Hi:

It wasn't clear to me precisely what you wanted, but here are a couple of
ideas in the hope that it will help.
I used ggplot2 for the graphics, so it requires some manipulation of your
dataset from 'wide' format to 'long'.
I also add an indicator for side of the ear (odd is side one (L?), even is
side 2) and a variable I call 'loc' to
indicate the value associated with the splxx variable.

I read the data into a data frame called spikelets. The first step is to
remove the rows of missing responses:

naind <- apply(spikelets[, -1], 1, function(x) all(is.na(x)))
spikelets2 <- spikelets[!naind, ]

Next, I use the plyr package and its melt() function to convert the data
frame from 'wide' to 'long' form:

library(ggplot2)         # attaches the plyr package in the loading process
spikes.long <- melt(spikelets2, id = 'cn')

The variable 'variable' contains the variable names as a vector (spl01,
spl02, ..., spl14)
Next, I create a variable called loc, which represents the numeric part of
the spl variables, and then
create a variable side to distinguish one side of the awn from the other.
'variable' is then removed...

spikes.long$loc <- as.numeric(substring(spikes.long$variable, 4))
spikes.long$side <- factor(2 - spikes.long$loc %% 2)
spikes.long$variable <- NULL

Now we're in a position to plot. The first is a scatterplot of the response
by location, stratified by cultivar;
it contains color to distinguish sides.

# With color:
p <- qplot(loc, value, data = spikes.long, group = cn,
           colour = side)
p + facet_grid(cn ~ .)

The color is not terribly informative, so to get rid of it, remove the
colour = side argument. One could
also merge the plots together and fit smooths to the different cultivars.

ggplot(spikes.long, aes(loc, value, colour = cn)) +
    geom_point() + geom_smooth(se = FALSE)

I also came up with boxplot pairs by side for each cultivar, which is shown
below:

q <- ggplot(spikes.long, aes(side, value))
q + geom_boxplot() + facet_grid(~ cultivar)

For some reason, I kept getting these messages from every ggplot2 call:

Error in recordGraphics(drawGTree(x), list(x = x), getNamespace("grid")) :
  invalid graphics state

but all of the plots rendered as expected.


