Weighted.Mean中的错误。为什么找不到专栏？

Question

我有一张像这样的数据

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))

我正在运行一个代码，在该代码中，我按区域分组，然后根据'Emp‘对所有“销售”列取平均值和加权平均值。

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))

但是，我得到了以下错误

Error in splice(dot_call(capture_dots, frame_env = frame_env, named = named,  : 
  object 'Emp' not found

不知道我做错了什么

Answer 1

未引用的Emp of [[告诉R搜索名为Emp的字符串变量，该变量可能包含包含权重的其他变量的名称，如下所示：

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.

因为，您没有这种Emp，所以R会抛出一个错误。

该怎么办呢？只需引用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.