Want to help out or contribute?

If you find any typos, errors, or places where the text could be improved, please let us know by providing feedback either in the feedback survey (given during class), by using GitLab, or directly in this document with hypothes.is annotations.

  • Open an issue or submit a merge request on GitLab with the feedback or suggestions.
  • Hypothesis Add an annotation using hypothes.is. To add an annotation, select some text and then click the on the pop-up menu. To see the annotations of others, click the in the upper right-hand corner of the page.

3 Pre-course tasks

Complete everything in this chapter, then complete the survey at the end. The deadline for these tasks is (at least) three days before the course starts.

3.1 Installing programs

The first things to do are to:

  1. Install the latest version of R (at least version 3.6.0, preferably >4.0.0).
  2. Install the latest version of RStudio (at least version 1.2.5001, preferably >1.3 version).
  3. Install Git.

For some Windows users, you may need to install Rtools in order for some R packages to be installed (which you’ll do shortly).

All these programs are required for the course, even Git. Git, which is a software program to formally manage versions of files, is used because of it’s popularity and the amount of documentation available for it. At the end of the course, you will be using Git and GitHub to manage your group assignment. Check out the online book Happy Git with R, especially the “Why Git” section, for an understanding on why we are teaching Git. Windows users tend to have more trouble with installing Git than macOS or Linux users. See the section on Installing Git for Windows for help.

A note to those who have or use work laptops with restrictive administrative privileges: You may encounter problems installing software due to administrative reasons (e.g. you don’t have permission to install things). Even if you have issues installing or updating the latest version of R or RStudio, you will likely be able to continue with the course as long as you have at least 3.6.0 for R and at least 1.2.5001 for RStudio. If you have versions of R and RStudio that are older than that, you may need to ask your IT department to update your software if you can’t do this yourself. Unfortunately, Git is not a commonly used software for some organizations, so you may not have it installed and you will need to ask IT to install it. We require it for the course, so please make sure to give IT enough time to be able to install it for you prior to the course.

Once R, RStudio, and Git have been installed, open RStudio. If you encounter any troubles during these pre-course tasks, try as best you can to complete the task and then let us know about the issues in the pre-course survey (at the end of this section). If you continue having problems, indicate on the survey that you need help and we can try to book a quick video call to fix the problem. Otherwise, you can come to the course 15-20 minutes earlier to get help.

If you’re unable to complete the setup procedure due to unfixable technical issues, you can use RStudio Cloud as a last-ditch solution to participate in the course. For help setting up RStudio Cloud for this course, refer to the RStudio Cloud setup guide.

3.2 What is R?

During this course, we will be spending most of our time in RStudio. RStudio is an environment that we use to interact with R. R is like an engine, while RStudio is like the tools we use to actually work with that engine. Prior to taking a look at what RStudio looks like, let’s talk briefly about R as a programming language.

R is a free programming language/environment used in statistical computing, data analytics, and scientific research. R is used to clean, organize, analyze, and report data. R has powerful visualization features, so it is a particularly useful tool for creating charts and figures. R is different from SPSS and other statistical programs in that you run analyses by typing commands in a console rather than using click-based, drop-down menus.

In recent years, R has become one of the most popular languages among statisticians and data scientists for several reasons:

  • It is open source, so you are able to see how exactly a, for instance, statistical method works.
  • It runs on all platforms (Windows, macOS, Linux).
  • It is highly compatible with other programming languages.
  • It provides access to a vast array of packages that can complete nearly any task or statistical approach.
  • There is a huge online community to help you problem-solve any issue.

However, like many programming languages, R is not easy to learn. Some functions are spread across packages, which means that you need to have prior knowledge of packages in order to implement some commands. R can also be slower than other programming languages. Nonetheless, R offers such a supportive community and rich functionality that it is worth the challenge!

3.3 Getting familiar with RStudio

Check out Figure 3.1 below. You can see that RStudio has four “panels,” dividing the screen into the four sections.

Interface to RStudio.

Figure 3.1: Interface to RStudio.

While you can customize where the individual panels go, the default layout is how the panels are shown.

  • Panel “A” is the panel that shows the “scripts,” which we will be using a lot during the course. You may or may not see this panel when you open RStudio for the first time. This panel is where you write R code that will be saved as a file.
  • Panel “B” is the Console. This is where R commands are sent and evaluated by R. This is the “engine.” No R code written here is saved. Almost all of the tasks in this course will be entered through the Console.
  • Panel “C” contains the Environment, History, Connections, and Git tabs. In this course, we will only be using the Environment and Git tab.
  • Panel “D” has the Files, Plots, Packages, Help, Build, and Viewer tabs. For this course, we will only be going over the Files, Plots, Packages, and Help tabs. There can be slight differences in your layout of tabs in each panel.

While we will spend most of the course using R script files to play around with code, we will also be learning and using RMarkdown (.Rmd). R Markdown is a dynamic and invaluable tool that will help make your analysis more reproducible. R Markdown allows you to enter chunks of code as well as text and images. R runs the code and inserts the code output into the R Markdown file. The R Markdown file can be converted into a wide range of document types, including MS Word, PDF, or HTML. Some researchers write and manage entire papers, theses, or books using R Markdown, as it can make things easier to organize and maintain. In fact, this website is written with R Markdown.

