Analysing data with the data verbs

Welcome! This tutorial is all about the 'data verbs' - a set of high-level operations that you can apply to data frames. Like the small, simple tools in the UNIX command line, these operations are individually simple but when put into pipelines

data %>% first operation %>% second operation %>% ...

can produce really useful results, really quickly.

Note

This tutorial has a cheat sheet! You should have been handed this, or right-click on the image below and save it to your computer.

Eight data verbs

You should learn these data verbs! Luckily there are only seven eight to worry about:

Operation	Description	In R with dplyr	In python with polars	In SQL databases
Select	Select columns, and maybe rename	select()	.select()	SELECT
Mutate	Add new columns	mutate()	.mutate()	ADD COLUMN
Filter	Filter rows based on column values	filter()	.filter()	SELECT
Slice	Takes a 'slice' of rows, such as the top few or the last few	head() or slice_head(), slice_tail(), or slice_sample()	?	(Varies by database)
Arrange or sort	Sort rows based on column values	arrange()	.sort()	ORDER BY
Summarise or aggregate	Combine values across rows	summarise()	.agg()	various functions, like SUM()
Group by	Group rows based on column values	group_by()	.groupby()	GROUP BY
Join	Join two data frames by column values	inner_join() or left_, right_, or full_join()	.join()	INNER JOIN or LEFT, RIGHT, or FULL JOIN

(Ok, there are also a few more once you get into this, like the pivot or reshape. But the above will get you a long way.)

Data verbs in different languages

We're going to use R for this tutorial, and specifically the tidyverse / dplyr implementation of the data verbs.

However the ideas are general and, as the table above suggests, you can use these same 'verbs' in other settings as well. For example:

• Most of the 'data verbs' are part of the SQL language used to talk to most relational databases (like MySQL or sqlite).
• You can use them in python in a similar way to in R (the polars library is a great way to do this).

Did I mention you should learn these verbs?

In this tutorial we'll use R, which has a particularly nice and easy-to-use implementation in the dplyr package (part of tidyverse), but feel free to translate these into other languages if you prefer.

Get started

Ready to start? Start an R session and let's go and get the data.