The goal of the workshop is to introduce git, RStudio, and some basics of R. If we have time we will also cover
data wrangling with dplyr, and data visualization with ggplot2. All of this is done
through the use of baseball data from package Lahman.
Downloading R, git, and RStudio
R: Choose link under 0-cloud, then choose appropriate install based on computer. https://cran.r-project.org/mirrors.html
Github: Create an account if you do not have one. https://github.com/
Git: Download git using instructions here https://www.theodinproject.com/paths/foundations/courses/foundations/lessons/setting-up-git
RStudio: Download RStudio Desktop https://www.rstudio.com/products/rstudio/download/
To bring these files into an RStudio session follow the steps below.
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Click on the green Code button in this repository, and copy the git URL (it should end in .git).
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Go to your RStudio Docker container at https://vm-manage.oit.duke.edu/containers/rstudio.
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In RStudio, go to File → New Project → Version Control → Git.
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Paste the git URL you copied in (1) into the dialog box labeled Repository URL. Adjust the folder to where you want this repository located.
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Click Create Project, and the files from your GitHub repo will be displayed in the Files pane in RStudio.
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Click class.Rmd to open the R Markdown file.
In R, the fundamental unit of shareable code is the package. A package bundles together code, data, documentation, and tests, and is easy to share with others.
-Hadley Wickham and Jenny Bryan
This analysis makes use of two packages: tidyverse and Lahman. The
tidyverse is a collection of packages. The analysis will use the dplyr and
gpplot2 packages that are automatically loaded when tidyverse is loaded.
If you find you are missing a package, install it with the below code.
install.packages("package_name")Be sure to spell the package correctly and put it in quotes.
Want to further your understanding of some of the functions used in the analysis? Check out the resources folder of the repository.
Want to learn on your own? See this document for a recommended learning pathway. https://docs.google.com/document/d/1fWET_GreI5qO2E-DDDojsmlfNeoQgZaCji-4D5M3PmA/edit#
The below books are also great resources. R Cookbook is for beginners, while R for Data Science does require some basic knowledge of R.
- R Cookbook - https://rc2e.com
- R for Data Science - https://r4ds.had.co.nz/