Update for dplyr >= 0.4.3.9000
In the dplyr development version 0.4.3.9000 (at time of writing), naming inside mutate_each and summarise_each has been simplified as noted in the News:
The naming behaviour of
summarise_each()andmutate_each()has been
tweaked so that you can force inclusion of both the function and the
variable name:summarise_each(mtcars, funs(mean = mean), everything())
This is mainly important if you want to apply only 1 function inside mutate_each / summarise_each and you want to give those column new names.
To show the difference, here’s the output from dplyr 0.4.3.9000 using the new naming functionality, in contrast to option a.2 below:
library(dplyr) # >= 0.4.3.9000
iris %>% mutate_each(funs(mysum = sum(.)), -Species) %>% head()
# Sepal.Length Sepal.Width Petal.Length Petal.Width Species Sepal.Length_mysum Sepal.Width_mysum
#1 5.1 3.5 1.4 0.2 setosa 876.5 458.6
#2 4.9 3.0 1.4 0.2 setosa 876.5 458.6
#3 4.7 3.2 1.3 0.2 setosa 876.5 458.6
#4 4.6 3.1 1.5 0.2 setosa 876.5 458.6
#5 5.0 3.6 1.4 0.2 setosa 876.5 458.6
#6 5.4 3.9 1.7 0.4 setosa 876.5 458.6
# Petal.Length_mysum Petal.Width_mysum
#1 563.7 179.9
#2 563.7 179.9
#3 563.7 179.9
#4 563.7 179.9
#5 563.7 179.9
#6 563.7 179.9
If you don’t supply new names and you only supply 1 function, dplyr will change the existing columns (as it did in previous versions):
iris %>% mutate_each(funs(sum), -Species) %>% head()
# Sepal.Length Sepal.Width Petal.Length Petal.Width Species
#1 876.5 458.6 563.7 179.9 setosa
#2 876.5 458.6 563.7 179.9 setosa
#3 876.5 458.6 563.7 179.9 setosa
#4 876.5 458.6 563.7 179.9 setosa
#5 876.5 458.6 563.7 179.9 setosa
#6 876.5 458.6 563.7 179.9 setosa
I assume that this new functionality will be available via CRAN in the next release version 0.4.4.
dplyr verions <= 0.4.3:
How can I give these new columns appropriate names, like I can in
mutate?
a) 1 function applied in mutate_each/summarise_each
If you apply only 1 function inside the mutate_each or summarise_each, the existing columns will be transformed and the names will be kept as they used to be, unless you supply a named vector to mutate_each_/summarise_each_ (see option a.4)
Here are some examples:
a.1 only 1 function -> will keep the existing names
iris %>% mutate_each(funs(sum), -Species) %>% head()
# Sepal.Length Sepal.Width Petal.Length Petal.Width Species
#1 876 459 564 180 setosa
#2 876 459 564 180 setosa
#3 876 459 564 180 setosa
#4 876 459 564 180 setosa
#5 876 459 564 180 setosa
#6 876 459 564 180 setosa
a.2 also if you specify a new column name extension:
iris %>% mutate_each(funs(mysum = sum(.)), -Species) %>% head()
# Sepal.Length Sepal.Width Petal.Length Petal.Width Species
#1 876 459 564 180 setosa
#2 876 459 564 180 setosa
#3 876 459 564 180 setosa
#4 876 459 564 180 setosa
#5 876 459 564 180 setosa
#6 876 459 564 180 setosa
a.3 Manually specify a new name per column (but only practical for few columns):
iris %>% mutate_each(funs(sum), SLsum = Sepal.Length,SWsum = Sepal.Width, -Species) %>% head()
# Sepal.Length Sepal.Width Petal.Length Petal.Width Species SLsum SWsum
#1 5.1 3.5 1.4 0.2 setosa 876 459
#2 4.9 3.0 1.4 0.2 setosa 876 459
#3 4.7 3.2 1.3 0.2 setosa 876 459
#4 4.6 3.1 1.5 0.2 setosa 876 459
#5 5.0 3.6 1.4 0.2 setosa 876 459
#6 5.4 3.9 1.7 0.4 setosa 876 459
a.4 Use a named vector to create additional columns with new names:
case 1: keep original columns
In contrast to options a.1, a.2 and a.3, dplyr will keep the existing columns unchanged and create new columns in this approach. The names of the new columns equal the names of the named vector you create in advance (vars in this case).
