Another solution would be
faclevels<-c("Not","Little","Somewhat","Very", NA)
group <- factor(1:6)
svdf<-data.frame(value = sample(group, 400, TRUE), category =
sample(faclevels,400,TRUE))
library(ggplot2)
ggplot(svdf, aes(x = value, fill = category)) + geom_histogram(position
= "dodge")
HTH,
Thierry
------------------------------------------------------------------------
----
ir. Thierry Onkelinx
Instituut voor natuur- en bosonderzoek / Research Institute for Nature
and Forest
Cel biometrie, methodologie en kwaliteitszorg / Section biometrics,
methodology and quality assurance
Gaverstraat 4
9500 Geraardsbergen
Belgium
tel. + 32 54/436 185
[EMAIL PROTECTED]
www.inbo.be
To call in the statistician after the experiment is done may be no more
than asking him to perform a post-mortem examination: he may be able to
say what the experiment died of.
~ Sir Ronald Aylmer Fisher
The plural of anecdote is not data.
~ Roger Brinner
The combination of some data and an aching desire for an answer does not
ensure that a reasonable answer can be extracted from a given body of
data.
~ John Tukey
-----Oorspronkelijk bericht-----
Van: [EMAIL PROTECTED] [mailto:[EMAIL PROTECTED]
Namens Jim Lemon
Verzonden: vrijdag 10 oktober 2008 14:26
Aan: [EMAIL PROTECTED]
CC: r-help@r-project.org
Onderwerp: Re: [R] Plot grouped histograms
[EMAIL PROTECTED] wrote:
> r11 -- r16 are variables showing a reason for usage of a product in 6
> different situations. Each variable is a factor with 4 levels imported
> from a SPSS sav file with labels ranging from "not important" to "very
> important", and NA's for a sample of N = 276.
>
> (1) I need a chi square test of independence showing that the reason
> does not differ depending on the situation.
>
> (2) I need a single coloured histogram plot. The x axis should be
> grouped by the 6 situation with small space between the groups, each
> group should show different bars for each factor value ("not
> important", "little...", ..., "NA's"), but NA's is not necessary.
>
> I've been googling the whole day, searching the mailing list and
> handbooks, and struggled through the somewhat R programmer specific
> documentation. Beeing a newby in R, I'm now afraid that I have to go
> back to SPSS and Excel where my tasks would be a work of an hour. But
> I was told "euphoric" that R may solve many of the problems I have
> (and don't like) with SPSS, or having to separate calculation (SPSS,
> Excel by hand) and plotting (GNUplot).
>
> So my two questions are: How can I easily solve my 2 tasks? Secondly,
> is R really recommended for R newbys in daily work?
Hi Soeren,
For number 1:
chisq.test(svfreqs) #see below
but I don't think that this is the best way to test this.
For number 2:
faclevels<-c("Not","Little","Somewhat","Very")
svdf<-data.frame(r11=sample(faclevels,276,TRUE),
r12=sample(faclevels,276,TRUE),
r13=sample(faclevels,276,TRUE),
r14=sample(faclevels,276,TRUE),
r15=sample(faclevels,276,TRUE),
r16=sample(faclevels,276,TRUE))
library(prettyR)
svfreqs<-t(matrix(unlist(sapply(svdf,freq,display.na=FALSE)),nrow=6,byro
w=TRUE))
library(plotrix)
barp(svfreqs,main="Frequencies of importance ratings",ylim=c(0,100),
names.arg=paste("r1",1:6,sep=""),col=2:5,xlab="Situation",
ylab="Frequency")
legend(2.3,98,c("Not","Little","Somewhat","Very"),fill=2:5)
Jim
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