How to arrange a dataframe by both ascending and descending within a function?

Hi,

I would like to write a function that can arrange a dataframe by several columns, some in an ascending way, some others in a descending way. I am using dplyr::arrange() and !!!rlang::syms() to do so, but this is not working.

Here what I need to get without a function:

# Library
library(tidyverse)

# Original tibble 
tbl_mtcars <- mtcars

# Without a function
tbl_mtcars_arrange_0 <-
  tbl_mtcars %>%
  dplyr::arrange(cyl ,desc(disp) ,hp)

It is working when I do not specify anything for the descending colums:

# With a function
func_arrange <- function(tbl ,arrangeby) {
  obj <-
    tbl %>%
    dplyr::arrange(!!!rlang::syms(arrangeby))
  
  return(obj)
}

## Working
tbl_mtcars_arrange_1 <- func_arrange(
  tbl        = tbl_mtcars
  ,arrangeby = c("cyl" ,"disp" ,"hp")
)

But not when I want to arrange by a descending colum:

## Not working
tbl_mtcars_arrange_2 <- func_arrange(
  tbl        = tbl_mtcars
  ,arrangeby = c("cyl" ,desc("disp") ,"hp")
)

Error in `dplyr::arrange()`:
! Problem with the implicit `transmute()` step.
x Problem while computing `..1 = c("cyl", "disp")`.
x `..1` must be size 32 or 1, not 2.

I'm using !!!rlang::syms() and not {{}} because {{}} is not working when I want to arrange by several columns (and I don't know why, it's working for dplyr::select() and dplyr::filter() for example).

Could you help me understand what's wrong in my code and how to solve it? Thank you very much!

When passing multiple columns, you can use the ... argument.

func_arrange = function(tbl, ...) {
  obj <- tbl %>%
    arrange(...)
  
  return(obj)  
}

func_arrange(tbl_mtcars, cyl, desc(gear), desc(hp))
#>                      mpg cyl  disp  hp drat    wt  qsec vs am gear carb
#> Lotus Europa        30.4   4  95.1 113 3.77 1.513 16.90  1  1    5    2
#> Porsche 914-2       26.0   4 120.3  91 4.43 2.140 16.70  0  1    5    2
#> Volvo 142E          21.4   4 121.0 109 4.11 2.780 18.60  1  1    4    2
#> Merc 230            22.8   4 140.8  95 3.92 3.150 22.90  1  0    4    2
#> Datsun 710          22.8   4 108.0  93 3.85 2.320 18.61  1  1    4    1
#> Fiat 128            32.4   4  78.7  66 4.08 2.200 19.47  1  1    4    1
#> Fiat X1-9           27.3   4  79.0  66 4.08 1.935 18.90  1  1    4    1
#> Toyota Corolla      33.9   4  71.1  65 4.22 1.835 19.90  1  1    4    1
#> Merc 240D           24.4   4 146.7  62 3.69 3.190 20.00  1  0    4    2
#> Honda Civic         30.4   4  75.7  52 4.93 1.615 18.52  1  1    4    2
#> Toyota Corona       21.5   4 120.1  97 3.70 2.465 20.01  1  0    3    1
#> Ferrari Dino        19.7   6 145.0 175 3.62 2.770 15.50  0  1    5    6
#> Merc 280            19.2   6 167.6 123 3.92 3.440 18.30  1  0    4    4
#> Merc 280C           17.8   6 167.6 123 3.92 3.440 18.90  1  0    4    4
#> Mazda RX4           21.0   6 160.0 110 3.90 2.620 16.46  0  1    4    4
#> Mazda RX4 Wag       21.0   6 160.0 110 3.90 2.875 17.02  0  1    4    4
#> Hornet 4 Drive      21.4   6 258.0 110 3.08 3.215 19.44  1  0    3    1
#> Valiant             18.1   6 225.0 105 2.76 3.460 20.22  1  0    3    1
#> Maserati Bora       15.0   8 301.0 335 3.54 3.570 14.60  0  1    5    8
#> Ford Pantera L      15.8   8 351.0 264 4.22 3.170 14.50  0  1    5    4
#> Duster 360          14.3   8 360.0 245 3.21 3.570 15.84  0  0    3    4
#> Camaro Z28          13.3   8 350.0 245 3.73 3.840 15.41  0  0    3    4
#> Chrysler Imperial   14.7   8 440.0 230 3.23 5.345 17.42  0  0    3    4
#> Lincoln Continental 10.4   8 460.0 215 3.00 5.424 17.82  0  0    3    4
#> Cadillac Fleetwood  10.4   8 472.0 205 2.93 5.250 17.98  0  0    3    4
#> Merc 450SE          16.4   8 275.8 180 3.07 4.070 17.40  0  0    3    3
#> Merc 450SL          17.3   8 275.8 180 3.07 3.730 17.60  0  0    3    3
#> Merc 450SLC         15.2   8 275.8 180 3.07 3.780 18.00  0  0    3    3
#> Hornet Sportabout   18.7   8 360.0 175 3.15 3.440 17.02  0  0    3    2
#> Pontiac Firebird    19.2   8 400.0 175 3.08 3.845 17.05  0  0    3    2
#> Dodge Challenger    15.5   8 318.0 150 2.76 3.520 16.87  0  0    3    2
#> AMC Javelin         15.2   8 304.0 150 3.15 3.435 17.30  0  0    3    2

Created on 2022-09-20 with reprex v2.0.2.9000

2 Likes

Thank you very much, this is working that way!

Still I am currently learning about the ... syntax and do not understand everything yet. I am wondering for example how it could work if I compute a more complex function with more statements (like dplyr::select() for example) and how it would work if I need to use the ... with other statements but with different columns.

For more complex applications, I would consult the documentation at https://dplyr.tidyverse.org/.

I found another way to solve this issue with rlang::parse_exprs() and !!!, in case of the user wants an alternative to ...:

func_arrange <- function(tbl ,arrangeby) {
  obj <-
    tbl %>%
    dplyr::arrange(!!!rlang::parse_exprs(arrangeby))
  
  return(obj)
}

## What we should get
test_0 <-
  mtcars %>%
  dplyr::arrange(cyl ,desc(disp) ,hp)

## With a function
test_1 <- func_arrange(
  tbl        = mtcars
  ,arrangeby = c("cyl" ,"desc(disp)" ,"hp")
)

This is working:

## Comparison
comp_0_1 <- dplyr::all_equal(
  test_0 
  ,test_1
  ,ignore_col_order = FALSE
  ,ignore_row_order = FALSE
)

print(comp_0_1)

Every element should be put between quotes though, otherwise it's nor working ("desc(col)" instead of desc("col")):

## Carefull to put also the "desc()" part between quotes
## Not working if not
test_2 <- func_arrange(
  tbl        = mtcars
  ,arrangeby = c("cyl" ,desc("disp") ,"hp")
)

comp_0_2 <- dplyr::all_equal(
  test_0 
  ,test_2
  ,ignore_col_order = FALSE
  ,ignore_row_order = FALSE
)

print(comp_0_2)