Data manipulation within groups

Source: R/group_dt.R

Analogous function for group_by in dplyr, but in another efficient way.

group_dt(data, by = NULL, ...)

Arguments

data

data.frame
by

Variables to group by,unquoted name of grouping variable of list of unquoted names of grouping variables.
...

Any data manipulation arguments that could be implemented on a data.frame.

Value

data.table

Examples

iris %>% group_dt(by = Species,slice_dt(1:2))
#>       Species Sepal.Length Sepal.Width Petal.Length Petal.Width
#> 1:     setosa          5.1         3.5          1.4         0.2
#> 2:     setosa          4.9         3.0          1.4         0.2
#> 3: versicolor          7.0         3.2          4.7         1.4
#> 4: versicolor          6.4         3.2          4.5         1.5
#> 5:  virginica          6.3         3.3          6.0         2.5
#> 6:  virginica          5.8         2.7          5.1         1.9
iris %>% group_dt(Species,filter_dt(Sepal.Length == max(Sepal.Length)))
#>       Species Sepal.Length Sepal.Width Petal.Length Petal.Width
#> 1:     setosa          5.8         4.0          1.2         0.2
#> 2: versicolor          7.0         3.2          4.7         1.4
#> 3:  virginica          7.9         3.8          6.4         2.0
iris %>% group_dt(Species,summarise_dt(new = max(Sepal.Length)))
#>       Species new
#> 1:     setosa 5.8
#> 2: versicolor 7.0
#> 3:  virginica 7.9

# you can pipe in the `group_dt`
iris %>% group_dt(Species,
                  mutate_dt(max= max(Sepal.Length)) %>%
                    summarise_dt(sum=sum(Sepal.Length)))
#>       Species   sum
#> 1:     setosa 250.3
#> 2: versicolor 296.8
#> 3:  virginica 329.4

# for summarise_dt, you can use "by" to calculate within the group
mtcars %>%
  summarise_dt(
   disp = mean(disp),
   hp = mean(hp),
   by = cyl
)
#>    cyl     disp        hp
#> 1:   6 183.3143 122.28571
#> 2:   4 105.1364  82.63636
#> 3:   8 353.1000 209.21429

 # but you could also, of course, use group_dt
mtcars %>%
  group_dt(by =.(vs,am),
    summarise_dt(avg = mean(mpg)))
#>    vs am      avg
#> 1:  0  1 19.75000
#> 2:  1  1 28.37143
#> 3:  1  0 20.74286
#> 4:  0  0 15.05000