I had a similar survey, and ended up stuffing everything into one field
using the bitops library.
On Thu, Jul 7, 2011 at 4:18 AM, jeroen00ms <jeroen.o...@stat.ucla.edu>wrote:
> I am working on a system to visualize survey responses. Survey responses
> typically include factors, numeric, timestamps, textfields and therefore
> fit
> perfectly nice in dataframes, making it easy to visualize using standard R
> functions.
>
> However I am currently working on a survey that also include questions in
> which the respondent can check more than one answer on a single multichoice
> item. I.e. this represents a factor for which every row has multiple
> responses. I am looking for a way to put this into a dataframe together
> with
> the other questions of the survey.
>
> I considered three workarounds, but both are problematic:
>
> - Column-wise expanding: convert a single multi-choice item into N binary
> column factors for every possible response (level) with 1/0 values
> representing if the answer was checked or not. Problem with this is that
> you
> lose the information that these N columns are in fact one question and it
> becomes very hard to vizualise this single question.
>
> - Row wise expanding: convert a single response into N rows, one for every
> response. Problem with this is that if the factor is part of the dataframe,
> also all of the other items have to be duplicated, leading to artificial
> results.
>
> I was wondering if there is a more natural datastructure to put a
> multi-choice item into a dataframe? Some code for illustration:
>
> people <- list(
> name=c("John", "Mary", "Jennifer", "Neil"),
> gender=factor(c("M","F","F","M")),
> age=c(34,23,40,30),
> residence=sapply(list("US", c("US", "CA"), "MX", c("MX", "US", "CA")),
> factor, levels=c("US", "CA", "MX"))
> );
>
>
>
> --
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>
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