DAPR1 flash cards (2026/27)

These flash cards are here to complement your lecture and workshop materials by offering additional examples and explanations of key R code.

You can use the flash cards as supporting documents whilst you work through the workshop exercises.

Find extra content down rabbit holes!

If you want to dive deeper or work toward a more complete understanding of certain topics, we’ve added “rabbit holes” (like this one!) throughout the flash cards.

We will not assess any content introduced in the rabbit holes. If you only want to know the basics, you can safely skip them. But if you’re interested in learning more (or if you find yourself doing more complex data analysis in the future), you might find the extra information useful.

These flash cards have been developed by Elizabeth Pankratz, Umberto Noè, and Josiah King (who also drew the cute rabbit)!

Tip: Develop your own glossary

A good understanding of R functions is essential for your success in the DAPR curriculum and the degree more generally. We strongly recommend you get into the habit of keeping track of any new R function that you encounter and write a short description of what the function does and which package it comes from. You could save this into a Word or Excel file. You should get into the habit of updating that document as you encounter new R functions. Here’s an example to help you get started:

Function	Use and package
`<-`	Assignment operator. Stores the value on the right into the named object on the left
`read_csv()`	For reading comma separated value files. Part of tidyverse package
`View()`	Opens a spreadsheet-like data viewer in RStudio. Base R function
`head()`	Shows the first 6 rows of a dataset (by default). Base R function
`nrow()`	Returns the number of rows. Base R function
`ncol()`	Returns the number of columns. Base R function
`dim()`	Returns the dimensions (rows and columns). Base R function
`glimpse()`	Similar to `str`. Displays an overview of the data variables with their types. Part of tidyverse package
`str()`	Similar to `glimpse`. Stands for “structure”. Displays an overview of the data variables with their types. Base R function
`summary()`	Produces summary statistics. Base R function
`factor()`	Creates or modifies a variable into a categorical one (factor) with levels. Base R function