Read Chapter 1 to help you complete the questions in this exercise.
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.
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).
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!).
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).
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.
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!
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).
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.
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
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.
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).
End of Exercise 1