R. Michael Weylandt <michael.weylandt <at> gmail.com> writes:
> It seems like the relevant plot would depend on what you are trying to
> investigate, but usually a scatterplot would well work for bivariate
> data with no other assumptions needed. I usually find ecdf() plots
> rather hard to interpret without playing around with the data
> elsewhere first and I'm not sure they make an enormous amount of sense
> for bivariate data in your case since they reorder inputs.
>
> Michael
[snip]
> On Sun, Oct 23, 2011 at 6:51 AM, gj <gawesh <at> gmail.com> wrote:
> > Hi,
> > I have the following data about courses (504) in a university, two
> > attributes about the proportion of resources used (#resources_used /
> > #resources_available), namely the average and the standard deviation.
> > Thus I have:
> > [1] n=504 rows
> > [2] 1 id column and 2 attributes
> >
> > Here's a sample of the data:
[snip]
You could make a "caterpillar plot" as follows:
X <- read.csv("coursetmp.dat")
library(ggplot2)
X <- transform(X,courseid=reorder(courseid,average))
ggplot(X,aes(x=courseid,y=average,
ymin=average-2*std,ymax=average+2*std))+geom_point()+
geom_linerange()+coord_flip()
(Here the x and y axes are flipped because it's easier to plot & read
the course ID labels that way)
Of course, the answer to "how should I visualize these data?" always
depends on what you want to find out ...
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