From cc42132a3265f0e5e9216dd326869c8505eb158b Mon Sep 17 00:00:00 2001
From: Nic Crane <thisisnic@gmail.com>
Date: Sat, 10 Aug 2024 22:23:31 +0100
Subject: [PATCH 1/2] Updates from the plane

---
 materials/1_hello_arrow.qmd                 |   2 +-
 materials/4_data_manipulation_2.qmd         |  95 +++++++++++++++++++-
 materials/7_continue_learning.qmd           |   6 ++
 materials/images/arrow-r-pkg-highlights.png | Bin 0 -> 339155 bytes
 materials/images/dummybookcover.png         | Bin 0 -> 33512 bytes
 5 files changed, 101 insertions(+), 2 deletions(-)
 create mode 100644 materials/images/arrow-r-pkg-highlights.png
 create mode 100644 materials/images/dummybookcover.png

diff --git a/materials/1_hello_arrow.qmd b/materials/1_hello_arrow.qmd
index b22557c..9954dba 100644
--- a/materials/1_hello_arrow.qmd
+++ b/materials/1_hello_arrow.qmd
@@ -210,7 +210,7 @@ The contiguous columnar layout enables vectorization using the latest SIMD (Sing
 
 <br>
 
-![](images/arrow-r-pkg.png){.absolute top="0" left="300" width="700" height="900"}
+![](images/arrow-r-pkg-highlights.png){.absolute top="0" left="300" width="700" height="900"}
 
 ## arrow 📦
 
diff --git a/materials/4_data_manipulation_2.qmd b/materials/4_data_manipulation_2.qmd
index 015e7a2..6350610 100644
--- a/materials/4_data_manipulation_2.qmd
+++ b/materials/4_data_manipulation_2.qmd
@@ -38,6 +38,11 @@ nyc_taxi <- open_dataset("data/nyc-taxi/")
 * Implement your own custom functions
 * Run in R not Arrow
 
+<!-- 
+In arrow 17.0.0 there will be less need to do this for custom functions
+
+-->
+
 ## How do function bindings usually work?
 
 ![](images/udf_normal.png)
@@ -66,13 +71,29 @@ nyc_taxi |>
 ```
 We get an error as we can't automatically convert the function to arrow.
 
+<!-- 
+* why did this happen?
+* Look at ?acero and show how difftime is implemented but only supports units= "secs"
+
+-->
+
 # User-defined functions (aka UDFs)
 
 -   Define your own functions
 -   Scalar functions - 1 row input and 1 row output
 
+<!-- 
+* Instead of having an Acero binding, Acero calls back to R to work out the result
+* Naturally slower than working entirely in Acero as now we don't have vectorisation, or any of the Arrow C++ optimisations
+* But, ultimately makes it possible
+-->
+
 ## User-defined functions - definition
 
+<!-- 
+show slide don't type
+-->
+
 ```{r}
 #| label: udf-define-1
 register_scalar_function(
@@ -93,6 +114,13 @@ register_scalar_function(
 
 This looks complicated, so let's look at it 1 part at a time!
 
+<!-- 
+UDFs *are* ugly!
+And that's because when we have bindings, we rely on the arrow R package's internals to handle all the complicated stuff like working out the types of the values.
+But it's not as bad as it looks, so we'll break it down!
+
+-->
+
 ## User-defined functions - definition
 
 Step 1. Give the function a name
@@ -115,6 +143,21 @@ register_scalar_function(
 )
 ```
 
+<!--
+show ?register_scalar_function help file
+
+set up the skeleton (i.e. register_scalar_function) and add in components 1 at a time
+
+name = some name to call it
+
+fun = the body of the function. first argument must be called "context" and you don't do anything with it - it'll be used by Acero to pass some metadata between Arrow and R. after that, define other arguments, and the function body like you otherwise would.
+
+in_type and out_type = so usually we don't have to worry about data types as the bindings do that for us, but now we're working more closely with Acero's internals, we need to do more things manually, so we have to tell it the data type of the input and output columns
+
+The `auto_convert` argument controls whether arrow automatically converts the inputs to R vectors or not.
+The default value `FALSE` can be used when working with R6 objects, but as we're working in an arrow dplyr pipeline here, we'll set it to `TRUE`.
+-->
+
 ## User-defined functions - definition
 
 Step 2. Define the body of the function - first argument *must* be `context`
@@ -216,6 +259,10 @@ nyc_taxi |>
   collect()
 ```
 
+<!-- 
+So, we call `register_scalar_function()` to create it, and then then when we run our pipeline it appears there
+-->
+
 ## Your Turn
 
 1.  Write a user-defined function which wraps the `stringr` function `str_replace_na()`, and use it to replace any `NA` values in the `vendor_name` column with the string "No vendor" instead. (Test it on the data from 2019 so you're not pulling everything into memory)
@@ -234,6 +281,18 @@ nyc_taxi |>
 
 ![](images/joins.png)
 
+<!-- 
+* combining data from different sources into a single source
+* fairly common to encounter when we have a dataset and some sort of reference table to combine it with
+* in the example here, we've done what's called a left-join:
+  * take everything from the table on the left
+  * decide which column to use for the join
+    * in this case, join vendor_name in the left table to code in the right table
+  * to do the join, goes through each row in the left table and finds the matching value in the right table, but then also brings the corresponding column (or multiple) from the right table
+* there are different type of joins, but the focus here isn't on joins, but is on what you need to know about doing joins in arrow
+* let's start off by writing out the code to do the join from the example
+-->
+
 ## Joining a reference table
 
 ```{r}
@@ -255,6 +314,11 @@ nyc_taxi |>
   collect()
 ```
 
+<!-- 
+* This was pretty straightforward here, and a lot of the time it should just work, but there are some things you should know about
+
+-->
+
 ## Traps for the unwary
 
 Question: which are the most common borough-to-borough journeys in the dataset?
@@ -270,6 +334,12 @@ nyc_taxi_zones <-
 nyc_taxi_zones
 ```
 
+<!-- 
+* the taxi data contains location IDs for the different pickup locations, but what if we want to be more generic and look at the different boroughs that people travel between?
+* one of the data files is a reference table which maps the location IDs to their Borough, and so we can answer this question by joining the reference table to our dataset
+* let's take a look
+-->
+
 ## Why didn't this work?
 
 ```{r}
@@ -281,13 +351,18 @@ nyc_taxi |>
   collect()
 ```
 
+<!-- 
+* The thing to do here feels like, let's do a join again then
+* it's complaining about data types, so let's look at the schema of both
+-->
+
 ## Schema for the `nyc_taxi` Dataset
 
 ```{r}
 #| label: get-schema
 schema(nyc_taxi)
 ```
-
+<!-- In the original data, pickup_location_id is an int64 -->
 ## Schema for the `nyc_taxi_zones` Table
 
 ```{r}
@@ -300,6 +375,14 @@ schema(nyc_taxi_zones_arrow)
 -   `pickup_location_id` is int64 in the `nyc_taxi` table
 -   `location_id` is int32 in the `nyc_taxi_zones` table
 
+<!-- 
+When we do a join here, the nyc_taxi_zones object we read in was a tibble as we collected it in memory.
+Arrow needs 2 Arrow objects to do a join, so behind the scenes, when we do the join, the tibble is converted into an Arrow Table.
+When we convert between R and Arrow objects, Arrow automatically does the type conversion for us.
+That's why here we call `arrow_table()` to see what the type conversion will be, and call schema()
+The thing to note here is that these are different types - and arrow needs for the join key column to be the same type. So what we need to do is manually specify the schema.
+-->
+
 ## Take control of the schema
 
 ```{r}
@@ -314,6 +397,11 @@ nyc_taxi_zones_arrow <- arrow_table(
 -   `schema()` takes variable name / types as input
 -   arrow has various "type" functions: `int64()`, `utf8()`, `boolean()`, `date32()` etc
 
+<!-- 
+This time we manually convert the data frame into a table and set the schema
+Alternatively, we could specify it when we read the data in
+-->
+
 ## Take control of the schema
 
 ```{r}
@@ -326,6 +414,11 @@ nyc_taxi_zones_arrow <- arrow_table(
 schema(nyc_taxi_zones_arrow)
 ```
 
+<!-- 
+Now we can see the schema matches so we should be able to run our analysis
+Let's do 1 final thing to answer our borough to borough question!
+-->
+
 ## Prepare the auxiliary tables
 
 ```{r}
diff --git a/materials/7_continue_learning.qmd b/materials/7_continue_learning.qmd
index 9911292..b6f91c5 100644
--- a/materials/7_continue_learning.qmd
+++ b/materials/7_continue_learning.qmd
@@ -12,6 +12,12 @@ editor: source
 
 # Continue Your Arrow Learning Journey {#more-learning}
 
+## Book
+
+<https://arrowrbook.com>
+
+![](images/dummybookcover.png)
+
 ## Docs
 
 ::: {style="font-size: 80%;"}
diff --git a/materials/images/arrow-r-pkg-highlights.png b/materials/images/arrow-r-pkg-highlights.png
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From 78feeb28626f37b60ae0e1f898152b074af5f3c3 Mon Sep 17 00:00:00 2001
From: Nic Crane <thisisnic@gmail.com>
Date: Sun, 11 Aug 2024 18:17:43 -0700
Subject: [PATCH 2/2] Updates

---
 .../0_housekeeping/execute-results/html.json  |    8 +-
 .../1_hello_arrow/execute-results/html.json   |    4 +-
 .../execute-results/html.json                 |    8 +-
 .../execute-results/html.json                 |    8 +-
 .../execute-results/html.json                 |    8 +-
 .../site_libs/revealjs/dist/theme/quarto.css  |    4 +-
 _site/index.html                              |   47 +-
 _site/license-web.html                        |   51 +-
 _site/materials/0_housekeeping.html           |   41 +-
 _site/materials/1_hello_arrow-exercises.html  |   53 +-
 _site/materials/1_hello_arrow.html            |   74 +-
 .../2_data_manipulation_1-exercises.html      |   53 +-
 _site/materials/2_data_manipulation_1.html    |  120 +-
 .../3_data_engineering-exercises.html         |   51 +-
 _site/materials/3_data_engineering.html       |   56 +-
 .../4_data_manipulation_2-exercises.html      |   53 +-
 _site/materials/4_data_manipulation_2.html    |  153 ++-
 .../5_arrow_single_file-exercises.html        |   53 +-
 _site/materials/5_arrow_single_file.html      |   50 +-
 _site/materials/6_wrapping_up.html            |   47 +-
 _site/materials/7_continue_learning.html      |   46 +-
 _site/materials/8_closing.html                |   41 +-
 .../images/arrow-r-pkg-highlights.png         |  Bin 0 -> 339155 bytes
 _site/materials/images/arrow-r-pkg.png        |  Bin 386695 -> 0 bytes
 _site/materials/images/dummybookcover.png     |  Bin 0 -> 33512 bytes
 _site/search.json                             | 1064 ++++++++---------
 _site/setup.html                              |   51 +-
 _site/site_libs/bootstrap/bootstrap.min.css   |    4 +-
 .../quarto-syntax-highlighting.css            |    2 +
 _site/site_libs/quarto-html/quarto.js         |   15 +-
 _site/site_libs/quarto-nav/quarto-nav.js      |   36 +
 .../site_libs/quarto-search/quarto-search.js  |    6 +-
 .../site_libs/revealjs/dist/theme/quarto.css  |    4 +-
 materials/0_housekeeping.qmd                  |    2 +-
 materials/1_hello_arrow.qmd                   |   11 -
 materials/2_data_manipulation_1.qmd           |   14 +-
 materials/4_data_manipulation_2.qmd           |    2 +
 37 files changed, 1279 insertions(+), 961 deletions(-)
 create mode 100644 _site/materials/images/arrow-r-pkg-highlights.png
 delete mode 100644 _site/materials/images/arrow-r-pkg.png
 create mode 100644 _site/materials/images/dummybookcover.png

