Hi RUsers
I like to keep the plots self contained and avoid changing the current
device parameters by using the par.settings.
To see what I could achieve by using par settings I tried the following and
several variants but could not get black points.
xyplot(yM + yF ~ x,
panel = panel.superpose,
type = c("l", "p"),
distribute.type = TRUE,
par.settings = list(superpose.line = list(lty = c(1,2),
col = c("black","black")),
superpose.points = list(pch = c(1,1), col =
c("black","black")),
plot.symbol = list(pch = c(1,1), col =
c("black","black"))
),
key = list(text = list(c("male", "female")),
lines = Rows(pset$superpose.line, 1:2),
pch = 1,
type = c("l", "p")))
What am I missing? Does the points reference refer to Grid settings?
Regards
Duncan Mackay
Department of Agronomy and Soil Science
University of New England
Armidale NSW 2350
Email Home: mac...@northnet.com.au
R version 2.9.1 (2009-06-26)
i386-pc-mingw32
attached base packages:
[1] datasets utils stats graphics grDevices
grid methods base
other attached packages:
[1] R.oo_1.4.8 R.methodsS3_1.0.3
foreign_0.8-36 chron_2.3-30 MASS_7.2-47 lattice_0.17-25
At 11:48 6/08/2009, Deepayan Sarkar wrote:
On Wed, Aug 5, 2009 at 2:24 PM, Jacob Wegelin<jacob.wege...@gmail.com>
wrote: > On Wed, 5 Aug 2009, Deepayan Sarkar wrote:
> >> On 8/5/09, Jacob Wegelin <jacob.wege...@gmail.com> wrote:
>>> I would like to use lattice graphics to plot multiple functions (or >
groups
>>> Â or subpopulations) on the same plot region, using different line types
> "lty"
>>> Â or colors "col" to distinguish the functions (or groups).
>>>
>>> Â In traditional graphics, this seems straightforward: First plot all
the
> data
>>> Â using 'type="n"', and subsequently execute a series of "points" or
> "lines"
>>> Â commands, one for each different group or function.
>>>
>>> Â What is the elegant way to do this using xyplot?
>>>
>>> Â To make this concrete, consider the following toy example:
>>>
>>> Â k<- 10
>>> Â x<- (1:k)/3
>>> Â yM<-6 + x^2
>>> Â yF<-12 + x^(1.5)
>>> Â xNA<-x[length(x)]
>>>
>>> Â # Insertion of NA row is necessary to prevent a meaningless line
>>> Â # from being drawn from the females to the males across the entire
plot.
>>>
>>> Â DAT<-data.frame(
>>> Â x=c(x, xNA, x) >>> Â , >>> Â y=c(yF, NA, yM) >>> Â , >>> Â sex=c(
rep(0, k ), 0, Â rep(1, k)) >>> Â ) >> >> It's much simpler in lattice,
and you don't need to play such tricks. > Option 1: >> >> xyplot(yM + yF
~ x, type = "l", auto.key = list(points = FALSE, lines = > TRUE)) >> >>
and if you want to control lty etc: >> >> xyplot(yM + yF ~ x, type = "l",
auto.key = list(points = FALSE, lines = > TRUE), >> Â Â Â par.settings
= simpleTheme(lty = c(2, 3))) >> >> >> Option 2 (a bit more work, but
less mysterious under the hood): >> >> DAT<- >> Â Â data.frame(x = c(x,
x), y=c(yF, yM), >> Â Â Â Â Â Â Â sex= rep(c("Female", "Male"),
each = length(x))) >> >> xyplot(y ~ x, data = DAT, groups = sex, type =
"l") > > Dear Bill and Deepayan, > > Thanks. This is helpful. Where can
one find a thorough documentation of all > these features like
par.settings, simpleTheme, the options for where to > place the legend or
"key", auto.key, the different locations besides "top" > where one can
place the "auto.key", etc.? Â I don't think this is all clearly > laid
out in the R help files or latticeLab.pdf. (Almost) everything is
mentioned in the help pages (?Lattice is a good place to start). Of
course finding the thing you are looking for is another matter. The book
does try to present things more systematically. > But using your hints I
found that the following worked: > > xyplot( > y ~ x > , groups= ~ sex >
, type="l" > , auto.key = list(columns=2, points = FALSE, lines = TRUE) >
, par.settings = simpleTheme(lty = c(1, 2), col="black") > , data=DAT >
) > > Now, how would I use lattice tools to plot males with a line and
females > with points--and still get an informative autokey?
xyplot(yM + yF ~ x,
type = c("l", "p"),
distribute.type = TRUE,
par.settings = simpleTheme(lty = c(1, 2), col="black"),
auto.key = list(points = FALSE, lines = TRUE, type = c("l", "p")))
...but this is pretty much impossible to figure out for a beginner.
On the other hand, reading the documentation carefully should lead you to
the following, which is almost there:
pset <- simpleTheme(lty = c(1, 2), col="black")
xyplot(yM + yF ~ x,
panel = panel.superpose,
type = c("l", "p"),
distribute.type = TRUE,
par.settings = pset,
key = list(text = list(c("male", "female")),
lines = Rows(pset$superpose.line, 1:2),
pch = 1,
type = c("l", "p")))
-Deepayan
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and provide commented, minimal, self-contained, reproducible code.
