为每个变量创建相对频率表的函数

Question

我有一个包含220个变量（列）的数据集。我的数据看起来像这样

    test      var1   var2      var3
1   Pretest   No    Sometimes   No
2   Postest   No    Sometimes   No
3   Pretest   No    Yes         No
4   Postest   No    Yes         No
5   Pretest   No    Sometimes   No
6   Postest   No    Yes         No
7   Pretest   No    Yes         No
8   Postest   No    No          No
9   Pretest   No    Yes         No
10  Postest   No    Sometimes   No
11  Pretest   No    Sometimes   No
12  Postest   No    No  No
13  Pretest   Yes   Yes No
14  Postest   No    Sometimes   No
15  Pretest   No    No  No
16  Postest   No    Sometimes   No
17  Pretest   No    Sometimes   No
18  Postest   No    No  No
19  Pretest   No    Yes No
20  Postest   No    Yes No

对于每个变量，我想按组生成相对频率表。为此，我正在使用dplyr

propvar1 <- dataprepost %>% 
  group_by(test,var1) %>%
  summarise(n = n()) %>%
  mutate(rel.freq = paste0(round(100 * n/sum(n), 0), "%"))

例如，这给了我想要的Var1表：

    test      var1      n     rel.freq
1   Postest   Sometimes 120   14%
2   Postest   No        667   79%
3   Postest   Yes       44    5%
4   Postest   NA        10    1%
5   Pretest   Sometimes 155   18%
6   Pretest   No        623   74%
7   Pretest   Yes       49    6%
8   Pretest   NA        14    2%

我该如何创建一个函数来对数据集中的每个变量自动执行此操作，所以最终为每个变量创建一个相似的表？

Answer 1

这不是最优雅的代码...但是可以！

数据

d <- structure(list(test = structure(c(2L, 1L, 2L, 1L, 2L, 1L, 2L, 
                                       1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L), .Label = c("Postest", 
                                                                                                       "Pretest"), class = "factor"), var1 = structure(c(1L, 1L, 1L, 
                                                                                                                                                         1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 
                                                                                                                                                         1L), .Label = c("No", "Yes"), class = "factor"), var2 = structure(c(2L, 
                                                                                                                                                                                                                             2L, 3L, 3L, 2L, 3L, 3L, 1L, 3L, 2L, 2L, 1L, 3L, 2L, 1L, 2L, 2L, 
                                                                                                                                                                                                                             1L, 3L, 3L), .Label = c("No", "Sometimes", "Yes"), class = "factor"), 
                    var3 = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
                                       1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L), .Label = "No", class = "factor")), .Names = c("test", 
                                                                                                                              "var1", "var2", "var3"), class = "data.frame", row.names = c("1", 
                                                                                                                                                                                           "2", "3", "4", "5", "6", "7", "8", "9", "10", "11", "12", "13", 
                                                                                                                                                                                           "14", "15", "16", "17", "18", "19", "20"))`

解决方案

output <- NULL

for (t_cat in unique(d$test)) {
  x <- d[d$test == t_cat, ]
  x$test <- NULL

  l <- lapply(x, function(x) {
    output <- data.frame(table(x))
    colnames(output) <- c('val', 'n')
    output$rel.freq <- output$n/length(x)
    output$category <- t_cat
    return(output)
  })

  r <- do.call("rbind", lapply(names(l), function(x) { cbind(l[[x]], 'var' = x)}))

  output <- rbind(output, r)
}

print(output)

# val  n rel.freq category  var
# 1         No  9      0.9  Pretest var1
# 2        Yes  1      0.1  Pretest var1
# 3         No  1      0.1  Pretest var2
# 4  Sometimes  4      0.4  Pretest var2
# 5        Yes  5      0.5  Pretest var2
# 6         No 10      1.0  Pretest var3
# 7         No 10      1.0  Postest var1
# 8        Yes  0      0.0  Postest var1
# 9         No  3      0.3  Postest var2
# 10 Sometimes  4      0.4  Postest var2
# 11       Yes  3      0.3  Postest var2
# 12        No 10      1.0  Postest var3