Grouped frequency table with multiple columns of each variable

Question

I have a dataset like this:

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         

Before I asked a question about how to make a frequency table of my data and received this answer:

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

And was able to get the following 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

However now I want to group by a new variable "class":

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

I have tried to just add class to the group by statement and I am getting an error. Any help is greatly appreciated!

Answer 1

We can modify the OP's code in the grouping ie grouped by 'class, 'Variable', calculate the 'Percent', and create the 'Total' row with adorn_totals before reshaping back to 'wide' with pivot_wider

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)

-output

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

---

The above output can be converted to a flextable

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

-output

data

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

You could use the datasummary function from the modelsummary package. (Disclaimer: I am the maintainer of this package.)

Load libraries and simulate data:

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

Reshape data:

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

Create table:

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

Answer 3

Another possible solution, based on purrr::reduce and 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