The big difference between the data sets is that many of your rows (16) have
all missing values. None of mine do. If you run my data and yours, you will see
that decast throws a warning "Aggregation function missing: defaulting to
length" with your data but not with mine. As a result, instead of using the
value of rank, dcast uses length(rank) which is always 1 except when there are
multiple missing values when it is the number of missing values. This problem
will occur whenever there is more than one missing value on a row. The simplest
way to handle this is to create a function that returns the first value of a
vector and use that with the fun.aggregate= argument:
> first <- function(x) {x[1]}
> d4<- dcast(d3, row~color, fun.aggregate=first, value.var="rank", fill=0)
The only drawback is that this will not warn you if a category was ranked twice
except that the NA column will be zero and one of the other columns will be
zero. The number of missing values is the number of zeroes in your category
columns (not including row or NA) and the value in NA is the lowest rank that
was missing.
David C
-----Original Message-----
From: Simon Kiss [mailto:sjk...@gmail.com]
Sent: Friday, September 5, 2014 10:22 AM
To: David L Carlson
Cc: r-help@r-project.org
Subject: Re: [R] Turn Rank Ordering Into Numerical Scores By Transposing A Data
Frame
HI, of course.
The a mini-version of my data-set is below, stored in d2. Then the code I'm
working follows.
library(reshape2)
#Create d2
structure(list(row = 1:50, rank1 = structure(c(3L, 3L, 3L, 4L,
3L, 3L, NA, NA, 3L, NA, 3L, 3L, 1L, NA, 2L, NA, 3L, NA, 2L, 1L,
1L, 3L, NA, 6L, NA, 1L, NA, 3L, 1L, NA, 1L, NA, NA, 6L, 3L, NA,
1L, 3L, 3L, 4L, 1L, NA, 3L, 3L, 3L, NA, 3L, 3L, NA, 1L), .Label =
c("accessible",
"alternatives", "information", "responsive", "social", "technical",
"trade"), class = "factor"), rank2 = structure(c(6L, 1L, 1L,
2L, 4L, 6L, NA, NA, 6L, NA, 6L, 4L, 2L, NA, 4L, NA, 6L, NA, 1L,
6L, 3L, 2L, NA, 3L, NA, 6L, NA, 6L, 6L, NA, 3L, NA, NA, 3L, 6L,
NA, 6L, 6L, 6L, 7L, 3L, NA, 1L, 6L, 6L, NA, 2L, 6L, NA, 2L), .Label =
c("accessible",
"alternatives", "information", "responsive", "social", "technical",
"trade"), class = "factor"), rank3 = structure(c(1L, 6L, 4L,
3L, 2L, 4L, NA, NA, 4L, NA, 1L, 1L, 6L, NA, 1L, NA, 1L, NA, 7L,
3L, 6L, 1L, NA, 2L, NA, 4L, NA, 1L, 3L, NA, 6L, NA, NA, 4L, 2L,
NA, 7L, 1L, 1L, 6L, 7L, NA, 6L, 1L, 1L, NA, 4L, 1L, NA, 3L), .Label =
c("accessible",
"alternatives", "information", "responsive", "social", "technical",
"trade"), class = "factor"), rank4 = structure(c(7L, 4L, 2L,
1L, 1L, 7L, NA, NA, 1L, NA, 7L, 2L, 7L, NA, 3L, NA, 2L, NA, 3L,
4L, 5L, 6L, NA, 4L, NA, 3L, NA, 4L, 4L, NA, 4L, NA, NA, 2L, 7L,
NA, 2L, 2L, 2L, 3L, 6L, NA, 2L, 5L, 4L, NA, 1L, 2L, NA, 4L), .Label =
c("accessible",
"alternatives", "information", "responsive", "social", "technical",
"trade"), class = "factor"), rank5 = structure(c(2L, 7L, 6L,
7L, 7L, 2L, NA, NA, 2L, NA, 2L, 7L, 3L, NA, 6L, NA, 7L, NA, 6L,
7L, 4L, 7L, NA, 7L, NA, 7L, NA, 2L, 2L, NA, 2L, NA, NA, 7L, 1L,
NA, 3L, 7L, 4L, 2L, 2L, NA, 4L, 2L, 2L, NA, 6L, 4L, NA, 5L), .Label =
c("accessible",
"alternatives", "information", "responsive", "social", "technical",
"trade"), class = "factor"), rank6 = structure(c(4L, 2L, 7L,
6L, 6L, 1L, NA, NA, 7L, NA, 4L, 5L, 4L, NA, 7L, NA, 4L, NA, 4L,
2L, 2L, 4L, NA, 1L, NA, 2L, NA, 7L, 7L, NA, 7L, NA, NA, 1L, 4L,
NA, 4L, 4L, 7L, 1L, 4L, NA, 7L, 7L, 7L, NA, 7L, 7L, NA, 7L), .Label =
c("accessible",
"alternatives", "information", "responsive", "social", "technical",
"trade"), class = "factor"), rank7 = structure(c(5L, 5L, 5L,
5L, 5L, 5L, NA, NA, 5L, NA, 5L, 6L, 5L, NA, 5L, NA, 5L, NA, 5L,
5L, 7L, 5L, NA, 5L, NA, 5L, NA, 5L, 5L, NA, 5L, NA, NA, 5L, 5L,
NA, 5L, NA, 5L, 5L, 5L, NA, 5L, 4L, 5L, NA, 5L, 5L, NA, 6L), .Label =
c("accessible",
"alternatives", "information", "responsive", "social", "technical",
"trade"), class = "factor")), .Names = c("row", "rank1", "rank2",
"rank3", "rank4", "rank5", "rank6", "rank7"), row.names = c(NA,
50L), class = "data.frame")
#This code is a replication of David Carlson's code (below) which works
splendidly, but does not work on my data-set
#Melt d2: Note, I've used value.name='color' to maximize comparability with
David's suggestion
d3 <- melt(d2, id.vars=1, measure.vars=2:8,
variable.name="rank",value.name="color")
#Make Rank Variable Numeric
d3$rank<-as.numeric(d3$rank)
#Recast d3 into d4
d4<- dcast(d3, row~color,value.var="rank", fill=0)
#Note that d4 appears to provide a binary variable for one if a respondent
checked the option, but does not provide information as to which rank they
assigned each option, but also seems to summarize the number of missing values
#David Carlson's Code
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")
mymelt <- melt(mydf, id.vars=1, measure.vars=2:5, variable.name="rank",
value.name="color")
mymelt$rank <- as.numeric(mymelt$rank)
mycast <- dcast(mymelt, row~color, value.var="rank", fill=0)
#Compare
str(mydf)
str(d2)
head(mycast)
head(d4)
Again, I'm grateful for assistance. I can't understand what how my data-set
differs from David's sample data-set.