diff --git a/_freeze/materials/0_housekeeping/execute-results/html.json b/_freeze/materials/0_housekeeping/execute-results/html.json
index a8fb9ae..ec8e46f 100644
--- a/_freeze/materials/0_housekeeping/execute-results/html.json
+++ b/_freeze/materials/0_housekeeping/execute-results/html.json
@@ -1,9 +1,11 @@
 {
-  "hash": "c2dd443d69d8b7a42084e265a7b740b7",
+  "hash": "97539e391b1f43d76b304efeb6c963b8",
   "result": {
     "engine": "knitr",
-    "markdown": "---\ntitle: \"Big Data in R with Arrow\"\nsubtitle: \"posit::conf(2024) 1-day workshop\"\nauthor: \"Nic Crane + Steph Hazlitt\"\nfooter: \"[🔗 pos.it/arrow-conf24](https://pos.it/arrow-conf24)\"\nlogo: \"images/logo.png\"\nexecute:\n  echo: true\nformat:\n  revealjs: \n    theme: default\nengine: knitr\neditor: source\n---\n\n\n# Welcome 👋\n\n## \n\n### WiFi `<svg aria-hidden=\"true\" role=\"img\" viewBox=\"0 0 640 512\" style=\"height:1em;width:1.25em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:currentColor;overflow:visible;position:relative;\"><path d=\"M54.2 202.9C123.2 136.7 216.8 96 320 96s196.8 40.7 265.8 106.9c12.8 12.2 33 11.8 45.2-.9s11.8-33-.9-45.2C549.7 79.5 440.4 32 320 32S90.3 79.5 9.8 156.7C-2.9 169-3.3 189.2 8.9 202s32.5 13.2 45.2 .9zM320 256c56.8 0 108.6 21.1 148.2 56c13.3 11.7 33.5 10.4 45.2-2.8s10.4-33.5-2.8-45.2C459.8 219.2 393 192 320 192s-139.8 27.2-190.5 72c-13.3 11.7-14.5 31.9-2.8 45.2s31.9 14.5 45.2 2.8c39.5-34.9 91.3-56 148.2-56zm64 160a64 64 0 1 0 -128 0 64 64 0 1 0 128 0z\"/></svg>`{=html}\n\n-   Username: **Posit Conf 2024**\n-   Password: **conf2024**\n\n<br>\n\n### Workshop `<svg aria-hidden=\"true\" role=\"img\" viewBox=\"0 0 496 512\" style=\"height:1em;width:0.97em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:currentColor;overflow:visible;position:relative;\"><path d=\"M165.9 397.4c0 2-2.3 3.6-5.2 3.6-3.3.3-5.6-1.3-5.6-3.6 0-2 2.3-3.6 5.2-3.6 3-.3 5.6 1.3 5.6 3.6zm-31.1-4.5c-.7 2 1.3 4.3 4.3 4.9 2.6 1 5.6 0 6.2-2s-1.3-4.3-4.3-5.2c-2.6-.7-5.5.3-6.2 2.3zm44.2-1.7c-2.9.7-4.9 2.6-4.6 4.9.3 2 2.9 3.3 5.9 2.6 2.9-.7 4.9-2.6 4.6-4.6-.3-1.9-3-3.2-5.9-2.9zM244.8 8C106.1 8 0 113.3 0 252c0 110.9 69.8 205.8 169.5 239.2 12.8 2.3 17.3-5.6 17.3-12.1 0-6.2-.3-40.4-.3-61.4 0 0-70 15-84.7-29.8 0 0-11.4-29.1-27.8-36.6 0 0-22.9-15.7 1.6-15.4 0 0 24.9 2 38.6 25.8 21.9 38.6 58.6 27.5 72.9 20.9 2.3-16 8.8-27.1 16-33.7-55.9-6.2-112.3-14.3-112.3-110.5 0-27.5 7.6-41.3 23.6-58.9-2.6-6.5-11.1-33.3 2.6-67.9 20.9-6.5 69 27 69 27 20-5.6 41.5-8.5 62.8-8.5s42.8 2.9 62.8 8.5c0 0 48.1-33.6 69-27 13.7 34.7 5.2 61.4 2.6 67.9 16 17.7 25.8 31.5 25.8 58.9 0 96.5-58.9 104.2-114.8 110.5 9.2 7.9 17 22.9 17 46.4 0 33.7-.3 75.4-.3 83.6 0 6.5 4.6 14.4 17.3 12.1C428.2 457.8 496 362.9 496 252 496 113.3 383.5 8 244.8 8zM97.2 352.9c-1.3 1-1 3.3.7 5.2 1.6 1.6 3.9 2.3 5.2 1 1.3-1 1-3.3-.7-5.2-1.6-1.6-3.9-2.3-5.2-1zm-10.8-8.1c-.7 1.3.3 2.9 2.3 3.9 1.6 1 3.6.7 4.3-.7.7-1.3-.3-2.9-2.3-3.9-2-.6-3.6-.3-4.3.7zm32.4 35.6c-1.6 1.3-1 4.3 1.3 6.2 2.3 2.3 5.2 2.6 6.5 1 1.3-1.3.7-4.3-1.3-6.2-2.2-2.3-5.2-2.6-6.5-1zm-11.4-14.7c-1.6 1-1.6 3.6 0 5.9 1.6 2.3 4.3 3.3 5.6 2.3 1.6-1.3 1.6-3.9 0-6.2-1.4-2.3-4-3.3-5.6-2z\"/></svg>`{=html}\n\n-   Website: [pos.it/arrow-conf24](https://pos.it/arrow-conf24)\n-   GitHub: [github.com/posit-conf-2024/arrow](https://github.com/posit-conf-2024/arrow)\n\n## Housekeeping\n\n<br>\n\n### Gender Neutral Bathrooms `<svg aria-hidden=\"true\" role=\"img\" viewBox=\"0 0 320 512\" style=\"height:1em;width:0.62em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:currentColor;overflow:visible;position:relative;\"><path d=\"M160 0a48 48 0 1 1 0 96 48 48 0 1 1 0-96zm8 352V128h6.9c33.7 0 64.9 17.7 82.3 46.6l58.3 97c9.1 15.1 4.2 34.8-10.9 43.9s-34.8 4.2-43.9-10.9L232 256.9V480c0 17.7-14.3 32-32 32s-32-14.3-32-32V352h0zM58.2 182.3c19.9-33.1 55.3-53.5 93.8-54.3V384h0v96c0 17.7-14.3 32-32 32s-32-14.3-32-32V384H70.2c-10.9 0-18.6-10.7-15.2-21.1L93.3 248.1 59.4 304.5c-9.1 15.1-28.8 20-43.9 10.9s-20-28.8-10.9-43.9l53.6-89.2z\"/></svg>`{=html}\n\n-   Located on levels 3, 4, 5, 6 & 7\n\n### Specialty Rooms `<svg aria-hidden=\"true\" role=\"img\" viewBox=\"0 0 448 512\" style=\"height:1em;width:0.88em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:currentColor;overflow:visible;position:relative;\"><path d=\"M301.1 34.8C312.6 40 320 51.4 320 64V448c0 12.6-7.4 24-18.9 29.2s-25 3.1-34.4-5.3L131.8 352H64c-35.3 0-64-28.7-64-64V224c0-35.3 28.7-64 64-64h67.8L266.7 40.1c9.4-8.4 22.9-10.4 34.4-5.3zM412.6 181.5C434.1 199.1 448 225.9 448 256s-13.9 56.9-35.4 74.5c-10.3 8.4-25.4 6.8-33.8-3.5s-6.8-25.4 3.5-33.8C393.1 284.4 400 271 400 256s-6.9-28.4-17.7-37.3c-10.3-8.4-11.8-23.5-3.5-33.8s23.5-11.8 33.8-3.5z\"/></svg>`{=html}\n\n-   Meditation/Prayer Room (503)\n-   Lactation Room (509)\n\n*Available Mon & Tues 7am - 7pm, and Wed 7am - 5pm\n\n\n## Photos\n\n<br>\n\n### Red Lanyards `<svg aria-hidden=\"true\" role=\"img\" viewBox=\"0 0 448 512\" style=\"height:1em;width:0.88em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:currentColor;overflow:visible;position:relative;\"><path d=\"M48 128c-17.7 0-32 14.3-32 32s14.3 32 32 32H400c17.7 0 32-14.3 32-32s-14.3-32-32-32H48zm0 192c-17.7 0-32 14.3-32 32s14.3 32 32 32H400c17.7 0 32-14.3 32-32s-14.3-32-32-32H48z\"/></svg>`{=html}`<svg aria-hidden=\"true\" role=\"img\" viewBox=\"0 0 448 512\" style=\"height:1em;width:0.88em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:currentColor;overflow:visible;position:relative;\"><path d=\"M48 128c-17.7 0-32 14.3-32 32s14.3 32 32 32H400c17.7 0 32-14.3 32-32s-14.3-32-32-32H48zm0 192c-17.7 0-32 14.3-32 32s14.3 32 32 32H400c17.7 0 32-14.3 32-32s-14.3-32-32-32H48z\"/></svg>`{=html} **NO** `<svg aria-hidden=\"true\" role=\"img\" viewBox=\"0 0 512 512\" style=\"height:1em;width:1em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:currentColor;overflow:visible;position:relative;\"><path d=\"M149.1 64.8L138.7 96H64C28.7 96 0 124.7 0 160V416c0 35.3 28.7 64 64 64H448c35.3 0 64-28.7 64-64V160c0-35.3-28.7-64-64-64H373.3L362.9 64.8C356.4 45.2 338.1 32 317.4 32H194.6c-20.7 0-39 13.2-45.5 32.8zM256 192a96 96 0 1 1 0 192 96 96 0 1 1 0-192z\"/></svg>`{=html}\n\n<br>\n\nPlease note everyone’s lanyard colors before taking a photo and respect their choices.\n\n## Code of Conduct\n\n<br>\n\n### `<svg aria-hidden=\"true\" role=\"img\" viewBox=\"0 0 640 512\" style=\"height:1em;width:1.25em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:currentColor;overflow:visible;position:relative;\"><path d=\"M579.8 267.7c56.5-56.5 56.5-148 0-204.5c-50-50-128.8-56.5-186.3-15.4l-1.6 1.1c-14.4 10.3-17.7 30.3-7.4 44.6s30.3 17.7 44.6 7.4l1.6-1.1c32.1-22.9 76-19.3 103.8 8.6c31.5 31.5 31.5 82.5 0 114L422.3 334.8c-31.5 31.5-82.5 31.5-114 0c-27.9-27.9-31.5-71.8-8.6-103.8l1.1-1.6c10.3-14.4 6.9-34.4-7.4-44.6s-34.4-6.9-44.6 7.4l-1.1 1.6C206.5 251.2 213 330 263 380c56.5 56.5 148 56.5 204.5 0L579.8 267.7zM60.2 244.3c-56.5 56.5-56.5 148 0 204.5c50 50 128.8 56.5 186.3 15.4l1.6-1.1c14.4-10.3 17.7-30.3 7.4-44.6s-30.3-17.7-44.6-7.4l-1.6 1.1c-32.1 22.9-76 19.3-103.8-8.6C74 372 74 321 105.5 289.5L217.7 177.2c31.5-31.5 82.5-31.5 114 0c27.9 27.9 31.5 71.8 8.6 103.9l-1.1 1.6c-10.3 14.4-6.9 34.4 7.4 44.6s34.4 6.9 44.6-7.4l1.1-1.6C433.5 260.8 427 182 377 132c-56.5-56.5-148-56.5-204.5 0L60.2 244.3z\"/></svg>`{=html} [posit.co/code-of-conduct/](https://posit.co/code-of-conduct/)\n\n-   Contact any posit::conf staff member, identifiable by their staff t-shirt, or visit the conference general information desk.\n-   Send a message to conf\\@posit.com; event organizers will respond promptly.\n-   Call +1-844-448-1212; this phone number will be monitored for the duration of the event.\n\n## Meet Your Teaching Team `<svg aria-hidden=\"true\" role=\"img\" viewBox=\"0 0 640 512\" style=\"height:1em;width:1.25em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:currentColor;overflow:visible;position:relative;\"><path d=\"M72 88a56 56 0 1 1 112 0A56 56 0 1 1 72 88zM64 245.7C54 256.9 48 271.8 48 288s6 31.1 16 42.3V245.7zm144.4-49.3C178.7 222.7 160 261.2 160 304c0 34.3 12 65.8 32 90.5V416c0 17.7-14.3 32-32 32H96c-17.7 0-32-14.3-32-32V389.2C26.2 371.2 0 332.7 0 288c0-61.9 50.1-112 112-112h32c24 0 46.2 7.5 64.4 20.3zM448 416V394.5c20-24.7 32-56.2 32-90.5c0-42.8-18.7-81.3-48.4-107.7C449.8 183.5 472 176 496 176h32c61.9 0 112 50.1 112 112c0 44.7-26.2 83.2-64 101.2V416c0 17.7-14.3 32-32 32H480c-17.7 0-32-14.3-32-32zm8-328a56 56 0 1 1 112 0A56 56 0 1 1 456 88zM576 245.7v84.7c10-11.3 16-26.1 16-42.3s-6-31.1-16-42.3zM320 32a64 64 0 1 1 0 128 64 64 0 1 1 0-128zM240 304c0 16.2 6 31 16 42.3V261.7c-10 11.3-16 26.1-16 42.3zm144-42.3v84.7c10-11.3 16-26.1 16-42.3s-6-31.1-16-42.3zM448 304c0 44.7-26.2 83.2-64 101.2V448c0 17.7-14.3 32-32 32H288c-17.7 0-32-14.3-32-32V405.2c-37.8-18-64-56.5-64-101.2c0-61.9 50.1-112 112-112h32c61.9 0 112 50.1 112 112z\"/></svg>`{=html}\n\n<br>\n\n### Co-Instructors\n\n-   Nic Crane\n-   Steph Hazlitt\n\n### Teaching Assistant\n\n-   Jonathan Keane\n\n## Meet Each Other `<svg aria-hidden=\"true\" role=\"img\" viewBox=\"0 0 640 512\" style=\"height:1em;width:1.25em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:currentColor;overflow:visible;position:relative;\"><path d=\"M272.2 64.6l-51.1 51.1c-15.3 4.2-29.5 11.9-41.5 22.5L153 161.9C142.8 171 129.5 176 115.8 176H96V304c20.4 .6 39.8 8.9 54.3 23.4l35.6 35.6 7 7 0 0L219.9 397c6.2 6.2 16.4 6.2 22.6 0c1.7-1.7 3-3.7 3.7-5.8c2.8-7.7 9.3-13.5 17.3-15.3s16.4 .6 22.2 6.5L296.5 393c11.6 11.6 30.4 11.6 41.9 0c5.4-5.4 8.3-12.3 8.6-19.4c.4-8.8 5.6-16.6 13.6-20.4s17.3-3 24.4 2.1c9.4 6.7 22.5 5.8 30.9-2.6c9.4-9.4 9.4-24.6 0-33.9L340.1 243l-35.8 33c-27.3 25.2-69.2 25.6-97 .9c-31.7-28.2-32.4-77.4-1.6-106.5l70.1-66.2C303.2 78.4 339.4 64 377.1 64c36.1 0 71 13.3 97.9 37.2L505.1 128H544h40 40c8.8 0 16 7.2 16 16V352c0 17.7-14.3 32-32 32H576c-11.8 0-22.2-6.4-27.7-16H463.4c-3.4 6.7-7.9 13.1-13.5 18.7c-17.1 17.1-40.8 23.8-63 20.1c-3.6 7.3-8.5 14.1-14.6 20.2c-27.3 27.3-70 30-100.4 8.1c-25.1 20.8-62.5 19.5-86-4.1L159 404l-7-7-35.6-35.6c-5.5-5.5-12.7-8.7-20.4-9.3C96 369.7 81.6 384 64 384H32c-17.7 0-32-14.3-32-32V144c0-8.8 7.2-16 16-16H56 96h19.8c2 0 3.9-.7 5.3-2l26.5-23.6C175.5 77.7 211.4 64 248.7 64H259c4.4 0 8.9 .2 13.2 .6zM544 320V176H496c-5.9 0-11.6-2.2-15.9-6.1l-36.9-32.8c-18.2-16.2-41.7-25.1-66.1-25.1c-25.4 0-49.8 9.7-68.3 27.1l-70.1 66.2c-10.3 9.8-10.1 26.3 .5 35.7c9.3 8.3 23.4 8.1 32.5-.3l71.9-66.4c9.7-9 24.9-8.4 33.9 1.4s8.4 24.9-1.4 33.9l-.8 .8 74.4 74.4c10 10 16.5 22.3 19.4 35.1H544zM64 336a16 16 0 1 0 -32 0 16 16 0 1 0 32 0zm528 16a16 16 0 1 0 0-32 16 16 0 1 0 0 32z\"/></svg>`{=html}\n\n<br>\n\n-   When did you use R for the first time?\n-   What is your favorite R package?\n-   Which package hex sticker would you like to find the most during posit::conf(2024)?\n\n## Getting Help Today `<svg aria-hidden=\"true\" role=\"img\" viewBox=\"0 0 448 512\" style=\"height:1em;width:0.88em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:currentColor;overflow:visible;position:relative;\"><path d=\"M48 24C48 10.7 37.3 0 24 0S0 10.7 0 24V64 350.5 400v88c0 13.3 10.7 24 24 24s24-10.7 24-24V388l80.3-20.1c41.1-10.3 84.6-5.5 122.5 13.4c44.2 22.1 95.5 24.8 141.7 7.4l34.7-13c12.5-4.7 20.8-16.6 20.8-30V66.1c0-23-24.2-38-44.8-27.7l-9.6 4.8c-46.3 23.2-100.8 23.2-147.1 0c-35.1-17.6-75.4-22-113.5-12.5L48 52V24zm0 77.5l96.6-24.2c27-6.7 55.5-3.6 80.4 8.8c54.9 27.4 118.7 29.7 175 6.8V334.7l-24.4 9.1c-33.7 12.6-71.2 10.7-103.4-5.4c-48.2-24.1-103.3-30.1-155.6-17.1L48 338.5v-237z\"/></svg>`{=html}\n\n<br>\n\n[TEAL]{style=\"color:teal;\"} sticky note: I am OK / I am done\n\n[PINK]{style=\"color:pink;\"} sticky note: I need support / I am working\n\n<br>\n\n`<svg aria-hidden=\"true\" role=\"img\" viewBox=\"0 0 512 512\" style=\"height:1em;width:1em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:currentColor;overflow:visible;position:relative;\"><path d=\"M256 0c-25.3 0-47.2 14.7-57.6 36c-7-2.6-14.5-4-22.4-4c-35.3 0-64 28.7-64 64V261.5l-2.7-2.7c-25-25-65.5-25-90.5 0s-25 65.5 0 90.5L106.5 437c48 48 113.1 75 181 75H296h8c1.5 0 3-.1 4.5-.4c91.7-6.2 165-79.4 171.1-171.1c.3-1.5 .4-3 .4-4.5V160c0-35.3-28.7-64-64-64c-5.5 0-10.9 .7-16 2V96c0-35.3-28.7-64-64-64c-7.9 0-15.4 1.4-22.4 4C303.2 14.7 281.3 0 256 0zM240 96.1c0 0 0-.1 0-.1V64c0-8.8 7.2-16 16-16s16 7.2 16 16V95.9c0 0 0 .1 0 .1V232c0 13.3 10.7 24 24 24s24-10.7 24-24V96c0 0 0 0 0-.1c0-8.8 7.2-16 16-16s16 7.2 16 16v55.9c0 0 0 .1 0 .1v80c0 13.3 10.7 24 24 24s24-10.7 24-24V160.1c0 0 0-.1 0-.1c0-8.8 7.2-16 16-16s16 7.2 16 16V332.9c-.1 .6-.1 1.3-.2 1.9c-3.4 69.7-59.3 125.6-129 129c-.6 0-1.3 .1-1.9 .2H296h-8.5c-55.2 0-108.1-21.9-147.1-60.9L52.7 315.3c-6.2-6.2-6.2-16.4 0-22.6s16.4-6.2 22.6 0L119 336.4c6.9 6.9 17.2 8.9 26.2 5.2s14.8-12.5 14.8-22.2V96c0-8.8 7.2-16 16-16c8.8 0 16 7.1 16 15.9V232c0 13.3 10.7 24 24 24s24-10.7 24-24V96.1z\"/></svg>`{=html} You can ask questions at any time during the workshop\n\n## Discord `<svg aria-hidden=\"true\" role=\"img\" viewBox=\"0 0 640 512\" style=\"height:1em;width:1.25em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:currentColor;overflow:visible;position:relative;\"><path d=\"M524.531,69.836a1.5,1.5,0,0,0-.764-.7A485.065,485.065,0,0,0,404.081,32.03a1.816,1.816,0,0,0-1.923.91,337.461,337.461,0,0,0-14.9,30.6,447.848,447.848,0,0,0-134.426,0,309.541,309.541,0,0,0-15.135-30.6,1.89,1.89,0,0,0-1.924-.91A483.689,483.689,0,0,0,116.085,69.137a1.712,1.712,0,0,0-.788.676C39.068,183.651,18.186,294.69,28.43,404.354a2.016,2.016,0,0,0,.765,1.375A487.666,487.666,0,0,0,176.02,479.918a1.9,1.9,0,0,0,2.063-.676A348.2,348.2,0,0,0,208.12,430.4a1.86,1.86,0,0,0-1.019-2.588,321.173,321.173,0,0,1-45.868-21.853,1.885,1.885,0,0,1-.185-3.126c3.082-2.309,6.166-4.711,9.109-7.137a1.819,1.819,0,0,1,1.9-.256c96.229,43.917,200.41,43.917,295.5,0a1.812,1.812,0,0,1,1.924.233c2.944,2.426,6.027,4.851,9.132,7.16a1.884,1.884,0,0,1-.162,3.126,301.407,301.407,0,0,1-45.89,21.83,1.875,1.875,0,0,0-1,2.611,391.055,391.055,0,0,0,30.014,48.815,1.864,1.864,0,0,0,2.063.7A486.048,486.048,0,0,0,610.7,405.729a1.882,1.882,0,0,0,.765-1.352C623.729,277.594,590.933,167.465,524.531,69.836ZM222.491,337.58c-28.972,0-52.844-26.587-52.844-59.239S193.056,219.1,222.491,219.1c29.665,0,53.306,26.82,52.843,59.239C275.334,310.993,251.924,337.58,222.491,337.58Zm195.38,0c-28.971,0-52.843-26.587-52.843-59.239S388.437,219.1,417.871,219.1c29.667,0,53.307,26.82,52.844,59.239C470.715,310.993,447.538,337.58,417.871,337.58Z\"/></svg>`{=html}\n\n-   [pos.it/conf-event-portal](http://pos.it/conf-event-portal) (login)\n-   Click on \"Join Discord, the virtual networking platform!\"\n-   Browse Channels -> `#workshop-arrow`\n\n## We Assume\n\n-   You know `<svg aria-hidden=\"true\" role=\"img\" viewBox=\"0 0 581 512\" style=\"height:1em;width:1.13em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:currentColor;overflow:visible;position:relative;\"><path d=\"M581 226.6C581 119.1 450.9 32 290.5 32S0 119.1 0 226.6C0 322.4 103.3 402 239.4 418.1V480h99.1v-61.5c24.3-2.7 47.6-7.4 69.4-13.9L448 480h112l-67.4-113.7c54.5-35.4 88.4-84.9 88.4-139.7zm-466.8 14.5c0-73.5 98.9-133 220.8-133s211.9 40.7 211.9 133c0 50.1-26.5 85-70.3 106.4-2.4-1.6-4.7-2.9-6.4-3.7-10.2-5.2-27.8-10.5-27.8-10.5s86.6-6.4 86.6-92.7-90.6-87.9-90.6-87.9h-199V361c-74.1-21.5-125.2-67.1-125.2-119.9zm225.1 38.3v-55.6c57.8 0 87.8-6.8 87.8 27.3 0 36.5-38.2 28.3-87.8 28.3zm-.9 72.5H365c10.8 0 18.9 11.7 24 19.2-16.1 1.9-33 2.8-50.6 2.9v-22.1z\"/></svg>`{=html}\n-   You are familiar with the [dplyr](https://dplyr.tidyverse.org/) package for data manipulation `<svg aria-hidden=\"true\" role=\"img\" viewBox=\"0 0 512 512\" style=\"height:1em;width:1em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:currentColor;overflow:visible;position:relative;\"><path d=\"M78.6 5C69.1-2.4 55.6-1.5 47 7L7 47c-8.5 8.5-9.4 22-2.1 31.6l80 104c4.5 5.9 11.6 9.4 19 9.4h54.1l109 109c-14.7 29-10 65.4 14.3 89.6l112 112c12.5 12.5 32.8 12.5 45.3 0l64-64c12.5-12.5 12.5-32.8 0-45.3l-112-112c-24.2-24.2-60.6-29-89.6-14.3l-109-109V104c0-7.5-3.5-14.5-9.4-19L78.6 5zM19.9 396.1C7.2 408.8 0 426.1 0 444.1C0 481.6 30.4 512 67.9 512c18 0 35.3-7.2 48-19.9L233.7 374.3c-7.8-20.9-9-43.6-3.6-65.1l-61.7-61.7L19.9 396.1zM512 144c0-10.5-1.1-20.7-3.2-30.5c-2.4-11.2-16.1-14.1-24.2-6l-63.9 63.9c-3 3-7.1 4.7-11.3 4.7H352c-8.8 0-16-7.2-16-16V102.6c0-4.2 1.7-8.3 4.7-11.3l63.9-63.9c8.1-8.1 5.2-21.8-6-24.2C388.7 1.1 378.5 0 368 0C288.5 0 224 64.5 224 144l0 .8 85.3 85.3c36-9.1 75.8 .5 104 28.7L429 274.5c49-23 83-72.8 83-130.5zM56 432a24 24 0 1 1 48 0 24 24 0 1 1 -48 0z\"/></svg>`{=html}\n-   You have data in your life that is too large to fit into memory or sluggish in memory\n-   You want to learn how to engineer your data storage for more performant access and analysis\n\n## Posit Workbench: Login 🛠️\n\n-   Join Workbench via URL in the #workshop-arrow Discord channel\n-   Select Posit Workbench >> Sign in with OpenID\n-   Use your GitHub credentials to log in (click the icon)\n\n![](images/wb-signin.png){.absolute left=\"200\" width=\"300\"}\n![](images/use-gh-creds.png){.absolute left=\"500\" width=\"300\"}\n\n## Posit Workbench: Setup 🍽️\n\n- 🖱 +New Session\n- 🖱 Start Session (defaults are fine)\n- Run `usethis::use_course(\"posit-conf-2024/arrow\")`\n\n![](images/wb-new-session.png){.absolute left=\"350\" width=\"400\"}\n\n\n## Posit Workbench: Setup 🍽️\n\n- Default location: 🖱 `2`\n- Unzip 📁: 🖱 `3`\n- Open Session dialog box: Resource Profile >> select `Large`\n- Open + run `data/setup.R` 🎉\n\n![](images/wb-usethis-session.png){.absolute left=\"350\" width=\"375\"}\n\n\n",
-    "supporting": [],
+    "markdown": "---\ntitle: \"Big Data in R with Arrow\"\nsubtitle: \"posit::conf(2024) 1-day workshop\"\nauthor: \"Nic Crane + Steph Hazlitt\"\nfooter: \"[🔗 pos.it/arrow-conf24](https://pos.it/arrow-conf24)\"\nlogo: \"images/logo.png\"\nexecute:\n  echo: true\nformat:\n  revealjs: \n    theme: default\nengine: knitr\neditor: source\n---\n\n\n\n\n# Welcome 👋\n\n## \n\n### WiFi `<svg aria-hidden=\"true\" role=\"img\" viewBox=\"0 0 640 512\" style=\"height:1em;width:1.25em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:currentColor;overflow:visible;position:relative;\"><path d=\"M54.2 202.9C123.2 136.7 216.8 96 320 96s196.8 40.7 265.8 106.9c12.8 12.2 33 11.8 45.2-.9s11.8-33-.9-45.2C549.7 79.5 440.4 32 320 32S90.3 79.5 9.8 156.7C-2.9 169-3.3 189.2 8.9 202s32.5 13.2 45.2 .9zM320 256c56.8 0 108.6 21.1 148.2 56c13.3 11.7 33.5 10.4 45.2-2.8s10.4-33.5-2.8-45.2C459.8 219.2 393 192 320 192s-139.8 27.2-190.5 72c-13.3 11.7-14.5 31.9-2.8 45.2s31.9 14.5 45.2 2.8c39.5-34.9 91.3-56 148.2-56zm64 160a64 64 0 1 0 -128 0 64 64 0 1 0 128 0z\"/></svg>`{=html}\n\n-   Username: **Posit Conf 2024**\n-   Password: **conf2024**\n\n<br>\n\n### Workshop `<svg aria-hidden=\"true\" role=\"img\" viewBox=\"0 0 496 512\" style=\"height:1em;width:0.97em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:currentColor;overflow:visible;position:relative;\"><path d=\"M165.9 397.4c0 2-2.3 3.6-5.2 3.6-3.3.3-5.6-1.3-5.6-3.6 0-2 2.3-3.6 5.2-3.6 3-.3 5.6 1.3 5.6 3.6zm-31.1-4.5c-.7 2 1.3 4.3 4.3 4.9 2.6 1 5.6 0 6.2-2s-1.3-4.3-4.3-5.2c-2.6-.7-5.5.3-6.2 2.3zm44.2-1.7c-2.9.7-4.9 2.6-4.6 4.9.3 2 2.9 3.3 5.9 2.6 2.9-.7 4.9-2.6 4.6-4.6-.3-1.9-3-3.2-5.9-2.9zM244.8 8C106.1 8 0 113.3 0 252c0 110.9 69.8 205.8 169.5 239.2 12.8 2.3 17.3-5.6 17.3-12.1 0-6.2-.3-40.4-.3-61.4 0 0-70 15-84.7-29.8 0 0-11.4-29.1-27.8-36.6 0 0-22.9-15.7 1.6-15.4 0 0 24.9 2 38.6 25.8 21.9 38.6 58.6 27.5 72.9 20.9 2.3-16 8.8-27.1 16-33.7-55.9-6.2-112.3-14.3-112.3-110.5 0-27.5 7.6-41.3 23.6-58.9-2.6-6.5-11.1-33.3 2.6-67.9 20.9-6.5 69 27 69 27 20-5.6 41.5-8.5 62.8-8.5s42.8 2.9 62.8 8.5c0 0 48.1-33.6 69-27 13.7 34.7 5.2 61.4 2.6 67.9 16 17.7 25.8 31.5 25.8 58.9 0 96.5-58.9 104.2-114.8 110.5 9.2 7.9 17 22.9 17 46.4 0 33.7-.3 75.4-.3 83.6 0 6.5 4.6 14.4 17.3 12.1C428.2 457.8 496 362.9 496 252 496 113.3 383.5 8 244.8 8zM97.2 352.9c-1.3 1-1 3.3.7 5.2 1.6 1.6 3.9 2.3 5.2 1 1.3-1 1-3.3-.7-5.2-1.6-1.6-3.9-2.3-5.2-1zm-10.8-8.1c-.7 1.3.3 2.9 2.3 3.9 1.6 1 3.6.7 4.3-.7.7-1.3-.3-2.9-2.3-3.9-2-.6-3.6-.3-4.3.7zm32.4 35.6c-1.6 1.3-1 4.3 1.3 6.2 2.3 2.3 5.2 2.6 6.5 1 1.3-1.3.7-4.3-1.3-6.2-2.2-2.3-5.2-2.6-6.5-1zm-11.4-14.7c-1.6 1-1.6 3.6 0 5.9 1.6 2.3 4.3 3.3 5.6 2.3 1.6-1.3 1.6-3.9 0-6.2-1.4-2.3-4-3.3-5.6-2z\"/></svg>`{=html}\n\n-   Website: [pos.it/arrow-conf24](https://pos.it/arrow-conf24)\n-   GitHub: [github.com/posit-conf-2024/arrow](https://github.com/posit-conf-2024/arrow)\n\n## Housekeeping\n\n<br>\n\n### Gender Neutral Bathrooms `<svg aria-hidden=\"true\" role=\"img\" viewBox=\"0 0 320 512\" style=\"height:1em;width:0.62em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:currentColor;overflow:visible;position:relative;\"><path d=\"M160 0a48 48 0 1 1 0 96 48 48 0 1 1 0-96zm8 352V128h6.9c33.7 0 64.9 17.7 82.3 46.6l58.3 97c9.1 15.1 4.2 34.8-10.9 43.9s-34.8 4.2-43.9-10.9L232 256.9V480c0 17.7-14.3 32-32 32s-32-14.3-32-32V352h0zM58.2 182.3c19.9-33.1 55.3-53.5 93.8-54.3V384h0v96c0 17.7-14.3 32-32 32s-32-14.3-32-32V384H70.2c-10.9 0-18.6-10.7-15.2-21.1L93.3 248.1 59.4 304.5c-9.1 15.1-28.8 20-43.9 10.9s-20-28.8-10.9-43.9l53.6-89.2z\"/></svg>`{=html}\n\n-   Located on levels 3, 4, 5, 6 & 7\n\n### Specialty Rooms `<svg aria-hidden=\"true\" role=\"img\" viewBox=\"0 0 448 512\" style=\"height:1em;width:0.88em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:currentColor;overflow:visible;position:relative;\"><path d=\"M301.1 34.8C312.6 40 320 51.4 320 64V448c0 12.6-7.4 24-18.9 29.2s-25 3.1-34.4-5.3L131.8 352H64c-35.3 0-64-28.7-64-64V224c0-35.3 28.7-64 64-64h67.8L266.7 40.1c9.4-8.4 22.9-10.4 34.4-5.3zM412.6 181.5C434.1 199.1 448 225.9 448 256s-13.9 56.9-35.4 74.5c-10.3 8.4-25.4 6.8-33.8-3.5s-6.8-25.4 3.5-33.8C393.1 284.4 400 271 400 256s-6.9-28.4-17.7-37.3c-10.3-8.4-11.8-23.5-3.5-33.8s23.5-11.8 33.8-3.5z\"/></svg>`{=html}\n\n-   Meditation/Prayer Room (503)\n-   Lactation Room (509)\n\n*Available Mon & Tues 7am - 7pm, and Wed 7am - 5pm\n\n\n## Photos\n\n<br>\n\n### Red Lanyards `<svg aria-hidden=\"true\" role=\"img\" viewBox=\"0 0 448 512\" style=\"height:1em;width:0.88em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:currentColor;overflow:visible;position:relative;\"><path d=\"M48 128c-17.7 0-32 14.3-32 32s14.3 32 32 32H400c17.7 0 32-14.3 32-32s-14.3-32-32-32H48zm0 192c-17.7 0-32 14.3-32 32s14.3 32 32 32H400c17.7 0 32-14.3 32-32s-14.3-32-32-32H48z\"/></svg>`{=html}`<svg aria-hidden=\"true\" role=\"img\" viewBox=\"0 0 448 512\" style=\"height:1em;width:0.88em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:currentColor;overflow:visible;position:relative;\"><path d=\"M48 128c-17.7 0-32 14.3-32 32s14.3 32 32 32H400c17.7 0 32-14.3 32-32s-14.3-32-32-32H48zm0 192c-17.7 0-32 14.3-32 32s14.3 32 32 32H400c17.7 0 32-14.3 32-32s-14.3-32-32-32H48z\"/></svg>`{=html} **NO** `<svg aria-hidden=\"true\" role=\"img\" viewBox=\"0 0 512 512\" style=\"height:1em;width:1em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:currentColor;overflow:visible;position:relative;\"><path d=\"M149.1 64.8L138.7 96H64C28.7 96 0 124.7 0 160V416c0 35.3 28.7 64 64 64H448c35.3 0 64-28.7 64-64V160c0-35.3-28.7-64-64-64H373.3L362.9 64.8C356.4 45.2 338.1 32 317.4 32H194.6c-20.7 0-39 13.2-45.5 32.8zM256 192a96 96 0 1 1 0 192 96 96 0 1 1 0-192z\"/></svg>`{=html}\n\n<br>\n\nPlease note everyone’s lanyard colors before taking a photo and respect their choices.\n\n## Code of Conduct\n\n<br>\n\n### `<svg aria-hidden=\"true\" role=\"img\" viewBox=\"0 0 640 512\" style=\"height:1em;width:1.25em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:currentColor;overflow:visible;position:relative;\"><path d=\"M579.8 267.7c56.5-56.5 56.5-148 0-204.5c-50-50-128.8-56.5-186.3-15.4l-1.6 1.1c-14.4 10.3-17.7 30.3-7.4 44.6s30.3 17.7 44.6 7.4l1.6-1.1c32.1-22.9 76-19.3 103.8 8.6c31.5 31.5 31.5 82.5 0 114L422.3 334.8c-31.5 31.5-82.5 31.5-114 0c-27.9-27.9-31.5-71.8-8.6-103.8l1.1-1.6c10.3-14.4 6.9-34.4-7.4-44.6s-34.4-6.9-44.6 7.4l-1.1 1.6C206.5 251.2 213 330 263 380c56.5 56.5 148 56.5 204.5 0L579.8 267.7zM60.2 244.3c-56.5 56.5-56.5 148 0 204.5c50 50 128.8 56.5 186.3 15.4l1.6-1.1c14.4-10.3 17.7-30.3 7.4-44.6s-30.3-17.7-44.6-7.4l-1.6 1.1c-32.1 22.9-76 19.3-103.8-8.6C74 372 74 321 105.5 289.5L217.7 177.2c31.5-31.5 82.5-31.5 114 0c27.9 27.9 31.5 71.8 8.6 103.9l-1.1 1.6c-10.3 14.4-6.9 34.4 7.4 44.6s34.4 6.9 44.6-7.4l1.1-1.6C433.5 260.8 427 182 377 132c-56.5-56.5-148-56.5-204.5 0L60.2 244.3z\"/></svg>`{=html} [posit.co/code-of-conduct/](https://posit.co/code-of-conduct/)\n\n-   Contact any posit::conf staff member, identifiable by their staff t-shirt, or visit the conference general information desk.\n-   Send a message to conf\\@posit.com; event organizers will respond promptly.\n-   Call +1-844-448-1212; this phone number will be monitored for the duration of the event.\n\n## Meet Your Teaching Team `<svg aria-hidden=\"true\" role=\"img\" viewBox=\"0 0 640 512\" style=\"height:1em;width:1.25em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:currentColor;overflow:visible;position:relative;\"><path d=\"M72 88a56 56 0 1 1 112 0A56 56 0 1 1 72 88zM64 245.7C54 256.9 48 271.8 48 288s6 31.1 16 42.3V245.7zm144.4-49.3C178.7 222.7 160 261.2 160 304c0 34.3 12 65.8 32 90.5V416c0 17.7-14.3 32-32 32H96c-17.7 0-32-14.3-32-32V389.2C26.2 371.2 0 332.7 0 288c0-61.9 50.1-112 112-112h32c24 0 46.2 7.5 64.4 20.3zM448 416V394.5c20-24.7 32-56.2 32-90.5c0-42.8-18.7-81.3-48.4-107.7C449.8 183.5 472 176 496 176h32c61.9 0 112 50.1 112 112c0 44.7-26.2 83.2-64 101.2V416c0 17.7-14.3 32-32 32H480c-17.7 0-32-14.3-32-32zm8-328a56 56 0 1 1 112 0A56 56 0 1 1 456 88zM576 245.7v84.7c10-11.3 16-26.1 16-42.3s-6-31.1-16-42.3zM320 32a64 64 0 1 1 0 128 64 64 0 1 1 0-128zM240 304c0 16.2 6 31 16 42.3V261.7c-10 11.3-16 26.1-16 42.3zm144-42.3v84.7c10-11.3 16-26.1 16-42.3s-6-31.1-16-42.3zM448 304c0 44.7-26.2 83.2-64 101.2V448c0 17.7-14.3 32-32 32H288c-17.7 0-32-14.3-32-32V405.2c-37.8-18-64-56.5-64-101.2c0-61.9 50.1-112 112-112h32c61.9 0 112 50.1 112 112z\"/></svg>`{=html}\n\n<br>\n\n### Co-Instructors\n\n-   Nic Crane\n-   Steph Hazlitt\n\n### Teaching Assistant\n\n-   Jonathan Keane\n\n## Meet Each Other `<svg aria-hidden=\"true\" role=\"img\" viewBox=\"0 0 640 512\" style=\"height:1em;width:1.25em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:currentColor;overflow:visible;position:relative;\"><path d=\"M272.2 64.6l-51.1 51.1c-15.3 4.2-29.5 11.9-41.5 22.5L153 161.9C142.8 171 129.5 176 115.8 176H96V304c20.4 .6 39.8 8.9 54.3 23.4l35.6 35.6 7 7 0 0L219.9 397c6.2 6.2 16.4 6.2 22.6 0c1.7-1.7 3-3.7 3.7-5.8c2.8-7.7 9.3-13.5 17.3-15.3s16.4 .6 22.2 6.5L296.5 393c11.6 11.6 30.4 11.6 41.9 0c5.4-5.4 8.3-12.3 8.6-19.4c.4-8.8 5.6-16.6 13.6-20.4s17.3-3 24.4 2.1c9.4 6.7 22.5 5.8 30.9-2.6c9.4-9.4 9.4-24.6 0-33.9L340.1 243l-35.8 33c-27.3 25.2-69.2 25.6-97 .9c-31.7-28.2-32.4-77.4-1.6-106.5l70.1-66.2C303.2 78.4 339.4 64 377.1 64c36.1 0 71 13.3 97.9 37.2L505.1 128H544h40 40c8.8 0 16 7.2 16 16V352c0 17.7-14.3 32-32 32H576c-11.8 0-22.2-6.4-27.7-16H463.4c-3.4 6.7-7.9 13.1-13.5 18.7c-17.1 17.1-40.8 23.8-63 20.1c-3.6 7.3-8.5 14.1-14.6 20.2c-27.3 27.3-70 30-100.4 8.1c-25.1 20.8-62.5 19.5-86-4.1L159 404l-7-7-35.6-35.6c-5.5-5.5-12.7-8.7-20.4-9.3C96 369.7 81.6 384 64 384H32c-17.7 0-32-14.3-32-32V144c0-8.8 7.2-16 16-16H56 96h19.8c2 0 3.9-.7 5.3-2l26.5-23.6C175.5 77.7 211.4 64 248.7 64H259c4.4 0 8.9 .2 13.2 .6zM544 320V176H496c-5.9 0-11.6-2.2-15.9-6.1l-36.9-32.8c-18.2-16.2-41.7-25.1-66.1-25.1c-25.4 0-49.8 9.7-68.3 27.1l-70.1 66.2c-10.3 9.8-10.1 26.3 .5 35.7c9.3 8.3 23.4 8.1 32.5-.3l71.9-66.4c9.7-9 24.9-8.4 33.9 1.4s8.4 24.9-1.4 33.9l-.8 .8 74.4 74.4c10 10 16.5 22.3 19.4 35.1H544zM64 336a16 16 0 1 0 -32 0 16 16 0 1 0 32 0zm528 16a16 16 0 1 0 0-32 16 16 0 1 0 0 32z\"/></svg>`{=html}\n\n<br>\n\n-   When did you use R for the first time?\n-   What is your favorite R package?\n-   Which package hex sticker would you like to find the most during posit::conf(2024)?\n\n## Getting Help Today `<svg aria-hidden=\"true\" role=\"img\" viewBox=\"0 0 448 512\" style=\"height:1em;width:0.88em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:currentColor;overflow:visible;position:relative;\"><path d=\"M48 24C48 10.7 37.3 0 24 0S0 10.7 0 24V64 350.5 400v88c0 13.3 10.7 24 24 24s24-10.7 24-24V388l80.3-20.1c41.1-10.3 84.6-5.5 122.5 13.4c44.2 22.1 95.5 24.8 141.7 7.4l34.7-13c12.5-4.7 20.8-16.6 20.8-30V66.1c0-23-24.2-38-44.8-27.7l-9.6 4.8c-46.3 23.2-100.8 23.2-147.1 0c-35.1-17.6-75.4-22-113.5-12.5L48 52V24zm0 77.5l96.6-24.2c27-6.7 55.5-3.6 80.4 8.8c54.9 27.4 118.7 29.7 175 6.8V334.7l-24.4 9.1c-33.7 12.6-71.2 10.7-103.4-5.4c-48.2-24.1-103.3-30.1-155.6-17.1L48 338.5v-237z\"/></svg>`{=html}\n\n<br>\n\n[GREEN]{style=\"color:green;\"} sticky note: I am OK / I am done\n\n[PINK]{style=\"color:pink;\"} sticky note: I need support / I am working\n\n<br>\n\n`<svg aria-hidden=\"true\" role=\"img\" viewBox=\"0 0 512 512\" style=\"height:1em;width:1em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:currentColor;overflow:visible;position:relative;\"><path d=\"M256 0c-25.3 0-47.2 14.7-57.6 36c-7-2.6-14.5-4-22.4-4c-35.3 0-64 28.7-64 64V261.5l-2.7-2.7c-25-25-65.5-25-90.5 0s-25 65.5 0 90.5L106.5 437c48 48 113.1 75 181 75H296h8c1.5 0 3-.1 4.5-.4c91.7-6.2 165-79.4 171.1-171.1c.3-1.5 .4-3 .4-4.5V160c0-35.3-28.7-64-64-64c-5.5 0-10.9 .7-16 2V96c0-35.3-28.7-64-64-64c-7.9 0-15.4 1.4-22.4 4C303.2 14.7 281.3 0 256 0zM240 96.1c0 0 0-.1 0-.1V64c0-8.8 7.2-16 16-16s16 7.2 16 16V95.9c0 0 0 .1 0 .1V232c0 13.3 10.7 24 24 24s24-10.7 24-24V96c0 0 0 0 0-.1c0-8.8 7.2-16 16-16s16 7.2 16 16v55.9c0 0 0 .1 0 .1v80c0 13.3 10.7 24 24 24s24-10.7 24-24V160.1c0 0 0-.1 0-.1c0-8.8 7.2-16 16-16s16 7.2 16 16V332.9c-.1 .6-.1 1.3-.2 1.9c-3.4 69.7-59.3 125.6-129 129c-.6 0-1.3 .1-1.9 .2H296h-8.5c-55.2 0-108.1-21.9-147.1-60.9L52.7 315.3c-6.2-6.2-6.2-16.4 0-22.6s16.4-6.2 22.6 0L119 336.4c6.9 6.9 17.2 8.9 26.2 5.2s14.8-12.5 14.8-22.2V96c0-8.8 7.2-16 16-16c8.8 0 16 7.1 16 15.9V232c0 13.3 10.7 24 24 24s24-10.7 24-24V96.1z\"/></svg>`{=html} You can ask questions at any time during the workshop\n\n## Discord `<svg aria-hidden=\"true\" role=\"img\" viewBox=\"0 0 640 512\" style=\"height:1em;width:1.25em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:currentColor;overflow:visible;position:relative;\"><path d=\"M524.531,69.836a1.5,1.5,0,0,0-.764-.7A485.065,485.065,0,0,0,404.081,32.03a1.816,1.816,0,0,0-1.923.91,337.461,337.461,0,0,0-14.9,30.6,447.848,447.848,0,0,0-134.426,0,309.541,309.541,0,0,0-15.135-30.6,1.89,1.89,0,0,0-1.924-.91A483.689,483.689,0,0,0,116.085,69.137a1.712,1.712,0,0,0-.788.676C39.068,183.651,18.186,294.69,28.43,404.354a2.016,2.016,0,0,0,.765,1.375A487.666,487.666,0,0,0,176.02,479.918a1.9,1.9,0,0,0,2.063-.676A348.2,348.2,0,0,0,208.12,430.4a1.86,1.86,0,0,0-1.019-2.588,321.173,321.173,0,0,1-45.868-21.853,1.885,1.885,0,0,1-.185-3.126c3.082-2.309,6.166-4.711,9.109-7.137a1.819,1.819,0,0,1,1.9-.256c96.229,43.917,200.41,43.917,295.5,0a1.812,1.812,0,0,1,1.924.233c2.944,2.426,6.027,4.851,9.132,7.16a1.884,1.884,0,0,1-.162,3.126,301.407,301.407,0,0,1-45.89,21.83,1.875,1.875,0,0,0-1,2.611,391.055,391.055,0,0,0,30.014,48.815,1.864,1.864,0,0,0,2.063.7A486.048,486.048,0,0,0,610.7,405.729a1.882,1.882,0,0,0,.765-1.352C623.729,277.594,590.933,167.465,524.531,69.836ZM222.491,337.58c-28.972,0-52.844-26.587-52.844-59.239S193.056,219.1,222.491,219.1c29.665,0,53.306,26.82,52.843,59.239C275.334,310.993,251.924,337.58,222.491,337.58Zm195.38,0c-28.971,0-52.843-26.587-52.843-59.239S388.437,219.1,417.871,219.1c29.667,0,53.307,26.82,52.844,59.239C470.715,310.993,447.538,337.58,417.871,337.58Z\"/></svg>`{=html}\n\n-   [pos.it/conf-event-portal](http://pos.it/conf-event-portal) (login)\n-   Click on \"Join Discord, the virtual networking platform!\"\n-   Browse Channels -> `#workshop-arrow`\n\n## We Assume\n\n-   You know `<svg aria-hidden=\"true\" role=\"img\" viewBox=\"0 0 581 512\" style=\"height:1em;width:1.13em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:currentColor;overflow:visible;position:relative;\"><path d=\"M581 226.6C581 119.1 450.9 32 290.5 32S0 119.1 0 226.6C0 322.4 103.3 402 239.4 418.1V480h99.1v-61.5c24.3-2.7 47.6-7.4 69.4-13.9L448 480h112l-67.4-113.7c54.5-35.4 88.4-84.9 88.4-139.7zm-466.8 14.5c0-73.5 98.9-133 220.8-133s211.9 40.7 211.9 133c0 50.1-26.5 85-70.3 106.4-2.4-1.6-4.7-2.9-6.4-3.7-10.2-5.2-27.8-10.5-27.8-10.5s86.6-6.4 86.6-92.7-90.6-87.9-90.6-87.9h-199V361c-74.1-21.5-125.2-67.1-125.2-119.9zm225.1 38.3v-55.6c57.8 0 87.8-6.8 87.8 27.3 0 36.5-38.2 28.3-87.8 28.3zm-.9 72.5H365c10.8 0 18.9 11.7 24 19.2-16.1 1.9-33 2.8-50.6 2.9v-22.1z\"/></svg>`{=html}\n-   You are familiar with the [dplyr](https://dplyr.tidyverse.org/) package for data manipulation `<svg aria-hidden=\"true\" role=\"img\" viewBox=\"0 0 512 512\" style=\"height:1em;width:1em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:currentColor;overflow:visible;position:relative;\"><path d=\"M78.6 5C69.1-2.4 55.6-1.5 47 7L7 47c-8.5 8.5-9.4 22-2.1 31.6l80 104c4.5 5.9 11.6 9.4 19 9.4h54.1l109 109c-14.7 29-10 65.4 14.3 89.6l112 112c12.5 12.5 32.8 12.5 45.3 0l64-64c12.5-12.5 12.5-32.8 0-45.3l-112-112c-24.2-24.2-60.6-29-89.6-14.3l-109-109V104c0-7.5-3.5-14.5-9.4-19L78.6 5zM19.9 396.1C7.2 408.8 0 426.1 0 444.1C0 481.6 30.4 512 67.9 512c18 0 35.3-7.2 48-19.9L233.7 374.3c-7.8-20.9-9-43.6-3.6-65.1l-61.7-61.7L19.9 396.1zM512 144c0-10.5-1.1-20.7-3.2-30.5c-2.4-11.2-16.1-14.1-24.2-6l-63.9 63.9c-3 3-7.1 4.7-11.3 4.7H352c-8.8 0-16-7.2-16-16V102.6c0-4.2 1.7-8.3 4.7-11.3l63.9-63.9c8.1-8.1 5.2-21.8-6-24.2C388.7 1.1 378.5 0 368 0C288.5 0 224 64.5 224 144l0 .8 85.3 85.3c36-9.1 75.8 .5 104 28.7L429 274.5c49-23 83-72.8 83-130.5zM56 432a24 24 0 1 1 48 0 24 24 0 1 1 -48 0z\"/></svg>`{=html}\n-   You have data in your life that is too large to fit into memory or sluggish in memory\n-   You want to learn how to engineer your data storage for more performant access and analysis\n\n## Posit Workbench: Login 🛠️\n\n-   Join Workbench via URL in the #workshop-arrow Discord channel\n-   Select Posit Workbench >> Sign in with OpenID\n-   Use your GitHub credentials to log in (click the icon)\n\n![](images/wb-signin.png){.absolute left=\"200\" width=\"300\"}\n![](images/use-gh-creds.png){.absolute left=\"500\" width=\"300\"}\n\n## Posit Workbench: Setup 🍽️\n\n- 🖱 +New Session\n- 🖱 Start Session (defaults are fine)\n- Run `usethis::use_course(\"posit-conf-2024/arrow\")`\n\n![](images/wb-new-session.png){.absolute left=\"350\" width=\"400\"}\n\n\n## Posit Workbench: Setup 🍽️\n\n- Default location: 🖱 `2`\n- Unzip 📁: 🖱 `3`\n- Open Session dialog box: Resource Profile >> select `Large`\n- Open + run `data/setup.R` 🎉\n\n![](images/wb-usethis-session.png){.absolute left=\"350\" width=\"375\"}\n\n\n",
+    "supporting": [
+      "0_housekeeping_files"
+    ],
     "filters": [
       "rmarkdown/pagebreak.lua"
     ],
diff --git a/_freeze/materials/1_hello_arrow/execute-results/html.json b/_freeze/materials/1_hello_arrow/execute-results/html.json
index 3b1fe43..ea02a0a 100644
--- a/_freeze/materials/1_hello_arrow/execute-results/html.json
+++ b/_freeze/materials/1_hello_arrow/execute-results/html.json
@@ -1,8 +1,8 @@
 {
-  "hash": "eaac9c74be87e906e161433327bd4053",
+  "hash": "05c528bbc963b71b6c8c7527e1aba81b",
   "result": {
     "engine": "knitr",
-    "markdown": "---\nfooter: \"[🔗 pos.it/arrow-conf24](https://pos.it/arrow-conf24)\"\nlogo: \"images/logo.png\"\nexecute:\n  echo: true\nformat:\n  revealjs: \n    theme: default\nengine: knitr\neditor: source\n---\n\n\n# Hello Arrow {#hello-arrow}\n\n\n## Kick-off Q&A\n\n<br>\n\n- What brings you to this workshop?\n- What challenges have you faced related to larger-than-memory data in R?\n- What is one thing you want to learn or achieve from today's workshop?\n- ...?\n\n\n## Poll: Arrow\n\n<br>\n\n**Have you used or experimented with Arrow before today?**\n\nVote using emojis on the #workshop-arrow discord channel! <br> \n\n1️⃣ Not yet\n\n2️⃣ Not yet, but I have read about it!\n\n3️⃣ A little\n\n4️⃣ A lot\n\n\n## Hello Arrow<br>Demo\n\n<br>\n\n![](images/logo.png){.absolute top=\"0\" left=\"250\" width=\"600\" height=\"800\"}\n\n## Some \"Big\" Data\n\n![](images/nyc-taxi-homepage.png){.absolute left=\"200\" width=\"600\"}\n\n::: {style=\"font-size: 60%; margin-top: 550px; margin-left: 200px;\"}\n<https://www.nyc.gov/site/tlc/about/tlc-trip-record-data.page>\n:::\n\n## NYC Taxi Data\n\n-   *big* NYC Taxi data set (\\~40GBs on disk)\n\n\n::: {.cell}\n\n```{.r .cell-code}\nopen_dataset(\"s3://voltrondata-labs-datasets/nyc-taxi\") |>\n  filter(year %in% 2012:2021) |>\n  write_dataset(\"data/nyc-taxi\", partitioning = c(\"year\", \"month\"))\n```\n:::\n\n\n-   *tiny* NYC Taxi data set (\\<1GB on disk)\n\n\n::: {.cell}\n\n```{.r .cell-code}\ndownload.file(url = \"https://github.com/posit-conf-2023/arrow/releases/download/v0.1.0/nyc-taxi-tiny.zip\",\n              destfile = \"data/nyc-taxi-tiny.zip\")\n\nunzip(\n  zipfile = \"data/nyc-taxi-tiny.zip\",\n  exdir = \"data/\"\n)\n```\n:::\n\n\n## Larger-Than-Memory Data\n\n<br>\n\n`arrow::open_dataset()`\n\n<br>\n\n::: notes\nArrow Datasets allow you to query against data that has been split across multiple files. This division of data into multiple files may indicate partitioning, which can accelerate queries that only touch some partitions (files). Call open_dataset() to point to a directory of data files and return a Dataset, then use dplyr methods to query it.\n:::\n\n## NYC Taxi Dataset\n\n\n::: {.cell}\n\n```{.r .cell-code}\nlibrary(arrow)\n\nnyc_taxi <- open_dataset(\"data/nyc-taxi\")\n```\n:::\n\n\n## NYC Taxi Dataset\n\n\n::: {.cell}\n\n```{.r .cell-code}\nnyc_taxi |> \n  nrow()\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n[1] 1150352666\n```\n\n\n:::\n:::\n\n\n<br>\n\n1.15 billion rows 🤯\n\n## NYC Taxi Dataset: A question\n\n<br>\n\nWhat percentage of taxi rides each year had more than 1 passenger?\n\n## NYC Taxi Dataset: A dplyr pipeline\n\n\n::: {.cell}\n\n```{.r .cell-code}\nlibrary(dplyr)\n\nnyc_taxi |>\n  group_by(year) |>\n  summarise(\n    all_trips = n(),\n    shared_trips = sum(passenger_count > 1, na.rm = TRUE)\n  ) |>\n  mutate(pct_shared = shared_trips / all_trips * 100) |>\n  collect()\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n# A tibble: 10 × 4\n    year all_trips shared_trips pct_shared\n   <int>     <int>        <int>      <dbl>\n 1  2012 178544324     53313752       29.9\n 2  2013 173179759     51215013       29.6\n 3  2014 165114361     48816505       29.6\n 4  2015 146112989     43081091       29.5\n 5  2017 113495512     32296166       28.5\n 6  2018 102797401     28796633       28.0\n 7  2019  84393604     23515989       27.9\n 8  2020  24647055      5837960       23.7\n 9  2021  30902618      7221844       23.4\n10  2016 131165043     38163870       29.1\n```\n\n\n:::\n:::\n\n\n## NYC Taxi Dataset: A dplyr pipeline\n\n\n::: {.cell}\n\n```{.r .cell-code}\nlibrary(tictoc)\n\ntic()\nnyc_taxi |>\n  group_by(year) |>\n  summarise(\n    all_trips = n(),\n    shared_trips = sum(passenger_count > 1, na.rm = TRUE)\n  ) |>\n  mutate(pct_shared = shared_trips / all_trips * 100) |>\n  collect()\ntoc()\n```\n:::\n\n\n> 6.077 sec elapsed\n\n## Your Turn\n\n1.  Calculate the longest trip distance for every month in 2019\n\n2.  How long did this query take to run?\n\n➡️ [Hello Arrow Exercises Page](1_hello_arrow-exercises.html)\n\n## What is Apache Arrow?\n\n::: columns\n::: {.column width=\"50%\"}\n> A multi-language toolbox for accelerated data interchange and in-memory processing\n:::\n\n::: {.column width=\"50%\"}\n> Arrow is designed to both improve the performance of analytical algorithms and the efficiency of moving data from one system or programming language to another\n:::\n:::\n\n::: {style=\"font-size: 70%;\"}\n<https://arrow.apache.org/overview/>\n:::\n\n## Apache Arrow Specification\n\nIn-memory columnar format: a standardized, language-agnostic specification for representing structured, table-like data sets in-memory.\n\n<br>\n\n![](images/arrow-rectangle.png){.absolute left=\"200\"}\n\n## A Multi-Language Toolbox\n\n![](images/arrow-libraries-structure.png)\n\n## Accelerated Data Interchange\n\n![](images/data-interchange-with-arrow.png)\n\n## Accelerated In-Memory Processing\n\nArrow's Columnar Format is Fast\n\n![](images/columnar-fast.png){.absolute top=\"120\" left=\"200\" height=\"600\"}\n\n::: notes\nThe contiguous columnar layout enables vectorization using the latest SIMD (Single Instruction, Multiple Data) operations included in modern processors.\n:::\n\n## arrow 📦\n\n<br>\n\n![](images/arrow-r-pkg.png){.absolute top=\"0\" left=\"300\" width=\"700\" height=\"900\"}\n\n## arrow 📦\n\n![](images/arrow-read-write-updated.png)\n\n## Today\n\n-   Module 1: Larger-than-memory data manipulation with Arrow---Part I\n-   Module 2: Data engineering with Arrow\n-   Module 3: In-memory workflows in R with Arrow\n-   Module 4: Larger-than-memory data manipulation with Arrow---Part II\n\n",
+    "markdown": "---\nfooter: \"[🔗 pos.it/arrow-conf24](https://pos.it/arrow-conf24)\"\nlogo: \"images/logo.png\"\nexecute:\n  echo: true\nformat:\n  revealjs: \n    theme: default\nengine: knitr\neditor: source\n---\n\n\n\n\n# Hello Arrow {#hello-arrow}\n\n## Poll: Arrow\n\n<br>\n\n**Have you used or experimented with Arrow before today?**\n\nVote using emojis on the #workshop-arrow discord channel! <br> \n\n1️⃣ Not yet\n\n2️⃣ Not yet, but I have read about it!\n\n3️⃣ A little\n\n4️⃣ A lot\n\n\n## Hello Arrow<br>Demo\n\n<br>\n\n![](images/logo.png){.absolute top=\"0\" left=\"250\" width=\"600\" height=\"800\"}\n\n## Some \"Big\" Data\n\n![](images/nyc-taxi-homepage.png){.absolute left=\"200\" width=\"600\"}\n\n::: {style=\"font-size: 60%; margin-top: 550px; margin-left: 200px;\"}\n<https://www.nyc.gov/site/tlc/about/tlc-trip-record-data.page>\n:::\n\n## NYC Taxi Data\n\n-   *big* NYC Taxi data set (\\~40GBs on disk)\n\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nopen_dataset(\"s3://voltrondata-labs-datasets/nyc-taxi\") |>\n  filter(year %in% 2012:2021) |>\n  write_dataset(\"data/nyc-taxi\", partitioning = c(\"year\", \"month\"))\n```\n:::\n\n\n\n\n-   *tiny* NYC Taxi data set (\\<1GB on disk)\n\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\ndownload.file(url = \"https://github.com/posit-conf-2023/arrow/releases/download/v0.1.0/nyc-taxi-tiny.zip\",\n              destfile = \"data/nyc-taxi-tiny.zip\")\n\nunzip(\n  zipfile = \"data/nyc-taxi-tiny.zip\",\n  exdir = \"data/\"\n)\n```\n:::\n\n\n\n\n## Larger-Than-Memory Data\n\n<br>\n\n`arrow::open_dataset()`\n\n<br>\n\n::: notes\nArrow Datasets allow you to query against data that has been split across multiple files. This division of data into multiple files may indicate partitioning, which can accelerate queries that only touch some partitions (files). Call open_dataset() to point to a directory of data files and return a Dataset, then use dplyr methods to query it.\n:::\n\n## NYC Taxi Dataset\n\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nlibrary(arrow)\n\nnyc_taxi <- open_dataset(\"data/nyc-taxi\")\n```\n:::\n\n\n\n\n## NYC Taxi Dataset\n\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nnyc_taxi |> \n  nrow()\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n[1] 1150352666\n```\n\n\n:::\n:::\n\n\n\n\n<br>\n\n1.15 billion rows 🤯\n\n## NYC Taxi Dataset: A question\n\n<br>\n\nWhat percentage of taxi rides each year had more than 1 passenger?\n\n## NYC Taxi Dataset: A dplyr pipeline\n\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nlibrary(dplyr)\n\nnyc_taxi |>\n  group_by(year) |>\n  summarise(\n    all_trips = n(),\n    shared_trips = sum(passenger_count > 1, na.rm = TRUE)\n  ) |>\n  mutate(pct_shared = shared_trips / all_trips * 100) |>\n  collect()\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n# A tibble: 10 × 4\n    year all_trips shared_trips pct_shared\n   <int>     <int>        <int>      <dbl>\n 1  2012 178544324     53313752       29.9\n 2  2013 173179759     51215013       29.6\n 3  2014 165114361     48816505       29.6\n 4  2015 146112989     43081091       29.5\n 5  2016 131165043     38163870       29.1\n 6  2017 113495512     32296166       28.5\n 7  2018 102797401     28796633       28.0\n 8  2019  84393604     23515989       27.9\n 9  2020  24647055      5837960       23.7\n10  2021  30902618      7221844       23.4\n```\n\n\n:::\n:::\n\n\n\n\n## NYC Taxi Dataset: A dplyr pipeline\n\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nlibrary(tictoc)\n\ntic()\nnyc_taxi |>\n  group_by(year) |>\n  summarise(\n    all_trips = n(),\n    shared_trips = sum(passenger_count > 1, na.rm = TRUE)\n  ) |>\n  mutate(pct_shared = shared_trips / all_trips * 100) |>\n  collect()\ntoc()\n```\n:::\n\n\n\n\n> 6.077 sec elapsed\n\n## Your Turn\n\n1.  Calculate the longest trip distance for every month in 2019\n\n2.  How long did this query take to run?\n\n➡️ [Hello Arrow Exercises Page](1_hello_arrow-exercises.html)\n\n## What is Apache Arrow?\n\n::: columns\n::: {.column width=\"50%\"}\n> A multi-language toolbox for accelerated data interchange and in-memory processing\n:::\n\n::: {.column width=\"50%\"}\n> Arrow is designed to both improve the performance of analytical algorithms and the efficiency of moving data from one system or programming language to another\n:::\n:::\n\n::: {style=\"font-size: 70%;\"}\n<https://arrow.apache.org/overview/>\n:::\n\n## Apache Arrow Specification\n\nIn-memory columnar format: a standardized, language-agnostic specification for representing structured, table-like data sets in-memory.\n\n<br>\n\n![](images/arrow-rectangle.png){.absolute left=\"200\"}\n\n## A Multi-Language Toolbox\n\n![](images/arrow-libraries-structure.png)\n\n## Accelerated Data Interchange\n\n![](images/data-interchange-with-arrow.png)\n\n## Accelerated In-Memory Processing\n\nArrow's Columnar Format is Fast\n\n![](images/columnar-fast.png){.absolute top=\"120\" left=\"200\" height=\"600\"}\n\n::: notes\nThe contiguous columnar layout enables vectorization using the latest SIMD (Single Instruction, Multiple Data) operations included in modern processors.\n:::\n\n## arrow 📦\n\n<br>\n\n![](images/arrow-r-pkg-highlights.png){.absolute top=\"0\" left=\"300\" width=\"700\" height=\"900\"}\n\n## arrow 📦\n\n![](images/arrow-read-write-updated.png)\n\n## Today\n\n-   Module 1: Larger-than-memory data manipulation with Arrow---Part I\n-   Module 2: Data engineering with Arrow\n-   Module 3: In-memory workflows in R with Arrow\n-   Module 4: Larger-than-memory data manipulation with Arrow---Part II\n\n",
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-    "markdown": "---\nfooter: \"[🔗 pos.it/arrow-conf24](https://pos.it/arrow-conf24)\"\nlogo: \"images/logo.png\"\nexecute:\n  echo: true\nformat:\n  revealjs: \n    theme: default\nengine: knitr\neditor: source\n---\n\n\n\n\n# Data Manipulation---Part 1 {#data-manip-1}\n\n\n\n\n::: {.cell}\n\n:::\n\n\n\n\n## Goals\n\nAvoiding these! But...don't worry!\n\n![](images/segfault.png)\n\n## An Arrow Dataset\n\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nlibrary(arrow)\n\nnyc_taxi <- open_dataset(\"data/nyc-taxi/\")\nnyc_taxi\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\nFileSystemDataset with 120 Parquet files\n24 columns\nvendor_name: string\npickup_datetime: timestamp[ms]\ndropoff_datetime: timestamp[ms]\npassenger_count: int64\ntrip_distance: double\npickup_longitude: double\npickup_latitude: double\nrate_code: string\nstore_and_fwd: string\ndropoff_longitude: double\ndropoff_latitude: double\npayment_type: string\nfare_amount: double\nextra: double\nmta_tax: double\ntip_amount: double\ntolls_amount: double\ntotal_amount: double\nimprovement_surcharge: double\ncongestion_surcharge: double\n...\n4 more columns\nUse `schema()` to see entire schema\n```\n\n\n:::\n:::\n\n\n\n## Arrow Datasets\n\n![](images/nyc_taxi_dataset.png)\n\n\n## Constructing queries\n\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nlibrary(dplyr)\n\nshared_rides <- nyc_taxi |>\n  group_by(year) |>\n  summarize(\n    all_trips = n(),\n    shared_trips = sum(passenger_count > 1, na.rm = TRUE)\n  ) |>\n  mutate(pct_shared = shared_trips / all_trips * 100) \n\nclass(shared_rides)\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n[1] \"arrow_dplyr_query\"\n```\n\n\n:::\n:::\n\n\n\n## arrow dplyr queries\n\n-   query has been constructed but not evaluated\n-   nothing has been pulled into memory\n\n## Running the query\n\n-   `collect()` evaluates the query, in-memory output returns to R\n\n## `collect()`\n\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\ncollect(shared_rides)\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n# A tibble: 10 × 4\n    year all_trips shared_trips pct_shared\n   <int>     <int>        <int>      <dbl>\n 1  2012 178544324     53313752       29.9\n 2  2013 173179759     51215013       29.6\n 3  2014 165114361     48816505       29.6\n 4  2015 146112989     43081091       29.5\n 5  2016 131165043     38163870       29.1\n 6  2017 113495512     32296166       28.5\n 7  2018 102797401     28796633       28.0\n 8  2019  84393604     23515989       27.9\n 9  2020  24647055      5837960       23.7\n10  2021  30902618      7221844       23.4\n```\n\n\n:::\n:::\n\n\n\n\n## Calling `nrow()` to see how much data\n\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nnyc_taxi |>\n  filter(year %in% 2017:2021) |>\n  nrow()\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n[1] 356236190\n```\n\n\n:::\n:::\n\n\n\n\n## Your Turn\n\nUse the function `nrow()` to work out the answers to these questions:\n\n1.  How many taxi fares in the dataset had a total amount greater than \\$100?\n\n➡️ [Data Manipulation Part I Exercises Page](2_data_manipulation_1-exercises.html)\n\n\n## Previewing output for large queries\n\nHow much were fares in GBP (£)?\n\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nfares_pounds <- nyc_taxi |>\n  mutate(\n    fare_amount_pounds = fare_amount * 0.79\n  )\n```\n:::\n\n\n\n\nHow many rows?\n\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nfares_pounds |>\n  nrow()\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n[1] 1150352666\n```\n\n\n:::\n:::\n\n\n\n\n## Use `head()` and `collect()` to preview results\n\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nnyc_taxi |>\n  filter(year == 2020) |>\n  mutate(fare_pounds = fare_amount * 0.79) |>\n  select(fare_amount, fare_pounds) |>\n  head() |>\n  collect()\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n# A tibble: 6 × 2\n  fare_amount fare_pounds\n        <dbl>       <dbl>\n1         8          6.32\n2        17         13.4 \n3         6.5        5.14\n4         7          5.53\n5         6.5        5.14\n6        42         33.2 \n```\n\n\n:::\n:::\n\n\n\n\n## Use `across()` to transform data in multiple columns\n\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nnyc_taxi |>\n  mutate(across(ends_with(\"amount\"), list(pounds = ~.x * 0.79))) |>\n  select(contains(\"amount\")) |>\n  head() |>\n  collect()\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n# A tibble: 6 × 8\n  fare_amount tip_amount tolls_amount total_amount fare_amount_pounds\n        <dbl>      <dbl>        <dbl>        <dbl>              <dbl>\n1        29.7       6.04            0        36.2               23.5 \n2         9.3       0               0         9.8                7.35\n3         4.1       1.38            0         5.98               3.24\n4         4.5       1               0         6                  3.56\n5         4.5       0               0         5.5                3.56\n6         4.1       0               0         5.6                3.24\n# ℹ 3 more variables: tip_amount_pounds <dbl>, tolls_amount_pounds <dbl>,\n#   total_amount_pounds <dbl>\n```\n\n\n:::\n:::\n\n\n\n\n## Summary\n\n-   Use `nrow()` to work out how many rows of data your analyses will return\n-   Use `collect()` to pull all of the data into your R session\n-   Use `head()` and `collect()` to preview results\n-   Use `across()` to manipulate data in multiple columns at once\n\n# dplyr verbs API in arrow - alternatives\n\n## Example - `slice()`\n\nFirst three trips in the dataset in 2021 where distance \\> 100 miles\n\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nlong_rides_2021 <- nyc_taxi |>\n  filter(year == 2021 & trip_distance > 100) |>\n  select(pickup_datetime, year, trip_distance)\n\nlong_rides_2021 |>\n  slice(1:3)\n```\n\n::: {.cell-output .cell-output-error}\n\n```\nError in UseMethod(\"slice\"): no applicable method for 'slice' applied to an object of class \"arrow_dplyr_query\"\n```\n\n\n:::\n:::\n\n\n\n\n## Head to the docs!\n\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\n?`arrow-dplyr`\n```\n:::\n\n\n\n\nor view them at <https://arrow.apache.org/docs/r/reference/acero.html>\n\n## A different function\n\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nlong_rides_2021 |>\n  slice_max(n = 3, order_by = trip_distance, with_ties = FALSE) |>\n  collect()\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n# A tibble: 3 × 3\n  pickup_datetime      year trip_distance\n  <dttm>              <int>         <dbl>\n1 2021-11-16 12:55:00  2021       351613.\n2 2021-10-27 17:46:00  2021       345124.\n3 2021-12-11 10:48:00  2021       335094.\n```\n\n\n:::\n:::\n\n\n\n\n## Or call `collect()` first\n\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nlong_rides_2021 |>\n  collect() |>\n  slice(1:3)\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n# A tibble: 3 × 3\n  pickup_datetime      year trip_distance\n  <dttm>              <int>         <dbl>\n1 2021-10-02 15:04:53  2021          188.\n2 2021-10-03 16:45:02  2021          134 \n3 2021-10-03 17:29:35  2021          218.\n```\n\n\n:::\n:::\n\n\n\n\n## tidyr functions - pivot\n\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nlibrary(tidyr)\n\nnyc_taxi |> \n  group_by(vendor_name) |>\n  summarise(max_fare = max(fare_amount)) |>\n  pivot_longer(!vendor_name, names_to = \"metric\") |> \n  collect()\n```\n\n::: {.cell-output .cell-output-error}\n\n```\nError in UseMethod(\"pivot_longer\"): no applicable method for 'pivot_longer' applied to an object of class \"arrow_dplyr_query\"\n```\n\n\n:::\n:::\n\n\n\n\n## duckdb\n\n* in-memory database\n* columnar\n* understands Arrow format\n\n## sharing data with duckdb and arrow\n\n![](images/dplyr-arrow-duckdb.png)\n\n## tidyr functions - pivot with duckdb!\n\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nlibrary(duckdb)\n\nnyc_taxi |> \n  group_by(vendor_name) |>\n  summarise(max_fare = max(fare_amount)) |>\n  to_duckdb() |> # send data to duckdb\n  pivot_longer(!vendor_name, names_to = \"metric\") |> \n  to_arrow() |> # return data back to arrow\n  collect()\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n# A tibble: 3 × 3\n  vendor_name metric     value\n  <chr>       <chr>      <dbl>\n1 CMT         max_fare 998310.\n2 VTS         max_fare  10000.\n3 <NA>        max_fare   3555.\n```\n\n\n:::\n:::\n\n\n\n\n# Using functions inside verbs\n\n## Using functions inside verbs\n\n-   lots of the [lubridate](https://lubridate.tidyverse.org/) and [stringr](https://stringr.tidyverse.org/) APIs supported!\n-   base R and others too - always good to check the docs\n\n## Morning vs afternoon with namespacing\n\n\n\n\n::: {.cell}\n\n```{.r .cell-code  code-line-numbers=\"3\"}\nnyc_taxi |>\n  group_by(\n    time_of_day = ifelse(lubridate::am(pickup_datetime), \"morning\", \"afternoon\")\n  ) |>\n  count() |>\n  collect()\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n# A tibble: 2 × 2\n# Groups:   time_of_day [2]\n  time_of_day         n\n  <chr>           <int>\n1 afternoon   736491676\n2 morning     413860990\n```\n\n\n:::\n:::\n\n\n\n\n## Morning vs afternoon - without namespacing\n\n\n\n\n::: {.cell}\n\n```{.r .cell-code  code-line-numbers=\"5\"}\nlibrary(lubridate)\n\nnyc_taxi |>\n  group_by(\n    time_of_day = ifelse(am(pickup_datetime), \"morning\", \"afternoon\")\n  ) |>\n  count() |>\n  collect()\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n# A tibble: 2 × 2\n# Groups:   time_of_day [2]\n  time_of_day         n\n  <chr>           <int>\n1 afternoon   736491676\n2 morning     413860990\n```\n\n\n:::\n:::\n\n\n\n\n## How does this work?\n\n![](images/dplyr-backend.png)\n\n## arrow C++\n\n![](images/arrow_cpp_functions.png)\n\n## arrow dplyr queries\n\n![](images/arrow_dplyr_query.png)\n\n## What if a function isn't implemented?\n\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nnyc_taxi |>\n  mutate(vendor_name = na_if(vendor_name, \"CMT\")) |>\n  head() |>\n  collect()\n```\n\n::: {.cell-output .cell-output-error}\n\n```\nError: Expression na_if(vendor_name, \"CMT\") not supported in Arrow\nCall collect() first to pull data into R.\n```\n\n\n:::\n:::\n\n\n\n\n## Head to the docs again to see what's implemented!\n\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\n?`arrow-dplyr`\n```\n:::\n\n\n\n\nor view them at <https://arrow.apache.org/docs/r/reference/acero.html>\n\n## Option 1 - find a workaround!\n\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nnyc_taxi |>\n  mutate(vendor_name = ifelse(vendor_name == \"CMT\", NA, vendor_name)) |>\n  head() |>\n  collect()\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n# A tibble: 6 × 24\n  vendor_name pickup_datetime     dropoff_datetime    passenger_count\n  <chr>       <dttm>              <dttm>                        <int>\n1 <NA>        2012-01-20 14:09:36 2012-01-20 14:42:25               1\n2 <NA>        2012-01-20 14:54:10 2012-01-20 15:06:55               1\n3 <NA>        2012-01-20 08:08:01 2012-01-20 08:11:02               1\n4 <NA>        2012-01-20 08:36:22 2012-01-20 08:39:44               1\n5 <NA>        2012-01-20 20:58:32 2012-01-20 21:03:04               1\n6 <NA>        2012-01-20 19:40:20 2012-01-20 19:43:43               2\n# ℹ 20 more variables: trip_distance <dbl>, pickup_longitude <dbl>,\n#   pickup_latitude <dbl>, rate_code <chr>, store_and_fwd <chr>,\n#   dropoff_longitude <dbl>, dropoff_latitude <dbl>, payment_type <chr>,\n#   fare_amount <dbl>, extra <dbl>, mta_tax <dbl>, tip_amount <dbl>,\n#   tolls_amount <dbl>, total_amount <dbl>, improvement_surcharge <dbl>,\n#   congestion_surcharge <dbl>, pickup_location_id <int>,\n#   dropoff_location_id <int>, year <int>, month <int>\n```\n\n\n:::\n:::\n\n\n\n\n## Option 2\n\n-   In data manipulation part 2!\n\n## Your Turn\n\n1.  Use the `dplyr::filter()` and `stringr::str_ends()` functions to return a subset of the data which is a) from September 2020, and b) the value in `vendor_name` ends with the letter \"S\".\n\n2.  Try to use the `stringr` function `str_replace_na()` to replace any `NA` values in the `vendor_name` column with the string \"No vendor\" instead. What happens, and why?\n\n3.  Bonus question: see if you can find a different way of completing the task in question 2.\n\n➡️ [Data Manipulation Part I Exercises Page](2_data_manipulation_1-exercises.html)\n\n## Working with custom functions\n\nArrow 17.0.0 or later!\n\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\ntime_text <- function(time){\n  day_of_week <- wday(time, label = TRUE, abbr = FALSE)\n  time_of_day <- ifelse(lubridate::am(time), \"AM\", \"PM\")\n  paste(day_of_week, time_of_day)\n}\n\nnyc_taxi |>\n  mutate(pickup_text = time_text(pickup_datetime)) |>\n  select(pickup_datetime, pickup_text) |>\n  head() |>\n  collect() \n```\n:::\n\n::: {.cell}\n::: {.cell-output .cell-output-stdout}\n\n```\n# A tibble: 6 × 2\n  pickup_datetime     pickup_text\n  <dttm>              <chr>      \n1 2012-01-08 20:50:38 Sunday PM  \n2 2012-01-08 20:52:01 Sunday PM  \n3 2012-01-08 02:39:26 Sunday AM  \n4 2012-01-08 02:40:49 Sunday AM  \n5 2012-01-09 03:42:37 Monday AM  \n6 2012-01-08 20:51:47 Sunday PM  \n```\n\n\n:::\n:::\n\n\n\n## How did that work?\n\nCustom function converted to Arrow Expression; query doesn't contain any reference to the `time_text()` function.\n\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nnyc_taxi |>\n  mutate(pickup_text = time_text(pickup_datetime)) |>\n  select(pickup_datetime, pickup_text)\n```\n:::\n\n::: {.cell}\n::: {.cell-output .cell-output-stdout}\n\n```\nFileSystemDataset (query)\npickup_datetime: timestamp[ms]\npickup_text: string (binary_join_element_wise(cast(strftime(pickup_datetime, {format=\"%A\"}), {to_type=string, allow_int_overflow=false, allow_time_truncate=false, allow_time_overflow=false, allow_decimal_truncate=false, allow_float_truncate=false, allow_invalid_utf8=false}), cast(if_else((hour(pickup_datetime) < 12), \"AM\", \"PM\"), {to_type=string, allow_int_overflow=false, allow_time_truncate=false, allow_time_overflow=false, allow_decimal_truncate=false, allow_float_truncate=false, allow_invalid_utf8=false}), \" \", {null_handling=REPLACE, null_replacement=\"NA\"}))\n\nSee $.data for the source Arrow object\n```\n\n\n:::\n:::\n\n\n\n## Anything else to be aware of?\n\n* arrow 17.0.0 or later\n* this will only work for functions which have Arrow bindings\n* use `` ?`arrow-dplyr` `` to see which ones do\n\n## Summary\n\n-   Working with Arrow Datasets allow you to manipulate data which is larger-than-memory\n-   You can use many dplyr functions with arrow - run `` ?`arrow-dplyr` `` to view the docs\n-   You can pass data to duckdb to use functions implemented in duckdb but not arrow\n",
-    "supporting": [],
+    "markdown": "---\nfooter: \"[🔗 pos.it/arrow-conf24](https://pos.it/arrow-conf24)\"\nlogo: \"images/logo.png\"\nexecute:\n  echo: true\nformat:\n  revealjs: \n    theme: default\nengine: knitr\neditor: source\n---\n\n\n\n\n# Data Manipulation---Part 1 {#data-manip-1}\n\n\n\n\n::: {.cell}\n\n:::\n\n\n\n\n## Goals\n\nAvoiding these! But...don't worry!\n\n![](images/segfault.png)\n\n## An Arrow Dataset\n\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nlibrary(arrow)\n\nnyc_taxi <- open_dataset(\"data/nyc-taxi/\")\nnyc_taxi\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\nFileSystemDataset with 120 Parquet files\n24 columns\nvendor_name: string\npickup_datetime: timestamp[ms]\ndropoff_datetime: timestamp[ms]\npassenger_count: int64\ntrip_distance: double\npickup_longitude: double\npickup_latitude: double\nrate_code: string\nstore_and_fwd: string\ndropoff_longitude: double\ndropoff_latitude: double\npayment_type: string\nfare_amount: double\nextra: double\nmta_tax: double\ntip_amount: double\ntolls_amount: double\ntotal_amount: double\nimprovement_surcharge: double\ncongestion_surcharge: double\n...\n4 more columns\nUse `schema()` to see entire schema\n```\n\n\n:::\n:::\n\n\n\n## Arrow Datasets\n\n![](images/nyc_taxi_dataset.png)\n\n\n## Constructing queries\n\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nlibrary(dplyr)\n\nshared_rides <- nyc_taxi |>\n  group_by(year) |>\n  summarize(\n    all_trips = n(),\n    shared_trips = sum(passenger_count > 1, na.rm = TRUE)\n  ) |>\n  mutate(pct_shared = shared_trips / all_trips * 100) \n\nclass(shared_rides)\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n[1] \"arrow_dplyr_query\"\n```\n\n\n:::\n:::\n\n\n\n## arrow dplyr queries\n\n-   query has been constructed but not evaluated\n-   nothing has been pulled into memory\n\n## Running the query\n\n-   `collect()` evaluates the query, in-memory output returns to R\n\n## `collect()`\n\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\ncollect(shared_rides)\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n# A tibble: 10 × 4\n    year all_trips shared_trips pct_shared\n   <int>     <int>        <int>      <dbl>\n 1  2012 178544324     53313752       29.9\n 2  2013 173179759     51215013       29.6\n 3  2014 165114361     48816505       29.6\n 4  2015 146112989     43081091       29.5\n 5  2016 131165043     38163870       29.1\n 6  2017 113495512     32296166       28.5\n 7  2018 102797401     28796633       28.0\n 8  2019  84393604     23515989       27.9\n 9  2020  24647055      5837960       23.7\n10  2021  30902618      7221844       23.4\n```\n\n\n:::\n:::\n\n\n\n\n## Calling `nrow()` to see how much data\n\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nnyc_taxi |>\n  filter(year %in% 2017:2021) |>\n  nrow()\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n[1] 356236190\n```\n\n\n:::\n:::\n\n\n\n\n## Your Turn\n\nUse the function `nrow()` to work out the answers to these questions:\n\n1.  How many taxi fares in the dataset had a total amount greater than \\$100?\n\n➡️ [Data Manipulation Part I Exercises Page](2_data_manipulation_1-exercises.html)\n\n\n## Previewing output for large queries\n\nHow much were fares in GBP (£)?\n\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nfares_pounds <- nyc_taxi |>\n  mutate(\n    fare_amount_pounds = fare_amount * 0.79\n  )\n```\n:::\n\n\n\n\nHow many rows?\n\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nfares_pounds |>\n  nrow()\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n[1] 1150352666\n```\n\n\n:::\n:::\n\n\n\n\n## Use `head()` and `collect()` to preview results\n\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nnyc_taxi |>\n  filter(year == 2020) |>\n  mutate(fare_pounds = fare_amount * 0.79) |>\n  select(fare_amount, fare_pounds) |>\n  head() |>\n  collect()\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n# A tibble: 6 × 2\n  fare_amount fare_pounds\n        <dbl>       <dbl>\n1        16.5       13.0 \n2        21.5       17.0 \n3         5          3.95\n4        10.5        8.30\n5        11          8.69\n6         5.5        4.35\n```\n\n\n:::\n:::\n\n\n\n\n## Use `across()` to transform data in multiple columns\n\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nnyc_taxi |>\n  mutate(across(ends_with(\"amount\"), list(pounds = ~.x * 0.79))) |>\n  select(contains(\"amount\")) |>\n  head() |>\n  collect()\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n# A tibble: 6 × 8\n  fare_amount tip_amount tolls_amount total_amount fare_amount_pounds\n        <dbl>      <dbl>        <dbl>        <dbl>              <dbl>\n1        29.7       6.04            0        36.2               23.5 \n2         9.3       0               0         9.8                7.35\n3         4.1       1.38            0         5.98               3.24\n4         4.5       1               0         6                  3.56\n5         4.5       0               0         5.5                3.56\n6         4.1       0               0         5.6                3.24\n# ℹ 3 more variables: tip_amount_pounds <dbl>, tolls_amount_pounds <dbl>,\n#   total_amount_pounds <dbl>\n```\n\n\n:::\n:::\n\n\n\n\n## Summary\n\n-   Use `nrow()` to work out how many rows of data your analyses will return\n-   Use `collect()` to pull all of the data into your R session\n-   Use `head()` and `collect()` to preview results\n-   Use `across()` to manipulate data in multiple columns at once\n\n# dplyr verbs API in arrow - alternatives\n\n## Example - `slice()`\n\nFirst three trips in the dataset in 2021 where distance \\> 100 miles\n\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nlong_rides_2021 <- nyc_taxi |>\n  filter(year == 2021 & trip_distance > 100) |>\n  select(pickup_datetime, year, trip_distance)\n\nlong_rides_2021 |>\n  slice(1:3)\n```\n\n::: {.cell-output .cell-output-error}\n\n```\nError in UseMethod(\"slice\"): no applicable method for 'slice' applied to an object of class \"arrow_dplyr_query\"\n```\n\n\n:::\n:::\n\n\n\n\n## Head to the docs!\n\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\n?acero\n```\n:::\n\n\n\n\nor view them at <https://arrow.apache.org/docs/r/reference/acero.html>\n\n## A different function\n\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nlong_rides_2021 |>\n  slice_max(n = 3, order_by = trip_distance, with_ties = FALSE) |>\n  collect()\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n# A tibble: 3 × 3\n  pickup_datetime      year trip_distance\n  <dttm>              <int>         <dbl>\n1 2021-11-16 04:55:00  2021       351613.\n2 2021-10-27 09:46:00  2021       345124.\n3 2021-12-11 02:48:00  2021       335094.\n```\n\n\n:::\n:::\n\n\n\n\n## Or call `collect()` first\n\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nlong_rides_2021 |>\n  collect() |>\n  slice(1:3)\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n# A tibble: 3 × 3\n  pickup_datetime      year trip_distance\n  <dttm>              <int>         <dbl>\n1 2021-01-03 01:01:26  2021          216.\n2 2021-01-03 03:36:52  2021          268.\n3 2021-10-02 07:04:53  2021          188.\n```\n\n\n:::\n:::\n\n\n\n\n## tidyr functions - pivot\n\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nlibrary(tidyr)\n\nnyc_taxi |> \n  group_by(vendor_name) |>\n  summarise(max_fare = max(fare_amount)) |>\n  pivot_longer(!vendor_name, names_to = \"metric\") |> \n  collect()\n```\n\n::: {.cell-output .cell-output-error}\n\n```\nError in UseMethod(\"pivot_longer\"): no applicable method for 'pivot_longer' applied to an object of class \"arrow_dplyr_query\"\n```\n\n\n:::\n:::\n\n\n\n\n## duckdb\n\n* in-memory database\n* columnar\n* understands Arrow format\n\n## sharing data with duckdb and arrow\n\n![](images/dplyr-arrow-duckdb.png)\n\n## tidyr functions - pivot with duckdb!\n\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nlibrary(duckdb)\n\nnyc_taxi |> \n  group_by(vendor_name) |>\n  summarise(max_fare = max(fare_amount)) |>\n  to_duckdb() |> # send data to duckdb\n  pivot_longer(!vendor_name, names_to = \"metric\") |> \n  to_arrow() |> # return data back to arrow\n  collect()\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n# A tibble: 3 × 3\n  vendor_name metric     value\n  <chr>       <chr>      <dbl>\n1 CMT         max_fare 998310.\n2 VTS         max_fare  10000.\n3 <NA>        max_fare   3555.\n```\n\n\n:::\n:::\n\n\n\n\n# Using functions inside verbs\n\n## Using functions inside verbs\n\n-   lots of the [lubridate](https://lubridate.tidyverse.org/) and [stringr](https://stringr.tidyverse.org/) APIs supported!\n-   base R and others too - always good to check the docs\n\n## Morning vs afternoon with namespacing\n\n\n\n\n::: {.cell}\n\n```{.r .cell-code  code-line-numbers=\"3\"}\nnyc_taxi |>\n  group_by(\n    time_of_day = ifelse(lubridate::am(pickup_datetime), \"morning\", \"afternoon\")\n  ) |>\n  count() |>\n  collect()\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n# A tibble: 2 × 2\n# Groups:   time_of_day [2]\n  time_of_day         n\n  <chr>           <int>\n1 afternoon   736491676\n2 morning     413860990\n```\n\n\n:::\n:::\n\n\n\n\n## Morning vs afternoon - without namespacing\n\n\n\n\n::: {.cell}\n\n```{.r .cell-code  code-line-numbers=\"5\"}\nlibrary(lubridate)\n\nnyc_taxi |>\n  group_by(\n    time_of_day = ifelse(am(pickup_datetime), \"morning\", \"afternoon\")\n  ) |>\n  count() |>\n  collect()\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n# A tibble: 2 × 2\n# Groups:   time_of_day [2]\n  time_of_day         n\n  <chr>           <int>\n1 afternoon   736491676\n2 morning     413860990\n```\n\n\n:::\n:::\n\n\n\n\n## How does this work?\n\n![](images/dplyr-backend.png)\n## Acero\n\n* arrow's query execution engine\n* use Arrow functions on Arrow Datasets\n\n## Acero\n\n![](images/arrow_cpp_functions.png)\n\n## arrow dplyr queries\n\n![](images/arrow_dplyr_query.png)\n\n## What if a function isn't implemented?\n\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nnyc_taxi |>\n  mutate(vendor_name = na_if(vendor_name, \"CMT\")) |>\n  head() |>\n  collect()\n```\n\n::: {.cell-output .cell-output-error}\n\n```\nError in `na_if()`:\n! Expression not supported in Arrow\n→ Call collect() first to pull data into R.\n```\n\n\n:::\n:::\n\n\n\n\n## Head to the docs again to see what's implemented!\n\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\n?acero\n```\n:::\n\n\n\n\nor view them at <https://arrow.apache.org/docs/r/reference/acero.html>\n\n## Option 1 - find a workaround!\n\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nnyc_taxi |>\n  mutate(vendor_name = ifelse(vendor_name == \"CMT\", NA, vendor_name)) |>\n  head() |>\n  collect()\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n# A tibble: 6 × 24\n  vendor_name pickup_datetime     dropoff_datetime    passenger_count\n  <chr>       <dttm>              <dttm>                        <int>\n1 <NA>        2012-01-20 06:09:36 2012-01-20 06:42:25               1\n2 <NA>        2012-01-20 06:54:10 2012-01-20 07:06:55               1\n3 <NA>        2012-01-20 00:08:01 2012-01-20 00:11:02               1\n4 <NA>        2012-01-20 00:36:22 2012-01-20 00:39:44               1\n5 <NA>        2012-01-20 12:58:32 2012-01-20 13:03:04               1\n6 <NA>        2012-01-20 11:40:20 2012-01-20 11:43:43               2\n# ℹ 20 more variables: trip_distance <dbl>, pickup_longitude <dbl>,\n#   pickup_latitude <dbl>, rate_code <chr>, store_and_fwd <chr>,\n#   dropoff_longitude <dbl>, dropoff_latitude <dbl>, payment_type <chr>,\n#   fare_amount <dbl>, extra <dbl>, mta_tax <dbl>, tip_amount <dbl>,\n#   tolls_amount <dbl>, total_amount <dbl>, improvement_surcharge <dbl>,\n#   congestion_surcharge <dbl>, pickup_location_id <int>,\n#   dropoff_location_id <int>, year <int>, month <int>\n```\n\n\n:::\n:::\n\n\n\n\n## Option 2\n\n-   In data manipulation part 2!\n\n## Your Turn\n\n1.  Use the `dplyr::filter()` and `stringr::str_ends()` functions to return a subset of the data which is a) from September 2020, and b) the value in `vendor_name` ends with the letter \"S\".\n\n2.  Try to use the `stringr` function `str_replace_na()` to replace any `NA` values in the `vendor_name` column with the string \"No vendor\" instead. What happens, and why?\n\n3.  Bonus question: see if you can find a different way of completing the task in question 2.\n\n➡️ [Data Manipulation Part I Exercises Page](2_data_manipulation_1-exercises.html)\n\n## Working with custom functions\n\nArrow 17.0.0 or later!\n\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\ntime_text <- function(time){\n  day_of_week <- wday(time, label = TRUE, abbr = FALSE)\n  time_of_day <- ifelse(lubridate::am(time), \"AM\", \"PM\")\n  paste(day_of_week, time_of_day)\n}\n\nnyc_taxi |>\n  mutate(pickup_text = time_text(pickup_datetime)) |>\n  select(pickup_datetime, pickup_text) |>\n  head() |>\n  collect() \n```\n:::\n\n::: {.cell}\n::: {.cell-output .cell-output-stdout}\n\n```\n# A tibble: 6 × 2\n  pickup_datetime     pickup_text\n  <dttm>              <chr>      \n1 2012-01-08 12:50:38 Sunday PM  \n2 2012-01-08 12:52:01 Sunday PM  \n3 2012-01-07 18:39:26 Sunday AM  \n4 2012-01-07 18:40:49 Sunday AM  \n5 2012-01-08 19:42:37 Monday AM  \n6 2012-01-08 12:51:47 Sunday PM  \n```\n\n\n:::\n:::\n\n\n\n## How did that work?\n\nCustom function converted to Arrow Expression; query doesn't contain any reference to the `time_text()` function.\n\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nnyc_taxi |>\n  mutate(pickup_text = time_text(pickup_datetime)) |>\n  select(pickup_datetime, pickup_text)\n```\n:::\n\n::: {.cell}\n::: {.cell-output .cell-output-stdout}\n\n```\nFileSystemDataset (query)\npickup_datetime: timestamp[ms]\npickup_text: string (binary_join_element_wise(cast(strftime(pickup_datetime, {format=\"%A\"}), {to_type=string, allow_int_overflow=false, allow_time_truncate=false, allow_time_overflow=false, allow_decimal_truncate=false, allow_float_truncate=false, allow_invalid_utf8=false}), cast(if_else((hour(pickup_datetime) < 12), \"AM\", \"PM\"), {to_type=string, allow_int_overflow=false, allow_time_truncate=false, allow_time_overflow=false, allow_decimal_truncate=false, allow_float_truncate=false, allow_invalid_utf8=false}), \" \", {null_handling=REPLACE, null_replacement=\"NA\"}))\n\nSee $.data for the source Arrow object\n```\n\n\n:::\n:::\n\n\n\n## Anything else to be aware of?\n\n* arrow 17.0.0 or later\n* this will only work for functions which have Arrow bindings\n* use `?acero` to see which ones do\n\n## Summary\n\n-   Working with Arrow Datasets allow you to manipulate data which is larger-than-memory\n-   You can use many dplyr functions with arrow - run `?acero` to view the docs\n-   You can pass data to duckdb to use functions implemented in duckdb but not arrow\n",
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-    "markdown": "---\nfooter: \"[🔗 pos.it/arrow-conf24](https://pos.it/arrow-conf24)\"\nlogo: \"images/logo.png\"\nexecute:\n  echo: true\nformat:\n  revealjs: \n    theme: default\nengine: knitr\neditor: source\n---\n\n\n\n\n# Data Manipulation---Part 2 {#data-manip-2}\n\n\n\n\n::: {.cell}\n\n:::\n\n\n\n\n## What if a function binding doesn't exist - revisited!\n\n-   Option 1 - find a workaround\n-   Option 2 - user-defined functions (UDFs)\n\n## Why use a UDF?\n\n* If no bindings for a function exist\n* Implement your own custom functions\n* Run in R not Arrow\n\n## How do function bindings usually work?\n\n![](images/udf_normal.png)\n## How do UDFs work?\n\n![](images/udf.png)\n\n\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\ntime_diff_minutes <- function(pickup, dropoff){\n  difftime(dropoff, pickup, units = \"mins\") |>\n      round() |>\n      as.integer()\n}\n\nnyc_taxi |>\n  mutate(\n    duration_minutes = time_diff_minutes(pickup_datetime, dropoff_datetime)\n  ) |> \n  select(pickup_datetime, dropoff_datetime, duration_minutes) |>\n  head() |>\n  collect()\n```\n\n::: {.cell-output .cell-output-error}\n\n```\nError: Expression time_diff_minutes(pickup_datetime, dropoff_datetime) not supported in Arrow\nCall collect() first to pull data into R.\n```\n\n\n:::\n:::\n\n\n\nWe get an error as we can't automatically convert the function to arrow.\n\n# User-defined functions (aka UDFs)\n\n-   Define your own functions\n-   Scalar functions - 1 row input and 1 row output\n\n## User-defined functions - definition\n\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nregister_scalar_function(\n  name = \"time_diff_minutes\",\n  function(context, pickup, dropoff) {\n    difftime(dropoff, pickup, units = \"mins\") |>\n      round() |>\n      as.integer()\n  },\n  in_type = schema(\n    pickup = timestamp(unit = \"ms\"),\n    dropoff = timestamp(unit = \"ms\")\n  ),\n  out_type = int32(),\n  auto_convert = TRUE\n)\n```\n:::\n\n\n\n\nThis looks complicated, so let's look at it 1 part at a time!\n\n## User-defined functions - definition\n\nStep 1. Give the function a name\n\n\n\n\n::: {.cell}\n\n```{.r .cell-code  code-line-numbers=\"2\"}\nregister_scalar_function(\n  name = \"time_diff_minutes\",\n  function(context, pickup, dropoff) {\n    difftime(dropoff, pickup, units = \"mins\") |>\n      round() |>\n      as.integer()\n  },\n  in_type = schema(\n    pickup = timestamp(unit = \"ms\"),\n    dropoff = timestamp(unit = \"ms\")\n  ),\n  out_type = int32(),\n  auto_convert = TRUE\n)\n```\n:::\n\n\n\n\n## User-defined functions - definition\n\nStep 2. Define the body of the function - first argument *must* be `context`\n\n\n\n\n::: {.cell}\n\n```{.r .cell-code  code-line-numbers=\"3,4,5,6,7\"}\nregister_scalar_function(\n  name = \"time_diff_minutes\",\n  function(context, pickup, dropoff) {\n    difftime(dropoff, pickup, units = \"mins\") |>\n      round() |>\n      as.integer()\n  },\n  in_type = schema(\n    pickup = timestamp(unit = \"ms\"),\n    dropoff = timestamp(unit = \"ms\")\n  ),\n  out_type = int32(),\n  auto_convert = TRUE\n)\n```\n:::\n\n\n\n\n## User-defined functions - definition\n\nStep 3. Set the schema of the input arguments\n\n\n\n\n::: {.cell}\n\n```{.r .cell-code  code-line-numbers=\"8,9,10,11\"}\nregister_scalar_function(\n  name = \"time_diff_minutes\",\n  function(context, pickup, dropoff) {\n    difftime(dropoff, pickup, units = \"mins\") |>\n      round() |>\n      as.integer()\n  },\n  in_type = schema(\n    pickup = timestamp(unit = \"ms\"),\n    dropoff = timestamp(unit = \"ms\")\n  ),\n  out_type = int32(),\n  auto_convert = TRUE\n)\n```\n:::\n\n\n\n\n## User-defined functions - definition\n\nStep 4. Set the data type of the output\n\n\n\n\n::: {.cell}\n\n```{.r .cell-code  code-line-numbers=\"12\"}\nregister_scalar_function(\n  name = \"time_diff_minutes\",\n  function(context, pickup, dropoff) {\n    difftime(dropoff, pickup, units = \"mins\") |>\n      round() |>\n      as.integer()\n  },\n  in_type = schema(\n    pickup = timestamp(unit = \"ms\"),\n    dropoff = timestamp(unit = \"ms\")\n  ),\n  out_type = int32(),\n  auto_convert = TRUE\n)\n```\n:::\n\n\n\n\n## User-defined functions - definition\n\nStep 5. Set `auto_convert = TRUE` if using in a dplyr pipeline\n\n\n\n\n::: {.cell}\n\n```{.r .cell-code  code-line-numbers=\"13\"}\nregister_scalar_function(\n  name = \"time_diff_minutes\",\n  function(context, pickup, dropoff) {\n    difftime(dropoff, pickup, units = \"mins\") |>\n      round() |>\n      as.integer()\n  },\n  in_type = schema(\n    pickup = timestamp(unit = \"ms\"),\n    dropoff = timestamp(unit = \"ms\")\n  ),\n  out_type = int32(),\n  auto_convert = TRUE\n)\n```\n:::\n\n\n\n\n## User-defined functions - usage\n\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nnyc_taxi |>\n  mutate(\n    duration_minutes = time_diff_minutes(pickup_datetime, dropoff_datetime)\n  ) |>\n  select(pickup_datetime, dropoff_datetime, duration_minutes) |>\n  head() |>\n  collect()\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n# A tibble: 6 × 3\n  pickup_datetime     dropoff_datetime    duration_minutes\n  <dttm>              <dttm>                         <int>\n1 2012-10-07 18:19:00 2012-10-07 18:29:00               10\n2 2012-10-07 18:19:00 2012-10-07 18:33:00               14\n3 2012-10-07 18:19:00 2012-10-07 18:35:00               16\n4 2012-10-07 18:19:00 2012-10-07 18:35:00               16\n5 2012-10-07 18:19:00 2012-10-07 18:42:00               23\n6 2012-10-07 18:19:00 2012-10-07 18:43:00               24\n```\n\n\n:::\n:::\n\n\n\n\n## Your Turn\n\n1.  Write a user-defined function which wraps the `stringr` function `str_replace_na()`, and use it to replace any `NA` values in the `vendor_name` column with the string \"No vendor\" instead. (Test it on the data from 2019 so you're not pulling everything into memory)\n\n➡️ [Data Manipulation Part II Exercises Page](4_data_manipulation_2-exercises.html)\n\n## Summary\n\n-   You can use UDFs to create your own bindings when they don't exist\n-   UDFs must be scalar (1 row in -\\> 1 row out) and stateless (no knowledge of other rows of data)\n-   Calculations done by R not Arrow, so slower than in-built bindings but still pretty fast\n\n# Joins\n\n## Joins\n\n![](images/joins.png)\n\n## Joining a reference table\n\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nvendors <- tibble::tibble(\n  code = c(\"VTS\", \"CMT\", \"DDS\"),\n  full_name = c(\n    \"Verifone Transportation Systems\",\n    \"Creative Mobile Technologies\",\n    \"Digital Dispatch Systems\"\n  )\n)\n\nnyc_taxi |>\n  left_join(vendors, by = c(\"vendor_name\" = \"code\")) |>\n  select(vendor_name, full_name, pickup_datetime) |>\n  head(3) |>\n  collect()\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n# A tibble: 3 × 3\n  vendor_name full_name                    pickup_datetime    \n  <chr>       <chr>                        <dttm>             \n1 CMT         Creative Mobile Technologies 2012-10-07 17:26:40\n2 CMT         Creative Mobile Technologies 2012-10-07 17:26:52\n3 CMT         Creative Mobile Technologies 2012-10-07 17:26:57\n```\n\n\n:::\n:::\n\n\n\n\n## Traps for the unwary\n\nQuestion: which are the most common borough-to-borough journeys in the dataset?\n\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nnyc_taxi_zones <- \n  read_csv_arrow(\"data/taxi_zone_lookup.csv\") |>\n  select(location_id = LocationID,\n         borough = Borough)\n\nnyc_taxi_zones\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n# A tibble: 265 × 2\n   location_id borough      \n         <int> <chr>        \n 1           1 EWR          \n 2           2 Queens       \n 3           3 Bronx        \n 4           4 Manhattan    \n 5           5 Staten Island\n 6           6 Staten Island\n 7           7 Queens       \n 8           8 Queens       \n 9           9 Queens       \n10          10 Queens       \n# ℹ 255 more rows\n```\n\n\n:::\n:::\n\n\n\n\n## Why didn't this work?\n\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nnyc_taxi |>\n  left_join(nyc_taxi_zones, by = c(\"pickup_location_id\" = \"location_id\")) |>\n  collect()\n```\n\n::: {.cell-output .cell-output-error}\n\n```\nError in `compute.arrow_dplyr_query()`:\n! Invalid: Incompatible data types for corresponding join field keys: FieldRef.Name(pickup_location_id) of type int64 and FieldRef.Name(location_id) of type int32\n```\n\n\n:::\n:::\n\n\n\n\n## Schema for the `nyc_taxi` Dataset\n\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nschema(nyc_taxi)\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\nSchema\nvendor_name: string\npickup_datetime: timestamp[ms]\ndropoff_datetime: timestamp[ms]\npassenger_count: int64\ntrip_distance: double\npickup_longitude: double\npickup_latitude: double\nrate_code: string\nstore_and_fwd: string\ndropoff_longitude: double\ndropoff_latitude: double\npayment_type: string\nfare_amount: double\nextra: double\nmta_tax: double\ntip_amount: double\ntolls_amount: double\ntotal_amount: double\nimprovement_surcharge: double\ncongestion_surcharge: double\npickup_location_id: int64\ndropoff_location_id: int64\nyear: int32\nmonth: int32\n```\n\n\n:::\n:::\n\n\n\n\n## Schema for the `nyc_taxi_zones` Table\n\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nnyc_taxi_zones_arrow <- arrow_table(nyc_taxi_zones)\nschema(nyc_taxi_zones_arrow)\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\nSchema\nlocation_id: int32\nborough: string\n```\n\n\n:::\n:::\n\n\n\n\n-   `pickup_location_id` is int64 in the `nyc_taxi` table\n-   `location_id` is int32 in the `nyc_taxi_zones` table\n\n## Take control of the schema\n\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nnyc_taxi_zones_arrow <- arrow_table(\n  nyc_taxi_zones, \n  schema = schema(location_id = int64(), borough = utf8())\n)\n```\n:::\n\n\n\n\n-   `schema()` takes variable name / types as input\n-   arrow has various \"type\" functions: `int64()`, `utf8()`, `boolean()`, `date32()` etc\n\n## Take control of the schema\n\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nnyc_taxi_zones_arrow <- arrow_table(\n  nyc_taxi_zones, \n  schema = schema(location_id = int64(), borough = utf8())\n)\nschema(nyc_taxi_zones_arrow)\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\nSchema\nlocation_id: int64\nborough: string\n```\n\n\n:::\n:::\n\n\n\n\n## Prepare the auxiliary tables\n\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\npickup <- nyc_taxi_zones_arrow |>\n  select(pickup_location_id = location_id,\n         pickup_borough = borough)\n\ndropoff <- nyc_taxi_zones_arrow |>\n  select(dropoff_location_id = location_id,\n         dropoff_borough = borough)\n```\n:::\n\n\n\n\n-   Join separately for the pickup and dropoff zones\n\n\n## Join and cross-tabulate\n\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nlibrary(tictoc)\n\ntic()\nborough_counts <- nyc_taxi |> \n  left_join(pickup) |>\n  left_join(dropoff) |>\n  count(pickup_borough, dropoff_borough) |>\n  arrange(desc(n)) |>\n  collect()\ntoc()\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n163.966 sec elapsed\n```\n\n\n:::\n:::\n\n\n\n\n<br>\n\n2-3 minutes to join twice and cross-tabulate on non-partition variables, with 1.15 billion rows of data 🙂\n\n## The results\n\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nborough_counts\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n# A tibble: 50 × 3\n   pickup_borough dropoff_borough         n\n   <chr>          <chr>               <int>\n 1 <NA>           <NA>            732357953\n 2 Manhattan      Manhattan       351198872\n 3 Queens         Manhattan        14440705\n 4 Manhattan      Queens           13052517\n 5 Manhattan      Brooklyn         11180867\n 6 Queens         Queens            7440356\n 7 Unknown        Unknown           4491811\n 8 Queens         Brooklyn          3662324\n 9 Brooklyn       Brooklyn          3550480\n10 Manhattan      Bronx             2071830\n# ℹ 40 more rows\n```\n\n\n:::\n:::\n\n\n\n\n## Your Turn\n\n1.  How many taxi pickups were recorded in 2019 from the three major airports covered by the NYC Taxis data set (JFK, LaGuardia, Newark)? (Hint: you can use `stringr::str_detect()` to help you find pickup zones with the word \"Airport\" in them)\n\n➡️ [Data Manipulation Part II Exercises Page](4_data_manipulation_2-exercises.html)\n\n## Summary\n\n-   You can join Arrow Tables and Datasets to R data frames and Arrow Tables\n-   The Arrow data type of join keys must always match\n\n",
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+    "markdown": "---\nfooter: \"[🔗 pos.it/arrow-conf24](https://pos.it/arrow-conf24)\"\nlogo: \"images/logo.png\"\nexecute:\n  echo: true\nformat:\n  revealjs: \n    theme: default\nengine: knitr\neditor: source\n---\n\n\n\n\n# Data Manipulation---Part 2 {#data-manip-2}\n\n\n\n\n::: {.cell}\n\n:::\n\n\n\n\n## What if a function binding doesn't exist - revisited!\n\n-   Option 1 - find a workaround\n-   Option 2 - user-defined functions (UDFs)\n\n## Why use a UDF?\n\n* If no bindings for a function exist\n* Implement your own custom functions\n* Run in R not Arrow\n\n<!-- \nIn arrow 17.0.0 there will be less need to do this for custom functions\n\n-->\n\n## How do function bindings usually work?\n\n![](images/udf_normal.png)\n## How do UDFs work?\n\n![](images/udf.png)\n\n\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\ntime_diff_minutes <- function(pickup, dropoff){\n  difftime(dropoff, pickup, units = \"mins\") |>\n      round() |>\n      as.integer()\n}\n\nnyc_taxi |>\n  mutate(\n    duration_minutes = time_diff_minutes(pickup_datetime, dropoff_datetime)\n  ) |> \n  select(pickup_datetime, dropoff_datetime, duration_minutes) |>\n  head() |>\n  collect()\n```\n\n::: {.cell-output .cell-output-error}\n\n```\nError in `time_diff_minutes()`:\n! `difftime()` with units other than `secs` not supported in Arrow\n→ Call collect() first to pull data into R.\n```\n\n\n:::\n:::\n\n\n\nWe get an error as we can't automatically convert the function to arrow.\n\n<!-- \n* why did this happen?\n* Look at ?acero and show how difftime is implemented but only supports units= \"secs\"\n\n-->\n\n# User-defined functions (aka UDFs)\n\n-   Define your own functions\n-   Scalar functions - 1 row input and 1 row output\n\n<!-- \n* Instead of having an Acero binding, Acero calls back to R to work out the result\n* Naturally slower than working entirely in Acero as now we don't have vectorisation, or any of the Arrow C++ optimisations\n* But, ultimately makes it possible\n-->\n\n## User-defined functions - definition\n\n<!-- \nshow slide don't type\n-->\n\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nregister_scalar_function(\n  name = \"time_diff_minutes\",\n  function(context, pickup, dropoff) {\n    difftime(dropoff, pickup, units = \"mins\") |>\n      round() |>\n      as.integer()\n  },\n  in_type = schema(\n    pickup = timestamp(unit = \"ms\"),\n    dropoff = timestamp(unit = \"ms\")\n  ),\n  out_type = int32(),\n  auto_convert = TRUE\n)\n```\n:::\n\n\n\n\nThis looks complicated, so let's look at it 1 part at a time!\n\n<!-- \nUDFs *are* ugly!\nAnd that's because when we have bindings, we rely on the arrow R package's internals to handle all the complicated stuff like working out the types of the values.\nBut it's not as bad as it looks, so we'll break it down!\n\n-->\n\n## User-defined functions - definition\n\nStep 1. Give the function a name\n\n\n\n\n::: {.cell}\n\n```{.r .cell-code  code-line-numbers=\"2\"}\nregister_scalar_function(\n  name = \"time_diff_minutes\",\n  function(context, pickup, dropoff) {\n    difftime(dropoff, pickup, units = \"mins\") |>\n      round() |>\n      as.integer()\n  },\n  in_type = schema(\n    pickup = timestamp(unit = \"ms\"),\n    dropoff = timestamp(unit = \"ms\")\n  ),\n  out_type = int32(),\n  auto_convert = TRUE\n)\n```\n:::\n\n\n\n\n<!--\nshow ?register_scalar_function help file\n\nset up the skeleton (i.e. register_scalar_function) and add in components 1 at a time\n\nname = some name to call it\n\nfun = the body of the function. first argument must be called \"context\" and you don't do anything with it - it'll be used by Acero to pass some metadata between Arrow and R. after that, define other arguments, and the function body like you otherwise would.\n\nin_type and out_type = so usually we don't have to worry about data types as the bindings do that for us, but now we're working more closely with Acero's internals, we need to do more things manually, so we have to tell it the data type of the input and output columns\n\nThe `auto_convert` argument controls whether arrow automatically converts the inputs to R vectors or not.\nThe default value `FALSE` can be used when working with R6 objects, but as we're working in an arrow dplyr pipeline here, we'll set it to `TRUE`.\n-->\n\n## User-defined functions - definition\n\nStep 2. Define the body of the function - first argument *must* be `context`\n\n\n\n\n::: {.cell}\n\n```{.r .cell-code  code-line-numbers=\"3,4,5,6,7\"}\nregister_scalar_function(\n  name = \"time_diff_minutes\",\n  function(context, pickup, dropoff) {\n    difftime(dropoff, pickup, units = \"mins\") |>\n      round() |>\n      as.integer()\n  },\n  in_type = schema(\n    pickup = timestamp(unit = \"ms\"),\n    dropoff = timestamp(unit = \"ms\")\n  ),\n  out_type = int32(),\n  auto_convert = TRUE\n)\n```\n:::\n\n\n\n\n## User-defined functions - definition\n\nStep 3. Set the schema of the input arguments\n\n\n\n\n::: {.cell}\n\n```{.r .cell-code  code-line-numbers=\"8,9,10,11\"}\nregister_scalar_function(\n  name = \"time_diff_minutes\",\n  function(context, pickup, dropoff) {\n    difftime(dropoff, pickup, units = \"mins\") |>\n      round() |>\n      as.integer()\n  },\n  in_type = schema(\n    pickup = timestamp(unit = \"ms\"),\n    dropoff = timestamp(unit = \"ms\")\n  ),\n  out_type = int32(),\n  auto_convert = TRUE\n)\n```\n:::\n\n\n\n\n## User-defined functions - definition\n\nStep 4. Set the data type of the output\n\n\n\n\n::: {.cell}\n\n```{.r .cell-code  code-line-numbers=\"12\"}\nregister_scalar_function(\n  name = \"time_diff_minutes\",\n  function(context, pickup, dropoff) {\n    difftime(dropoff, pickup, units = \"mins\") |>\n      round() |>\n      as.integer()\n  },\n  in_type = schema(\n    pickup = timestamp(unit = \"ms\"),\n    dropoff = timestamp(unit = \"ms\")\n  ),\n  out_type = int32(),\n  auto_convert = TRUE\n)\n```\n:::\n\n\n\n\n## User-defined functions - definition\n\nStep 5. Set `auto_convert = TRUE` if using in a dplyr pipeline\n\n\n\n\n::: {.cell}\n\n```{.r .cell-code  code-line-numbers=\"13\"}\nregister_scalar_function(\n  name = \"time_diff_minutes\",\n  function(context, pickup, dropoff) {\n    difftime(dropoff, pickup, units = \"mins\") |>\n      round() |>\n      as.integer()\n  },\n  in_type = schema(\n    pickup = timestamp(unit = \"ms\"),\n    dropoff = timestamp(unit = \"ms\")\n  ),\n  out_type = int32(),\n  auto_convert = TRUE\n)\n```\n:::\n\n\n\n\n## User-defined functions - usage\n\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nnyc_taxi |>\n  mutate(\n    duration_minutes = time_diff_minutes(pickup_datetime, dropoff_datetime)\n  ) |>\n  select(pickup_datetime, dropoff_datetime, duration_minutes) |>\n  head() |>\n  collect()\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n# A tibble: 6 × 3\n  pickup_datetime     dropoff_datetime    duration_minutes\n  <dttm>              <dttm>                         <int>\n1 2012-11-02 16:40:32 2012-11-02 16:58:16               18\n2 2012-11-02 16:40:41 2012-11-02 16:45:56                5\n3 2012-11-02 16:40:50 2012-11-02 16:49:20                8\n4 2012-11-02 16:40:52 2012-11-02 16:46:15                5\n5 2012-11-02 16:41:00 2012-11-02 16:44:00                3\n6 2012-11-02 16:41:00 2012-11-02 16:45:00                4\n```\n\n\n:::\n:::\n\n\n\n\n<!-- \nSo, we call `register_scalar_function()` to create it, and then then when we run our pipeline it appears there\n-->\n\n## Your Turn\n\n1.  Write a user-defined function which wraps the `stringr` function `str_replace_na()`, and use it to replace any `NA` values in the `vendor_name` column with the string \"No vendor\" instead. (Test it on the data from 2019 so you're not pulling everything into memory)\n\n➡️ [Data Manipulation Part II Exercises Page](4_data_manipulation_2-exercises.html)\n\n## Summary\n\n-   You can use UDFs to create your own bindings when they don't exist\n-   UDFs must be scalar (1 row in -\\> 1 row out) and stateless (no knowledge of other rows of data)\n-   Calculations done by R not Arrow, so slower than in-built bindings but still pretty fast\n\n# Joins\n\n## Joins\n\n![](images/joins.png)\n\n<!-- \n* combining data from different sources into a single source\n* fairly common to encounter when we have a dataset and some sort of reference table to combine it with\n* in the example here, we've done what's called a left-join:\n  * take everything from the table on the left\n  * decide which column to use for the join\n    * in this case, join vendor_name in the left table to code in the right table\n  * to do the join, goes through each row in the left table and finds the matching value in the right table, but then also brings the corresponding column (or multiple) from the right table\n* there are different type of joins, but the focus here isn't on joins, but is on what you need to know about doing joins in arrow\n* let's start off by writing out the code to do the join from the example\n-->\n\n## Joining a reference table\n\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nvendors <- tibble::tibble(\n  code = c(\"VTS\", \"CMT\", \"DDS\"),\n  full_name = c(\n    \"Verifone Transportation Systems\",\n    \"Creative Mobile Technologies\",\n    \"Digital Dispatch Systems\"\n  )\n)\n\nnyc_taxi |>\n  left_join(vendors, by = c(\"vendor_name\" = \"code\")) |>\n  select(vendor_name, full_name, pickup_datetime) |>\n  head(3) |>\n  collect()\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n# A tibble: 3 × 3\n  vendor_name full_name                    pickup_datetime    \n  <chr>       <chr>                        <dttm>             \n1 CMT         Creative Mobile Technologies 2012-01-27 15:35:26\n2 CMT         Creative Mobile Technologies 2012-01-27 06:56:04\n3 CMT         Creative Mobile Technologies 2012-01-27 08:12:50\n```\n\n\n:::\n:::\n\n\n\n\n<!-- \n* This was pretty straightforward here, and a lot of the time it should just work, but there are some things you should know about\n\n-->\n\n## Traps for the unwary\n\nQuestion: which are the most common borough-to-borough journeys in the dataset?\n\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nnyc_taxi_zones <- \n  read_csv_arrow(\"data/taxi_zone_lookup.csv\") |>\n  select(location_id = LocationID,\n         borough = Borough)\n\nnyc_taxi_zones\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n# A tibble: 265 × 2\n   location_id borough      \n         <int> <chr>        \n 1           1 EWR          \n 2           2 Queens       \n 3           3 Bronx        \n 4           4 Manhattan    \n 5           5 Staten Island\n 6           6 Staten Island\n 7           7 Queens       \n 8           8 Queens       \n 9           9 Queens       \n10          10 Queens       \n# ℹ 255 more rows\n```\n\n\n:::\n:::\n\n\n\n\n<!-- \n* the taxi data contains location IDs for the different pickup locations, but what if we want to be more generic and look at the different boroughs that people travel between?\n* one of the data files is a reference table which maps the location IDs to their Borough, and so we can answer this question by joining the reference table to our dataset\n* let's take a look\n-->\n\n## Why didn't this work?\n\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nnyc_taxi |>\n  left_join(nyc_taxi_zones, by = c(\"pickup_location_id\" = \"location_id\")) |>\n  collect()\n```\n\n::: {.cell-output .cell-output-error}\n\n```\nError in `compute.arrow_dplyr_query()`:\n! Invalid: Incompatible data types for corresponding join field keys: FieldRef.Name(pickup_location_id) of type int64 and FieldRef.Name(location_id) of type int32\n```\n\n\n:::\n:::\n\n\n\n\n<!-- \n* The thing to do here feels like, let's do a join again then\n* it's complaining about data types, so let's look at the schema of both\n-->\n\n## Schema for the `nyc_taxi` Dataset\n\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nschema(nyc_taxi)\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\nSchema\nvendor_name: string\npickup_datetime: timestamp[ms]\ndropoff_datetime: timestamp[ms]\npassenger_count: int64\ntrip_distance: double\npickup_longitude: double\npickup_latitude: double\nrate_code: string\nstore_and_fwd: string\ndropoff_longitude: double\ndropoff_latitude: double\npayment_type: string\nfare_amount: double\nextra: double\nmta_tax: double\ntip_amount: double\ntolls_amount: double\ntotal_amount: double\nimprovement_surcharge: double\ncongestion_surcharge: double\npickup_location_id: int64\ndropoff_location_id: int64\nyear: int32\nmonth: int32\n```\n\n\n:::\n:::\n\n\n\n<!-- In the original data, pickup_location_id is an int64 -->\n## Schema for the `nyc_taxi_zones` Table\n\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nnyc_taxi_zones_arrow <- arrow_table(nyc_taxi_zones)\nschema(nyc_taxi_zones_arrow)\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\nSchema\nlocation_id: int32\nborough: string\n```\n\n\n:::\n:::\n\n\n\n\n-   `pickup_location_id` is int64 in the `nyc_taxi` table\n-   `location_id` is int32 in the `nyc_taxi_zones` table\n\n<!-- \nWhen we do a join here, the nyc_taxi_zones object we read in was a tibble as we collected it in memory.\nArrow needs 2 Arrow objects to do a join, so behind the scenes, when we do the join, the tibble is converted into an Arrow Table.\nWhen we convert between R and Arrow objects, Arrow automatically does the type conversion for us.\nThat's why here we call `arrow_table()` to see what the type conversion will be, and call schema()\nThe thing to note here is that these are different types - and arrow needs for the join key column to be the same type. So what we need to do is manually specify the schema.\n-->\n\n## Take control of the schema\n\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nnyc_taxi_zones_arrow <- arrow_table(\n  nyc_taxi_zones, \n  schema = schema(location_id = int64(), borough = utf8())\n)\n```\n:::\n\n\n\n\n-   `schema()` takes variable name / types as input\n-   arrow has various \"type\" functions: `int64()`, `utf8()`, `boolean()`, `date32()` etc\n\n<!-- \nThis time we manually convert the data frame into a table and set the schema\nAlternatively, we could specify it when we read the data in\n-->\n\n## Take control of the schema\n\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nnyc_taxi_zones_arrow <- arrow_table(\n  nyc_taxi_zones, \n  schema = schema(location_id = int64(), borough = utf8())\n)\nschema(nyc_taxi_zones_arrow)\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\nSchema\nlocation_id: int64\nborough: string\n```\n\n\n:::\n:::\n\n\n\n\n<!-- \nNow we can see the schema matches so we should be able to run our analysis\nLet's do 1 final thing to answer our borough to borough question!\n-->\n\n## Prepare the auxiliary tables\n\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\npickup <- nyc_taxi_zones_arrow |>\n  select(pickup_location_id = location_id,\n         pickup_borough = borough)\n\ndropoff <- nyc_taxi_zones_arrow |>\n  select(dropoff_location_id = location_id,\n         dropoff_borough = borough)\n```\n:::\n\n\n\n\n-   Join separately for the pickup and dropoff zones\n\n\n## Join and cross-tabulate\n\n<!-- Run the code and recap the section while the timer runs! -->\n\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nlibrary(tictoc)\n\ntic()\nborough_counts <- nyc_taxi |> \n  left_join(pickup) |>\n  left_join(dropoff) |>\n  count(pickup_borough, dropoff_borough) |>\n  arrange(desc(n)) |>\n  collect()\ntoc()\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n177.061 sec elapsed\n```\n\n\n:::\n:::\n\n\n\n\n<br>\n\n2-3 minutes to join twice and cross-tabulate on non-partition variables, with 1.15 billion rows of data 🙂\n\n## The results\n\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nborough_counts\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n# A tibble: 50 × 3\n   pickup_borough dropoff_borough         n\n   <chr>          <chr>               <int>\n 1 <NA>           <NA>            732357953\n 2 Manhattan      Manhattan       351198872\n 3 Queens         Manhattan        14440705\n 4 Manhattan      Queens           13052517\n 5 Manhattan      Brooklyn         11180867\n 6 Queens         Queens            7440356\n 7 Unknown        Unknown           4491811\n 8 Queens         Brooklyn          3662324\n 9 Brooklyn       Brooklyn          3550480\n10 Manhattan      Bronx             2071830\n# ℹ 40 more rows\n```\n\n\n:::\n:::\n\n\n\n\n## Your Turn\n\n1.  How many taxi pickups were recorded in 2019 from the three major airports covered by the NYC Taxis data set (JFK, LaGuardia, Newark)? (Hint: you can use `stringr::str_detect()` to help you find pickup zones with the word \"Airport\" in them)\n\n➡️ [Data Manipulation Part II Exercises Page](4_data_manipulation_2-exercises.html)\n\n## Summary\n\n-   You can join Arrow Tables and Datasets to R data frames and Arrow Tables\n-   The Arrow data type of join keys must always match\n\n",
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diff --git a/_freeze/materials/7_continue_learning/execute-results/html.json b/_freeze/materials/7_continue_learning/execute-results/html.json
index 2fccdec..066516a 100644
--- a/_freeze/materials/7_continue_learning/execute-results/html.json
+++ b/_freeze/materials/7_continue_learning/execute-results/html.json
@@ -1,9 +1,11 @@
 {
-  "hash": "c6646851b6ea0f9c2ad145e6e6e8ef7d",
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   "result": {
     "engine": "knitr",
-    "markdown": "---\nfooter: \"[🔗 pos.it/arrow-conf24](https://pos.it/arrow-conf24)\"\nlogo: \"images/logo.png\"\nexecute:\n  echo: true\nformat:\n  revealjs: \n    theme: default\nengine: knitr\neditor: source\n---\n\n\n\n\n# Continue Your Arrow Learning Journey {#more-learning}\n\n## Docs\n\n::: {style=\"font-size: 80%;\"}\n<https://arrow.apache.org/docs/r/>\n:::\n\n![](images/docs.png)\n\n## Cookbook\n\n<https://arrow.apache.org/cookbook/r/>\n\n![](images/cookbook.png)\n\n## Cheatsheet\n\n::: {style=\"font-size: 60%;\"}\n<https://github.com/apache/arrow/blob/master/r/cheatsheet/arrow-cheatsheet.pdf>\n:::\n\n![](https://arrow.apache.org/img/20220427-arrow-r-cheatsheet-thumbnail.png){.absolute top=\"125\" width=\"700\"}\n\n## Awesome Arrow\n\n<https://github.com/thisisnic/awesome-arrow-r>\n\n![](images/awesomearrow.png)\n\n# How to Get Help\n\n## GitHub Issues\n\n<https://github.com/apache/arrow/issues>\n\n![](images/issues.png)\n\n## Community\n\n<https://arrow.apache.org/community/>\n\n![](images/mailing-list.png){.absolute width=\"600\"}\n\n::: notes\nalso Stack Overflow, Posit Community\n:::\n",
-    "supporting": [],
+    "markdown": "---\nfooter: \"[🔗 pos.it/arrow-conf24](https://pos.it/arrow-conf24)\"\nlogo: \"images/logo.png\"\nexecute:\n  echo: true\nformat:\n  revealjs: \n    theme: default\nengine: knitr\neditor: source\n---\n\n\n\n\n# Continue Your Arrow Learning Journey {#more-learning}\n\n## Book\n\n<https://arrowrbook.com>\n\n![](images/dummybookcover.png)\n\n## Docs\n\n::: {style=\"font-size: 80%;\"}\n<https://arrow.apache.org/docs/r/>\n:::\n\n![](images/docs.png)\n\n## Cookbook\n\n<https://arrow.apache.org/cookbook/r/>\n\n![](images/cookbook.png)\n\n## Cheatsheet\n\n::: {style=\"font-size: 60%;\"}\n<https://github.com/apache/arrow/blob/master/r/cheatsheet/arrow-cheatsheet.pdf>\n:::\n\n![](https://arrow.apache.org/img/20220427-arrow-r-cheatsheet-thumbnail.png){.absolute top=\"125\" width=\"700\"}\n\n## Awesome Arrow\n\n<https://github.com/thisisnic/awesome-arrow-r>\n\n![](images/awesomearrow.png)\n\n# How to Get Help\n\n## GitHub Issues\n\n<https://github.com/apache/arrow/issues>\n\n![](images/issues.png)\n\n## Community\n\n<https://arrow.apache.org/community/>\n\n![](images/mailing-list.png){.absolute width=\"600\"}\n\n::: notes\nalso Stack Overflow, Posit Community\n:::\n",
+    "supporting": [
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diff --git a/_freeze/site_libs/revealjs/dist/theme/quarto.css b/_freeze/site_libs/revealjs/dist/theme/quarto.css
index 2577a64..58f87c5 100644
--- a/_freeze/site_libs/revealjs/dist/theme/quarto.css
+++ b/_freeze/site_libs/revealjs/dist/theme/quarto.css
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diff --git a/_site/index.html b/_site/index.html
index 34ec940..90a6c08 100644
--- a/_site/index.html
+++ b/_site/index.html
@@ -2,7 +2,7 @@
 <html xmlns="http://www.w3.org/1999/xhtml" lang="en" xml:lang="en"><head>
 
