Install R

This page has 30 images

Download pdf of this page

Introduction

The first time installing R can seem intimidating. To start, be clear about the overall goal of the procedure: providing the student with an accessible environment for solving statistics problems.

In brief, this page explains how to get R set up on your computer. First, you need to download the R installer from the official CRAN website. When you run the installer,in general, accept the default choices. However, for Windows users, it’s important to right-click the file and choose “Run as administrator.” This step ensures that R has the proper permissions to install correctly and avoids problems with user access later on. Once installed, you can open R and test it by typing a simple command like `2 + 2` in the console to confirm everything is working.

The flow chart presented in Fig 1 suggests a way to orient the student to solving the task. First, understand which type of computing environment you have available. Second, if a macOS, then additional software (XQuartz) is required to provide full function to the R software. Third, download and install the base R software appropriate for the computer system.

Figure 1 suggests an R installation flow chart.

Figure 1. Suggested flow chart for R installation. 

It’s possible to follow the steps in Figure 1, accepting all default options presented along the way, to end up with a working R environment. As with many software processes, there are choices beyond defaults that can be made to improve the software use.

This page presents a detailed guide about how to install R onto your computer — this is referred to as building a local development environment or LDE. Additional install R help was provided in Chapter 1.1 – A quick look at R and R Commander.

Instructions for RStudio are also provided (optional for BI311 students). A guide to install R Commander is provided in Install R Commander.

Instructions for how to run R via a “cloud computing” (serverless) option — a remote development environment — are also provided, Use R in the Cloud.

For help upgrading installed packages after upgrading new R version, see R packages.

Run R on your computer (LDE install)

CoLab, skip this step: Instead, go to Use R in the cloud.

So why in this day in age should you install and build R on your own computer? The remote options to run R in the cloud are a wonderful option, convenient: you can access anywhere you have internet, from any device that connects to the internet. It’s easy to share and work together on projects, particularly those based on Jupyter Notebooks.

I think the main benefits to a local installation is it’s a more efficient environment to work in — you have control of everything and, provided your PC has power, a working R install on your computer will always be available to you. Since you can control the update cycle for your computer, you won’t run into times you cannot access the remote server to work on your project. Testing code is faster on a local install, feedback — think error messages — apply to your installed version. And, while remote R servers may come with low initial costs to students, any significant use will quickly require paid accounts. As a reminder, the good folks at the R-project continue to offer R as free software. All you need to do is work through the install process.

Start at the R-project homepage, r-project.org. To download software, first click on CRAN link,  located on left hand side of the screen (here, highlighted by green arrow, Fig 2).

Figure 2. Screenshot homepage for R-project.org. 

Figure 3 shows a screenshot of the CRAN mirror page. The idea is to select the mirror site closest to your location. In Hawaiʻi, that’s likely to be any of the sites in California. However, I recommend selecting the first in the list, 0-cloud, at cloud.r-project.org (highlighted by green arrow, Fig 3).

Figure 3. Screenshot of portion of R-Project CRAN mirror page.

After selecting the mirror site, the download page is presented (Fig 4). Click on the link that corresponds to your computer system (Linux, macOS, or Windows).

Figure 4. Screenshot of portion of base R download page.

Once the installation file is located onto your computer, proceed to install base R.

Detailed instructions

For screenshots of installation steps on WinPC, see Win10/11 setup, screenshots

1. Windows PCs, download the base application from selected CRAN Mirror site, select Download R for Windows, and install the R software as you would any other software. All of you are likely to have the 64-bit version of Windows 11, so install the 64-bit version of R. Follow the instructions as they are presented. Screenshots of the install process are available at the end of this page (click here or scroll down to Win11 setup, Screenshots).
   • Current versions of Microsoft Windows come in several flavors, the simplest distinction is between home and pro. R runs perfectly well on both.
     • Windows 10 is reaching end of life cycle.
     • Some inexpensive Microsoft Windows PCs are built on ARM64, not Intel or AMD64 CPU. Thus, installing R and or RStudio may prove problematic.
     • Also note: Windows in S mode only run applications from the Microsoft store. To install R, you first may need to switch out of S mode (see Microsoft FAQ about S mode).
   • You should install R with Administrator privileges. Highlight the install file, right-click the file, and select “Run as administrator” from the popup menu.
   • When you first try to run R you may get a popup screen “Windows protected your PC,” locate and click on the “More info” link and select “Run anyway.”
     • This in no way will harm your computer — provided you have downloaded from official sites. R is a verified program. Microsoft has taken an aggressive line on developers and favors apps that are part of their app store.
     • It is advisable to confirm for yourself: check the md5sum against the fingerprint on the CRAN server
   • When prompted, I recommend that you change the install directory to root folder, e.g., C:\R\R-4.5.1. This will allow for installation of packages to the common library as opposed to a personal library.
     • I recommend this change because of how Windows assigns home folders. During initial setup Windows 10 prompted you to choose a username and whether you wanted your work stored locally or in your OneDrive folder. A worse case scenario? You select a username with spaces, e.g.,”Mike Dohm,” and you selected OneDrive. Both will cause challenges later for running and or installing packages for R.
       • If you install R anywhere but the default Program files folder on your Win10/11 PC, chances are you will need to add the folder containing the executable, r.exe, to you path.
         • Search: “env”
         • Open “Edit the system environment variables” in the Control Panel
         • Click Advanced tab, then click on Environment Variables… button (lower right of panel)
         • Under System variables, scroll to and select Path.
         • Click Edit… button, then click New button.
         • Type in the path to the folder containing R.exe. That’s likely to be C:\R\R-4.5.1, assuming R-4.5.1 is the latest version of R installed on your computer.
     • I made a video for you. Video is about 26 minutes long; at 22 minute mark, video includes how to install R Commander (instructions provided Install R Commander).
       • Apologies for the production quality — videos are not my thing.

