# [R] Turn Rank Ordering Into Numerical Scores By Transposing A Data Frame

David L Carlson dcarlson at tamu.edu
Thu Sep 4 20:35:43 CEST 2014

```I think we would need enough of the data you are using to figure out how to modify the process. Can you use dput() to send a small data set that fails to work?

David C

-----Original Message-----
From: Simon Kiss [mailto:sjkiss at gmail.com]
Sent: Thursday, September 4, 2014 1:28 PM
To: David L Carlson
Cc: r-help at r-project.org
Subject: Re: [R] Turn Rank Ordering Into Numerical Scores By Transposing A Data Frame

Hi David and list:
This is working, except at this command
mycast <- dcast(mymelt, row~color, value.var="rank", fill=0)

dcast is using "length" as the default aggregating function. This results in not accurate results. It tells me, for example how many choices were missing values and it tells me if a person selected any given option (value is reported as 1).
When I try to run your reproducible research, it works great, but something with the aggregating function is not working properly with mine.
Any other thoughts?
Simon
On Aug 18, 2014, at 10:44 AM, David L Carlson <dcarlson at tamu.edu> wrote:

> Another approach using reshape2:
>
>> library(reshape2)
>> # Construct data/ add column of row numbers
>> set.seed(42)
>> mydf <- data.frame(t(replicate(100, sample(c("red", "blue",
> +   "green", "yellow", NA), 4))))
>> mydf <- data.frame(rows=1:100, mydf)
>> colnames(mydf) <- c("row", "rank1", "rank2", "rank3", "rank4")
>  row  rank1  rank2  rank3 rank4
> 1   1   <NA> yellow    red  blue
> 2   2 yellow  green   <NA>   red
> 3   3 yellow  green   blue  <NA>
> 4   4   <NA>   blue yellow green
> 5   5   <NA>    red   blue green
> 6   6   <NA>    red  green  blue
>> # Reshape
>> mymelt <- melt(mydf, id.vars=1, measure.vars=2:5,
> +     variable.name="rank", value.name="color")
>> # Convert rank to numeric
>> mymelt\$rank <- as.numeric(mymelt\$rank)
>> mycast <- dcast(mymelt, row~color, value.var="rank", fill=0)
>  row blue green red yellow NA
> 1   1    4     0   3      2  1
> 2   2    0     2   4      1  3
> 3   3    3     2   0      1  4
> 4   4    2     4   0      3  1
> 5   5    3     4   2      0  1
> 6   6    4     3   2      0  1
>
> David C
>
> -----Original Message-----
> From: r-help-bounces at r-project.org [mailto:r-help-bounces at r-project.org] On Behalf Of David L Carlson
> Sent: Sunday, August 17, 2014 6:32 PM
> To: Simon Kiss; r-help at r-project.org
> Subject: Re: [R] Turn Rank Ordering Into Numerical Scores By Transposing A Data Frame
>
> There is probably an easier way to do this, but
>
>> set.seed(42)
>> mydf <- data.frame(t(replicate(100, sample(c("red", "blue",
> +  "green", "yellow", NA), 4))))
>> colnames(mydf) <- c("rank1", "rank2", "rank3", "rank4")
>   rank1  rank2  rank3 rank4
> 1   <NA> yellow    red  blue
> 2 yellow  green   <NA>   red
> 3 yellow  green   blue  <NA>
> 4   <NA>   blue yellow green
> 5   <NA>    red   blue green
> 6   <NA>    red  green  blue
>> lvls <- levels(mydf\$rank1)
>> # convert color factors to numeric
>> for (i in seq_along(mydf)) mydf[,i] <- as.numeric(mydf[,i])
>> # stack the columns
>> mydf2 <- stack(mydf)
>> # convert rank factor to numeric
>> mydf2\$ind <- as.numeric(mydf2\$ind)
>> mydf2 <- data.frame(rows=1:100, mydf2)
>> # Create table
>> mytbl <- xtabs(ind~rows+values, mydf2)
>> # convert to data frame
>> mydf3 <- data.frame(unclass(mytbl))
>> colnames(mydf3) <- lvls
>  blue green red yellow
> 1    4     0   3      2
> 2    0     2   4      1
> 3    3     2   0      1
> 4    2     4   0      3
> 5    3     4   2      0
> 6    4     3   2      0
>
> David C
>
> -----Original Message-----
> From: r-help-bounces at r-project.org [mailto:r-help-bounces at r-project.org] On Behalf Of Simon Kiss
> Sent: Friday, August 15, 2014 3:58 PM
> To: r-help at r-project.org
> Subject: Re: [R] Turn Rank Ordering Into Numerical Scores By Transposing A Data Frame
>
>
> Both the suggestions I got work very well, but what I didn't realize is that NA values would cause serious problems.  Where there is a missing value, using the argument na.last=NA to order just returns the the order of the factor levels, but excludes the missing values, but I have no idea where those occur in the or rather which of those variables were actually missing.
