UPDATED: Using R and H2O to identify product anomalies during the manufacturing process.

Note.  This is an update to article: - It has some updates but also code optimization from  Yana Kane-Esrig( ) , as she mentioned in a message: The code you posted has two nested for() {} loops. It took a very long time to run. I used just one for() loop. It was much faster   Here her original code: num_rows=nrow(allData) for(i in 1:ncol(allData)) {   temp = allData [,i]   cat( "Processing column:", i, ", number missing:", sum(, "\n" )    temp_mising allData[, i])    temp_values = allData[,i][! temp_mising]    temp_random = sample(temp_values, size = num_rows, replace = TRUE)      temp_imputed = temp   temp_imputed[temp_mising]= temp_random [temp_mising]   # describe(temp_imputed)   allData [,i] = temp_imputed      cat( "Processed column:", i, ", number missing:", sum( [,i
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