https://youtu.be/upjmBieh3bM

For screenshots of installation steps on WinPC, see MacOS setup, screenshots

1. macOS PCs, first you must download and install XQuartz from https://www.xquartz.org. Best to restart your mac after installing XQuartz then proceed to install R.
   After installing XQuartz, then return to https://cran.r-project.org, select Download for Mac(OS) X, and run the installer. Screenshots of the install process are available at the end of this page (click here or scroll down to Macos setup, Screenshots).
   • As of August 2025, be advised that there are two distinct R versions for your MacBook or iMac.
     • For MacBook or iMac with Apple’s M1 or M2 ARM chip sets, download and install R-4.5.1-arm64.pkg.
       • If you recently purchased a new MacBook or iMac (2020 to present), then you probably have the M2 or M3 chipset (check by clicking the Apple icon, then selecting About this Mac or System Information (/Applications/Utilities/System Information.app)).
       • XQuartz version 2.8.5 works on macs with either the M1 or Intel chipsets.
     • For older MacBook or iMacs with Intel processors, download R-4.5.1-x86_64.pkg.
     • Depreciated 8/4/2021: Be advised that these instructions are for Intel-based macs. At the time of writing these instructions (April 2021), the installation of XQuartz and R should work on new M1-based macs. At the time of this writing (April 2021), however, R will not run natively on your M1 mac. It will run using Rosetta 2, an emulator that is included with your M1 mac. The R folks are busy working on a version that will run natively, which may be ready within a few months.
   • At the completion of the install process, don’t forget to drag the R app to your Applications folder.
   • When you first try to run R, you may get a popup screen which provides no option to start the app, and perhaps even a rather ominous option to move the app to Trash. Just close the warning message and right-click on the R app. A new screen pops up, which looks very much like the previous warning, but now you will see and option to open the app. Click on open to start R.
   • Like the message to Windows PC users, bypassing Apple’s Gatekeeper to run R in no way will harm your computer — provided you have downloaded from official sites — R is a verified program. Apple has taken an aggressive line on developers and favors apps that are part of their app store.
2. LINUX distros. If your PC platform is Linux, then you should be comfortable with installation and updating of software. R base is already included in Debian distributions (e.g., Mint, Ubuntu). See https://cloud.r-project.org/ for additional instructions.
   • For Chromebook users, if you can install a Linux subsystem, then you can also install and run R. For instructions to install R see Levi’s excellent writeup at levente.littvay.hu/chromebook/.

Test R

For both macOS and Windows PCs, successful installation of R on your computer installs base R programming language and a simple graphical user interface. Test your install by running code in the terminal (one line at a time) or via script:

1. Rgui.exe (Windows PC)
2. File → New script

Enter code in script editor, e.g.,

myX <- c(1,2,3,4)
myY <- c(5,10,15,20)
plot(myY,myX)

3. Run code: Ctrl+R

Figure 5 shows screenshot of R running on WinPC. RGui.exe (1), script editor (2), and results of plot() (3) on WinPC.

Figure 5. Screenshot of RGui.exe (1), script editor (2), and results of plot() (3) on WinPC.

1. R.app (macOS): run code in the terminal or via script
2. File → New Document

Enter code in script editor, e.g.,

myX <- c(1,2,3,4)
myY <- c(5,10,15,20)
plot(myY,myX)

3. Run code: Cmd+Enter

Figure 6 shows screenshot of R running on macOS. R.app (1), script editor (2), and results of plot() (3).

Figure 6. Screenshot of R.app (1), script editor (2), and results of plot() (3) on macOS.

Many of you would like a video. Do a little search and you’ll find plenty, although most are also showing how to install RStudio in addition to base R.

For BI311, we also use R Commander package

R Commander is a package that adds function to R; it provides a familiar point-and-click interface to R, which allows the user to access functions via a drop-down menu system (Fox 2017).

Go to Install R Commander guide.

