Select distinct/unique rows in data.frame

Source: R/distinct_dt.R

Analogous function for distinct in dplyr

distinct_dt(data, ..., .keep_all = FALSE)

Arguments

data

data.frame
...

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.frame. If a combination of ... is not distinct, this keeps the first row of values.

Value

data.table

See also

distinct

Examples

iris %>% distinct_dt()
#>      Sepal.Length Sepal.Width Petal.Length Petal.Width   Species
#>   1:          5.1         3.5          1.4         0.2    setosa
#>   2:          4.9         3.0          1.4         0.2    setosa
#>   3:          4.7         3.2          1.3         0.2    setosa
#>   4:          4.6         3.1          1.5         0.2    setosa
#>   5:          5.0         3.6          1.4         0.2    setosa
#>  ---                                                            
#> 145:          6.7         3.0          5.2         2.3 virginica
#> 146:          6.3         2.5          5.0         1.9 virginica
#> 147:          6.5         3.0          5.2         2.0 virginica
#> 148:          6.2         3.4          5.4         2.3 virginica
#> 149:          5.9         3.0          5.1         1.8 virginica
iris %>% distinct_dt(Species)
#>       Species
#> 1:     setosa
#> 2: versicolor
#> 3:  virginica
iris %>% distinct_dt(Species,.keep_all = TRUE)
#>       Species Sepal.Length Sepal.Width Petal.Length Petal.Width
#> 1:     setosa          5.1         3.5          1.4         0.2
#> 2: versicolor          7.0         3.2          4.7         1.4
#> 3:  virginica          6.3         3.3          6.0         2.5
mtcars %>% distinct_dt(cyl,vs)
#>    cyl vs
#> 1:   6  0
#> 2:   4  1
#> 3:   6  1
#> 4:   8  0
#> 5:   4  0
mtcars %>% distinct_dt(cyl,vs,.keep_all = TRUE)
#>    cyl vs  mpg  disp  hp drat    wt  qsec am gear carb
#> 1:   6  0 21.0 160.0 110 3.90 2.620 16.46  1    4    4
#> 2:   4  1 22.8 108.0  93 3.85 2.320 18.61  1    4    1
#> 3:   6  1 21.4 258.0 110 3.08 3.215 19.44  0    3    1
#> 4:   8  0 18.7 360.0 175 3.15 3.440 17.02  0    3    2
#> 5:   4  0 26.0 120.3  91 4.43 2.140 16.70  1    5    2