What strategies did you use to eliminate NA
Substituting with feature average
Step 1 – Get the row mean
> s_data
s_1 s_2 s_3
1 103 102 113
2 101 108 122
3 98 106 88
4 101 102 106
5 98 NA 101
6 108 103 95
7 100 99 106
8 94 80 107
9 97 104 99
10 93 96 97
> mean(s_data$s_2,na.rm = TRUE)
[1] 100
Step 2 – Replace the NA with row mean
> s_data[5,"s_2"] = mean(s_data$s_2,na.rm = TRUE)
> s_data
s_1 s_2 s_3
1 103 102 113
2 101 108 122
3 98 106 88
4 101 102 106
5 98 100 101
6 108 103 95
7 100 99 106
8 94 80 107
9 97 104 99
10 93 96 97
If you wanted to do this pro grammatically ( for any number of rows,columns ), do this
replaceNA = function(data) {
# Loop through each column
for ( var in 1:ncol(data)){
# Get the mean odf the column.
mean = mean ( data[,var], na.rm = TRUE)
# Replace the NA with the mean
# is.na ( data[,var]) - Gets the T/F vector where value is NA
data[ is.na ( data[,var]),var ] = mean
}
# return the dataset
data
}
> replaceNA(s_data)
s_1 s_2 s_3
1 103 102 113
2 101 108 122
3 98 106 88
4 101 102 106
5 98 100 101
6 108 103 95
7 100 99 106
8 94 80 107
9 97 104 99
10 93 96 97
Eliminate the entire observation
If the number of observations with NAs is quite small, the easy way is to get rid of the entire observation.
For example, getting rid of the 6th row above with NA is very easy. Just use the function omit.na()
> s_data
s_1 s_2 s_3
1 103 102 113
2 101 108 122
3 98 106 88
4 101 102 106
5 98 NA 101
6 108 103 95
7 100 99 106
8 94 80 107
9 97 104 99
10 93 96 97
> na.omit(s_data)
s_1 s_2 s_3
1 103 102 113
2 101 108 122
3 98 106 88
4 101 102 106
6 108 103 95
7 100 99 106
8 94 80 107
9 97 104 99
10 93 96 97
As you can see, the 5th row is gone.