每个变量的多列分组频率表

Question

我有一个这样的数据集：

ID    color_1  color_2  shape_1   shape_2       size     class
55    red      blue     circle    triangle      small     A                                         
83    blue     yellow   circle    NA            large     B
78    red      yellow   square    circle        large     B
43    green    NA       square    circle        small     C
29    yellow   green    circle    triangle      medium    A         

在我问一个关于如何制作我的数据频率表的问题并收到这个答案之前：

pat_words <- c("color", "shape", "size")
out <-  df %>% 
  pivot_longer(
    -ID,
    names_to = "Question",
    values_to = "Response"
  ) %>% mutate(Question = str_extract(Question, str_c(pat_words, collapse="|"))) %>% group_by(Question, Response) %>% 
  count(Response, name = "Freq") %>% 
  na.omit() %>% 
  group_by(Question) %>% 
  mutate(Percent = round(Freq/sum(Freq)*100, 2)) %>% 
  group_split() %>% 
  adorn_totals() %>% 
  bind_rows() %>% 
  mutate(Response = ifelse(Response == last(Response), last(Question), Response)) %>% 
  mutate(Question = ifelse(duplicated(Question) |
                             Question == "Total", NA, Question)) %>% 
  as.data.frame

并且能够得到以下output：

Variable      Level        Freq        Percent 
 
color         blue          2           22.22
              red           2           22.22
              yellow        3           33.33
              green         2           22.22
              total         9           100.00

shape         circle        5           50.0       
              triangle      3           30.0
              square        2           20.0
              total         10          100.0

size          small         2           33.3
              medium        2           33.3
              large         2           33.3
              total         6           100.0

但是现在我想按一个新变量“类”进行分组：

Class                       A                              B                           C             
Variable      Level        Freq        Percent       Freq    Percent             Freq   Percent                
 
color         blue          1           25            1        25                 0       0
              red           1           25            1        25                 0       0
              yellow        1           25            2        50                 0       0
              green         1           25            0        0                  1       100
              total         4           100.00        4        100.00             1       100
  
shape         circle        2           50            2        66.7               1       50
              triangle      2           50            0        0                  0       0
              square        0           0             1        33.3               1       50
              total         4          100.0          3        100.0              2       100

size          small         1           50            0        0                  1       100   
              medium        1           50            0        0                  0       0
              large         0           0             2        100                0       0
              total         2           100.0         2        100                1       100

我试图将 class 添加到 group by 语句中，但出现错误。 任何帮助是极大的赞赏！

Answer 1

我们可以修改分组中的 OP 代码，即按“类”、“变量”分组，计算“百分比”，并使用pivot_wider创建“总计”行，然后使用adorn_totals重新调整为“宽”

library(dplyr)
library(tidyr)
library(janitor)
library(stringr)
out <- df %>% 
   pivot_longer(cols = -c(ID, class), names_to = 'Variable', 
      values_to = 'Level', values_drop_na = TRUE) %>% 
   mutate(Variable = str_remove(Variable, "_\\d+")) %>% 
   count(class, Variable, Level, name = "Freq") %>% 
   group_by(class, Variable)  %>% 
   mutate(Percent = round(100 * Freq/sum(Freq))) %>% 
   group_modify(~ .x %>% 
   adorn_totals()) %>% 
   ungroup %>% 
   pivot_wider(names_from = class, values_from = c(Freq, Percent), 
        values_fill = 0) %>%
   select(Variable, Level, ends_with('A'), ends_with('B'), ends_with('C')) %>% 
   arrange(Variable)

-输出

out
# A tibble: 13 × 8
   Variable Level    Freq_A Percent_A Freq_B Percent_B Freq_C Percent_C
   <chr>    <chr>     <int>     <dbl>  <int>     <dbl>  <int>     <dbl>
 1 color    blue          1        25      1        25      0         0
 2 color    green         1        25      0         0      1       100
 3 color    red           1        25      1        25      0         0
 4 color    yellow        1        25      2        50      0         0
 5 color    Total         4       100      4       100      1       100
 6 shape    circle        2        50      2        67      1        50
 7 shape    triangle      2        50      0         0      0         0
 8 shape    Total         4       100      3       100      2       100
 9 shape    square        0         0      1        33      1        50
10 size     medium        1        50      0         0      0         0
11 size     small         1        50      0         0      1       100
12 size     Total         2       100      2       100      1       100
13 size     large         0         0      2       100      0         0

