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?
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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