diff --git a/404.html b/404.html
index bc27b3c..793e142 100644
--- a/404.html
+++ b/404.html
@@ -20,7 +20,7 @@
 
     <a class="navbar-brand me-2" href="https://NIEHS.github.io/chopin/index.html">chopin</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.7.8.20240814</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.7.8.20240817</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/CODE_OF_CONDUCT.html b/CODE_OF_CONDUCT.html
index 4f05277..1b98434 100644
--- a/CODE_OF_CONDUCT.html
+++ b/CODE_OF_CONDUCT.html
@@ -7,7 +7,7 @@
 
     <a class="navbar-brand me-2" href="index.html">chopin</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.7.8.20240814</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.7.8.20240817</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/CONTRIBUTING.html b/CONTRIBUTING.html
index 2e62978..9463921 100644
--- a/CONTRIBUTING.html
+++ b/CONTRIBUTING.html
@@ -7,7 +7,7 @@
 
     <a class="navbar-brand me-2" href="index.html">chopin</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.7.8.20240814</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.7.8.20240817</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/LICENSE-text.html b/LICENSE-text.html
index 11fe3ea..4d7b877 100644
--- a/LICENSE-text.html
+++ b/LICENSE-text.html
@@ -7,7 +7,7 @@
 
     <a class="navbar-brand me-2" href="index.html">chopin</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.7.8.20240814</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.7.8.20240817</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/LICENSE.html b/LICENSE.html
index 623bf81..a00aeec 100644
--- a/LICENSE.html
+++ b/LICENSE.html
@@ -7,7 +7,7 @@
 
     <a class="navbar-brand me-2" href="index.html">chopin</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.7.8.20240814</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.7.8.20240817</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/articles/index.html b/articles/index.html
index c6123c4..57b6579 100644
--- a/articles/index.html
+++ b/articles/index.html
@@ -7,7 +7,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">chopin</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.7.8.20240814</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.7.8.20240817</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/articles/v01_start.html b/articles/v01_start.html
index 80651f0..8e76533 100644
--- a/articles/v01_start.html
+++ b/articles/v01_start.html
@@ -20,7 +20,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">chopin</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.7.8.20240814</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.7.8.20240817</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -295,21 +295,21 @@ <h2 id="multiraster-processing">Multiraster processing<a class="anchor" aria-lab
 <div class="sourceCode" id="cb19"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="fu"><a href="https://rspatial.github.io/terra/reference/headtail.html" class="external-link">head</a></span><span class="op">(</span><span class="va">pm</span><span class="op">)</span></span></code></pre></div>
 <pre><code><span><span class="co">##          mean               base_raster</span></span>
-<span><span class="co">## 1   12.443567 /tmp/Rtmpe09POE/test1.tif</span></span>
-<span><span class="co">## 2   74.934341 /tmp/Rtmpe09POE/test1.tif</span></span>
-<span><span class="co">## 3  179.088806 /tmp/Rtmpe09POE/test1.tif</span></span>
-<span><span class="co">## 4    8.542515 /tmp/Rtmpe09POE/test1.tif</span></span>
-<span><span class="co">## 5 1087.089722 /tmp/Rtmpe09POE/test1.tif</span></span>
-<span><span class="co">## 6  110.032043 /tmp/Rtmpe09POE/test1.tif</span></span></code></pre>
+<span><span class="co">## 1   12.443567 /tmp/RtmpUmoTIK/test1.tif</span></span>
+<span><span class="co">## 2   74.934341 /tmp/RtmpUmoTIK/test1.tif</span></span>
+<span><span class="co">## 3  179.088806 /tmp/RtmpUmoTIK/test1.tif</span></span>
+<span><span class="co">## 4    8.542515 /tmp/RtmpUmoTIK/test1.tif</span></span>
+<span><span class="co">## 5 1087.089722 /tmp/RtmpUmoTIK/test1.tif</span></span>
+<span><span class="co">## 6  110.032043 /tmp/RtmpUmoTIK/test1.tif</span></span></code></pre>
 <div class="sourceCode" id="cb21"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="fu"><a href="https://rspatial.github.io/terra/reference/headtail.html" class="external-link">tail</a></span><span class="op">(</span><span class="va">pm</span><span class="op">)</span></span></code></pre></div>
 <pre><code><span><span class="co">##            mean               base_raster</span></span>
-<span><span class="co">## 2495 101.370438 /tmp/Rtmpe09POE/test5.tif</span></span>
-<span><span class="co">## 2496  -2.881788 /tmp/Rtmpe09POE/test5.tif</span></span>
-<span><span class="co">## 2497   7.347102 /tmp/Rtmpe09POE/test5.tif</span></span>
-<span><span class="co">## 2498   8.790645 /tmp/Rtmpe09POE/test5.tif</span></span>
-<span><span class="co">## 2499  21.443277 /tmp/Rtmpe09POE/test5.tif</span></span>
-<span><span class="co">## 2500  56.097313 /tmp/Rtmpe09POE/test5.tif</span></span></code></pre>
+<span><span class="co">## 2495 101.370438 /tmp/RtmpUmoTIK/test5.tif</span></span>
+<span><span class="co">## 2496  -2.881788 /tmp/RtmpUmoTIK/test5.tif</span></span>
+<span><span class="co">## 2497   7.347102 /tmp/RtmpUmoTIK/test5.tif</span></span>
+<span><span class="co">## 2498   8.790645 /tmp/RtmpUmoTIK/test5.tif</span></span>
+<span><span class="co">## 2499  21.443277 /tmp/RtmpUmoTIK/test5.tif</span></span>
+<span><span class="co">## 2500  56.097313 /tmp/RtmpUmoTIK/test5.tif</span></span></code></pre>
 </div>
 <div class="section level2">
 <h2 id="why-parallelization-is-slower-than-the-ordinary-function-run">Why parallelization is slower than the ordinary function run?<a class="anchor" aria-label="anchor" href="#why-parallelization-is-slower-than-the-ordinary-function-run"></a>
diff --git a/articles/v02_good_practice.html b/articles/v02_good_practice.html
index b12fc02..41b4994 100644
--- a/articles/v02_good_practice.html
+++ b/articles/v02_good_practice.html
@@ -20,7 +20,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">chopin</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.7.8.20240814</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.7.8.20240817</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/articles/v03_par_pad_grid.html b/articles/v03_par_pad_grid.html
index da9b1a0..f6407e8 100644
--- a/articles/v03_par_pad_grid.html
+++ b/articles/v03_par_pad_grid.html
@@ -20,7 +20,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">chopin</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.7.8.20240814</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.7.8.20240817</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/articles/v04_climate_examples.html b/articles/v04_climate_examples.html
index 66f485f..339e551 100644
--- a/articles/v04_climate_examples.html
+++ b/articles/v04_climate_examples.html
@@ -20,7 +20,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">chopin</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.7.8.20240814</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.7.8.20240817</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/articles/v05_targets.html b/articles/v05_targets.html
index 5eeb9aa..41ff648 100644
--- a/articles/v05_targets.html
+++ b/articles/v05_targets.html
@@ -20,7 +20,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">chopin</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.7.8.20240814</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.7.8.20240817</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/authors.html b/authors.html
index b885cb7..159393e 100644
--- a/authors.html
+++ b/authors.html
@@ -7,7 +7,7 @@
 
     <a class="navbar-brand me-2" href="index.html">chopin</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.7.8.20240814</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.7.8.20240817</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -57,13 +57,13 @@ <h2 id="citation">Citation</h2>
 
       <p>Song I, Messier K (2024).
 <em>chopin: Computation of Spatial Data by Hierarchical and Objective Partitioning of Inputs for Parallel Processing</em>.
-R package version 0.7.8.20240814, <a href="https://github.com/NIEHS/chopin" class="external-link">https://github.com/NIEHS/chopin</a>.
+R package version 0.7.8.20240817, <a href="https://github.com/NIEHS/chopin" class="external-link">https://github.com/NIEHS/chopin</a>.
 </p>
       <pre>@Manual{,
   title = {chopin: Computation of Spatial Data by Hierarchical and Objective Partitioning of Inputs for Parallel Processing},
   author = {Insang Song and Kyle Messier},
   year = {2024},
-  note = {R package version 0.7.8.20240814},
+  note = {R package version 0.7.8.20240817},
   url = {https://github.com/NIEHS/chopin},
 }</pre>
     </div>
diff --git a/index.html b/index.html
index fac574a..3c383ba 100644
--- a/index.html
+++ b/index.html
@@ -22,7 +22,7 @@
 
     <a class="navbar-brand me-2" href="index.html">chopin</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.7.8.20240814</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.7.8.20240817</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -120,9 +120,16 @@ <h2 id="overview">Overview<a class="anchor" aria-label="anchor" href="#overview"
 <span><span class="co"># future::multisession, future::cluster are available,</span></span>
 <span><span class="co"># See future.batchtools and future.callr for other options</span></span>
 <span><span class="co"># the number of workers are up to users' choice</span></span></code></pre></div>
+<div class="section level3">
+<h3 id="function-selection-guide">Function selection guide<a class="anchor" aria-label="anchor" href="#function-selection-guide"></a>
+</h3>
+<p>We provide two flowcharts to help users choose the right function for parallel processing. The raster-oriented flowchart is for users who want to start with raster data, and the vector-oriented flowchart is for users with large vector data.</p>
+<p>In <strong>raster-oriented selection</strong>, we suggest four factors to consider: - Number of raster files: for multiple files, <code>par_multirasters</code> is recommended. When there are multiple rasters that share the same extent and resolution, consider stacking the rasters into multilayer SpatRaster object by calling <code>terra::rast(filenames)</code>. - Raster resolution: We suggest 100 meters as a threshold. Rasters with resolution coarser than 100 meters and a few layers would be better for the direct call of <code><a href="https://isciences.gitlab.io/exactextractr/reference/exact_extract.html" class="external-link">exactextractr::exact_extract()</a></code>. - Raster extent: Using <code>SpatRaster</code> in <code><a href="https://isciences.gitlab.io/exactextractr/reference/exact_extract.html" class="external-link">exactextractr::exact_extract()</a></code> is often minimally affected by the raster extent. - Memory size: <code>max_cells_in_memory</code> argument value of <code><a href="https://isciences.gitlab.io/exactextractr/reference/exact_extract.html" class="external-link">exactextractr::exact_extract()</a></code>, raster resolution, and the number of layers in <code>SpatRaster</code> are multiplicatively related to the memory usage.</p>
 <p><img src="reference/figures/README-flowchart-mermaid-raster-1.png" width="100%"></p>
+<p>For <strong>vector-oriented selection</strong>, we suggest three factors to consider: - Number of features: When the number of features is over 100,000, consider using <code>par_grid</code> or <code>par_hierarchy</code> to split the data into smaller chunks. - Hierarchical structure: If the data has a hierarchical structure, consider using <code>par_hierarchy</code> to parallelize the operation. - Data grouping: If the data needs to be grouped in similar sizes, consider using <code>par_pad_balanced</code> or <code>par_pad_grid</code> with <code>mode = "grid_quantile"</code>.</p>
 <p><img src="reference/figures/README-flowchart-mermaid-vector-1.png" width="100%"></p>
 </div>
+</div>
 <div class="section level2">
 <h2 id="installation">Installation<a class="anchor" aria-label="anchor" href="#installation"></a>
 </h2>
@@ -239,7 +246,7 @@ <h4 id="target-raster-dataset-shuttle-radar-topography-mission">Target raster da
 <span><span class="op">)</span></span>
 <span><span class="co">#&gt; Input is a character. Attempt to read it with terra::rast...</span></span>
 <span><span class="co">#&gt;    user  system elapsed </span></span>
-<span><span class="co">#&gt;   7.205   0.206   7.669</span></span></code></pre></div>
+<span><span class="co">#&gt;   7.115   0.227   7.537</span></span></code></pre></div>
 </div>
 <div class="section level4">
 <h4 id="generate-regular-grid-computational-regions">Generate regular grid computational regions<a class="anchor" aria-label="anchor" href="#generate-regular-grid-computational-regions"></a>
@@ -320,7 +327,7 @@ <h4 id="parallel-processing">Parallel processing<a class="anchor" aria-label="an
 <span><span class="co">#&gt; Input is a character. Attempt to read it with terra::rast...</span></span>
 <span><span class="co">#&gt; ℹ Task at CGRIDID: 4 is successfully dispatched.</span></span>
 <span><span class="co">#&gt;    user  system elapsed </span></span>
-<span><span class="co">#&gt;   0.329   0.021   8.337</span></span>
+<span><span class="co">#&gt;   0.360   0.010   8.089</span></span>
 <span></span>
 <span><span class="va">ncpoints_srtm</span> <span class="op">&lt;-</span></span>
 <span>  <span class="fu"><a href="reference/extract_at.html">extract_at</a></span><span class="op">(</span></span>
@@ -369,7 +376,7 @@ <h4 id="read-data">Read data<a class="anchor" aria-label="anchor" href="#read-da
 <span><span class="va">nc_data</span> <span class="op">&lt;-</span> <span class="va">path_nchrchy</span></span>
 <span><span class="va">nc_county</span> <span class="op">&lt;-</span> <span class="fu">sf</span><span class="fu">::</span><span class="fu"><a href="https://r-spatial.github.io/sf/reference/st_read.html" class="external-link">st_read</a></span><span class="op">(</span><span class="va">nc_data</span>, layer <span class="op">=</span> <span class="st">"county"</span><span class="op">)</span></span>
 <span><span class="co">#&gt; Reading layer `county' from data source </span></span>
-<span><span class="co">#&gt;   `/tmp/RtmpkigVUL/temp_libpath3cdb0b6b55ee11/chopin/extdata/nc_hierarchy.gpkg' </span></span>
+<span><span class="co">#&gt;   `/tmp/RtmpPipkp9/temp_libpath2270313b1b582/chopin/extdata/nc_hierarchy.gpkg' </span></span>
 <span><span class="co">#&gt;   using driver `GPKG'</span></span>
 <span><span class="co">#&gt; Simple feature collection with 100 features and 1 field</span></span>
 <span><span class="co">#&gt; Geometry type: POLYGON</span></span>
@@ -378,7 +385,7 @@ <h4 id="read-data">Read data<a class="anchor" aria-label="anchor" href="#read-da
 <span><span class="co">#&gt; Projected CRS: NAD83 / Conus Albers</span></span>
 <span><span class="va">nc_tracts</span> <span class="op">&lt;-</span> <span class="fu">sf</span><span class="fu">::</span><span class="fu"><a href="https://r-spatial.github.io/sf/reference/st_read.html" class="external-link">st_read</a></span><span class="op">(</span><span class="va">nc_data</span>, layer <span class="op">=</span> <span class="st">"tracts"</span><span class="op">)</span></span>
 <span><span class="co">#&gt; Reading layer `tracts' from data source </span></span>
-<span><span class="co">#&gt;   `/tmp/RtmpkigVUL/temp_libpath3cdb0b6b55ee11/chopin/extdata/nc_hierarchy.gpkg' </span></span>
+<span><span class="co">#&gt;   `/tmp/RtmpPipkp9/temp_libpath2270313b1b582/chopin/extdata/nc_hierarchy.gpkg' </span></span>
 <span><span class="co">#&gt;   using driver `GPKG'</span></span>
 <span><span class="co">#&gt; Simple feature collection with 2672 features and 1 field</span></span>
 <span><span class="co">#&gt; Geometry type: MULTIPOLYGON</span></span>
@@ -406,7 +413,7 @@ <h4 id="extract-average-srtm-elevations-by-single-and-multiple-threads">Extract
 <span><span class="op">)</span></span>
 <span><span class="co">#&gt; Input is a character. Attempt to read it with terra::rast...</span></span>
 <span><span class="co">#&gt;    user  system elapsed </span></span>
-<span><span class="co">#&gt;   0.910   0.003   0.916</span></span>
+<span><span class="co">#&gt;   0.897   0.003   0.904</span></span>
 <span></span>
 <span><span class="co"># hierarchical parallelization</span></span>
 <span><span class="fu"><a href="https://rdrr.io/r/base/system.time.html" class="external-link">system.time</a></span><span class="op">(</span></span>
@@ -524,7 +531,7 @@ <h4 id="extract-average-srtm-elevations-by-single-and-multiple-threads">Extract
 <span><span class="co">#&gt; Input is a character. Attempt to read it with terra::rast...ℹ Your input function at 37055 is dispatched.</span></span>
 <span><span class="co">#&gt; Input is a character. Attempt to read it with terra::rast...ℹ Your input function at 37047 is dispatched.</span></span>
 <span><span class="co">#&gt;    user  system elapsed </span></span>
-<span><span class="co">#&gt;   0.258   0.073   2.005</span></span></code></pre></div>
+<span><span class="co">#&gt;   0.249   0.077   2.016</span></span></code></pre></div>
 </div>
 </div>
 <div class="section level3">
@@ -552,9 +559,9 @@ <h3 id="par_multirasters-parallelize-over-multiple-rasters">
 <span><span class="co"># check if the raster files were exported as expected</span></span>
 <span><span class="va">testfiles</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/list.files.html" class="external-link">list.files</a></span><span class="op">(</span><span class="va">tdir</span>, pattern <span class="op">=</span> <span class="st">"*.tif$"</span>, full.names <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span>
 <span><span class="va">testfiles</span></span>
-<span><span class="co">#&gt; [1] "/tmp/Rtmpqu68xF/test1.tif" "/tmp/Rtmpqu68xF/test2.tif"</span></span>
-<span><span class="co">#&gt; [3] "/tmp/Rtmpqu68xF/test3.tif" "/tmp/Rtmpqu68xF/test4.tif"</span></span>
-<span><span class="co">#&gt; [5] "/tmp/Rtmpqu68xF/test5.tif"</span></span></code></pre></div>
+<span><span class="co">#&gt; [1] "/tmp/RtmpxL0QL4/test1.tif" "/tmp/RtmpxL0QL4/test2.tif"</span></span>
+<span><span class="co">#&gt; [3] "/tmp/RtmpxL0QL4/test3.tif" "/tmp/RtmpxL0QL4/test4.tif"</span></span>
+<span><span class="co">#&gt; [5] "/tmp/RtmpxL0QL4/test5.tif"</span></span></code></pre></div>
 <div class="sourceCode" id="cb17"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/system.time.html" class="external-link">system.time</a></span><span class="op">(</span></span>
 <span>  <span class="va">res</span> <span class="op">&lt;-</span></span>
@@ -569,21 +576,21 @@ <h3 id="par_multirasters-parallelize-over-multiple-rasters">
 <span><span class="op">)</span></span>
 <span><span class="co">#&gt; ℹ Input is not a character.</span></span>
 <span><span class="co">#&gt; Input is a character. Attempt to read it with terra::rast...</span></span>
-<span><span class="co">#&gt; ℹ Your input function at /tmp/Rtmpqu68xF/test1.tif is dispatched.</span></span>
+<span><span class="co">#&gt; ℹ Your input function at /tmp/RtmpxL0QL4/test1.tif is dispatched.</span></span>
 <span><span class="co">#&gt; </span></span>
 <span><span class="co">#&gt; Input is a character. Attempt to read it with terra::rast...</span></span>
-<span><span class="co">#&gt; ℹ Your input function at /tmp/Rtmpqu68xF/test2.tif is dispatched.</span></span>
+<span><span class="co">#&gt; ℹ Your input function at /tmp/RtmpxL0QL4/test2.tif is dispatched.</span></span>
 <span><span class="co">#&gt; </span></span>
 <span><span class="co">#&gt; Input is a character. Attempt to read it with terra::rast...</span></span>
-<span><span class="co">#&gt; ℹ Your input function at /tmp/Rtmpqu68xF/test3.tif is dispatched.</span></span>
+<span><span class="co">#&gt; ℹ Your input function at /tmp/RtmpxL0QL4/test3.tif is dispatched.</span></span>
 <span><span class="co">#&gt; </span></span>
 <span><span class="co">#&gt; Input is a character. Attempt to read it with terra::rast...</span></span>
-<span><span class="co">#&gt; ℹ Your input function at /tmp/Rtmpqu68xF/test4.tif is dispatched.</span></span>
+<span><span class="co">#&gt; ℹ Your input function at /tmp/RtmpxL0QL4/test4.tif is dispatched.</span></span>
 <span><span class="co">#&gt; </span></span>
 <span><span class="co">#&gt; Input is a character. Attempt to read it with terra::rast...</span></span>
-<span><span class="co">#&gt; ℹ Your input function at /tmp/Rtmpqu68xF/test5.tif is dispatched.</span></span>
+<span><span class="co">#&gt; ℹ Your input function at /tmp/RtmpxL0QL4/test5.tif is dispatched.</span></span>
 <span><span class="co">#&gt;    user  system elapsed </span></span>
-<span><span class="co">#&gt;   1.713   0.147   2.747</span></span>
+<span><span class="co">#&gt;   1.714   0.139   2.718</span></span>
 <span><span class="fu">knitr</span><span class="fu">::</span><span class="fu"><a href="https://rdrr.io/pkg/knitr/man/kable.html" class="external-link">kable</a></span><span class="op">(</span><span class="fu"><a href="https://rspatial.github.io/terra/reference/headtail.html" class="external-link">head</a></span><span class="op">(</span><span class="va">res</span><span class="op">)</span><span class="op">)</span></span></code></pre></div>
 <table class="table">
 <thead><tr class="header">
@@ -593,27 +600,27 @@ <h3 id="par_multirasters-parallelize-over-multiple-rasters">
 <tbody>
 <tr class="odd">
 <td align="right">136.80203</td>
-<td align="left">/tmp/Rtmpqu68xF/test1.tif</td>
+<td align="left">/tmp/RtmpxL0QL4/test1.tif</td>
 </tr>
 <tr class="even">
 <td align="right">189.76170</td>
-<td align="left">/tmp/Rtmpqu68xF/test1.tif</td>
+<td align="left">/tmp/RtmpxL0QL4/test1.tif</td>
 </tr>
 <tr class="odd">
 <td align="right">231.16968</td>
-<td align="left">/tmp/Rtmpqu68xF/test1.tif</td>
+<td align="left">/tmp/RtmpxL0QL4/test1.tif</td>
 </tr>
 <tr class="even">
 <td align="right">98.03845</td>
-<td align="left">/tmp/Rtmpqu68xF/test1.tif</td>
+<td align="left">/tmp/RtmpxL0QL4/test1.tif</td>
 </tr>
 <tr class="odd">
 <td align="right">41.23463</td>
-<td align="left">/tmp/Rtmpqu68xF/test1.tif</td>
+<td align="left">/tmp/RtmpxL0QL4/test1.tif</td>
 </tr>
 <tr class="even">
 <td align="right">270.96933</td>
-<td align="left">/tmp/Rtmpqu68xF/test1.tif</td>
+<td align="left">/tmp/RtmpxL0QL4/test1.tif</td>
 </tr>
 </tbody>
 </table>
@@ -649,7 +656,7 @@ <h2 id="parallelization-of-a-generic-geospatial-operation">Parallelization of a
 <span><span class="va">pnts</span><span class="op">$</span><span class="va">pid</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/sprintf.html" class="external-link">sprintf</a></span><span class="op">(</span><span class="st">"RPID-%04d"</span>, <span class="fu"><a href="https://rdrr.io/r/base/seq.html" class="external-link">seq</a></span><span class="op">(</span><span class="fl">1</span>, <span class="fl">5000</span><span class="op">)</span><span class="op">)</span></span>
 <span><span class="va">rd1</span> <span class="op">&lt;-</span> <span class="fu">sf</span><span class="fu">::</span><span class="fu"><a href="https://r-spatial.github.io/sf/reference/st_read.html" class="external-link">st_read</a></span><span class="op">(</span><span class="va">path_ncrd1</span><span class="op">)</span></span>
 <span><span class="co">#&gt; Reading layer `ncroads_first' from data source </span></span>
-<span><span class="co">#&gt;   `/tmp/RtmpkigVUL/temp_libpath3cdb0b6b55ee11/chopin/extdata/ncroads_first.gpkg' </span></span>
+<span><span class="co">#&gt;   `/tmp/RtmpPipkp9/temp_libpath2270313b1b582/chopin/extdata/ncroads_first.gpkg' </span></span>
 <span><span class="co">#&gt;   using driver `GPKG'</span></span>
 <span><span class="co">#&gt; Simple feature collection with 620 features and 4 fields</span></span>
 <span><span class="co">#&gt; Geometry type: MULTILINESTRING</span></span>
@@ -694,11 +701,11 @@ <h2 id="parallelization-of-a-generic-geospatial-operation">Parallelization of a
 <span>  <span class="va">restr</span> <span class="op">&lt;-</span> <span class="fu">terra</span><span class="fu">::</span><span class="fu"><a href="https://rspatial.github.io/terra/reference/nearby.html" class="external-link">nearest</a></span><span class="op">(</span>x <span class="op">=</span> <span class="fu">terra</span><span class="fu">::</span><span class="fu"><a href="https://rspatial.github.io/terra/reference/vect.html" class="external-link">vect</a></span><span class="op">(</span><span class="va">pntst</span><span class="op">)</span>, y <span class="op">=</span> <span class="fu">terra</span><span class="fu">::</span><span class="fu"><a href="https://rspatial.github.io/terra/reference/vect.html" class="external-link">vect</a></span><span class="op">(</span><span class="va">rd1t</span><span class="op">)</span><span class="op">)</span></span>
 <span><span class="op">)</span></span>
 <span><span class="co">#&gt;    user  system elapsed </span></span>
-<span><span class="co">#&gt;   0.603   0.000   0.604</span></span>
+<span><span class="co">#&gt;   0.602   0.000   0.603</span></span>
 <span></span>
 <span><span class="va">pnt_path</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/file.path.html" class="external-link">file.path</a></span><span class="op">(</span><span class="va">tdir</span>, <span class="st">"pntst.gpkg"</span><span class="op">)</span></span>
 <span><span class="fu">sf</span><span class="fu">::</span><span class="fu"><a href="https://r-spatial.github.io/sf/reference/st_write.html" class="external-link">st_write</a></span><span class="op">(</span><span class="va">pntst</span>, <span class="va">pnt_path</span><span class="op">)</span></span>
-<span><span class="co">#&gt; Writing layer `pntst' to data source `/tmp/Rtmpqu68xF/pntst.gpkg' using driver `GPKG'</span></span>
+<span><span class="co">#&gt; Writing layer `pntst' to data source `/tmp/RtmpxL0QL4/pntst.gpkg' using driver `GPKG'</span></span>
 <span><span class="co">#&gt; Writing 5000 features with 1 fields and geometry type Point.</span></span>
 <span></span>
 <span><span class="co"># we use four threads that were configured above</span></span>
@@ -744,7 +751,7 @@ <h2 id="parallelization-of-a-generic-geospatial-operation">Parallelization of a
 <span><span class="co">#&gt; ℹ Input is a character. Trying to read with terra .</span></span>
 <span><span class="co">#&gt; ℹ Task at CGRIDID: 8 is successfully dispatched.</span></span>
 <span><span class="co">#&gt;    user  system elapsed </span></span>
-<span><span class="co">#&gt;   0.085   0.000   0.534</span></span></code></pre></div>
+<span><span class="co">#&gt;   0.079   0.005   0.448</span></span></code></pre></div>
 <ul>
 <li>We will compare the results from the single-thread and multi-thread calculation.</li>
 </ul>
diff --git a/news/index.html b/news/index.html
index cfca4ee..4bd2f22 100644
--- a/news/index.html
+++ b/news/index.html
@@ -7,7 +7,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">chopin</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.7.8.20240814</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.7.8.20240817</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/pkgdown.yml b/pkgdown.yml
index b156d55..11df501 100644
--- a/pkgdown.yml
+++ b/pkgdown.yml
@@ -7,7 +7,7 @@ articles:
   v03_par_pad_grid: v03_par_pad_grid.html
   v04_climate_examples: v04_climate_examples.html
   v05_targets: v05_targets.html
-last_built: 2024-08-17T03:39Z
+last_built: 2024-08-18T18:15Z
 urls:
   reference: https://NIEHS.github.io/chopin/reference
   article: https://NIEHS.github.io/chopin/articles
diff --git a/reference/chopin-package.html b/reference/chopin-package.html
index 09816d6..28e005c 100644
--- a/reference/chopin-package.html
+++ b/reference/chopin-package.html
@@ -9,7 +9,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">chopin</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.7.8.20240814</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.7.8.20240817</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/reference/clip_ras_ext.html b/reference/clip_ras_ext.html
index bb4f8c5..7f24158 100644
--- a/reference/clip_ras_ext.html
+++ b/reference/clip_ras_ext.html
@@ -9,7 +9,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">chopin</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.7.8.20240814</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.7.8.20240817</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/reference/clip_vec_ext.html b/reference/clip_vec_ext.html
index ddb4787..0754ad5 100644
--- a/reference/clip_vec_ext.html
+++ b/reference/clip_vec_ext.html
@@ -9,7 +9,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">chopin</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.7.8.20240814</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.7.8.20240817</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/reference/datamod.html b/reference/datamod.html
index 66558f1..62bcea6 100644
--- a/reference/datamod.html
+++ b/reference/datamod.html
@@ -9,7 +9,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">chopin</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.7.8.20240814</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.7.8.20240817</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/reference/dep_check.html b/reference/dep_check.html
index 5011e87..133daef 100644
--- a/reference/dep_check.html
+++ b/reference/dep_check.html
@@ -7,7 +7,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">chopin</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.7.8.20240814</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.7.8.20240817</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/reference/dep_switch.html b/reference/dep_switch.html
index 4487f4b..d1fb0fe 100644
--- a/reference/dep_switch.html
+++ b/reference/dep_switch.html
@@ -7,7 +7,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">chopin</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.7.8.20240814</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.7.8.20240817</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/reference/dot-check_character.html b/reference/dot-check_character.html
index d4eda35..7ca5e9e 100644
--- a/reference/dot-check_character.html
+++ b/reference/dot-check_character.html
@@ -11,7 +11,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">chopin</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.7.8.20240814</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.7.8.20240817</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/reference/dot-check_raster.html b/reference/dot-check_raster.html
index aa7932a..9a471e6 100644
--- a/reference/dot-check_raster.html
+++ b/reference/dot-check_raster.html
@@ -11,7 +11,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">chopin</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.7.8.20240814</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.7.8.20240817</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/reference/dot-check_vector.html b/reference/dot-check_vector.html
index 3f5b780..e573eee 100644
--- a/reference/dot-check_vector.html
+++ b/reference/dot-check_vector.html
@@ -9,7 +9,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">chopin</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.7.8.20240814</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.7.8.20240817</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/reference/dot-intersect_extent.html b/reference/dot-intersect_extent.html
index 4af7861..f05028b 100644
--- a/reference/dot-intersect_extent.html
+++ b/reference/dot-intersect_extent.html
@@ -7,7 +7,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">chopin</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.7.8.20240814</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.7.8.20240817</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/reference/dot-par_screen.html b/reference/dot-par_screen.html
index 424ac09..d1ddb8f 100644
--- a/reference/dot-par_screen.html
+++ b/reference/dot-par_screen.html
@@ -9,7 +9,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">chopin</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.7.8.20240814</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.7.8.20240817</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/reference/extract_at.html b/reference/extract_at.html
index 291b693..12f60fa 100644
--- a/reference/extract_at.html
+++ b/reference/extract_at.html
@@ -7,7 +7,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">chopin</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.7.8.20240814</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.7.8.20240817</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/reference/get_clip_ext.html b/reference/get_clip_ext.html
index 277a34e..63b2084 100644
--- a/reference/get_clip_ext.html
+++ b/reference/get_clip_ext.html
@@ -9,7 +9,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">chopin</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.7.8.20240814</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.7.8.20240817</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/reference/index.html b/reference/index.html
index 7ff1f99..45dd158 100644
--- a/reference/index.html
+++ b/reference/index.html
@@ -7,7 +7,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">chopin</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.7.8.20240814</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.7.8.20240817</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/reference/indexing.html b/reference/indexing.html
index f0aca73..2617b43 100644
--- a/reference/indexing.html
+++ b/reference/indexing.html
@@ -9,7 +9,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">chopin</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.7.8.20240814</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.7.8.20240817</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/reference/kernelfunction.html b/reference/kernelfunction.html
index c54c8d3..6a4d6cb 100644
--- a/reference/kernelfunction.html
+++ b/reference/kernelfunction.html
@@ -7,7 +7,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">chopin</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.7.8.20240814</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.7.8.20240817</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/reference/ncpoints.html b/reference/ncpoints.html
index 1d6a7af..f88d861 100644
--- a/reference/ncpoints.html
+++ b/reference/ncpoints.html
@@ -7,7 +7,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">chopin</a>
 
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diff --git a/reference/par_cut_coords.html b/reference/par_cut_coords.html
index 3581faa..e1f5039 100644
--- a/reference/par_cut_coords.html
+++ b/reference/par_cut_coords.html
@@ -7,7 +7,7 @@
 
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diff --git a/reference/par_def_q.html b/reference/par_def_q.html
index af9111d..84d853e 100644
--- a/reference/par_def_q.html
+++ b/reference/par_def_q.html
@@ -7,7 +7,7 @@
 
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diff --git a/reference/par_grid.html b/reference/par_grid.html
index 1170c4a..4eef843 100644
--- a/reference/par_grid.html
+++ b/reference/par_grid.html
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diff --git a/reference/par_hierarchy.html b/reference/par_hierarchy.html
index 545ff05..64e7217 100644
--- a/reference/par_hierarchy.html
+++ b/reference/par_hierarchy.html
@@ -35,7 +35,7 @@
 
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diff --git a/reference/par_make_balanced.html b/reference/par_make_balanced.html
index 3495d8a..8abbb2e 100644
--- a/reference/par_make_balanced.html
+++ b/reference/par_make_balanced.html
@@ -17,7 +17,7 @@
 
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diff --git a/reference/par_make_grid.html b/reference/par_make_grid.html
index 73bc3e2..d56d139 100644
--- a/reference/par_make_grid.html
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@@ -11,7 +11,7 @@
 
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     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/reference/par_map_args.html b/reference/par_map_args.html
index c6d130b..395fc8b 100644
--- a/reference/par_map_args.html
+++ b/reference/par_map_args.html
@@ -19,7 +19,7 @@
 
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diff --git a/reference/par_merge_grid.html b/reference/par_merge_grid.html
index b89a4a7..4267f7b 100644
--- a/reference/par_merge_grid.html
+++ b/reference/par_merge_grid.html
@@ -15,7 +15,7 @@
 
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diff --git a/reference/par_multirasters.html b/reference/par_multirasters.html
index 2a9fad5..1aa6b65 100644
--- a/reference/par_multirasters.html
+++ b/reference/par_multirasters.html
@@ -41,7 +41,7 @@
 
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     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -188,9 +188,9 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="op">)</span></span></span>
 <span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BBBB;">ℹ</span> Input is not a character.</span>
 <span class="r-msg co"><span class="r-pr">#&gt;</span> Input is a character. Attempt to read it with terra::rast...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> ℹ Your input function at /tmp/RtmprV25By/test1.tif is dispatched.</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> ℹ Your input function at /tmp/Rtmpc1MfKT/test1.tif is dispatched.</span>
 <span class="r-msg co"><span class="r-pr">#&gt;</span> Input is a character. Attempt to read it with terra::rast...</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> ℹ Your input function at /tmp/RtmprV25By/test2.tif is dispatched.</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> ℹ Your input function at /tmp/Rtmpc1MfKT/test2.tif is dispatched.</span>
 </code></pre></div>
     </div>
   </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
diff --git a/reference/par_pad_balanced.html b/reference/par_pad_balanced.html
index 2a2d9a8..2ef34bf 100644
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index 41e3352..3a3721e 100644
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diff --git a/reference/par_split_list.html b/reference/par_split_list.html
index eaf8654..2fb6366 100644
--- a/reference/par_split_list.html
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diff --git a/reference/prediction_grid.html b/reference/prediction_grid.html
index 5b0b8e5..6eb92e2 100644
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diff --git a/reference/reproject_std.html b/reference/reproject_std.html
index 88e6478..5202a7d 100644
--- a/reference/reproject_std.html
+++ b/reference/reproject_std.html
@@ -11,7 +11,7 @@
 
