diff --git a/README.md b/README.md index 5adc25d..92c9ec7 100644 --- a/README.md +++ b/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) diff --git a/code/session_4.R b/code/session_4.R index cdb1596..00179cd 100644 --- a/code/session_4.R +++ b/code/session_4.R @@ -1,4 +1,4 @@ -## R MIEF Skills Workshop — Session 4 +## R Practical Skills Course — Session 4 ### Session Description diff --git a/slides/session_4/session_4.Rmd b/slides/session_4/session_4.Rmd index 89fc929..df6cb36 100644 --- a/slides/session_4/session_4.Rmd +++ b/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 + +**More Complex Data Manipulation** + +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)**. + +**Publishing Your R Work** + +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 + +**Data Visualization Using Plots** + +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 + +**Geospatial Data Visualization** + +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/)**. + diff --git a/slides/session_4/session_4.html b/slides/session_4/session_4.html index 3881914..2ebd691 100644 --- a/slides/session_4/session_4.html +++ b/slides/session_4/session_4.html @@ -1,7 +1,7 @@ - R MIEF Skills Workshop + R Professional Skills Course @@ -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 & 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 + +<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/)**. +