tally.DataFrame | R Documentation |
count()
lets you quickly count the unique values of one or more variables:
df %>% count(a, b)
is roughly equivalent to
df %>% group_by(a, b) %>% summarise(n = n())
.
count()
is paired with tally()
, a lower-level helper that is equivalent
to df %>% summarise(n = n())
. Supply wt
to perform weighted counts,
switching the summary from n = n()
to n = sum(wt)
.
add_count()
and add_tally()
are equivalents to count()
and tally()
but use mutate()
instead of summarise()
so that they add a new column
with group-wise counts.
## S3 method for class 'DataFrame'
tally(x, wt = NULL, sort = FALSE, name = NULL)
x |
A data frame, data frame extension (e.g. a tibble), or a lazy data frame (e.g. from dbplyr or dtplyr). |
wt |
<
|
sort |
If |
name |
The name of the new column in the output. If omitted, it will default to |
An object of the same type as .data
. count()
and add_count()
group transiently, so the output has the same groups as the input.
# count() is a convenient way to get a sense of the distribution of
# values in a dataset
starwars %>% count(species)
starwars %>% count(species, sort = TRUE)
starwars %>% count(sex, gender, sort = TRUE)
starwars %>% count(birth_decade = round(birth_year, -1))
# use the `wt` argument to perform a weighted count. This is useful
# when the data has already been aggregated once
df <- tribble(
~name, ~gender, ~runs,
"Max", "male", 10,
"Sandra", "female", 1,
"Susan", "female", 4
)
# counts rows:
df %>% count(gender)
# counts runs:
df %>% count(gender, wt = runs)
# When factors are involved, `.drop = FALSE` can be used to retain factor
# levels that don't appear in the data
df2 <- tibble(
id = 1:5,
type = factor(c("a", "c", "a", NA, "a"), levels = c("a", "b", "c"))
)
df2 %>% count(type)
df2 %>% count(type, .drop = FALSE)
# Or, using `group_by()`:
df2 %>% group_by(type, .drop = FALSE) %>% count()
# tally() is a lower-level function that assumes you've done the grouping
starwars %>% tally()
starwars %>% group_by(species) %>% tally()
# both count() and tally() have add_ variants that work like
# mutate() instead of summarise
df %>% add_count(gender, wt = runs)
df %>% add_tally(wt = runs)
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