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index ebde512..200aa86 100644
--- a/reference/reproject_to_raster.html
+++ b/reference/reproject_to_raster.html
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index ac06c7c..0eda348 100644
--- a/reference/summarize_aw.html
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@@ -184,7 +184,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="op">)</span></span></span>
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 <span class="r-out co"><span class="r-pr">#&gt;</span>      BIR74             BIR79                  geometry  </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>  Min.   :  159.6   Min.   :  244.7   POLYGON      :300  </span>
@@ -216,7 +216,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="op">)</span></span></span>
 <span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>[intersect] different crs</span>
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 <span class="r-out co"><span class="r-pr">#&gt;</span>        id             BIR74               BIR79          </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>  Min.   :  1.00   Min.   :2.453e+08   Min.   :2.453e+08  </span>
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index d457ab0..5a0ca88 100644
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index c0b9a4c..d74553b 100644
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diff --git a/search.json b/search.json
index 27fdf9b..526e0f7 100644
--- a/search.json
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-[{"path":[]},{"path":"https://NIEHS.github.io/chopin/CODE_OF_CONDUCT.html","id":"our-pledge","dir":"","previous_headings":"","what":"Our Pledge","title":"Contributor Covenant Code of Conduct","text":"members, contributors, leaders pledge make participation community harassment-free experience everyone, regardless age, body size, visible invisible disability, ethnicity, sex characteristics, gender identity expression, level experience, education, socio-economic status, nationality, personal appearance, race, caste, color, religion, sexual identity orientation. pledge act interact ways contribute open, welcoming, diverse, inclusive, healthy community.","code":""},{"path":"https://NIEHS.github.io/chopin/CODE_OF_CONDUCT.html","id":"our-standards","dir":"","previous_headings":"","what":"Our Standards","title":"Contributor Covenant Code of Conduct","text":"Examples behavior contributes positive environment community include: Demonstrating empathy kindness toward people respectful differing opinions, viewpoints, experiences Giving gracefully accepting constructive feedback Accepting responsibility apologizing affected mistakes, learning experience Focusing best just us individuals, overall community Examples unacceptable behavior include: use sexualized language imagery, sexual attention advances kind Trolling, insulting derogatory comments, personal political attacks Public private harassment Publishing others’ private information, physical email address, without explicit permission conduct reasonably considered inappropriate professional setting","code":""},{"path":"https://NIEHS.github.io/chopin/CODE_OF_CONDUCT.html","id":"enforcement-responsibilities","dir":"","previous_headings":"","what":"Enforcement Responsibilities","title":"Contributor Covenant Code of Conduct","text":"Community leaders responsible clarifying enforcing standards acceptable behavior take appropriate fair corrective action response behavior deem inappropriate, threatening, offensive, harmful. 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Examples representing community include using official e-mail address, posting via official social media account, acting appointed representative online offline event.","code":""},{"path":"https://NIEHS.github.io/chopin/CODE_OF_CONDUCT.html","id":"enforcement","dir":"","previous_headings":"","what":"Enforcement","title":"Contributor Covenant Code of Conduct","text":"Instances abusive, harassing, otherwise unacceptable behavior may reported community leaders responsible enforcement geoissong@gmail.com. complaints reviewed investigated promptly fairly. community leaders obligated respect privacy security reporter incident.","code":""},{"path":"https://NIEHS.github.io/chopin/CODE_OF_CONDUCT.html","id":"enforcement-guidelines","dir":"","previous_headings":"","what":"Enforcement Guidelines","title":"Contributor Covenant Code of Conduct","text":"Community leaders follow Community Impact Guidelines determining consequences action deem violation Code Conduct:","code":""},{"path":"https://NIEHS.github.io/chopin/CODE_OF_CONDUCT.html","id":"id_1-correction","dir":"","previous_headings":"Enforcement Guidelines","what":"1. Correction","title":"Contributor Covenant Code of Conduct","text":"Community Impact: Use inappropriate language behavior deemed unprofessional unwelcome community. Consequence: private, written warning community leaders, providing clarity around nature violation explanation behavior inappropriate. public apology may requested.","code":""},{"path":"https://NIEHS.github.io/chopin/CODE_OF_CONDUCT.html","id":"id_2-warning","dir":"","previous_headings":"Enforcement Guidelines","what":"2. Warning","title":"Contributor Covenant Code of Conduct","text":"Community Impact: violation single incident series actions. Consequence: warning consequences continued behavior. interaction people involved, including unsolicited interaction enforcing Code Conduct, specified period time. includes avoiding interactions community spaces well external channels like social media. Violating terms may lead temporary permanent ban.","code":""},{"path":"https://NIEHS.github.io/chopin/CODE_OF_CONDUCT.html","id":"id_3-temporary-ban","dir":"","previous_headings":"Enforcement Guidelines","what":"3. Temporary Ban","title":"Contributor Covenant Code of Conduct","text":"Community Impact: serious violation community standards, including sustained inappropriate behavior. Consequence: temporary ban sort interaction public communication community specified period time. public private interaction people involved, including unsolicited interaction enforcing Code Conduct, allowed period. Violating terms may lead permanent ban.","code":""},{"path":"https://NIEHS.github.io/chopin/CODE_OF_CONDUCT.html","id":"id_4-permanent-ban","dir":"","previous_headings":"Enforcement Guidelines","what":"4. Permanent Ban","title":"Contributor Covenant Code of Conduct","text":"Community Impact: Demonstrating pattern violation community standards, including sustained inappropriate behavior, harassment individual, aggression toward disparagement classes individuals. Consequence: permanent ban sort public interaction within community.","code":""},{"path":"https://NIEHS.github.io/chopin/CODE_OF_CONDUCT.html","id":"attribution","dir":"","previous_headings":"","what":"Attribution","title":"Contributor Covenant Code of Conduct","text":"Code Conduct adapted Contributor Covenant, version 2.1, available https://www.contributor-covenant.org/version/2/1/code_of_conduct.html. Community Impact Guidelines inspired [Mozilla’s code conduct enforcement ladder][https://github.com/mozilla/inclusion]. answers common questions code conduct, see FAQ https://www.contributor-covenant.org/faq. Translations available https://www.contributor-covenant.org/translations.","code":""},{"path":"https://NIEHS.github.io/chopin/CONTRIBUTING.html","id":null,"dir":"","previous_headings":"","what":"Contributing to chopin","title":"Contributing to chopin","text":"outlines propose change chopin.","code":""},{"path":"https://NIEHS.github.io/chopin/CONTRIBUTING.html","id":"fixing-typos","dir":"","previous_headings":"","what":"Fixing typos","title":"Contributing to chopin","text":"can fix typos, spelling mistakes, grammatical errors documentation directly using GitHub web interface, long changes made source file. generally means ’ll need edit roxygen2 comments .R, .Rd file. can find .R file generates .Rd reading comment first line.","code":""},{"path":"https://NIEHS.github.io/chopin/CONTRIBUTING.html","id":"bigger-changes","dir":"","previous_headings":"","what":"Bigger changes","title":"Contributing to chopin","text":"want make bigger change, ’s good idea first file issue make sure someone team agrees ’s needed. ’ve found bug, please file issue illustrates bug minimal reprex (also help write unit test, needed). See tidyverse team’s guide create great issue advice. adding new function package, always add working examples new function roxygen2 documentation #' @examples.","code":""},{"path":"https://NIEHS.github.io/chopin/CONTRIBUTING.html","id":"pull-request-process","dir":"","previous_headings":"Bigger changes","what":"Pull request process","title":"Contributing to chopin","text":"Fork package clone onto computer. haven’t done , recommend using usethis::create_from_github(\"NIEHS/chopin\", fork = TRUE). Install development dependencies devtools::install_dev_deps(), make sure package passes R CMD check running devtools::check(). R CMD check doesn’t pass cleanly, ’s good idea ask help continuing. Create Git branch pull request (PR). recommend using usethis::pr_init(\"brief-description--change\"). Make changes, commit git, create PR running usethis::pr_push(), following prompts browser. title PR briefly describe change. body PR contain Fixes #issue-number. user-facing changes, add bullet top NEWS.md (.e. just first header).","code":""},{"path":"https://NIEHS.github.io/chopin/CONTRIBUTING.html","id":"code-style","dir":"","previous_headings":"Bigger changes","what":"Code style","title":"Contributing to chopin","text":"Please abide 80-character line rules (default settings lintr) use roxygen2, Markdown syntax, documentation. use testthat unit tests. Contributions test cases included easier accept.","code":""},{"path":"https://NIEHS.github.io/chopin/CONTRIBUTING.html","id":"code-of-conduct","dir":"","previous_headings":"","what":"Code of Conduct","title":"Contributing to chopin","text":"Please note chopin project released Contributor Code Conduct. contributing project agree abide terms.","code":""},{"path":"https://NIEHS.github.io/chopin/LICENSE.html","id":null,"dir":"","previous_headings":"","what":"MIT License","title":"MIT License","text":"Copyright (c) 2024 chopin authors Permission hereby granted, free charge, person obtaining copy software associated documentation files (“Software”), deal Software without restriction, including without limitation rights use, copy, modify, merge, publish, distribute, sublicense, /sell copies Software, permit persons Software furnished , subject following conditions: copyright notice permission notice shall included copies substantial portions Software. SOFTWARE PROVIDED “”, WITHOUT WARRANTY KIND, EXPRESS IMPLIED, INCLUDING LIMITED WARRANTIES MERCHANTABILITY, FITNESS PARTICULAR PURPOSE NONINFRINGEMENT. EVENT SHALL AUTHORS COPYRIGHT HOLDERS LIABLE CLAIM, DAMAGES LIABILITY, WHETHER ACTION CONTRACT, TORT OTHERWISE, ARISING , CONNECTION SOFTWARE USE DEALINGS SOFTWARE.","code":""},{"path":"https://NIEHS.github.io/chopin/articles/v01_start.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Getting started with chopin","text":"chopin automatically distributes geospatial data computation multiple threads. Function centered workflow streamline process parallelizing geospatial computation minimal effort.","code":""},{"path":"https://NIEHS.github.io/chopin/articles/v01_start.html","id":"chopin-workflow","dir":"Articles","previous_headings":"","what":"chopin workflow","title":"Getting started with chopin","text":"simplest way parallelizing generic geospatial computation start par_pad_* functions par_grid, running par_hierarchy, par_multirasters functions .","code":"library(chopin) library(terra) library(sf) library(collapse) library(dplyr) library(future) library(future.mirai) library(future.apply)"},{"path":"https://NIEHS.github.io/chopin/articles/v01_start.html","id":"example-data","dir":"Articles","previous_headings":"","what":"Example data","title":"Getting started with chopin","text":"North Carolinian counties raster elevation data used example data.","code":"nccnty_path <- system.file(\"extdata\", \"nc_hierarchy.gpkg\", package = \"chopin\") ncelev_path <-   system.file(\"extdata/nc_srtm15_otm.tif\", package = \"chopin\") nccnty <- terra::vect(nccnty_path)  ncelev <- terra::rast(ncelev_path)"},{"path":"https://NIEHS.github.io/chopin/articles/v01_start.html","id":"generating-random-points-in-north-carolina","dir":"Articles","previous_headings":"Example data","what":"Generating random points in North Carolina","title":"Getting started with chopin","text":"demonstrate chopin functions, generate 10,000 random points North Carolina","code":"ncsamp <-   terra::spatSample(     nccnty,     1e4L   ) ncsamp$pid <- 1:nrow(ncsamp)"},{"path":"https://NIEHS.github.io/chopin/articles/v01_start.html","id":"creating-grids","dir":"Articles","previous_headings":"","what":"Creating grids","title":"Getting started with chopin","text":"example generate regular grid random point data.","code":"ncgrid <- par_pad_grid(ncsamp, mode = \"grid\", nx = 4L, ny = 2L, padding = 10000)  plot(ncgrid$original)"},{"path":"https://NIEHS.github.io/chopin/articles/v01_start.html","id":"extracting-values-from-raster","dir":"Articles","previous_headings":"","what":"Extracting values from raster","title":"Getting started with chopin","text":"Since par_* functions operate future backends, users define future plan running functions. multicore plan supports terra objects may lead faster computation, supported Windows. alternative future.mirai’s mirai_multisession plan, supported many platforms generally faster plain future multisession plan. workers argument defined integer value specify number threads used. dispatch multiple extract_at runs grid polygons. proceed, terra object converted sf object.","code":"future::plan(future.mirai::mirai_multisession, workers = 2L) pg <-   par_grid(     grids = ncgrid,     pad_y = FALSE,     .debug = TRUE,     fun_dist = extract_at,     x = ncelev_path,     y = sf::st_as_sf(ncsamp),     id = \"pid\",     radius = 1e4,     func = \"mean\"   )"},{"path":"https://NIEHS.github.io/chopin/articles/v01_start.html","id":"hierarchical-processing","dir":"Articles","previous_headings":"","what":"Hierarchical processing","title":"Getting started with chopin","text":"demonstrate hierarchical processing random points using census tract polygons. example parallelize summarizing mean elevation 10 kilometers circular buffers random sample points first five characters census tract unique identifiers, county codes. example demonstrates hierarchy can defined given polygons unique identifiers suitably formatted defining hierarchy.","code":"nccnty <- sf::st_read(nccnty_path, layer = \"county\") ## Reading layer `county' from data source  ##   `/home/runner/work/_temp/Library/chopin/extdata/nc_hierarchy.gpkg'  ##   using driver `GPKG' ## Simple feature collection with 100 features and 1 field ## Geometry type: POLYGON ## Dimension:     XY ## Bounding box:  xmin: 1054155 ymin: 1341756 xmax: 1838923 ymax: 1690176 ## Projected CRS: NAD83 / Conus Albers nctrct <- sf::st_read(nccnty_path, layer = \"tracts\") ## Reading layer `tracts' from data source  ##   `/home/runner/work/_temp/Library/chopin/extdata/nc_hierarchy.gpkg'  ##   using driver `GPKG' ## Simple feature collection with 2672 features and 1 field ## Geometry type: MULTIPOLYGON ## Dimension:     XY ## Bounding box:  xmin: 1054155 ymin: 1341756 xmax: 1838923 ymax: 1690176 ## Projected CRS: NAD83 / Conus Albers px <-   par_hierarchy(     # from here the par_hierarchy-specific arguments     regions = nctrct,     regions_id = \"GEOID\",     length_left = 5,     pad = 10000,     pad_y = FALSE,     .debug = TRUE,     # from here are the dispatched function definition     # for parallel workers     fun_dist = extract_at,     # below should follow the arguments of the dispatched function     x = ncelev,     y = sf::st_as_sf(ncsamp),     id = \"pid\",     radius = 1e4,     func = \"mean\"   )  dim(px) ## [1] 10000     2 head(px) ##   pid      mean ## 1  79 13.664886 ## 2 198  7.980108 ## 3 337 12.189816 ## 4 454  2.763836 ## 5 467 19.170189 ## 6 485 13.523435 tail(px) ##        pid      mean ## 9995  9542 -1.184563 ## 9996  9703  5.548240 ## 9997  9756  5.548804 ## 9998  9759  6.540437 ## 9999  9841  6.414054 ## 10000 9963  4.990165"},{"path":"https://NIEHS.github.io/chopin/articles/v01_start.html","id":"multiraster-processing","dir":"Articles","previous_headings":"","what":"Multiraster processing","title":"Getting started with chopin","text":"demonstrate multiraster processing random points using multiple rasters.","code":"ncelev <-   system.file(\"extdata/nc_srtm15_otm.tif\", package = \"chopin\") ncelev <- terra::rast(ncelev) tdir <- tempdir(check = TRUE) terra::writeRaster(ncelev, file.path(tdir, \"test1.tif\"), overwrite = TRUE) terra::writeRaster(ncelev, file.path(tdir, \"test2.tif\"), overwrite = TRUE) terra::writeRaster(ncelev, file.path(tdir, \"test3.tif\"), overwrite = TRUE) terra::writeRaster(ncelev, file.path(tdir, \"test4.tif\"), overwrite = TRUE) terra::writeRaster(ncelev, file.path(tdir, \"test5.tif\"), overwrite = TRUE)  rasts <- list.files(tdir, pattern = \"tif$\", full.names = TRUE)  pm <-   par_multirasters(     filenames = rasts,     fun_dist = extract_at,     x = NA,     y = sf::st_as_sf(ncsamp)[1:500, ],     id = \"pid\",     radius = 1e4,     func = \"mean\",     .debug = TRUE   )  dim(pm) ## [1] 2500    2 head(pm) ##          mean               base_raster ## 1   12.443567 /tmp/Rtmpe09POE/test1.tif ## 2   74.934341 /tmp/Rtmpe09POE/test1.tif ## 3  179.088806 /tmp/Rtmpe09POE/test1.tif ## 4    8.542515 /tmp/Rtmpe09POE/test1.tif ## 5 1087.089722 /tmp/Rtmpe09POE/test1.tif ## 6  110.032043 /tmp/Rtmpe09POE/test1.tif tail(pm) ##            mean               base_raster ## 2495 101.370438 /tmp/Rtmpe09POE/test5.tif ## 2496  -2.881788 /tmp/Rtmpe09POE/test5.tif ## 2497   7.347102 /tmp/Rtmpe09POE/test5.tif ## 2498   8.790645 /tmp/Rtmpe09POE/test5.tif ## 2499  21.443277 /tmp/Rtmpe09POE/test5.tif ## 2500  56.097313 /tmp/Rtmpe09POE/test5.tif"},{"path":"https://NIEHS.github.io/chopin/articles/v01_start.html","id":"why-parallelization-is-slower-than-the-ordinary-function-run","dir":"Articles","previous_headings":"","what":"Why parallelization is slower than the ordinary function run?","title":"Getting started with chopin","text":"Parallelization may underperform datasets small take advantage divide--compute approach, parallelization overhead involved. Overhead refers required amount computational resources transferring objects multiple processes. Since demonstrations use quite small datasets, advantage parallelization noticeable expected. large amount data (spatial/temporal resolution number files, example) processed, users find efficiency package. vignette package demonstrates use cases extracting various climate/weather datasets.","code":""},{"path":"https://NIEHS.github.io/chopin/articles/v02_good_practice.html","id":"assumptions","dir":"Articles","previous_headings":"","what":"Assumptions","title":"Good practice of using `chopin` in HPC","text":"Users can access multi-threaded central processing units sufficient memory (.e., least 4GB/thread) Single-node calculation: version 0.3.0, chopin supports single-node","code":""},{"path":[]},{"path":"https://NIEHS.github.io/chopin/articles/v02_good_practice.html","id":"minimize-errors","dir":"Articles","previous_headings":"Basic workflow","what":"Minimize errors","title":"Good practice of using `chopin` in HPC","text":"Consider using try() tryCatch() let error halt work without results, especially using higher-level functions","code":""},{"path":"https://NIEHS.github.io/chopin/articles/v02_good_practice.html","id":"raster-vector-overlay","dir":"Articles","previous_headings":"Basic workflow","what":"Raster-Vector overlay","title":"Good practice of using `chopin` in HPC","text":"Use chopin::par_pad_grid make exhaustive padded grid objects SpatVector class chopin::par_grid filters features intersect grid. Post-processing necessary polygons may taken twice parallelized calculation. users field containing divisible hierarchical information, dataset can processed chopin::par_hierarchy.","code":""},{"path":"https://NIEHS.github.io/chopin/articles/v02_good_practice.html","id":"customization","dir":"Articles","previous_headings":"Basic workflow","what":"Customization","title":"Good practice of using `chopin` in HPC","text":"chopin way parallelize iterate calculation list use future.apply function extent vector element Preprocessed vector objects respect extent vector first list Preferably lists named Use file database table paths instead SpatRaster objects future parallel processing workflow","code":""},{"path":"https://NIEHS.github.io/chopin/articles/v02_good_practice.html","id":"save-computing-costs","dir":"Articles","previous_headings":"Basic workflow","what":"Save computing costs","title":"Good practice of using `chopin` in HPC","text":"HPC systems CPU-memory usage quota users profvis::profvis() helps summarize runtime per function call memory usage testing, use future::plan(future::sequential) see single-thread. Keep chopin::par_pad_grid arguments take one two grids using lapply. Preferably hardest case tested estimate maximum peak memory usage per thread. Submit job proper amount computational assets: HPC management system offers tools see statistics task summaries. Use records plan asset allocation next time.","code":""},{"path":"https://NIEHS.github.io/chopin/articles/v03_par_pad_grid.html","id":"computational-grids","dir":"Articles","previous_headings":"","what":"Computational grids","title":"Generate computational grids","text":"Computational grids refer set polygons cover entire spatial domain interest. grids used split spatial domain smaller pieces parallel processing. chopin provides functions generate computational grids parallel processing par_grid() function. Generate computational grids par_pad_grid() par_pad_balanced(). step give set grid polygons, one original splits padded consideration buffer radius subsequent spatial operations. Run par_grid() input dataset grid polygons. Input data: data computed value stored. example, extracting raster values vector objects, vector objects input data. Target data: data values come. example , raster object target data. Original padded grids used split main input original grid target dataset computation padded grid, respectively.","code":""},{"path":"https://NIEHS.github.io/chopin/articles/v03_par_pad_grid.html","id":"types-of-computational-grids-and-their-generation","dir":"Articles","previous_headings":"","what":"Types of computational grids and their generation","title":"Generate computational grids","text":"two approaches generate computational grids. One use par_pad_grid() one three modes use par_group_grid(). Thus, users four options total generate computational grids. padding argument important ensure accurate parallel operations case buffering involved. Suppose user point geometry inputs apply circular buffer certain radius. Since original grid (without padding = overlap adjacent grids) filters original input parallel worker, points near grid border may miss target data buffered polygons exceed grid.","code":""},{"path":"https://NIEHS.github.io/chopin/articles/v03_par_pad_grid.html","id":"par_pad_grid-standard-interface","dir":"Articles","previous_headings":"Types of computational grids and their generation","what":"par_pad_grid(): standard interface","title":"Generate computational grids","text":"par_pad_grid() generates regular grid polygons padding parallel processing. Padding distance overlapping grid polygons, essentially buffer radius points buffer polygons concerned. mode = \"grid\": generates regular grid polygons padding, nx ny arguments determine number columns rows grid, respectively. mode = \"grid_quantile\": generates regular grid polygons padding based quantiles number points grid. grids look irregular points per grid balanced grid mode. mode = \"grid_advanced\": generates regular grid polygons padding based number points grid number points entire dataset. nx ny arguments determine number columns rows grid, merge_max argument controls many adjacent grids merged one grid. grid_min_features argument determines minimum number points grid, means grids fewer points value merged adjacent grids. Adjusting arguments can balance computational load among threads reduce overhead parallelization.","code":""},{"path":"https://NIEHS.github.io/chopin/articles/v03_par_pad_grid.html","id":"par_pad_balanced-focusing-on-getting-the-balanced-clusters","dir":"Articles","previous_headings":"Types of computational grids and their generation","what":"par_pad_balanced(): focusing on getting the balanced clusters","title":"Generate computational grids","text":"par_pad_balanced() groups inputs equal size, generates padded rectangles cover number points per grid. Users can use output function par_grid() parallel processing.","code":""},{"path":"https://NIEHS.github.io/chopin/articles/v03_par_pad_grid.html","id":"random-points-in-nc","dir":"Articles","previous_headings":"Types of computational grids and their generation","what":"Random points in NC","title":"Generate computational grids","text":"demonstration par_pad_grid(), use moderately clustered point locations generated inside counties North Carolina.","code":"ncpoly <- system.file(\"shape/nc.shp\", package = \"sf\") ncsf <- sf::read_sf(ncpoly) ncsf <- sf::st_transform(ncsf, \"EPSG:5070\") plot(sf::st_geometry(ncsf)) # sampling clustered point library(spatstat.random) ## Loading required package: spatstat.data ## Loading required package: spatstat.univar ## spatstat.univar 3.0-0 ## Loading required package: spatstat.geom ## spatstat.geom 3.3-2 ##  ## Attaching package: 'spatstat.geom' ## The following objects are masked from 'package:igraph': ##  ##     diameter, edges, is.connected, vertices ## The following objects are masked from 'package:terra': ##  ##     area, delaunay, is.empty, rescale, rotate, shift, where.max, ##     where.min ## spatstat.random 3.3-1 set.seed(202404) ncpoints <-   sf::st_sample(     x = ncsf,     type = \"Thomas\",     mu = 20,     scale = 1e4,     kappa = 1.25e-9   ) ncpoints <- ncpoints[seq_len(2e3L), ]  ncpoints <- sf::st_as_sf(ncpoints) ncpoints <- sf::st_set_crs(ncpoints, \"EPSG:5070\") ncpoints$pid <- sprintf(\"PID-%05d\", seq(1, nrow(ncpoints))) plot(sf::st_geometry(ncpoints)) # convert to terra SpatVector ncpoints_tr <- terra::vect(ncpoints)"},{"path":"https://NIEHS.github.io/chopin/articles/v03_par_pad_grid.html","id":"visualize-computational-grids","dir":"Articles","previous_headings":"","what":"Visualize computational grids","title":"Generate computational grids","text":"output par_pad_grid() par_pad_balanced() length 2 list. significant difference two first element output. par_pad_grid(), always sf SpatVector object polygon geometries. hand, par_pad_balanced() first element sf SpatVector object geometry type original input. example, point geometries input point.","code":"compregions <-   chopin::par_pad_grid(     ncpoints_tr,     mode = \"grid\",     nx = 8L,     ny = 5L,     padding = 1e4L   )  # a list object class(compregions) ## [1] \"list\" # length of 2 names(compregions) ## [1] \"original\" \"padded\" par(mfrow = c(2, 1)) plot(compregions$original, main = \"Original grids\") plot(compregions$padded, main = \"Padded grids\")"},{"path":"https://NIEHS.github.io/chopin/articles/v03_par_pad_grid.html","id":"generate-regular-grid-computational-regions","dir":"Articles","previous_headings":"Visualize computational grids","what":"Generate regular grid computational regions","title":"Generate computational grids","text":"chopin::par_pad_grid() takes spatial dataset generate regular grid polygons nx ny arguments padding. Users overlapping (degree radius) non-overlapping grids, utilized split locations target datasets sub-datasets efficient processing. output par_pad_grid() list object two elements named original (non-overlapping grid polygons) padded (overlapping padding). class element depends input dataset class. figures illustrate grid polygons without overlaps.","code":"compregions <-   chopin::par_pad_grid(     ncpoints_tr,     mode = \"grid\",     nx = 8L,     ny = 5L,     padding = 1e4L   ) names(compregions) ## [1] \"original\" \"padded\" oldpar <- par() par(mfrow = c(2, 1)) terra::plot(compregions$original, main = \"Original grids\") terra::plot(compregions$padded, main = \"Padded grids\") par(mfrow = c(1, 1)) terra::plot(compregions$original, main = \"Original grids\") terra::plot(ncpoints_tr, add = TRUE, col = \"red\", cex = 0.4)"},{"path":"https://NIEHS.github.io/chopin/articles/v03_par_pad_grid.html","id":"split-the-points-by-two-1d-quantiles","dir":"Articles","previous_headings":"Visualize computational grids","what":"Split the points by two 1D quantiles","title":"Generate computational grids","text":"mode = \"grid_quantile\" generates regular grid polygons padding based quantiles coordinates dimension. using mode, users define quantiles argument, used get number quantiles dimension. convenient way define seq() function length.argument. example uses length.= 5, give quartiles.","code":"grid_quantiles <-   chopin::par_pad_grid(     input = ncpoints_tr,     mode = \"grid_quantile\",     quantiles = seq(0, 1, length.out = 5),     padding = 1e4L   )  names(grid_quantiles) ## [1] \"original\" \"padded\" par(mfrow = c(2, 1)) terra::plot(grid_quantiles$original, main = \"Original grids\") terra::plot(grid_quantiles$padded, main = \"Padded grids\") par(mfrow = c(1, 1)) terra::plot(grid_quantiles$original, main = \"Original grids\") terra::plot(ncpoints_tr, add = TRUE, col = \"red\", cex = 0.4)"},{"path":"https://NIEHS.github.io/chopin/articles/v03_par_pad_grid.html","id":"merge-the-grids-based-on-the-number-of-points","dir":"Articles","previous_headings":"Visualize computational grids","what":"Merge the grids based on the number of points","title":"Generate computational grids","text":"mode = \"grid_advanced\" utilizes finer grids merge results finer grids coarser grids. behavior can balance computational load among threads reduce overhead parallelization. said, mode internally generates grids mode = \"grid\" merges based number points grid. determine adjacency merging behavior, minimum spanning tree (MST) identified. function utilizes igraph::mst() MST identification graph summary functions hood. note, users can adjust merging behavior changing arguments grid_min_features merge_max.","code":"grid_advanced1 <-   chopin::par_pad_grid(     input = ncpoints_tr,     mode = \"grid_advanced\",     nx = 15L,     ny = 8L,     padding = 1e4L,     grid_min_features = 25L,     merge_max = 5L   ) ## Switch terra class to sf... ## Switch terra class to sf... ## ℹ The merged polygons have too complex shapes. ## Increase threshold or use the original grids. ##  ## Switch sf class to terra... par(mfrow = c(2, 1)) terra::plot(grid_advanced1$original, main = \"Original grids\") terra::plot(grid_advanced1$padded, main = \"Padded grids\") par(mfrow = c(1, 1)) terra::plot(grid_advanced1$original, main = \"Merged grids (merge_max = 5)\") terra::plot(ncpoints_tr, add = TRUE, col = \"red\", cex = 0.4) ncpoints_tr$n <- 1 n_points <-   terra::zonal(     ncpoints_tr,     grid_advanced1$original,     fun = \"sum\")[[\"n\"]]  grid_advanced1g <- grid_advanced1$original grid_advanced1g$n_points <- n_points  terra::plot(grid_advanced1g, \"n_points\", main = \"Number of points in each grid\")"},{"path":"https://NIEHS.github.io/chopin/articles/v03_par_pad_grid.html","id":"different-values-in-merge_max","dir":"Articles","previous_headings":"Visualize computational grids > Merge the grids based on the number of points","what":"Different values in merge_max","title":"Generate computational grids","text":"Keeping arguments , can see difference number merged grids changing merge_max argument.","code":"grid_advanced2 <-   chopin::par_pad_grid(     input = ncpoints_tr,     mode = \"grid_advanced\",     nx = 15L,     ny = 8L,     padding = 1e4L,     grid_min_features = 15L,     merge_max = 4L   ) ## Switch terra class to sf... ## Switch terra class to sf... ## ℹ The merged polygons have too complex shapes. ## Increase threshold or use the original grids. ##  ## Switch sf class to terra... par(mfrow = c(2, 1)) terra::plot(grid_advanced2$original, main = \"Original grids\") terra::plot(grid_advanced2$padded, main = \"Padded grids\") par(mfrow = c(1, 1)) terra::plot(grid_advanced2$original, main = \"Merged grids (merge_max = 8)\") terra::plot(ncpoints_tr, add = TRUE, col = \"red\", cex = 0.4) grid_advanced3 <-   chopin::par_pad_grid(     input = ncpoints_tr,     mode = \"grid_advanced\",     nx = 15L,     ny = 8L,     padding = 1e4L,     grid_min_features = 25L,     merge_max = 3L   ) ## Switch terra class to sf... ## Switch terra class to sf... ## ℹ The merged polygons have too complex shapes. ## Increase threshold or use the original grids. ##  ## Switch sf class to terra... par(mfrow = c(2, 1)) terra::plot(grid_advanced3$original, main = \"Original grids\") terra::plot(grid_advanced3$padded, main = \"Padded grids\") par(mfrow = c(1, 1)) terra::plot(grid_advanced3$original, main = \"Merged grids (merge_max = 3)\") terra::plot(ncpoints_tr, add = TRUE, col = \"red\", cex = 0.4)"},{"path":"https://NIEHS.github.io/chopin/articles/v03_par_pad_grid.html","id":"par_make_balanced","dir":"Articles","previous_headings":"Visualize computational grids > Merge the grids based on the number of points","what":"par_make_balanced()","title":"Generate computational grids","text":"background, par_pad_balanced() run first generate equally sized clusters input. , padding applied extent cluster compatible par_grid(), original padded grids used. Please note ngroups argument value must exact divisor number points. example, example , one changes ngroups 10L, fail number points divisible 10. Consult anticlust package details algorithm. par_pad_balanced() makes compatible object output par_pad_grid() directly input points. illustrated figure , points split ngroups clusters number points processed parallel using output object par_grid().","code":"# ngroups should be the exact divisor of the number of points! group_bal_grid <-   chopin::par_pad_balanced(     points_in = ncpoints_tr,     ngroups = 10L,     padding = 1e4   ) group_bal_grid$original$CGRIDID <- as.factor(group_bal_grid$original$CGRIDID)  par(mfrow = c(2, 1)) terra::plot(group_bal_grid$original, \"CGRIDID\",             legend = FALSE,             main = \"Assigned points (ngroups = 10)\") terra::plot(group_bal_grid$padded, main = \"Padded grids\")"},{"path":"https://NIEHS.github.io/chopin/articles/v04_climate_examples.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Extracting Weather/Climate Geospatial Data with `chopin`","text":"document demonstrates parallelize weather/climate geospatial data processing chopin cases users may take advantage parallel processing . cover following formats: consider TerraClimate PRISM data data format . Parameter-elevation Regressions Independent Slopes Model (PRISM) high-resolution (800-1,000 meters) gridded climate dataset available BIL (band interleaved line) format readable GDAL. TerraClimate high-resolution (5 km) gridded climate dataset NetCDF format readable GDAL. spatial resolution TerraClimate data commensurates 5 km equator, whereas PRISM data approximately 1 km. data available NetCDF format readable GDAL. use terra package read data.","code":""},{"path":"https://NIEHS.github.io/chopin/articles/v04_climate_examples.html","id":"prepare-target-datasets","dir":"Articles","previous_headings":"Introduction","what":"Prepare target datasets","title":"Extracting Weather/Climate Geospatial Data with `chopin`","text":"consider populated places centroids mainland United States (.e., excluding Alaska, Hawaii, territories). use tigris package download data.","code":"pkgs <-   c(\"chopin\", \"terra\", \"stars\", \"sf\",     \"future\", \"doFuture\", \"dplyr\", \"parallelly\", \"tigris\", \"tictoc\") invisible(sapply(pkgs, library, character.only = TRUE, quietly = TRUE)) options(tigris_use_cache = TRUE, sf_use_s2 = FALSE)"},{"path":"https://NIEHS.github.io/chopin/articles/v04_climate_examples.html","id":"hardware-specification","dir":"Articles","previous_headings":"Introduction","what":"Hardware specification","title":"Extracting Weather/Climate Geospatial Data with `chopin`","text":"used machine 112 physical CPU cores 750 GB memory, used portion cores (20) demonstration. maximum possible memory usage set. However, typical HPC management systems, users required request number cores memory jobs. number cores memory capacity considered users parallelize extraction process.","code":""},{"path":[]},{"path":"https://NIEHS.github.io/chopin/articles/v04_climate_examples.html","id":"download","dir":"Articles","previous_headings":"Introduction > Download and preprocess","what":"Download","title":"Extracting Weather/Climate Geospatial Data with `chopin`","text":"","code":"# populated places state <- tigris::states(year = 2022) statemain <-   state[!state$STUSPS %in% c(\"AK\", \"HI\", \"PR\", \"VI\", \"GU\", \"MP\", \"AS\"), ] target_states <- statemain$GEOID  popplace <-   lapply(target_states, function(x) tigris::places(x, year = 2022)) %>%   do.call(rbind, .) saveRDS(popplace, \"./input/populated_place_2022.rds\", compress = \"xz\")"},{"path":"https://NIEHS.github.io/chopin/articles/v04_climate_examples.html","id":"read","dir":"Articles","previous_headings":"Introduction > Download and preprocess","what":"Read","title":"Extracting Weather/Climate Geospatial Data with `chopin`","text":"","code":"state <- tigris::states(year = 2022) statemain <-   state[!state$STUSPS %in% c(\"AK\", \"HI\", \"PR\", \"VI\", \"GU\", \"MP\", \"AS\"), ] target_states <- statemain$GEOID  # prepared populated places popplace <- readRDS(\"./input/populated_place_2022.rds\")  # generate circular buffers with 10 km radius popplacep <- sf::st_centroid(popplace, of_largest_polygon = TRUE) %>%   sf::st_transform(\"EPSG:5070\") popplacep2 <- sf::st_centroid(popplace, of_largest_polygon = TRUE) popplaceb <- sf::st_buffer(popplacep, dist = units::set_units(10, \"km\"))"},{"path":"https://NIEHS.github.io/chopin/articles/v04_climate_examples.html","id":"terraclimate","dir":"Articles","previous_headings":"","what":"TerraClimate","title":"Extracting Weather/Climate Geospatial Data with `chopin`","text":"TerraClimate data provided yearly NetCDF files, contains monthly layers. read data terra package preprocess data extract annual mean sum bands types columns.","code":"# wbd ext_mainland <- c(xmin = -126, xmax = -64, ymin = 22, ymax = 51) ext_mainland <- terra::ext(ext_mainland)  path_tc <- file.path(\"input\", \"terraClimate\") path_tc_files <- list.files(   path = path_tc, pattern = \"*.nc$\",   full.names = TRUE ) path_tc_files #>  [1] \"input/terraClimate/TerraClimate_aet_2000.nc\"  #>  [2] \"input/terraClimate/TerraClimate_aet_2001.nc\" #>  [truncated] #> [321] \"input/terraClimate/TerraClimate_ws_2021.nc\"   #> [322] \"input/terraClimate/TerraClimate_ws_2022.nc\""},{"path":"https://NIEHS.github.io/chopin/articles/v04_climate_examples.html","id":"preprocessing","dir":"Articles","previous_headings":"TerraClimate","what":"Preprocessing","title":"Extracting Weather/Climate Geospatial Data with `chopin`","text":"Fourteen variables available TerraClimate data. table TerraClimate website. variables can categorized two types: summed averaged. Sum: aet, def, pet, ppt, q, soil, swe. Mean: PDSI, srad, tmax, tmin, vap, vpd, ws. Following rationale, preprocess data summing first seven layers averaging rest layers. code blocks follow “split-apply-combine” strategy. Note terra::tapp aggregates layers attributes time custom indices. !WARNING!WARNING Stacking raster data may take long time consume large amount memory depending users’ area interest data resolution. Users need consider memory capacity system stacking rasters. 14 data elements 23 years 12 months . demonstrates summary data layers averaged circular buffer polygons 10 kilometers (10,000 meters) radius. leverage multiple cores machine, run future::availableCores() check number cores set number workers future::plan accordingly. Typically, two logical cores physical core modern central processing units. number workers set number physical cores machine optimal performance. example uses future::multicore shares memory across workers. !CAUTION!CAUTION Windows users RStudio users (platforms) able use future::multicore due restriction future package. Please future::multisession instead note option runs slower future::multicore case.","code":"options(future.globals = FALSE) # some bands should be summed bandnames <- c(   \"aet\", \"def\", \"PDSI\", \"pet\", \"ppt\", \"q\", \"soil\", \"srad\",   \"swe\", \"tmax\", \"tmin\", \"vap\", \"vpd\", \"ws\" ) bandnames_sorted <- sort(bandnames)  # single nc file, yearly aggregation by fun value # band for summation bandnames_sum <- c(\"aet\", \"def\", \"pet\", \"ppt\", \"q\", \"soil\", \"swe\")  # band for averaging bandnames_avg <- c(\"PDSI\", \"srad\", \"tmax\", \"tmin\", \"vap\", \"vpd\", \"ws\")  # mean: temporally marginal pixel mean (i.e., monthly -> yearly) # sum: temporally marginal pixel sum (i.e., monthly -> yearly) # Preprocessed data are stored in tictoc::tic(\"sum: 7 layers\") netcdf_read_sum <-   split(bandnames_sum, bandnames_sum) |>   lapply(function(x) {     grep(paste0(\"(\", x, \")\"), path_tc_files, value = TRUE)   }) |>   lapply(function(x) {     terra::tapp(terra::rast(x, win = ext_mainland, snap = \"out\"), index = \"years\", fun = \"sum\")   }) netcdf_read_sum <- Reduce(c, netcdf_read_sum) tictoc::toc() #> sum: 7 layers: 177.974 sec elapsed names(netcdf_read_sum) <- paste0(rep(bandnames_sum, each = 23), \"_\", rep(2000:2022, 7)) netcdf_read_sum #> class       : SpatRaster  #> dimensions  : 696, 1488, 161  (nrow, ncol, nlyr) #> resolution  : 0.04166667, 0.04166667  (x, y) #> extent      : -126, -64, 22, 51  (xmin, xmax, ymin, ymax) #> coord. ref. : +proj=longlat +ellps=WGS84 +no_defs  #> source(s)   : memory #> names       : aet_2000,  aet_2001,  aet_2002,  aet_2003, ...  #> min values  :     22.9,      27.3,        11,      30.8, ...  #> max values  :   1270.9,    1366.6,      1399,    1411.1, ...  #> time (years):   2000 to 2022 # tictoc::tic(\"mean: 7 layers\") # netcdf_read_mean <- #   split(bandnames_avg, bandnames_avg) |> #   lapply(function(x) { #     grep(paste0(\"(\", x, \")\"), path_tc_files, value = TRUE) #   }) |> #   lapply(function(x) { #     terra::tapp(terra::rast(x, win = ext_mainland, snap = \"out\"), index = \"years\", fun = \"mean\") #   }) |> #   Reduce(f = c, x = _) # tictoc::toc()  # names(netcdf_read_mean) <- #   sprintf(\"%s_%d\", rep(bandnames_avg, each = 23), rep(2000:2022, 7)) # netcdf_read_mean tic(\"multi threads (grid)\") doFuture::registerDoFuture() future::plan(future::multicore, workers = 8L) grid_init <-   chopin::par_pad_grid(     popplacep2,     mode = \"grid\",     padding = 1e4,     nx = 4L,     ny = 2L   ) multi_grid <-   chopin::par_grid(     grids = grid_init,     fun_dist = extract_at,     y = popplacep2,     x = netcdf_read_sum,     id = \"GEOID\",     func = \"mean\",     radius = 1e4   ) toc() #> multi threads (grid): 16.668 sec elapsed system.time(   multi <-     grep(       paste0(\"(\", paste(bandnames_sum, collapse = \"|\"), \")\"),       path_tc_files,       value = TRUE     ) %>%     chopin::par_multirasters(       filenames = .,       fun_dist = extract_at,       y = popplacep2,       x = rast(), # ignored       id = \"GEOID\",       func = \"mean\",       radius = 1e4     ) ) #>     user   system  elapsed  #> 5377.495  231.654  762.147 future::plan(future::sequential)   # single thread tic(\"single thread\") single <-   exactextractr::exact_extract(     netcdf_read_sum,     sf::st_as_sf(popplaceb),     fun = \"mean\",     stack_apply = TRUE,     force_df = TRUE,     append_cols = c(\"GEOID\"),     progress = FALSE   ) toc() #> single thread: 21.161 sec elapsed"},{"path":"https://NIEHS.github.io/chopin/articles/v04_climate_examples.html","id":"prism-dataset","dir":"Articles","previous_headings":"","what":"PRISM dataset","title":"Extracting Weather/Climate Geospatial Data with `chopin`","text":"PRISM data provided monthly 30-year normal BIL files. Assuming user wants summarize 30-year normal precipitation 10 kilometers circular buffers geogrpahic centroids US Census Places (p.26), demonstrate extraction process chopin package.","code":""},{"path":[]},{"path":"https://NIEHS.github.io/chopin/articles/v04_climate_examples.html","id":"download-1","dir":"Articles","previous_headings":"PRISM dataset > Download and preprocess","what":"Download","title":"Extracting Weather/Climate Geospatial Data with `chopin`","text":"","code":"# populated places # mainland states state <- tigris::states(year = 2022) statemain <-   state[!state$STUSPS %in% c(\"AK\", \"HI\", \"PR\", \"VI\", \"GU\", \"MP\", \"AS\"), ] target_states <- statemain$GEOID  popplace <-   lapply(target_states, function(x) tigris::places(x, year = 2022)) %>%   do.call(rbind, .) saveRDS(popplace, \"./input/populated_place_2022.rds\", compress = \"xz\")"},{"path":"https://NIEHS.github.io/chopin/articles/v04_climate_examples.html","id":"read-1","dir":"Articles","previous_headings":"PRISM dataset > Download and preprocess","what":"Read","title":"Extracting Weather/Climate Geospatial Data with `chopin`","text":"!IMPORTANT!IMPORTANTchopin::par_pad_grid works best point datasets since grid clip input features parallelized. Polygon inputs result duplicate values output lead take longer complete.","code":"bils <- list.files(\"input\", \"bil$\", recursive = TRUE, full.names = TRUE) bilssds <- terra::rast(bils[-13]) popplace2 <- sf::st_transform(popplace, crs = terra::crs(bilssds)) popplaceb2 <- sf::st_transform(popplaceb, crs = terra::crs(bilssds))"},{"path":"https://NIEHS.github.io/chopin/articles/v04_climate_examples.html","id":"grid-parallelization","dir":"Articles","previous_headings":"PRISM dataset","what":"Grid parallelization","title":"Extracting Weather/Climate Geospatial Data with `chopin`","text":"vein TerraClimate data, use chopin package parallelize extraction process grid strategy.","code":"exgrid <-   chopin::par_pad_grid(     popplacep2,     mode = \"grid\",     padding = 1e4,     nx = 4L,     ny = 2L   )  doFuture::registerDoFuture() future::plan(future::multicore, workers = 8L)  system.time(   exmulti <-     chopin::par_grid(       exgrid,       fun_dist = extract_at,       y = popplacep2,       x = bilssds,       radius = units::set_units(1e4, \"meter\"),       id = \"GEOID\",       func = \"mean\"     ) ) #> Your input function was successfully run at CGRIDID: 1 #> Your input function was successfully run at CGRIDID: 2 #> Your input function was successfully run at CGRIDID: 3 #> Your input function was successfully run at CGRIDID: 4 #> Your input function was successfully run at CGRIDID: 5 #> Your input function was successfully run at CGRIDID: 6 #> Your input function was successfully run at CGRIDID: 7 #> Your input function was successfully run at CGRIDID: 8 #>    user  system elapsed  #>  33.090  13.254  14.571 system.time(   exsingle <-     exactextractr::exact_extract(       bilssds,       popplaceb2,       fun = \"mean\",       stack_apply = TRUE,       force_df = TRUE,       append_cols = \"GEOID\",       max_cells_in_memory = 2.14e9,       progress = FALSE     ) ) #>    user  system elapsed  #>  19.347   1.927  21.716 future::plan(future::sequential)"},{"path":[]},{"path":"https://NIEHS.github.io/chopin/articles/v04_climate_examples.html","id":"larger-buffer-sizes","dir":"Articles","previous_headings":"Scaled up examples","what":"Larger buffer sizes","title":"Extracting Weather/Climate Geospatial Data with `chopin`","text":"Examples showed difference parallelized single-threaded extraction process significant. increase buffer size 50 kilometers compare performance parallelized single-threaded extraction process. !NOTE!NOTE example used strings raster file paths surf argument chopin::extract_at_buffer. terra::rast multiple file paths return SpatRaster multiple layers rasters extent resolution.","code":"# make buffers popplaceb5 <- sf::st_buffer(popplacep, dist = units::set_units(50, \"km\")) %>%   sf::st_transform(terra::crs(bilssds))  system.time(   exsingle5 <-     exactextractr::exact_extract(       bilssds,       popplaceb5,       fun = \"mean\",       stack_apply = TRUE,       force_df = TRUE,       append_cols = \"GEOID\",       max_cells_in_memory = 2.14e9,       progress = FALSE     ) ) #>    user  system elapsed  #> 140.373   2.302 144.552 exgrid5k <-   chopin::par_pad_grid(     popplacep2,     mode = \"grid\",     padding = 5e4,     nx = 4L,     ny = 2L   )   doFuture::registerDoFuture() future::plan(future::multicore, workers = 8L)  system.time(   exmulti5k <-     chopin::par_grid(       exgrid5k,       fun_dist = extract_at,       y = popplacep2,       x = bilssds,       radius = 5e4,       id = \"GEOID\",       func = \"mean\"     ) ) #> Your input function was successfully run at CGRIDID: 1 #> Your input function was successfully run at CGRIDID: 2 #> Your input function was successfully run at CGRIDID: 3 #> Your input function was successfully run at CGRIDID: 4 #> Your input function was successfully run at CGRIDID: 5 #> Your input function was successfully run at CGRIDID: 6 #> Your input function was successfully run at CGRIDID: 7 #> Your input function was successfully run at CGRIDID: 8 #>    user  system elapsed  #> 152.344  11.003  60.409 future::plan(future::sequential)"},{"path":"https://NIEHS.github.io/chopin/articles/v04_climate_examples.html","id":"larger-number-of-features","dir":"Articles","previous_headings":"Scaled up examples","what":"Larger number of features","title":"Extracting Weather/Climate Geospatial Data with `chopin`","text":"example uses 1,204,934 1-km grid points southeastern United States summarize seven layers TerraClimate.","code":"## generate 1km grid points in the southeastern US States stt <- tigris::states(year = 2020) targ_states <- c(\"NC\", \"SC\", \"GA\", \"FL\", \"AL\", \"MS\", \"TN\", \"LA\", \"AR\") stt_targ <- stt[stt$STUSPS %in% targ_states, ] stt_t <- sf::st_transform(stt_targ, \"EPSG:5070\") stt_g <- sf::st_sample(stt_t, type = \"regular\", 1204934) stt_g <- sf::st_as_sf(stt_g) sf::st_geometry(stt_g) <- \"geometry\" stt_g$pid <- seq(1, nrow(stt_g)) stt_gb <- sf::st_buffer(stt_g, units::set_units(10, \"km\"))  tic() single_2m <-   exactextractr::exact_extract(     netcdf_read_sum,     stt_gb,     fun = \"mean\",     stack_apply = TRUE,     force_df = TRUE,     max_cells_in_memory = 2.14e9,     progress = FALSE   ) toc() #> 855.908 sec elapsed stt_gbg <-   chopin::par_pad_grid(     stt_g,     mode = \"grid\",     padding = 5e3,     nx = 5L,     ny = 5L   )   doFuture::registerDoFuture() future::plan(future::multicore, workers = 8L) system.time(   stt5k <-     chopin::par_grid(       stt_gbg,       fun_dist = extract_at,       y = stt_g,       x = netcdf_read_sum,       id = \"pid\",       radius = 1e4,       func = \"mean\",       max_cells = 2e7     ) ) #>   user  system elapsed  #>  6.745   4.102 434.041 future::plan(future::sequential)"},{"path":"https://NIEHS.github.io/chopin/articles/v04_climate_examples.html","id":"finely-resolved-vector","dir":"Articles","previous_headings":"Scaled up examples","what":"Finely resolved vector","title":"Extracting Weather/Climate Geospatial Data with `chopin`","text":"Using PRISM data, example summarizes mean values data element census block groups.","code":""},{"path":"https://NIEHS.github.io/chopin/articles/v04_climate_examples.html","id":"download-2","dir":"Articles","previous_headings":"Scaled up examples > Finely resolved vector","what":"Download","title":"Extracting Weather/Climate Geospatial Data with `chopin`","text":"","code":"# set state=NULL and cb=TRUE will download the block groups for the entire US bg <- tigris::block_groups(state = NULL, cb = TRUE, year = 2020) sf::write_sf(bg, file.path(\"input\", \"Blockgroups_2020.gpkg\"))"},{"path":"https://NIEHS.github.io/chopin/articles/v04_climate_examples.html","id":"extract","dir":"Articles","previous_headings":"Scaled up examples > Finely resolved vector","what":"Extract","title":"Extracting Weather/Climate Geospatial Data with `chopin`","text":"","code":"## extract prism by par_hierarchy bgsf <- sf::st_read(\"input/Blockgroups_2020.gpkg\") #> Reading layer `Blockgroups_2020' from data source  #>   `/ddn/gs1/home/songi2/projects/chopin/input/Blockgroups_2020.gpkg'  #>   using driver `GPKG' #> Simple feature collection with 242298 features and 11 fields #> Geometry type: MULTIPOLYGON #> Dimension:     XY #> Bounding box:  xmin: -179.1467 ymin: -14.5487 xmax: 179.7785 ymax: 71.38782 #> Geodetic CRS:  NAD83 bgsf_main <- bgsf %>%   dplyr::filter(!STATEFP %in% c(\"02\", \"15\", \"72\", \"66\", \"78\", \"60\", \"69\"))  ## extract prism at bg system.time(   exsingle <-     exactextractr::exact_extract(       bilssds,       bgsf_main,       fun = \"mean\",       stack_apply = TRUE,       force_df = TRUE,       append_cols = \"GEOID\",       max_cells_in_memory = 2.14e9,       progress = FALSE     ) ) #>    user  system elapsed  #>  60.387   2.059  64.147 nmain <- c(\"AS\", \"AK\", \"HI\", \"PR\", \"VI\", \"GU\", \"MP\") stt_nmain <- stt[!stt$STUSPS %in% nmain, ]   future::plan(future.mirai::mirai_multisession, workers = 20L) system.time(   exhierarchy <-     chopin::par_hierarchy(       bgsf_main,       regions_id = \"STATEFP\",       fun_dist = extract_at,       y = bgsf_main,       x = bilssds,       id = \"GEOID\"     ) ) #>    user  system elapsed  #>   2.205   0.558 158.905 future::plan(future::sequential)"},{"path":"https://NIEHS.github.io/chopin/articles/v04_climate_examples.html","id":"finely-resolved-raster","dir":"Articles","previous_headings":"Scaled up examples","what":"Finely resolved raster","title":"Extracting Weather/Climate Geospatial Data with `chopin`","text":"demonstrate extraction process CropScape dataset resolution 30 meters. case, use frac option exact_extract tabulates fraction area cell category covered polygon accounting partial coverage polygon segments raster cells. balancing computation time, split block groups nine subsets parallelize. Note mode = \"grid_quantile\" used par_pad_grid balance number block groups per grid. input argument par_pad_grid polygons, polygons duplicate rows output data.frame since block groups overlapping grid selected.","code":"cdl <- terra::rast(\"input/2022_cdls/2022_30m_cdls.tif\")  system.time(   bgsf_cdl_single <-     exactextractr::exact_extract(       cdl,       bgsf_main,       fun = \"frac\",       stack_apply = TRUE,       force_df = TRUE,       append_cols = \"GEOID\",       max_cells_in_memory = 2.14e9,       progress = FALSE     ) ) #>     user   system  elapsed  #> 1013.411   44.322 1369.053 pkgs <-   c(\"chopin\", \"terra\", \"stars\", \"sf\", \"future\", \"doFuture\", \"dplyr\", \"parallelly\", \"tigris\", \"tictoc\") invisible(sapply(pkgs, library, character.only = TRUE, quietly = TRUE)) options(tigris_use_cache = TRUE, sf_use_s2 = FALSE) cdl <- terra::rast(\"input/2022_cdls/2022_30m_cdls.tif\") bgsf <- sf::st_read(\"input/Blockgroups_2020.gpkg\") bgsf_main <- bgsf %>%   dplyr::filter(!STATEFP %in% c(\"02\", \"15\", \"72\", \"66\", \"78\", \"60\", \"69\"))   # balancing the number of features assigned per workers # by splitting block groups by splitting centroids bgsf_9grids_plain <- bgsf_main %>%   chopin::par_pad_grid(     mode = \"grid\",     nx = 3L, ny = 3L, padding = 1e4   ) bgsf_9grids <- bgsf_main %>%   chopin::par_pad_grid(     mode = \"grid_quantile\",     padding = 1e4,     quantiles = chopin::par_def_q(3L)   )   future::plan(future.mirai::mirai_multisession, workers = 9L) # Note that bgsf_9grids were converted to sf # for exporting the objects to the parallel workers system.time(   bgsf_cdl_par <-     chopin::par_grid(       lapply(bgsf_9grids, sf::st_as_sf),       fun_dist = extract_at,       y = bgsf_main,       x = cdl,       id = \"GEOID\",       func = \"frac\",       max_cells = 2e7     ) ) #> Your input function was successfully run at CGRIDID: 1 #> Your input function was successfully run at CGRIDID: 2 #> Your input function was successfully run at CGRIDID: 3 #> Your input function was successfully run at CGRIDID: 4 #> Your input function was successfully run at CGRIDID: 5 #> Your input function was successfully run at CGRIDID: 6 #> Your input function was successfully run at CGRIDID: 7 #> Your input function was successfully run at CGRIDID: 8 #> Your input function was successfully run at CGRIDID: 9 #>   user  system elapsed  #>  6.632   1.821 347.638"},{"path":"https://NIEHS.github.io/chopin/articles/v04_climate_examples.html","id":"discussion-which-strategy-is-better-stacked-vs-file-based-parallelization","dir":"Articles","previous_headings":"","what":"Discussion: which strategy is better? stacked vs file-based parallelization","title":"Extracting Weather/Climate Geospatial Data with `chopin`","text":"!NOTE!NOTEIt’s users’ system specification size data. faster computation, two strategies. One parallelization implemented chopin example. strategy stack rasters extract values . Adjustment arguments exactextractr::exact_extract benefit many people dealing sizable data able stacked. compare two strategies terms computation time. proceeding, users need consider hardware specification. example, memory storage leverage maximal performance exact_extract. Specifically speaking, memory capacity crucial store stacked rasters memory rather read proxy rasters disk implemented terra. max_cells_in_memory key argument control memory usage. maximum possible value argument 231−1=2,147,483,6472^{31} - 1 = 2,147,483,647, roughly 2.147e9, applied example . memory bandwidth much faster disk input/output specification, stacked rasters high max_cells_in_memory applied run faster file-based parallelization extraction lower value max_cells_in_memory. performance come without cost. memory-intensive setting suitable system limited memory, example, consumer laptops around 16 GB RAM.","code":""},{"path":"https://NIEHS.github.io/chopin/articles/v04_climate_examples.html","id":"more-tips-to-save-time-and-memory","dir":"Articles","previous_headings":"Discussion: which strategy is better? stacked vs file-based parallelization","what":"More tips to save time and memory","title":"Extracting Weather/Climate Geospatial Data with `chopin`","text":"Always consider stacking rasters large number separate raster files resolution extent machine’s memory capacity allows. Reading multiple raster files terra::rast automatically stack single SpatRaster object. !NOTE!NOTE Stacking rasters usually takes large amount memory. Users need consider memory capacity system stacking rasters. Read vector data sf package first. bit faster terra save time processing exactextractr designed work sf objects vector inputs. users want use exactextractr raster-vector overlay, pre-cropping raster data may help saving time processing. exactextractr read raster data extent vector data ad hoc. analysis require high precision vector data, simplification geometries (e.g., using rmapshaper, .e., ms_simplify) result considerable time savings.","code":""},{"path":"https://NIEHS.github.io/chopin/articles/v04_climate_examples.html","id":"see-also","dir":"Articles","previous_headings":"Discussion: which strategy is better? stacked vs file-based parallelization","what":"See also","title":"Extracting Weather/Climate Geospatial Data with `chopin`","text":"Garnett, R. (2023). Geospatial distributed processing furrr Dyba, K. (n.d.). Parallel raster processing stars","code":""},{"path":"https://NIEHS.github.io/chopin/articles/v05_targets.html","id":"objective","dir":"Articles","previous_headings":"","what":"Objective","title":"targets and grid objects","text":"targets powerful workflow management reproducibility. chopin grid partitioning way parallelize repeated tasks across unit grids applying patterns. vignette demonstrates use targets chopin together.","code":""},{"path":"https://NIEHS.github.io/chopin/articles/v05_targets.html","id":"installation","dir":"Articles","previous_headings":"","what":"Installation","title":"targets and grid objects","text":"Despite targets referenced DESCRIPTION file, required install targets package run code vignette.","code":"rlang::check_installed(\"targets\")"},{"path":"https://NIEHS.github.io/chopin/articles/v05_targets.html","id":"example","dir":"Articles","previous_headings":"","what":"Example","title":"targets and grid objects","text":"par_pad_grid() par_pad_balanced() functions argument return_wkt return grid partition well-known text (WKT) format characters. format exported parallel workers regardless parallel backend future::multisession mirai::daemons, interoperate externalpnt objects C++ functions. Using WKT character objects, can easily convert sf terra objects inside function running parallel worker use targets workflow standard branching/patterning interface map(), cross(), others. example generate grid partition North Carolina state demonstrate use grid partition targets workflow.","code":""},{"path":"https://NIEHS.github.io/chopin/articles/v05_targets.html","id":"random-points-in-nc","dir":"Articles","previous_headings":"Example","what":"Random points in NC","title":"targets and grid objects","text":"demonstration par_pad_grid(), use moderately clustered point locations generated inside counties North Carolina.","code":"library(chopin) library(sf) library(spatstat.random)  sf::sf_use_s2(FALSE) set.seed(202404) ncpoly <- system.file(\"shape/nc.shp\", package = \"sf\") ncsf <- sf::read_sf(ncpoly) ncsf <- sf::st_transform(ncsf, \"EPSG:5070\") plot(sf::st_geometry(ncsf)) ncpoints <-   sf::st_sample(     x = ncsf,     type = \"Thomas\",     mu = 20,     scale = 1e4,     kappa = 1.25e-9   ) ncpoints <- sf::st_as_sf(ncpoints) ncpoints <- sf::st_set_crs(ncpoints, \"EPSG:5070\") ncpoints$pid <- sprintf(\"PID-%05d\", seq(1, nrow(ncpoints))) plot(sf::st_geometry(ncpoints))"},{"path":"https://NIEHS.github.io/chopin/articles/v05_targets.html","id":"grid-partition-of-nc","dir":"Articles","previous_headings":"Example","what":"Grid partition of NC","title":"targets and grid objects","text":"Since sf objects exportable parallel workers, can also consider part targets workflow.","code":"ncgrid_sf <-   par_pad_grid(     input = ncpoints,     mode = \"grid\",     nx = 6L,     ny = 3L,     padding = 1e4L,     return_wkt = FALSE   )  ncgrid_sf$original ## Simple feature collection with 18 features and 1 field ## Geometry type: POLYGON ## Dimension:     XY ## Bounding box:  xmin: 1057207 ymin: 1355826 xmax: 1830521 ymax: 1676488 ## Projected CRS: NAD83 / Conus Albers ## First 10 features: ##                          geometry CGRIDID ## 1  POLYGON ((1057207 1355826, ...       1 ## 2  POLYGON ((1186092 1355826, ...       2 ## 3  POLYGON ((1314978 1355826, ...       3 ## 4  POLYGON ((1443864 1355826, ...       4 ## 5  POLYGON ((1572750 1355826, ...       5 ## 6  POLYGON ((1701635 1355826, ...       6 ## 7  POLYGON ((1057207 1462714, ...       7 ## 8  POLYGON ((1186092 1462714, ...       8 ## 9  POLYGON ((1314978 1462714, ...       9 ## 10 POLYGON ((1443864 1462714, ...      