Session 4 initial update

README.md

@@ -26,5 +26,6 @@ Click on the links below to get access to:
     - Slides ([.html](https://mfiorina.github.io/sais_r_course/slides/session_3/session_3.html) or [.pdf](https://mfiorina.github.io/sais_r_course/slides/session_3/session_3.pdf))
     - [Code](https://mfiorina.github.io/sais_r_course/code/session_3.R)
 
-- Session 4 slides TBA
-[]([.html](https://mfiorina.github.io/sais_r_course/slides/session_4/session_4.html))
+- Session 4:
+    - Slides ([.html](https://mfiorina.github.io/sais_r_course/slides/session_4/session_4.html) or [.pdf](https://mfiorina.github.io/sais_r_course/slides/session_4/session_4.pdf))
+    - [Code](https://mfiorina.github.io/sais_r_course/code/session_4.R)

code/session_4.R

@@ -1,4 +1,4 @@
-## R MIEF Skills Workshop — Session 4
+## R Practical Skills Course — Session 4
 
   ### Session Description
 

slides/session_4/session_4.Rmd

@@ -1,8 +1,8 @@
 ---
-title: "R MIEF Skills Workshop"
+title: "R Professional Skills Course"
 subtitle: "Session 4"
 author: "Marc-Andrea Fiorina"
-date: "2024/09/27 (updated: `r Sys.Date()`)"
+date: "2024/11/13 (updated: `r Sys.Date()`)"
 output:
   xaringan::moon_reader:
     seal: false
@@ -41,11 +41,11 @@ xaringanExtra::use_panelset()
 
 class: center, middle
 
-# R MIEF Skills Workshop
+# Programming for Professional Research Using R
 
 ## Session 4
 
-### September 27, 2024
+### November 13, 2024
 
 ---
 
@@ -336,11 +336,11 @@ class: middle
 
 2. Work on your final assignment! The final assignment is to complete the Session 2, 3, and 4 challenges. You can find the challenges rewritten together on the next slide.
 
-**NOTE** — You should refer to documentation for the dataset, which can be found in the "Module" section, "Course Resources" Module on Canvas, for details on the variables and their given values.
+**NOTE** — You should refer to documentation for the dataset, which can be found at [https://mfiorina.github.io/sais_r_course/](https://mfiorina.github.io/sais_r_course/)
 
 ---
 
-### End of Course Assignment (**Due on Thursday, October 10**)
+### End of Course Assignment (**Due on Wednesday, November 27**)
 
 .panelset[
 
@@ -411,8 +411,6 @@ class: middle
 
 ---
 
-class: middle
-
 ## Links
 
 Hadley Wickham, Mine Çetinkaya-Rundel & Garrett Grolemund, **[R for Data Science, 2e — Custom Functions](https://r4ds.hadley.nz/functions)**
@@ -423,3 +421,54 @@ Rebecca Barter, **["Learn to purrr"](https://www.rebeccabarter.com/blog/2019-08-
 
 RStudio, **[RStudio Cheatsheets](https://www.rstudio.com/resources/cheatsheets/)**
 
