On Apr 3, 2011, at 1:44 PM, Tyler Rinker wrote:
Quick question,
I tried to find a function in available packages to find NA's for an
entire data set (or single variables) and report the row of missing
values (NA's for each column). I searched the typical routes
through the blogs and the help manuals for 15 minutes. Rather than
spend any more time searching I created my own function to do this
(probably in less time than it would have taken me to find the
function).
Now I still have the same question: Is this function (NAhunter I
call it) already in existence? If so please direct me (because I'm
sure they've written better code more efficiently). I highly doubt
I'm this first person to want to find all the missing values in a
data set so I assume there is a function for it but I just didn't
spend enough time looking. If there is no existing function (big if
here), is this something people feel is worthwhile for me to put
into a package of some sort?
I'm not sure that it would have occurred to people to include it in a
package. Consider:
getNa <- function(dfrm) lapply(dfrm, function(x) which(is.na(x) ) )
> cities
long lat city pop
1 -58.38194 -34.59972 Buenos Aires NA
2 14.25000 40.83333 <NA> NA
> getNa(cities)
$long
integer(0)
$lat
integer(0)
$city
[1] 2
$pop
[1] 1 2
There are several packages with functions by the name `describe` that
do most or all of rest of what you have proposed. I happen to use
Harrell's Hmisc but the other versions should also be reviewed if you
want to avoid re-inventing the wheel.
--
David.
Tyler
Here's the code:
NAhunter<-function(dataset)
{
find.NA<-function(variable)
{
if(is.numeric(variable)){
n<-length(variable)
mean<-mean(variable, na.rm=T)
median<-median(variable, na.rm=T)
sd<-sd(variable, na.rm=T)
NAs<-is.na(variable)
total.NA<-sum(NAs)
percent.missing<-total.NA/n
descriptives<-data.frame(n,mean,median,sd,total.NA,percent.missing)
rownames(descriptives)<-c(" ")
Case.Number<-1:n
Missing.Values<-ifelse(NAs>0,"Missing Value"," ")
missing.value<-data.frame(Case.Number,Missing.Values)
missing.values<-missing.value[ which(Missing.Values=='Missing
Value'),]
list("NUMERIC DATA","DESCRIPTIVES"=t(descriptives),"CASE # OF
MISSING VALUES"=missing.values[,1])
}
else{
n<-length(variable)
NAs<-is.na(variable)
total.NA<-sum(NAs)
percent.missing<-total.NA/n
descriptives<-data.frame(n,total.NA,percent.missing)
rownames(descriptives)<-c(" ")
Case.Number<-1:n
Missing.Values<-ifelse(NAs>0,"Missing Value"," ")
missing.value<-data.frame(Case.Number,Missing.Values)
missing.values<-missing.value[ which(Missing.Values=='Missing
Value'),]
list("CATEGORICAL DATA","DESCRIPTIVES"=t(descriptives),"CASE # OF
MISSING VALUES"=missing.values[,1])
}
}
dataset<-data.frame(dataset)
options(scipen=100)
options(digits=2)
lapply(dataset,find.NA)
}
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David Winsemius, MD
West Hartford, CT
______________________________________________
R-help@r-project.org mailing list
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PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
