Read Chapter 1 to help you complete the questions in this exercise. We’ll also bounce occasionally to Chapter 2 for a few questions where links will be provided.
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 - see Section 1.9 of the Introduction to R book for a discussion on naming files and directories) 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. If you’re confused see Section 1.6 of the introduction to R Book which covers RStudio Projects or watch the ‘RStudio Projects’ video here.
2. 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 (remember, files names should not contain spaces!). 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.
3. 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).
4. 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). See
Section 2.5.1 of the
Introduction to R book for more information about help files.
5. 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!).
6. Next, let’s practice creating a variable and assigning a value to
this variable. Take a look at Section 2.2 of the
Introduction to R book for further information on how to do this or if
you prefer watch the Objects in R
video. Create a variable called first_num and assign it
the value 42. 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". 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"
7. Remove the variable first_num from your environment
using the rm() function. Use the code
rm(first_num) to do this. Check out the ‘Environment’ tab
to ensure the variable has been removed. Alternatively, use the
ls() function to list all objects in your environment.
8. 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!
9. 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).
10. 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. 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’ll 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.
11. 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 (Hint: the function name probably has the words ‘plot’ and
‘design’ in it!).
12. 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’. Take a look at Section 2.5.1 for more
information. Also, 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()).
help.search("plot")
??plot # shortcut for help.search function
help.search("plot", package = "nlme")
13. 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.
14. 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
here 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 ?list.files or Section 1.8 of the course
book).
15. 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