Installing R and RStudio: A Beginner’s Checklist

Installing R and RStudio involves two related applications. R runs the statistical calculations. RStudio Desktop provides an interface for writing scripts, inspecting objects and managing projects. Installing the interface alone does not replace installing R.

This guide takes you from choosing official downloads to running a small analysis from a saved script. Installer names and supported operating systems change, so use the linked project pages for current requirements rather than downloading a package copied from an old tutorial.

1. Understand what you need to install

Start with R and the appropriate RStudio Desktop release. Do not install a long list of packages simply because another person’s script uses them. The examples in our ANOVA in R guide and correlation in R guide use functions supplied with R.

2. Download R from the official project network

Open CRAN, the Comprehensive R Archive Network, and choose the instructions for your operating system. Check whether your computer uses an architecture supported by the chosen build. Avoid third-party download sites that bundle unrelated software or present an advertisement as the download button.

On Windows, use the official Windows installation route and follow its installer instructions. On macOS, select the package appropriate for the system and processor. On Linux, use the instructions for your distribution; commands for one distribution should not be copied into another without checking compatibility.

The R Installation and Administration manual provides the authoritative installation details. On a managed work or university computer, follow the organization’s software installation process rather than bypassing device controls.

3. Confirm that R itself starts

Launch R using the installed application or the appropriate command for your system. Run a simple calculation in its console:

1 + 1
R.version.string
sessionInfo()

The first expression should return 2. The other commands identify the R version and session environment. Keep the session information when asking for help: it tells someone much more than “R does not work.” Do not share private file paths or credentials with a support request.

If R cannot start, solve that problem before troubleshooting the RStudio interface. Distinguishing the two applications reduces unnecessary reinstallations and helps you describe the problem accurately.

4. Install a compatible RStudio Desktop release

Use Posit’s official RStudio download page. Check its current operating-system requirements and choose the desktop installer for your platform. The newest release may require a newer operating system than a tutorial written several years ago assumes.

Install and open RStudio after R is available. In its Console pane, repeat R.version.string. Compare the result with the R installation you intended to use. If the versions differ, inspect RStudio’s selected R installation rather than assuming the newer application automatically selected the newer runtime.

For machines that cannot run a supported desktop release, review the official support information or discuss an approved alternative with your administrator. Do not download an unidentified installer or grant remote access to someone offering an unsolicited fix.

5. Create a project and a saved script

Create a new RStudio project in a folder dedicated to your analysis. A simple folder layout might contain data, scripts and outputs. Keep original input files separate from files your script generates. A project is easier to move or reopen when file paths are relative to its own folder.

Create an R script, save it and enter the following code. Run the lines from the script rather than typing everything only into the console:

scores <- c(62, 65, 64, 67)
mean(scores)
sd(scores)
summary(scores)
plot(scores, type = "b",
     xlab = "Observation", ylab = "Score")

The mean should be 64.5. The plot should contain four observations. These are teaching values, not a real learner assessment. Close and reopen the project, then run the saved script again. This checks that your analysis is stored as reproducible instructions rather than only as objects in a previous session.

6. Install packages only when they are needed

Installing a package places it in an R package library. Loading it makes its exported functions available in the current session. These are different actions:

# Install when needed, not on every script run:
# install.packages("ggplot2")

# Load in a session that uses the package:
# library(ggplot2)

# Inspect where packages are installed:
.libPaths()

The example is commented so it will not download software unexpectedly. Remove the relevant comment marker when you intentionally want to perform that action. R’s install.packages reference explains repository, library and installation options.

If installation fails, read the first meaningful error as well as the final message. Network restrictions, a non-writable library, unavailable dependencies and a package requiring another R version need different fixes. Installing a package from source can also require build tools.

7. Troubleshoot the layer that actually failed

Copy the relevant error text when asking for assistance. Include a short example that reproduces the problem without sharing confidential data. A screenshot can show the interface, but searchable error text is usually easier to investigate.

8. Record a reproducible starting point

Save the script, record where input data came from and keep any cleaning steps in code. Use sessionInfo() to document R and loaded package versions. This does not freeze the whole environment, but it provides a useful record when another machine produces a different result.

Before updating a working environment, retain the scripts and data needed to rerun an important analysis. For larger projects, review the environment-management options in the official documentation. Avoid assuming that installing the newest package version guarantees compatibility with an older project.

• R starts and a basic calculation works.
• RStudio uses the intended R installation.
• The project opens in the correct folder.
• A saved script runs from a fresh session.
• Input files and generated outputs are separated.
• Package dependencies and session details are documented.

You are ready to begin when those checks pass. Start with a small dataset you can inspect manually before moving to a large analysis. Our R analysis workflow guide provides the next reading step.

Editorial update: Oxford Training and Research. Prepared with AI-assisted drafting; the original author record is retained. Worked examples are illustrative. No independent field study or specialist review is claimed.