Include column names as function input with dplyr - r

Question

I often have to transform long tidy data frames into wide format. To do so I use the following standard procedure:
# Example data frame
df <- data.frame("ID" = rep(1:5, each = 4), "score" = runif(20, 0, 100), "location" = rep(c("a", "b", "c", "d"), 5))
# Transform into wide format
df_wide <- df %>%
group_by_at(vars(-score)) %>% # group by everything other than the value column.
mutate(row_id=1:n()) %>% ungroup() %>% # build group index
spread(key=location, value=score) %>% # spread
dplyr::select(-row_id)
Instead of typing this little script over and over again, I wanted to define a function to do it automatically. I found many useful posts on how to include column names as function inputs, but somehow it doesn't work or I get error messages. What am I doing wrong?
Below a few of my attempts (neither of them work), following these and this suggestions:
wide_fun <- function(dat, key_name, value_name) {
group_by_at(vars(- !! sym(value_name))) %>% # group by everything other than the value column.
mutate(row_id=1:n()) %>% ungroup() %>% # build group index
spread(key=!! sym(key_name), value=!! sym(value_name)) %>% # spread
dplyr::select(-row_id)
}
wide_fun2 <- function(dat, key_name, value_name) {
key_col <- enquo(key_name)
value_col <- enquo(value_name)
group_by_at(vars(- !!value_col)) %>% # group by everything other than the value column.
mutate(row_id=1:n()) %>% ungroup() %>% # build group index
spread(key= !!key_col, value= !!value_col) %>% # spread
dplyr::select(-row_id)
}
wide_fun3 <- function(dat, key_name, value_name) {
group_by_at(vars(- value_name)) %>% # group by everything other than the value column.
mutate(row_id=1:n()) %>% ungroup() %>% # build group index
spread(key=key_name, value=value_name) %>% # spread
dplyr::select(-row_id)
}
wide_fun3(df, quote(location), quote(score))
Thanks for your help!

Answer 1

I've slightly updated your code to dplyr 1.0.0 and tidyr. Then you can make use of the new dplyr programming feature {{}} to specify variables that are arguments of a function.
# Example data frame
df <- data.frame("ID" = rep(1:5, each = 4), "score" = runif(20, 0, 100), "location" = rep(c("a", "b", "c", "d"), 5))
library(dplyr)
wide_fun <- function(.data, key_name, value_name) {
.data %>%
group_by(across(-{{value_name}})) %>% # group by everything other than the value column.
mutate(row_id = 1:n()) %>% ungroup() %>% # build group index
tidyr::pivot_wider(
names_from = {{key_name}},
values_from = {{value_name}}) %>% # spread
select(-row_id)
}
wide_fun(df, location, score)
#> # A tibble: 5 x 5
#> ID a b c d
#> <int> <dbl> <dbl> <dbl> <dbl>
#> 1 1 90.8 38.9 28.7 39.0
#> 2 2 94.5 24.9 84.6 54.6
#> 3 3 61.1 97.2 12.2 57.7
#> 4 4 52.7 85.6 41.4 100.
#> 5 5 17.8 86.1 92.3 33.7
Created on 2020-09-11 by the reprex package (v0.3.0)
Edit
This function should also work with older versions of dplyr:
library(dplyr)
wide_fun_2 <- function(.data, key_name, value_name) {
.data %>%
group_by_at(vars(-!!ensym(value_name))) %>% # group by everything other than the value column.
mutate(row_id = 1:n()) %>% ungroup() %>% # build group index
tidyr::pivot_wider(
names_from = !!ensym(key_name),
values_from = !!ensym(value_name)) %>% # spread
select(-row_id)
}
df %>%
wide_fun_2(location, score)
A tibble: 5 x 5
ID a b c d
<int> <dbl> <dbl> <dbl> <dbl>
1 1 72.2 81.4 52.5 48.8
2 2 36.1 27.5 82.2 73.0
3 3 83.9 68.2 80.9 15.7
4 4 0.451 70.0 18.5 43.2
5 5 82.6 68.2 22.8 63.0
If you just provide the argument that specifies the column, you only need to deal with symbols and not quosures, therefore you need to use ensym.