Simon Kiss
On Sep 4, 2014, at 2:35 PM, David L Carlson <dcarl...@tamu.edu> wrote:
> 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:sjk...@gmail.com]
> Sent: Thursday, September 4, 2014 1:28 PM
> To: David L Carlson
> Cc: r-help@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 <dcarl...@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")
>>> head(mydf)
>> 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)
>>> head(mycast)
>> 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-boun...@r-project.org [mailto:r-help-boun...@r-project.org] On
>> Behalf Of David L Carlson
>> Sent: Sunday, August 17, 2014 6:32 PM
>> To: Simon Kiss; r-help@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")
>>> head(mydf)
>> 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$ind)
>>> # add row numbers
>>> mydf2 <- data.frame(rows=1:100, mydf2)
>>> # Create table
>>> mytbl <- xtabs(ind~rows+values, mydf2)
>>> # convert to data frame
>>> mydf3 <- data.frame(unclass(mytbl))
>>> colnames(mydf3) <- lvls
>>> head(mydf3)
>> blue green red yellow
>> 1 4 0 3 2
>> 2 0 2 4 1
>> 3 3 2 0 1
>> 4 2 4 0 3
>> 5 3 4 2 0
>> 6 4 3 2 0
>>
>> David C
>>
>> -----Original Message-----
>> From: r-help-boun...@r-project.org [mailto:r-help-boun...@r-project.org] On
>> Behalf Of Simon Kiss
>> Sent: Friday, August 15, 2014 3:58 PM
>> To: r-help@r-project.org
>> Subject: Re: [R] Turn Rank Ordering Into Numerical Scores By Transposing A
>> Data Frame
>>
>>
>> Both the suggestions I got work very well, but what I didn't realize is that
>> NA values would cause serious problems. Where there is a missing value,
>> using the argument na.last=NA to order just returns the the order of the
>> factor levels, but excludes the missing values, but I have no idea where
>> those occur in the or rather which of those variables were actually missing.
>>
>> Have I explained this problem sufficiently?
>> I didn't think it would cause such a problem so I didn't include it in the
>> original problem definition.
>> Yours, Simon
>> On Jul 25, 2014, at 4:58 PM, David L Carlson <dcarl...@tamu.edu> wrote:
>>
>>> I think this gets what you want. But your data are not reproducible since
>>> they are randomly drawn without setting a seed and the two data sets have
>>> no relationship to one another.
>>>
>>>> set.seed(42)
>>>> mydf <- data.frame(t(replicate(100, sample(c("red", "blue",
>>> + "green", "yellow")))))
>>>> colnames(mydf) <- c("rank1", "rank2", "rank3", "rank4")
>>>> mydf2 <- data.frame(t(apply(mydf, 1, order)))
>>>> colnames(mydf2) <- levels(mydf$rank1)
>>>> head(mydf)
>>> 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
>>>> head(mydf2)
>>> 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-boun...@r-project.org [mailto:r-help-boun...@r-project.org] On
>>> Behalf Of Simon Kiss
>>> Sent: Friday, July 25, 2014 2:34 PM
>>> To: r-help@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
>>> Brantford, Ontario, Canada
>>> N3T 2C9
>>>
>>> ______________________________________________
>>> R-help@r-project.org 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.
>>
>> *********************************
>> Simon J. Kiss, PhD
>> Assistant Professor, Wilfrid Laurier University
>> 73 George Street
>> Brantford, Ontario, Canada
>> N3T 2C9
>> Cell: +1 905 746 7606
>>
>> ______________________________________________
>> R-help@r-project.org 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.
>>
>> ______________________________________________
>> R-help@r-project.org 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.
>
> *********************************
> Simon J. Kiss, PhD
> Assistant Professor, Wilfrid Laurier University
> 73 George Street
> Brantford, Ontario, Canada
> N3T 2C9
> Cell: +1 905 746 7606
>
>
>
*********************************
Simon J. Kiss, PhD
Assistant Professor, Wilfrid Laurier University
73 George Street
Brantford, Ontario, Canada
N3T 2C9
Cell: +1 905 746 7606
______________________________________________
R-help@r-project.org 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.
