Computing Setup

36-315: Statistical Graphics and Visualization, Summer 2026

R and RStudio

R is an open-source programming language for statistical computing. R is widely-used in both academia and industry, due to its capacity for statistical analysis and data science.

In order to use R effectively, you need a suitable editing environment, i.e. RStudio. For more context, RStudio is an integrated development environment (IDE) developed specifically for R programming. Although R can be run without RStudio, RStudio provides a more user-friendly experience with additional functionality.

To download R and RStudio, visit https://posit.co/download/rstudio-desktop.

You will see the following two tasks to be completed:

  • 1: Install R
  • 2: Install RStudio

Step 1: Install R

(Note that the following instructions apply to the latest R version (4.6.0) as of May 1, 2026)

Click on DOWNLOAD AND INSTALL R. This will direct you to the CRAN (Comprehensive R Archive Network) website.

macOS

  • Click on Download R for macOS.

  • Choose the .pkg file suitable for your Mac (Apple silicon (M1,2,..) Macs or older Intel Macs.)

  • Open the .pkg file after the download is complete.

  • Follow the installation instructions.

Windows

  • Click on Download R for Windows.

  • Click on install R for the first time (on the same line as the base subdirectory.)

  • Choose Download R-4.6.0 for Windows.

  • Open the .exe file after the download is complete.

  • Follow the installation instructions.

Step 2: Install RStudio

ImportantImportant

R must be installed before RStudio.

Click on DOWNLOAD RSTUDIO DESKTOP… (If your operating system is not detected, scroll down and choose the right version.)

macOS

  • Open the .dmg file after the download is complete.

  • Drag and drop it to your Applications folder.

Windows

  • Open the .exe file after the download is complete.

  • Follow the installation instructions.

Step 3: Check R and RStudio installations

Open RStudio and type in the following command in the Console pane.

version

This will print out the current version of R on your machine. The output should look similar to what shown below.

               _                           
platform       aarch64-apple-darwin23      
arch           aarch64                     
os             darwin23                    
system         aarch64, darwin23           
status                                     
major          4                           
minor          6.0                         
year           2026                        
month          04                          
day            24                          
svn rev        89956                       
language       R                           
version.string R version 4.6.0 (2026-04-24)
nickname       Because it was There

Step 4: Install an R package

In R, a package is a collection of functions, data, and compiled code. In addition to a set of built-in base packages, there are numerous external R packages written by the community to add specific functionality.

In general, to install an R package, you can use the install.packages() function and pass in the package name.

The following example shows how to install the tidyverse package in R. The tidyverse is a suite of R packages we will be using throughout this course. It features popular packages such as ggplot2 for data visualization and dplyr for data manipulation.

After installing R and RStudio, open RStudio and enter the following command in the Console pane.

install.packages("tidyverse")

To verify that tidyverse is successfully installed, run the following command:

library(tidyverse)

You should get a message similar to the output below.

── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
✔ dplyr     1.2.1     ✔ readr     2.2.0
✔ forcats   1.0.1     ✔ stringr   1.6.0
✔ ggplot2   4.0.3     ✔ tibble    3.3.1
✔ lubridate 1.9.5     ✔ tidyr     1.3.2
✔ purrr     1.2.2     
── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag()    masks stats::lag()
ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors

Optional: Customize RStudio

RStudio can be customized with different themes. To explore built-in themes,

  • Navigate to the menu bar at the top of your screen

  • Choose Tools > Global Options... > Appearance

  • Change your RStudio theme under Editor theme

Note that within the Appearance tab, there are also options for changing your Editor font, Editor font size, etc.

Feel free to explore other options within the Tools > Global Options... menu.