Personalize R startup

At startup, R app looks to a series of environment files to instruct how it will provide a working session environment. One important file to know about is the .Rprofile file, which will reside in your home directory on your computer. This file generally won’t be present after the first installation, but can be easily added.

First, verify the home directory where R will first look for an .Rprofile file.

Identify home directory

At the R prompt, verify your home directory, type and run

path.expand("~")

On a macOS, this should return something like

"~Users/USERNAME"

On a Windows 11 machine, this may return

"C:/Users/USERNAME/OneDrive/Documents"

if default instructions were accepted during initial Windows setup.

How to modify .Rprofile

Good practice suggests setting a project folder to contain work done for a project. For biostatistics students, minimally, this might be the course name, BIOSTATS. Within this folder we’d expect to find all course data files, homework, R coding scripts and related for the class. While it is easy enough to point R to a data file (see Part 07. Working with your own data, Mike’s Workbook for Biostatistics), it’s often easier to tell R in advance where to expect to find these files by setting a working folder at the beginning of a session using the setwd() command.

Alternatively, you can edit .Rprofile so that R always starts in the folder you want.

To open (or create if the first time) .Rprofile, at the R prompt type and submit

file.edit("~/.Rprofile")

options(test_success = "Success! .Rprofile is loaded.")

getOption("test_success")

Windows 11 Setup

• Open your local home documents folder (usually C:\Users\YOUR_NAME\Documents).
• Create or open a file named exactly .Rprofile (Ensure it does not end in .txt).

Copy this code for Windows:

.First <- function() {
# 1. Grab the literal, local hard drive user folder
local_root <- Sys.getenv("USERPROFILE")

# 2. Open a native Windows browse window for folder selection
message("Please select your local project folder...")
flush.console()

chosen_path <- choose.dir(
default = local_root,
caption = "Select your local project folder"
)

# 3. Set the directory to what they clicked on
if (!is.na(chosen_path)) {
setwd(chosen_path)
message("Working directory successfully set to: ", chosen_path)
} else {
warning("No folder selected. Working directory unchanged.")
}
}

macOS Setup

• Ensure R.app has been granted Full Disk Access in your Mac’s System Settings > Privacy & Security.
• Open Terminal and run open -a TextEdit ~/.Rprofile to open your profile configuration.

Copy this code for Mac:

.First <- function() {
# 1. Open a macOS Finder folder selection window
message("Please select your local project folder...")
flush.console()

# Calls native macOS AppleScript to open a secure folder picker
chosen_path <- tryCatch({
system("osascript -e 'POSIX path of (choose folder with prompt \"Select your local project folder:\")'", intern = TRUE)
}, error = function(e) {
return(NA)
})

# 2. Set the directory to what they clicked on
if (!is.na(chosen_path) && length(chosen_path) > 0) {
setwd(chosen_path)
message("Working directory successfully set to: ", chosen_path)
} else {
warning("No folder selected. Working directory unchanged. Keep in mind R.app needs 'Full Disk Access' in macOS System Settings.")
}
}

Set your CRAN mirror site

To add function to R, you’ll download many R packages. These, too, are stored at the mirror sites. Each time you run install.packages(), R will ask for the mirror site. The R app on Windows pops up a long selection box; macOS R.app is a bit more friendly, but regardless, it’s best to choose a mirror site at the beginning of a session. At the R prompt, type and submit the command

options(repos = c(CRAN = "https://cloud.r-project.org"))

this will remain in effect for the current R session.

To make the change permanent, add to .Rprofile in your personal home directory

local({
r <- getOption("repos")
r["CRAN"] <- "https://cloud.r-project.org"
options(repos = r)
})

If you use textedit or notepad, make sure to return after the }); the file requires an empty new line to run.

Restart R, then enter at the prompt

file.exists("~/.Rprofile")

If you’ve created the Rprofile file correctly, R should repeat.

Run R from the terminal

Whether yours is a mac or win11 pc, you have a powerful computing environment lurking beneath the glossy graphic user interface. It’s called the terminal. The terminal is a place where text-based instructions may be written and submitted to command your computer to do something.

On win11 pc, the modern terminal is the Power Shell. Search “PowerShell,” then open Windows PowerShell. Alternatively, shortcut + X, then I key. This opens terminal in your home folder.

• You’ll want to navigate to your working folder, e.g., BI311 on the Desktop. Setting your working folder can be done in R of course, but at the terminal type the command “cd \users\default\desktop\bi311“.

On macOS, Spotlight (search) “Terminal.app“. Alternatively, right-click on your working folder icon and select New terminal at Folder from the popup menu.

On Ubuntu Linux, shortcut keys + alt, then t key.

