To get started, you first need to install R. Next, we highly recommend using RStudio, an integrated development environment (IDE) that makes working with R much easier (RStudio’s setup is familiar to those of who have prior experience with Matlab). RStudio comes with the very handy RProj environment and provides many built-in features that help facilitate data analysis and modelling tasks.

Install R

Visit https://cloud.r-project.org/ and click on the download link that fits your operating system.

Install RStudio

Visit https://www.rstudio.com/ website and click the download button. The free version covers (way) more than what you need for this course.

Three recommended RStudio settings:

  1. Always use a project-oriented workflow, where each task, assignment, etc. is a standalone project. Remember: If you need your R script to start withsetwd() and rm(list=ls()) than are doing something wrong!

  2. Go to Tools -> General Options -> General and set the Save workspace to .RData on exit to NEVER. This option improves the reproducability of your projects since it causes you to start every project in a new R session, i.e., a clean environment without any objects or packages pre-loaded.

  3. Go to Tools -> General Options -> Git/SVN and check the “Enable version control interface to RStudio projects” box. Next, make sure that the “Git executable” points to the git binary file (e.g., “C:/Program Files/Git/bin/git.exe”).

Alternatives to R and RStudio

If for some reason you insist on not using RStudio, we can suggest a couple alternative open-source IDEs:

  1. JupyterLab coupled with the IRkernel extension.
  2. Atom coupled with the ide-r and Hydrogen extensions.

Note that all IDEs, RStudio included depend on R being installed.

Further Resources


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