library(fscaret)
# Make sample matrix
testData <- matrix(data=rep(1:5),ncol=10,nrow=15)
# Replace random values with NA's
n <- 15
replace <- TRUE
set.seed(1)
rand.sample <- sample(length(testData), n, replace=replace)
testData[rand.sample] <- NA
# Print out input matrix
testData
# Record cols with missing values
missing.colsTestMatrix <- which(colSums(is.na(testData))>0)
for(i in 1:length(missing.colsTestMatrix)){
rowToReplace <- missing.colsTestMatrix[i]
testData[,rowToReplace] <- impute.mean(testData[,rowToReplace])
}
# Print out matrix with replaced NA's by column mean
testData
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