Open the included script editor

Use RStudio

Rstudio is a very popular data science IDE, specifically written for R programming. Therefore, if your goal is to become adept at R programming, go with RStudio. Like R Commander, Rstudio provides an environment to write an manage code, generate markdown reports, and importantly, manage files in R projects. Figure 7 shows a screenshot of RStudio: Clockwise, four panes: Source, Environment, Output, Console on WinPC. BI311 students, please note that I recommend use of R Commander and Google CoLab.

Figure 7. Screenshot of RStudio IDE.

Installation of RStudio desktop is straightforward.

Go to https://posit.co/download/rstudio-desktop/

Install R first. Since at this point you’ve likely already installed R, go directly to 2 (green box, Fig 8).

Figure 8. Screenshot RStudio desktop download page, current as of September 2025.

After downloading the installation software, double click the file to begin. Follow on screen instructions for your computer system.

Run R in the “Cloud”

If you do not wish to install R, or, if you have a Chromebook and, therefore cannot gracefully install R, then there are alternatives; Run R in the Cloud. I’ll list three ways to run R in the cloud for free. Go to Use R in the Cloud guide.

MacOS setup, Screenshots

Download R install package from R-project.org, then select the R install package from your Download folder (Fig 9).

Figure 9. Screenshot — Find the R install file in your download folder.

First screen, R install for macOS (Fig 10). Select continue.

Figure 10. Screenshot: first instructions pop-up window.

Second screen, R install on macOS (Fig 11). Select continue.

Figure 11. Screenshot: second instructions pop-up window.

Third screen, R install on macOS (Fig 12). Select continue.

Figure 12. Screenshot: third instructions pop-up window.

Fourth screen, R install on macOS (Fig 13). Agree to continue.

Figure 13. Screenshot: fourth instructions pop-up window.

Fifth screen, R install on macOS (Fig 14). Select Install.

Figure 14. Screenshot: fifth instructions pop-up window.

Sixth screen, R install on macOS (Fig 15). Enter your username and password for your computer, then select Install Software.

Figure 15. Screenshot: sixth instructions pop-up window.

Seventh screen, R install on macOS (Fig 16). Several screens will popup, reporting progress. Be patient!

Figure 16. Screenshot: seventh instructions pop-up window.

Eighth and final screen, R install on macOS (Fig 17). Select close.

Figure 17. Screenshot: eighth and final instructions pop-up window.

Optional — Keep or discard the install file (Fig 18). I keep and then do manual delete after I’ve confirmed the installation.

Figure 18. Screenshot: MacOS will prompt with option to delete the installation file. This has no effect on the installation of R.

From Applications folder, start R.app. You should see the R Console (Fig 19).

Figure 19. Screenshot: R Console in the R.app on macOS.

Wind10/11 setup, screenshots

Download from R-project.org, then right-click the R install package from your Download folder. Run as administrator (Fig 20).

Figure 20. WinPC  screenshot: Pop-up menu after right-click on the R installation file in windows Explorer.

First screen, select language (Fig 21). Select OK to continue

Figure 21. WinPC screenshot: first instructions pop-up window.

Second screen (Fig 22), click Next to continue

Figure 22. WinPC screenshot: second instructions pop-up window.

Third screen (Fig 23). Change the default location (show in the screenshot) to root folder, e.g., C:\R\R-4.5.1 (current version as of August 2025).

Figure 23. WinPC screenshot: third instructions pop-up window. Note — R-4.5.1 (current version as of August 2025).

Fourth screen (Fig 24). Change startup options. Select Yes (customized startup) to continue.

Figure 24. WinPC screenshot: fourth instructions pop-up window.

Fifth screen (Fig 25), select SDI — single document interface — not the default MDI — multiple document interface, then Next to continue.

Figure 25. WinPC screenshot: fifth instructions pop-up window. Recommend setting to SDI.

Sixth screen (Fig 26), select HTML help, then Next to continue.

Figure 26. WinPC screenshot: sixth instructions pop-up window.

Seventh screen (Fig 27), leave start menu folder as is (R), then Next to continue.

Figure 27. WinPC screenshot: seventh instructions pop-up window.

Eighth screen (Fig 28), check all boxes, then Next to continue.

Figure 28. WinPC screenshot: eighth instructions pop-up window.

Ninth screen (Fig 29), a series of status updates during the installation.

Figure 29. WinPC screenshot: ninth instructions pop-up window.

Final screen (Fig 30), successful install.

Figure 30. WinPC screenshot: final instructions pop-up window — successful installation.

/MD

2.2 – Why do we use R Software?

If this page is TL;DR, and you’re in a rush, then instructions to install R onto your macOS or Windows PC are provided in Install R.  To run in the cloud only, see Use R in the cloud.

Why R? The case for using R in biostatistics education.

.

Why do we use R Software? Or put another way: DrD, Why are you making me use R?

Truth? You can use just about any acceptable statistical application to get the work done and achieve the learning objectives we have for beginning biostatistics. However, we will use the R statistical language as our primary statistical software in this course. Part of the justification is that all statistical software applications come with a learning curve, so you’d start at zero regardless of which application I used for the course.