 <meta charset="utf-8">
-<meta name="generator" content="quarto-1.4.555">
+<meta name="generator" content="quarto-1.5.53">
 
 <meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=yes">
 
@@ -86,7 +86,7 @@
     </a>
   </div>
             <div id="quarto-search" class="" title="Search"></div>
-          <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbarCollapse" aria-controls="navbarCollapse" aria-expanded="false" aria-label="Toggle navigation" onclick="if (window.quartoToggleHeadroom) { window.quartoToggleHeadroom(); }">
+          <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbarCollapse" aria-controls="navbarCollapse" role="menu" aria-expanded="false" aria-label="Toggle navigation" onclick="if (window.quartoToggleHeadroom) { window.quartoToggleHeadroom(); }">
   <span class="navbar-toggler-icon"></span>
 </button>
           <div class="collapse navbar-collapse" id="navbarCollapse">
@@ -100,7 +100,7 @@
 <span class="menu-text">Packages &amp; Data</span></a>
   </li>  
   <li class="nav-item dropdown ">
-    <a class="nav-link dropdown-toggle" href="#" id="nav-menu-slides" role="button" data-bs-toggle="dropdown" aria-expanded="false">
+    <a class="nav-link dropdown-toggle" href="#" id="nav-menu-slides" role="link" data-bs-toggle="dropdown" aria-expanded="false">
  <span class="menu-text">Slides</span>
     </a>
     <ul class="dropdown-menu" aria-labelledby="nav-menu-slides">    
@@ -143,7 +143,7 @@
     </ul>
   </li>
   <li class="nav-item dropdown ">
-    <a class="nav-link dropdown-toggle" href="#" id="nav-menu-exercises" role="button" data-bs-toggle="dropdown" aria-expanded="false">
+    <a class="nav-link dropdown-toggle" href="#" id="nav-menu-exercises" role="link" data-bs-toggle="dropdown" aria-expanded="false">
  <span class="menu-text">Exercises</span>
     </a>
     <ul class="dropdown-menu" aria-labelledby="nav-menu-exercises">    
@@ -183,7 +183,7 @@
   </li>  
 </ul>
           </div> <!-- /navcollapse -->
-          <div class="quarto-navbar-tools">
+            <div class="quarto-navbar-tools">
 </div>
       </div> <!-- /container-fluid -->
     </nav>
@@ -352,18 +352,7 @@ <h3 class="anchored" data-anchor-id="acknowledgements">Acknowledgements</h3>
     }
     return false;
   }
-  const clipboard = new window.ClipboardJS('.code-copy-button', {
-    text: function(trigger) {
-      const codeEl = trigger.previousElementSibling.cloneNode(true);
-      for (const childEl of codeEl.children) {
-        if (isCodeAnnotation(childEl)) {
-          childEl.remove();
-        }
-      }
-      return codeEl.innerText;
-    }
-  });
-  clipboard.on('success', function(e) {
+  const onCopySuccess = function(e) {
     // button target
     const button = e.trigger;
     // don't keep focus
@@ -395,7 +384,29 @@ <h3 class="anchored" data-anchor-id="acknowledgements">Acknowledgements</h3>
     }, 1000);
     // clear code selection
     e.clearSelection();
+  }
+  const getTextToCopy = function(trigger) {
+      const codeEl = trigger.previousElementSibling.cloneNode(true);
+      for (const childEl of codeEl.children) {
+        if (isCodeAnnotation(childEl)) {
+          childEl.remove();
+        }
+      }
+      return codeEl.innerText;
+  }
+  const clipboard = new window.ClipboardJS('.code-copy-button:not([data-in-quarto-modal])', {
+    text: getTextToCopy
   });
+  clipboard.on('success', onCopySuccess);
+  if (window.document.getElementById('quarto-embedded-source-code-modal')) {
+    // For code content inside modals, clipBoardJS needs to be initialized with a container option
+    // TODO: Check when it could be a function (https://github.com/zenorocha/clipboard.js/issues/860)
+    const clipboardModal = new window.ClipboardJS('.code-copy-button[data-in-quarto-modal]', {
+      text: getTextToCopy,
+      container: window.document.getElementById('quarto-embedded-source-code-modal')
+    });
+    clipboardModal.on('success', onCopySuccess);
+  }
     var localhostRegex = new RegExp(/^(?:http|https):\/\/localhost\:?[0-9]*\//);
     var mailtoRegex = new RegExp(/^mailto:/);
       var filterRegex = new RegExp('/' + window.location.host + '/');
@@ -403,7 +414,7 @@ <h3 class="anchored" data-anchor-id="acknowledgements">Acknowledgements</h3>
         return filterRegex.test(href) || localhostRegex.test(href) || mailtoRegex.test(href);
     }
     // Inspect non-navigation links and adorn them if external
- 	var links = window.document.querySelectorAll('a[href]:not(.nav-link):not(.navbar-brand):not(.toc-action):not(.sidebar-link):not(.sidebar-item-toggle):not(.pagination-link):not(.no-external):not([aria-hidden]):not(.dropdown-item):not(.quarto-navigation-tool)');
+ 	var links = window.document.querySelectorAll('a[href]:not(.nav-link):not(.navbar-brand):not(.toc-action):not(.sidebar-link):not(.sidebar-item-toggle):not(.pagination-link):not(.no-external):not([aria-hidden]):not(.dropdown-item):not(.quarto-navigation-tool):not(.about-link)');
     for (var i=0; i<links.length; i++) {
       const link = links[i];
       if (!isInternal(link.href)) {
diff --git a/_site/license-web.html b/_site/license-web.html
index 24a9d80..befe3db 100644
--- a/_site/license-web.html
+++ b/_site/license-web.html
@@ -2,12 +2,12 @@
 <html xmlns="http://www.w3.org/1999/xhtml" lang="en" xml:lang="en"><head>
 
 <meta charset="utf-8">
-<meta name="generator" content="quarto-1.4.555">
+<meta name="generator" content="quarto-1.5.53">
 
 <meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=yes">
 
 
-<title>Big Data in R with Arrow - License</title>
+<title>License – Big Data in R with Arrow</title>
 <style>
 code{white-space: pre-wrap;}
 span.smallcaps{font-variant: small-caps;}
@@ -86,7 +86,7 @@
     </a>
   </div>
             <div id="quarto-search" class="" title="Search"></div>
-          <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbarCollapse" aria-controls="navbarCollapse" aria-expanded="false" aria-label="Toggle navigation" onclick="if (window.quartoToggleHeadroom) { window.quartoToggleHeadroom(); }">
+          <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbarCollapse" aria-controls="navbarCollapse" role="menu" aria-expanded="false" aria-label="Toggle navigation" onclick="if (window.quartoToggleHeadroom) { window.quartoToggleHeadroom(); }">
   <span class="navbar-toggler-icon"></span>
 </button>
           <div class="collapse navbar-collapse" id="navbarCollapse">
@@ -100,7 +100,7 @@
 <span class="menu-text">Packages &amp; Data</span></a>
   </li>  
   <li class="nav-item dropdown ">
-    <a class="nav-link dropdown-toggle" href="#" id="nav-menu-slides" role="button" data-bs-toggle="dropdown" aria-expanded="false">
+    <a class="nav-link dropdown-toggle" href="#" id="nav-menu-slides" role="link" data-bs-toggle="dropdown" aria-expanded="false">
  <span class="menu-text">Slides</span>
     </a>
     <ul class="dropdown-menu" aria-labelledby="nav-menu-slides">    
@@ -143,7 +143,7 @@
     </ul>
   </li>
   <li class="nav-item dropdown ">
-    <a class="nav-link dropdown-toggle" href="#" id="nav-menu-exercises" role="button" data-bs-toggle="dropdown" aria-expanded="false">
+    <a class="nav-link dropdown-toggle" href="#" id="nav-menu-exercises" role="link" data-bs-toggle="dropdown" aria-expanded="false">
  <span class="menu-text">Exercises</span>
     </a>
     <ul class="dropdown-menu" aria-labelledby="nav-menu-exercises">    
@@ -183,7 +183,7 @@
   </li>  
 </ul>
           </div> <!-- /navcollapse -->
-          <div class="quarto-navbar-tools">
+            <div class="quarto-navbar-tools">
 </div>
       </div> <!-- /container-fluid -->
     </nav>
@@ -192,7 +192,7 @@
 <div id="quarto-content" class="quarto-container page-columns page-rows-contents page-layout-article page-navbar">
 <!-- sidebar -->
 <!-- margin-sidebar -->
-    <div id="quarto-margin-sidebar" class="sidebar margin-sidebar">
+    <div id="quarto-margin-sidebar" class="sidebar margin-sidebar zindex-bottom">
         
     </div>
 <!-- main -->
@@ -258,18 +258,7 @@ <h1 class="title">License</h1>
     }
     return false;
   }
-  const clipboard = new window.ClipboardJS('.code-copy-button', {
-    text: function(trigger) {
-      const codeEl = trigger.previousElementSibling.cloneNode(true);
-      for (const childEl of codeEl.children) {
-        if (isCodeAnnotation(childEl)) {
-          childEl.remove();
-        }
-      }
-      return codeEl.innerText;
-    }
-  });
-  clipboard.on('success', function(e) {
+  const onCopySuccess = function(e) {
     // button target
     const button = e.trigger;
     // don't keep focus
@@ -301,7 +290,29 @@ <h1 class="title">License</h1>
     }, 1000);
     // clear code selection
     e.clearSelection();
+  }
+  const getTextToCopy = function(trigger) {
+      const codeEl = trigger.previousElementSibling.cloneNode(true);
+      for (const childEl of codeEl.children) {
+        if (isCodeAnnotation(childEl)) {
+          childEl.remove();
+        }
+      }
+      return codeEl.innerText;
+  }
+  const clipboard = new window.ClipboardJS('.code-copy-button:not([data-in-quarto-modal])', {
+    text: getTextToCopy
   });
+  clipboard.on('success', onCopySuccess);
+  if (window.document.getElementById('quarto-embedded-source-code-modal')) {
+    // For code content inside modals, clipBoardJS needs to be initialized with a container option
+    // TODO: Check when it could be a function (https://github.com/zenorocha/clipboard.js/issues/860)
+    const clipboardModal = new window.ClipboardJS('.code-copy-button[data-in-quarto-modal]', {
+      text: getTextToCopy,
+      container: window.document.getElementById('quarto-embedded-source-code-modal')
+    });
+    clipboardModal.on('success', onCopySuccess);
+  }
     var localhostRegex = new RegExp(/^(?:http|https):\/\/localhost\:?[0-9]*\//);
     var mailtoRegex = new RegExp(/^mailto:/);
       var filterRegex = new RegExp('/' + window.location.host + '/');
@@ -309,7 +320,7 @@ <h1 class="title">License</h1>
         return filterRegex.test(href) || localhostRegex.test(href) || mailtoRegex.test(href);
     }
     // Inspect non-navigation links and adorn them if external
- 	var links = window.document.querySelectorAll('a[href]:not(.nav-link):not(.navbar-brand):not(.toc-action):not(.sidebar-link):not(.sidebar-item-toggle):not(.pagination-link):not(.no-external):not([aria-hidden]):not(.dropdown-item):not(.quarto-navigation-tool)');
+ 	var links = window.document.querySelectorAll('a[href]:not(.nav-link):not(.navbar-brand):not(.toc-action):not(.sidebar-link):not(.sidebar-item-toggle):not(.pagination-link):not(.no-external):not([aria-hidden]):not(.dropdown-item):not(.quarto-navigation-tool):not(.about-link)');
     for (var i=0; i<links.length; i++) {
       const link = links[i];
       if (!isInternal(link.href)) {
diff --git a/_site/materials/0_housekeeping.html b/_site/materials/0_housekeeping.html
index 6bb28b8..cceb9c7 100644
--- a/_site/materials/0_housekeeping.html
+++ b/_site/materials/0_housekeeping.html
@@ -8,7 +8,7 @@
 <link href="../site_libs/quarto-html/light-border.css" rel="stylesheet">
 <link href="../site_libs/quarto-html/quarto-html.min.css" rel="stylesheet" data-mode="light">
 <link href="../site_libs/quarto-html/quarto-syntax-highlighting.css" rel="stylesheet" id="quarto-text-highlighting-styles"><meta charset="utf-8">
-  <meta name="generator" content="quarto-1.4.555">
+  <meta name="generator" content="quarto-1.5.53">
 
   <meta name="author" content="Nic Crane + Steph Hazlitt">
   <title>Big Data in R with Arrow</title>
@@ -413,7 +413,7 @@ <h2>Meet Each Other <svg aria-hidden="true" role="img" viewbox="0 0 640 512" sty
 <section id="getting-help-today" class="slide level2">
 <h2>Getting Help Today <svg aria-hidden="true" role="img" viewbox="0 0 448 512" style="height:1em;width:0.88em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:currentColor;overflow:visible;position:relative;"><path d="M48 24C48 10.7 37.3 0 24 0S0 10.7 0 24V64 350.5 400v88c0 13.3 10.7 24 24 24s24-10.7 24-24V388l80.3-20.1c41.1-10.3 84.6-5.5 122.5 13.4c44.2 22.1 95.5 24.8 141.7 7.4l34.7-13c12.5-4.7 20.8-16.6 20.8-30V66.1c0-23-24.2-38-44.8-27.7l-9.6 4.8c-46.3 23.2-100.8 23.2-147.1 0c-35.1-17.6-75.4-22-113.5-12.5L48 52V24zm0 77.5l96.6-24.2c27-6.7 55.5-3.6 80.4 8.8c54.9 27.4 118.7 29.7 175 6.8V334.7l-24.4 9.1c-33.7 12.6-71.2 10.7-103.4-5.4c-48.2-24.1-103.3-30.1-155.6-17.1L48 338.5v-237z"></path></svg></h2>
 <p><br></p>
-<p><span style="color:teal;">TEAL</span> sticky note: I am OK / I am done</p>
+<p><span style="color:green;">GREEN</span> sticky note: I am OK / I am done</p>
 <p><span style="color:pink;">PINK</span> sticky note: I need support / I am working</p>
 <p><br></p>
 <p><svg aria-hidden="true" role="img" viewbox="0 0 512 512" style="height:1em;width:1em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:currentColor;overflow:visible;position:relative;"><path d="M256 0c-25.3 0-47.2 14.7-57.6 36c-7-2.6-14.5-4-22.4-4c-35.3 0-64 28.7-64 64V261.5l-2.7-2.7c-25-25-65.5-25-90.5 0s-25 65.5 0 90.5L106.5 437c48 48 113.1 75 181 75H296h8c1.5 0 3-.1 4.5-.4c91.7-6.2 165-79.4 171.1-171.1c.3-1.5 .4-3 .4-4.5V160c0-35.3-28.7-64-64-64c-5.5 0-10.9 .7-16 2V96c0-35.3-28.7-64-64-64c-7.9 0-15.4 1.4-22.4 4C303.2 14.7 281.3 0 256 0zM240 96.1c0 0 0-.1 0-.1V64c0-8.8 7.2-16 16-16s16 7.2 16 16V95.9c0 0 0 .1 0 .1V232c0 13.3 10.7 24 24 24s24-10.7 24-24V96c0 0 0 0 0-.1c0-8.8 7.2-16 16-16s16 7.2 16 16v55.9c0 0 0 .1 0 .1v80c0 13.3 10.7 24 24 24s24-10.7 24-24V160.1c0 0 0-.1 0-.1c0-8.8 7.2-16 16-16s16 7.2 16 16V332.9c-.1 .6-.1 1.3-.2 1.9c-3.4 69.7-59.3 125.6-129 129c-.6 0-1.3 .1-1.9 .2H296h-8.5c-55.2 0-108.1-21.9-147.1-60.9L52.7 315.3c-6.2-6.2-6.2-16.4 0-22.6s16.4-6.2 22.6 0L119 336.4c6.9 6.9 17.2 8.9 26.2 5.2s14.8-12.5 14.8-22.2V96c0-8.8 7.2-16 16-16c8.8 0 16 7.1 16 15.9V232c0 13.3 10.7 24 24 24s24-10.7 24-24V96.1z"></path></svg> You can ask questions at any time during the workshop</p>
@@ -750,18 +750,7 @@ <h2>Posit Workbench: Setup 🍽️</h2>
         }
         return false;
       }
-      const clipboard = new window.ClipboardJS('.code-copy-button', {
-        text: function(trigger) {
-          const codeEl = trigger.previousElementSibling.cloneNode(true);
-          for (const childEl of codeEl.children) {
-            if (isCodeAnnotation(childEl)) {
-              childEl.remove();
-            }
-          }
-          return codeEl.innerText;
-        }
-      });
-      clipboard.on('success', function(e) {
+      const onCopySuccess = function(e) {
         // button target
         const button = e.trigger;
         // don't keep focus
@@ -793,7 +782,29 @@ <h2>Posit Workbench: Setup 🍽️</h2>
         }, 1000);
         // clear code selection
         e.clearSelection();
+      }
+      const getTextToCopy = function(trigger) {
+          const codeEl = trigger.previousElementSibling.cloneNode(true);
+          for (const childEl of codeEl.children) {
+            if (isCodeAnnotation(childEl)) {
+              childEl.remove();
+            }
+          }
+          return codeEl.innerText;
+      }
+      const clipboard = new window.ClipboardJS('.code-copy-button:not([data-in-quarto-modal])', {
+        text: getTextToCopy
       });
+      clipboard.on('success', onCopySuccess);
+      if (window.document.getElementById('quarto-embedded-source-code-modal')) {
+        // For code content inside modals, clipBoardJS needs to be initialized with a container option
+        // TODO: Check when it could be a function (https://github.com/zenorocha/clipboard.js/issues/860)
+        const clipboardModal = new window.ClipboardJS('.code-copy-button[data-in-quarto-modal]', {
+          text: getTextToCopy,
+          container: window.document.getElementById('quarto-embedded-source-code-modal')
+        });
+        clipboardModal.on('success', onCopySuccess);
+      }
         var localhostRegex = new RegExp(/^(?:http|https):\/\/localhost\:?[0-9]*\//);
         var mailtoRegex = new RegExp(/^mailto:/);
           var filterRegex = new RegExp('/' + window.location.host + '/');
@@ -801,7 +812,7 @@ <h2>Posit Workbench: Setup 🍽️</h2>
             return filterRegex.test(href) || localhostRegex.test(href) || mailtoRegex.test(href);
         }
         // Inspect non-navigation links and adorn them if external
-     	var links = window.document.querySelectorAll('a[href]:not(.nav-link):not(.navbar-brand):not(.toc-action):not(.sidebar-link):not(.sidebar-item-toggle):not(.pagination-link):not(.no-external):not([aria-hidden]):not(.dropdown-item):not(.quarto-navigation-tool)');
+     	var links = window.document.querySelectorAll('a[href]:not(.nav-link):not(.navbar-brand):not(.toc-action):not(.sidebar-link):not(.sidebar-item-toggle):not(.pagination-link):not(.no-external):not([aria-hidden]):not(.dropdown-item):not(.quarto-navigation-tool):not(.about-link)');
         for (var i=0; i<links.length; i++) {
           const link = links[i];
           if (!isInternal(link.href)) {
diff --git a/_site/materials/1_hello_arrow-exercises.html b/_site/materials/1_hello_arrow-exercises.html
index 9b41371..b48c75f 100644
--- a/_site/materials/1_hello_arrow-exercises.html
+++ b/_site/materials/1_hello_arrow-exercises.html
@@ -2,12 +2,12 @@
 <html xmlns="http://www.w3.org/1999/xhtml" lang="en" xml:lang="en"><head>
 
 <meta charset="utf-8">
-<meta name="generator" content="quarto-1.4.555">
+<meta name="generator" content="quarto-1.5.53">
 
 <meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=yes">
 
 
-<title>Big Data in R with Arrow - Hello Arrow Exercises</title>
+<title>Hello Arrow Exercises – Big Data in R with Arrow</title>
 <style>
 code{white-space: pre-wrap;}
 span.smallcaps{font-variant: small-caps;}
@@ -33,7 +33,7 @@
 }
 @media print {
 pre > code.sourceCode { white-space: pre-wrap; }
-pre > code.sourceCode > span { text-indent: -5em; padding-left: 5em; }
+pre > code.sourceCode > span { display: inline-block; text-indent: -5em; padding-left: 5em; }
 }
 pre.numberSource code
   { counter-reset: source-line 0; }
@@ -120,7 +120,7 @@
     </a>
   </div>
             <div id="quarto-search" class="" title="Search"></div>
-          <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbarCollapse" aria-controls="navbarCollapse" aria-expanded="false" aria-label="Toggle navigation" onclick="if (window.quartoToggleHeadroom) { window.quartoToggleHeadroom(); }">
+          <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbarCollapse" aria-controls="navbarCollapse" role="menu" aria-expanded="false" aria-label="Toggle navigation" onclick="if (window.quartoToggleHeadroom) { window.quartoToggleHeadroom(); }">
   <span class="navbar-toggler-icon"></span>
 </button>
           <div class="collapse navbar-collapse" id="navbarCollapse">
@@ -134,7 +134,7 @@
 <span class="menu-text">Packages &amp; Data</span></a>
   </li>  
   <li class="nav-item dropdown ">
-    <a class="nav-link dropdown-toggle" href="#" id="nav-menu-slides" role="button" data-bs-toggle="dropdown" aria-expanded="false">
+    <a class="nav-link dropdown-toggle" href="#" id="nav-menu-slides" role="link" data-bs-toggle="dropdown" aria-expanded="false">
  <span class="menu-text">Slides</span>
     </a>
     <ul class="dropdown-menu" aria-labelledby="nav-menu-slides">    
@@ -177,7 +177,7 @@
     </ul>
   </li>
   <li class="nav-item dropdown ">
-    <a class="nav-link dropdown-toggle" href="#" id="nav-menu-exercises" role="button" data-bs-toggle="dropdown" aria-expanded="false">
+    <a class="nav-link dropdown-toggle" href="#" id="nav-menu-exercises" role="link" data-bs-toggle="dropdown" aria-expanded="false">
  <span class="menu-text">Exercises</span>
     </a>
     <ul class="dropdown-menu" aria-labelledby="nav-menu-exercises">    
@@ -217,7 +217,7 @@
   </li>  
 </ul>
           </div> <!-- /navcollapse -->
-          <div class="quarto-navbar-tools">
+            <div class="quarto-navbar-tools">
 </div>
       </div> <!-- /container-fluid -->
     </nav>
@@ -226,7 +226,7 @@
 <div id="quarto-content" class="quarto-container page-columns page-rows-contents page-layout-article page-navbar">
 <!-- sidebar -->
 <!-- margin-sidebar -->
-    <div id="quarto-margin-sidebar" class="sidebar margin-sidebar">
+    <div id="quarto-margin-sidebar" class="sidebar margin-sidebar zindex-bottom">
         
     </div>
 <!-- main -->
@@ -398,18 +398,7 @@ <h1 class="title">Hello Arrow Exercises</h1>
     }
     return false;
   }
-  const clipboard = new window.ClipboardJS('.code-copy-button', {
-    text: function(trigger) {
-      const codeEl = trigger.previousElementSibling.cloneNode(true);
-      for (const childEl of codeEl.children) {
-        if (isCodeAnnotation(childEl)) {
-          childEl.remove();
-        }
-      }
-      return codeEl.innerText;
-    }
-  });
-  clipboard.on('success', function(e) {
+  const onCopySuccess = function(e) {
     // button target
     const button = e.trigger;
     // don't keep focus
@@ -441,7 +430,29 @@ <h1 class="title">Hello Arrow Exercises</h1>
     }, 1000);
     // clear code selection
     e.clearSelection();
+  }
+  const getTextToCopy = function(trigger) {
+      const codeEl = trigger.previousElementSibling.cloneNode(true);
+      for (const childEl of codeEl.children) {
+        if (isCodeAnnotation(childEl)) {
+          childEl.remove();
+        }
+      }
+      return codeEl.innerText;
+  }
+  const clipboard = new window.ClipboardJS('.code-copy-button:not([data-in-quarto-modal])', {
+    text: getTextToCopy
   });
+  clipboard.on('success', onCopySuccess);
+  if (window.document.getElementById('quarto-embedded-source-code-modal')) {
+    // For code content inside modals, clipBoardJS needs to be initialized with a container option
+    // TODO: Check when it could be a function (https://github.com/zenorocha/clipboard.js/issues/860)
+    const clipboardModal = new window.ClipboardJS('.code-copy-button[data-in-quarto-modal]', {
+      text: getTextToCopy,
+      container: window.document.getElementById('quarto-embedded-source-code-modal')
+    });
+    clipboardModal.on('success', onCopySuccess);
+  }
     var localhostRegex = new RegExp(/^(?:http|https):\/\/localhost\:?[0-9]*\//);
     var mailtoRegex = new RegExp(/^mailto:/);
       var filterRegex = new RegExp('/' + window.location.host + '/');
@@ -449,7 +460,7 @@ <h1 class="title">Hello Arrow Exercises</h1>
         return filterRegex.test(href) || localhostRegex.test(href) || mailtoRegex.test(href);
     }
     // Inspect non-navigation links and adorn them if external
- 	var links = window.document.querySelectorAll('a[href]:not(.nav-link):not(.navbar-brand):not(.toc-action):not(.sidebar-link):not(.sidebar-item-toggle):not(.pagination-link):not(.no-external):not([aria-hidden]):not(.dropdown-item):not(.quarto-navigation-tool)');
+ 	var links = window.document.querySelectorAll('a[href]:not(.nav-link):not(.navbar-brand):not(.toc-action):not(.sidebar-link):not(.sidebar-item-toggle):not(.pagination-link):not(.no-external):not([aria-hidden]):not(.dropdown-item):not(.quarto-navigation-tool):not(.about-link)');
     for (var i=0; i<links.length; i++) {
       const link = links[i];
       if (!isInternal(link.href)) {
diff --git a/_site/materials/1_hello_arrow.html b/_site/materials/1_hello_arrow.html
index 60c9d12..2ad6ff9 100644
--- a/_site/materials/1_hello_arrow.html
+++ b/_site/materials/1_hello_arrow.html
@@ -8,9 +8,9 @@
 <link href="../site_libs/quarto-html/light-border.css" rel="stylesheet">
 <link href="../site_libs/quarto-html/quarto-html.min.css" rel="stylesheet" data-mode="light">
 <link href="../site_libs/quarto-html/quarto-syntax-highlighting.css" rel="stylesheet" id="quarto-text-highlighting-styles"><meta charset="utf-8">
-  <meta name="generator" content="quarto-1.4.555">
+  <meta name="generator" content="quarto-1.5.53">
 
-  <title>Big Data in R with Arrow</title>
+  <title>Big Data in R with Arrow – hello_arrow</title>
   <meta name="apple-mobile-web-app-capable" content="yes">
   <meta name="apple-mobile-web-app-status-bar-style" content="black-translucent">
   <meta name="viewport" content="width=device-width, initial-scale=1.0, maximum-scale=1.0, user-scalable=no, minimal-ui">
@@ -41,7 +41,7 @@
     }
     @media print {
     pre > code.sourceCode { white-space: pre-wrap; }
-    pre > code.sourceCode > span { text-indent: -5em; padding-left: 5em; }
+    pre > code.sourceCode > span { display: inline-block; text-indent: -5em; padding-left: 5em; }
     }
     pre.numberSource code
       { counter-reset: source-line 0; }
@@ -69,7 +69,7 @@
     code span.at { color: #657422; } /* Attribute */
     code span.bn { color: #ad0000; } /* BaseN */
     code span.bu { } /* BuiltIn */
-    code span.cf { color: #003b4f; } /* ControlFlow */
+    code span.cf { color: #003b4f; font-weight: bold; } /* ControlFlow */
     code span.ch { color: #20794d; } /* Char */
     code span.cn { color: #8f5902; } /* Constant */
     code span.co { color: #5e5e5e; } /* Comment */
@@ -83,7 +83,7 @@
     code span.fu { color: #4758ab; } /* Function */
     code span.im { color: #00769e; } /* Import */
     code span.in { color: #5e5e5e; } /* Information */
-    code span.kw { color: #003b4f; } /* Keyword */
+    code span.kw { color: #003b4f; font-weight: bold; } /* Keyword */
     code span.op { color: #5e5e5e; } /* Operator */
     code span.ot { color: #003b4f; } /* Other */
     code span.pp { color: #ad0000; } /* Preprocessor */
@@ -394,16 +394,6 @@
 <section id="hello-arrow" class="title-slide slide level1 center">
 <h1>Hello Arrow</h1>
 
-</section>
-<section id="kick-off-qa" class="slide level2">
-<h2>Kick-off Q&amp;A</h2>
-<p><br></p>
-<ul>
-<li>What brings you to this workshop?</li>
-<li>What challenges have you faced related to larger-than-memory data in R?</li>
-<li>What is one thing you want to learn or achieve from today’s workshop?</li>
-<li>…?</li>
-</ul>
 </section>
 <section id="poll-arrow" class="slide level2">
 <h2>Poll: Arrow</h2>
@@ -515,12 +505,12 @@ <h2>NYC Taxi Dataset: A dplyr pipeline</h2>
  2  2013 173179759     51215013       29.6
  3  2014 165114361     48816505       29.6
  4  2015 146112989     43081091       29.5
- 5  2017 113495512     32296166       28.5
- 6  2018 102797401     28796633       28.0
- 7  2019  84393604     23515989       27.9
- 8  2020  24647055      5837960       23.7
- 9  2021  30902618      7221844       23.4
-10  2016 131165043     38163870       29.1</code></pre>
+ 5  2016 131165043     38163870       29.1
+ 6  2017 113495512     32296166       28.5
+ 7  2018 102797401     28796633       28.0
+ 8  2019  84393604     23515989       27.9
+ 9  2020  24647055      5837960       23.7
+10  2021  30902618      7221844       23.4</code></pre>
 </div>
 </div>
 </section>
@@ -563,8 +553,7 @@ <h2>What is Apache Arrow?</h2>
 <blockquote>
 <p>Arrow is designed to both improve the performance of analytical algorithms and the efficiency of moving data from one system or programming language to another</p>
 </blockquote>
-</div>
-</div>
+</div></div>
 <div style="font-size: 70%;">
 <p><a href="https://arrow.apache.org/overview/" class="uri">https://arrow.apache.org/overview/</a></p>
 </div>
@@ -604,7 +593,7 @@ <h2>Accelerated In-Memory Processing</h2>
 <section id="arrow" class="slide level2">
 <h2>arrow 📦</h2>
 <p><br></p>
-<p><img data-src="images/arrow-r-pkg.png" class="absolute" style="top: 0px; left: 300px; width: 700px; height: 900px; "></p>
+<p><img data-src="images/arrow-r-pkg-highlights.png" class="absolute" style="top: 0px; left: 300px; width: 700px; height: 900px; "></p>
 </section>
 <section id="arrow-1" class="slide level2">
 <h2>arrow 📦</h2>
@@ -906,18 +895,7 @@ <h2>Today</h2>
         }
         return false;
       }
-      const clipboard = new window.ClipboardJS('.code-copy-button', {
-        text: function(trigger) {
-          const codeEl = trigger.previousElementSibling.cloneNode(true);
-          for (const childEl of codeEl.children) {
-            if (isCodeAnnotation(childEl)) {
-              childEl.remove();
-            }
-          }
-          return codeEl.innerText;
-        }
-      });
-      clipboard.on('success', function(e) {
+      const onCopySuccess = function(e) {
         // button target
         const button = e.trigger;
         // don't keep focus
@@ -949,7 +927,29 @@ <h2>Today</h2>
         }, 1000);
         // clear code selection
         e.clearSelection();
+      }
+      const getTextToCopy = function(trigger) {
+          const codeEl = trigger.previousElementSibling.cloneNode(true);
+          for (const childEl of codeEl.children) {
+            if (isCodeAnnotation(childEl)) {
+              childEl.remove();
+            }
+          }
+          return codeEl.innerText;
+      }
+      const clipboard = new window.ClipboardJS('.code-copy-button:not([data-in-quarto-modal])', {
+        text: getTextToCopy
       });
+      clipboard.on('success', onCopySuccess);
+      if (window.document.getElementById('quarto-embedded-source-code-modal')) {
+        // For code content inside modals, clipBoardJS needs to be initialized with a container option
+        // TODO: Check when it could be a function (https://github.com/zenorocha/clipboard.js/issues/860)
+        const clipboardModal = new window.ClipboardJS('.code-copy-button[data-in-quarto-modal]', {
+          text: getTextToCopy,
+          container: window.document.getElementById('quarto-embedded-source-code-modal')
+        });
+        clipboardModal.on('success', onCopySuccess);
+      }
         var localhostRegex = new RegExp(/^(?:http|https):\/\/localhost\:?[0-9]*\//);
         var mailtoRegex = new RegExp(/^mailto:/);
           var filterRegex = new RegExp('/' + window.location.host + '/');
@@ -957,7 +957,7 @@ <h2>Today</h2>
             return filterRegex.test(href) || localhostRegex.test(href) || mailtoRegex.test(href);
         }
         // Inspect non-navigation links and adorn them if external
-     	var links = window.document.querySelectorAll('a[href]:not(.nav-link):not(.navbar-brand):not(.toc-action):not(.sidebar-link):not(.sidebar-item-toggle):not(.pagination-link):not(.no-external):not([aria-hidden]):not(.dropdown-item):not(.quarto-navigation-tool)');
+     	var links = window.document.querySelectorAll('a[href]:not(.nav-link):not(.navbar-brand):not(.toc-action):not(.sidebar-link):not(.sidebar-item-toggle):not(.pagination-link):not(.no-external):not([aria-hidden]):not(.dropdown-item):not(.quarto-navigation-tool):not(.about-link)');
         for (var i=0; i<links.length; i++) {
           const link = links[i];
           if (!isInternal(link.href)) {
diff --git a/_site/materials/2_data_manipulation_1-exercises.html b/_site/materials/2_data_manipulation_1-exercises.html
index b8fe93e..f4f067b 100644
--- a/_site/materials/2_data_manipulation_1-exercises.html
+++ b/_site/materials/2_data_manipulation_1-exercises.html
@@ -2,12 +2,12 @@
 <html xmlns="http://www.w3.org/1999/xhtml" lang="en" xml:lang="en"><head>
 
 <meta charset="utf-8">
-<meta name="generator" content="quarto-1.4.555">
+<meta name="generator" content="quarto-1.5.53">
 
 <meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=yes">
 
 
-<title>Big Data in R with Arrow - Data Manipulation Part 1 - Exercises</title>
+<title>Data Manipulation Part 1 - Exercises – Big Data in R with Arrow</title>
 <style>
 code{white-space: pre-wrap;}
 span.smallcaps{font-variant: small-caps;}
@@ -33,7 +33,7 @@
 }
 @media print {
 pre > code.sourceCode { white-space: pre-wrap; }
-pre > code.sourceCode > span { text-indent: -5em; padding-left: 5em; }
+pre > code.sourceCode > span { display: inline-block; text-indent: -5em; padding-left: 5em; }
 }
 pre.numberSource code
   { counter-reset: source-line 0; }
@@ -120,7 +120,7 @@
     </a>
   </div>
             <div id="quarto-search" class="" title="Search"></div>
-          <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbarCollapse" aria-controls="navbarCollapse" aria-expanded="false" aria-label="Toggle navigation" onclick="if (window.quartoToggleHeadroom) { window.quartoToggleHeadroom(); }">
+          <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbarCollapse" aria-controls="navbarCollapse" role="menu" aria-expanded="false" aria-label="Toggle navigation" onclick="if (window.quartoToggleHeadroom) { window.quartoToggleHeadroom(); }">
   <span class="navbar-toggler-icon"></span>
 </button>
           <div class="collapse navbar-collapse" id="navbarCollapse">
@@ -134,7 +134,7 @@
 <span class="menu-text">Packages &amp; Data</span></a>
   </li>  
   <li class="nav-item dropdown ">
-    <a class="nav-link dropdown-toggle" href="#" id="nav-menu-slides" role="button" data-bs-toggle="dropdown" aria-expanded="false">
+    <a class="nav-link dropdown-toggle" href="#" id="nav-menu-slides" role="link" data-bs-toggle="dropdown" aria-expanded="false">
  <span class="menu-text">Slides</span>
     </a>
     <ul class="dropdown-menu" aria-labelledby="nav-menu-slides">    
@@ -177,7 +177,7 @@
     </ul>
   </li>
   <li class="nav-item dropdown ">
-    <a class="nav-link dropdown-toggle" href="#" id="nav-menu-exercises" role="button" data-bs-toggle="dropdown" aria-expanded="false">
+    <a class="nav-link dropdown-toggle" href="#" id="nav-menu-exercises" role="link" data-bs-toggle="dropdown" aria-expanded="false">
  <span class="menu-text">Exercises</span>
     </a>
     <ul class="dropdown-menu" aria-labelledby="nav-menu-exercises">    
@@ -217,7 +217,7 @@
   </li>  
 </ul>
           </div> <!-- /navcollapse -->
-          <div class="quarto-navbar-tools">
+            <div class="quarto-navbar-tools">
 </div>
       </div> <!-- /container-fluid -->
     </nav>
@@ -226,7 +226,7 @@
 <div id="quarto-content" class="quarto-container page-columns page-rows-contents page-layout-article page-navbar">
 <!-- sidebar -->
 <!-- margin-sidebar -->
-    <div id="quarto-margin-sidebar" class="sidebar margin-sidebar">
+    <div id="quarto-margin-sidebar" class="sidebar margin-sidebar zindex-bottom">
         
     </div>
 <!-- main -->
@@ -384,18 +384,7 @@ <h1 class="title">Data Manipulation Part 1 - Exercises</h1>
     }
     return false;
   }
-  const clipboard = new window.ClipboardJS('.code-copy-button', {
-    text: function(trigger) {
-      const codeEl = trigger.previousElementSibling.cloneNode(true);
-      for (const childEl of codeEl.children) {
-        if (isCodeAnnotation(childEl)) {
-          childEl.remove();
-        }
-      }
-      return codeEl.innerText;
-    }
-  });
-  clipboard.on('success', function(e) {
+  const onCopySuccess = function(e) {
     // button target
     const button = e.trigger;
     // don't keep focus
@@ -427,7 +416,29 @@ <h1 class="title">Data Manipulation Part 1 - Exercises</h1>
     }, 1000);
     // clear code selection
     e.clearSelection();
+  }
+  const getTextToCopy = function(trigger) {
+      const codeEl = trigger.previousElementSibling.cloneNode(true);
+      for (const childEl of codeEl.children) {
+        if (isCodeAnnotation(childEl)) {
+          childEl.remove();
+        }
+      }
+      return codeEl.innerText;
+  }
+  const clipboard = new window.ClipboardJS('.code-copy-button:not([data-in-quarto-modal])', {
+    text: getTextToCopy
   });
+  clipboard.on('success', onCopySuccess);
+  if (window.document.getElementById('quarto-embedded-source-code-modal')) {
+    // For code content inside modals, clipBoardJS needs to be initialized with a container option
+    // TODO: Check when it could be a function (https://github.com/zenorocha/clipboard.js/issues/860)
+    const clipboardModal = new window.ClipboardJS('.code-copy-button[data-in-quarto-modal]', {
+      text: getTextToCopy,
+      container: window.document.getElementById('quarto-embedded-source-code-modal')
+    });
+    clipboardModal.on('success', onCopySuccess);
+  }
     var localhostRegex = new RegExp(/^(?:http|https):\/\/localhost\:?[0-9]*\//);
     var mailtoRegex = new RegExp(/^mailto:/);
       var filterRegex = new RegExp('/' + window.location.host + '/');
@@ -435,7 +446,7 @@ <h1 class="title">Data Manipulation Part 1 - Exercises</h1>
         return filterRegex.test(href) || localhostRegex.test(href) || mailtoRegex.test(href);
     }
     // Inspect non-navigation links and adorn them if external
- 	var links = window.document.querySelectorAll('a[href]:not(.nav-link):not(.navbar-brand):not(.toc-action):not(.sidebar-link):not(.sidebar-item-toggle):not(.pagination-link):not(.no-external):not([aria-hidden]):not(.dropdown-item):not(.quarto-navigation-tool)');
+ 	var links = window.document.querySelectorAll('a[href]:not(.nav-link):not(.navbar-brand):not(.toc-action):not(.sidebar-link):not(.sidebar-item-toggle):not(.pagination-link):not(.no-external):not([aria-hidden]):not(.dropdown-item):not(.quarto-navigation-tool):not(.about-link)');
     for (var i=0; i<links.length; i++) {
       const link = links[i];
       if (!isInternal(link.href)) {
diff --git a/_site/materials/2_data_manipulation_1.html b/_site/materials/2_data_manipulation_1.html
index d11dde5..6e1a362 100644
--- a/_site/materials/2_data_manipulation_1.html
+++ b/_site/materials/2_data_manipulation_1.html
@@ -8,9 +8,9 @@
 <link href="../site_libs/quarto-html/light-border.css" rel="stylesheet">
 <link href="../site_libs/quarto-html/quarto-html.min.css" rel="stylesheet" data-mode="light">
 <link href="../site_libs/quarto-html/quarto-syntax-highlighting.css" rel="stylesheet" id="quarto-text-highlighting-styles"><meta charset="utf-8">
-  <meta name="generator" content="quarto-1.4.555">
+  <meta name="generator" content="quarto-1.5.53">
 
-  <title>Big Data in R with Arrow</title>
+  <title>Big Data in R with Arrow – data_manipulation_1</title>
   <meta name="apple-mobile-web-app-capable" content="yes">
   <meta name="apple-mobile-web-app-status-bar-style" content="black-translucent">
   <meta name="viewport" content="width=device-width, initial-scale=1.0, maximum-scale=1.0, user-scalable=no, minimal-ui">
@@ -41,7 +41,7 @@
     }
     @media print {
     pre > code.sourceCode { white-space: pre-wrap; }
-    pre > code.sourceCode > span { text-indent: -5em; padding-left: 5em; }
+    pre > code.sourceCode > span { display: inline-block; text-indent: -5em; padding-left: 5em; }
     }
     pre.numberSource code
       { counter-reset: source-line 0; }
@@ -69,7 +69,7 @@
     code span.at { color: #657422; } /* Attribute */
     code span.bn { color: #ad0000; } /* BaseN */
     code span.bu { } /* BuiltIn */
-    code span.cf { color: #003b4f; } /* ControlFlow */
+    code span.cf { color: #003b4f; font-weight: bold; } /* ControlFlow */
     code span.ch { color: #20794d; } /* Char */
     code span.cn { color: #8f5902; } /* Constant */
     code span.co { color: #5e5e5e; } /* Comment */
@@ -83,7 +83,7 @@
     code span.fu { color: #4758ab; } /* Function */
     code span.im { color: #00769e; } /* Import */
     code span.in { color: #5e5e5e; } /* Information */
-    code span.kw { color: #003b4f; } /* Keyword */
+    code span.kw { color: #003b4f; font-weight: bold; } /* Keyword */
     code span.op { color: #5e5e5e; } /* Operator */
     code span.ot { color: #003b4f; } /* Other */
     code span.pp { color: #ad0000; } /* Preprocessor */
@@ -543,12 +543,12 @@ <h2>Use <code>head()</code> and <code>collect()</code> to preview results</h2>
 <pre><code># A tibble: 6 × 2
   fare_amount fare_pounds
         &lt;dbl&gt;       &lt;dbl&gt;
-1         8          6.32
-2        17         13.4 
-3         6.5        5.14
-4         7          5.53
-5         6.5        5.14
-6        42         33.2 </code></pre>
+1        16.5       13.0 
+2        21.5       17.0 
+3         5          3.95
+4        10.5        8.30
+5        11          8.69
+6         5.5        4.35</code></pre>
 </div>
 </div>
 </section>
@@ -607,7 +607,7 @@ <h2>Example - <code>slice()</code></h2>
 <section id="head-to-the-docs" class="slide level2">
 <h2>Head to the docs!</h2>
 <div class="cell">
-<div class="sourceCode cell-code" id="cb18"><pre class="sourceCode numberSource r number-lines code-with-copy"><code class="sourceCode r"><span id="cb18-1"><a></a>?<span class="st">`</span><span class="at">arrow-dplyr</span><span class="st">`</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div class="sourceCode cell-code" id="cb18"><pre class="sourceCode numberSource r number-lines code-with-copy"><code class="sourceCode r"><span id="cb18-1"><a></a>?acero</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </div>
 <p>or view them at <a href="https://arrow.apache.org/docs/r/reference/acero.html" class="uri">https://arrow.apache.org/docs/r/reference/acero.html</a></p>
 </section>
@@ -621,9 +621,9 @@ <h2>A different function</h2>
 <pre><code># A tibble: 3 × 3
   pickup_datetime      year trip_distance
   &lt;dttm&gt;              &lt;int&gt;         &lt;dbl&gt;
-1 2021-11-16 12:55:00  2021       351613.
-2 2021-10-27 17:46:00  2021       345124.
-3 2021-12-11 10:48:00  2021       335094.</code></pre>
+1 2021-11-16 04:55:00  2021       351613.
+2 2021-10-27 09:46:00  2021       345124.
+3 2021-12-11 02:48:00  2021       335094.</code></pre>
 </div>
 </div>
 </section>
@@ -637,9 +637,9 @@ <h2>Or call <code>collect()</code> first</h2>
 <pre><code># A tibble: 3 × 3
   pickup_datetime      year trip_distance
   &lt;dttm&gt;              &lt;int&gt;         &lt;dbl&gt;
-1 2021-10-02 15:04:53  2021          188.
-2 2021-10-03 16:45:02  2021          134 
-3 2021-10-03 17:29:35  2021          218.</code></pre>
+1 2021-01-03 01:01:26  2021          216.
+2 2021-01-03 03:36:52  2021          268.
+3 2021-10-02 07:04:53  2021          188.</code></pre>
 </div>
 </div>
 </section>
@@ -746,10 +746,14 @@ <h2>Morning vs afternoon - without namespacing</h2>
 </section>
 <section id="how-does-this-work" class="slide level2">
 <h2>How does this work?</h2>
-
-<img data-src="images/dplyr-backend.png" class="r-stretch"></section>
-<section id="arrow-c" class="slide level2">
-<h2>arrow C++</h2>
+<p><img data-src="images/dplyr-backend.png"> ## Acero</p>
+<ul>
+<li>arrow’s query execution engine</li>
+<li>use Arrow functions on Arrow Datasets</li>
+</ul>
+</section>
+<section id="acero" class="slide level2">
+<h2>Acero</h2>
 
 <img data-src="images/arrow_cpp_functions.png" class="r-stretch"></section>
 <section id="arrow-dplyr-queries-1" class="slide level2">
@@ -764,15 +768,16 @@ <h2>What if a function isn’t implemented?</h2>
 <span id="cb31-3"><a></a>  <span class="fu">head</span>() <span class="sc">|&gt;</span></span>
 <span id="cb31-4"><a></a>  <span class="fu">collect</span>()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-error">
-<pre><code>Error: Expression na_if(vendor_name, "CMT") not supported in Arrow
-Call collect() first to pull data into R.</code></pre>
+<pre><code>Error in `na_if()`:
+! Expression not supported in Arrow
+→ Call collect() first to pull data into R.</code></pre>
 </div>
 </div>
 </section>
 <section id="head-to-the-docs-again-to-see-whats-implemented" class="slide level2">
 <h2>Head to the docs again to see what’s implemented!</h2>
 <div class="cell">
-<div class="sourceCode cell-code" id="cb33"><pre class="sourceCode numberSource r number-lines code-with-copy"><code class="sourceCode r"><span id="cb33-1"><a></a>?<span class="st">`</span><span class="at">arrow-dplyr</span><span class="st">`</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div class="sourceCode cell-code" id="cb33"><pre class="sourceCode numberSource r number-lines code-with-copy"><code class="sourceCode r"><span id="cb33-1"><a></a>?acero</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </div>
 <p>or view them at <a href="https://arrow.apache.org/docs/r/reference/acero.html" class="uri">https://arrow.apache.org/docs/r/reference/acero.html</a></p>
 </section>
@@ -787,12 +792,12 @@ <h2>Option 1 - find a workaround!</h2>
 <pre><code># A tibble: 6 × 24
   vendor_name pickup_datetime     dropoff_datetime    passenger_count
   &lt;chr&gt;       &lt;dttm&gt;              &lt;dttm&gt;                        &lt;int&gt;
-1 &lt;NA&gt;        2012-01-20 14:09:36 2012-01-20 14:42:25               1
-2 &lt;NA&gt;        2012-01-20 14:54:10 2012-01-20 15:06:55               1
-3 &lt;NA&gt;        2012-01-20 08:08:01 2012-01-20 08:11:02               1
-4 &lt;NA&gt;        2012-01-20 08:36:22 2012-01-20 08:39:44               1
-5 &lt;NA&gt;        2012-01-20 20:58:32 2012-01-20 21:03:04               1
-6 &lt;NA&gt;        2012-01-20 19:40:20 2012-01-20 19:43:43               2
+1 &lt;NA&gt;        2012-01-20 06:09:36 2012-01-20 06:42:25               1
+2 &lt;NA&gt;        2012-01-20 06:54:10 2012-01-20 07:06:55               1
+3 &lt;NA&gt;        2012-01-20 00:08:01 2012-01-20 00:11:02               1
+4 &lt;NA&gt;        2012-01-20 00:36:22 2012-01-20 00:39:44               1
+5 &lt;NA&gt;        2012-01-20 12:58:32 2012-01-20 13:03:04               1
+6 &lt;NA&gt;        2012-01-20 11:40:20 2012-01-20 11:43:43               2
 # ℹ 20 more variables: trip_distance &lt;dbl&gt;, pickup_longitude &lt;dbl&gt;,
 #   pickup_latitude &lt;dbl&gt;, rate_code &lt;chr&gt;, store_and_fwd &lt;chr&gt;,
 #   dropoff_longitude &lt;dbl&gt;, dropoff_latitude &lt;dbl&gt;, payment_type &lt;chr&gt;,
@@ -839,12 +844,12 @@ <h2>Working with custom functions</h2>
 <pre><code># A tibble: 6 × 2
   pickup_datetime     pickup_text
   &lt;dttm&gt;              &lt;chr&gt;      
-1 2012-01-08 20:50:38 Sunday PM  
-2 2012-01-08 20:52:01 Sunday PM  
-3 2012-01-08 02:39:26 Sunday AM  
-4 2012-01-08 02:40:49 Sunday AM  
-5 2012-01-09 03:42:37 Monday AM  
-6 2012-01-08 20:51:47 Sunday PM  </code></pre>
+1 2012-01-08 12:50:38 Sunday PM  
+2 2012-01-08 12:52:01 Sunday PM  
+3 2012-01-07 18:39:26 Sunday AM  
+4 2012-01-07 18:40:49 Sunday AM  
+5 2012-01-08 19:42:37 Monday AM  
+6 2012-01-08 12:51:47 Sunday PM  </code></pre>
 </div>
 </div>
 </section>
@@ -871,14 +876,14 @@ <h2>Anything else to be aware of?</h2>
 <ul>
 <li>arrow 17.0.0 or later</li>
 <li>this will only work for functions which have Arrow bindings</li>
-<li>use <code>?`arrow-dplyr`</code> to see which ones do</li>
+<li>use <code>?acero</code> to see which ones do</li>
 </ul>
 </section>
 <section id="summary-1" class="slide level2">
 <h2>Summary</h2>
 <ul>
 <li>Working with Arrow Datasets allow you to manipulate data which is larger-than-memory</li>
-<li>You can use many dplyr functions with arrow - run <code>?`arrow-dplyr`</code> to view the docs</li>
+<li>You can use many dplyr functions with arrow - run <code>?acero</code> to view the docs</li>
 <li>You can pass data to duckdb to use functions implemented in duckdb but not arrow</li>
 </ul>
 
