you could try something like this:
dat <- rbind(c(1, 2, 5, 2, 3, NA, 5, NA, 1, 4),
c(3, 1, 4, 5, 2, 6, NA, 4, NA, 1))
##########
# (1)
rowSums(is.na(dat[, 6:10]))## (2) dat. <- dat[, 1:5] dat.[is.na(dat[, 6:10])] <- NA rowSums(dat., na.rm=TRUE) rowMeans(dat., na.rm=TRUE)
I hope it helps.
Best, Dimitris
---- Dimitris Rizopoulos Ph.D. Student Biostatistical Centre School of Public Health Catholic University of Leuven
Address: Kapucijnenvoer 35, Leuven, Belgium Tel: +32/16/336899 Fax: +32/16/337015 Web: http://www.med.kuleuven.ac.be/biostat/ http://www.student.kuleuven.ac.be/~m0390867/dimitris.htm
----- Original Message ----- From: "Christoph Scherber" <[EMAIL PROTECTED]>
To: <r-help@stat.math.ethz.ch>
Sent: Monday, May 02, 2005 4:52 PM
Subject: [R] "apply" question
Dear R users,
Iīve got a simple question but somehow I canīt 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 NAīs at some places.
I now want to calculate
(1) the number of values in each row of columns 6-10 that were NAīs
(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: (letīs 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 NAīs in columns 6-10."
(2) The mean of Columns 1-5 was 2+2+3=7" (because there were NAīs 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 donīt know how to condition these on the values of columns 6-10
rowSums(data[,1:5]) #thatīs straightforward apply(data[,6:19],1,function(x)sum(is.na(x))) #this also works fine
But I donīt 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
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