distinct: Select distinct/unique rows

Description Usage Arguments Details Examples

View source: R/distinct.R

Description

Retain only unique/distinct rows from an input tbl. This is similar to unique.data.frame(), but considerably faster.

Usage

1
distinct(.data, ..., .keep_all = FALSE)

Arguments

.data

a tbl

...

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.

.keep_all

If TRUE, keep all variables in .data. If a combination of ... is not distinct, this keeps the first row of values.

Details

Comparing list columns is not fully supported. Elements in list columns are compared by reference. A warning will be given when trying to include list columns in the computation. This behavior is kept for compatibility reasons and may change in a future version. See examples.

Examples

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
df <- tibble(
  x = sample(10, 100, rep = TRUE),
  y = sample(10, 100, rep = TRUE)
)
nrow(df)
nrow(distinct(df))
nrow(distinct(df, x, y))

distinct(df, x)
distinct(df, y)

# Can choose to keep all other variables as well
distinct(df, x, .keep_all = TRUE)
distinct(df, y, .keep_all = TRUE)

# You can also use distinct on computed variables
distinct(df, diff = abs(x - y))

# The same behaviour applies for grouped data frames
# except that the grouping variables are always included
df <- tibble(
  g = c(1, 1, 2, 2),
  x = c(1, 1, 2, 1)
) %>% group_by(g)
df %>% distinct()
df %>% distinct(x)

# Values in list columns are compared by reference, this can lead to
# surprising results
tibble(a = as.list(c(1, 1, 2))) %>% glimpse() %>% distinct()
tibble(a = as.list(1:2)[c(1, 1, 2)]) %>% glimpse() %>% distinct()

tidyverse/dplyr documentation built on Jan. 11, 2019, 11:08 a.m.