vars <- names(iris)[1:2] # choose which columns should be mutated
vars <- setNames(vars, paste0(vars, "_sum")) # create new column names
iris %>% mutate_each_(funs(sum), vars) %>% head
# Sepal.Length Sepal.Width Petal.Length Petal.Width Species Sepal.Length_sum Sepal.Width_sum
#1 5.1 3.5 1.4 0.2 setosa 876.5 458.6
#2 4.9 3.0 1.4 0.2 setosa 876.5 458.6
#3 4.7 3.2 1.3 0.2 setosa 876.5 458.6
#4 4.6 3.1 1.5 0.2 setosa 876.5 458.6
#5 5.0 3.6 1.4 0.2 setosa 876.5 458.6
#6 5.4 3.9 1.7 0.4 setosa 876.5 458.6
case 2: remove original columns
As you can see, this approach keeps the existing columns unchanged and adds new columns with specified names. In case you don’t want to keep the original columns, but just the newly created columns (and the other columns) you can just add a select statement afterwards:
iris %>% mutate_each_(funs(sum), vars) %>% select(-one_of(vars)) %>% head
# Petal.Length Petal.Width Species Sepal.Length_sum Sepal.Width_sum
#1 1.4 0.2 setosa 876.5 458.6
#2 1.4 0.2 setosa 876.5 458.6
#3 1.3 0.2 setosa 876.5 458.6
#4 1.5 0.2 setosa 876.5 458.6
#5 1.4 0.2 setosa 876.5 458.6
#6 1.7 0.4 setosa 876.5 458.6
b) more than 1 function applied in mutate_each/summarise_each
b.1 Let dplyr figure out new names
If you applied more than 1 function, you can let dplyr figure out names by itself (and it will keep the existing columns):
iris %>% mutate_each(funs(sum, mean), -Species) %>% head()
# Sepal.Length Sepal.Width Petal.Length Petal.Width Species Sepal.Length_sum Sepal.Width_sum Petal.Length_sum
#1 5.1 3.5 1.4 0.2 setosa 876 459 564
#2 4.9 3.0 1.4 0.2 setosa 876 459 564
#3 4.7 3.2 1.3 0.2 setosa 876 459 564
#4 4.6 3.1 1.5 0.2 setosa 876 459 564
#5 5.0 3.6 1.4 0.2 setosa 876 459 564
#6 5.4 3.9 1.7 0.4 setosa 876 459 564
# Petal.Width_sum Sepal.Length_mean Sepal.Width_mean Petal.Length_mean Petal.Width_mean
#1 180 5.84 3.06 3.76 1.2
#2 180 5.84 3.06 3.76 1.2
#3 180 5.84 3.06 3.76 1.2
#4 180 5.84 3.06 3.76 1.2
#5 180 5.84 3.06 3.76 1.2
#6 180 5.84 3.06 3.76 1.2
b.2 Manually specify new column names
Another option, when using more than 1 function, is to specify the column name extension on your own:
iris %>% mutate_each(funs(MySum = sum(.), MyMean = mean(.)), -Species) %>% head()
# Sepal.Length Sepal.Width Petal.Length Petal.Width Species Sepal.Length_MySum Sepal.Width_MySum Petal.Length_MySum
#1 5.1 3.5 1.4 0.2 setosa 876 459 564
#2 4.9 3.0 1.4 0.2 setosa 876 459 564
#3 4.7 3.2 1.3 0.2 setosa 876 459 564
#4 4.6 3.1 1.5 0.2 setosa 876 459 564
#5 5.0 3.6 1.4 0.2 setosa 876 459 564
#6 5.4 3.9 1.7 0.4 setosa 876 459 564
# Petal.Width_MySum Sepal.Length_MyMean Sepal.Width_MyMean Petal.Length_MyMean Petal.Width_MyMean
#1 180 5.84 3.06 3.76 1.2
#2 180 5.84 3.06 3.76 1.2
#3 180 5.84 3.06 3.76 1.2
#4 180 5.84 3.06 3.76 1.2
#5 180 5.84 3.06 3.76 1.2
#6 180 5.84 3.06 3.76 1.2
How can I select certain columns that I wish to mutate, like I did
with select in the first case?
You can do that by referencing the columns to be mutated (or left out) by giving their names like here (mutate Sepal.Length, but not Species):
iris %>% mutate_each(funs(sum), Sepal.Length, -Species) %>% head()
In addition, you can use special functions to select columns to be mutated, all columns that start with or contain a certain word etc by using for example:
iris %>% mutate_each(funs(sum), contains("Sepal"), -Species) %>% head()
For more information of those functions, see ?mutate_each and ?select.
Edit 1 after comment:
If you want to use standard evaluation, dplyr supplies SE-versions of most functions ending with an addtional “_”. So in this case you would use:
x <- c("Sepal.Width", "Sepal.Length") # vector of column names
iris %>% mutate_each_(funs(sum), x) %>% head()
Notice the mutate_each_ I used here.
Edit 2: updated with option a.4