In selecting software for statistics I have several criteria. The software should be:

• if not exactly easy, the software should have a reasonable learning curve
• widely accessible and compatible with all most personal computers
• well-respected and widely used by professionals
• free software
• open source
• well-supported for the purposes of data analysis and data processing
• really good for making graphics, from the basics to advanced
• capable to handle diverse kinds of statistical tests

R meets all of these criteria. R history began back in 1993 and has always been available as free software under the terms of the Free Software Foundation’s GNU General Public License in source code form. R compiles and runs on a wide variety of UNIX platforms and similar systems, including GNU/LINUX, FreeBSD, and various Linux distros like the popular Ubuntu^®, in addition to their more famous Microsoft Windows^® and Apple macOS^® distributions. To facilitate access to the software, numerous mirror sites are available from sites around the world, with cloud.r-project.org supported by RStudio perhaps the most widely used. From January 2024 to December 2024, more than 8.5 million downloads of base R were made from the RStudio CRAN mirror site (CRAN stands for Comprehensive R Archive Network.

install.packages("cranlogs")
library(cranlogs)
# How many downloads of base R around start of Fall semester?
 out <- cran_downloads("R", from = "2025-08-01", to = "2025-08-31")
sum(out$count)

[1] 582524

R is straightforward to use once you learn how to work with the language, but has a steep learning curve; after all, it’s a programming language. I recommend installing the program onto your computer, but ready to go R is also available at multiple cloud services, including Google CoLab and Posit Cloud. The remainder of this page is directed at users who choose to install R to their computer, although much of the discussion holds regardless of the R environment choice, local or Cloud.

For local installs of R, I also recommend use of the GUI R Commander.  Use of R Commander helps with the learning curve, and eventually, your use of code will become second nature. We don’t use RStudio, but as your skills improve, I recommend transitioning to the IDE RStudio; after the initial growing pains are behind you, RStudio likely will be a better solution over R Commander.

This discussion implies learning R programming. However, while we need statistical software to do statistics, students in my BI311 course must keep in mind that learning objectives for a biostatistics course is about the concepts and interpretation of statistics, not just use of the software. In other words, learning how to use R is not the focus of BI311 nor will you likely achieve R programming competency by the end of the semester. I certainly encourage students to strive for competency and I give frequent bonus opportunities to demonstrate coding skills during the semester.

Why R and not some other statistics software?

Thus, you might ask if the purpose of the course isn’t to learn R, why work with R instead of a more familiar app or software, eg, Microsoft Excel^® (hereafter simply referred to as Excel), or Google Sheets, or even my favorite open-source office alternative, LibreOffice Calc? Or, perhaps even just one of the many online calculators, if the course learning objective is to “just” learn about statistics?

First, I believe that real data derived from real biology or biomedical problems are essential elements to a first course in biostatistics. That’s not a particularly unique perspective although I don’t have survey results of other statistics instructors to back up the claim. Real problems involve observations on multiple subjects, many variables — large data sets; this alone precludes use of hand calculations and calculators. As a corollary, we will not spend a great deal of time learning the in’s and out’s of the algorithms that form particular statistical tests. Now, do understand that there is a tremendous benefit to understanding statistics by working through the equations, by looking at the algorithms, and there’s no escaping the need for understanding that probability provides the foundation of statistics inference (Chapter 8). Thus, for most of us, the statistical software available to us provides an appropriate framework for applying correct statistical tests to our projects. Therefore, the decision is about which statistical package we should use.

Second, R is perhaps the choice in academia for statistical software. A PUBMED search found more than 1500 citations of R. Visit Robert A. Muenchen’s web page (The popularity of data analysis software, r4stats.com) to see updated statistics on statistical software use. Those of you continuing on to graduate school or to professional schools will find that many of your statistically literate colleagues use R and not one of the commercial programs. While there are many excellent commercial packages (Table 1), and in some cases you can make spreadsheet programs do statistics (typically add-ins are required), all statistical software come with steep learning curves. Thus, part of my selling point to you is that learning to use R is at the cutting-edge in your field and, given that all of the software you could use can have have their challenges, it is best to work with something that will be around and is in wide use, without the burden of a financial investment.

Table 1. A selective list of statistical software.

see Wikipedia for list of additional software

Third, what about online sites like plot.ly where, for free, you can plot and, in some cases, calculate statistics? What about the web application at Brightstat, which claims to provide an SPSS-like experience online (Stricker 2008)? While it is true that there are many wonderful websites that can perform many of the statistical tests we will use this semester, these sites are not suitable for more than occasional use.

How to get started with R.

.

A few words about my approach to the software in my biostatistics course. I note to students during the semester that our course is not a programming course, but rather, programming skills are acquired along the way. Our focus is how to do statistical analysis, how to design experiments, etc. Coding skills without understanding of the why we’re doing it is not unlike learning to cook by following a great recipe as opposed to learning about the art and science of flavor (NPR Science Friday, March 15, 2024) — the homework is solved but the why we can support our conclusions may be obscure.

