Summarize Missing Data for all Variables in a Data Frame in R

Something like this probably already exists in an R package somewhere out there, but I needed a function to summarize how much missing data I have in each variable of a data frame in R. Pass a data frame to this function and for each variable it'll give you the number of missing values, the total N, and the proportion missing.

propmiss <- function(dataframe) lapply(dataframe,function(x) data.frame(nmiss=sum(is.na(x)), n=length(x), propmiss=sum(is.na(x))/length(x)))

Let's try it out.

#simulate some fake data

fakedata=data.frame(var1=c(1,2,NA,4,NA,6,7,8,9,10),var2=c(11,NA,NA,14,NA,16,17,NA,19,NA))

print(fakedata)
   var1 var2
1     1   11
2     2   NA
3    NA   NA
4     4   14
5    NA   NA
6     6   16
7     7   17
8     8   NA
9     9   19
10   10   NA

# summarize the missing data

propmiss(fakedata)

$var1
  nmiss  n propmiss
1     2 10      0.2

$var2
  nmiss  n propmiss
1     5 10      0.5

Running that function returns a list of data.frame objects. You can access the proportion missing for var1 by running propmiss(fakedata)$var1$propmis.

*Edit 2011-02-23*

Commenter A. Friedman asked for a version of this function that gives you the output as a data frame. The function's a bit uglier because something was being coerced as a list, but this does the trick:

	propmiss <- function(dataframe) {
	m <- sapply(dataframe, function(x) {
	data.frame(
	nmiss=sum(is.na(x)),
	n=length(x),
	propmiss=sum(is.na(x))/length(x)
	)
	})
	d <- data.frame(t(m))
	d <- sapply(d, unlist)
	d <- as.data.frame(d)
	d$variable <- row.names(d)
	row.names(d) <- NULL
	d <- cbind(d[ncol(d)],d[-ncol(d)])
	return(d[order(d$propmiss), ])
	}

view raw propmiss.r hosted with ❤ by GitHub