Installing R and RStudio
6.2 Installing R and RStudio
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6.2 Installing R and RStudio
Before using R, learners need to understand the relationship between R and RStudio. R is the language and computational engine. RStudio is an integrated development environment that makes it easier to write, run, organize, and review R code. R can be used without RStudio, but RStudio provides a more user-friendly interface for beginners and is widely used in academic and applied research settings [@posit2024rstudio].
The first installation step is to install R from the Comprehensive R Archive Network, commonly called CRAN. CRAN provides R installers for Windows, macOS, and Linux. Once R is installed, RStudio Desktop can be installed separately. RStudio detects the installed version of R and provides a working environment with panes for scripts, console output, objects, files, plots, packages, help, and version control integration.
In an institutional environment, installation may require information technology support. Some organizations restrict software installation or require approved versions. Clinical research teams should follow local information security and governance procedures, especially when working with identifiable or sensitive health data. Installing R and RStudio is not only a technical task; it is part of establishing a controlled computing environment. Teams should know where data will be stored, whether the workstation is encrypted, whether network drives are approved for research data, and whether scripts will be version controlled.
It is also important to distinguish between installing software and installing packages. Installing R and RStudio is usually done once per computer or when a new version is required. Installing R packages is done from within R and may occur more often. Packages extend the base capabilities of R. For example, the tidyverse provides tools for data import, transformation, visualization, and programming using a consistent grammar [@wickham2019tidyverse]. Other packages support Excel import, data cleaning, table production, reporting, and connection to databases.
| Component | What it is | Why it matters in clinical data management |
|---|---|---|
| R | The programming language and statistical computing environment | Runs the code that imports, checks, transforms, and summarizes data |
| RStudio | The interface used to write and manage R code | Helps beginners organize scripts, projects, files, objects, and output |
| CRAN | Main repository for R and many R packages | Provides stable package installation for common workflows |
| R package | A reusable collection of functions and documentation | Adds specific capabilities such as importing Excel files or cleaning names |
| R project | A project-level workspace managed by RStudio | Keeps code, data, and outputs organized within one study folder |
| Script | A text file containing R commands | Documents the data management process so it can be rerun |
For learners working on this course, a recommended installation sequence is:
1. Install R from CRAN.
2. Install RStudio Desktop from Posit.
3. Create a course practice folder.
4. Open RStudio and create a new R project inside that folder.
5. Install the packages needed for the exercises.
6. Save all practice scripts inside a `scripts` folder.
7. Keep imported raw data separate from cleaned or derived data.
The installation should be tested before any course session that relies on practical R exercises. A simple test is to open RStudio, type `1 + 1` in the console, and confirm that R returns `2`. This test is simple, but it confirms that RStudio can communicate with the installed R engine.
```r
1 + 1
```
The expected output is:
```text
[1] 2
```
This first command is not clinically meaningful, but it confirms that the environment is working. Once learners can run a command, the next step is to organize work using an R project.