3.4 Installing R packages

Now that you have RStudio and R on your computer, we need to install the R packages we’ll use in the course. R packages are bundles of R code that other people have written. There are so many R packages available that there is likely an R package for anything you’d like to do in R. Making use of R packages can greatly help you out when doing your research.

For this course, we will be focusing on R packages that are powerful and general-purpose enough to help you in multiple aspects of your research. To install these packages, we’ll need to install the r3 helper package. For that, we’ll need to first install the remotes package. Watch the video below to see how to do this:

Copy and paste the command below into the RStudio Console. Hit Enter and the r3 helper package will be installed. Watch the video below to see how to do this:

remotes::install_gitlab("rostools/r3", upgrade = TRUE)

It is important to understand what you are commanding when you enter a function like something::something(). In the example of remotes::install_gitlab(), you would “read” this as:

R, can you please use the install_gitlab function from the remotes package?

You could load the package with library(remotes) and then run the install_gitlab function. However, using the :: tells R that we want to use a function directly from a package. We prefer this way as we only want to use the install_gitlab() function from the remotes package without having to load all the other functions. We will be using :: often during this course.

Most of the packages we will be using in this course are bundled together into one package called tidyverse. tidyverse is a collection of packages that are designed for common tasks in data science, ranging from data exploration to data visualization. As the name suggests, tidyverse is an attempt to organize the “universe” of data analysis by providing packages that guide workflows and lead to more reproducible analysis projects. Tidyverse will be installed for you now that you’ve installed r3, as it was already a part of that helper package. If you wanted to install tidyverse normally, you would type in the Console:


The specific packages from tidyverse that we will use are ggplot2, dplyr, and rmarkdown. These packages provide a set of tools for the most common data analysis tasks, and have excellent documentation and tutorials on how to use them.

There are two core packages contained within tidyverse that we will use more regularly.

  • dplyr (along with a complementary package tidyr) is a package that is very popular and contains important data manipulation functions, including functions that select and/or create variables depending on certain conditions. dplyr is built to work directly with data frames (rectangular data like those found in spreadsheets), and has an additional feature to interact directly with data stored in an external database such as in SQL. Working with databases is a powerful way to work with massive datasets (100s of GB), more than what your computer could normally handle. Working with massive data won’t be covered in this course, but see this resource from Data Carpentry to learn more.
  • ggplot2 is a data visualization package that can be used to create bar charts, pie charts, histograms, scatterplots, error charts, and more. It uses high-level API that means you are able to customize the aesthetics of your plot and add different components or layers.

3.5 Setting up Git and GitHub

We’ll cover what Git and GitHub are during the course, but for now, we need you to prepare things so that you are ready for the course. In order to use Git properly, you need to inform your computer that you are using Git. Since we’ve installed the r3 package and we only want to use a specific function from it, we’ll be using r3:: often. So, type in the RStudio Console:


Hit enter and follow the instructions. Finally, type and run this next function to make sure everything is working with your setup. When you complete the survey later, you will need to copy and paste the output of this function.


After you are done, you need to create a GitHub account. See Figure 3.2 for a demonstration of how to do that. Make note of your username, as we will ask you for it in the pre-course survey.

Note: GitHub is a company and website, while Git is a software. There is sometimes confusion about these two things since they both say “Git.” It’s important to distinguish that they are two separate things.

Creating a GitHub account.

Figure 3.2: Creating a GitHub account.

3.6 Course introduction

Most of the description of the course is found in the syllabus. A shortcut to quickly access the syllabus from RStudio is to enter r3::open_syllabus() or click it from the menu bar on the side.

While you may have signed up to this course to learn more about R, you should know that conducting reproducible research goes beyond R and RStudio. As such, we will be spending a lot of time exploring other tools that are used in conjunction with R, to improve the structure and transparency of your work. This course is designed to not only introduce you to R, but also to show ways of conducting reproducible research and data analysis in R.

If you haven’t read the syllabus, please read it now. Read over what the course will cover, what we expect you to learn by the end of it, and what our basic assumptions are about who you are and what you already know. At the end of this section, we’ll ask you a few questions to see if you understand what you’ll learn in the course.

One goal of the course is to teach about open science, and true to our mission, we practice what we preach. The course material is publicly accessible (all on this website) and openly licensed so you can (re-)use it for free! The material is organized in the order that we will cover it in the course. While the course will include lots of hands-on work during the sessions, the final group project assignment will allow you to practice everything you’ve learned in a team setting. To quickly access the final project assignment from inside RStudio, run the function r3::open_assignment().

We have a Code of Conduct. If you haven’t read it, read it now. The survey involves some questions about Conduct. We want to make sure this course is a supportive and safe environment for learning, so this Code of Conduct is quite important.

You’re almost done. Please fill out the pre-course survey to finish this section, either at this link or with this function:


See you at the course!