HTH,
Dennis

2010/1/10 Carl-Göran CG. Pettersson <CG.Pettersson@vpe.slu.se>

> Dear all
>
> R2.10  WinXP
>
> I have a dataset dealing with the way different wheat cultivars build their
> yield.
> Wheat ears are organised in spikelets where the spikelets can be numbered
> from the bottom, with even numbers on one side and odd on the other.
> I know how many kernels there were in each spikelet after some months spent
> counting them...
>
> Now I want to illustrate the differences between the cultivars in how the
> kernels are distributed in the ears.
> In the best of all possible worlds it would be possible to place histograms
> or boxplots on adjecent sides of vertical lines representing different
> cultivars.
> I have done some experimenting using boxplot() but I am stuck and out of
> ideas right now.
>
> All ideas are welcome!
> /CG
>
>
> Here is a sample dataset with the countings of kernels for the first 14
> spikelets:
>
> cn      spl01   spl02   spl03   spl04   spl05   spl06   spl07   spl08
> spl09   spl10   spl11   spl12   spl13   spl14
> Lans    1.8     3.1     3.5     3.8     3.8     4.1     4.2     4.3     4.4
>     4.5     4.2     4.1     3.9     3.8
> Kranich 0.6     2.4     3.4     4.2     4.5     4.7     4.9     4.9     4.8
>     4.7     4.4     4.1     4.1     3.9
> Loyal   1.1     2.7     3.6     3.7     4.1     4.4     4.4     4.6     4.3
>     4.5     4.3     4.1     3.8     3.7
> Boomer  NA      NA      NA      NA      NA      NA      NA      NA      NA
>      NA      NA      NA      NA      NA
> Oakley  NA      NA      NA      NA      NA      NA      NA      NA      NA
>      NA      NA      NA      NA      NA
> Hereford        0.6     2.3     3.3     3.6     3.9     4       4.2     4.1
>     4.1     3.9     3.9     3.6     3.4     3.2
> Kranich 0.3     2.5     3.6     4       4.4     4.5     4.3     4.8     4.7
>     4.6     4.4     4.3     4.1     4
> Oakley  0.5     2.1     3.2     3.4     3.8     4.4     4.3     4.3     4.3
>     4.2     4.2     3.9     3.8     3.6
> Loyal   1.6     3.3     3.9     4.2     4.3     4.4     4.4     4.6     4.6
>     4.5     4.3     4.3     4.2     4
> Hereford        NA      NA      NA      NA      NA      NA      NA      NA
>      NA      NA      NA      NA      NA      NA
> Oakley  0.5     2.1     3.2     3.6     4       4       4.1     4.4     4.4
>     4.2     4.1     3.8     3.8     4
> Kranich NA      NA      NA      NA      NA      NA      NA      NA      NA
>      NA      NA      NA      NA      NA
> Lans    1.4     3       3.3     3.8     3.9     4.3     4       4.3     4.3
>     4.3     4       4.1     4       4
> Hereford        1.2     2.7     3.6     3.8     4       4       4.1     4.2
>     4.1     4.1     3.9     3.6     3.8     3.3
> Boomer  0.3     2.5     3.1     3.8     3.9     4.4     4.1     4.2     4.3
>     4       4.2     4       3.8     3.7
> Lans    NA      NA      NA      NA      NA      NA      NA      NA      NA
>      NA      NA      NA      NA      NA
> Boomer  0.2     1.9     3       3.4     3.7     3.9     3.9     4       4
>     4       3.8     3.8     3.6     3.4
> Loyal   NA      NA      NA      NA      NA      NA      NA      NA      NA
>      NA      NA      NA      NA      NA
> Boomer  NA      NA      NA      NA      NA      NA      NA      NA      NA
>      NA      NA      NA      NA      NA
> Kranich NA      NA      NA      NA      NA      NA      NA      NA      NA
>      NA      NA      NA      NA      NA
> Kranich 0.3     1.1     2.9     3.5     3.9     4.3     4.4     4.4     4
>     4.2     4.2     4       3.9     3.8
> Hereford        0.5     2.1     3.1     3.6     3.7     3.9     4       3.8
>     4       3.8     3.6     3.6     3.1     3
> Loyal   NA      NA      NA      NA      NA      NA      NA      NA      NA
>      NA      NA      NA      NA      NA
> Boomer  0.3     0.8     2.8     3       3.6     3.7     3.8     4       3.8
>     3.5     3.3     3.2     3.2     2.9
> Oakley  0.5     2.7     3.4     3.8     4       3.9     4.2     4.5     4.3
>     4.4     4       4       3.9     3.9
> Loyal   0.9     2.6     3.6     3.8     3.8     4.4     4.2     4.4     4.2
>     3.9     3.8     4       3.4     3.7
> Oakley  NA      NA      NA      NA      NA      NA      NA      NA      NA
>      NA      NA      NA      NA      NA
> Hereford        0.7     2.9     3.6     4       4       3.9     4       4
>     4       3.9     3.8     3.7     3       3
> Hereford        NA      NA      NA      NA      NA      NA      NA      NA
>      NA      NA      NA      NA      NA      NA
> Loyal   0.7     2.3     3.5     3.7     3.9     3.8     4.2     4.1     4.1
>     4.1     4       4       3.4     3.6
> Boomer  0.7     2       3.3     3.5     3.9     3.7     4       3.9     3.8
>     4       3.7     3.8     3.5     3.4
> Lans    NA      NA      NA      NA      NA      NA      NA      NA      NA
>      NA      NA      NA      NA      NA
> Lans    1.9     3       3.7     3.8     3.9     4       3.9     4.3     4.1
>     4.1     4.1     3.8     3.8     3.9
> Lans    1.1     2.6     3.3     3.7     4.1     4       4.2     4.2     4.2
>     4       4.1     4.1     3.8     3.6
> Kranich 0.5     1.3     2.9     3.8     3.8     4.3     4.3     4.4     4.4
>     4       4.3     3.9     3.6     3.4
> Oakley  0.1     2       3.1     3.5     4.1     3.9     4.1     4.2     4.2
>     4.2     4.1     4       3.9     3.8
> ______________________________________________
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