Grouped data

knitr::opts_chunk$set(collapse = T, comment = "#>")
options(tibble.print_min = 4L, tibble.print_max = 4L)

dplyr verbs are particularly powerful when you apply them to grouped data frames (grouped_df objects). This vignette shows you:

We'll start by loading dplyr:

library(dplyr)

group_by()

The most important grouping verb is group_by(): it takes a data frame and one or more variables to group by:

by_species <- starwars %>% group_by(species)
by_sex_gender <- starwars %>% group_by(sex, gender)

You can see the grouping when you print the data:

by_species
by_sex_gender

Or use tally() to count the number of rows in each group. The sort argument is useful if you want to see the largest groups up front.

by_species %>% tally()

by_sex_gender %>% tally(sort = TRUE)

As well as grouping by existing variables, you can group by any function of existing variables. This is equivalent to performing a mutate() before the group_by():

bmi_breaks <- c(0, 18.5, 25, 30, Inf)

starwars %>%
  group_by(bmi_cat = cut(mass/(height/100)^2, breaks=bmi_breaks)) %>%
  tally()

Group metadata

You can see underlying group data with group_keys(). It has one row for each group and one column for each grouping variable:

by_species %>% group_keys()

by_sex_gender %>% group_keys()

You can see which group each row belongs to with group_indices():

by_species %>% group_indices()

And which rows each group contains with group_rows():

by_species %>% group_rows() %>% head()

Use group_vars() if you just want the names of the grouping variables:

by_species %>% group_vars()
by_sex_gender %>% group_vars()

Changing and adding to grouping variables

If you apply group_by() to an already grouped dataset, will overwrite the existing grouping variables. For example, the following code groups by homeworld instead of species:

by_species %>%
  group_by(homeworld) %>%
  tally()

To augment the grouping, using .add = TRUE[^add]. For example, the following code groups by species and homeworld:

by_species %>%
  group_by(homeworld, .add = TRUE) %>%
  tally()

[^add]: Note that the argument changed from add = TRUE to .add = TRUE in dplyr 1.0.0.

Removing grouping variables

To remove all grouping variables, use ungroup():

by_species %>%
  ungroup() %>%
  tally()

You can also choose to selectively ungroup by listing the variables you want to remove:

by_sex_gender %>% 
  ungroup(sex) %>% 
  tally()

Verbs

The following sections describe how grouping affects the main dplyr verbs.

summarise()

summarise() computes a summary for each group. This means that it starts from group_keys(), adding summary variables to the right hand side:

by_species %>%
  summarise(
    n = n(),
    height = mean(height, na.rm = TRUE)
  )

The .groups= argument controls the grouping structure of the output. The historical behaviour of removing the right hand side grouping variable corresponds to .groups = "drop_last" without a message or .groups = NULL with a message (the default).

by_sex_gender %>% 
  summarise(n = n()) %>% 
  group_vars()

by_sex_gender %>% 
  summarise(n = n(), .groups = "drop_last") %>% 
  group_vars()

Since version 1.0.0 the groups may also be kept (.groups = "keep") or dropped (.groups = "drop").

by_sex_gender %>% 
  summarise(n = n(), .groups = "keep") %>% 
  group_vars()

by_sex_gender %>% 
  summarise(n = n(), .groups = "drop") %>% 
  group_vars()

When the output no longer have grouping variables, it becomes ungrouped (i.e. a regular tibble).

select(), rename(), and relocate()

rename() and relocate() behave identically with grouped and ungrouped data because they only affect the name or position of existing columns. Grouped select() is almost identical to ungrouped select, except that it always includes the grouping variables:

by_species %>% select(mass)

If you don't want the grouping variables, you'll have to first ungroup(). (This design is possibly a mistake, but we're stuck with it for now.)

arrange()

Grouped arrange() is the same as ungrouped arrange(), unless you set .by_group = TRUE, in which case it will order first by the grouping variables.

by_species %>%
  arrange(desc(mass)) %>%
  relocate(species, mass)

by_species %>%
  arrange(desc(mass), .by_group = TRUE) %>%
  relocate(species, mass)

Note that second example is sorted by species (from the group_by() statement) and then by mass (within species).

mutate() and transmute()

In simple cases with vectorised functions, grouped and ungrouped mutate() give the same results. They differ when used with summary functions:

# Subtract off global mean
starwars %>% 
  select(name, homeworld, mass) %>% 
  mutate(standard_mass = mass - mean(mass, na.rm = TRUE))

# Subtract off homeworld mean
starwars %>% 
  select(name, homeworld, mass) %>% 
  group_by(homeworld) %>% 
  mutate(standard_mass = mass - mean(mass, na.rm = TRUE))

Or with window functions like min_rank():

# Overall rank
starwars %>% 
  select(name, homeworld, height) %>% 
  mutate(rank = min_rank(height))

# Rank per homeworld
starwars %>% 
  select(name, homeworld, height) %>% 
  group_by(homeworld) %>% 
  mutate(rank = min_rank(height))

filter()

A grouped filter() effectively does a mutate() to generate a logical variable, and then only keeps the rows where the variable is TRUE. This means that grouped filters can be used with summary functions. For example, we can find the tallest character of each species:

by_species %>%
  select(name, species, height) %>% 
  filter(height == max(height))

You can also use filter() to remove entire groups. For example, the following code eliminates all groups that only have a single member:

by_species %>%
  filter(n() != 1) %>% 
  tally()

slice() and friends

slice() and friends (slice_head(), slice_tail(), slice_sample(), slice_min() and slice_max()) select rows within a group. For example, we can select the first observation within each species:

by_species %>%
  relocate(species) %>% 
  slice(1)

Similarly, we can use slice_min() to select the smallest n values of a variable:

by_species %>%
  filter(!is.na(height)) %>% 
  slice_min(height, n = 2)

Computing on grouping information

Inside dplyr verbs, you can access various properties of the "current" group using a family of functions with the cur_ prefix. These functions are typically needed for everyday usage of dplyr, but can be useful because they allow you to free from some of the typical constraints of dplyr verbs.

cur_data()

cur_data() returns the current group, excluding grouping variables. It's useful to feed to functions that take a whole data frame. For example, the following code fits a linear model of mass ~ height to each species:

by_species %>%
  filter(n() > 1) %>% 
  mutate(mod = list(lm(mass ~ height, data = cur_data())))

cur_group() and cur_group_id()

cur_group_id() gives a unique numeric identifier for the current group. This is sometimes useful if you want to index into an external data structure.

by_species %>%
  arrange(species) %>% 
  select(name, species, homeworld) %>% 
  mutate(id = cur_group_id())


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dplyr documentation built on June 19, 2021, 1:07 a.m.