Thanks,
ggplot is on my list of things to learn before Hadley comes here to the
bay area
to give a session on interactive graphics in R
On Fri, May 27, 2011 at 10:29 PM, Joshua Wiley <jwiley.ps...@gmail.com>wrote:
> Hi Steven,
>
> This is not, strictly speaking, the answer to your question (hopefully
> Tom already answered that). Rather, it is the answer to questions you
> *might* have asked (and perhaps one of them will be one you wished you
> had asked).
>
> Barplots have a low data:ink ratio...you are using an entire plot to
> convey 8 means. A variety of alternatives exist. As a minimal first
> step, you could just use points to show the means and skip all the
> wasted bar space, and you might add error bars in (A). You could also
> use boxplots to give your viewers (or just yourself) a sense of the
> distribution along with the medians (B). Another elegant option is
> violin plots. These are kind of like (exactly like?) mirrored density
> plots. A measure of central tendency is not explicitly shown, but the
> *entire* distribution and range is shown (C).
>
> Cheers,
>
> Josh
>
> (P.S. I hit send too soon before and sent you an offlist message with
> PDF examples)
>
> ## Create your data
> DF <- data.frame(
> Incidents = factor(rep(c("a", "b", "d", "e"), each = 25)),
> Months = factor(rep(1:2, each = 10)),
> Time = rnorm(100))
>
> ## Load required packages
> require(ggplot2)
> require(Hmisc)
>
> ## Option A
> ggplot(DF, aes(x = Incidents, y = Time, colour = Months)) +
> stat_summary(fun.y = "mean", geom = "point",
> position = position_dodge(width = .90), size = 3) +
> stat_summary(fun.data = "mean_cl_normal", geom = "errorbar",
> position = "dodge")
>
> ## Option B
> ggplot(DF, aes(x = Incidents, y = Time, fill = Months)) +
> geom_boxplot(position = position_dodge(width = .8))
>
> ## Option C
> ggplot(DF, aes(x = Time, fill = Months)) +
> geom_ribbon(aes(ymax = ..density.., ymin = -..density..),
> alpha = .2, stat = "density") +
> facet_grid( ~ Incidents) +
> coord_flip()
>
> ## Option C altered
> ggplot(DF, aes(x = Time, fill = Months)) +
> geom_ribbon(aes(ymax = ..density.., ymin = -..density..),
> alpha = .2, stat = "density") +
> facet_grid( ~ Incidents + Months) +
> scale_y_continuous(name = "density", breaks = NA, labels = NA) +
> coord_flip()
>
> On Fri, May 27, 2011 at 3:08 PM, steven mosher <mosherste...@gmail.com>
> wrote:
> > Hi,
> >
> > I'm really struggling with barplot
> >
> > I have a data.frame with 3 columns. The first column represents an
> > "incident" type
> > The second column represents a "month"
> > The third column represents a "time"
> >
> > Code for a sample data.frame
> >
> > incidents <- rep(c('a','b','d','e'), each =25)
> > months <- rep(c(1,2), each =10)
> > times <-rnorm(100)
> >
> > # make my sample data
> >
> > DF <-
> >
> data.frame(Incidents=as.factor(incidents),Months=as.factor(months),Time=times)
> >
> > # now calculate a mean for the "by" groups of incident type and month
> >
> > pivot <-
> >
> aggregate(DF$Time,by=list(Incidents=DF$Incidents,Months=DF$Month),FUN=mean,simplify=TRUE)
> >
> > What I want to create is a bar plot where I have groupings by incident
> type
> > ( a,b,d,e) and within each group
> > I have the months in order.
> >
> > So group 1 would be Type "a"; month 1,2;
> > group 2 would be Type "b"; month 1,2;
> > group 3 would be Type "d"; month 1,2;
> > group 4 would be Type "3"; month 1,2;
> >
> > I know barplot is probably the right function but I'm a bit lost on how
> to
> > specify groupings etc
> >
> > TIA
> >
> > [[alternative HTML version deleted]]
> >
> > ______________________________________________
> > 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.
> >
>
>
>
> --
> Joshua Wiley
> Ph.D. Student, Health Psychology
> University of California, Los Angeles
> http://www.joshuawiley.com/
>
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