@@ -1169,18 +1174,7 @@ <h2>Summary</h2>
         }
         return false;
       }
-      const clipboard = new window.ClipboardJS('.code-copy-button', {
-        text: function(trigger) {
-          const codeEl = trigger.previousElementSibling.cloneNode(true);
-          for (const childEl of codeEl.children) {
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-              childEl.remove();
-            }
-          }
-          return codeEl.innerText;
-        }
-      });
-      clipboard.on('success', function(e) {
+      const onCopySuccess = function(e) {
         // button target
         const button = e.trigger;
         // don't keep focus
@@ -1212,7 +1206,29 @@ <h2>Summary</h2>
         }, 1000);
         // clear code selection
         e.clearSelection();
+      }
+      const getTextToCopy = function(trigger) {
+          const codeEl = trigger.previousElementSibling.cloneNode(true);
+          for (const childEl of codeEl.children) {
+            if (isCodeAnnotation(childEl)) {
+              childEl.remove();
+            }
+          }
+          return codeEl.innerText;
+      }
+      const clipboard = new window.ClipboardJS('.code-copy-button:not([data-in-quarto-modal])', {
+        text: getTextToCopy
       });
+      clipboard.on('success', onCopySuccess);
+      if (window.document.getElementById('quarto-embedded-source-code-modal')) {
+        // For code content inside modals, clipBoardJS needs to be initialized with a container option
+        // TODO: Check when it could be a function (https://github.com/zenorocha/clipboard.js/issues/860)
+        const clipboardModal = new window.ClipboardJS('.code-copy-button[data-in-quarto-modal]', {
+          text: getTextToCopy,
+          container: window.document.getElementById('quarto-embedded-source-code-modal')
+        });
+        clipboardModal.on('success', onCopySuccess);
+      }
         var localhostRegex = new RegExp(/^(?:http|https):\/\/localhost\:?[0-9]*\//);
         var mailtoRegex = new RegExp(/^mailto:/);
           var filterRegex = new RegExp('/' + window.location.host + '/');
@@ -1220,7 +1236,7 @@ <h2>Summary</h2>
             return filterRegex.test(href) || localhostRegex.test(href) || mailtoRegex.test(href);
         }
         // Inspect non-navigation links and adorn them if external
-     	var links = window.document.querySelectorAll('a[href]:not(.nav-link):not(.navbar-brand):not(.toc-action):not(.sidebar-link):not(.sidebar-item-toggle):not(.pagination-link):not(.no-external):not([aria-hidden]):not(.dropdown-item):not(.quarto-navigation-tool)');
+     	var links = window.document.querySelectorAll('a[href]:not(.nav-link):not(.navbar-brand):not(.toc-action):not(.sidebar-link):not(.sidebar-item-toggle):not(.pagination-link):not(.no-external):not([aria-hidden]):not(.dropdown-item):not(.quarto-navigation-tool):not(.about-link)');
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diff --git a/_site/materials/3_data_engineering-exercises.html b/_site/materials/3_data_engineering-exercises.html
index 1cda294..e7bd4c8 100644
--- a/_site/materials/3_data_engineering-exercises.html
+++ b/_site/materials/3_data_engineering-exercises.html
@@ -2,12 +2,12 @@
 <html xmlns="http://www.w3.org/1999/xhtml" lang="en" xml:lang="en"><head>
 
 <meta charset="utf-8">
-<meta name="generator" content="quarto-1.4.555">
+<meta name="generator" content="quarto-1.5.53">
 
 <meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=yes">
 
 
-<title>Big Data in R with Arrow - Data Engineering with Arrow Exercises</title>
+<title>Data Engineering with Arrow Exercises – Big Data in R with Arrow</title>
 <style>
 code{white-space: pre-wrap;}
 span.smallcaps{font-variant: small-caps;}
@@ -33,7 +33,7 @@
 }
 @media print {
 pre > code.sourceCode { white-space: pre-wrap; }
-pre > code.sourceCode > span { text-indent: -5em; padding-left: 5em; }
+pre > code.sourceCode > span { display: inline-block; text-indent: -5em; padding-left: 5em; }
 }
 pre.numberSource code
   { counter-reset: source-line 0; }
@@ -120,7 +120,7 @@
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   </div>
             <div id="quarto-search" class="" title="Search"></div>
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+          <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbarCollapse" aria-controls="navbarCollapse" role="menu" aria-expanded="false" aria-label="Toggle navigation" onclick="if (window.quartoToggleHeadroom) { window.quartoToggleHeadroom(); }">
   <span class="navbar-toggler-icon"></span>
 </button>
           <div class="collapse navbar-collapse" id="navbarCollapse">
@@ -134,7 +134,7 @@
 <span class="menu-text">Packages &amp; Data</span></a>
   </li>  
   <li class="nav-item dropdown ">
-    <a class="nav-link dropdown-toggle" href="#" id="nav-menu-slides" role="button" data-bs-toggle="dropdown" aria-expanded="false">
+    <a class="nav-link dropdown-toggle" href="#" id="nav-menu-slides" role="link" data-bs-toggle="dropdown" aria-expanded="false">
  <span class="menu-text">Slides</span>
     </a>
     <ul class="dropdown-menu" aria-labelledby="nav-menu-slides">    
@@ -177,7 +177,7 @@
     </ul>
   </li>
   <li class="nav-item dropdown ">
-    <a class="nav-link dropdown-toggle" href="#" id="nav-menu-exercises" role="button" data-bs-toggle="dropdown" aria-expanded="false">
+    <a class="nav-link dropdown-toggle" href="#" id="nav-menu-exercises" role="link" data-bs-toggle="dropdown" aria-expanded="false">
  <span class="menu-text">Exercises</span>
     </a>
     <ul class="dropdown-menu" aria-labelledby="nav-menu-exercises">    
@@ -217,7 +217,7 @@
   </li>  
 </ul>
           </div> <!-- /navcollapse -->
-          <div class="quarto-navbar-tools">
+            <div class="quarto-navbar-tools">
 </div>
       </div> <!-- /container-fluid -->
     </nav>
@@ -594,18 +594,7 @@ <h1>Partitioning</h1>
     }
     return false;
   }
-  const clipboard = new window.ClipboardJS('.code-copy-button', {
-    text: function(trigger) {
-      const codeEl = trigger.previousElementSibling.cloneNode(true);
-      for (const childEl of codeEl.children) {
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-          childEl.remove();
-        }
-      }
-      return codeEl.innerText;
-    }
-  });
-  clipboard.on('success', function(e) {
+  const onCopySuccess = function(e) {
     // button target
     const button = e.trigger;
     // don't keep focus
@@ -637,7 +626,29 @@ <h1>Partitioning</h1>
     }, 1000);
     // clear code selection
     e.clearSelection();
+  }
+  const getTextToCopy = function(trigger) {
+      const codeEl = trigger.previousElementSibling.cloneNode(true);
+      for (const childEl of codeEl.children) {
+        if (isCodeAnnotation(childEl)) {
+          childEl.remove();
+        }
+      }
+      return codeEl.innerText;
+  }
+  const clipboard = new window.ClipboardJS('.code-copy-button:not([data-in-quarto-modal])', {
+    text: getTextToCopy
   });
+  clipboard.on('success', onCopySuccess);
+  if (window.document.getElementById('quarto-embedded-source-code-modal')) {
+    // For code content inside modals, clipBoardJS needs to be initialized with a container option
+    // TODO: Check when it could be a function (https://github.com/zenorocha/clipboard.js/issues/860)
+    const clipboardModal = new window.ClipboardJS('.code-copy-button[data-in-quarto-modal]', {
+      text: getTextToCopy,
+      container: window.document.getElementById('quarto-embedded-source-code-modal')
+    });
+    clipboardModal.on('success', onCopySuccess);
+  }
     var localhostRegex = new RegExp(/^(?:http|https):\/\/localhost\:?[0-9]*\//);
     var mailtoRegex = new RegExp(/^mailto:/);
       var filterRegex = new RegExp('/' + window.location.host + '/');
@@ -645,7 +656,7 @@ <h1>Partitioning</h1>
         return filterRegex.test(href) || localhostRegex.test(href) || mailtoRegex.test(href);
     }
     // Inspect non-navigation links and adorn them if external
- 	var links = window.document.querySelectorAll('a[href]:not(.nav-link):not(.navbar-brand):not(.toc-action):not(.sidebar-link):not(.sidebar-item-toggle):not(.pagination-link):not(.no-external):not([aria-hidden]):not(.dropdown-item):not(.quarto-navigation-tool)');
+ 	var links = window.document.querySelectorAll('a[href]:not(.nav-link):not(.navbar-brand):not(.toc-action):not(.sidebar-link):not(.sidebar-item-toggle):not(.pagination-link):not(.no-external):not([aria-hidden]):not(.dropdown-item):not(.quarto-navigation-tool):not(.about-link)');
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diff --git a/_site/materials/3_data_engineering.html b/_site/materials/3_data_engineering.html
index c6994a8..7f30c46 100644
--- a/_site/materials/3_data_engineering.html
+++ b/_site/materials/3_data_engineering.html
@@ -8,9 +8,9 @@
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 <link href="../site_libs/quarto-html/quarto-syntax-highlighting.css" rel="stylesheet" id="quarto-text-highlighting-styles"><meta charset="utf-8">
-  <meta name="generator" content="quarto-1.4.555">
+  <meta name="generator" content="quarto-1.5.53">
 
-  <title>Big Data in R with Arrow</title>
+  <title>Big Data in R with Arrow – data_engineering</title>
   <meta name="apple-mobile-web-app-capable" content="yes">
   <meta name="apple-mobile-web-app-status-bar-style" content="black-translucent">
   <meta name="viewport" content="width=device-width, initial-scale=1.0, maximum-scale=1.0, user-scalable=no, minimal-ui">
@@ -41,7 +41,7 @@
     }
     @media print {
     pre > code.sourceCode { white-space: pre-wrap; }
-    pre > code.sourceCode > span { text-indent: -5em; padding-left: 5em; }
+    pre > code.sourceCode > span { display: inline-block; text-indent: -5em; padding-left: 5em; }
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     pre.numberSource code
       { counter-reset: source-line 0; }
@@ -69,7 +69,7 @@
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-    code span.cf { color: #003b4f; } /* ControlFlow */
+    code span.cf { color: #003b4f; font-weight: bold; } /* ControlFlow */
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     code span.im { color: #00769e; } /* Import */
     code span.in { color: #5e5e5e; } /* Information */
-    code span.kw { color: #003b4f; } /* Keyword */
+    code span.kw { color: #003b4f; font-weight: bold; } /* Keyword */
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@@ -806,8 +806,7 @@ <h2>File Storage:<br>Partitioning</h2>
 <p>Dividing data into smaller pieces, making it more easily accessible and manageable</p>
 </div><div class="column" style="width:50%;">
 <p><img data-src="images/partitions.png" class="absolute" style="top: 0px; "></p>
-</div>
-</div>
+</div></div>
 <aside class="notes">
 <p>also called multi-files or sometimes shards</p>
 <style type="text/css">
@@ -945,8 +944,7 @@ <h2>Partition Design</h2>
 </ul>
 </div><div class="column" style="width:50%;">
 <p><img data-src="images/partitions.png" class="absolute" style="top: 0px; "></p>
-</div>
-</div>
+</div></div>
 </section>
 <section id="partitions-na-values" class="slide level2">
 <h2>Partitions &amp; NA Values</h2>
@@ -1055,8 +1053,7 @@ <h2>R for Data Science (2e)</h2>
 <p><a href="https://r4ds.hadley.nz/arrow.html">Chapter 23: Arrow</a></p>
 <p><br></p>
 <p><a href="https://r4ds.hadley.nz/" class="uri">https://r4ds.hadley.nz/</a></p>
-</div>
-</div>
+</div></div>
 
 <div class="quarto-auto-generated-content">
 <p><img src="images/logo.png" class="slide-logo"></p>
@@ -1345,18 +1342,7 @@ <h2>R for Data Science (2e)</h2>
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+      const onCopySuccess = function(e) {
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@@ -1388,7 +1374,29 @@ <h2>R for Data Science (2e)</h2>
         }, 1000);
         // clear code selection
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+        text: getTextToCopy
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+      clipboard.on('success', onCopySuccess);
+      if (window.document.getElementById('quarto-embedded-source-code-modal')) {
+        // For code content inside modals, clipBoardJS needs to be initialized with a container option
+        // TODO: Check when it could be a function (https://github.com/zenorocha/clipboard.js/issues/860)
+        const clipboardModal = new window.ClipboardJS('.code-copy-button[data-in-quarto-modal]', {
+          text: getTextToCopy,
+          container: window.document.getElementById('quarto-embedded-source-code-modal')
+        });
+        clipboardModal.on('success', onCopySuccess);
+      }
         var localhostRegex = new RegExp(/^(?:http|https):\/\/localhost\:?[0-9]*\//);
         var mailtoRegex = new RegExp(/^mailto:/);
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@@ -1396,7 +1404,7 @@ <h2>R for Data Science (2e)</h2>
             return filterRegex.test(href) || localhostRegex.test(href) || mailtoRegex.test(href);
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         // Inspect non-navigation links and adorn them if external
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+     	var links = window.document.querySelectorAll('a[href]:not(.nav-link):not(.navbar-brand):not(.toc-action):not(.sidebar-link):not(.sidebar-item-toggle):not(.pagination-link):not(.no-external):not([aria-hidden]):not(.dropdown-item):not(.quarto-navigation-tool):not(.about-link)');
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diff --git a/_site/materials/4_data_manipulation_2-exercises.html b/_site/materials/4_data_manipulation_2-exercises.html
index 48f0bfd..cce6516 100644
--- a/_site/materials/4_data_manipulation_2-exercises.html
+++ b/_site/materials/4_data_manipulation_2-exercises.html
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+<meta name="generator" content="quarto-1.5.53">
 
 <meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=yes">
 
 
-<title>Big Data in R with Arrow - Data Manipulation Part 2 - Exercises</title>
+<title>Data Manipulation Part 2 - Exercises – Big Data in R with Arrow</title>
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@@ -33,7 +33,7 @@
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+pre > code.sourceCode > span { display: inline-block; text-indent: -5em; padding-left: 5em; }
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@@ -120,7 +120,7 @@
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+          <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbarCollapse" aria-controls="navbarCollapse" role="menu" aria-expanded="false" aria-label="Toggle navigation" onclick="if (window.quartoToggleHeadroom) { window.quartoToggleHeadroom(); }">
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@@ -134,7 +134,7 @@
 <span class="menu-text">Packages &amp; Data</span></a>
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-    <a class="nav-link dropdown-toggle" href="#" id="nav-menu-slides" role="button" data-bs-toggle="dropdown" aria-expanded="false">
+    <a class="nav-link dropdown-toggle" href="#" id="nav-menu-slides" role="link" data-bs-toggle="dropdown" aria-expanded="false">
  <span class="menu-text">Slides</span>
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@@ -177,7 +177,7 @@
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   <li class="nav-item dropdown ">
-    <a class="nav-link dropdown-toggle" href="#" id="nav-menu-exercises" role="button" data-bs-toggle="dropdown" aria-expanded="false">
+    <a class="nav-link dropdown-toggle" href="#" id="nav-menu-exercises" role="link" data-bs-toggle="dropdown" aria-expanded="false">
  <span class="menu-text">Exercises</span>
     </a>
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@@ -217,7 +217,7 @@
   </li>  
 </ul>
           </div> <!-- /navcollapse -->
-          <div class="quarto-navbar-tools">
+            <div class="quarto-navbar-tools">
 </div>
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     </nav>
@@ -226,7 +226,7 @@
 <div id="quarto-content" class="quarto-container page-columns page-rows-contents page-layout-article page-navbar">
 <!-- sidebar -->
 <!-- margin-sidebar -->
-    <div id="quarto-margin-sidebar" class="sidebar margin-sidebar">
+    <div id="quarto-margin-sidebar" class="sidebar margin-sidebar zindex-bottom">
         
     </div>
 <!-- main -->
@@ -422,18 +422,7 @@ <h1 class="title">Data Manipulation Part 2 - Exercises</h1>
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+  const onCopySuccess = function(e) {
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@@ -465,7 +454,29 @@ <h1 class="title">Data Manipulation Part 2 - Exercises</h1>
     }, 1000);
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+  }
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+      const codeEl = trigger.previousElementSibling.cloneNode(true);
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+  }
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+  clipboard.on('success', onCopySuccess);
+  if (window.document.getElementById('quarto-embedded-source-code-modal')) {
+    // For code content inside modals, clipBoardJS needs to be initialized with a container option
+    // TODO: Check when it could be a function (https://github.com/zenorocha/clipboard.js/issues/860)
+    const clipboardModal = new window.ClipboardJS('.code-copy-button[data-in-quarto-modal]', {
+      text: getTextToCopy,
+      container: window.document.getElementById('quarto-embedded-source-code-modal')
+    });
+    clipboardModal.on('success', onCopySuccess);
+  }
     var localhostRegex = new RegExp(/^(?:http|https):\/\/localhost\:?[0-9]*\//);
     var mailtoRegex = new RegExp(/^mailto:/);
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@@ -473,7 +484,7 @@ <h1 class="title">Data Manipulation Part 2 - Exercises</h1>
         return filterRegex.test(href) || localhostRegex.test(href) || mailtoRegex.test(href);
     }
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+ 	var links = window.document.querySelectorAll('a[href]:not(.nav-link):not(.navbar-brand):not(.toc-action):not(.sidebar-link):not(.sidebar-item-toggle):not(.pagination-link):not(.no-external):not([aria-hidden]):not(.dropdown-item):not(.quarto-navigation-tool):not(.about-link)');
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diff --git a/_site/materials/4_data_manipulation_2.html b/_site/materials/4_data_manipulation_2.html
index f56c993..895e296 100644
--- a/_site/materials/4_data_manipulation_2.html
+++ b/_site/materials/4_data_manipulation_2.html
@@ -8,9 +8,9 @@
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-  <meta name="generator" content="quarto-1.4.555">
+  <meta name="generator" content="quarto-1.5.53">
 
-  <title>Big Data in R with Arrow</title>
+  <title>Big Data in R with Arrow – data_manipulation_2</title>
   <meta name="apple-mobile-web-app-capable" content="yes">
   <meta name="apple-mobile-web-app-status-bar-style" content="black-translucent">
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@@ -41,7 +41,7 @@
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+    pre > code.sourceCode > span { display: inline-block; text-indent: -5em; padding-left: 5em; }
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@@ -69,7 +69,7 @@
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@@ -83,7 +83,7 @@
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     code span.im { color: #00769e; } /* Import */
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@@ -409,6 +409,10 @@ <h2>Why use a UDF?</h2>
 <li>Implement your own custom functions</li>
 <li>Run in R not Arrow</li>
 </ul>
+<!-- 
+In arrow 17.0.0 there will be less need to do this for custom functions
+
+-->
 </section>
 <section id="how-do-function-bindings-usually-work" class="slide level2">
 <h2>How do function bindings usually work?</h2>
@@ -429,11 +433,17 @@ <h2>How do function bindings usually work?</h2>
 <span id="cb1-12"><a></a>  <span class="fu">head</span>() <span class="sc">|&gt;</span></span>
 <span id="cb1-13"><a></a>  <span class="fu">collect</span>()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-error">
-<pre><code>Error: Expression time_diff_minutes(pickup_datetime, dropoff_datetime) not supported in Arrow
-Call collect() first to pull data into R.</code></pre>
+<pre><code>Error in `time_diff_minutes()`:
+! `difftime()` with units other than `secs` not supported in Arrow
+→ Call collect() first to pull data into R.</code></pre>
 </div>
 </div>
 <p>We get an error as we can’t automatically convert the function to arrow.</p>
+<!-- 
+* why did this happen?
+* Look at ?acero and show how difftime is implemented but only supports units= "secs"
+
+-->
 </section></section>
 <section>
 <section id="user-defined-functions-aka-udfs" class="title-slide slide level1 center">
@@ -442,9 +452,17 @@ <h1>User-defined functions (aka UDFs)</h1>
 <li>Define your own functions</li>
 <li>Scalar functions - 1 row input and 1 row output</li>
 </ul>
+<!-- 
+* Instead of having an Acero binding, Acero calls back to R to work out the result
+* Naturally slower than working entirely in Acero as now we don't have vectorisation, or any of the Arrow C++ optimisations
+* But, ultimately makes it possible
+-->
 </section>
 <section id="user-defined-functions---definition" class="slide level2">
 <h2>User-defined functions - definition</h2>
+<!-- 
+show slide don't type
+-->
 <div class="cell">
 <div class="sourceCode cell-code" id="cb3"><pre class="sourceCode numberSource r number-lines code-with-copy"><code class="sourceCode r"><span id="cb3-1"><a></a><span class="fu">register_scalar_function</span>(</span>
 <span id="cb3-2"><a></a>  <span class="at">name =</span> <span class="st">"time_diff_minutes"</span>,</span>
@@ -462,6 +480,12 @@ <h2>User-defined functions - definition</h2>
 <span id="cb3-14"><a></a>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </div>
 <p>This looks complicated, so let’s look at it 1 part at a time!</p>
+<!-- 
+UDFs *are* ugly!
+And that's because when we have bindings, we rely on the arrow R package's internals to handle all the complicated stuff like working out the types of the values.
+But it's not as bad as it looks, so we'll break it down!
+
+-->
 </section>
 <section id="user-defined-functions---definition-1" class="slide level2">
 <h2>User-defined functions - definition</h2>
@@ -482,6 +506,20 @@ <h2>User-defined functions - definition</h2>
 <span id="cb4-13"><a></a>  <span class="at">auto_convert =</span> <span class="cn">TRUE</span></span>
 <span id="cb4-14"><a></a>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </div>
+<!--
+show ?register_scalar_function help file
+
+set up the skeleton (i.e. register_scalar_function) and add in components 1 at a time
+
+name = some name to call it
+
+fun = the body of the function. first argument must be called "context" and you don't do anything with it - it'll be used by Acero to pass some metadata between Arrow and R. after that, define other arguments, and the function body like you otherwise would.
+
+in_type and out_type = so usually we don't have to worry about data types as the bindings do that for us, but now we're working more closely with Acero's internals, we need to do more things manually, so we have to tell it the data type of the input and output columns
+
+The `auto_convert` argument controls whether arrow automatically converts the inputs to R vectors or not.
+The default value `FALSE` can be used when working with R6 objects, but as we're working in an arrow dplyr pipeline here, we'll set it to `TRUE`.
+-->
 </section>
 <section id="user-defined-functions---definition-2" class="slide level2">
 <h2>User-defined functions - definition</h2>
@@ -577,14 +615,17 @@ <h2>User-defined functions - usage</h2>
 <pre><code># A tibble: 6 × 3
   pickup_datetime     dropoff_datetime    duration_minutes
   &lt;dttm&gt;              &lt;dttm&gt;                         &lt;int&gt;
-1 2012-10-07 18:19:00 2012-10-07 18:29:00               10
-2 2012-10-07 18:19:00 2012-10-07 18:33:00               14
-3 2012-10-07 18:19:00 2012-10-07 18:35:00               16
-4 2012-10-07 18:19:00 2012-10-07 18:35:00               16
-5 2012-10-07 18:19:00 2012-10-07 18:42:00               23
-6 2012-10-07 18:19:00 2012-10-07 18:43:00               24</code></pre>
+1 2012-11-02 16:40:32 2012-11-02 16:58:16               18
+2 2012-11-02 16:40:41 2012-11-02 16:45:56                5
+3 2012-11-02 16:40:50 2012-11-02 16:49:20                8
+4 2012-11-02 16:40:52 2012-11-02 16:46:15                5
+5 2012-11-02 16:41:00 2012-11-02 16:44:00                3
+6 2012-11-02 16:41:00 2012-11-02 16:45:00                4</code></pre>
 </div>
 </div>
+<!-- 
+So, we call `register_scalar_function()` to create it, and then then when we run our pipeline it appears there
+-->
 </section>
 <section id="your-turn" class="slide level2">
 <h2>Your Turn</h2>
@@ -609,6 +650,17 @@ <h1>Joins</h1>
 <section id="joins-1" class="slide level2">
 <h2>Joins</h2>
 
+<!-- 
+* combining data from different sources into a single source
+* fairly common to encounter when we have a dataset and some sort of reference table to combine it with
+* in the example here, we've done what's called a left-join:
+  * take everything from the table on the left
+  * decide which column to use for the join
+    * in this case, join vendor_name in the left table to code in the right table
+  * to do the join, goes through each row in the left table and finds the matching value in the right table, but then also brings the corresponding column (or multiple) from the right table
+* there are different type of joins, but the focus here isn't on joins, but is on what you need to know about doing joins in arrow
+* let's start off by writing out the code to do the join from the example
+-->
 <img data-src="images/joins.png" class="r-stretch"></section>
 <section id="joining-a-reference-table" class="slide level2">
 <h2>Joining a reference table</h2>
@@ -631,11 +683,15 @@ <h2>Joining a reference table</h2>
 <pre><code># A tibble: 3 × 3
   vendor_name full_name                    pickup_datetime    
   &lt;chr&gt;       &lt;chr&gt;                        &lt;dttm&gt;             
-1 CMT         Creative Mobile Technologies 2012-10-07 17:26:40
-2 CMT         Creative Mobile Technologies 2012-10-07 17:26:52
-3 CMT         Creative Mobile Technologies 2012-10-07 17:26:57</code></pre>
+1 CMT         Creative Mobile Technologies 2012-01-27 15:35:26
+2 CMT         Creative Mobile Technologies 2012-01-27 06:56:04
+3 CMT         Creative Mobile Technologies 2012-01-27 08:12:50</code></pre>
 </div>
 </div>
+<!-- 
+* This was pretty straightforward here, and a lot of the time it should just work, but there are some things you should know about
+
+-->
 </section>
 <section id="traps-for-the-unwary" class="slide level2">
 <h2>Traps for the unwary</h2>
@@ -664,6 +720,11 @@ <h2>Traps for the unwary</h2>
 # ℹ 255 more rows</code></pre>
 </div>
 </div>
+<!-- 
+* the taxi data contains location IDs for the different pickup locations, but what if we want to be more generic and look at the different boroughs that people travel between?
+* one of the data files is a reference table which maps the location IDs to their Borough, and so we can answer this question by joining the reference table to our dataset
+* let's take a look
+-->
 </section>
 <section id="why-didnt-this-work" class="slide level2">
 <h2>Why didn’t this work?</h2>
@@ -676,6 +737,10 @@ <h2>Why didn’t this work?</h2>
 ! Invalid: Incompatible data types for corresponding join field keys: FieldRef.Name(pickup_location_id) of type int64 and FieldRef.Name(location_id) of type int32</code></pre>
 </div>
 </div>
+<!-- 
+* The thing to do here feels like, let's do a join again then
+* it's complaining about data types, so let's look at the schema of both
+-->
 </section>
 <section id="schema-for-the-nyc_taxi-dataset" class="slide level2">
 <h2>Schema for the <code>nyc_taxi</code> Dataset</h2>
@@ -709,6 +774,7 @@ <h2>Schema for the <code>nyc_taxi</code> Dataset</h2>
 month: int32</code></pre>
 </div>
 </div>
+<!-- In the original data, pickup_location_id is an int64 -->
 </section>
 <section id="schema-for-the-nyc_taxi_zones-table" class="slide level2">
 <h2>Schema for the <code>nyc_taxi_zones</code> Table</h2>
@@ -725,6 +791,13 @@ <h2>Schema for the <code>nyc_taxi_zones</code> Table</h2>
 <li><code>pickup_location_id</code> is int64 in the <code>nyc_taxi</code> table</li>
 <li><code>location_id</code> is int32 in the <code>nyc_taxi_zones</code> table</li>
 </ul>
+<!-- 
+When we do a join here, the nyc_taxi_zones object we read in was a tibble as we collected it in memory.
+Arrow needs 2 Arrow objects to do a join, so behind the scenes, when we do the join, the tibble is converted into an Arrow Table.
+When we convert between R and Arrow objects, Arrow automatically does the type conversion for us.
+That's why here we call `arrow_table()` to see what the type conversion will be, and call schema()
+The thing to note here is that these are different types - and arrow needs for the join key column to be the same type. So what we need to do is manually specify the schema.
+-->
 </section>
 <section id="take-control-of-the-schema" class="slide level2">
 <h2>Take control of the schema</h2>
@@ -738,6 +811,10 @@ <h2>Take control of the schema</h2>
 <li><code>schema()</code> takes variable name / types as input</li>
 <li>arrow has various “type” functions: <code>int64()</code>, <code>utf8()</code>, <code>boolean()</code>, <code>date32()</code> etc</li>
 </ul>
+<!-- 
+This time we manually convert the data frame into a table and set the schema
+Alternatively, we could specify it when we read the data in
+-->
 </section>
 <section id="take-control-of-the-schema-1" class="slide level2">
 <h2>Take control of the schema</h2>
@@ -753,6 +830,10 @@ <h2>Take control of the schema</h2>
 borough: string</code></pre>
 </div>
 </div>
+<!-- 
+Now we can see the schema matches so we should be able to run our analysis
+Let's do 1 final thing to answer our borough to borough question!
+-->
 </section>
 <section id="prepare-the-auxiliary-tables" class="slide level2">
 <h2>Prepare the auxiliary tables</h2>
@@ -771,6 +852,7 @@ <h2>Prepare the auxiliary tables</h2>
 </section>
 <section id="join-and-cross-tabulate" class="slide level2">
 <h2>Join and cross-tabulate</h2>
+<!-- Run the code and recap the section while the timer runs! -->
 <div class="cell">
 <div class="sourceCode cell-code" id="cb25"><pre class="sourceCode numberSource r number-lines code-with-copy"><code class="sourceCode r"><span id="cb25-1"><a></a><span class="fu">library</span>(tictoc)</span>
 <span id="cb25-2"><a></a></span>
@@ -783,7 +865,7 @@ <h2>Join and cross-tabulate</h2>
 <span id="cb25-9"><a></a>  <span class="fu">collect</span>()</span>
 <span id="cb25-10"><a></a><span class="fu">toc</span>()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-stdout">
-<pre><code>163.966 sec elapsed</code></pre>
+<pre><code>177.061 sec elapsed</code></pre>
 </div>
 </div>
 <p><br></p>
@@ -1112,18 +1194,7 @@ <h2>Summary</h2>
         }
         return false;
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-      });
-      clipboard.on('success', function(e) {
+      const onCopySuccess = function(e) {
         // button target
         const button = e.trigger;
         // don't keep focus
@@ -1155,7 +1226,29 @@ <h2>Summary</h2>
         }, 1000);
         // clear code selection
         e.clearSelection();
+      }
+      const getTextToCopy = function(trigger) {
+          const codeEl = trigger.previousElementSibling.cloneNode(true);
+          for (const childEl of codeEl.children) {
+            if (isCodeAnnotation(childEl)) {
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+          return codeEl.innerText;
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+      const clipboard = new window.ClipboardJS('.code-copy-button:not([data-in-quarto-modal])', {
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       });
+      clipboard.on('success', onCopySuccess);
+      if (window.document.getElementById('quarto-embedded-source-code-modal')) {
+        // For code content inside modals, clipBoardJS needs to be initialized with a container option
+        // TODO: Check when it could be a function (https://github.com/zenorocha/clipboard.js/issues/860)
+        const clipboardModal = new window.ClipboardJS('.code-copy-button[data-in-quarto-modal]', {
+          text: getTextToCopy,
+          container: window.document.getElementById('quarto-embedded-source-code-modal')
+        });
+        clipboardModal.on('success', onCopySuccess);
+      }
         var localhostRegex = new RegExp(/^(?:http|https):\/\/localhost\:?[0-9]*\//);
         var mailtoRegex = new RegExp(/^mailto:/);
           var filterRegex = new RegExp('/' + window.location.host + '/');
@@ -1163,7 +1256,7 @@ <h2>Summary</h2>
             return filterRegex.test(href) || localhostRegex.test(href) || mailtoRegex.test(href);
         }
         // Inspect non-navigation links and adorn them if external
-     	var links = window.document.querySelectorAll('a[href]:not(.nav-link):not(.navbar-brand):not(.toc-action):not(.sidebar-link):not(.sidebar-item-toggle):not(.pagination-link):not(.no-external):not([aria-hidden]):not(.dropdown-item):not(.quarto-navigation-tool)');
+     	var links = window.document.querySelectorAll('a[href]:not(.nav-link):not(.navbar-brand):not(.toc-action):not(.sidebar-link):not(.sidebar-item-toggle):not(.pagination-link):not(.no-external):not([aria-hidden]):not(.dropdown-item):not(.quarto-navigation-tool):not(.about-link)');
         for (var i=0; i<links.length; i++) {
           const link = links[i];
           if (!isInternal(link.href)) {
diff --git a/_site/materials/5_arrow_single_file-exercises.html b/_site/materials/5_arrow_single_file-exercises.html
index c04e30c..4a93994 100644
--- a/_site/materials/5_arrow_single_file-exercises.html
+++ b/_site/materials/5_arrow_single_file-exercises.html
@@ -2,12 +2,12 @@
 <html xmlns="http://www.w3.org/1999/xhtml" lang="en" xml:lang="en"><head>
 
 <meta charset="utf-8">
-<meta name="generator" content="quarto-1.4.555">
+<meta name="generator" content="quarto-1.5.53">
 
 <meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=yes">
 
 
-<title>Big Data in R with Arrow - Arrow In-Memory Exercise</title>
+<title>Arrow In-Memory Exercise – Big Data in R with Arrow</title>
 <style>
 code{white-space: pre-wrap;}
 span.smallcaps{font-variant: small-caps;}
@@ -33,7 +33,7 @@
 }
 @media print {
 pre > code.sourceCode { white-space: pre-wrap; }
-pre > code.sourceCode > span { text-indent: -5em; padding-left: 5em; }
+pre > code.sourceCode > span { display: inline-block; text-indent: -5em; padding-left: 5em; }
 }
 pre.numberSource code
   { counter-reset: source-line 0; }
@@ -120,7 +120,7 @@
     </a>
   </div>
             <div id="quarto-search" class="" title="Search"></div>
-          <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbarCollapse" aria-controls="navbarCollapse" aria-expanded="false" aria-label="Toggle navigation" onclick="if (window.quartoToggleHeadroom) { window.quartoToggleHeadroom(); }">
+          <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbarCollapse" aria-controls="navbarCollapse" role="menu" aria-expanded="false" aria-label="Toggle navigation" onclick="if (window.quartoToggleHeadroom) { window.quartoToggleHeadroom(); }">
   <span class="navbar-toggler-icon"></span>
 </button>
           <div class="collapse navbar-collapse" id="navbarCollapse">
@@ -134,7 +134,7 @@
 <span class="menu-text">Packages &amp; Data</span></a>
   </li>  
   <li class="nav-item dropdown ">
-    <a class="nav-link dropdown-toggle" href="#" id="nav-menu-slides" role="button" data-bs-toggle="dropdown" aria-expanded="false">
+    <a class="nav-link dropdown-toggle" href="#" id="nav-menu-slides" role="link" data-bs-toggle="dropdown" aria-expanded="false">
  <span class="menu-text">Slides</span>
     </a>
     <ul class="dropdown-menu" aria-labelledby="nav-menu-slides">    
@@ -177,7 +177,7 @@
     </ul>
   </li>
   <li class="nav-item dropdown ">
-    <a class="nav-link dropdown-toggle" href="#" id="nav-menu-exercises" role="button" data-bs-toggle="dropdown" aria-expanded="false">
+    <a class="nav-link dropdown-toggle" href="#" id="nav-menu-exercises" role="link" data-bs-toggle="dropdown" aria-expanded="false">
  <span class="menu-text">Exercises</span>
     </a>
     <ul class="dropdown-menu" aria-labelledby="nav-menu-exercises">    
@@ -217,7 +217,7 @@
   </li>  
 </ul>
           </div> <!-- /navcollapse -->
-          <div class="quarto-navbar-tools">
+            <div class="quarto-navbar-tools">
 </div>
       </div> <!-- /container-fluid -->
     </nav>
@@ -226,7 +226,7 @@
 <div id="quarto-content" class="quarto-container page-columns page-rows-contents page-layout-article page-navbar">
 <!-- sidebar -->
 <!-- margin-sidebar -->
-    <div id="quarto-margin-sidebar" class="sidebar margin-sidebar">
+    <div id="quarto-margin-sidebar" class="sidebar margin-sidebar zindex-bottom">
         
     </div>
 <!-- main -->
@@ -442,18 +442,7 @@ <h1 class="title">Arrow In-Memory Exercise</h1>
     }
     return false;
   }
-  const clipboard = new window.ClipboardJS('.code-copy-button', {
-    text: function(trigger) {
-      const codeEl = trigger.previousElementSibling.cloneNode(true);
-      for (const childEl of codeEl.children) {
-        if (isCodeAnnotation(childEl)) {
-          childEl.remove();
-        }
-      }
-      return codeEl.innerText;
-    }
-  });
-  clipboard.on('success', function(e) {
+  const onCopySuccess = function(e) {
     // button target
     const button = e.trigger;
     // don't keep focus
@@ -485,7 +474,29 @@ <h1 class="title">Arrow In-Memory Exercise</h1>
     }, 1000);
     // clear code selection
     e.clearSelection();
+  }
+  const getTextToCopy = function(trigger) {
+      const codeEl = trigger.previousElementSibling.cloneNode(true);
+      for (const childEl of codeEl.children) {
+        if (isCodeAnnotation(childEl)) {
+          childEl.remove();
+        }
+      }
+      return codeEl.innerText;
+  }
+  const clipboard = new window.ClipboardJS('.code-copy-button:not([data-in-quarto-modal])', {
+    text: getTextToCopy
   });
+  clipboard.on('success', onCopySuccess);
+  if (window.document.getElementById('quarto-embedded-source-code-modal')) {
+    // For code content inside modals, clipBoardJS needs to be initialized with a container option
+    // TODO: Check when it could be a function (https://github.com/zenorocha/clipboard.js/issues/860)
+    const clipboardModal = new window.ClipboardJS('.code-copy-button[data-in-quarto-modal]', {
+      text: getTextToCopy,
+      container: window.document.getElementById('quarto-embedded-source-code-modal')
+    });
+    clipboardModal.on('success', onCopySuccess);
+  }
     var localhostRegex = new RegExp(/^(?:http|https):\/\/localhost\:?[0-9]*\//);
     var mailtoRegex = new RegExp(/^mailto:/);
       var filterRegex = new RegExp('/' + window.location.host + '/');
@@ -493,7 +504,7 @@ <h1 class="title">Arrow In-Memory Exercise</h1>
         return filterRegex.test(href) || localhostRegex.test(href) || mailtoRegex.test(href);
     }
     // Inspect non-navigation links and adorn them if external
- 	var links = window.document.querySelectorAll('a[href]:not(.nav-link):not(.navbar-brand):not(.toc-action):not(.sidebar-link):not(.sidebar-item-toggle):not(.pagination-link):not(.no-external):not([aria-hidden]):not(.dropdown-item):not(.quarto-navigation-tool)');
+ 	var links = window.document.querySelectorAll('a[href]:not(.nav-link):not(.navbar-brand):not(.toc-action):not(.sidebar-link):not(.sidebar-item-toggle):not(.pagination-link):not(.no-external):not([aria-hidden]):not(.dropdown-item):not(.quarto-navigation-tool):not(.about-link)');
     for (var i=0; i<links.length; i++) {
       const link = links[i];
       if (!isInternal(link.href)) {
diff --git a/_site/materials/5_arrow_single_file.html b/_site/materials/5_arrow_single_file.html
index bbc5840..152a17e 100644
--- a/_site/materials/5_arrow_single_file.html
+++ b/_site/materials/5_arrow_single_file.html
@@ -8,9 +8,9 @@
 <link href="../site_libs/quarto-html/light-border.css" rel="stylesheet">
 <link href="../site_libs/quarto-html/quarto-html.min.css" rel="stylesheet" data-mode="light">
 <link href="../site_libs/quarto-html/quarto-syntax-highlighting.css" rel="stylesheet" id="quarto-text-highlighting-styles"><meta charset="utf-8">
-  <meta name="generator" content="quarto-1.4.555">
+  <meta name="generator" content="quarto-1.5.53">
 
-  <title>Big Data in R with Arrow</title>
+  <title>Big Data in R with Arrow – arrow_single_file</title>
   <meta name="apple-mobile-web-app-capable" content="yes">
   <meta name="apple-mobile-web-app-status-bar-style" content="black-translucent">
   <meta name="viewport" content="width=device-width, initial-scale=1.0, maximum-scale=1.0, user-scalable=no, minimal-ui">
@@ -41,7 +41,7 @@
     }
     @media print {
     pre > code.sourceCode { white-space: pre-wrap; }
-    pre > code.sourceCode > span { text-indent: -5em; padding-left: 5em; }
+    pre > code.sourceCode > span { display: inline-block; text-indent: -5em; padding-left: 5em; }
     }
     pre.numberSource code
       { counter-reset: source-line 0; }
@@ -69,7 +69,7 @@
     code span.at { color: #657422; } /* Attribute */
     code span.bn { color: #ad0000; } /* BaseN */
     code span.bu { } /* BuiltIn */
-    code span.cf { color: #003b4f; } /* ControlFlow */
+    code span.cf { color: #003b4f; font-weight: bold; } /* ControlFlow */
     code span.ch { color: #20794d; } /* Char */
     code span.cn { color: #8f5902; } /* Constant */
     code span.co { color: #5e5e5e; } /* Comment */
@@ -83,7 +83,7 @@
     code span.fu { color: #4758ab; } /* Function */
     code span.im { color: #00769e; } /* Import */
     code span.in { color: #5e5e5e; } /* Information */
-    code span.kw { color: #003b4f; } /* Keyword */
+    code span.kw { color: #003b4f; font-weight: bold; } /* Keyword */
     code span.op { color: #5e5e5e; } /* Operator */
     code span.ot { color: #003b4f; } /* Other */
     code span.pp { color: #ad0000; } /* Preprocessor */
@@ -567,8 +567,7 @@ <h2>Watch Your Schemas 👀</h2>
 dropoff_location_id: int64</code></pre>
 </div>
 </div>
-</div>
-</div>
+</div></div>
 </section>
 <section id="data-frames" class="slide level2">
 <h2>Data frames</h2>
@@ -1042,18 +1041,7 @@ <h2>Arrow for Improving Those Sluggish Worklows</h2>
         }
         return false;
       }
-      const clipboard = new window.ClipboardJS('.code-copy-button', {
-        text: function(trigger) {
-          const codeEl = trigger.previousElementSibling.cloneNode(true);
-          for (const childEl of codeEl.children) {
-            if (isCodeAnnotation(childEl)) {
-              childEl.remove();
-            }
-          }
-          return codeEl.innerText;
-        }
-      });
-      clipboard.on('success', function(e) {
+      const onCopySuccess = function(e) {
         // button target
         const button = e.trigger;
         // don't keep focus
@@ -1085,7 +1073,29 @@ <h2>Arrow for Improving Those Sluggish Worklows</h2>
         }, 1000);
         // clear code selection
         e.clearSelection();
+      }
+      const getTextToCopy = function(trigger) {
+          const codeEl = trigger.previousElementSibling.cloneNode(true);
+          for (const childEl of codeEl.children) {
+            if (isCodeAnnotation(childEl)) {
+              childEl.remove();
+            }
+          }
+          return codeEl.innerText;
+      }
+      const clipboard = new window.ClipboardJS('.code-copy-button:not([data-in-quarto-modal])', {
+        text: getTextToCopy
       });
+      clipboard.on('success', onCopySuccess);
+      if (window.document.getElementById('quarto-embedded-source-code-modal')) {
+        // For code content inside modals, clipBoardJS needs to be initialized with a container option
+        // TODO: Check when it could be a function (https://github.com/zenorocha/clipboard.js/issues/860)
+        const clipboardModal = new window.ClipboardJS('.code-copy-button[data-in-quarto-modal]', {
+          text: getTextToCopy,
+          container: window.document.getElementById('quarto-embedded-source-code-modal')
+        });
+        clipboardModal.on('success', onCopySuccess);
+      }
         var localhostRegex = new RegExp(/^(?:http|https):\/\/localhost\:?[0-9]*\//);
         var mailtoRegex = new RegExp(/^mailto:/);
           var filterRegex = new RegExp('/' + window.location.host + '/');
@@ -1093,7 +1103,7 @@ <h2>Arrow for Improving Those Sluggish Worklows</h2>
             return filterRegex.test(href) || localhostRegex.test(href) || mailtoRegex.test(href);
         }
         // Inspect non-navigation links and adorn them if external
-     	var links = window.document.querySelectorAll('a[href]:not(.nav-link):not(.navbar-brand):not(.toc-action):not(.sidebar-link):not(.sidebar-item-toggle):not(.pagination-link):not(.no-external):not([aria-hidden]):not(.dropdown-item):not(.quarto-navigation-tool)');
+     	var links = window.document.querySelectorAll('a[href]:not(.nav-link):not(.navbar-brand):not(.toc-action):not(.sidebar-link):not(.sidebar-item-toggle):not(.pagination-link):not(.no-external):not([aria-hidden]):not(.dropdown-item):not(.quarto-navigation-tool):not(.about-link)');
         for (var i=0; i<links.length; i++) {
           const link = links[i];
           if (!isInternal(link.href)) {
diff --git a/_site/materials/6_wrapping_up.html b/_site/materials/6_wrapping_up.html
index 51470f3..4154b91 100644
--- a/_site/materials/6_wrapping_up.html
+++ b/_site/materials/6_wrapping_up.html
@@ -8,9 +8,9 @@
 <link href="../site_libs/quarto-html/light-border.css" rel="stylesheet">
 <link href="../site_libs/quarto-html/quarto-html.min.css" rel="stylesheet" data-mode="light">
 <link href="../site_libs/quarto-html/quarto-syntax-highlighting.css" rel="stylesheet" id="quarto-text-highlighting-styles"><meta charset="utf-8">
-  <meta name="generator" content="quarto-1.4.555">
+  <meta name="generator" content="quarto-1.5.53">
 
-  <title>Big Data in R with Arrow</title>
+  <title>Big Data in R with Arrow – wrapping_up</title>
   <meta name="apple-mobile-web-app-capable" content="yes">
   <meta name="apple-mobile-web-app-status-bar-style" content="black-translucent">
   <meta name="viewport" content="width=device-width, initial-scale=1.0, maximum-scale=1.0, user-scalable=no, minimal-ui">
@@ -41,7 +41,7 @@
     }
     @media print {
     pre > code.sourceCode { white-space: pre-wrap; }
-    pre > code.sourceCode > span { text-indent: -5em; padding-left: 5em; }
+    pre > code.sourceCode > span { display: inline-block; text-indent: -5em; padding-left: 5em; }
     }
     pre.numberSource code
       { counter-reset: source-line 0; }
@@ -69,7 +69,7 @@
     code span.at { color: #657422; } /* Attribute */
     code span.bn { color: #ad0000; } /* BaseN */
     code span.bu { } /* BuiltIn */
-    code span.cf { color: #003b4f; } /* ControlFlow */
+    code span.cf { color: #003b4f; font-weight: bold; } /* ControlFlow */
     code span.ch { color: #20794d; } /* Char */
     code span.cn { color: #8f5902; } /* Constant */
     code span.co { color: #5e5e5e; } /* Comment */
@@ -83,7 +83,7 @@
     code span.fu { color: #4758ab; } /* Function */
     code span.im { color: #00769e; } /* Import */
     code span.in { color: #5e5e5e; } /* Information */
-    code span.kw { color: #003b4f; } /* Keyword */
+    code span.kw { color: #003b4f; font-weight: bold; } /* Keyword */
     code span.op { color: #5e5e5e; } /* Operator */
     code span.ot { color: #003b4f; } /* Other */
     code span.pp { color: #ad0000; } /* Preprocessor */
@@ -773,18 +773,7 @@ <h2>Arrow + R Together: {arrow}</h2>
         }
         return false;
       }
-      const clipboard = new window.ClipboardJS('.code-copy-button', {
-        text: function(trigger) {
-          const codeEl = trigger.previousElementSibling.cloneNode(true);
-          for (const childEl of codeEl.children) {
-            if (isCodeAnnotation(childEl)) {
-              childEl.remove();
-            }
-          }
-          return codeEl.innerText;
-        }
-      });
-      clipboard.on('success', function(e) {
+      const onCopySuccess = function(e) {
         // button target
         const button = e.trigger;
         // don't keep focus
@@ -816,7 +805,29 @@ <h2>Arrow + R Together: {arrow}</h2>
         }, 1000);
         // clear code selection
         e.clearSelection();
+      }
+      const getTextToCopy = function(trigger) {
+          const codeEl = trigger.previousElementSibling.cloneNode(true);
+          for (const childEl of codeEl.children) {
+            if (isCodeAnnotation(childEl)) {
+              childEl.remove();
+            }
+          }
+          return codeEl.innerText;
+      }
+      const clipboard = new window.ClipboardJS('.code-copy-button:not([data-in-quarto-modal])', {
+        text: getTextToCopy
       });
+      clipboard.on('success', onCopySuccess);
+      if (window.document.getElementById('quarto-embedded-source-code-modal')) {
+        // For code content inside modals, clipBoardJS needs to be initialized with a container option
+        // TODO: Check when it could be a function (https://github.com/zenorocha/clipboard.js/issues/860)
+        const clipboardModal = new window.ClipboardJS('.code-copy-button[data-in-quarto-modal]', {
+          text: getTextToCopy,
+          container: window.document.getElementById('quarto-embedded-source-code-modal')
+        });
+        clipboardModal.on('success', onCopySuccess);
+      }
         var localhostRegex = new RegExp(/^(?:http|https):\/\/localhost\:?[0-9]*\//);
         var mailtoRegex = new RegExp(/^mailto:/);
           var filterRegex = new RegExp('/' + window.location.host + '/');
@@ -824,7 +835,7 @@ <h2>Arrow + R Together: {arrow}</h2>
             return filterRegex.test(href) || localhostRegex.test(href) || mailtoRegex.test(href);
         }
         // Inspect non-navigation links and adorn them if external
-     	var links = window.document.querySelectorAll('a[href]:not(.nav-link):not(.navbar-brand):not(.toc-action):not(.sidebar-link):not(.sidebar-item-toggle):not(.pagination-link):not(.no-external):not([aria-hidden]):not(.dropdown-item):not(.quarto-navigation-tool)');
+     	var links = window.document.querySelectorAll('a[href]:not(.nav-link):not(.navbar-brand):not(.toc-action):not(.sidebar-link):not(.sidebar-item-toggle):not(.pagination-link):not(.no-external):not([aria-hidden]):not(.dropdown-item):not(.quarto-navigation-tool):not(.about-link)');
         for (var i=0; i<links.length; i++) {
           const link = links[i];
           if (!isInternal(link.href)) {
diff --git a/_site/materials/7_continue_learning.html b/_site/materials/7_continue_learning.html
index ea59956..2dd2384 100644
--- a/_site/materials/7_continue_learning.html
+++ b/_site/materials/7_continue_learning.html
@@ -8,9 +8,9 @@
 <link href="../site_libs/quarto-html/light-border.css" rel="stylesheet">
 <link href="../site_libs/quarto-html/quarto-html.min.css" rel="stylesheet" data-mode="light">
 <link href="../site_libs/quarto-html/quarto-syntax-highlighting.css" rel="stylesheet" id="quarto-text-highlighting-styles"><meta charset="utf-8">
-  <meta name="generator" content="quarto-1.4.555">
+  <meta name="generator" content="quarto-1.5.53">
 
-  <title>Big Data in R with Arrow</title>
+  <title>Big Data in R with Arrow – continue_learning</title>
   <meta name="apple-mobile-web-app-capable" content="yes">
   <meta name="apple-mobile-web-app-status-bar-style" content="black-translucent">
   <meta name="viewport" content="width=device-width, initial-scale=1.0, maximum-scale=1.0, user-scalable=no, minimal-ui">
@@ -330,6 +330,11 @@
 <h1>Continue Your Arrow Learning Journey</h1>
 
 </section>
+<section id="book" class="slide level2">
+<h2>Book</h2>
+<p><a href="https://arrowrbook.com" class="uri">https://arrowrbook.com</a></p>
+
+<img data-src="images/dummybookcover.png" class="r-stretch"></section>
 <section id="docs" class="slide level2">
 <h2>Docs</h2>
 <div style="font-size: 80%;">
@@ -669,18 +674,7 @@ <h2>Community</h2>
         }
         return false;
       }
-      const clipboard = new window.ClipboardJS('.code-copy-button', {
-        text: function(trigger) {
-          const codeEl = trigger.previousElementSibling.cloneNode(true);
-          for (const childEl of codeEl.children) {
-            if (isCodeAnnotation(childEl)) {
-              childEl.remove();
-            }
-          }
-          return codeEl.innerText;
-        }
-      });
-      clipboard.on('success', function(e) {
+      const onCopySuccess = function(e) {
         // button target
         const button = e.trigger;
         // don't keep focus
@@ -712,7 +706,29 @@ <h2>Community</h2>
         }, 1000);
         // clear code selection
         e.clearSelection();
+      }
+      const getTextToCopy = function(trigger) {
+          const codeEl = trigger.previousElementSibling.cloneNode(true);
+          for (const childEl of codeEl.children) {
+            if (isCodeAnnotation(childEl)) {
+              childEl.remove();
+            }
+          }
+          return codeEl.innerText;
+      }
+      const clipboard = new window.ClipboardJS('.code-copy-button:not([data-in-quarto-modal])', {
+        text: getTextToCopy
       });
+      clipboard.on('success', onCopySuccess);
+      if (window.document.getElementById('quarto-embedded-source-code-modal')) {
+        // For code content inside modals, clipBoardJS needs to be initialized with a container option
+        // TODO: Check when it could be a function (https://github.com/zenorocha/clipboard.js/issues/860)
+        const clipboardModal = new window.ClipboardJS('.code-copy-button[data-in-quarto-modal]', {
+          text: getTextToCopy,
+          container: window.document.getElementById('quarto-embedded-source-code-modal')
+        });
+        clipboardModal.on('success', onCopySuccess);
+      }
         var localhostRegex = new RegExp(/^(?:http|https):\/\/localhost\:?[0-9]*\//);
         var mailtoRegex = new RegExp(/^mailto:/);
           var filterRegex = new RegExp('/' + window.location.host + '/');
@@ -720,7 +736,7 @@ <h2>Community</h2>
             return filterRegex.test(href) || localhostRegex.test(href) || mailtoRegex.test(href);
         }
         // Inspect non-navigation links and adorn them if external
-     	var links = window.document.querySelectorAll('a[href]:not(.nav-link):not(.navbar-brand):not(.toc-action):not(.sidebar-link):not(.sidebar-item-toggle):not(.pagination-link):not(.no-external):not([aria-hidden]):not(.dropdown-item):not(.quarto-navigation-tool)');
+     	var links = window.document.querySelectorAll('a[href]:not(.nav-link):not(.navbar-brand):not(.toc-action):not(.sidebar-link):not(.sidebar-item-toggle):not(.pagination-link):not(.no-external):not([aria-hidden]):not(.dropdown-item):not(.quarto-navigation-tool):not(.about-link)');
         for (var i=0; i<links.length; i++) {
           const link = links[i];
           if (!isInternal(link.href)) {
diff --git a/_site/materials/8_closing.html b/_site/materials/8_closing.html
index ef2b694..005f0ad 100644
--- a/_site/materials/8_closing.html
+++ b/_site/materials/8_closing.html
@@ -8,9 +8,9 @@
 <link href="../site_libs/quarto-html/light-border.css" rel="stylesheet">
 <link href="../site_libs/quarto-html/quarto-html.min.css" rel="stylesheet" data-mode="light">
 <link href="../site_libs/quarto-html/quarto-syntax-highlighting.css" rel="stylesheet" id="quarto-text-highlighting-styles"><meta charset="utf-8">
-  <meta name="generator" content="quarto-1.4.555">
+  <meta name="generator" content="quarto-1.5.53">
 