We set aside the first couple of class meetings simply to setup student’s Windows PC or Apple macOS computers to do data science; for students with access to Chromebooks or iPad tablets, we need to set them up to run R in the Cloud (eg., Google Colaboratory) — in other words, there’s a lot of pre-work that can present barriers to actual doing statistics in R (or other software). Once we have the environment set up for the students, we utilize a “tell-show-do” approach to learning how to code. The “tell” part includes sharing script; the “do” part is mostly done by students outside of class as part of completing homework.

First work with the language can be frustrating, so I also encourage a “five minute” rule — if it ain’t working, stop. So, by all means when Run through a troubleshooting checklist, but please don’t continue to struggle — the gap is between the instructions and where the student is with the material — it’s a me-as-instructor problem, not a student must work harder problem! 

All statistics work follows a similar workflow.

The basic workflow for a data project, using R as the primary statistical programming tool, looks like that illustrated in Figure 1.

Figure 1. A basic workflow with R.

The workflow begins with the assumption that a data set already exists. Getting the data into an R session depends on the source of the data, for example stored in spreadsheet file or from a table on a webpage. Data cleaning and processing of the data may be in order — for small and modest-sized data sets, my own preference is to do most of the data cleaning in my spreadsheet app, but R has full capabilities to handle or “wrangle” data. Next, the raw data should be stored in R session in a data frame object. Once the data frame object is available, exploratory data analysis, including summary statistics and data visualization can proceed. Higher order analytics, including statistical inference and modeling, can then begin.

The R statistical language, accompanied by additional packages to extend its capabilities beyond basic math and statistical functions, provided a complete statistical environment. R is best viewed as a programming language for statistics (data analysis), and data processing. Power users of R learn how to write scripts that do t-tests, ANOVA, regression, etc. The scripts are just lines of code that R understands and it provides the user tremendous control over analysis and inference of data sets. Because of this flexibility and power, however, R can be intimidating at first. So, we’ll start slowly with scripts, introducing just what we need to get started and build from there. We’ll be addressing R issues in more depth over the next several weeks, but for the first week, our goal(s) should be to make sure each of you knows how to start/exit R, how to create and utilize a working directory, and how to use R as a calculator. You obtain your copy of R from the R Project for Statistical Computing, available at https://www.r-project.org. Instructions to install R are provided in Install R.  A ten-part tutorial to get started using R is provided in Mike’s Workbook for Biostatistics.

 /Users/username/Desktop/BI311

Why R Commander, why not RStudio?

.

We utilize an R package that provides a menu-driven context to much of the typical statistics one needs to do biostatistics. The package is called R Commander (Rcmdr), which provides a graphic user interface or GUI. Rcmdr therefore significantly eases the learning curve for doing statistics with R. We use a package called R Commander, which provides drop down menus for most of the typical kinds of analyses. Rcmdr is in use in many courses across the world (more than 17K downloads in August 2025), and among the other GUI available for R, Rcmdr is among the best supported GUI available for R. R Commander function is extended by plug-ins; as of August 2025, there were 31 plugins that extend Rcmdr’s capabilities. Instructions to install R Commander are provided in Install R Commander. 

Wait! Why don’t we use Excel? My instructor in {insert course here} used Excel…

.

A very reasonable question for you to ask — why don’t we use Microsoft Excel or Google Sheets for statistics? Moreover, it is highly likely that you have gained at least some introduction to descriptive statistics and graphing with spreadsheets in former courses — shouldn’t we learn statistics within a software framework you are already familiar?

After all, “Can’t Microsoft Excel do statistics?” Mostly the answer is, no, not really (Fig 2).

Figure 2. “Spreadsheets,” xkcd.com no. 2180.

MS Excel, Google Sheets, Apple Numbers, and for that matter, Calc, the spreadsheet application in my favorite office app LibreOffice (LibreOffice is a free, open-source alternative to Microsoft Office), can be used to calculate many descriptive statistics. With some effort, these applications can be extended by use of either Analysis ToolPak or Solver Add-ins to do more complicated statistics like regression and analysis of variance, and curve fitting.

However, use of MS Excel for statistical analysis involves learning a number of commands, syntax, and developing workflows that are neither intuitive nor standard. Some publishers have provided add-ins that are reportedly designed to simplify this process (eg, XLStat, UNISTAT, Real Statistics using Excel). None of these options are free and none are in use in any major way by scientists (see Robert Muenchen’s The popularity of data analysis software). The free add-ins of Analysis ToolPak and Solver may work for you if you own a Windows PC, but only Solver is included for the Mac versions of Excel. MacOS users may download and install StatPlus:MacLE, which is a limited, but free alternative to the Analysis ToolPak add-in; for a complete package a Pro version is available (licenses started at $89, web site: www.analystsoft.com/en/products/statplusmacle/).

