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Make a custom function of an dplyr procedure

Time:12-18

I would like to make a custom function of this modified dplyr procedure:

randomly replacing percentage of values per group with NA in R dataframe

library(dplyr)
mtcars %>%
    mutate(mpg =  replace(mpg, sample(row_number(),  
           size = ceiling(0.3 * n()), replace = FALSE), NA))

The arguments should be:

  • df = dataframe
  • x = column
  • y = double number (here 0.3)

My approach so far:

my_func <- function(df,x,y){
  df %>%
  mutate(x =  replace({{x}}, sample(row_number(),  
                                    size = ceiling(y * n()), replace = FALSE), NA))
}

When applying this function:

my_func(mtcars, mtcars$mpg, 0.3)

#gives:

                     mpg cyl  disp  hp drat    wt  qsec vs am gear carb    x
Mazda RX4           21.0   6 160.0 110 3.90 2.620 16.46  0  1    4    4 21.0
Mazda RX4 Wag       21.0   6 160.0 110 3.90 2.875 17.02  0  1    4    4   NA
Datsun 710          22.8   4 108.0  93 3.85 2.320 18.61  1  1    4    1 22.8
Hornet 4 Drive      21.4   6 258.0 110 3.08 3.215 19.44  1  0    3    1 21.4
Hornet Sportabout   18.7   8 360.0 175 3.15 3.440 17.02  0  0    3    2   NA
Valiant             18.1   6 225.0 105 2.76 3.460 20.22  1  0    3    1 18.1
#....etc..

My question:

  • I want to change mpg column by adding NA's there, not adding a new column x
  • Putting the first x = in {{x}} = throws an error:
Error: unexpected '=' in:
"  df %>%
  mutate({{x}} ="
>                                     size = ceiling(y * n()), replace = FALSE), NA))
Error: unexpected ',' in "                                    size = ceiling(y * n()),"
> }
Error: unexpected '}' in "}"
> 

CodePudding user response:

This works; does it solve your problem?

library(dplyr)
#> 
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:stats':
#> 
#>     filter, lag
#> The following objects are masked from 'package:base':
#> 
#>     intersect, setdiff, setequal, union

my_func <- function(df,x,y){
  df %>%
    mutate({{x}} :=  replace({{x}}, sample(row_number(),  
                                      size = ceiling(y * n()), replace = FALSE), NA))
}

my_func(mtcars, mpg, 0.3)
#>                      mpg cyl  disp  hp drat    wt  qsec vs am gear carb
#> Mazda RX4             NA   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
#> Datsun 710            NA   4 108.0  93 3.85 2.320 18.61  1  1    4    1
#> Hornet 4 Drive      21.4   6 258.0 110 3.08 3.215 19.44  1  0    3    1
#> Hornet Sportabout   18.7   8 360.0 175 3.15 3.440 17.02  0  0    3    2
#> Valiant             18.1   6 225.0 105 2.76 3.460 20.22  1  0    3    1
#> Duster 360          14.3   8 360.0 245 3.21 3.570 15.84  0  0    3    4
#> Merc 240D           24.4   4 146.7  62 3.69 3.190 20.00  1  0    4    2
#> Merc 230              NA   4 140.8  95 3.92 3.150 22.90  1  0    4    2
#> 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
#> Merc 450SE            NA   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
#> Cadillac Fleetwood    NA   8 472.0 205 2.93 5.250 17.98  0  0    3    4
#> Lincoln Continental   NA   8 460.0 215 3.00 5.424 17.82  0  0    3    4
#> Chrysler Imperial     NA   8 440.0 230 3.23 5.345 17.42  0  0    3    4
#> Fiat 128            32.4   4  78.7  66 4.08 2.200 19.47  1  1    4    1
#> Honda Civic         30.4   4  75.7  52 4.93 1.615 18.52  1  1    4    2
#> Toyota Corolla      33.9   4  71.1  65 4.22 1.835 19.90  1  1    4    1
#> Toyota Corona         NA   4 120.1  97 3.70 2.465 20.01  1  0    3    1
#> 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
#> Camaro Z28          13.3   8 350.0 245 3.73 3.840 15.41  0  0    3    4
#> Pontiac Firebird    19.2   8 400.0 175 3.08 3.845 17.05  0  0    3    2
#> Fiat X1-9           27.3   4  79.0  66 4.08 1.935 18.90  1  1    4    1
#> Porsche 914-2       26.0   4 120.3  91 4.43 2.140 16.70  0  1    5    2
#> Lotus Europa        30.4   4  95.1 113 3.77 1.513 16.90  1  1    5    2
#> Ford Pantera L        NA   8 351.0 264 4.22 3.170 14.50  0  1    5    4
#> Ferrari Dino        19.7   6 145.0 175 3.62 2.770 15.50  0  1    5    6
#> Maserati Bora         NA   8 301.0 335 3.54 3.570 14.60  0  1    5    8
#> Volvo 142E          21.4   4 121.0 109 4.11 2.780 18.60  1  1    4    2

Created on 2021-12-18 by the reprex package (v2.0.1)

From https://adv-r.hadley.nz/quasiquotation.html?q=:=#tidy-dots :

:= is like a vestigial organ: it’s recognised by R’s parser, but it doesn’t have any code associated with it. It looks like an = but allows expressions on either side, making it a more flexible alternative to =. It is used in data.table for similar reasons.

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