Question

I am trying to clean up some data that has been incorrectly entered. The question for the variable allows for multiple responses out of five choices, numbered as 1 to 5. The data has been entered in the following manner (this is just an example–there are many more variables and many more observations in the actual data frame):

data
          V1
1    1, 2, 3
2    1, 2, 4
3 2, 3, 4, 5
4    1, 3, 4
5    1, 3, 5
6 2, 3, 4, 5

Here’s some code to recreate that example data:

data = data.frame(V1 = c("1, 2, 3", "1, 2, 4", "2, 3, 4, 5", 
                         "1, 3, 4", "1, 3, 5", "2, 3, 4, 5"))

What I actually need is the data to be treated more… binary–like a set of “yes/no” questions–entered in a data frame that looks more like:

data
    V1.1  V1.2  V1.3  V1.4  V1.5
1      1     1     1    NA    NA
2      1     1    NA     1    NA
3     NA     1     1     1     1
4      1    NA     1     1    NA
5      1    NA     1    NA     1
6     NA     1     1     1     1

The actual variable names don’t matter at the moment–I can easily fix that. Also, it doesn’t matter too much whether the missing elements are “O”, “NA”, or blank–again, that’s something I can fix later.

I’ve tried using the transform function from the reshape package as well as a fed different things with strsplit, but I can’t get either to do what I am looking for.
I’ve also looked at many other related questions on Stackoverflow, but they don’t seem to be quite the same problem.

Answer 1

You just need to write a function and use apply. First some dummy data:

##Make sure you're not using factors
dd = data.frame(V1 = c("1, 2, 3", "1, 2, 4", "2, 3, 4, 5", 
                         "1, 3, 4", "1, 3, 5", "2, 3, 4, 5"), 
                     stringsAsFactors=FALSE)

Next, create a function that takes in a row and transforms as necessary

make_row = function(i, ncol=5) {
  ##Could make the default NA if needed
  m = numeric(ncol)
  v = as.numeric(strsplit(i, ",")[[1]])
  m[v] = 1
  return(m)
}

Then use apply and transpose the result

t(apply(dd, 1, make_row))