playwith {playwith} | R Documentation |
A GTK+ graphical user interface for exploring and editing R plots.
playwith(expr, new = playwith.getOption("new"), title = NULL, labels = NULL, data.points = NULL, viewport = NULL, parameters = list(), tools = list(), update.actions = list(), init.actions = list(), ..., width = playwith.getOption("width"), height = playwith.getOption("height"), pointsize = playwith.getOption("pointsize"), eval.args = playwith.getOption("eval.args"), on.close = playwith.getOption("on.close"), modal = FALSE, link.to = NULL, playState = if (!new) playDevCur(), plot.call, main.function)
expr |
an expression to create a plot, like plot(mydata) .
Note, arguments and nested calls are allowed, just like a normal
plot call (see examples).
Could also be a chunk of code in { braces} .
For quoted calls, use the plot.call argument.
|
new |
if TRUE open in a new window, otherwise replace the
current window (if one exists).
|
title |
optional window title; otherwise derived from the plot call. |
labels |
a character vector of labels for data points. If missing, it will be guessed from the plot call arguments if possible. |
data.points |
a data frame (or other suitable plotting
structure: see xy.coords ) giving locations of data
points, in case these can not be guessed from the plot call
arguments. If a data frame, extra variables may be included; these
can be used to label or locate points in the GUI. Note, if a
suitable data argument is found in the plot call, that plays the
same role.
|
viewport |
name or vpPath of the
viewport representing the data space. This
allows interaction with grid graphics plots (but ignore this for
Lattice plots). Experimental: can also be a named list.
|
parameters |
defines simple tools for controlling values of any parameters
appearing in the plot call. This must be a named list, where the value given for
each name defines the possible or initial values of that parameter. The
supported values are:
|
tools |
a list of tool specifications. These are technically
GtkActionEntry s but should be specified as
lists with the following structure. Elements can be specified in
this order, or named (as with a function call).
|
update.actions |
a list of actions to be run after the plot is
drawn (and each time it is redrawn). These may be functions,
or names of functions, or expressions. Functions are passed one
argument, which is the playState . Note, these
are in addition to any given in
playwith.options("update.actions") .
|
init.actions |
a list of actions to be run before the plot is
drawn, whenever the plot type changes or its data changes. These are
not run when only simple arguments to the call change, but they are
run whenever the plot call is edited manually. Same format
as update.actions .
|
... |
extra arguments are stored in the playState object.
These can then be accessed by tools. The default tools
will recognise the following extra arguments:
|
width, height |
initial size of the plot device in inches. |
pointsize |
default point size for text in the
Cairo device.
|
eval.args |
whether to evaluate the plot call arguments: can be
TRUE , FALSE , NA (don't eval global vars)
or a regular expression matching symbols to evaluate.
Or a list. See below.
|
on.close |
a function to be called when the user closes the plot
window. The playState object
will passed to the function. If the function returns TRUE ,
the window will not be closed.
|
modal |
whether the window is modal: if TRUE ,
the session will freeze until the window is closed.
|
link.to |
an existing playState (i.e. playwith
plot) to link to. The set of brushed data points will then be
synchronised between them. It is assumed that the data subscripts of
the two plots correspond directly. Links can be broken with
playUnlink .
|
playState |
the playState object for an existing plot window.
If given, the new plot will appear in that window, replacing the old plot.
This over-rides the new argument.
|
plot.call |
a plot call (call object), if given
this is used instead of expr .
|
main.function |
the function (or its name) appearing in the call
which accepts typical plot arguments like xlim or
ylab . This will only be needed in unusual cases when the
default guess fails.
|
This function opens a GTK+ window containing a plot device
(from the cairoDevice package), a menubar and toolbars.
There is a call toolbar (similar to the "address bar" of a web browser) at the top,
showing the current plot call, which can be edited in-place.
Then there are up to four toolbars, one on each side of the plot.
The user interface is customisable: see playwith.options
.
With the autoplay
facility, playwith
can function
like a default graphics device (although it is not technically a
graphics device itself, it is a wrapper around one).
See playwith.API for help on controlling the plot once open, as
well as defining new tools.
For the special case of tools to control parameter values, it is possible
to create the tools automatically using the parameters
argument.
Four types of plots are handled somewhat differently:
trellis
. This is the best-supported case.
ggplot
.
This case is rather poorly supported.
viewport
argument to enable interaction.
par()
.
Some forms of interaction are based on evaluating and changing arguments to the plot call.
This is designed to work in common cases, but could never work for all
types of plots. To enable zooming, ensure that the main call accepts xlim
and ylim
arguments. Furthermore, you may need to specify main.function
if the
relevant high-level call is nested in a complex block of expressions.
To enable identification of data points, the locations of data points
are required, along with appropriate labels.
By default, these locations and labels will be guessed from the plot call,
but this may fail.
You can pass the correct values in as data.points
and/or labels
.
Please also contact the maintainer to help improve the guesses.
