How do I add random `NA`s into a data frame

Question

I created a data frame with random values

n <- 50
df <- data.frame(id = seq (1:n),
age = sample(c(20:90), n, rep = TRUE), 
sex = sample(c("m", "f"), n, rep = TRUE, prob = c(0.55, 0.45))
)

and would like to introduce a few NA values to simulate real world data. I am trying to use apply but cannot get there. The line

apply(subset(df,select=-id), 2, function(x) {x[sample(c(1:n),floor(n/10))]})

will retrieve random values alright, but

apply(subset(df,select=-id), 2, function(x) {x[sample(c(1:n),floor(n/10))]<-NA}) 

will not set them to NA. Have tried with and within, too.

Brute force works:

for (i in (1:floor(n/10))) {
  df[sample(c(1:n), 1), sample(c(2:ncol(df)), 1)] <- NA
  }

But I'd prefer to use the apply family.

Answer 1

Simply pass your dataframe into the following function. The only arguments are the frame you want to add NAs to and the number of features (columns) you want to have with NAs.

add_random_nas_to_frame <- function(frame, num_features) {
   col_order <- names(frame) 
   rand_cols <- sample(ncol(frame), num_features)
   left_overs <- which(!names(frame) %in% names(frame[,rand_cols]))
   other_frame <- frame[,left_overs]
   nas_added <- data.frame(lapply(frame[,rand_cols], function(x) x[sample(c(TRUE, NA), prob = c(sample(100, 1)/100, 0.15), size = length(x), replace = TRUE)]))
   final_frame <- cbind(other_frame, nas_added)
   final_frame <- final_frame[,col_order]
   return(final_frame)
}

For example, using the full dataset from banking dataset from UCI:

https://archive.ics.uci.edu/ml/datasets/Bank+Marketing

bank <- read.table(file='path_to_data', sep =";", stringsAsFactors = F, header = T)

And viewing the original missing data:

We can see there is no missing data in the original frame.

Now applying our function:

bank_nas <- add_random_nas_to_frame(bank, 5)

Answer 2

I think you need to return the x value from the function:

apply(subset(df,select=-id), 2, function(x) 
     {x[sample(c(1:n),floor(n/10))]<-NA; x}) 

but you also need to assign this back to the relevant subset of the data frame (and subset(...) <- ... doesn't work)

idCol <- names(df)=="id"
df[,!idCol] <- apply(df[,!idCol], 2, function(x) 
     {x[sample(1:n,floor(n/10))] <- NA; x})

(if you have only a single non-ID column you'll need df[,!idCol,drop=FALSE])

Answer 3

Return x within your function:

> df <- apply (df, 2, function(x) {x[sample( c(1:n), floor(n/10))] <- NA; x} )
> tail(df)
      id   age  sex
[45,] "45" "41" NA 
[46,] "46" NA   "f"
[47,] "47" "38" "f"
[48,] "48" "32" "f"
[49,] "49" "53" NA 
[50,] "50" "74" "f"

Answer 4

Apply returns an array, thereby converting all columns to the same type. You could use this instead:

df[,-1] <- do.call(cbind.data.frame, 
                   lapply(df[,-1], function(x) {
                     x[sample(c(1:n),floor(n/10))]<-NA
                     x
                   })
                   )

Or use a for loop:

for (i in seq_along(df[,-1])+1) {
  is.na(df[sample(seq_len(n), floor(n/10)),i]) <- TRUE
}

Answer 5

here is another simple way to go at it

your data frame

df<-mtcars

Number of missing required

nbr_missing<-20

sample row and column indices

y<-data.frame(row=sample(nrow(df),size=nbr_missing,replace = T),
          col=sample(ncol(df),size = nbr_missing,replace = T))

remove duplication

y<-y[!duplicated(y),]

use matrix indexing

df[as.matrix(y)]<-NA