How to use dplyr group by in R
06.13.2021
Intro
The group_by method allows you to group data which allows for easy visualization and summarizing over groups. Tidyverse makes single and multiple groups easy. In this article, we will learn how to us dplyr group by.
If you are in a hurry
If you don’t have time to read, here is a quick code snippet for you.
library(tidyverse)## -- Attaching packages --------------------------------------- tidyverse 1.3.1 --
## v ggplot2 3.3.3 v purrr 0.3.4
## v tibble 3.1.0 v dplyr 1.0.5
## v tidyr 1.1.3 v stringr 1.4.0
## v readr 1.4.0 v forcats 0.5.1
## -- Conflicts ------------------------------------------ tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()mtcars %>% group_by(cyl, hp)## # A tibble: 32 x 11
## # Groups: cyl, hp [23]
## mpg cyl disp hp drat wt qsec vs am gear carb
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 21 6 160 110 3.9 2.62 16.5 0 1 4 4
## 2 21 6 160 110 3.9 2.88 17.0 0 1 4 4
## 3 22.8 4 108 93 3.85 2.32 18.6 1 1 4 1
## 4 21.4 6 258 110 3.08 3.22 19.4 1 0 3 1
## 5 18.7 8 360 175 3.15 3.44 17.0 0 0 3 2
## 6 18.1 6 225 105 2.76 3.46 20.2 1 0 3 1
## 7 14.3 8 360 245 3.21 3.57 15.8 0 0 3 4
## 8 24.4 4 147. 62 3.69 3.19 20 1 0 4 2
## 9 22.8 4 141. 95 3.92 3.15 22.9 1 0 4 2
## 10 19.2 6 168. 123 3.92 3.44 18.3 1 0 4 4
## # ... with 22 more rowsLoading the Library
We can load the dplyr package directly, but I recommend loading the
tidyverse package as we will use some other features in side.
library(tidyverse)Loading the Dataset
For this tutorial, we will use the mtcars data set the comes with
tidyverse. We take a look at this data set below.
data(mtcars)
glimpse(mtcars)## Rows: 32
## Columns: 11
## $ mpg <dbl> 21.0, 21.0, 22.8, 21.4, 18.7, 18.1, 14.3, 24.4, 22.8, 19.2, 17.8,~
## $ cyl <dbl> 6, 6, 4, 6, 8, 6, 8, 4, 4, 6, 6, 8, 8, 8, 8, 8, 8, 4, 4, 4, 4, 8,~
## $ disp <dbl> 160.0, 160.0, 108.0, 258.0, 360.0, 225.0, 360.0, 146.7, 140.8, 16~
## $ hp <dbl> 110, 110, 93, 110, 175, 105, 245, 62, 95, 123, 123, 180, 180, 180~
## $ drat <dbl> 3.90, 3.90, 3.85, 3.08, 3.15, 2.76, 3.21, 3.69, 3.92, 3.92, 3.92,~
## $ wt <dbl> 2.620, 2.875, 2.320, 3.215, 3.440, 3.460, 3.570, 3.190, 3.150, 3.~
## $ qsec <dbl> 16.46, 17.02, 18.61, 19.44, 17.02, 20.22, 15.84, 20.00, 22.90, 18~
## $ vs <dbl> 0, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0,~
## $ am <dbl> 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0,~
## $ gear <dbl> 4, 4, 4, 3, 3, 3, 3, 4, 4, 4, 4, 3, 3, 3, 3, 3, 3, 4, 4, 4, 3, 3,~
## $ carb <dbl> 4, 4, 1, 1, 2, 1, 4, 2, 2, 4, 4, 3, 3, 3, 4, 4, 4, 1, 2, 1, 1, 2,~Basic dplyr group by
To use group_by, we pass our data and the column we want to group by as parameters to the method.
