计算分组 dataframe 中多个列中因子的出现次数

Question

I have the following dataframe and want to group by the grp column to see how many of each column-value appears in each group.

> data.frame(grp = unlist(strsplit("aabbccca", "")), col1=unlist(strsplit("ABAABBAB", "")), col2=unlist(strsplit("BBCCCCDD", "")))
  grp col1 col2
1   a    A    B
2   a    B    B
3   b    A    C
4   b    A    C
5   c    B    C
6   c    B    C
7   c    A    D
8   a    B    D

Desired result:

  grp col1A col1B col2B col2C col2D
1   a    1    2     2     0     1
2   b    2    0     0     2     0
3   c    1    2     0     2     1

If I only look at the grp and col1 columns, it is easy to solve this using table() and when there are only 2 columns, I could merge table(df[c('grp', 'col1')]) with table(df[c('grp', 'col2')]) . However, this gets extremely cumbersome as the number of factor columns grows, and is problematic if there are shared values between col1 and col2 .

Note that dplyr's count doesn't work, as it looks for unique combinations of the col1 and col2.

I've tried melting and spreading the dataframe using tidyr, without any luck

> pivot_longer(df, c(col1, col2), names_to= "key", values_to = "val") %>% pivot_wider("grp", names_from = c("key", "val"), values_from = 1, values_fn = sum)
Error in `stop_subscript()`:
! Can't subset columns that don't exist.
x Column `grp` doesn't exist.

I can find plenty of solutions that work for the case where I have 1 group column and 1 value column, but I can't figure out how to generalize them to more columns.

Answer 1

You can stack col1 & col2 together, count the number of each combination, and then transform the table to a wide form.

library(dplyr)
library(tidyr)

df %>%
  pivot_longer(col1:col2) %>%
  count(grp, name, value) %>%
  pivot_wider(grp, names_from = c(name, value), names_sort = TRUE,
              values_from = n, values_fill = 0)

# A tibble: 3 x 6
  grp   col1_A col1_B col2_B col2_C col2_D
  <chr>  <int>  <int>  <int>  <int>  <int>
1 a          1      2      2      0      1
2 b          2      0      0      2      0
3 c          1      2      0      2      1

A base solution (Thank @GKi to refine the code):

table(cbind(df["grp"], col=do.call(paste0, stack(df[-1])[2:1])))

   col
grp col1A col1B col2B col2C col2D
  a     1     2     2     0     1
  b     2     0     0     2     0
  c     1     2     0     2     1

Answer 2

Use recast from reshape2 package:

reshape2::recast(df, grp~variable+value,id.var = 'grp', fun = length)

  grp col1_A col1_B col2_B col2_C col2_D
1   a      1      2      2      0      1
2   b      2      0      0      2      0
3   c      1      2      0      2      1

In base R you could do:

with(df, cbind(table(grp, paste0('col1_', col1)), table(grp, paste0('col2_', col2))))

  col1_A col1_B col2_B col2_C col2_D
a      1      2      2      0      1
b      2      0      0      2      0
c      1      2      0      2      1

If you have many columns consider doing:

do.call(cbind, Map(function(x, y) table(df$grp, paste(x,y, sep = '_')),
                        names(df)[-1], df[,-1]))

  col1_A col1_B col2_B col2_C col2_D
a      1      2      2      0      1
b      2      0      0      2      0
c      1      2      0      2      1

You can then turn this to a dataframe

Answer 3

You were on the right track with melt and spread . Here's a tidyverse solution. I first use pivot_longer to generalise to an arbitrary number of columns and then pivot_wider to return to the desired output format. The order of columns in the output data frame is data dependent. If this is an issue, simply append a select to the end of the pipe to obtain the desired order. (Or use names_sort as in @DarrenTsai's answer.)

library(tidyverse)

d %>% 
  pivot_longer(
    starts_with("col"),
    names_to="Column",
    values_to="Value"
  ) %>% 
  group_by(grp, Column, Value) %>% 
  summarise(N=n(), .groups="drop") %>% 
  group_by(grp) %>% 
  pivot_wider(
    id_cols=grp,
    values_from=N,
    names_from=c(Column, Value),
    names_sep="",
    values_fill=0
  ) %>%
  ungroup()
# A tibble: 3 × 6
  grp   col1A col1B col2B col2D col2C
  <chr> <int> <int> <int> <int> <int>
1 a         1     2     2     1     0
2 b         2     0     0     0     2
3 c         1     2     0     1     2

Answer 4

Another possible solution, based on a tidyr::pivot_longer followed by a tidyr::pivot_wider and using values_fn = length :

library(tidyverse)

df %>% 
  pivot_longer(c(col1, col2)) %>% 
  mutate(name = str_c(name, value)) %>% 
  pivot_wider(grp, values_fn = length, values_fill = 0, names_sort = T)

#> # A tibble: 3 x 6
#>   grp   col1A col1B col2B col2C col2D
#>   <chr> <int> <int> <int> <int> <int>
#> 1 a         1     2     2     0     1
#> 2 b         2     0     0     2     0
#> 3 c         1     2     0     2     1

Answer 5

In data.table , we can use dcast + melt like below

dcast(
    melt(setDT(df), id.vars = "grp")[
        , value := paste(variable, value, sep = "_")
    ], grp ~ value
)

to produce

   grp col1_A col1_B col2_B col2_C col2_D
1:   a      1      2      2      0      1
2:   b      2      0      0      2      0
3:   c      1      2      0      2      1