-  <title>Big Data in R with Arrow</title>
+  <title>Big Data in R with Arrow – closing</title>
   <meta name="apple-mobile-web-app-capable" content="yes">
   <meta name="apple-mobile-web-app-status-bar-style" content="black-translucent">
   <meta name="viewport" content="width=device-width, initial-scale=1.0, maximum-scale=1.0, user-scalable=no, minimal-ui">
@@ -669,18 +669,7 @@ <h2>Grab a sticker!</h2>
         }
         return false;
       }
-      const clipboard = new window.ClipboardJS('.code-copy-button', {
-        text: function(trigger) {
-          const codeEl = trigger.previousElementSibling.cloneNode(true);
-          for (const childEl of codeEl.children) {
-            if (isCodeAnnotation(childEl)) {
-              childEl.remove();
-            }
-          }
-          return codeEl.innerText;
-        }
-      });
-      clipboard.on('success', function(e) {
+      const onCopySuccess = function(e) {
         // button target
         const button = e.trigger;
         // don't keep focus
@@ -712,7 +701,29 @@ <h2>Grab a sticker!</h2>
         }, 1000);
         // clear code selection
         e.clearSelection();
+      }
+      const getTextToCopy = function(trigger) {
+          const codeEl = trigger.previousElementSibling.cloneNode(true);
+          for (const childEl of codeEl.children) {
+            if (isCodeAnnotation(childEl)) {
+              childEl.remove();
+            }
+          }
+          return codeEl.innerText;
+      }
+      const clipboard = new window.ClipboardJS('.code-copy-button:not([data-in-quarto-modal])', {
+        text: getTextToCopy
       });
+      clipboard.on('success', onCopySuccess);
+      if (window.document.getElementById('quarto-embedded-source-code-modal')) {
+        // For code content inside modals, clipBoardJS needs to be initialized with a container option
+        // TODO: Check when it could be a function (https://github.com/zenorocha/clipboard.js/issues/860)
+        const clipboardModal = new window.ClipboardJS('.code-copy-button[data-in-quarto-modal]', {
+          text: getTextToCopy,
+          container: window.document.getElementById('quarto-embedded-source-code-modal')
+        });
+        clipboardModal.on('success', onCopySuccess);
+      }
         var localhostRegex = new RegExp(/^(?:http|https):\/\/localhost\:?[0-9]*\//);
         var mailtoRegex = new RegExp(/^mailto:/);
           var filterRegex = new RegExp('/' + window.location.host + '/');
@@ -720,7 +731,7 @@ <h2>Grab a sticker!</h2>
             return filterRegex.test(href) || localhostRegex.test(href) || mailtoRegex.test(href);
         }
         // Inspect non-navigation links and adorn them if external
-     	var links = window.document.querySelectorAll('a[href]:not(.nav-link):not(.navbar-brand):not(.toc-action):not(.sidebar-link):not(.sidebar-item-toggle):not(.pagination-link):not(.no-external):not([aria-hidden]):not(.dropdown-item):not(.quarto-navigation-tool)');
+     	var links = window.document.querySelectorAll('a[href]:not(.nav-link):not(.navbar-brand):not(.toc-action):not(.sidebar-link):not(.sidebar-item-toggle):not(.pagination-link):not(.no-external):not([aria-hidden]):not(.dropdown-item):not(.quarto-navigation-tool):not(.about-link)');
         for (var i=0; i<links.length; i++) {
           const link = links[i];
           if (!isInternal(link.href)) {
diff --git a/_site/materials/images/arrow-r-pkg-highlights.png b/_site/materials/images/arrow-r-pkg-highlights.png
new file mode 100644
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diff --git a/_site/search.json b/_site/search.json
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+++ b/_site/search.json
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-    "text": "Welcome to the Big Data in R with Arrow workshop. On this page you will find information about the software, packages and data we will be using during the 1-day workshop.\nWe will be using Posit Workbench to learn together. Workbench will be setup with all the below software and data—all participants need to bring is a laptop that can connect to wifi! If you would prefer to run code locally on your own laptop on the day, or if you want to know how to get set-up locally, you can use the below instructions to install the software & packages and download the data."
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-    "title": "Packages & Data",
-    "section": "Larger-Than-Memory Data Option",
-    "text": "Larger-Than-Memory Data Option\n\n1. NYC Taxi Data\nThis is the main dataset we will use on the day. It’s pretty hefty—40 GB in total—and there are a couple of options for how to acquire it, depending on your internet connection speed.\n\nOption 1—the simplest option—for those with a good internet connection and happy to let things run\nIf you have a solid internet connection, and especially if you’re in the US/Canada, this option is the simplest. You can use arrow itself to download the data. Note that there are no progress bars displayed during download, and so your session will appear to hang, but you can check progress by inspecting the contents of the download directory. When we tested this with Steph’s laptop and a fast internet connection, it took 67 minutes, though results will likely vary widely.\nThis method requires the arrow R package to have been built with S3 support enabled. This is on by default for most MacOS and Windows users, but if you’re on Linux, take a look at the instructions here.\nAfter installing arrow, run the following code:\n\nlibrary(arrow)\nlibrary(dplyr)\n\ndata_path &lt;- \"data/nyc-taxi\" # Or set your own preferred path\n\nopen_dataset(\"s3://voltrondata-labs-datasets/nyc-taxi\") |&gt;\n    filter(year %in% 2012:2021) |&gt; \n    write_dataset(data_path, partitioning = c(\"year\", \"month\"))\n\nOnce this has completed, you can check everything has downloaded correctly by calling:\n\nopen_dataset(data_path) |&gt;\n    nrow()\n\nIt might take a moment to run (the data has over a billion rows!), but you should expect to see:\n[1] 1150352666\nIf you get an error message, your download may have been interrupted at some point. The error message will name the file which could not be read. Manually delete this file and run the nrow() code snippet again until you successfully load the remaining data. You can then download any missing files individually using option 2.\n\n\nOption 2—one file at a time via https\nIf you have a slower internet connection or are further away from the data S3 bucket location, it’s probably going to be simpler to download the data file-by-file. Or, if you had any interruptions to your download process in the previous step, you can either try instead with this method, or delete the files which weren’t downloaded properly, and use this method to just download the files you need.\nWe’ve created a script for you which downloads the data one file at a time via https. The script also checks for previously downloaded data, so if you encounter problems downloading any files, just delete the partially downloaded file and run again—the script will only download files which are missing.\n\ndownload_via_https &lt;- function(data_dir, years = 2012:2021){\n\n    # Set this option as we'll be downloading large files and R has a default\n    # timeout of 60 seconds, so we've updated this to 30 mins\n    options(timeout = 1800)\n    \n    # The S3 bucket where the data is stored\n    bucket &lt;- \"https://voltrondata-labs-datasets.s3.us-east-2.amazonaws.com\"\n    \n    # Collect any errors raised during the download process\n    problems &lt;- c()\n    \n    # Download the data from S3 - loops through the data files, downloading 1 file at a time\n    for (year in years) {\n      \n      # We only have 2 months for 2022 data\n      if(year ==2022){\n        months = 1:2\n      } else {\n        months = 1:12\n      }\n      \n      for (month in months) {\n        \n        # Work out where we're going to be saving the data\n        partition_dir &lt;- paste0(\"year=\", year, \"/month=\", month)\n        dest_dir &lt;- file.path(data_dir, partition_dir)\n        dest_file_path &lt;- file.path(dest_dir, \"part-0.parquet\")\n        \n        # If the file doesn't exist\n        if (!file.exists(dest_file_path)) {\n          \n          # Create the partition to store the data in\n          if(!dir.exists(dest_dir)){\n            dir.create(dest_dir, recursive = TRUE)\n          }\n           \n          # Work out where we are going to be retrieving the data from\n          source_path &lt;- file.path(bucket, \"nyc-taxi\", partition_dir, \"part-0.parquet\")\n          \n          # Download the data - save any error messages that occur\n          tryCatch(\n            download.file(source_path, dest_file_path, mode = \"wb\"),\n            error = function(e){\n              problems &lt;- c(problems, e$message)\n            }\n          )\n        }\n      }\n    }\n    \n    print(\"Downloads complete\")\n    \n    if(length(problems) &gt; 0){\n      warning(call. = FALSE, \"The following errors occurred during download:\\n\", paste(problems, collapse =  \"\\n\"))\n    }\n}\n\n\ndata_path &lt;- \"data/nyc-taxi\" # Or set your own preferred path\n\ndownload_via_https(data_path)\n\nOnce this has completed, you can check everything has downloaded correctly by calling:\n\nopen_dataset(data_path) |&gt;\n    nrow()\n\nIt might take a moment to run (the data has over a billion rows), but you should expect to see:\n[1] 1150352666\nIf you get an error message, your download may have been interrupted at some point. The error message will name the file which could not be read. Manually delete this file and run the nrow() code snippet again until you successfully load the data. You can then download any missing files by re-running download_via_https(data_path).\n\n\n\n2. Seattle Checkouts by Title Data\nThis is the data we use to explore some data storage and engineering options. It’s a good sized, single CSV file—9GB on-disk in total, which can be downloaded from the an AWS S3 bucket via https:\n\noptions(timeout = 1800)\ndownload.file(\n  url = \"https://r4ds.s3.us-west-2.amazonaws.com/seattle-library-checkouts.csv\",\n  destfile = \"data/seattle-library-checkouts.csv\"\n)"
+    "objectID": "materials/2_data_manipulation_1.html#goals",
+    "href": "materials/2_data_manipulation_1.html#goals",
+    "title": "Big Data in R with Arrow",
+    "section": "Goals",
+    "text": "Goals\nAvoiding these! But…don’t worry!"
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-    "title": "Packages & Data",
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-    "text": "Tiny Data Option\nIf you don’t have time or disk space to download the larger-than-memory datasets (and still have disk space do the exercises), you can run the code and exercises in the course with “tiny” versions of these data. Although the focus in this course is working with larger-than-memory data, you can still learn about the concepts and workflows with smaller data—although note you may not see the same performance improvements that you would get when working with larger data.\n\n1. Tiny NYC Taxi Data\nWe’ve created a “tiny” NYC Taxi dataset which contains only 1 in 1000 rows from the original dataset. So instead of working with 1.15 billion rows of data and about 40GB of files, the tiny taxi dataset is 1.15 million rows and about 50MB of files. You can download the tiny NYC Taxi data directly from this repo via https:\n\noptions(timeout = 1800)\ndownload.file(\n  url = \"https://github.com/posit-conf-2023/arrow/releases/download/v0.1.0/nyc-taxi-tiny.zip\",\n  destfile = \"data/nyc-taxi-tiny.zip\"\n)\n\n# Extract the partitioned parquet files from the zip folder:\nunzip(\n  zipfile = \"data/nyc-taxi-tiny.zip\", \n  exdir = \"data/\"\n)\n\n\n\n2. Tiny Seattle Checkouts by Title Data\nWe’ve created a “tiny” Seattle Checkouts by Title dataset which contains only 1 in 100 rows from the original dataset. So instead of working with ~41 million rows of data in a 9GB file, the tiny Seattle checkouts dataset is ~410 thousand rows and in an 90MB file. You can download the tiny Seattle Checkouts by Title data directly from this repo via https:\n\noptions(timeout = 1800)\ndownload.file(\n  url = \"https://github.com/posit-conf-2023/arrow/releases/download/v0.1.0/seattle-library-checkouts-tiny.csv\",\n  destfile = \"data/seattle-library-checkouts-tiny.csv\"\n)"
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+    "title": "Big Data in R with Arrow",
+    "section": "An Arrow Dataset",
+    "text": "An Arrow Dataset\n\nlibrary(arrow)\n\nnyc_taxi &lt;- open_dataset(\"data/nyc-taxi/\")\nnyc_taxi\n\nFileSystemDataset with 120 Parquet files\n24 columns\nvendor_name: string\npickup_datetime: timestamp[ms]\ndropoff_datetime: timestamp[ms]\npassenger_count: int64\ntrip_distance: double\npickup_longitude: double\npickup_latitude: double\nrate_code: string\nstore_and_fwd: string\ndropoff_longitude: double\ndropoff_latitude: double\npayment_type: string\nfare_amount: double\nextra: double\nmta_tax: double\ntip_amount: double\ntolls_amount: double\ntotal_amount: double\nimprovement_surcharge: double\ncongestion_surcharge: double\n...\n4 more columns\nUse `schema()` to see entire schema"
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-    "section": "Both Data Options / Everyone",
-    "text": "Both Data Options / Everyone\n\n3. Taxi Zone Lookup CSV Table & Taxi Zone Shapefile\nYou can download the two NYC Taxi trip ancillary data files directly from this repo via https:\n\noptions(timeout = 1800)\ndownload.file(\n  url = \"https://github.com/posit-conf-2023/arrow/releases/download/v0.1.0/taxi_zone_lookup.csv\",\n  destfile = \"data/taxi_zone_lookup.csv\"\n)\n\ndownload.file(\n  url = \"https://github.com/posit-conf-2023/arrow/releases/download/v0.1.0/taxi_zones.zip\",\n  destfile = \"data/taxi_zones.zip\"\n)\n\n# Extract the spatial files from the zip folder:\nunzip(\n  zipfile = \"data/taxi_zones.zip\", \n  exdir = \"data/taxi_zones\"\n)"
+    "objectID": "materials/2_data_manipulation_1.html#arrow-datasets",
+    "href": "materials/2_data_manipulation_1.html#arrow-datasets",
+    "title": "Big Data in R with Arrow",
+    "section": "Arrow Datasets",
+    "text": "Arrow Datasets"
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+    "objectID": "materials/2_data_manipulation_1.html#constructing-queries",
+    "href": "materials/2_data_manipulation_1.html#constructing-queries",
     "title": "Big Data in R with Arrow",
-    "section": "Docs",
-    "text": "Docs\n\nhttps://arrow.apache.org/docs/r/"
+    "section": "Constructing queries",
+    "text": "Constructing queries\n\nlibrary(dplyr)\n\nshared_rides &lt;- nyc_taxi |&gt;\n  group_by(year) |&gt;\n  summarize(\n    all_trips = n(),\n    shared_trips = sum(passenger_count &gt; 1, na.rm = TRUE)\n  ) |&gt;\n  mutate(pct_shared = shared_trips / all_trips * 100) \n\nclass(shared_rides)\n\n[1] \"arrow_dplyr_query\""
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+    "href": "materials/2_data_manipulation_1.html#arrow-dplyr-queries",
     "title": "Big Data in R with Arrow",
-    "section": "Cookbook",
-    "text": "Cookbook\nhttps://arrow.apache.org/cookbook/r/"
+    "section": "arrow dplyr queries",
+    "text": "arrow dplyr queries\n\nquery has been constructed but not evaluated\nnothing has been pulled into memory"
   },
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-    "objectID": "materials/7_continue_learning.html#cheatsheet",
-    "href": "materials/7_continue_learning.html#cheatsheet",
+    "objectID": "materials/2_data_manipulation_1.html#running-the-query",
+    "href": "materials/2_data_manipulation_1.html#running-the-query",
     "title": "Big Data in R with Arrow",
-    "section": "Cheatsheet",
-    "text": "Cheatsheet\n\nhttps://github.com/apache/arrow/blob/master/r/cheatsheet/arrow-cheatsheet.pdf"
+    "section": "Running the query",
+    "text": "Running the query\n\ncollect() evaluates the query, in-memory output returns to R"
   },
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-    "objectID": "materials/7_continue_learning.html#awesome-arrow",
-    "href": "materials/7_continue_learning.html#awesome-arrow",
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+    "href": "materials/2_data_manipulation_1.html#collect",
     "title": "Big Data in R with Arrow",
-    "section": "Awesome Arrow",
-    "text": "Awesome Arrow\nhttps://github.com/thisisnic/awesome-arrow-r"
+    "section": "collect()",
+    "text": "collect()\n\ncollect(shared_rides)\n\n# A tibble: 10 × 4\n    year all_trips shared_trips pct_shared\n   &lt;int&gt;     &lt;int&gt;        &lt;int&gt;      &lt;dbl&gt;\n 1  2012 178544324     53313752       29.9\n 2  2013 173179759     51215013       29.6\n 3  2014 165114361     48816505       29.6\n 4  2015 146112989     43081091       29.5\n 5  2016 131165043     38163870       29.1\n 6  2017 113495512     32296166       28.5\n 7  2018 102797401     28796633       28.0\n 8  2019  84393604     23515989       27.9\n 9  2020  24647055      5837960       23.7\n10  2021  30902618      7221844       23.4"
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-    "objectID": "materials/7_continue_learning.html#github-issues",
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+    "objectID": "materials/2_data_manipulation_1.html#calling-nrow-to-see-how-much-data",
+    "href": "materials/2_data_manipulation_1.html#calling-nrow-to-see-how-much-data",
     "title": "Big Data in R with Arrow",
-    "section": "GitHub Issues",
-    "text": "GitHub Issues\nhttps://github.com/apache/arrow/issues"
+    "section": "Calling nrow() to see how much data",
+    "text": "Calling nrow() to see how much data\n\nnyc_taxi |&gt;\n  filter(year %in% 2017:2021) |&gt;\n  nrow()\n\n[1] 356236190"
   },
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+    "href": "materials/2_data_manipulation_1.html#your-turn",
     "title": "Big Data in R with Arrow",
-    "section": "Community",
-    "text": "Community\nhttps://arrow.apache.org/community/\n\n\nalso Stack Overflow, Posit Community\n\n\n\n\n\n🔗 pos.it/arrow-conf24"
+    "section": "Your Turn",
+    "text": "Your Turn\nUse the function nrow() to work out the answers to these questions:\n\nHow many taxi fares in the dataset had a total amount greater than $100?\n\n➡️ Data Manipulation Part I Exercises Page"
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   {
-    "objectID": "materials/5_arrow_single_file.html#arrow",
-    "href": "materials/5_arrow_single_file.html#arrow",
+    "objectID": "materials/2_data_manipulation_1.html#previewing-output-for-large-queries",
+    "href": "materials/2_data_manipulation_1.html#previewing-output-for-large-queries",
     "title": "Big Data in R with Arrow",
-    "section": "arrow 📦",
-    "text": "arrow 📦"
+    "section": "Previewing output for large queries",
+    "text": "Previewing output for large queries\nHow much were fares in GBP (£)?\n\nfares_pounds &lt;- nyc_taxi |&gt;\n  mutate(\n    fare_amount_pounds = fare_amount * 0.79\n  )\n\nHow many rows?\n\nfares_pounds |&gt;\n  nrow()\n\n[1] 1150352666"
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     "title": "Big Data in R with Arrow",
-    "section": "Arrow & Single Files",
-    "text": "Arrow & Single Files\n\nlibrary(arrow)\n\nread_parquet()\nread_csv_arrow()\nread_feather()\nread_json_arrow()\n\nValue: tibble (the default), or an Arrow Table if as_data_frame = FALSE — both in-memory"
+    "section": "Use head() and collect() to preview results",
+    "text": "Use head() and collect() to preview results\n\nnyc_taxi |&gt;\n  filter(year == 2020) |&gt;\n  mutate(fare_pounds = fare_amount * 0.79) |&gt;\n  select(fare_amount, fare_pounds) |&gt;\n  head() |&gt;\n  collect()\n\n# A tibble: 6 × 2\n  fare_amount fare_pounds\n        &lt;dbl&gt;       &lt;dbl&gt;\n1        16.5       13.0 \n2        21.5       17.0 \n3         5          3.95\n4        10.5        8.30\n5        11          8.69\n6         5.5        4.35"
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     "title": "Big Data in R with Arrow",
-    "section": "Your Turn",
-    "text": "Your Turn\n\nRead in a single NYC Taxi parquet file using read_parquet() as an Arrow Table\nConvert your Arrow Table object to a data.frame or a tibble"
+    "section": "Use across() to transform data in multiple columns",
+    "text": "Use across() to transform data in multiple columns\n\nnyc_taxi |&gt;\n  mutate(across(ends_with(\"amount\"), list(pounds = ~.x * 0.79))) |&gt;\n  select(contains(\"amount\")) |&gt;\n  head() |&gt;\n  collect()\n\n# A tibble: 6 × 8\n  fare_amount tip_amount tolls_amount total_amount fare_amount_pounds\n        &lt;dbl&gt;      &lt;dbl&gt;        &lt;dbl&gt;        &lt;dbl&gt;              &lt;dbl&gt;\n1        29.7       6.04            0        36.2               23.5 \n2         9.3       0               0         9.8                7.35\n3         4.1       1.38            0         5.98               3.24\n4         4.5       1               0         6                  3.56\n5         4.5       0               0         5.5                3.56\n6         4.1       0               0         5.6                3.24\n# ℹ 3 more variables: tip_amount_pounds &lt;dbl&gt;, tolls_amount_pounds &lt;dbl&gt;,\n#   total_amount_pounds &lt;dbl&gt;"
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     "title": "Big Data in R with Arrow",
-    "section": "Read a Parquet File (tibble)",
-    "text": "Read a Parquet File (tibble)\n\nlibrary(arrow)\n\nparquet_file &lt;- \"data/nyc-taxi/year=2019/month=9/part-0.parquet\"\n\ntaxi_df &lt;- read_parquet(file = parquet_file)\ntaxi_df\n\n# A tibble: 6,567,396 × 22\n   vendor_name pickup_datetime     dropoff_datetime    passenger_count\n   &lt;chr&gt;       &lt;dttm&gt;              &lt;dttm&gt;                        &lt;int&gt;\n 1 CMT         2019-08-31 18:09:30 2019-08-31 18:15:42               1\n 2 CMT         2019-08-31 18:26:30 2019-08-31 18:44:31               1\n 3 CMT         2019-08-31 18:39:35 2019-08-31 19:15:55               2\n 4 VTS         2019-08-31 18:12:26 2019-08-31 18:15:17               4\n 5 VTS         2019-08-31 18:43:16 2019-08-31 18:53:50               1\n 6 VTS         2019-08-31 18:26:13 2019-08-31 18:45:35               1\n 7 CMT         2019-08-31 18:34:52 2019-08-31 18:42:03               1\n 8 CMT         2019-08-31 18:50:02 2019-08-31 18:58:16               1\n 9 CMT         2019-08-31 18:08:02 2019-08-31 18:14:44               0\n10 VTS         2019-08-31 18:11:38 2019-08-31 18:26:47               1\n# ℹ 6,567,386 more rows\n# ℹ 18 more variables: trip_distance &lt;dbl&gt;, pickup_longitude &lt;dbl&gt;,\n#   pickup_latitude &lt;dbl&gt;, rate_code &lt;chr&gt;, store_and_fwd &lt;chr&gt;,\n#   dropoff_longitude &lt;dbl&gt;, dropoff_latitude &lt;dbl&gt;, payment_type &lt;chr&gt;,\n#   fare_amount &lt;dbl&gt;, extra &lt;dbl&gt;, mta_tax &lt;dbl&gt;, tip_amount &lt;dbl&gt;,\n#   tolls_amount &lt;dbl&gt;, total_amount &lt;dbl&gt;, improvement_surcharge &lt;dbl&gt;,\n#   congestion_surcharge &lt;dbl&gt;, pickup_location_id &lt;int&gt;, …"
+    "section": "Summary",
+    "text": "Summary\n\nUse nrow() to work out how many rows of data your analyses will return\nUse collect() to pull all of the data into your R session\nUse head() and collect() to preview results\nUse across() to manipulate data in multiple columns at once"
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     "title": "Big Data in R with Arrow",
-    "section": "Read a Parquet File (Table)",
-    "text": "Read a Parquet File (Table)\n\ntaxi_table &lt;- read_parquet(file = parquet_file, as_data_frame = FALSE)\ntaxi_table\n\nTable\n6567396 rows x 22 columns\n$vendor_name &lt;string&gt;\n$pickup_datetime &lt;timestamp[ms]&gt;\n$dropoff_datetime &lt;timestamp[ms]&gt;\n$passenger_count &lt;int64&gt;\n$trip_distance &lt;double&gt;\n$pickup_longitude &lt;double&gt;\n$pickup_latitude &lt;double&gt;\n$rate_code &lt;string&gt;\n$store_and_fwd &lt;string&gt;\n$dropoff_longitude &lt;double&gt;\n$dropoff_latitude &lt;double&gt;\n$payment_type &lt;string&gt;\n$fare_amount &lt;double&gt;\n$extra &lt;double&gt;\n$mta_tax &lt;double&gt;\n$tip_amount &lt;double&gt;\n$tolls_amount &lt;double&gt;\n$total_amount &lt;double&gt;\n$improvement_surcharge &lt;double&gt;\n$congestion_surcharge &lt;double&gt;\n...\n2 more columns\nUse `schema()` to see entire schema"
+    "section": "Example - slice()",
+    "text": "Example - slice()\nFirst three trips in the dataset in 2021 where distance &gt; 100 miles\n\nlong_rides_2021 &lt;- nyc_taxi |&gt;\n  filter(year == 2021 & trip_distance &gt; 100) |&gt;\n  select(pickup_datetime, year, trip_distance)\n\nlong_rides_2021 |&gt;\n  slice(1:3)\n\nError in UseMethod(\"slice\"): no applicable method for 'slice' applied to an object of class \"arrow_dplyr_query\""
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     "title": "Big Data in R with Arrow",
-    "section": "tibble <-> Table <-> data.frame",
-    "text": "tibble &lt;-&gt; Table &lt;-&gt; data.frame\n\nlibrary(dplyr)\n\n#change a df to a table\narrow_table(taxi_df)\n\n#change a table to a tibble\ntaxi_table |&gt; collect()\nas_tibble(taxi_table)\n\n#change a table to a data.frame\nas.data.frame(taxi_table)\n\n\n\ndata.frame & tibble are R objects in-memory\nTable is an Arrow object in-memory"
+    "section": "Head to the docs!",
+    "text": "Head to the docs!\n\n?acero\n\nor view them at https://arrow.apache.org/docs/r/reference/acero.html"
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     "title": "Big Data in R with Arrow",
-    "section": "Watch Your Schemas 👀",
-    "text": "Watch Your Schemas 👀\n\n\n\nschema(taxi_df)\n\nSchema\nvendor_name: string\npickup_datetime: timestamp[us, tz=America/Vancouver]\ndropoff_datetime: timestamp[us, tz=America/Vancouver]\npassenger_count: int32\ntrip_distance: double\npickup_longitude: double\npickup_latitude: double\nrate_code: string\nstore_and_fwd: string\ndropoff_longitude: double\ndropoff_latitude: double\npayment_type: string\nfare_amount: double\nextra: double\nmta_tax: double\ntip_amount: double\ntolls_amount: double\ntotal_amount: double\nimprovement_surcharge: double\ncongestion_surcharge: double\npickup_location_id: int32\ndropoff_location_id: int32\n\n\n\n\nschema(taxi_table)\n\nSchema\nvendor_name: string\npickup_datetime: timestamp[ms]\ndropoff_datetime: timestamp[ms]\npassenger_count: int64\ntrip_distance: double\npickup_longitude: double\npickup_latitude: double\nrate_code: string\nstore_and_fwd: string\ndropoff_longitude: double\ndropoff_latitude: double\npayment_type: string\nfare_amount: double\nextra: double\nmta_tax: double\ntip_amount: double\ntolls_amount: double\ntotal_amount: double\nimprovement_surcharge: double\ncongestion_surcharge: double\npickup_location_id: int64\ndropoff_location_id: int64"
+    "section": "A different function",
+    "text": "A different function\n\nlong_rides_2021 |&gt;\n  slice_max(n = 3, order_by = trip_distance, with_ties = FALSE) |&gt;\n  collect()\n\n# A tibble: 3 × 3\n  pickup_datetime      year trip_distance\n  &lt;dttm&gt;              &lt;int&gt;         &lt;dbl&gt;\n1 2021-11-16 04:55:00  2021       351613.\n2 2021-10-27 09:46:00  2021       345124.\n3 2021-12-11 02:48:00  2021       335094."
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     "title": "Big Data in R with Arrow",
-    "section": "Data frames",
-    "text": "Data frames"
+    "section": "Or call collect() first",
+    "text": "Or call collect() first\n\nlong_rides_2021 |&gt;\n  collect() |&gt;\n  slice(1:3)\n\n# A tibble: 3 × 3\n  pickup_datetime      year trip_distance\n  &lt;dttm&gt;              &lt;int&gt;         &lt;dbl&gt;\n1 2021-01-03 01:01:26  2021          216.\n2 2021-01-03 03:36:52  2021          268.\n3 2021-10-02 07:04:53  2021          188."
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     "title": "Big Data in R with Arrow",
-    "section": "Arrow Tables",
-    "text": "Arrow Tables\n\n\nArrow Tables are collections of chunked arrays"
+    "section": "tidyr functions - pivot",
+    "text": "tidyr functions - pivot\n\nlibrary(tidyr)\n\nnyc_taxi |&gt; \n  group_by(vendor_name) |&gt;\n  summarise(max_fare = max(fare_amount)) |&gt;\n  pivot_longer(!vendor_name, names_to = \"metric\") |&gt; \n  collect()\n\nError in UseMethod(\"pivot_longer\"): no applicable method for 'pivot_longer' applied to an object of class \"arrow_dplyr_query\""
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     "title": "Big Data in R with Arrow",
-    "section": "Table | Dataset: A dplyr pipeline",
-    "text": "Table | Dataset: A dplyr pipeline\n\nparquet_file |&gt;\n  read_parquet(as_data_frame = FALSE) |&gt;\n  group_by(vendor_name) |&gt;\n  summarise(all_trips = n(),\n            shared_trips = sum(passenger_count &gt; 1, na.rm = TRUE)) |&gt;\n  mutate(pct_shared = shared_trips / all_trips * 100) |&gt;\n  collect()\n\n# A tibble: 3 × 4\n  vendor_name all_trips shared_trips pct_shared\n  &lt;chr&gt;           &lt;int&gt;        &lt;int&gt;      &lt;dbl&gt;\n1 VTS           4238808      1339478       31.6\n2 CMT           2294473       470344       20.5\n3 &lt;NA&gt;            34115            0        0  \n\n\n\nFunctions available in Arrow dplyr queries: https://arrow.apache.org/docs/r/reference/acero.html\n\nAll the same capabilities as you practiced with Arrow Dataset"
+    "section": "duckdb",
+    "text": "duckdb\n\nin-memory database\ncolumnar\nunderstands Arrow format"
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-    "section": "Arrow for Efficient In-Memory Processing",
-    "text": "Arrow for Efficient In-Memory Processing\n\nparquet_file |&gt;\n  read_parquet() |&gt;\n  nrow()\n\n[1] 6567396\n\n\n\n\nparquet_file |&gt;\n  read_parquet() |&gt;\n  group_by(vendor_name) |&gt;\n  summarise(all_trips = n(),\n            shared_trips = sum(passenger_count &gt; 1, na.rm = TRUE)) |&gt;\n  mutate(pct_shared = shared_trips / all_trips * 100) |&gt;\n  collect() |&gt;\n  system.time()\n\n   user  system elapsed \n  1.157   0.261   0.509"
+    "section": "sharing data with duckdb and arrow",
+    "text": "sharing data with duckdb and arrow"
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     "title": "Big Data in R with Arrow",
-    "section": "Arrow for Efficient In-Memory Processing",
-    "text": "Arrow for Efficient In-Memory Processing\n\nparquet_file |&gt;\n  read_parquet(as_data_frame = FALSE) |&gt;\n  nrow()\n\n[1] 6567396\n\n\n\n\nparquet_file |&gt;\n  read_parquet(as_data_frame = FALSE) |&gt;\n  group_by(vendor_name) |&gt;\n  summarise(all_trips = n(),\n            shared_trips = sum(passenger_count &gt; 1, na.rm = TRUE)) |&gt;\n  mutate(pct_shared = shared_trips / all_trips * 100) |&gt;\n  collect() |&gt;\n  system.time()\n\n   user  system elapsed \n  1.047   0.203   0.220"
+    "section": "tidyr functions - pivot with duckdb!",
+    "text": "tidyr functions - pivot with duckdb!\n\nlibrary(duckdb)\n\nnyc_taxi |&gt; \n  group_by(vendor_name) |&gt;\n  summarise(max_fare = max(fare_amount)) |&gt;\n  to_duckdb() |&gt; # send data to duckdb\n  pivot_longer(!vendor_name, names_to = \"metric\") |&gt; \n  to_arrow() |&gt; # return data back to arrow\n  collect()\n\n# A tibble: 3 × 3\n  vendor_name metric     value\n  &lt;chr&gt;       &lt;chr&gt;      &lt;dbl&gt;\n1 CMT         max_fare 998310.\n2 VTS         max_fare  10000.\n3 &lt;NA&gt;        max_fare   3555."
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     "title": "Big Data in R with Arrow",
-    "section": "Read a Parquet File Selectively",
-    "text": "Read a Parquet File Selectively\n\nparquet_file |&gt;\n  read_parquet(\n    col_select = c(\"vendor_name\", \"passenger_count\"),\n    as_data_frame = FALSE\n  )\n\nTable\n6567396 rows x 2 columns\n$vendor_name &lt;string&gt;\n$passenger_count &lt;int64&gt;"
+    "section": "Using functions inside verbs",
+    "text": "Using functions inside verbs\n\nlots of the lubridate and stringr APIs supported!\nbase R and others too - always good to check the docs"
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     "title": "Big Data in R with Arrow",
-    "section": "Selective Reads Are Faster",
-    "text": "Selective Reads Are Faster\n\nparquet_file |&gt;\n  read_parquet(\n    col_select = c(\"vendor_name\", \"passenger_count\"),\n    as_data_frame = FALSE\n  ) |&gt; \n  group_by(vendor_name) |&gt;\n  summarise(all_trips = n(),\n            shared_trips = sum(passenger_count &gt; 1, na.rm = TRUE)) |&gt;\n  mutate(pct_shared = shared_trips / all_trips * 100) |&gt;\n  collect() |&gt;\n  system.time()\n\n   user  system elapsed \n  0.258   0.011   0.131 \n\n\n\nNotes: row-based format readers often allow you to specify which columns to read in but the entire row must be read in and the unwanted columns discarded. Parquet’s columnar format allows you to read in only the columns you need, which is faster when you only need a subset of the data."
+    "section": "Morning vs afternoon with namespacing",
+    "text": "Morning vs afternoon with namespacing\n\nnyc_taxi |&gt;\n  group_by(\n    time_of_day = ifelse(lubridate::am(pickup_datetime), \"morning\", \"afternoon\")\n  ) |&gt;\n  count() |&gt;\n  collect()\n\n# A tibble: 2 × 2\n# Groups:   time_of_day [2]\n  time_of_day         n\n  &lt;chr&gt;           &lt;int&gt;\n1 afternoon   736491676\n2 morning     413860990"
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     "title": "Big Data in R with Arrow",
-    "section": "Arrow Table or Dataset?",
-    "text": "Arrow Table or Dataset?\n\n\nhttps://francoismichonneau.net/2022/10/import-big-csv/"
+    "section": "Morning vs afternoon - without namespacing",
+    "text": "Morning vs afternoon - without namespacing\n\nlibrary(lubridate)\n\nnyc_taxi |&gt;\n  group_by(\n    time_of_day = ifelse(am(pickup_datetime), \"morning\", \"afternoon\")\n  ) |&gt;\n  count() |&gt;\n  collect()\n\n# A tibble: 2 × 2\n# Groups:   time_of_day [2]\n  time_of_day         n\n  &lt;chr&gt;           &lt;int&gt;\n1 afternoon   736491676\n2 morning     413860990"
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     "title": "Big Data in R with Arrow",
-    "section": "Arrow for Improving Those Sluggish Worklows",
-    "text": "Arrow for Improving Those Sluggish Worklows\n\na “drop-in” for many dplyr workflows (Arrow Table or Dataset)\nworks when your tabular data get too big for your RAM (Arrow Dataset)\nprovides tools for re-engineering data storage for better performance (arrow::write_dataset())\n\n\nLot’s of ways to speed up sluggish workflows e.g. writing more performant tidyverse code, use other data frame libraries like data.table or polars, use duckDB or other databases, Spark + splarklyr … However, Arrow offers some attractive features for tackling this challenge, especially for dplyr users.\n\n\n\n\n\n🔗 pos.it/arrow-conf24"
+    "section": "How does this work?",
+    "text": "How does this work?\n ## Acero\n\narrow’s query execution engine\nuse Arrow functions on Arrow Datasets"
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     "title": "Big Data in R with Arrow",
-    "section": "What if a function binding doesn’t exist - revisited!",
-    "text": "What if a function binding doesn’t exist - revisited!\n\nOption 1 - find a workaround\nOption 2 - user-defined functions (UDFs)"
+    "section": "Acero",
+    "text": "Acero"
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     "title": "Big Data in R with Arrow",
-    "section": "Why use a UDF?",
-    "text": "Why use a UDF?\n\nIf no bindings for a function exist\nImplement your own custom functions\nRun in R not Arrow"
+    "section": "arrow dplyr queries",
+    "text": "arrow dplyr queries"
   },
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+    "href": "materials/2_data_manipulation_1.html#what-if-a-function-isnt-implemented",
     "title": "Big Data in R with Arrow",
-    "section": "How do function bindings usually work?",
-    "text": "How do function bindings usually work?\n ## How do UDFs work?\n\n\ntime_diff_minutes &lt;- function(pickup, dropoff){\n  difftime(dropoff, pickup, units = \"mins\") |&gt;\n      round() |&gt;\n      as.integer()\n}\n\nnyc_taxi |&gt;\n  mutate(\n    duration_minutes = time_diff_minutes(pickup_datetime, dropoff_datetime)\n  ) |&gt; \n  select(pickup_datetime, dropoff_datetime, duration_minutes) |&gt;\n  head() |&gt;\n  collect()\n\nError: Expression time_diff_minutes(pickup_datetime, dropoff_datetime) not supported in Arrow\nCall collect() first to pull data into R.\n\n\nWe get an error as we can’t automatically convert the function to arrow."
+    "section": "What if a function isn’t implemented?",
+    "text": "What if a function isn’t implemented?\n\nnyc_taxi |&gt;\n  mutate(vendor_name = na_if(vendor_name, \"CMT\")) |&gt;\n  head() |&gt;\n  collect()\n\nError in `na_if()`:\n! Expression not supported in Arrow\n→ Call collect() first to pull data into R."
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     "title": "Big Data in R with Arrow",
-    "section": "User-defined functions - definition",
-    "text": "User-defined functions - definition\n\nregister_scalar_function(\n  name = \"time_diff_minutes\",\n  function(context, pickup, dropoff) {\n    difftime(dropoff, pickup, units = \"mins\") |&gt;\n      round() |&gt;\n      as.integer()\n  },\n  in_type = schema(\n    pickup = timestamp(unit = \"ms\"),\n    dropoff = timestamp(unit = \"ms\")\n  ),\n  out_type = int32(),\n  auto_convert = TRUE\n)\n\nThis looks complicated, so let’s look at it 1 part at a time!"
+    "section": "Head to the docs again to see what’s implemented!",
+    "text": "Head to the docs again to see what’s implemented!\n\n?acero\n\nor view them at https://arrow.apache.org/docs/r/reference/acero.html"
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+    "href": "materials/2_data_manipulation_1.html#option-1---find-a-workaround",
     "title": "Big Data in R with Arrow",
-    "section": "User-defined functions - definition",
-    "text": "User-defined functions - definition\nStep 1. Give the function a name\n\nregister_scalar_function(\n  name = \"time_diff_minutes\",\n  function(context, pickup, dropoff) {\n    difftime(dropoff, pickup, units = \"mins\") |&gt;\n      round() |&gt;\n      as.integer()\n  },\n  in_type = schema(\n    pickup = timestamp(unit = \"ms\"),\n    dropoff = timestamp(unit = \"ms\")\n  ),\n  out_type = int32(),\n  auto_convert = TRUE\n)"
+    "section": "Option 1 - find a workaround!",
+    "text": "Option 1 - find a workaround!\n\nnyc_taxi |&gt;\n  mutate(vendor_name = ifelse(vendor_name == \"CMT\", NA, vendor_name)) |&gt;\n  head() |&gt;\n  collect()\n\n# A tibble: 6 × 24\n  vendor_name pickup_datetime     dropoff_datetime    passenger_count\n  &lt;chr&gt;       &lt;dttm&gt;              &lt;dttm&gt;                        &lt;int&gt;\n1 &lt;NA&gt;        2012-01-20 06:09:36 2012-01-20 06:42:25               1\n2 &lt;NA&gt;        2012-01-20 06:54:10 2012-01-20 07:06:55               1\n3 &lt;NA&gt;        2012-01-20 00:08:01 2012-01-20 00:11:02               1\n4 &lt;NA&gt;        2012-01-20 00:36:22 2012-01-20 00:39:44               1\n5 &lt;NA&gt;        2012-01-20 12:58:32 2012-01-20 13:03:04               1\n6 &lt;NA&gt;        2012-01-20 11:40:20 2012-01-20 11:43:43               2\n# ℹ 20 more variables: trip_distance &lt;dbl&gt;, pickup_longitude &lt;dbl&gt;,\n#   pickup_latitude &lt;dbl&gt;, rate_code &lt;chr&gt;, store_and_fwd &lt;chr&gt;,\n#   dropoff_longitude &lt;dbl&gt;, dropoff_latitude &lt;dbl&gt;, payment_type &lt;chr&gt;,\n#   fare_amount &lt;dbl&gt;, extra &lt;dbl&gt;, mta_tax &lt;dbl&gt;, tip_amount &lt;dbl&gt;,\n#   tolls_amount &lt;dbl&gt;, total_amount &lt;dbl&gt;, improvement_surcharge &lt;dbl&gt;,\n#   congestion_surcharge &lt;dbl&gt;, pickup_location_id &lt;int&gt;,\n#   dropoff_location_id &lt;int&gt;, year &lt;int&gt;, month &lt;int&gt;"
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+    "href": "materials/2_data_manipulation_1.html#option-2",
     "title": "Big Data in R with Arrow",
-    "section": "User-defined functions - definition",
-    "text": "User-defined functions - definition\nStep 2. Define the body of the function - first argument must be context\n\nregister_scalar_function(\n  name = \"time_diff_minutes\",\n  function(context, pickup, dropoff) {\n    difftime(dropoff, pickup, units = \"mins\") |&gt;\n      round() |&gt;\n      as.integer()\n  },\n  in_type = schema(\n    pickup = timestamp(unit = \"ms\"),\n    dropoff = timestamp(unit = \"ms\")\n  ),\n  out_type = int32(),\n  auto_convert = TRUE\n)"
+    "section": "Option 2",
+    "text": "Option 2\n\nIn data manipulation part 2!"
   },
   {
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+    "href": "materials/2_data_manipulation_1.html#your-turn-1",
     "title": "Big Data in R with Arrow",
-    "section": "User-defined functions - definition",
-    "text": "User-defined functions - definition\nStep 3. Set the schema of the input arguments\n\nregister_scalar_function(\n  name = \"time_diff_minutes\",\n  function(context, pickup, dropoff) {\n    difftime(dropoff, pickup, units = \"mins\") |&gt;\n      round() |&gt;\n      as.integer()\n  },\n  in_type = schema(\n    pickup = timestamp(unit = \"ms\"),\n    dropoff = timestamp(unit = \"ms\")\n  ),\n  out_type = int32(),\n  auto_convert = TRUE\n)"
+    "section": "Your Turn",
+    "text": "Your Turn\n\nUse the dplyr::filter() and stringr::str_ends() functions to return a subset of the data which is a) from September 2020, and b) the value in vendor_name ends with the letter “S”.\nTry to use the stringr function str_replace_na() to replace any NA values in the vendor_name column with the string “No vendor” instead. What happens, and why?\nBonus question: see if you can find a different way of completing the task in question 2.\n\n➡️ Data Manipulation Part I Exercises Page"
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-    "title": "Big Data in R with Arrow",
-    "section": "User-defined functions - definition",
-    "text": "User-defined functions - definition\nStep 4. Set the data type of the output\n\nregister_scalar_function(\n  name = \"time_diff_minutes\",\n  function(context, pickup, dropoff) {\n    difftime(dropoff, pickup, units = \"mins\") |&gt;\n      round() |&gt;\n      as.integer()\n  },\n  in_type = schema(\n    pickup = timestamp(unit = \"ms\"),\n    dropoff = timestamp(unit = \"ms\")\n  ),\n  out_type = int32(),\n  auto_convert = TRUE\n)"
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-    "title": "Big Data in R with Arrow",
-    "section": "User-defined functions - definition",
-    "text": "User-defined functions - definition\nStep 5. Set auto_convert = TRUE if using in a dplyr pipeline\n\nregister_scalar_function(\n  name = \"time_diff_minutes\",\n  function(context, pickup, dropoff) {\n    difftime(dropoff, pickup, units = \"mins\") |&gt;\n      round() |&gt;\n      as.integer()\n  },\n  in_type = schema(\n    pickup = timestamp(unit = \"ms\"),\n    dropoff = timestamp(unit = \"ms\")\n  ),\n  out_type = int32(),\n  auto_convert = TRUE\n)"
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-    "title": "Big Data in R with Arrow",
-    "section": "User-defined functions - usage",
-    "text": "User-defined functions - usage\n\nnyc_taxi |&gt;\n  mutate(\n    duration_minutes = time_diff_minutes(pickup_datetime, dropoff_datetime)\n  ) |&gt;\n  select(pickup_datetime, dropoff_datetime, duration_minutes) |&gt;\n  head() |&gt;\n  collect()\n\n# A tibble: 6 × 3\n  pickup_datetime     dropoff_datetime    duration_minutes\n  &lt;dttm&gt;              &lt;dttm&gt;                         &lt;int&gt;\n1 2012-10-07 18:19:00 2012-10-07 18:29:00               10\n2 2012-10-07 18:19:00 2012-10-07 18:33:00               14\n3 2012-10-07 18:19:00 2012-10-07 18:35:00               16\n4 2012-10-07 18:19:00 2012-10-07 18:35:00               16\n5 2012-10-07 18:19:00 2012-10-07 18:42:00               23\n6 2012-10-07 18:19:00 2012-10-07 18:43:00               24"
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-    "href": "materials/4_data_manipulation_2.html#your-turn",
-    "title": "Big Data in R with Arrow",
-    "section": "Your Turn",
-    "text": "Your Turn\n\nWrite a user-defined function which wraps the stringr function str_replace_na(), and use it to replace any NA values in the vendor_name column with the string “No vendor” instead. (Test it on the data from 2019 so you’re not pulling everything into memory)\n\n➡️ Data Manipulation Part II Exercises Page"
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-    "title": "Big Data in R with Arrow",
-    "section": "Summary",
-    "text": "Summary\n\nYou can use UDFs to create your own bindings when they don’t exist\nUDFs must be scalar (1 row in -&gt; 1 row out) and stateless (no knowledge of other rows of data)\nCalculations done by R not Arrow, so slower than in-built bindings but still pretty fast"
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-    "section": "Joins",
-    "text": "Joins"
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-    "title": "Big Data in R with Arrow",
-    "section": "Joining a reference table",
-    "text": "Joining a reference table\n\nvendors &lt;- tibble::tibble(\n  code = c(\"VTS\", \"CMT\", \"DDS\"),\n  full_name = c(\n    \"Verifone Transportation Systems\",\n    \"Creative Mobile Technologies\",\n    \"Digital Dispatch Systems\"\n  )\n)\n\nnyc_taxi |&gt;\n  left_join(vendors, by = c(\"vendor_name\" = \"code\")) |&gt;\n  select(vendor_name, full_name, pickup_datetime) |&gt;\n  head(3) |&gt;\n  collect()\n\n# A tibble: 3 × 3\n  vendor_name full_name                    pickup_datetime    \n  &lt;chr&gt;       &lt;chr&gt;                        &lt;dttm&gt;             \n1 CMT         Creative Mobile Technologies 2012-10-07 17:26:40\n2 CMT         Creative Mobile Technologies 2012-10-07 17:26:52\n3 CMT         Creative Mobile Technologies 2012-10-07 17:26:57"
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-    "title": "Big Data in R with Arrow",
-    "section": "Traps for the unwary",
-    "text": "Traps for the unwary\nQuestion: which are the most common borough-to-borough journeys in the dataset?\n\nnyc_taxi_zones &lt;- \n  read_csv_arrow(\"data/taxi_zone_lookup.csv\") |&gt;\n  select(location_id = LocationID,\n         borough = Borough)\n\nnyc_taxi_zones\n\n# A tibble: 265 × 2\n   location_id borough      \n         &lt;int&gt; &lt;chr&gt;        \n 1           1 EWR          \n 2           2 Queens       \n 3           3 Bronx        \n 4           4 Manhattan    \n 5           5 Staten Island\n 6           6 Staten Island\n 7           7 Queens       \n 8           8 Queens       \n 9           9 Queens       \n10          10 Queens       \n# ℹ 255 more rows"
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-    "title": "Big Data in R with Arrow",
-    "section": "Why didn’t this work?",
-    "text": "Why didn’t this work?\n\nnyc_taxi |&gt;\n  left_join(nyc_taxi_zones, by = c(\"pickup_location_id\" = \"location_id\")) |&gt;\n  collect()\n\nError in `compute.arrow_dplyr_query()`:\n! Invalid: Incompatible data types for corresponding join field keys: FieldRef.Name(pickup_location_id) of type int64 and FieldRef.Name(location_id) of type int32"
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-    "title": "Big Data in R with Arrow",
-    "section": "Schema for the nyc_taxi Dataset",
-    "text": "Schema for the nyc_taxi Dataset\n\nschema(nyc_taxi)\n\nSchema\nvendor_name: string\npickup_datetime: timestamp[ms]\ndropoff_datetime: timestamp[ms]\npassenger_count: int64\ntrip_distance: double\npickup_longitude: double\npickup_latitude: double\nrate_code: string\nstore_and_fwd: string\ndropoff_longitude: double\ndropoff_latitude: double\npayment_type: string\nfare_amount: double\nextra: double\nmta_tax: double\ntip_amount: double\ntolls_amount: double\ntotal_amount: double\nimprovement_surcharge: double\ncongestion_surcharge: double\npickup_location_id: int64\ndropoff_location_id: int64\nyear: int32\nmonth: int32"
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-    "title": "Big Data in R with Arrow",
-    "section": "Schema for the nyc_taxi_zones Table",
-    "text": "Schema for the nyc_taxi_zones Table\n\nnyc_taxi_zones_arrow &lt;- arrow_table(nyc_taxi_zones)\nschema(nyc_taxi_zones_arrow)\n\nSchema\nlocation_id: int32\nborough: string\n\n\n\npickup_location_id is int64 in the nyc_taxi table\nlocation_id is int32 in the nyc_taxi_zones table"
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-    "title": "Big Data in R with Arrow",
-    "section": "Take control of the schema",
-    "text": "Take control of the schema\n\nnyc_taxi_zones_arrow &lt;- arrow_table(\n  nyc_taxi_zones, \n  schema = schema(location_id = int64(), borough = utf8())\n)\n\n\nschema() takes variable name / types as input\narrow has various “type” functions: int64(), utf8(), boolean(), date32() etc"
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-    "title": "Big Data in R with Arrow",
-    "section": "Take control of the schema",
-    "text": "Take control of the schema\n\nnyc_taxi_zones_arrow &lt;- arrow_table(\n  nyc_taxi_zones, \n  schema = schema(location_id = int64(), borough = utf8())\n)\nschema(nyc_taxi_zones_arrow)\n\nSchema\nlocation_id: int64\nborough: string"
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-    "href": "materials/4_data_manipulation_2.html#prepare-the-auxiliary-tables",
-    "title": "Big Data in R with Arrow",
-    "section": "Prepare the auxiliary tables",
-    "text": "Prepare the auxiliary tables\n\npickup &lt;- nyc_taxi_zones_arrow |&gt;\n  select(pickup_location_id = location_id,\n         pickup_borough = borough)\n\ndropoff &lt;- nyc_taxi_zones_arrow |&gt;\n  select(dropoff_location_id = location_id,\n         dropoff_borough = borough)\n\n\nJoin separately for the pickup and dropoff zones"
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+    "href": "materials/2_data_manipulation_1.html#working-with-custom-functions",
     "title": "Big Data in R with Arrow",
-    "section": "Join and cross-tabulate",
-    "text": "Join and cross-tabulate\n\nlibrary(tictoc)\n\ntic()\nborough_counts &lt;- nyc_taxi |&gt; \n  left_join(pickup) |&gt;\n  left_join(dropoff) |&gt;\n  count(pickup_borough, dropoff_borough) |&gt;\n  arrange(desc(n)) |&gt;\n  collect()\ntoc()\n\n163.966 sec elapsed\n\n\n\n2-3 minutes to join twice and cross-tabulate on non-partition variables, with 1.15 billion rows of data 🙂"
+    "section": "Working with custom functions",
+    "text": "Working with custom functions\nArrow 17.0.0 or later!\n\ntime_text &lt;- function(time){\n  day_of_week &lt;- wday(time, label = TRUE, abbr = FALSE)\n  time_of_day &lt;- ifelse(lubridate::am(time), \"AM\", \"PM\")\n  paste(day_of_week, time_of_day)\n}\n\nnyc_taxi |&gt;\n  mutate(pickup_text = time_text(pickup_datetime)) |&gt;\n  select(pickup_datetime, pickup_text) |&gt;\n  head() |&gt;\n  collect() \n\n\n\n# A tibble: 6 × 2\n  pickup_datetime     pickup_text\n  &lt;dttm&gt;              &lt;chr&gt;      \n1 2012-01-08 12:50:38 Sunday PM  \n2 2012-01-08 12:52:01 Sunday PM  \n3 2012-01-07 18:39:26 Sunday AM  \n4 2012-01-07 18:40:49 Sunday AM  \n5 2012-01-08 19:42:37 Monday AM  \n6 2012-01-08 12:51:47 Sunday PM"
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+    "href": "materials/2_data_manipulation_1.html#how-did-that-work",
     "title": "Big Data in R with Arrow",
-    "section": "The results",
-    "text": "The results\n\nborough_counts\n\n# A tibble: 50 × 3\n   pickup_borough dropoff_borough         n\n   &lt;chr&gt;          &lt;chr&gt;               &lt;int&gt;\n 1 &lt;NA&gt;           &lt;NA&gt;            732357953\n 2 Manhattan      Manhattan       351198872\n 3 Queens         Manhattan        14440705\n 4 Manhattan      Queens           13052517\n 5 Manhattan      Brooklyn         11180867\n 6 Queens         Queens            7440356\n 7 Unknown        Unknown           4491811\n 8 Queens         Brooklyn          3662324\n 9 Brooklyn       Brooklyn          3550480\n10 Manhattan      Bronx             2071830\n# ℹ 40 more rows"
+    "section": "How did that work?",
+    "text": "How did that work?\nCustom function converted to Arrow Expression; query doesn’t contain any reference to the time_text() function.\n\nnyc_taxi |&gt;\n  mutate(pickup_text = time_text(pickup_datetime)) |&gt;\n  select(pickup_datetime, pickup_text)\n\n\n\nFileSystemDataset (query)\npickup_datetime: timestamp[ms]\npickup_text: string (binary_join_element_wise(cast(strftime(pickup_datetime, {format=\"%A\"}), {to_type=string, allow_int_overflow=false, allow_time_truncate=false, allow_time_overflow=false, allow_decimal_truncate=false, allow_float_truncate=false, allow_invalid_utf8=false}), cast(if_else((hour(pickup_datetime) &lt; 12), \"AM\", \"PM\"), {to_type=string, allow_int_overflow=false, allow_time_truncate=false, allow_time_overflow=false, allow_decimal_truncate=false, allow_float_truncate=false, allow_invalid_utf8=false}), \" \", {null_handling=REPLACE, null_replacement=\"NA\"}))\n\nSee $.data for the source Arrow object"
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-    "href": "materials/4_data_manipulation_2.html#your-turn-1",
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+    "href": "materials/2_data_manipulation_1.html#anything-else-to-be-aware-of",
     "title": "Big Data in R with Arrow",
-    "section": "Your Turn",
-    "text": "Your Turn\n\nHow many taxi pickups were recorded in 2019 from the three major airports covered by the NYC Taxis data set (JFK, LaGuardia, Newark)? (Hint: you can use stringr::str_detect() to help you find pickup zones with the word “Airport” in them)\n\n➡️ Data Manipulation Part II Exercises Page"
+    "section": "Anything else to be aware of?",
+    "text": "Anything else to be aware of?\n\narrow 17.0.0 or later\nthis will only work for functions which have Arrow bindings\nuse ?acero to see which ones do"
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     "title": "Big Data in R with Arrow",
     "section": "Summary",
-    "text": "Summary\n\nYou can join Arrow Tables and Datasets to R data frames and Arrow Tables\nThe Arrow data type of join keys must always match\n\n\n\n\n\n🔗 pos.it/arrow-conf24"
+    "text": "Summary\n\nWorking with Arrow Datasets allow you to manipulate data which is larger-than-memory\nYou can use many dplyr functions with arrow - run ?acero to view the docs\nYou can pass data to duckdb to use functions implemented in duckdb but not arrow\n\n\n\n\n\n🔗 pos.it/arrow-conf24"
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-    "text": "File Storage:Partitioning\n\n\n\nDividing data into smaller pieces, making it more easily accessible and manageable\n\n\n\n\n\nalso called multi-files or sometimes shards"
+    "text": "File Storage:Partitioning\n\n\n\nDividing data into smaller pieces, making it more easily accessible and manageable\n\n\n\n\nalso called multi-files or sometimes shards"
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-    "text": "R for Data Science (2e)\n\n\n\n\n\nChapter 23: Arrow\n\nhttps://r4ds.hadley.nz/\n\n\n\n\n\n\n🔗 pos.it/arrow-conf24"
+    "text": "R for Data Science (2e)\n\n\n\n\n\nChapter 23: Arrow\n\nhttps://r4ds.hadley.nz/\n\n\n\n\n\n🔗 pos.it/arrow-conf24"
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-    "text": "Goals\nAvoiding these! But…don’t worry!"
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+    "text": "Schemas\n\nlibrary(arrow)\nlibrary(dplyr)\n\n\nseattle_csv &lt;- open_dataset(sources = \"data/seattle-library-checkouts.csv\",\n                            format = \"csv\")\n\n\n\n\n\n\n\nData Types & Controlling the Schema\n\n\n\n\nProblemsSolution 1Solution 2\n\n\n\nThe first few thousand rows of ISBN are blank in the Seattle Checkouts CSV file. Read in the Seattle Checkouts CSV file with open_dataset() and ensure the correct data type for ISBN is &lt;string&gt; (or the alias &lt;utf8&gt;) instead of the &lt;null&gt; interpreted by Arrow.\nOnce you have a Dataset object with the metadata you are after, count the number of Checkouts by CheckoutYear and arrange the result by CheckoutYear.\n\n\n\n\nseattle_csv &lt;- open_dataset(sources = \"data/seattle-library-checkouts.csv\",\n  format = \"csv\",\n  schema(\n    UsageClass = utf8(),\n    CheckoutType = utf8(),\n    MaterialType = utf8(),\n    CheckoutYear = int64(),\n    CheckoutMonth = int64(),\n    Checkouts = int64(),\n    Title = utf8(),\n    ISBN = string(), #or utf8()\n    Creator = utf8(),\n    Subjects = utf8(),\n    Publisher = utf8(),\n    PublicationYear = utf8()\n  ),\n    skip = 1,\n)\nseattle_csv\n\nFileSystemDataset with 1 csv file\n12 columns\nUsageClass: string\nCheckoutType: string\nMaterialType: string\nCheckoutYear: int64\nCheckoutMonth: int64\nCheckouts: int64\nTitle: string\nISBN: string\nCreator: string\nSubjects: string\nPublisher: string\nPublicationYear: string\n\n\nor\n\nseattle_csv &lt;- open_dataset(sources = \"data/seattle-library-checkouts.csv\",\n  format = \"csv\",\n  col_types = schema(ISBN = string()) # or utf8()\n)\nseattle_csv\n\nFileSystemDataset with 1 csv file\n12 columns\nUsageClass: string\nCheckoutType: string\nMaterialType: string\nCheckoutYear: int64\nCheckoutMonth: int64\nCheckouts: int64\nTitle: string\nISBN: string\nCreator: string\nSubjects: string\nPublisher: string\nPublicationYear: string\n\n\n\n\nThe number of Checkouts by CheckoutYear arranged by CheckoutYear:\n\nseattle_csv |&gt;\n  group_by(CheckoutYear) |&gt;\n  summarise(sum(Checkouts)) |&gt;\n  arrange(CheckoutYear) |&gt; \n  collect()\n\n# A tibble: 18 × 2\n   CheckoutYear `sum(Checkouts)`\n          &lt;int&gt;            &lt;int&gt;\n 1         2005          3798685\n 2         2006          6599318\n 3         2007          7126627\n 4         2008          8438486\n 5         2009          9135167\n 6         2010          8608966\n 7         2011          8321732\n 8         2012          8163046\n 9         2013          9057096\n10         2014          9136081\n11         2015          9084179\n12         2016          9021051\n13         2017          9231648\n14         2018          9149176\n15         2019          9199083\n16         2020          6053717\n17         2021          7361031\n18         2022          7001989\n\n\nor\n\nseattle_csv |&gt; \n  count(CheckoutYear, wt = Checkouts) |&gt; \n  arrange(CheckoutYear) |&gt; \n  collect()\n\n# A tibble: 18 × 2\n   CheckoutYear       n\n          &lt;int&gt;   &lt;int&gt;\n 1         2005 3798685\n 2         2006 6599318\n 3         2007 7126627\n 4         2008 8438486\n 5         2009 9135167\n 6         2010 8608966\n 7         2011 8321732\n 8         2012 8163046\n 9         2013 9057096\n10         2014 9136081\n11         2015 9084179\n12         2016 9021051\n13         2017 9231648\n14         2018 9149176\n15         2019 9199083\n16         2020 6053717\n17         2021 7361031\n18         2022 7001989\n\n\nTiming the query:\n\nseattle_csv |&gt;\n  group_by(CheckoutYear) |&gt;\n  summarise(sum(Checkouts)) |&gt;\n  arrange(CheckoutYear) |&gt; \n  collect() |&gt; \n  system.time()\n\n   user  system elapsed \n 10.696   1.227  10.449 \n\n\nQuerying 42 million rows of data stored in a CSV on disk in ~10 seconds, not too bad.\n\n\n\n\n\n\n\nParquet\n\nseattle_parquet &lt;- \"data/seattle-library-checkouts-parquet\"\n\nseattle_csv |&gt;\n  write_dataset(path = seattle_parquet,\n                format = \"parquet\")\n\n\n\n\n\n\n\nParquet\n\n\n\n\nProblemSolution 1\n\n\n\nRe-run the query counting the number of Checkouts by CheckoutYear and arranging the result by CheckoutYear, this time using the Seattle Checkout data saved to disk as a single, Parquet file. Did you notice a difference in compute time?\n\n\n\n\nseattle_parquet &lt;- \"data/seattle-library-checkouts-parquet\"\n\nopen_dataset(sources = seattle_parquet, \n             format = \"parquet\") |&gt;\n  group_by(CheckoutYear) |&gt;\n  summarise(sum(Checkouts)) |&gt;\n  arrange(CheckoutYear) |&gt; \n  collect() |&gt; \n  system.time()\n\n   user  system elapsed \n  1.576   0.377   0.548 \n\n\nA much faster compute time for the query when the on-disk data is stored in the Parquet format.\n\n\n\n\n\n\n\nPartitioning\n\nseattle_parquet_part &lt;- \"data/seattle-library-checkouts\"\n\nseattle_csv |&gt;\n  group_by(CheckoutYear) |&gt;\n  write_dataset(path = seattle_parquet_part,\n                format = \"parquet\")\n\n\n\n\n\n\n\nPartitioning\n\n\n\n\nProblemsSolution 1Solution 2\n\n\n\nLet’s write the Seattle Checkout CSV data to a multi-file dataset just one more time! This time, write the data partitioned by CheckoutType as Parquet files.\nNow compare the compute time between our Parquet data partitioned by CheckoutYear and our Parquet data partitioned by CheckoutType with a query of the total number of checkouts in September of 2019. Did you find a difference in compute time?\n\n\n\nWriting the data:\n\nseattle_checkouttype &lt;- \"data/seattle-library-checkouts-type\"\n\nseattle_csv |&gt;\n  group_by(CheckoutType) |&gt;\n  write_dataset(path = seattle_checkouttype,\n                format = \"parquet\")\n\n\n\nTotal number of Checkouts in September of 2019 using partitioned Parquet data by CheckoutType:\n\nopen_dataset(sources = \"data/seattle-library-checkouts-type\") |&gt; \n  filter(CheckoutYear == 2019, CheckoutMonth == 9) |&gt; \n  summarise(TotalCheckouts = sum(Checkouts)) |&gt;\n  collect() |&gt; \n  system.time()\n\n   user  system elapsed \n  0.779   0.080   0.301 \n\n\nTotal number of Checkouts in September of 2019 using partitioned Parquet data by CheckoutYear and CheckoutMonth:\n\nopen_dataset(\"data/seattle-library-checkouts\") |&gt; \n  filter(CheckoutYear == 2019, CheckoutMonth == 9) |&gt; \n  summarise(TotalCheckouts = sum(Checkouts)) |&gt;\n  collect() |&gt; \n  system.time()\n\n   user  system elapsed \n  0.035   0.005   0.029 \n\n\nFaster compute time because the filter() call is based on the partitions."
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-    "text": "An Arrow Dataset\n\nlibrary(arrow)\n\nnyc_taxi &lt;- open_dataset(\"data/nyc-taxi/\")\nnyc_taxi\n\nFileSystemDataset with 120 Parquet files\n24 columns\nvendor_name: string\npickup_datetime: timestamp[ms]\ndropoff_datetime: timestamp[ms]\npassenger_count: int64\ntrip_distance: double\npickup_longitude: double\npickup_latitude: double\nrate_code: string\nstore_and_fwd: string\ndropoff_longitude: double\ndropoff_latitude: double\npayment_type: string\nfare_amount: double\nextra: double\nmta_tax: double\ntip_amount: double\ntolls_amount: double\ntotal_amount: double\nimprovement_surcharge: double\ncongestion_surcharge: double\n...\n4 more columns\nUse `schema()` to see entire schema"
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+    "text": "library(arrow)\nlibrary(dplyr)\nlibrary(tictoc)\n\n\nnyc_taxi &lt;- open_dataset(\"data/nyc-taxi/\")\n\n\n\n\n\n\n\nFirst dplyr pipeline with Arrow\n\n\n\n\nProblemsSolution 1Solution 2\n\n\n\nCalculate the longest trip distance for every month in 2019\nHow long did this query take to run?\n\n\n\nLongest trip distance for every month in 2019:\n\nnyc_taxi |&gt; \n  filter(year == 2019) |&gt;\n  group_by(month) |&gt;\n  summarize(longest_trip = max(trip_distance, na.rm = TRUE)) |&gt;\n  arrange(month) |&gt; \n  collect()\n\n# A tibble: 12 × 2\n   month longest_trip\n   &lt;int&gt;        &lt;dbl&gt;\n 1     1         832.\n 2     2         702.\n 3     3         237.\n 4     4         831.\n 5     5         401.\n 6     6       45977.\n 7     7         312.\n 8     8         602.\n 9     9         604.\n10    10         308.\n11    11         701.\n12    12       19130.\n\n\n\n\nCompute time:\n\nlibrary(tictoc)\n\ntic()\nnyc_taxi |&gt; \n  filter(year == 2019) |&gt;\n  group_by(month) |&gt;\n  summarize(longest_trip = max(trip_distance, na.rm = TRUE)) |&gt;\n  arrange(month) |&gt; \n  collect()\n\n# A tibble: 12 × 2\n   month longest_trip\n   &lt;int&gt;        &lt;dbl&gt;\n 1     1         832.\n 2     2         702.\n 3     3         237.\n 4     4         831.\n 5     5         401.\n 6     6       45977.\n 7     7         312.\n 8     8         602.\n 9     9         604.\n10    10         308.\n11    11         701.\n12    12       19130.\n\ntoc()\n\n0.56 sec elapsed\n\n\nor\n\nnyc_taxi |&gt; \n  filter(year == 2019) |&gt;\n  group_by(month) |&gt;\n  summarize(longest_trip = max(trip_distance, na.rm = TRUE)) |&gt;\n  arrange(month) |&gt; \n  collect() |&gt; \n  system.time()\n\n   user  system elapsed \n  2.532   0.316   0.452"
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+    "text": "library(arrow)\nlibrary(dplyr)\nlibrary(stringr)\n\n\nnyc_taxi &lt;- open_dataset(\"data/nyc-taxi/\")\n\n\n\n\n\n\n\nUsing collect() to run a query\n\n\n\n\nProblemsSolution 1\n\n\nUse the function nrow() to work out the answers to these questions:\n\nHow many taxi fares in the dataset had a total amount greater than $100?\n\n\n\n\nnyc_taxi |&gt;\n  filter(total_amount &gt; 100) |&gt;\n  nrow()\n\n[1] 1518869\n\n\n\n\n\n\n\n\n\n\n\n\n\nUsing the dplyr API in arrow\n\n\n\n\nProblemsSolution 1Solution 2 and 3\n\n\n\nUse the dplyr::filter() and stringr::str_ends() functions to return a subset of the data which is a) from September 2020, and b) the value in vendor_name ends with the letter “S”.\nTry to use the stringr function str_replace_na() to replace any NA values in the vendor_name column with the string “No vendor” instead. What happens, and why?\nBonus question: see if you can find a different way of completing the task in question 2.\n\n\n\n\nnyc_taxi |&gt;\n  filter(str_ends(vendor_name, \"S\"), year == 2020,  month == 9) |&gt;\n  collect()\n\n\n\n\nnyc_taxi |&gt;\n  mutate(vendor_name = stringr::str_replace_na(vendor_name, \"No vendor\")) |&gt;\n  head() |&gt;\n  collect()\n\nThis won’t work as stringr::str_replace_na() hasn’t been implemented in Arrow. You could try using mutate() and ifelse() here instead.\n\nnyc_taxi |&gt;\n  mutate(vendor_name = ifelse(is.na(vendor_name), \"No vendor\", vendor_name)) |&gt;\n  head() |&gt;\n  collect()\n\nOr, if you only needed a subset of the data, you could apply the function after collecting it into R memory.\n\nnyc_taxi |&gt;\n  filter(year == 2019, month == 10) |&gt; # smaller subset of the data\n  collect() |&gt;\n  mutate(vendor_name = stringr::str_replace_na(vendor_name, \"No vendor\"))"
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-    "text": "Constructing queries\n\nlibrary(dplyr)\n\nshared_rides &lt;- nyc_taxi |&gt;\n  group_by(year) |&gt;\n  summarize(\n    all_trips = n(),\n    shared_trips = sum(passenger_count &gt; 1, na.rm = TRUE)\n  ) |&gt;\n  mutate(pct_shared = shared_trips / all_trips * 100) \n\nclass(shared_rides)\n\n[1] \"arrow_dplyr_query\""
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+    "text": "library(arrow)\nlibrary(dplyr)\n\n\nnyc_taxi &lt;- open_dataset(\"data/nyc-taxi/\")\n\n\n\n\n\n\n\nUser-defined functions\n\n\n\n\nProblemSolution 1\n\n\n\nWrite a user-defined function which wraps the stringr function str_replace_na(), and use it to replace any NA values in the vendor_name column with the string “No vendor” instead. (Test it on the data from 2019 so you’re not pulling everything into memory)\n\n\n\n\n# Preview the distinct vendor names before we start\nnyc_taxi |&gt;\n  filter(year == 2019) |&gt; # smaller subset of the data\n  distinct(vendor_name) |&gt;\n  collect()\n\n# A tibble: 3 × 1\n  vendor_name\n  &lt;chr&gt;      \n1 CMT        \n2 VTS        \n3 &lt;NA&gt;       \n\n\n\nregister_scalar_function(\n  name = \"replace_vendor_na\",\n  function(context, string) {\n    stringr::str_replace_na(string, \"No vendor\")\n  },\n  in_type = schema(string = string()),\n  out_type = string(),\n  auto_convert = TRUE\n)\n\nvendor_names_fixed &lt;- nyc_taxi |&gt;\n  mutate(vendor_name = replace_vendor_na(vendor_name)) \n\n# Preview the distinct vendor names to check it's worked\nvendor_names_fixed |&gt;\n  filter(year == 2019) |&gt; # smaller subset of the data\n  distinct(vendor_name) |&gt;\n  collect()\n\n# A tibble: 3 × 1\n  vendor_name\n  &lt;chr&gt;      \n1 CMT        \n2 VTS        \n3 No vendor  \n\n\n\n\n\n\n\n\n\n\n\n\n\nJoins\n\n\n\n\nProblemSolution 1\n\n\n\nHow many taxi pickups were recorded in 2019 from the three major airports covered by the NYC Taxis data set (JFK, LaGuardia, Newark)? (Hint: you can use stringr::str_detect() to help you find pickup zones with the word “Airport” in them)\n\n\n\n\npickup_location &lt;- read_csv_arrow(\"data/taxi_zone_lookup.csv\")\n\npickup_location &lt;- pickup_location |&gt;\n  select(\n    pickup_location_id = LocationID,\n    borough = Borough,\n    pickup_zone = Zone\n  ) \n\n\npickup_location_arrow &lt;- arrow_table(\n  pickup_location, \n  schema = schema(\n    pickup_location_id = int64(),\n    borough = utf8(),\n    pickup_zone = utf8()\n  ))\n\nnyc_taxi |&gt;\n  filter(year == 2019) |&gt;\n  left_join(pickup_location_arrow) |&gt;\n  filter(str_detect(pickup_zone, \"Airport\")) |&gt;\n  count(pickup_zone) |&gt;\n  collect()\n\n# A tibble: 3 × 2\n  pickup_zone             n\n  &lt;chr&gt;               &lt;int&gt;\n1 JFK Airport       2729336\n2 LaGuardia Airport 2159224\n3 Newark Airport       8643"
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-    "text": "arrow dplyr queries\n\nquery has been constructed but not evaluated\nnothing has been pulled into memory"
+    "section": "Feedback",
+    "text": "Feedback\n\nPlease complete the post-workshop survey 🙏\nYour feedback is crucial! Data from the survey informs curriculum and format decisions for future conf workshops, and we really appreciate you taking the time to provide it.\n\npos.it/conf-workshop-survey"
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-    "text": "Running the query\n\ncollect() evaluates the query, in-memory output returns to R"
+    "section": "Course Materials",
+    "text": "Course Materials\n\n\nhttps://github.com/posit-conf-2024/arrow\nmaterials open-licensed: Creative Commons Attribution 4.0 International License\nPlease open an Issue with any glitches, gotchas or comments!"
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-    "text": "collect()\n\ncollect(shared_rides)\n\n# A tibble: 10 × 4\n    year all_trips shared_trips pct_shared\n   &lt;int&gt;     &lt;int&gt;        &lt;int&gt;      &lt;dbl&gt;\n 1  2012 178544324     53313752       29.9\n 2  2013 173179759     51215013       29.6\n 3  2014 165114361     48816505       29.6\n 4  2015 146112989     43081091       29.5\n 5  2016 131165043     38163870       29.1\n 6  2017 113495512     32296166       28.5\n 7  2018 102797401     28796633       28.0\n 8  2019  84393604     23515989       27.9\n 9  2020  24647055      5837960       23.7\n10  2021  30902618      7221844       23.4"
+    "section": "Grab a sticker!",
+    "text": "Grab a sticker!\n\n\n\ngrab a hex sticker before you go!\n\n\n\n\n\n🔗 pos.it/arrow-conf24"
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-    "section": "Calling nrow() to see how much data",
-    "text": "Calling nrow() to see how much data\n\nnyc_taxi |&gt;\n  filter(year %in% 2017:2021) |&gt;\n  nrow()\n\n[1] 356236190"
+    "section": "",
+    "text": "WiFi \n\nUsername: Posit Conf 2024\nPassword: conf2024\n\n\nWorkshop \n\nWebsite: pos.it/arrow-conf24\nGitHub: github.com/posit-conf-2024/arrow"
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-    "section": "Your Turn",
-    "text": "Your Turn\nUse the function nrow() to work out the answers to these questions:\n\nHow many taxi fares in the dataset had a total amount greater than $100?\n\n➡️ Data Manipulation Part I Exercises Page"
+    "section": "Housekeeping",
+    "text": "Housekeeping\n\nGender Neutral Bathrooms \n\nLocated on levels 3, 4, 5, 6 & 7\n\nSpecialty Rooms \n\nMeditation/Prayer Room (503)\nLactation Room (509)\n\n*Available Mon & Tues 7am - 7pm, and Wed 7am - 5pm"
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+    "section": "Photos",
+    "text": "Photos\n\nRed Lanyards  NO \n\nPlease note everyone’s lanyard colors before taking a photo and respect their choices."
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-    "section": "Use head() and collect() to preview results",
-    "text": "Use head() and collect() to preview results\n\nnyc_taxi |&gt;\n  filter(year == 2020) |&gt;\n  mutate(fare_pounds = fare_amount * 0.79) |&gt;\n  select(fare_amount, fare_pounds) |&gt;\n  head() |&gt;\n  collect()\n\n# A tibble: 6 × 2\n  fare_amount fare_pounds\n        &lt;dbl&gt;       &lt;dbl&gt;\n1         8          6.32\n2        17         13.4 \n3         6.5        5.14\n4         7          5.53\n5         6.5        5.14\n6        42         33.2"
+    "section": "Code of Conduct",
+    "text": "Code of Conduct\n\n posit.co/code-of-conduct/\n\nContact any posit::conf staff member, identifiable by their staff t-shirt, or visit the conference general information desk.\nSend a message to conf@posit.com; event organizers will respond promptly.\nCall +1-844-448-1212; this phone number will be monitored for the duration of the event."
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-    "section": "Use across() to transform data in multiple columns",
-    "text": "Use across() to transform data in multiple columns\n\nnyc_taxi |&gt;\n  mutate(across(ends_with(\"amount\"), list(pounds = ~.x * 0.79))) |&gt;\n  select(contains(\"amount\")) |&gt;\n  head() |&gt;\n  collect()\n\n# A tibble: 6 × 8\n  fare_amount tip_amount tolls_amount total_amount fare_amount_pounds\n        &lt;dbl&gt;      &lt;dbl&gt;        &lt;dbl&gt;        &lt;dbl&gt;              &lt;dbl&gt;\n1        29.7       6.04            0        36.2               23.5 \n2         9.3       0               0         9.8                7.35\n3         4.1       1.38            0         5.98               3.24\n4         4.5       1               0         6                  3.56\n5         4.5       0               0         5.5                3.56\n6         4.1       0               0         5.6                3.24\n# ℹ 3 more variables: tip_amount_pounds &lt;dbl&gt;, tolls_amount_pounds &lt;dbl&gt;,\n#   total_amount_pounds &lt;dbl&gt;"
+    "section": "Meet Your Teaching Team ",
+    "text": "Meet Your Teaching Team \n\nCo-Instructors\n\nNic Crane\nSteph Hazlitt\n\nTeaching Assistant\n\nJonathan Keane"
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-    "text": "Summary\n\nUse nrow() to work out how many rows of data your analyses will return\nUse collect() to pull all of the data into your R session\nUse head() and collect() to preview results\nUse across() to manipulate data in multiple columns at once"
+    "section": "Meet Each Other ",
+    "text": "Meet Each Other \n\n\nWhen did you use R for the first time?\nWhat is your favorite R package?\nWhich package hex sticker would you like to find the most during posit::conf(2024)?"
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-    "text": "Example - slice()\nFirst three trips in the dataset in 2021 where distance &gt; 100 miles\n\nlong_rides_2021 &lt;- nyc_taxi |&gt;\n  filter(year == 2021 & trip_distance &gt; 100) |&gt;\n  select(pickup_datetime, year, trip_distance)\n\nlong_rides_2021 |&gt;\n  slice(1:3)\n\nError in UseMethod(\"slice\"): no applicable method for 'slice' applied to an object of class \"arrow_dplyr_query\""
+    "section": "Getting Help Today ",
+    "text": "Getting Help Today \n\nGREEN sticky note: I am OK / I am done\nPINK sticky note: I need support / I am working\n\n You can ask questions at any time during the workshop"
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-    "text": "Head to the docs!\n\n?`arrow-dplyr`\n\nor view them at https://arrow.apache.org/docs/r/reference/acero.html"
+    "section": "Discord ",
+    "text": "Discord \n\npos.it/conf-event-portal (login)\nClick on “Join Discord, the virtual networking platform!”\nBrowse Channels -&gt; #workshop-arrow"
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-    "text": "A different function\n\nlong_rides_2021 |&gt;\n  slice_max(n = 3, order_by = trip_distance, with_ties = FALSE) |&gt;\n  collect()\n\n# A tibble: 3 × 3\n  pickup_datetime      year trip_distance\n  &lt;dttm&gt;              &lt;int&gt;         &lt;dbl&gt;\n1 2021-11-16 12:55:00  2021       351613.\n2 2021-10-27 17:46:00  2021       345124.\n3 2021-12-11 10:48:00  2021       335094."
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+    "text": "We Assume\n\nYou know \nYou are familiar with the dplyr package for data manipulation \nYou have data in your life that is too large to fit into memory or sluggish in memory\nYou want to learn how to engineer your data storage for more performant access and analysis"
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-    "section": "Or call collect() first",
-    "text": "Or call collect() first\n\nlong_rides_2021 |&gt;\n  collect() |&gt;\n  slice(1:3)\n\n# A tibble: 3 × 3\n  pickup_datetime      year trip_distance\n  &lt;dttm&gt;              &lt;int&gt;         &lt;dbl&gt;\n1 2021-10-02 15:04:53  2021          188.\n2 2021-10-03 16:45:02  2021          134 \n3 2021-10-03 17:29:35  2021          218."
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-    "section": "tidyr functions - pivot",
-    "text": "tidyr functions - pivot\n\nlibrary(tidyr)\n\nnyc_taxi |&gt; \n  group_by(vendor_name) |&gt;\n  summarise(max_fare = max(fare_amount)) |&gt;\n  pivot_longer(!vendor_name, names_to = \"metric\") |&gt; \n  collect()\n\nError in UseMethod(\"pivot_longer\"): no applicable method for 'pivot_longer' applied to an object of class \"arrow_dplyr_query\""
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-    "section": "tidyr functions - pivot with duckdb!",
-    "text": "tidyr functions - pivot with duckdb!\n\nlibrary(duckdb)\n\nnyc_taxi |&gt; \n  group_by(vendor_name) |&gt;\n  summarise(max_fare = max(fare_amount)) |&gt;\n  to_duckdb() |&gt; # send data to duckdb\n  pivot_longer(!vendor_name, names_to = \"metric\") |&gt; \n  to_arrow() |&gt; # return data back to arrow\n  collect()\n\n# A tibble: 3 × 3\n  vendor_name metric     value\n  &lt;chr&gt;       &lt;chr&gt;      &lt;dbl&gt;\n1 CMT         max_fare 998310.\n2 VTS         max_fare  10000.\n3 &lt;NA&gt;        max_fare   3555."
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-    "section": "Using functions inside verbs",
-    "text": "Using functions inside verbs\n\nlots of the lubridate and stringr APIs supported!\nbase R and others too - always good to check the docs"
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-    "section": "Morning vs afternoon with namespacing",
-    "text": "Morning vs afternoon with namespacing\n\nnyc_taxi |&gt;\n  group_by(\n    time_of_day = ifelse(lubridate::am(pickup_datetime), \"morning\", \"afternoon\")\n  ) |&gt;\n  count() |&gt;\n  collect()\n\n# A tibble: 2 × 2\n# Groups:   time_of_day [2]\n  time_of_day         n\n  &lt;chr&gt;           &lt;int&gt;\n1 afternoon   736491676\n2 morning     413860990"
+    "section": "Posit Workbench: Login 🛠️",
+    "text": "Posit Workbench: Login 🛠️\n\nJoin Workbench via URL in the #workshop-arrow Discord channel\nSelect Posit Workbench &gt;&gt; Sign in with OpenID\nUse your GitHub credentials to log in (click the icon)"
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-    "text": "Morning vs afternoon - without namespacing\n\nlibrary(lubridate)\n\nnyc_taxi |&gt;\n  group_by(\n    time_of_day = ifelse(am(pickup_datetime), \"morning\", \"afternoon\")\n  ) |&gt;\n  count() |&gt;\n  collect()\n\n# A tibble: 2 × 2\n# Groups:   time_of_day [2]\n  time_of_day         n\n  &lt;chr&gt;           &lt;int&gt;\n1 afternoon   736491676\n2 morning     413860990"
+    "section": "Posit Workbench: Setup 🍽️",
+    "text": "Posit Workbench: Setup 🍽️\n\n🖱 +New Session\n🖱 Start Session (defaults are fine)\nRun usethis::use_course(\"posit-conf-2024/arrow\")"
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-    "text": "How does this work?"