group_by(mtcars, cyl)## # A tibble: 32 x 11
## # Groups: cyl [3]
## mpg cyl disp hp drat wt qsec vs am gear carb
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 21 6 160 110 3.9 2.62 16.5 0 1 4 4
## 2 21 6 160 110 3.9 2.88 17.0 0 1 4 4
## 3 22.8 4 108 93 3.85 2.32 18.6 1 1 4 1
## 4 21.4 6 258 110 3.08 3.22 19.4 1 0 3 1
## 5 18.7 8 360 175 3.15 3.44 17.0 0 0 3 2
## 6 18.1 6 225 105 2.76 3.46 20.2 1 0 3 1
## 7 14.3 8 360 245 3.21 3.57 15.8 0 0 3 4
## 8 24.4 4 147. 62 3.69 3.19 20 1 0 4 2
## 9 22.8 4 141. 95 3.92 3.15 22.9 1 0 4 2
## 10 19.2 6 168. 123 3.92 3.44 18.3 1 0 4 4
## # ... with 22 more rowsNotice how the look does not change, but there is a new item at the top saying we have 3 cyl groups.
When working with dplyr and the tidyverse, we often use the pipe, %>% operator. With this, we can send the data set to our method to use. Here is a rewrite of the code above.
mtcars %>% group_by(cyl)## # A tibble: 32 x 11
## # Groups: cyl [3]
## mpg cyl disp hp drat wt qsec vs am gear carb
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 21 6 160 110 3.9 2.62 16.5 0 1 4 4
## 2 21 6 160 110 3.9 2.88 17.0 0 1 4 4
## 3 22.8 4 108 93 3.85 2.32 18.6 1 1 4 1
## 4 21.4 6 258 110 3.08 3.22 19.4 1 0 3 1
## 5 18.7 8 360 175 3.15 3.44 17.0 0 0 3 2
## 6 18.1 6 225 105 2.76 3.46 20.2 1 0 3 1
## 7 14.3 8 360 245 3.21 3.57 15.8 0 0 3 4
## 8 24.4 4 147. 62 3.69 3.19 20 1 0 4 2
## 9 22.8 4 141. 95 3.92 3.15 22.9 1 0 4 2
## 10 19.2 6 168. 123 3.92 3.44 18.3 1 0 4 4
## # ... with 22 more rowsUsually, group_by is used in conjunction with summarize. Here is an example of getting the avgerage horsepower per a car’s cyl.
mtcars %>%
group_by(cyl) %>%
summarize(avg_hp = mean(hp))## # A tibble: 3 x 2
## cyl avg_hp
## <dbl> <dbl>
## 1 4 82.6
## 2 6 122.
## 3 8 209.More dplyr group by
Let’s go over some other features of the group_by method. First of all, if you us group_by on an existing grouped data frame, the old group will be removed.
group1 = mtcars %>%
group_by(cyl)
group1 %>%
group_by(vs, am)## # A tibble: 32 x 11
## # Groups: vs, am [4]
## mpg cyl disp hp drat wt qsec vs am gear carb
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 21 6 160 110 3.9 2.62 16.5 0 1 4 4
## 2 21 6 160 110 3.9 2.88 17.0 0 1 4 4
## 3 22.8 4 108 93 3.85 2.32 18.6 1 1 4 1
## 4 21.4 6 258 110 3.08 3.22 19.4 1 0 3 1
## 5 18.7 8 360 175 3.15 3.44 17.0 0 0 3 2
## 6 18.1 6 225 105 2.76 3.46 20.2 1 0 3 1
## 7 14.3 8 360 245 3.21 3.57 15.8 0 0 3 4
## 8 24.4 4 147. 62 3.69 3.19 20 1 0 4 2
## 9 22.8 4 141. 95 3.92 3.15 22.9 1 0 4 2
## 10 19.2 6 168. 123 3.92 3.44 18.3 1 0 4 4
## # ... with 22 more rowsIf we would like to add groups instead, we can use the .add parameter.
group1 %>%
group_by(vs, am, .add = TRUE)## # A tibble: 32 x 11
## # Groups: cyl, vs, am [7]
## mpg cyl disp hp drat wt qsec vs am gear carb
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 21 6 160 110 3.9 2.62 16.5 0 1 4 4
## 2 21 6 160 110 3.9 2.88 17.0 0 1 4 4
## 3 22.8 4 108 93 3.85 2.32 18.6 1 1 4 1
## 4 21.4 6 258 110 3.08 3.22 19.4 1 0 3 1
## 5 18.7 8 360 175 3.15 3.44 17.0 0 0 3 2
## 6 18.1 6 225 105 2.76 3.46 20.2 1 0 3 1
## 7 14.3 8 360 245 3.21 3.57 15.8 0 0 3 4
## 8 24.4 4 147. 62 3.69 3.19 20 1 0 4 2
## 9 22.8 4 141. 95 3.92 3.15 22.9 1 0 4 2
## 10 19.2 6 168. 123 3.92 3.44 18.3 1 0 4 4
## # ... with 22 more rowsUngroup
We can remove group by using the ungroup method.
