using the bitops library.

> 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"))

> );

>

>

>

> --

> View this message in context:

>

http://r.789695.n4.nabble.com/datastructure-for-multi-choice-factors-tp3650940p3650940.html> Sent from the R help mailing list archive at Nabble.com.

>

> ______________________________________________

>

[hidden email] mailing list

>

https://stat.ethz.ch/mailman/listinfo/r-help> PLEASE do read the posting guide

>

http://www.R-project.org/posting-guide.html> and provide commented, minimal, self-contained, reproducible code.

>