Delete columns/rows with more than x% missing

Question

I want to delete all columns or rows with more than 50% NAs in a data frame.

This is my solution:

# delete columns with more than 50% missings miss <- c() for(i in 1:ncol(data)) {   if(length(which(is.na(data[,i]))) > 0.5*nrow(data)) miss <- append(miss,i)  } data2 <- data[,-miss]   # delete rows with more than 50% percent missing miss2 <- c() for(i in 1:nrow(data)) {   if(length(which(is.na(data[i,]))) > 0.5*ncol(data)) miss2 <- append(miss2,i)  } data <- data[-miss,] 

but I'm looking for a nicer/faster solution.

I would also appreciate a dplyr solution

Answer 1

To remove columns with some amount of NA, you can use colMeans(is.na(...))

## Some sample data set.seed(0) dat <- matrix(1:100, 10, 10) dat[sample(1:100, 50)] <- NA dat <- data.frame(dat)  ## Remove columns with more than 50% NA dat[, which(colMeans(!is.na(dat)) > 0.5)]  ## Remove rows with more than 50% NA dat[which(rowMeans(!is.na(dat)) > 0.5), ]  ## Remove columns and rows with more than 50% NA dat[which(rowMeans(!is.na(dat)) > 0.5), which(colMeans(!is.na(dat)) > 0.5)]