mtcars %>%
group_by(cyl) %>%
ungroup()## # A tibble: 32 x 11
## mpg cyl disp hp drat wt qsec vs am gear carb
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 21 6 160 110 3.9 2.62 16.5 0 1 4 4
## 2 21 6 160 110 3.9 2.88 17.0 0 1 4 4
## 3 22.8 4 108 93 3.85 2.32 18.6 1 1 4 1
## 4 21.4 6 258 110 3.08 3.22 19.4 1 0 3 1
## 5 18.7 8 360 175 3.15 3.44 17.0 0 0 3 2
## 6 18.1 6 225 105 2.76 3.46 20.2 1 0 3 1
## 7 14.3 8 360 245 3.21 3.57 15.8 0 0 3 4
## 8 24.4 4 147. 62 3.69 3.19 20 1 0 4 2
## 9 22.8 4 141. 95 3.92 3.15 22.9 1 0 4 2
## 10 19.2 6 168. 123 3.92 3.44 18.3 1 0 4 4
## # ... with 22 more rowsGroup by Computed
We can also group by a computed column by passing the new parameter name and the computing function. This is a short hand for using the mutate function.
mtcars %>% group_by(vsam = vs + am)## # A tibble: 32 x 12
## # Groups: vsam [3]
## mpg cyl disp hp drat wt qsec vs am gear carb vsam
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 21 6 160 110 3.9 2.62 16.5 0 1 4 4 1
## 2 21 6 160 110 3.9 2.88 17.0 0 1 4 4 1
## 3 22.8 4 108 93 3.85 2.32 18.6 1 1 4 1 2
## 4 21.4 6 258 110 3.08 3.22 19.4 1 0 3 1 1
## 5 18.7 8 360 175 3.15 3.44 17.0 0 0 3 2 0
## 6 18.1 6 225 105 2.76 3.46 20.2 1 0 3 1 1
## 7 14.3 8 360 245 3.21 3.57 15.8 0 0 3 4 0
## 8 24.4 4 147. 62 3.69 3.19 20 1 0 4 2 1
## 9 22.8 4 141. 95 3.92 3.15 22.9 1 0 4 2 1
## 10 19.2 6 168. 123 3.92 3.44 18.3 1 0 4 4 1
## # ... with 22 more rowsNotice how the above is the same as using the mutate function.
mtcars %>%
mutate(vsam = vs + am) %>%
group_by(vsam)## # A tibble: 32 x 12
## # Groups: vsam [3]
## mpg cyl disp hp drat wt qsec vs am gear carb vsam
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 21 6 160 110 3.9 2.62 16.5 0 1 4 4 1
## 2 21 6 160 110 3.9 2.88 17.0 0 1 4 4 1
## 3 22.8 4 108 93 3.85 2.32 18.6 1 1 4 1 2
## 4 21.4 6 258 110 3.08 3.22 19.4 1 0 3 1 1
## 5 18.7 8 360 175 3.15 3.44 17.0 0 0 3 2 0
## 6 18.1 6 225 105 2.76 3.46 20.2 1 0 3 1 1
## 7 14.3 8 360 245 3.21 3.57 15.8 0 0 3 4 0
## 8 24.4 4 147. 62 3.69 3.19 20 1 0 4 2 1
## 9 22.8 4 141. 95 3.92 3.15 22.9 1 0 4 2 1
## 10 19.2 6 168. 123 3.92 3.44 18.3 1 0 4 4 1
## # ... with 22 more rowsGroup Vars
We an use the group_vars function to get the list of groups.
mtcars %>%
group_by(vs, am) %>%
group_vars()## [1] "vs" "am"