Merging june 2024 training into main

materials/_quarto.yml

@@ -3,8 +3,8 @@ project:
   output-dir: _book
 
 book: 
-  title: "UCSB Faculty Seminar Series: Grow Your Data & Team Science Skills"
-  # date: "January 22-26, 2024"
+  title: "NCEAS coreR for Delta Science Program"
+  subtitle: "June 10 - 13, 2024"
   reader-mode: false
 
   # NoteHD: cover image is not working
@@ -23,28 +23,28 @@ book:
       - icon: twitter
         href: https://twitter.com/ucsb_nceas
       - icon: github
-        href: https://github.com/NCEAS/nceas-training/tree/2024-03-ucsb-faculty
+        href: https://github.com/NCEAS/nceas-training/tree/2024-06-delta
 
   chapters:
     - index.qmd # preface 
-    - session_01.qmd # Intro and Lego
-    - session_02.qmd # Data Visualization
-    - session_03.qmd # Text Analysis (Casey)
-    - session_04.qmd # Survey data
-    - session_05.qmd # Visualizing spatial data (Rachel)
-    - session_06.qmd # GitHub for project management 
-    - session_07.qmd # Publishing to Web
-    - session_08.qmd # GitHub Workflows
-    - session_09.qmd # Message Box
-    - session_10.qmd # Quarto Dashboards
-    - session_11.qmd # Practice Session
-    - session_12.qmd # reproducibility and provenance
-    - session_13.qmd # check PAT
-    # - session_14.qmd # Social Aspects of collab
-    # - session_15.qmd # Functions
-    # - session_16.qmd # Packages
-    # - session_17.qmd # Practice Session II
-    # - session_18.qmd # Data Portals
+    - session_01.qmd # Introduction
+    - session_02.qmd # R Setup
+    - session_03.qmd # Git and GitHub set up
+    - session_04.qmd # Literate Analysis
+    - session_05.qmd # Intro to Git and GitHub
+    - session_06.qmd # Tidy data
+    - session_07.qmd # Cleaning and Wrangling
+    - session_08.qmd # Practice Session I - joins
+    - session_09.qmd # Collaborating with Git
+    - session_10.qmd # Data Management
+    - session_11.qmd # Publishing to the Web
+    - session_12.qmd # Intro to Data Viz
+    - session_13.qmd # Spatial Data
+    - session_14.qmd # Practice Session II -lobter report 
+    - session_15.qmd # Shiny
+    - session_16.qmd # Reproducibility and Provenence
+    - session_17.qmd # Appendix: Set up in personla comp
+    - session_18.qmd # Intro to R & RStudio
     # - session_19.qmd # Spatial Data
     # - session_20.qmd # Repro and Provenance
 

materials/index.qmd

@@ -4,27 +4,29 @@
 
 ![](cover.png){width="80%" fig-align="center"}
 
-![](images/logo-ucsb-office-research.png){width="80%" fig-align="center"}
+![](images/delta/delta-logo.png){width="80%" fig-align="center"}
 
 :::
 
 ## About the course {.unnumbered}
+As part of a collaboration between NCEAS and the Delta Science Program, this course has been adapted from [NCEAS coreR course](https://www.nceas.ucsb.edu/learning-hub/core-r), an in-person immersion in R programming for environmental data science. Researchers will gain experience with essential data science tools and best practices to increase their capacity as collaborators, reproducible coders, and open scientists.
 
-Hosted by the [National Center for Ecological Anlysis and Synthesis (NCEAS)](https://www.nceas.ucsb.edu/) and in partnership with [UCSB's Office of Research](https://www.research.ucsb.edu/), this seminar series is a training program where participants engage in synthesis research in a small cohort. It will equip participants with data science and team science tools, and provide them with a platform to conduct collaborative synthesis research. The overall aim is to grow cross-departmental relationships and interdisciplinary research outputs.
 
 ## NCEAS Expertise
 
-NCEAS, a research affiliate of UCSB, is a leading expert on interdisciplinary data science and works collaboratively to answer the world's largest and most complex questions. The NCEAS approach leverages existing data and employs a team science philosophy to squeeze out all potential insights and solutions efficiently - this is called [synthesis science](https://www.nceas.ucsb.edu/our-approach). NCEAS has over 25 years of success with this model among working groups and environmental professionals.
+The [National Center for Ecological Analysis and Synthesis (NCEAS)](https://www.nceas.ucsb.edu/), a research affiliate of UCSB, is a leading expert on interdisciplinary data science and works collaboratively to answer the world's largest and most complex questions. The NCEAS approach leverages existing data and employs a team science philosophy to squeeze out all potential insights and solutions efficiently - this is called [synthesis science](https://www.nceas.ucsb.edu/our-approach).
 
-## Week Three: Communicating your Science and Reproducible Workflows
+NCEAS has over 25 years of success with this model among working groups and environmental professionals. Together with the Delta Science Program and the Delta Stewardship Council we are excited to pass along skills, workflows, mindsets learn throughout the years.
 
