New variable for missing values across multiple columns

Question

I'm trying to see if R has a command similar to Stata. In Stata, the !mi(a, b, c,...) command creates a new variable and indicates a 1/0 if the indicated variable(s) have no missing data. 1 = no missing data across variables x, 0 = missing data in one of the variables x.

I'm looking for a simple code because sometimes I have about 15-20 variables (mainly to mark listwise deletion cases). It takes a little more work but I specify the column names instead of using the : marker. The options I've found creates a new dataframe (na.omit), but I want to retain all the cases.

I know that ifelse can accomplish this using:

df$test <- ifelse(!is.na(df$ID) & !is.na(df$STATUS), 1,0)

I like to know if there's another way with less code where I don't need to write "!is.na(df$ )" over and over. Maybe a $global code (similar to Stata)?

G5W · Accepted Answer · 2019-06-07 00:46:25Z

3

You should be able to do this using complete.cases

df$test <- as.numeric(complete.cases(df))

answered Jun 7, 2019 at 0:46

G5W

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Marius Over a year ago

And you can specify certain columns by picking them out of df: as.numeric(complete.cases(df[, c("a", "b", "d")])

Andrew · Accepted Answer · 2019-06-07 00:58:51Z

0

You could also use rowSums:

df$test <- as.numeric(rowSums(is.na(df)) == 0)

answered Jun 7, 2019 at 0:58

Andrew

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