An additional caution: you should be aware that there have been reports over the years that algorithms selected by Microsoft for Excel have not always been to industry standards (eg, McCullogh and Wilson 2005). In short, the fit of Excel and other spreadsheet apps for use in statistics is not a simple one. To do the kinds of statistics we will use routinely in class, Excel would need to be modified with add-ins, and the add-ins would be the result of programming by someone. And you would still need to learn how to write the code.

What about graphics? You may like Microsoft Excel’s ability to do graphics. Indeed, Excel, Google Sheets, and LibreOffice Calc can be used to generate many typical kinds of statistical plots. But again, in comparison to R, spreadsheet app graphics are limited and require a deal of effort to generate acceptable plots. I think you’ll be surprised at how straight-forward R is. Here’s an example, first rendered in Microsoft Excel, then in base R. And importantly, the kinds of plots Excel does well at are not necessarily the plots suitable for research publication. For example, Excel allows you to make bar charts easily, but cannot do box plots. Box plots are preferred over bar (column) charts for ratio scale data.

Statistics comparisons between R and MS Excel.

.

About that learning curve. Let’s compare R and MS Excel for basic functions common in data analysis. Similar conclusions hold for comparisons to Google Sheets and LibreOffice Calcs.

Table 2 lists the observations we can use to conduct comparisons of the applications.

A simple data set

One of the first steps in data analysis is to produce what are called descriptive statistics. Common descriptive statistics are the mean and the sample standard deviation. Let’s compare Excel and R for retrieving these two statistics.

With Excel, to calculate the arithmetic mean of 24 numbers, enter the values into a single column of 24 rows, then enter “=average(A2:A25)“, without the quotes, into a new cell of the spreadsheet. “A2:A25” refers to where data would be contained in column A rows 2 through 25. Typically the first row in a worksheet would contain the name of the variable, eg, “A.” Depending on the significant figures set, the estimate returned by Excel for the mean of A is 59.58333333.

Similarly, to obtain the standard deviation, type =stdev(A2:A25), into a new cell of the spreadsheet. Again, depending on the significant figures set, Excel returns a value of 37.05215674 for the standard deviation of A.

In contrast, to obtain the mean and standard deviation for a variable in an R data set, all you would type at the R prompt (>), or in the script window

and then submit the code is:

myA <- c(12, 14, 20, 25, 28, 29, 32, 34, 35, 39, 47, 47, 50, 53, 54, 71, 79, 87, 89, 96, 105, 122, 130, 132)

where the “c” is a function to combine arguments into a vector and saved to the object myA, followed at the new line by

mean(myA)

Hit enter after entering the command) and R returns

[1] 59.58333

For the standard deviation, write the R base function sd()

sd(myA)

Hit enter after entering the command and R returns

[1] 37.05216

It’s not much of a difference, but note that to get the mean (arithmetic average) I typed seven characters in R, but 16 characters in Excel; similarly, for the standard deviation I typed in 5 characters in R, but 13 characters in Excel. That’s a savings of 56% and 62%, respectively. Excel tries to help by using AutoComplete to anticipate what you want to enter, but AutoComplete doesn’t always work properly (eg, see gene name errors generated by use of default Microsoft Excel settings, Ziemann et al 2016).

In conclusion, R is quicker for descriptive statistics.

Graphics comparison between R and MS Excel.

.

MS Excel is often cited for its graphics capabilities (Camões 2016). We can make the familiar scatter plots, bar charts, and pie charts in Excel. These plots and more are easily obtained in R. I won’t elaborate here about graphics, we talk at some length about graphics in Chapter 4. But here’s one example in R.

Let’s plot myB vs myA. We already provided the data for variable A, here’s the data for variable B.

17, 21, 21, 26, 27, 32, 28, 42, 40, 30, 71, 53, 56, 61, 55, 89, 82, 63, 116, 162, 116, 154, 137, 149

Don’t recall how to assign a set of numbers to an object, B, in R? See above and look again at how we assigned the numbers to object myA.

To get a simple scatter plot (Fig 3), I may write at the R prompt.

plot(myA,myB)

Figure 3. Basic scatter plot made in R, plot(myA,myB).

And here’s the comparable default plot (Fig 4) from Microsoft Excel, Office 365

Figure 4. Basic scatterplot made in Microsoft Excel.

Now, both graphs need some work, and to be fair, these are just the defaults. With some effort, you can make an Excel graph look pretty good. But note — the defaults in Excel don’t generate axis labels, while R default plot does. Excel adds a useless title and legend; both need to be removed. Excel also adds grid lines where typically one would not include these in a scientific plot; for example, in the case of scatterplots, grid lines can distract the viewer from the data points and reduce the data-to-ink ratio Tufte 1984).

