Silvia Canelón

Our illustrated penguins have reached the dplyr package! The photo backdrop is a snowy Antarctic wonderland featuring a Gentoo penguin with outstretched flippers

dplyr: info

Overview
Cheatsheet
Reading

Data transformation helps you get the data in exactly the right form you need

With dplyr you can:

create new variables
create summaries
rename variables
reorder observations
…and more!

Pick observations by their values with filter().
Reorder the rows with arrange().
Pick variables by their names select().
Create new variables with functions of existing variables with mutate().
Collapse many values down to a single summary with summarize().
group_by() gets the above functions to operate group-by-group rather than on the entire dataset.
and count() + add_count() simplify group_by() + summarize() when you just want to count

PDF: https://github.com/rstudio/cheatsheets/raw/main/data-transformation.pdf dplyr cheatsheet

R4DS book cover

R for Data Science: Ch 11 Data transformation

Package documentation: https://dplyr.tidyverse.org

Can you spot the difference in these operations?

select(penguins, 
       species,
       sex,
       body_mass_g)

# A tibble: 344 × 3
   species sex    body_mass_g
   <fct>   <fct>        <int>
 1 Adelie  male          3750
 2 Adelie  female        3800
 3 Adelie  female        3250
 4 Adelie  <NA>            NA
 5 Adelie  female        3450
 6 Adelie  male          3650
 7 Adelie  female        3625
 8 Adelie  male          4675
 9 Adelie  <NA>          3475
10 Adelie  <NA>          4250
# … with 334 more rows

penguins |> 
  select(species,
         sex,
         body_mass_g)

# A tibble: 344 × 3
   species sex    body_mass_g
   <fct>   <fct>        <int>
 1 Adelie  male          3750
 2 Adelie  female        3800
 3 Adelie  female        3250
 4 Adelie  <NA>            NA
 5 Adelie  female        3450
 6 Adelie  male          3650
 7 Adelie  female        3625
 8 Adelie  male          4675
 9 Adelie  <NA>          3475
10 Adelie  <NA>          4250
# … with 334 more rows

We can use arrange() to arrange our data in descending order by body_mass_g

glimpse(penguins)
## Rows: 344
## Columns: 8
## $ species           <fct> Adelie, Adelie, Adelie, Adelie, Adelie, Adelie, Adel…
## $ island            <fct> Torgersen, Torgersen, Torgersen, Torgersen, Torgerse…
## $ bill_length_mm    <dbl> 39.1, 39.5, 40.3, NA, 36.7, 39.3, 38.9, 39.2, 34.1, …
## $ bill_depth_mm     <dbl> 18.7, 17.4, 18.0, NA, 19.3, 20.6, 17.8, 19.6, 18.1, …
## $ flipper_length_mm <int> 181, 186, 195, NA, 193, 190, 181, 195, 193, 190, 186…
## $ body_mass_g       <int> 3750, 3800, 3250, NA, 3450, 3650, 3625, 4675, 3475, …
## $ sex               <fct> male, female, female, NA, female, male, female, male…
## $ year              <int> 2007, 2007, 2007, 2007, 2007, 2007, 2007, 2007, 2007…

penguins |>
  select(species, sex, body_mass_g) |>
  arrange(desc(body_mass_g))

# A tibble: 344 × 3
   species sex   body_mass_g
   <fct>   <fct>       <int>
 1 Gentoo  male         6300
 2 Gentoo  male         6050
 3 Gentoo  male         6000
 4 Gentoo  male         6000
 5 Gentoo  male         5950
 6 Gentoo  male         5950
 7 Gentoo  male         5850
 8 Gentoo  male         5850
 9 Gentoo  male         5850
10 Gentoo  male         5800
# … with 334 more rows

We can use group_by() to group our data by species and sex

We can use summarize() to calculate the average body_mass_g for each grouping

penguins |>
  select(species, sex, body_mass_g) |>
  group_by(species, sex) |>         
  summarize(mean = mean(body_mass_g))

# A tibble: 8 × 3
# Groups:   species [3]
  species   sex     mean
  <fct>     <fct>  <dbl>
1 Adelie    female 3369.
2 Adelie    male   4043.
3 Adelie    <NA>     NA 
4 Chinstrap female 3527.
5 Chinstrap male   3939.
6 Gentoo    female 4680.
7 Gentoo    male   5485.
8 Gentoo    <NA>     NA

If we’re just interested in counting the observations in each grouping, we can group and summarize with special functions count() and add_count().

Counting can be done with group_by() and summarize(), but it’s a little cumbersome.

It involves…

using mutate() to create an intermediate variable n_species that adds up all observations per species, and
an ungroup()-ing step

penguins |> 
  group_by(species) |>
  mutate(n_species = n()) |> 
  ungroup() |> 
  group_by(species, sex, n_species) |>
  summarize(n = n())

# A tibble: 8 × 4
# Groups:   species, sex [8]
  species   sex    n_species     n
  <fct>     <fct>      <int> <int>
1 Adelie    female       152    73
2 Adelie    male         152    73
3 Adelie    <NA>         152     6
4 Chinstrap female        68    34
5 Chinstrap male          68    34
6 Gentoo    female       124    58
7 Gentoo    male         124    61
8 Gentoo    <NA>         124     5

If we’re just interested in counting the observations in each grouping, we can group and summarize with special functions count() and add_count().

In contrast, count() and add_count() offer a simplified approach

Example kindly contributed by Alison Hill

penguins |>
  count(species, sex) |>
  add_count(species, wt = n, 
            name = "n_species")

# A tibble: 8 × 4
  species   sex        n n_species
  <fct>     <fct>  <int>     <int>
1 Adelie    female    73       152
2 Adelie    male      73       152
3 Adelie    <NA>       6       152
4 Chinstrap female    34        68
5 Chinstrap male      34        68
6 Gentoo    female    58       124
7 Gentoo    male      61       124
8 Gentoo    <NA>       5       124

We can add to our counting example by using mutate() to create a new variable prop

prop represents the proportion of penguins of each sex, grouped by species

Example kindly contributed by Alison Hill

penguins |>
  count(species, sex) |>
  add_count(species, wt = n, 
            name = "n_species") |>
  mutate(prop = n/n_species*100)

# A tibble: 8 × 5
  species   sex        n n_species  prop
  <fct>     <fct>  <int>     <int> <dbl>
1 Adelie    female    73       152 48.0 
2 Adelie    male      73       152 48.0 
3 Adelie    <NA>       6       152  3.95
4 Chinstrap female    34        68 50   
5 Chinstrap male      34        68 50   
6 Gentoo    female    58       124 46.8 
7 Gentoo    male      61       124 49.2 
8 Gentoo    <NA>       5       124  4.03

Finally, we can filter rows to only show us Chinstrap penguin summaries by adding filter() to our pipeline

penguins |>
  count(species, sex) |>
  add_count(species, wt = n, 
            name = "n_species") |>
  mutate(prop = n/n_species*100) |>
  filter(species == "Chinstrap")

# A tibble: 2 × 5
  species   sex        n n_species  prop
  <fct>     <fct>  <int>     <int> <dbl>
1 Chinstrap female    34        68    50
2 Chinstrap male      34        68    50