how bout:

dat<-data.frame(id=1:4,city=c('berlin','munich'),likeability=c(5,4,6,5),uniqueness=c(3,4,4,4))

ggplot(ddply(melt(dat,
                          id.vars=c('id','city')),
                  .(variable,city),
                  summarise,
                  value=mean(value)),
          aes(x=factor(city),y=value)) +
geom_point() +
facet_wrap(~variable)

the line drawing is a bit more tricky...  Since the x values are factors
rather than continuous, fitting a line to them is kind of nonsense.  It
matters which order they are in for example.  If instead you want to plot
something like:

ggplot(dat,aes(x=likeability,y=uniqueness,colour=city))+geom_point()+geom_smooth(aes(group=city),method='lm')

You could draw fit lines that make a bit more sense.  Forgive me if I'm
over simplifying your problem!


Justin

On Thu, Jan 5, 2012 at 7:46 AM, Mario Giesel <rr.giesel@yahoo.de> wrote:

> Hello, R friends,
>
>  I've been struggling quite a bit with ggplot2.
> Having worked through Hadleys book twice I still wonder how to solve this
> task.
>
>
> 1. Short example Dataframe:
>
> id    city    Likeability    Uniqueness
> 1    Berlin    5    3
> 2    Munich    4    4
> 3    Berlin    6    4
> 4    Munich    5    4
>
> 2. Task:
>
> a) Facetting plots for each attitude (1 plot for likeability and
> uniqueness each, horizontally on one page)
> b) Showing Berlin and Munich together on x axis
> c) Showing the means of Berlin and Munich on y axis (means of cities in
> likeability on first plot, means of cities in uniqueness on second plot)
> d) Drawing a line through mean points on each plot
>
>
>
> Hope I could explain it understandably. Any help is appreciated!
>
> Thanks a lot,
>  Mario
>
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>
>
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>
>

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