10 ncgrid_sf$padded ## Simple feature collection with 18 features and 1 field ## Geometry type: POLYGON ## Dimension:     XY ## Bounding box:  xmin: 1047207 ymin: 1345826 xmax: 1840521 ymax: 1686488 ## Projected CRS: NAD83 / Conus Albers ## First 10 features: ##    CGRIDID                       geometry ## 1        1 POLYGON ((1047207 1345826, ... ## 2        2 POLYGON ((1176092 1345826, ... ## 3        3 POLYGON ((1304978 1345826, ... ## 4        4 POLYGON ((1433864 1345826, ... ## 5        5 POLYGON ((1562750 1345826, ... ## 6        6 POLYGON ((1691635 1345826, ... ## 7        7 POLYGON ((1047207 1452714, ... ## 8        8 POLYGON ((1176092 1452714, ... ## 9        9 POLYGON ((1304978 1452714, ... ## 10      10 POLYGON ((1433864 1452714, ... ncgrid_wkt <-   par_pad_grid(     input = ncpoints,     mode = \"grid\",     nx = 6L,     ny = 3L,     padding = 1e4L,     return_wkt = TRUE   )  ncgrid_wkt$original ##  [1] \"POLYGON ((1057207 1355826, 1186092 1355826, 1186092 1462714, 1057207 1462714, 1057207 1355826))\" ##  [2] \"POLYGON ((1186092 1355826, 1314978 1355826, 1314978 1462714, 1186092 1462714, 1186092 1355826))\" ##  [3] \"POLYGON ((1314978 1355826, 1443864 1355826, 1443864 1462714, 1314978 1462714, 1314978 1355826))\" ##  [4] \"POLYGON ((1443864 1355826, 1572750 1355826, 1572750 1462714, 1443864 1462714, 1443864 1355826))\" ##  [5] \"POLYGON ((1572750 1355826, 1701635 1355826, 1701635 1462714, 1572750 1462714, 1572750 1355826))\" ##  [6] \"POLYGON ((1701635 1355826, 1830521 1355826, 1830521 1462714, 1701635 1462714, 1701635 1355826))\" ##  [7] \"POLYGON ((1057207 1462714, 1186092 1462714, 1186092 1569601, 1057207 1569601, 1057207 1462714))\" ##  [8] \"POLYGON ((1186092 1462714, 1314978 1462714, 1314978 1569601, 1186092 1569601, 1186092 1462714))\" ##  [9] \"POLYGON ((1314978 1462714, 1443864 1462714, 1443864 1569601, 1314978 1569601, 1314978 1462714))\" ## [10] \"POLYGON ((1443864 1462714, 1572750 1462714, 1572750 1569601, 1443864 1569601, 1443864 1462714))\" ## [11] \"POLYGON ((1572750 1462714, 1701635 1462714, 1701635 1569601, 1572750 1569601, 1572750 1462714))\" ## [12] \"POLYGON ((1701635 1462714, 1830521 1462714, 1830521 1569601, 1701635 1569601, 1701635 1462714))\" ## [13] \"POLYGON ((1057207 1569601, 1186092 1569601, 1186092 1676488, 1057207 1676488, 1057207 1569601))\" ## [14] \"POLYGON ((1186092 1569601, 1314978 1569601, 1314978 1676488, 1186092 1676488, 1186092 1569601))\" ## [15] \"POLYGON ((1314978 1569601, 1443864 1569601, 1443864 1676488, 1314978 1676488, 1314978 1569601))\" ## [16] \"POLYGON ((1443864 1569601, 1572750 1569601, 1572750 1676488, 1443864 1676488, 1443864 1569601))\" ## [17] \"POLYGON ((1572750 1569601, 1701635 1569601, 1701635 1676488, 1572750 1676488, 1572750 1569601))\" ## [18] \"POLYGON ((1701635 1569601, 1830521 1569601, 1830521 1676488, 1701635 1676488, 1701635 1569601))\" ncgrid_wkt$padded ##  [1] \"POLYGON ((1047207 1345826, 1047207 1472714, 1196092 1472714, 1196092 1345826, 1047207 1345826))\" ##  [2] \"POLYGON ((1176092 1345826, 1176092 1472714, 1324978 1472714, 1324978 1345826, 1176092 1345826))\" ##  [3] \"POLYGON ((1304978 1345826, 1304978 1472714, 1453864 1472714, 1453864 1345826, 1304978 1345826))\" ##  [4] \"POLYGON ((1433864 1345826, 1433864 1472714, 1582750 1472714, 1582750 1345826, 1433864 1345826))\" ##  [5] \"POLYGON ((1562750 1345826, 1562750 1472714, 1711635 1472714, 1711635 1345826, 1562750 1345826))\" ##  [6] \"POLYGON ((1691635 1345826, 1691635 1472714, 1840521 1472714, 1840521 1345826, 1691635 1345826))\" ##  [7] \"POLYGON ((1047207 1452714, 1047207 1579601, 1196092 1579601, 1196092 1452714, 1047207 1452714))\" ##  [8] \"POLYGON ((1176092 1452714, 1176092 1579601, 1324978 1579601, 1324978 1452714, 1176092 1452714))\" ##  [9] \"POLYGON ((1304978 1452714, 1304978 1579601, 1453864 1579601, 1453864 1452714, 1304978 1452714))\" ## [10] \"POLYGON ((1433864 1452714, 1433864 1579601, 1582750 1579601, 1582750 1452714, 1433864 1452714))\" ## [11] \"POLYGON ((1562750 1452714, 1562750 1579601, 1711635 1579601, 1711635 1452714, 1562750 1452714))\" ## [12] \"POLYGON ((1691635 1452714, 1691635 1579601, 1840521 1579601, 1840521 1452714, 1691635 1452714))\" ## [13] \"POLYGON ((1047207 1559601, 1047207 1686488, 1196092 1686488, 1196092 1559601, 1047207 1559601))\" ## [14] \"POLYGON ((1176092 1559601, 1176092 1686488, 1324978 1686488, 1324978 1559601, 1176092 1559601))\" ## [15] \"POLYGON ((1304978 1559601, 1304978 1686488, 1453864 1686488, 1453864 1559601, 1304978 1559601))\" ## [16] \"POLYGON ((1433864 1559601, 1433864 1686488, 1582750 1686488, 1582750 1559601, 1433864 1559601))\" ## [17] \"POLYGON ((1562750 1559601, 1562750 1686488, 1711635 1686488, 1711635 1559601, 1562750 1559601))\" ## [18] \"POLYGON ((1691635 1559601, 1691635 1686488, 1840521 1686488, 1840521 1559601, 1691635 1559601))\""},{"path":"https://NIEHS.github.io/chopin/articles/v05_targets.html","id":"targets-workflow","dir":"Articles","previous_headings":"Example","what":"Targets workflow","title":"targets and grid objects","text":"Assume design function calc_something() calculates something grid partition. can use grid partition input function. sf object centered workflow, can use sf functions interact exported grid partition objects. Let’s consider binary spatial operation x y involved. x dataset variable calculated whereas y raster file path extract values. Please note SpatRaster objects exported parallel workers . read object parallel workers. branch across grid partition, function unit grid handle subsetting x narrow calculation scope grid. Therefore, synopsis function look like : map(unit_grid, pad_grid) pattern argument tar_target() . sf object inherits data.frame class. align object targets branching, clear convert object list object pattern across grid partition. par_split_list chopin . WKT format used, function modified restore grid partition sf objects. function modified follows: tar_target can use list object function calc_something branch . workable example tar_target proper _targets.R file follows: target result list data.frames contain calculation results.","code":"calc_something <- function(x, y, unit_grid, pad_grid, ...) {   # 0. restore unit_grid and pad_grid to sf objects if they are in WKT format   # 1-1. make x subset using intersect logic between x and unit_grid   # 1-2. read y subset using intersect logic between y and pad_grid   # 2. make buffer of x   # 3. do actual calculation (use ... wisely to pass additional arguments)   # 4. return the result } calc_something <- function(x, y, unit_grid, pad_grid, ...) {   # 1-1. make x subset using intersect logic between x and unit_grid   x <- x[unit_grid, ]   # 1-2. read y subset using intersect logic between y and pad_grid   yext <- terra::ext(sf::st_bbox(pad_grid))   yras <- terra::rast(y, win = yext)   # 2. make buffer of x   xbuffer <- sf::st_buffer(x, units::set_units(10, \"km\"))   # 3. do actual calculation (use ... wisely to pass additional arguments)   xycalc <- exactextractr::exact_extract(     yras,     xbuffer,     force_df = TRUE,     fun = \"mean\",     append_cols = \"pid\", # assume that pid is a unique identifier     progress = FALSE   )   # 4. return the result   return(xycalc) } ncgrid_sflist <-   par_split_list(ncgrid_sf) calc_something <- function(x, y, unit_grid, pad_grid, ...) {   # 0. restore unit_grid and pad_grid to sf objects if they are in WKT format   unit_grid <- sf::st_as_sf(wkt = unit_grid)   pad_grid <- sf::st_as_sf(wkt = pad_grid)   # 1-1. make x subset using intersect logic between x and unit_grid   x <- x[unit_grid, ]   # 1-2. read y subset using intersect logic between y and pad_grid   yext <- terra::ext(sf::st_bbox(pad_grid))   yras <- terra::rast(y, win = yext)   # 2. make buffer of x   xbuffer <- sf::st_buffer(x, units::set_units(10, \"km\"))   # 3. do actual calculation (use ... wisely to pass additional arguments)   xycalc <- exactextractr::exact_extract(     yras,     xbuffer,     fun = \"mean\",     force_df = TRUE,     append_cols = \"pid\", # assume that pid is a unique identifier     progress = FALSE   )   # 4. return the result   return(xycalc) } ncgrid_wktlist <-   par_split_list(ncgrid_wkt) list(   tar_target(     name = points,     command = sf::st_read(\"path_to_points.format\")   ),   tar_target(     name = raster,     command = \"path_to_raster.format\",     format = \"file\"   ),   tar_target(     name = chopingrid,     command = par_pad_grid(points, input = points, nx = 6L, ny = 3L, padding = 1e4L, return_wkt = FALSE)   ),   tar_target(     name = chopingrid_split,     command = mapply(       function(listorig, row) {         list(listorig$original[row, ], listorig$padded[row, ])       },       chopingrid, seq_len(nrow(chopingrid$original)),       SIMPLIFY = FALSE     ),     iteration = \"list\"   ),   tar_target(     name = result,     command =     calc_something(       points, raster,       chopingrid_split[[1]], chopingrid_split[[2]]     ),     pattern = map(chopingrid_split),     iteration = \"list\"   ) )"},{"path":"https://NIEHS.github.io/chopin/authors.html","id":null,"dir":"","previous_headings":"","what":"Authors","title":"Authors and Citation","text":"Insang Song. Author, maintainer. Kyle Messier. Author, contributor.","code":""},{"path":"https://NIEHS.github.io/chopin/authors.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Authors and Citation","text":"Song , Messier K (2024). chopin: Computation Spatial Data Hierarchical Objective Partitioning Inputs Parallel Processing. R package version 0.7.8.20240814, https://github.com/NIEHS/chopin.","code":"@Manual{,   title = {chopin: Computation of Spatial Data by Hierarchical and Objective Partitioning of Inputs for Parallel Processing},   author = {Insang Song and Kyle Messier},   year = {2024},   note = {R package version 0.7.8.20240814},   url = {https://github.com/NIEHS/chopin}, }"},{"path":[]},{"path":[]},{"path":"https://NIEHS.github.io/chopin/index.html","id":"objective","dir":"","previous_headings":"Objective and target users","what":"Objective","title":"Computation of Spatial Data by Hierarchical and Objective Partitioning of Inputs for Parallel Processing","text":"package automates parallelization spatial operations chopin functions well sf/terra functions. GDAL-compatible files database tables, chopin functions help calculate spatial variables vector raster data external software requirements.","code":""},{"path":"https://NIEHS.github.io/chopin/index.html","id":"for-whom-chopin-is-useful","dir":"","previous_headings":"Objective and target users","what":"For whom chopin is useful","title":"Computation of Spatial Data by Hierarchical and Objective Partitioning of Inputs for Parallel Processing","text":"need perform geospatial operations large datasets may find package useful accelerate covariate calculation process analysis modeling. basic knowledge geographic information system data models, coordinate systems transformations, spatial operations, raster-vector overlay; Understood planned want calculate; Collected datasets need","code":""},{"path":"https://NIEHS.github.io/chopin/index.html","id":"overview","dir":"","previous_headings":"","what":"Overview","title":"Computation of Spatial Data by Hierarchical and Objective Partitioning of Inputs for Parallel Processing","text":"Processing functions accept terra/sf classes spatial data. Raster-vector overlay done exactextractr. extract_at: extract raster values point buffers polygons without kernel weights summarize_sedc: calculate sums exponentially decaying contributions summarize_aw: area-weighted covariates based target reference polygons par_grid: parallelize artificial grid polygons generated maximum extent inputs. par_pad_grid used generate grid polygons running function. par_hierarchy: parallelize hierarchy coded identifier fields (example, census blocks county US) par_multirasters: parallelize multiple raster files functions designed used future future.mirai packages parallelize multiple CPU threads. Users can choose number threads used parallelization process. Users always need register parallel workers future running three functions .","code":"future::plan(future.mirai::mirai_multisession, workers = 4L) # future::multisession, future::cluster are available, # See future.batchtools and future.callr for other options # the number of workers are up to users' choice"},{"path":"https://NIEHS.github.io/chopin/index.html","id":"installation","dir":"","previous_headings":"","what":"Installation","title":"Computation of Spatial Data by Hierarchical and Objective Partitioning of Inputs for Parallel Processing","text":"chopin can installed using remotes::install_github (also possible pak::pak devtools::install_github).","code":"rlang::check_installed(\"remotes\") remotes::install_github(\"NIEHS/chopin\")"},{"path":"https://NIEHS.github.io/chopin/index.html","id":"examples","dir":"","previous_headings":"","what":"Examples","title":"Computation of Spatial Data by Hierarchical and Objective Partitioning of Inputs for Parallel Processing","text":"Examples navigate par_grid, par_hierarchy, par_multirasters functions chopin parallelize geospatial operations.","code":"# check and install packages to run examples pkgs <- c(\"chopin\", \"dplyr\", \"sf\", \"terra\", \"future\", \"future.mirai\", \"mirai\") # install packages if anything is unavailable rlang::check_installed(pkgs)  library(chopin) library(dplyr) #>  #> Attaching package: 'dplyr' #> The following objects are masked from 'package:stats': #>  #>     filter, lag #> The following objects are masked from 'package:base': #>  #>     intersect, setdiff, setequal, union library(sf) #> Linking to GEOS 3.12.1, GDAL 3.8.5, PROJ 9.4.0; sf_use_s2() is TRUE library(terra) #> terra 1.7.78 library(future) library(future.mirai) library(mirai)  # disable spherical geometries sf::sf_use_s2(FALSE) #> Spherical geometry (s2) switched off  # parallelization-safe random number generator set.seed(2024, kind = \"L'Ecuyer-CMRG\")"},{"path":"https://NIEHS.github.io/chopin/index.html","id":"par_grid-parallelize-over-artificial-grid-polygons","dir":"","previous_headings":"Examples","what":"par_grid: parallelize over artificial grid polygons","title":"Computation of Spatial Data by Hierarchical and Objective Partitioning of Inputs for Parallel Processing","text":"Please refer small example extracting mean altitude values circular point buffers census tracts North Carolina. running code chunks , set cloned chopin repository working directory setwd()","code":"ncpoly <- system.file(\"shape/nc.shp\", package = \"sf\") ncsf <- sf::read_sf(ncpoly) ncsf <- sf::st_transform(ncsf, \"EPSG:5070\") plot(sf::st_geometry(ncsf))"},{"path":"https://NIEHS.github.io/chopin/index.html","id":"generate-random-points-in-nc","dir":"","previous_headings":"Examples > par_grid: parallelize over artificial grid polygons","what":"Generate random points in NC","title":"Computation of Spatial Data by Hierarchical and Objective Partitioning of Inputs for Parallel Processing","text":"Ten thousands random point locations generated inside counties North Carolina.","code":"ncpoints <- sf::st_sample(ncsf, 1e4) ncpoints <- sf::st_as_sf(ncpoints) ncpoints$pid <- sprintf(\"PID-%05d\", seq(1, 1e4)) plot(sf::st_geometry(ncpoints))"},{"path":"https://NIEHS.github.io/chopin/index.html","id":"target-raster-dataset-shuttle-radar-topography-mission","dir":"","previous_headings":"Examples > par_grid: parallelize over artificial grid polygons","what":"Target raster dataset: Shuttle Radar Topography Mission","title":"Computation of Spatial Data by Hierarchical and Objective Partitioning of Inputs for Parallel Processing","text":"use elevation dataset moderate spatial resolution (approximately 400 meters 0.25 miles).","code":"# data preparation wdir <- system.file(\"extdata\", package = \"chopin\") srtm <- file.path(wdir, \"nc_srtm15_otm.tif\")  # terra SpatRaster objects are wrapped when exported to rds file srtm_ras <- terra::rast(srtm) terra::crs(srtm_ras) <- \"EPSG:5070\" srtm_ras #> class       : SpatRaster  #> dimensions  : 1534, 2281, 1  (nrow, ncol, nlyr) #> resolution  : 391.5026, 391.5026  (x, y) #> extent      : 1012872, 1905890, 1219961, 1820526  (xmin, xmax, ymin, ymax) #> coord. ref. : NAD83 / Conus Albers (EPSG:5070)  #> source      : nc_srtm15_otm.tif  #> name        :    srtm15  #> min value   : -3589.291  #> max value   :  1946.400 terra::plot(srtm_ras) # ncpoints_tr <- terra::vect(ncpoints) system.time(   ncpoints_srtm <-     chopin::extract_at(       x = srtm,       y = ncpoints,       id = \"pid\",       mode = \"buffer\",       radius = 1e4L  # 10,000 meters (10 km)     ) ) #> Input is a character. Attempt to read it with terra::rast... #>    user  system elapsed  #>   7.205   0.206   7.669"},{"path":"https://NIEHS.github.io/chopin/index.html","id":"generate-regular-grid-computational-regions","dir":"","previous_headings":"Examples > par_grid: parallelize over artificial grid polygons","what":"Generate regular grid computational regions","title":"Computation of Spatial Data by Hierarchical and Objective Partitioning of Inputs for Parallel Processing","text":"chopin::par_pad_grid takes spatial dataset generate regular grid polygons nx ny arguments padding. Users overlapping (degree radius) non-overlapping grids, utilized split locations target datasets sub-datasets efficient processing. compregions list object two elements named original (non-overlapping grid polygons) padded (overlapping padding). figures illustrate grid polygons without overlaps.","code":"compregions <-   chopin::par_pad_grid(     ncpoints,     mode = \"grid\",     nx = 2L,     ny = 2L,     padding = 1e4L   ) #> Switch sf class to terra... #> Switch terra class to sf... names(compregions) #> [1] \"original\" \"padded\"  oldpar <- par() par(mfrow = c(2, 1)) terra::plot(   terra::vect(compregions$original),   main = \"Original grids\" ) terra::plot(   terra::vect(compregions$padded),   main = \"Padded grids\" )"},{"path":"https://NIEHS.github.io/chopin/index.html","id":"parallel-processing","dir":"","previous_headings":"Examples > par_grid: parallelize over artificial grid polygons","what":"Parallel processing","title":"Computation of Spatial Data by Hierarchical and Objective Partitioning of Inputs for Parallel Processing","text":"Using grid polygons, distribute task averaging elevations 10,000 circular buffer polygons, generated random locations, 10 kilometers radius chopin::par_grid. Users always need register multiple CPU threads (logical cores) parallelization. Users can inject generic functions’ arguments (parameters) writing ellipsis (...) arguments, demonstrated :","code":"future::plan(future.mirai::mirai_multisession, workers = 4L)  system.time(   ncpoints_srtm_mthr <-     par_grid(       grids = compregions,       fun_dist = extract_at,       x = srtm,       y = ncpoints,       id = \"pid\",       radius = 1e4L,       .standalone = FALSE     ) ) #> ℹ Input is not a character. #> Input is a character. Attempt to read it with terra::rast... #> ℹ Task at CGRIDID: 1 is successfully dispatched. #>  #> Input is a character. Attempt to read it with terra::rast... #> ℹ Task at CGRIDID: 2 is successfully dispatched. #>  #> Input is a character. Attempt to read it with terra::rast... #> ℹ Task at CGRIDID: 3 is successfully dispatched. #>  #> Input is a character. Attempt to read it with terra::rast... #> ℹ Task at CGRIDID: 4 is successfully dispatched. #>    user  system elapsed  #>   0.329   0.021   8.337  ncpoints_srtm <-   extract_at(     x = srtm,     y = ncpoints,     id = \"pid\",     radius = 1e4L   ) #> Input is a character. Attempt to read it with terra::rast... colnames(ncpoints_srtm_mthr)[2] <- \"mean_par\" ncpoints_compar <- merge(ncpoints_srtm, ncpoints_srtm_mthr) # Are the calculations equal? all.equal(ncpoints_compar$mean, ncpoints_compar$mean_par) #> [1] TRUE ncpoints_s <-   merge(ncpoints, ncpoints_srtm) ncpoints_m <-   merge(ncpoints, ncpoints_srtm_mthr)  plot(ncpoints_s[, \"mean\"], main = \"Single-thread\", pch = 19, cex = 0.33) plot(ncpoints_m[, \"mean_par\"], main = \"Multi-thread\", pch = 19, cex = 0.33)"},{"path":"https://NIEHS.github.io/chopin/index.html","id":"chopinpar_hierarchy-parallelize-geospatial-computations-using-intrinsic-data-hierarchy","dir":"","previous_headings":"Examples","what":"chopin::par_hierarchy: parallelize geospatial computations using intrinsic data hierarchy","title":"Computation of Spatial Data by Hierarchical and Objective Partitioning of Inputs for Parallel Processing","text":"real world datasets, usually nested/exhaustive hierarchies. example, land organized administrative/jurisdictional borders multiple levels exist. U.S. context, state consists several counties, counties split census tracts, group block groups. chopin::par_hierarchy leverages hierarchies parallelize geospatial operations, means group lower-level geographic units higher-level geography assigned process. demonstration shows census tracts grouped counties county processed CPU thread.","code":""},{"path":"https://NIEHS.github.io/chopin/index.html","id":"read-data","dir":"","previous_headings":"Examples > chopin::par_hierarchy: parallelize geospatial computations using intrinsic data hierarchy","what":"Read data","title":"Computation of Spatial Data by Hierarchical and Objective Partitioning of Inputs for Parallel Processing","text":"","code":"# nc_hierarchy.gpkg includes two layers: county and tracts path_nchrchy <- file.path(wdir, \"nc_hierarchy.gpkg\")  nc_data <- path_nchrchy nc_county <- sf::st_read(nc_data, layer = \"county\") #> Reading layer `county' from data source  #>   `/tmp/RtmpkigVUL/temp_libpath3cdb0b6b55ee11/chopin/extdata/nc_hierarchy.gpkg'  #>   using driver `GPKG' #> Simple feature collection with 100 features and 1 field #> Geometry type: POLYGON #> Dimension:     XY #> Bounding box:  xmin: 1054155 ymin: 1341756 xmax: 1838923 ymax: 1690176 #> Projected CRS: NAD83 / Conus Albers nc_tracts <- sf::st_read(nc_data, layer = \"tracts\") #> Reading layer `tracts' from data source  #>   `/tmp/RtmpkigVUL/temp_libpath3cdb0b6b55ee11/chopin/extdata/nc_hierarchy.gpkg'  #>   using driver `GPKG' #> Simple feature collection with 2672 features and 1 field #> Geometry type: MULTIPOLYGON #> Dimension:     XY #> Bounding box:  xmin: 1054155 ymin: 1341756 xmax: 1838923 ymax: 1690176 #> Projected CRS: NAD83 / Conus Albers  # reproject to Conus Albers Equal Area nc_county <- sf::st_transform(nc_county, \"EPSG:5070\") nc_tracts <- sf::st_transform(nc_tracts, \"EPSG:5070\") nc_tracts$COUNTY <- substr(nc_tracts$GEOID, 1, 5)"},{"path":"https://NIEHS.github.io/chopin/index.html","id":"extract-average-srtm-elevations-by-single-and-multiple-threads","dir":"","previous_headings":"Examples > chopin::par_hierarchy: parallelize geospatial computations using intrinsic data hierarchy","what":"Extract average SRTM elevations by single and multiple threads","title":"Computation of Spatial Data by Hierarchical and Objective Partitioning of Inputs for Parallel Processing","text":"","code":"# single-thread system.time(   nc_elev_tr_single <-     chopin::extract_at(       x = srtm,       y = nc_tracts,       id = \"GEOID\"     ) ) #> Input is a character. Attempt to read it with terra::rast... #>    user  system elapsed  #>   0.910   0.003   0.916  # hierarchical parallelization system.time(   nc_elev_tr_distr <-     chopin::par_hierarchy(       regions = nc_county, # higher level geometry       regions_id = \"GEOID\", # higher level unique id       fun_dist = extract_at,       x = srtm,       y = nc_tracts, # lower level geometry       id = \"GEOID\", # lower level unique id       func = \"mean\"     ) ) #> ℹ Input is not a character. #> ℹ GEOID is used to stratify the process. #> Input is a character. Attempt to read it with terra::rast...ℹ Your input function at 37037 is dispatched. #> Input is a character. Attempt to read it with terra::rast...ℹ Your input function at 37001 is dispatched. #> Input is a character. Attempt to read it with terra::rast...ℹ Your input function at 37057 is dispatched. #> Input is a character. Attempt to read it with terra::rast...ℹ Your input function at 37069 is dispatched. #> Input is a character. Attempt to read it with terra::rast...ℹ Your input function at 37155 is dispatched. #> Input is a character. Attempt to read it with terra::rast...ℹ Your input function at 37109 is dispatched. #> Input is a character. Attempt to read it with terra::rast...ℹ Your input function at 37027 is dispatched. #> Input is a character. Attempt to read it with terra::rast...ℹ Your input function at 37063 is dispatched. #> Input is a character. Attempt to read it with terra::rast...ℹ Your input function at 37145 is dispatched. #> Input is a character. Attempt to read it with terra::rast...ℹ Your input function at 37115 is dispatched. #> Input is a character. Attempt to read it with terra::rast...ℹ Your input function at 37151 is dispatched. #> Input is a character. Attempt to read it with terra::rast...ℹ Your input function at 37131 is dispatched. #> Input is a character. Attempt to read it with terra::rast...ℹ Your input function at 37013 is dispatched. #> Input is a character. Attempt to read it with terra::rast...ℹ Your input function at 37159 is dispatched. #> Input is a character. Attempt to read it with terra::rast...ℹ Your input function at 37051 is dispatched. #> Input is a character. Attempt to read it with terra::rast...ℹ Your input function at 37153 is dispatched. #> Input is a character. Attempt to read it with terra::rast...ℹ Your input function at 37093 is dispatched. #> Input is a character. Attempt to read it with terra::rast...ℹ Your input function at 37025 is dispatched. #> Input is a character. Attempt to read it with terra::rast...ℹ Your input function at 37029 is dispatched. #> Input is a character. Attempt to read it with terra::rast...ℹ Your input function at 37169 is dispatched. #> Input is a character. Attempt to read it with terra::rast...ℹ Your input function at 37031 is dispatched. #> Input is a character. Attempt to read it with terra::rast...ℹ Your input function at 37005 is dispatched. #> Input is a character. Attempt to read it with terra::rast...ℹ Your input function at 37139 is dispatched. #> Input is a character. Attempt to read it with terra::rast...ℹ Your input function at 37193 is dispatched. #> Input is a character. Attempt to read it with terra::rast...ℹ Your input function at 37003 is dispatched. #> Input is a character. Attempt to read it with terra::rast...ℹ Your input function at 37083 is dispatched. #> Input is a character. Attempt to read it with terra::rast...ℹ Your input function at 37163 is dispatched. #> Input is a character. Attempt to read it with terra::rast...ℹ Your input function at 37189 is dispatched. #> Input is a character. Attempt to read it with terra::rast...ℹ Your input function at 37173 is dispatched. #> Input is a character. Attempt to read it with terra::rast...ℹ Your input function at 37011 is dispatched. #> Input is a character. Attempt to read it with terra::rast...ℹ Your input function at 37045 is dispatched. #> Input is a character. Attempt to read it with terra::rast...ℹ Your input function at 37125 is dispatched. #> Input is a character. Attempt to read it with terra::rast...ℹ Your input function at 37067 is dispatched. #> Input is a character. Attempt to read it with terra::rast...ℹ Your input function at 37077 is dispatched. #> Input is a character. Attempt to read it with terra::rast...ℹ Your input function at 37185 is dispatched. #> Input is a character. Attempt to read it with terra::rast...ℹ Your input function at 37137 is dispatched. #> Input is a character. Attempt to read it with terra::rast...ℹ Your input function at 37033 is dispatched. #> Input is a character. Attempt to read it with terra::rast...ℹ Your input function at 37107 is dispatched. #> Input is a character. Attempt to read it with terra::rast...ℹ Your input function at 37075 is dispatched. #> Input is a character. Attempt to read it with terra::rast...ℹ Your input function at 37073 is dispatched. #> Input is a character. Attempt to read it with terra::rast...ℹ Your input function at 37161 is dispatched. #> Input is a character. Attempt to read it with terra::rast...ℹ Your input function at 37187 is dispatched. #> Input is a character. Attempt to read it with terra::rast...ℹ Your input function at 37007 is dispatched. #> Input is a character. Attempt to read it with terra::rast...ℹ Your input function at 37135 is dispatched. #> Input is a character. Attempt to read it with terra::rast...ℹ Your input function at 37049 is dispatched. #> Input is a character. Attempt to read it with terra::rast...ℹ Your input function at 37195 is dispatched. #> Input is a character. Attempt to read it with terra::rast...ℹ Your input function at 37061 is dispatched. #> Input is a character. Attempt to read it with terra::rast...ℹ Your input function at 37087 is dispatched. #> Input is a character. Attempt to read it with terra::rast...ℹ Your input function at 37081 is dispatched. #> Input is a character. Attempt to read it with terra::rast...ℹ Your input function at 37099 is dispatched. #> Input is a character. Attempt to read it with terra::rast...ℹ Your input function at 37097 is dispatched. #> Input is a character. Attempt to read it with terra::rast...ℹ Your input function at 37091 is dispatched. #> Input is a character. Attempt to read it with terra::rast...ℹ Your input function at 37149 is dispatched. #> Input is a character. Attempt to read it with terra::rast...ℹ Your input function at 37165 is dispatched. #> Input is a character. Attempt to read it with terra::rast...ℹ Your input function at 37085 is dispatched. #> Input is a character. Attempt to read it with terra::rast...ℹ Your input function at 37105 is dispatched. #> Input is a character. Attempt to read it with terra::rast...ℹ Your input function at 37017 is dispatched. #> Input is a character. Attempt to read it with terra::rast...ℹ Your input function at 37039 is dispatched. #> Input is a character. Attempt to read it with terra::rast...ℹ Your input function at 37035 is dispatched. #> Input is a character. Attempt to read it with terra::rast...ℹ Your input function at 37177 is dispatched. #> Input is a character. Attempt to read it with terra::rast...ℹ Your input function at 37113 is dispatched. #> Input is a character. Attempt to read it with terra::rast...ℹ Your input function at 37143 is dispatched. #> Input is a character. Attempt to read it with terra::rast...ℹ Your input function at 37095 is dispatched. #> Input is a character. Attempt to read it with terra::rast...ℹ Your input function at 37071 is dispatched. #> Input is a character. Attempt to read it with terra::rast...ℹ Your input function at 37101 is dispatched. #> Input is a character. Attempt to read it with terra::rast...ℹ Your input function at 37015 is dispatched. #> Input is a character. Attempt to read it with terra::rast...ℹ Your input function at 37167 is dispatched. #> Input is a character. Attempt to read it with terra::rast...ℹ Your input function at 37079 is dispatched. #> Input is a character. Attempt to read it with terra::rast...ℹ Your input function at 37129 is dispatched. #> Input is a character. Attempt to read it with terra::rast...ℹ Your input function at 37147 is dispatched. #> Input is a character. Attempt to read it with terra::rast...ℹ Your input function at 37141 is dispatched. #> Input is a character. Attempt to read it with terra::rast...ℹ Your input function at 37179 is dispatched. #> Input is a character. Attempt to read it with terra::rast...ℹ Your input function at 37121 is dispatched. #> Input is a character. Attempt to read it with terra::rast...ℹ Your input function at 37133 is dispatched. #> Input is a character. Attempt to read it with terra::rast...ℹ Your input function at 37065 is dispatched. #> Input is a character. Attempt to read it with terra::rast...ℹ Your input function at 37119 is dispatched. #> Input is a character. Attempt to read it with terra::rast...ℹ Your input function at 37199 is dispatched. #> Input is a character. Attempt to read it with terra::rast...ℹ Your input function at 37197 is dispatched. #> Input is a character. Attempt to read it with terra::rast...ℹ Your input function at 37023 is dispatched. #> Input is a character. Attempt to read it with terra::rast...ℹ Your input function at 37191 is dispatched. #> Input is a character. Attempt to read it with terra::rast...ℹ Your input function at 37059 is dispatched. #> Input is a character. Attempt to read it with terra::rast...ℹ Your input function at 37111 is dispatched. #> Input is a character. Attempt to read it with terra::rast...ℹ Your input function at 37183 is dispatched. #> Input is a character. Attempt to read it with terra::rast...ℹ Your input function at 37053 is dispatched. #> Input is a character. Attempt to read it with terra::rast...ℹ Your input function at 37103 is dispatched. #> Input is a character. Attempt to read it with terra::rast...ℹ Your input function at 37041 is dispatched. #> Input is a character. Attempt to read it with terra::rast...ℹ Your input function at 37021 is dispatched. #> Input is a character. Attempt to read it with terra::rast...ℹ Your input function at 37157 is dispatched. #> Input is a character. Attempt to read it with terra::rast...ℹ Your input function at 37117 is dispatched. #> Input is a character. Attempt to read it with terra::rast...ℹ Your input function at 37089 is dispatched. #> Input is a character. Attempt to read it with terra::rast...ℹ Your input function at 37127 is dispatched. #> Input is a character. Attempt to read it with terra::rast...ℹ Your input function at 37009 is dispatched. #> Input is a character. Attempt to read it with terra::rast...ℹ Your input function at 37019 is dispatched. #> Input is a character. Attempt to read it with terra::rast...ℹ Your input function at 37123 is dispatched. #> Input is a character. Attempt to read it with terra::rast...ℹ Your input function at 37181 is dispatched. #> Input is a character. Attempt to read it with terra::rast...ℹ Your input function at 37175 is dispatched. #> Input is a character. Attempt to read it with terra::rast...ℹ Your input function at 37171 is dispatched. #> Input is a character. Attempt to read it with terra::rast...ℹ Your input function at 37043 is dispatched. #> Input is a character. Attempt to read it with terra::rast...ℹ Your input function at 37055 is dispatched. #> Input is a character. Attempt to read it with terra::rast...ℹ Your input function at 37047 is dispatched. #>    user  system elapsed  #>   0.258   0.073   2.005"},{"path":"https://NIEHS.github.io/chopin/index.html","id":"par_multirasters-parallelize-over-multiple-rasters","dir":"","previous_headings":"Examples","what":"par_multirasters: parallelize over multiple rasters","title":"Computation of Spatial Data by Hierarchical and Objective Partitioning of Inputs for Parallel Processing","text":"common case large group raster files operation performed. chopin::par_multirasters cases. example demonstrates five elevation raster files calculate average elevation counties North Carolina.","code":"# nccnty <- sf::st_read(nc_data, layer = \"county\") ncelev <- terra::rast(srtm) terra::crs(ncelev) <- \"EPSG:5070\" names(ncelev) <- c(\"srtm15\") tdir <- tempdir()  terra::writeRaster(ncelev, file.path(tdir, \"test1.tif\"), overwrite = TRUE) terra::writeRaster(ncelev, file.path(tdir, \"test2.tif\"), overwrite = TRUE) terra::writeRaster(ncelev, file.path(tdir, \"test3.tif\"), overwrite = TRUE) terra::writeRaster(ncelev, file.path(tdir, \"test4.tif\"), overwrite = TRUE) terra::writeRaster(ncelev, file.path(tdir, \"test5.tif\"), overwrite = TRUE)  # check if the raster files were exported as expected testfiles <- list.files(tdir, pattern = \"*.tif$\", full.names = TRUE) testfiles #> [1] \"/tmp/Rtmpqu68xF/test1.tif\" \"/tmp/Rtmpqu68xF/test2.tif\" #> [3] \"/tmp/Rtmpqu68xF/test3.tif\" \"/tmp/Rtmpqu68xF/test4.tif\" #> [5] \"/tmp/Rtmpqu68xF/test5.tif\" system.time(   res <-     chopin::par_multirasters(       filenames = testfiles,       fun_dist = extract_at,       x = ncelev,       y = nc_county,       id = \"GEOID\",       func = \"mean\"     ) ) #> ℹ Input is not a character. #> Input is a character. Attempt to read it with terra::rast... #> ℹ Your input function at /tmp/Rtmpqu68xF/test1.tif is dispatched. #>  #> Input is a character. Attempt to read it with terra::rast... #> ℹ Your input function at /tmp/Rtmpqu68xF/test2.tif is dispatched. #>  #> Input is a character. Attempt to read it with terra::rast... #> ℹ Your input function at /tmp/Rtmpqu68xF/test3.tif is dispatched. #>  #> Input is a character. Attempt to read it with terra::rast... #> ℹ Your input function at /tmp/Rtmpqu68xF/test4.tif is dispatched. #>  #> Input is a character. Attempt to read it with terra::rast... #> ℹ Your input function at /tmp/Rtmpqu68xF/test5.tif is dispatched. #>    user  system elapsed  #>   1.713   0.147   2.747 knitr::kable(head(res)) # remove temporary raster files file.remove(testfiles) #> [1] TRUE TRUE TRUE TRUE TRUE"},{"path":"https://NIEHS.github.io/chopin/index.html","id":"parallelization-of-a-generic-geospatial-operation","dir":"","previous_headings":"","what":"Parallelization of a generic geospatial operation","title":"Computation of Spatial Data by Hierarchical and Objective Partitioning of Inputs for Parallel Processing","text":"chopin internal macros, chopin::par_* functions support generic geospatial operations. example uses terra::nearest, gets nearest feature’s attributes, inside chopin::par_grid. figure shows padded grids (50 kilometers), primary roads, points. Primary roads selected padded grid per iteration used calculate distance point nearest primary road. Padded grids overlapping areas look different according padding argument chopin::par_pad_grid.  compare results single-thread multi-thread calculation. example, one wants calculate nearest interstate rural homes fine grids, grids may interstates homes grids get distance nearest interstate. problems can avoided choosing nx, ny, padding values par_pad_grid meticulously.","code":"path_ncrd1 <- file.path(wdir, \"ncroads_first.gpkg\")  # Generate 5000 random points pnts <- sf::st_sample(nc_county, 5000) pnts <- sf::st_as_sf(pnts) # assign identifiers pnts$pid <- sprintf(\"RPID-%04d\", seq(1, 5000)) rd1 <- sf::st_read(path_ncrd1) #> Reading layer `ncroads_first' from data source  #>   `/tmp/RtmpkigVUL/temp_libpath3cdb0b6b55ee11/chopin/extdata/ncroads_first.gpkg'  #>   using driver `GPKG' #> Simple feature collection with 620 features and 4 fields #> Geometry type: MULTILINESTRING #> Dimension:     XY #> Bounding box:  xmin: 1152512 ymin: 1390719 xmax: 1748367 ymax: 1662294 #> Projected CRS: NAD83 / Conus Albers  # reproject pntst <- sf::st_transform(pnts, \"EPSG:5070\") rd1t <- sf::st_transform(rd1, \"EPSG:5070\")  # generate grids nccompreg <-   chopin::par_pad_grid(     input = pntst,     mode = \"grid\",     nx = 4L,     ny = 2L,     padding = 5e4L   ) #> Switch sf class to terra... #> Switch terra class to sf... # plot terra::plot(nccompreg$padded, border = \"orange\") terra::plot(terra::vect(ncsf), add = TRUE) terra::plot(rd1t, col = \"blue\", add = TRUE) #> Warning in plot.sf(rd1t, col = \"blue\", add = TRUE): ignoring all but the first #> attribute terra::plot(pntst, add = TRUE, cex = 0.3) legend(1.02e6, 1.72e6,        legend = c(\"Computation grids (50km padding)\", \"Major roads\"),        lty = 1, lwd = 1, col = c(\"orange\", \"blue\"),        cex = 0.5) # terra::nearest run system.time(   restr <- terra::nearest(x = terra::vect(pntst), y = terra::vect(rd1t)) ) #>    user  system elapsed  #>   0.603   0.000   0.604  pnt_path <- file.path(tdir, \"pntst.gpkg\") sf::st_write(pntst, pnt_path) #> Writing layer `pntst' to data source `/tmp/Rtmpqu68xF/pntst.gpkg' using driver `GPKG' #> Writing 5000 features with 1 fields and geometry type Point.  # we use four threads that were configured above system.time(   resd <-     chopin::par_grid(       grids = nccompreg,       fun_dist = nearest,       x = pnt_path,       y = path_ncrd1,       pad_y = TRUE     ) ) #> ℹ Input is a character. Trying to read with terra . #> ℹ Input is a character. Trying to read with terra . #> ℹ Task at CGRIDID: 1 is successfully dispatched. #>  #> ℹ Input is a character. Trying to read with terra . #> ℹ Input is a character. Trying to read with terra . #> ℹ Task at CGRIDID: 2 is successfully dispatched. #>  #> ℹ Input is a character. Trying to read with terra . #> ℹ Input is a character. Trying to read with terra . #> ℹ Task at CGRIDID: 3 is successfully dispatched. #>  #> ℹ Input is a character. Trying to read with terra . #> ℹ Input is a character. Trying to read with terra . #> ℹ Task at CGRIDID: 4 is successfully dispatched. #>  #> ℹ Input is a character. Trying to read with terra . #> ℹ Input is a character. Trying to read with terra . #> ℹ Task at CGRIDID: 5 is successfully dispatched. #>  #> ℹ Input is a character. Trying to read with terra . #> ℹ Input is a character. Trying to read with terra . #> ℹ Task at CGRIDID: 6 is successfully dispatched. #>  #> ℹ Input is a character. Trying to read with terra . #> ℹ Input is a character. Trying to read with terra . #> ℹ Task at CGRIDID: 7 is successfully dispatched. #>  #> ℹ Input is a character. Trying to read with terra . #> ℹ Input is a character. Trying to read with terra . #> ℹ Task at CGRIDID: 8 is successfully dispatched. #>    user  system elapsed  #>   0.085   0.000   0.534 resj <- merge(restr, resd, by = c(\"from_x\", \"from_y\")) all.equal(resj$distance.x, resj$distance.y) #> [1] TRUE"},{"path":"https://NIEHS.github.io/chopin/index.html","id":"notes-on-data-restrictions","dir":"","previous_headings":"Parallelization of a generic geospatial operation","what":"Notes on data restrictions","title":"Computation of Spatial Data by Hierarchical and Objective Partitioning of Inputs for Parallel Processing","text":"chopin works best two-dimensional (planar) geometries. Users disable s2 spherical geometry mode sf setting. Running chopin functions spherical three-dimensional (e.g., including M/Z dimensions) geometries may produce incorrect unexpected results.","code":"sf::sf_use_s2(FALSE)"},{"path":"https://NIEHS.github.io/chopin/reference/chopin-package.html","id":null,"dir":"Reference","previous_headings":"","what":"Computation of spatial data by hierarchical and objective partitioning of inputs for parallel processing — chopin-package","title":"Computation of spatial data by hierarchical and objective partitioning of inputs for parallel processing — chopin-package","text":"chopin package provides set functions compute divided geospatial data.","code":""},{"path":"https://NIEHS.github.io/chopin/reference/chopin-package.html","id":"basic-functionalities","dir":"Reference","previous_headings":"","what":"Basic functionalities","title":"Computation of spatial data by hierarchical and objective partitioning of inputs for parallel processing — chopin-package","text":"Distribute terra, sf, chopin functions parallel workers set future Set parallelization strategies based artificial grids, equal-size clusters, hierarchy, multiple raster files Convenience functions raster-vector overlay weighted summary vector dataset","code":""},{"path":"https://NIEHS.github.io/chopin/reference/chopin-package.html","id":"chopin-workflow","dir":"Reference","previous_headings":"","what":"chopin workflow","title":"Computation of spatial data by hierarchical and objective partitioning of inputs for parallel processing — chopin-package","text":"simplest way parallelizing generic geospatial computation start par_pad_* functions par_grid, running par_hierarchy, par_multirasters functions .   Example data North Carolinian counties raster elevation data used example data.   demonstrate chopin functions, generate 10,000 random points North Carolina   Creating grids example generate regular grid random point data.   Extracting values raster Since par_* functions operate future backends, users define future plan running functions. multicore plan supports terra objects may lead faster computation, supported Windows. alternative future.mirai's mirai_multisession plan, supported many platforms generally faster plain future multisession plan. workers argument defined integer value specify number threads used.   dispatch multiple extract_at runs grid polygons. proceed, terra object converted sf object.   Hierarchical processing demonstrate hierarchical processing random points using census tract polygons.   example parallelize summarizing mean elevation 10 kilometers circular buffers random sample points first five characters census tract unique identifiers, county codes. example demonstrates hierarchy can defined given polygons unique identifiers suitably formatted defining hierarchy.   Multiraster processing demonstrate multiraster processing random points using multiple rasters.","code":"library(chopin) library(terra) library(sf) library(collapse) library(dplyr) library(future) library(future.mirai) library(future.apply) nccnty_path <- system.file(\"extdata\", \"nc_hierarchy.gpkg\", package = \"chopin\") ncelev_path <-   system.file(\"extdata/nc_srtm15_otm.tif\", package = \"chopin\") nccnty <- terra::vect(nccnty_path) ncelev <- terra::rast(ncelev_path) ncsamp <-   terra::spatSample(     nccnty,     1e4L   ) ncsamp$pid <- 1:nrow(ncsamp) ncgrid <- par_pad_grid(ncsamp, mode = \"grid\", nx = 4L, ny = 2L, padding = 10000) plot(ncgrid$original) future::plan(future.mirai::mirai_multisession, workers = 2L) pg <-   par_grid(     grids = ncgrid,     pad_y = FALSE,     .debug = TRUE,     fun_dist = extract_at,     x = ncelev_path,     y = sf::st_as_sf(ncsamp),     id = \"pid\",     radius = 1e4,     func = \"mean\"   ) nccnty <- sf::st_read(nccnty_path, layer = \"county\") nctrct <- sf::st_read(nccnty_path, layer = \"tracts\") px <-   par_hierarchy(     # from here the par_hierarchy-specific arguments     regions = nctrct,     regions_id = \"GEOID\",     length_left = 5,     pad = 10000,     pad_y = FALSE,     .debug = TRUE,     # from here are the dispatched function definition     # for parallel workers     fun_dist = extract_at,     # below should follow the arguments of the dispatched function     x = ncelev,     y = sf::st_as_sf(ncsamp),     id = \"pid\",     radius = 1e4,     func = \"mean\"   ) ncelev <-   system.file(\"extdata/nc_srtm15_otm.tif\", package = \"chopin\") ncelev <- terra::rast(ncelev) tdir <- tempdir(check = TRUE) terra::writeRaster(ncelev, file.path(tdir, \"test1.tif\"), overwrite = TRUE) terra::writeRaster(ncelev, file.path(tdir, \"test2.tif\"), overwrite = TRUE) terra::writeRaster(ncelev, file.path(tdir, \"test3.tif\"), overwrite = TRUE) terra::writeRaster(ncelev, file.path(tdir, \"test4.tif\"), overwrite = TRUE) terra::writeRaster(ncelev, file.path(tdir, \"test5.tif\"), overwrite = TRUE) rasts <- list.files(tdir, pattern = \"tif$\", full.names = TRUE)  pm <-   par_multirasters(     filenames = rasts,     fun_dist = extract_at,     x = NA,     y = sf::st_as_sf(ncsamp)[1:500, ],     id = \"pid\",     radius = 1e4,     func = \"mean\",     .debug = TRUE   )"},{"path":[]},{"path":"https://NIEHS.github.io/chopin/reference/chopin-package.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Computation of spatial data by hierarchical and objective partitioning of inputs for parallel processing — chopin-package","text":"Maintainer: Insang Song geoissong@gmail.com (ORCID) Authors: Kyle Messier (ORCID) [contributor]","code":""},{"path":"https://NIEHS.github.io/chopin/reference/clip_ras_ext.html","id":null,"dir":"Reference","previous_headings":"","what":"Clip input raster with a buffered vector extent. — clip_ras_ext","title":"Clip input raster with a buffered vector extent. — clip_ras_ext","text":"Clip input raster expected maximum extent computation.","code":""},{"path":"https://NIEHS.github.io/chopin/reference/clip_ras_ext.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Clip input raster with a buffered vector extent. — clip_ras_ext","text":"","code":"clip_ras_ext(x = NULL, y = NULL, radius = NULL, nqsegs = 180L, extrusion = 1.1)"},{"path":"https://NIEHS.github.io/chopin/reference/clip_ras_ext.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Clip input raster with a buffered vector extent. — clip_ras_ext","text":"x SpatRaster object clipped y sf SpatVector object radius numeric(1). buffer radius. value automatically multiplied extrusion nqsegs integer(1). number points per quarter circle extrusion numeric(1). Extrusion factor extent. Default 1.1","code":""},{"path":"https://NIEHS.github.io/chopin/reference/clip_ras_ext.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Clip input raster with a buffered vector extent. — clip_ras_ext","text":"clipped SpatRaster object.","code":""},{"path":[]},{"path":"https://NIEHS.github.io/chopin/reference/clip_ras_ext.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Clip input raster with a buffered vector extent. — clip_ras_ext","text":"Insang Song","code":""},{"path":"https://NIEHS.github.io/chopin/reference/clip_vec_ext.html","id":null,"dir":"Reference","previous_headings":"","what":"Clip to the buffered extent of input vector — clip_vec_ext","title":"Clip to the buffered extent of input vector — clip_vec_ext","text":"Clip input vector expected maximum extent computation.","code":""},{"path":"https://NIEHS.github.io/chopin/reference/clip_vec_ext.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Clip to the buffered extent of input vector — clip_vec_ext","text":"","code":"clip_vec_ext(x, y, radius)"},{"path":"https://NIEHS.github.io/chopin/reference/clip_vec_ext.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Clip to the buffered extent of input vector — clip_vec_ext","text":"x sf SpatVector object clipped y sf SpatVector object radius numeric(1). Circular buffer radius. value automatically multiplied 1.1","code":""},{"path":"https://NIEHS.github.io/chopin/reference/clip_vec_ext.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Clip to the buffered extent of input vector — clip_vec_ext","text":"clipped sf SpatVector object.","code":""},{"path":[]},{"path":"https://NIEHS.github.io/chopin/reference/clip_vec_ext.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Clip to the buffered extent of input vector — clip_vec_ext","text":"Insang Song","code":""},{"path":"https://NIEHS.github.io/chopin/reference/clip_vec_ext.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Clip to the buffered extent of input vector — clip_vec_ext","text":"","code":"if (FALSE) { # \\dontrun{ library(sf) library(stars) library(terra) options(sf_use_s2 = FALSE)  bcsd_path <- system.file(package = \"stars\", \"nc/bcsd_obs_1999.nc\") bcsd <- stars::read_stars(bcsd_path) bcsd <- sf::st_as_sf(bcsd) bcsd_rpnt <- sf::st_as_sf(sf::st_sample(bcsd, 4L)) bcsd_rpntm <- sf::st_as_sf(sf::st_sample(bcsd, 1000L)) clip_vec_ext(bcsd_rpntm, 1000, bcsd_rpnt) } # }"},{"path":"https://NIEHS.github.io/chopin/reference/datamod.html","id":null,"dir":"Reference","previous_headings":"","what":"Return the input's GIS data model type — datamod","title":"Return the input's GIS data model type — datamod","text":"function returns one 'vector' 'raster' depending input class.","code":""},{"path":"https://NIEHS.github.io/chopin/reference/datamod.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Return the input's GIS data model type — datamod","text":"","code":"datamod(input)"},{"path":"https://NIEHS.github.io/chopin/reference/datamod.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Return the input's GIS data model type — datamod","text":"input Spat*/sf/stars object.","code":""},{"path":"https://NIEHS.github.io/chopin/reference/datamod.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Return the input's GIS data model type — datamod","text":"character(1). One \"vector\" \"raster\".","code":""},{"path":"https://NIEHS.github.io/chopin/reference/datamod.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Return the input's GIS data model type — datamod","text":"Although stars object little ambiguous whether classify vector raster, considered raster package.","code":""},{"path":[]},{"path":"https://NIEHS.github.io/chopin/reference/datamod.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Return the input's GIS data model type — datamod","text":"Insang Song","code":""},{"path":"https://NIEHS.github.io/chopin/reference/datamod.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Return the input's GIS data model type — datamod","text":"","code":"if (FALSE) { # \\dontrun{ library(sf) library(terra) options(sf_use_s2 = FALSE)  nc_path <- system.file(\"gpkg/nc.gpkg\", package = \"sf\") nc_sf <- sf::st_read(nc_path) datamod(nc_sf)  ra_path <- system.file(\"ex/elev.tif\", package = \"terra\") ra <- terra::rast(ra_path) datamod(ra) } # }"},{"path":"https://NIEHS.github.io/chopin/reference/dep_check.html","id":null,"dir":"Reference","previous_headings":"","what":"Return the package the input object is based on — dep_check","title":"Return the package the input object is based on — dep_check","text":"Detect whether input object sf Spat* object.","code":""},{"path":"https://NIEHS.github.io/chopin/reference/dep_check.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Return the package the input object is based on — dep_check","text":"","code":"dep_check(input)"},{"path":"https://NIEHS.github.io/chopin/reference/dep_check.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Return the package the input object is based on — dep_check","text":"input Spat* terra sf object.","code":""},{"path":"https://NIEHS.github.io/chopin/reference/dep_check.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Return the package the input object is based on — dep_check","text":"character object; one \"character\", \"terra\" \"sf\"","code":""},{"path":[]},{"path":"https://NIEHS.github.io/chopin/reference/dep_check.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Return the package the input object is based on — dep_check","text":"Insang Song","code":""},{"path":"https://NIEHS.github.io/chopin/reference/dep_check.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Return the package the input object is based on — dep_check","text":"","code":"if (FALSE) { # \\dontrun{ library(sf) library(terra) options(sf_use_s2 = FALSE)  nc_path <- system.file(\"gpkg/nc.gpkg\", package = \"sf\") nc_sf <- sf::st_read(nc_path) dep_check(nc_sf) nc_vect <- terra::vect(nc_sf) dep_check(nc_vect) } # }"},{"path":"https://NIEHS.github.io/chopin/reference/dep_switch.html","id":null,"dir":"Reference","previous_headings":"","what":"Switch spatial data class — dep_switch","title":"Switch spatial data class — dep_switch","text":"Convert class sf/stars-terra","code":""},{"path":"https://NIEHS.github.io/chopin/reference/dep_switch.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Switch spatial data class — dep_switch","text":"","code":"dep_switch(input)"},{"path":"https://NIEHS.github.io/chopin/reference/dep_switch.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Switch spatial data class — dep_switch","text":"input Spat* terra sf object.","code":""},{"path":"https://NIEHS.github.io/chopin/reference/dep_switch.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Switch spatial data class — dep_switch","text":"Data converted package class (sf, terra; terra, sf)","code":""},{"path":[]},{"path":"https://NIEHS.github.io/chopin/reference/dep_switch.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Switch spatial data class — dep_switch","text":"Insang Song","code":""},{"path":"https://NIEHS.github.io/chopin/reference/dep_switch.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Switch spatial data class — dep_switch","text":"","code":"if (FALSE) { # \\dontrun{ library(sf) library(stars) library(terra) options(sf_use_s2 = FALSE)  ## generate a random raster ras_rand <- terra::rast(nrow = 30, ncol = 30) terra::values(ras_rand) <- runif(900) stars_rand <- dep_switch(ras_rand) stars_rand inherits(sf_rand, \"stars\") # TRUE # should return stars object  vec_rand <- terra::spatSample(ras_rand, size = 10L, as.points = TRUE) sf_rand <- dep_switch(vec_rand) inherits(sf_rand, \"sf\") # TRUE sf_rand # should return sf object } # }"},{"path":"https://NIEHS.github.io/chopin/reference/dot-check_character.html","id":null,"dir":"Reference","previous_headings":"","what":"Check the class of an input object — .check_character","title":"Check the class of an input object — .check_character","text":"function checks class input object returns \"raster\" raster object, \"vector\" vector object.","code":""},{"path":"https://NIEHS.github.io/chopin/reference/dot-check_character.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Check the class of an input object — .check_character","text":"","code":".check_character(input)"},{"path":"https://NIEHS.github.io/chopin/reference/dot-check_character.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Check the class of an input object — .check_character","text":"input input object checked","code":""},{"path":"https://NIEHS.github.io/chopin/reference/dot-check_character.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Check the class of an input object — .check_character","text":"character string indicating class input object (\"raster\" \"vector\")","code":""},{"path":"https://NIEHS.github.io/chopin/reference/dot-check_raster.html","id":null,"dir":"Reference","previous_headings":"","what":"Check Raster Input — .check_raster","title":"Check Raster Input — .check_raster","text":"function checks input object ensure valid raster object character path raster file. also provides warnings informative messages based input type.","code":""},{"path":"https://NIEHS.github.io/chopin/reference/dot-check_raster.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Check Raster Input — .check_raster","text":"","code":".check_raster(input, extent = NULL, ...)"},{"path":"https://NIEHS.github.io/chopin/reference/dot-check_raster.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Check Raster Input — .check_raster","text":"input input object checked. can either SpatRaster object character path raster file. extent extent raster. Defaults NULL. Numeric vector put order c(xmin, xmax, ymin, ymax). ... Placeholder.","code":""},{"path":"https://NIEHS.github.io/chopin/reference/dot-check_raster.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Check Raster Input — .check_raster","text":"validated input object.","code":""},{"path":"https://NIEHS.github.io/chopin/reference/dot-check_raster.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Check Raster Input — .check_raster","text":"","code":"if (FALSE) { # \\dontrun{ .check_raster(system.file(\"extdata/nc_srtm15_otm.tif\", package = \"chopin\")) } # }"},{"path":"https://NIEHS.github.io/chopin/reference/dot-check_vector.html","id":null,"dir":"Reference","previous_headings":"","what":"Check the subject object and perform necessary conversions if needed. — .check_vector","title":"Check the subject object and perform necessary conversions if needed. — .check_vector","text":"function checks class input object performs necessary conversions needed.","code":""},{"path":"https://NIEHS.github.io/chopin/reference/dot-check_vector.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Check the subject object and perform necessary conversions if needed. — .check_vector","text":"","code":".check_vector(   input,   input_id = NULL,   extent = NULL,   out_class = character(1),   ... )"},{"path":"https://NIEHS.github.io/chopin/reference/dot-check_vector.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Check the subject object and perform necessary conversions if needed. — .check_vector","text":"input sf/SpatVector/character. input object checked. input_id character(1). ID field subject object. extent numeric(4). extent subject object. Numeric vector put order c(xmin, xmax, ymin, ymax). out_class character(1). class output object. one c(\"sf\", \"terra\"). ... Placeholder.","code":""},{"path":"https://NIEHS.github.io/chopin/reference/dot-check_vector.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Check the subject object and perform necessary conversions if needed. — .check_vector","text":"checked converted subject object.","code":""},{"path":"https://NIEHS.github.io/chopin/reference/dot-check_vector.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Check the subject object and perform necessary conversions if needed. — .check_vector","text":"","code":"if (FALSE) { # \\dontrun{ # Check a SpatVector object ncpath <- system.file(\"gpkg/nc.gpkg\", package = \"sf\") nc <- terra::vect(ncpath) extent <- c(-80, -77, 35, 36) .check_vector(input = nc, extent = extent, input_id = \"FIPS\")  # Check a sf object ncsf <- sf::st_read(ncpath) .check_vector(input = ncsf, extent = extent, input_id = \"FIPS\")  # Check a character object .check_vector(   input = ncpath,   extent = extent,   out_class = \"terra\",   input_id = \"FIPS\" ) } # }"},{"path":"https://NIEHS.github.io/chopin/reference/dot-intersect_extent.html","id":null,"dir":"Reference","previous_headings":"","what":"Get intersection extent — .intersect_extent","title":"Get intersection extent — .intersect_extent","text":"Get intersection extent","code":""},{"path":"https://NIEHS.github.io/chopin/reference/dot-intersect_extent.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get intersection extent — .intersect_extent","text":"","code":".intersect_extent(input, out_class, ...)  # S4 method for class 'sf,ANY' .intersect_extent(input, out_class = NULL, ...)  # S4 method for class 'SpatExtent,ANY' .intersect_extent(input, out_class = NULL, ...)  # S4 method for class 'SpatVector,ANY' .intersect_extent(input, out_class = NULL, ...)  # S4 method for class 'numeric,character' .intersect_extent(input, out_class = NULL, ...)"},{"path":"https://NIEHS.github.io/chopin/reference/dot-intersect_extent.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get intersection extent — .intersect_extent","text":"input sf/SpatExtent/SpatVector/numeric out_class character(1). \"sf\" \"terra\" ... arguments. Placeholder.","code":""},{"path":"https://NIEHS.github.io/chopin/reference/dot-par_screen.html","id":null,"dir":"Reference","previous_headings":"","what":"Prescreen input data for parallelization — .par_screen","title":"Prescreen input data for parallelization — .par_screen","text":"function takes input object type character ingest input object return object desired class.","code":""},{"path":"https://NIEHS.github.io/chopin/reference/dot-par_screen.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Prescreen input data for parallelization — .par_screen","text":"","code":".par_screen(type, input, input_id = NULL, out_class = \"terra\", .window = NULL)"},{"path":"https://NIEHS.github.io/chopin/reference/dot-par_screen.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Prescreen input data for parallelization — .par_screen","text":"type character(1). \"raster\" \"vector\". input object. Input object. input_id character(1). Default NULL. NULL, function check object ID column. out_class character(1). Default NULL, one c(\"sf\", \"terra\"). Default \"terra\". .window numeric(4)/SpatExtent/st_bbox object. Loading window.","code":""},{"path":"https://NIEHS.github.io/chopin/reference/extract_at.html","id":null,"dir":"Reference","previous_headings":"","what":"Extract raster values with point buffers or polygons — extract_at","title":"Extract raster values with point buffers or polygons — extract_at","text":"Extract raster values point buffers polygons","code":""},{"path":"https://NIEHS.github.io/chopin/reference/extract_at.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extract raster values with point buffers or polygons — extract_at","text":"","code":"extract_at(x, y, ...)  # S4 method for class 'SpatRaster,sf' extract_at(   x = NULL,   y = NULL,   id = NULL,   func = \"mean\",   extent = NULL,   radius = NULL,   out_class = \"sf\",   kernel = NULL,   kernel_func = stats::weighted.mean,   bandwidth = NULL,   max_cells = 3e+07,   .standalone = TRUE,   ... )  # S4 method for class 'character,character' extract_at(   x = NULL,   y = NULL,   id = NULL,   func = \"mean\",   extent = NULL,   radius = NULL,   out_class = \"sf\",   kernel = NULL,   kernel_func = stats::weighted.mean,   bandwidth = NULL,   max_cells = 3e+07,   .standalone = TRUE,   ... )  # S4 method for class 'SpatRaster,character' extract_at(   x = NULL,   y = NULL,   id = NULL,   func = \"mean\",   extent = NULL,   radius = NULL,   out_class = \"sf\",   kernel = NULL,   kernel_func = stats::weighted.mean,   bandwidth = NULL,   max_cells = 3e+07,   .standalone = TRUE,   ... )  # S4 method for class 'SpatRaster,SpatVector' extract_at(   x = NULL,   y = NULL,   id = NULL,   func = \"mean\",   extent = NULL,   radius = NULL,   out_class = \"sf\",   kernel = NULL,   kernel_func = stats::weighted.mean,   bandwidth = NULL,   max_cells = 3e+07,   .standalone = TRUE,   ... )  # S4 method for class 'character,sf' extract_at(   x = NULL,   y = NULL,   id = NULL,   func = \"mean\",   extent = NULL,   radius = NULL,   out_class = \"sf\",   kernel = NULL,   kernel_func = stats::weighted.mean,   bandwidth = NULL,   max_cells = 3e+07,   .standalone = TRUE,   ... )  # S4 method for class 'character,SpatVector' extract_at(   x = NULL,   y = NULL,   id = NULL,   func = \"mean\",   extent,   radius,   out_class = \"sf\",   kernel = NULL,   kernel_func = stats::weighted.mean,   bandwidth,   max_cells = 3e+07,   .standalone = TRUE,   ... )"},{"path":"https://NIEHS.github.io/chopin/reference/extract_at.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extract raster values with point buffers or polygons — extract_at","text":"x SpatRaster object file path(s) extensions GDAL-compatible. multiple file paths used, rasters must extent resolution. y sf/SpatVector object file path. ... Placeholder. id character(1). Unique identifier point. func function taking one numeric vector argument. extent numeric(4) SpatExtent. Extent clipping vector. works points character(1) file path. radius numeric(1). Buffer radius. out_class character(1). Output class. One sf terra. kernel character(1). Name kernel function One \"uniform\", \"triweight\", \"quartic\", \"epanechnikov\" kernel_func function. Kernel function apply extracted values. bandwidth numeric(1). Kernel bandwidth. max_cells integer(1). Maximum number cells memory. .standalone logical(1). Default TRUE, means function executed standalone mode. using function par_* functions, set FALSE.","code":""},{"path":"https://NIEHS.github.io/chopin/reference/extract_at.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Extract raster values with point buffers or polygons — extract_at","text":"data.frame object summarized raster values respect mode (polygon buffer) function.","code":""},{"path":"https://NIEHS.github.io/chopin/reference/extract_at.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Extract raster values with point buffers or polygons — extract_at","text":"Inputs preprocessed different ways depending class. Vector inputs y: sf preferred, thus character SpatVector inputs converted sf object. radius NULL, sf::st_buffer used generate circular buffers subsequent raster-vector overlay done exactextractr::exact_extract. Raster input x: SpatRaster preferred. input SpatRaster, converted SpatRaster object.","code":""},{"path":[]},{"path":"https://NIEHS.github.io/chopin/reference/extract_at.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Extract raster values with point buffers or polygons — extract_at","text":"Insang Song geoissong@gmail.com","code":""},{"path":"https://NIEHS.github.io/chopin/reference/extract_at.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Extract raster values with point buffers or polygons — extract_at","text":"","code":"ncpath <- system.file(\"gpkg/nc.gpkg\", package = \"sf\") rastpath <- system.file(\"extdata/nc_srtm15_otm.tif\", package = \"chopin\")  nc <- terra::vect(ncpath) nc <- terra::project(nc, \"EPSG:5070\") rrast <- terra::rast(nc, nrow = 100, ncol = 220) ncr <- terra::rasterize(nc, rrast) terra::values(rrast) <- rgamma(2.2e4, 4, 2) rpnt <- terra::spatSample(rrast, 16L, as.points = TRUE) rpnt$pid <- sprintf(\"ID-%02d\", seq(1, 16))  extract_at(rrast, rpnt, \"pid\", \"mean\", radius = 1000) #> Switch terra class to sf... #>      pid      mean #> 1  ID-01 1.9822180 #> 2  ID-02 3.4319303 #> 3  ID-03 3.9273469 #> 4  ID-04 0.2887684 #> 5  ID-05 1.0191861 #> 6  ID-06 1.4394056 #> 7  ID-07 3.3074858 #> 8  ID-08 3.7643452 #> 9  ID-09 1.9196675 #> 10 ID-10 3.5066707 #> 11 ID-11 1.7782123 #> 12 ID-12 0.7785873 #> 13 ID-13 2.0458314 #> 14 ID-14 1.1224172 #> 15 ID-15 3.9863424 #> 16 ID-16 2.9632106 extract_at(rrast, nc, \"NAME\", \"mean\") #> Switch terra class to sf... #>             NAME     mean #> 1           Ashe 1.947390 #> 2      Alleghany 1.967787 #> 3          Surry 1.979185 #> 4      Currituck 2.087879 #> 5    Northampton 1.995564 #> 6       Hertford 2.055216 #> 7         Camden 2.007895 #> 8          Gates 1.988872 #> 9         Warren 2.030980 #> 10        Stokes 1.948919 #> 11       Caswell 2.259248 #> 12    Rockingham 1.933288 #> 13     Granville 1.968703 #> 14        Person 2.050707 #> 15         Vance 2.038214 #> 16       Halifax 2.146045 #> 17    Pasquotank 1.875991 #> 18        Wilkes 1.915943 #> 19       Watauga 1.895707 #> 20    Perquimans 1.864332 #> 21        Chowan 2.067013 #> 22         Avery 2.087210 #> 23        Yadkin 2.030051 #> 24      Franklin 1.952200 #> 25       Forsyth 2.081115 #> 26      Guilford 1.971956 #> 27      Alamance 1.851228 #> 28        Bertie 1.861882 #> 29        Orange 1.943042 #> 30        Durham 2.267813 #> 31          Nash 2.116162 #> 32      Mitchell 2.020578 #> 33     Edgecombe 2.139719 #> 34      Caldwell 2.003097 #> 35        Yancey 1.859106 #> 36        Martin 1.997395 #> 37          Wake 1.919064 #> 38       Madison 1.956803 #> 39       Iredell 1.979584 #> 40         Davie 2.242437 #> 41     Alexander 1.812335 #> 42      Davidson 2.031528 #> 43         Burke 1.928709 #> 44    Washington 2.051182 #> 45       Tyrrell 2.120088 #> 46      McDowell 1.951825 #> 47      Randolph 1.997749 #> 48       Chatham 1.956700 #> 49        Wilson 2.065812 #> 50         Rowan 1.879703 #> 51          Pitt 1.969181 #> 52       Catawba 2.094155 #> 53      Buncombe 2.115411 #> 54      Johnston 2.030387 #> 55       Haywood 1.925143 #> 56          Dare 2.003934 #> 57      Beaufort 1.995797 #> 58         Swain 1.995029 #> 59        Greene 1.892647 #> 60           Lee 1.786664 #> 61    Rutherford 2.080569 #> 62         Wayne 2.036669 #> 63       Harnett 2.025678 #> 64     Cleveland 2.155540 #> 65       Lincoln 2.244004 #> 66       Jackson 2.096978 #> 67         Moore 2.074827 #> 68   Mecklenburg 1.946310 #> 69      Cabarrus 1.985471 #> 70    Montgomery 1.982364 #> 71        Stanly 1.831006 #> 72     Henderson 2.046879 #> 73        Graham 1.807080 #> 74        Lenoir 1.992867 #> 75  Transylvania 2.139678 #> 76        Gaston 1.860849 #> 77          Polk 2.001973 #> 78         Macon 2.087381 #> 79       Sampson 2.149392 #> 80       Pamlico 1.884383 #> 81      Cherokee 1.974908 #> 82    Cumberland 1.973660 #> 83         Jones 1.979061 #> 84         Union 1.981828 #> 85         Anson 2.053549 #> 86          Hoke 1.982197 #> 87          Hyde 2.087430 #> 88        Duplin 2.001727 #> 89      Richmond 2.093516 #> 90          Clay 1.872995 #> 91        Craven 1.985506 #> 92      Scotland 1.972766 #> 93        Onslow 1.982934 #> 94       Robeson 2.052670 #> 95      Carteret 2.064187 #> 96        Bladen 2.083948 #> 97        Pender 1.920480 #> 98      Columbus 2.075454 #> 99   New Hanover 1.874795 #> 100    Brunswick 1.911686 extract_at(rrast, ncpath, \"NAME\", \"mean\") #> ℹ Input is a character. Trying to read with sf. #> Reading layer `nc.gpkg' from data source  #>   `/home/runner/work/_temp/Library/sf/gpkg/nc.gpkg' using driver `GPKG' #> Simple feature collection with 100 features and 14 fields #> Geometry type: MULTIPOLYGON #> Dimension:     XY #> Bounding box:  xmin: -84.32385 ymin: 33.88199 xmax: -75.45698 ymax: 36.58965 #> Geodetic CRS:  NAD27 #>             NAME     mean #> 1           Ashe 1.947390 #> 2      Alleghany 1.967787 #> 3          Surry 1.979185 #> 4      Currituck 2.087879 #> 5    Northampton 1.995564 #> 6       Hertford 2.055216 #> 7         Camden 2.007895 #> 8          Gates 1.988872 #> 9         Warren 2.030980 #> 10        Stokes 1.948919 #> 11       Caswell 2.259248 #> 12    Rockingham 1.933288 #> 13     Granville 1.968703 #> 14        Person 2.050707 #> 15         Vance 2.038214 #> 16       Halifax 2.146045 #> 17    Pasquotank 1.875991 #> 18        Wilkes 1.915943 #> 19       Watauga 1.895707 #> 20    Perquimans 1.864332 #> 21        Chowan 2.067013 #> 22         Avery 2.087210 #> 23        Yadkin 2.030051 #> 24      Franklin 1.952200 #> 25       Forsyth 2.081115 #> 26      Guilford 1.971956 #> 27      Alamance 1.851228 #> 28        Bertie 1.861882 #> 29        Orange 1.943042 #> 30        Durham 2.267813 #> 31          Nash 2.116162 #> 32      Mitchell 2.020578 #> 33     Edgecombe 2.139719 #> 34      Caldwell 2.003097 #> 35        Yancey 1.859106 #> 36        Martin 1.997395 #> 37          Wake 1.919064 #> 38       Madison 1.956803 #> 39       Iredell 1.979584 #> 40         Davie 2.242437 #> 41     Alexander 1.812335 #> 42      Davidson 2.031528 #> 43         Burke 1.928709 #> 44    Washington 2.051182 #> 45       Tyrrell 2.120088 #> 46      McDowell 1.951825 #> 47      Randolph 1.997749 #> 48       Chatham 1.956700 #> 49        Wilson 2.065812 #> 50         Rowan 1.879703 #> 51          Pitt 1.969181 #> 52       Catawba 2.094155 #> 53      Buncombe 2.115411 #> 54      Johnston 2.030387 #> 55       Haywood 1.925143 #> 56          Dare 2.003934 #> 57      Beaufort 1.995797 #> 58         Swain 1.995029 #> 59        Greene 1.892647 #> 60           Lee 1.786664 #> 61    Rutherford 2.080569 #> 62         Wayne 2.036669 #> 63       Harnett 2.025678 #> 64     Cleveland 2.155540 #> 65       Lincoln 2.244004 #> 66       Jackson 2.096978 #> 67         Moore 2.074827 #> 68   Mecklenburg 1.946310 #> 69      Cabarrus 1.985471 #> 70    Montgomery 1.982364 #> 71        Stanly 1.831006 #> 72     Henderson 2.046879 #> 73        Graham 1.807080 #> 74        Lenoir 1.992867 #> 75  Transylvania 2.139678 #> 76        Gaston 1.860849 #> 77          Polk 2.001973 #> 78         Macon 2.087381 #> 79       Sampson 2.149392 #> 80       Pamlico 1.884383 #> 81      Cherokee 1.974908 #> 82    Cumberland 1.973660 #> 83         Jones 1.979061 #> 84         Union 1.981828 #> 85         Anson 2.053549 #> 86          Hoke 1.982197 #> 87          Hyde 2.087430 #> 88        Duplin 2.001727 #> 89      Richmond 2.093516 #> 90          Clay 1.872995 #> 91        Craven 1.985506 #> 92      Scotland 1.972766 #> 93        Onslow 1.982934 #> 94       Robeson 2.052670 #> 95      Carteret 2.064187 #> 96        Bladen 2.083948 #> 97        Pender 1.920480 #> 98      Columbus 2.075454 #> 99   New Hanover 1.874795 #> 100    Brunswick 1.911686 extract_at(   rrast, ncpath, \"NAME\", \"mean\",   kernel = \"epanechnikov\",   bandwidth = 1e5 ) #> ℹ Input is a character. Trying to read with sf. #> Reading layer `nc.gpkg' from data source  #>   `/home/runner/work/_temp/Library/sf/gpkg/nc.gpkg' using driver `GPKG' #> Simple feature collection with 100 features and 14 fields #> Geometry type: MULTIPOLYGON #> Dimension:     XY #> Bounding box:  xmin: -84.32385 ymin: 33.88199 xmax: -75.45698 ymax: 36.58965 #> Geodetic CRS:  NAD27 #> Kernel function [ epanechnikov ] is applied to calculate weights... #> Switch sf class to terra... #> Warning: Point geometries are acceptable for kernel weighting. Convert to points... #> (Note: inside = TRUE is applied) #> # A tibble: 100 × 2 #>    NAME      value #>    <chr>     <dbl> #>  1 Alamance   1.85 #>  2 Alexander  1.81 #>  3 Alleghany  1.97 #>  4 Anson      2.06 #>  5 Ashe       1.95 #>  6 Avery      2.08 #>  7 Beaufort   1.99 #>  8 Bertie     1.86 #>  9 Bladen     2.08 #> 10 Brunswick  1.92 #> # ℹ 90 more rows extract_at(   rastpath, ncpath, \"NAME\", \"mean\",   kernel = \"epanechnikov\",   bandwidth = 1e5 ) #> Input is a character. Attempt to read it with terra::rast... #> ℹ Input is a character. Trying to read with sf. #> Reading layer `nc.gpkg' from data source  #>   `/home/runner/work/_temp/Library/sf/gpkg/nc.gpkg' using driver `GPKG' #> Simple feature collection with 100 features and 14 fields #> Geometry type: MULTIPOLYGON #> Dimension:     XY #> Bounding box:  xmin: -84.32385 ymin: 33.88199 xmax: -75.45698 ymax: 36.58965 #> Geodetic CRS:  NAD27 #> Kernel function [ epanechnikov ] is applied to calculate weights... #> Switch sf class to terra... #> Warning: Point geometries are acceptable for kernel weighting. Convert to points... #> (Note: inside = TRUE is applied) #> # A tibble: 100 × 2 #>    NAME       value #>    <chr>      <dbl> #>  1 Alamance   189.  #>  2 Alexander  358.  #>  3 Alleghany  877.  #>  4 Anson      106.  #>  5 Ashe       981.  #>  6 Avery     1086.  #>  7 Beaufort    11.8 #>  8 Bertie      18.9 #>  9 Bladen      26.7 #> 10 Brunswick   11.7 #> # ℹ 90 more rows"},{"path":"https://NIEHS.github.io/chopin/reference/get_clip_ext.html","id":null,"dir":"Reference","previous_headings":"","what":"Setting the clipping extent — get_clip_ext","title":"Setting the clipping extent — get_clip_ext","text":"Return clipping extent buffer radius. assumes input CRS projected linear unit meters.","code":""},{"path":"https://NIEHS.github.io/chopin/reference/get_clip_ext.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Setting the clipping extent — get_clip_ext","text":"","code":"get_clip_ext(pnts, radius, extrusion = 1.1)"},{"path":"https://NIEHS.github.io/chopin/reference/get_clip_ext.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Setting the clipping extent — get_clip_ext","text":"pnts One sf SpatVector object. Target points computation. radius numeric(1). Buffer radius. assumed meters extrusion numeric(1). extent extrusion factor.","code":""},{"path":"https://NIEHS.github.io/chopin/reference/get_clip_ext.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Setting the clipping extent — get_clip_ext","text":"terra::ext sfc_POLYGON object computation extent.","code":""},{"path":[]},{"path":"https://NIEHS.github.io/chopin/reference/get_clip_ext.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Setting the clipping extent — get_clip_ext","text":"Insang Song","code":""},{"path":"https://NIEHS.github.io/chopin/reference/get_clip_ext.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Setting the clipping extent — get_clip_ext","text":"","code":"if (FALSE) { # \\dontrun{ library(sf) library(terra) options(sf_use_s2 = FALSE)  nc_path <- system.file(\"gpkg/nc.gpkg\", package = \"sf\") nc_sf <- sf::st_read(nc_path) nc_sf <- sf::st_transform(nc_sf, \"EPSG:5070\") get_clip_ext(nc_sf, 2.5e4) nc_vect <- terra::vect(nc_sf) get_clip_ext(nc_vect, 2.5e4) } # }"},{"path":"https://NIEHS.github.io/chopin/reference/indexing.html","id":null,"dir":"Reference","previous_headings":"","what":"Subset for nonidentical package class objects — indexing","title":"Subset for nonidentical package class objects — indexing","text":"Subset nonidentical package class objects Intersect different data model objects","code":""},{"path":"https://NIEHS.github.io/chopin/reference/indexing.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Subset for nonidentical package class objects — indexing","text":"","code":"# S4 method for class 'SpatVector,bbox,missing,ANY' x[i, j]  # S4 method for class 'SpatVector,sf,missing,ANY' x[i, j]  # S4 method for class 'SpatVector,sfc,missing,ANY' x[i, j]  # S4 method for class 'SpatVector,SpatExtent,missing,ANY' x[i, j]  .intersect(x, y)"},{"path":"https://NIEHS.github.io/chopin/reference/indexing.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Subset for nonidentical package class objects — indexing","text":"x SpatVector/sf/SpatRaster object intersected. Dataset used subset x. j Column indices names. y SpatVector/sf object. Intersecting object.","code":""},{"path":"https://NIEHS.github.io/chopin/reference/kernelfunction.html","id":null,"dir":"Reference","previous_headings":"","what":"Kernel functions — kernelfunction","title":"Kernel functions — kernelfunction","text":"Kernel functions","code":""},{"path":"https://NIEHS.github.io/chopin/reference/kernelfunction.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Kernel functions — kernelfunction","text":"","code":"kernelfunction(   d,   bw,   kernel = c(\"uniform\", \"quartic\", \"triweight\", \"epanechnikov\") )"},{"path":"https://NIEHS.github.io/chopin/reference/kernelfunction.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Kernel functions — kernelfunction","text":"d Distance bw Bandwidth kernel kernel Kernel type. One \"uniform\", \"quartic\", \"triweight\", \"epanechnikov\"","code":""},{"path":"https://NIEHS.github.io/chopin/reference/kernelfunction.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Kernel functions — kernelfunction","text":"numeric. Kernel weights.","code":""},{"path":"https://NIEHS.github.io/chopin/reference/kernelfunction.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Kernel functions — kernelfunction","text":"https://github.com/JanCaha/SpatialKDE","code":""},{"path":[]},{"path":"https://NIEHS.github.io/chopin/reference/kernelfunction.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Kernel functions — kernelfunction","text":"","code":"v_dist <- c(1, 10, 100, 25, 50, 0.1) bw_dist1 <- 1 bw_dist2 <- 10 kernelfunction(v_dist, bw_dist1, \"uniform\") #> [1] 0.5 0.0 0.0 0.0 0.0 0.5 kernelfunction(v_dist, bw_dist1, \"quartic\") #> [1] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.9188437 kernelfunction(v_dist, bw_dist1, \"triweight\") #> [1] 0.000 0.000 0.000 0.000 0.000 0.999 kernelfunction(v_dist, bw_dist1, \"epanechnikov\") #> [1] 0.0000 0.0000 0.0000 0.0000 0.0000 0.7425 kernelfunction(v_dist, bw_dist2, \"uniform\") #> [1] 0.5 0.5 0.0 0.0 0.0 0.5 kernelfunction(v_dist, bw_dist2, \"quartic\") #> [1] 0.9188437 0.0000000 0.0000000 0.0000000 0.0000000 0.9373125 kernelfunction(v_dist, bw_dist2, \"triweight\") #> [1] 0.999000 0.000000 0.000000 0.000000 0.000000 0.999999 kernelfunction(v_dist, bw_dist2, \"epanechnikov\") #> [1] 0.742500 0.000000 0.000000 0.000000 0.000000 0.749925"},{"path":"https://NIEHS.github.io/chopin/reference/ncpoints.html","id":null,"dir":"Reference","previous_headings":"","what":"Mildly clustered points in North Carolina, United States — ncpoints","title":"Mildly clustered points in North Carolina, United States — ncpoints","text":"Mildly clustered points North Carolina, United States","code":""},{"path":"https://NIEHS.github.io/chopin/reference/ncpoints.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Mildly clustered points in North Carolina, United States — ncpoints","text":"","code":"ncpoints"},{"path":"https://NIEHS.github.io/chopin/reference/ncpoints.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Mildly clustered points in North Carolina, United States — ncpoints","text":"data frame 2,304 rows two variables: X X coordinate Y Y coordinate","code":""},{"path":"https://NIEHS.github.io/chopin/reference/ncpoints.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Mildly clustered points in North Carolina, United States — ncpoints","text":"sf package data nc","code":""},{"path":"https://NIEHS.github.io/chopin/reference/ncpoints.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Mildly clustered points in North Carolina, United States — ncpoints","text":"Coordinates EPSG:5070 (Conus Albers Equal Area)","code":""},{"path":[]},{"path":"https://NIEHS.github.io/chopin/reference/ncpoints.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Mildly clustered points in North Carolina, United States — ncpoints","text":"","code":"data(\"ncpoints\", package = \"chopin\")"},{"path":"https://NIEHS.github.io/chopin/reference/par_cut_coords.html","id":null,"dir":"Reference","previous_headings":"","what":"Partition coordinates into quantile polygons — par_cut_coords","title":"Partition coordinates into quantile polygons — par_cut_coords","text":"Partition coordinates quantile polygons","code":""},{"path":"https://NIEHS.github.io/chopin/reference/par_cut_coords.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Partition coordinates into quantile polygons — par_cut_coords","text":"","code":"par_cut_coords(x = NULL, y = NULL, quantiles)"},{"path":"https://NIEHS.github.io/chopin/reference/par_cut_coords.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Partition coordinates into quantile polygons — par_cut_coords","text":"x numeric/sf/SpatVector. x-coordinates (numeric). y numeric. y-coordinates. quantiles numeric vector. Quantiles.","code":""},{"path":"https://NIEHS.github.io/chopin/reference/par_cut_coords.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Partition coordinates into quantile polygons — par_cut_coords","text":"SpatVector object field CGRIDID.","code":""},{"path":"https://NIEHS.github.io/chopin/reference/par_cut_coords.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Partition coordinates into quantile polygons — par_cut_coords","text":"function two-dimensional points.","code":""},{"path":[]},{"path":"https://NIEHS.github.io/chopin/reference/par_cut_coords.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Partition coordinates into quantile polygons — par_cut_coords","text":"","code":"if (FALSE) { # \\dontrun{ library(terra)  random_points <-   data.frame(x = runif(1000, 0, 100), y = runif(1000, 0, 100)) quantiles <- seq(0, 1, length.out = 5L) qpoly <- par_cut_coords(random_points$x, random_points$y, quantiles) clustered_points <-   data.frame(x = rgamma(1000, 1, 1), y = rgamma(1000, 4, 1))  qpoly_c <- par_cut_coords(clustered_points$x, clustered_points$y, quantiles)  par(mfcol = c(1, 2)) plot(qpoly) plot(qpoly_c) par(mfcol = c(1, 1))  cvect <- terra::vect(clustered_points, geom = c(\"x\", \"y\")) plot(cvect) plot(qpoly_c, add = TRUE, col = \"transparent\", border = \"red\")  qcv <- intersect(cvect, qpoly_c) table(qcv$CGRIDID) sum(table(qcv$CGRIDID)) # should be 1000 } # }"},{"path":"https://NIEHS.github.io/chopin/reference/par_def_q.html","id":null,"dir":"Reference","previous_headings":"","what":"Quantile definition — par_def_q","title":"Quantile definition — par_def_q","text":"Quantile definition","code":""},{"path":"https://NIEHS.github.io/chopin/reference/par_def_q.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Quantile definition — par_def_q","text":"","code":"par_def_q(steps = 4L)"},{"path":"https://NIEHS.github.io/chopin/reference/par_def_q.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Quantile definition — par_def_q","text":"steps integer(1). number quantiles.","code":""},{"path":"https://NIEHS.github.io/chopin/reference/par_def_q.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Quantile definition — par_def_q","text":"numeric vector quantiles.","code":""},{"path":[]},{"path":"https://NIEHS.github.io/chopin/reference/par_def_q.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Quantile definition — par_def_q","text":"","code":"if (FALSE) { # \\dontrun{ par_def_q(5L) } # }"},{"path":"https://NIEHS.github.io/chopin/reference/par_grid.html","id":null,"dir":"Reference","previous_headings":"","what":"Parallelize spatial computation over the computational grids — par_grid","title":"Parallelize spatial computation over the computational grids — par_grid","text":"future::multicore, future::multisession, future::cluster future.mirai::mirai_multisession future::plan parallelize work grid. details terminology future package, refer future::plan. function assumes users one raster file sizable spatially distributed target locations. thread process nearest integer $|N_g| / |N_t|$ grids $|N_g|$ denotes number grids $|N_t|$ denotes number threads.","code":""},{"path":"https://NIEHS.github.io/chopin/reference/par_grid.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Parallelize spatial computation over the computational grids — par_grid","text":"","code":"par_grid(grids, fun_dist, ..., pad_y = FALSE, .debug = FALSE)"},{"path":"https://NIEHS.github.io/chopin/reference/par_grid.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Parallelize spatial computation over the computational grids — par_grid","text":"grids List two sf/SpatVector objects. Computational grids. takes strict assumption grid input output `par_pad_grid“. fun_dist sf, terra chopin functions. function x y arguments. ... Arguments passed argument fun_dist. pad_y logical(1). Whether filter y padded grid. TRUE x values calculated. Default FALSE. reverse case, like terra::extent exactextractr::exact_extract, raster (x) extent set padded grid. .debug logical(1). Default FALSE. Otherwise, unit computation fails, error message CGRIDID value error occurred included output.","code":""},{"path":"https://NIEHS.github.io/chopin/reference/par_grid.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Parallelize spatial computation over the computational grids — par_grid","text":"data.frame object computation results. entries results, consult documentation function put fun_dist argument.","code":""},{"path":"https://NIEHS.github.io/chopin/reference/par_grid.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Parallelize spatial computation over the computational grids — par_grid","text":"dynamic dots (...), fun_dist arguments include x y sf/terra class objects file paths accepted. Virtually sf/terra functions accept two arguments can put fun_dist, please advised spatial operations necessarily give exact result done single-thread. example, distance calculated function may return lower value actual computational region reduced. case especially target features spatially sparsely distributed.","code":""},{"path":[]},{"path":"https://NIEHS.github.io/chopin/reference/par_grid.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Parallelize spatial computation over the computational grids — par_grid","text":"Insang Song geoissong@gmail.com","code":""},{"path":"https://NIEHS.github.io/chopin/reference/par_grid.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Parallelize spatial computation over the computational grids — par_grid","text":"","code":"library(sf) #> Linking to GEOS 3.10.2, GDAL 3.4.1, PROJ 8.2.1; sf_use_s2() is TRUE library(future) library(future.mirai) plan(mirai_multisession, workers = 2) ncpath <- system.file(\"shape/nc.shp\", package = \"sf\") ncpoly <- sf::st_read(ncpath) #> Reading layer `nc' from data source  #>   `/home/runner/work/_temp/Library/sf/shape/nc.shp' using driver `ESRI Shapefile' #> Simple feature collection with 100 features and 14 fields #> Geometry type: MULTIPOLYGON #> Dimension:     XY #> Bounding box:  xmin: -84.32385 ymin: 33.88199 xmax: -75.45698 ymax: 36.58965 #> Geodetic CRS:  NAD27 # sf object ncpnts <-   readRDS(     system.file(\"extdata/nc_random_point.rds\", package = \"chopin\")   ) # file path ncelev <-     system.file(\"extdata/nc_srtm15_otm.tif\", package = \"chopin\")  nccompreg <-   chopin::par_pad_grid(     input = ncpnts,     mode = \"grid\",     nx = 4L,     ny = 2L,     padding = 5e3L   ) #> Switch sf class to terra... #> Switch terra class to sf... res <-   par_grid(     grids = nccompreg,     fun_dist = extract_at,     x = ncelev,     y = ncpnts,     qsegs = 90L,     radius = 5e3L,     id = \"pid\"   ) #> Spherical geometry (s2) switched off #> ℹ Input is not a character. #> Input is a character. Attempt to read it with terra::rast... #> ℹ Task at CGRIDID: 1 is successfully dispatched. #> Input is a character. Attempt to read it with terra::rast... #> ℹ Task at CGRIDID: 2 is successfully dispatched. #> Input is a character. Attempt to read it with terra::rast... #> ℹ Task at CGRIDID: 3 is successfully dispatched. #> Input is a character. Attempt to read it with terra::rast... #> ℹ Task at CGRIDID: 4 is successfully dispatched. #> Input is a character. Attempt to read it with terra::rast... #> ℹ Task at CGRIDID: 5 is successfully dispatched. #> Input is a character. Attempt to read it with terra::rast... #> ℹ Task at CGRIDID: 6 is successfully dispatched. #> Input is a character. Attempt to read it with terra::rast... #> ℹ Task at CGRIDID: 7 is successfully dispatched. #> Input is a character. Attempt to read it with terra::rast... #> ℹ Task at CGRIDID: 8 is successfully dispatched."},{"path":"https://NIEHS.github.io/chopin/reference/par_hierarchy.html","id":null,"dir":"Reference","previous_headings":"","what":"Parallelize spatial computation by hierarchy in input data — par_hierarchy","title":"Parallelize spatial computation by hierarchy in input data — par_hierarchy","text":"\"Hierarchy\" refers system, divides entire study region multiple subregions. oftentimes reflected area code system (e.g., FIPS US Census geographies Nomenclature Territorial Units Statistics (NUTS), etc.). future::multisession, future::multicore, future::cluster, future.mirai::mirai_multisession future::plan parallelize work splitting lower level features several higher level feature group. details terminology future package, please refer future::plan documentation. thread process number lower level features higher level feature. Please advised accessing file simultaneously multiple processes may result errors.","code":""},{"path":"https://NIEHS.github.io/chopin/reference/par_hierarchy.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Parallelize spatial computation by hierarchy in input data — par_hierarchy","text":"","code":"par_hierarchy(   regions,   regions_id = NULL,   length_left = NULL,   pad = 0,   pad_y = FALSE,   fun_dist,   ...,   .debug = FALSE )"},{"path":"https://NIEHS.github.io/chopin/reference/par_hierarchy.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Parallelize spatial computation by hierarchy in input data — par_hierarchy","text":"regions sf/SpatVector object. Computational regions. polygons accepted. regions_id character(1). Name unique ID field regions. regions split common level value. length_left integer(1). Length first characters regions_id values. Default NULL, manipulate regions_id values. number characters consistent (example, numerics), function alert user. pad numeric(1). Padding distance subregion defined regions_id trimmed regions_id values. linear unit coordinate system. Default 0, means subregion used . value greater 0, subregion buffered value. padding distance applied x (pad_y = FALSE) y (pad_y = TRUE) filter data. pad_y logical(1). Whether filter y padded grid. TRUE x values calculated. Default FALSE. reverse case, like terra::extent exactextractr::exact_extract, raster (x) scoped padded grid. fun_dist sf, terra, chopin functions. function x y arguments. ... Arguments passed argument fun_dist. .debug logical(1). Default FALSE unit computation fails, error message regions_id value error occurred included output.","code":""},{"path":"https://NIEHS.github.io/chopin/reference/par_hierarchy.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Parallelize spatial computation by hierarchy in input data — par_hierarchy","text":"data.frame object computation results. entries results, consult function used fun_dist argument.","code":""},{"path":"https://NIEHS.github.io/chopin/reference/par_hierarchy.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Parallelize spatial computation by hierarchy in input data — par_hierarchy","text":"dynamic dots (...), fun_dist arguments include x y sf/terra class objects file paths accepted. Hierarchy interpreted regions_id argument first. regions_id assumed field name x y argument object. expected regions represents higher level boundaries x y fun_dist lower level boundaries. However, case, trim argument, function generate higher level codes regions_id extracting left-t Whether x y searched determined pad_y value. pad_y = TRUE make function attempt find regions_id x, whereas pad_y = FALSE look regions_id y. regions_id exist x y, function utilize spatial relationship (intersects) filter data. Note dispatching computation subregions based spatial relationship may lead slight discrepancy result. example, higher lower level features perfectly aligned, may features included duplicated subregions. function alert user spatial relation- ship used filter data.","code":""},{"path":"https://NIEHS.github.io/chopin/reference/par_hierarchy.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Parallelize spatial computation by hierarchy in input data — par_hierarchy","text":"Virtually sf/terra functions accept two arguments can put fun_dist, please advised spatial operations necessarily give exact result done single thread. example, distance calculated function may return lower value actual computational region reduced. case especially target features spatially sparsely distributed.","code":""},{"path":[]},{"path":"https://NIEHS.github.io/chopin/reference/par_hierarchy.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Parallelize spatial computation by hierarchy in input data — par_hierarchy","text":"Insang Song geoissong@gmail.com","code":""},{"path":"https://NIEHS.github.io/chopin/reference/par_hierarchy.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Parallelize spatial computation by hierarchy in input data — par_hierarchy","text":"","code":"library(terra) #> terra 1.7.78 library(sf) library(future) library(future.mirai) options(sf_use_s2 = FALSE) future::plan(future.mirai::mirai_multisession, workers = 2)  ncpath <- system.file(\"extdata/nc_hierarchy.gpkg\", package = \"chopin\") nccnty <- sf::st_read(ncpath, layer = \"county\") #> Reading layer `county' from data source  #>   `/home/runner/work/_temp/Library/chopin/extdata/nc_hierarchy.gpkg'  #>   using driver `GPKG' #> Simple feature collection with 100 features and 1 field #> Geometry type: POLYGON #> Dimension:     XY #> Bounding box:  xmin: 1054155 ymin: 1341756 xmax: 1838923 ymax: 1690176 #> Projected CRS: NAD83 / Conus Albers nctrct <- sf::st_read(ncpath, layer = \"tracts\") #> Reading layer `tracts' from data source  #>   `/home/runner/work/_temp/Library/chopin/extdata/nc_hierarchy.gpkg'  #>   using driver `GPKG' #> Simple feature collection with 2672 features and 1 field #> Geometry type: MULTIPOLYGON #> Dimension:     XY #> Bounding box:  xmin: 1054155 ymin: 1341756 xmax: 1838923 ymax: 1690176 #> Projected CRS: NAD83 / Conus Albers ncelev <-   system.file(\"extdata/nc_srtm15_otm.tif\", package = \"chopin\")  ncsamp <-   sf::st_sample(     nccnty,     size = 1e4L   ) # sfc to sf ncsamp <- sf::st_as_sf(ncsamp) # assign ID ncsamp$kid <- sprintf(\"K-%05d\", seq_len(nrow(ncsamp))) res <-   par_hierarchy(     regions = nccnty,     regions_id = \"GEOID\",     fun_dist = extract_at,     y = nctrct,     x = ncelev,     id = \"GEOID\",     func = \"mean\"   ) #> ℹ Input is not a character. #> ℹ GEOID is used to stratify the process. #> Input is a character. Attempt to read it with terra::rast... #> ℹ Your input function at 37037 is dispatched. #> Input is a character. Attempt to read it with terra::rast... #> ℹ Your input function at 37001 is dispatched. #> Input is a character. Attempt to read it with terra::rast... #> ℹ Your input function at 37057 is dispatched. #> Input is a character. Attempt to read it with terra::rast... #> ℹ Your input function at 37069 is dispatched. #> Input is a character. Attempt to read it with terra::rast... #> ℹ Your input function at 37155 is dispatched. #> Input is a character. Attempt to read it with terra::rast... #> ℹ Your input function at 37109 is dispatched. #> Input is a character. Attempt to read it with terra::rast... #> ℹ Your input function at 37027 is dispatched. #> Input is a character. Attempt to read it with terra::rast... #> ℹ Your input function at 37063 is dispatched. #> Input is a character. Attempt to read it with terra::rast... #> ℹ Your input function at 37145 is dispatched. #> Input is a character. Attempt to read it with terra::rast... #> ℹ Your input function at 37115 is dispatched. #> Input is a character. Attempt to read it with terra::rast... #> ℹ Your input function at 37151 is dispatched. #> Input is a character. Attempt to read it with terra::rast... #> ℹ Your input function at 37131 is dispatched. #> Input is a character. Attempt to read it with terra::rast... #> ℹ Your input function at 37013 is dispatched. #> Input is a character. Attempt to read it with terra::rast... #> ℹ Your input function at 37159 is dispatched. #> Input is a character. Attempt to read it with terra::rast... #> ℹ Your input function at 37051 is dispatched. #> Input is a character. Attempt to read it with terra::rast... #> ℹ Your input function at 37153 is dispatched. #> Input is a character. Attempt to read it with terra::rast... #> ℹ Your input function at 37093 is dispatched. #> Input is a character. Attempt to read it with terra::rast... #> ℹ Your input function at 37025 is dispatched. #> Input is a character. Attempt to read it with terra::rast... #> ℹ Your input function at 37029 is dispatched. #> Input is a character. Attempt to read it with terra::rast... #> ℹ Your input function at 37169 is dispatched. #> Input is a character. Attempt to read it with terra::rast... #> ℹ Your input function at 37031 is dispatched. #> Input is a character. Attempt to read it with terra::rast... #> ℹ Your input function at 37005 is dispatched. #> Input is a character. Attempt to read it with terra::rast... #> ℹ Your input function at 37139 is dispatched. #> Input is a character. Attempt to read it with terra::rast... #> ℹ Your input function at 37193 is dispatched. #> Input is a character. Attempt to read it with terra::rast... #> ℹ Your input function at 37003 is dispatched. #> Input is a character. Attempt to read it with terra::rast... #> ℹ Your input function at 37083 is dispatched. #> Input is a character. Attempt to read it with terra::rast... #> ℹ Your input function at 37163 is dispatched. #> Input is a character. Attempt to read it with terra::rast... #> ℹ Your input function at 37189 is dispatched. #> Input is a character. Attempt to read it with terra::rast... #> ℹ Your input function at 37173 is dispatched. #> Input is a character. Attempt to read it with terra::rast... #> ℹ Your input function at 37011 is dispatched. #> Input is a character. Attempt to read it with terra::rast... #> ℹ Your input function at 37045 is dispatched. #> Input is a character. Attempt to read it with terra::rast... #> ℹ Your input function at 37125 is dispatched. #> Input is a character. Attempt to read it with terra::rast... #> ℹ Your input function at 37067 is dispatched. #> Input is a character. Attempt to read it with terra::rast... #> ℹ Your input function at 37077 is dispatched. #> Input is a character. Attempt to read it with terra::rast... #> ℹ Your input function at 37185 is dispatched. #> Input is a character. Attempt to read it with terra::rast... #> ℹ Your input function at 37137 is dispatched. #> Input is a character. Attempt to read it with terra::rast... #> ℹ Your input function at 37033 is dispatched. #> Input is a character. Attempt to read it with terra::rast... #> ℹ Your input function at 37107 is dispatched. #> Input is a character. Attempt to read it with terra::rast... #> ℹ Your input function at 37075 is dispatched. #> Input is a character. Attempt to read it with terra::rast... #> ℹ Your input function at 37073 is dispatched. #> Input is a character. Attempt to read it with terra::rast... #> ℹ Your input function at 37161 is dispatched. #> Input is a character. Attempt to read it with terra::rast... #> ℹ Your input function at 37187 is dispatched. #> Input is a character. Attempt to read it with terra::rast... #> ℹ Your input function at 37007 is dispatched. #> Input is a character. Attempt to read it with terra::rast... #> ℹ Your input function at 37135 is dispatched. #> Input is a character. Attempt to read it with terra::rast... #> ℹ Your input function at 37049 is dispatched. #> Input is a character. Attempt to read it with terra::rast... #> ℹ Your input function at 37195 is dispatched. #> Input is a character. Attempt to read it with terra::rast... #> ℹ Your input function at 37061 is dispatched. #> Input is a character. Attempt to read it with terra::rast... #> ℹ Your input function at 37087 is dispatched. #> Input is a character. Attempt to read it with terra::rast... #> ℹ Your input function at 37081 is dispatched. #> Input is a character. Attempt to read it with terra::rast... #> ℹ Your input function at 37099 is dispatched. #> Input is a character. Attempt to read it with terra::rast... #> ℹ Your input function at 37097 is dispatched. #> Input is a character. Attempt to read it with terra::rast... #> ℹ Your input function at 37091 is dispatched. #> Input is a character. Attempt to read it with terra::rast... #> ℹ Your input function at 37149 is dispatched. #> Input is a character. Attempt to read it with terra::rast... #> ℹ Your input function at 37165 is dispatched. #> Input is a character. Attempt to read it with terra::rast... #> ℹ Your input function at 37085 is dispatched. #> Input is a character. Attempt to read it with terra::rast... #> ℹ Your input function at 37105 is dispatched. #> Input is a character. Attempt to read it with terra::rast... #> ℹ Your input function at 37017 is dispatched. #> Input is a character. Attempt to read it with terra::rast... #> ℹ Your input function at 37039 is dispatched. #> Input is a character. Attempt to read it with terra::rast... #> ℹ Your input function at 37035 is dispatched. #> Input is a character. Attempt to read it with terra::rast... #> ℹ Your input function at 37177 is dispatched. #> Input is a character. Attempt to read it with terra::rast... #> ℹ Your input function at 37113 is dispatched. #> Input is a character. Attempt to read it with terra::rast... #> ℹ Your input function at 37143 is dispatched. #> Input is a character. Attempt to read it with terra::rast... #> ℹ Your input function at 37095 is dispatched. #> Input is a character. Attempt to read it with terra::rast... #> ℹ Your input function at 37071 is dispatched. #> Input is a character. Attempt to read it with terra::rast... #> ℹ Your input function at 37101 is dispatched. #> Input is a character. Attempt to read it with terra::rast... #> ℹ Your input function at 37015 is dispatched. #> Input is a character. Attempt to read it with terra::rast... #> ℹ Your input function at 37167 is dispatched. #> Input is a character. Attempt to read it with terra::rast... #> ℹ Your input function at 37079 is dispatched. #> Input is a character. Attempt to read it with terra::rast... #> ℹ Your input function at 37129 is dispatched. #> Input is a character. Attempt to read it with terra::rast... #> ℹ Your input function at 37147 is dispatched. #> Input is a character. Attempt to read it with terra::rast... #> ℹ Your input function at 37141 is dispatched. #> Input is a character. Attempt to read it with terra::rast... #> ℹ Your input function at 37179 is dispatched. #> Input is a character. Attempt to read it with terra::rast... #> ℹ Your input function at 37121 is dispatched. #> Input is a character. Attempt to read it with terra::rast... #> ℹ Your input function at 37133 is dispatched. #> Input is a character. Attempt to read it with terra::rast... #> ℹ Your input function at 37065 is dispatched. #> Input is a character. Attempt to read it with terra::rast... #> ℹ Your input function at 37119 is dispatched. #> Input is a character. Attempt to read it with terra::rast... #> ℹ Your input function at 37199 is dispatched. #> Input is a character. Attempt to read it with terra::rast... #> ℹ Your input function at 37197 is dispatched. #> Input is a character. Attempt to read it with terra::rast... #> ℹ Your input function at 37023 is dispatched. #> Input is a character. Attempt to read it with terra::rast... #> ℹ Your input function at 37191 is dispatched. #> Input is a character. Attempt to read it with terra::rast... #> ℹ Your input function at 37059 is dispatched. #> Input is a character. Attempt to read it with terra::rast... #> ℹ Your input function at 37111 is dispatched. #> Input is a character. Attempt to read it with terra::rast... #> ℹ Your input function at 37183 is dispatched. #> Input is a character. Attempt to read it with terra::rast... #> ℹ Your input function at 37053 is dispatched. #> Input is a character. Attempt to read it with terra::rast... #> ℹ Your input function at 37103 is dispatched. #> Input is a character. Attempt to read it with terra::rast... #> ℹ Your input function at 37041 is dispatched. #> Input is a character. Attempt to read it with terra::rast... #> ℹ Your input function at 37021 is dispatched. #> Input is a character. Attempt to read it with terra::rast... #> ℹ Your input function at 37157 is dispatched. #> Input is a character. Attempt to read it with terra::rast... #> ℹ Your input function at 37117 is dispatched. #> Input is a character. Attempt to read it with terra::rast... #> ℹ Your input function at 37089 is dispatched. #> Input is a character. Attempt to read it with terra::rast... #> ℹ Your input function at 37127 is dispatched. #> Input is a character. Attempt to read it with terra::rast... #> ℹ Your input function at 37009 is dispatched. #> Input is a character. Attempt to read it with terra::rast... #> ℹ Your input function at 37019 is dispatched. #> Input is a character. Attempt to read it with terra::rast... #> ℹ Your input function at 37123 is dispatched. #> Input is a character. Attempt to read it with terra::rast... #> ℹ Your input function at 37181 is dispatched. #> Input is a character. Attempt to read it with terra::rast... #> ℹ Your input function at 37175 is dispatched. #> Input is a character. Attempt to read it with terra::rast... #> ℹ Your input function at 37171 is dispatched. #> Input is a character. Attempt to read it with terra::rast... #> ℹ Your input function at 37043 is dispatched. #> Input is a character. Attempt to read it with terra::rast... #> ℹ Your input function at 37055 is dispatched. #> Input is a character. Attempt to read it with terra::rast... #> ℹ Your input function at 37047 is dispatched."},{"path":"https://NIEHS.github.io/chopin/reference/par_make_balanced.html","id":null,"dir":"Reference","previous_headings":"","what":"Generate groups based on balanced clustering — par_make_balanced","title":"Generate groups based on balanced clustering — par_make_balanced","text":"balancing computational loads, function uses anticlust package cluster input points. number clusters determined num_cluster argument. cluster equal number points. Grids generated based cluster extents. lower level, function uses terra::distance() function calculate Euclidean distance points.","code":""},{"path":"https://NIEHS.github.io/chopin/reference/par_make_balanced.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generate groups based on balanced clustering — par_make_balanced","text":"","code":"par_make_balanced(points_in = NULL, n_clusters = NULL)"},{"path":"https://NIEHS.github.io/chopin/reference/par_make_balanced.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generate groups based on balanced clustering — par_make_balanced","text":"points_in sf SpatVector object. Target points computation. n_clusters integer(1). number clusters.","code":""},{"path":"https://NIEHS.github.io/chopin/reference/par_make_balanced.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Generate groups based on balanced clustering — par_make_balanced","text":"SpatVector object field \"CGRIDID\".","code":""},{"path":"https://NIEHS.github.io/chopin/reference/par_make_balanced.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Generate groups based on balanced clustering — par_make_balanced","text":"function two-dimensional points. results irregular grids without overlapping parts.","code":""},{"path":"https://NIEHS.github.io/chopin/reference/par_make_balanced.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Generate groups based on balanced clustering — par_make_balanced","text":"Insang Song","code":""},{"path":"https://NIEHS.github.io/chopin/reference/par_make_balanced.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Generate groups based on balanced clustering — par_make_balanced","text":"","code":"if (FALSE) { # \\dontrun{ library(terra) library(anticlust) data(ncpoints, package = \"chopin\")  ncp <- terra::vect(   ncpoints, geom = c(\"X\", \"Y\"),   keepgeom = FALSE, crs = \"EPSG:5070\" )  # 2,304 points / 12 = 192 points per cluster ncpbal <- par_make_balanced(ncp, 12) ncpbal } # }"},{"path":"https://NIEHS.github.io/chopin/reference/par_make_grid.html","id":null,"dir":"Reference","previous_headings":"","what":"Generate grid polygons — par_make_grid","title":"Generate grid polygons — par_make_grid","text":"Returns sf object includes x- y- index using two inputs ncutsx ncutsy, x- y-directional splits, respectively.","code":""},{"path":"https://NIEHS.github.io/chopin/reference/par_make_grid.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generate grid polygons — par_make_grid","text":"","code":"par_make_grid(points_in = NULL, ncutsx = NULL, ncutsy = NULL)"},{"path":"https://NIEHS.github.io/chopin/reference/par_make_grid.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generate grid polygons — par_make_grid","text":"points_in sf SpatVector object. Target points computation. character(1) file path also acceptable. ncutsx integer(1). number splits along x-axis. ncutsy integer(1). number splits along y-axis.","code":""},{"path":"https://NIEHS.github.io/chopin/reference/par_make_grid.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Generate grid polygons — par_make_grid","text":"sf SpatVector object computation grids unique grid id (CGRIDID).","code":""},{"path":"https://NIEHS.github.io/chopin/reference/par_make_grid.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Generate grid polygons — par_make_grid","text":"Grids generated based extent points_in first, exhaustive grids filtered intersection points_in. Thus, number generated grids may smaller ncutsx * ncutsy.","code":""},{"path":[]},{"path":"https://NIEHS.github.io/chopin/reference/par_make_grid.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Generate grid polygons — par_make_grid","text":"Insang Song","code":""},{"path":"https://NIEHS.github.io/chopin/reference/par_make_grid.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Generate grid polygons — par_make_grid","text":"","code":"if (FALSE) { # \\dontrun{ library(sf) library(terra) options(sf_use_s2 = FALSE)  nc_path <- system.file(\"gpkg/nc.gpkg\", package = \"sf\") nc <- terra::vect(nc_path) nc_rp <- terra::spatSample(nc, 1000) nc_gr <- par_make_grid(nc_rp, 10L, 6L)  plot(nc_rp) plot(nc_gr, add = TRUE) } # }"},{"path":"https://NIEHS.github.io/chopin/reference/par_map_args.html","id":null,"dir":"Reference","previous_headings":"","what":"Map arguments to the desired names — par_map_args","title":"Map arguments to the desired names — par_map_args","text":"function maps arguments target function desired names. Users use named list name_match standardize argument names, least x y, target function. function particularly useful parallelize functions spatial data outside sf terra packages arguments named x /y. par_* functions detect functions wrapping nonstandardized functions parallelize computation.","code":""},{"path":"https://NIEHS.github.io/chopin/reference/par_map_args.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Map arguments to the desired names — par_map_args","text":"","code":"par_map_args(fun, name_match = list(), ...)"},{"path":"https://NIEHS.github.io/chopin/reference/par_map_args.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Map arguments to the desired names — par_map_args","text":"fun target function called. name_match named list arguments mapped. ... Arguments passed target function.","code":""},{"path":"https://NIEHS.github.io/chopin/reference/par_map_args.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Map arguments to the desired names — par_map_args","text":"result target function.","code":""},{"path":[]},{"path":"https://NIEHS.github.io/chopin/reference/par_map_args.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Map arguments to the desired names — par_map_args","text":"","code":"# Example target function example_fun <- function(x, y, z = 1) {   return(c(x = x, y = y, z = z)) }  # Example usage of map_args_xy result <- par_map_args(fun = example_fun,                        name_match = list(a = \"x\", b = \"y\"),                        a = 10, b = 20, z = 5) print(result) #>  x  y  z  #> 10 20  5"},{"path":"https://NIEHS.github.io/chopin/reference/par_merge_grid.html","id":null,"dir":"Reference","previous_headings":"","what":"Merge adjacent grid polygons with given rules — par_merge_grid","title":"Merge adjacent grid polygons with given rules — par_merge_grid","text":"Merge boundary-sharing (\"Rook\" contiguity) grids fewer target features threshold. function strongly assumes input returned par_make_grid, \"CGRIDID\" unique id field.","code":""},{"path":"https://NIEHS.github.io/chopin/reference/par_merge_grid.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Merge adjacent grid polygons with given rules — par_merge_grid","text":"","code":"par_merge_grid(   points_in = NULL,   grid_in = NULL,   grid_min_features = NULL,   merge_max = 4L )"},{"path":"https://NIEHS.github.io/chopin/reference/par_merge_grid.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Merge adjacent grid polygons with given rules — par_merge_grid","text":"points_in sf SpatVector object. Target points computation. grid_in sf SpatVector object. grid generated internal function par_make_grid. grid_min_features integer(1). Threshold merge adjacent grids. merge_max integer(1). Maximum number grids merge per merged set. Default 4. example, number grids merge 20 merge_max 10, function split 20 grids two sets 10 grids.","code":""},{"path":"https://NIEHS.github.io/chopin/reference/par_merge_grid.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Merge adjacent grid polygons with given rules — par_merge_grid","text":"sf SpatVector object computation grids.","code":""},{"path":"https://NIEHS.github.io/chopin/reference/par_merge_grid.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Merge adjacent grid polygons with given rules — par_merge_grid","text":"function work properly grid_in one million grids.","code":""},{"path":"https://NIEHS.github.io/chopin/reference/par_merge_grid.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Merge adjacent grid polygons with given rules — par_merge_grid","text":"Polsby DD, Popper FJ. (1991). Third Criterion: Compactness Procedural Safeguard Partisan Gerrymandering. Yale Law & Policy Review, 9(2), 301–353.","code":""},{"path":[]},{"path":"https://NIEHS.github.io/chopin/reference/par_merge_grid.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Merge adjacent grid polygons with given rules — par_merge_grid","text":"Insang Song","code":""},{"path":"https://NIEHS.github.io/chopin/reference/par_merge_grid.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Merge adjacent grid polygons with given rules — par_merge_grid","text":"","code":"library(sf) library(igraph) #>  #> Attaching package: ‘igraph’ #> The following objects are masked from ‘package:terra’: #>  #>     blocks, compare, union #> The following objects are masked from ‘package:future’: #>  #>     %->%, %<-% #> The following objects are masked from ‘package:stats’: #>  #>     decompose, spectrum #> The following object is masked from ‘package:base’: #>  #>     union library(dplyr) #>  #> Attaching package: ‘dplyr’ #> The following objects are masked from ‘package:igraph’: #>  #>     as_data_frame, groups, union #> The following objects are masked from ‘package:terra’: #>  #>     intersect, union #> The following objects are masked from ‘package:stats’: #>  #>     filter, lag #> The following objects are masked from ‘package:base’: #>  #>     intersect, setdiff, setequal, union library(spatstat.random) #> Loading required package: spatstat.data #> Loading required package: spatstat.univar #> spatstat.univar 3.0-0 #> Loading required package: spatstat.geom #> spatstat.geom 3.3-2 #>  #> Attaching package: ‘spatstat.geom’ #> The following objects are masked from ‘package:igraph’: #>  #>     diameter, edges, is.connected, vertices #> The following objects are masked from ‘package:terra’: #>  #>     area, delaunay, is.empty, rescale, rotate, shift, where.max, #>     where.min #> spatstat.random 3.3-1 options(sf_use_s2 = FALSE)  dg <- sf::st_as_sfc(st_bbox(c(xmin = 0, ymin = 0, xmax = 8e5, ymax = 6e5))) sf::st_crs(dg) <- 5070 dgs <- sf::st_as_sf(st_make_grid(dg, n = c(20, 15))) dgs$CGRIDID <- seq(1, nrow(dgs))  dg_sample <- sf::st_sample(dg, kappa = 5e-9, mu = 15, scale = 15000, type = \"Thomas\") sf::st_crs(dg_sample) <- sf::st_crs(dg) dg_merged <- par_merge_grid(sf::st_as_sf(dg_sample), dgs, 100) #> ℹ The merged polygons have too complex shapes. #> Increase threshold or use the original grids.  plot(sf::st_geometry(dg_merged))"},{"path":"https://NIEHS.github.io/chopin/reference/par_multirasters.html","id":null,"dir":"Reference","previous_headings":"","what":"Parallelize spatial computation over multiple raster files — par_multirasters","title":"Parallelize spatial computation over multiple raster files — par_multirasters","text":"Large raster files usually exceed memory capacity size. function can helpful process heterogenous raster files homogeneous summary functions. Heterogenous raster files refer rasters different spatial extents resolutions. Cropping large raster small subset even consumes lot memory adds processing time. function leverages terra SpatRaster distribute computation jobs multiple threads. assumed users multiple large raster files disk, file path assigned thread. thread directly read raster values disk using C++ pointers operate terra functions. use, strongly recommended use vector data small confined spatial extent computation avoid --memory error. y argument fun_dist used -. means preprocessing subsetting applied. Please aware spatial extent size inputs.","code":""},{"path":"https://NIEHS.github.io/chopin/reference/par_multirasters.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Parallelize spatial computation over multiple raster files — par_multirasters","text":"","code":"par_multirasters(filenames, fun_dist, ..., .debug = FALSE)"},{"path":"https://NIEHS.github.io/chopin/reference/par_multirasters.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Parallelize spatial computation over multiple raster files — par_multirasters","text":"filenames character. vector list full file paths raster files. n total number raster files. fun_dist terra chopin functions accept SpatRaster object argument. particular, x y arguments present x SpatRaster. ... Arguments passed argument fun_dist. .debug logical(1). Default FALSE. TRUE unit computation fails, error message file path error occurred included output.","code":""},{"path":"https://NIEHS.github.io/chopin/reference/par_multirasters.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Parallelize spatial computation over multiple raster files — par_multirasters","text":"data.frame object computation results. entries results, consult function used fun_dist argument.","code":""},{"path":[]},{"path":"https://NIEHS.github.io/chopin/reference/par_multirasters.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Parallelize spatial computation over multiple raster files — par_multirasters","text":"Insang Song geoissong@gmail.com","code":""},{"path":"https://NIEHS.github.io/chopin/reference/par_multirasters.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Parallelize spatial computation over multiple raster files — par_multirasters","text":"","code":"library(terra) library(sf) library(future) library(future.mirai) sf::sf_use_s2(FALSE) future::plan(future.mirai::mirai_multisession, workers = 2)  ncpath <- system.file(\"extdata/nc_hierarchy.gpkg\", package = \"chopin\") nccnty <- sf::st_read(ncpath, layer = \"county\") #> Reading layer `county' from data source  #>   `/home/runner/work/_temp/Library/chopin/extdata/nc_hierarchy.gpkg'  #>   using driver `GPKG' #> Simple feature collection with 100 features and 1 field #> Geometry type: POLYGON #> Dimension:     XY #> Bounding box:  xmin: 1054155 ymin: 1341756 xmax: 1838923 ymax: 1690176 #> Projected CRS: NAD83 / Conus Albers ncelev <-   system.file(\"extdata/nc_srtm15_otm.tif\", package = \"chopin\") ncelevras <- terra::rast(ncelev)  tdir <- tempdir(check = TRUE) terra::writeRaster(ncelevras, file.path(tdir, \"test1.tif\"), overwrite = TRUE) terra::writeRaster(ncelevras, file.path(tdir, \"test2.tif\"), overwrite = TRUE) testfiles <- list.files(tdir, pattern = \"tif$\", full.names = TRUE)  res <- par_multirasters(   filenames = testfiles,   fun_dist = extract_at,   x = ncelev,   y = nccnty,   id = \"GEOID\",   func = \"mean\" ) #> ℹ Input is not a character. #> Input is a character. Attempt to read it with terra::rast... #> ℹ Your input function at /tmp/RtmprV25By/test1.tif is dispatched. #> Input is a character. Attempt to read it with terra::rast... #> ℹ Your input function at /tmp/RtmprV25By/test2.tif is dispatched."},{"path":"https://NIEHS.github.io/chopin/reference/par_pad_balanced.html","id":null,"dir":"Reference","previous_headings":"","what":"Extension of par_make_balanced for padded grids — par_pad_balanced","title":"Extension of par_make_balanced for padded grids — par_pad_balanced","text":"function extension par_make_balanced compatible par_make_grid, set padded grids extent input point subsets (recorded field named \"CGRIDID\") generated input points along output par_make_balanced.","code":""},{"path":"https://NIEHS.github.io/chopin/reference/par_pad_balanced.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extension of par_make_balanced for padded grids — par_pad_balanced","text":"","code":"par_pad_balanced(points_in = NULL, ngroups, padding)"},{"path":"https://NIEHS.github.io/chopin/reference/par_pad_balanced.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extension of par_make_balanced for padded grids — par_pad_balanced","text":"points_in sf SpatVector object. Point geometries. ngroups integer(1). number groups. padding numeric(1). extrusion factor make buffer clip actual datasets. Depending length unit CRS input.","code":""},{"path":"https://NIEHS.github.io/chopin/reference/par_pad_balanced.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Extension of par_make_balanced for padded grids — par_pad_balanced","text":"list two, original: exhaustive non-overlapping grid polygons class input padded: square buffer polygon original. Used computation.","code":""},{"path":[]},{"path":"https://NIEHS.github.io/chopin/reference/par_pad_balanced.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Extension of par_make_balanced for padded grids — par_pad_balanced","text":"Insang Song","code":""},{"path":"https://NIEHS.github.io/chopin/reference/par_pad_balanced.