Error in Weighted.Mean. Why can't it find column? - r

Question

I have a dataframe that looks like this
df <- data.frame(Region = c("Asia","Asia","Africa","Europe","Europe"),
Emp = c(120,40,10,67,110),
Sales18 = c(12310, 4510, 1140, 5310, 16435),
Sales19 = c(15670, 6730, 1605, 6120, 1755))
I am running a code where I group by region and then take average and weighted average for all 'sales' columns by 'Emp'
Result <- df %>% group_by(Region) %>%
summarise(sales18 = mean(Sales18, na.rm = T),
sales19 = mean(Sales19, na.rm = T),
weightedsales18 = weighted.mean(Sales18, .data[[Emp]], na.rm = T),
weightedsales19 = weighted.mean(Sales19, .data[[Emp]], na.rm = T))
However, I get the following err
Error in splice(dot_call(capture_dots, frame_env = frame_env, named = named, :
object 'Emp' not found
Can't figure out what I am doing wrong

Answer 1

An option could be:
library(tidyverse)
df <- data.frame(Region = c("Asia","Asia","Africa","Europe","Europe"),
Emp = c(120,40,10,67,110),
Sales18 = c(12310, 4510, 1140, 5310, 16435),
Sales19 = c(15670, 6730, 1605, 6120, 1755))
df %>%
group_by(Region) %>%
summarise(across(
.cols = starts_with("Sales"),
.fns = list(w_mean = ~ weighted.mean(.x, w = Emp), mean = ~ mean(.x)),
.names = "{.col}_{.fn}")
)
#> # A tibble: 3 x 5
#> Region Sales18_w_mean Sales18_mean Sales19_w_mean Sales19_mean
#> <chr> <dbl> <dbl> <dbl> <dbl>
#> 1 Africa 1140 1140 1605 1605
#> 2 Asia 10360 8410 13435 11200
#> 3 Europe 12224. 10872. 3407. 3938.
Created on 2021-05-25 by the reprex package (v2.0.0)

Answer 2

This works. The data masking already takes place, you don't need the .data pronoun.
library(tidyverse)
df <- data.frame(Region = c("Asia","Asia","Africa","Europe","Europe"),
Emp = c(120,40,10,67,110),
Sales18 = c(12310, 4510, 1140, 5310, 16435),
Sales19 = c(15670, 6730, 1605, 6120, 1755))
Result <- df %>% group_by(Region) %>%
summarise(sales18 = mean(Sales18, na.rm = T),
sales19 = mean(Sales19, na.rm = T),
weightedsales18 = weighted.mean(Sales18, Emp, na.rm = T),
weightedsales19 = weighted.mean(Sales19, Emp, na.rm = T))
Result
#> # A tibble: 3 x 5
#> Region sales18 sales19 weightedsales18 weightedsales19
#> <chr> <dbl> <dbl> <dbl> <dbl>
#> 1 Africa 1140 1605 1140 1605
#> 2 Asia 8410 11200 10360 13435
#> 3 Europe 10872. 3938. 12224. 3407.
Created on 2021-05-25 by the reprex package (v2.0.0)

Answer 3

Unquoted Emp inside [[ tells R to search for string variable called Emp that presumably contains name of other variable that contains weights, like here:
df <- data.frame(Region = c("Asia","Asia","Africa","Europe","Europe"),
x = c(120,40,10,67,110),
Sales18 = c(12310, 4510, 1140, 5310, 16435),
Sales19 = c(15670, 6730, 1605, 6120, 1755))
Emp <- 'x'
df %>% group_by(Region) %>%
summarise(sales18 = mean(Sales18, na.rm = T),
sales19 = mean(Sales19, na.rm = T),
weightedsales18 = weighted.mean(Sales18, .data[[Emp]], na.rm = T),
weightedsales19 = weighted.mean(Sales19, .data[[Emp]], na.rm = T))
# A tibble: 3 x 5
Region sales18 sales19 weightedsales18 weightedsales19
<chr> <dbl> <dbl> <dbl> <dbl>
1 Africa 1140 1605 1140 1605
2 Asia 8410 11200 10360 13435
3 Europe 10872. 3938. 12224. 3407.
Since, you do not have this kind of Emp, R throws an error.
What to do? Just quote Emp inside [[:
df <- data.frame(Region = c("Asia","Asia","Africa","Europe","Europe"),
Emp = c(120,40,10,67,110),
Sales18 = c(12310, 4510, 1140, 5310, 16435),
Sales19 = c(15670, 6730, 1605, 6120, 1755))
df %>% group_by(Region) %>%
summarise(sales18 = mean(Sales18, na.rm = T),
sales19 = mean(Sales19, na.rm = T),
weightedsales18 = weighted.mean(Sales18, .data[['Emp']], na.rm = T),
weightedsales19 = weighted.mean(Sales19, .data[['Emp']], na.rm = T))
# A tibble: 3 x 5
Region sales18 sales19 weightedsales18 weightedsales19
<chr> <dbl> <dbl> <dbl> <dbl>
1 Africa 1140 1605 1140 1605
2 Asia 8410 11200 10360 13435
3 Europe 10872. 3938. 12224. 3407.