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?
CodePudding user response：
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