Summarise group of values into one value for each group. If there is only one group, then only one value would be returned. The summarise function should always return a single value.
summarise_dt(.data, ..., by = NULL)
summarize_dt(.data, ..., by = NULL)
summarise_when(.data, when, ..., by = NULL)
summarize_when(.data, when, ..., by = NULL)
summarise_vars(.data, .cols = NULL, .func, ..., by)
summarize_vars(.data, .cols = NULL, .func, ..., by)data.frame
List of variables or name-value pairs of summary/modifications
functions for summarise_dt.Additional parameters to be passed to
parameter '.func' in summarise_vars.
unquoted name of grouping variable of list of unquoted names of grouping variables. For details see data.table
An object which can be coerced to logical mode
Columns to be summarised.
Function to be run within each column, should return a value or vectors with same length.
data.table
summarise_vars could complete summarise on specific columns.
iris %>% summarise_dt(avg = mean(Sepal.Length))
#> avg
#> <num>
#> 1: 5.843333
iris %>% summarise_dt(avg = mean(Sepal.Length),by = Species)
#> Species avg
#> <fctr> <num>
#> 1: setosa 5.006
#> 2: versicolor 5.936
#> 3: virginica 6.588
mtcars %>% summarise_dt(avg = mean(hp),by = .(cyl,vs))
#> cyl vs avg
#> <num> <num> <num>
#> 1: 6 0 131.6667
#> 2: 4 1 81.8000
#> 3: 6 1 115.2500
#> 4: 8 0 209.2143
#> 5: 4 0 91.0000
# the data.table way
mtcars %>% summarise_dt(cyl_n = .N, by = .(cyl, vs)) # `.` is short for list
#> cyl vs cyl_n
#> <num> <num> <int>
#> 1: 6 0 3
#> 2: 4 1 10
#> 3: 6 1 4
#> 4: 8 0 14
#> 5: 4 0 1
iris %>% summarise_vars(is.numeric,min)
#> Sepal.Length Sepal.Width Petal.Length Petal.Width
#> <num> <num> <num> <num>
#> 1: 4.3 2 1 0.1
iris %>% summarise_vars(-is.factor,min)
#> Sepal.Length Sepal.Width Petal.Length Petal.Width
#> <num> <num> <num> <num>
#> 1: 4.3 2 1 0.1
iris %>% summarise_vars(1:4,min)
#> Sepal.Length Sepal.Width Petal.Length Petal.Width
#> <num> <num> <num> <num>
#> 1: 4.3 2 1 0.1
iris %>% summarise_vars(is.numeric,min,by ="Species")
#> Species Sepal.Length Sepal.Width Petal.Length Petal.Width
#> <fctr> <num> <num> <num> <num>
#> 1: setosa 4.3 2.3 1.0 0.1
#> 2: versicolor 4.9 2.0 3.0 1.0
#> 3: virginica 4.9 2.2 4.5 1.4
mtcars %>% summarise_vars(is.numeric,mean,by = c("vs", "am"))
#> vs am mpg cyl disp hp drat wt qsec
#> <num> <num> <num> <num> <num> <num> <num> <num> <num>
#> 1: 0 1 19.75000 6.333333 206.2167 180.83333 3.935000 2.857500 15.79667
#> 2: 1 1 28.37143 4.000000 89.8000 80.57143 4.148571 2.028286 18.70000
#> 3: 1 0 20.74286 5.142857 175.1143 102.14286 3.570000 3.194286 19.96714
#> 4: 0 0 15.05000 8.000000 357.6167 194.16667 3.120833 4.104083 17.14250
#> gear carb
#> <num> <num>
#> 1: 4.666667 4.666667
#> 2: 4.142857 1.428571
#> 3: 3.571429 2.142857
#> 4: 3.000000 3.083333
# use multiple functions on multiple columns
iris %>%
summarise_vars(is.numeric,.func = list(mean,sd,median))
#> Sepal.Length Sepal.Width Petal.Length Petal.Width fun_name
#> <num> <num> <num> <num> <char>
#> 1: 5.8433333 3.0573333 3.758000 1.1993333 mean
#> 2: 0.8280661 0.4358663 1.765298 0.7622377 sd
#> 3: 5.8000000 3.0000000 4.350000 1.3000000 median
iris %>%
summarise_vars(is.numeric,.func = list(mean,sd,median),by = Species)
#> Species Sepal.Length Sepal.Width Petal.Length Petal.Width fun_name
#> <fctr> <num> <num> <num> <num> <char>
#> 1: setosa 5.0060000 3.4280000 1.4620000 0.2460000 mean
#> 2: versicolor 5.9360000 2.7700000 4.2600000 1.3260000 mean
#> 3: virginica 6.5880000 2.9740000 5.5520000 2.0260000 mean
#> 4: setosa 0.3524897 0.3790644 0.1736640 0.1053856 sd
#> 5: versicolor 0.5161711 0.3137983 0.4699110 0.1977527 sd
#> 6: virginica 0.6358796 0.3224966 0.5518947 0.2746501 sd
#> 7: setosa 5.0000000 3.4000000 1.5000000 0.2000000 median
#> 8: versicolor 5.9000000 2.8000000 4.3500000 1.3000000 median
#> 9: virginica 6.5000000 3.0000000 5.5500000 2.0000000 median