na.omit may do what you want. It will remove any row with missing data. Depending on how your data looks this can work well or be the the equivalent of using a sledge hammer to shell an almond. It might help if we saw a bit of your data.
new_data <- na.omit(Ch2_Raw_Data)
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A handy way to supply some sample data is the dput() function. In the case of a large dataset something like dput(head(mydata, 100)) should supply the data we need. Just do dput(mydata) where mydata is your data. Copy the output and paste it here.