How to subset rows containing NA in a chosen column of a data frame

Question

Consider a data frame from a csv file. The chosen data frame has observed values and a column that contains the date( a measurement that has been taken).

Just in case if the record isn't present then it contains the value as NA for missing data.

Col1  Col2 
10   2018/01/01
20   NA
30   2018/05/01

We would like to use the subset command to define a new data frame. This new data frame contains only rows taht have NA values from the column(Col2).

In the example given, only Row 2 will be contained in the new data frame.

The command is as follows:

new_data<-subset(data,data$Col2=="NA") 

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

In the original csv file, the values NA are exchanged with NULL.

The same command produces the desired result:

new_data<-subset(data,data$Col2=="NULL").

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

Answer 1

I would suggest you, to never to use =='NA' to test for missing values.

Use is.na() instead.

This should serve your purpose:

new_data <- data[rowSums(is.na(data)) > 0,]

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

new_data <- data[is.na(data$Col),]

OR In case you have NA character values, first run

data[data=='NA'] <- NA

to replace them with missing values.

Answer 2

You can give this a try.

subset(dataframe, is.na(dataframe$col2)) .

This will return you a subset of a data frame with col2 having NA.