Macy
> The data in the sample data frame are already the means of
> the 5 samples measured for each time T. Does this mean that I need to
> 1) calculate the means and standard deviations separately per
> variable per time,
> 2) compile those results in a new data frame, then
> 3) use the ggplot code you provided?
That seems to be the easiest thing to do.
You said you already used aggregate() to get the means; you can extend that a
little to get the sd's or (assuming a simple stderr) the standard errors in the
same data frame; for example
aggregate(count~site+time+benthic.component, data=yourdataframe,
FUN=function(x) c(mean=mean(x), sd=sd(x), stderr=sqrt(var(x)/length(x))))
#assuming i) that you have a column-format data frame with one row for
each individual count identified by site, time and component
#ii) that's how you want the stderr calculated!
Then you'll have the means, standard deviations and standard errors in the
right form for ggplot to work with stat="identity".
Do check the variable names in the resulting aggregated data frame; aggregate
would give them names like count.mean, count.sd etc so you will need to use
those names in ggplot.
Steve E
>
>
>
> On Wed, Oct 24, 2012 at 11:22 PM, S Ellison
> <s.elli...@lgcgroup.com> wrote:
>
>
>
> > I'd greatly appreciate your help in making a bar
> graph with multiple
> > variables plotted on it. All the help sites I've seen
> so far only plot 1
> > variable on the y-axis
>
> > ...
>
> >
> > I've spent several hours looking for code to do this
> but didn't find
> > anything. I'd use the Excel graph except that it
> doesn't have the sd or se
> > bars.
> >
>
> Perhaps something like lattice or ggplot would serve better?
>
> Here's something using ggplot (which has prettier
> colours than lattice)
>
>
> #Something like your data - with considerable licence
> on unreadable names!
> abst <-structure(list(Count = c(17.03, 22.94, 28.38,
> 29.72, 28.37, 14.45,
> 1.51, 0.54, 0.62, 1.52, 62.3, 70.6, 68.82, 64.75, 63.77, 3.17,
> 2.78, 2.22, 2.03, 1.94, 0.61, 0.33, 0.74, 0.74, 0.58,
> 0.44, 0.12,
> 0.37, 0.08, 0.41, 0.04, 0, 0.08, 0, 0.08, 1.96, 1.68,
> 2.84, 2.06,
> 3.32, 0, 0, 0, 0, 0), Benthic = structure(c(2L, 2L, 2L, 2L, 2L,
> 3L, 3L, 3L, 3L, 3L, 1L, 1L, 1L, 1L, 1L, 9L, 9L, 9L, 9L, 9L, 4L,
> 4L, 4L, 4L, 4L, 5L, 5L, 5L, 5L, 5L, 6L, 6L, 6L, 6L, 6L, 8L, 8L,
> 8L, 8L, 8L, 7L, 7L, 7L, 7L, 7L), .Label = c("Algae", "Coral",
> "Deadcoral", "Ind", "other", "softcoral", "something",
> "sponges",
> "xBiotic"), class = "factor"), Time = structure(c(1L, 2L, 3L,
> 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L,
> 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L,
> 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L), .Label = c("T
> 1", "T 2",
> "T 3", "T 4", "T 5"), class = "factor")), .Names = c("Count",
> "Benthic", "Time"), row.names = c(NA, -45L), class =
> "data.frame")
>
> head(abst)
>
> #Add an arbitrary 'std error'
>
> abst$stderr <- 0.05*abst$Count
>
> library(ggplot2)
> b <- ggplot(subset(abst, Benthic!="something"), aes(x
> = Time, y = Count, fill=Time))
> bptot<-b + geom_bar(stat = "identity", position = "dodge")
> bptot + facet_grid(. ~ Benthic , scales="free_y", margins=T)
>
> #or, for unequal scale heights - much easier to see
> individual trends
> bwrap <- bptot + facet_wrap( ~ Benthic , scales="free", nrow=2)
> bwrap
>
> #Now add error bars
> bwrap+geom_errorbar(aes(ymin=Count-stderr,
> ymax=Count+stderr), width=.3)
>
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