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Extension of par_make_balanced for padded grids — par_pad_balanced","text":"","code":"library(terra) library(sf) options(sf_use_s2 = FALSE)  ncpath <- system.file(\"gpkg/nc.gpkg\", package = \"sf\") nc <- terra::vect(ncpath) nc_rp <- terra::spatSample(nc, 1000)  nc_gr <- par_pad_balanced(nc_rp, 10L, 1000) nc_gr #> $original #>  class       : SpatVector  #>  geometry    : points  #>  dimensions  : 1000, 15  (geometries, attributes) #>  extent      : -84.24941, -75.79698, 33.88802, 36.55712  (xmin, xmax, ymin, ymax) #>  coord. ref. : lon/lat NAD27 (EPSG:4267)  #>  names       :  AREA PERIMETER CNTY_ CNTY_ID        NAME  FIPS    FIPSNO #>  type        : <num>     <num> <num>   <num>       <chr> <chr>     <num> #>  values      : 0.078     1.202  2056    2056      Graham 37075 3.708e+04 #>                0.125     2.868  2156    2156    Carteret 37031 3.703e+04 #>                0.153     2.206  1832    1832 Northampton 37131 3.713e+04 #>  CRESS_ID BIR74 SID74 (and 5 more) #>     <int> <num> <num>              #>        38   415     0              #>        16  2414     5              #>        66  1421     9              #>  #> $padded #>  class       : SpatVector  #>  geometry    : polygons  #>  dimensions  : 10, 1  (geometries, attributes) #>  extent      : -1084.249, 924.203, -966.112, 1036.557  (xmin, xmax, ymin, ymax) #>  coord. ref. :   #>  names       : CGRIDID #>  type        :   <num> #>  values      :       1 #>                      2 #>                      3 #>"},{"path":"https://NIEHS.github.io/chopin/reference/par_pad_grid.html","id":null,"dir":"Reference","previous_headings":"","what":"Get a set of computational grids — par_pad_grid","title":"Get a set of computational grids — par_pad_grid","text":"Using input points, bounding box split predefined numbers columns rows. grid buffered radius.","code":""},{"path":"https://NIEHS.github.io/chopin/reference/par_pad_grid.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get a set of computational grids — par_pad_grid","text":"","code":"par_pad_grid(   input,   mode = c(\"grid\", \"grid_advanced\", \"grid_quantile\"),   nx = 10L,   ny = 10L,   grid_min_features = 30L,   padding = NULL,   unit = NULL,   quantiles = NULL,   merge_max = NULL,   return_wkt = FALSE,   ... )"},{"path":"https://NIEHS.github.io/chopin/reference/par_pad_grid.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get a set of computational grids — par_pad_grid","text":"input sf Spat* object. mode character(1). Mode region construction. One \"grid\" (simple grid regardless number features grid) \"grid_advanced\" (merging adjacent grids smaller number features grid_min_features). argument grid_min_features specified. \"grid_quantile\" (x y quantiles): argument quantiles specified. nx integer(1). number grids along x-axis. ny integer(1). number grids along y-axis. grid_min_features integer(1). threshold merging adjacent grids padding numeric(1). extrusion factor make buffer clip actual datasets. Depending length unit CRS input. unit character(1). length unit padding (optional). units::set_units used padding sf object used. See link list acceptable unit forms. quantiles numeric. Quantiles grid_quantile mode. merge_max integer(1). Maximum number grids merge per merged set. return_wkt logical(1). Return WKT format. TRUE, return value list two WKT strings. ... arguments passed internal function","code":""},{"path":"https://NIEHS.github.io/chopin/reference/par_pad_grid.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get a set of computational grids — par_pad_grid","text":"list two, original: exhaustive (filling completely) non-overlapping grid polygons class input padded: square buffer polygon original. Used computation.","code":""},{"path":[]},{"path":"https://NIEHS.github.io/chopin/reference/par_pad_grid.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Get a set of computational grids — par_pad_grid","text":"Insang Song","code":""},{"path":"https://NIEHS.github.io/chopin/reference/par_pad_grid.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Get a set of computational grids — par_pad_grid","text":"","code":"# data library(sf) options(sf_use_s2 = FALSE) ncpath <- system.file(\"shape/nc.shp\", package = \"sf\") nc <- read_sf(ncpath) nc <- st_transform(nc, \"EPSG:5070\")  # run: nx and ny should strictly be integers nc_comp_region <-   par_pad_grid(     nc,     mode = \"grid\",     nx = 4L, ny = 2L,     padding = 10000) #> Switch sf class to terra... #> Switch terra class to sf... par(mfcol = c(1, 2)) plot(nc_comp_region$original$geometry) plot(nc_comp_region$padded$geometry)   nc_comp_region_wkt <-   par_pad_grid(     nc,     mode = \"grid\",     nx = 4L, ny = 2L,     padding = 10000,     return_wkt = TRUE) #> Switch sf class to terra... #> Switch terra class to sf... nc_comp_region_wkt$original #> [1] \"POLYGON ((1054293 1348025, 1249094 1348025, 1249094 1518630, 1054293 1518630, 1054293 1348025))\" #> [2] \"POLYGON ((1249094 1348025, 1443896 1348025, 1443896 1518630, 1249094 1518630, 1249094 1348025))\" #> [3] \"POLYGON ((1443896 1348025, 1638697 1348025, 1638697 1518630, 1443896 1518630, 1443896 1348025))\" #> [4] \"POLYGON ((1638697 1348025, 1833499 1348025, 1833499 1518630, 1638697 1518630, 1638697 1348025))\" #> [5] \"POLYGON ((1054293 1518630, 1249094 1518630, 1249094 1689236, 1054293 1689236, 1054293 1518630))\" #> [6] \"POLYGON ((1249094 1518630, 1443896 1518630, 1443896 1689236, 1249094 1689236, 1249094 1518630))\" #> [7] \"POLYGON ((1443896 1518630, 1638697 1518630, 1638697 1689236, 1443896 1689236, 1443896 1518630))\" #> [8] \"POLYGON ((1638697 1518630, 1833499 1518630, 1833499 1689236, 1638697 1689236, 1638697 1518630))\" nc_comp_region_wkt$padded #> [1] \"POLYGON ((1044293 1338025, 1044293 1528630, 1259094 1528630, 1259094 1338025, 1044293 1338025))\" #> [2] \"POLYGON ((1239094 1338025, 1239094 1528630, 1453896 1528630, 1453896 1338025, 1239094 1338025))\" #> [3] \"POLYGON ((1433896 1338025, 1433896 1528630, 1648697 1528630, 1648697 1338025, 1433896 1338025))\" #> [4] \"POLYGON ((1628697 1338025, 1628697 1528630, 1843499 1528630, 1843499 1338025, 1628697 1338025))\" #> [5] \"POLYGON ((1044293 1508630, 1044293 1699236, 1259094 1699236, 1259094 1508630, 1044293 1508630))\" #> [6] \"POLYGON ((1239094 1508630, 1239094 1699236, 1453896 1699236, 1453896 1508630, 1239094 1508630))\" #> [7] \"POLYGON ((1433896 1508630, 1433896 1699236, 1648697 1699236, 1648697 1508630, 1433896 1508630))\" #> [8] \"POLYGON ((1628697 1508630, 1628697 1699236, 1843499 1699236, 1843499 1508630, 1628697 1508630))\""},{"path":"https://NIEHS.github.io/chopin/reference/par_split_list.html","id":null,"dir":"Reference","previous_headings":"","what":"Split grid list to a nested list of row-wise data frames — par_split_list","title":"Split grid list to a nested list of row-wise data frames — par_split_list","text":"Split grid list nested list row-wise data frames","code":""},{"path":"https://NIEHS.github.io/chopin/reference/par_split_list.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Split grid list to a nested list of row-wise data frames — par_split_list","text":"","code":"par_split_list(gridlist)"},{"path":"https://NIEHS.github.io/chopin/reference/par_split_list.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Split grid list to a nested list of row-wise data frames — par_split_list","text":"gridlist list. Output par_pad_grid par_pad_balanced","code":""},{"path":"https://NIEHS.github.io/chopin/reference/par_split_list.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Split grid list to a nested list of row-wise data frames — par_split_list","text":"nested list data frames WKT strings.","code":""},{"path":"https://NIEHS.github.io/chopin/reference/par_split_list.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Split grid list to a nested list of row-wise data frames — par_split_list","text":"input data frame, function return list two data frames: original padded. input WKT vector, function return list two WKT strings: original padded.","code":""},{"path":[]},{"path":"https://NIEHS.github.io/chopin/reference/par_split_list.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Split grid list to a nested list of row-wise data frames — par_split_list","text":"","code":"library(sf) library(terra) options(sf_use_s2 = FALSE)  ncpath <- system.file(\"shape/nc.shp\", package = \"sf\") nc <- read_sf(ncpath) nc <- st_transform(nc, \"EPSG:5070\") nc_comp_region <-   par_pad_grid(     nc,     mode = \"grid\",     nx = 4L, ny = 2L,     padding = 10000) #> Switch sf class to terra... #> Switch terra class to sf... par_split_list(nc_comp_region) #> [[1]] #> [[1]]$original #> Simple feature collection with 1 feature and 1 field #> Geometry type: POLYGON #> Dimension:     XY #> Bounding box:  xmin: 1054293 ymin: 1348025 xmax: 1249094 ymax: 1518630 #> Projected CRS: NAD83 / Conus Albers #>                         geometry CGRIDID #> 1 POLYGON ((1054293 1348025, ...       1 #>  #> [[1]]$padded #> Simple feature collection with 1 feature and 1 field #> Geometry type: POLYGON #> Dimension:     XY #> Bounding box:  xmin: 1044293 ymin: 1338025 xmax: 1259094 ymax: 1528630 #> Projected CRS: NAD83 / Conus Albers #>   CGRIDID                       geometry #> 1       1 POLYGON ((1044293 1338025, ... #>  #>  #> [[2]] #> [[2]]$original #> Simple feature collection with 1 feature and 1 field #> Geometry type: POLYGON #> Dimension:     XY #> Bounding box:  xmin: 1249094 ymin: 1348025 xmax: 1443896 ymax: 1518630 #> Projected CRS: NAD83 / Conus Albers #>                         geometry CGRIDID #> 2 POLYGON ((1249094 1348025, ...       2 #>  #> [[2]]$padded #> Simple feature collection with 1 feature and 1 field #> Geometry type: POLYGON #> Dimension:     XY #> Bounding box:  xmin: 1239094 ymin: 1338025 xmax: 1453896 ymax: 1528630 #> Projected CRS: NAD83 / Conus Albers #>   CGRIDID                       geometry #> 2       2 POLYGON ((1239094 1338025, ... #>  #>  #> [[3]] #> [[3]]$original #> Simple feature collection with 1 feature and 1 field #> Geometry type: POLYGON #> Dimension:     XY #> Bounding box:  xmin: 1443896 ymin: 1348025 xmax: 1638697 ymax: 1518630 #> Projected CRS: NAD83 / Conus Albers #>                         geometry CGRIDID #> 3 POLYGON ((1443896 1348025, ...       3 #>  #> [[3]]$padded #> Simple feature collection with 1 feature and 1 field #> Geometry type: POLYGON #> Dimension:     XY #> Bounding box:  xmin: 1433896 ymin: 1338025 xmax: 1648697 ymax: 1528630 #> Projected CRS: NAD83 / Conus Albers #>   CGRIDID                       geometry #> 3       3 POLYGON ((1433896 1338025, ... #>  #>  #> [[4]] #> [[4]]$original #> Simple feature collection with 1 feature and 1 field #> Geometry type: POLYGON #> Dimension:     XY #> Bounding box:  xmin: 1638697 ymin: 1348025 xmax: 1833499 ymax: 1518630 #> Projected CRS: NAD83 / Conus Albers #>                         geometry CGRIDID #> 4 POLYGON ((1638697 1348025, ...       4 #>  #> [[4]]$padded #> Simple feature collection with 1 feature and 1 field #> Geometry type: POLYGON #> Dimension:     XY #> Bounding box:  xmin: 1628697 ymin: 1338025 xmax: 1843499 ymax: 1528630 #> Projected CRS: NAD83 / Conus Albers #>   CGRIDID                       geometry #> 4       4 POLYGON ((1628697 1338025, ... #>  #>  #> [[5]] #> [[5]]$original #> Simple feature collection with 1 feature and 1 field #> Geometry type: POLYGON #> Dimension:     XY #> Bounding box:  xmin: 1054293 ymin: 1518630 xmax: 1249094 ymax: 1689236 #> Projected CRS: NAD83 / Conus Albers #>                         geometry CGRIDID #> 5 POLYGON ((1054293 1518630, ...       5 #>  #> [[5]]$padded #> Simple feature collection with 1 feature and 1 field #> Geometry type: POLYGON #> Dimension:     XY #> Bounding box:  xmin: 1044293 ymin: 1508630 xmax: 1259094 ymax: 1699236 #> Projected CRS: NAD83 / Conus Albers #>   CGRIDID                       geometry #> 5       5 POLYGON ((1044293 1508630, ... #>  #>  #> [[6]] #> [[6]]$original #> Simple feature collection with 1 feature and 1 field #> Geometry type: POLYGON #> Dimension:     XY #> Bounding box:  xmin: 1249094 ymin: 1518630 xmax: 1443896 ymax: 1689236 #> Projected CRS: NAD83 / Conus Albers #>                         geometry CGRIDID #> 6 POLYGON ((1249094 1518630, ...       6 #>  #> [[6]]$padded #> Simple feature collection with 1 feature and 1 field #> Geometry type: POLYGON #> Dimension:     XY #> Bounding box:  xmin: 1239094 ymin: 1508630 xmax: 1453896 ymax: 1699236 #> Projected CRS: NAD83 / Conus Albers #>   CGRIDID                       geometry #> 6       6 POLYGON ((1239094 1508630, ... #>  #>  #> [[7]] #> [[7]]$original #> Simple feature collection with 1 feature and 1 field #> Geometry type: POLYGON #> Dimension:     XY #> Bounding box:  xmin: 1443896 ymin: 1518630 xmax: 1638697 ymax: 1689236 #> Projected CRS: NAD83 / Conus Albers #>                         geometry CGRIDID #> 7 POLYGON ((1443896 1518630, ...       7 #>  #> [[7]]$padded #> Simple feature collection with 1 feature and 1 field #> Geometry type: POLYGON #> Dimension:     XY #> Bounding box:  xmin: 1433896 ymin: 1508630 xmax: 1648697 ymax: 1699236 #> Projected CRS: NAD83 / Conus Albers #>   CGRIDID                       geometry #> 7       7 POLYGON ((1433896 1508630, ... #>  #>  #> [[8]] #> [[8]]$original #> Simple feature collection with 1 feature and 1 field #> Geometry type: POLYGON #> Dimension:     XY #> Bounding box:  xmin: 1638697 ymin: 1518630 xmax: 1833499 ymax: 1689236 #> Projected CRS: NAD83 / Conus Albers #>                         geometry CGRIDID #> 8 POLYGON ((1638697 1518630, ...       8 #>  #> [[8]]$padded #> Simple feature collection with 1 feature and 1 field #> Geometry type: POLYGON #> Dimension:     XY #> Bounding box:  xmin: 1628697 ymin: 1508630 xmax: 1843499 ymax: 1699236 #> Projected CRS: NAD83 / Conus Albers #>   CGRIDID                       geometry #> 8       8 POLYGON ((1628697 1508630, ... #>  #>"},{"path":"https://NIEHS.github.io/chopin/reference/prediction_grid.html","id":null,"dir":"Reference","previous_headings":"","what":"Regular grid points in the mainland United States at 1km spatial resolution — prediction_grid","title":"Regular grid points in the mainland United States at 1km spatial resolution — prediction_grid","text":"Regular grid points mainland United States 1km spatial resolution","code":""},{"path":"https://NIEHS.github.io/chopin/reference/prediction_grid.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Regular grid points in the mainland United States at 1km spatial resolution — prediction_grid","text":"","code":"prediction_grid"},{"path":"https://NIEHS.github.io/chopin/reference/prediction_grid.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Regular grid points in the mainland United States at 1km spatial resolution — prediction_grid","text":"data frame 8,092,995 rows three variables: site_id Unique point identifier. Arbitrarily generated. lon Longitude lat Latitude","code":""},{"path":"https://NIEHS.github.io/chopin/reference/prediction_grid.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Regular grid points in the mainland United States at 1km spatial resolution — prediction_grid","text":"Mainland United States polygon obtained US Census Bureau.","code":""},{"path":"https://NIEHS.github.io/chopin/reference/prediction_grid.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Regular grid points in the mainland United States at 1km spatial resolution — prediction_grid","text":"Coordinates EPSG:5070 (Conus Albers Equal Area)","code":""},{"path":[]},{"path":"https://NIEHS.github.io/chopin/reference/prediction_grid.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Regular grid points in the mainland United States at 1km spatial resolution — prediction_grid","text":"","code":"data(\"prediction_grid\", package = \"chopin\")"},{"path":"https://NIEHS.github.io/chopin/reference/reproject_std.html","id":null,"dir":"Reference","previous_headings":"","what":"Check coordinate system then reproject — reproject_std","title":"Check coordinate system then reproject — reproject_std","text":"input checked whether coordinate system present. , reprojected CRS specified crs_standard.","code":""},{"path":"https://NIEHS.github.io/chopin/reference/reproject_std.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Check coordinate system then reproject — reproject_std","text":"","code":"reproject_std(input, crs_standard = \"EPSG:4326\")"},{"path":"https://NIEHS.github.io/chopin/reference/reproject_std.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Check coordinate system then reproject — reproject_std","text":"input Input object one sf terra::Spat* object crs_standard character(1). standard definition coordinate reference system. Default \"EPSG:4326\" Consult epsg.io details CRS.","code":""},{"path":"https://NIEHS.github.io/chopin/reference/reproject_std.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Check coordinate system then reproject — reproject_std","text":"(reprojected) sf SpatVector object.","code":""},{"path":"https://NIEHS.github.io/chopin/reference/reproject_std.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Check coordinate system then reproject — reproject_std","text":"function works well EPSG codes.","code":""},{"path":[]},{"path":"https://NIEHS.github.io/chopin/reference/reproject_std.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Check coordinate system then reproject — reproject_std","text":"Insang Song","code":""},{"path":"https://NIEHS.github.io/chopin/reference/reproject_std.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Check coordinate system then reproject — reproject_std","text":"","code":"if (FALSE) { # \\dontrun{ library(sf) library(terra) options(sf_use_s2 = FALSE)  base_crs <- \"EPSG:5070\" nc_path <- system.file(\"gpkg/nc.gpkg\", package = \"sf\") nc_sf <- sf::st_read(nc_path) reproject_std(nc_sf, base_crs)  nc_vect <- terra::vect(nc_sf) reproject_std(nc_vect, base_crs) } # }"},{"path":"https://NIEHS.github.io/chopin/reference/reproject_to_raster.html","id":null,"dir":"Reference","previous_headings":"","what":"Align vector CRS to raster's — reproject_to_raster","title":"Align vector CRS to raster's — reproject_to_raster","text":"Align vector CRS raster's","code":""},{"path":"https://NIEHS.github.io/chopin/reference/reproject_to_raster.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Align vector CRS to raster's — reproject_to_raster","text":"","code":"reproject_to_raster(vector = NULL, raster = NULL)"},{"path":"https://NIEHS.github.io/chopin/reference/reproject_to_raster.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Align vector CRS to raster's — reproject_to_raster","text":"vector sf/stars/SpatVector/SpatRaster object raster SpatRaster object","code":""},{"path":"https://NIEHS.github.io/chopin/reference/reproject_to_raster.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Align vector CRS to raster's — reproject_to_raster","text":"Reprojected object class vector","code":""},{"path":[]},{"path":"https://NIEHS.github.io/chopin/reference/reproject_to_raster.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Align vector CRS to raster's — reproject_to_raster","text":"Insang Song","code":""},{"path":"https://NIEHS.github.io/chopin/reference/reproject_to_raster.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Align vector CRS to raster's — reproject_to_raster","text":"","code":"if (FALSE) { # \\dontrun{ library(terra) library(sf) options(sf_use_s2 = FALSE)  ncpath <- system.file(\"gpkg/nc.gpkg\", package = \"sf\") elev <- system.file(\"ex/elev.tif\", package = \"terra\") nc <- terra::vect(ncpath) elev <- terra::rast(elev) reproject_to_raster(nc, elev) } # }"},{"path":"https://NIEHS.github.io/chopin/reference/summarize_aw.html","id":null,"dir":"Reference","previous_headings":"","what":"Area weighted summary using two polygon objects — summarize_aw","title":"Area weighted summary using two polygon objects — summarize_aw","text":"x y different classes, poly_weight converted class x.","code":""},{"path":"https://NIEHS.github.io/chopin/reference/summarize_aw.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Area weighted summary using two polygon objects — summarize_aw","text":"","code":"summarize_aw(x, y, ...)  # S4 method for class 'SpatVector,SpatVector' summarize_aw(   x,   y,   target_fields = NULL,   id_x = \"ID\",   fun = stats::weighted.mean,   extent = NULL,   ... )  # S4 method for class 'character,character' summarize_aw(   x,   y,   target_fields = NULL,   id_x = \"ID\",   fun = stats::weighted.mean,   out_class = \"terra\",   extent = NULL,   ... )  # S4 method for class 'sf,sf' summarize_aw(   x,   y,   target_fields = NULL,   id_x = \"ID\",   fun = NULL,   extent = NULL,   ... )"},{"path":"https://NIEHS.github.io/chopin/reference/summarize_aw.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Area weighted summary using two polygon objects — summarize_aw","text":"x sf/SpatVector object file path polygons detectable GDAL driver weighted means calculated. y sf/SpatVector object file path polygons weighted means calculated. ... Placeholder. target_fields character. Field names calculate area-weighted. id_x character(1). unique identifier polygon x. Default \"ID\". fun function(1)/character(1). function calculate weighted summary. Default stats::weighted.mean. function must w argument. x y sf, one c(\"sum\", \"mean\"). determine extensive argument sf::st_interpolate_aw. extent numeric(4) SpatExtent object. Extent clipping x. works x character(1) file path. See terra::ext details. Coordinate systems match. out_class character(1). \"sf\" \"terra\". Output class.","code":""},{"path":"https://NIEHS.github.io/chopin/reference/summarize_aw.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Area weighted summary using two polygon objects — summarize_aw","text":"data.frame numeric fields area-weighted means.","code":""},{"path":"https://NIEHS.github.io/chopin/reference/summarize_aw.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Area weighted summary using two polygon objects — summarize_aw","text":"x y classes match. x y characters, read sf objects.","code":""},{"path":[]},{"path":"https://NIEHS.github.io/chopin/reference/summarize_aw.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Area weighted summary using two polygon objects — summarize_aw","text":"Insang Song geoissong@gmail.com","code":""},{"path":"https://NIEHS.github.io/chopin/reference/summarize_aw.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Area weighted summary using two polygon objects — summarize_aw","text":"","code":"# package library(sf) options(sf_use_s2 = FALSE) nc <- sf::st_read(system.file(\"shape/nc.shp\", package=\"sf\")) #> Reading layer `nc' from data source  #>   `/home/runner/work/_temp/Library/sf/shape/nc.shp' using driver `ESRI Shapefile' #> Simple feature collection with 100 features and 14 fields #> Geometry type: MULTIPOLYGON #> Dimension:     XY #> Bounding box:  xmin: -84.32385 ymin: 33.88199 xmax: -75.45698 ymax: 36.58965 #> Geodetic CRS:  NAD27 nc <- sf::st_transform(nc, 5070) pp <- sf::st_sample(nc, size = 300) pp <- sf::st_as_sf(pp) pp[[\"id\"]] <- seq(1, nrow(pp)) sf::st_crs(pp) <- \"EPSG:5070\" ppb <- sf::st_buffer(pp, nQuadSegs=180, dist = units::set_units(20, \"km\"))  system.time(   ppb_nc_aw <-     summarize_aw(       ppb, nc, c(\"BIR74\", \"BIR79\"),       \"id\", fun = \"sum\"     ) ) #> Warning: st_interpolate_aw assumes attributes are constant or uniform over areas of x #>    user  system elapsed  #>   0.370   0.000   0.371  summary(ppb_nc_aw) #>      BIR74             BIR79                  geometry   #>  Min.   :  159.6   Min.   :  244.7   POLYGON      :300   #>  1st Qu.: 1278.6   1st Qu.: 1568.8   epsg:5070    :  0   #>  Median : 2231.9   Median : 2724.9   +proj=aea ...:  0   #>  Mean   : 3000.0   Mean   : 3832.5                       #>  3rd Qu.: 3821.2   3rd Qu.: 4872.5                       #>  Max.   :16384.9   Max.   :23159.3                        # terra examples library(terra) ncpath <- system.file(\"gpkg/nc.gpkg\", package = \"sf\") elev <- system.file(\"ex/elev.tif\", package = \"terra\") nc <- terra::vect(ncpath) elev <- terra::rast(elev) pp <- terra::spatSample(nc, size = 300) pp <- terra::project(pp, crs(elev)) pp <- terra::as.points(pp) #> Warning: [as.points] returning a copy pp[[\"id\"]] <- seq(1, nrow(pp)) ppb <- terra::buffer(pp, 20000)  system.time(   ppb_nc_aw <-     summarize_aw(       ppb, nc, c(\"BIR74\", \"BIR79\"), \"id\",       fun = sum     ) ) #> Warning: [intersect] different crs #>    user  system elapsed  #>   0.083   0.000   0.084  summary(ppb_nc_aw) #>        id             BIR74               BIR79           #>  Min.   :  1.00   Min.   :2.453e+08   Min.   :2.453e+08   #>  1st Qu.: 75.75   1st Qu.:1.171e+09   1st Qu.:1.171e+09   #>  Median :150.50   Median :1.251e+09   Median :1.251e+09   #>  Mean   :150.50   Mean   :1.169e+09   Mean   :1.169e+09   #>  3rd Qu.:225.25   3rd Qu.:1.251e+09   3rd Qu.:1.251e+09   #>  Max.   :300.00   Max.   :1.252e+09   Max.   :1.252e+09"},{"path":"https://NIEHS.github.io/chopin/reference/summarize_sedc.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate Sum of Exponentially Decaying Contributions (SEDC) covariates — summarize_sedc","title":"Calculate Sum of Exponentially Decaying Contributions (SEDC) covariates — summarize_sedc","text":"Calculate Sum Exponentially Decaying Contributions (SEDC) covariates","code":""},{"path":"https://NIEHS.github.io/chopin/reference/summarize_sedc.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate Sum of Exponentially Decaying Contributions (SEDC) covariates — summarize_sedc","text":"","code":"summarize_sedc(   point_from = NULL,   point_to = NULL,   id = NULL,   sedc_bandwidth = NULL,   threshold = NULL,   target_fields = NULL,   extent_from = NULL,   extent_to = NULL,   ... )"},{"path":"https://NIEHS.github.io/chopin/reference/summarize_sedc.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate Sum of Exponentially Decaying Contributions (SEDC) covariates — summarize_sedc","text":"point_from SpatVector object. Locations sum SEDCs calculated. point_to SpatVector object. Locations SEDC calculated. id character(1). Name unique id field point_to. sedc_bandwidth numeric(1). Distance source concentration reduced exp(-3) (approximately -95 %) threshold numeric(1). computational efficiency, nearest points threshold selected. 2 * sedc_bandwidth applied value remains NULL. target_fields character. Field names calculate SEDC. extent_from numeric(4) SpatExtent. Extent clipping point_from. works point_from character(1) file path. See terra::ext details. Coordinate systems match. extent_to numeric(4) SpatExtent. Extent clipping point_to. ... Placeholder.","code":""},{"path":"https://NIEHS.github.io/chopin/reference/summarize_sedc.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate Sum of Exponentially Decaying Contributions (SEDC) covariates — summarize_sedc","text":"data.frame object input field names suffix \"_sedc\" sums EDC stored. Additional attributes attached EDC information. attr(result, \"sedc_bandwidth\"): bandwidth concentration reduces approximately five percent attr(result, \"sedc_threshold\"): threshold distance emission source points excluded beyond ","code":""},{"path":"https://NIEHS.github.io/chopin/reference/summarize_sedc.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Calculate Sum of Exponentially Decaying Contributions (SEDC) covariates — summarize_sedc","text":"SEDC specialized vector vector summary covariates exponential decay. Decaying slope defined sedc_bandwidth, concentration source reduced $\\exp(-3)$ (approximately 5 \\ attenuating concentration distance sources. can thought fixed bandwidth kernel weighted sum covariates, encapsulates three steps: Calculate distance source target points. Calculate weight source point exponential decay. Summarize weighted covariates.","code":""},{"path":"https://NIEHS.github.io/chopin/reference/summarize_sedc.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Calculate Sum of Exponentially Decaying Contributions (SEDC) covariates — summarize_sedc","text":"Distance calculation done terra functions internally. Thus, function internally converts sf objects point_* arguments terra. Please note NA values input ignored SEDC calculation.","code":""},{"path":"https://NIEHS.github.io/chopin/reference/summarize_sedc.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Calculate Sum of Exponentially Decaying Contributions (SEDC) covariates — summarize_sedc","text":"Messier KP, Akita Y, Serre ML. (2012). Integrating Address Geocoding, Land Use Regression, Spatiotemporal Geostatistical Estimation Groundwater Tetrachloroethylene. Environmental Science & Technology 46(5), 2772-2780. Wiesner C. (n.d.). Euclidean Sum Exponentially Decaying Contributions Tutorial.","code":""},{"path":[]},{"path":"https://NIEHS.github.io/chopin/reference/summarize_sedc.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Calculate Sum of Exponentially Decaying Contributions (SEDC) covariates — summarize_sedc","text":"Insang Song","code":""},{"path":"https://NIEHS.github.io/chopin/reference/summarize_sedc.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calculate Sum of Exponentially Decaying Contributions (SEDC) covariates — summarize_sedc","text":"","code":"library(terra) library(sf) set.seed(101) ncpath <- system.file(\"gpkg/nc.gpkg\", package = \"sf\") nc <- terra::vect(ncpath) nc <- terra::project(nc, \"EPSG:5070\") pnt_from <- terra::centroids(nc, inside = TRUE) pnt_from <- pnt_from[, \"NAME\"] pnt_to <- terra::spatSample(nc, 100L) pnt_to$pid <- seq(1, 100) pnt_to <- pnt_to[, \"pid\"] pnt_to$val1 <- rgamma(100L, 1, 0.05) pnt_to$val2 <- rgamma(100L, 2, 1)  vals <- c(\"val1\", \"val2\") summarize_sedc(pnt_from, pnt_to, \"NAME\", 1e5, 2e5, vals) #> # A tibble: 100 × 3 #>    NAME      val1_sedc val2_sedc #>    <chr>         <dbl>     <dbl> #>  1 Alamance       66.3      6.71 #>  2 Alexander      98.0     11.7  #>  3 Alleghany     135.       8.57 #>  4 Anson          50.0      5.23 #>  5 Ashe          143.       8.42 #>  6 Avery          76.1      8.76 #>  7 Beaufort       58.8      6.29 #>  8 Bertie         70.1      7.85 #>  9 Bladen         79.5      6.82 #> 10 Brunswick      54.3      5.34 #> # ℹ 90 more rows"},{"path":"https://NIEHS.github.io/chopin/reference/vect_validate.html","id":null,"dir":"Reference","previous_headings":"","what":"Validate and repair input vector data — vect_validate","title":"Validate and repair input vector data — vect_validate","text":"tries repairing input vector data. Vector validity violation usually appears polygon data self-crossing hole orders. function pass input_vector object sf::st_make_valid (input_vector sf) terra::makeValid (input_vector SpatVector). May take time depending geometry complexity.","code":""},{"path":"https://NIEHS.github.io/chopin/reference/vect_validate.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Validate and repair input vector data — vect_validate","text":"","code":"vect_validate(input_vector)"},{"path":"https://NIEHS.github.io/chopin/reference/vect_validate.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Validate and repair input vector data — vect_validate","text":"input_vector One sf vect class. Target points computation.","code":""},{"path":"https://NIEHS.github.io/chopin/reference/vect_validate.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Validate and repair input vector data — vect_validate","text":"repaired sf SpatVector object depending class input_vector.","code":""},{"path":"https://NIEHS.github.io/chopin/reference/vect_validate.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Validate and repair input vector data — vect_validate","text":"function works GEOS (>=3.8).","code":""},{"path":[]},{"path":"https://NIEHS.github.io/chopin/reference/vect_validate.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Validate and repair input vector data — vect_validate","text":"Insang Song","code":""},{"path":"https://NIEHS.github.io/chopin/reference/vect_validate.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Validate and repair input vector data — vect_validate","text":"","code":"if (FALSE) { # \\dontrun{ library(terra) library(sf) ncpath <- system.file(\"gpkg/nc.gpkg\", package = \"sf\") nc <- terra::vect(ncpath)  nc_valid <- vect_validate(nc) } # }"}]
+[{"path":[]},{"path":"https://NIEHS.github.io/chopin/CODE_OF_CONDUCT.html","id":"our-pledge","dir":"","previous_headings":"","what":"Our Pledge","title":"Contributor Covenant Code of Conduct","text":"members, contributors, leaders pledge make participation community harassment-free experience everyone, regardless age, body size, visible invisible disability, ethnicity, sex characteristics, gender identity expression, level experience, education, socio-economic status, nationality, personal appearance, race, caste, color, religion, sexual identity orientation. pledge act interact ways contribute open, welcoming, diverse, inclusive, healthy community.","code":""},{"path":"https://NIEHS.github.io/chopin/CODE_OF_CONDUCT.html","id":"our-standards","dir":"","previous_headings":"","what":"Our Standards","title":"Contributor Covenant Code of Conduct","text":"Examples behavior contributes positive environment community include: Demonstrating empathy kindness toward people respectful differing opinions, viewpoints, experiences Giving gracefully accepting constructive feedback Accepting responsibility apologizing affected mistakes, learning experience Focusing best just us individuals, overall community Examples unacceptable behavior include: use sexualized language imagery, sexual attention advances kind Trolling, insulting derogatory comments, personal political attacks Public private harassment Publishing others’ private information, physical email address, without explicit permission conduct reasonably considered inappropriate professional setting","code":""},{"path":"https://NIEHS.github.io/chopin/CODE_OF_CONDUCT.html","id":"enforcement-responsibilities","dir":"","previous_headings":"","what":"Enforcement Responsibilities","title":"Contributor Covenant Code of Conduct","text":"Community leaders responsible clarifying enforcing standards acceptable behavior take appropriate fair corrective action response behavior deem inappropriate, threatening, offensive, harmful. Community leaders right responsibility remove, edit, reject comments, commits, code, wiki edits, issues, contributions aligned Code Conduct, communicate reasons moderation decisions appropriate.","code":""},{"path":"https://NIEHS.github.io/chopin/CODE_OF_CONDUCT.html","id":"scope","dir":"","previous_headings":"","what":"Scope","title":"Contributor Covenant Code of Conduct","text":"Code Conduct applies within community spaces, also applies individual officially representing community public spaces. Examples representing community include using official e-mail address, posting via official social media account, acting appointed representative online offline event.","code":""},{"path":"https://NIEHS.github.io/chopin/CODE_OF_CONDUCT.html","id":"enforcement","dir":"","previous_headings":"","what":"Enforcement","title":"Contributor Covenant Code of Conduct","text":"Instances abusive, harassing, otherwise unacceptable behavior may reported community leaders responsible enforcement geoissong@gmail.com. complaints reviewed investigated promptly fairly. community leaders obligated respect privacy security reporter incident.","code":""},{"path":"https://NIEHS.github.io/chopin/CODE_OF_CONDUCT.html","id":"enforcement-guidelines","dir":"","previous_headings":"","what":"Enforcement Guidelines","title":"Contributor Covenant Code of Conduct","text":"Community leaders follow Community Impact Guidelines determining consequences action deem violation Code Conduct:","code":""},{"path":"https://NIEHS.github.io/chopin/CODE_OF_CONDUCT.html","id":"id_1-correction","dir":"","previous_headings":"Enforcement Guidelines","what":"1. Correction","title":"Contributor Covenant Code of Conduct","text":"Community Impact: Use inappropriate language behavior deemed unprofessional unwelcome community. Consequence: private, written warning community leaders, providing clarity around nature violation explanation behavior inappropriate. public apology may requested.","code":""},{"path":"https://NIEHS.github.io/chopin/CODE_OF_CONDUCT.html","id":"id_2-warning","dir":"","previous_headings":"Enforcement Guidelines","what":"2. Warning","title":"Contributor Covenant Code of Conduct","text":"Community Impact: violation single incident series actions. Consequence: warning consequences continued behavior. interaction people involved, including unsolicited interaction enforcing Code Conduct, specified period time. includes avoiding interactions community spaces well external channels like social media. Violating terms may lead temporary permanent ban.","code":""},{"path":"https://NIEHS.github.io/chopin/CODE_OF_CONDUCT.html","id":"id_3-temporary-ban","dir":"","previous_headings":"Enforcement Guidelines","what":"3. Temporary Ban","title":"Contributor Covenant Code of Conduct","text":"Community Impact: serious violation community standards, including sustained inappropriate behavior. Consequence: temporary ban sort interaction public communication community specified period time. public private interaction people involved, including unsolicited interaction enforcing Code Conduct, allowed period. Violating terms may lead permanent ban.","code":""},{"path":"https://NIEHS.github.io/chopin/CODE_OF_CONDUCT.html","id":"id_4-permanent-ban","dir":"","previous_headings":"Enforcement Guidelines","what":"4. Permanent Ban","title":"Contributor Covenant Code of Conduct","text":"Community Impact: Demonstrating pattern violation community standards, including sustained inappropriate behavior, harassment individual, aggression toward disparagement classes individuals. Consequence: permanent ban sort public interaction within community.","code":""},{"path":"https://NIEHS.github.io/chopin/CODE_OF_CONDUCT.html","id":"attribution","dir":"","previous_headings":"","what":"Attribution","title":"Contributor Covenant Code of Conduct","text":"Code Conduct adapted Contributor Covenant, version 2.1, available https://www.contributor-covenant.org/version/2/1/code_of_conduct.html. Community Impact Guidelines inspired [Mozilla’s code conduct enforcement ladder][https://github.com/mozilla/inclusion]. answers common questions code conduct, see FAQ https://www.contributor-covenant.org/faq. Translations available https://www.contributor-covenant.org/translations.","code":""},{"path":"https://NIEHS.github.io/chopin/CONTRIBUTING.html","id":null,"dir":"","previous_headings":"","what":"Contributing to chopin","title":"Contributing to chopin","text":"outlines propose change chopin.","code":""},{"path":"https://NIEHS.github.io/chopin/CONTRIBUTING.html","id":"fixing-typos","dir":"","previous_headings":"","what":"Fixing typos","title":"Contributing to chopin","text":"can fix typos, spelling mistakes, grammatical errors documentation directly using GitHub web interface, long changes made source file. generally means ’ll need edit roxygen2 comments .R, .Rd file. can find .R file generates .Rd reading comment first line.","code":""},{"path":"https://NIEHS.github.io/chopin/CONTRIBUTING.html","id":"bigger-changes","dir":"","previous_headings":"","what":"Bigger changes","title":"Contributing to chopin","text":"want make bigger change, ’s good idea first file issue make sure someone team agrees ’s needed. ’ve found bug, please file issue illustrates bug minimal reprex (also help write unit test, needed). See tidyverse team’s guide create great issue advice. adding new function package, always add working examples new function roxygen2 documentation #' @examples.","code":""},{"path":"https://NIEHS.github.io/chopin/CONTRIBUTING.html","id":"pull-request-process","dir":"","previous_headings":"Bigger changes","what":"Pull request process","title":"Contributing to chopin","text":"Fork package clone onto computer. haven’t done , recommend using usethis::create_from_github(\"NIEHS/chopin\", fork = TRUE). Install development dependencies devtools::install_dev_deps(), make sure package passes R CMD check running devtools::check(). R CMD check doesn’t pass cleanly, ’s good idea ask help continuing. Create Git branch pull request (PR). recommend using usethis::pr_init(\"brief-description--change\"). Make changes, commit git, create PR running usethis::pr_push(), following prompts browser. title PR briefly describe change. body PR contain Fixes #issue-number. user-facing changes, add bullet top NEWS.md (.e. just first header).","code":""},{"path":"https://NIEHS.github.io/chopin/CONTRIBUTING.html","id":"code-style","dir":"","previous_headings":"Bigger changes","what":"Code style","title":"Contributing to chopin","text":"Please abide 80-character line rules (default settings lintr) use roxygen2, Markdown syntax, documentation. use testthat unit tests. Contributions test cases included easier accept.","code":""},{"path":"https://NIEHS.github.io/chopin/CONTRIBUTING.html","id":"code-of-conduct","dir":"","previous_headings":"","what":"Code of Conduct","title":"Contributing to chopin","text":"Please note chopin project released Contributor Code Conduct. contributing project agree abide terms.","code":""},{"path":"https://NIEHS.github.io/chopin/LICENSE.html","id":null,"dir":"","previous_headings":"","what":"MIT License","title":"MIT License","text":"Copyright (c) 2024 chopin authors Permission hereby granted, free charge, person obtaining copy software associated documentation files (“Software”), deal Software without restriction, including without limitation rights use, copy, modify, merge, publish, distribute, sublicense, /sell copies Software, permit persons Software furnished , subject following conditions: copyright notice permission notice shall included copies substantial portions Software. SOFTWARE PROVIDED “”, WITHOUT WARRANTY KIND, EXPRESS IMPLIED, INCLUDING LIMITED WARRANTIES MERCHANTABILITY, FITNESS PARTICULAR PURPOSE NONINFRINGEMENT. EVENT SHALL AUTHORS COPYRIGHT HOLDERS LIABLE CLAIM, DAMAGES LIABILITY, WHETHER ACTION CONTRACT, TORT OTHERWISE, ARISING , CONNECTION SOFTWARE USE DEALINGS SOFTWARE.","code":""},{"path":"https://NIEHS.github.io/chopin/articles/v01_start.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Getting started with chopin","text":"chopin automatically distributes geospatial data computation multiple threads. Function centered workflow streamline process parallelizing geospatial computation minimal effort.","code":""},{"path":"https://NIEHS.github.io/chopin/articles/v01_start.html","id":"chopin-workflow","dir":"Articles","previous_headings":"","what":"chopin workflow","title":"Getting started with chopin","text":"simplest way parallelizing generic geospatial computation start par_pad_* functions par_grid, running par_hierarchy, par_multirasters functions .","code":"library(chopin) library(terra) library(sf) library(collapse) library(dplyr) library(future) library(future.mirai) library(future.apply)"},{"path":"https://NIEHS.github.io/chopin/articles/v01_start.html","id":"example-data","dir":"Articles","previous_headings":"","what":"Example data","title":"Getting started with chopin","text":"North Carolinian counties raster elevation data used example data.","code":"nccnty_path <- system.file(\"extdata\", \"nc_hierarchy.gpkg\", package = \"chopin\") ncelev_path <-   system.file(\"extdata/nc_srtm15_otm.tif\", package = \"chopin\") nccnty <- terra::vect(nccnty_path)  ncelev <- terra::rast(ncelev_path)"},{"path":"https://NIEHS.github.io/chopin/articles/v01_start.html","id":"generating-random-points-in-north-carolina","dir":"Articles","previous_headings":"Example data","what":"Generating random points in North Carolina","title":"Getting started with chopin","text":"demonstrate chopin functions, generate 10,000 random points North Carolina","code":"ncsamp <-   terra::spatSample(     nccnty,     1e4L   ) ncsamp$pid <- 1:nrow(ncsamp)"},{"path":"https://NIEHS.github.io/chopin/articles/v01_start.html","id":"creating-grids","dir":"Articles","previous_headings":"","what":"Creating grids","title":"Getting started with chopin","text":"example generate regular grid random point data.","code":"ncgrid <- par_pad_grid(ncsamp, mode = \"grid\", nx = 4L, ny = 2L, padding = 10000)  plot(ncgrid$original)"},{"path":"https://NIEHS.github.io/chopin/articles/v01_start.html","id":"extracting-values-from-raster","dir":"Articles","previous_headings":"","what":"Extracting values from raster","title":"Getting started with chopin","text":"Since par_* functions operate future backends, users define future plan running functions. multicore plan supports terra objects may lead faster computation, supported Windows. alternative future.mirai’s mirai_multisession plan, supported many platforms generally faster plain future multisession plan. workers argument defined integer value specify number threads used. dispatch multiple extract_at runs grid polygons. proceed, terra object converted sf object.","code":"future::plan(future.mirai::mirai_multisession, workers = 2L) pg <-   par_grid(     grids = ncgrid,     pad_y = FALSE,     .debug = TRUE,     fun_dist = extract_at,     x = ncelev_path,     y = sf::st_as_sf(ncsamp),     id = \"pid\",     radius = 1e4,     func = \"mean\"   )"},{"path":"https://NIEHS.github.io/chopin/articles/v01_start.html","id":"hierarchical-processing","dir":"Articles","previous_headings":"","what":"Hierarchical processing","title":"Getting started with chopin","text":"demonstrate hierarchical processing random points using census tract polygons. example parallelize summarizing mean elevation 10 kilometers circular buffers random sample points first five characters census tract unique identifiers, county codes. example demonstrates hierarchy can defined given polygons unique identifiers suitably formatted defining hierarchy.","code":"nccnty <- sf::st_read(nccnty_path, layer = \"county\") ## Reading layer `county' from data source  ##   `/home/runner/work/_temp/Library/chopin/extdata/nc_hierarchy.gpkg'  ##   using driver `GPKG' ## Simple feature collection with 100 features and 1 field ## Geometry type: POLYGON ## Dimension:     XY ## Bounding box:  xmin: 1054155 ymin: 1341756 xmax: 1838923 ymax: 1690176 ## Projected CRS: NAD83 / Conus Albers nctrct <- sf::st_read(nccnty_path, layer = \"tracts\") ## Reading layer `tracts' from data source  ##   `/home/runner/work/_temp/Library/chopin/extdata/nc_hierarchy.gpkg'  ##   using driver `GPKG' ## Simple feature collection with 2672 features and 1 field ## Geometry type: MULTIPOLYGON ## Dimension:     XY ## Bounding box:  xmin: 1054155 ymin: 1341756 xmax: 1838923 ymax: 1690176 ## Projected CRS: NAD83 / Conus Albers px <-   par_hierarchy(     # from here the par_hierarchy-specific arguments     regions = nctrct,     regions_id = \"GEOID\",     length_left = 5,     pad = 10000,     pad_y = FALSE,     .debug = TRUE,     # from here are the dispatched function definition     # for parallel workers     fun_dist = extract_at,     # below should follow the arguments of the dispatched function     x = ncelev,     y = sf::st_as_sf(ncsamp),     id = \"pid\",     radius = 1e4,     func = \"mean\"   )  dim(px) ## [1] 10000     2 head(px) ##   pid      mean ## 1  79 13.664886 ## 2 198  7.980108 ## 3 337 12.189816 ## 4 454  2.763836 ## 5 467 19.170189 ## 6 485 13.523435 tail(px) ##        pid      mean ## 9995  9542 -1.184563 ## 9996  9703  5.548240 ## 9997  9756  5.548804 ## 9998  9759  6.540437 ## 9999  9841  6.414054 ## 10000 9963  4.990165"},{"path":"https://NIEHS.github.io/chopin/articles/v01_start.html","id":"multiraster-processing","dir":"Articles","previous_headings":"","what":"Multiraster processing","title":"Getting started with chopin","text":"demonstrate multiraster processing random points using multiple rasters.","code":"ncelev <-   system.file(\"extdata/nc_srtm15_otm.tif\", package = \"chopin\") ncelev <- terra::rast(ncelev) tdir <- tempdir(check = TRUE) terra::writeRaster(ncelev, file.path(tdir, \"test1.tif\"), overwrite = TRUE) terra::writeRaster(ncelev, file.path(tdir, \"test2.tif\"), overwrite = TRUE) terra::writeRaster(ncelev, file.path(tdir, \"test3.tif\"), overwrite = TRUE) terra::writeRaster(ncelev, file.path(tdir, \"test4.tif\"), overwrite = TRUE) terra::writeRaster(ncelev, file.path(tdir, \"test5.tif\"), overwrite = TRUE)  rasts <- list.files(tdir, pattern = \"tif$\", full.names = TRUE)  pm <-   par_multirasters(     filenames = rasts,     fun_dist = extract_at,     x = NA,     y = sf::st_as_sf(ncsamp)[1:500, ],     id = \"pid\",     radius = 1e4,     func = \"mean\",     .debug = TRUE   )  dim(pm) ## [1] 2500    2 head(pm) ##          mean               base_raster ## 1   12.443567 /tmp/RtmpUmoTIK/test1.tif ## 2   74.934341 /tmp/RtmpUmoTIK/test1.tif ## 3  179.088806 /tmp/RtmpUmoTIK/test1.tif ## 4    8.542515 /tmp/RtmpUmoTIK/test1.tif ## 5 1087.089722 /tmp/RtmpUmoTIK/test1.tif ## 6  110.032043 /tmp/RtmpUmoTIK/test1.tif tail(pm) ##            mean               base_raster ## 2495 101.370438 /tmp/RtmpUmoTIK/test5.tif ## 2496  -2.881788 /tmp/RtmpUmoTIK/test5.tif ## 2497   7.347102 /tmp/RtmpUmoTIK/test5.tif ## 2498   8.790645 /tmp/RtmpUmoTIK/test5.tif ## 2499  21.443277 /tmp/RtmpUmoTIK/test5.tif ## 2500  56.097313 /tmp/RtmpUmoTIK/test5.tif"},{"path":"https://NIEHS.github.io/chopin/articles/v01_start.html","id":"why-parallelization-is-slower-than-the-ordinary-function-run","dir":"Articles","previous_headings":"","what":"Why parallelization is slower than the ordinary function run?","title":"Getting started with chopin","text":"Parallelization may underperform datasets small take advantage divide--compute approach, parallelization overhead involved. Overhead refers required amount computational resources transferring objects multiple processes. Since demonstrations use quite small datasets, advantage parallelization noticeable expected. large amount data (spatial/temporal resolution number files, example) processed, users find efficiency package. vignette package demonstrates use cases extracting various climate/weather datasets.","code":""},{"path":"https://NIEHS.github.io/chopin/articles/v02_good_practice.html","id":"assumptions","dir":"Articles","previous_headings":"","what":"Assumptions","title":"Good practice of using `chopin` in HPC","text":"Users can access multi-threaded central processing units sufficient memory (.e., least 4GB/thread) Single-node calculation: version 0.3.0, chopin supports single-node","code":""},{"path":[]},{"path":"https://NIEHS.github.io/chopin/articles/v02_good_practice.html","id":"minimize-errors","dir":"Articles","previous_headings":"Basic workflow","what":"Minimize errors","title":"Good practice of using `chopin` in HPC","text":"Consider using try() tryCatch() let error halt work without results, especially using higher-level functions","code":""},{"path":"https://NIEHS.github.io/chopin/articles/v02_good_practice.html","id":"raster-vector-overlay","dir":"Articles","previous_headings":"Basic workflow","what":"Raster-Vector overlay","title":"Good practice of using `chopin` in HPC","text":"Use chopin::par_pad_grid make exhaustive padded grid objects SpatVector class chopin::par_grid filters features intersect grid. Post-processing necessary polygons may taken twice parallelized calculation. users field containing divisible hierarchical information, dataset can processed chopin::par_hierarchy.","code":""},{"path":"https://NIEHS.github.io/chopin/articles/v02_good_practice.html","id":"customization","dir":"Articles","previous_headings":"Basic workflow","what":"Customization","title":"Good practice of using `chopin` in HPC","text":"chopin way parallelize iterate calculation list use future.apply function extent vector element Preprocessed vector objects respect extent vector first list Preferably lists named Use file database table paths instead SpatRaster objects future parallel processing workflow","code":""},{"path":"https://NIEHS.github.io/chopin/articles/v02_good_practice.html","id":"save-computing-costs","dir":"Articles","previous_headings":"Basic workflow","what":"Save computing costs","title":"Good practice of using `chopin` in HPC","text":"HPC systems CPU-memory usage quota users profvis::profvis() helps summarize runtime per function call memory usage testing, use future::plan(future::sequential) see single-thread. Keep chopin::par_pad_grid arguments take one two grids using lapply. Preferably hardest case tested estimate maximum peak memory usage per thread. Submit job proper amount computational assets: HPC management system offers tools see statistics task summaries. Use records plan asset allocation next time.","code":""},{"path":"https://NIEHS.github.io/chopin/articles/v03_par_pad_grid.html","id":"computational-grids","dir":"Articles","previous_headings":"","what":"Computational grids","title":"Generate computational grids","text":"Computational grids refer set polygons cover entire spatial domain interest. grids used split spatial domain smaller pieces parallel processing. chopin provides functions generate computational grids parallel processing par_grid() function. Generate computational grids par_pad_grid() par_pad_balanced(). step give set grid polygons, one original splits padded consideration buffer radius subsequent spatial operations. Run par_grid() input dataset grid polygons. Input data: data computed value stored. example, extracting raster values vector objects, vector objects input data. Target data: data values come. example , raster object target data. Original padded grids used split main input original grid target dataset computation padded grid, respectively.","code":""},{"path":"https://NIEHS.github.io/chopin/articles/v03_par_pad_grid.html","id":"types-of-computational-grids-and-their-generation","dir":"Articles","previous_headings":"","what":"Types of computational grids and their generation","title":"Generate computational grids","text":"two approaches generate computational grids. One use par_pad_grid() one three modes use par_group_grid(). Thus, users four options total generate computational grids. padding argument important ensure accurate parallel operations case buffering involved. Suppose user point geometry inputs apply circular buffer certain radius. Since original grid (without padding = overlap adjacent grids) filters original input parallel worker, points near grid border may miss target data buffered polygons exceed grid.","code":""},{"path":"https://NIEHS.github.io/chopin/articles/v03_par_pad_grid.html","id":"par_pad_grid-standard-interface","dir":"Articles","previous_headings":"Types of computational grids and their generation","what":"par_pad_grid(): standard interface","title":"Generate computational grids","text":"par_pad_grid() generates regular grid polygons padding parallel processing. Padding distance overlapping grid polygons, essentially buffer radius points buffer polygons concerned. mode = \"grid\": generates regular grid polygons padding, nx ny arguments determine number columns rows grid, respectively. mode = \"grid_quantile\": generates regular grid polygons padding based quantiles number points grid. grids look irregular points per grid balanced grid mode. mode = \"grid_advanced\": generates regular grid polygons padding based number points grid number points entire dataset. nx ny arguments determine number columns rows grid, merge_max argument controls many adjacent grids merged one grid. grid_min_features argument determines minimum number points grid, means grids fewer points value merged adjacent grids. Adjusting arguments can balance computational load among threads reduce overhead parallelization.","code":""},{"path":"https://NIEHS.github.io/chopin/articles/v03_par_pad_grid.html","id":"par_pad_balanced-focusing-on-getting-the-balanced-clusters","dir":"Articles","previous_headings":"Types of computational grids and their generation","what":"par_pad_balanced(): focusing on getting the balanced clusters","title":"Generate computational grids","text":"par_pad_balanced() groups inputs equal size, generates padded rectangles cover number points per grid. Users can use output function par_grid() parallel processing.","code":""},{"path":"https://NIEHS.github.io/chopin/articles/v03_par_pad_grid.html","id":"random-points-in-nc","dir":"Articles","previous_headings":"Types of computational grids and their generation","what":"Random points in NC","title":"Generate computational grids","text":"demonstration par_pad_grid(), use moderately clustered point locations generated inside counties North Carolina.","code":"ncpoly <- system.file(\"shape/nc.shp\", package = \"sf\") ncsf <- sf::read_sf(ncpoly) ncsf <- sf::st_transform(ncsf, \"EPSG:5070\") plot(sf::st_geometry(ncsf)) # sampling clustered point library(spatstat.random) ## Loading required package: spatstat.data ## Loading required package: spatstat.univar ## spatstat.univar 3.0-0 ## Loading required package: spatstat.geom ## spatstat.geom 3.3-2 ##  ## Attaching package: 'spatstat.geom' ## The following objects are masked from 'package:igraph': ##  ##     diameter, edges, is.connected, vertices ## The following objects are masked from 'package:terra': ##  ##     area, delaunay, is.empty, rescale, rotate, shift, where.max, ##     where.min ## spatstat.random 3.3-1 set.seed(202404) ncpoints <-   sf::st_sample(     x = ncsf,     type = \"Thomas\",     mu = 20,     scale = 1e4,     kappa = 1.25e-9   ) ncpoints <- ncpoints[seq_len(2e3L), ]  ncpoints <- sf::st_as_sf(ncpoints) ncpoints <- sf::st_set_crs(ncpoints, \"EPSG:5070\") ncpoints$pid <- sprintf(\"PID-%05d\", seq(1, nrow(ncpoints))) plot(sf::st_geometry(ncpoints)) # convert to terra SpatVector ncpoints_tr <- terra::vect(ncpoints)"},{"path":"https://NIEHS.github.io/chopin/articles/v03_par_pad_grid.html","id":"visualize-computational-grids","dir":"Articles","previous_headings":"","what":"Visualize computational grids","title":"Generate computational grids","text":"output par_pad_grid() par_pad_balanced() length 2 list. significant difference two first element output. par_pad_grid(), always sf SpatVector object polygon geometries. hand, par_pad_balanced() first element sf SpatVector object geometry type original input. example, point geometries input point.","code":"compregions <-   chopin::par_pad_grid(     ncpoints_tr,     mode = \"grid\",     nx = 8L,     ny = 5L,     padding = 1e4L   )  # a list object class(compregions) ## [1] \"list\" # length of 2 names(compregions) ## [1] \"original\" \"padded\" par(mfrow = c(2, 1)) plot(compregions$original, main = \"Original grids\") plot(compregions$padded, main = \"Padded grids\")"},{"path":"https://NIEHS.github.io/chopin/articles/v03_par_pad_grid.html","id":"generate-regular-grid-computational-regions","dir":"Articles","previous_headings":"Visualize computational grids","what":"Generate regular grid computational regions","title":"Generate computational grids","text":"chopin::par_pad_grid() takes spatial dataset generate regular grid polygons nx ny arguments padding. Users overlapping (degree radius) non-overlapping grids, utilized split locations target datasets sub-datasets efficient processing. output par_pad_grid() list object two elements named original (non-overlapping grid polygons) padded (overlapping padding). class element depends input dataset class. figures illustrate grid polygons without overlaps.","code":"compregions <-   chopin::par_pad_grid(     ncpoints_tr,     mode = \"grid\",     nx = 8L,     ny = 5L,     padding = 1e4L   ) names(compregions) ## [1] \"original\" \"padded\" oldpar <- par() par(mfrow = c(2, 1)) terra::plot(compregions$original, main = \"Original grids\") terra::plot(compregions$padded, main = \"Padded grids\") par(mfrow = c(1, 1)) terra::plot(compregions$original, main = \"Original grids\") terra::plot(ncpoints_tr, add = TRUE, col = \"red\", cex = 0.4)"},{"path":"https://NIEHS.github.io/chopin/articles/v03_par_pad_grid.html","id":"split-the-points-by-two-1d-quantiles","dir":"Articles","previous_headings":"Visualize computational grids","what":"Split the points by two 1D quantiles","title":"Generate computational grids","text":"mode = \"grid_quantile\" generates regular grid polygons padding based quantiles coordinates dimension. using mode, users define quantiles argument, used get number quantiles dimension. convenient way define seq() function length.argument. example uses length.= 5, give quartiles.","code":"grid_quantiles <-   chopin::par_pad_grid(     input = ncpoints_tr,     mode = \"grid_quantile\",     quantiles = seq(0, 1, length.out = 5),     padding = 1e4L   )  names(grid_quantiles) ## [1] \"original\" \"padded\" par(mfrow = c(2, 1)) terra::plot(grid_quantiles$original, main = \"Original grids\") terra::plot(grid_quantiles$padded, main = \"Padded grids\") par(mfrow = c(1, 1)) terra::plot(grid_quantiles$original, main = \"Original grids\") terra::plot(ncpoints_tr, add = TRUE, col = \"red\", cex = 0.4)"},{"path":"https://NIEHS.github.io/chopin/articles/v03_par_pad_grid.html","id":"merge-the-grids-based-on-the-number-of-points","dir":"Articles","previous_headings":"Visualize computational grids","what":"Merge the grids based on the number of points","title":"Generate computational grids","text":"mode = \"grid_advanced\" utilizes finer grids merge results finer grids coarser grids. behavior can balance computational load among threads reduce overhead parallelization. said, mode internally generates grids mode = \"grid\" merges based number points grid. determine adjacency merging behavior, minimum spanning tree (MST) identified. function utilizes igraph::mst() MST identification graph summary functions hood. note, users can adjust merging behavior changing arguments grid_min_features merge_max.","code":"grid_advanced1 <-   chopin::par_pad_grid(     input = ncpoints_tr,     mode = \"grid_advanced\",     nx = 15L,     ny = 8L,     padding = 1e4L,     grid_min_features = 25L,     merge_max = 5L   ) ## Switch terra class to sf... ## Switch terra class to sf... ## ℹ The merged polygons have too complex shapes. ## Increase threshold or use the original grids. ##  ## Switch sf class to terra... par(mfrow = c(2, 1)) terra::plot(grid_advanced1$original, main = \"Original grids\") terra::plot(grid_advanced1$padded, main = \"Padded grids\") par(mfrow = c(1, 1)) terra::plot(grid_advanced1$original, main = \"Merged grids (merge_max = 5)\") terra::plot(ncpoints_tr, add = TRUE, col = \"red\", cex = 0.4) ncpoints_tr$n <- 1 n_points <-   terra::zonal(     ncpoints_tr,     grid_advanced1$original,     fun = \"sum\")[[\"n\"]]  grid_advanced1g <- grid_advanced1$original grid_advanced1g$n_points <- n_points  terra::plot(grid_advanced1g, \"n_points\", main = \"Number of points in each grid\")"},{"path":"https://NIEHS.github.io/chopin/articles/v03_par_pad_grid.html","id":"different-values-in-merge_max","dir":"Articles","previous_headings":"Visualize computational grids > Merge the grids based on the number of points","what":"Different values in merge_max","title":"Generate computational grids","text":"Keeping arguments , can see difference number merged grids changing merge_max argument.","code":"grid_advanced2 <-   chopin::par_pad_grid(     input = ncpoints_tr,     mode = \"grid_advanced\",     nx = 15L,     ny = 8L,     padding = 1e4L,     grid_min_features = 15L,     merge_max = 4L   ) ## Switch terra class to sf... ## Switch terra class to sf... ## ℹ The merged polygons have too complex shapes. ## Increase threshold or use the original grids. ##  ## Switch sf class to terra... par(mfrow = c(2, 1)) terra::plot(grid_advanced2$original, main = \"Original grids\") terra::plot(grid_advanced2$padded, main = \"Padded grids\") par(mfrow = c(1, 1)) terra::plot(grid_advanced2$original, main = \"Merged grids (merge_max = 8)\") terra::plot(ncpoints_tr, add = TRUE, col = \"red\", cex = 0.4) grid_advanced3 <-   chopin::par_pad_grid(     input = ncpoints_tr,     mode = \"grid_advanced\",     nx = 15L,     ny = 8L,     padding = 1e4L,     grid_min_features = 25L,     merge_max = 3L   ) ## Switch terra class to sf... ## Switch terra class to sf... ## ℹ The merged polygons have too complex shapes. ## Increase threshold or use the original grids. ##  ## Switch sf class to terra... par(mfrow = c(2, 1)) terra::plot(grid_advanced3$original, main = \"Original grids\") terra::plot(grid_advanced3$padded, main = \"Padded grids\") par(mfrow = c(1, 1)) terra::plot(grid_advanced3$original, main = \"Merged grids (merge_max = 3)\") terra::plot(ncpoints_tr, add = TRUE, col = \"red\", cex = 0.4)"},{"path":"https://NIEHS.github.io/chopin/articles/v03_par_pad_grid.html","id":"par_make_balanced","dir":"Articles","previous_headings":"Visualize computational grids > Merge the grids based on the number of points","what":"par_make_balanced()","title":"Generate computational grids","text":"background, par_pad_balanced() run first generate equally sized clusters input. , padding applied extent cluster compatible par_grid(), original padded grids used. Please note ngroups argument value must exact divisor number points. example, example , one changes ngroups 10L, fail number points divisible 10. Consult anticlust package details algorithm. par_pad_balanced() makes compatible object output par_pad_grid() directly input points. illustrated figure , points split ngroups clusters number points processed parallel using output object par_grid().","code":"# ngroups should be the exact divisor of the number of points! group_bal_grid <-   chopin::par_pad_balanced(     points_in = ncpoints_tr,     ngroups = 10L,     padding = 1e4   ) group_bal_grid$original$CGRIDID <- as.factor(group_bal_grid$original$CGRIDID)  par(mfrow = c(2, 1)) terra::plot(group_bal_grid$original, \"CGRIDID\",             legend = FALSE,             main = \"Assigned points (ngroups = 10)\") terra::plot(group_bal_grid$padded, main = \"Padded grids\")"},{"path":"https://NIEHS.github.io/chopin/articles/v04_climate_examples.