Subset of rows containing NA (missing) values in a chosen column of a data frame

We have a data frame from a CSV file. The data frame DF has columns that contain observed values and a column (VaR2) that contains the date at which a measurement has been taken. If the date was not recorded, the CSV file contains the value NA, for missing data.

Var1  Var2 
10   2010/01/01
20   NA
30   2010/03/01

We would like to use the subset command to define a new data frame new_DF such that it only contains rows that have an NA' value from the column (VaR2). In the example given, only Row 2 will be contained in the new DF.

The command

new_DF<-subset(DF,DF$Var2=="NA") 

does not work, the resulting data frame has no row entries.

If in the original CSV file the Value NA are exchanged with NULL, the same command produces the desired result: new_DF<-subset(DF,DF$Var2=="NULL").

How can I get this method working, if for the character string the value NA is provided in the original CSV file?

Never use =='NA' to test for missing values. Use is.na() instead. This should do it:

new_DF <- DF[rowSums(is.na(DF)) > 0,]

or in case you want to check a particular column, you can also use

new_DF <- DF[is.na(DF$Var),]

In case you have NA character values, first run

Df[Df=='NA'] <- NA

to replace them with missing values.

complete.cases gives TRUE when all values in a row are not NA

DF[!complete.cases(DF), ]