Instructor Notes

Dataset

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: https://ndownloader.figshare.com/files/11492171.

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.

Narrative

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 a 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.

Data wrangling 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_by should be, and when to use filter() and select().

Data wrangling with tidyr

  • The dplyr and tidyr episode was recently split up into two separate episodes to try to make the information about these two important packages more digestible.
  • There is some rather advanced topics in this episode, but it is really useful for learners to become familiar with some ways to deal with data that is not in a format they want.
  • This episode is also important in that it produces the interviews_plotting tibble that is used in the next episode on ggplot2.
  • 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 group in the aes() function, (3) basic customization of the plots.

Getting started with R Markdown (Optional)

  • This is an optional lesson intended to introduce learners to R Markdown.
  • While it is listed after the core lessons, some instructors may prefer to teach it early in the workshop, depending on the audience.

Processing JSON data (Optional)

  • This is an optional lessons intended to introduce learners to JSON data, as well as how to read JSON data into R and how to convert the data into a data frame or array.
  • Note that his lesson was community-contributed and remains a work in progress. As such, it could benefit from feedback from instructors and/or workshop participants.

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 | key. This character can be created using:

`alt` + `1`

Other Resources

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.

Before we Start

Introduction to R

Starting with Data

Data Wrangling with dplyr

Data Wrangling with tidyr

Data Visualisation with ggplot2

Getting started with R Markdown (Optional)

Processing JSON data (Optional)