-*March 25 - 27, 2024*
 
-### Learning Objectives
+<!-- *June 10 - 13, 2024* -->
 
--   Explore visualization tools such as `ggplot2`, Quarto Dashboards and different packages to plot spatial data.
--   Broaden the tool kit of data science analytical tools by exploring text analysis methods and reproducible workflows to access survey data.
--   Recap and wrap up the concept of reproducibility and how to apply it into the everyday workflow.
+### Learning Objectives
+-   Effectively manage data using `tidy` data practices
+-   Implement reproducible scientific workflows throughout all aspects of a project
+-   Establish best practices and utilize tools like Git & GitHub to optimize your collaboration
+-   Better communicate scientific analyses and results using Markdown, GitHub webpages, and R packages like `ggplot2` and `Shiny`
+- Increase your familiarity and confidence with data science tools
 
 ### Week's Schedule
 
@@ -43,10 +45,12 @@ These written materials are the result of a continuous and collaborative effort
 
 This work is licensed under a [Creative Commons Attribution 4.0 International License](http://creativecommons.org/licenses/by/4.0/).
 
-**Citation:** Camila Vargas Poulsen, Rachel King, Casey O'Hara (2024), UCSB Faculty Seminar Series: Grow Your Data & Team Science Skills, March 25-27, NCEAS Learning Hub. URL <https://learning.nceas.ucsb.edu/2024-03-ucsb-faculty>.
+**Citation:** Camila Vargas Poulsen & Angel Chen (2024), NCEAS coreR for Delta Science Program, June 2024, NCEAS Learning Hub. URL <https://learning.nceas.ucsb.edu/2024-06-delta>.
 
 **Additional contributors:** Ben Bolker, Amber E. Budden, Julien Brun, Samantha Csik, Halina Do-Linh, Natasha Haycock-Chavez, S. Jeanette Clark, Julie Lowndes, Stephanie Hampton, Matt Jone, Samanta Katz, Erin McLean, Bryce Mecum, Deanna Pennington, Karthik Ram, Jim Regetz, Tracy Teal, Daphne Virlar-Knight, Leah Wasser.
 
+
+
 This is a Quarto book. To learn more about Quarto books visit <https://quarto.org/docs/books>.
 
 

materials/sections/clean-wrangle-data.qmd

@@ -154,7 +154,7 @@ Now that we have introduced some data wrangling libraries, let's get the data th
 
 1. Go to [KNB Data Package Alaska commercial salmon catches by management region (1886- 1997)](https://knb.ecoinformatics.org/view/df35b.304.2)
 
-2. Find the data file `byerlySalmonByRegion.csv`. Right click the "Download" button and select "Copy Link Address"
+2. Find the data file `df35b.302.1`. Right click the "Download" button and select "Copy Link Address"
 
 3. Paste the copied URL into the `read_csv()` function
 
@@ -189,18 +189,19 @@ Before we get too much further, spend a minute or two outlining your Quarto docu
 - Clean and Reshape data
   - Using `select()` function
   - Check column types
-  - Replace values in a column
-  - Reshape data
+  - Replace values in a column with `mutate()`
+  - Reshape data with `pivot_longer()` and `pivot_wider()`
   - Rename columns `rename()`
+  - Add columns with `mutate()`
   - Summary stats using `group_by()` and `summarize()`
   - Filtering rows using `filter()`
   - Sort data using `arrange()`
-  - Split and combine values in columns
+  - Split and combine values in columns with `separate()` and `unite()`
 
 :::
 
 ## Data exploration
-Similar to what we did in our [Intro to Literate Analysis](https://learning.nceas.ucsb.edu/2023-10-coreR/session_04.html) lesson, it is good practice to skim through the data you just read in. 
+Similar to what we did in our [Literate Analysis](https://learning.nceas.ucsb.edu/2024-06-delta/session_04.html) lesson, it is good practice to skim through the data you just read in. 
 
 Doing so is important to make sure the data is read as you were expecting and to familiarize yourself with the data.
 
@@ -340,7 +341,7 @@ It looks like there is only one problem row, lets have a look at it in the origi
 catch_data[i,]
 ```
 
-Well that's odd: The value in `catch_thousands` is the letter `I`. It turns out that this data set is from a PDF which was automatically converted into a `csv` and this value of `I` is actually a 1.
+Well that's odd: The value in `Chinook` is the letter `I`. It turns out that this data set is from a PDF which was automatically converted into a `csv` and this value of `I` is actually a 1.
 
 Let's fix it by incorporating the `if_else()` function to our `mutate()` call, which will change the value of the `Chinook` column to 1 if the value is equal to `I`, then will use `as.numeric()` to turn the character representations of numbers into numeric typed values.
 
@@ -677,12 +678,11 @@ We just ran through the various things we can do with `dplyr` and `tidyr` but if
 ```{r}
 #| catch: true
 #| message: false
-catch_original <- read_csv(url("https://knb.ecoinformatics.org/knb/d1/mn/v2/object/df35b.302.1", 
-                               method = "libcurl"))
+catch_original <- read_csv("https://knb.ecoinformatics.org/knb/d1/mn/v2/object/df35b.302.1")
 
 mean_region <- catch_original %>%
   select(-All, -notesRegCode) %>% 
-  mutate(Chinook = ifelse(Chinook == "I", 1, Chinook)) %>% 
+  mutate(Chinook = if_else(Chinook == "I", "1", Chinook)) %>% 
   mutate(Chinook = as.numeric(Chinook)) %>% 
   pivot_longer(-c(Region, Year), 
                names_to = "species",