unique values of rows

Question

I often encounter data that looks like this:

#create dummy data frame
data <- as.data.frame(diag(4))
data[data==0] <- NA
data[2,2] <- NA
data

#V1 V2 V3 V4
#1  1 NA NA NA
#2 NA NA NA NA
#3 NA NA  1 NA
#4 NA NA NA  1

Rows represent participants and columns V1 through V4 represent the condition that the participant is in (e.g., a 1 under V1 means this participant is in condition 1, a 1 under V4 means this participant is in condition 4). Sidenote: The data are not symmetric, so there are a lot more participants spread over the 4 conditions.

What I want is a vector with the condition for each participant:

1 NA  3  4

I wrote the following bit, but was wondering if there was a more efficient way (i.e., using fewer lines of code)?

#replace entries with condition numbers 
cond <- data + matrix(rep(0:3, 4), 4, byrow=TRUE) #add 0 to 1 for condition 1...

#get all unique elements (ignore NAs)
cond <- apply(cond, 1, function(x)unique(x[!is.na(x)]))

#because I ignored NAs just now, cond[2,2] is numeric(0)
#assign NA to all values that are numeric(0)
cond[sapply(cond, function(x) length(x)==0)] <- NA

cond <- unlist(cond)
cond
#[1]  1 NA  3  4

Answer 1

Less clever and efficient than other solutions, but perhaps more readable?

apply(data,
      MARGIN = 1, 
      FUN = function(x) { 
          if(all(is.na(x))) return(NA)
          return(which(!is.na(x)))
      }
)
# [1]  1 NA  3  4

Answer 2

We can use max.col with ties.method='first' on the logical matrix of non-NA elements in 'data'. To make the rows that have only NA elements as NA, we multiply the max.col index with rowSums of logical matrix with 0 non-NA rows converted to NA (NA^).

 max.col(!is.na(data), 'first')* NA^!rowSums(!is.na(data))
 #[1]  1 NA  3  4

Or another option is pmax. We multiply the column index with the data so that the non-NA elements get replaced by the index. Then, use pmax with na.rm=TRUE and get the max value per each row.

 do.call(pmax, c(col(data)*data, na.rm=TRUE))
 #[1]  1 NA  3  4

Answer 3

Using the reshape2 package:

> data$ID <- rownames(data)
> melt(data, 'ID', na.rm=TRUE)
   ID variable value
1   1       V1     1
11  3       V3     1
16  4       V4     1

IMHO, this has the advantage of keeping the ID variable along with the treatment factor; also if you have a response measurement it comes along too in the value column.

EDIT:

If you want to include the subject under no conditions, you can reconstruct that indicator variable explicitly:

data$VNA <- ifelse(apply(is.na(data), 1, all), 1, NA)