"apply" question

## Number of NAs in columns 6-10.
colSums(is.na(data[6:10]))

## Number of NAs in each row of columns 6-10.
rowSums(is.na(data[6:10]))

## Sums of rows 1-5 omitting corresponding NAs in cols 6-10.
rowSums(data[,1:5] * !is.na(data[,6:10]))

From: Christoph Scherber

Dear R users,

I??ve got a simple question but somehow I can??t find the solution:

I have a data frame with columns 1-5 containing one set of integer 
values, and columns 6-10 containing another set of integer values. 
Columns 6-10 contain NA??s at some places.

I now want to calculate
(1) the number of values in each row of columns 6-10 that were NA??s
(2) the sum of all values on columns 1-5 for which there were 
no missing 
values in the corresponding cells of columns 6-10.


Example: (let??s call the data frame "data")

Col1   Col2   Col3   Col4   Col5   Col6   Col7   Col8   Col9   Col10
1      2      5      2      3      NA      5      NA    1      4
3      1      4      5      2      6      NA      4     NA     1

The result would then be (for the first row)
(1) "There were 2 NA??s in columns 6-10."
(2) The mean of Columns 1-5 was 2+2+3=7" (because there were 
NA??s in the 
1st and 3rd position in rows 6-10)

So far, I know how to calculate the rowSums for the data.frame, but I 
don??t know how to condition these on the values of columns 6-10

rowSums(data[,1:5]) #that??s straightforward
apply(data[,6:19],1,function(x)sum(is.na(x))) #this also works fine

But I don??t know how to select just the desired values of columns 1-5 
(as described above)


Can anyone help me? Thanks a lot in advance!

Best regards
Christoph