---

上面的output可以转换成flextable

library(flextable)
out %>% 
   mutate(Variable = replace(Variable, duplicated(Variable), NA)) %>% 
   flextable()

-输出

数据

df <- structure(list(ID = c(55L, 83L, 78L, 43L, 29L), color_1 = c("red", 
"blue", "red", "green", "yellow"), color_2 = c("blue", "yellow", 
"yellow", NA, "green"), shape_1 = c("circle", "circle", "square", 
"square", "circle"), shape_2 = c("triangle", NA, "circle", "circle", 
"triangle"), size = c("small", "large", "large", "small", "medium"
), class = c("A", "B", "B", "C", "A")), class = "data.frame", row.names = c(NA, 
-5L))

Answer 2

您可以使用datasummary中的modelsummary 。 （免责声明：我是这个 package 的维护者。）

加载库并模拟数据：

library(tidyverse)
library(modelsummary)

N <- 100
colors <- c(NA, "red", "blue", "yellow")
shapes <- c(NA, "circle", "triangle", "square")
sizes <- c("small", "medium", "large")
classes <- c("A", "B", "C")
dat <- data.frame(
    ID = 1:N,
    color_1 = sample(colors, N, replace = TRUE),
    color_2 = sample(colors, N, replace = TRUE),
    shape_1 = sample(shapes, N, replace = TRUE),
    shape_2 = sample(shapes, N, replace = TRUE),
    size = sample(sizes, N, replace = TRUE),
    class = sample(classes, N, replace = TRUE))

head(dat)

##   ID color_1 color_2  shape_1 shape_2   size class
## 1  1  yellow     red   square  circle medium     B
## 2  2    blue    <NA> triangle  circle  large     B
## 3  3    <NA>  yellow     <NA>  square  small     C
## 4  4  yellow    blue     <NA>    <NA>  small     C
## 5  5     red    blue triangle    <NA>  large     B
## 6  6    <NA>    blue     <NA>  circle  small     C

重塑数据：

dat <- dat %>%
       pivot_longer(cols = matches("color|shape"),
                    names_sep = "_",
                    names_to = c("feature", "entry")) %>%
       pivot_wider(names_from = "feature", values_from = "value")

创建表：

datasummary(color + shape + size ~ 
            class * ((Freq = 1) + Percent(denom = "col")), 
            fmt = 0,
            data = dat)

Answer 3

另一种可能的解决方案，基于purrr::reduce和dplyr::full_join ：

library(tidyverse)

df %>% 
  pivot_longer(cols = !contains(c("ID", "class")), names_to = "Variable", 
     values_to = "Level") %>% drop_na %>% 
  mutate(Variable = str_remove(Variable,"_\\d$")) %>% 
  add_count(class, Variable) %>% 
  group_by(class, Variable, Level) %>% 
  summarise(count = n(), perc = 100*count/n, .groups = "drop") %>% 
  group_split(class) %>% 
  reduce(full_join, by=c("Variable", "Level"), suffix=c(".A",".B")) %>% 
  select(-starts_with("class")) %>% distinct %>% 
  arrange(Variable) %>% 
  rename("count.C" = "count", "perc.C" = "perc") %>% 
  mutate(across(contains("."), ~ replace_na(.x,0)))

#> # A tibble: 10 × 8
#>    Variable Level    count.A perc.A count.B perc.B count.C perc.C
#>    <chr>    <chr>      <dbl>  <dbl>   <dbl>  <dbl>   <dbl>  <dbl>
#>  1 color    blue           1     25       1   25         0      0
#>  2 color    green          1     25       0    0         1    100
#>  3 color    red            1     25       1   25         0      0
#>  4 color    yellow         1     25       2   50         0      0
#>  5 shape    circle         2     50       2   66.7       1     50
#>  6 shape    triangle       2     50       0    0         0      0
#>  7 shape    square         0      0       1   33.3       1     50
#>  8 size     medium         1     50       0    0         0      0
#>  9 size     small          1     50       0    0         1    100
#> 10 size     large          0      0       2  100         0      0