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Using tidyr::gather() to tidy data

Joyce Robbins originally published: 1/31/2018

tidyr::gather() has been superceded by tidyr::pivot_longer(). Please see this tutorial on pivot_longer() instead.

This tutorial focuses on one function: tidyr::gather(), which is used to convert data from messy to tidy. I won’t get into the distinction here – I’m going to assume that you know the terms and just need help figuring how to get from A to B. If, however, this is all new to you, I suggest reading “Tidy Data” by Hadley Wickham, the seminal paper on the topic, which lays out the concept in detail.

Example 1: City vs. Highway

Observe

Before getting into the nitty-gritty of gather() let’s study what our messy data looks like, and what it will look like when it’s tidied:

Study the differences carefully. What did you observe?


Hopefully you noticed the following:

  1. The id column appears in both versions, though it’s double the length in the tidy form.

  2. We have two new columns: roadtype and mpg.

  3. The city and hwy column names became the contents of the new roadtype (key) column.

  4. The values of the city and hwy columns became the values of the new mpg (value) column.

Plan

Now here’s the trick: the only parts we need to concern ourselves with are 1. and 2.: deciding which columns to keep unchanged, and what names to give the new columns, which are called the key and value columns.

The columns to keep are the ones that are already tidy. The ones to dump are the ones that aren’t true variables but in fact are levels of another variable. In this case, city and hwy should be levels of a new variable called roadtype (or something similar), according to the theory of tidy data. On the other hand, id should remain as is.

The bottom line: keep id as is

The second (and final) step is to choose names for the new key and value columns. We’ve already chosen roadtype for key; let’s pick mpg for value. You can also choose to stick with the defaults for the new key and value column names: key and value (surprise). Using key and value may be helpful at first to keep track of which is which, though ultimately I find more descriptive names to be more useful.

The bottom line: key = roadtype, value = mpg

Code

The code to carry out the transformation from messy to tidy is one call to gather(), with parameters that reflect our conclusions about the columns to keep and the names we’ve chosen:

tidydata <- messydata %>% 
  gather(key = "roadtype",  value = "mpg", -id)

The most important thing to note is that each parameter corresponds to one of the columns in the tidy data set.

Now let’s look at them one at a time in more detail:

key = "roadtype" sounds like it’s setting the key to a preexisting column called “roadtype”, but that’s not what’s going on. Rather, think of this as an instruction to create a new key column and call it “roadtype”.

value = "mpg" likewise is an instruction to create a new value column and call it “mpg”.

-id is the piece that says “Don’t touch my id column! Leave it as is!”

Nothing else needs to be specified: every value in a column not marked “don’t touch” will be moved to the value (“mpg”) column, paired with its old column name (in this case “city” or “hwy”) from the messy data set, which now appears in the key column.

Heads up

There are a few problems I’ve seen with the way in which people use gather():

Not separating the messy data columns properly into “keep” and “dump”.

If we had made this mistake in the example above, the result would be something like:

messydata %>% 
  gather(key = "roadtype", value = "mpg")
## # A tibble: 9 x 2
##   roadtype mpg  
##   <chr>    <chr>
## 1 id       car1 
## 2 id       car2 
## 3 id       car3 
## 4 city     19   
## 5 city     20   
## 6 city     29   
## 7 hwy      24   
## 8 hwy      30   
## 9 hwy      35

Yikes, not what we wanted. Looking at the mpg column, we see that “car1”, “car2”, and “car3” don’t belong. The solution is to add the -id parameter – remember, think: “don’t touch id” – so that it isn’t “dumped” into the key column.

Note: it is possible to specify the columns to dump rather then the columns to keep:

messydata %>% 
  gather(key = "roadtype", value = "mpg", city, hwy)
## # A tibble: 6 x 3
##   id    roadtype   mpg
##   <chr> <chr>    <dbl>
## 1 car1  city        19
## 2 car2  city        20
## 3 car3  city        29
## 4 car1  hwy         24
## 5 car2  hwy         30
## 6 car3  hwy         35

I find this method less intuitive, but of course it’s your choice.

Missing id column

Note in our example that the id column is important for linking the city and highway miles per gallon. For example, we might want to plot the data as follows, which requires knowing which car had which city and highway mpg:

Often, however, there is no id column: it’s not necessary since each row represents one item – a car in this case. If we try to tidy messy data without an id, it looks like this:

The problem is that we’ve lost the natural connection we had between city and highway for each car since the two values no longer reside in the same row. The solution here is to move the row names – or row numbers in this case – to a column to be used as an id:

messy2 %>% rownames_to_column("id") %>% 
  gather(key, value, -id)
## # A tibble: 6 x 3
##   id    key   value
##   <chr> <chr> <dbl>
## 1 1     city     19
## 2 2     city     20
## 3 3     city     29
## 4 1     hwy      24
## 5 2     hwy      30
## 6 3     hwy      35

Note that we must specify the “don’t touch” id column that we just created. Also note that we used the default key and value column names rather than choose our own.

Example 2: MASS::painters dataset

Let’s take the painters dataset from the MASS package:

##               Composition Drawing Colour Expression School
## Da Udine               10       8     16          3      A
## Da Vinci               15      16      4         14      A
## Del Piombo              8      13     16          7      A
## Del Sarto              12      16      9          8      A
## Fr. Penni               0      15      8          0      A
## Guilio Romano          15      16      4         14      A

…and tidy it into four columns as such:

##            Name School       Skill Score
## 1      Da Udine      A Composition    10
## 2      Da Vinci      A Composition    15
## 3    Del Piombo      A Composition     8
## 4     Del Sarto      A Composition    12
## 5     Fr. Penni      A Composition     0
## 6 Guilio Romano      A Composition    15

This example is a little more complex than the previous one, since it’s missing an id column, and there is more than one “keep” column. (You are encouraged to try this on your own before looking at the solution!)

Observe

Plan

  • Move rownames to a new column and call it Name (see “missing id column” in the Heads up section above to understand the need for this new column.)

  • Keep Name and School columns as is. (“Don’t Touch!”)

  • Since the four columns Composition, Drawing, Colour, and Expression are really levels of another variable, they do not get the “keep” as is designation. That means we are staging them to be dumped into a new key variable which we’ll call Skill. In addition, the values contained in these columns will move to a new value column, which we’ll call Score.

Code

library(MASS)
library(tidyverse)
tidypaint <- painters %>% 
  rownames_to_column("Name") %>% 
  gather(key = "Skill", value = "Score", -Name, -School)

The breakdown:

Finally, if the visuals aren’t your style, here’s a running commentary of the instructions:

“Take the (messy) dataset painters. Move the contents of the rownames to a new column called Name. Now let’s start tidying by gathering multiple columns into key-value pairs. Do this by creating a new key column called Skill. While you’re at it, create a new value column called Score. Absolutely do not touch or change the Name and School columns (other than to replicate as necessary). It goes without saying that the four other column names will fill the new Skill column, and the values of those columns will fill the new Score column, since it’s understood from what I’ve said already! Much appreciated!”

Thank you to @angelotrivelli @dch4n @jschoeley @jspncr_ @kierisi @s_lump for providing feedback and helpful suggestions in response to this Twitter post.