Welcome to the official site of the R office hours materials.
You can catch up on the fall semester topics using the links below!
February 13: Making summary tables code
February 20: Review and de-bugging practice Rmd file
February 27: Writing functions
March 13: Functions and test review
March 27: Loops
May 8: A new way to reshape data
In the spring semester, all sessions will be held Wednesdays from 3:30 to 4:30.
| Date | Leader | Room |
|---|---|---|
| February 13 | Emma | Kresge 204 |
| February 20 | Emma | Kresge 204 |
| February 27 | Emma | Kresge 204 |
| March 6 | CANCELLED | - |
| March 13 | Louisa | Kresge 204 |
| March 20 | SPRING BREAK | - |
| March 27 | Louisa | Kresge 200 |
| April 3 | Emma | Kresge 200 |
| April 10 | Louisa | Kresge 200 |
| April 17 | Emma | Kresge 200 |
| April 24 | Louisa | Kresge 200 |
| May 1 | Emma | Kresge 200 |
| May 8 | Louisa | Kresge 200 |
(Right-click and download links to save data files)
September 5: Introduction to ggplot; data
September 12: Filtering data; data
September 19: Selecting columns with select()
September 25/26: Making new variables with mutate()
October 2/3: Using factors to work with categorical variables
October 9/10: Using summarise() and group_by() to summarise your dataset
October 16/17: Review and challenges
October 22/23: Improving data visualizations with facets and themes
October 30/31: Tidying data using gather()
November 6/7: Going back to wide format using spread()
November 20: Separating and uniting variables in your dataset
November 27/28: Merging datasets
December 4/5: Working with dates and times
December 11/12: Tips and tricks for de-bugging code
Used to SAS or STATA? These guides ( SAS and Stata ( 1 & 2)) can help you translate from those languages to R.
Some people love learning R with Swirl, which teaches you to code interactively.
Here’s RStudio’s great list of online resources. In particular, there are some more online tutorials here.
Hadley Wickham is probably the #1 R guru and has written several books about R, which you can read on his website, where you can also learn more about the packages he’s written, including ggplot2.
Here’s another book with a good introduction to data science R, including data visualization.
The fivethirtyeight package has a ton of cool datasets that you can play around with.
If you’re not understanding an error message, clear your workspace and/or restart RStudio and try again. Does the error still show up? Then try writing a minimal working example. What does it take to reproduce the error? Is the problem with your data, your code, or both?
Watch this video of an expert walk through her process of debugging code (even experts get error messages all the time!).
It may sound silly, but copying and pasting error messages into Google is usually the fastest way to solve a tricky problem. You will almost certainly end up on the relevant stack overflow question page, because someone somewhere has experienced the error you’ve encountered.
Struggling with ggplot? Take a look at this cheat sheet or this gallery. This website is another great resource.
Ready to make your plots beautiful? Choose your color scheme with the RColorBrewer package. Explore ColorBrewer palettes here. “Set1” and “Dark2” are favorites for qualitative data and “BuGn” is nice for sequential gradients.
This Rmarkdown cheat sheet is helpful for getting started.
Can’t remember the name of a certain Greek letter? Try detexify.