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Extracting Weather/Climate Geospatial Data with `chopin`","text":"document demonstrates parallelize weather/climate geospatial data processing chopin cases users may take advantage parallel processing . cover following formats: consider TerraClimate PRISM data data format . Parameter-elevation Regressions Independent Slopes Model (PRISM) high-resolution (800-1,000 meters) gridded climate dataset available BIL (band interleaved line) format readable GDAL. TerraClimate high-resolution (5 km) gridded climate dataset NetCDF format readable GDAL. spatial resolution TerraClimate data commensurates 5 km equator, whereas PRISM data approximately 1 km. data available NetCDF format readable GDAL. use terra package read data.","code":""},{"path":"https://NIEHS.github.io/chopin/articles/v04_climate_examples.html","id":"prepare-target-datasets","dir":"Articles","previous_headings":"Introduction","what":"Prepare target datasets","title":"Extracting Weather/Climate Geospatial Data with `chopin`","text":"consider populated places centroids mainland United States (.e., excluding Alaska, Hawaii, territories). use tigris package download data.","code":"pkgs <-   c(\"chopin\", \"terra\", \"stars\", \"sf\",     \"future\", \"doFuture\", \"dplyr\", \"parallelly\", \"tigris\", \"tictoc\") invisible(sapply(pkgs, library, character.only = TRUE, quietly = TRUE)) options(tigris_use_cache = TRUE, sf_use_s2 = FALSE)"},{"path":"https://NIEHS.github.io/chopin/articles/v04_climate_examples.html","id":"hardware-specification","dir":"Articles","previous_headings":"Introduction","what":"Hardware specification","title":"Extracting Weather/Climate Geospatial Data with `chopin`","text":"used machine 112 physical CPU cores 750 GB memory, used portion cores (20) demonstration. maximum possible memory usage set. However, typical HPC management systems, users required request number cores memory jobs. number cores memory capacity considered users parallelize extraction process.","code":""},{"path":[]},{"path":"https://NIEHS.github.io/chopin/articles/v04_climate_examples.html","id":"download","dir":"Articles","previous_headings":"Introduction > Download and preprocess","what":"Download","title":"Extracting Weather/Climate Geospatial Data with `chopin`","text":"","code":"# populated places state <- tigris::states(year = 2022) statemain <-   state[!state$STUSPS %in% c(\"AK\", \"HI\", \"PR\", \"VI\", \"GU\", \"MP\", \"AS\"), ] target_states <- statemain$GEOID  popplace <-   lapply(target_states, function(x) tigris::places(x, year = 2022)) %>%   do.call(rbind, .) saveRDS(popplace, \"./input/populated_place_2022.rds\", compress = \"xz\")"},{"path":"https://NIEHS.github.io/chopin/articles/v04_climate_examples.html","id":"read","dir":"Articles","previous_headings":"Introduction > Download and preprocess","what":"Read","title":"Extracting Weather/Climate Geospatial Data with `chopin`","text":"","code":"state <- tigris::states(year = 2022) statemain <-   state[!state$STUSPS %in% c(\"AK\", \"HI\", \"PR\", \"VI\", \"GU\", \"MP\", \"AS\"), ] target_states <- statemain$GEOID  # prepared populated places popplace <- readRDS(\"./input/populated_place_2022.rds\")  # generate circular buffers with 10 km radius popplacep <- sf::st_centroid(popplace, of_largest_polygon = TRUE) %>%   sf::st_transform(\"EPSG:5070\") popplacep2 <- sf::st_centroid(popplace, of_largest_polygon = TRUE) popplaceb <- sf::st_buffer(popplacep, dist = units::set_units(10, \"km\"))"},{"path":"https://NIEHS.github.io/chopin/articles/v04_climate_examples.html","id":"terraclimate","dir":"Articles","previous_headings":"","what":"TerraClimate","title":"Extracting Weather/Climate Geospatial Data with `chopin`","text":"TerraClimate data provided yearly NetCDF files, contains monthly layers. read data terra package preprocess data extract annual mean sum bands types columns.","code":"# wbd ext_mainland <- c(xmin = -126, xmax = -64, ymin = 22, ymax = 51) ext_mainland <- terra::ext(ext_mainland)  path_tc <- file.path(\"input\", \"terraClimate\") path_tc_files <- list.files(   path = path_tc, pattern = \"*.nc$\",   full.names = TRUE ) path_tc_files #>  [1] \"input/terraClimate/TerraClimate_aet_2000.nc\"  #>  [2] \"input/terraClimate/TerraClimate_aet_2001.nc\" #>  [truncated] #> [321] \"input/terraClimate/TerraClimate_ws_2021.nc\"   #> [322] \"input/terraClimate/TerraClimate_ws_2022.nc\""},{"path":"https://NIEHS.github.io/chopin/articles/v04_climate_examples.html","id":"preprocessing","dir":"Articles","previous_headings":"TerraClimate","what":"Preprocessing","title":"Extracting Weather/Climate Geospatial Data with `chopin`","text":"Fourteen variables available TerraClimate data. table TerraClimate website. variables can categorized two types: summed averaged. Sum: aet, def, pet, ppt, q, soil, swe. Mean: PDSI, srad, tmax, tmin, vap, vpd, ws. Following rationale, preprocess data summing first seven layers averaging rest layers. code blocks follow “split-apply-combine” strategy. Note terra::tapp aggregates layers attributes time custom indices. !WARNING!WARNING Stacking raster data may take long time consume large amount memory depending users’ area interest data resolution. Users need consider memory capacity system stacking rasters. 14 data elements 23 years 12 months . demonstrates summary data layers averaged circular buffer polygons 10 kilometers (10,000 meters) radius. leverage multiple cores machine, run future::availableCores() check number cores set number workers future::plan accordingly. Typically, two logical cores physical core modern central processing units. number workers set number physical cores machine optimal performance. example uses future::multicore shares memory across workers. !CAUTION!CAUTION Windows users RStudio users (platforms) able use future::multicore due restriction future package. Please future::multisession instead note option runs slower future::multicore case.","code":"options(future.globals = FALSE) # some bands should be summed bandnames <- c(   \"aet\", \"def\", \"PDSI\", \"pet\", \"ppt\", \"q\", \"soil\", \"srad\",   \"swe\", \"tmax\", \"tmin\", \"vap\", \"vpd\", \"ws\" ) bandnames_sorted <- sort(bandnames)  # single nc file, yearly aggregation by fun value # band for summation bandnames_sum <- c(\"aet\", \"def\", \"pet\", \"ppt\", \"q\", \"soil\", \"swe\")  # band for averaging bandnames_avg <- c(\"PDSI\", \"srad\", \"tmax\", \"tmin\", \"vap\", \"vpd\", \"ws\")  # mean: temporally marginal pixel mean (i.e., monthly -> yearly) # sum: temporally marginal pixel sum (i.e., monthly -> yearly) # Preprocessed data are stored in tictoc::tic(\"sum: 7 layers\") netcdf_read_sum <-   split(bandnames_sum, bandnames_sum) |>   lapply(function(x) {     grep(paste0(\"(\", x, \")\"), path_tc_files, value = TRUE)   }) |>   lapply(function(x) {     terra::tapp(terra::rast(x, win = ext_mainland, snap = \"out\"), index = \"years\", fun = \"sum\")   }) netcdf_read_sum <- Reduce(c, netcdf_read_sum) tictoc::toc() #> sum: 7 layers: 177.974 sec elapsed names(netcdf_read_sum) <- paste0(rep(bandnames_sum, each = 23), \"_\", rep(2000:2022, 7)) netcdf_read_sum #> class       : SpatRaster  #> dimensions  : 696, 1488, 161  (nrow, ncol, nlyr) #> resolution  : 0.04166667, 0.04166667  (x, y) #> extent      : -126, -64, 22, 51  (xmin, xmax, ymin, ymax) #> coord. ref. : +proj=longlat +ellps=WGS84 +no_defs  #> source(s)   : memory #> names       : aet_2000,  aet_2001,  aet_2002,  aet_2003, ...  #> min values  :     22.9,      27.3,        11,      30.8, ...  #> max values  :   1270.9,    1366.6,      1399,    1411.1, ...  #> time (years):   2000 to 2022 # tictoc::tic(\"mean: 7 layers\") # netcdf_read_mean <- #   split(bandnames_avg, bandnames_avg) |> #   lapply(function(x) { #     grep(paste0(\"(\", x, \")\"), path_tc_files, value = TRUE) #   }) |> #   lapply(function(x) { #     terra::tapp(terra::rast(x, win = ext_mainland, snap = \"out\"), index = \"years\", fun = \"mean\") #   }) |> #   Reduce(f = c, x = _) # tictoc::toc()  # names(netcdf_read_mean) <- #   sprintf(\"%s_%d\", rep(bandnames_avg, each = 23), rep(2000:2022, 7)) # netcdf_read_mean tic(\"multi threads (grid)\") doFuture::registerDoFuture() future::plan(future::multicore, workers = 8L) grid_init <-   chopin::par_pad_grid(     popplacep2,     mode = \"grid\",     padding = 1e4,     nx = 4L,     ny = 2L   ) multi_grid <-   chopin::par_grid(     grids = grid_init,     fun_dist = extract_at,     y = popplacep2,     x = netcdf_read_sum,     id = \"GEOID\",     func = \"mean\",     radius = 1e4   ) toc() #> multi threads (grid): 16.668 sec elapsed system.time(   multi <-     grep(       paste0(\"(\", paste(bandnames_sum, collapse = \"|\"), \")\"),       path_tc_files,       value = TRUE     ) %>%     chopin::par_multirasters(       filenames = .,       fun_dist = extract_at,       y = popplacep2,       x = rast(), # ignored       id = \"GEOID\",       func = \"mean\",       radius = 1e4     ) ) #>     user   system  elapsed  #> 5377.495  231.654  762.147 future::plan(future::sequential)   # single thread tic(\"single thread\") single <-   exactextractr::exact_extract(     netcdf_read_sum,     sf::st_as_sf(popplaceb),     fun = \"mean\",     stack_apply = TRUE,     force_df = TRUE,     append_cols = c(\"GEOID\"),     progress = FALSE   ) toc() #> single thread: 21.161 sec elapsed"},{"path":"https://NIEHS.github.io/chopin/articles/v04_climate_examples.html","id":"prism-dataset","dir":"Articles","previous_headings":"","what":"PRISM dataset","title":"Extracting Weather/Climate Geospatial Data with `chopin`","text":"PRISM data provided monthly 30-year normal BIL files. Assuming user wants summarize 30-year normal precipitation 10 kilometers circular buffers geogrpahic centroids US Census Places (p.26), demonstrate extraction process chopin package.","code":""},{"path":[]},{"path":"https://NIEHS.github.io/chopin/articles/v04_climate_examples.html","id":"download-1","dir":"Articles","previous_headings":"PRISM dataset > Download and preprocess","what":"Download","title":"Extracting Weather/Climate Geospatial Data with `chopin`","text":"","code":"# populated places # mainland states state <- tigris::states(year = 2022) statemain <-   state[!state$STUSPS %in% c(\"AK\", \"HI\", \"PR\", \"VI\", \"GU\", \"MP\", \"AS\"), ] target_states <- statemain$GEOID  popplace <-   lapply(target_states, function(x) tigris::places(x, year = 2022)) %>%   do.call(rbind, .) saveRDS(popplace, \"./input/populated_place_2022.rds\", compress = \"xz\")"},{"path":"https://NIEHS.github.io/chopin/articles/v04_climate_examples.html","id":"read-1","dir":"Articles","previous_headings":"PRISM dataset > Download and preprocess","what":"Read","title":"Extracting Weather/Climate Geospatial Data with `chopin`","text":"!IMPORTANT!IMPORTANTchopin::par_pad_grid works best point datasets since grid clip input features parallelized. Polygon inputs result duplicate values output lead take longer complete.","code":"bils <- list.files(\"input\", \"bil$\", recursive = TRUE, full.names = TRUE) bilssds <- terra::rast(bils[-13]) popplace2 <- sf::st_transform(popplace, crs = terra::crs(bilssds)) popplaceb2 <- sf::st_transform(popplaceb, crs = terra::crs(bilssds))"},{"path":"https://NIEHS.github.io/chopin/articles/v04_climate_examples.html","id":"grid-parallelization","dir":"Articles","previous_headings":"PRISM dataset","what":"Grid parallelization","title":"Extracting Weather/Climate Geospatial Data with `chopin`","text":"vein TerraClimate data, use chopin package parallelize extraction process grid strategy.","code":"exgrid <-   chopin::par_pad_grid(     popplacep2,     mode = \"grid\",     padding = 1e4,     nx = 4L,     ny = 2L   )  doFuture::registerDoFuture() future::plan(future::multicore, workers = 8L)  system.time(   exmulti <-     chopin::par_grid(       exgrid,       fun_dist = extract_at,       y = popplacep2,       x = bilssds,       radius = units::set_units(1e4, \"meter\"),       id = \"GEOID\",       func = \"mean\"     ) ) #> Your input function was successfully run at CGRIDID: 1 #> Your input function was successfully run at CGRIDID: 2 #> Your input function was successfully run at CGRIDID: 3 #> Your input function was successfully run at CGRIDID: 4 #> Your input function was successfully run at CGRIDID: 5 #> Your input function was successfully run at CGRIDID: 6 #> Your input function was successfully run at CGRIDID: 7 #> Your input function was successfully run at CGRIDID: 8 #>    user  system elapsed  #>  33.090  13.254  14.571 system.time(   exsingle <-     exactextractr::exact_extract(       bilssds,       popplaceb2,       fun = \"mean\",       stack_apply = TRUE,       force_df = TRUE,       append_cols = \"GEOID\",       max_cells_in_memory = 2.14e9,       progress = FALSE     ) ) #>    user  system elapsed  #>  19.347   1.927  21.716 future::plan(future::sequential)"},{"path":[]},{"path":"https://NIEHS.github.io/chopin/articles/v04_climate_examples.html","id":"larger-buffer-sizes","dir":"Articles","previous_headings":"Scaled up examples","what":"Larger buffer sizes","title":"Extracting Weather/Climate Geospatial Data with `chopin`","text":"Examples showed difference parallelized single-threaded extraction process significant. increase buffer size 50 kilometers compare performance parallelized single-threaded extraction process. !NOTE!NOTE example used strings raster file paths surf argument chopin::extract_at_buffer. terra::rast multiple file paths return SpatRaster multiple layers rasters extent resolution.","code":"# make buffers popplaceb5 <- sf::st_buffer(popplacep, dist = units::set_units(50, \"km\")) %>%   sf::st_transform(terra::crs(bilssds))  system.time(   exsingle5 <-     exactextractr::exact_extract(       bilssds,       popplaceb5,       fun = \"mean\",       stack_apply = TRUE,       force_df = TRUE,       append_cols = \"GEOID\",       max_cells_in_memory = 2.14e9,       progress = FALSE     ) ) #>    user  system elapsed  #> 140.373   2.302 144.552 exgrid5k <-   chopin::par_pad_grid(     popplacep2,     mode = \"grid\",     padding = 5e4,     nx = 4L,     ny = 2L   )   doFuture::registerDoFuture() future::plan(future::multicore, workers = 8L)  system.time(   exmulti5k <-     chopin::par_grid(       exgrid5k,       fun_dist = extract_at,       y = popplacep2,       x = bilssds,       radius = 5e4,       id = \"GEOID\",       func = \"mean\"     ) ) #> Your input function was successfully run at CGRIDID: 1 #> Your input function was successfully run at CGRIDID: 2 #> Your input function was successfully run at CGRIDID: 3 #> Your input function was successfully run at CGRIDID: 4 #> Your input function was successfully run at CGRIDID: 5 #> Your input function was successfully run at CGRIDID: 6 #> Your input function was successfully run at CGRIDID: 7 #> Your input function was successfully run at CGRIDID: 8 #>    user  system elapsed  #> 152.344  11.003  60.409 future::plan(future::sequential)"},{"path":"https://NIEHS.github.io/chopin/articles/v04_climate_examples.html","id":"larger-number-of-features","dir":"Articles","previous_headings":"Scaled up examples","what":"Larger number of features","title":"Extracting Weather/Climate Geospatial Data with `chopin`","text":"example uses 1,204,934 1-km grid points southeastern United States summarize seven layers TerraClimate.","code":"## generate 1km grid points in the southeastern US States stt <- tigris::states(year = 2020) targ_states <- c(\"NC\", \"SC\", \"GA\", \"FL\", \"AL\", \"MS\", \"TN\", \"LA\", \"AR\") stt_targ <- stt[stt$STUSPS %in% targ_states, ] stt_t <- sf::st_transform(stt_targ, \"EPSG:5070\") stt_g <- sf::st_sample(stt_t, type = \"regular\", 1204934) stt_g <- sf::st_as_sf(stt_g) sf::st_geometry(stt_g) <- \"geometry\" stt_g$pid <- seq(1, nrow(stt_g)) stt_gb <- sf::st_buffer(stt_g, units::set_units(10, \"km\"))  tic() single_2m <-   exactextractr::exact_extract(     netcdf_read_sum,     stt_gb,     fun = \"mean\",     stack_apply = TRUE,     force_df = TRUE,     max_cells_in_memory = 2.14e9,     progress = FALSE   ) toc() #> 855.908 sec elapsed stt_gbg <-   chopin::par_pad_grid(     stt_g,     mode = \"grid\",     padding = 5e3,     nx = 5L,     ny = 5L   )   doFuture::registerDoFuture() future::plan(future::multicore, workers = 8L) system.time(   stt5k <-     chopin::par_grid(       stt_gbg,       fun_dist = extract_at,       y = stt_g,       x = netcdf_read_sum,       id = \"pid\",       radius = 1e4,       func = \"mean\",       max_cells = 2e7     ) ) #>   user  system elapsed  #>  6.745   4.102 434.041 future::plan(future::sequential)"},{"path":"https://NIEHS.github.io/chopin/articles/v04_climate_examples.html","id":"finely-resolved-vector","dir":"Articles","previous_headings":"Scaled up examples","what":"Finely resolved vector","title":"Extracting Weather/Climate Geospatial Data with `chopin`","text":"Using PRISM data, example summarizes mean values data element census block groups.","code":""},{"path":"https://NIEHS.github.io/chopin/articles/v04_climate_examples.html","id":"download-2","dir":"Articles","previous_headings":"Scaled up examples > Finely resolved vector","what":"Download","title":"Extracting Weather/Climate Geospatial Data with `chopin`","text":"","code":"# set state=NULL and cb=TRUE will download the block groups for the entire US bg <- tigris::block_groups(state = NULL, cb = TRUE, year = 2020) sf::write_sf(bg, file.path(\"input\", \"Blockgroups_2020.gpkg\"))"},{"path":"https://NIEHS.github.io/chopin/articles/v04_climate_examples.html","id":"extract","dir":"Articles","previous_headings":"Scaled up examples > Finely resolved vector","what":"Extract","title":"Extracting Weather/Climate Geospatial Data with `chopin`","text":"","code":"## extract prism by par_hierarchy bgsf <- sf::st_read(\"input/Blockgroups_2020.gpkg\") #> Reading layer `Blockgroups_2020' from data source  #>   `/ddn/gs1/home/songi2/projects/chopin/input/Blockgroups_2020.gpkg'  #>   using driver `GPKG' #> Simple feature collection with 242298 features and 11 fields #> Geometry type: MULTIPOLYGON #> Dimension:     XY #> Bounding box:  xmin: -179.1467 ymin: -14.5487 xmax: 179.7785 ymax: 71.38782 #> Geodetic CRS:  NAD83 bgsf_main <- bgsf %>%   dplyr::filter(!STATEFP %in% c(\"02\", \"15\", \"72\", \"66\", \"78\", \"60\", \"69\"))  ## extract prism at bg system.time(   exsingle <-     exactextractr::exact_extract(       bilssds,       bgsf_main,       fun = \"mean\",       stack_apply = TRUE,       force_df = TRUE,       append_cols = \"GEOID\",       max_cells_in_memory = 2.14e9,       progress = FALSE     ) ) #>    user  system elapsed  #>  60.387   2.059  64.147 nmain <- c(\"AS\", \"AK\", \"HI\", \"PR\", \"VI\", \"GU\", \"MP\") stt_nmain <- stt[!stt$STUSPS %in% nmain, ]   future::plan(future.mirai::mirai_multisession, workers = 20L) system.time(   exhierarchy <-     chopin::par_hierarchy(       bgsf_main,       regions_id = \"STATEFP\",       fun_dist = extract_at,       y = bgsf_main,       x = bilssds,       id = \"GEOID\"     ) ) #>    user  system elapsed  #>   2.205   0.558 158.905 future::plan(future::sequential)"},{"path":"https://NIEHS.github.io/chopin/articles/v04_climate_examples.html","id":"finely-resolved-raster","dir":"Articles","previous_headings":"Scaled up examples","what":"Finely resolved raster","title":"Extracting Weather/Climate Geospatial Data with `chopin`","text":"demonstrate extraction process CropScape dataset resolution 30 meters. case, use frac option exact_extract tabulates fraction area cell category covered polygon accounting partial coverage polygon segments raster cells. balancing computation time, split block groups nine subsets parallelize. Note mode = \"grid_quantile\" used par_pad_grid balance number block groups per grid. input argument par_pad_grid polygons, polygons duplicate rows output data.frame since block groups overlapping grid selected.","code":"cdl <- terra::rast(\"input/2022_cdls/2022_30m_cdls.tif\")  system.time(   bgsf_cdl_single <-     exactextractr::exact_extract(       cdl,       bgsf_main,       fun = \"frac\",       stack_apply = TRUE,       force_df = TRUE,       append_cols = \"GEOID\",       max_cells_in_memory = 2.14e9,       progress = FALSE     ) ) #>     user   system  elapsed  #> 1013.411   44.322 1369.053 pkgs <-   c(\"chopin\", \"terra\", \"stars\", \"sf\", \"future\", \"doFuture\", \"dplyr\", \"parallelly\", \"tigris\", \"tictoc\") invisible(sapply(pkgs, library, character.only = TRUE, quietly = TRUE)) options(tigris_use_cache = TRUE, sf_use_s2 = FALSE) cdl <- terra::rast(\"input/2022_cdls/2022_30m_cdls.tif\") bgsf <- sf::st_read(\"input/Blockgroups_2020.gpkg\") bgsf_main <- bgsf %>%   dplyr::filter(!STATEFP %in% c(\"02\", \"15\", \"72\", \"66\", \"78\", \"60\", \"69\"))   # balancing the number of features assigned per workers # by splitting block groups by splitting centroids bgsf_9grids_plain <- bgsf_main %>%   chopin::par_pad_grid(     mode = \"grid\",     nx = 3L, ny = 3L, padding = 1e4   ) bgsf_9grids <- bgsf_main %>%   chopin::par_pad_grid(     mode = \"grid_quantile\",     padding = 1e4,     quantiles = chopin::par_def_q(3L)   )   future::plan(future.mirai::mirai_multisession, workers = 9L) # Note that bgsf_9grids were converted to sf # for exporting the objects to the parallel workers system.time(   bgsf_cdl_par <-     chopin::par_grid(       lapply(bgsf_9grids, sf::st_as_sf),       fun_dist = extract_at,       y = bgsf_main,       x = cdl,       id = \"GEOID\",       func = \"frac\",       max_cells = 2e7     ) ) #> Your input function was successfully run at CGRIDID: 1 #> Your input function was successfully run at CGRIDID: 2 #> Your input function was successfully run at CGRIDID: 3 #> Your input function was successfully run at CGRIDID: 4 #> Your input function was successfully run at CGRIDID: 5 #> Your input function was successfully run at CGRIDID: 6 #> Your input function was successfully run at CGRIDID: 7 #> Your input function was successfully run at CGRIDID: 8 #> Your input function was successfully run at CGRIDID: 9 #>   user  system elapsed  #>  6.632   1.821 347.638"},{"path":"https://NIEHS.github.io/chopin/articles/v04_climate_examples.html","id":"discussion-which-strategy-is-better-stacked-vs-file-based-parallelization","dir":"Articles","previous_headings":"","what":"Discussion: which strategy is better? stacked vs file-based parallelization","title":"Extracting Weather/Climate Geospatial Data with `chopin`","text":"!NOTE!NOTEIt’s users’ system specification size data. faster computation, two strategies. One parallelization implemented chopin example. strategy stack rasters extract values . Adjustment arguments exactextractr::exact_extract benefit many people dealing sizable data able stacked. compare two strategies terms computation time. proceeding, users need consider hardware specification. example, memory storage leverage maximal performance exact_extract. Specifically speaking, memory capacity crucial store stacked rasters memory rather read proxy rasters disk implemented terra. max_cells_in_memory key argument control memory usage. maximum possible value argument 231−1=2,147,483,6472^{31} - 1 = 2,147,483,647, roughly 2.147e9, applied example . memory bandwidth much faster disk input/output specification, stacked rasters high max_cells_in_memory applied run faster file-based parallelization extraction lower value max_cells_in_memory. performance come without cost. memory-intensive setting suitable system limited memory, example, consumer laptops around 16 GB RAM.","code":""},{"path":"https://NIEHS.github.io/chopin/articles/v04_climate_examples.html","id":"more-tips-to-save-time-and-memory","dir":"Articles","previous_headings":"Discussion: which strategy is better? stacked vs file-based parallelization","what":"More tips to save time and memory","title":"Extracting Weather/Climate Geospatial Data with `chopin`","text":"Always consider stacking rasters large number separate raster files resolution extent machine’s memory capacity allows. Reading multiple raster files terra::rast automatically stack single SpatRaster object. !NOTE!NOTE Stacking rasters usually takes large amount memory. Users need consider memory capacity system stacking rasters. Read vector data sf package first. bit faster terra save time processing exactextractr designed work sf objects vector inputs. users want use exactextractr raster-vector overlay, pre-cropping raster data may help saving time processing. exactextractr read raster data extent vector data ad hoc. analysis require high precision vector data, simplification geometries (e.g., using rmapshaper, .e., ms_simplify) result considerable time savings.","code":""},{"path":"https://NIEHS.github.io/chopin/articles/v04_climate_examples.html","id":"see-also","dir":"Articles","previous_headings":"Discussion: which strategy is better? stacked vs file-based parallelization","what":"See also","title":"Extracting Weather/Climate Geospatial Data with `chopin`","text":"Garnett, R. (2023). Geospatial distributed processing furrr Dyba, K. (n.d.). Parallel raster processing stars","code":""},{"path":"https://NIEHS.github.io/chopin/articles/v05_targets.html","id":"objective","dir":"Articles","previous_headings":"","what":"Objective","title":"targets and grid objects","text":"targets powerful workflow management reproducibility. chopin grid partitioning way parallelize repeated tasks across unit grids applying patterns. vignette demonstrates use targets chopin together.","code":""},{"path":"https://NIEHS.github.io/chopin/articles/v05_targets.html","id":"installation","dir":"Articles","previous_headings":"","what":"Installation","title":"targets and grid objects","text":"Despite targets referenced DESCRIPTION file, required install targets package run code vignette.","code":"rlang::check_installed(\"targets\")"},{"path":"https://NIEHS.github.io/chopin/articles/v05_targets.html","id":"example","dir":"Articles","previous_headings":"","what":"Example","title":"targets and grid objects","text":"par_pad_grid() par_pad_balanced() functions argument return_wkt return grid partition well-known text (WKT) format characters. format exported parallel workers regardless parallel backend future::multisession mirai::daemons, interoperate externalpnt objects C++ functions. Using WKT character objects, can easily convert sf terra objects inside function running parallel worker use targets workflow standard branching/patterning interface map(), cross(), others. example generate grid partition North Carolina state demonstrate use grid partition targets workflow.","code":""},{"path":"https://NIEHS.github.io/chopin/articles/v05_targets.html","id":"random-points-in-nc","dir":"Articles","previous_headings":"Example","what":"Random points in NC","title":"targets and grid objects","text":"demonstration par_pad_grid(), use moderately clustered point locations generated inside counties North Carolina.","code":"library(chopin) library(sf) library(spatstat.random)  sf::sf_use_s2(FALSE) set.seed(202404) ncpoly <- system.file(\"shape/nc.shp\", package = \"sf\") ncsf <- sf::read_sf(ncpoly) ncsf <- sf::st_transform(ncsf, \"EPSG:5070\") plot(sf::st_geometry(ncsf)) ncpoints <-   sf::st_sample(     x = ncsf,     type = \"Thomas\",     mu = 20,     scale = 1e4,     kappa = 1.25e-9   ) ncpoints <- sf::st_as_sf(ncpoints) ncpoints <- sf::st_set_crs(ncpoints, \"EPSG:5070\") ncpoints$pid <- sprintf(\"PID-%05d\", seq(1, nrow(ncpoints))) plot(sf::st_geometry(ncpoints))"},{"path":"https://NIEHS.github.io/chopin/articles/v05_targets.html","id":"grid-partition-of-nc","dir":"Articles","previous_headings":"Example","what":"Grid partition of NC","title":"targets and grid objects","text":"Since sf objects exportable parallel workers, can also consider part targets workflow.","code":"ncgrid_sf <-   par_pad_grid(     input = ncpoints,     mode = \"grid\",     nx = 6L,     ny = 3L,     padding = 1e4L,     return_wkt = FALSE   )  ncgrid_sf$original ## Simple feature collection with 18 features and 1 field ## Geometry type: POLYGON ## Dimension:     XY ## Bounding box:  xmin: 1057207 ymin: 1355826 xmax: 1830521 ymax: 1676488 ## Projected CRS: NAD83 / Conus Albers ## First 10 features: ##                          geometry CGRIDID ## 1  POLYGON ((1057207 1355826, ...       1 ## 2  POLYGON ((1186092 1355826, ...       2 ## 3  POLYGON ((1314978 1355826, ...       3 ## 4  POLYGON ((1443864 1355826, ...       4 ## 5  POLYGON ((1572750 1355826, ...       5 ## 6  POLYGON ((1701635 1355826, ...       6 ## 7  POLYGON ((1057207 1462714, ...       7 ## 8  POLYGON ((1186092 1462714, ...       8 ## 9  POLYGON ((1314978 1462714, ...       9 ## 10 POLYGON ((1443864 1462714, ...      10 ncgrid_sf$padded ## Simple feature collection with 18 features and 1 field ## Geometry type: POLYGON ## Dimension:     XY ## Bounding box:  xmin: 1047207 ymin: 1345826 xmax: 1840521 ymax: 1686488 ## Projected CRS: NAD83 / Conus Albers ## First 10 features: ##    CGRIDID                       geometry ## 1        1 POLYGON ((1047207 1345826, ... ## 2        2 POLYGON ((1176092 1345826, ... ## 3        3 POLYGON ((1304978 1345826, ... ## 4        4 POLYGON ((1433864 1345826, ... ## 5        5 POLYGON ((1562750 1345826, ... ## 6        6 POLYGON ((1691635 1345826, ... ## 7        7 POLYGON ((1047207 1452714, ... ## 8        8 POLYGON ((1176092 1452714, ... ## 9        9 POLYGON ((1304978 1452714, ... ## 10      10 POLYGON ((1433864 1452714, ... ncgrid_wkt <-   par_pad_grid(     input = ncpoints,     mode = \"grid\",     nx = 6L,     ny = 3L,     padding = 1e4L,     return_wkt = TRUE   )  ncgrid_wkt$original ##  [1] \"POLYGON ((1057207 1355826, 1186092 1355826, 1186092 1462714, 1057207 1462714, 1057207 1355826))\" ##  [2] \"POLYGON ((1186092 1355826, 1314978 1355826, 1314978 1462714, 1186092 1462714, 1186092 1355826))\" ##  [3] \"POLYGON ((1314978 1355826, 1443864 1355826, 1443864 1462714, 1314978 1462714, 1314978 1355826))\" ##  [4] \"POLYGON ((1443864 1355826, 1572750 1355826, 1572750 1462714, 1443864 1462714, 1443864 1355826))\" ##  [5] \"POLYGON ((1572750 1355826, 1701635 1355826, 1701635 1462714, 1572750 1462714, 1572750 1355826))\" ##  [6] \"POLYGON ((1701635 1355826, 1830521 1355826, 1830521 1462714, 1701635 1462714, 1701635 1355826))\" ##  [7] \"POLYGON ((1057207 1462714, 1186092 1462714, 1186092 1569601, 1057207 1569601, 1057207 1462714))\" ##  [8] \"POLYGON ((1186092 1462714, 1314978 1462714, 1314978 1569601, 1186092 1569601, 1186092 1462714))\" ##  [9] \"POLYGON ((1314978 1462714, 1443864 1462714, 1443864 1569601, 1314978 1569601, 1314978 1462714))\" ## [10] \"POLYGON ((1443864 1462714, 1572750 1462714, 1572750 1569601, 1443864 1569601, 1443864 1462714))\" ## [11] \"POLYGON ((1572750 1462714, 1701635 1462714, 1701635 1569601, 1572750 1569601, 1572750 1462714))\" ## [12] \"POLYGON ((1701635 1462714, 1830521 1462714, 1830521 1569601, 1701635 1569601, 1701635 1462714))\" ## [13] \"POLYGON ((1057207 1569601, 1186092 1569601, 1186092 1676488, 1057207 1676488, 1057207 1569601))\" ## [14] \"POLYGON ((1186092 1569601, 1314978 1569601, 1314978 1676488, 1186092 1676488, 1186092 1569601))\" ## [15] \"POLYGON ((1314978 1569601, 1443864 1569601, 1443864 1676488, 1314978 1676488, 1314978 1569601))\" ## [16] \"POLYGON ((1443864 1569601, 1572750 1569601, 1572750 1676488, 1443864 1676488, 1443864 1569601))\" ## [17] \"POLYGON ((1572750 1569601, 1701635 1569601, 1701635 1676488, 1572750 1676488, 1572750 1569601))\" ## [18] \"POLYGON ((1701635 1569601, 1830521 1569601, 1830521 1676488, 1701635 1676488, 1701635 1569601))\" ncgrid_wkt$padded ##  [1] \"POLYGON ((1047207 1345826, 1047207 1472714, 1196092 1472714, 1196092 1345826, 1047207 1345826))\" ##  [2] \"POLYGON ((1176092 1345826, 1176092 1472714, 1324978 1472714, 1324978 1345826, 1176092 1345826))\" ##  [3] \"POLYGON ((1304978 1345826, 1304978 1472714, 1453864 1472714, 1453864 1345826, 1304978 1345826))\" ##  [4] \"POLYGON ((1433864 1345826, 1433864 1472714, 1582750 1472714, 1582750 1345826, 1433864 1345826))\" ##  [5] \"POLYGON ((1562750 1345826, 1562750 1472714, 1711635 1472714, 1711635 1345826, 1562750 1345826))\" ##  [6] \"POLYGON ((1691635 1345826, 1691635 1472714, 1840521 1472714, 1840521 1345826, 1691635 1345826))\" ##  [7] \"POLYGON ((1047207 1452714, 1047207 1579601, 1196092 1579601, 1196092 1452714, 1047207 1452714))\" ##  [8] \"POLYGON ((1176092 1452714, 1176092 1579601, 1324978 1579601, 1324978 1452714, 1176092 1452714))\" ##  [9] \"POLYGON ((1304978 1452714, 1304978 1579601, 1453864 1579601, 1453864 1452714, 1304978 1452714))\" ## [10] \"POLYGON ((1433864 1452714, 1433864 1579601, 1582750 1579601, 1582750 1452714, 1433864 1452714))\" ## [11] \"POLYGON ((1562750 1452714, 1562750 1579601, 1711635 1579601, 1711635 1452714, 1562750 1452714))\" ## [12] \"POLYGON ((1691635 1452714, 1691635 1579601, 1840521 1579601, 1840521 1452714, 1691635 1452714))\" ## [13] \"POLYGON ((1047207 1559601, 1047207 1686488, 1196092 1686488, 1196092 1559601, 1047207 1559601))\" ## [14] \"POLYGON ((1176092 1559601, 1176092 1686488, 1324978 1686488, 1324978 1559601, 1176092 1559601))\" ## [15] \"POLYGON ((1304978 1559601, 1304978 1686488, 1453864 1686488, 1453864 1559601, 1304978 1559601))\" ## [16] \"POLYGON ((1433864 1559601, 1433864 1686488, 1582750 1686488, 1582750 1559601, 1433864 1559601))\" ## [17] \"POLYGON ((1562750 1559601, 1562750 1686488, 1711635 1686488, 1711635 1559601, 1562750 1559601))\" ## [18] \"POLYGON ((1691635 1559601, 1691635 1686488, 1840521 1686488, 1840521 1559601, 1691635 1559601))\""},{"path":"https://NIEHS.github.io/chopin/articles/v05_targets.html","id":"targets-workflow","dir":"Articles","previous_headings":"Example","what":"Targets workflow","title":"targets and grid objects","text":"Assume design function calc_something() calculates something grid partition. can use grid partition input function. sf object centered workflow, can use sf functions interact exported grid partition objects. Let’s consider binary spatial operation x y involved. x dataset variable calculated whereas y raster file path extract values. Please note SpatRaster objects exported parallel workers . read object parallel workers. branch across grid partition, function unit grid handle subsetting x narrow calculation scope grid. Therefore, synopsis function look like : map(unit_grid, pad_grid) pattern argument tar_target() . sf object inherits data.frame class. align object targets branching, clear convert object list object pattern across grid partition. par_split_list chopin . WKT format used, function modified restore grid partition sf objects. function modified follows: tar_target can use list object function calc_something branch . workable example tar_target proper _targets.R file follows: target result list data.frames contain calculation results.","code":"calc_something <- function(x, y, unit_grid, pad_grid, ...) {   # 0. restore unit_grid and pad_grid to sf objects if they are in WKT format   # 1-1. make x subset using intersect logic between x and unit_grid   # 1-2. read y subset using intersect logic between y and pad_grid   # 2. make buffer of x   # 3. do actual calculation (use ... wisely to pass additional arguments)   # 4. return the result } calc_something <- function(x, y, unit_grid, pad_grid, ...) {   # 1-1. make x subset using intersect logic between x and unit_grid   x <- x[unit_grid, ]   # 1-2. read y subset using intersect logic between y and pad_grid   yext <- terra::ext(sf::st_bbox(pad_grid))   yras <- terra::rast(y, win = yext)   # 2. make buffer of x   xbuffer <- sf::st_buffer(x, units::set_units(10, \"km\"))   # 3. do actual calculation (use ... wisely to pass additional arguments)   xycalc <- exactextractr::exact_extract(     yras,     xbuffer,     force_df = TRUE,     fun = \"mean\",     append_cols = \"pid\", # assume that pid is a unique identifier     progress = FALSE   )   # 4. return the result   return(xycalc) } ncgrid_sflist <-   par_split_list(ncgrid_sf) calc_something <- function(x, y, unit_grid, pad_grid, ...) {   # 0. restore unit_grid and pad_grid to sf objects if they are in WKT format   unit_grid <- sf::st_as_sf(wkt = unit_grid)   pad_grid <- sf::st_as_sf(wkt = pad_grid)   # 1-1. make x subset using intersect logic between x and unit_grid   x <- x[unit_grid, ]   # 1-2. read y subset using intersect logic between y and pad_grid   yext <- terra::ext(sf::st_bbox(pad_grid))   yras <- terra::rast(y, win = yext)   # 2. make buffer of x   xbuffer <- sf::st_buffer(x, units::set_units(10, \"km\"))   # 3. do actual calculation (use ... wisely to pass additional arguments)   xycalc <- exactextractr::exact_extract(     yras,     xbuffer,     fun = \"mean\",     force_df = TRUE,     append_cols = \"pid\", # assume that pid is a unique identifier     progress = FALSE   )   # 4. return the result   return(xycalc) } ncgrid_wktlist <-   par_split_list(ncgrid_wkt) list(   tar_target(     name = points,     command = sf::st_read(\"path_to_points.format\")   ),   tar_target(     name = raster,     command = \"path_to_raster.format\",     format = \"file\"   ),   tar_target(     name = chopingrid,     command = par_pad_grid(points, input = points, nx = 6L, ny = 3L, padding = 1e4L, return_wkt = FALSE)   ),   tar_target(     name = chopingrid_split,     command = mapply(       function(listorig, row) {         list(listorig$original[row, ], listorig$padded[row, ])       },       chopingrid, seq_len(nrow(chopingrid$original)),       SIMPLIFY = FALSE     ),     iteration = \"list\"   ),   tar_target(     name = result,     command =     calc_something(       points, raster,       chopingrid_split[[1]], chopingrid_split[[2]]     ),     pattern = map(chopingrid_split),     iteration = \"list\"   ) )"},{"path":"https://NIEHS.github.io/chopin/authors.html","id":null,"dir":"","previous_headings":"","what":"Authors","title":"Authors and Citation","text":"Insang Song. Author, maintainer. Kyle Messier. Author, contributor.","code":""},{"path":"https://NIEHS.github.io/chopin/authors.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Authors and Citation","text":"Song , Messier K (2024). chopin: Computation Spatial Data Hierarchical Objective Partitioning Inputs Parallel Processing. R package version 0.7.8.20240817, https://github.com/NIEHS/chopin.","code":"@Manual{,   title = {chopin: Computation of Spatial Data by Hierarchical and Objective Partitioning of Inputs for Parallel Processing},   author = {Insang Song and Kyle Messier},   year = {2024},   note = {R package version 0.7.8.20240817},   url = {https://github.com/NIEHS/chopin}, }"},{"path":[]},{"path":[]},{"path":"https://NIEHS.github.io/chopin/index.html","id":"objective","dir":"","previous_headings":"Objective and target users","what":"Objective","title":"Computation of Spatial Data by Hierarchical and Objective Partitioning of Inputs for Parallel Processing","text":"package automates parallelization spatial operations chopin functions well sf/terra functions. GDAL-compatible files database tables, chopin functions help calculate spatial variables vector raster data external software requirements.","code":""},{"path":"https://NIEHS.github.io/chopin/index.html","id":"for-whom-chopin-is-useful","dir":"","previous_headings":"Objective and target users","what":"For whom chopin is useful","title":"Computation of Spatial Data by Hierarchical and Objective Partitioning of Inputs for Parallel Processing","text":"need perform geospatial operations large datasets may find package useful accelerate covariate calculation process analysis modeling. basic knowledge geographic information system data models, coordinate systems transformations, spatial operations, raster-vector overlay; Understood planned want calculate; Collected datasets need","code":""},{"path":"https://NIEHS.github.io/chopin/index.html","id":"overview","dir":"","previous_headings":"","what":"Overview","title":"Computation of Spatial Data by Hierarchical and Objective Partitioning of Inputs for Parallel Processing","text":"Processing functions accept terra/sf classes spatial data. Raster-vector overlay done exactextractr. extract_at: extract raster values point buffers polygons without kernel weights summarize_sedc: calculate sums exponentially decaying contributions summarize_aw: area-weighted covariates based target reference polygons par_grid: parallelize artificial grid polygons generated maximum extent inputs. par_pad_grid used generate grid polygons running function. par_hierarchy: parallelize hierarchy coded identifier fields (example, census blocks county US) par_multirasters: parallelize multiple raster files functions designed used future future.mirai packages parallelize multiple CPU threads. Users can choose number threads used parallelization process. Users always need register parallel workers future running three functions .","code":"future::plan(future.mirai::mirai_multisession, workers = 4L) # future::multisession, future::cluster are available, # See future.batchtools and future.callr for other options # the number of workers are up to users' choice"},{"path":"https://NIEHS.github.io/chopin/index.html","id":"function-selection-guide","dir":"","previous_headings":"Overview","what":"Function selection guide","title":"Computation of Spatial Data by Hierarchical and Objective Partitioning of Inputs for Parallel Processing","text":"provide two flowcharts help users choose right function parallel processing. raster-oriented flowchart users want start raster data, vector-oriented flowchart users large vector data. raster-oriented selection, suggest four factors consider: - Number raster files: multiple files, par_multirasters recommended. multiple rasters share extent resolution, consider stacking rasters multilayer SpatRaster object calling terra::rast(filenames). - Raster resolution: suggest 100 meters threshold. Rasters resolution coarser 100 meters layers better direct call exactextractr::exact_extract(). - Raster extent: Using SpatRaster exactextractr::exact_extract() often minimally affected raster extent. - Memory size: max_cells_in_memory argument value exactextractr::exact_extract(), raster resolution, number layers SpatRaster multiplicatively related memory usage.  vector-oriented selection, suggest three factors consider: - Number features: number features 100,000, consider using par_grid par_hierarchy split data smaller chunks. - Hierarchical structure: data hierarchical structure, consider using par_hierarchy parallelize operation. - Data grouping: data needs grouped similar sizes, consider using par_pad_balanced par_pad_grid mode = \"grid_quantile\".","code":""},{"path":"https://NIEHS.github.io/chopin/index.html","id":"installation","dir":"","previous_headings":"","what":"Installation","title":"Computation of Spatial Data by Hierarchical and Objective Partitioning of Inputs for Parallel Processing","text":"chopin can installed using remotes::install_github (also possible pak::pak devtools::install_github).","code":"rlang::check_installed(\"remotes\") remotes::install_github(\"NIEHS/chopin\")"},{"path":"https://NIEHS.github.io/chopin/index.html","id":"examples","dir":"","previous_headings":"","what":"Examples","title":"Computation of Spatial Data by Hierarchical and Objective Partitioning of Inputs for Parallel Processing","text":"Examples navigate par_grid, par_hierarchy, par_multirasters functions chopin parallelize geospatial operations.","code":"# check and install packages to run examples pkgs <- c(\"chopin\", \"dplyr\", \"sf\", \"terra\", \"future\", \"future.mirai\", \"mirai\") # install packages if anything is unavailable rlang::check_installed(pkgs)  library(chopin) library(dplyr) #>  #> Attaching package: 'dplyr' #> The following objects are masked from 'package:stats': #>  #>     filter, lag #> The following objects are masked from 'package:base': #>  #>     intersect, setdiff, setequal, union library(sf) #> Linking to GEOS 3.12.1, GDAL 3.8.5, PROJ 9.4.0; sf_use_s2() is TRUE library(terra) #> terra 1.7.78 library(future) library(future.mirai) library(mirai)  # disable spherical geometries sf::sf_use_s2(FALSE) #> Spherical geometry (s2) switched off  # parallelization-safe random number generator set.seed(2024, kind = \"L'Ecuyer-CMRG\")"},{"path":"https://NIEHS.github.io/chopin/index.html","id":"par_grid-parallelize-over-artificial-grid-polygons","dir":"","previous_headings":"Examples","what":"par_grid: parallelize over artificial grid polygons","title":"Computation of Spatial Data by Hierarchical and Objective Partitioning of Inputs for Parallel Processing","text":"Please refer small example extracting mean altitude values circular point buffers census tracts North Carolina. running code chunks , set cloned chopin repository working directory setwd()","code":"ncpoly <- system.file(\"shape/nc.shp\", package = \"sf\") ncsf <- sf::read_sf(ncpoly) ncsf <- sf::st_transform(ncsf, \"EPSG:5070\") plot(sf::st_geometry(ncsf))"},{"path":"https://NIEHS.github.io/chopin/index.html","id":"generate-random-points-in-nc","dir":"","previous_headings":"Examples > par_grid: parallelize over artificial grid polygons","what":"Generate random points in NC","title":"Computation of Spatial Data by Hierarchical and Objective Partitioning of Inputs for Parallel Processing","text":"Ten thousands random point locations generated inside counties North Carolina.","code":"ncpoints <- sf::st_sample(ncsf, 1e4) ncpoints <- sf::st_as_sf(ncpoints) ncpoints$pid <- sprintf(\"PID-%05d\", seq(1, 1e4)) plot(sf::st_geometry(ncpoints))"},{"path":"https://NIEHS.github.io/chopin/index.html","id":"target-raster-dataset-shuttle-radar-topography-mission","dir":"","previous_headings":"Examples > par_grid: parallelize over artificial grid polygons","what":"Target raster dataset: Shuttle Radar Topography Mission","title":"Computation of Spatial Data by Hierarchical and Objective Partitioning of Inputs for Parallel Processing","text":"use elevation dataset moderate spatial resolution (approximately 400 meters 0.25 miles).","code":"# data preparation wdir <- system.file(\"extdata\", package = \"chopin\") srtm <- file.path(wdir, \"nc_srtm15_otm.tif\")  # terra SpatRaster objects are wrapped when exported to rds file srtm_ras <- terra::rast(srtm) terra::crs(srtm_ras) <- \"EPSG:5070\" srtm_ras #> class       : SpatRaster  #> dimensions  : 1534, 2281, 1  (nrow, ncol, nlyr) #> resolution  : 391.5026, 391.5026  (x, y) #> extent      : 1012872, 1905890, 1219961, 1820526  (xmin, xmax, ymin, ymax) #> coord. ref. : NAD83 / Conus Albers (EPSG:5070)  #> source      : nc_srtm15_otm.tif  #> name        :    srtm15  #> min value   : -3589.291  #> max value   :  1946.400 terra::plot(srtm_ras) # ncpoints_tr <- terra::vect(ncpoints) system.time(   ncpoints_srtm <-     chopin::extract_at(       x = srtm,       y = ncpoints,       id = \"pid\",       mode = \"buffer\",       radius = 1e4L  # 10,000 meters (10 km)     ) ) #> Input is a character. Attempt to read it with terra::rast... #>    user  system elapsed  #>   7.115   0.227   7.537"},{"path":"https://NIEHS.github.io/chopin/index.html","id":"generate-regular-grid-computational-regions","dir":"","previous_headings":"Examples > par_grid: parallelize over artificial grid polygons","what":"Generate regular grid computational regions","title":"Computation of Spatial Data by Hierarchical and Objective Partitioning of Inputs for Parallel Processing","text":"chopin::par_pad_grid takes spatial dataset generate regular grid polygons nx ny arguments padding. Users overlapping (degree radius) non-overlapping grids, utilized split locations target datasets sub-datasets efficient processing. compregions list object two elements named original (non-overlapping grid polygons) padded (overlapping padding). figures illustrate grid polygons without overlaps.","code":"compregions <-   chopin::par_pad_grid(     ncpoints,     mode = \"grid\",     nx = 2L,     ny = 2L,     padding = 1e4L   ) #> Switch sf class to terra... #> Switch terra class to sf... names(compregions) #> [1] \"original\" \"padded\"  oldpar <- par() par(mfrow = c(2, 1)) terra::plot(   terra::vect(compregions$original),   main = \"Original grids\" ) terra::plot(   terra::vect(compregions$padded),   main = \"Padded grids\" )"},{"path":"https://NIEHS.github.io/chopin/index.html","id":"parallel-processing","dir":"","previous_headings":"Examples > par_grid: parallelize over artificial grid polygons","what":"Parallel processing","title":"Computation of Spatial Data by Hierarchical and Objective Partitioning of Inputs for Parallel Processing","text":"Using grid polygons, distribute task averaging elevations 10,000 circular buffer polygons, generated random locations, 10 kilometers radius chopin::par_grid. Users always need register multiple CPU threads (logical cores) parallelization. Users can inject generic functions’ arguments (parameters) writing ellipsis (...) arguments, demonstrated :","code":"future::plan(future.mirai::mirai_multisession, workers = 4L)  system.time(   ncpoints_srtm_mthr <-     par_grid(       grids = compregions,       fun_dist = extract_at,       x = srtm,       y = ncpoints,       id = \"pid\",       radius = 1e4L,       .standalone = FALSE     ) ) #> ℹ Input is not a character. #> Input is a character. Attempt to read it with terra::rast... #> ℹ Task at CGRIDID: 1 is successfully dispatched. #>  #> Input is a character. Attempt to read it with terra::rast... #> ℹ Task at CGRIDID: 2 is successfully dispatched. #>  #> Input is a character. Attempt to read it with terra::rast... #> ℹ Task at CGRIDID: 3 is successfully dispatched. #>  #> Input is a character. Attempt to read it with terra::rast... #> ℹ Task at CGRIDID: 4 is successfully dispatched. #>    user  system elapsed  #>   0.360   0.010   8.089  ncpoints_srtm <-   extract_at(     x = srtm,     y = ncpoints,     id = \"pid\",     radius = 1e4L   ) #> Input is a character. Attempt to read it with terra::rast... colnames(ncpoints_srtm_mthr)[2] <- \"mean_par\" ncpoints_compar <- merge(ncpoints_srtm, ncpoints_srtm_mthr) # Are the calculations equal? all.equal(ncpoints_compar$mean, ncpoints_compar$mean_par) #> [1] TRUE ncpoints_s <-   merge(ncpoints, ncpoints_srtm) ncpoints_m <-   merge(ncpoints, ncpoints_srtm_mthr)  plot(ncpoints_s[, \"mean\"], main = \"Single-thread\", pch = 19, cex = 0.33) plot(ncpoints_m[, \"mean_par\"], main = \"Multi-thread\", pch = 19, cex = 0.33)"},{"path":"https://NIEHS.github.io/chopin/index.html","id":"chopinpar_hierarchy-parallelize-geospatial-computations-using-intrinsic-data-hierarchy","dir":"","previous_headings":"Examples","what":"chopin::par_hierarchy: parallelize geospatial computations using intrinsic data hierarchy","title":"Computation of Spatial Data by Hierarchical and Objective Partitioning of Inputs for Parallel Processing","text":"real world datasets, usually nested/exhaustive hierarchies. example, land organized administrative/jurisdictional borders multiple levels exist. U.S. context, state consists several counties, counties split census tracts, group block groups. chopin::par_hierarchy leverages hierarchies parallelize geospatial operations, means group lower-level geographic units higher-level geography assigned process. demonstration shows census tracts grouped counties county processed CPU thread.","code":""},{"path":"https://NIEHS.github.io/chopin/index.html","id":"read-data","dir":"","previous_headings":"Examples > chopin::par_hierarchy: parallelize geospatial computations using intrinsic data hierarchy","what":"Read data","title":"Computation of Spatial Data by Hierarchical and Objective Partitioning of Inputs for Parallel Processing","text":"","code":"# nc_hierarchy.gpkg includes two layers: county and tracts path_nchrchy <- file.path(wdir, \"nc_hierarchy.gpkg\")  nc_data <- path_nchrchy nc_county <- sf::st_read(nc_data, layer = \"county\") #> Reading layer `county' from data source  #>   `/tmp/RtmpPipkp9/temp_libpath2270313b1b582/chopin/extdata/nc_hierarchy.gpkg'  #>   using driver `GPKG' #> Simple feature collection with 100 features and 1 field #> Geometry type: POLYGON #> Dimension:     XY #> Bounding box:  xmin: 1054155 ymin: 1341756 xmax: 1838923 ymax: 1690176 #> Projected CRS: NAD83 / Conus Albers nc_tracts <- sf::st_read(nc_data, layer = \"tracts\") #> Reading layer `tracts' from data source  #>   `/tmp/RtmpPipkp9/temp_libpath2270313b1b582/chopin/extdata/nc_hierarchy.gpkg'  #>   using driver `GPKG' #> Simple feature collection with 2672 features and 1 field #> Geometry type: MULTIPOLYGON #> Dimension:     XY #> Bounding box:  xmin: 1054155 ymin: 1341756 xmax: 1838923 ymax: 1690176 #> Projected CRS: NAD83 / Conus Albers  # reproject to Conus Albers Equal Area nc_county <- sf::st_transform(nc_county, \"EPSG:5070\") nc_tracts <- sf::st_transform(nc_tracts, \"EPSG:5070\") nc_tracts$COUNTY <- substr(nc_tracts$GEOID, 1, 5)"},{"path":"https://NIEHS.github.io/chopin/index.html","id":"extract-average-srtm-elevations-by-single-and-multiple-threads","dir":"","previous_headings":"Examples > chopin::par_hierarchy: parallelize geospatial computations using intrinsic data hierarchy","what":"Extract average SRTM elevations by single and multiple threads","title":"Computation of Spatial Data by Hierarchical and Objective Partitioning of Inputs for Parallel Processing","text":"","code":"# single-thread system.time(   nc_elev_tr_single <-     chopin::extract_at(       x = srtm,       y = nc_tracts,       id = \"GEOID\"     ) ) #> Input is a character. Attempt to read it with terra::rast... #>    user  system elapsed  #>   0.897   0.003   0.904  # hierarchical parallelization system.time(   nc_elev_tr_distr <-     chopin::par_hierarchy(       regions = nc_county, # higher level geometry       regions_id = \"GEOID\", # higher level unique id       fun_dist = extract_at,       x = srtm,       y = nc_tracts, # lower level geometry       id = \"GEOID\", # lower level unique id       func = \"mean\"     ) ) #> ℹ Input is not a character. #> ℹ GEOID is used to stratify the process. #> Input is a character. Attempt to read it with terra::rast...ℹ Your input function at 37037 is dispatched. #> Input is a character. Attempt to read it with terra::rast...ℹ Your input function at 37001 is dispatched. #> Input is a character. Attempt to read it with terra::rast...ℹ Your input function at 37057 is dispatched. #> Input is a character. Attempt to read it with terra::rast...ℹ Your input function at 37069 is dispatched. #> Input is a character. Attempt to read it with terra::rast...ℹ Your input function at 37155 is dispatched. #> Input is a character. Attempt to read it with terra::rast...ℹ Your input function at 37109 is dispatched. #> Input is a character. Attempt to read it with terra::rast...ℹ Your input function at 37027 is dispatched. #> Input is a character. Attempt to read it with terra::rast...ℹ Your input function at 37063 is dispatched. #> Input is a character. Attempt to read it with terra::rast...ℹ Your input function at 37145 is dispatched. #> Input is a character. Attempt to read it with terra::rast...ℹ Your input function at 37115 is dispatched. #> Input is a character. Attempt to read it with terra::rast...ℹ Your input function at 37151 is dispatched. #> Input is a character. Attempt to read it with terra::rast...ℹ Your input function at 37131 is dispatched. #> Input is a character. Attempt to read it with terra::rast...ℹ Your input function at 37013 is dispatched. #> Input is a character. Attempt to read it with terra::rast...ℹ Your input function at 37159 is dispatched. #> Input is a character. Attempt to read it with terra::rast...ℹ Your input function at 37051 is dispatched. #> Input is a character. Attempt to read it with terra::rast...ℹ Your input function at 37153 is dispatched. #> Input is a character. Attempt to read it with terra::rast...ℹ Your input function at 37093 is dispatched. #> Input is a character. Attempt to read it with terra::rast...ℹ Your input function at 37025 is dispatched. #> Input is a character. Attempt to read it with terra::rast...ℹ Your input function at 37029 is dispatched. #> Input is a character. Attempt to read it with terra::rast...ℹ Your input function at 37169 is dispatched. #> Input is a character. Attempt to read it with terra::rast...ℹ Your input function at 37031 is dispatched. #> Input is a character. Attempt to read it with terra::rast...ℹ Your input function at 37005 is dispatched. #> Input is a character. Attempt to read it with terra::rast...ℹ Your input function at 37139 is dispatched. #> Input is a character. Attempt to read it with terra::rast...ℹ Your input function at 37193 is dispatched. #> Input is a character. Attempt to read it with terra::rast...ℹ Your input function at 37003 is dispatched. #> Input is a character. Attempt to read it with terra::rast...ℹ Your input function at 37083 is dispatched. #> Input is a character. Attempt to read it with terra::rast...ℹ Your input function at 37163 is dispatched. #> Input is a character. Attempt to read it with terra::rast...ℹ Your input function at 37189 is dispatched. #> Input is a character. Attempt to read it with terra::rast...ℹ Your input function at 37173 is dispatched. #> Input is a character. Attempt to read it with terra::rast...ℹ Your input function at 37011 is dispatched. #> Input is a character. Attempt to read it with terra::rast...ℹ Your input function at 37045 is dispatched. #> Input is a character. Attempt to read it with terra::rast...ℹ Your input function at 37125 is dispatched. #> Input is a character. Attempt to read it with terra::rast...ℹ Your input function at 37067 is dispatched. #> Input is a character. Attempt to read it with terra::rast...ℹ Your input function at 37077 is dispatched. #> Input is a character. Attempt to read it with terra::rast...ℹ Your input function at 37185 is dispatched. #> Input is a character. Attempt to read it with terra::rast...ℹ Your input function at 37137 is dispatched. #> Input is a character. Attempt to read it with terra::rast...ℹ Your input function at 37033 is dispatched. #> Input is a character. Attempt to read it with terra::rast...ℹ Your input function at 37107 is dispatched. #> Input is a character. Attempt to read it with terra::rast...ℹ Your input function at 37075 is dispatched. #> Input is a character. Attempt to read it with terra::rast...ℹ Your input function at 37073 is dispatched. #> Input is a character. Attempt to read it with terra::rast...ℹ Your input function at 37161 is dispatched. #> Input is a character. Attempt to read it with terra::rast...ℹ Your input function at 37187 is dispatched. #> Input is a character. Attempt to read it with terra::rast...ℹ Your input function at 37007 is dispatched. #> Input is a character. Attempt to read it with terra::rast...ℹ Your input function at 37135 is dispatched. #> Input is a character. Attempt to read it with terra::rast...ℹ Your input function at 37049 is dispatched. #> Input is a character. Attempt to read it with terra::rast...ℹ Your input function at 37195 is dispatched. #> Input is a character. Attempt to read it with terra::rast...ℹ Your input function at 37061 is dispatched. #> Input is a character. Attempt to read it with terra::rast...ℹ Your input function at 37087 is dispatched. #> Input is a character. Attempt to read it with terra::rast...ℹ Your input function at 37081 is dispatched. #> Input is a character. Attempt to read it with terra::rast...ℹ Your input function at 37099 is dispatched. #> Input is a character. Attempt to read it with terra::rast...ℹ Your input function at 37097 is dispatched. #> Input is a character. Attempt to read it with terra::rast...ℹ Your input function at 37091 is dispatched. #> Input is a character. Attempt to read it with terra::rast...ℹ Your input function at 37149 is dispatched. #> Input is a character. Attempt to read it with terra::rast...ℹ Your input function at 37165 is dispatched. #> Input is a character. Attempt to read it with terra::rast...ℹ Your input function at 37085 is dispatched. #> Input is a character. Attempt to read it with terra::rast...ℹ Your input function at 37105 is dispatched. #> Input is a character. Attempt to read it with terra::rast...ℹ Your input function at 37017 is dispatched. #> Input is a character. Attempt to read it with terra::rast...ℹ Your input function at 37039 is dispatched. #> Input is a character. Attempt to read it with terra::rast...ℹ Your input function at 37035 is dispatched. #> Input is a character. Attempt to read it with terra::rast...ℹ Your input function at 37177 is dispatched. #> Input is a character. Attempt to read it with terra::rast...ℹ Your input function at 37113 is dispatched. #> Input is a character. Attempt to read it with terra::rast...ℹ Your input function at 37143 is dispatched. #> Input is a character. Attempt to read it with terra::rast...ℹ Your input function at 37095 is dispatched. #> Input is a character. Attempt to read it with terra::rast...ℹ Your input function at 37071 is dispatched. #> Input is a character. Attempt to read it with terra::rast...ℹ Your input function at 37101 is dispatched. #> Input is a character. Attempt to read it with terra::rast...ℹ Your input function at 37015 is dispatched. #> Input is a character. Attempt to read it with terra::rast...ℹ Your input function at 37167 is dispatched. #> Input is a character. Attempt to read it with terra::rast...ℹ Your input function at 37079 is dispatched. #> Input is a character. Attempt to read it with terra::rast...ℹ Your input function at 37129 is dispatched. #> Input is a character. Attempt to read it with terra::rast...ℹ Your input function at 37147 is dispatched. #> Input is a character. Attempt to read it with terra::rast...ℹ Your input function at 37141 is dispatched. #> Input is a character. Attempt to read it with terra::rast...ℹ Your input function at 37179 is dispatched. #> Input is a character. Attempt to read it with terra::rast...ℹ Your input function at 37121 is dispatched. #> Input is a character. Attempt to read it with terra::rast...ℹ Your input function at 37133 is dispatched. #> Input is a character. Attempt to read it with terra::rast...ℹ Your input function at 37065 is dispatched. #> Input is a character. Attempt to read it with terra::rast...