Exercises for using R for data science

This package contains exercises for getting started with the programming language R. They were designed to align with the courses I am teaching at the Europa-University Flensburg, but the material they cover is most likely part of many introductions to R.

Before you start

You need two things installed on your computer:

1. R (version 4.3 or newer) - download from cran.r-project.org
2. RStudio Desktop - download from posit.co

Everything below is typed into the console in RStudio, the pane with the > prompt.

Installation

Copy the following two lines into the console and press enter:

install.packages("DataScienceExercises",
  repos = c("https://teaching-datascience.r-universe.dev",
            "https://cloud.r-project.org"))

This installs the package together with everything it needs. It may take a few minutes the first time.

To check that everything worked:

DataScienceExercises::check_setup()

Updating

The exercises are part of the package, so corrections and new exercises only reach you when you install it again. If an exercise behaves strangely, run the same two lines from above once more - this updates the package. The version you have is shown by packageVersion("DataScienceExercises").

Doing the exercises

To see which exercises are available:

library(DataScienceExercises)
list_exercises()

To start one of them:

run_exercise("Basics")

The exercise opens in your web browser. The first start of an exercise takes a while (often a minute or more) because it has to be prepared for you - this is normal, please be patient. Later starts are much faster.

To stop an exercise, click the red stop button in RStudio, or click into the console and press Esc.

Available exercises

Exercise code	Description
Basics	Exercises on how to conduct basic tasks in R
Functions	Exercises on how to define your own functions
ObjectTypes1	Exercises on how to handle basic object types
ObjectTypes2	Exercises on how to use advanced object types, such as data frames, tibbles or factors
Visualization1	Building plots using ggplot2
ProjectOrga	How to set up an R project and use the here function
ImportData	How to import data, especially csv files using data.table::fread()
RMarkdown	An older version that contains some exercises on R Markdown
Quarto	A newer version that contains some exercises on Quarto
Wrangling1	The basics of manipulating and reshaping data frames
Wrangling2	More exercises on making data wider or longer
LinearRegression1	Basics of linear regression models
LinearRegression2	More advanced applications for linear regression models
Sampling	Simulating random processes and conduct Monte Carlo simulations
Models	Multiple choice questions on the theory of models

If something goes wrong

Run DataScienceExercises::check_setup() first. It prints your R version and tells you whether any package is missing. Send its output to your lecturer if you need help - it contains everything needed to understand the problem.

Warnings are not errors. While an exercise is being prepared, R often prints lines starting with Warning or [WARNING], for example about deprecated pandoc options. These come from the packages working in the background, not from your code, and you can ignore them. Only a message starting with Error actually stops something from working.

Some common situations:

• "there is no package called 'DataScienceExercises'" - the installation did not finish. Run the installation command again and read the output for errors.
• An exercise name is not found - names are listed by list_exercises(). run_exercise() ignores capitalisation and suggests the closest match.
• The browser does not open - the exercise may still be preparing. Wait, and check the console for a link you can open manually.
• Installation asks "Do you want to install from sources?" - answer no.

Alternative installation from GitHub

If the installation above does not work, you can install directly from GitHub. This route needs the remotes package first, and is slower:

install.packages("remotes")
remotes::install_github(
  repo = "teaching-datascience/DataScienceExercises",
  upgrade = "never")

The upgrade = "never" matters: without it you are asked which of your other packages to update, and answering "all" can start lengthy installations that may fail.

Feedback

For bug reports and feedback please use the Github issue tracker.