3

I have created this custom function with the help of @jared_mamrot Make a custom function of an dplyr procedure

It basically takes a dataframe, a column and a number as argument and replaces in that column a defined percent (y) of values with NA's:

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

Now I would like to apply this function to multiple columns using mutate(across...

My try so far:

mtcars %>% 
  mutate(across(1:3, ~my_func(mtcars, ., 0.3)))

This does essentially what the function should do but the whole dataframe is repeated x times.

What I want is:

The function should only be applied to column 1:3.

Adding the .names = argument does not solve the issue.

So I guess I have to modify the function?

TarJae
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1 Answers1

8

The (quasi-)function(s) in across(..., ***) iterate over vectors, so they never see the whole frame. I suggest you modified your function to deal with vectors, not frames.

my_func2 <- function(x, prop) replace(x, sample(length(x), size = ceiling(prop * length(x)), replace = FALSE), NA)
set.seed(42)
out <- mtcars %>%
  mutate(across(1:3, ~ my_func2(., 0.3)))
out
#                      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    NA 110 3.90 2.875 17.02  0  1    4    4
# Datsun 710          22.8  NA    NA  93 3.85 2.320 18.61  1  1    4    1
# Hornet 4 Drive        NA  NA    NA 110 3.08 3.215 19.44  1  0    3    1
# Hornet Sportabout     NA  NA    NA 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            NA   8 360.0 245 3.21 3.570 15.84  0  0    3    4
# Merc 240D           24.4   4    NA  62 3.69 3.190 20.00  1  0    4    2
# Merc 230            22.8  NA 140.8  95 3.92 3.150 22.90  1  0    4    2
# Merc 280              NA   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          16.4   8 275.8 180 3.07 4.070 17.40  0  0    3    3
# Merc 450SL          17.3   8    NA 180 3.07 3.730 17.60  0  0    3    3
# Merc 450SLC         15.2  NA 275.8 180 3.07 3.780 18.00  0  0    3    3
# Cadillac Fleetwood    NA  NA 472.0 205 2.93 5.250 17.98  0  0    3    4
# Lincoln Continental 10.4   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              NA  NA  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  NA    NA  65 4.22 1.835 19.90  1  1    4    1
# Toyota Corona       21.5   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      NA  NA    NA 175 3.08 3.845 17.05  0  0    3    2
# Fiat X1-9           27.3  NA  79.0  66 4.08 1.935 18.90  1  1    4    1
# Porsche 914-2       26.0   4    NA  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      15.8   8    NA 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       15.0   8 301.0 335 3.54 3.570 14.60  0  1    5    8
# Volvo 142E            NA   4 121.0 109 4.11 2.780 18.60  1  1    4    2

sapply(out, function(z) sum(is.na(z)) / length(z))
#    mpg    cyl   disp     hp   drat     wt   qsec     vs     am   gear   carb 
# 0.3125 0.3125 0.3125 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
r2evans
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