如何使用具有多个变量的 prop.table 显示计数和百分比？

Question

我正在努力使用

prop.table

函数，我试图让它显示变量的每个计数的百分比。目前它只显示每个意见中 owner 变量 N 和 Y 的百分比。我希望它显示所有意见中每个 Y /N 变量的百分比，因此整个百分比输出列的总和为 100.

这是我使用的代码和输出。

hey <- data %>% group_by(opinion, owner) %>% 
  summarize(count = n()) %>% mutate(percentage=prop.table(count)*100)
hey

   opinion           owner     count      percentage
   <chr>             <chr>     <int>      <dbl>
 1 Agree             N            22       56.4
 2 Agree             Y            17       43.6
 3 Disagree          N             2       16.7
 4 Disagree          Y            10       83.3
 5 Not Sure          N             1       16.7
 6 Not Sure          Y             5       83.3
 7 Strongly Agree    N            57       66.3
 8 Strongly Agree    Y            29       33.7
 9 Strongly Disagree N             1       20  
10 Strongly Disagree Y             4       80 

Answer 1

您可以使用

xtabs

和

cbind

proportions

。 （请注意，“

prop.table

是一个较早的名称，保留用于向后兼容。”）

as.data.frame(xtabs(~ owner + opinion, dat)) |> {\(.) cbind(., perc=proportions(.$Freq)*100)}()
#    owner           opinion Freq       perc
# 1      N             Agree   22 14.8648649
# 2      Y             Agree   17 11.4864865
# 3      N          Disagree    2  1.3513514
# 4      Y          Disagree   10  6.7567568
# 5      N          Not Sure    1  0.6756757
# 6      Y          Not Sure    5  3.3783784
# 7      N    Strongly Agree   57 38.5135135
# 8      Y    Strongly Agree   29 19.5945946
# 9      N Strongly Disagree    1  0.6756757
# 10     Y Strongly Disagree    4  2.7027027

---

资料：

dat <- structure(list(opinion = c("Agree", "Agree", "Agree", "Agree", 
"Agree", "Agree", "Agree", "Agree", "Agree", "Agree", "Agree", 
"Agree", "Agree", "Agree", "Agree", "Agree", "Agree", "Agree", 
"Agree", "Agree", "Agree", "Agree", "Agree", "Agree", "Agree", 
"Agree", "Agree", "Agree", "Agree", "Agree", "Agree", "Agree", 
"Agree", "Agree", "Agree", "Agree", "Agree", "Agree", "Agree", 
"Disagree", "Disagree", "Disagree", "Disagree", "Disagree", "Disagree", 
"Disagree", "Disagree", "Disagree", "Disagree", "Disagree", "Disagree", 
"Not Sure", "Not Sure", "Not Sure", "Not Sure", "Not Sure", "Not Sure", 
"Strongly Agree", "Strongly Agree", "Strongly Agree", "Strongly Agree", 
"Strongly Agree", "Strongly Agree", "Strongly Agree", "Strongly Agree", 
"Strongly Agree", "Strongly Agree", "Strongly Agree", "Strongly Agree", 
"Strongly Agree", "Strongly Agree", "Strongly Agree", "Strongly Agree", 
"Strongly Agree", "Strongly Agree", "Strongly Agree", "Strongly Agree", 
"Strongly Agree", "Strongly Agree", "Strongly Agree", "Strongly Agree", 
"Strongly Agree", "Strongly Agree", "Strongly Agree", "Strongly Agree", 
"Strongly Agree", "Strongly Agree", "Strongly Agree", "Strongly Agree", 
"Strongly Agree", "Strongly Agree", "Strongly Agree", "Strongly Agree", 
"Strongly Agree", "Strongly Agree", "Strongly Agree", "Strongly Agree", 
"Strongly Agree", "Strongly Agree", "Strongly Agree", "Strongly Agree", 
"Strongly Agree", "Strongly Agree", "Strongly Agree", "Strongly Agree", 
"Strongly Agree", "Strongly Agree", "Strongly Agree", "Strongly Agree", 
"Strongly Agree", "Strongly Agree", "Strongly Agree", "Strongly Agree", 
"Strongly Agree", "Strongly Agree", "Strongly Agree", "Strongly Agree", 
"Strongly Agree", "Strongly Agree", "Strongly Agree", "Strongly Agree", 
"Strongly Agree", "Strongly Agree", "Strongly Agree", "Strongly Agree", 
"Strongly Agree", "Strongly Agree", "Strongly Agree", "Strongly Agree", 
"Strongly Agree", "Strongly Agree", "Strongly Agree", "Strongly Agree", 
"Strongly Agree", "Strongly Agree", "Strongly Agree", "Strongly Agree", 
"Strongly Agree", "Strongly Agree", "Strongly Agree", "Strongly Agree", 
"Strongly Agree", "Strongly Agree", "Strongly Disagree", "Strongly Disagree", 
"Strongly Disagree", "Strongly Disagree", "Strongly Disagree"
), owner = c("N", "N", "N", "N", "N", "N", "N", "N", "N", "N", 
"N", "N", "N", "N", "N", "N", "N", "N", "N", "N", "N", "N", "Y", 
"Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y", 
"Y", "Y", "Y", "N", "N", "Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y", 
"Y", "Y", "N", "Y", "Y", "Y", "Y", "Y", "N", "N", "N", "N", "N", 
"N", "N", "N", "N", "N", "N", "N", "N", "N", "N", "N", "N", "N", 
"N", "N", "N", "N", "N", "N", "N", "N", "N", "N", "N", "N", "N", 
"N", "N", "N", "N", "N", "N", "N", "N", "N", "N", "N", "N", "N", 
"N", "N", "N", "N", "N", "N", "N", "N", "N", "N", "N", "N", "N", 
"Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y", 
"Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y", 
"Y", "Y", "Y", "N", "Y", "Y", "Y", "Y"), n = c(1, 1, 1, 1, 1, 
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1)), row.names = c(NA, 
-148L), class = "data.frame")