> Have I explained this problem sufficiently?
> I didn't think it would cause such a problem so I didn't include it in the original problem definition.
> Yours, Simon
> On Jul 25, 2014, at 4:58 PM, David L Carlson <dcarlson at tamu.edu> wrote:
>
>> I think this gets what you want. But your data are not reproducible since they are randomly drawn without setting a seed and the two data sets have no relationship to one another.
>>
>>> set.seed(42)
>>> mydf <- data.frame(t(replicate(100, sample(c("red", "blue",
>> + "green", "yellow")))))
>>> colnames(mydf) <- c("rank1", "rank2", "rank3", "rank4")
>>> mydf2 <- data.frame(t(apply(mydf, 1, order)))
>>> colnames(mydf2) <- levels(mydf\$rank1)
>>  rank1  rank2  rank3 rank4
>> 1 yellow  green    red  blue
>> 2  green   blue yellow   red
>> 3  green yellow    red  blue
>> 4 yellow    red  green  blue
>> 5 yellow    red  green  blue
>> 6 yellow    red   blue green
>> blue green red yellow
>> 1    4     2   3      1
>> 2    2     1   4      3
>> 3    4     1   3      2
>> 4    4     3   2      1
>> 5    4     3   2      1
>> 6    3     4   2      1
>>
>> -------------------------------------
>> David L Carlson
>> Department of Anthropology
>> Texas A&M University
>> College Station, TX 77840-4352
>>
>> -----Original Message-----
>> From: r-help-bounces at r-project.org [mailto:r-help-bounces at r-project.org] On Behalf Of Simon Kiss
>> Sent: Friday, July 25, 2014 2:34 PM
>> To: r-help at r-project.org
>> Subject: [R] Turn Rank Ordering Into Numerical Scores By Transposing A Data Frame
>>
>> Hello:
>> I have data that looks like mydf, below.  It is the results of a survey where participants were to put a number of statements (in this case colours) in their order of preference. In this case, the rank number is the variable, and the factor level for each respondent is which colour they assigned to that rank.  I would like to find a way to effectively transpose the data frame so that it looks like mydf2, also below, where the colours the participants were able to choose are the variables and the variable score is what that person ranked that variable.
>>
>> Ultimately what I would like to do is a factor analysis on these items, so I'd like to be able to see if people ranked red and yellow higher together but ranked green and blue together lower, that sort of thing.
>> I have played around with different variations of t(), melt(), ifelse() and if() but can't find a solution.
>> Thank you
>> Simon
>> #Reproducible code
>> mydf<-data.frame(rank1=sample(c('red', 'blue', 'green', 'yellow'), replace=TRUE, size=100), rank2=sample(c('red', 'blue', 'green', 'yellow'), replace=TRUE, size=100), rank3=sample(c('red', 'blue', 'green', 'yellow'), replace=TRUE, size=100), rank4=sample(c('red', 'blue', 'green', 'yellow'), replace=TRUE, size=100))
>>
>> mydf2<-data.frame(red=sample(c(1,2,3,4), replace=TRUE,size=100),blue=sample(c(1,2,3,4), replace=TRUE,size=100),green=sample(c(1,2,3,4), replace=TRUE,size=100) ,yellow=sample(c(1,2,3,4), replace=TRUE,size=100))
>> *********************************
>> Simon J. Kiss, PhD
>> Assistant Professor, Wilfrid Laurier University
>> 73 George Street
>> N3T 2C9
>>
>> ______________________________________________
>> R-help at r-project.org mailing list
>> https://stat.ethz.ch/mailman/listinfo/r-help
>> and provide commented, minimal, self-contained, reproducible code.
>
> *********************************
> Simon J. Kiss, PhD
> Assistant Professor, Wilfrid Laurier University
> 73 George Street
> N3T 2C9
> Cell: +1 905 746 7606
>
> ______________________________________________
> R-help at r-project.org mailing list
> https://stat.ethz.ch/mailman/listinfo/r-help
> and provide commented, minimal, self-contained, reproducible code.
>
> ______________________________________________
> R-help at r-project.org mailing list
> https://stat.ethz.ch/mailman/listinfo/r-help
> and provide commented, minimal, self-contained, reproducible code.

*********************************
Simon J. Kiss, PhD
Assistant Professor, Wilfrid Laurier University
73 George Street