Use variable for column in dplyr's group_by [duplicate] - r

Question

I want to use use the dplyr::group_by function inside another function, but I do not know how to pass the arguments to this function.
Can someone provide a working example?
library(dplyr)
data(iris)
iris %.% group_by(Species) %.% summarise(n = n()) #
## Source: local data frame [3 x 2]
## Species n
## 1 virginica 50
## 2 versicolor 50
## 3 setosa 50
mytable0 <- function(x, ...) x %.% group_by(...) %.% summarise(n = n())
mytable0(iris, "Species") # OK
## Source: local data frame [3 x 2]
## Species n
## 1 virginica 50
## 2 versicolor 50
## 3 setosa 50
mytable1 <- function(x, key) x %.% group_by(as.name(key)) %.% summarise(n = n())
mytable1(iris, "Species") # Wrong!
# Error: unsupported type for column 'as.name(key)' (SYMSXP)
mytable2 <- function(x, key) x %.% group_by(key) %.% summarise(n = n())
mytable2(iris, "Species") # Wrong!
# Error: index out of bounds

Answer 1

For programming, group_by_ is the counterpart to group_by:
library(dplyr)
mytable <- function(x, ...) x %>% group_by_(...) %>% summarise(n = n())
mytable(iris, "Species")
# or iris %>% mytable("Species")
which gives:
Species n
1 setosa 50
2 versicolor 50
3 virginica 50
Update At the time this was written dplyr used %.% which is what was originally used above but now %>% is favored so have changed above to that to keep this relevant.
Update 2 regroup is now deprecated, use group_by_ instead.
Update 3 group_by_(list(...)) now becomes group_by_(...) in new version of dplyr as per Roberto's comment.
Update 4 Added minor variation suggested in comments.
Update 5: With rlang/tidyeval it is now possible to do this:
library(rlang)
mytable <- function(x, ...) {
group_ <- syms(...)
x %>%
group_by(!!!group_) %>%
summarise(n = n())
}
mytable(iris, "Species")
or passing Species unevaluated, i.e. no quotes around it:
library(rlang)
mytable <- function(x, ...) {
group_ <- enquos(...)
x %>%
group_by(!!!group_) %>%
summarise(n = n())
}
mytable(iris, Species)
Update 6: There is now a {{...}} notation that works if there is just one grouping variable:
mytable <- function(x, group) {
x %>%
group_by({{group}}) %>%
summarise(n = n())
}
mytable(iris, Species)

Answer 2

UPDATE: As of dplyr 0.7.0 you can use tidy eval to accomplish this.
See http://dplyr.tidyverse.org/articles/programming.html for more details.
library(tidyverse)
data("iris")
my_table <- function(df, group_var) {
group_var <- enquo(group_var) # Create quosure
df %>%
group_by(!!group_var) %>% # Use !! to unquote the quosure
summarise(n = n())
}
my_table(iris, Species)
> my_table(iris, Species)
# A tibble: 3 x 2
Species n
<fctr> <int>
1 setosa 50
2 versicolor 50
3 virginica 50

Answer 3

As a complement to the Update 6 in the answer by #G. Grothendieck, if you want to use a string as an argument in your summary function, instead of embracing the argument with doubled braces ({{), you should use the .data pronoun as described in the Programming vignette: Loop over multiple variables:
mytable <- function( x, group ) {
x %>%
group_by( .data[[group]] ) %>%
summarise( n = n() )
}
group_string <- 'Species'
mytable( iris, group_string )
`summarise()` ungrouping output (override with `.groups` argument)
# A tibble: 3 x 2
Species n
<fct> <int>
1 setosa 50
2 versicolor 50
3 virginica 50

Answer 4

Ugly as they come, but she works:
mytable3 <- function(x, key) {
my.call <- bquote(summarise(group_by(.(substitute(x)), NULL), n = n()))
my.call[[2]][[3]] <- as.name(key)
eval(my.call, parent.frame())
}
mytable3(iris, "Species")
# Source: local data frame [3 x 2]
#
# Species n
# 1 virginica 50
# 2 versicolor 50
# 3 setosa 50
There are almost certainly cases that will cause this to break, but you get the idea. I don't think you can get around messing with the call. One other thing that did work but was even uglier is:
mytable4 <- function(x, key) summarise(group_by(x, x[[key]]), n = n())