(This article was first published on

Two new quick tips from 'almost regular' contributor Jason:**One R Tip A Day**, and kindly contributed to R-bloggers)Handling missing values in R can be tricky. Let's say you have a table

with missing values you'd like to read from disk. Reading in the table

with,

read.table( fileName )

might fail. If your table is properly formatted, then R can determine

what's a missing value by using the "sep" option in read.table:

read.table( fileName, sep="\t" )

This tells R that all my columns will be separated by TABS regardless of

whether there's data there or not. So, make sure that your file on disk

really is fully TAB separated: if there is a missing data point you must

have a TAB to tell R that this datum is missing and to move to the next

field for processing.

Lastly, don't forget the "header=T" option if you have a header line in

your file.

Here's the 2nd tip:

Some algorithms in R don't support missing (NA) values. If you have a

data.frame with missing values and quickly want the ROWS with any

missing data to be removed then try:

myData[rowSums(is.na(myData))==0, ]

To find NA values in your data you have to use the "is.na" function.

To

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