+---
+
+## Explore R Further
+
+<ins>**More Complex Data Manipulation**</ins>
+
+Iterative coding (using loops for repetitive code) – purrr ’s map function is your friend. I recommend Thomas Mock, **[“Functional programming in R with Purrr”](https://themockup.blog/posts/2018-12-11-functional-progamming-in-r-with-purrr/)** to get you started.
+
+User-made functions in R – At some point, it will become time-effective to create your own functions to apply to your work. Hadley Wickham, **[Advanced R Chapter 6 – Functions](https://advr.hadley.nz/functions.html)**.
+
+<ins>**Publishing Your R Work**</ins>
+
+RStudio, **[“Introduction to RMarkdown”](https://rmarkdown.rstudio.com/lesson-1.html)**. Summarizes the uses and utility of the RMarkdown framework.
+
+Yihui Xie, **[“xaringan Presentations” – book chapter](https://bookdown.org/yihui/rmarkdown/xaringan.html)** and **[presentation](https://slides.yihui.org/xaringan/)**. Introduction to xaringan , a package
+that allows you to create slide decks using R. Also explore the **[xaringanExtra package](https://pkg.garrickadenbuie.com/xaringanExtra/#/)**.
+
+With RMarkdown, create books using **[bookdown](https://bookdown.org/)** or a blog using **[blogdown](https://bookdown.org/yihui/blogdown/)**.
+
+---
+
+## Explore R Further
+
+<ins>**Data Visualization Using Plots**</ins>
+
+The R community organizes **[“Tidy Tuesday”](https://www.tidytuesday.com/)**. This is a weekly challenge where users are provided a dataset and participants then swap graphs and scripts used to create their visualizations.
+
+David Robinson’s **[Tidy Tuesday live screencasts](https://www.youtube.com/user/safe4democracy)** on YouTube. The perfect resource to follow along and try to replicate a professional coder’s scripts.
+
+Yan Holtz and Conor Healy, **[“From Data to Viz”](https://www.data-toviz.com/)**. An amazing repository of methods to create different data visualizations using R.
+
+---
+
+## Explore R Further
+
+<ins>**Geospatial Data Visualization**</ins>
+
+Robin Lovelace, Jakub Nowosad, and Jannes Muenchow, **[Geocomputation with R](https://geocompr.robinlovelace.net/index.html)**. A great introduction to manipulating geospatial data (shapefiles and rasters) in R.
+
+Edzer Pebesma, **[“Simple Features for R”](https://rspatial.github.io/sf/articles/sf1.html)**. An introduction to the sf package, commonly used for geospatial work in R.
+
+Edzer Pebesma, **[“Plotting Simple Features”](https://rspatial.github.io/sf/articles/sf5.html)**. How to use sf and ggplot2 to visualize data using maps.
+
+---
+
+## Explore R Further
+
+For those interested in conducting data work in the development world: Kristoffer Bjarkefur, Luiza Cardoso de Andrade, Benjamin Daniels, and Maria Ruth Jones, **[Development Research in Practice – The DIME Analytics Data Handbook](https://worldbank.github.io/dime-data-handbook/)**. A comprehensive account of tools and instruments to conduct quantitative development research.
+
+For those looking for more hands-on, real-world data work: Ben Baldwin, **[“A beginner’s guide to nflfastR”](https://www.nflfastr.com/articles/beginners_guide.html)**. How to download and explore NFL play-by-play data. This is how I learnt how to use R. Further tutorials using this data can be found at the **[“Open Source Football” blog](https://www.opensourcefootball.com/)**.
+

slides/session_4/session_4.html

@@ -1,7 +1,7 @@
 <!DOCTYPE html>
 <html lang="" xml:lang="">
   <head>
-    <title>R MIEF Skills Workshop</title>
+    <title>R Professional Skills Course</title>
     <meta charset="utf-8" />
     <meta name="author" content="Marc-Andrea Fiorina" />
     <script src="libs/header-attrs/header-attrs.js"></script>
@@ -23,11 +23,11 @@
 
 class: center, middle
 
-# R MIEF Skills Workshop
+# Programming for Professional Research Using R
 
 ## Session 4
 
-### September 27, 2024
+### November 13, 2024
 
 ---
 
@@ -407,11 +407,11 @@
 
 2. Work on your final assignment! The final assignment is to complete the Session 2, 3, and 4 challenges. You can find the challenges rewritten together on the next slide.
 
-**NOTE** — You should refer to documentation for the dataset, which can be found in the "Module" section, "Course Resources" Module on Canvas, for details on the variables and their given values.
+**NOTE** — You should refer to documentation for the dataset, which can be found at [https://mfiorina.github.io/sais_r_course/](https://mfiorina.github.io/sais_r_course/)
 
 ---
 
-### End of Course Assignment (**Due on Thursday, October 10**)
+### End of Course Assignment (**Due on Wednesday, November 27**)
 
