Keep only unique/distinct rows from a data frame. This is similar
to unique.data.frame() but considerably faster.
Arguments
- .data
A data frame, data frame extension (e.g. a tibble), or a lazy data frame (e.g. from dbplyr or dtplyr). See Methods, below, for more details.
- ...
<
data-masking> Optional variables to use when determining uniqueness. If there are multiple rows for a given combination of inputs, only the first row will be preserved. If omitted, will use all variables in the data frame.- .keep_all
If
TRUE, keep all variables in.data. If a combination of...is not distinct, this keeps the first row of values.
Value
An object of the same type as .data. The output has the following
properties:
Rows are a subset of the input but appear in the same order.
Columns are not modified if
...is empty or.keep_allisTRUE. Otherwise,distinct()first callsmutate()to create new columns.Groups are not modified.
Data frame attributes are preserved.
Methods
This function is a generic, which means that packages can provide implementations (methods) for other classes. See the documentation of individual methods for extra arguments and differences in behaviour.
The following methods are currently available in loaded packages:
dbplyr (tbl_lazy), dplyr (data.frame)
.
Examples
df <- tibble(
x = sample(10, 100, rep = TRUE),
y = sample(10, 100, rep = TRUE)
)
nrow(df)
#> [1] 100
nrow(distinct(df))
#> [1] 71
nrow(distinct(df, x, y))
#> [1] 71
distinct(df, x)
#> # A tibble: 10 × 1
#> x
#> <int>
#> 1 3
#> 2 1
#> 3 9
#> 4 10
#> 5 5
#> 6 2
#> 7 8
#> 8 4
#> 9 7
#> 10 6
distinct(df, y)
#> # A tibble: 10 × 1
#> y
#> <int>
#> 1 7
#> 2 6
#> 3 3
#> 4 4
#> 5 10
#> 6 9
#> 7 1
#> 8 2
#> 9 5
#> 10 8
# You can choose to keep all other variables as well
distinct(df, x, .keep_all = TRUE)
#> # A tibble: 10 × 2
#> x y
#> <int> <int>
#> 1 3 7
#> 2 1 6
#> 3 9 4
#> 4 10 3
#> 5 5 4
#> 6 2 10
#> 7 8 9
#> 8 4 1
#> 9 7 3
#> 10 6 7
distinct(df, y, .keep_all = TRUE)
#> # A tibble: 10 × 2
#> x y
#> <int> <int>
#> 1 3 7
#> 2 1 6
#> 3 1 3
#> 4 9 4
#> 5 2 10
#> 6 8 9
#> 7 4 1
#> 8 5 2
#> 9 3 5
#> 10 3 8
# You can also use distinct on computed variables
distinct(df, diff = abs(x - y))
#> # A tibble: 10 × 1
#> diff
#> <int>
#> 1 4
#> 2 5
#> 3 2
#> 4 7
#> 5 1
#> 6 8
#> 7 3
#> 8 6
#> 9 0
#> 10 9
# Use `pick()` to select columns with tidy-select
distinct(starwars, pick(contains("color")))
#> # A tibble: 67 × 3
#> hair_color skin_color eye_color
#> <chr> <chr> <chr>
#> 1 blond fair blue
#> 2 NA gold yellow
#> 3 NA white, blue red
#> 4 none white yellow
#> 5 brown light brown
#> 6 brown, grey light blue
#> 7 brown light blue
#> 8 NA white, red red
#> 9 black light brown
#> 10 auburn, white fair blue-gray
#> # ℹ 57 more rows
# Grouping -------------------------------------------------
df <- tibble(
g = c(1, 1, 2, 2, 2),
x = c(1, 1, 2, 1, 2),
y = c(3, 2, 1, 3, 1)
)
df <- df %>% group_by(g)
# With grouped data frames, distinctness is computed within each group
df %>% distinct(x)
#> # A tibble: 3 × 2
#> # Groups: g [2]
#> g x
#> <dbl> <dbl>
#> 1 1 1
#> 2 2 2
#> 3 2 1
# When `...` are omitted, `distinct()` still computes distinctness using
# all variables in the data frame
df %>% distinct()
#> # A tibble: 4 × 3
#> # Groups: g [2]
#> g x y
#> <dbl> <dbl> <dbl>
#> 1 1 1 3
#> 2 1 1 2
#> 3 2 2 1
#> 4 2 1 3