组合：rowwise()、mutate()、cross()，用于多个函数

Question

This is somehow related to this question : In principle I try to understand how rowwise operations with mutate across multiple columns applying more then 1 functions like ( mean() , sum() , min() etc..) work.

I have learned that across does this job and not c_across . I have learned that the function mean() is different to the function min() in that way that mean() doesn't work on dataframes and we need to change it to vector which can be done with unlist or as.matrix -> learned from Ronak Shah here Understanding rowwise() and c_across()

Now with my actual case: I was able to do this task but I loose one column d . How can I avoid the loose of the column d in this setting.

My df:

df <- structure(list(a = 1:5, b = 6:10, c = 11:15, d = c("a", "b", 
"c", "d", "e"), e = 1:5), row.names = c(NA, -5L), class = c("tbl_df", 
"tbl", "data.frame"))

Works not:

df %>% 
  rowwise() %>% 
  mutate(across(a:e), 
         avg = mean(unlist(cur_data()), na.rm = TRUE),
         min = min(unlist(cur_data()), na.rm = TRUE), 
         max = max(unlist(cur_data()), na.rm = TRUE)
  )

# Output:
      a     b     c d         e   avg min   max  
  <int> <int> <int> <chr> <int> <dbl> <chr> <chr>
1     1     6    11 a         1    NA 1     a    
2     2     7    12 b         2    NA 12    b    
3     3     8    13 c         3    NA 13    c    
4     4     9    14 d         4    NA 14    d    
5     5    10    15 e         5    NA 10    e 

Works, but I loose column d :

df %>% 
  select(-d) %>% 
  rowwise() %>% 
  mutate(across(a:e), 
         avg = mean(unlist(cur_data()), na.rm = TRUE),
         min = min(unlist(cur_data()), na.rm = TRUE), 
         max = max(unlist(cur_data()), na.rm = TRUE)
  )

      a     b     c     e   avg   min   max
  <int> <int> <int> <int> <dbl> <dbl> <dbl>
1     1     6    11     1  4.75     1    11
2     2     7    12     2  5.75     2    12
3     3     8    13     3  6.75     3    13
4     4     9    14     4  7.75     4    14
5     5    10    15     5  8.75     5    15

Answer 1

Using pmap() from purrr might be more preferable since you need to select the data just once and you can use the select helpers:

df %>% 
 mutate(pmap_dfr(across(where(is.numeric)),
                 ~ data.frame(max = max(c(...)),
                              min = min(c(...)),
                              avg = mean(c(...)))))

      a     b     c d         e   max   min   avg
  <int> <int> <int> <chr> <int> <int> <int> <dbl>
1     1     6    11 a         1    11     1  4.75
2     2     7    12 b         2    12     2  5.75
3     3     8    13 c         3    13     3  6.75
4     4     9    14 d         4    14     4  7.75
5     5    10    15 e         5    15     5  8.75

Or with the addition of tidyr :

df %>% 
 mutate(res = pmap(across(where(is.numeric)),
                   ~ list(max = max(c(...)),
                          min = min(c(...)),
                          avg = mean(c(...))))) %>%
 unnest_wider(res)

Answer 2

Edit:

Best way out here

df %>%
  rowwise() %>% 
  mutate(min = min(c_across(a:e & where(is.numeric)), na.rm = TRUE),
         max = max(c_across(a:e & where(is.numeric)), na.rm = TRUE), 
         avg = mean(c_across(a:e & where(is.numeric)), na.rm = TRUE)
  )

# A tibble: 5 x 8
# Rowwise: 
      a     b     c d         e   min   max   avg
  <int> <int> <int> <chr> <int> <int> <int> <dbl>
1     1     6    11 a         1     1    11  4.75
2     2     7    12 b         2     2    12  5.75
3     3     8    13 c         3     3    13  6.75
4     4     9    14 d         4     4    14  7.75
5     5    10    15 e         5     5    15  8.75

Earlier Answer Your this will work won't even work properly, if you change the output sequence, see

df %>% 
  select(-d) %>% 
  rowwise() %>% 
  mutate(across(a:e), 
         min = min(unlist(cur_data()), na.rm = TRUE),
         max = max(unlist(cur_data()), na.rm = TRUE), 
         avg = mean(unlist(cur_data()), na.rm = TRUE)
  )

# A tibble: 5 x 7
# Rowwise: 
      a     b     c     e   min   max   avg
  <int> <int> <int> <int> <int> <int> <dbl>
1     1     6    11     1     1    11  5.17
2     2     7    12     2     2    12  6.17
3     3     8    13     3     3    13  7.17
4     4     9    14     4     4    14  8.17
5     5    10    15     5     5    15  9.17

Therefore, it is advised to do it like this-

df %>% 
  select(-d) %>% 
  rowwise() %>% 
  mutate(min = min(c_across(a:e), na.rm = TRUE),
         max = max(c_across(a:e), na.rm = TRUE), 
         avg = mean(c_across(a:e), na.rm = TRUE)
  )

# A tibble: 5 x 7
# Rowwise: 
      a     b     c     e   min   max   avg
  <int> <int> <int> <int> <int> <int> <dbl>
1     1     6    11     1     1    11  4.75
2     2     7    12     2     2    12  5.75
3     3     8    13     3     3    13  6.75
4     4     9    14     4     4    14  7.75
5     5    10    15     5     5    15  8.75

One more alternative is

cols <- c('a', 'b', 'c', 'e')
df %>%
  rowwise() %>% 
  mutate(min = min(c_across(cols), na.rm = TRUE),
         max = max(c_across(cols), na.rm = TRUE), 
         avg = mean(c_across(cols), na.rm = TRUE)
  )

# A tibble: 5 x 8
# Rowwise: 
      a     b     c d         e   min   max   avg
  <int> <int> <int> <chr> <int> <int> <int> <dbl>
1     1     6    11 a         1     1    11  4.75
2     2     7    12 b         2     2    12  5.75
3     3     8    13 c         3     3    13  6.75
4     4     9    14 d         4     4    14  7.75
5     5    10    15 e         5     5    15  8.75

Even @Sinh suggested approach of group_by won't work properly in these cases.

Answer 3

Here is one method which would preserve the data.frame attribute in mutate if we want to set a particular column to row name attribute ( column_to_rownames ) and then return the attribute after the transformation

library(dplyr)
library(tibble)
library(purrr)
df %>% 
   column_to_rownames('d') %>%
   mutate(max = reduce(., pmax), min = reduce(., pmin), 
         avg = rowMeans(.)) %>% 
   rownames_to_column('d')
#  d a  b  c e max min  avg
#1 a 1  6 11 1  11   1 4.75
#2 b 2  7 12 2  12   2 5.75
#3 c 3  8 13 3  13   3 6.75
#4 d 4  9 14 4  14   4 7.75
#5 e 5 10 15 5  15   5 8.75