I would like to obtain a generic formula to arrange dataframes with a varying number of columns.

For example, in this case the dataframe contains "categ_1, categ_2, points_1, points_2":

  library(tidyverse)
  set.seed(1)
  nrows <- 20
  df <- tibble(
    other_text = sample(letters,
                        nrows, replace = TRUE),
    categ_1 = sample(c("A", "B"), nrows, replace = TRUE),
    categ_2 = sample(c("A", "B"), nrows, replace = TRUE),
    points_1 = sample(20:25, nrows, replace = TRUE),
    points_2 = sample(20:25, nrows, replace = TRUE),
  ) %>%
    rowwise() %>%
    mutate(total = sum(c_across(starts_with("points_")))) %>%
    ungroup()

And the formula to arrange:

df %>%
  arrange(
    desc(total),
    categ_1, categ_2,
    desc(points_1), desc(points_2)
  )

But df could have more columns: "categ_1, categ_2, categ_3, points_1, points_2, points_3". So, in that case, the formula should be:

df %>%
  mutate(
    categ_3 = sample(c("A", "B"), nrows, replace = TRUE),
    points_3 = sample(20:25, nrows, replace = TRUE),
  ) %>%
    rowwise() %>%
    mutate(total = sum(c_across(starts_with("points_")))) %>%
    ungroup() %>%
    arrange(
      desc(total),
      categ_1, categ_2, categ_3,
      desc(points_1), desc(points_2), desc(points_3)
    )

I tried writing a generic formula (using across):

  library(daff)

  daff::diff_data(
    df %>%
      arrange(
        desc(total),
        categ_1, categ_2,
        desc(points_1), desc(points_2)
      )
    ,
    df %>%
      arrange(
        desc(total),
        across(starts_with("categ_")),
        across(starts_with("points_"), desc)
      )
  )
#> Daff Comparison: ‘df %>% arrange(desc(total), categ_1, categ_2, desc(points_1), ’ ‘    desc(points_2))’ vs. ‘df %>% arrange(desc(total), across(starts_with("categ_")), across(starts_with("points_"), ’ ‘    desc))’
#>           A:A        B:B     ... E:E      F:F
#>       @@  other_text categ_1 ... points_2 total
#>       ... ...        ...     ... ...      ...
#> 10:9      z          A       ... 23       45
#> 9:10  :   v          A       ... 22       45
#> 11:11     s          B       ... 23       45
#>       ... ...        ...     ... ...      ...

It seems like a bug in arrange: arrange only considers the parameters until the first across.

I also tried writing the conditions inside a case_when but couldn't find the correct syntax:

  # not working
  df %>%
    arrange(
      across(everything(), ~ case_when(
        . == "total" ~ .,
        str_detect(., "categ_") ~ .,
        str_detect(., "points_") ~ desc(.),
        TRUE ~ 1
      )
      )
    )
#> Error in `arrange()`:
#> ! Problem with the implicit `transmute()` step.

What would be the generic way of writing that formula inside arrange? (Other alternatives are welcome but I would prefer a tidyverse solution.)

CodePudding user response：

An easy (sort of) solution would be to use cur_column() to determine the column name which eventually determines the sorting order:

Data

library(dplyr)
library(daff)

set.seed(32211)
nrows <- 20

df <- tibble(
   other_text = sample(letters,
                       nrows, replace = TRUE),
   categ_1 = sample(c("A", "B"), nrows, replace = TRUE),
   categ_2 = sample(c("A", "B"), nrows, replace = TRUE),
   points_1 = sample(20:25, nrows, replace = TRUE),
   points_2 = sample(20:25, nrows, replace = TRUE),
) %>%
   rowwise() %>%
   mutate(total = sum(c_across(starts_with("points_")))) %>%
   ungroup()

Comparison

by_hand <- df %>%
   arrange(
      desc(total),
      categ_1, categ_2,
      desc(points_1), desc(points_2)
   )

with_across <- df %>%
   arrange(
      across(
         c(total, starts_with("categ_"), starts_with("points_")),
         .fns = function(x) {
            if (grepl("^total$|^points_.*$", cur_column()))
               desc(x)
            else
               x
         })
   )

diff_data(by_hand, with_across)
# Daff Comparison: ‘by_hand’ vs. ‘with_across’ 
#      other_text categ_1 categ_2 points_1 points_2 total

CodePudding user response：

Latest, super-simple fix

Install development version:

# remotes::install_github("tidyverse/dplyr")
library(tidyverse)

set.seed(144)
nrows <- 20
df <- tibble(
  other_text = sample(letters,
                      nrows, replace = FALSE),
  categ_1 = sample(c("A", "B"), nrows, replace = TRUE),
  categ_2 = sample(c("A", "B"), nrows, replace = TRUE),
  points_1 = sample(1:25, nrows, replace = FALSE),
  points_2 = sample(100:125, nrows, replace = FALSE),
) %>%
  rowwise() %>%
  mutate(total = sum(c_across(starts_with("points_")))) %>%
  ungroup()

out1 <- df %>%
  arrange(
    desc(total),
    categ_1, categ_2,
    desc(points_1), desc(points_2)
  )

out2 <- df %>%
  arrange(
    desc(total),
    across(starts_with("categ_")),
    across(starts_with("points_"), desc)
  )

daff::diff_data(out1, out2)
#> Daff Comparison: 'out1' vs. 'out2' 
#>      other_text categ_1 ...

CodePudding user response：

You could try just wrapping everything in the arrange() in a data frame. Looks like arrange() does some code manipulation to handle top-level desc() calls in a special manner, which has a bad interaction with the data frames created by across(). But that can be avoided using the data frame unpacking feature.

library(tidyverse)

set.seed(3)
nrows <- 20

df <- tibble(
  other_text = sample(letters, nrows, replace = TRUE),
  categ_1 = sample(c("A", "B"), nrows, replace = TRUE),
  categ_2 = sample(c("A", "B"), nrows, replace = TRUE),
  points_1 = sample(20:25, nrows, replace = TRUE),
  points_2 = sample(20:25, nrows, replace = TRUE),
) %>%
  rowwise() %>%
  mutate(total = sum(c_across(starts_with("points_")))) %>%
  ungroup()

identical(
  df %>%
    arrange(
      desc(total),
      categ_1, categ_2,
      desc(points_1), desc(points_2)
    ),
  df %>%
    arrange(
      tibble(
        desc(total),
        across(starts_with("categ_")),
        across(starts_with("points_"), desc)
      )
    )
)
#> [1] TRUE