Exercise 1: Getting to know R and RStudio


Read Chapter 1 to help you complete the questions in this exercise.


  1. Start RStudio on your computer. If you haven’t already done so, create a new RStudio Project (select File –> New Project on the main menu). Create the Project in a new directory by selecting ‘New Directory’ and then select ‘New Project’. Give the Project a suitable name (intro2r maybe) in the ‘Directory name:’ box and choose where you would like to create this Project directory by clicking on the ‘Browse’ button. Finally create the project by clicking on the ‘Create Project’ button. This will be your main RStudio Project file and directory which you will use throughout this course. See Section 1.6 of the Introduction to R book for more information about RStudio Projects and here for a short video.


  1. Now create a new R script inside this Project by selecting File –> New File –> R Script from the main menu (or use the shortcut button). Before you start writing any code save this script by selecting File –> Save from the main menu. Call this script ‘exercise_1’ or something similar. Click on the ‘Files’ tab in the bottom right RStudio pane to see whether your file has been saved in the correct location. Ok, at the top of almost every R script (there are very few exceptions to this!) you should include some metadata to help your collaborators (and the future you) know who wrote the script, when it was written and what the script does (amongst other things). Include this information at the top of your R script making sure that you place a # at the beginning of every line to let R know this is a comment. See Section 1.10 for a little more detail.

    # An Introduction to R course Exercise 1
    # Date: 27/01/21
    # Created by: Professor Plum


  1. Explore RStudio making sure you understand the functionality of each of the four windows (see Section 1.3 of the Introduction to R book for a summary and/or watch this video). Take your time and check out each of the tabs in the windows. The function of some of these tabs will be obvious whereas others won’t be useful right now. In general, you will write your R code in the script editor window (usually top left window) and then source your code into the R console (usually bottom left) by clicking anywhere in the relevant line of code with your mouse and then clicking on the ‘Run’ button at the top of the script editor window. If you don’t like clicking buttons (I don’t!) then you can use the keyboard shortcut ‘ctrl + enter’ (on Windows) or ‘command + enter’ (on Mac OSX).


  1. Now to practice writing code in the script editor and sourcing this code into the R console. Let’s display the help file for the function mean. In your script type help('mean') and source this code into the console. Notice that the help file is displayed in the bottom right window (if not then click on the ‘Help’ tab). Examine the different components of the help file (especially the examples section at the end of the help file).

    help(mean)          # different methods of using help


  1. The content displayed in the bottom right window is context dependent. For example if we write the code plot(1:10) and source it into the R console the bottom right window will display this plot (don’t worry about understanding the R code right now, hopefully this will become clear later on in the course!).

    plot(1:10)    #dont worry about what 1:10 does just yet


  1. Next, let’s practice creating a variable and assigning a value to this variable. Don’t worry if this doesn’t make complete sense to you, we’re just getting familiar with RStudio right now (see Section 2.2 of the Introduction to R book if you want more detail). Create a variable called first_num and assign it the value 42 using the assignment operator <-. Click on the ‘Environment’ tab in the top right window to display the variable and value. Now create another variable called first_char and assign it a value "my first character" (remember to include the quotes, you need them). Notice this variable is now also displayed in the ‘Environment’ along with it’s value and class (chr - short for character class).

    first_num <- 42    # create variable first_num and assign the value 42
    first_char <- "my first character"


  2. Remove the variable first_num from your environment using the rm() function. Check out the ‘Environment’ tab to ensure the variable has been removed. Alternatively, use the ls() function to list all objects in your environment.

    ls()          # list all variables in the workspace 


  3. Let’s see what happens if we assign another value to an existing variable. Assign the value "my second character" to the variable first_char you created in Q6. Notice the value has changed in the ‘Environment’. To display the value of first.char enter the name of the variable in the console. Don’t to forget to save your R script periodically!

    first_char <- "my second variable"
    first_char    # display the value 


  4. OK, let’s leave RStudio for a minute. Using your favourite web browser, navigate to the R-project website and explore links that catch your eye. Make sure you find the R manuals page and the user contributed documents section. Download any manuals that you think you might find useful and save them on your computer (or USB drive).


  1. Click on the ‘Search’ link on the R-Project website. Use ‘Rseek’ to search for the term ‘mixed model p values’ (this is a controversial subject!) and explore anything that looks interesting. Also experiment with the ‘R site search’ and ‘Nabble R Forum’ links. Learning how to search for help when you run into a problem when using R is an acquired skill and something you get better at over time. One note of caution, often you will find many different solutions to solving a problem in R, some written by experienced R users and others by people with less experience. Whichever solution you choose make sure you understand what the code is doing and thoroughly test it to make sure it’s doing what you want.


  1. OK, back to RStudio. Sometimes you may forget the exact name of a function you want to use so it would be useful to be able to search through all the function names. For example, you want to create a design plot but can only remember that the name of the function has the word ‘plot’ in it. Use the apropos() function to list all the functions with the word plot in their name (see Section 2.5.1 of the Introduction to R book). Look through the list and once you have figured what the correct function is then bring up the help file for this function.



  2. Another strategy would be to use the help.search() function to search through R’s help files. Search the R help system for instances of the character string ‘plot’. See if you can figure out how to narrow your search by only searching for ‘plot’ in the nlme package (hint: see the help page for help.search().

    ??plot     # shortcut for help.search function
    help.search("plot", package = "nlme")


  3. R’s working directory is the default location of any files you read into R, or export from R. Although you won’t be importing or exporting files just yet (that’s tomorrows job) it’s useful to be able to determine what your current working directory is. So, read Section 1.7 of the Introduction to R book to introduce yourself to working directories and figure out how to display your current working directory.

    getwd()    # displays the current working directory 


  4. Let’s finish up by creating some additional useful directories in your Project directory. If you’ve followed the Data instructions when downloading datasets for this course you will already have a directory called data in your Project (if you didn’t then take a look at the instructions under Data to create this directory). Now let’s create another directory called output where you’ll save data files and plots you generate later on during this course. This time, instead of clicking on the ‘New Folder’ icon in RStudio we’ll create a new directory using R code directly in the R console (you can also interact with your computer’s operating system in all sorts of useful ways). To do this use the dir.create() function to create a directory called output (See Section 1.8 of the Introduction to R book for more details). If you fancy it, you can also create a subdirectory in your output directory called figures to store all your fancy R plots for your thesis. You can list all the files in your directories using the list.files() function. Can you figure out how to list the directories as well? (hint: see ?listfiles or Section 1.8 of the course book).

    dir.create(path = 'output')
    dir.create(path = 'output/figures')
    list.files(include.dirs = TRUE)


  1. Don’t to forget to save your R script. Close your Project by selecting File –> Close Project on the main menu.


End of Exercise 1