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+    "text": "Posit Workbench: Setup 🍽️\n\nDefault location: 🖱 2\nUnzip 📁: 🖱 3\nOpen Session dialog box: Resource Profile &gt;&gt; select Large\nOpen + run data/setup.R 🎉\n\n\n\n\n\n\n🔗 pos.it/arrow-conf24"
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+    "text": "by Nic Crane & Steph Hazlitt\n\n🗓️ August 12th, 2024\n⏰ 09:00 - 17:00\n🏨 305 | Chelais\n✍️ pos.it/conf\n\n\nWorkshop Overview\nData analysis pipelines with larger-than-memory data are becoming more and more commonplace. In this workshop you will learn how to use Apache Arrow, a multi-language toolbox for working with larger-than-memory tabular data, to create seamless “big” data analysis pipelines with R.\nThe workshop will focus on using the the arrow R package—a mature R interface to Apache Arrow—to process larger-than-memory files and multi-file datasets with arrow using familiar dplyr syntax. You’ll learn to create and use interoperable data file formats like Parquet for efficient data storage and access, with data stored both on disk and in the cloud, and also how to exercise fine control over data types to avoid common large data pipeline problems. This workshop will provide a foundation for using Arrow, giving you access to a powerful suite of tools for performant analysis of larger-than-memory data in R.\nThis course is for you if you:\n\nwant to learn how to work with tabular data that is too large to fit in memory using existing R and tidyverse syntax implemented in Arrow\nwant to learn about Parquet and other file formats that are powerful alternatives to CSV files\nwant to learn how to engineer your tabular data storage for more performant access and analysis with Apache Arrow\n\n\n\nWorkshop Prework\nAll participants need to bring is a laptop that can connect to wifi. We will be using Posit Workbench to learn together—Workbench will be setup with all the software and data needed for the day. If you would prefer to run code locally on your own laptop, detailed instructions for software requirements and data sources are covered in Packages & Data.\n\n\nWorkshop Schedule\n“This schedule is more what you would call a ‘guideline’ than an actual schedule” — Barbossa, Pirates of the Caribbean\n\n\n\n\n\n\n\nTime\nActivity\n\n\n\n\n09:00 - 10:30\nSession 1: Hello Arrow + Data Manipulation with Arrow I\n\n\n10:30 - 11:00\nCoffee break\n\n\n11:00 - 12:30\nSession 2: Data Engineering with Arrow\n\n\n12:30 - 13:30\nLunch break\n\n\n13:30 - 15:00\nSession 3: Arrow In-Memory Workflows\n\n\n15:00 - 15:30\nCoffee break\n\n\n15:30 - 17:00\nSession 4: Data Manipulation with Arrow II + Wrapping Up\n\n\n\n\n\nInstructors\nNic Crane is an R consultant with a background in data science and software engineering. They are passionate about open source, and learning and teaching all things R. Nic is part of the core team that maintain the Arrow R package, and a co-author of “Scaling up with R and Arrow”, due to be published by CRC Press later this year.\nSteph Hazlitt is a data scientist, researcher and R enthusiast. She has spent the better part of her career wrangling data with R and supporting people and teams in creating and sharing data science-related products and open source software. Steph is the Director of Data Science Partnerships with BC Stats.\n\n\nAcknowledgements\nSome of this Big Data in R with Arrow workshop materials draw on other open-licensed teaching content which we would like to acknowledge:\n\nuseR!2022 virtual Larger-Than-Memory Data Workflows with Apache Arrow tutorial authored by Danielle Navarro\nR for Data Science (2e) written by Hadley Wickham, Mine Çetinkaya-Rundel, and Garrett Grolemund—with thanks to Danielle Navarro who contributed the initial version of the Arrow chapter\nHow to use Arrow to work with large CSV files? blog post by François Michonneau, which introduces the single vs multi-file API models for learning/teaching Arrow\nBig Data in R with Arrow 1-Day Posit::Conf (2023) Workshop by Steph Hazlitt & Nic Crane, an earlier version of this 1-day course.\n\n\n This work is licensed under a Creative Commons Attribution 4.0 International License."
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-    "text": "What if a function isn’t implemented?\n\nnyc_taxi |&gt;\n  mutate(vendor_name = na_if(vendor_name, \"CMT\")) |&gt;\n  head() |&gt;\n  collect()\n\nError: Expression na_if(vendor_name, \"CMT\") not supported in Arrow\nCall collect() first to pull data into R."
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+    "text": "Book\nhttps://arrowrbook.com"
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-    "text": "Head to the docs again to see what’s implemented!\n\n?`arrow-dplyr`\n\nor view them at https://arrow.apache.org/docs/r/reference/acero.html"
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+    "text": "Docs\n\nhttps://arrow.apache.org/docs/r/"
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-    "text": "Option 1 - find a workaround!\n\nnyc_taxi |&gt;\n  mutate(vendor_name = ifelse(vendor_name == \"CMT\", NA, vendor_name)) |&gt;\n  head() |&gt;\n  collect()\n\n# A tibble: 6 × 24\n  vendor_name pickup_datetime     dropoff_datetime    passenger_count\n  &lt;chr&gt;       &lt;dttm&gt;              &lt;dttm&gt;                        &lt;int&gt;\n1 &lt;NA&gt;        2012-01-20 14:09:36 2012-01-20 14:42:25               1\n2 &lt;NA&gt;        2012-01-20 14:54:10 2012-01-20 15:06:55               1\n3 &lt;NA&gt;        2012-01-20 08:08:01 2012-01-20 08:11:02               1\n4 &lt;NA&gt;        2012-01-20 08:36:22 2012-01-20 08:39:44               1\n5 &lt;NA&gt;        2012-01-20 20:58:32 2012-01-20 21:03:04               1\n6 &lt;NA&gt;        2012-01-20 19:40:20 2012-01-20 19:43:43               2\n# ℹ 20 more variables: trip_distance &lt;dbl&gt;, pickup_longitude &lt;dbl&gt;,\n#   pickup_latitude &lt;dbl&gt;, rate_code &lt;chr&gt;, store_and_fwd &lt;chr&gt;,\n#   dropoff_longitude &lt;dbl&gt;, dropoff_latitude &lt;dbl&gt;, payment_type &lt;chr&gt;,\n#   fare_amount &lt;dbl&gt;, extra &lt;dbl&gt;, mta_tax &lt;dbl&gt;, tip_amount &lt;dbl&gt;,\n#   tolls_amount &lt;dbl&gt;, total_amount &lt;dbl&gt;, improvement_surcharge &lt;dbl&gt;,\n#   congestion_surcharge &lt;dbl&gt;, pickup_location_id &lt;int&gt;,\n#   dropoff_location_id &lt;int&gt;, year &lt;int&gt;, month &lt;int&gt;"
+    "section": "Cookbook",
+    "text": "Cookbook\nhttps://arrow.apache.org/cookbook/r/"
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-    "text": "Option 2\n\nIn data manipulation part 2!"
+    "section": "Cheatsheet",
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-    "text": "Your Turn\n\nUse the dplyr::filter() and stringr::str_ends() functions to return a subset of the data which is a) from September 2020, and b) the value in vendor_name ends with the letter “S”.\nTry to use the stringr function str_replace_na() to replace any NA values in the vendor_name column with the string “No vendor” instead. What happens, and why?\nBonus question: see if you can find a different way of completing the task in question 2.\n\n➡️ Data Manipulation Part I Exercises Page"
+    "section": "Awesome Arrow",
+    "text": "Awesome Arrow\nhttps://github.com/thisisnic/awesome-arrow-r"
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-    "text": "Working with custom functions\nArrow 17.0.0 or later!\n\ntime_text &lt;- function(time){\n  day_of_week &lt;- wday(time, label = TRUE, abbr = FALSE)\n  time_of_day &lt;- ifelse(lubridate::am(time), \"AM\", \"PM\")\n  paste(day_of_week, time_of_day)\n}\n\nnyc_taxi |&gt;\n  mutate(pickup_text = time_text(pickup_datetime)) |&gt;\n  select(pickup_datetime, pickup_text) |&gt;\n  head() |&gt;\n  collect() \n\n\n\n# A tibble: 6 × 2\n  pickup_datetime     pickup_text\n  &lt;dttm&gt;              &lt;chr&gt;      \n1 2012-01-08 20:50:38 Sunday PM  \n2 2012-01-08 20:52:01 Sunday PM  \n3 2012-01-08 02:39:26 Sunday AM  \n4 2012-01-08 02:40:49 Sunday AM  \n5 2012-01-09 03:42:37 Monday AM  \n6 2012-01-08 20:51:47 Sunday PM"
+    "section": "GitHub Issues",
+    "text": "GitHub Issues\nhttps://github.com/apache/arrow/issues"
   },
   {
-    "objectID": "materials/2_data_manipulation_1.html#how-did-that-work",
-    "href": "materials/2_data_manipulation_1.html#how-did-that-work",
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+    "href": "materials/7_continue_learning.html#community",
     "title": "Big Data in R with Arrow",
-    "section": "How did that work?",
-    "text": "How did that work?\nCustom function converted to Arrow Expression; query doesn’t contain any reference to the time_text() function.\n\nnyc_taxi |&gt;\n  mutate(pickup_text = time_text(pickup_datetime)) |&gt;\n  select(pickup_datetime, pickup_text)\n\n\n\nFileSystemDataset (query)\npickup_datetime: timestamp[ms]\npickup_text: string (binary_join_element_wise(cast(strftime(pickup_datetime, {format=\"%A\"}), {to_type=string, allow_int_overflow=false, allow_time_truncate=false, allow_time_overflow=false, allow_decimal_truncate=false, allow_float_truncate=false, allow_invalid_utf8=false}), cast(if_else((hour(pickup_datetime) &lt; 12), \"AM\", \"PM\"), {to_type=string, allow_int_overflow=false, allow_time_truncate=false, allow_time_overflow=false, allow_decimal_truncate=false, allow_float_truncate=false, allow_invalid_utf8=false}), \" \", {null_handling=REPLACE, null_replacement=\"NA\"}))\n\nSee $.data for the source Arrow object"
+    "section": "Community",
+    "text": "Community\nhttps://arrow.apache.org/community/\n\n\nalso Stack Overflow, Posit Community\n\n\n\n\n\n🔗 pos.it/arrow-conf24"
   },
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+    "objectID": "materials/6_wrapping_up.html#arrow",
+    "href": "materials/6_wrapping_up.html#arrow",
     "title": "Big Data in R with Arrow",
-    "section": "Anything else to be aware of?",
-    "text": "Anything else to be aware of?\n\narrow 17.0.0 or later\nthis will only work for functions which have Arrow bindings\nuse ?`arrow-dplyr` to see which ones do"
+    "section": "Arrow",
+    "text": "Arrow\n\nefficiently read + filter + join + summarise 1.15 billion rows\n\n\nlibrary(arrow)\nlibrary(dplyr)\nlibrary(janitor)\nlibrary(stringr)\n\nnyc_taxi_zones &lt;- read_csv_arrow(\"data/taxi_zone_lookup.csv\",\n                                 as_data_frame = FALSE) |&gt;\n  clean_names()\n  \nairport_zones &lt;- nyc_taxi_zones |&gt;\n  filter(str_detect(zone, \"Airport\")) |&gt;\n  pull(location_id, as_vector = TRUE)\n\ndropoff_zones &lt;- nyc_taxi_zones |&gt;\n  select(dropoff_location_id = location_id,\n         dropoff_zone = zone) |&gt; \n  collect(as_data_frame = FALSE)\n\nairport_pickups &lt;- open_dataset(\"data/nyc-taxi/\") |&gt;\n  filter(pickup_location_id %in% airport_zones) |&gt;\n  select(\n    matches(\"datetime\"),\n    matches(\"location_id\")\n  ) |&gt;\n  left_join(dropoff_zones) |&gt;\n  count(dropoff_zone) |&gt;\n  arrange(desc(n)) |&gt;\n  collect()"
   },
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-    "href": "materials/2_data_manipulation_1.html#summary-1",
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+    "href": "materials/6_wrapping_up.html#r",
     "title": "Big Data in R with Arrow",
-    "section": "Summary",
-    "text": "Summary\n\nWorking with Arrow Datasets allow you to manipulate data which is larger-than-memory\nYou can use many dplyr functions with arrow - run ?`arrow-dplyr` to view the docs\nYou can pass data to duckdb to use functions implemented in duckdb but not arrow\n\n\n\n\n\n🔗 pos.it/arrow-conf24"
+    "section": "R",
+    "text": "R\n\nread + wrangle spatial data + 🤩 graphics\n\n\nlibrary(sf)\nlibrary(ggplot2)\nlibrary(ggrepel)\nlibrary(stringr)\nlibrary(scales)\n\nmap &lt;- read_sf(\"data/taxi_zones/taxi_zones.shp\") |&gt;\n  clean_names() |&gt;\n  left_join(airport_pickups,\n            by = c(\"zone\" = \"dropoff_zone\")) |&gt;\n  arrange(desc(n))\n\narrow_r_together &lt;- ggplot(data = map, aes(fill = n)) +\n  geom_sf(size = .1) +\n  scale_fill_distiller(\n    name = \"Number of trips\",\n    labels = label_comma(),\n    palette = \"Reds\",\n    direction = 1\n  ) +\n  geom_label_repel(\n    stat = \"sf_coordinates\",\n    data = map |&gt;\n      mutate(zone_label = case_when(\n        str_detect(zone, \"Airport\") ~ zone,\n        str_detect(zone, \"Times\") ~ zone,\n        .default = \"\"\n      )),\n    mapping = aes(label = zone_label, geometry = geometry),\n    max.overlaps = 60,\n    label.padding = .3,\n    fill = \"white\"\n  ) +\n  theme_void()"
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-    "href": "materials/1_hello_arrow.html#kick-off-qa",
+    "objectID": "materials/6_wrapping_up.html#arrow-r-together-arrow",
+    "href": "materials/6_wrapping_up.html#arrow-r-together-arrow",
     "title": "Big Data in R with Arrow",
-    "section": "Kick-off Q&A",
-    "text": "Kick-off Q&A\n\n\nWhat brings you to this workshop?\nWhat challenges have you faced related to larger-than-memory data in R?\nWhat is one thing you want to learn or achieve from today’s workshop?\n…?"
+    "section": "Arrow + R Together: {arrow}",
+    "text": "Arrow + R Together: {arrow}\n\narrow_r_together\n\n\n\n\n\n\n\n\n\n\n\n\n🔗 pos.it/arrow-conf24"
   },
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@@ -956,7 +809,7 @@
     "href": "materials/1_hello_arrow.html#nyc-taxi-dataset-a-dplyr-pipeline",
     "title": "Big Data in R with Arrow",
     "section": "NYC Taxi Dataset: A dplyr pipeline",
-    "text": "NYC Taxi Dataset: A dplyr pipeline\n\nlibrary(dplyr)\n\nnyc_taxi |&gt;\n  group_by(year) |&gt;\n  summarise(\n    all_trips = n(),\n    shared_trips = sum(passenger_count &gt; 1, na.rm = TRUE)\n  ) |&gt;\n  mutate(pct_shared = shared_trips / all_trips * 100) |&gt;\n  collect()\n\n# A tibble: 10 × 4\n    year all_trips shared_trips pct_shared\n   &lt;int&gt;     &lt;int&gt;        &lt;int&gt;      &lt;dbl&gt;\n 1  2012 178544324     53313752       29.9\n 2  2013 173179759     51215013       29.6\n 3  2014 165114361     48816505       29.6\n 4  2015 146112989     43081091       29.5\n 5  2017 113495512     32296166       28.5\n 6  2018 102797401     28796633       28.0\n 7  2019  84393604     23515989       27.9\n 8  2020  24647055      5837960       23.7\n 9  2021  30902618      7221844       23.4\n10  2016 131165043     38163870       29.1"
+    "text": "NYC Taxi Dataset: A dplyr pipeline\n\nlibrary(dplyr)\n\nnyc_taxi |&gt;\n  group_by(year) |&gt;\n  summarise(\n    all_trips = n(),\n    shared_trips = sum(passenger_count &gt; 1, na.rm = TRUE)\n  ) |&gt;\n  mutate(pct_shared = shared_trips / all_trips * 100) |&gt;\n  collect()\n\n# A tibble: 10 × 4\n    year all_trips shared_trips pct_shared\n   &lt;int&gt;     &lt;int&gt;        &lt;int&gt;      &lt;dbl&gt;\n 1  2012 178544324     53313752       29.9\n 2  2013 173179759     51215013       29.6\n 3  2014 165114361     48816505       29.6\n 4  2015 146112989     43081091       29.5\n 5  2016 131165043     38163870       29.1\n 6  2017 113495512     32296166       28.5\n 7  2018 102797401     28796633       28.0\n 8  2019  84393604     23515989       27.9\n 9  2020  24647055      5837960       23.7\n10  2021  30902618      7221844       23.4"
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@@ -977,7 +830,7 @@
     "href": "materials/1_hello_arrow.html#what-is-apache-arrow",
     "title": "Big Data in R with Arrow",
     "section": "What is Apache Arrow?",
-    "text": "What is Apache Arrow?\n\n\n\nA multi-language toolbox for accelerated data interchange and in-memory processing\n\n\n\nArrow is designed to both improve the performance of analytical algorithms and the efficiency of moving data from one system or programming language to another\n\n\n\n\nhttps://arrow.apache.org/overview/"
+    "text": "What is Apache Arrow?\n\n\n\nA multi-language toolbox for accelerated data interchange and in-memory processing\n\n\n\nArrow is designed to both improve the performance of analytical algorithms and the efficiency of moving data from one system or programming language to another\n\n\n\nhttps://arrow.apache.org/overview/"
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@@ -1029,178 +882,325 @@
     "text": "Today\n\nModule 1: Larger-than-memory data manipulation with Arrow—Part I\nModule 2: Data engineering with Arrow\nModule 3: In-memory workflows in R with Arrow\nModule 4: Larger-than-memory data manipulation with Arrow—Part II\n\n\n\n\n\n🔗 pos.it/arrow-conf24"
   },
   {
-    "objectID": "materials/0_housekeeping.html#section",
-    "href": "materials/0_housekeeping.html#section",
-    "title": "Big Data in R with Arrow",
+    "objectID": "materials/5_arrow_single_file-exercises.html",
+    "href": "materials/5_arrow_single_file-exercises.html",
+    "title": "Arrow In-Memory Exercise",
     "section": "",
-    "text": "WiFi \n\nUsername: Posit Conf 2024\nPassword: conf2024\n\n\nWorkshop \n\nWebsite: pos.it/arrow-conf24\nGitHub: github.com/posit-conf-2024/arrow"
+    "text": "library(arrow)\nlibrary(dplyr)\n\n\n\n\n\n\n\nArrow Table\n\n\n\n\nProblemsSolution 1Solution 2\n\n\n\nRead in a single NYC Taxi parquet file using read_parquet() as an Arrow Table\nConvert your Arrow Table object to a data.frame or a tibble\n\n\n\n\nparquet_file &lt;- \"data/nyc-taxi/year=2019/month=9/part-0.parquet\"\n\ntaxi_table &lt;- read_parquet(parquet_file, as_data_frame = FALSE)\ntaxi_table\n\nTable\n6567396 rows x 22 columns\n$vendor_name &lt;string&gt;\n$pickup_datetime &lt;timestamp[ms]&gt;\n$dropoff_datetime &lt;timestamp[ms]&gt;\n$passenger_count &lt;int64&gt;\n$trip_distance &lt;double&gt;\n$pickup_longitude &lt;double&gt;\n$pickup_latitude &lt;double&gt;\n$rate_code &lt;string&gt;\n$store_and_fwd &lt;string&gt;\n$dropoff_longitude &lt;double&gt;\n$dropoff_latitude &lt;double&gt;\n$payment_type &lt;string&gt;\n$fare_amount &lt;double&gt;\n$extra &lt;double&gt;\n$mta_tax &lt;double&gt;\n$tip_amount &lt;double&gt;\n$tolls_amount &lt;double&gt;\n$total_amount &lt;double&gt;\n$improvement_surcharge &lt;double&gt;\n$congestion_surcharge &lt;double&gt;\n...\n2 more columns\nUse `schema()` to see entire schema\n\n\n\n\n\ntaxi_table |&gt; collect()\n\n# A tibble: 6,567,396 × 22\n   vendor_name pickup_datetime     dropoff_datetime    passenger_count\n   &lt;chr&gt;       &lt;dttm&gt;              &lt;dttm&gt;                        &lt;int&gt;\n 1 CMT         2019-08-31 18:09:30 2019-08-31 18:15:42               1\n 2 CMT         2019-08-31 18:26:30 2019-08-31 18:44:31               1\n 3 CMT         2019-08-31 18:39:35 2019-08-31 19:15:55               2\n 4 VTS         2019-08-31 18:12:26 2019-08-31 18:15:17               4\n 5 VTS         2019-08-31 18:43:16 2019-08-31 18:53:50               1\n 6 VTS         2019-08-31 18:26:13 2019-08-31 18:45:35               1\n 7 CMT         2019-08-31 18:34:52 2019-08-31 18:42:03               1\n 8 CMT         2019-08-31 18:50:02 2019-08-31 18:58:16               1\n 9 CMT         2019-08-31 18:08:02 2019-08-31 18:14:44               0\n10 VTS         2019-08-31 18:11:38 2019-08-31 18:26:47               1\n# ℹ 6,567,386 more rows\n# ℹ 18 more variables: trip_distance &lt;dbl&gt;, pickup_longitude &lt;dbl&gt;,\n#   pickup_latitude &lt;dbl&gt;, rate_code &lt;chr&gt;, store_and_fwd &lt;chr&gt;,\n#   dropoff_longitude &lt;dbl&gt;, dropoff_latitude &lt;dbl&gt;, payment_type &lt;chr&gt;,\n#   fare_amount &lt;dbl&gt;, extra &lt;dbl&gt;, mta_tax &lt;dbl&gt;, tip_amount &lt;dbl&gt;,\n#   tolls_amount &lt;dbl&gt;, total_amount &lt;dbl&gt;, improvement_surcharge &lt;dbl&gt;,\n#   congestion_surcharge &lt;dbl&gt;, pickup_location_id &lt;int&gt;, …\n\n\nor\n\nas_tibble(taxi_table)\n\n# A tibble: 6,567,396 × 22\n   vendor_name pickup_datetime     dropoff_datetime    passenger_count\n   &lt;chr&gt;       &lt;dttm&gt;              &lt;dttm&gt;                        &lt;int&gt;\n 1 CMT         2019-08-31 18:09:30 2019-08-31 18:15:42               1\n 2 CMT         2019-08-31 18:26:30 2019-08-31 18:44:31               1\n 3 CMT         2019-08-31 18:39:35 2019-08-31 19:15:55               2\n 4 VTS         2019-08-31 18:12:26 2019-08-31 18:15:17               4\n 5 VTS         2019-08-31 18:43:16 2019-08-31 18:53:50               1\n 6 VTS         2019-08-31 18:26:13 2019-08-31 18:45:35               1\n 7 CMT         2019-08-31 18:34:52 2019-08-31 18:42:03               1\n 8 CMT         2019-08-31 18:50:02 2019-08-31 18:58:16               1\n 9 CMT         2019-08-31 18:08:02 2019-08-31 18:14:44               0\n10 VTS         2019-08-31 18:11:38 2019-08-31 18:26:47               1\n# ℹ 6,567,386 more rows\n# ℹ 18 more variables: trip_distance &lt;dbl&gt;, pickup_longitude &lt;dbl&gt;,\n#   pickup_latitude &lt;dbl&gt;, rate_code &lt;chr&gt;, store_and_fwd &lt;chr&gt;,\n#   dropoff_longitude &lt;dbl&gt;, dropoff_latitude &lt;dbl&gt;, payment_type &lt;chr&gt;,\n#   fare_amount &lt;dbl&gt;, extra &lt;dbl&gt;, mta_tax &lt;dbl&gt;, tip_amount &lt;dbl&gt;,\n#   tolls_amount &lt;dbl&gt;, total_amount &lt;dbl&gt;, improvement_surcharge &lt;dbl&gt;,\n#   congestion_surcharge &lt;dbl&gt;, pickup_location_id &lt;int&gt;, …\n\n\nor\n\nas.data.frame(taxi_table)\n\n  vendor_name     pickup_datetime    dropoff_datetime passenger_count\n1         CMT 2019-08-31 18:09:30 2019-08-31 18:15:42               1\n2         CMT 2019-08-31 18:26:30 2019-08-31 18:44:31               1\n3         CMT 2019-08-31 18:39:35 2019-08-31 19:15:55               2\n4         VTS 2019-08-31 18:12:26 2019-08-31 18:15:17               4\n5         VTS 2019-08-31 18:43:16 2019-08-31 18:53:50               1\n  trip_distance pickup_longitude pickup_latitude     rate_code store_and_fwd\n1          0.80               NA              NA Standard rate            No\n2          3.70               NA              NA Standard rate            No\n3          8.10               NA              NA Standard rate            No\n4          0.58               NA              NA Standard rate            No\n5          3.32               NA              NA Standard rate            No\n  dropoff_longitude dropoff_latitude payment_type fare_amount extra mta_tax\n1                NA               NA         Cash         6.0   3.0     0.5\n2                NA               NA  Credit card        15.0   3.0     0.5\n3                NA               NA  Credit card        29.5   3.0     0.5\n4                NA               NA  Credit card         4.0   0.5     0.5\n5                NA               NA  Credit card        12.5   0.5     0.5\n  tip_amount tolls_amount total_amount improvement_surcharge\n1       0.00            0         9.80                   0.3\n2       3.00            0        21.80                   0.3\n3       0.00            0        33.30                   0.3\n4       1.06            0         6.36                   0.3\n5       3.26            0        19.56                   0.3\n  congestion_surcharge pickup_location_id dropoff_location_id\n1                  2.5                148                  79\n2                  2.5                148                 230\n3                  2.5                 79                 188\n4                  0.0                 80                  80\n5                  2.5                148                  87\n [ reached 'max' / getOption(\"max.print\") -- omitted 6567391 rows ]"
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-    "href": "materials/0_housekeeping.html#housekeeping",
+    "objectID": "setup.html",
+    "href": "setup.html",
+    "title": "Packages & Data",
+    "section": "",
+    "text": "Welcome to the Big Data in R with Arrow workshop. On this page you will find information about the software, packages and data we will be using during the 1-day workshop.\nWe will be using Posit Workbench to learn together. Workbench will be setup with all the below software and data—all participants need to bring is a laptop that can connect to wifi! If you would prefer to run code locally on your own laptop on the day, or if you want to know how to get set-up locally, you can use the below instructions to install the software & packages and download the data."
+  },
+  {
+    "objectID": "setup.html#larger-than-memory-data-option",
+    "href": "setup.html#larger-than-memory-data-option",
+    "title": "Packages & Data",
+    "section": "Larger-Than-Memory Data Option",
+    "text": "Larger-Than-Memory Data Option\n\n1. NYC Taxi Data\nThis is the main dataset we will use on the day. It’s pretty hefty—40 GB in total—and there are a couple of options for how to acquire it, depending on your internet connection speed.\n\nOption 1—the simplest option—for those with a good internet connection and happy to let things run\nIf you have a solid internet connection, and especially if you’re in the US/Canada, this option is the simplest. You can use arrow itself to download the data. Note that there are no progress bars displayed during download, and so your session will appear to hang, but you can check progress by inspecting the contents of the download directory. When we tested this with Steph’s laptop and a fast internet connection, it took 67 minutes, though results will likely vary widely.\nThis method requires the arrow R package to have been built with S3 support enabled. This is on by default for most MacOS and Windows users, but if you’re on Linux, take a look at the instructions here.\nAfter installing arrow, run the following code:\n\nlibrary(arrow)\nlibrary(dplyr)\n\ndata_path &lt;- \"data/nyc-taxi\" # Or set your own preferred path\n\nopen_dataset(\"s3://voltrondata-labs-datasets/nyc-taxi\") |&gt;\n    filter(year %in% 2012:2021) |&gt; \n    write_dataset(data_path, partitioning = c(\"year\", \"month\"))\n\nOnce this has completed, you can check everything has downloaded correctly by calling:\n\nopen_dataset(data_path) |&gt;\n    nrow()\n\nIt might take a moment to run (the data has over a billion rows!), but you should expect to see:\n[1] 1150352666\nIf you get an error message, your download may have been interrupted at some point. The error message will name the file which could not be read. Manually delete this file and run the nrow() code snippet again until you successfully load the remaining data. You can then download any missing files individually using option 2.\n\n\nOption 2—one file at a time via https\nIf you have a slower internet connection or are further away from the data S3 bucket location, it’s probably going to be simpler to download the data file-by-file. Or, if you had any interruptions to your download process in the previous step, you can either try instead with this method, or delete the files which weren’t downloaded properly, and use this method to just download the files you need.\nWe’ve created a script for you which downloads the data one file at a time via https. The script also checks for previously downloaded data, so if you encounter problems downloading any files, just delete the partially downloaded file and run again—the script will only download files which are missing.\n\ndownload_via_https &lt;- function(data_dir, years = 2012:2021){\n\n    # Set this option as we'll be downloading large files and R has a default\n    # timeout of 60 seconds, so we've updated this to 30 mins\n    options(timeout = 1800)\n    \n    # The S3 bucket where the data is stored\n    bucket &lt;- \"https://voltrondata-labs-datasets.s3.us-east-2.amazonaws.com\"\n    \n    # Collect any errors raised during the download process\n    problems &lt;- c()\n    \n    # Download the data from S3 - loops through the data files, downloading 1 file at a time\n    for (year in years) {\n      \n      # We only have 2 months for 2022 data\n      if(year ==2022){\n        months = 1:2\n      } else {\n        months = 1:12\n      }\n      \n      for (month in months) {\n        \n        # Work out where we're going to be saving the data\n        partition_dir &lt;- paste0(\"year=\", year, \"/month=\", month)\n        dest_dir &lt;- file.path(data_dir, partition_dir)\n        dest_file_path &lt;- file.path(dest_dir, \"part-0.parquet\")\n        \n        # If the file doesn't exist\n        if (!file.exists(dest_file_path)) {\n          \n          # Create the partition to store the data in\n          if(!dir.exists(dest_dir)){\n            dir.create(dest_dir, recursive = TRUE)\n          }\n           \n          # Work out where we are going to be retrieving the data from\n          source_path &lt;- file.path(bucket, \"nyc-taxi\", partition_dir, \"part-0.parquet\")\n          \n          # Download the data - save any error messages that occur\n          tryCatch(\n            download.file(source_path, dest_file_path, mode = \"wb\"),\n            error = function(e){\n              problems &lt;- c(problems, e$message)\n            }\n          )\n        }\n      }\n    }\n    \n    print(\"Downloads complete\")\n    \n    if(length(problems) &gt; 0){\n      warning(call. = FALSE, \"The following errors occurred during download:\\n\", paste(problems, collapse =  \"\\n\"))\n    }\n}\n\n\ndata_path &lt;- \"data/nyc-taxi\" # Or set your own preferred path\n\ndownload_via_https(data_path)\n\nOnce this has completed, you can check everything has downloaded correctly by calling:\n\nopen_dataset(data_path) |&gt;\n    nrow()\n\nIt might take a moment to run (the data has over a billion rows), but you should expect to see:\n[1] 1150352666\nIf you get an error message, your download may have been interrupted at some point. The error message will name the file which could not be read. Manually delete this file and run the nrow() code snippet again until you successfully load the data. You can then download any missing files by re-running download_via_https(data_path).\n\n\n\n2. Seattle Checkouts by Title Data\nThis is the data we use to explore some data storage and engineering options. It’s a good sized, single CSV file—9GB on-disk in total, which can be downloaded from the an AWS S3 bucket via https:\n\noptions(timeout = 1800)\ndownload.file(\n  url = \"https://r4ds.s3.us-west-2.amazonaws.com/seattle-library-checkouts.csv\",\n  destfile = \"data/seattle-library-checkouts.csv\"\n)"
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+    "text": "Tiny Data Option\nIf you don’t have time or disk space to download the larger-than-memory datasets (and still have disk space do the exercises), you can run the code and exercises in the course with “tiny” versions of these data. Although the focus in this course is working with larger-than-memory data, you can still learn about the concepts and workflows with smaller data—although note you may not see the same performance improvements that you would get when working with larger data.\n\n1. Tiny NYC Taxi Data\nWe’ve created a “tiny” NYC Taxi dataset which contains only 1 in 1000 rows from the original dataset. So instead of working with 1.15 billion rows of data and about 40GB of files, the tiny taxi dataset is 1.15 million rows and about 50MB of files. You can download the tiny NYC Taxi data directly from this repo via https:\n\noptions(timeout = 1800)\ndownload.file(\n  url = \"https://github.com/posit-conf-2023/arrow/releases/download/v0.1.0/nyc-taxi-tiny.zip\",\n  destfile = \"data/nyc-taxi-tiny.zip\"\n)\n\n# Extract the partitioned parquet files from the zip folder:\nunzip(\n  zipfile = \"data/nyc-taxi-tiny.zip\", \n  exdir = \"data/\"\n)\n\n\n\n2. Tiny Seattle Checkouts by Title Data\nWe’ve created a “tiny” Seattle Checkouts by Title dataset which contains only 1 in 100 rows from the original dataset. So instead of working with ~41 million rows of data in a 9GB file, the tiny Seattle checkouts dataset is ~410 thousand rows and in an 90MB file. You can download the tiny Seattle Checkouts by Title data directly from this repo via https:\n\noptions(timeout = 1800)\ndownload.file(\n  url = \"https://github.com/posit-conf-2023/arrow/releases/download/v0.1.0/seattle-library-checkouts-tiny.csv\",\n  destfile = \"data/seattle-library-checkouts-tiny.csv\"\n)"
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+    "text": "Both Data Options / Everyone\n\n3. Taxi Zone Lookup CSV Table & Taxi Zone Shapefile\nYou can download the two NYC Taxi trip ancillary data files directly from this repo via https:\n\noptions(timeout = 1800)\ndownload.file(\n  url = \"https://github.com/posit-conf-2023/arrow/releases/download/v0.1.0/taxi_zone_lookup.csv\",\n  destfile = \"data/taxi_zone_lookup.csv\"\n)\n\ndownload.file(\n  url = \"https://github.com/posit-conf-2023/arrow/releases/download/v0.1.0/taxi_zones.zip\",\n  destfile = \"data/taxi_zones.zip\"\n)\n\n# Extract the spatial files from the zip folder:\nunzip(\n  zipfile = \"data/taxi_zones.zip\", \n  exdir = \"data/taxi_zones\"\n)"
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-    "section": "Housekeeping",
-    "text": "Housekeeping\n\nGender Neutral Bathrooms \n\nLocated on levels 3, 4, 5, 6 & 7\n\nSpecialty Rooms \n\nMeditation/Prayer Room (503)\nLactation Room (509)\n\n*Available Mon & Tues 7am - 7pm, and Wed 7am - 5pm"
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+    "text": "arrow 📦"
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-    "section": "Photos",
-    "text": "Photos\n\nRed Lanyards  NO \n\nPlease note everyone’s lanyard colors before taking a photo and respect their choices."
+    "section": "Arrow & Single Files",
+    "text": "Arrow & Single Files\n\nlibrary(arrow)\n\nread_parquet()\nread_csv_arrow()\nread_feather()\nread_json_arrow()\n\nValue: tibble (the default), or an Arrow Table if as_data_frame = FALSE — both in-memory"
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-    "section": "Code of Conduct",
-    "text": "Code of Conduct\n\n posit.co/code-of-conduct/\n\nContact any posit::conf staff member, identifiable by their staff t-shirt, or visit the conference general information desk.\nSend a message to conf@posit.com; event organizers will respond promptly.\nCall +1-844-448-1212; this phone number will be monitored for the duration of the event."
+    "section": "Your Turn",
+    "text": "Your Turn\n\nRead in a single NYC Taxi parquet file using read_parquet() as an Arrow Table\nConvert your Arrow Table object to a data.frame or a tibble"
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-    "section": "Meet Your Teaching Team ",
-    "text": "Meet Your Teaching Team \n\nCo-Instructors\n\nNic Crane\nSteph Hazlitt\n\nTeaching Assistant\n\nJonathan Keane"
+    "section": "Read a Parquet File (tibble)",
+    "text": "Read a Parquet File (tibble)\n\nlibrary(arrow)\n\nparquet_file &lt;- \"data/nyc-taxi/year=2019/month=9/part-0.parquet\"\n\ntaxi_df &lt;- read_parquet(file = parquet_file)\ntaxi_df\n\n# A tibble: 6,567,396 × 22\n   vendor_name pickup_datetime     dropoff_datetime    passenger_count\n   &lt;chr&gt;       &lt;dttm&gt;              &lt;dttm&gt;                        &lt;int&gt;\n 1 CMT         2019-08-31 18:09:30 2019-08-31 18:15:42               1\n 2 CMT         2019-08-31 18:26:30 2019-08-31 18:44:31               1\n 3 CMT         2019-08-31 18:39:35 2019-08-31 19:15:55               2\n 4 VTS         2019-08-31 18:12:26 2019-08-31 18:15:17               4\n 5 VTS         2019-08-31 18:43:16 2019-08-31 18:53:50               1\n 6 VTS         2019-08-31 18:26:13 2019-08-31 18:45:35               1\n 7 CMT         2019-08-31 18:34:52 2019-08-31 18:42:03               1\n 8 CMT         2019-08-31 18:50:02 2019-08-31 18:58:16               1\n 9 CMT         2019-08-31 18:08:02 2019-08-31 18:14:44               0\n10 VTS         2019-08-31 18:11:38 2019-08-31 18:26:47               1\n# ℹ 6,567,386 more rows\n# ℹ 18 more variables: trip_distance &lt;dbl&gt;, pickup_longitude &lt;dbl&gt;,\n#   pickup_latitude &lt;dbl&gt;, rate_code &lt;chr&gt;, store_and_fwd &lt;chr&gt;,\n#   dropoff_longitude &lt;dbl&gt;, dropoff_latitude &lt;dbl&gt;, payment_type &lt;chr&gt;,\n#   fare_amount &lt;dbl&gt;, extra &lt;dbl&gt;, mta_tax &lt;dbl&gt;, tip_amount &lt;dbl&gt;,\n#   tolls_amount &lt;dbl&gt;, total_amount &lt;dbl&gt;, improvement_surcharge &lt;dbl&gt;,\n#   congestion_surcharge &lt;dbl&gt;, pickup_location_id &lt;int&gt;, …"
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-    "text": "Meet Each Other \n\n\nWhen did you use R for the first time?\nWhat is your favorite R package?\nWhich package hex sticker would you like to find the most during posit::conf(2024)?"
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-    "text": "Getting Help Today \n\nTEAL sticky note: I am OK / I am done\nPINK sticky note: I need support / I am working\n\n You can ask questions at any time during the workshop"
+    "section": "tibble <-> Table <-> data.frame",
+    "text": "tibble &lt;-&gt; Table &lt;-&gt; data.frame\n\nlibrary(dplyr)\n\n#change a df to a table\narrow_table(taxi_df)\n\n#change a table to a tibble\ntaxi_table |&gt; collect()\nas_tibble(taxi_table)\n\n#change a table to a data.frame\nas.data.frame(taxi_table)\n\n\n\ndata.frame & tibble are R objects in-memory\nTable is an Arrow object in-memory"
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-    "text": "Discord \n\npos.it/conf-event-portal (login)\nClick on “Join Discord, the virtual networking platform!”\nBrowse Channels -&gt; #workshop-arrow"
+    "section": "Watch Your Schemas 👀",
+    "text": "Watch Your Schemas 👀\n\n\n\nschema(taxi_df)\n\nSchema\nvendor_name: string\npickup_datetime: timestamp[us, tz=America/Vancouver]\ndropoff_datetime: timestamp[us, tz=America/Vancouver]\npassenger_count: int32\ntrip_distance: double\npickup_longitude: double\npickup_latitude: double\nrate_code: string\nstore_and_fwd: string\ndropoff_longitude: double\ndropoff_latitude: double\npayment_type: string\nfare_amount: double\nextra: double\nmta_tax: double\ntip_amount: double\ntolls_amount: double\ntotal_amount: double\nimprovement_surcharge: double\ncongestion_surcharge: double\npickup_location_id: int32\ndropoff_location_id: int32\n\n\n\n\nschema(taxi_table)\n\nSchema\nvendor_name: string\npickup_datetime: timestamp[ms]\ndropoff_datetime: timestamp[ms]\npassenger_count: int64\ntrip_distance: double\npickup_longitude: double\npickup_latitude: double\nrate_code: string\nstore_and_fwd: string\ndropoff_longitude: double\ndropoff_latitude: double\npayment_type: string\nfare_amount: double\nextra: double\nmta_tax: double\ntip_amount: double\ntolls_amount: double\ntotal_amount: double\nimprovement_surcharge: double\ncongestion_surcharge: double\npickup_location_id: int64\ndropoff_location_id: int64"
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-    "text": "We Assume\n\nYou know \nYou are familiar with the dplyr package for data manipulation \nYou have data in your life that is too large to fit into memory or sluggish in memory\nYou want to learn how to engineer your data storage for more performant access and analysis"
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-    "section": "Posit Workbench: Login 🛠️",
-    "text": "Posit Workbench: Login 🛠️\n\nJoin Workbench via URL in the #workshop-arrow Discord channel\nSelect Posit Workbench &gt;&gt; Sign in with OpenID\nUse your GitHub credentials to log in (click the icon)"
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-    "section": "Posit Workbench: Setup 🍽️",
-    "text": "Posit Workbench: Setup 🍽️\n\n🖱 +New Session\n🖱 Start Session (defaults are fine)\nRun usethis::use_course(\"posit-conf-2024/arrow\")"
+    "section": "Table | Dataset: A dplyr pipeline",
+    "text": "Table | Dataset: A dplyr pipeline\n\nparquet_file |&gt;\n  read_parquet(as_data_frame = FALSE) |&gt;\n  group_by(vendor_name) |&gt;\n  summarise(all_trips = n(),\n            shared_trips = sum(passenger_count &gt; 1, na.rm = TRUE)) |&gt;\n  mutate(pct_shared = shared_trips / all_trips * 100) |&gt;\n  collect()\n\n# A tibble: 3 × 4\n  vendor_name all_trips shared_trips pct_shared\n  &lt;chr&gt;           &lt;int&gt;        &lt;int&gt;      &lt;dbl&gt;\n1 VTS           4238808      1339478       31.6\n2 CMT           2294473       470344       20.5\n3 &lt;NA&gt;            34115            0        0  \n\n\n\nFunctions available in Arrow dplyr queries: https://arrow.apache.org/docs/r/reference/acero.html\n\nAll the same capabilities as you practiced with Arrow Dataset"
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-    "section": "Posit Workbench: Setup 🍽️",
-    "text": "Posit Workbench: Setup 🍽️\n\nDefault location: 🖱 2\nUnzip 📁: 🖱 3\nOpen Session dialog box: Resource Profile &gt;&gt; select Large\nOpen + run data/setup.R 🎉\n\n\n\n\n\n\n🔗 pos.it/arrow-conf24"
+    "section": "Arrow for Efficient In-Memory Processing",
+    "text": "Arrow for Efficient In-Memory Processing\n\nparquet_file |&gt;\n  read_parquet() |&gt;\n  nrow()\n\n[1] 6567396\n\n\n\n\nparquet_file |&gt;\n  read_parquet() |&gt;\n  group_by(vendor_name) |&gt;\n  summarise(all_trips = n(),\n            shared_trips = sum(passenger_count &gt; 1, na.rm = TRUE)) |&gt;\n  mutate(pct_shared = shared_trips / all_trips * 100) |&gt;\n  collect() |&gt;\n  system.time()\n\n   user  system elapsed \n  1.157   0.261   0.509"
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-    "text": "by Nic Crane & Steph Hazlitt\n\n🗓️ August 12th, 2024\n⏰ 09:00 - 17:00\n🏨 305 | Chelais\n✍️ pos.it/conf\n\n\nWorkshop Overview\nData analysis pipelines with larger-than-memory data are becoming more and more commonplace. In this workshop you will learn how to use Apache Arrow, a multi-language toolbox for working with larger-than-memory tabular data, to create seamless “big” data analysis pipelines with R.\nThe workshop will focus on using the the arrow R package—a mature R interface to Apache Arrow—to process larger-than-memory files and multi-file datasets with arrow using familiar dplyr syntax. You’ll learn to create and use interoperable data file formats like Parquet for efficient data storage and access, with data stored both on disk and in the cloud, and also how to exercise fine control over data types to avoid common large data pipeline problems. This workshop will provide a foundation for using Arrow, giving you access to a powerful suite of tools for performant analysis of larger-than-memory data in R.\nThis course is for you if you:\n\nwant to learn how to work with tabular data that is too large to fit in memory using existing R and tidyverse syntax implemented in Arrow\nwant to learn about Parquet and other file formats that are powerful alternatives to CSV files\nwant to learn how to engineer your tabular data storage for more performant access and analysis with Apache Arrow\n\n\n\nWorkshop Prework\nAll participants need to bring is a laptop that can connect to wifi. We will be using Posit Workbench to learn together—Workbench will be setup with all the software and data needed for the day. If you would prefer to run code locally on your own laptop, detailed instructions for software requirements and data sources are covered in Packages & Data.\n\n\nWorkshop Schedule\n“This schedule is more what you would call a ‘guideline’ than an actual schedule” — Barbossa, Pirates of the Caribbean\n\n\n\n\n\n\n\nTime\nActivity\n\n\n\n\n09:00 - 10:30\nSession 1: Hello Arrow + Data Manipulation with Arrow I\n\n\n10:30 - 11:00\nCoffee break\n\n\n11:00 - 12:30\nSession 2: Data Engineering with Arrow\n\n\n12:30 - 13:30\nLunch break\n\n\n13:30 - 15:00\nSession 3: Arrow In-Memory Workflows\n\n\n15:00 - 15:30\nCoffee break\n\n\n15:30 - 17:00\nSession 4: Data Manipulation with Arrow II + Wrapping Up\n\n\n\n\n\nInstructors\nNic Crane is an R consultant with a background in data science and software engineering. They are passionate about open source, and learning and teaching all things R. Nic is part of the core team that maintain the Arrow R package, and a co-author of “Scaling up with R and Arrow”, due to be published by CRC Press later this year.\nSteph Hazlitt is a data scientist, researcher and R enthusiast. She has spent the better part of her career wrangling data with R and supporting people and teams in creating and sharing data science-related products and open source software. Steph is the Director of Data Science Partnerships with BC Stats.\n\n\nAcknowledgements\nSome of this Big Data in R with Arrow workshop materials draw on other open-licensed teaching content which we would like to acknowledge:\n\nuseR!2022 virtual Larger-Than-Memory Data Workflows with Apache Arrow tutorial authored by Danielle Navarro\nR for Data Science (2e) written by Hadley Wickham, Mine Çetinkaya-Rundel, and Garrett Grolemund—with thanks to Danielle Navarro who contributed the initial version of the Arrow chapter\nHow to use Arrow to work with large CSV files? blog post by François Michonneau, which introduces the single vs multi-file API models for learning/teaching Arrow\nBig Data in R with Arrow 1-Day Posit::Conf (2023) Workshop by Steph Hazlitt & Nic Crane, an earlier version of this 1-day course.\n\n\n This work is licensed under a Creative Commons Attribution 4.0 International License."
+    "section": "Arrow for Efficient In-Memory Processing",
+    "text": "Arrow for Efficient In-Memory Processing\n\nparquet_file |&gt;\n  read_parquet(as_data_frame = FALSE) |&gt;\n  nrow()\n\n[1] 6567396\n\n\n\n\nparquet_file |&gt;\n  read_parquet(as_data_frame = FALSE) |&gt;\n  group_by(vendor_name) |&gt;\n  summarise(all_trips = n(),\n            shared_trips = sum(passenger_count &gt; 1, na.rm = TRUE)) |&gt;\n  mutate(pct_shared = shared_trips / all_trips * 100) |&gt;\n  collect() |&gt;\n  system.time()\n\n   user  system elapsed \n  1.047   0.203   0.220"
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+    "text": "Read a Parquet File Selectively\n\nparquet_file |&gt;\n  read_parquet(\n    col_select = c(\"vendor_name\", \"passenger_count\"),\n    as_data_frame = FALSE\n  )\n\nTable\n6567396 rows x 2 columns\n$vendor_name &lt;string&gt;\n$passenger_count &lt;int64&gt;"
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-    "text": "library(arrow)\nlibrary(dplyr)\nlibrary(tictoc)\n\n\nnyc_taxi &lt;- open_dataset(\"data/nyc-taxi/\")\n\n\n\n\n\n\n\nFirst dplyr pipeline with Arrow\n\n\n\n\nProblemsSolution 1Solution 2\n\n\n\nCalculate the longest trip distance for every month in 2019\nHow long did this query take to run?\n\n\n\nLongest trip distance for every month in 2019:\n\nnyc_taxi |&gt; \n  filter(year == 2019) |&gt;\n  group_by(month) |&gt;\n  summarize(longest_trip = max(trip_distance, na.rm = TRUE)) |&gt;\n  arrange(month) |&gt; \n  collect()\n\n# A tibble: 12 × 2\n   month longest_trip\n   &lt;int&gt;        &lt;dbl&gt;\n 1     1         832.\n 2     2         702.\n 3     3         237.\n 4     4         831.\n 5     5         401.\n 6     6       45977.\n 7     7         312.\n 8     8         602.\n 9     9         604.\n10    10         308.\n11    11         701.\n12    12       19130.\n\n\n\n\nCompute time:\n\nlibrary(tictoc)\n\ntic()\nnyc_taxi |&gt; \n  filter(year == 2019) |&gt;\n  group_by(month) |&gt;\n  summarize(longest_trip = max(trip_distance, na.rm = TRUE)) |&gt;\n  arrange(month) |&gt; \n  collect()\n\n# A tibble: 12 × 2\n   month longest_trip\n   &lt;int&gt;        &lt;dbl&gt;\n 1     1         832.\n 2     2         702.\n 3     3         237.\n 4     4         831.\n 5     5         401.\n 6     6       45977.\n 7     7         312.\n 8     8         602.\n 9     9         604.\n10    10         308.\n11    11         701.\n12    12       19130.\n\ntoc()\n\n0.56 sec elapsed\n\n\nor\n\nnyc_taxi |&gt; \n  filter(year == 2019) |&gt;\n  group_by(month) |&gt;\n  summarize(longest_trip = max(trip_distance, na.rm = TRUE)) |&gt;\n  arrange(month) |&gt; \n  collect() |&gt; \n  system.time()\n\n   user  system elapsed \n  2.532   0.316   0.452"
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+    "section": "Selective Reads Are Faster",
+    "text": "Selective Reads Are Faster\n\nparquet_file |&gt;\n  read_parquet(\n    col_select = c(\"vendor_name\", \"passenger_count\"),\n    as_data_frame = FALSE\n  ) |&gt; \n  group_by(vendor_name) |&gt;\n  summarise(all_trips = n(),\n            shared_trips = sum(passenger_count &gt; 1, na.rm = TRUE)) |&gt;\n  mutate(pct_shared = shared_trips / all_trips * 100) |&gt;\n  collect() |&gt;\n  system.time()\n\n   user  system elapsed \n  0.258   0.011   0.131 \n\n\n\nNotes: row-based format readers often allow you to specify which columns to read in but the entire row must be read in and the unwanted columns discarded. Parquet’s columnar format allows you to read in only the columns you need, which is faster when you only need a subset of the data."
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-    "text": "library(arrow)\nlibrary(dplyr)\nlibrary(stringr)\n\n\nnyc_taxi &lt;- open_dataset(\"data/nyc-taxi/\")\n\n\n\n\n\n\n\nUsing collect() to run a query\n\n\n\n\nProblemsSolution 1\n\n\nUse the function nrow() to work out the answers to these questions:\n\nHow many taxi fares in the dataset had a total amount greater than $100?\n\n\n\n\nnyc_taxi |&gt;\n  filter(total_amount &gt; 100) |&gt;\n  nrow()\n\n[1] 1518869\n\n\n\n\n\n\n\n\n\n\n\n\n\nUsing the dplyr API in arrow\n\n\n\n\nProblemsSolution 1Solution 2 and 3\n\n\n\nUse the dplyr::filter() and stringr::str_ends() functions to return a subset of the data which is a) from September 2020, and b) the value in vendor_name ends with the letter “S”.\nTry to use the stringr function str_replace_na() to replace any NA values in the vendor_name column with the string “No vendor” instead. What happens, and why?\nBonus question: see if you can find a different way of completing the task in question 2.\n\n\n\n\nnyc_taxi |&gt;\n  filter(str_ends(vendor_name, \"S\"), year == 2020,  month == 9) |&gt;\n  collect()\n\n\n\n\nnyc_taxi |&gt;\n  mutate(vendor_name = stringr::str_replace_na(vendor_name, \"No vendor\")) |&gt;\n  head() |&gt;\n  collect()\n\nThis won’t work as stringr::str_replace_na() hasn’t been implemented in Arrow. You could try using mutate() and ifelse() here instead.\n\nnyc_taxi |&gt;\n  mutate(vendor_name = ifelse(is.na(vendor_name), \"No vendor\", vendor_name)) |&gt;\n  head() |&gt;\n  collect()\n\nOr, if you only needed a subset of the data, you could apply the function after collecting it into R memory.\n\nnyc_taxi |&gt;\n  filter(year == 2019, month == 10) |&gt; # smaller subset of the data\n  collect() |&gt;\n  mutate(vendor_name = stringr::str_replace_na(vendor_name, \"No vendor\"))"
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+    "title": "Big Data in R with Arrow",
+    "section": "Arrow Table or Dataset?",
+    "text": "Arrow Table or Dataset?\n\n\nhttps://francoismichonneau.net/2022/10/import-big-csv/"
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-    "text": "Schemas\n\nlibrary(arrow)\nlibrary(dplyr)\n\n\nseattle_csv &lt;- open_dataset(sources = \"data/seattle-library-checkouts.csv\",\n                            format = \"csv\")\n\n\n\n\n\n\n\nData Types & Controlling the Schema\n\n\n\n\nProblemsSolution 1Solution 2\n\n\n\nThe first few thousand rows of ISBN are blank in the Seattle Checkouts CSV file. Read in the Seattle Checkouts CSV file with open_dataset() and ensure the correct data type for ISBN is &lt;string&gt; (or the alias &lt;utf8&gt;) instead of the &lt;null&gt; interpreted by Arrow.\nOnce you have a Dataset object with the metadata you are after, count the number of Checkouts by CheckoutYear and arrange the result by CheckoutYear.\n\n\n\n\nseattle_csv &lt;- open_dataset(sources = \"data/seattle-library-checkouts.csv\",\n  format = \"csv\",\n  schema(\n    UsageClass = utf8(),\n    CheckoutType = utf8(),\n    MaterialType = utf8(),\n    CheckoutYear = int64(),\n    CheckoutMonth = int64(),\n    Checkouts = int64(),\n    Title = utf8(),\n    ISBN = string(), #or utf8()\n    Creator = utf8(),\n    Subjects = utf8(),\n    Publisher = utf8(),\n    PublicationYear = utf8()\n  ),\n    skip = 1,\n)\nseattle_csv\n\nFileSystemDataset with 1 csv file\n12 columns\nUsageClass: string\nCheckoutType: string\nMaterialType: string\nCheckoutYear: int64\nCheckoutMonth: int64\nCheckouts: int64\nTitle: string\nISBN: string\nCreator: string\nSubjects: string\nPublisher: string\nPublicationYear: string\n\n\nor\n\nseattle_csv &lt;- open_dataset(sources = \"data/seattle-library-checkouts.csv\",\n  format = \"csv\",\n  col_types = schema(ISBN = string()) # or utf8()\n)\nseattle_csv\n\nFileSystemDataset with 1 csv file\n12 columns\nUsageClass: string\nCheckoutType: string\nMaterialType: string\nCheckoutYear: int64\nCheckoutMonth: int64\nCheckouts: int64\nTitle: string\nISBN: string\nCreator: string\nSubjects: string\nPublisher: string\nPublicationYear: string\n\n\n\n\nThe number of Checkouts by CheckoutYear arranged by CheckoutYear:\n\nseattle_csv |&gt;\n  group_by(CheckoutYear) |&gt;\n  summarise(sum(Checkouts)) |&gt;\n  arrange(CheckoutYear) |&gt; \n  collect()\n\n# A tibble: 18 × 2\n   CheckoutYear `sum(Checkouts)`\n          &lt;int&gt;            &lt;int&gt;\n 1         2005          3798685\n 2         2006          6599318\n 3         2007          7126627\n 4         2008          8438486\n 5         2009          9135167\n 6         2010          8608966\n 7         2011          8321732\n 8         2012          8163046\n 9         2013          9057096\n10         2014          9136081\n11         2015          9084179\n12         2016          9021051\n13         2017          9231648\n14         2018          9149176\n15         2019          9199083\n16         2020          6053717\n17         2021          7361031\n18         2022          7001989\n\n\nor\n\nseattle_csv |&gt; \n  count(CheckoutYear, wt = Checkouts) |&gt; \n  arrange(CheckoutYear) |&gt; \n  collect()\n\n# A tibble: 18 × 2\n   CheckoutYear       n\n          &lt;int&gt;   &lt;int&gt;\n 1         2005 3798685\n 2         2006 6599318\n 3         2007 7126627\n 4         2008 8438486\n 5         2009 9135167\n 6         2010 8608966\n 7         2011 8321732\n 8         2012 8163046\n 9         2013 9057096\n10         2014 9136081\n11         2015 9084179\n12         2016 9021051\n13         2017 9231648\n14         2018 9149176\n15         2019 9199083\n16         2020 6053717\n17         2021 7361031\n18         2022 7001989\n\n\nTiming the query:\n\nseattle_csv |&gt;\n  group_by(CheckoutYear) |&gt;\n  summarise(sum(Checkouts)) |&gt;\n  arrange(CheckoutYear) |&gt; \n  collect() |&gt; \n  system.time()\n\n   user  system elapsed \n 10.696   1.227  10.449 \n\n\nQuerying 42 million rows of data stored in a CSV on disk in ~10 seconds, not too bad.\n\n\n\n\n\n\n\nParquet\n\nseattle_parquet &lt;- \"data/seattle-library-checkouts-parquet\"\n\nseattle_csv |&gt;\n  write_dataset(path = seattle_parquet,\n                format = \"parquet\")\n\n\n\n\n\n\n\nParquet\n\n\n\n\nProblemSolution 1\n\n\n\nRe-run the query counting the number of Checkouts by CheckoutYear and arranging the result by CheckoutYear, this time using the Seattle Checkout data saved to disk as a single, Parquet file. Did you notice a difference in compute time?\n\n\n\n\nseattle_parquet &lt;- \"data/seattle-library-checkouts-parquet\"\n\nopen_dataset(sources = seattle_parquet, \n             format = \"parquet\") |&gt;\n  group_by(CheckoutYear) |&gt;\n  summarise(sum(Checkouts)) |&gt;\n  arrange(CheckoutYear) |&gt; \n  collect() |&gt; \n  system.time()\n\n   user  system elapsed \n  1.576   0.377   0.548 \n\n\nA much faster compute time for the query when the on-disk data is stored in the Parquet format.\n\n\n\n\n\n\n\nPartitioning\n\nseattle_parquet_part &lt;- \"data/seattle-library-checkouts\"\n\nseattle_csv |&gt;\n  group_by(CheckoutYear) |&gt;\n  write_dataset(path = seattle_parquet_part,\n                format = \"parquet\")\n\n\n\n\n\n\n\nPartitioning\n\n\n\n\nProblemsSolution 1Solution 2\n\n\n\nLet’s write the Seattle Checkout CSV data to a multi-file dataset just one more time! This time, write the data partitioned by CheckoutType as Parquet files.\nNow compare the compute time between our Parquet data partitioned by CheckoutYear and our Parquet data partitioned by CheckoutType with a query of the total number of checkouts in September of 2019. Did you find a difference in compute time?\n\n\n\nWriting the data:\n\nseattle_checkouttype &lt;- \"data/seattle-library-checkouts-type\"\n\nseattle_csv |&gt;\n  group_by(CheckoutType) |&gt;\n  write_dataset(path = seattle_checkouttype,\n                format = \"parquet\")\n\n\n\nTotal number of Checkouts in September of 2019 using partitioned Parquet data by CheckoutType:\n\nopen_dataset(sources = \"data/seattle-library-checkouts-type\") |&gt; \n  filter(CheckoutYear == 2019, CheckoutMonth == 9) |&gt; \n  summarise(TotalCheckouts = sum(Checkouts)) |&gt;\n  collect() |&gt; \n  system.time()\n\n   user  system elapsed \n  0.779   0.080   0.301 \n\n\nTotal number of Checkouts in September of 2019 using partitioned Parquet data by CheckoutYear and CheckoutMonth:\n\nopen_dataset(\"data/seattle-library-checkouts\") |&gt; \n  filter(CheckoutYear == 2019, CheckoutMonth == 9) |&gt; \n  summarise(TotalCheckouts = sum(Checkouts)) |&gt;\n  collect() |&gt; \n  system.time()\n\n   user  system elapsed \n  0.035   0.005   0.029 \n\n\nFaster compute time because the filter() call is based on the partitions."
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+    "title": "Big Data in R with Arrow",
+    "section": "Arrow for Improving Those Sluggish Worklows",
+    "text": "Arrow for Improving Those Sluggish Worklows\n\na “drop-in” for many dplyr workflows (Arrow Table or Dataset)\nworks when your tabular data get too big for your RAM (Arrow Dataset)\nprovides tools for re-engineering data storage for better performance (arrow::write_dataset())\n\n\nLot’s of ways to speed up sluggish workflows e.g. writing more performant tidyverse code, use other data frame libraries like data.table or polars, use duckDB or other databases, Spark + splarklyr … However, Arrow offers some attractive features for tackling this challenge, especially for dplyr users.\n\n\n\n\n\n🔗 pos.it/arrow-conf24"
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-    "text": "library(arrow)\nlibrary(dplyr)\n\n\nnyc_taxi &lt;- open_dataset(\"data/nyc-taxi/\")\n\n\n\n\n\n\n\nUser-defined functions\n\n\n\n\nProblemSolution 1\n\n\n\nWrite a user-defined function which wraps the stringr function str_replace_na(), and use it to replace any NA values in the vendor_name column with the string “No vendor” instead. (Test it on the data from 2019 so you’re not pulling everything into memory)\n\n\n\n\n# Preview the distinct vendor names before we start\nnyc_taxi |&gt;\n  filter(year == 2019) |&gt; # smaller subset of the data\n  distinct(vendor_name) |&gt;\n  collect()\n\n# A tibble: 3 × 1\n  vendor_name\n  &lt;chr&gt;      \n1 CMT        \n2 VTS        \n3 &lt;NA&gt;       \n\n\n\nregister_scalar_function(\n  name = \"replace_vendor_na\",\n  function(context, string) {\n    stringr::str_replace_na(string, \"No vendor\")\n  },\n  in_type = schema(string = string()),\n  out_type = string(),\n  auto_convert = TRUE\n)\n\nvendor_names_fixed &lt;- nyc_taxi |&gt;\n  mutate(vendor_name = replace_vendor_na(vendor_name)) \n\n# Preview the distinct vendor names to check it's worked\nvendor_names_fixed |&gt;\n  filter(year == 2019) |&gt; # smaller subset of the data\n  distinct(vendor_name) |&gt;\n  collect()\n\n# A tibble: 3 × 1\n  vendor_name\n  &lt;chr&gt;      \n1 CMT        \n2 VTS        \n3 No vendor  \n\n\n\n\n\n\n\n\n\n\n\n\n\nJoins\n\n\n\n\nProblemSolution 1\n\n\n\nHow many taxi pickups were recorded in 2019 from the three major airports covered by the NYC Taxis data set (JFK, LaGuardia, Newark)? (Hint: you can use stringr::str_detect() to help you find pickup zones with the word “Airport” in them)\n\n\n\n\npickup_location &lt;- read_csv_arrow(\"data/taxi_zone_lookup.csv\")\n\npickup_location &lt;- pickup_location |&gt;\n  select(\n    pickup_location_id = LocationID,\n    borough = Borough,\n    pickup_zone = Zone\n  ) \n\n\npickup_location_arrow &lt;- arrow_table(\n  pickup_location, \n  schema = schema(\n    pickup_location_id = int64(),\n    borough = utf8(),\n    pickup_zone = utf8()\n  ))\n\nnyc_taxi |&gt;\n  filter(year == 2019) |&gt;\n  left_join(pickup_location_arrow) |&gt;\n  filter(str_detect(pickup_zone, \"Airport\")) |&gt;\n  count(pickup_zone) |&gt;\n  collect()\n\n# A tibble: 3 × 2\n  pickup_zone             n\n  &lt;chr&gt;               &lt;int&gt;\n1 JFK Airport       2729336\n2 LaGuardia Airport 2159224\n3 Newark Airport       8643"
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+    "title": "Big Data in R with Arrow",
+    "section": "What if a function binding doesn’t exist - revisited!",
+    "text": "What if a function binding doesn’t exist - revisited!\n\nOption 1 - find a workaround\nOption 2 - user-defined functions (UDFs)"
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-    "text": "library(arrow)\nlibrary(dplyr)\n\n\n\n\n\n\n\nArrow Table\n\n\n\n\nProblemsSolution 1Solution 2\n\n\n\nRead in a single NYC Taxi parquet file using read_parquet() as an Arrow Table\nConvert your Arrow Table object to a data.frame or a tibble\n\n\n\n\nparquet_file &lt;- \"data/nyc-taxi/year=2019/month=9/part-0.parquet\"\n\ntaxi_table &lt;- read_parquet(parquet_file, as_data_frame = FALSE)\ntaxi_table\n\nTable\n6567396 rows x 22 columns\n$vendor_name &lt;string&gt;\n$pickup_datetime &lt;timestamp[ms]&gt;\n$dropoff_datetime &lt;timestamp[ms]&gt;\n$passenger_count &lt;int64&gt;\n$trip_distance &lt;double&gt;\n$pickup_longitude &lt;double&gt;\n$pickup_latitude &lt;double&gt;\n$rate_code &lt;string&gt;\n$store_and_fwd &lt;string&gt;\n$dropoff_longitude &lt;double&gt;\n$dropoff_latitude &lt;double&gt;\n$payment_type &lt;string&gt;\n$fare_amount &lt;double&gt;\n$extra &lt;double&gt;\n$mta_tax &lt;double&gt;\n$tip_amount &lt;double&gt;\n$tolls_amount &lt;double&gt;\n$total_amount &lt;double&gt;\n$improvement_surcharge &lt;double&gt;\n$congestion_surcharge &lt;double&gt;\n...\n2 more columns\nUse `schema()` to see entire schema\n\n\n\n\n\ntaxi_table |&gt; collect()\n\n# A tibble: 6,567,396 × 22\n   vendor_name pickup_datetime     dropoff_datetime    passenger_count\n   &lt;chr&gt;       &lt;dttm&gt;              &lt;dttm&gt;                        &lt;int&gt;\n 1 CMT         2019-08-31 18:09:30 2019-08-31 18:15:42               1\n 2 CMT         2019-08-31 18:26:30 2019-08-31 18:44:31               1\n 3 CMT         2019-08-31 18:39:35 2019-08-31 19:15:55               2\n 4 VTS         2019-08-31 18:12:26 2019-08-31 18:15:17               4\n 5 VTS         2019-08-31 18:43:16 2019-08-31 18:53:50               1\n 6 VTS         2019-08-31 18:26:13 2019-08-31 18:45:35               1\n 7 CMT         2019-08-31 18:34:52 2019-08-31 18:42:03               1\n 8 CMT         2019-08-31 18:50:02 2019-08-31 18:58:16               1\n 9 CMT         2019-08-31 18:08:02 2019-08-31 18:14:44               0\n10 VTS         2019-08-31 18:11:38 2019-08-31 18:26:47               1\n# ℹ 6,567,386 more rows\n# ℹ 18 more variables: trip_distance &lt;dbl&gt;, pickup_longitude &lt;dbl&gt;,\n#   pickup_latitude &lt;dbl&gt;, rate_code &lt;chr&gt;, store_and_fwd &lt;chr&gt;,\n#   dropoff_longitude &lt;dbl&gt;, dropoff_latitude &lt;dbl&gt;, payment_type &lt;chr&gt;,\n#   fare_amount &lt;dbl&gt;, extra &lt;dbl&gt;, mta_tax &lt;dbl&gt;, tip_amount &lt;dbl&gt;,\n#   tolls_amount &lt;dbl&gt;, total_amount &lt;dbl&gt;, improvement_surcharge &lt;dbl&gt;,\n#   congestion_surcharge &lt;dbl&gt;, pickup_location_id &lt;int&gt;, …\n\n\nor\n\nas_tibble(taxi_table)\n\n# A tibble: 6,567,396 × 22\n   vendor_name pickup_datetime     dropoff_datetime    passenger_count\n   &lt;chr&gt;       &lt;dttm&gt;              &lt;dttm&gt;                        &lt;int&gt;\n 1 CMT         2019-08-31 18:09:30 2019-08-31 18:15:42               1\n 2 CMT         2019-08-31 18:26:30 2019-08-31 18:44:31               1\n 3 CMT         2019-08-31 18:39:35 2019-08-31 19:15:55               2\n 4 VTS         2019-08-31 18:12:26 2019-08-31 18:15:17               4\n 5 VTS         2019-08-31 18:43:16 2019-08-31 18:53:50               1\n 6 VTS         2019-08-31 18:26:13 2019-08-31 18:45:35               1\n 7 CMT         2019-08-31 18:34:52 2019-08-31 18:42:03               1\n 8 CMT         2019-08-31 18:50:02 2019-08-31 18:58:16               1\n 9 CMT         2019-08-31 18:08:02 2019-08-31 18:14:44               0\n10 VTS         2019-08-31 18:11:38 2019-08-31 18:26:47               1\n# ℹ 6,567,386 more rows\n# ℹ 18 more variables: trip_distance &lt;dbl&gt;, pickup_longitude &lt;dbl&gt;,\n#   pickup_latitude &lt;dbl&gt;, rate_code &lt;chr&gt;, store_and_fwd &lt;chr&gt;,\n#   dropoff_longitude &lt;dbl&gt;, dropoff_latitude &lt;dbl&gt;, payment_type &lt;chr&gt;,\n#   fare_amount &lt;dbl&gt;, extra &lt;dbl&gt;, mta_tax &lt;dbl&gt;, tip_amount &lt;dbl&gt;,\n#   tolls_amount &lt;dbl&gt;, total_amount &lt;dbl&gt;, improvement_surcharge &lt;dbl&gt;,\n#   congestion_surcharge &lt;dbl&gt;, pickup_location_id &lt;int&gt;, …\n\n\nor\n\nas.data.frame(taxi_table)\n\n  vendor_name     pickup_datetime    dropoff_datetime passenger_count\n1         CMT 2019-08-31 18:09:30 2019-08-31 18:15:42               1\n2         CMT 2019-08-31 18:26:30 2019-08-31 18:44:31               1\n3         CMT 2019-08-31 18:39:35 2019-08-31 19:15:55               2\n4         VTS 2019-08-31 18:12:26 2019-08-31 18:15:17               4\n5         VTS 2019-08-31 18:43:16 2019-08-31 18:53:50               1\n  trip_distance pickup_longitude pickup_latitude     rate_code store_and_fwd\n1          0.80               NA              NA Standard rate            No\n2          3.70               NA              NA Standard rate            No\n3          8.10               NA              NA Standard rate            No\n4          0.58               NA              NA Standard rate            No\n5          3.32               NA              NA Standard rate            No\n  dropoff_longitude dropoff_latitude payment_type fare_amount extra mta_tax\n1                NA               NA         Cash         6.0   3.0     0.5\n2                NA               NA  Credit card        15.0   3.0     0.5\n3                NA               NA  Credit card        29.5   3.0     0.5\n4                NA               NA  Credit card         4.0   0.5     0.5\n5                NA               NA  Credit card        12.5   0.5     0.5\n  tip_amount tolls_amount total_amount improvement_surcharge\n1       0.00            0         9.80                   0.3\n2       3.00            0        21.80                   0.3\n3       0.00            0        33.30                   0.3\n4       1.06            0         6.36                   0.3\n5       3.26            0        19.56                   0.3\n  congestion_surcharge pickup_location_id dropoff_location_id\n1                  2.5                148                  79\n2                  2.5                148                 230\n3                  2.5                 79                 188\n4                  0.0                 80                  80\n5                  2.5                148                  87\n [ reached 'max' / getOption(\"max.print\") -- omitted 6567391 rows ]"
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+    "section": "Why use a UDF?",
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     "title": "Big Data in R with Arrow",
-    "section": "Arrow",
-    "text": "Arrow\n\nefficiently read + filter + join + summarise 1.15 billion rows\n\n\nlibrary(arrow)\nlibrary(dplyr)\nlibrary(janitor)\nlibrary(stringr)\n\nnyc_taxi_zones &lt;- read_csv_arrow(\"data/taxi_zone_lookup.csv\",\n                                 as_data_frame = FALSE) |&gt;\n  clean_names()\n  \nairport_zones &lt;- nyc_taxi_zones |&gt;\n  filter(str_detect(zone, \"Airport\")) |&gt;\n  pull(location_id, as_vector = TRUE)\n\ndropoff_zones &lt;- nyc_taxi_zones |&gt;\n  select(dropoff_location_id = location_id,\n         dropoff_zone = zone) |&gt; \n  collect(as_data_frame = FALSE)\n\nairport_pickups &lt;- open_dataset(\"data/nyc-taxi/\") |&gt;\n  filter(pickup_location_id %in% airport_zones) |&gt;\n  select(\n    matches(\"datetime\"),\n    matches(\"location_id\")\n  ) |&gt;\n  left_join(dropoff_zones) |&gt;\n  count(dropoff_zone) |&gt;\n  arrange(desc(n)) |&gt;\n  collect()"
+    "section": "How do function bindings usually work?",
+    "text": "How do function bindings usually work?\n ## How do UDFs work?\n\n\ntime_diff_minutes &lt;- function(pickup, dropoff){\n  difftime(dropoff, pickup, units = \"mins\") |&gt;\n      round() |&gt;\n      as.integer()\n}\n\nnyc_taxi |&gt;\n  mutate(\n    duration_minutes = time_diff_minutes(pickup_datetime, dropoff_datetime)\n  ) |&gt; \n  select(pickup_datetime, dropoff_datetime, duration_minutes) |&gt;\n  head() |&gt;\n  collect()\n\nError in `time_diff_minutes()`:\n! `difftime()` with units other than `secs` not supported in Arrow\n→ Call collect() first to pull data into R.\n\n\nWe get an error as we can’t automatically convert the function to arrow."
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+    "href": "materials/4_data_manipulation_2.html#user-defined-functions---definition",
     "title": "Big Data in R with Arrow",
-    "section": "R",
-    "text": "R\n\nread + wrangle spatial data + 🤩 graphics\n\n\nlibrary(sf)\nlibrary(ggplot2)\nlibrary(ggrepel)\nlibrary(stringr)\nlibrary(scales)\n\nmap &lt;- read_sf(\"data/taxi_zones/taxi_zones.shp\") |&gt;\n  clean_names() |&gt;\n  left_join(airport_pickups,\n            by = c(\"zone\" = \"dropoff_zone\")) |&gt;\n  arrange(desc(n))\n\narrow_r_together &lt;- ggplot(data = map, aes(fill = n)) +\n  geom_sf(size = .1) +\n  scale_fill_distiller(\n    name = \"Number of trips\",\n    labels = label_comma(),\n    palette = \"Reds\",\n    direction = 1\n  ) +\n  geom_label_repel(\n    stat = \"sf_coordinates\",\n    data = map |&gt;\n      mutate(zone_label = case_when(\n        str_detect(zone, \"Airport\") ~ zone,\n        str_detect(zone, \"Times\") ~ zone,\n        .default = \"\"\n      )),\n    mapping = aes(label = zone_label, geometry = geometry),\n    max.overlaps = 60,\n    label.padding = .3,\n    fill = \"white\"\n  ) +\n  theme_void()"
+    "section": "User-defined functions - definition",
+    "text": "User-defined functions - definition\n\n\nregister_scalar_function(\n  name = \"time_diff_minutes\",\n  function(context, pickup, dropoff) {\n    difftime(dropoff, pickup, units = \"mins\") |&gt;\n      round() |&gt;\n      as.integer()\n  },\n  in_type = schema(\n    pickup = timestamp(unit = \"ms\"),\n    dropoff = timestamp(unit = \"ms\")\n  ),\n  out_type = int32(),\n  auto_convert = TRUE\n)\n\nThis looks complicated, so let’s look at it 1 part at a time!"
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-    "href": "materials/6_wrapping_up.html#arrow-r-together-arrow",
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+    "href": "materials/4_data_manipulation_2.html#user-defined-functions---definition-1",
     "title": "Big Data in R with Arrow",
-    "section": "Arrow + R Together: {arrow}",
-    "text": "Arrow + R Together: {arrow}\n\narrow_r_together\n\n\n\n\n\n\n\n\n\n\n\n\n🔗 pos.it/arrow-conf24"
+    "section": "User-defined functions - definition",
+    "text": "User-defined functions - definition\nStep 1. Give the function a name\n\nregister_scalar_function(\n  name = \"time_diff_minutes\",\n  function(context, pickup, dropoff) {\n    difftime(dropoff, pickup, units = \"mins\") |&gt;\n      round() |&gt;\n      as.integer()\n  },\n  in_type = schema(\n    pickup = timestamp(unit = \"ms\"),\n    dropoff = timestamp(unit = \"ms\")\n  ),\n  out_type = int32(),\n  auto_convert = TRUE\n)"
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+    "href": "materials/4_data_manipulation_2.html#user-defined-functions---definition-2",
     "title": "Big Data in R with Arrow",
-    "section": "Feedback",
-    "text": "Feedback\n\nPlease complete the post-workshop survey 🙏\nYour feedback is crucial! Data from the survey informs curriculum and format decisions for future conf workshops, and we really appreciate you taking the time to provide it.\n\npos.it/conf-workshop-survey"
+    "section": "User-defined functions - definition",
+    "text": "User-defined functions - definition\nStep 2. Define the body of the function - first argument must be context\n\nregister_scalar_function(\n  name = \"time_diff_minutes\",\n  function(context, pickup, dropoff) {\n    difftime(dropoff, pickup, units = \"mins\") |&gt;\n      round() |&gt;\n      as.integer()\n  },\n  in_type = schema(\n    pickup = timestamp(unit = \"ms\"),\n    dropoff = timestamp(unit = \"ms\")\n  ),\n  out_type = int32(),\n  auto_convert = TRUE\n)"
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-    "objectID": "materials/8_closing.html#course-materials",
-    "href": "materials/8_closing.html#course-materials",
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+    "href": "materials/4_data_manipulation_2.html#user-defined-functions---definition-3",
     "title": "Big Data in R with Arrow",
-    "section": "Course Materials",
-    "text": "Course Materials\n\n\nhttps://github.com/posit-conf-2024/arrow\nmaterials open-licensed: Creative Commons Attribution 4.0 International License\nPlease open an Issue with any glitches, gotchas or comments!"
+    "section": "User-defined functions - definition",
+    "text": "User-defined functions - definition\nStep 3. Set the schema of the input arguments\n\nregister_scalar_function(\n  name = \"time_diff_minutes\",\n  function(context, pickup, dropoff) {\n    difftime(dropoff, pickup, units = \"mins\") |&gt;\n      round() |&gt;\n      as.integer()\n  },\n  in_type = schema(\n    pickup = timestamp(unit = \"ms\"),\n    dropoff = timestamp(unit = \"ms\")\n  ),\n  out_type = int32(),\n  auto_convert = TRUE\n)"
   },
   {
-    "objectID": "materials/8_closing.html#grab-a-sticker",
-    "href": "materials/8_closing.html#grab-a-sticker",
+    "objectID": "materials/4_data_manipulation_2.html#user-defined-functions---definition-4",
+    "href": "materials/4_data_manipulation_2.html#user-defined-functions---definition-4",
     "title": "Big Data in R with Arrow",
-    "section": "Grab a sticker!",
-    "text": "Grab a sticker!\n\n\n\ngrab a hex sticker before you go!\n\n\n\n\n\n🔗 pos.it/arrow-conf24"
+    "section": "User-defined functions - definition",
+    "text": "User-defined functions - definition\nStep 4. Set the data type of the output\n\nregister_scalar_function(\n  name = \"time_diff_minutes\",\n  function(context, pickup, dropoff) {\n    difftime(dropoff, pickup, units = \"mins\") |&gt;\n      round() |&gt;\n      as.integer()\n  },\n  in_type = schema(\n    pickup = timestamp(unit = \"ms\"),\n    dropoff = timestamp(unit = \"ms\")\n  ),\n  out_type = int32(),\n  auto_convert = TRUE\n)"
+  },
+  {
+    "objectID": "materials/4_data_manipulation_2.html#user-defined-functions---definition-5",
+    "href": "materials/4_data_manipulation_2.html#user-defined-functions---definition-5",
+    "title": "Big Data in R with Arrow",
+    "section": "User-defined functions - definition",
+    "text": "User-defined functions - definition\nStep 5. Set auto_convert = TRUE if using in a dplyr pipeline\n\nregister_scalar_function(\n  name = \"time_diff_minutes\",\n  function(context, pickup, dropoff) {\n    difftime(dropoff, pickup, units = \"mins\") |&gt;\n      round() |&gt;\n      as.integer()\n  },\n  in_type = schema(\n    pickup = timestamp(unit = \"ms\"),\n    dropoff = timestamp(unit = \"ms\")\n  ),\n  out_type = int32(),\n  auto_convert = TRUE\n)"
+  },
+  {
+    "objectID": "materials/4_data_manipulation_2.html#user-defined-functions---usage",
+    "href": "materials/4_data_manipulation_2.html#user-defined-functions---usage",
+    "title": "Big Data in R with Arrow",
+    "section": "User-defined functions - usage",
+    "text": "User-defined functions - usage\n\nnyc_taxi |&gt;\n  mutate(\n    duration_minutes = time_diff_minutes(pickup_datetime, dropoff_datetime)\n  ) |&gt;\n  select(pickup_datetime, dropoff_datetime, duration_minutes) |&gt;\n  head() |&gt;\n  collect()\n\n# A tibble: 6 × 3\n  pickup_datetime     dropoff_datetime    duration_minutes\n  &lt;dttm&gt;              &lt;dttm&gt;                         &lt;int&gt;\n1 2012-11-02 16:40:32 2012-11-02 16:58:16               18\n2 2012-11-02 16:40:41 2012-11-02 16:45:56                5\n3 2012-11-02 16:40:50 2012-11-02 16:49:20                8\n4 2012-11-02 16:40:52 2012-11-02 16:46:15                5\n5 2012-11-02 16:41:00 2012-11-02 16:44:00                3\n6 2012-11-02 16:41:00 2012-11-02 16:45:00                4"
+  },
+  {
+    "objectID": "materials/4_data_manipulation_2.html#your-turn",
+    "href": "materials/4_data_manipulation_2.html#your-turn",
+    "title": "Big Data in R with Arrow",
+    "section": "Your Turn",
+    "text": "Your Turn\n\nWrite a user-defined function which wraps the stringr function str_replace_na(), and use it to replace any NA values in the vendor_name column with the string “No vendor” instead. (Test it on the data from 2019 so you’re not pulling everything into memory)\n\n➡️ Data Manipulation Part II Exercises Page"
+  },
+  {
+    "objectID": "materials/4_data_manipulation_2.html#summary",
+    "href": "materials/4_data_manipulation_2.html#summary",
+    "title": "Big Data in R with Arrow",
+    "section": "Summary",
+    "text": "Summary\n\nYou can use UDFs to create your own bindings when they don’t exist\nUDFs must be scalar (1 row in -&gt; 1 row out) and stateless (no knowledge of other rows of data)\nCalculations done by R not Arrow, so slower than in-built bindings but still pretty fast"
+  },
+  {
+    "objectID": "materials/4_data_manipulation_2.html#joins-1",
+    "href": "materials/4_data_manipulation_2.html#joins-1",
+    "title": "Big Data in R with Arrow",
+    "section": "Joins",
+    "text": "Joins"
+  },
+  {
+    "objectID": "materials/4_data_manipulation_2.html#joining-a-reference-table",
+    "href": "materials/4_data_manipulation_2.html#joining-a-reference-table",
+    "title": "Big Data in R with Arrow",
+    "section": "Joining a reference table",
+    "text": "Joining a reference table\n\nvendors &lt;- tibble::tibble(\n  code = c(\"VTS\", \"CMT\", \"DDS\"),\n  full_name = c(\n    \"Verifone Transportation Systems\",\n    \"Creative Mobile Technologies\",\n    \"Digital Dispatch Systems\"\n  )\n)\n\nnyc_taxi |&gt;\n  left_join(vendors, by = c(\"vendor_name\" = \"code\")) |&gt;\n  select(vendor_name, full_name, pickup_datetime) |&gt;\n  head(3) |&gt;\n  collect()\n\n# A tibble: 3 × 3\n  vendor_name full_name                    pickup_datetime    \n  &lt;chr&gt;       &lt;chr&gt;                        &lt;dttm&gt;             \n1 CMT         Creative Mobile Technologies 2012-01-27 15:35:26\n2 CMT         Creative Mobile Technologies 2012-01-27 06:56:04\n3 CMT         Creative Mobile Technologies 2012-01-27 08:12:50"
+  },
+  {
+    "objectID": "materials/4_data_manipulation_2.html#traps-for-the-unwary",
+    "href": "materials/4_data_manipulation_2.html#traps-for-the-unwary",
+    "title": "Big Data in R with Arrow",
+    "section": "Traps for the unwary",
+    "text": "Traps for the unwary\nQuestion: which are the most common borough-to-borough journeys in the dataset?\n\nnyc_taxi_zones &lt;- \n  read_csv_arrow(\"data/taxi_zone_lookup.csv\") |&gt;\n  select(location_id = LocationID,\n         borough = Borough)\n\nnyc_taxi_zones\n\n# A tibble: 265 × 2\n   location_id borough      \n         &lt;int&gt; &lt;chr&gt;        \n 1           1 EWR          \n 2           2 Queens       \n 3           3 Bronx        \n 4           4 Manhattan    \n 5           5 Staten Island\n 6           6 Staten Island\n 7           7 Queens       \n 8           8 Queens       \n 9           9 Queens       \n10          10 Queens       \n# ℹ 255 more rows"
+  },
+  {
+    "objectID": "materials/4_data_manipulation_2.html#why-didnt-this-work",
+    "href": "materials/4_data_manipulation_2.html#why-didnt-this-work",
+    "title": "Big Data in R with Arrow",
+    "section": "Why didn’t this work?",
