mutate_when
integrates mutate
and case_when
in dplyr and make a new tidy verb for data.table. mutate_cols
is
a super function to do updates in specific columns according to conditions.
mutate_when(data, when, ...) mutate_cols(data, .cols = NULL, .func, ...)
data | data.frame |
---|---|
when | An object which can be coerced to logical mode |
... | Name-value pairs of expressions for |
.cols | Any types that can be accepted by |
.func | Function to be run within each column, should return a value or vectors with same length. |
data.table
select_if
, case_when
iris[3:8,]#> Sepal.Length Sepal.Width Petal.Length Petal.Width Species #> 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 #> 6 5.4 3.9 1.7 0.4 setosa #> 7 4.6 3.4 1.4 0.3 setosa #> 8 5.0 3.4 1.5 0.2 setosairis[3:8,] %>% mutate_when(Petal.Width == .2, one = 1,Sepal.Length=2)#> Sepal.Length Sepal.Width Petal.Length Petal.Width Species one #> <num> <num> <num> <num> <fctr> <num> #> 1: 2.0 3.2 1.3 0.2 setosa 1 #> 2: 2.0 3.1 1.5 0.2 setosa 1 #> 3: 2.0 3.6 1.4 0.2 setosa 1 #> 4: 5.4 3.9 1.7 0.4 setosa NA #> 5: 4.6 3.4 1.4 0.3 setosa NA #> 6: 2.0 3.4 1.5 0.2 setosa 1iris %>% mutate_cols("Pe",scale)#> Sepal.Length Sepal.Width Petal.Length Petal.Width Species #> <num> <num> <num> <num> <fctr> #> 1: 5.1 3.5 -1.3357516 -1.3110521 setosa #> 2: 4.9 3.0 -1.3357516 -1.3110521 setosa #> 3: 4.7 3.2 -1.3923993 -1.3110521 setosa #> 4: 4.6 3.1 -1.2791040 -1.3110521 setosa #> 5: 5.0 3.6 -1.3357516 -1.3110521 setosa #> --- #> 146: 6.7 3.0 0.8168591 1.4439941 virginica #> 147: 6.3 2.5 0.7035638 0.9192234 virginica #> 148: 6.5 3.0 0.8168591 1.0504160 virginica #> 149: 6.2 3.4 0.9301544 1.4439941 virginica #> 150: 5.9 3.0 0.7602115 0.7880307 virginicairis %>% mutate_cols(is.numeric,scale)#> Sepal.Length Sepal.Width Petal.Length Petal.Width Species #> <num> <num> <num> <num> <fctr> #> 1: -0.89767388 1.01560199 -1.3357516 -1.3110521 setosa #> 2: -1.13920048 -0.13153881 -1.3357516 -1.3110521 setosa #> 3: -1.38072709 0.32731751 -1.3923993 -1.3110521 setosa #> 4: -1.50149039 0.09788935 -1.2791040 -1.3110521 setosa #> 5: -1.01843718 1.24503015 -1.3357516 -1.3110521 setosa #> --- #> 146: 1.03453895 -0.13153881 0.8168591 1.4439941 virginica #> 147: 0.55148575 -1.27867961 0.7035638 0.9192234 virginica #> 148: 0.79301235 -0.13153881 0.8168591 1.0504160 virginica #> 149: 0.43072244 0.78617383 0.9301544 1.4439941 virginica #> 150: 0.06843254 -0.13153881 0.7602115 0.7880307 virginicairis %>% mutate_cols(1:2,scale)#> Sepal.Length Sepal.Width Petal.Length Petal.Width Species #> <num> <num> <num> <num> <fctr> #> 1: -0.89767388 1.01560199 1.4 0.2 setosa #> 2: -1.13920048 -0.13153881 1.4 0.2 setosa #> 3: -1.38072709 0.32731751 1.3 0.2 setosa #> 4: -1.50149039 0.09788935 1.5 0.2 setosa #> 5: -1.01843718 1.24503015 1.4 0.2 setosa #> --- #> 146: 1.03453895 -0.13153881 5.2 2.3 virginica #> 147: 0.55148575 -1.27867961 5.0 1.9 virginica #> 148: 0.79301235 -0.13153881 5.2 2.0 virginica #> 149: 0.43072244 0.78617383 5.4 2.3 virginica #> 150: 0.06843254 -0.13153881 5.1 1.8 virginicairis %>% mutate_cols(.func = as.character)#> Sepal.Length Sepal.Width Petal.Length Petal.Width Species #> <char> <char> <char> <char> <char> #> 1: 5.1 3.5 1.4 0.2 setosa #> 2: 4.9 3 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 3.6 1.4 0.2 setosa #> --- #> 146: 6.7 3 5.2 2.3 virginica #> 147: 6.3 2.5 5 1.9 virginica #> 148: 6.5 3 5.2 2 virginica #> 149: 6.2 3.4 5.4 2.3 virginica #> 150: 5.9 3 5.1 1.8 virginica