I have two functions that evaluate ellipses arguments as column names. These functions (sub_fun_1 and sub_fun_2) use these column names to group the inputted data-frames.
main_fun <- function(df1, df2, ...) {
columns <- enquos(...) ### '...' arguments used to specify columns in data-sets
df1_grouped <- sub_fun_1(df1, columns) ### Evaluates 'columns' and groups df1 by column names
df2_grouped <- sub_fun_2(df2, columns) ### Evaluates 'columns' and groups df2 by column names
}
sub_fun_1 <- function(df1, columns) {
df1_grouped <- df1 %>%
group_by(!!! columns)
}
sub_fun_2 <- function(df2, columns) {
df2_grouped <- df2 %>%
group_by(!!! columns)
}
However, I cannot use the same column names for both data-sets, as that would result in an error, i.e. both data-sets will need to be grouped by column names unique to them.
Is there a way I can specify which ellipses arguments correspond to which function/data-frame? Or possibly use two unique sets of ellipses arguments?
I found some similar questions/answers pages, but am still confused on the topic and how to best implement a solution toward my particular problem.
One option is to use intersect after converting to characters
library(dplyr)
library(purrr)
main_fun <- function(data1, data2, ...) {
columns <- map_chr(enquos(...), rlang::quo_name)
sub_fun1 <- data1 %>%
group_by_at(vars(intersect(names(.), columns)))
sub_fun2 <- data2 %>%
group_by_at(vars(intersect(names(.), columns)))
list(sub_fun1, sub_fun2)
}
main_fun(iris, mtcars, gear, vs, Species)
#[[1]]
# A tibble: 150 x 5
# Groups: Species [3]
# Sepal.Length Sepal.Width Petal.Length Petal.Width Species
# <dbl> <dbl> <dbl> <dbl> <fct>
# 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
# 6 5.4 3.9 1.7 0.4 setosa
# 7 4.6 3.4 1.4 0.3 setosa
# 8 5 3.4 1.5 0.2 setosa
# 9 4.4 2.9 1.4 0.2 setosa
#10 4.9 3.1 1.5 0.1 setosa
# … with 140 more rows
#[[2]]
# A tibble: 32 x 11
# Groups: vs, gear [6]
# mpg cyl disp hp drat wt qsec vs am gear carb
# <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
# 1 21 6 160 110 3.9 2.62 16.5 0 1 4 4
# 2 21 6 160 110 3.9 2.88 17.0 0 1 4 4
# 3 22.8 4 108 93 3.85 2.32 18.6 1 1 4 1
# 4 21.4 6 258 110 3.08 3.22 19.4 1 0 3 1
# 5 18.7 8 360 175 3.15 3.44 17.0 0 0 3 2
# 6 18.1 6 225 105 2.76 3.46 20.2 1 0 3 1
# 7 14.3 8 360 245 3.21 3.57 15.8 0 0 3 4
# 8 24.4 4 147. 62 3.69 3.19 20 1 0 4 2
# 9 22.8 4 141. 95 3.92 3.15 22.9 1 0 4 2
#10 19.2 6 168. 123 3.92 3.44 18.3 1 0 4 4
# … with 22 more rows