 .panelset[
 
@@ -482,8 +482,6 @@
 
 ---
 
-class: middle
-
 ## Links
 
 Hadley Wickham, Mine Çetinkaya-Rundel &amp; Garrett Grolemund, **[R for Data Science, 2e — Custom Functions](https://r4ds.hadley.nz/functions)**
@@ -494,6 +492,57 @@
 
 RStudio, **[RStudio Cheatsheets](https://www.rstudio.com/resources/cheatsheets/)**
 
+---
+
+## Explore R Further
+
+&lt;ins&gt;**More Complex Data Manipulation**&lt;/ins&gt;
+
+Iterative coding (using loops for repetitive code) – purrr ’s map function is your friend. I recommend Thomas Mock, **[“Functional programming in R with Purrr”](https://themockup.blog/posts/2018-12-11-functional-progamming-in-r-with-purrr/)** to get you started.
+
+User-made functions in R – At some point, it will become time-effective to create your own functions to apply to your work. Hadley Wickham, **[Advanced R Chapter 6 – Functions](https://advr.hadley.nz/functions.html)**.
+
+&lt;ins&gt;**Publishing Your R Work**&lt;/ins&gt;
+
+RStudio, **[“Introduction to RMarkdown”](https://rmarkdown.rstudio.com/lesson-1.html)**. Summarizes the uses and utility of the RMarkdown framework.
+
+Yihui Xie, **[“xaringan Presentations” – book chapter](https://bookdown.org/yihui/rmarkdown/xaringan.html)** and **[presentation](https://slides.yihui.org/xaringan/)**. Introduction to xaringan , a package
+that allows you to create slide decks using R. Also explore the **[xaringanExtra package](https://pkg.garrickadenbuie.com/xaringanExtra/#/)**.
+
+With RMarkdown, create books using **[bookdown](https://bookdown.org/)** or a blog using **[blogdown](https://bookdown.org/yihui/blogdown/)**.
+
+---
+
+## Explore R Further
+
+&lt;ins&gt;**Data Visualization Using Plots**&lt;/ins&gt;
+
+The R community organizes **[“Tidy Tuesday”](https://www.tidytuesday.com/)**. This is a weekly challenge where users are provided a dataset and participants then swap graphs and scripts used to create their visualizations.
+
+David Robinson’s **[Tidy Tuesday live screencasts](https://www.youtube.com/user/safe4democracy)** on YouTube. The perfect resource to follow along and try to replicate a professional coder’s scripts.
+
+Yan Holtz and Conor Healy, **[“From Data to Viz”](https://www.data-toviz.com/)**. An amazing repository of methods to create different data visualizations using R.
+
+---
+
+## Explore R Further
+
+&lt;ins&gt;**Geospatial Data Visualization**&lt;/ins&gt;
+
+Robin Lovelace, Jakub Nowosad, and Jannes Muenchow, **[Geocomputation with R](https://geocompr.robinlovelace.net/index.html)**. A great introduction to manipulating geospatial data (shapefiles and rasters) in R.
+
+Edzer Pebesma, **[“Simple Features for R”](https://rspatial.github.io/sf/articles/sf1.html)**. An introduction to the sf package, commonly used for geospatial work in R.
+
+Edzer Pebesma, **[“Plotting Simple Features”](https://rspatial.github.io/sf/articles/sf5.html)**. How to use sf and ggplot2 to visualize data using maps.
+
+---
+
+## Explore R Further
+
+For those interested in conducting data work in the development world: Kristoffer Bjarkefur, Luiza Cardoso de Andrade, Benjamin Daniels, and Maria Ruth Jones, **[Development Research in Practice – The DIME Analytics Data Handbook](https://worldbank.github.io/dime-data-handbook/)**. A comprehensive account of tools and instruments to conduct quantitative development research.
+
+For those looking for more hands-on, real-world data work: Ben Baldwin, **[“A beginner’s guide to nflfastR”](https://www.nflfastr.com/articles/beginners_guide.html)**. How to download and explore NFL play-by-play data. This is how I learnt how to use R. Further tutorials using this data can be found at the **[“Open Source Football” blog](https://www.opensourcefootball.com/)**.
+
     </textarea>
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