ℹ Your input function at 37119 is dispatched. #> Input is a character. Attempt to read it with terra::rast...ℹ Your input function at 37199 is dispatched. #> Input is a character. Attempt to read it with terra::rast...ℹ Your input function at 37197 is dispatched. #> Input is a character. Attempt to read it with terra::rast...ℹ Your input function at 37023 is dispatched. #> Input is a character. Attempt to read it with terra::rast...ℹ Your input function at 37191 is dispatched. #> Input is a character. Attempt to read it with terra::rast...ℹ Your input function at 37059 is dispatched. #> Input is a character. Attempt to read it with terra::rast...ℹ Your input function at 37111 is dispatched. #> Input is a character. Attempt to read it with terra::rast...ℹ Your input function at 37183 is dispatched. #> Input is a character. Attempt to read it with terra::rast...ℹ Your input function at 37053 is dispatched. #> Input is a character. Attempt to read it with terra::rast...ℹ Your input function at 37103 is dispatched. #> Input is a character. Attempt to read it with terra::rast...ℹ Your input function at 37041 is dispatched. #> Input is a character. Attempt to read it with terra::rast...ℹ Your input function at 37021 is dispatched. #> Input is a character. Attempt to read it with terra::rast...ℹ Your input function at 37157 is dispatched. #> Input is a character. Attempt to read it with terra::rast...ℹ Your input function at 37117 is dispatched. #> Input is a character. Attempt to read it with terra::rast...ℹ Your input function at 37089 is dispatched. #> Input is a character. Attempt to read it with terra::rast...ℹ Your input function at 37127 is dispatched. #> Input is a character. Attempt to read it with terra::rast...ℹ Your input function at 37009 is dispatched. #> Input is a character. Attempt to read it with terra::rast...ℹ Your input function at 37019 is dispatched. #> Input is a character. Attempt to read it with terra::rast...ℹ Your input function at 37123 is dispatched. #> Input is a character. Attempt to read it with terra::rast...ℹ Your input function at 37181 is dispatched. #> Input is a character. Attempt to read it with terra::rast...ℹ Your input function at 37175 is dispatched. #> Input is a character. Attempt to read it with terra::rast...ℹ Your input function at 37171 is dispatched. #> Input is a character. Attempt to read it with terra::rast...ℹ Your input function at 37043 is dispatched. #> Input is a character. Attempt to read it with terra::rast...ℹ Your input function at 37055 is dispatched. #> Input is a character. Attempt to read it with terra::rast...ℹ Your input function at 37047 is dispatched. #>    user  system elapsed  #>   0.249   0.077   2.016"},{"path":"https://NIEHS.github.io/chopin/index.html","id":"par_multirasters-parallelize-over-multiple-rasters","dir":"","previous_headings":"Examples","what":"par_multirasters: parallelize over multiple rasters","title":"Computation of Spatial Data by Hierarchical and Objective Partitioning of Inputs for Parallel Processing","text":"common case large group raster files operation performed. chopin::par_multirasters cases. example demonstrates five elevation raster files calculate average elevation counties North Carolina.","code":"# nccnty <- sf::st_read(nc_data, layer = \"county\") ncelev <- terra::rast(srtm) terra::crs(ncelev) <- \"EPSG:5070\" names(ncelev) <- c(\"srtm15\") tdir <- tempdir()  terra::writeRaster(ncelev, file.path(tdir, \"test1.tif\"), overwrite = TRUE) terra::writeRaster(ncelev, file.path(tdir, \"test2.tif\"), overwrite = TRUE) terra::writeRaster(ncelev, file.path(tdir, \"test3.tif\"), overwrite = TRUE) terra::writeRaster(ncelev, file.path(tdir, \"test4.tif\"), overwrite = TRUE) terra::writeRaster(ncelev, file.path(tdir, \"test5.tif\"), overwrite = TRUE)  # check if the raster files were exported as expected testfiles <- list.files(tdir, pattern = \"*.tif$\", full.names = TRUE) testfiles #> [1] \"/tmp/RtmpxL0QL4/test1.tif\" \"/tmp/RtmpxL0QL4/test2.tif\" #> [3] \"/tmp/RtmpxL0QL4/test3.tif\" \"/tmp/RtmpxL0QL4/test4.tif\" #> [5] \"/tmp/RtmpxL0QL4/test5.tif\" system.time(   res <-     chopin::par_multirasters(       filenames = testfiles,       fun_dist = extract_at,       x = ncelev,       y = nc_county,       id = \"GEOID\",       func = \"mean\"     ) ) #> ℹ Input is not a character. #> Input is a character. Attempt to read it with terra::rast... #> ℹ Your input function at /tmp/RtmpxL0QL4/test1.tif is dispatched. #>  #> Input is a character. Attempt to read it with terra::rast... #> ℹ Your input function at /tmp/RtmpxL0QL4/test2.tif is dispatched. #>  #> Input is a character. Attempt to read it with terra::rast... #> ℹ Your input function at /tmp/RtmpxL0QL4/test3.tif is dispatched. #>  #> Input is a character. Attempt to read it with terra::rast... #> ℹ Your input function at /tmp/RtmpxL0QL4/test4.tif is dispatched. #>  #> Input is a character. Attempt to read it with terra::rast... #> ℹ Your input function at /tmp/RtmpxL0QL4/test5.tif is dispatched. #>    user  system elapsed  #>   1.714   0.139   2.718 knitr::kable(head(res)) # remove temporary raster files file.remove(testfiles) #> [1] TRUE TRUE TRUE TRUE TRUE"},{"path":"https://NIEHS.github.io/chopin/index.html","id":"parallelization-of-a-generic-geospatial-operation","dir":"","previous_headings":"","what":"Parallelization of a generic geospatial operation","title":"Computation of Spatial Data by Hierarchical and Objective Partitioning of Inputs for Parallel Processing","text":"chopin internal macros, chopin::par_* functions support generic geospatial operations. example uses terra::nearest, gets nearest feature’s attributes, inside chopin::par_grid. figure shows padded grids (50 kilometers), primary roads, points. Primary roads selected padded grid per iteration used calculate distance point nearest primary road. Padded grids overlapping areas look different according padding argument chopin::par_pad_grid.  compare results single-thread multi-thread calculation. example, one wants calculate nearest interstate rural homes fine grids, grids may interstates homes grids get distance nearest interstate. problems can avoided choosing nx, ny, padding values par_pad_grid meticulously.","code":"path_ncrd1 <- file.path(wdir, \"ncroads_first.gpkg\")  # Generate 5000 random points pnts <- sf::st_sample(nc_county, 5000) pnts <- sf::st_as_sf(pnts) # assign identifiers pnts$pid <- sprintf(\"RPID-%04d\", seq(1, 5000)) rd1 <- sf::st_read(path_ncrd1) #> Reading layer `ncroads_first' from data source  #>   `/tmp/RtmpPipkp9/temp_libpath2270313b1b582/chopin/extdata/ncroads_first.gpkg'  #>   using driver `GPKG' #> Simple feature collection with 620 features and 4 fields #> Geometry type: MULTILINESTRING #> Dimension:     XY #> Bounding box:  xmin: 1152512 ymin: 1390719 xmax: 1748367 ymax: 1662294 #> Projected CRS: NAD83 / Conus Albers  # reproject pntst <- sf::st_transform(pnts, \"EPSG:5070\") rd1t <- sf::st_transform(rd1, \"EPSG:5070\")  # generate grids nccompreg <-   chopin::par_pad_grid(     input = pntst,     mode = \"grid\",     nx = 4L,     ny = 2L,     padding = 5e4L   ) #> Switch sf class to terra... #> Switch terra class to sf... # plot terra::plot(nccompreg$padded, border = \"orange\") terra::plot(terra::vect(ncsf), add = TRUE) terra::plot(rd1t, col = \"blue\", add = TRUE) #> Warning in plot.sf(rd1t, col = \"blue\", add = TRUE): ignoring all but the first #> attribute terra::plot(pntst, add = TRUE, cex = 0.3) legend(1.02e6, 1.72e6,        legend = c(\"Computation grids (50km padding)\", \"Major roads\"),        lty = 1, lwd = 1, col = c(\"orange\", \"blue\"),        cex = 0.5) # terra::nearest run system.time(   restr <- terra::nearest(x = terra::vect(pntst), y = terra::vect(rd1t)) ) #>    user  system elapsed  #>   0.602   0.000   0.603  pnt_path <- file.path(tdir, \"pntst.gpkg\") sf::st_write(pntst, pnt_path) #> Writing layer `pntst' to data source `/tmp/RtmpxL0QL4/pntst.gpkg' using driver `GPKG' #> Writing 5000 features with 1 fields and geometry type Point.  # we use four threads that were configured above system.time(   resd <-     chopin::par_grid(       grids = nccompreg,       fun_dist = nearest,       x = pnt_path,       y = path_ncrd1,       pad_y = TRUE     ) ) #> ℹ Input is a character. Trying to read with terra . #> ℹ Input is a character. Trying to read with terra . #> ℹ Task at CGRIDID: 1 is successfully dispatched. #>  #> ℹ Input is a character. Trying to read with terra . #> ℹ Input is a character. Trying to read with terra . #> ℹ Task at CGRIDID: 2 is successfully dispatched. #>  #> ℹ Input is a character. Trying to read with terra . #> ℹ Input is a character. Trying to read with terra . #> ℹ Task at CGRIDID: 3 is successfully dispatched. #>  #> ℹ Input is a character. Trying to read with terra . #> ℹ Input is a character. Trying to read with terra . #> ℹ Task at CGRIDID: 4 is successfully dispatched. #>  #> ℹ Input is a character. Trying to read with terra . #> ℹ Input is a character. Trying to read with terra . #> ℹ Task at CGRIDID: 5 is successfully dispatched. #>  #> ℹ Input is a character. Trying to read with terra . #> ℹ Input is a character. Trying to read with terra . #> ℹ Task at CGRIDID: 6 is successfully dispatched. #>  #> ℹ Input is a character. Trying to read with terra . #> ℹ Input is a character. Trying to read with terra . #> ℹ Task at CGRIDID: 7 is successfully dispatched. #>  #> ℹ Input is a character. Trying to read with terra . #> ℹ Input is a character. Trying to read with terra . #> ℹ Task at CGRIDID: 8 is successfully dispatched. #>    user  system elapsed  #>   0.079   0.005   0.448 resj <- merge(restr, resd, by = c(\"from_x\", \"from_y\")) all.equal(resj$distance.x, resj$distance.y) #> [1] TRUE"},{"path":"https://NIEHS.github.io/chopin/index.html","id":"notes-on-data-restrictions","dir":"","previous_headings":"Parallelization of a generic geospatial operation","what":"Notes on data restrictions","title":"Computation of Spatial Data by Hierarchical and Objective Partitioning of Inputs for Parallel Processing","text":"chopin works best two-dimensional (planar) geometries. Users disable s2 spherical geometry mode sf setting. Running chopin functions spherical three-dimensional (e.g., including M/Z dimensions) geometries may produce incorrect unexpected results.","code":"sf::sf_use_s2(FALSE)"},{"path":"https://NIEHS.github.io/chopin/reference/chopin-package.html","id":null,"dir":"Reference","previous_headings":"","what":"Computation of spatial data by hierarchical and objective partitioning of inputs for parallel processing — chopin-package","title":"Computation of spatial data by hierarchical and objective partitioning of inputs for parallel processing — chopin-package","text":"chopin package provides set functions compute divided geospatial data.","code":""},{"path":"https://NIEHS.github.io/chopin/reference/chopin-package.html","id":"basic-functionalities","dir":"Reference","previous_headings":"","what":"Basic functionalities","title":"Computation of spatial data by hierarchical and objective partitioning of inputs for parallel processing — chopin-package","text":"Distribute terra, sf, chopin functions parallel workers set future Set parallelization strategies based artificial grids, equal-size clusters, hierarchy, multiple raster files Convenience functions raster-vector overlay weighted summary vector dataset","code":""},{"path":"https://NIEHS.github.io/chopin/reference/chopin-package.html","id":"chopin-workflow","dir":"Reference","previous_headings":"","what":"chopin workflow","title":"Computation of spatial data by hierarchical and objective partitioning of inputs for parallel processing — chopin-package","text":"simplest way parallelizing generic geospatial computation start par_pad_* functions par_grid, running par_hierarchy, par_multirasters functions .   Example data North Carolinian counties raster elevation data used example data.   demonstrate chopin functions, generate 10,000 random points North Carolina   Creating grids example generate regular grid random point data.   Extracting values raster Since par_* functions operate future backends, users define future plan running functions. multicore plan supports terra objects may lead faster computation, supported Windows. alternative future.mirai's mirai_multisession plan, supported many platforms generally faster plain future multisession plan. workers argument defined integer value specify number threads used.   dispatch multiple extract_at runs grid polygons. proceed, terra object converted sf object.   Hierarchical processing demonstrate hierarchical processing random points using census tract polygons.   example parallelize summarizing mean elevation 10 kilometers circular buffers random sample points first five characters census tract unique identifiers, county codes. example demonstrates hierarchy can defined given polygons unique identifiers suitably formatted defining hierarchy.   Multiraster processing demonstrate multiraster processing random points using multiple rasters.","code":"library(chopin) library(terra) library(sf) library(collapse) library(dplyr) library(future) library(future.mirai) library(future.apply) nccnty_path <- system.file(\"extdata\", \"nc_hierarchy.gpkg\", package = \"chopin\") ncelev_path <-   system.file(\"extdata/nc_srtm15_otm.tif\", package = \"chopin\") nccnty <- terra::vect(nccnty_path) ncelev <- terra::rast(ncelev_path) ncsamp <-   terra::spatSample(     nccnty,     1e4L   ) ncsamp$pid <- 1:nrow(ncsamp) ncgrid <- par_pad_grid(ncsamp, mode = \"grid\", nx = 4L, ny = 2L, padding = 10000) plot(ncgrid$original) future::plan(future.mirai::mirai_multisession, workers = 2L) pg <-   par_grid(     grids = ncgrid,     pad_y = FALSE,     .debug = TRUE,     fun_dist = extract_at,     x = ncelev_path,     y = sf::st_as_sf(ncsamp),     id = \"pid\",     radius = 1e4,     func = \"mean\"   ) nccnty <- sf::st_read(nccnty_path, layer = \"county\") nctrct <- sf::st_read(nccnty_path, layer = \"tracts\") px <-   par_hierarchy(     # from here the par_hierarchy-specific arguments     regions = nctrct,     regions_id = \"GEOID\",     length_left = 5,     pad = 10000,     pad_y = FALSE,     .debug = TRUE,     # from here are the dispatched function definition     # for parallel workers     fun_dist = extract_at,     # below should follow the arguments of the dispatched function     x = ncelev,     y = sf::st_as_sf(ncsamp),     id = \"pid\",     radius = 1e4,     func = \"mean\"   ) ncelev <-   system.file(\"extdata/nc_srtm15_otm.tif\", package = \"chopin\") ncelev <- terra::rast(ncelev) tdir <- tempdir(check = TRUE) terra::writeRaster(ncelev, file.path(tdir, \"test1.tif\"), overwrite = TRUE) terra::writeRaster(ncelev, file.path(tdir, \"test2.tif\"), overwrite = TRUE) terra::writeRaster(ncelev, file.path(tdir, \"test3.tif\"), overwrite = TRUE) terra::writeRaster(ncelev, file.path(tdir, \"test4.tif\"), overwrite = TRUE) terra::writeRaster(ncelev, file.path(tdir, \"test5.tif\"), overwrite = TRUE) rasts <- list.files(tdir, pattern = \"tif$\", full.names = TRUE)  pm <-   par_multirasters(     filenames = rasts,     fun_dist = extract_at,     x = NA,     y = sf::st_as_sf(ncsamp)[1:500, ],     id = \"pid\",     radius = 1e4,     func = \"mean\",     .debug = TRUE   )"},{"path":[]},{"path":"https://NIEHS.github.io/chopin/reference/chopin-package.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Computation of spatial data by hierarchical and objective partitioning of inputs for parallel processing — chopin-package","text":"Maintainer: Insang Song geoissong@gmail.com (ORCID) Authors: Kyle Messier (ORCID) [contributor]","code":""},{"path":"https://NIEHS.github.io/chopin/reference/clip_ras_ext.html","id":null,"dir":"Reference","previous_headings":"","what":"Clip input raster with a buffered vector extent. — clip_ras_ext","title":"Clip input raster with a buffered vector extent. — clip_ras_ext","text":"Clip input raster expected maximum extent computation.","code":""},{"path":"https://NIEHS.github.io/chopin/reference/clip_ras_ext.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Clip input raster with a buffered vector extent. — clip_ras_ext","text":"","code":"clip_ras_ext(x = NULL, y = NULL, radius = NULL, nqsegs = 180L, extrusion = 1.1)"},{"path":"https://NIEHS.github.io/chopin/reference/clip_ras_ext.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Clip input raster with a buffered vector extent. — clip_ras_ext","text":"x SpatRaster object clipped y sf SpatVector object radius numeric(1). buffer radius. value automatically multiplied extrusion nqsegs integer(1). number points per quarter circle extrusion numeric(1). Extrusion factor extent. Default 1.1","code":""},{"path":"https://NIEHS.github.io/chopin/reference/clip_ras_ext.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Clip input raster with a buffered vector extent. — clip_ras_ext","text":"clipped SpatRaster object.","code":""},{"path":[]},{"path":"https://NIEHS.github.io/chopin/reference/clip_ras_ext.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Clip input raster with a buffered vector extent. — clip_ras_ext","text":"Insang Song","code":""},{"path":"https://NIEHS.github.io/chopin/reference/clip_vec_ext.html","id":null,"dir":"Reference","previous_headings":"","what":"Clip to the buffered extent of input vector — clip_vec_ext","title":"Clip to the buffered extent of input vector — clip_vec_ext","text":"Clip input vector expected maximum extent computation.","code":""},{"path":"https://NIEHS.github.io/chopin/reference/clip_vec_ext.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Clip to the buffered extent of input vector — clip_vec_ext","text":"","code":"clip_vec_ext(x, y, radius)"},{"path":"https://NIEHS.github.io/chopin/reference/clip_vec_ext.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Clip to the buffered extent of input vector — clip_vec_ext","text":"x sf SpatVector object clipped y sf SpatVector object radius numeric(1). Circular buffer radius. value automatically multiplied 1.1","code":""},{"path":"https://NIEHS.github.io/chopin/reference/clip_vec_ext.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Clip to the buffered extent of input vector — clip_vec_ext","text":"clipped sf SpatVector object.","code":""},{"path":[]},{"path":"https://NIEHS.github.io/chopin/reference/clip_vec_ext.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Clip to the buffered extent of input vector — clip_vec_ext","text":"Insang Song","code":""},{"path":"https://NIEHS.github.io/chopin/reference/clip_vec_ext.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Clip to the buffered extent of input vector — clip_vec_ext","text":"","code":"if (FALSE) { # \\dontrun{ library(sf) library(stars) library(terra) options(sf_use_s2 = FALSE)  bcsd_path <- system.file(package = \"stars\", \"nc/bcsd_obs_1999.nc\") bcsd <- stars::read_stars(bcsd_path) bcsd <- sf::st_as_sf(bcsd) bcsd_rpnt <- sf::st_as_sf(sf::st_sample(bcsd, 4L)) bcsd_rpntm <- sf::st_as_sf(sf::st_sample(bcsd, 1000L)) clip_vec_ext(bcsd_rpntm, 1000, bcsd_rpnt) } # }"},{"path":"https://NIEHS.github.io/chopin/reference/datamod.html","id":null,"dir":"Reference","previous_headings":"","what":"Return the input's GIS data model type — datamod","title":"Return the input's GIS data model type — datamod","text":"function returns one 'vector' 'raster' depending input class.","code":""},{"path":"https://NIEHS.github.io/chopin/reference/datamod.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Return the input's GIS data model type — datamod","text":"","code":"datamod(input)"},{"path":"https://NIEHS.github.io/chopin/reference/datamod.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Return the input's GIS data model type — datamod","text":"input Spat*/sf/stars object.","code":""},{"path":"https://NIEHS.github.io/chopin/reference/datamod.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Return the input's GIS data model type — datamod","text":"character(1). One \"vector\" \"raster\".","code":""},{"path":"https://NIEHS.github.io/chopin/reference/datamod.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Return the input's GIS data model type — datamod","text":"Although stars object little ambiguous whether classify vector raster, considered raster package.","code":""},{"path":[]},{"path":"https://NIEHS.github.io/chopin/reference/datamod.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Return the input's GIS data model type — datamod","text":"Insang Song","code":""},{"path":"https://NIEHS.github.io/chopin/reference/datamod.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Return the input's GIS data model type — datamod","text":"","code":"if (FALSE) { # \\dontrun{ library(sf) library(terra) options(sf_use_s2 = FALSE)  nc_path <- system.file(\"gpkg/nc.gpkg\", package = \"sf\") nc_sf <- sf::st_read(nc_path) datamod(nc_sf)  ra_path <- system.file(\"ex/elev.tif\", package = \"terra\") ra <- terra::rast(ra_path) datamod(ra) } # }"},{"path":"https://NIEHS.github.io/chopin/reference/dep_check.html","id":null,"dir":"Reference","previous_headings":"","what":"Return the package the input object is based on — dep_check","title":"Return the package the input object is based on — dep_check","text":"Detect whether input object sf Spat* object.","code":""},{"path":"https://NIEHS.github.io/chopin/reference/dep_check.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Return the package the input object is based on — dep_check","text":"","code":"dep_check(input)"},{"path":"https://NIEHS.github.io/chopin/reference/dep_check.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Return the package the input object is based on — dep_check","text":"input Spat* terra sf object.","code":""},{"path":"https://NIEHS.github.io/chopin/reference/dep_check.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Return the package the input object is based on — dep_check","text":"character object; one \"character\", \"terra\" \"sf\"","code":""},{"path":[]},{"path":"https://NIEHS.github.io/chopin/reference/dep_check.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Return the package the input object is based on — dep_check","text":"Insang Song","code":""},{"path":"https://NIEHS.github.io/chopin/reference/dep_check.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Return the package the input object is based on — dep_check","text":"","code":"if (FALSE) { # \\dontrun{ library(sf) library(terra) options(sf_use_s2 = FALSE)  nc_path <- system.file(\"gpkg/nc.gpkg\", package = \"sf\") nc_sf <- sf::st_read(nc_path) dep_check(nc_sf) nc_vect <- terra::vect(nc_sf) dep_check(nc_vect) } # }"},{"path":"https://NIEHS.github.io/chopin/reference/dep_switch.html","id":null,"dir":"Reference","previous_headings":"","what":"Switch spatial data class — dep_switch","title":"Switch spatial data class — dep_switch","text":"Convert class sf/stars-terra","code":""},{"path":"https://NIEHS.github.io/chopin/reference/dep_switch.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Switch spatial data class — dep_switch","text":"","code":"dep_switch(input)"},{"path":"https://NIEHS.github.io/chopin/reference/dep_switch.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Switch spatial data class — dep_switch","text":"input Spat* terra sf object.","code":""},{"path":"https://NIEHS.github.io/chopin/reference/dep_switch.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Switch spatial data class — dep_switch","text":"Data converted package class (sf, terra; terra, sf)","code":""},{"path":[]},{"path":"https://NIEHS.github.io/chopin/reference/dep_switch.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Switch spatial data class — dep_switch","text":"Insang Song","code":""},{"path":"https://NIEHS.github.io/chopin/reference/dep_switch.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Switch spatial data class — dep_switch","text":"","code":"if (FALSE) { # \\dontrun{ library(sf) library(stars) library(terra) options(sf_use_s2 = FALSE)  ## generate a random raster ras_rand <- terra::rast(nrow = 30, ncol = 30) terra::values(ras_rand) <- runif(900) stars_rand <- dep_switch(ras_rand) stars_rand inherits(sf_rand, \"stars\") # TRUE # should return stars object  vec_rand <- terra::spatSample(ras_rand, size = 10L, as.points = TRUE) sf_rand <- dep_switch(vec_rand) inherits(sf_rand, \"sf\") # TRUE sf_rand # should return sf object } # }"},{"path":"https://NIEHS.github.io/chopin/reference/dot-check_character.html","id":null,"dir":"Reference","previous_headings":"","what":"Check the class of an input object — .check_character","title":"Check the class of an input object — .check_character","text":"function checks class input object returns \"raster\" raster object, \"vector\" vector object.","code":""},{"path":"https://NIEHS.github.io/chopin/reference/dot-check_character.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Check the class of an input object — .check_character","text":"","code":".check_character(input)"},{"path":"https://NIEHS.github.io/chopin/reference/dot-check_character.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Check the class of an input object — .check_character","text":"input input object checked","code":""},{"path":"https://NIEHS.github.io/chopin/reference/dot-check_character.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Check the class of an input object — .check_character","text":"character string indicating class input object (\"raster\" \"vector\")","code":""},{"path":"https://NIEHS.github.io/chopin/reference/dot-check_raster.html","id":null,"dir":"Reference","previous_headings":"","what":"Check Raster Input — .check_raster","title":"Check Raster Input — .check_raster","text":"function checks input object ensure valid raster object character path raster file. also provides warnings informative messages based input type.","code":""},{"path":"https://NIEHS.github.io/chopin/reference/dot-check_raster.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Check Raster Input — .check_raster","text":"","code":".check_raster(input, extent = NULL, ...)"},{"path":"https://NIEHS.github.io/chopin/reference/dot-check_raster.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Check Raster Input — .check_raster","text":"input input object checked. can either SpatRaster object character path raster file. extent extent raster. Defaults NULL. Numeric vector put order c(xmin, xmax, ymin, ymax). ... Placeholder.","code":""},{"path":"https://NIEHS.github.io/chopin/reference/dot-check_raster.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Check Raster Input — .check_raster","text":"validated input object.","code":""},{"path":"https://NIEHS.github.io/chopin/reference/dot-check_raster.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Check Raster Input — .check_raster","text":"","code":"if (FALSE) { # \\dontrun{ .check_raster(system.file(\"extdata/nc_srtm15_otm.tif\", package = \"chopin\")) } # }"},{"path":"https://NIEHS.github.io/chopin/reference/dot-check_vector.html","id":null,"dir":"Reference","previous_headings":"","what":"Check the subject object and perform necessary conversions if needed. — .check_vector","title":"Check the subject object and perform necessary conversions if needed. — .check_vector","text":"function checks class input object performs necessary conversions needed.","code":""},{"path":"https://NIEHS.github.io/chopin/reference/dot-check_vector.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Check the subject object and perform necessary conversions if needed. — .check_vector","text":"","code":".check_vector(   input,   input_id = NULL,   extent = NULL,   out_class = character(1),   ... )"},{"path":"https://NIEHS.github.io/chopin/reference/dot-check_vector.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Check the subject object and perform necessary conversions if needed. — .check_vector","text":"input sf/SpatVector/character. input object checked. input_id character(1). ID field subject object. extent numeric(4). extent subject object. Numeric vector put order c(xmin, xmax, ymin, ymax). out_class character(1). class output object. one c(\"sf\", \"terra\"). ... Placeholder.","code":""},{"path":"https://NIEHS.github.io/chopin/reference/dot-check_vector.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Check the subject object and perform necessary conversions if needed. — .check_vector","text":"checked converted subject object.","code":""},{"path":"https://NIEHS.github.io/chopin/reference/dot-check_vector.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Check the subject object and perform necessary conversions if needed. — .check_vector","text":"","code":"if (FALSE) { # \\dontrun{ # Check a SpatVector object ncpath <- system.file(\"gpkg/nc.gpkg\", package = \"sf\") nc <- terra::vect(ncpath) extent <- c(-80, -77, 35, 36) .check_vector(input = nc, extent = extent, input_id = \"FIPS\")  # Check a sf object ncsf <- sf::st_read(ncpath) .check_vector(input = ncsf, extent = extent, input_id = \"FIPS\")  # Check a character object .check_vector(   input = ncpath,   extent = extent,   out_class = \"terra\",   input_id = \"FIPS\" ) } # }"},{"path":"https://NIEHS.github.io/chopin/reference/dot-intersect_extent.html","id":null,"dir":"Reference","previous_headings":"","what":"Get intersection extent — .intersect_extent","title":"Get intersection extent — .intersect_extent","text":"Get intersection extent","code":""},{"path":"https://NIEHS.github.io/chopin/reference/dot-intersect_extent.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get intersection extent — .intersect_extent","text":"","code":".intersect_extent(input, out_class, ...)  # S4 method for class 'sf,ANY' .intersect_extent(input, out_class = NULL, ...)  # S4 method for class 'SpatExtent,ANY' .intersect_extent(input, out_class = NULL, ...)  # S4 method for class 'SpatVector,ANY' .intersect_extent(input, out_class = NULL, ...)  # S4 method for class 'numeric,character' .intersect_extent(input, out_class = NULL, ...)"},{"path":"https://NIEHS.github.io/chopin/reference/dot-intersect_extent.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get intersection extent — .intersect_extent","text":"input sf/SpatExtent/SpatVector/numeric out_class character(1). \"sf\" \"terra\" ... arguments. Placeholder.","code":""},{"path":"https://NIEHS.github.io/chopin/reference/dot-par_screen.html","id":null,"dir":"Reference","previous_headings":"","what":"Prescreen input data for parallelization — .par_screen","title":"Prescreen input data for parallelization — .par_screen","text":"function takes input object type character ingest input object return object desired class.","code":""},{"path":"https://NIEHS.github.io/chopin/reference/dot-par_screen.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Prescreen input data for parallelization — .par_screen","text":"","code":".par_screen(type, input, input_id = NULL, out_class = \"terra\", .window = NULL)"},{"path":"https://NIEHS.github.io/chopin/reference/dot-par_screen.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Prescreen input data for parallelization — .par_screen","text":"type character(1). \"raster\" \"vector\". input object. Input object. input_id character(1). Default NULL. NULL, function check object ID column. out_class character(1). Default NULL, one c(\"sf\", \"terra\"). Default \"terra\". .window numeric(4)/SpatExtent/st_bbox object. Loading window.","code":""},{"path":"https://NIEHS.github.io/chopin/reference/extract_at.html","id":null,"dir":"Reference","previous_headings":"","what":"Extract raster values with point buffers or polygons — extract_at","title":"Extract raster values with point buffers or polygons — extract_at","text":"Extract raster values point buffers polygons","code":""},{"path":"https://NIEHS.github.io/chopin/reference/extract_at.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extract raster values with point buffers or polygons — extract_at","text":"","code":"extract_at(x, y, ...)  # S4 method for class 'SpatRaster,sf' extract_at(   x = NULL,   y = NULL,   id = NULL,   func = \"mean\",   extent = NULL,   radius = NULL,   out_class = \"sf\",   kernel = NULL,   kernel_func = stats::weighted.mean,   bandwidth = NULL,   max_cells = 3e+07,   .standalone = TRUE,   ... )  # S4 method for class 'character,character' extract_at(   x = NULL,   y = NULL,   id = NULL,   func = \"mean\",   extent = NULL,   radius = NULL,   out_class = \"sf\",   kernel = NULL,   kernel_func = stats::weighted.mean,   bandwidth = NULL,   max_cells = 3e+07,   .standalone = TRUE,   ... )  # S4 method for class 'SpatRaster,character' extract_at(   x = NULL,   y = NULL,   id = NULL,   func = \"mean\",   extent = NULL,   radius = NULL,   out_class = \"sf\",   kernel = NULL,   kernel_func = stats::weighted.mean,   bandwidth = NULL,   max_cells = 3e+07,   .standalone = TRUE,   ... )  # S4 method for class 'SpatRaster,SpatVector' extract_at(   x = NULL,   y = NULL,   id = NULL,   func = \"mean\",   extent = NULL,   radius = NULL,   out_class = \"sf\",   kernel = NULL,   kernel_func = stats::weighted.mean,   bandwidth = NULL,   max_cells = 3e+07,   .standalone = TRUE,   ... )  # S4 method for class 'character,sf' extract_at(   x = NULL,   y = NULL,   id = NULL,   func = \"mean\",   extent = NULL,   radius = NULL,   out_class = \"sf\",   kernel = NULL,   kernel_func = stats::weighted.mean,   bandwidth = NULL,   max_cells = 3e+07,   .standalone = TRUE,   ... )  # S4 method for class 'character,SpatVector' extract_at(   x = NULL,   y = NULL,   id = NULL,   func = \"mean\",   extent,   radius,   out_class = \"sf\",   kernel = NULL,   kernel_func = stats::weighted.mean,   bandwidth,   max_cells = 3e+07,   .standalone = TRUE,   ... )"},{"path":"https://NIEHS.github.io/chopin/reference/extract_at.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extract raster values with point buffers or polygons — extract_at","text":"x SpatRaster object file path(s) extensions GDAL-compatible. multiple file paths used, rasters must extent resolution. y sf/SpatVector object file path. ... Placeholder. id character(1). Unique identifier point. func function taking one numeric vector argument. extent numeric(4) SpatExtent. Extent clipping vector. works points character(1) file path. radius numeric(1). Buffer radius. out_class character(1). Output class. One sf terra. kernel character(1). Name kernel function One \"uniform\", \"triweight\", \"quartic\", \"epanechnikov\" kernel_func function. Kernel function apply extracted values. bandwidth numeric(1). Kernel bandwidth. max_cells integer(1). Maximum number cells memory. .standalone logical(1). Default TRUE, means function executed standalone mode. using function par_* functions, set FALSE.","code":""},{"path":"https://NIEHS.github.io/chopin/reference/extract_at.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Extract raster values with point buffers or polygons — extract_at","text":"data.frame object summarized raster values respect mode (polygon buffer) function.","code":""},{"path":"https://NIEHS.github.io/chopin/reference/extract_at.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Extract raster values with point buffers or polygons — extract_at","text":"Inputs preprocessed different ways depending class. Vector inputs y: sf preferred, thus character SpatVector inputs converted sf object. radius NULL, sf::st_buffer used generate circular buffers subsequent raster-vector overlay done exactextractr::exact_extract. Raster input x: SpatRaster preferred. input SpatRaster, converted SpatRaster object.","code":""},{"path":[]},{"path":"https://NIEHS.github.io/chopin/reference/extract_at.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Extract raster values with point buffers or polygons — extract_at","text":"Insang Song geoissong@gmail.com","code":""},{"path":"https://NIEHS.github.io/chopin/reference/extract_at.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Extract raster values with point buffers or polygons — extract_at","text":"","code":"ncpath <- system.file(\"gpkg/nc.gpkg\", package = \"sf\") rastpath <- system.file(\"extdata/nc_srtm15_otm.tif\", package = \"chopin\")  nc <- terra::vect(ncpath) nc <- terra::project(nc, \"EPSG:5070\") rrast <- terra::rast(nc, nrow = 100, ncol = 220) ncr <- terra::rasterize(nc, rrast) terra::values(rrast) <- rgamma(2.2e4, 4, 2) rpnt <- terra::spatSample(rrast, 16L, as.points = TRUE) rpnt$pid <- sprintf(\"ID-%02d\", seq(1, 16))  extract_at(rrast, rpnt, \"pid\", \"mean\", radius = 1000) #> Switch terra class to sf... #>      pid      mean #> 1  ID-01 1.9822180 #> 2  ID-02 3.4319303 #> 3  ID-03 3.9273469 #> 4  ID-04 0.2887684 #> 5  ID-05 1.0191861 #> 6  ID-06 1.4394056 #> 7  ID-07 3.3074858 #> 8  ID-08 3.7643452 #> 9  ID-09 1.9196675 #> 10 ID-10 3.5066707 #> 11 ID-11 1.7782123 #> 12 ID-12 0.7785873 #> 13 ID-13 2.0458314 #> 14 ID-14 1.1224172 #> 15 ID-15 3.9863424 #> 16 ID-16 2.9632106 extract_at(rrast, nc, \"NAME\", \"mean\") #> Switch terra class to sf... #>             NAME     mean #> 1           Ashe 1.947390 #> 2      Alleghany 1.967787 #> 3          Surry 1.979185 #> 4      Currituck 2.087879 #> 5    Northampton 1.995564 #> 6       Hertford 2.055216 #> 7         Camden 2.007895 #> 8          Gates 1.988872 #> 9         Warren 2.030980 #> 10        Stokes 1.948919 #> 11       Caswell 2.259248 #> 12    Rockingham 1.933288 #> 13     Granville 1.968703 #> 14        Person 2.050707 #> 15         Vance 2.038214 #> 16       Halifax 2.146045 #> 17    Pasquotank 1.875991 #> 18        Wilkes 1.915943 #> 19       Watauga 1.895707 #> 20    Perquimans 1.864332 #> 21        Chowan 2.067013 #> 22         Avery 2.087210 #> 23        Yadkin 2.030051 #> 24      Franklin 1.952200 #> 25       Forsyth 2.081115 #> 26      Guilford 1.971956 #> 27      Alamance 1.851228 #> 28        Bertie 1.861882 #> 29        Orange 1.943042 #> 30        Durham 2.267813 #> 31          Nash 2.116162 #> 32      Mitchell 2.020578 #> 33     Edgecombe 2.139719 #> 34      Caldwell 2.003097 #> 35        Yancey 1.859106 #> 36        Martin 1.997395 #> 37          Wake 1.919064 #> 38       Madison 1.956803 #> 39       Iredell 1.979584 #> 40         Davie 2.242437 #> 41     Alexander 1.812335 #> 42      Davidson 2.031528 #> 43         Burke 1.928709 #> 44    Washington 2.051182 #> 45       Tyrrell 2.120088 #> 46      McDowell 1.951825 #> 47      Randolph 1.997749 #> 48       Chatham 1.956700 #> 49        Wilson 2.065812 #> 50         Rowan 1.879703 #> 51          Pitt 1.969181 #> 52       Catawba 2.094155 #> 53      Buncombe 2.115411 #> 54      Johnston 2.030387 #> 55       Haywood 1.925143 #> 56          Dare 2.003934 #> 57      Beaufort 1.995797 #> 58         Swain 1.995029 #> 59        Greene 1.892647 #> 60           Lee 1.786664 #> 61    Rutherford 2.080569 #> 62         Wayne 2.036669 #> 63       Harnett 2.025678 #> 64     Cleveland 2.155540 #> 65       Lincoln 2.244004 #> 66       Jackson 2.096978 #> 67         Moore 2.074827 #> 68   Mecklenburg 1.946310 #> 69      Cabarrus 1.985471 #> 70    Montgomery 1.982364 #> 71        Stanly 1.831006 #> 72     Henderson 2.046879 #> 73        Graham 1.807080 #> 74        Lenoir 1.992867 #> 75  Transylvania 2.139678 #> 76        Gaston 1.860849 #> 77          Polk 2.001973 #> 78         Macon 2.087381 #> 79       Sampson 2.149392 #> 80       Pamlico 1.884383 #> 81      Cherokee 1.974908 #> 82    Cumberland 1.973660 #> 83         Jones 1.979061 #> 84         Union 1.981828 #> 85         Anson 2.053549 #> 86          Hoke 1.982197 #> 87          Hyde 2.087430 #> 88        Duplin 2.001727 #> 89      Richmond 2.093516 #> 90          Clay 1.872995 #> 91        Craven 1.985506 #> 92      Scotland 1.972766 #> 93        Onslow 1.982934 #> 94       Robeson 2.052670 #> 95      Carteret 2.064187 #> 96        Bladen 2.083948 #> 97        Pender 1.920480 #> 98      Columbus 2.075454 #> 99   New Hanover 1.874795 #> 100    Brunswick 1.911686 extract_at(rrast, ncpath, \"NAME\", \"mean\") #> ℹ Input is a character. Trying to read with sf. #> Reading layer `nc.gpkg' from data source  #>   `/home/runner/work/_temp/Library/sf/gpkg/nc.gpkg' using driver `GPKG' #> Simple feature collection with 100 features and 14 fields #> Geometry type: MULTIPOLYGON #> Dimension:     XY #> Bounding box:  xmin: -84.32385 ymin: 33.88199 xmax: -75.45698 ymax: 36.58965 #> Geodetic CRS:  NAD27 #>             NAME     mean #> 1           Ashe 1.947390 #> 2      Alleghany 1.967787 #> 3          Surry 1.979185 #> 4      Currituck 2.087879 #> 5    Northampton 1.995564 #> 6       Hertford 2.055216 #> 7         Camden 2.007895 #> 8          Gates 1.988872 #> 9         Warren 2.030980 #> 10        Stokes 1.948919 #> 11       Caswell 2.259248 #> 12    Rockingham 1.933288 #> 13     Granville 1.968703 #> 14        Person 2.050707 #> 15         Vance 2.038214 #> 16       Halifax 2.146045 #> 17    Pasquotank 1.875991 #> 18        Wilkes 1.915943 #> 19       Watauga 1.895707 #> 20    Perquimans 1.864332 #> 21        Chowan 2.067013 #> 22         Avery 2.087210 #> 23        Yadkin 2.030051 #> 24      Franklin 1.952200 #> 25       Forsyth 2.081115 #> 26      Guilford 1.971956 #> 27      Alamance 1.851228 #> 28        Bertie 1.861882 #> 29        Orange 1.943042 #> 30        Durham 2.267813 #> 31          Nash 2.116162 #> 32      Mitchell 2.020578 #> 33     Edgecombe 2.139719 #> 34      Caldwell 2.003097 #> 35        Yancey 1.859106 #> 36        Martin 1.997395 #> 37          Wake 1.919064 #> 38       Madison 1.956803 #> 39       Iredell 1.979584 #> 40         Davie 2.242437 #> 41     Alexander 1.812335 #> 42      Davidson 2.031528 #> 43         Burke 1.928709 #> 44    Washington 2.051182 #> 45       Tyrrell 2.120088 #> 46      McDowell 1.951825 #> 47      Randolph 1.997749 #> 48       Chatham 1.956700 #> 49        Wilson 2.065812 #> 50         Rowan 1.879703 #> 51          Pitt 1.969181 #> 52       Catawba 2.094155 #> 53      Buncombe 2.115411 #> 54      Johnston 2.030387 #> 55       Haywood 1.925143 #> 56          Dare 2.003934 #> 57      Beaufort 1.995797 #> 58         Swain 1.995029 #> 59        Greene 1.892647 #> 60           Lee 1.786664 #> 61    Rutherford 2.080569 #> 62         Wayne 2.036669 #> 63       Harnett 2.025678 #> 64     Cleveland 2.155540 #> 65       Lincoln 2.244004 #> 66       Jackson 2.096978 #> 67         Moore 2.074827 #> 68   Mecklenburg 1.946310 #> 69      Cabarrus 1.985471 #> 70    Montgomery 1.982364 #> 71        Stanly 1.831006 #> 72     Henderson 2.046879 #> 73        Graham 1.807080 #> 74        Lenoir 1.992867 #> 75  Transylvania 2.139678 #> 76        Gaston 1.860849 #> 77          Polk 2.001973 #> 78         Macon 2.087381 #> 79       Sampson 2.149392 #> 80       Pamlico 1.884383 #> 81      Cherokee 1.974908 #> 82    Cumberland 1.973660 #> 83         Jones 1.979061 #> 84         Union 1.981828 #> 85         Anson 2.053549 #> 86          Hoke 1.982197 #> 87          Hyde 2.087430 #> 88        Duplin 2.001727 #> 89      Richmond 2.093516 #> 90          Clay 1.872995 #> 91        Craven 1.985506 #> 92      Scotland 1.972766 #> 93        Onslow 1.982934 #> 94       Robeson 2.052670 #> 95      Carteret 2.064187 #> 96        Bladen 2.083948 #> 97        Pender 1.920480 #> 98      Columbus 2.075454 #> 99   New Hanover 1.874795 #> 100    Brunswick 1.911686 extract_at(   rrast, ncpath, \"NAME\", \"mean\",   kernel = \"epanechnikov\",   bandwidth = 1e5 ) #> ℹ Input is a character. Trying to read with sf. #> Reading layer `nc.gpkg' from data source  #>   `/home/runner/work/_temp/Library/sf/gpkg/nc.gpkg' using driver `GPKG' #> Simple feature collection with 100 features and 14 fields #> Geometry type: MULTIPOLYGON #> Dimension:     XY #> Bounding box:  xmin: -84.32385 ymin: 33.88199 xmax: -75.45698 ymax: 36.58965 #> Geodetic CRS:  NAD27 #> Kernel function [ epanechnikov ] is applied to calculate weights... #> Switch sf class to terra... #> Warning: Point geometries are acceptable for kernel weighting. Convert to points... #> (Note: inside = TRUE is applied) #> # A tibble: 100 × 2 #>    NAME      value #>    <chr>     <dbl> #>  1 Alamance   1.85 #>  2 Alexander  1.81 #>  3 Alleghany  1.97 #>  4 Anson      2.06 #>  5 Ashe       1.95 #>  6 Avery      2.08 #>  7 Beaufort   1.99 #>  8 Bertie     1.86 #>  9 Bladen     2.08 #> 10 Brunswick  1.92 #> # ℹ 90 more rows extract_at(   rastpath, ncpath, \"NAME\", \"mean\",   kernel = \"epanechnikov\",   bandwidth = 1e5 ) #> Input is a character. Attempt to read it with terra::rast... #> ℹ Input is a character. Trying to read with sf. #> Reading layer `nc.gpkg' from data source  #>   `/home/runner/work/_temp/Library/sf/gpkg/nc.gpkg' using driver `GPKG' #> Simple feature collection with 100 features and 14 fields #> Geometry type: MULTIPOLYGON #> Dimension:     XY #> Bounding box:  xmin: -84.32385 ymin: 33.88199 xmax: -75.45698 ymax: 36.58965 #> Geodetic CRS:  NAD27 #> Kernel function [ epanechnikov ] is applied to calculate weights... #> Switch sf class to terra... #> Warning: Point geometries are acceptable for kernel weighting. Convert to points... #> (Note: inside = TRUE is applied) #> # A tibble: 100 × 2 #>    NAME       value #>    <chr>      <dbl> #>  1 Alamance   189.  #>  2 Alexander  358.  #>  3 Alleghany  877.  #>  4 Anson      106.  #>  5 Ashe       981.  #>  6 Avery     1086.  #>  7 Beaufort    11.8 #>  8 Bertie      18.9 #>  9 Bladen      26.7 #> 10 Brunswick   11.7 #> # ℹ 90 more rows"},{"path":"https://NIEHS.github.io/chopin/reference/get_clip_ext.html","id":null,"dir":"Reference","previous_headings":"","what":"Setting the clipping extent — get_clip_ext","title":"Setting the clipping extent — get_clip_ext","text":"Return clipping extent buffer radius. assumes input CRS projected linear unit meters.","code":""},{"path":"https://NIEHS.github.io/chopin/reference/get_clip_ext.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Setting the clipping extent — get_clip_ext","text":"","code":"get_clip_ext(pnts, radius, extrusion = 1.1)"},{"path":"https://NIEHS.github.io/chopin/reference/get_clip_ext.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Setting the clipping extent — get_clip_ext","text":"pnts One sf SpatVector object. Target points computation. radius numeric(1). Buffer radius. assumed meters extrusion numeric(1). extent extrusion factor.","code":""},{"path":"https://NIEHS.github.io/chopin/reference/get_clip_ext.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Setting the clipping extent — get_clip_ext","text":"terra::ext sfc_POLYGON object computation extent.","code":""},{"path":[]},{"path":"https://NIEHS.github.io/chopin/reference/get_clip_ext.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Setting the clipping extent — get_clip_ext","text":"Insang Song","code":""},{"path":"https://NIEHS.github.io/chopin/reference/get_clip_ext.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Setting the clipping extent — get_clip_ext","text":"","code":"if (FALSE) { # \\dontrun{ library(sf) library(terra) options(sf_use_s2 = FALSE)  nc_path <- system.file(\"gpkg/nc.gpkg\", package = \"sf\") nc_sf <- sf::st_read(nc_path) nc_sf <- sf::st_transform(nc_sf, \"EPSG:5070\") get_clip_ext(nc_sf, 2.5e4) nc_vect <- terra::vect(nc_sf) get_clip_ext(nc_vect, 2.5e4) } # }"},{"path":"https://NIEHS.github.io/chopin/reference/indexing.html","id":null,"dir":"Reference","previous_headings":"","what":"Subset for nonidentical package class objects — indexing","title":"Subset for nonidentical package class objects — indexing","text":"Subset nonidentical package class objects Intersect different data model objects","code":""},{"path":"https://NIEHS.github.io/chopin/reference/indexing.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Subset for nonidentical package class objects — indexing","text":"","code":"# S4 method for class 'SpatVector,bbox,missing,ANY' x[i, j]  # S4 method for class 'SpatVector,sf,missing,ANY' x[i, j]  # S4 method for class 'SpatVector,sfc,missing,ANY' x[i, j]  # S4 method for class 'SpatVector,SpatExtent,missing,ANY' x[i, j]  .intersect(x, y)"},{"path":"https://NIEHS.github.io/chopin/reference/indexing.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Subset for nonidentical package class objects — indexing","text":"x SpatVector/sf/SpatRaster object intersected. Dataset used subset x. j Column indices names. y SpatVector/sf object. Intersecting object.","code":""},{"path":"https://NIEHS.github.io/chopin/reference/kernelfunction.html","id":null,"dir":"Reference","previous_headings":"","what":"Kernel functions — kernelfunction","title":"Kernel functions — kernelfunction","text":"Kernel functions","code":""},{"path":"https://NIEHS.github.io/chopin/reference/kernelfunction.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Kernel functions — kernelfunction","text":"","code":"kernelfunction(   d,   bw,   kernel = c(\"uniform\", \"quartic\", \"triweight\", \"epanechnikov\") )"},{"path":"https://NIEHS.github.io/chopin/reference/kernelfunction.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Kernel functions — kernelfunction","text":"d Distance bw Bandwidth kernel kernel Kernel type. One \"uniform\", \"quartic\", \"triweight\", \"epanechnikov\"","code":""},{"path":"https://NIEHS.github.io/chopin/reference/kernelfunction.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Kernel functions — kernelfunction","text":"numeric. Kernel weights.","code":""},{"path":"https://NIEHS.github.io/chopin/reference/kernelfunction.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Kernel functions — kernelfunction","text":"https://github.com/JanCaha/SpatialKDE","code":""},{"path":[]},{"path":"https://NIEHS.github.io/chopin/reference/kernelfunction.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Kernel functions — kernelfunction","text":"","code":"v_dist <- c(1, 10, 100, 25, 50, 0.1) bw_dist1 <- 1 bw_dist2 <- 10 kernelfunction(v_dist, bw_dist1, \"uniform\") #> [1] 0.5 0.0 0.0 0.0 0.0 0.5 kernelfunction(v_dist, bw_dist1, \"quartic\") #> [1] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.9188437 kernelfunction(v_dist, bw_dist1, \"triweight\") #> [1] 0.000 0.000 0.000 0.000 0.000 0.999 kernelfunction(v_dist, bw_dist1, \"epanechnikov\") #> [1] 0.0000 0.0000 0.0000 0.0000 0.0000 0.7425 kernelfunction(v_dist, bw_dist2, \"uniform\") #> [1] 0.5 0.5 0.0 0.0 0.0 0.5 kernelfunction(v_dist, bw_dist2, \"quartic\") #> [1] 0.9188437 0.0000000 0.0000000 0.0000000 0.0000000 0.9373125 kernelfunction(v_dist, bw_dist2, \"triweight\") #> [1] 0.999000 0.000000 0.000000 0.000000 0.000000 0.999999 kernelfunction(v_dist, bw_dist2, \"epanechnikov\") #> [1] 0.742500 0.000000 0.000000 0.000000 0.000000 0.749925"},{"path":"https://NIEHS.github.io/chopin/reference/ncpoints.html","id":null,"dir":"Reference","previous_headings":"","what":"Mildly clustered points in North Carolina, United States — ncpoints","title":"Mildly clustered points in North Carolina, United States — ncpoints","text":"Mildly clustered points North Carolina, United States","code":""},{"path":"https://NIEHS.github.io/chopin/reference/ncpoints.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Mildly clustered points in North Carolina, United States — ncpoints","text":"","code":"ncpoints"},{"path":"https://NIEHS.github.io/chopin/reference/ncpoints.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Mildly clustered points in North Carolina, United States — ncpoints","text":"data frame 2,304 rows two variables: X X coordinate Y Y coordinate","code":""},{"path":"https://NIEHS.github.io/chopin/reference/ncpoints.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Mildly clustered points in North Carolina, United States — ncpoints","text":"sf package data nc","code":""},{"path":"https://NIEHS.github.io/chopin/reference/ncpoints.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Mildly clustered points in North Carolina, United States — ncpoints","text":"Coordinates EPSG:5070 (Conus Albers Equal Area)","code":""},{"path":[]},{"path":"https://NIEHS.github.io/chopin/reference/ncpoints.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Mildly clustered points in North Carolina, United States — ncpoints","text":"","code":"data(\"ncpoints\", package = \"chopin\")"},{"path":"https://NIEHS.github.io/chopin/reference/par_cut_coords.html","id":null,"dir":"Reference","previous_headings":"","what":"Partition coordinates into quantile polygons — par_cut_coords","title":"Partition coordinates into quantile polygons — par_cut_coords","text":"Partition coordinates quantile polygons","code":""},{"path":"https://NIEHS.github.io/chopin/reference/par_cut_coords.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Partition coordinates into quantile polygons — par_cut_coords","text":"","code":"par_cut_coords(x = NULL, y = NULL, quantiles)"},{"path":"https://NIEHS.github.io/chopin/reference/par_cut_coords.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Partition coordinates into quantile polygons — par_cut_coords","text":"x numeric/sf/SpatVector. x-coordinates (numeric). y numeric. y-coordinates. quantiles numeric vector. Quantiles.","code":""},{"path":"https://NIEHS.github.io/chopin/reference/par_cut_coords.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Partition coordinates into quantile polygons — par_cut_coords","text":"SpatVector object field CGRIDID.","code":""},{"path":"https://NIEHS.github.io/chopin/reference/par_cut_coords.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Partition coordinates into quantile polygons — par_cut_coords","text":"function two-dimensional points.","code":""},{"path":[]},{"path":"https://NIEHS.github.io/chopin/reference/par_cut_coords.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Partition coordinates into quantile polygons — par_cut_coords","text":"","code":"if (FALSE) { # \\dontrun{ library(terra)  random_points <-   data.frame(x = runif(1000, 0, 100), y = runif(1000, 0, 100)) quantiles <- seq(0, 1, length.out = 5L) qpoly <- par_cut_coords(random_points$x, random_points$y, quantiles) clustered_points <-   data.frame(x = rgamma(1000, 1, 1), y = rgamma(1000, 4, 1))  qpoly_c <- par_cut_coords(clustered_points$x, clustered_points$y, quantiles)  par(mfcol = c(1, 2)) plot(qpoly) plot(qpoly_c) par(mfcol = c(1, 1))  cvect <- terra::vect(clustered_points, geom = c(\"x\", \"y\")) plot(cvect) plot(qpoly_c, add = TRUE, col = \"transparent\", border = \"red\")  qcv <- intersect(cvect, qpoly_c) table(qcv$CGRIDID) sum(table(qcv$CGRIDID)) # should be 1000 } # }"},{"path":"https://NIEHS.github.io/chopin/reference/par_def_q.html","id":null,"dir":"Reference","previous_headings":"","what":"Quantile definition — par_def_q","title":"Quantile definition — par_def_q","text":"Quantile definition","code":""},{"path":"https://NIEHS.github.io/chopin/reference/par_def_q.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Quantile definition — par_def_q","text":"","code":"par_def_q(steps = 4L)"},{"path":"https://NIEHS.github.io/chopin/reference/par_def_q.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Quantile definition — par_def_q","text":"steps integer(1). number quantiles.","code":""},{"path":"https://NIEHS.github.io/chopin/reference/par_def_q.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Quantile definition — par_def_q","text":"numeric vector quantiles.","code":""},{"path":[]},{"path":"https://NIEHS.github.io/chopin/reference/par_def_q.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Quantile definition — par_def_q","text":"","code":"if (FALSE) { # \\dontrun{ par_def_q(5L) } # }"},{"path":"https://NIEHS.github.io/chopin/reference/par_grid.html","id":null,"dir":"Reference","previous_headings":"","what":"Parallelize spatial computation over the computational grids — par_grid","title":"Parallelize spatial computation over the computational grids — par_grid","text":"future::multicore, future::multisession, future::cluster future.mirai::mirai_multisession future::plan parallelize work grid. details terminology future package, refer future::plan. function assumes users one raster file sizable spatially distributed target locations. thread process nearest integer $|N_g| / |N_t|$ grids $|N_g|$ denotes number grids $|N_t|$ denotes number threads.","code":""},{"path":"https://NIEHS.github.io/chopin/reference/par_grid.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Parallelize spatial computation over the computational grids — par_grid","text":"","code":"par_grid(grids, fun_dist, ..., pad_y = FALSE, .debug = FALSE)"},{"path":"https://NIEHS.github.io/chopin/reference/par_grid.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Parallelize spatial computation over the computational grids — par_grid","text":"grids List two sf/SpatVector objects. Computational grids. takes strict assumption grid input output `par_pad_grid“. fun_dist sf, terra chopin functions. function x y arguments. ... Arguments passed argument fun_dist. pad_y logical(1). Whether filter y padded grid. TRUE x values calculated. Default FALSE. reverse case, like terra::extent exactextractr::exact_extract, raster (x) extent set padded grid. .debug logical(1). Default FALSE. Otherwise, unit computation fails, error message CGRIDID value error occurred included output.","code":""},{"path":"https://NIEHS.github.io/chopin/reference/par_grid.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Parallelize spatial computation over the computational grids — par_grid","text":"data.frame object computation results. entries results, consult documentation function put fun_dist argument.","code":""},{"path":"https://NIEHS.github.io/chopin/reference/par_grid.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Parallelize spatial computation over the computational grids — par_grid","text":"dynamic dots (...), fun_dist arguments include x y sf/terra class objects file paths accepted. Virtually sf/terra functions accept two arguments can put fun_dist, please advised spatial operations necessarily give exact result done single-thread. example, distance calculated function may return lower value actual computational region reduced. case especially target features spatially sparsely distributed.","code":""},{"path":[]},{"path":"https://NIEHS.github.io/chopin/reference/par_grid.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Parallelize spatial computation over the computational grids — par_grid","text":"Insang Song geoissong@gmail.com","code":""},{"path":"https://NIEHS.github.io/chopin/reference/par_grid.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Parallelize spatial computation over the computational grids — par_grid","text":"","code":"library(sf) #> Linking to GEOS 3.10.2, GDAL 3.4.1, PROJ 8.2.1; sf_use_s2() is TRUE library(future) library(future.mirai) plan(mirai_multisession, workers = 2) ncpath <- system.file(\"shape/nc.shp\", package = \"sf\") ncpoly <- sf::st_read(ncpath) #> Reading layer `nc' from data source  #>   `/home/runner/work/_temp/Library/sf/shape/nc.shp' using driver `ESRI Shapefile' #> Simple feature collection with 100 features and 14 fields #> Geometry type: MULTIPOLYGON #> Dimension:     XY #> Bounding box:  xmin: -84.32385 ymin: 33.88199 xmax: -75.45698 ymax: 36.58965 #> Geodetic CRS:  NAD27 # sf object ncpnts <-   readRDS(     system.file(\"extdata/nc_random_point.rds\", package = \"chopin\")   ) # file path ncelev <-     system.file(\"extdata/nc_srtm15_otm.tif\", package = \"chopin\")  nccompreg <-   chopin::par_pad_grid(     input = ncpnts,     mode = \"grid\",     nx = 4L,     ny = 2L,     padding = 5e3L   ) #> Switch sf class to terra... #> Switch terra class to sf... res <-   par_grid(     grids = nccompreg,     fun_dist = extract_at,     x = ncelev,     y = ncpnts,     qsegs = 90L,     radius = 5e3L,     id = \"pid\"   ) #> Spherical geometry (s2) switched off #> ℹ Input is not a character. #> Input is a character. Attempt to read it with terra::rast... #> ℹ Task at CGRIDID: 1 is successfully dispatched. #> Input is a character. Attempt to read it with terra::rast... #> ℹ Task at CGRIDID: 2 is successfully dispatched. #> Input is a character. Attempt to read it with terra::rast... #> ℹ Task at CGRIDID: 3 is successfully dispatched. #> Input is a character. Attempt to read it with terra::rast... #> ℹ Task at CGRIDID: 4 is successfully dispatched. #> Input is a character. Attempt to read it with terra::rast... #> ℹ Task at CGRIDID: 5 is successfully dispatched. #> Input is a character. Attempt to read it with terra::rast... #> ℹ Task at CGRIDID: 6 is successfully dispatched. #> Input is a character. Attempt to read it with terra::rast... #> ℹ Task at CGRIDID: 7 is successfully dispatched. #> Input is a character. Attempt to read it with terra::rast... #> ℹ Task at CGRIDID: 8 is successfully dispatched."},{"path":"https://NIEHS.github.io/chopin/reference/par_hierarchy.html","id":null,"dir":"Reference","previous_headings":"","what":"Parallelize spatial computation by hierarchy in input data — par_hierarchy","title":"Parallelize spatial computation by hierarchy in input data — par_hierarchy","text":"\"Hierarchy\" refers system, divides entire study region multiple subregions. oftentimes reflected area code system (e.g., FIPS US Census geographies Nomenclature Territorial Units Statistics (NUTS), etc.). future::multisession, future::multicore, future::cluster, future.mirai::mirai_multisession future::plan parallelize work splitting lower level features several higher level feature group. details terminology future package, please refer future::plan documentation. thread process number lower level features higher level feature. Please advised accessing file simultaneously multiple processes may result errors.","code":""},{"path":"https://NIEHS.github.io/chopin/reference/par_hierarchy.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Parallelize spatial computation by hierarchy in input data — par_hierarchy","text":"","code":"par_hierarchy(   regions,   regions_id = NULL,   length_left = NULL,   pad = 0,   pad_y = FALSE,   fun_dist,   ...,   .debug = FALSE )"},{"path":"https://NIEHS.github.io/chopin/reference/par_hierarchy.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Parallelize spatial computation by hierarchy in input data — par_hierarchy","text":"regions sf/SpatVector object. Computational regions. polygons accepted. regions_id character(1). Name unique ID field regions. regions split common level value. length_left integer(1). Length first characters regions_id values. Default NULL, manipulate regions_id values. number characters consistent (example, numerics), function alert user. pad numeric(1). Padding distance subregion defined regions_id trimmed regions_id values. linear unit coordinate system. Default 0, means subregion used . value greater 0, subregion buffered value. padding distance applied x (pad_y = FALSE) y (pad_y = TRUE) filter data. pad_y logical(1). Whether filter y padded grid. TRUE x values calculated. Default FALSE. reverse case, like terra::extent exactextractr::exact_extract, raster (x) scoped padded grid. fun_dist sf, terra, chopin functions. function x y arguments. ... Arguments passed argument fun_dist. .debug logical(1). Default FALSE unit computation fails, error message regions_id value error occurred included output.","code":""},{"path":"https://NIEHS.github.io/chopin/reference/par_hierarchy.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Parallelize spatial computation by hierarchy in input data — par_hierarchy","text":"data.frame object computation results. entries results, consult function used fun_dist argument.","code":""},{"path":"https://NIEHS.github.io/chopin/reference/par_hierarchy.