The data used for this lesson are in the figshare repository at: https://figshare.com/articles/SAFI_Survey_Results/6262019.
This lesson uses
SAFI_clean.csv. The direct download link for this file is:
When time comes in the lesson to use this file, we recommend that the
instructors place the
download.file() command in the Etherpad, and that the
learners copy and paste it in their scripts to download the file directly from
figshare in their working directory. If the learners haven’t created the
data/ directory and/or are not in the correct working directory, the
download.file() command will produce an error. Therefore, it is important to use
the stickies at this point.
RStudio and Multiple R Installs
Some learners may have previous R installations. On Mac, if a new install is performed, the learner’s system will create a symbolic link, pointing to the new install as ‘Current.’ Sometimes this process does not occur, and, even though a new R is installed and can be accessed via the R console, RStudio does not find it. The net result of this is that the learner’s RStudio will be running an older R install. This will cause package installations to fail. This can be fixed at the terminal. First, check for the appropriate R installation in the library:
ls -l /Library/Frameworks/R.framework/Versions/
We are currently using R >=3.2. If it isn’t there, they will need to install it. If it is present, you will need to set the symbolic link to Current to point to the R >=3.2 directory:
ln -s /Library/Frameworks/R.framework/Versions/3.x.y /Library/Frameworks/R.framework/Version/Current
Then restart RStudio.
Before we start
- The main goal here is to help the learners be comfortable with the RStudio interface. We use RStudio because it helps make using R more organized and user friendly.
- Go very slowly in the “Getting setup” section. Make sure everyone is following
along (remind learners to use the stickies). Plan with the helpers at this
point to go around the room, and be available to help. It’s important to make
sure that learners are in the correct working directory, and that they create
data(all lowercase) subfolder.
Intro to R
- Why use assignment arrows (<-) over equal signs? Historically, the assignment arrow dates back to S. In S, the <- was inspired by APL, which had a key for <-. At that time, <- was used for variable assignment, because == didn’t exist for equality comparisons. Instead, equality was tested with =. So, you needed a different variable for assignment.
Fast forward to today, there really are only a few mechanical reasons why <- is preferred over =. Assignment ranks higher in operator precedence than =. If you wish to perform variable assignment inside a function, <- is the only option.
- When going over the section on assignments, make sure to pause for at least 30
seconds when asking “What do you think is the current content of the object
area_acres? 123.5 or 6.175?”. For learners with no programming experience, this is a new and important concept.
- Given that the concept of missing data is an important feature of the R language, it is worth spending enough time on it.
Starting with data
The two main goals for this lessons are:
- To make sure that learners are comfortable with working with data frames, and can use the bracket notation to select slices/columns.
- To expose learners to factors. Their behavior is not necessarily intuitive, and so it is important that they are guided through it the first time they are exposed to it. The content of the lesson should be enough for learners to avoid common mistakes with them.
Manipulating data with dplyr
- This lesson works better if you have graphics demonstrating dplyr commands. You can modify this Google Slides deck and use it for your workshop.
- For this lesson make sure that learners are comfortable using pipes.
- There is also sometimes some confusion on what the arguments of
group_byshould be, and when to use
- If the code that generates the output for the table
interviews_plotting(which is used in the following episode) causes the following error:
Error: Can’t rename columns that don’t exist.
x Column NA doesn’t exist.
Make sure you have read in the CSV file with the option that interprets the
"NULL" string as
NA, like so:
interviews <- read_csv("data/SAFI_clean.csv", na = "NULL")
Visualizing data with ggplot2
- This lesson is a broad overview of ggplot2 and focuses on (1) getting familiar
with the layering system of ggplot2, (2) using the argument
aes()function, (3) basic customization of the plots.
Technical Tips and Tricks
Show how to use the ‘zoom’ button to blow up graphs without constantly resizing windows.
Sometimes a package will not install. You can try a different CRAN mirror:
- Tools > Global Options > Packages > CRAN Mirror
Alternatively you can go to CRAN and download the package and install from ZIP file:
- Tools > Install Packages > set to ‘from Zip/TAR’
It is important that R, and the R packages be installed locally, not on a network drive. If a learner is using a machine with multiple users where their account is not based locally this can create a variety of issues (this often happens on university computers). Hopefully the learner will realize these issues beforehand, but depending on the machine and how the IT folks that service the computer have things set up, it may be very difficult to impossible to make R work without their help.
If learners are having issues with one package, they may have issues with another. It’s often easier to make sure they have all the needed packages installed at one time, rather than deal with these issues over and over. Here is a list of all necessary packages for these lessons.
| character on Spanish keyboards: The Spanish Mac keyboard does not have a
This character can be created using:
`alt` + `1`
If you encounter a problem during a workshop, feel free to contact the maintainers by email or open an issue.
For a more in-depth coverage of topics of the workshops, you may want to read “R for Data Science” by Hadley Wickham and Garrett Grolemund.