R - Create missingness in DataFrame for testing - r

Question

I need to test some imputation evaluation software I'm creating and am struggling to get benchmark datasets.
Does anyone know of a way to delete a certain amount of data from a dataframe.
As an example of what I need:
You have a dataset and you want a random 20% of the rows to have a random amounts of variables in that row removed (ie. NA)
Or: Something that can turn
> head(mtcars,n=10)
mpg cyl disp hp drat wt qsec vs am gear carb
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
Datsun 710 22.8 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 22.8 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
Into:
> head(mtcars,n=10)
mpg cyl disp hp drat wt qsec vs am gear carb
Mazda RX4 NA 6 160.0 NA 3.90 2.620 NA 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 22.8 NA 108.0 93 NA NA 18.61 NA 1 NA 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 22.8 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
I have tried a couple of methods that manipulate the columns but these have some fundamental flaws in them which render them useless.
This is my first every question on here, if I have missed out anything or done something wrong, please do let me know.
All the best

Answer 1

This should do it:
df_new <- as.data.frame(apply(mtcars,2,function(x){
x[sample(1:length(x),round(length(x)*0.2))] <- NA
return(x)
}))
Apply() goes through the columns and in each column sample() is used to randomly select 20% of the values to be set to NA.
New answer after comment:
This randomly adds NA in 10% of all rows.
df <- mtcars
random_rows <- sample(1:nrow(df),round(nrow(df)*0.2))
for(i_row in random_rows){
df[i_row,sample(1:ncol(df),sample(1:ncol(df),1))] <- NA
}