+    "text": "Why didn’t this work?\n\nnyc_taxi |&gt;\n  left_join(nyc_taxi_zones, by = c(\"pickup_location_id\" = \"location_id\")) |&gt;\n  collect()\n\nError in `compute.arrow_dplyr_query()`:\n! Invalid: Incompatible data types for corresponding join field keys: FieldRef.Name(pickup_location_id) of type int64 and FieldRef.Name(location_id) of type int32"
+  },
+  {
+    "objectID": "materials/4_data_manipulation_2.html#schema-for-the-nyc_taxi-dataset",
+    "href": "materials/4_data_manipulation_2.html#schema-for-the-nyc_taxi-dataset",
+    "title": "Big Data in R with Arrow",
+    "section": "Schema for the nyc_taxi Dataset",
+    "text": "Schema for the nyc_taxi Dataset\n\nschema(nyc_taxi)\n\nSchema\nvendor_name: string\npickup_datetime: timestamp[ms]\ndropoff_datetime: timestamp[ms]\npassenger_count: int64\ntrip_distance: double\npickup_longitude: double\npickup_latitude: double\nrate_code: string\nstore_and_fwd: string\ndropoff_longitude: double\ndropoff_latitude: double\npayment_type: string\nfare_amount: double\nextra: double\nmta_tax: double\ntip_amount: double\ntolls_amount: double\ntotal_amount: double\nimprovement_surcharge: double\ncongestion_surcharge: double\npickup_location_id: int64\ndropoff_location_id: int64\nyear: int32\nmonth: int32"
+  },
+  {
+    "objectID": "materials/4_data_manipulation_2.html#schema-for-the-nyc_taxi_zones-table",
+    "href": "materials/4_data_manipulation_2.html#schema-for-the-nyc_taxi_zones-table",
+    "title": "Big Data in R with Arrow",
+    "section": "Schema for the nyc_taxi_zones Table",
+    "text": "Schema for the nyc_taxi_zones Table\n\nnyc_taxi_zones_arrow &lt;- arrow_table(nyc_taxi_zones)\nschema(nyc_taxi_zones_arrow)\n\nSchema\nlocation_id: int32\nborough: string\n\n\n\npickup_location_id is int64 in the nyc_taxi table\nlocation_id is int32 in the nyc_taxi_zones table"
+  },
+  {
+    "objectID": "materials/4_data_manipulation_2.html#take-control-of-the-schema",
+    "href": "materials/4_data_manipulation_2.html#take-control-of-the-schema",
+    "title": "Big Data in R with Arrow",
+    "section": "Take control of the schema",
+    "text": "Take control of the schema\n\nnyc_taxi_zones_arrow &lt;- arrow_table(\n  nyc_taxi_zones, \n  schema = schema(location_id = int64(), borough = utf8())\n)\n\n\nschema() takes variable name / types as input\narrow has various “type” functions: int64(), utf8(), boolean(), date32() etc"
+  },
+  {
+    "objectID": "materials/4_data_manipulation_2.html#take-control-of-the-schema-1",
+    "href": "materials/4_data_manipulation_2.html#take-control-of-the-schema-1",
+    "title": "Big Data in R with Arrow",
+    "section": "Take control of the schema",
+    "text": "Take control of the schema\n\nnyc_taxi_zones_arrow &lt;- arrow_table(\n  nyc_taxi_zones, \n  schema = schema(location_id = int64(), borough = utf8())\n)\nschema(nyc_taxi_zones_arrow)\n\nSchema\nlocation_id: int64\nborough: string"
+  },
+  {
+    "objectID": "materials/4_data_manipulation_2.html#prepare-the-auxiliary-tables",
+    "href": "materials/4_data_manipulation_2.html#prepare-the-auxiliary-tables",
+    "title": "Big Data in R with Arrow",
+    "section": "Prepare the auxiliary tables",
+    "text": "Prepare the auxiliary tables\n\npickup &lt;- nyc_taxi_zones_arrow |&gt;\n  select(pickup_location_id = location_id,\n         pickup_borough = borough)\n\ndropoff &lt;- nyc_taxi_zones_arrow |&gt;\n  select(dropoff_location_id = location_id,\n         dropoff_borough = borough)\n\n\nJoin separately for the pickup and dropoff zones"
+  },
+  {
+    "objectID": "materials/4_data_manipulation_2.html#join-and-cross-tabulate",
+    "href": "materials/4_data_manipulation_2.html#join-and-cross-tabulate",
+    "title": "Big Data in R with Arrow",
+    "section": "Join and cross-tabulate",
+    "text": "Join and cross-tabulate\n\n\nlibrary(tictoc)\n\ntic()\nborough_counts &lt;- nyc_taxi |&gt; \n  left_join(pickup) |&gt;\n  left_join(dropoff) |&gt;\n  count(pickup_borough, dropoff_borough) |&gt;\n  arrange(desc(n)) |&gt;\n  collect()\ntoc()\n\n177.061 sec elapsed\n\n\n\n2-3 minutes to join twice and cross-tabulate on non-partition variables, with 1.15 billion rows of data 🙂"
+  },
+  {
+    "objectID": "materials/4_data_manipulation_2.html#the-results",
+    "href": "materials/4_data_manipulation_2.html#the-results",
+    "title": "Big Data in R with Arrow",
+    "section": "The results",
+    "text": "The results\n\nborough_counts\n\n# A tibble: 50 × 3\n   pickup_borough dropoff_borough         n\n   &lt;chr&gt;          &lt;chr&gt;               &lt;int&gt;\n 1 &lt;NA&gt;           &lt;NA&gt;            732357953\n 2 Manhattan      Manhattan       351198872\n 3 Queens         Manhattan        14440705\n 4 Manhattan      Queens           13052517\n 5 Manhattan      Brooklyn         11180867\n 6 Queens         Queens            7440356\n 7 Unknown        Unknown           4491811\n 8 Queens         Brooklyn          3662324\n 9 Brooklyn       Brooklyn          3550480\n10 Manhattan      Bronx             2071830\n# ℹ 40 more rows"
+  },
+  {
+    "objectID": "materials/4_data_manipulation_2.html#your-turn-1",
+    "href": "materials/4_data_manipulation_2.html#your-turn-1",
+    "title": "Big Data in R with Arrow",
+    "section": "Your Turn",
+    "text": "Your Turn\n\nHow many taxi pickups were recorded in 2019 from the three major airports covered by the NYC Taxis data set (JFK, LaGuardia, Newark)? (Hint: you can use stringr::str_detect() to help you find pickup zones with the word “Airport” in them)\n\n➡️ Data Manipulation Part II Exercises Page"
+  },
+  {
+    "objectID": "materials/4_data_manipulation_2.html#summary-1",
+    "href": "materials/4_data_manipulation_2.html#summary-1",
+    "title": "Big Data in R with Arrow",
+    "section": "Summary",
+    "text": "Summary\n\nYou can join Arrow Tables and Datasets to R data frames and Arrow Tables\nThe Arrow data type of join keys must always match\n\n\n\n\n\n🔗 pos.it/arrow-conf24"
   }
 ]
\ No newline at end of file
diff --git a/_site/setup.html b/_site/setup.html
index f53ee4a..67c4bb2 100644
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+++ b/_site/setup.html
@@ -2,12 +2,12 @@
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+          <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbarCollapse" aria-controls="navbarCollapse" role="menu" aria-expanded="false" aria-label="Toggle navigation" onclick="if (window.quartoToggleHeadroom) { window.quartoToggleHeadroom(); }">
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@@ -134,7 +134,7 @@
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-    <a class="nav-link dropdown-toggle" href="#" id="nav-menu-slides" role="button" data-bs-toggle="dropdown" aria-expanded="false">
+    <a class="nav-link dropdown-toggle" href="#" id="nav-menu-slides" role="link" data-bs-toggle="dropdown" aria-expanded="false">
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@@ -177,7 +177,7 @@
     </ul>
   </li>
   <li class="nav-item dropdown ">
-    <a class="nav-link dropdown-toggle" href="#" id="nav-menu-exercises" role="button" data-bs-toggle="dropdown" aria-expanded="false">
+    <a class="nav-link dropdown-toggle" href="#" id="nav-menu-exercises" role="link" data-bs-toggle="dropdown" aria-expanded="false">
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@@ -217,7 +217,7 @@
   </li>  
 </ul>
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-          <div class="quarto-navbar-tools">
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       </div> <!-- /container-fluid -->
     </nav>
@@ -537,18 +537,7 @@ <h3 class="anchored" data-anchor-id="taxi-zone-lookup-csv-table-taxi-zone-shapef
     }
     return false;
   }
-  const clipboard = new window.ClipboardJS('.code-copy-button', {
-    text: function(trigger) {
-      const codeEl = trigger.previousElementSibling.cloneNode(true);
-      for (const childEl of codeEl.children) {
-        if (isCodeAnnotation(childEl)) {
-          childEl.remove();
-        }
-      }
-      return codeEl.innerText;
-    }
-  });
-  clipboard.on('success', function(e) {
+  const onCopySuccess = function(e) {
     // button target
     const button = e.trigger;
     // don't keep focus
@@ -580,7 +569,29 @@ <h3 class="anchored" data-anchor-id="taxi-zone-lookup-csv-table-taxi-zone-shapef
     }, 1000);
     // clear code selection
     e.clearSelection();
+  }
+  const getTextToCopy = function(trigger) {
+      const codeEl = trigger.previousElementSibling.cloneNode(true);
+      for (const childEl of codeEl.children) {
+        if (isCodeAnnotation(childEl)) {
+          childEl.remove();
+        }
+      }
+      return codeEl.innerText;
+  }
+  const clipboard = new window.ClipboardJS('.code-copy-button:not([data-in-quarto-modal])', {
+    text: getTextToCopy
   });
+  clipboard.on('success', onCopySuccess);
+  if (window.document.getElementById('quarto-embedded-source-code-modal')) {
+    // For code content inside modals, clipBoardJS needs to be initialized with a container option
+    // TODO: Check when it could be a function (https://github.com/zenorocha/clipboard.js/issues/860)
+    const clipboardModal = new window.ClipboardJS('.code-copy-button[data-in-quarto-modal]', {
+      text: getTextToCopy,
+      container: window.document.getElementById('quarto-embedded-source-code-modal')
+    });
+    clipboardModal.on('success', onCopySuccess);
+  }
     var localhostRegex = new RegExp(/^(?:http|https):\/\/localhost\:?[0-9]*\//);
     var mailtoRegex = new RegExp(/^mailto:/);
       var filterRegex = new RegExp('/' + window.location.host + '/');
@@ -588,7 +599,7 @@ <h3 class="anchored" data-anchor-id="taxi-zone-lookup-csv-table-taxi-zone-shapef
         return filterRegex.test(href) || localhostRegex.test(href) || mailtoRegex.test(href);
     }
     // Inspect non-navigation links and adorn them if external
- 	var links = window.document.querySelectorAll('a[href]:not(.nav-link):not(.navbar-brand):not(.toc-action):not(.sidebar-link):not(.sidebar-item-toggle):not(.pagination-link):not(.no-external):not([aria-hidden]):not(.dropdown-item):not(.quarto-navigation-tool)');
+ 	var links = window.document.querySelectorAll('a[href]:not(.nav-link):not(.navbar-brand):not(.toc-action):not(.sidebar-link):not(.sidebar-item-toggle):not(.pagination-link):not(.no-external):not([aria-hidden]):not(.dropdown-item):not(.quarto-navigation-tool):not(.about-link)');
     for (var i=0; i<links.length; i++) {
       const link = links[i];
       if (!isInternal(link.href)) {
diff --git a/_site/site_libs/bootstrap/bootstrap.min.css b/_site/site_libs/bootstrap/bootstrap.min.css
index 563b6a9..6875898 100644
--- a/_site/site_libs/bootstrap/bootstrap.min.css
+++ b/_site/site_libs/bootstrap/bootstrap.min.css
@@ -2,11 +2,11 @@
  * Bootstrap  v5.3.1 (https://getbootstrap.com/)
  * Copyright 2011-2023 The Bootstrap Authors
  * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)
- */@import"https://fonts.googleapis.com/css2?family=Source+Sans+Pro:wght@300;400;700&display=swap";:root,[data-bs-theme=light]{--bs-blue: #2780e3;--bs-indigo: #6610f2;--bs-purple: #613d7c;--bs-pink: #e83e8c;--bs-red: #ff0039;--bs-orange: #f0ad4e;--bs-yellow: #ff7518;--bs-green: #3fb618;--bs-teal: #20c997;--bs-cyan: #9954bb;--bs-black: #000;--bs-white: #fff;--bs-gray: #6c757d;--bs-gray-dark: #343a40;--bs-gray-100: #f8f9fa;--bs-gray-200: #e9ecef;--bs-gray-300: #dee2e6;--bs-gray-400: #ced4da;--bs-gray-500: #adb5bd;--bs-gray-600: #6c757d;--bs-gray-700: #495057;--bs-gray-800: #343a40;--bs-gray-900: #212529;--bs-default: #343a40;--bs-primary: #2780e3;--bs-secondary: #343a40;--bs-success: #3fb618;--bs-info: #9954bb;--bs-warning: #ff7518;--bs-danger: #ff0039;--bs-light: #f8f9fa;--bs-dark: #343a40;--bs-default-rgb: 52, 58, 64;--bs-primary-rgb: 39, 128, 227;--bs-secondary-rgb: 52, 58, 64;--bs-success-rgb: 63, 182, 24;--bs-info-rgb: 153, 84, 187;--bs-warning-rgb: 255, 117, 24;--bs-danger-rgb: 255, 0, 57;--bs-light-rgb: 248, 249, 250;--bs-dark-rgb: 52, 58, 64;--bs-primary-text-emphasis: #10335b;--bs-secondary-text-emphasis: #15171a;--bs-success-text-emphasis: #19490a;--bs-info-text-emphasis: #3d224b;--bs-warning-text-emphasis: #662f0a;--bs-danger-text-emphasis: #660017;--bs-light-text-emphasis: #495057;--bs-dark-text-emphasis: #495057;--bs-primary-bg-subtle: #d4e6f9;--bs-secondary-bg-subtle: #d6d8d9;--bs-success-bg-subtle: #d9f0d1;--bs-info-bg-subtle: #ebddf1;--bs-warning-bg-subtle: #ffe3d1;--bs-danger-bg-subtle: #ffccd7;--bs-light-bg-subtle: #fcfcfd;--bs-dark-bg-subtle: #ced4da;--bs-primary-border-subtle: #a9ccf4;--bs-secondary-border-subtle: #aeb0b3;--bs-success-border-subtle: #b2e2a3;--bs-info-border-subtle: #d6bbe4;--bs-warning-border-subtle: #ffc8a3;--bs-danger-border-subtle: #ff99b0;--bs-light-border-subtle: #e9ecef;--bs-dark-border-subtle: #adb5bd;--bs-white-rgb: 255, 255, 255;--bs-black-rgb: 0, 0, 0;--bs-font-sans-serif: "Source Sans Pro", -apple-system, BlinkMacSystemFont, "Segoe UI", Roboto, "Helvetica Neue", Arial, sans-serif, "Apple Color Emoji", "Segoe UI Emoji", "Segoe UI Symbol";--bs-font-monospace: SFMono-Regular, Menlo, Monaco, Consolas, "Liberation Mono", "Courier New", monospace;--bs-gradient: linear-gradient(180deg, rgba(255, 255, 255, 0.15), rgba(255, 255, 255, 0));--bs-root-font-size: 17px;--bs-body-font-family: "Source Sans Pro", -apple-system, BlinkMacSystemFont, "Segoe UI", Roboto, "Helvetica Neue", Arial, sans-serif, "Apple Color Emoji", "Segoe UI Emoji", "Segoe UI Symbol";--bs-body-font-size:1rem;--bs-body-font-weight: 400;--bs-body-line-height: 1.5;--bs-body-color: #343a40;--bs-body-color-rgb: 52, 58, 64;--bs-body-bg: #fff;--bs-body-bg-rgb: 255, 255, 255;--bs-emphasis-color: #000;--bs-emphasis-color-rgb: 0, 0, 0;--bs-secondary-color: rgba(52, 58, 64, 0.75);--bs-secondary-color-rgb: 52, 58, 64;--bs-secondary-bg: #e9ecef;--bs-secondary-bg-rgb: 233, 236, 239;--bs-tertiary-color: rgba(52, 58, 64, 0.5);--bs-tertiary-color-rgb: 52, 58, 64;--bs-tertiary-bg: #f8f9fa;--bs-tertiary-bg-rgb: 248, 249, 250;--bs-heading-color: inherit;--bs-link-color: #2761e3;--bs-link-color-rgb: 39, 97, 227;--bs-link-decoration: underline;--bs-link-hover-color: #1f4eb6;--bs-link-hover-color-rgb: 31, 78, 182;--bs-code-color: #7d12ba;--bs-highlight-bg: #ffe3d1;--bs-border-width: 1px;--bs-border-style: solid;--bs-border-color: #dee2e6;--bs-border-color-translucent: rgba(0, 0, 0, 0.175);--bs-border-radius: 0.25rem;--bs-border-radius-sm: 0.2em;--bs-border-radius-lg: 0.5rem;--bs-border-radius-xl: 1rem;--bs-border-radius-xxl: 2rem;--bs-border-radius-2xl: var(--bs-border-radius-xxl);--bs-border-radius-pill: 50rem;--bs-box-shadow: 0 0.5rem 1rem rgba(0, 0, 0, 0.15);--bs-box-shadow-sm: 0 0.125rem 0.25rem rgba(0, 0, 0, 0.075);--bs-box-shadow-lg: 0 1rem 3rem rgba(0, 0, 0, 0.175);--bs-box-shadow-inset: inset 0 1px 2px rgba(0, 0, 0, 0.075);--bs-focus-ring-width: 0.25rem;--bs-focus-ring-opacity: 0.25;--bs-focus-ring-color: rgba(39, 128, 227, 0.25);--bs-form-valid-color: #3fb618;--bs-form-valid-border-color: #3fb618;--bs-form-invalid-color: #ff0039;--bs-form-invalid-border-color: #ff0039}[data-bs-theme=dark]{color-scheme:dark;--bs-body-color: #dee2e6;--bs-body-color-rgb: 222, 226, 230;--bs-body-bg: #212529;--bs-body-bg-rgb: 33, 37, 41;--bs-emphasis-color: #fff;--bs-emphasis-color-rgb: 255, 255, 255;--bs-secondary-color: rgba(222, 226, 230, 0.75);--bs-secondary-color-rgb: 222, 226, 230;--bs-secondary-bg: #343a40;--bs-secondary-bg-rgb: 52, 58, 64;--bs-tertiary-color: rgba(222, 226, 230, 0.5);--bs-tertiary-color-rgb: 222, 226, 230;--bs-tertiary-bg: #2b3035;--bs-tertiary-bg-rgb: 43, 48, 53;--bs-primary-text-emphasis: #7db3ee;--bs-secondary-text-emphasis: #85898c;--bs-success-text-emphasis: #8cd374;--bs-info-text-emphasis: #c298d6;--bs-warning-text-emphasis: #ffac74;--bs-danger-text-emphasis: #ff6688;--bs-light-text-emphasis: #f8f9fa;--bs-dark-text-emphasis: #dee2e6;--bs-primary-bg-subtle: #081a2d;--bs-secondary-bg-subtle: #0a0c0d;--bs-success-bg-subtle: #0d2405;--bs-info-bg-subtle: #1f1125;--bs-warning-bg-subtle: #331705;--bs-danger-bg-subtle: #33000b;--bs-light-bg-subtle: #343a40;--bs-dark-bg-subtle: #1a1d20;--bs-primary-border-subtle: #174d88;--bs-secondary-border-subtle: #1f2326;--bs-success-border-subtle: #266d0e;--bs-info-border-subtle: #5c3270;--bs-warning-border-subtle: #99460e;--bs-danger-border-subtle: #990022;--bs-light-border-subtle: #495057;--bs-dark-border-subtle: #343a40;--bs-heading-color: inherit;--bs-link-color: #7db3ee;--bs-link-hover-color: #97c2f1;--bs-link-color-rgb: 125, 179, 238;--bs-link-hover-color-rgb: 151, 194, 241;--bs-code-color: white;--bs-border-color: #495057;--bs-border-color-translucent: rgba(255, 255, 255, 0.15);--bs-form-valid-color: #8cd374;--bs-form-valid-border-color: #8cd374;--bs-form-invalid-color: #ff6688;--bs-form-invalid-border-color: #ff6688}*,*::before,*::after{box-sizing:border-box}:root{font-size:var(--bs-root-font-size)}body{margin:0;font-family:var(--bs-body-font-family);font-size:var(--bs-body-font-size);font-weight:var(--bs-body-font-weight);line-height:var(--bs-body-line-height);color:var(--bs-body-color);text-align:var(--bs-body-text-align);background-color:var(--bs-body-bg);-webkit-text-size-adjust:100%;-webkit-tap-highlight-color:rgba(0,0,0,0)}hr{margin:1rem 0;color:inherit;border:0;border-top:1px solid;opacity:.25}h6,.h6,h5,.h5,h4,.h4,h3,.h3,h2,.h2,h1,.h1{margin-top:0;margin-bottom:.5rem;font-weight:400;line-height:1.2;color:var(--bs-heading-color)}h1,.h1{font-size:calc(1.325rem + 0.9vw)}@media(min-width: 1200px){h1,.h1{font-size:2rem}}h2,.h2{font-size:calc(1.29rem + 0.48vw)}@media(min-width: 1200px){h2,.h2{font-size:1.65rem}}h3,.h3{font-size:calc(1.27rem + 0.24vw)}@media(min-width: 1200px){h3,.h3{font-size:1.45rem}}h4,.h4{font-size:1.25rem}h5,.h5{font-size:1.1rem}h6,.h6{font-size:1rem}p{margin-top:0;margin-bottom:1rem}abbr[title]{text-decoration:underline dotted;-webkit-text-decoration:underline dotted;-moz-text-decoration:underline dotted;-ms-text-decoration:underline dotted;-o-text-decoration:underline dotted;cursor:help;text-decoration-skip-ink:none}address{margin-bottom:1rem;font-style:normal;line-height:inherit}ol,ul{padding-left:2rem}ol,ul,dl{margin-top:0;margin-bottom:1rem}ol ol,ul ul,ol ul,ul ol{margin-bottom:0}dt{font-weight:700}dd{margin-bottom:.5rem;margin-left:0}blockquote{margin:0 0 1rem;padding:.625rem 1.25rem;border-left:.25rem solid #e9ecef}blockquote p:last-child,blockquote ul:last-child,blockquote ol:last-child{margin-bottom:0}b,strong{font-weight:bolder}small,.small{font-size:0.875em}mark,.mark{padding:.1875em;background-color:var(--bs-highlight-bg)}sub,sup{position:relative;font-size:0.75em;line-height:0;vertical-align:baseline}sub{bottom:-0.25em}sup{top:-0.5em}a{color:rgba(var(--bs-link-color-rgb), var(--bs-link-opacity, 1));text-decoration:underline;-webkit-text-decoration:underline;-moz-text-decoration:underline;-ms-text-decoration:underline;-o-text-decoration:underline}a:hover{--bs-link-color-rgb: var(--bs-link-hover-color-rgb)}a:not([href]):not([class]),a:not([href]):not([class]):hover{color:inherit;text-decoration:none}pre,code,kbd,samp{font-family:SFMono-Regular,Menlo,Monaco,Consolas,"Liberation Mono","Courier New",monospace;font-size:1em}pre{display:block;margin-top:0;margin-bottom:1rem;overflow:auto;font-size:0.875em;color:#000;background-color:#f8f9fa;padding:.5rem;border:1px solid var(--bs-border-color, #dee2e6)}pre code{background-color:rgba(0,0,0,0);font-size:inherit;color:inherit;word-break:normal}code{font-size:0.875em;color:var(--bs-code-color);background-color:#f8f9fa;padding:.125rem .25rem;word-wrap:break-word}a>code{color:inherit}kbd{padding:.4rem .4rem;font-size:0.875em;color:#fff;background-color:#343a40}kbd kbd{padding:0;font-size:1em}figure{margin:0 0 1rem}img,svg{vertical-align:middle}table{caption-side:bottom;border-collapse:collapse}caption{padding-top:.5rem;padding-bottom:.5rem;color:rgba(52,58,64,.75);text-align:left}th{text-align:inherit;text-align:-webkit-match-parent}thead,tbody,tfoot,tr,td,th{border-color:inherit;border-style:solid;border-width:0}label{display:inline-block}button{border-radius:0}button:focus:not(:focus-visible){outline:0}input,button,select,optgroup,textarea{margin:0;font-family:inherit;font-size:inherit;line-height:inherit}button,select{text-transform:none}[role=button]{cursor:pointer}select{word-wrap:normal}select:disabled{opacity:1}[list]:not([type=date]):not([type=datetime-local]):not([type=month]):not([type=week]):not([type=time])::-webkit-calendar-picker-indicator{display:none !important}button,[type=button],[type=reset],[type=submit]{-webkit-appearance:button}button:not(:disabled),[type=button]:not(:disabled),[type=reset]:not(:disabled),[type=submit]:not(:disabled){cursor:pointer}::-moz-focus-inner{padding:0;border-style:none}textarea{resize:vertical}fieldset{min-width:0;padding:0;margin:0;border:0}legend{float:left;width:100%;padding:0;margin-bottom:.5rem;font-size:calc(1.275rem + 0.3vw);line-height:inherit}@media(min-width: 1200px){legend{font-size:1.5rem}}legend+*{clear:left}::-webkit-datetime-edit-fields-wrapper,::-webkit-datetime-edit-text,::-webkit-datetime-edit-minute,::-webkit-datetime-edit-hour-field,::-webkit-datetime-edit-day-field,::-webkit-datetime-edit-month-field,::-webkit-datetime-edit-year-field{padding:0}::-webkit-inner-spin-button{height:auto}[type=search]{-webkit-appearance:textfield;outline-offset:-2px}::-webkit-search-decoration{-webkit-appearance:none}::-webkit-color-swatch-wrapper{padding:0}::file-selector-button{font:inherit;-webkit-appearance:button}output{display:inline-block}iframe{border:0}summary{display:list-item;cursor:pointer}progress{vertical-align:baseline}[hidden]{display:none !important}.lead{font-size:1.25rem;font-weight:300}.display-1{font-size:calc(1.625rem + 4.5vw);font-weight:300;line-height:1.2}@media(min-width: 1200px){.display-1{font-size:5rem}}.display-2{font-size:calc(1.575rem + 3.9vw);font-weight:300;line-height:1.2}@media(min-width: 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0;width:100%;padding-right:calc(var(--bs-gutter-x)*.5);padding-left:calc(var(--bs-gutter-x)*.5);margin-right:auto;margin-left:auto}@media(min-width: 576px){.container-sm,.container{max-width:540px}}@media(min-width: 768px){.container-md,.container-sm,.container{max-width:720px}}@media(min-width: 992px){.container-lg,.container-md,.container-sm,.container{max-width:960px}}@media(min-width: 1200px){.container-xl,.container-lg,.container-md,.container-sm,.container{max-width:1140px}}@media(min-width: 1400px){.container-xxl,.container-xl,.container-lg,.container-md,.container-sm,.container{max-width:1320px}}:root{--bs-breakpoint-xs: 0;--bs-breakpoint-sm: 576px;--bs-breakpoint-md: 768px;--bs-breakpoint-lg: 992px;--bs-breakpoint-xl: 1200px;--bs-breakpoint-xxl: 1400px}.grid{display:grid;grid-template-rows:repeat(var(--bs-rows, 1), 1fr);grid-template-columns:repeat(var(--bs-columns, 12), 1fr);gap:var(--bs-gap, 1.5rem)}.grid .g-col-1{grid-column:auto/span 1}.grid .g-col-2{grid-column:auto/span 2}.grid .g-col-3{grid-column:auto/span 3}.grid .g-col-4{grid-column:auto/span 4}.grid .g-col-5{grid-column:auto/span 5}.grid .g-col-6{grid-column:auto/span 6}.grid .g-col-7{grid-column:auto/span 7}.grid .g-col-8{grid-column:auto/span 8}.grid .g-col-9{grid-column:auto/span 9}.grid .g-col-10{grid-column:auto/span 10}.grid .g-col-11{grid-column:auto/span 11}.grid .g-col-12{grid-column:auto/span 12}.grid .g-start-1{grid-column-start:1}.grid .g-start-2{grid-column-start:2}.grid .g-start-3{grid-column-start:3}.grid .g-start-4{grid-column-start:4}.grid .g-start-5{grid-column-start:5}.grid .g-start-6{grid-column-start:6}.grid .g-start-7{grid-column-start:7}.grid .g-start-8{grid-column-start:8}.grid .g-start-9{grid-column-start:9}.grid .g-start-10{grid-column-start:10}.grid .g-start-11{grid-column-start:11}@media(min-width: 576px){.grid .g-col-sm-1{grid-column:auto/span 1}.grid .g-col-sm-2{grid-column:auto/span 2}.grid .g-col-sm-3{grid-column:auto/span 3}.grid .g-col-sm-4{grid-column:auto/span 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992px){.navbar-expand-lg{flex-wrap:nowrap;-webkit-flex-wrap:nowrap;justify-content:flex-start;-webkit-justify-content:flex-start}.navbar-expand-lg .navbar-nav{flex-direction:row;-webkit-flex-direction:row}.navbar-expand-lg .navbar-nav .dropdown-menu{position:absolute}.navbar-expand-lg .navbar-nav .nav-link{padding-right:var(--bs-navbar-nav-link-padding-x);padding-left:var(--bs-navbar-nav-link-padding-x)}.navbar-expand-lg .navbar-nav-scroll{overflow:visible}.navbar-expand-lg .navbar-collapse{display:flex !important;display:-webkit-flex !important;flex-basis:auto;-webkit-flex-basis:auto}.navbar-expand-lg .navbar-toggler{display:none}.navbar-expand-lg .offcanvas{position:static;z-index:auto;flex-grow:1;-webkit-flex-grow:1;width:auto !important;height:auto !important;visibility:visible !important;background-color:rgba(0,0,0,0) !important;border:0 !important;transform:none !important;transition:none}.navbar-expand-lg .offcanvas .offcanvas-header{display:none}.navbar-expand-lg .offcanvas 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!important;flex-basis:auto;-webkit-flex-basis:auto}.navbar-expand .navbar-toggler{display:none}.navbar-expand .offcanvas{position:static;z-index:auto;flex-grow:1;-webkit-flex-grow:1;width:auto !important;height:auto !important;visibility:visible !important;background-color:rgba(0,0,0,0) !important;border:0 !important;transform:none !important;transition:none}.navbar-expand .offcanvas .offcanvas-header{display:none}.navbar-expand .offcanvas .offcanvas-body{display:flex;display:-webkit-flex;flex-grow:0;-webkit-flex-grow:0;padding:0;overflow-y:visible}.navbar-dark,.navbar[data-bs-theme=dark]{--bs-navbar-color: #cacccd;--bs-navbar-hover-color: rgba(184, 203, 246, 0.8);--bs-navbar-disabled-color: rgba(202, 204, 205, 0.75);--bs-navbar-active-color: #b8cbf6;--bs-navbar-brand-color: #cacccd;--bs-navbar-brand-hover-color: #b8cbf6;--bs-navbar-toggler-border-color: rgba(202, 204, 205, 0);--bs-navbar-toggler-icon-bg: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 30 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var(--bs-card-spacer-x);color:var(--bs-card-color)}.card-title{margin-bottom:var(--bs-card-title-spacer-y);color:var(--bs-card-title-color)}.card-subtitle{margin-top:calc(-0.5*var(--bs-card-title-spacer-y));margin-bottom:0;color:var(--bs-card-subtitle-color)}.card-text:last-child{margin-bottom:0}.card-link+.card-link{margin-left:var(--bs-card-spacer-x)}.card-header{padding:var(--bs-card-cap-padding-y) var(--bs-card-cap-padding-x);margin-bottom:0;color:var(--bs-card-cap-color);background-color:var(--bs-card-cap-bg);border-bottom:var(--bs-card-border-width) solid var(--bs-card-border-color)}.card-footer{padding:var(--bs-card-cap-padding-y) var(--bs-card-cap-padding-x);color:var(--bs-card-cap-color);background-color:var(--bs-card-cap-bg);border-top:var(--bs-card-border-width) solid var(--bs-card-border-color)}.card-header-tabs{margin-right:calc(-0.5*var(--bs-card-cap-padding-x));margin-bottom:calc(-1*var(--bs-card-cap-padding-y));margin-left:calc(-0.5*var(--bs-card-cap-padding-x));border-bottom:0}.card-header-tabs .nav-link.active{background-color:var(--bs-card-bg);border-bottom-color:var(--bs-card-bg)}.card-header-pills{margin-right:calc(-0.5*var(--bs-card-cap-padding-x));margin-left:calc(-0.5*var(--bs-card-cap-padding-x))}.card-img-overlay{position:absolute;top:0;right:0;bottom:0;left:0;padding:var(--bs-card-img-overlay-padding)}.card-img,.card-img-top,.card-img-bottom{width:100%}.card-group>.card{margin-bottom:var(--bs-card-group-margin)}@media(min-width: 576px){.card-group{display:flex;display:-webkit-flex;flex-flow:row wrap;-webkit-flex-flow:row wrap}.card-group>.card{flex:1 0 0%;-webkit-flex:1 0 0%;margin-bottom:0}.card-group>.card+.card{margin-left:0;border-left:0}}.accordion{--bs-accordion-color: #343a40;--bs-accordion-bg: #fff;--bs-accordion-transition: 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viewBox='0 0 16 16' fill='%2310335b'%3e%3cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e");--bs-accordion-btn-focus-border-color: #93c0f1;--bs-accordion-btn-focus-box-shadow: 0 0 0 0.25rem rgba(39, 128, 227, 0.25);--bs-accordion-body-padding-x: 1.25rem;--bs-accordion-body-padding-y: 1rem;--bs-accordion-active-color: #10335b;--bs-accordion-active-bg: #d4e6f9}.accordion-button{position:relative;display:flex;display:-webkit-flex;align-items:center;-webkit-align-items:center;width:100%;padding:var(--bs-accordion-btn-padding-y) var(--bs-accordion-btn-padding-x);font-size:1rem;color:var(--bs-accordion-btn-color);text-align:left;background-color:var(--bs-accordion-btn-bg);border:0;overflow-anchor:none;transition:var(--bs-accordion-transition)}@media(prefers-reduced-motion: reduce){.accordion-button{transition:none}}.accordion-button:not(.collapsed){color:var(--bs-accordion-active-color);background-color:var(--bs-accordion-active-bg);box-shadow:inset 0 calc(-1*var(--bs-accordion-border-width)) 0 var(--bs-accordion-border-color)}.accordion-button:not(.collapsed)::after{background-image:var(--bs-accordion-btn-active-icon);transform:var(--bs-accordion-btn-icon-transform)}.accordion-button::after{flex-shrink:0;-webkit-flex-shrink:0;width:var(--bs-accordion-btn-icon-width);height:var(--bs-accordion-btn-icon-width);margin-left:auto;content:"";background-image:var(--bs-accordion-btn-icon);background-repeat:no-repeat;background-size:var(--bs-accordion-btn-icon-width);transition:var(--bs-accordion-btn-icon-transition)}@media(prefers-reduced-motion: reduce){.accordion-button::after{transition:none}}.accordion-button:hover{z-index:2}.accordion-button:focus{z-index:3;border-color:var(--bs-accordion-btn-focus-border-color);outline:0;box-shadow:var(--bs-accordion-btn-focus-box-shadow)}.accordion-header{margin-bottom:0}.accordion-item{color:var(--bs-accordion-color);background-color:var(--bs-accordion-bg);border:var(--bs-accordion-border-width) solid var(--bs-accordion-border-color)}.accordion-item:not(:first-of-type){border-top:0}.accordion-body{padding:var(--bs-accordion-body-padding-y) var(--bs-accordion-body-padding-x)}.accordion-flush .accordion-collapse{border-width:0}.accordion-flush .accordion-item{border-right:0;border-left:0}.accordion-flush .accordion-item:first-child{border-top:0}.accordion-flush .accordion-item:last-child{border-bottom:0}[data-bs-theme=dark] .accordion-button::after{--bs-accordion-btn-icon: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%237db3ee'%3e%3cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e");--bs-accordion-btn-active-icon: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%237db3ee'%3e%3cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e")}.breadcrumb{--bs-breadcrumb-padding-x: 0;--bs-breadcrumb-padding-y: 0;--bs-breadcrumb-margin-bottom: 1rem;--bs-breadcrumb-bg: ;--bs-breadcrumb-border-radius: ;--bs-breadcrumb-divider-color: rgba(52, 58, 64, 0.75);--bs-breadcrumb-item-padding-x: 0.5rem;--bs-breadcrumb-item-active-color: rgba(52, 58, 64, 0.75);display:flex;display:-webkit-flex;flex-wrap:wrap;-webkit-flex-wrap:wrap;padding:var(--bs-breadcrumb-padding-y) var(--bs-breadcrumb-padding-x);margin-bottom:var(--bs-breadcrumb-margin-bottom);font-size:var(--bs-breadcrumb-font-size);list-style:none;background-color:var(--bs-breadcrumb-bg)}.breadcrumb-item+.breadcrumb-item{padding-left:var(--bs-breadcrumb-item-padding-x)}.breadcrumb-item+.breadcrumb-item::before{float:left;padding-right:var(--bs-breadcrumb-item-padding-x);color:var(--bs-breadcrumb-divider-color);content:var(--bs-breadcrumb-divider, ">") /* rtl: var(--bs-breadcrumb-divider, ">") */}.breadcrumb-item.active{color:var(--bs-breadcrumb-item-active-color)}.pagination{--bs-pagination-padding-x: 0.75rem;--bs-pagination-padding-y: 0.375rem;--bs-pagination-font-size:1rem;--bs-pagination-color: #2761e3;--bs-pagination-bg: #fff;--bs-pagination-border-width: 1px;--bs-pagination-border-color: #dee2e6;--bs-pagination-border-radius: 0.25rem;--bs-pagination-hover-color: #1f4eb6;--bs-pagination-hover-bg: #f8f9fa;--bs-pagination-hover-border-color: #dee2e6;--bs-pagination-focus-color: #1f4eb6;--bs-pagination-focus-bg: #e9ecef;--bs-pagination-focus-box-shadow: 0 0 0 0.25rem rgba(39, 128, 227, 0.25);--bs-pagination-active-color: #fff;--bs-pagination-active-bg: #2780e3;--bs-pagination-active-border-color: #2780e3;--bs-pagination-disabled-color: rgba(52, 58, 64, 0.75);--bs-pagination-disabled-bg: #e9ecef;--bs-pagination-disabled-border-color: #dee2e6;display:flex;display:-webkit-flex;padding-left:0;list-style:none}.page-link{position:relative;display:block;padding:var(--bs-pagination-padding-y) var(--bs-pagination-padding-x);font-size:var(--bs-pagination-font-size);color:var(--bs-pagination-color);text-decoration:none;-webkit-text-decoration:none;-moz-text-decoration:none;-ms-text-decoration:none;-o-text-decoration:none;background-color:var(--bs-pagination-bg);border:var(--bs-pagination-border-width) solid var(--bs-pagination-border-color);transition:color .15s ease-in-out,background-color .15s ease-in-out,border-color .15s ease-in-out,box-shadow .15s ease-in-out}@media(prefers-reduced-motion: reduce){.page-link{transition:none}}.page-link:hover{z-index:2;color:var(--bs-pagination-hover-color);background-color:var(--bs-pagination-hover-bg);border-color:var(--bs-pagination-hover-border-color)}.page-link:focus{z-index:3;color:var(--bs-pagination-focus-color);background-color:var(--bs-pagination-focus-bg);outline:0;box-shadow:var(--bs-pagination-focus-box-shadow)}.page-link.active,.active>.page-link{z-index:3;color:var(--bs-pagination-active-color);background-color:var(--bs-pagination-active-bg);border-color:var(--bs-pagination-active-border-color)}.page-link.disabled,.disabled>.page-link{color:var(--bs-pagination-disabled-color);pointer-events:none;background-color:var(--bs-pagination-disabled-bg);border-color:var(--bs-pagination-disabled-border-color)}.page-item:not(:first-child) .page-link{margin-left:calc(1px*-1)}.pagination-lg{--bs-pagination-padding-x: 1.5rem;--bs-pagination-padding-y: 0.75rem;--bs-pagination-font-size:1.25rem;--bs-pagination-border-radius: 0.5rem}.pagination-sm{--bs-pagination-padding-x: 0.5rem;--bs-pagination-padding-y: 0.25rem;--bs-pagination-font-size:0.875rem;--bs-pagination-border-radius: 0.2em}.badge{--bs-badge-padding-x: 0.65em;--bs-badge-padding-y: 0.35em;--bs-badge-font-size:0.75em;--bs-badge-font-weight: 700;--bs-badge-color: #fff;--bs-badge-border-radius: 0.25rem;display:inline-block;padding:var(--bs-badge-padding-y) var(--bs-badge-padding-x);font-size:var(--bs-badge-font-size);font-weight:var(--bs-badge-font-weight);line-height:1;color:var(--bs-badge-color);text-align:center;white-space:nowrap;vertical-align:baseline}.badge:empty{display:none}.btn .badge{position:relative;top:-1px}.alert{--bs-alert-bg: transparent;--bs-alert-padding-x: 1rem;--bs-alert-padding-y: 1rem;--bs-alert-margin-bottom: 1rem;--bs-alert-color: inherit;--bs-alert-border-color: transparent;--bs-alert-border: 0 solid var(--bs-alert-border-color);--bs-alert-border-radius: 0.25rem;--bs-alert-link-color: inherit;position:relative;padding:var(--bs-alert-padding-y) var(--bs-alert-padding-x);margin-bottom:var(--bs-alert-margin-bottom);color:var(--bs-alert-color);background-color:var(--bs-alert-bg);border:var(--bs-alert-border)}.alert-heading{color:inherit}.alert-link{font-weight:700;color:var(--bs-alert-link-color)}.alert-dismissible{padding-right:3rem}.alert-dismissible .btn-close{position:absolute;top:0;right:0;z-index:2;padding:1.25rem 1rem}.alert-default{--bs-alert-color: var(--bs-default-text-emphasis);--bs-alert-bg: var(--bs-default-bg-subtle);--bs-alert-border-color: var(--bs-default-border-subtle);--bs-alert-link-color: var(--bs-default-text-emphasis)}.alert-primary{--bs-alert-color: var(--bs-primary-text-emphasis);--bs-alert-bg: var(--bs-primary-bg-subtle);--bs-alert-border-color: var(--bs-primary-border-subtle);--bs-alert-link-color: var(--bs-primary-text-emphasis)}.alert-secondary{--bs-alert-color: var(--bs-secondary-text-emphasis);--bs-alert-bg: var(--bs-secondary-bg-subtle);--bs-alert-border-color: var(--bs-secondary-border-subtle);--bs-alert-link-color: var(--bs-secondary-text-emphasis)}.alert-success{--bs-alert-color: var(--bs-success-text-emphasis);--bs-alert-bg: var(--bs-success-bg-subtle);--bs-alert-border-color: var(--bs-success-border-subtle);--bs-alert-link-color: var(--bs-success-text-emphasis)}.alert-info{--bs-alert-color: var(--bs-info-text-emphasis);--bs-alert-bg: var(--bs-info-bg-subtle);--bs-alert-border-color: var(--bs-info-border-subtle);--bs-alert-link-color: var(--bs-info-text-emphasis)}.alert-warning{--bs-alert-color: var(--bs-warning-text-emphasis);--bs-alert-bg: var(--bs-warning-bg-subtle);--bs-alert-border-color: var(--bs-warning-border-subtle);--bs-alert-link-color: var(--bs-warning-text-emphasis)}.alert-danger{--bs-alert-color: var(--bs-danger-text-emphasis);--bs-alert-bg: var(--bs-danger-bg-subtle);--bs-alert-border-color: var(--bs-danger-border-subtle);--bs-alert-link-color: var(--bs-danger-text-emphasis)}.alert-light{--bs-alert-color: var(--bs-light-text-emphasis);--bs-alert-bg: var(--bs-light-bg-subtle);--bs-alert-border-color: var(--bs-light-border-subtle);--bs-alert-link-color: var(--bs-light-text-emphasis)}.alert-dark{--bs-alert-color: var(--bs-dark-text-emphasis);--bs-alert-bg: var(--bs-dark-bg-subtle);--bs-alert-border-color: var(--bs-dark-border-subtle);--bs-alert-link-color: var(--bs-dark-text-emphasis)}@keyframes progress-bar-stripes{0%{background-position-x:.5rem}}.progress,.progress-stacked{--bs-progress-height: 0.5rem;--bs-progress-font-size:0.75rem;--bs-progress-bg: #e9ecef;--bs-progress-border-radius: 0.25rem;--bs-progress-box-shadow: inset 0 1px 2px rgba(0, 0, 0, 0.075);--bs-progress-bar-color: #fff;--bs-progress-bar-bg: #2780e3;--bs-progress-bar-transition: width 0.6s ease;display:flex;display:-webkit-flex;height:var(--bs-progress-height);overflow:hidden;font-size:var(--bs-progress-font-size);background-color:var(--bs-progress-bg)}.progress-bar{display:flex;display:-webkit-flex;flex-direction:column;-webkit-flex-direction:column;justify-content:center;-webkit-justify-content:center;overflow:hidden;color:var(--bs-progress-bar-color);text-align:center;white-space:nowrap;background-color:var(--bs-progress-bar-bg);transition:var(--bs-progress-bar-transition)}@media(prefers-reduced-motion: reduce){.progress-bar{transition:none}}.progress-bar-striped{background-image:linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);background-size:var(--bs-progress-height) var(--bs-progress-height)}.progress-stacked>.progress{overflow:visible}.progress-stacked>.progress>.progress-bar{width:100%}.progress-bar-animated{animation:1s linear infinite progress-bar-stripes}@media(prefers-reduced-motion: reduce){.progress-bar-animated{animation:none}}.list-group{--bs-list-group-color: #343a40;--bs-list-group-bg: #fff;--bs-list-group-border-color: #dee2e6;--bs-list-group-border-width: 1px;--bs-list-group-border-radius: 0.25rem;--bs-list-group-item-padding-x: 1rem;--bs-list-group-item-padding-y: 0.5rem;--bs-list-group-action-color: rgba(52, 58, 64, 0.75);--bs-list-group-action-hover-color: #000;--bs-list-group-action-hover-bg: #f8f9fa;--bs-list-group-action-active-color: #343a40;--bs-list-group-action-active-bg: #e9ecef;--bs-list-group-disabled-color: rgba(52, 58, 64, 0.75);--bs-list-group-disabled-bg: #fff;--bs-list-group-active-color: #fff;--bs-list-group-active-bg: #2780e3;--bs-list-group-active-border-color: #2780e3;display:flex;display:-webkit-flex;flex-direction:column;-webkit-flex-direction:column;padding-left:0;margin-bottom:0}.list-group-numbered{list-style-type:none;counter-reset:section}.list-group-numbered>.list-group-item::before{content:counters(section, ".") ". ";counter-increment:section}.list-group-item-action{width:100%;color:var(--bs-list-group-action-color);text-align:inherit}.list-group-item-action:hover,.list-group-item-action:focus{z-index:1;color:var(--bs-list-group-action-hover-color);text-decoration:none;background-color:var(--bs-list-group-action-hover-bg)}.list-group-item-action:active{color:var(--bs-list-group-action-active-color);background-color:var(--bs-list-group-action-active-bg)}.list-group-item{position:relative;display:block;padding:var(--bs-list-group-item-padding-y) var(--bs-list-group-item-padding-x);color:var(--bs-list-group-color);text-decoration:none;-webkit-text-decoration:none;-moz-text-decoration:none;-ms-text-decoration:none;-o-text-decoration:none;background-color:var(--bs-list-group-bg);border:var(--bs-list-group-border-width) solid var(--bs-list-group-border-color)}.list-group-item.disabled,.list-group-item:disabled{color:var(--bs-list-group-disabled-color);pointer-events:none;background-color:var(--bs-list-group-disabled-bg)}.list-group-item.active{z-index:2;color:var(--bs-list-group-active-color);background-color:var(--bs-list-group-active-bg);border-color:var(--bs-list-group-active-border-color)}.list-group-item+.list-group-item{border-top-width:0}.list-group-item+.list-group-item.active{margin-top:calc(-1*var(--bs-list-group-border-width));border-top-width:var(--bs-list-group-border-width)}.list-group-horizontal{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal>.list-group-item.active{margin-top:0}.list-group-horizontal>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}@media(min-width: 576px){.list-group-horizontal-sm{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-sm>.list-group-item.active{margin-top:0}.list-group-horizontal-sm>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-sm>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media(min-width: 768px){.list-group-horizontal-md{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-md>.list-group-item.active{margin-top:0}.list-group-horizontal-md>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-md>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media(min-width: 992px){.list-group-horizontal-lg{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-lg>.list-group-item.active{margin-top:0}.list-group-horizontal-lg>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-lg>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media(min-width: 1200px){.list-group-horizontal-xl{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-xl>.list-group-item.active{margin-top:0}.list-group-horizontal-xl>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-xl>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media(min-width: 1400px){.list-group-horizontal-xxl{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-xxl>.list-group-item.active{margin-top:0}.list-group-horizontal-xxl>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-xxl>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}.list-group-flush>.list-group-item{border-width:0 0 var(--bs-list-group-border-width)}.list-group-flush>.list-group-item:last-child{border-bottom-width:0}.list-group-item-default{--bs-list-group-color: var(--bs-default-text-emphasis);--bs-list-group-bg: var(--bs-default-bg-subtle);--bs-list-group-border-color: var(--bs-default-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-default-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-default-border-subtle);--bs-list-group-active-color: var(--bs-default-bg-subtle);--bs-list-group-active-bg: var(--bs-default-text-emphasis);--bs-list-group-active-border-color: var(--bs-default-text-emphasis)}.list-group-item-primary{--bs-list-group-color: var(--bs-primary-text-emphasis);--bs-list-group-bg: var(--bs-primary-bg-subtle);--bs-list-group-border-color: var(--bs-primary-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-primary-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-primary-border-subtle);--bs-list-group-active-color: var(--bs-primary-bg-subtle);--bs-list-group-active-bg: var(--bs-primary-text-emphasis);--bs-list-group-active-border-color: var(--bs-primary-text-emphasis)}.list-group-item-secondary{--bs-list-group-color: var(--bs-secondary-text-emphasis);--bs-list-group-bg: var(--bs-secondary-bg-subtle);--bs-list-group-border-color: var(--bs-secondary-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-secondary-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-secondary-border-subtle);--bs-list-group-active-color: var(--bs-secondary-bg-subtle);--bs-list-group-active-bg: var(--bs-secondary-text-emphasis);--bs-list-group-active-border-color: var(--bs-secondary-text-emphasis)}.list-group-item-success{--bs-list-group-color: var(--bs-success-text-emphasis);--bs-list-group-bg: var(--bs-success-bg-subtle);--bs-list-group-border-color: var(--bs-success-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-success-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-success-border-subtle);--bs-list-group-active-color: var(--bs-success-bg-subtle);--bs-list-group-active-bg: var(--bs-success-text-emphasis);--bs-list-group-active-border-color: var(--bs-success-text-emphasis)}.list-group-item-info{--bs-list-group-color: var(--bs-info-text-emphasis);--bs-list-group-bg: var(--bs-info-bg-subtle);--bs-list-group-border-color: var(--bs-info-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-info-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-info-border-subtle);--bs-list-group-active-color: var(--bs-info-bg-subtle);--bs-list-group-active-bg: var(--bs-info-text-emphasis);--bs-list-group-active-border-color: var(--bs-info-text-emphasis)}.list-group-item-warning{--bs-list-group-color: var(--bs-warning-text-emphasis);--bs-list-group-bg: var(--bs-warning-bg-subtle);--bs-list-group-border-color: var(--bs-warning-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-warning-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-warning-border-subtle);--bs-list-group-active-color: var(--bs-warning-bg-subtle);--bs-list-group-active-bg: var(--bs-warning-text-emphasis);--bs-list-group-active-border-color: var(--bs-warning-text-emphasis)}.list-group-item-danger{--bs-list-group-color: var(--bs-danger-text-emphasis);--bs-list-group-bg: var(--bs-danger-bg-subtle);--bs-list-group-border-color: var(--bs-danger-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-danger-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-danger-border-subtle);--bs-list-group-active-color: var(--bs-danger-bg-subtle);--bs-list-group-active-bg: var(--bs-danger-text-emphasis);--bs-list-group-active-border-color: var(--bs-danger-text-emphasis)}.list-group-item-light{--bs-list-group-color: var(--bs-light-text-emphasis);--bs-list-group-bg: var(--bs-light-bg-subtle);--bs-list-group-border-color: var(--bs-light-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-light-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-light-border-subtle);--bs-list-group-active-color: var(--bs-light-bg-subtle);--bs-list-group-active-bg: var(--bs-light-text-emphasis);--bs-list-group-active-border-color: var(--bs-light-text-emphasis)}.list-group-item-dark{--bs-list-group-color: var(--bs-dark-text-emphasis);--bs-list-group-bg: var(--bs-dark-bg-subtle);--bs-list-group-border-color: var(--bs-dark-border-subtle);--bs-list-group-action-hover-color: 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var(--bg-gradient-start, 36%), #3fb618 var(--bg-gradient-end, 180%)) #757b7a;color:#fff}.bg-gradient-cyan-teal{--bslib-color-fg: #fff;--bslib-color-bg: #6983ad;background:linear-gradient(var(--bg-gradient-deg, 140deg), #9954bb var(--bg-gradient-start, 36%), #20c997 var(--bg-gradient-end, 180%)) #6983ad;color:#fff}:root{--bslib-value-box-shadow: none;--bslib-value-box-border-width-auto-yes: var(--bslib-value-box-border-width-baseline);--bslib-value-box-border-width-auto-no: 0;--bslib-value-box-border-width-baseline: 1px}.bslib-value-box{border-width:var(--bslib-value-box-border-width-auto-no, var(--bslib-value-box-border-width-baseline));container-name:bslib-value-box;container-type:inline-size}.bslib-value-box.card{box-shadow:var(--bslib-value-box-shadow)}.bslib-value-box.border-auto{border-width:var(--bslib-value-box-border-width-auto-yes, var(--bslib-value-box-border-width-baseline))}.bslib-value-box.default{--bslib-value-box-bg-default: var(--bs-card-bg, #fff);--bslib-value-box-border-color-default: var(--bs-card-border-color, rgba(0, 0, 0, 0.175));color:var(--bslib-value-box-color);background-color:var(--bslib-value-box-bg, var(--bslib-value-box-bg-default));border-color:var(--bslib-value-box-border-color, var(--bslib-value-box-border-color-default))}.bslib-value-box .value-box-grid{display:grid;grid-template-areas:"left right";align-items:center;overflow:hidden}.bslib-value-box .value-box-showcase{height:100%;max-height:var(---bslib-value-box-showcase-max-h, 100%)}.bslib-value-box .value-box-showcase,.bslib-value-box .value-box-showcase>.html-fill-item{width:100%}.bslib-value-box[data-full-screen=true] .value-box-showcase{max-height:var(---bslib-value-box-showcase-max-h-fs, 100%)}@media screen and (min-width: 575.98px){@container bslib-value-box (max-width: 300px){.bslib-value-box:not(.showcase-bottom) .value-box-grid{grid-template-columns:1fr !important;grid-template-rows:auto auto;grid-template-areas:"top" "bottom"}.bslib-value-box:not(.showcase-bottom) .value-box-grid .value-box-showcase{grid-area:top !important}.bslib-value-box:not(.showcase-bottom) .value-box-grid .value-box-area{grid-area:bottom !important;justify-content:end}}}.bslib-value-box .value-box-area{justify-content:center;padding:1.5rem 1rem;font-size:.9rem;font-weight:500}.bslib-value-box .value-box-area *{margin-bottom:0;margin-top:0}.bslib-value-box .value-box-title{font-size:1rem;margin-top:0;margin-bottom:.5rem;font-weight:400;line-height:1.2}.bslib-value-box .value-box-title:empty::after{content:" "}.bslib-value-box .value-box-value{font-size:calc(1.29rem + 0.48vw);margin-top:0;margin-bottom:.5rem;font-weight:400;line-height:1.2}@media(min-width: 1200px){.bslib-value-box .value-box-value{font-size:1.65rem}}.bslib-value-box .value-box-value:empty::after{content:" "}.bslib-value-box .value-box-showcase{align-items:center;justify-content:center;margin-top:auto;margin-bottom:auto;padding:1rem}.bslib-value-box .value-box-showcase .bi,.bslib-value-box .value-box-showcase .fa,.bslib-value-box .value-box-showcase .fab,.bslib-value-box .value-box-showcase .fas,.bslib-value-box .value-box-showcase .far{opacity:.85;min-width:50px;max-width:125%}.bslib-value-box .value-box-showcase .bi,.bslib-value-box .value-box-showcase .fa,.bslib-value-box .value-box-showcase .fab,.bslib-value-box .value-box-showcase .fas,.bslib-value-box .value-box-showcase .far{font-size:4rem}.bslib-value-box.showcase-top-right .value-box-grid{grid-template-columns:1fr var(---bslib-value-box-showcase-w, 50%)}.bslib-value-box.showcase-top-right .value-box-grid .value-box-showcase{grid-area:right;margin-left:auto;align-self:start;align-items:end;padding-left:0;padding-bottom:0}.bslib-value-box.showcase-top-right .value-box-grid .value-box-area{grid-area:left;align-self:end}.bslib-value-box.showcase-top-right[data-full-screen=true] .value-box-grid{grid-template-columns:auto var(---bslib-value-box-showcase-w-fs, 1fr)}.bslib-value-box.showcase-top-right[data-full-screen=true] .value-box-grid>div{align-self:center}.bslib-value-box.showcase-top-right:not([data-full-screen=true]) .value-box-showcase{margin-top:0}@container bslib-value-box (max-width: 300px){.bslib-value-box.showcase-top-right:not([data-full-screen=true]) .value-box-grid .value-box-showcase{padding-left:1rem}}.bslib-value-box.showcase-left-center .value-box-grid{grid-template-columns:var(---bslib-value-box-showcase-w, 30%) auto}.bslib-value-box.showcase-left-center[data-full-screen=true] .value-box-grid{grid-template-columns:var(---bslib-value-box-showcase-w-fs, 1fr) auto}.bslib-value-box.showcase-left-center:not([data-fill-screen=true]) .value-box-grid .value-box-showcase{grid-area:left}.bslib-value-box.showcase-left-center:not([data-fill-screen=true]) .value-box-grid .value-box-area{grid-area:right}.bslib-value-box.showcase-bottom .value-box-grid{grid-template-columns:1fr;grid-template-rows:1fr var(---bslib-value-box-showcase-h, auto);grid-template-areas:"top" "bottom";overflow:hidden}.bslib-value-box.showcase-bottom .value-box-grid .value-box-showcase{grid-area:bottom;padding:0;margin:0}.bslib-value-box.showcase-bottom .value-box-grid .value-box-area{grid-area:top}.bslib-value-box.showcase-bottom[data-full-screen=true] .value-box-grid{grid-template-rows:1fr var(---bslib-value-box-showcase-h-fs, 2fr)}.bslib-value-box.showcase-bottom[data-full-screen=true] .value-box-grid .value-box-showcase{padding:1rem}[data-bs-theme=dark] .bslib-value-box{--bslib-value-box-shadow: 0 0.5rem 1rem rgb(0 0 0 / 50%)}@media(min-width: 576px){.nav:not(.nav-hidden){display:flex !important;display:-webkit-flex !important}.nav:not(.nav-hidden):not(.nav-stacked):not(.flex-column){float:none !important}.nav:not(.nav-hidden):not(.nav-stacked):not(.flex-column)>.bslib-nav-spacer{margin-left:auto !important}.nav:not(.nav-hidden):not(.nav-stacked):not(.flex-column)>.form-inline{margin-top:auto;margin-bottom:auto}.nav:not(.nav-hidden).nav-stacked{flex-direction:column;-webkit-flex-direction:column;height:100%}.nav:not(.nav-hidden).nav-stacked>.bslib-nav-spacer{margin-top:auto !important}}.bslib-card{overflow:auto}.bslib-card .card-body+.card-body{padding-top:0}.bslib-card .card-body{overflow:auto}.bslib-card .card-body p{margin-top:0}.bslib-card .card-body p:last-child{margin-bottom:0}.bslib-card .card-body{max-height:var(--bslib-card-body-max-height, none)}.bslib-card[data-full-screen=true]>.card-body{max-height:var(--bslib-card-body-max-height-full-screen, none)}.bslib-card .card-header .form-group{margin-bottom:0}.bslib-card .card-header .selectize-control{margin-bottom:0}.bslib-card .card-header .selectize-control .item{margin-right:1.15rem}.bslib-card .card-footer{margin-top:auto}.bslib-card .bslib-navs-card-title{display:flex;flex-wrap:wrap;justify-content:space-between;align-items:center}.bslib-card .bslib-navs-card-title .nav{margin-left:auto}.bslib-card .bslib-sidebar-layout:not([data-bslib-sidebar-border=true]){border:none}.bslib-card .bslib-sidebar-layout:not([data-bslib-sidebar-border-radius=true]){border-top-left-radius:0;border-top-right-radius:0}[data-full-screen=true]{position:fixed;inset:3.5rem 1rem 1rem;height:auto !important;max-height:none !important;width:auto !important;z-index:1070}.bslib-full-screen-enter{display:none;position:absolute;bottom:var(--bslib-full-screen-enter-bottom, 0.2rem);right:var(--bslib-full-screen-enter-right, 0);top:var(--bslib-full-screen-enter-top);left:var(--bslib-full-screen-enter-left);color:var(--bslib-color-fg, var(--bs-card-color));background-color:var(--bslib-color-bg, var(--bs-card-bg, var(--bs-body-bg)));border:var(--bs-card-border-width) solid var(--bslib-color-fg, var(--bs-card-border-color));box-shadow:0 2px 4px rgba(0,0,0,.15);margin:.2rem .4rem;padding:.55rem !important;font-size:.8rem;cursor:pointer;opacity:.7;z-index:1070}.bslib-full-screen-enter:hover{opacity:1}.card[data-full-screen=false]:hover>*>.bslib-full-screen-enter{display:block}.bslib-has-full-screen .card:hover>*>.bslib-full-screen-enter{display:none}@media(max-width: 575.98px){.bslib-full-screen-enter{display:none !important}}.bslib-full-screen-exit{position:relative;top:1.35rem;font-size:.9rem;cursor:pointer;text-decoration:none;display:flex;float:right;margin-right:2.15rem;align-items:center;color:rgba(var(--bs-body-bg-rgb), 0.8)}.bslib-full-screen-exit:hover{color:rgba(var(--bs-body-bg-rgb), 1)}.bslib-full-screen-exit svg{margin-left:.5rem;font-size:1.5rem}#bslib-full-screen-overlay{position:fixed;inset:0;background-color:rgba(var(--bs-body-color-rgb), 0.6);backdrop-filter:blur(2px);-webkit-backdrop-filter:blur(2px);z-index:1069;animation:bslib-full-screen-overlay-enter 400ms cubic-bezier(0.6, 0.02, 0.65, 1) 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992px){.navbar-expand-lg{flex-wrap:nowrap;-webkit-flex-wrap:nowrap;justify-content:flex-start;-webkit-justify-content:flex-start}.navbar-expand-lg .navbar-nav{flex-direction:row;-webkit-flex-direction:row}.navbar-expand-lg .navbar-nav .dropdown-menu{position:absolute}.navbar-expand-lg .navbar-nav .nav-link{padding-right:var(--bs-navbar-nav-link-padding-x);padding-left:var(--bs-navbar-nav-link-padding-x)}.navbar-expand-lg .navbar-nav-scroll{overflow:visible}.navbar-expand-lg .navbar-collapse{display:flex !important;display:-webkit-flex !important;flex-basis:auto;-webkit-flex-basis:auto}.navbar-expand-lg .navbar-toggler{display:none}.navbar-expand-lg .offcanvas{position:static;z-index:auto;flex-grow:1;-webkit-flex-grow:1;width:auto !important;height:auto !important;visibility:visible !important;background-color:rgba(0,0,0,0) !important;border:0 !important;transform:none !important;transition:none}.navbar-expand-lg .offcanvas .offcanvas-header{display:none}.navbar-expand-lg .offcanvas 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!important;flex-basis:auto;-webkit-flex-basis:auto}.navbar-expand .navbar-toggler{display:none}.navbar-expand .offcanvas{position:static;z-index:auto;flex-grow:1;-webkit-flex-grow:1;width:auto !important;height:auto !important;visibility:visible !important;background-color:rgba(0,0,0,0) !important;border:0 !important;transform:none !important;transition:none}.navbar-expand .offcanvas .offcanvas-header{display:none}.navbar-expand .offcanvas .offcanvas-body{display:flex;display:-webkit-flex;flex-grow:0;-webkit-flex-grow:0;padding:0;overflow-y:visible}.navbar-dark,.navbar[data-bs-theme=dark]{--bs-navbar-color: #cacccd;--bs-navbar-hover-color: rgba(184, 203, 246, 0.8);--bs-navbar-disabled-color: rgba(202, 204, 205, 0.75);--bs-navbar-active-color: #b8cbf6;--bs-navbar-brand-color: #cacccd;--bs-navbar-brand-hover-color: #b8cbf6;--bs-navbar-toggler-border-color: rgba(202, 204, 205, 0);--bs-navbar-toggler-icon-bg: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 30 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var(--bs-card-spacer-x);color:var(--bs-card-color)}.card-title{margin-bottom:var(--bs-card-title-spacer-y);color:var(--bs-card-title-color)}.card-subtitle{margin-top:calc(-0.5*var(--bs-card-title-spacer-y));margin-bottom:0;color:var(--bs-card-subtitle-color)}.card-text:last-child{margin-bottom:0}.card-link+.card-link{margin-left:var(--bs-card-spacer-x)}.card-header{padding:var(--bs-card-cap-padding-y) var(--bs-card-cap-padding-x);margin-bottom:0;color:var(--bs-card-cap-color);background-color:var(--bs-card-cap-bg);border-bottom:var(--bs-card-border-width) solid var(--bs-card-border-color)}.card-footer{padding:var(--bs-card-cap-padding-y) var(--bs-card-cap-padding-x);color:var(--bs-card-cap-color);background-color:var(--bs-card-cap-bg);border-top:var(--bs-card-border-width) solid var(--bs-card-border-color)}.card-header-tabs{margin-right:calc(-0.5*var(--bs-card-cap-padding-x));margin-bottom:calc(-1*var(--bs-card-cap-padding-y));margin-left:calc(-0.5*var(--bs-card-cap-padding-x));border-bottom:0}.card-header-tabs .nav-link.active{background-color:var(--bs-card-bg);border-bottom-color:var(--bs-card-bg)}.card-header-pills{margin-right:calc(-0.5*var(--bs-card-cap-padding-x));margin-left:calc(-0.5*var(--bs-card-cap-padding-x))}.card-img-overlay{position:absolute;top:0;right:0;bottom:0;left:0;padding:var(--bs-card-img-overlay-padding)}.card-img,.card-img-top,.card-img-bottom{width:100%}.card-group>.card{margin-bottom:var(--bs-card-group-margin)}@media(min-width: 576px){.card-group{display:flex;display:-webkit-flex;flex-flow:row wrap;-webkit-flex-flow:row wrap}.card-group>.card{flex:1 0 0%;-webkit-flex:1 0 0%;margin-bottom:0}.card-group>.card+.card{margin-left:0;border-left:0}}.accordion{--bs-accordion-color: #343a40;--bs-accordion-bg: #fff;--bs-accordion-transition: 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viewBox='0 0 16 16' fill='%2310335b'%3e%3cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e");--bs-accordion-btn-focus-border-color: #93c0f1;--bs-accordion-btn-focus-box-shadow: 0 0 0 0.25rem rgba(39, 128, 227, 0.25);--bs-accordion-body-padding-x: 1.25rem;--bs-accordion-body-padding-y: 1rem;--bs-accordion-active-color: #10335b;--bs-accordion-active-bg: #d4e6f9}.accordion-button{position:relative;display:flex;display:-webkit-flex;align-items:center;-webkit-align-items:center;width:100%;padding:var(--bs-accordion-btn-padding-y) var(--bs-accordion-btn-padding-x);font-size:1rem;color:var(--bs-accordion-btn-color);text-align:left;background-color:var(--bs-accordion-btn-bg);border:0;overflow-anchor:none;transition:var(--bs-accordion-transition)}@media(prefers-reduced-motion: reduce){.accordion-button{transition:none}}.accordion-button:not(.collapsed){color:var(--bs-accordion-active-color);background-color:var(--bs-accordion-active-bg);box-shadow:inset 0 calc(-1*var(--bs-accordion-border-width)) 0 var(--bs-accordion-border-color)}.accordion-button:not(.collapsed)::after{background-image:var(--bs-accordion-btn-active-icon);transform:var(--bs-accordion-btn-icon-transform)}.accordion-button::after{flex-shrink:0;-webkit-flex-shrink:0;width:var(--bs-accordion-btn-icon-width);height:var(--bs-accordion-btn-icon-width);margin-left:auto;content:"";background-image:var(--bs-accordion-btn-icon);background-repeat:no-repeat;background-size:var(--bs-accordion-btn-icon-width);transition:var(--bs-accordion-btn-icon-transition)}@media(prefers-reduced-motion: reduce){.accordion-button::after{transition:none}}.accordion-button:hover{z-index:2}.accordion-button:focus{z-index:3;border-color:var(--bs-accordion-btn-focus-border-color);outline:0;box-shadow:var(--bs-accordion-btn-focus-box-shadow)}.accordion-header{margin-bottom:0}.accordion-item{color:var(--bs-accordion-color);background-color:var(--bs-accordion-bg);border:var(--bs-accordion-border-width) solid var(--bs-accordion-border-color)}.accordion-item:not(:first-of-type){border-top:0}.accordion-body{padding:var(--bs-accordion-body-padding-y) var(--bs-accordion-body-padding-x)}.accordion-flush .accordion-collapse{border-width:0}.accordion-flush .accordion-item{border-right:0;border-left:0}.accordion-flush .accordion-item:first-child{border-top:0}.accordion-flush .accordion-item:last-child{border-bottom:0}[data-bs-theme=dark] .accordion-button::after{--bs-accordion-btn-icon: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%237db3ee'%3e%3cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e");--bs-accordion-btn-active-icon: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%237db3ee'%3e%3cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e")}.breadcrumb{--bs-breadcrumb-padding-x: 0;--bs-breadcrumb-padding-y: 0;--bs-breadcrumb-margin-bottom: 1rem;--bs-breadcrumb-bg: ;--bs-breadcrumb-border-radius: ;--bs-breadcrumb-divider-color: rgba(52, 58, 64, 0.75);--bs-breadcrumb-item-padding-x: 0.5rem;--bs-breadcrumb-item-active-color: rgba(52, 58, 64, 0.75);display:flex;display:-webkit-flex;flex-wrap:wrap;-webkit-flex-wrap:wrap;padding:var(--bs-breadcrumb-padding-y) var(--bs-breadcrumb-padding-x);margin-bottom:var(--bs-breadcrumb-margin-bottom);font-size:var(--bs-breadcrumb-font-size);list-style:none;background-color:var(--bs-breadcrumb-bg)}.breadcrumb-item+.breadcrumb-item{padding-left:var(--bs-breadcrumb-item-padding-x)}.breadcrumb-item+.breadcrumb-item::before{float:left;padding-right:var(--bs-breadcrumb-item-padding-x);color:var(--bs-breadcrumb-divider-color);content:var(--bs-breadcrumb-divider, ">") /* rtl: var(--bs-breadcrumb-divider, ">") */}.breadcrumb-item.active{color:var(--bs-breadcrumb-item-active-color)}.pagination{--bs-pagination-padding-x: 0.75rem;--bs-pagination-padding-y: 0.375rem;--bs-pagination-font-size:1rem;--bs-pagination-color: #2761e3;--bs-pagination-bg: #fff;--bs-pagination-border-width: 1px;--bs-pagination-border-color: #dee2e6;--bs-pagination-border-radius: 0.25rem;--bs-pagination-hover-color: #1f4eb6;--bs-pagination-hover-bg: #f8f9fa;--bs-pagination-hover-border-color: #dee2e6;--bs-pagination-focus-color: #1f4eb6;--bs-pagination-focus-bg: #e9ecef;--bs-pagination-focus-box-shadow: 0 0 0 0.25rem rgba(39, 128, 227, 0.25);--bs-pagination-active-color: #fff;--bs-pagination-active-bg: #2780e3;--bs-pagination-active-border-color: #2780e3;--bs-pagination-disabled-color: rgba(52, 58, 64, 0.75);--bs-pagination-disabled-bg: #e9ecef;--bs-pagination-disabled-border-color: #dee2e6;display:flex;display:-webkit-flex;padding-left:0;list-style:none}.page-link{position:relative;display:block;padding:var(--bs-pagination-padding-y) var(--bs-pagination-padding-x);font-size:var(--bs-pagination-font-size);color:var(--bs-pagination-color);text-decoration:none;-webkit-text-decoration:none;-moz-text-decoration:none;-ms-text-decoration:none;-o-text-decoration:none;background-color:var(--bs-pagination-bg);border:var(--bs-pagination-border-width) solid var(--bs-pagination-border-color);transition:color .15s ease-in-out,background-color .15s ease-in-out,border-color .15s ease-in-out,box-shadow .15s ease-in-out}@media(prefers-reduced-motion: reduce){.page-link{transition:none}}.page-link:hover{z-index:2;color:var(--bs-pagination-hover-color);background-color:var(--bs-pagination-hover-bg);border-color:var(--bs-pagination-hover-border-color)}.page-link:focus{z-index:3;color:var(--bs-pagination-focus-color);background-color:var(--bs-pagination-focus-bg);outline:0;box-shadow:var(--bs-pagination-focus-box-shadow)}.page-link.active,.active>.page-link{z-index:3;color:var(--bs-pagination-active-color);background-color:var(--bs-pagination-active-bg);border-color:var(--bs-pagination-active-border-color)}.page-link.disabled,.disabled>.page-link{color:var(--bs-pagination-disabled-color);pointer-events:none;background-color:var(--bs-pagination-disabled-bg);border-color:var(--bs-pagination-disabled-border-color)}.page-item:not(:first-child) .page-link{margin-left:calc(1px*-1)}.pagination-lg{--bs-pagination-padding-x: 1.5rem;--bs-pagination-padding-y: 0.75rem;--bs-pagination-font-size:1.25rem;--bs-pagination-border-radius: 0.5rem}.pagination-sm{--bs-pagination-padding-x: 0.5rem;--bs-pagination-padding-y: 0.25rem;--bs-pagination-font-size:0.875rem;--bs-pagination-border-radius: 0.2em}.badge{--bs-badge-padding-x: 0.65em;--bs-badge-padding-y: 0.35em;--bs-badge-font-size:0.75em;--bs-badge-font-weight: 700;--bs-badge-color: #fff;--bs-badge-border-radius: 0.25rem;display:inline-block;padding:var(--bs-badge-padding-y) var(--bs-badge-padding-x);font-size:var(--bs-badge-font-size);font-weight:var(--bs-badge-font-weight);line-height:1;color:var(--bs-badge-color);text-align:center;white-space:nowrap;vertical-align:baseline}.badge:empty{display:none}.btn .badge{position:relative;top:-1px}.alert{--bs-alert-bg: transparent;--bs-alert-padding-x: 1rem;--bs-alert-padding-y: 1rem;--bs-alert-margin-bottom: 1rem;--bs-alert-color: inherit;--bs-alert-border-color: transparent;--bs-alert-border: 0 solid var(--bs-alert-border-color);--bs-alert-border-radius: 0.25rem;--bs-alert-link-color: inherit;position:relative;padding:var(--bs-alert-padding-y) var(--bs-alert-padding-x);margin-bottom:var(--bs-alert-margin-bottom);color:var(--bs-alert-color);background-color:var(--bs-alert-bg);border:var(--bs-alert-border)}.alert-heading{color:inherit}.alert-link{font-weight:700;color:var(--bs-alert-link-color)}.alert-dismissible{padding-right:3rem}.alert-dismissible .btn-close{position:absolute;top:0;right:0;z-index:2;padding:1.25rem 1rem}.alert-default{--bs-alert-color: var(--bs-default-text-emphasis);--bs-alert-bg: var(--bs-default-bg-subtle);--bs-alert-border-color: var(--bs-default-border-subtle);--bs-alert-link-color: var(--bs-default-text-emphasis)}.alert-primary{--bs-alert-color: var(--bs-primary-text-emphasis);--bs-alert-bg: var(--bs-primary-bg-subtle);--bs-alert-border-color: var(--bs-primary-border-subtle);--bs-alert-link-color: var(--bs-primary-text-emphasis)}.alert-secondary{--bs-alert-color: var(--bs-secondary-text-emphasis);--bs-alert-bg: var(--bs-secondary-bg-subtle);--bs-alert-border-color: var(--bs-secondary-border-subtle);--bs-alert-link-color: var(--bs-secondary-text-emphasis)}.alert-success{--bs-alert-color: var(--bs-success-text-emphasis);--bs-alert-bg: var(--bs-success-bg-subtle);--bs-alert-border-color: var(--bs-success-border-subtle);--bs-alert-link-color: var(--bs-success-text-emphasis)}.alert-info{--bs-alert-color: var(--bs-info-text-emphasis);--bs-alert-bg: var(--bs-info-bg-subtle);--bs-alert-border-color: var(--bs-info-border-subtle);--bs-alert-link-color: var(--bs-info-text-emphasis)}.alert-warning{--bs-alert-color: var(--bs-warning-text-emphasis);--bs-alert-bg: var(--bs-warning-bg-subtle);--bs-alert-border-color: var(--bs-warning-border-subtle);--bs-alert-link-color: var(--bs-warning-text-emphasis)}.alert-danger{--bs-alert-color: var(--bs-danger-text-emphasis);--bs-alert-bg: var(--bs-danger-bg-subtle);--bs-alert-border-color: var(--bs-danger-border-subtle);--bs-alert-link-color: var(--bs-danger-text-emphasis)}.alert-light{--bs-alert-color: var(--bs-light-text-emphasis);--bs-alert-bg: var(--bs-light-bg-subtle);--bs-alert-border-color: var(--bs-light-border-subtle);--bs-alert-link-color: var(--bs-light-text-emphasis)}.alert-dark{--bs-alert-color: var(--bs-dark-text-emphasis);--bs-alert-bg: var(--bs-dark-bg-subtle);--bs-alert-border-color: var(--bs-dark-border-subtle);--bs-alert-link-color: var(--bs-dark-text-emphasis)}@keyframes progress-bar-stripes{0%{background-position-x:.5rem}}.progress,.progress-stacked{--bs-progress-height: 0.5rem;--bs-progress-font-size:0.75rem;--bs-progress-bg: #e9ecef;--bs-progress-border-radius: 0.25rem;--bs-progress-box-shadow: inset 0 1px 2px rgba(0, 0, 0, 0.075);--bs-progress-bar-color: #fff;--bs-progress-bar-bg: #2780e3;--bs-progress-bar-transition: width 0.6s ease;display:flex;display:-webkit-flex;height:var(--bs-progress-height);overflow:hidden;font-size:var(--bs-progress-font-size);background-color:var(--bs-progress-bg)}.progress-bar{display:flex;display:-webkit-flex;flex-direction:column;-webkit-flex-direction:column;justify-content:center;-webkit-justify-content:center;overflow:hidden;color:var(--bs-progress-bar-color);text-align:center;white-space:nowrap;background-color:var(--bs-progress-bar-bg);transition:var(--bs-progress-bar-transition)}@media(prefers-reduced-motion: reduce){.progress-bar{transition:none}}.progress-bar-striped{background-image:linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);background-size:var(--bs-progress-height) var(--bs-progress-height)}.progress-stacked>.progress{overflow:visible}.progress-stacked>.progress>.progress-bar{width:100%}.progress-bar-animated{animation:1s linear infinite progress-bar-stripes}@media(prefers-reduced-motion: reduce){.progress-bar-animated{animation:none}}.list-group{--bs-list-group-color: #343a40;--bs-list-group-bg: #fff;--bs-list-group-border-color: #dee2e6;--bs-list-group-border-width: 1px;--bs-list-group-border-radius: 0.25rem;--bs-list-group-item-padding-x: 1rem;--bs-list-group-item-padding-y: 0.5rem;--bs-list-group-action-color: rgba(52, 58, 64, 0.75);--bs-list-group-action-hover-color: #000;--bs-list-group-action-hover-bg: #f8f9fa;--bs-list-group-action-active-color: #343a40;--bs-list-group-action-active-bg: #e9ecef;--bs-list-group-disabled-color: rgba(52, 58, 64, 0.75);--bs-list-group-disabled-bg: #fff;--bs-list-group-active-color: #fff;--bs-list-group-active-bg: #2780e3;--bs-list-group-active-border-color: #2780e3;display:flex;display:-webkit-flex;flex-direction:column;-webkit-flex-direction:column;padding-left:0;margin-bottom:0}.list-group-numbered{list-style-type:none;counter-reset:section}.list-group-numbered>.list-group-item::before{content:counters(section, ".") ". ";counter-increment:section}.list-group-item-action{width:100%;color:var(--bs-list-group-action-color);text-align:inherit}.list-group-item-action:hover,.list-group-item-action:focus{z-index:1;color:var(--bs-list-group-action-hover-color);text-decoration:none;background-color:var(--bs-list-group-action-hover-bg)}.list-group-item-action:active{color:var(--bs-list-group-action-active-color);background-color:var(--bs-list-group-action-active-bg)}.list-group-item{position:relative;display:block;padding:var(--bs-list-group-item-padding-y) var(--bs-list-group-item-padding-x);color:var(--bs-list-group-color);text-decoration:none;-webkit-text-decoration:none;-moz-text-decoration:none;-ms-text-decoration:none;-o-text-decoration:none;background-color:var(--bs-list-group-bg);border:var(--bs-list-group-border-width) solid var(--bs-list-group-border-color)}.list-group-item.disabled,.list-group-item:disabled{color:var(--bs-list-group-disabled-color);pointer-events:none;background-color:var(--bs-list-group-disabled-bg)}.list-group-item.active{z-index:2;color:var(--bs-list-group-active-color);background-color:var(--bs-list-group-active-bg);border-color:var(--bs-list-group-active-border-color)}.list-group-item+.list-group-item{border-top-width:0}.list-group-item+.list-group-item.active{margin-top:calc(-1*var(--bs-list-group-border-width));border-top-width:var(--bs-list-group-border-width)}.list-group-horizontal{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal>.list-group-item.active{margin-top:0}.list-group-horizontal>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}@media(min-width: 576px){.list-group-horizontal-sm{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-sm>.list-group-item.active{margin-top:0}.list-group-horizontal-sm>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-sm>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media(min-width: 768px){.list-group-horizontal-md{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-md>.list-group-item.active{margin-top:0}.list-group-horizontal-md>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-md>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media(min-width: 992px){.list-group-horizontal-lg{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-lg>.list-group-item.active{margin-top:0}.list-group-horizontal-lg>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-lg>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media(min-width: 1200px){.list-group-horizontal-xl{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-xl>.list-group-item.active{margin-top:0}.list-group-horizontal-xl>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-xl>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media(min-width: 1400px){.list-group-horizontal-xxl{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-xxl>.list-group-item.active{margin-top:0}.list-group-horizontal-xxl>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-xxl>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}.list-group-flush>.list-group-item{border-width:0 0 var(--bs-list-group-border-width)}.list-group-flush>.list-group-item:last-child{border-bottom-width:0}.list-group-item-default{--bs-list-group-color: var(--bs-default-text-emphasis);--bs-list-group-bg: var(--bs-default-bg-subtle);--bs-list-group-border-color: var(--bs-default-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-default-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-default-border-subtle);--bs-list-group-active-color: var(--bs-default-bg-subtle);--bs-list-group-active-bg: var(--bs-default-text-emphasis);--bs-list-group-active-border-color: var(--bs-default-text-emphasis)}.list-group-item-primary{--bs-list-group-color: var(--bs-primary-text-emphasis);--bs-list-group-bg: var(--bs-primary-bg-subtle);--bs-list-group-border-color: var(--bs-primary-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-primary-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-primary-border-subtle);--bs-list-group-active-color: var(--bs-primary-bg-subtle);--bs-list-group-active-bg: var(--bs-primary-text-emphasis);--bs-list-group-active-border-color: var(--bs-primary-text-emphasis)}.list-group-item-secondary{--bs-list-group-color: var(--bs-secondary-text-emphasis);--bs-list-group-bg: var(--bs-secondary-bg-subtle);--bs-list-group-border-color: var(--bs-secondary-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-secondary-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-secondary-border-subtle);--bs-list-group-active-color: var(--bs-secondary-bg-subtle);--bs-list-group-active-bg: var(--bs-secondary-text-emphasis);--bs-list-group-active-border-color: var(--bs-secondary-text-emphasis)}.list-group-item-success{--bs-list-group-color: var(--bs-success-text-emphasis);--bs-list-group-bg: var(--bs-success-bg-subtle);--bs-list-group-border-color: var(--bs-success-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-success-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-success-border-subtle);--bs-list-group-active-color: var(--bs-success-bg-subtle);--bs-list-group-active-bg: var(--bs-success-text-emphasis);--bs-list-group-active-border-color: var(--bs-success-text-emphasis)}.list-group-item-info{--bs-list-group-color: var(--bs-info-text-emphasis);--bs-list-group-bg: var(--bs-info-bg-subtle);--bs-list-group-border-color: var(--bs-info-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-info-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-info-border-subtle);--bs-list-group-active-color: var(--bs-info-bg-subtle);--bs-list-group-active-bg: var(--bs-info-text-emphasis);--bs-list-group-active-border-color: var(--bs-info-text-emphasis)}.list-group-item-warning{--bs-list-group-color: var(--bs-warning-text-emphasis);--bs-list-group-bg: var(--bs-warning-bg-subtle);--bs-list-group-border-color: var(--bs-warning-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-warning-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-warning-border-subtle);--bs-list-group-active-color: var(--bs-warning-bg-subtle);--bs-list-group-active-bg: var(--bs-warning-text-emphasis);--bs-list-group-active-border-color: var(--bs-warning-text-emphasis)}.list-group-item-danger{--bs-list-group-color: var(--bs-danger-text-emphasis);--bs-list-group-bg: var(--bs-danger-bg-subtle);--bs-list-group-border-color: var(--bs-danger-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-danger-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-danger-border-subtle);--bs-list-group-active-color: var(--bs-danger-bg-subtle);--bs-list-group-active-bg: var(--bs-danger-text-emphasis);--bs-list-group-active-border-color: var(--bs-danger-text-emphasis)}.list-group-item-light{--bs-list-group-color: var(--bs-light-text-emphasis);--bs-list-group-bg: var(--bs-light-bg-subtle);--bs-list-group-border-color: var(--bs-light-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-light-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-light-border-subtle);--bs-list-group-active-color: var(--bs-light-bg-subtle);--bs-list-group-active-bg: var(--bs-light-text-emphasis);--bs-list-group-active-border-color: var(--bs-light-text-emphasis)}.list-group-item-dark{--bs-list-group-color: var(--bs-dark-text-emphasis);--bs-list-group-bg: var(--bs-dark-bg-subtle);--bs-list-group-border-color: var(--bs-dark-border-subtle);--bs-list-group-action-hover-color: 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 * that seems to be what ansi_up emits
 *
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.quarto-title-meta{display:grid;grid-template-columns:repeat(2, 1fr);grid-column-gap:1em}#title-block-header.quarto-title-block.default .quarto-title .title{margin-bottom:0}#title-block-header.quarto-title-block.default .quarto-title-author-orcid img{margin-top:-0.2em;height:.8em;width:.8em}#title-block-header.quarto-title-block.default .quarto-title-author-email{opacity:.7}#title-block-header.quarto-title-block.default .quarto-description p:last-of-type{margin-bottom:0}#title-block-header.quarto-title-block.default .quarto-title-meta-contents p,#title-block-header.quarto-title-block.default .quarto-title-authors p,#title-block-header.quarto-title-block.default .quarto-title-affiliations p{margin-bottom:.1em}#title-block-header.quarto-title-block.default .quarto-title-meta-heading{text-transform:uppercase;margin-top:1em;font-size:.8em;opacity:.8;font-weight:400}#title-block-header.quarto-title-block.default 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diff --git a/_site/site_libs/quarto-html/quarto-syntax-highlighting.css b/_site/site_libs/quarto-html/quarto-syntax-highlighting.css
index d9fd98f..b30ce57 100644
--- a/_site/site_libs/quarto-html/quarto-syntax-highlighting.css
+++ b/_site/site_libs/quarto-html/quarto-syntax-highlighting.css
@@ -85,6 +85,7 @@ code span.st {
 
