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

[Alpydev]

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!

[scottyd22]

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

[Alpydev]

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.

[scottyd22]

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

[Alpydev]

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)