如何使用 dplyr 对这些变量进行分组以进行分组汇总？

Question

这是我的输入：

structure(list(Students = c(300L, 1600L, 100L, 90L, 2000L, 200L, 
300L, 340L, 1500L, 500L, 360L, 820L, 150L, 1380L, NA, 360L, 400L, 
1000L, 1600L, 142L, 250L, 2000L), Students_Primary = c(150L, 
NA, 100L, 90L, 800L, NA, NA, 150L, NA, 250L, 220L, 400L, NA, 
750L, NA, NA, NA, 600L, NA, 142L, NA, 500L), Chinese_Spoken = c("Mandarin", 
"Mandarin", "Mandarin", "Mandarin", "Mandarin", "Mandarin", "Mandarin", 
"Mandarin", "Mandarin", "Mandarin", "Cantonese", "Mandarin", 
"Mandarin", "Mandarin", "Mandarin", "Mandarin", "Mandarin", "Mandarin", 
"Mandarin", "Both", "Mandarin", "Both"), Chinese_Written = c("Simplified", 
"Traditional", "Simplified", "Traditional", "Both", "Traditional", 
"Traditional", "Simplified", "Simplified", NA, "Traditional", 
"Both", NA, "Both", "Both", "Simplified", "Both", "Traditional", 
"Traditional", "Traditional", "Simplified", "Both")), class = "data.frame", row.names = c(NA, 
-22L))

我想总结一下有多少学生使用不同的中文写作，所以我尝试使用以下代码：

school %>% 
  select(Chinese_Written, Students) %>%
  group_by(Chinese_Written) %>% 
  arrange(Chinese_Written) %>% 
  na.omit()

它吐出这个：

   Chinese_Written Students
   <chr>              <int>
 1 Both                2000
 2 Both                 820
 3 Both                1380
 4 Both                 400
 5 Both                2000
 6 Simplified           300
 7 Simplified           100
 8 Simplified           340
 9 Simplified          1500
10 Simplified           360
11 Simplified           250
12 Traditional         1600
13 Traditional           90
14 Traditional          200
15 Traditional          300
16 Traditional          360
17 Traditional         1000
18 Traditional         1600
19 Traditional          142

是否有某些原因它们没有被组合在一起？我希望将所有“Both”、“Simplified”和“Traditional”分别归为一组。

Answer 1

group_by 单独不做任何事情，它使下面的命令被分组。所以你可以在sum变量Students之后使用summariseChinese_Written

library(dplyr)

school %>% 
  group_by(Chinese_Written) %>% 
  summarise(Students = sum(Students,na.rm = TRUE))

# A tibble: 4 x 2
  Chinese_Written Students
  <chr>              <int>
1 Both                6600
2 Simplified          2850
3 Traditional         5292
4 NA                   650