I am trying to write a custom function where I use rlang's quasiquotation. This function also internally uses dplyr's join functions. I have provided below a minimal working example that illustrated my problem.
# needed libraries
library(tidyverse)
# function definition
df_combiner <- function(data, x, group.by) {
# check how many variables were entered for this grouping variable
group.by <- as.list(rlang::quo_squash(rlang::enquo(group.by)))
# based on number of arguments, select `group.by` in cases like `c(cyl)`,
# the first list element after `quo_squash` will be `c` which we don't need,
# but if we pass just `cyl`, there is no `c`, this will take care of that
# issue
group.by <-
if (length(group.by) == 1) {
group.by
} else {
group.by[-1]
}
# creating internal dataframe
df <- dplyr::group_by(.data = data, !!!group.by, .drop = TRUE)
# creating dataframes to be joined: one with tally, one with summary
df_tally <- dplyr::tally(df)
df_mean <- dplyr::summarise(df, mean = mean({{ x }}, na.rm = TRUE))
# without specifying `by` argument, this works but prints a message I want to avoid
print(dplyr::left_join(x = df_tally, y = df_mean))
# joining by specifying `by` argument (my failed attempt)
dplyr::left_join(x = df_tally, y = df_mean, by = !!!group.by)
}
# using the function
df_combiner(diamonds, carat, c(cut, clarity))
#> Joining, by = c("cut", "clarity")
#> # A tibble: 40 x 4
#> # Groups: cut [5]
#> cut clarity n mean
#> <ord> <ord> <int> <dbl>
#> 1 Fair I1 210 1.36
#> 2 Fair SI2 466 1.20
#> 3 Fair SI1 408 0.965
#> 4 Fair VS2 261 0.885
#> 5 Fair VS1 170 0.880
#> 6 Fair VVS2 69 0.692
#> 7 Fair VVS1 17 0.665
#> 8 Fair IF 9 0.474
#> 9 Good I1 96 1.20
#> 10 Good SI2 1081 1.04
#> # ... with 30 more rows
#> Error in !group.by: invalid argument type
As can be seen here, I want to avoid the message #> Joining, by = c("cut", "clarity") and so explicitly want to input the by argument for the _join function but I am not sure how to do this.
(I've tried rlang::as_string, rlang::quo_name, etc.).
We can convert to string with as_string
dplyr::left_join(x = df_tally, y = df_mean,
by = map_chr(group.by, rlang::as_string))
df_combiner <- function(data, x, group.by) {
# check how many variables were entered for this grouping variable
group.by <- as.list(rlang::quo_squash(rlang::enquo(group.by)))
# based on number of arguments, select `group.by` in cases like `c(cyl)`,
# the first list element after `quo_squash` will be `c` which we don't need,
# but if we pass just `cyl`, there is no `c`, this will take care of that
# issue
group.by <-
if (length(group.by) == 1) {
group.by
} else {
group.by[-1]
}
# creating internal dataframe
df <- dplyr::group_by(.data = data, !!!group.by, .drop = TRUE)
# creating dataframes to be joined: one with tally, one with summary
df_tally <- dplyr::tally(df)
df_mean <- dplyr::summarise(df, mean = mean({{ x }}, na.rm = TRUE))
# without specifying `by` argument, this works but prints a message I want to avoid
#print(dplyr::left_join(x = df_tally, y = df_mean))
# joining by specifying `by` argument (my failed attempt)
dplyr::left_join(x = df_tally, y = df_mean, by = map_chr(group.by, rlang::as_string))
}
-checking
df_combiner(diamonds, carat, c(cut, clarity))
# A tibble: 40 x 4
# Groups: cut [5]
# cut clarity n mean
# <ord> <ord> <int> <dbl>
# 1 Fair I1 210 1.36
# 2 Fair SI2 466 1.20
# 3 Fair SI1 408 0.965
# 4 Fair VS2 261 0.885
# 5 Fair VS1 170 0.880
# 6 Fair VVS2 69 0.692
# 7 Fair VVS1 17 0.665
# 8 Fair IF 9 0.474
# 9 Good I1 96 1.20
#10 Good SI2 1081 1.04
# … with 30 more rows