Count the steps to generate an acceptable scatter plot (Table 3). I’ve also added R Commander (Rcmdr) steps for comparisons (Rcmdr lets you use drop-down menus like Excel or Google Sheets or LibreOffice Calcs).

Table 3. Steps needed to make a simple scatterplot in R, R Commander, or Microsoft Excel.

Conclusion? R is quicker for routine statistical plots like a scatter plot. And I didn’t even count the steps needed to change MS Excel’s dreadful diamond icon points.

That’s one step in R, four steps in Rcmdr, but eight steps for Microsoft Excel. LibreOffice Calc is a little better at only four steps, but like MS Excel, you’d need to change several components to the graph (Fig 5).

Figure 5. Basic scatterplot made in LibreOffice Calc.

plot(myA,myB, pch = 22)

So, you’re telling me I don’t need a spreadsheet application?

.

No, not at all. We use spreadsheets, and more generally, databases, to store data. Spreadsheets apps are designed to make data entry and data management approachable and efficient. They remain an important tool for researchers (Browman and Woo 2017).

R is not that great of a spreadsheet; packages are available to seamlessly tie your spreadsheet and database data to R via ODBC. We will routinely enter and manipulate data in MS Excel, then import the data into R for analysis.

Spreadsheet apps like MS Excel and Google Sheets (see also LibreOffice Calc) are great at being a spreadsheet program, R is great at being a statistical software program. You should take advantage of what the tools do best.

Still not convinced?

.

R is in use all over the world, by students and professionals alike and if one is going to spend the time to learn how to use a statistics software program, you should learn a standard program, like R.

And it’s not just me. Read about R in this 2009 New York Times piece, “Data analysts captivated by R’s power.” Look who purchased (April 2015) Revolution Analytics, a major player in the development of the R programming language.

Why install R on your computer?

.

Convenience. Control. Offline.

At the Biology department of Chaminade University, we have installed and maintain R, Rcmdr, and RStudio along with all required packages on our Macbook Pro® Lab computers for your use during class and during optional, proctored biostatistics work sessions. Since 2018, R is increasingly available “in the cloud” (eg, RStudio Cloud), which would mean you could run R in your browser and avoid installation on your computer. You can run significant analysis with R in the cloud via the free Google Colaboratory and CoCalc are now available: I encourage you to look into these platforms. Unfortunately, these services are not quite ready for the classroom. For example, RStudio in the Cloud is free to use on a limited basis, but quickly requires a significant subscription cost with increasing use. Google Colab and GoCalcs require use of Jupyter notebooks, which add yet another layer to the learning curve without focusing on learning statistics. Second, although access to their servers is easy, running simultaneous connections via Chaminade’s single public IP address is likely to lead to problems for us. Third, I want you to use R Commander (Rcmdr) to assist in the learning curve — Rcmdr cannot be run in the Cloud (ie, RStudio in the Cloud, Google Colaboratory, or CoCalc).

Therefore, you are encouraged to install R, Rcmdr, and even RStudio, onto your own computers, in part because of the convenience, but also because R is not generally available to students on campus, ie, only the Biology department’s computers have the up-to-date R software installed.

To get started, go to your Canvas website and view How to install R on your own computer.

An additional benefit to installing a version of R on your computer, you’ll understand more about the software if you take the time to install and if need be, troubleshoot your installation of the software. Moreover, there’s a considerable amount of help out there for R. For example, a simple Google search(keywords: tutorial “install R”), returns more than 700K hits, and more than 40K January 2023 alone (add “after:2023-01-01” to Google search box). In fact, there’s so much out there that you’ll want to sample from several sites and select the voice that works best for you.  

Questions

1. Write up three learning outcomes for this page. Hint: Point your favorite generative AI to this page and ask for help.
2. I listed several spreadsheet apps: Microsoft Excel, Google Sheets, Apple Numbers, LibreOffice Calc. Which of these are free to download and install to your computer? Which are freely available via the Cloud?
3. What level of confidence do you have to this statement: I am confident in my ability to use spreadsheet apps for my BI-311 work this semester. Response options: 
   1. Strongly disagree.
   2. Disagree.
   3. Agree.
   4. Strongly agree.
4. What level of confidence do you have to this statement: I am confident in my ability to use the R programming software for my BI-311 work this semester. Response options: 
   1. Strongly disagree.
   2. Disagree.
   3. Agree.
   4. Strongly agree.

.

Quiz Chapter 2.2

Why do we use R software?

Start Quiz

Question

✕

✓

Your answer:

Correct answer:

Next

Please wait..

↺

You got {{SCORE_CORRECT}} out of {{SCORE_TOTAL}}

Your Answers

Chapter 2 contents

.

• Introduction
• Why biostatistics?
• Why do we use R Software?
• A brief history of (bio)statistics
• Experimental Design and rise of statistics in medical research
• Scientific method and where statistics fits
• Statistical reasoning
• Chapter 2 – References and suggested reading