Subset columns based on certain columns missing value - r

Question

My dataset is pretty big. I have about 2,000 variables and 1,000 observations.
I want to run a model for each variable using other variables.
To do so, I need to drop variables which have missing values where the dependent variable doesn't have.
I meant that for instance, for variable "A" I need to drop variable C and D because those have missing values where variable A doesn't have. for variable "C" I can keep variable "D".
data <- read.table(text="
A B C D
1 3 9 4
2 1 3 4
NA NA 3 5
4 2 NA NA
2 5 4 3
1 1 1 2",header=T,sep="")
I think I need to make a loop to go through each variable.

Answer 1

I think this gets what you need:
for (i in 1:ncol(data)) {
# filter out rows with NA's in on column 'i'
# which is the column we currently care about
tmp <- data[!is.na(data[,i]),]
# now column 'i' has no NA values, so remove other columns
# that have NAs in them from the data frame
tmp <- tmp[sapply(tmp, function(x) !any(is.na(x)))]
#run your model on 'tmp'
}
For each iteration of i, the tmp data frame looks like:
'data.frame': 5 obs. of 2 variables:
$ A: int 1 2 4 2 1
$ B: int 3 1 2 5 1
'data.frame': 5 obs. of 2 variables:
$ A: int 1 2 4 2 1
$ B: int 3 1 2 5 1
'data.frame': 4 obs. of 2 variables:
$ C: int 3 3 4 1
$ D: int 4 5 3 2
'data.frame': 5 obs. of 1 variable:
$ D: int 4 4 5 3 2

Answer 2

I'll provide a way to get the usable vadiables for each column you choose:
getVars <- function(data, col){
tmp<-!sapply(data[!is.na(data[[col]]),], function(x) { any(is.na(x)) })
names(data)[tmp & names(data) != col]
}
PS: I'm on my phone so I didn't test the above nor had the chance for a good code styling.
EDIT: Styling fixed!