If identification of data points is not required, passing
data.points = NA, labels = NA
may speed things up.
Some lattice functions need to be called with subscripts = TRUE
in order to correctly
identify points in a multiple-panel layout. Otherwise the subscripts used will then
refer to the data in each panel separately, rather than the original dataset.
In this case a warning dialog box will be shown.
In order to interact with a plot, its supporting data needs to be stored:
i.e. all variables appearing in the plot call must remain accessible.
By default (eval.args = NA
), objects that are not globally
accessible will be copied into an attached environment and stored with
the plot window.
I.e. objects are stored unless they exist in the global environment
(user workspace) or in an attached namespace.
This method should work in most cases.
However, it may end up copying more data than is really necessary,
potentially using up memory. Note that if e.g. foo$bar
appears
in the call, the whole of foo
will be copied.
If eval.args = TRUE
then variables appearing in the plot call will be
evaluated and stored even if they are defined in the global environment.
Use this if the global variables might change (or be removed) before the
plot is destroyed.
If eval.args = FALSE
then the plot call will be left alone
and no objects will be copied. This is OK if all the data are
globally accessible, and will speed things up.
If a regular expression is given for eval.args
then only variables
whose names match it will be evaluated, and this includes global variables,
as with eval.args=TRUE
. In this case you can set invert.match=TRUE
to store variables that are not matched.
For example eval.args="^tmp"
will store variables whose names
begin with "tmp"; eval.args=list("^foo$", invert.match=TRUE)
will store
everything except foo
.
Note: function calls appearing in the plot call will be evaluated each
time the plot is updated – so random data as in plot(rnorm(100))
will keep changing, with confusing consequences! You should therefore
generate random data prior to the plot call. Changes to variables
in the workspace (if they are not stored locally) may also cause
inconsistencies in previously generated plots.
Warning: the playwith device will tend to make itself the active device any time it is clicked on, so be careful if any other devices are left open.
playwith
invisibly returns the playState
object representing
the plot, window and device. The result of the plot call is available
as component $result
.
Felix Andrews felix@nfrac.org
playwith.options
,
autoplay
,
playwith.API
if (interactive()) { options(device.ask.default = FALSE) ## Scatterplot (Lattice graphics). ## Labels are taken from rownames of data. ## Right-click on the plot to identify points. playwith(xyplot(Income ~ log(Population / Area), data = data.frame(state.x77), groups = state.region, type = c("p", "smooth"), span = 1, auto.key = TRUE, xlab = "Population density, 1974 (log scale)", ylab = "Income per capita, 1974")) ## Scatterplot (base graphics); similar. ## Note that label style can be set from a menu item. urbAss <- USArrests[,c("UrbanPop", "Assault")] playwith(plot(urbAss, panel.first = lines(lowess(urbAss)), col = "blue", main = "Assault vs urbanisation", xlab = "Percent urban population, 1973", ylab = "Assault arrests per 100k, 1973")) ## Time series plot (Lattice). ## Date-time range can be entered directly in "time mode" ## (supports numeric, Date, POSIXct, yearmon and yearqtr). ## Click and drag to zoom in, holding Shift to constrain; ## or use the scrollbar to move along the x-axis. library(zoo) playwith(xyplot(sunspots ~ yearmon(time(sunspots)), xlim = c(1900, 1930), type = "l"), time.mode = TRUE) ## Time series plot (base graphics); similar. ## Custom labels are passed directly to playwith. tt <- time(treering) treeyears <- paste(abs(tt) + (tt <= 0), ifelse(tt > 0, "CE", "BCE")) playwith(plot(treering, xlim = c(1000, 1300)), labels = treeyears, time.mode = TRUE) ## Multi-panel Lattice plot. ## Need subscripts = TRUE to correctly identify points. ## Scales are "same" so zooming applies to all panels. ## Use the 'Panel' tool to expand a single panel, then use ## the vertical scrollbar to change pages. Depth <- equal.count(quakes$depth, number = 3, overlap = 0.1) playwith(xyplot(lat ~ long | Depth, data = quakes, subscripts = TRUE, aspect = "iso", pch = ".", cex = 2), labels = paste("mag", quakes$mag)) ## Spin and brush for a 3D Lattice plot. ## Drag on the plot to rotate in 3D (can be confusing). ## Brushing is linked to the previous xyplot (if still open). ## Note, brushing 'cloud' requires a recent version of Lattice. playwith(cloud(-depth ~ long * lat, quakes, zlab = "altitude"), new = TRUE, link.to = playDevCur(), click.mode = "Brush") ## Set brushed points according to a logical condition. playSetIDs(value = which(quakes$mag >= 6)) ## Interactive control of a parameter with a slider. xx <- rnorm(50) playwith(plot(density(xx, bw = bandwidth), panel.last = rug(xx)), parameters = list(bandwidth = seq(0.05, 1, by = 0.01))) ## The same with a spinbutton (use I() to force spinbutton). ## Initial value is set as the first in the vector of values. ## This also shows a combobox for selecting text options. xx <- rnorm(50) kernels <- c("gaussian", "epanechnikov", "rectangular", "triangular", "biweight", "cosine", "optcosine") playwith(plot(density(xx, bw = bandwidth, kern = kernel), lty = lty), parameters = list(bandwidth = I(c(0.1, 1:50/50)), kernel = kernels, lty = 1:6)) ## More parameters (logical, numeric, text). playwith(stripplot(yield ~ site, data = barley, jitter = TRUE, type = c("p", "a"), aspect = aspect, groups = barley[[groups]], scales = list(abbreviate = abbrev), par.settings = list(plot.line = list(col = linecol))), parameters = list(abbrev = FALSE, aspect = 0.5, groups = c("none", "year", "variety"), linecol = "red")) ## Composite plot (base graphics). ## Adapted from an example in help("legend"). ## In this case, the initial plot() call is detected correctly; ## in more complex cases may need e.g. main.function="plot". ## Here we also construct data points and labels manually. x <- seq(-4*pi, 4*pi, by = pi/24) pts <- data.frame(x = x, y = c(sin(x), cos(x), tan(x))) labs <- rep(c("sin", "cos", "tan"), each = length(x)) labs <- paste(labs, round(180 * x / pi) %% 360) playwith( { plot(x, sin(x), type = "l", xlim = c(-pi, pi), ylim = c(-1.2, 1.8), col = 3, lty = 2) points(x, cos(x), pch = 3, col = 4) lines(x, tan(x), type = "b", lty = 1, pch = 4, col = 6) legend("topright", c("sin", "cos", "tan"), col = c(3,4,6), lty = c(2, -1, 1), pch = c(-1, 3, 4), merge = TRUE, bg = 'gray90') }, data.points = pts, labels = labs) ## A ggplot example. ## NOTE: only qplot()-based calls will work. ## Labels are taken from rownames of the data. library(ggplot2) playwith(qplot(qsec, wt, data = mtcars) + stat_smooth()) ## A minimalist grid plot. ## This shows how to get playwith to work with custom plots: ## accept xlim/ylim and pass "viewport" to enable zooming. myGridPlot <- function(x, y, xlim = NULL, ylim = NULL, ...) { if (is.null(xlim)) xlim <- extendrange(x) if (is.null(ylim)) ylim <- extendrange(y) grid.newpage() pushViewport(plotViewport()) grid.rect() pushViewport(viewport(xscale = xlim, yscale = ylim, name = "theData")) grid.points(x, y, ...) grid.xaxis() grid.yaxis() upViewport(0) } playwith(myGridPlot(1:10, 11:20, pch = 17), viewport = "theData") ## Presenting the window as a modal dialog box. ## When the window is closed, ask user to confirm. confirmClose <- function(playState) { if (gconfirm("Close window and report IDs?", parent = playState$win)) { cat("Indices of identified data points:\n") print(playGetIDs(playState)) return(FALSE) ## close } else TRUE ## don't close } xy <- data.frame(x = 1:20, y = rnorm(20), row.names = letters[1:20]) playwith(xyplot(y ~ x, xy, main = "Select points, then close"), width = 4, height = 3.5, show.toolbars = FALSE, on.close = confirmClose, modal = TRUE, click.mode = "Brush") ## Demonstrate cacheing of objects in local environment. ## By default, only local variables in the plot call are stored. x_global <- rnorm(100) doLocalStuff <- function(...) { y_local <- rnorm(100) angle <- (atan2(y_local, x_global) / (2*pi)) + 0.5 color <- hsv(h = angle, v = 0.75) doRays <- function(x, y, col) { segments(0, 0, x, y, col = col) } playwith(plot(x_global, y_local, pch = 8, col = color, panel.first = doRays(x_global, y_local, color)), ...) } doLocalStuff(title = "locals only") ## eval.args = NA is default ## List objects that have been copied and stored: ## Note: if you rm(x_global) now, redraws will fail. ls(playDevCur()$env) ## Next: store all data objects (in a new window): doLocalStuff(title = "all stored", eval.args = TRUE, new = TRUE) ls(playDevCur()$env) ## Now there are two devices open: str(playDevList()) playDevCur() playDevOff() playDevCur() ## Not run: ## Memory usage test. ## Big data object, do not try to guess labels or time.mode. gc() bigobj <- rpois(5000000, 1) object.size(bigobj) / 1048576 ## in MB gc() playwith(qqmath(~ bigobj, f.value = ppoints(500)), data.points = NA, labels = NA) playDevOff() gc() ## or generate the trellis object first: trel <- qqmath(~ bigobj, f.value = ppoints(500)) playwith(trel) rm(trel) ## in this case, better to compute the sample first: subobj <- quantile(foo, ppoints(500), na.rm = TRUE) playwith(qqmath(~ subobj)) rm(subobj) rm(bigobj) ## End(Not run) ## See demo(package = "playwith") for examples of new tools. }