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Parallelize spatial computation by hierarchy in input data — par_hierarchy","text":"dynamic dots (...), fun_dist arguments include x y sf/terra class objects file paths accepted. Hierarchy interpreted regions_id argument first. regions_id assumed field name x y argument object. expected regions represents higher level boundaries x y fun_dist lower level boundaries. However, case, trim argument, function generate higher level codes regions_id extracting left-t Whether x y searched determined pad_y value. pad_y = TRUE make function attempt find regions_id x, whereas pad_y = FALSE look regions_id y. regions_id exist x y, function utilize spatial relationship (intersects) filter data. Note dispatching computation subregions based spatial relationship may lead slight discrepancy result. example, higher lower level features perfectly aligned, may features included duplicated subregions. function alert user spatial relation- ship used filter data.","code":""},{"path":"https://NIEHS.github.io/chopin/reference/par_hierarchy.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Parallelize spatial computation by hierarchy in input data — par_hierarchy","text":"Virtually sf/terra functions accept two arguments can put fun_dist, please advised spatial operations necessarily give exact result done single thread. example, distance calculated function may return lower value actual computational region reduced. case especially target features spatially sparsely distributed.","code":""},{"path":[]},{"path":"https://NIEHS.github.io/chopin/reference/par_hierarchy.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Parallelize spatial computation by hierarchy in input data — par_hierarchy","text":"Insang Song geoissong@gmail.com","code":""},{"path":"https://NIEHS.github.io/chopin/reference/par_hierarchy.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Parallelize spatial computation by hierarchy in input data — par_hierarchy","text":"","code":"library(terra) #> terra 1.7.78 library(sf) library(future) library(future.mirai) options(sf_use_s2 = FALSE) future::plan(future.mirai::mirai_multisession, workers = 2)  ncpath <- system.file(\"extdata/nc_hierarchy.gpkg\", package = \"chopin\") nccnty <- sf::st_read(ncpath, layer = \"county\") #> Reading layer `county' from data source  #>   `/home/runner/work/_temp/Library/chopin/extdata/nc_hierarchy.gpkg'  #>   using driver `GPKG' #> Simple feature collection with 100 features and 1 field #> Geometry type: POLYGON #> Dimension:     XY #> Bounding box:  xmin: 1054155 ymin: 1341756 xmax: 1838923 ymax: 1690176 #> Projected CRS: NAD83 / Conus Albers nctrct <- sf::st_read(ncpath, layer = \"tracts\") #> Reading layer `tracts' from data source  #>   `/home/runner/work/_temp/Library/chopin/extdata/nc_hierarchy.gpkg'  #>   using driver `GPKG' #> Simple feature collection with 2672 features and 1 field #> Geometry type: MULTIPOLYGON #> Dimension:     XY #> Bounding box:  xmin: 1054155 ymin: 1341756 xmax: 1838923 ymax: 1690176 #> Projected CRS: NAD83 / Conus Albers ncelev <-   system.file(\"extdata/nc_srtm15_otm.tif\", package = \"chopin\")  ncsamp <-   sf::st_sample(     nccnty,     size = 1e4L   ) # sfc to sf ncsamp <- sf::st_as_sf(ncsamp) # assign ID ncsamp$kid <- sprintf(\"K-%05d\", seq_len(nrow(ncsamp))) res <-   par_hierarchy(     regions = nccnty,     regions_id = \"GEOID\",     fun_dist = extract_at,     y = nctrct,     x = ncelev,     id = \"GEOID\",     func = \"mean\"   ) #> ℹ Input is not a character. #> ℹ GEOID is used to stratify the process. #> Input is a character. Attempt to read it with terra::rast... #> ℹ Your input function at 37037 is dispatched. #> Input is a character. Attempt to read it with terra::rast... #> ℹ Your input function at 37001 is dispatched. #> Input is a character. Attempt to read it with terra::rast... #> ℹ Your input function at 37057 is dispatched. #> Input is a character. Attempt to read it with terra::rast... #> ℹ Your input function at 37069 is dispatched. #> Input is a character. Attempt to read it with terra::rast... #> ℹ Your input function at 37155 is dispatched. #> Input is a character. Attempt to read it with terra::rast... #> ℹ Your input function at 37109 is dispatched. #> Input is a character. Attempt to read it with terra::rast... #> ℹ Your input function at 37027 is dispatched. #> Input is a character. Attempt to read it with terra::rast... #> ℹ Your input function at 37063 is dispatched. #> Input is a character. Attempt to read it with terra::rast... #> ℹ Your input function at 37145 is dispatched. #> Input is a character. Attempt to read it with terra::rast... #> ℹ Your input function at 37115 is dispatched. #> Input is a character. Attempt to read it with terra::rast... #> ℹ Your input function at 37151 is dispatched. #> Input is a character. Attempt to read it with terra::rast... #> ℹ Your input function at 37131 is dispatched. #> Input is a character. Attempt to read it with terra::rast... #> ℹ Your input function at 37013 is dispatched. #> Input is a character. Attempt to read it with terra::rast... #> ℹ Your input function at 37159 is dispatched. #> Input is a character. Attempt to read it with terra::rast... #> ℹ Your input function at 37051 is dispatched. #> Input is a character. Attempt to read it with terra::rast... #> ℹ Your input function at 37153 is dispatched. #> Input is a character. Attempt to read it with terra::rast... #> ℹ Your input function at 37093 is dispatched. #> Input is a character. Attempt to read it with terra::rast... #> ℹ Your input function at 37025 is dispatched. #> Input is a character. Attempt to read it with terra::rast... #> ℹ Your input function at 37029 is dispatched. #> Input is a character. Attempt to read it with terra::rast... #> ℹ Your input function at 37169 is dispatched. #> Input is a character. Attempt to read it with terra::rast... #> ℹ Your input function at 37031 is dispatched. #> Input is a character. Attempt to read it with terra::rast... #> ℹ Your input function at 37005 is dispatched. #> Input is a character. Attempt to read it with terra::rast... #> ℹ Your input function at 37139 is dispatched. #> Input is a character. Attempt to read it with terra::rast... #> ℹ Your input function at 37193 is dispatched. #> Input is a character. Attempt to read it with terra::rast... #> ℹ Your input function at 37003 is dispatched. #> Input is a character. Attempt to read it with terra::rast... #> ℹ Your input function at 37083 is dispatched. #> Input is a character. Attempt to read it with terra::rast... #> ℹ Your input function at 37163 is dispatched. #> Input is a character. Attempt to read it with terra::rast... #> ℹ Your input function at 37189 is dispatched. #> Input is a character. Attempt to read it with terra::rast... #> ℹ Your input function at 37173 is dispatched. #> Input is a character. Attempt to read it with terra::rast... #> ℹ Your input function at 37011 is dispatched. #> Input is a character. Attempt to read it with terra::rast... #> ℹ Your input function at 37045 is dispatched. #> Input is a character. Attempt to read it with terra::rast... #> ℹ Your input function at 37125 is dispatched. #> Input is a character. Attempt to read it with terra::rast... #> ℹ Your input function at 37067 is dispatched. #> Input is a character. Attempt to read it with terra::rast... #> ℹ Your input function at 37077 is dispatched. #> Input is a character. Attempt to read it with terra::rast... #> ℹ Your input function at 37185 is dispatched. #> Input is a character. Attempt to read it with terra::rast... #> ℹ Your input function at 37137 is dispatched. #> Input is a character. Attempt to read it with terra::rast... #> ℹ Your input function at 37033 is dispatched. #> Input is a character. Attempt to read it with terra::rast... #> ℹ Your input function at 37107 is dispatched. #> Input is a character. Attempt to read it with terra::rast... #> ℹ Your input function at 37075 is dispatched. #> Input is a character. Attempt to read it with terra::rast... #> ℹ Your input function at 37073 is dispatched. #> Input is a character. Attempt to read it with terra::rast... #> ℹ Your input function at 37161 is dispatched. #> Input is a character. Attempt to read it with terra::rast... #> ℹ Your input function at 37187 is dispatched. #> Input is a character. Attempt to read it with terra::rast... #> ℹ Your input function at 37007 is dispatched. #> Input is a character. Attempt to read it with terra::rast... #> ℹ Your input function at 37135 is dispatched. #> Input is a character. Attempt to read it with terra::rast... #> ℹ Your input function at 37049 is dispatched. #> Input is a character. Attempt to read it with terra::rast... #> ℹ Your input function at 37195 is dispatched. #> Input is a character. Attempt to read it with terra::rast... #> ℹ Your input function at 37061 is dispatched. #> Input is a character. Attempt to read it with terra::rast... #> ℹ Your input function at 37087 is dispatched. #> Input is a character. Attempt to read it with terra::rast... #> ℹ Your input function at 37081 is dispatched. #> Input is a character. Attempt to read it with terra::rast... #> ℹ Your input function at 37099 is dispatched. #> Input is a character. Attempt to read it with terra::rast... #> ℹ Your input function at 37097 is dispatched. #> Input is a character. Attempt to read it with terra::rast... #> ℹ Your input function at 37091 is dispatched. #> Input is a character. Attempt to read it with terra::rast... #> ℹ Your input function at 37149 is dispatched. #> Input is a character. Attempt to read it with terra::rast... #> ℹ Your input function at 37165 is dispatched. #> Input is a character. Attempt to read it with terra::rast... #> ℹ Your input function at 37085 is dispatched. #> Input is a character. Attempt to read it with terra::rast... #> ℹ Your input function at 37105 is dispatched. #> Input is a character. Attempt to read it with terra::rast... #> ℹ Your input function at 37017 is dispatched. #> Input is a character. Attempt to read it with terra::rast... #> ℹ Your input function at 37039 is dispatched. #> Input is a character. Attempt to read it with terra::rast... #> ℹ Your input function at 37035 is dispatched. #> Input is a character. Attempt to read it with terra::rast... #> ℹ Your input function at 37177 is dispatched. #> Input is a character. Attempt to read it with terra::rast... #> ℹ Your input function at 37113 is dispatched. #> Input is a character. Attempt to read it with terra::rast... #> ℹ Your input function at 37143 is dispatched. #> Input is a character. Attempt to read it with terra::rast... #> ℹ Your input function at 37095 is dispatched. #> Input is a character. Attempt to read it with terra::rast... #> ℹ Your input function at 37071 is dispatched. #> Input is a character. Attempt to read it with terra::rast... #> ℹ Your input function at 37101 is dispatched. #> Input is a character. Attempt to read it with terra::rast... #> ℹ Your input function at 37015 is dispatched. #> Input is a character. Attempt to read it with terra::rast... #> ℹ Your input function at 37167 is dispatched. #> Input is a character. Attempt to read it with terra::rast... #> ℹ Your input function at 37079 is dispatched. #> Input is a character. Attempt to read it with terra::rast... #> ℹ Your input function at 37129 is dispatched. #> Input is a character. Attempt to read it with terra::rast... #> ℹ Your input function at 37147 is dispatched. #> Input is a character. Attempt to read it with terra::rast... #> ℹ Your input function at 37141 is dispatched. #> Input is a character. Attempt to read it with terra::rast... #> ℹ Your input function at 37179 is dispatched. #> Input is a character. Attempt to read it with terra::rast... #> ℹ Your input function at 37121 is dispatched. #> Input is a character. Attempt to read it with terra::rast... #> ℹ Your input function at 37133 is dispatched. #> Input is a character. Attempt to read it with terra::rast... #> ℹ Your input function at 37065 is dispatched. #> Input is a character. Attempt to read it with terra::rast... #> ℹ Your input function at 37119 is dispatched. #> Input is a character. Attempt to read it with terra::rast... #> ℹ Your input function at 37199 is dispatched. #> Input is a character. Attempt to read it with terra::rast... #> ℹ Your input function at 37197 is dispatched. #> Input is a character. Attempt to read it with terra::rast... #> ℹ Your input function at 37023 is dispatched. #> Input is a character. Attempt to read it with terra::rast... #> ℹ Your input function at 37191 is dispatched. #> Input is a character. Attempt to read it with terra::rast... #> ℹ Your input function at 37059 is dispatched. #> Input is a character. Attempt to read it with terra::rast... #> ℹ Your input function at 37111 is dispatched. #> Input is a character. Attempt to read it with terra::rast... #> ℹ Your input function at 37183 is dispatched. #> Input is a character. Attempt to read it with terra::rast... #> ℹ Your input function at 37053 is dispatched. #> Input is a character. Attempt to read it with terra::rast... #> ℹ Your input function at 37103 is dispatched. #> Input is a character. Attempt to read it with terra::rast... #> ℹ Your input function at 37041 is dispatched. #> Input is a character. Attempt to read it with terra::rast... #> ℹ Your input function at 37021 is dispatched. #> Input is a character. Attempt to read it with terra::rast... #> ℹ Your input function at 37157 is dispatched. #> Input is a character. Attempt to read it with terra::rast... #> ℹ Your input function at 37117 is dispatched. #> Input is a character. Attempt to read it with terra::rast... #> ℹ Your input function at 37089 is dispatched. #> Input is a character. Attempt to read it with terra::rast... #> ℹ Your input function at 37127 is dispatched. #> Input is a character. Attempt to read it with terra::rast... #> ℹ Your input function at 37009 is dispatched. #> Input is a character. Attempt to read it with terra::rast... #> ℹ Your input function at 37019 is dispatched. #> Input is a character. Attempt to read it with terra::rast... #> ℹ Your input function at 37123 is dispatched. #> Input is a character. Attempt to read it with terra::rast... #> ℹ Your input function at 37181 is dispatched. #> Input is a character. Attempt to read it with terra::rast... #> ℹ Your input function at 37175 is dispatched. #> Input is a character. Attempt to read it with terra::rast... #> ℹ Your input function at 37171 is dispatched. #> Input is a character. Attempt to read it with terra::rast... #> ℹ Your input function at 37043 is dispatched. #> Input is a character. Attempt to read it with terra::rast... #> ℹ Your input function at 37055 is dispatched. #> Input is a character. Attempt to read it with terra::rast... #> ℹ Your input function at 37047 is dispatched."},{"path":"https://NIEHS.github.io/chopin/reference/par_make_balanced.html","id":null,"dir":"Reference","previous_headings":"","what":"Generate groups based on balanced clustering — par_make_balanced","title":"Generate groups based on balanced clustering — par_make_balanced","text":"balancing computational loads, function uses anticlust package cluster input points. number clusters determined num_cluster argument. cluster equal number points. Grids generated based cluster extents. lower level, function uses terra::distance() function calculate Euclidean distance points.","code":""},{"path":"https://NIEHS.github.io/chopin/reference/par_make_balanced.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generate groups based on balanced clustering — par_make_balanced","text":"","code":"par_make_balanced(points_in = NULL, n_clusters = NULL)"},{"path":"https://NIEHS.github.io/chopin/reference/par_make_balanced.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generate groups based on balanced clustering — par_make_balanced","text":"points_in sf SpatVector object. Target points computation. n_clusters integer(1). number clusters.","code":""},{"path":"https://NIEHS.github.io/chopin/reference/par_make_balanced.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Generate groups based on balanced clustering — par_make_balanced","text":"SpatVector object field \"CGRIDID\".","code":""},{"path":"https://NIEHS.github.io/chopin/reference/par_make_balanced.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Generate groups based on balanced clustering — par_make_balanced","text":"function two-dimensional points. results irregular grids without overlapping parts.","code":""},{"path":"https://NIEHS.github.io/chopin/reference/par_make_balanced.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Generate groups based on balanced clustering — par_make_balanced","text":"Insang Song","code":""},{"path":"https://NIEHS.github.io/chopin/reference/par_make_balanced.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Generate groups based on balanced clustering — par_make_balanced","text":"","code":"if (FALSE) { # \\dontrun{ library(terra) library(anticlust) data(ncpoints, package = \"chopin\")  ncp <- terra::vect(   ncpoints, geom = c(\"X\", \"Y\"),   keepgeom = FALSE, crs = \"EPSG:5070\" )  # 2,304 points / 12 = 192 points per cluster ncpbal <- par_make_balanced(ncp, 12) ncpbal } # }"},{"path":"https://NIEHS.github.io/chopin/reference/par_make_grid.html","id":null,"dir":"Reference","previous_headings":"","what":"Generate grid polygons — par_make_grid","title":"Generate grid polygons — par_make_grid","text":"Returns sf object includes x- y- index using two inputs ncutsx ncutsy, x- y-directional splits, respectively.","code":""},{"path":"https://NIEHS.github.io/chopin/reference/par_make_grid.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generate grid polygons — par_make_grid","text":"","code":"par_make_grid(points_in = NULL, ncutsx = NULL, ncutsy = NULL)"},{"path":"https://NIEHS.github.io/chopin/reference/par_make_grid.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generate grid polygons — par_make_grid","text":"points_in sf SpatVector object. Target points computation. character(1) file path also acceptable. ncutsx integer(1). number splits along x-axis. ncutsy integer(1). number splits along y-axis.","code":""},{"path":"https://NIEHS.github.io/chopin/reference/par_make_grid.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Generate grid polygons — par_make_grid","text":"sf SpatVector object computation grids unique grid id (CGRIDID).","code":""},{"path":"https://NIEHS.github.io/chopin/reference/par_make_grid.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Generate grid polygons — par_make_grid","text":"Grids generated based extent points_in first, exhaustive grids filtered intersection points_in. Thus, number generated grids may smaller ncutsx * ncutsy.","code":""},{"path":[]},{"path":"https://NIEHS.github.io/chopin/reference/par_make_grid.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Generate grid polygons — par_make_grid","text":"Insang Song","code":""},{"path":"https://NIEHS.github.io/chopin/reference/par_make_grid.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Generate grid polygons — par_make_grid","text":"","code":"if (FALSE) { # \\dontrun{ library(sf) library(terra) options(sf_use_s2 = FALSE)  nc_path <- system.file(\"gpkg/nc.gpkg\", package = \"sf\") nc <- terra::vect(nc_path) nc_rp <- terra::spatSample(nc, 1000) nc_gr <- par_make_grid(nc_rp, 10L, 6L)  plot(nc_rp) plot(nc_gr, add = TRUE) } # }"},{"path":"https://NIEHS.github.io/chopin/reference/par_map_args.html","id":null,"dir":"Reference","previous_headings":"","what":"Map arguments to the desired names — par_map_args","title":"Map arguments to the desired names — par_map_args","text":"function maps arguments target function desired names. Users use named list name_match standardize argument names, least x y, target function. function particularly useful parallelize functions spatial data outside sf terra packages arguments named x /y. par_* functions detect functions wrapping nonstandardized functions parallelize computation.","code":""},{"path":"https://NIEHS.github.io/chopin/reference/par_map_args.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Map arguments to the desired names — par_map_args","text":"","code":"par_map_args(fun, name_match = list(), ...)"},{"path":"https://NIEHS.github.io/chopin/reference/par_map_args.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Map arguments to the desired names — par_map_args","text":"fun target function called. name_match named list arguments mapped. ... Arguments passed target function.","code":""},{"path":"https://NIEHS.github.io/chopin/reference/par_map_args.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Map arguments to the desired names — par_map_args","text":"result target function.","code":""},{"path":[]},{"path":"https://NIEHS.github.io/chopin/reference/par_map_args.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Map arguments to the desired names — par_map_args","text":"","code":"# Example target function example_fun <- function(x, y, z = 1) {   return(c(x = x, y = y, z = z)) }  # Example usage of map_args_xy result <- par_map_args(fun = example_fun,                        name_match = list(a = \"x\", b = \"y\"),                        a = 10, b = 20, z = 5) print(result) #>  x  y  z  #> 10 20  5"},{"path":"https://NIEHS.github.io/chopin/reference/par_merge_grid.html","id":null,"dir":"Reference","previous_headings":"","what":"Merge adjacent grid polygons with given rules — par_merge_grid","title":"Merge adjacent grid polygons with given rules — par_merge_grid","text":"Merge boundary-sharing (\"Rook\" contiguity) grids fewer target features threshold. function strongly assumes input returned par_make_grid, \"CGRIDID\" unique id field.","code":""},{"path":"https://NIEHS.github.io/chopin/reference/par_merge_grid.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Merge adjacent grid polygons with given rules — par_merge_grid","text":"","code":"par_merge_grid(   points_in = NULL,   grid_in = NULL,   grid_min_features = NULL,   merge_max = 4L )"},{"path":"https://NIEHS.github.io/chopin/reference/par_merge_grid.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Merge adjacent grid polygons with given rules — par_merge_grid","text":"points_in sf SpatVector object. Target points computation. grid_in sf SpatVector object. grid generated internal function par_make_grid. grid_min_features integer(1). Threshold merge adjacent grids. merge_max integer(1). Maximum number grids merge per merged set. Default 4. example, number grids merge 20 merge_max 10, function split 20 grids two sets 10 grids.","code":""},{"path":"https://NIEHS.github.io/chopin/reference/par_merge_grid.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Merge adjacent grid polygons with given rules — par_merge_grid","text":"sf SpatVector object computation grids.","code":""},{"path":"https://NIEHS.github.io/chopin/reference/par_merge_grid.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Merge adjacent grid polygons with given rules — par_merge_grid","text":"function work properly grid_in one million grids.","code":""},{"path":"https://NIEHS.github.io/chopin/reference/par_merge_grid.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Merge adjacent grid polygons with given rules — par_merge_grid","text":"Polsby DD, Popper FJ. (1991). Third Criterion: Compactness Procedural Safeguard Partisan Gerrymandering. Yale Law & Policy Review, 9(2), 301–353.","code":""},{"path":[]},{"path":"https://NIEHS.github.io/chopin/reference/par_merge_grid.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Merge adjacent grid polygons with given rules — par_merge_grid","text":"Insang Song","code":""},{"path":"https://NIEHS.github.io/chopin/reference/par_merge_grid.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Merge adjacent grid polygons with given rules — par_merge_grid","text":"","code":"library(sf) library(igraph) #>  #> Attaching package: ‘igraph’ #> The following objects are masked from ‘package:terra’: #>  #>     blocks, compare, union #> The following objects are masked from ‘package:future’: #>  #>     %->%, %<-% #> The following objects are masked from ‘package:stats’: #>  #>     decompose, spectrum #> The following object is masked from ‘package:base’: #>  #>     union library(dplyr) #>  #> Attaching package: ‘dplyr’ #> The following objects are masked from ‘package:igraph’: #>  #>     as_data_frame, groups, union #> The following objects are masked from ‘package:terra’: #>  #>     intersect, union #> The following objects are masked from ‘package:stats’: #>  #>     filter, lag #> The following objects are masked from ‘package:base’: #>  #>     intersect, setdiff, setequal, union library(spatstat.random) #> Loading required package: spatstat.data #> Loading required package: spatstat.univar #> spatstat.univar 3.0-0 #> Loading required package: spatstat.geom #> spatstat.geom 3.3-2 #>  #> Attaching package: ‘spatstat.geom’ #> The following objects are masked from ‘package:igraph’: #>  #>     diameter, edges, is.connected, vertices #> The following objects are masked from ‘package:terra’: #>  #>     area, delaunay, is.empty, rescale, rotate, shift, where.max, #>     where.min #> spatstat.random 3.3-1 options(sf_use_s2 = FALSE)  dg <- sf::st_as_sfc(st_bbox(c(xmin = 0, ymin = 0, xmax = 8e5, ymax = 6e5))) sf::st_crs(dg) <- 5070 dgs <- sf::st_as_sf(st_make_grid(dg, n = c(20, 15))) dgs$CGRIDID <- seq(1, nrow(dgs))  dg_sample <- sf::st_sample(dg, kappa = 5e-9, mu = 15, scale = 15000, type = \"Thomas\") sf::st_crs(dg_sample) <- sf::st_crs(dg) dg_merged <- par_merge_grid(sf::st_as_sf(dg_sample), dgs, 100) #> ℹ The merged polygons have too complex shapes. #> Increase threshold or use the original grids.  plot(sf::st_geometry(dg_merged))"},{"path":"https://NIEHS.github.io/chopin/reference/par_multirasters.html","id":null,"dir":"Reference","previous_headings":"","what":"Parallelize spatial computation over multiple raster files — par_multirasters","title":"Parallelize spatial computation over multiple raster files — par_multirasters","text":"Large raster files usually exceed memory capacity size. function can helpful process heterogenous raster files homogeneous summary functions. Heterogenous raster files refer rasters different spatial extents resolutions. Cropping large raster small subset even consumes lot memory adds processing time. function leverages terra SpatRaster distribute computation jobs multiple threads. assumed users multiple large raster files disk, file path assigned thread. thread directly read raster values disk using C++ pointers operate terra functions. use, strongly recommended use vector data small confined spatial extent computation avoid --memory error. y argument fun_dist used -. means preprocessing subsetting applied. Please aware spatial extent size inputs.","code":""},{"path":"https://NIEHS.github.io/chopin/reference/par_multirasters.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Parallelize spatial computation over multiple raster files — par_multirasters","text":"","code":"par_multirasters(filenames, fun_dist, ..., .debug = FALSE)"},{"path":"https://NIEHS.github.io/chopin/reference/par_multirasters.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Parallelize spatial computation over multiple raster files — par_multirasters","text":"filenames character. vector list full file paths raster files. n total number raster files. fun_dist terra chopin functions accept SpatRaster object argument. particular, x y arguments present x SpatRaster. ... Arguments passed argument fun_dist. .debug logical(1). Default FALSE. TRUE unit computation fails, error message file path error occurred included output.","code":""},{"path":"https://NIEHS.github.io/chopin/reference/par_multirasters.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Parallelize spatial computation over multiple raster files — par_multirasters","text":"data.frame object computation results. entries results, consult function used fun_dist argument.","code":""},{"path":[]},{"path":"https://NIEHS.github.io/chopin/reference/par_multirasters.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Parallelize spatial computation over multiple raster files — par_multirasters","text":"Insang Song geoissong@gmail.com","code":""},{"path":"https://NIEHS.github.io/chopin/reference/par_multirasters.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Parallelize spatial computation over multiple raster files — par_multirasters","text":"","code":"library(terra) library(sf) library(future) library(future.mirai) sf::sf_use_s2(FALSE) future::plan(future.mirai::mirai_multisession, workers = 2)  ncpath <- system.file(\"extdata/nc_hierarchy.gpkg\", package = \"chopin\") nccnty <- sf::st_read(ncpath, layer = \"county\") #> Reading layer `county' from data source  #>   `/home/runner/work/_temp/Library/chopin/extdata/nc_hierarchy.gpkg'  #>   using driver `GPKG' #> Simple feature collection with 100 features and 1 field #> Geometry type: POLYGON #> Dimension:     XY #> Bounding box:  xmin: 1054155 ymin: 1341756 xmax: 1838923 ymax: 1690176 #> Projected CRS: NAD83 / Conus Albers ncelev <-   system.file(\"extdata/nc_srtm15_otm.tif\", package = \"chopin\") ncelevras <- terra::rast(ncelev)  tdir <- tempdir(check = TRUE) terra::writeRaster(ncelevras, file.path(tdir, \"test1.tif\"), overwrite = TRUE) terra::writeRaster(ncelevras, file.path(tdir, \"test2.tif\"), overwrite = TRUE) testfiles <- list.files(tdir, pattern = \"tif$\", full.names = TRUE)  res <- par_multirasters(   filenames = testfiles,   fun_dist = extract_at,   x = ncelev,   y = nccnty,   id = \"GEOID\",   func = \"mean\" ) #> ℹ Input is not a character. #> Input is a character. Attempt to read it with terra::rast... #> ℹ Your input function at /tmp/Rtmpc1MfKT/test1.tif is dispatched. #> Input is a character. Attempt to read it with terra::rast... #> ℹ Your input function at /tmp/Rtmpc1MfKT/test2.tif is dispatched."},{"path":"https://NIEHS.github.io/chopin/reference/par_pad_balanced.html","id":null,"dir":"Reference","previous_headings":"","what":"Extension of par_make_balanced for padded grids — par_pad_balanced","title":"Extension of par_make_balanced for padded grids — par_pad_balanced","text":"function extension par_make_balanced compatible par_make_grid, set padded grids extent input point subsets (recorded field named \"CGRIDID\") generated input points along output par_make_balanced.","code":""},{"path":"https://NIEHS.github.io/chopin/reference/par_pad_balanced.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extension of par_make_balanced for padded grids — par_pad_balanced","text":"","code":"par_pad_balanced(points_in = NULL, ngroups, padding)"},{"path":"https://NIEHS.github.io/chopin/reference/par_pad_balanced.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extension of par_make_balanced for padded grids — par_pad_balanced","text":"points_in sf SpatVector object. Point geometries. ngroups integer(1). number groups. padding numeric(1). extrusion factor make buffer clip actual datasets. Depending length unit CRS input.","code":""},{"path":"https://NIEHS.github.io/chopin/reference/par_pad_balanced.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Extension of par_make_balanced for padded grids — par_pad_balanced","text":"list two, original: exhaustive non-overlapping grid polygons class input padded: square buffer polygon original. Used computation.","code":""},{"path":[]},{"path":"https://NIEHS.github.io/chopin/reference/par_pad_balanced.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Extension of par_make_balanced for padded grids — par_pad_balanced","text":"Insang Song","code":""},{"path":"https://NIEHS.github.io/chopin/reference/par_pad_balanced.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Extension of par_make_balanced for padded grids — par_pad_balanced","text":"","code":"library(terra) library(sf) options(sf_use_s2 = FALSE)  ncpath <- system.file(\"gpkg/nc.gpkg\", package = \"sf\") nc <- terra::vect(ncpath) nc_rp <- terra::spatSample(nc, 1000)  nc_gr <- par_pad_balanced(nc_rp, 10L, 1000) nc_gr #> $original #>  class       : SpatVector  #>  geometry    : points  #>  dimensions  : 1000, 15  (geometries, attributes) #>  extent      : -84.24941, -75.79698, 33.88802, 36.55712  (xmin, xmax, ymin, ymax) #>  coord. ref. : lon/lat NAD27 (EPSG:4267)  #>  names       :  AREA PERIMETER CNTY_ CNTY_ID        NAME  FIPS    FIPSNO #>  type        : <num>     <num> <num>   <num>       <chr> <chr>     <num> #>  values      : 0.078     1.202  2056    2056      Graham 37075 3.708e+04 #>                0.125     2.868  2156    2156    Carteret 37031 3.703e+04 #>                0.153     2.206  1832    1832 Northampton 37131 3.713e+04 #>  CRESS_ID BIR74 SID74 (and 5 more) #>     <int> <num> <num>              #>        38   415     0              #>        16  2414     5              #>        66  1421     9              #>  #> $padded #>  class       : SpatVector  #>  geometry    : polygons  #>  dimensions  : 10, 1  (geometries, attributes) #>  extent      : -1084.249, 924.203, -966.112, 1036.557  (xmin, xmax, ymin, ymax) #>  coord. ref. :   #>  names       : CGRIDID #>  type        :   <num> #>  values      :       1 #>                      2 #>                      3 #>"},{"path":"https://NIEHS.github.io/chopin/reference/par_pad_grid.html","id":null,"dir":"Reference","previous_headings":"","what":"Get a set of computational grids — par_pad_grid","title":"Get a set of computational grids — par_pad_grid","text":"Using input points, bounding box split predefined numbers columns rows. grid buffered radius.","code":""},{"path":"https://NIEHS.github.io/chopin/reference/par_pad_grid.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get a set of computational grids — par_pad_grid","text":"","code":"par_pad_grid(   input,   mode = c(\"grid\", \"grid_advanced\", \"grid_quantile\"),   nx = 10L,   ny = 10L,   grid_min_features = 30L,   padding = NULL,   unit = NULL,   quantiles = NULL,   merge_max = NULL,   return_wkt = FALSE,   ... )"},{"path":"https://NIEHS.github.io/chopin/reference/par_pad_grid.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get a set of computational grids — par_pad_grid","text":"input sf Spat* object. mode character(1). Mode region construction. One \"grid\" (simple grid regardless number features grid) \"grid_advanced\" (merging adjacent grids smaller number features grid_min_features). argument grid_min_features specified. \"grid_quantile\" (x y quantiles): argument quantiles specified. nx integer(1). number grids along x-axis. ny integer(1). number grids along y-axis. grid_min_features integer(1). threshold merging adjacent grids padding numeric(1). extrusion factor make buffer clip actual datasets. Depending length unit CRS input. unit character(1). length unit padding (optional). units::set_units used padding sf object used. See link list acceptable unit forms. quantiles numeric. Quantiles grid_quantile mode. merge_max integer(1). Maximum number grids merge per merged set. return_wkt logical(1). Return WKT format. TRUE, return value list two WKT strings. ... arguments passed internal function","code":""},{"path":"https://NIEHS.github.io/chopin/reference/par_pad_grid.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get a set of computational grids — par_pad_grid","text":"list two, original: exhaustive (filling completely) non-overlapping grid polygons class input padded: square buffer polygon original. Used computation.","code":""},{"path":[]},{"path":"https://NIEHS.github.io/chopin/reference/par_pad_grid.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Get a set of computational grids — par_pad_grid","text":"Insang Song","code":""},{"path":"https://NIEHS.github.io/chopin/reference/par_pad_grid.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Get a set of computational grids — par_pad_grid","text":"","code":"# data library(sf) options(sf_use_s2 = FALSE) ncpath <- system.file(\"shape/nc.shp\", package = \"sf\") nc <- read_sf(ncpath) nc <- st_transform(nc, \"EPSG:5070\")  # run: nx and ny should strictly be integers nc_comp_region <-   par_pad_grid(     nc,     mode = \"grid\",     nx = 4L, ny = 2L,     padding = 10000) #> Switch sf class to terra... #> Switch terra class to sf... par(mfcol = c(1, 2)) plot(nc_comp_region$original$geometry) plot(nc_comp_region$padded$geometry)   nc_comp_region_wkt <-   par_pad_grid(     nc,     mode = \"grid\",     nx = 4L, ny = 2L,     padding = 10000,     return_wkt = TRUE) #> Switch sf class to terra... #> Switch terra class to sf... nc_comp_region_wkt$original #> [1] \"POLYGON ((1054293 1348025, 1249094 1348025, 1249094 1518630, 1054293 1518630, 1054293 1348025))\" #> [2] \"POLYGON ((1249094 1348025, 1443896 1348025, 1443896 1518630, 1249094 1518630, 1249094 1348025))\" #> [3] \"POLYGON ((1443896 1348025, 1638697 1348025, 1638697 1518630, 1443896 1518630, 1443896 1348025))\" #> [4] \"POLYGON ((1638697 1348025, 1833499 1348025, 1833499 1518630, 1638697 1518630, 1638697 1348025))\" #> [5] \"POLYGON ((1054293 1518630, 1249094 1518630, 1249094 1689236, 1054293 1689236, 1054293 1518630))\" #> [6] \"POLYGON ((1249094 1518630, 1443896 1518630, 1443896 1689236, 1249094 1689236, 1249094 1518630))\" #> [7] \"POLYGON ((1443896 1518630, 1638697 1518630, 1638697 1689236, 1443896 1689236, 1443896 1518630))\" #> [8] \"POLYGON ((1638697 1518630, 1833499 1518630, 1833499 1689236, 1638697 1689236, 1638697 1518630))\" nc_comp_region_wkt$padded #> [1] \"POLYGON ((1044293 1338025, 1044293 1528630, 1259094 1528630, 1259094 1338025, 1044293 1338025))\" #> [2] \"POLYGON ((1239094 1338025, 1239094 1528630, 1453896 1528630, 1453896 1338025, 1239094 1338025))\" #> [3] \"POLYGON ((1433896 1338025, 1433896 1528630, 1648697 1528630, 1648697 1338025, 1433896 1338025))\" #> [4] \"POLYGON ((1628697 1338025, 1628697 1528630, 1843499 1528630, 1843499 1338025, 1628697 1338025))\" #> [5] \"POLYGON ((1044293 1508630, 1044293 1699236, 1259094 1699236, 1259094 1508630, 1044293 1508630))\" #> [6] \"POLYGON ((1239094 1508630, 1239094 1699236, 1453896 1699236, 1453896 1508630, 1239094 1508630))\" #> [7] \"POLYGON ((1433896 1508630, 1433896 1699236, 1648697 1699236, 1648697 1508630, 1433896 1508630))\" #> [8] \"POLYGON ((1628697 1508630, 1628697 1699236, 1843499 1699236, 1843499 1508630, 1628697 1508630))\""},{"path":"https://NIEHS.github.io/chopin/reference/par_split_list.html","id":null,"dir":"Reference","previous_headings":"","what":"Split grid list to a nested list of row-wise data frames — par_split_list","title":"Split grid list to a nested list of row-wise data frames — par_split_list","text":"Split grid list nested list row-wise data frames","code":""},{"path":"https://NIEHS.github.io/chopin/reference/par_split_list.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Split grid list to a nested list of row-wise data frames — par_split_list","text":"","code":"par_split_list(gridlist)"},{"path":"https://NIEHS.github.io/chopin/reference/par_split_list.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Split grid list to a nested list of row-wise data frames — par_split_list","text":"gridlist list. Output par_pad_grid par_pad_balanced","code":""},{"path":"https://NIEHS.github.io/chopin/reference/par_split_list.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Split grid list to a nested list of row-wise data frames — par_split_list","text":"nested list data frames WKT strings.","code":""},{"path":"https://NIEHS.github.io/chopin/reference/par_split_list.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Split grid list to a nested list of row-wise data frames — par_split_list","text":"input data frame, function return list two data frames: original padded. input WKT vector, function return list two WKT strings: original padded.","code":""},{"path":[]},{"path":"https://NIEHS.github.io/chopin/reference/par_split_list.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Split grid list to a nested list of row-wise data frames — par_split_list","text":"","code":"library(sf) library(terra) options(sf_use_s2 = FALSE)  ncpath <- system.file(\"shape/nc.shp\", package = \"sf\") nc <- read_sf(ncpath) nc <- st_transform(nc, \"EPSG:5070\") nc_comp_region <-   par_pad_grid(     nc,     mode = \"grid\",     nx = 4L, ny = 2L,     padding = 10000) #> Switch sf class to terra... #> Switch terra class to sf... par_split_list(nc_comp_region) #> [[1]] #> [[1]]$original #> Simple feature collection with 1 feature and 1 field #> Geometry type: POLYGON #> Dimension:     XY #> Bounding box:  xmin: 1054293 ymin: 1348025 xmax: 1249094 ymax: 1518630 #> Projected CRS: NAD83 / Conus Albers #>                         geometry CGRIDID #> 1 POLYGON ((1054293 1348025, ...       1 #>  #> [[1]]$padded #> Simple feature collection with 1 feature and 1 field #> Geometry type: POLYGON #> Dimension:     XY #> Bounding box:  xmin: 1044293 ymin: 1338025 xmax: 1259094 ymax: 1528630 #> Projected CRS: NAD83 / Conus Albers #>   CGRIDID                       geometry #> 1       1 POLYGON ((1044293 1338025, ... #>  #>  #> [[2]] #> [[2]]$original #> Simple feature collection with 1 feature and 1 field #> Geometry type: POLYGON #> Dimension:     XY #> Bounding box:  xmin: 1249094 ymin: 1348025 xmax: 1443896 ymax: 1518630 #> Projected CRS: NAD83 / Conus Albers #>                         geometry CGRIDID #> 2 POLYGON ((1249094 1348025, ...       2 #>  #> [[2]]$padded #> Simple feature collection with 1 feature and 1 field #> Geometry type: POLYGON #> Dimension:     XY #> Bounding box:  xmin: 1239094 ymin: 1338025 xmax: 1453896 ymax: 1528630 #> Projected CRS: NAD83 / Conus Albers #>   CGRIDID                       geometry #> 2       2 POLYGON ((1239094 1338025, ... #>  #>  #> [[3]] #> [[3]]$original #> Simple feature collection with 1 feature and 1 field #> Geometry type: POLYGON #> Dimension:     XY #> Bounding box:  xmin: 1443896 ymin: 1348025 xmax: 1638697 ymax: 1518630 #> Projected CRS: NAD83 / Conus Albers #>                         geometry CGRIDID #> 3 POLYGON ((1443896 1348025, ...       3 #>  #> [[3]]$padded #> Simple feature collection with 1 feature and 1 field #> Geometry type: POLYGON #> Dimension:     XY #> Bounding box:  xmin: 1433896 ymin: 1338025 xmax: 1648697 ymax: 1528630 #> Projected CRS: NAD83 / Conus Albers #>   CGRIDID                       geometry #> 3       3 POLYGON ((1433896 1338025, ... #>  #>  #> [[4]] #> [[4]]$original #> Simple feature collection with 1 feature and 1 field #> Geometry type: POLYGON #> Dimension:     XY #> Bounding box:  xmin: 1638697 ymin: 1348025 xmax: 1833499 ymax: 1518630 #> Projected CRS: NAD83 / Conus Albers #>                         geometry CGRIDID #> 4 POLYGON ((1638697 1348025, ...       4 #>  #> [[4]]$padded #> Simple feature collection with 1 feature and 1 field #> Geometry type: POLYGON #> Dimension:     XY #> Bounding box:  xmin: 1628697 ymin: 1338025 xmax: 1843499 ymax: 1528630 #> Projected CRS: NAD83 / Conus Albers #>   CGRIDID                       geometry #> 4       4 POLYGON ((1628697 1338025, ... #>  #>  #> [[5]] #> [[5]]$original #> Simple feature collection with 1 feature and 1 field #> Geometry type: POLYGON #> Dimension:     XY #> Bounding box:  xmin: 1054293 ymin: 1518630 xmax: 1249094 ymax: 1689236 #> Projected CRS: NAD83 / Conus Albers #>                         geometry CGRIDID #> 5 POLYGON ((1054293 1518630, ...       5 #>  #> [[5]]$padded #> Simple feature collection with 1 feature and 1 field #> Geometry type: POLYGON #> Dimension:     XY #> Bounding box:  xmin: 1044293 ymin: 1508630 xmax: 1259094 ymax: 1699236 #> Projected CRS: NAD83 / Conus Albers #>   CGRIDID                       geometry #> 5       5 POLYGON ((1044293 1508630, ... #>  #>  #> [[6]] #> [[6]]$original #> Simple feature collection with 1 feature and 1 field #> Geometry type: POLYGON #> Dimension:     XY #> Bounding box:  xmin: 1249094 ymin: 1518630 xmax: 1443896 ymax: 1689236 #> Projected CRS: NAD83 / Conus Albers #>                         geometry CGRIDID #> 6 POLYGON ((1249094 1518630, ...       6 #>  #> [[6]]$padded #> Simple feature collection with 1 feature and 1 field #> Geometry type: POLYGON #> Dimension:     XY #> Bounding box:  xmin: 1239094 ymin: 1508630 xmax: 1453896 ymax: 1699236 #> Projected CRS: NAD83 / Conus Albers #>   CGRIDID                       geometry #> 6       6 POLYGON ((1239094 1508630, ... #>  #>  #> [[7]] #> [[7]]$original #> Simple feature collection with 1 feature and 1 field #> Geometry type: POLYGON #> Dimension:     XY #> Bounding box:  xmin: 1443896 ymin: 1518630 xmax: 1638697 ymax: 1689236 #> Projected CRS: NAD83 / Conus Albers #>                         geometry CGRIDID #> 7 POLYGON ((1443896 1518630, ...       7 #>  #> [[7]]$padded #> Simple feature collection with 1 feature and 1 field #> Geometry type: POLYGON #> Dimension:     XY #> Bounding box:  xmin: 1433896 ymin: 1508630 xmax: 1648697 ymax: 1699236 #> Projected CRS: NAD83 / Conus Albers #>   CGRIDID                       geometry #> 7       7 POLYGON ((1433896 1508630, ... #>  #>  #> [[8]] #> [[8]]$original #> Simple feature collection with 1 feature and 1 field #> Geometry type: POLYGON #> Dimension:     XY #> Bounding box:  xmin: 1638697 ymin: 1518630 xmax: 1833499 ymax: 1689236 #> Projected CRS: NAD83 / Conus Albers #>                         geometry CGRIDID #> 8 POLYGON ((1638697 1518630, ...       8 #>  #> [[8]]$padded #> Simple feature collection with 1 feature and 1 field #> Geometry type: POLYGON #> Dimension:     XY #> Bounding box:  xmin: 1628697 ymin: 1508630 xmax: 1843499 ymax: 1699236 #> Projected CRS: NAD83 / Conus Albers #>   CGRIDID                       geometry #> 8       8 POLYGON ((1628697 1508630, ... #>  #>"},{"path":"https://NIEHS.github.io/chopin/reference/prediction_grid.html","id":null,"dir":"Reference","previous_headings":"","what":"Regular grid points in the mainland United States at 1km spatial resolution — prediction_grid","title":"Regular grid points in the mainland United States at 1km spatial resolution — prediction_grid","text":"Regular grid points mainland United States 1km spatial resolution","code":""},{"path":"https://NIEHS.github.io/chopin/reference/prediction_grid.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Regular grid points in the mainland United States at 1km spatial resolution — prediction_grid","text":"","code":"prediction_grid"},{"path":"https://NIEHS.github.io/chopin/reference/prediction_grid.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Regular grid points in the mainland United States at 1km spatial resolution — prediction_grid","text":"data frame 8,092,995 rows three variables: site_id Unique point identifier. Arbitrarily generated. lon Longitude lat Latitude","code":""},{"path":"https://NIEHS.github.io/chopin/reference/prediction_grid.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Regular grid points in the mainland United States at 1km spatial resolution — prediction_grid","text":"Mainland United States polygon obtained US Census Bureau.","code":""},{"path":"https://NIEHS.github.io/chopin/reference/prediction_grid.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Regular grid points in the mainland United States at 1km spatial resolution — prediction_grid","text":"Coordinates EPSG:5070 (Conus Albers Equal Area)","code":""},{"path":[]},{"path":"https://NIEHS.github.io/chopin/reference/prediction_grid.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Regular grid points in the mainland United States at 1km spatial resolution — prediction_grid","text":"","code":"data(\"prediction_grid\", package = \"chopin\")"},{"path":"https://NIEHS.github.io/chopin/reference/reproject_std.html","id":null,"dir":"Reference","previous_headings":"","what":"Check coordinate system then reproject — reproject_std","title":"Check coordinate system then reproject — reproject_std","text":"input checked whether coordinate system present. , reprojected CRS specified crs_standard.","code":""},{"path":"https://NIEHS.github.io/chopin/reference/reproject_std.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Check coordinate system then reproject — reproject_std","text":"","code":"reproject_std(input, crs_standard = \"EPSG:4326\")"},{"path":"https://NIEHS.github.io/chopin/reference/reproject_std.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Check coordinate system then reproject — reproject_std","text":"input Input object one sf terra::Spat* object crs_standard character(1). standard definition coordinate reference system. Default \"EPSG:4326\" Consult epsg.io details CRS.","code":""},{"path":"https://NIEHS.github.io/chopin/reference/reproject_std.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Check coordinate system then reproject — reproject_std","text":"(reprojected) sf SpatVector object.","code":""},{"path":"https://NIEHS.github.io/chopin/reference/reproject_std.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Check coordinate system then reproject — reproject_std","text":"function works well EPSG codes.","code":""},{"path":[]},{"path":"https://NIEHS.github.io/chopin/reference/reproject_std.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Check coordinate system then reproject — reproject_std","text":"Insang Song","code":""},{"path":"https://NIEHS.github.io/chopin/reference/reproject_std.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Check coordinate system then reproject — reproject_std","text":"","code":"if (FALSE) { # \\dontrun{ library(sf) library(terra) options(sf_use_s2 = FALSE)  base_crs <- \"EPSG:5070\" nc_path <- system.file(\"gpkg/nc.gpkg\", package = \"sf\") nc_sf <- sf::st_read(nc_path) reproject_std(nc_sf, base_crs)  nc_vect <- terra::vect(nc_sf) reproject_std(nc_vect, base_crs) } # }"},{"path":"https://NIEHS.github.io/chopin/reference/reproject_to_raster.html","id":null,"dir":"Reference","previous_headings":"","what":"Align vector CRS to raster's — reproject_to_raster","title":"Align vector CRS to raster's — reproject_to_raster","text":"Align vector CRS raster's","code":""},{"path":"https://NIEHS.github.io/chopin/reference/reproject_to_raster.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Align vector CRS to raster's — reproject_to_raster","text":"","code":"reproject_to_raster(vector = NULL, raster = NULL)"},{"path":"https://NIEHS.github.io/chopin/reference/reproject_to_raster.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Align vector CRS to raster's — reproject_to_raster","text":"vector sf/stars/SpatVector/SpatRaster object raster SpatRaster object","code":""},{"path":"https://NIEHS.github.io/chopin/reference/reproject_to_raster.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Align vector CRS to raster's — reproject_to_raster","text":"Reprojected object class vector","code":""},{"path":[]},{"path":"https://NIEHS.github.io/chopin/reference/reproject_to_raster.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Align vector CRS to raster's — reproject_to_raster","text":"Insang Song","code":""},{"path":"https://NIEHS.github.io/chopin/reference/reproject_to_raster.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Align vector CRS to raster's — reproject_to_raster","text":"","code":"if (FALSE) { # \\dontrun{ library(terra) library(sf) options(sf_use_s2 = FALSE)  ncpath <- system.file(\"gpkg/nc.gpkg\", package = \"sf\") elev <- system.file(\"ex/elev.tif\", package = \"terra\") nc <- terra::vect(ncpath) elev <- terra::rast(elev) reproject_to_raster(nc, elev) } # }"},{"path":"https://NIEHS.github.io/chopin/reference/summarize_aw.html","id":null,"dir":"Reference","previous_headings":"","what":"Area weighted summary using two polygon objects — summarize_aw","title":"Area weighted summary using two polygon objects — summarize_aw","text":"x y different classes, poly_weight converted class x.","code":""},{"path":"https://NIEHS.github.io/chopin/reference/summarize_aw.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Area weighted summary using two polygon objects — summarize_aw","text":"","code":"summarize_aw(x, y, ...)  # S4 method for class 'SpatVector,SpatVector' summarize_aw(   x,   y,   target_fields = NULL,   id_x = \"ID\",   fun = stats::weighted.mean,   extent = NULL,   ... )  # S4 method for class 'character,character' summarize_aw(   x,   y,   target_fields = NULL,   id_x = \"ID\",   fun = stats::weighted.mean,   out_class = \"terra\",   extent = NULL,   ... )  # S4 method for class 'sf,sf' summarize_aw(   x,   y,   target_fields = NULL,   id_x = \"ID\",   fun = NULL,   extent = NULL,   ... )"},{"path":"https://NIEHS.github.io/chopin/reference/summarize_aw.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Area weighted summary using two polygon objects — summarize_aw","text":"x sf/SpatVector object file path polygons detectable GDAL driver weighted means calculated. y sf/SpatVector object file path polygons weighted means calculated. ... Placeholder. target_fields character. Field names calculate area-weighted. id_x character(1). unique identifier polygon x. Default \"ID\". fun function(1)/character(1). function calculate weighted summary. Default stats::weighted.mean. function must w argument. x y sf, one c(\"sum\", \"mean\"). determine extensive argument sf::st_interpolate_aw. extent numeric(4) SpatExtent object. Extent clipping x. works x character(1) file path. See terra::ext details. Coordinate systems match. out_class character(1). \"sf\" \"terra\". Output class.","code":""},{"path":"https://NIEHS.github.io/chopin/reference/summarize_aw.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Area weighted summary using two polygon objects — summarize_aw","text":"data.frame numeric fields area-weighted means.","code":""},{"path":"https://NIEHS.github.io/chopin/reference/summarize_aw.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Area weighted summary using two polygon objects — summarize_aw","text":"x y classes match. x y characters, read sf objects.","code":""},{"path":[]},{"path":"https://NIEHS.github.io/chopin/reference/summarize_aw.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Area weighted summary using two polygon objects — summarize_aw","text":"Insang Song geoissong@gmail.com","code":""},{"path":"https://NIEHS.github.io/chopin/reference/summarize_aw.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Area weighted summary using two polygon objects — summarize_aw","text":"","code":"# package library(sf) options(sf_use_s2 = FALSE) nc <- sf::st_read(system.file(\"shape/nc.shp\", package=\"sf\")) #> Reading layer `nc' from data source  #>   `/home/runner/work/_temp/Library/sf/shape/nc.shp' using driver `ESRI Shapefile' #> Simple feature collection with 100 features and 14 fields #> Geometry type: MULTIPOLYGON #> Dimension:     XY #> Bounding box:  xmin: -84.32385 ymin: 33.88199 xmax: -75.45698 ymax: 36.58965 #> Geodetic CRS:  NAD27 nc <- sf::st_transform(nc, 5070) pp <- sf::st_sample(nc, size = 300) pp <- sf::st_as_sf(pp) pp[[\"id\"]] <- seq(1, nrow(pp)) sf::st_crs(pp) <- \"EPSG:5070\" ppb <- sf::st_buffer(pp, nQuadSegs=180, dist = units::set_units(20, \"km\"))  system.time(   ppb_nc_aw <-     summarize_aw(       ppb, nc, c(\"BIR74\", \"BIR79\"),       \"id\", fun = \"sum\"     ) ) #> Warning: st_interpolate_aw assumes attributes are constant or uniform over areas of x #>    user  system elapsed  #>   0.367   0.000   0.366  summary(ppb_nc_aw) #>      BIR74             BIR79                  geometry   #>  Min.   :  159.6   Min.   :  244.7   POLYGON      :300   #>  1st Qu.: 1278.6   1st Qu.: 1568.8   epsg:5070    :  0   #>  Median : 2231.9   Median : 2724.9   +proj=aea ...:  0   #>  Mean   : 3000.0   Mean   : 3832.5                       #>  3rd Qu.: 3821.2   3rd Qu.: 4872.5                       #>  Max.   :16384.9   Max.   :23159.3                        # terra examples library(terra) ncpath <- system.file(\"gpkg/nc.gpkg\", package = \"sf\") elev <- system.file(\"ex/elev.tif\", package = \"terra\") nc <- terra::vect(ncpath) elev <- terra::rast(elev) pp <- terra::spatSample(nc, size = 300) pp <- terra::project(pp, crs(elev)) pp <- terra::as.points(pp) #> Warning: [as.points] returning a copy pp[[\"id\"]] <- seq(1, nrow(pp)) ppb <- terra::buffer(pp, 20000)  system.time(   ppb_nc_aw <-     summarize_aw(       ppb, nc, c(\"BIR74\", \"BIR79\"), \"id\",       fun = sum     ) ) #> Warning: [intersect] different crs #>    user  system elapsed  #>   0.086   0.000   0.086  summary(ppb_nc_aw) #>        id             BIR74               BIR79           #>  Min.   :  1.00   Min.   :2.453e+08   Min.   :2.453e+08   #>  1st Qu.: 75.75   1st Qu.:1.171e+09   1st Qu.:1.171e+09   #>  Median :150.50   Median :1.251e+09   Median :1.251e+09   #>  Mean   :150.50   Mean   :1.169e+09   Mean   :1.169e+09   #>  3rd Qu.:225.25   3rd Qu.:1.251e+09   3rd Qu.:1.251e+09   #>  Max.   :300.00   Max.   :1.252e+09   Max.   :1.252e+09"},{"path":"https://NIEHS.github.io/chopin/reference/summarize_sedc.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate Sum of Exponentially Decaying Contributions (SEDC) covariates — summarize_sedc","title":"Calculate Sum of Exponentially Decaying Contributions (SEDC) covariates — summarize_sedc","text":"Calculate Sum Exponentially Decaying Contributions (SEDC) covariates","code":""},{"path":"https://NIEHS.github.io/chopin/reference/summarize_sedc.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate Sum of Exponentially Decaying Contributions (SEDC) covariates — summarize_sedc","text":"","code":"summarize_sedc(   point_from = NULL,   point_to = NULL,   id = NULL,   sedc_bandwidth = NULL,   threshold = NULL,   target_fields = NULL,   extent_from = NULL,   extent_to = NULL,   ... )"},{"path":"https://NIEHS.github.io/chopin/reference/summarize_sedc.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate Sum of Exponentially Decaying Contributions (SEDC) covariates — summarize_sedc","text":"point_from SpatVector object. Locations sum SEDCs calculated. point_to SpatVector object. Locations SEDC calculated. id character(1). Name unique id field point_to. sedc_bandwidth numeric(1). Distance source concentration reduced exp(-3) (approximately -95 %) threshold numeric(1). computational efficiency, nearest points threshold selected. 2 * sedc_bandwidth applied value remains NULL. target_fields character. Field names calculate SEDC. extent_from numeric(4) SpatExtent. Extent clipping point_from. works point_from character(1) file path. See terra::ext details. Coordinate systems match. extent_to numeric(4) SpatExtent. Extent clipping point_to. ... Placeholder.","code":""},{"path":"https://NIEHS.github.io/chopin/reference/summarize_sedc.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate Sum of Exponentially Decaying Contributions (SEDC) covariates — summarize_sedc","text":"data.frame object input field names suffix \"_sedc\" sums EDC stored. Additional attributes attached EDC information. attr(result, \"sedc_bandwidth\"): bandwidth concentration reduces approximately five percent attr(result, \"sedc_threshold\"): threshold distance emission source points excluded beyond ","code":""},{"path":"https://NIEHS.github.io/chopin/reference/summarize_sedc.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Calculate Sum of Exponentially Decaying Contributions (SEDC) covariates — summarize_sedc","text":"SEDC specialized vector vector summary covariates exponential decay. Decaying slope defined sedc_bandwidth, concentration source reduced $\\exp(-3)$ (approximately 5 \\ attenuating concentration distance sources. can thought fixed bandwidth kernel weighted sum covariates, encapsulates three steps: Calculate distance source target points. Calculate weight source point exponential decay. Summarize weighted covariates.","code":""},{"path":"https://NIEHS.github.io/chopin/reference/summarize_sedc.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Calculate Sum of Exponentially Decaying Contributions (SEDC) covariates — summarize_sedc","text":"Distance calculation done terra functions internally. Thus, function internally converts sf objects point_* arguments terra. Please note NA values input ignored SEDC calculation.","code":""},{"path":"https://NIEHS.github.io/chopin/reference/summarize_sedc.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Calculate Sum of Exponentially Decaying Contributions (SEDC) covariates — summarize_sedc","text":"Messier KP, Akita Y, Serre ML. (2012). Integrating Address Geocoding, Land Use Regression, Spatiotemporal Geostatistical Estimation Groundwater Tetrachloroethylene. Environmental Science & Technology 46(5), 2772-2780. Wiesner C. (n.d.). Euclidean Sum Exponentially Decaying Contributions Tutorial.","code":""},{"path":[]},{"path":"https://NIEHS.github.io/chopin/reference/summarize_sedc.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Calculate Sum of Exponentially Decaying Contributions (SEDC) covariates — summarize_sedc","text":"Insang Song","code":""},{"path":"https://NIEHS.github.io/chopin/reference/summarize_sedc.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calculate Sum of Exponentially Decaying Contributions (SEDC) covariates — summarize_sedc","text":"","code":"library(terra) library(sf) set.seed(101) ncpath <- system.file(\"gpkg/nc.gpkg\", package = \"sf\") nc <- terra::vect(ncpath) nc <- terra::project(nc, \"EPSG:5070\") pnt_from <- terra::centroids(nc, inside = TRUE) pnt_from <- pnt_from[, \"NAME\"] pnt_to <- terra::spatSample(nc, 100L) pnt_to$pid <- seq(1, 100) pnt_to <- pnt_to[, \"pid\"] pnt_to$val1 <- rgamma(100L, 1, 0.05) pnt_to$val2 <- rgamma(100L, 2, 1)  vals <- c(\"val1\", \"val2\") summarize_sedc(pnt_from, pnt_to, \"NAME\", 1e5, 2e5, vals) #> # A tibble: 100 × 3 #>    NAME      val1_sedc val2_sedc #>    <chr>         <dbl>     <dbl> #>  1 Alamance       66.3      6.71 #>  2 Alexander      98.0     11.7  #>  3 Alleghany     135.       8.57 #>  4 Anson          50.0      5.23 #>  5 Ashe          143.       8.42 #>  6 Avery          76.1      8.76 #>  7 Beaufort       58.8      6.29 #>  8 Bertie         70.1      7.85 #>  9 Bladen         79.5      6.82 #> 10 Brunswick      54.3      5.34 #> # ℹ 90 more rows"},{"path":"https://NIEHS.github.io/chopin/reference/vect_validate.html","id":null,"dir":"Reference","previous_headings":"","what":"Validate and repair input vector data — vect_validate","title":"Validate and repair input vector data — vect_validate","text":"tries repairing input vector data. Vector validity violation usually appears polygon data self-crossing hole orders. function pass input_vector object sf::st_make_valid (input_vector sf) terra::makeValid (input_vector SpatVector). May take time depending geometry complexity.","code":""},{"path":"https://NIEHS.github.io/chopin/reference/vect_validate.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Validate and repair input vector data — vect_validate","text":"","code":"vect_validate(input_vector)"},{"path":"https://NIEHS.github.io/chopin/reference/vect_validate.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Validate and repair input vector data — vect_validate","text":"input_vector One sf vect class. Target points computation.","code":""},{"path":"https://NIEHS.github.io/chopin/reference/vect_validate.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Validate and repair input vector data — vect_validate","text":"repaired sf SpatVector object depending class input_vector.","code":""},{"path":"https://NIEHS.github.io/chopin/reference/vect_validate.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Validate and repair input vector data — vect_validate","text":"function works GEOS (>=3.8).","code":""},{"path":[]},{"path":"https://NIEHS.github.io/chopin/reference/vect_validate.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Validate and repair input vector data — vect_validate","text":"Insang Song","code":""},{"path":"https://NIEHS.github.io/chopin/reference/vect_validate.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Validate and repair input vector data — vect_validate","text":"","code":"if (FALSE) { # \\dontrun{ library(terra) library(sf) ncpath <- system.file(\"gpkg/nc.gpkg\", package = \"sf\") nc <- terra::vect(ncpath)  nc_valid <- vect_validate(nc) } # }"}]