Instructor Notes

Dataset


The data used for this lesson are a slightly cleaned up version of the SAFI Survey Results available on GitHub. The original data is on figshare.

This lesson uses SAFI_clean.csv. The direct download link for the data file is: https://raw.githubusercontent.com/datacarpentry/r-socialsci/main/episodes/data/SAFI_clean.csv.

Lesson Plans


The lesson contains much more material than can be taught in a day. Instructors will need to pick an appropriate subset of episodes to use in a standard one day course.

Suggested path for half-day course:

  • Before we Start
  • Introduction to R
  • Starting with Data

Suggested path for full-day course:

  • Before we Start
  • Introduction to R
  • Starting with Data
  • Data Wranging with dplyr
  • (OPTIONAL) Data Wrangling with tidyr
  • Data Visualisation with ggplot2

For a two-day workshop, it may be possible to cover all of the episodes. Feedback from the community on successful lesson plans is always appreciated!

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’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


Instructor Note

  • The main goal here is to help the learners be comfortable with the RStudio interface.
  • Go very slowly in the “Getting set up” 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.


Introduction to R


Instructor Note

  • The main goal is to introduce users to the various objects in R, from atomic types to creating your own objects.
  • While this epsiode is foundational, be careful not to get caught in the weeds as the variety of types and operations can be overwhelming for new users, especially before they understand how this fits into their own “workflow.”


Starting with Data


Instructor Note

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


Instructor Note

  • 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


Data Visualisation with ggplot2


Instructor Note

  • 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)


Instructor Note

  • 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)


Instructor Note

  • 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.