"如何使用 dplyr 计算两个分组变量的加权平均值"

Question

I know this must be super easy, but I'm having trouble finding the right dplyr commands to do this. Let's say I want to group a dataset by two variables, and then summarize the count for each row. For this we simply have:

mtcars %>% group_by(cyl, mpg) %>% summarize(Count = n())

Answer 1

If I have understood you correctly, you need weighted.mean

library(dplyr)
mtcars %>% 
   group_by(cyl, mpg) %>% 
   summarize(Count = n()) %>%
   group_by(cyl) %>%
   summarise(avg_mpg = weighted.mean(mpg, Count))

# A tibble: 3 x 2
#    cyl   avg_mpg
#  <dbl>   <dbl>
#1  4.00    26.7
#2  6.00    19.7
#3  8.00    15.1

---

which is equivalent to

mtcars %>% 
  group_by(cyl, mpg) %>% 
  summarize(Count = n()) %>%
  group_by(cyl) %>%
  summarise(avg_mpg = sum(mpg * Count)/sum(Count))

Answer 2

You are effectively performing a simple mean because the weights are the grouping variable:

library(dplyr)
options(pillar.sigfig=10) # To check they are identical results
    
mtcars %>%
  group_by(cyl) %>%
  summarise(avg_mpg = mean(mpg))

Output:

The result is identical to the ones proposed above:

# A tibble: 3 x 2
    cyl     avg_mpg
  <dbl>       <dbl>
1     4 26.66363636
2     6 19.74285714
3     8 15.1  

If you need a weighted mean based on another variable:

mtcars %>%
  group_by(cyl) %>%
  summarise(avg_mpg = weighted.mean(mpg, disp))

# A tibble: 3 x 2
    cyl     avg_mpg
  <dbl>       <dbl>
1     4 25.81985300
2     6 19.77197631
3     8 14.86285148