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

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

rowwise_dt(.data, ...)

Arguments

.data

A 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

Details

If you want to use summarise_dt and mutate_dt in group_dt, it is better to use the "by" parameter in those functions, that would be much faster because you don't have to use .SD (which takes extra time to copy).

References

https://stackoverflow.com/questions/36802385/use-by-each-row-for-data-table

Examples

iris %>% group_dt(by = Species,slice_dt(1:2))
#> Species Sepal.Length Sepal.Width Petal.Length Petal.Width #> <fctr> <num> <num> <num> <num> #> 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 #> <fctr> <num> <num> <num> <num> #> 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 #> <fctr> <num> #> 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 #> <fctr> <num> #> 1: setosa 250.3 #> 2: versicolor 296.8 #> 3: virginica 329.4
# for users familiar with data.table, you can work on .SD directly # following codes get the first and last row from each group iris %>% group_dt( by = Species, rbind(.SD[1],.SD[.N]) )
#> Species Sepal.Length Sepal.Width Petal.Length Petal.Width #> <fctr> <num> <num> <num> <num> #> 1: setosa 5.1 3.5 1.4 0.2 #> 2: setosa 5.0 3.3 1.4 0.2 #> 3: versicolor 7.0 3.2 4.7 1.4 #> 4: versicolor 5.7 2.8 4.1 1.3 #> 5: virginica 6.3 3.3 6.0 2.5 #> 6: virginica 5.9 3.0 5.1 1.8
#' # 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 #> <num> <num> <num> #> 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 #> <num> <num> <num> #> 1: 0 1 19.75000 #> 2: 1 1 28.37143 #> 3: 1 0 20.74286 #> 4: 0 0 15.05000
# and list of variables could also be used mtcars %>% group_dt(by =list(vs,am), summarise_dt(avg = mean(mpg)))
#> vs am avg #> <num> <num> <num> #> 1: 0 1 19.75000 #> 2: 1 1 28.37143 #> 3: 1 0 20.74286 #> 4: 0 0 15.05000
# examples for `rowwise_dt` df <- data.table(x = 1:2, y = 3:4, z = 4:5) df %>% mutate_dt(m = mean(c(x, y, z)))
#> x y z m #> <int> <int> <int> <num> #> 1: 1 3 4 3.166667 #> 2: 2 4 5 3.166667
df %>% rowwise_dt( mutate_dt(m = mean(c(x, y, z))) )
#> x y z m #> <int> <int> <int> <num> #> 1: 1 3 4 2.666667 #> 2: 2 4 5 3.666667