 code span.cf {
   color: #003B4F;
+  font-weight: bold;
   font-style: inherit;
 }
 
@@ -193,6 +194,7 @@ code span.dv {
 
 code span.kw {
   color: #003B4F;
+  font-weight: bold;
   font-style: inherit;
 }
 
diff --git a/_site/site_libs/quarto-html/quarto.js b/_site/site_libs/quarto-html/quarto.js
index 3ebd49c..39e6869 100644
--- a/_site/site_libs/quarto-html/quarto.js
+++ b/_site/site_libs/quarto-html/quarto.js
@@ -94,7 +94,7 @@ window.document.addEventListener("DOMContentLoaded", function (_event) {
       if (link.href.indexOf("#") !== -1) {
         const anchor = link.href.split("#")[1];
         const heading = window.document.querySelector(
-          `[data-anchor-id=${anchor}]`
+          `[data-anchor-id="${anchor}"]`
         );
         if (heading) {
           // Add the class
@@ -134,8 +134,10 @@ window.document.addEventListener("DOMContentLoaded", function (_event) {
       window.innerHeight + window.pageYOffset >=
       window.document.body.offsetHeight
     ) {
+      // This is the no-scroll case where last section should be the active one
       sectionIndex = 0;
     } else {
+      // This finds the last section visible on screen that should be made active
       sectionIndex = [...sections].reverse().findIndex((section) => {
         if (section) {
           return window.pageYOffset >= section.offsetTop - sectionMargin;
@@ -317,6 +319,7 @@ window.document.addEventListener("DOMContentLoaded", function (_event) {
           for (const child of el.children) {
             child.style.opacity = 0;
             child.style.overflow = "hidden";
+            child.style.pointerEvents = "none";
           }
 
           nexttick(() => {
@@ -358,6 +361,7 @@ window.document.addEventListener("DOMContentLoaded", function (_event) {
 
               const clone = child.cloneNode(true);
               clone.style.opacity = 1;
+              clone.style.pointerEvents = null;
               clone.style.display = null;
               toggleContents.append(clone);
             }
@@ -432,6 +436,7 @@ window.document.addEventListener("DOMContentLoaded", function (_event) {
           for (const child of el.children) {
             child.style.opacity = 1;
             child.style.overflow = null;
+            child.style.pointerEvents = null;
           }
 
           const placeholderEl = window.document.getElementById(
@@ -739,6 +744,7 @@ window.document.addEventListener("DOMContentLoaded", function (_event) {
     // Process the collapse state if this is an UL
     if (el.tagName === "UL") {
       if (tocOpenDepth === -1 && depth > 1) {
+        // toc-expand: false
         el.classList.add("collapse");
       } else if (
         depth <= tocOpenDepth ||
@@ -757,10 +763,9 @@ window.document.addEventListener("DOMContentLoaded", function (_event) {
   };
 
   // walk the TOC and expand / collapse any items that should be shown
-
   if (tocEl) {
-    walk(tocEl, 0);
     updateActiveLink();
+    walk(tocEl, 0);
   }
 
   // Throttle the scroll event and walk peridiocally
@@ -779,6 +784,10 @@ window.document.addEventListener("DOMContentLoaded", function (_event) {
   window.addEventListener(
     "resize",
     throttle(() => {
+      if (tocEl) {
+        updateActiveLink();
+        walk(tocEl, 0);
+      }
       if (!isReaderMode()) {
         hideOverlappedSidebars();
       }
diff --git a/_site/site_libs/quarto-nav/quarto-nav.js b/_site/site_libs/quarto-nav/quarto-nav.js
index f6a53b1..38cc430 100644
--- a/_site/site_libs/quarto-nav/quarto-nav.js
+++ b/_site/site_libs/quarto-nav/quarto-nav.js
@@ -5,9 +5,45 @@ const headroomChanged = new CustomEvent("quarto-hrChanged", {
   composed: false,
 });
 
+const announceDismiss = () => {
+  const annEl = window.document.getElementById("quarto-announcement");
+  if (annEl) {
+    annEl.remove();
+
+    const annId = annEl.getAttribute("data-announcement-id");
+    window.localStorage.setItem(`quarto-announce-${annId}`, "true");
+  }
+};
+
+const announceRegister = () => {
+  const annEl = window.document.getElementById("quarto-announcement");
+  if (annEl) {
+    const annId = annEl.getAttribute("data-announcement-id");
+    const isDismissed =
+      window.localStorage.getItem(`quarto-announce-${annId}`) || false;
+    if (isDismissed) {
+      announceDismiss();
+      return;
+    } else {
+      annEl.classList.remove("hidden");
+    }
+
+    const actionEl = annEl.querySelector(".quarto-announcement-action");
+    if (actionEl) {
+      actionEl.addEventListener("click", function (e) {
+        e.preventDefault();
+        // Hide the bar immediately
+        announceDismiss();
+      });
+    }
+  }
+};
+
 window.document.addEventListener("DOMContentLoaded", function () {
   let init = false;
 
+  announceRegister();
+
   // Manage the back to top button, if one is present.
   let lastScrollTop = window.pageYOffset || document.documentElement.scrollTop;
   const scrollDownBuffer = 5;
diff --git a/_site/site_libs/quarto-search/quarto-search.js b/_site/site_libs/quarto-search/quarto-search.js
index 5f723d7..d788a95 100644
--- a/_site/site_libs/quarto-search/quarto-search.js
+++ b/_site/site_libs/quarto-search/quarto-search.js
@@ -1275,7 +1275,11 @@ async function fuseSearch(query, fuse, fuseOptions) {
 
   // If we don't have a subfuse and the query is long enough, go ahead
   // and create a subfuse to use for subsequent queries
-  if (now - then > kFuseMaxWait && subSearchFuse === undefined) {
+  if (
+    now - then > kFuseMaxWait &&
+    subSearchFuse === undefined &&
+    resultsRaw.length < fuseOptions.limit
+  ) {
     subSearchTerm = query;
     subSearchFuse = new window.Fuse([], kFuseIndexOptions);
     resultsRaw.forEach((rr) => {
diff --git a/_site/site_libs/revealjs/dist/theme/quarto.css b/_site/site_libs/revealjs/dist/theme/quarto.css
index 2577a64..58f87c5 100644
--- a/_site/site_libs/revealjs/dist/theme/quarto.css
+++ b/_site/site_libs/revealjs/dist/theme/quarto.css
@@ -1,8 +1,8 @@
-@import"./fonts/source-sans-pro/source-sans-pro.css";:root{--r-background-color: #fff;--r-main-font: Source Sans Pro, Helvetica, sans-serif;--r-main-font-size: 40px;--r-main-color: #222;--r-block-margin: 12px;--r-heading-margin: 0 0 12px 0;--r-heading-font: Source Sans Pro, Helvetica, sans-serif;--r-heading-color: #222;--r-heading-line-height: 1.2;--r-heading-letter-spacing: normal;--r-heading-text-transform: none;--r-heading-text-shadow: none;--r-heading-font-weight: 600;--r-heading1-text-shadow: none;--r-heading1-size: 2.5em;--r-heading2-size: 1.6em;--r-heading3-size: 1.3em;--r-heading4-size: 1em;--r-code-font: SFMono-Regular, Menlo, Monaco, Consolas, Liberation Mono, Courier New, monospace;--r-link-color: #2a76dd;--r-link-color-dark: #1a53a1;--r-link-color-hover: #5692e4;--r-selection-background-color: #98bdef;--r-selection-color: #fff}.reveal-viewport{background:#fff;background-color:var(--r-background-color)}.reveal{font-family:var(--r-main-font);font-size:var(--r-main-font-size);font-weight:normal;color:var(--r-main-color)}.reveal ::selection{color:var(--r-selection-color);background:var(--r-selection-background-color);text-shadow:none}.reveal ::-moz-selection{color:var(--r-selection-color);background:var(--r-selection-background-color);text-shadow:none}.reveal .slides section,.reveal .slides section>section{line-height:1.3;font-weight:inherit}.reveal h1,.reveal h2,.reveal h3,.reveal h4,.reveal h5,.reveal h6{margin:var(--r-heading-margin);color:var(--r-heading-color);font-family:var(--r-heading-font);font-weight:var(--r-heading-font-weight);line-height:var(--r-heading-line-height);letter-spacing:var(--r-heading-letter-spacing);text-transform:var(--r-heading-text-transform);text-shadow:var(--r-heading-text-shadow);word-wrap:break-word}.reveal h1{font-size:var(--r-heading1-size)}.reveal 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auto;padding:5px;font-style:italic;background:rgba(255,255,255,.05);box-shadow:0px 0px 2px rgba(0,0,0,.2)}.reveal blockquote p:first-child,.reveal blockquote p:last-child{display:inline-block}.reveal q{font-style:italic}.reveal pre{display:block;position:relative;width:90%;margin:var(--r-block-margin) auto;text-align:left;font-size:.55em;font-family:var(--r-code-font);line-height:1.2em;word-wrap:break-word;box-shadow:0px 5px 15px rgba(0,0,0,.15)}.reveal code{font-family:var(--r-code-font);text-transform:none;tab-size:2}.reveal pre code{display:block;padding:5px;overflow:auto;max-height:400px;word-wrap:normal}.reveal .code-wrapper{white-space:normal}.reveal .code-wrapper code{white-space:pre}.reveal table{margin:auto;border-collapse:collapse;border-spacing:0}.reveal table th{font-weight:bold}.reveal table th,.reveal table td{text-align:left;padding:.2em .5em .2em .5em;border-bottom:1px solid}.reveal table th[align=center],.reveal table td[align=center]{text-align:center}.reveal table 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diff --git a/materials/0_housekeeping.qmd b/materials/0_housekeeping.qmd
index d27a24c..66ecc86 100644
--- a/materials/0_housekeeping.qmd
+++ b/materials/0_housekeeping.qmd
@@ -90,7 +90,7 @@ Please note everyone’s lanyard colors before taking a photo and respect their
 
 <br>
 
-[TEAL]{style="color:teal;"} sticky note: I am OK / I am done
+[GREEN]{style="color:green;"} sticky note: I am OK / I am done
 
 [PINK]{style="color:pink;"} sticky note: I need support / I am working
 
diff --git a/materials/1_hello_arrow.qmd b/materials/1_hello_arrow.qmd
index 9954dba..a48b196 100644
--- a/materials/1_hello_arrow.qmd
+++ b/materials/1_hello_arrow.qmd
@@ -12,17 +12,6 @@ editor: source
 
 # Hello Arrow {#hello-arrow}
 
-
-## Kick-off Q&A
-
-<br>
-
-- What brings you to this workshop?
-- What challenges have you faced related to larger-than-memory data in R?
-- What is one thing you want to learn or achieve from today's workshop?
-- ...?
-
-
 ## Poll: Arrow
 
 <br>
diff --git a/materials/2_data_manipulation_1.qmd b/materials/2_data_manipulation_1.qmd
index fbbc8f1..e755615 100644
--- a/materials/2_data_manipulation_1.qmd
+++ b/materials/2_data_manipulation_1.qmd
@@ -175,7 +175,7 @@ long_rides_2021 |>
 #| label: get-help
 #| eval: false
 
-?`arrow-dplyr`
+?acero
 ```
 
 or view them at <https://arrow.apache.org/docs/r/reference/acero.html>
@@ -279,8 +279,12 @@ nyc_taxi |>
 ## How does this work?
 
 ![](images/dplyr-backend.png)
+## Acero
 
-## arrow C++
+* arrow's query execution engine
+* use Arrow functions on Arrow Datasets
+
+## Acero
 
 ![](images/arrow_cpp_functions.png)
 
@@ -304,7 +308,7 @@ nyc_taxi |>
 ```{r}
 #| label: get-help-funcs
 
-?`arrow-dplyr`
+?acero
 ```
 
 or view them at <https://arrow.apache.org/docs/r/reference/acero.html>
@@ -385,10 +389,10 @@ See $.data for the source Arrow object'
 
 * arrow 17.0.0 or later
 * this will only work for functions which have Arrow bindings
-* use `` ?`arrow-dplyr` `` to see which ones do
+* use `?acero` to see which ones do
 
 ## Summary
 
 -   Working with Arrow Datasets allow you to manipulate data which is larger-than-memory
--   You can use many dplyr functions with arrow - run `` ?`arrow-dplyr` `` to view the docs
+-   You can use many dplyr functions with arrow - run `?acero` to view the docs
 -   You can pass data to duckdb to use functions implemented in duckdb but not arrow
diff --git a/materials/4_data_manipulation_2.qmd b/materials/4_data_manipulation_2.qmd
index 6350610..c3207d8 100644
--- a/materials/4_data_manipulation_2.qmd
+++ b/materials/4_data_manipulation_2.qmd
@@ -438,6 +438,8 @@ dropoff <- nyc_taxi_zones_arrow |>
 
 ## Join and cross-tabulate
 
+<!-- Run the code and recap the section while the timer runs! -->
+
 ```{r}
 #| label: join-crosstab