Description Usage Arguments Details Value Author(s) See Also Examples
A GTK+ graphical user interface for exploring and editing R plots.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 | playwith(expr,
new = playwith.getOption("new"),
title = NULL,
labels = NULL,
data.points = NULL,
viewport = NULL,
parameters = list(),
tools = list(),
init.actions = list(),
preplot.actions = list(),
update.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 |
new |
if |
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 |
viewport |
name or |
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:
These can also be lists, where the first item is the value as
above. In this case an item named |
tools |
a list of tool specifications. These are technically
|
preplot.actions, update.actions |
a list of actions to be run, respectively, before and
after the plot is drawn (and each time it is redrawn).
Note that These may be functions,
or names of functions, or expressions. Functions are passed one
argument, which is the |
init.actions |
|
... |
extra arguments are stored in the
|
width, height |
initial size of the plot device in inches. |
pointsize |
default point size for text in the
|
eval.args |
whether to evaluate the plot call arguments: can be
|
on.close |
a function to be called when the user closes the plot
window. The |
modal |
whether the window is modal: if |
link.to |
an existing |
playState |
the |
plot.call |
a plot call ( |
main.function |
the function (or its name) appearing in the call
which accepts typical plot arguments like |
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:
Lattice graphics: recognised by returning an object of class
trellis
. This is the best-supported case.
ggplot2 graphics: recognised by returning an object of class ggplot
.
This case is rather poorly supported.
other grid graphics: you must give the viewport
argument to enable interaction.
base graphics: this is the default case. If a multiple-plot
layout is used, interaction can only work in the last sub-plot, i.e.
the settings defined by 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
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 | 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"))
## Looking through 100 time series and comparing to a reference;
## Use buttons to save the current series number or its mean value.
dat <- ts(matrix(cumsum(rnorm(100*100)), ncol = 100), start = 1900)
colnames(dat) <- paste("Series", 1:100)
ref <- (dat[,3] + dat[,4]) / 2
playwith(xyplot(cbind(dat[,i], ref = ref)),
parameters = list(i = 1:100,
print_i = function(playState) print(playState$env$i),
print_mean = function(p) print(mean(dat[,p$env$i])),
save_to_ii = function(playState)
.GlobalEnv$ii <- playState$env$i,
append_to_ii = function(playState) {
if (!exists("ii")) ii <- c()
.GlobalEnv$ii <- c(ii, playState$env$i)
})
)
## 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.
if (require(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$win)) {
cat("Indices of identified data points:\n")
print(playGetIDs(playState))
return(FALSE) ## close
} else TRUE ## don't close
}
xy <- data.frame(x = 1:20, y = rnorm(20),
row.names = letters[1:20])
playwith(xyplot(y ~ x, xy, main = "Select points, then close"),
width = 4, height = 3.5, show.toolbars = FALSE,
on.close = confirmClose, modal = TRUE,
click.mode = "Brush")
## Ask user to save plot to PNG when window is closed:
saveOnClose <- function(playState) {
playDevSet(playState)
if (!gconfirm("Save plot to PNG file? (Cancel = no)")) return(FALSE)
fname <- gfile("Save PNG file as:", type = "save")
if (is.na(fname)) return(TRUE) ## cancel
dev.off(dev.copy(Cairo_png, file = fname,
width = dev.size()[1], height = dev.size()[2]))
FALSE
}
#playwith.options(on.close = saveOnClose)
## Demonstrate cacheing of objects in local environment.
## By default, only local variables in the plot call are stored.
x_global <- rnorm(100)
doLocalStuff <- function(...) {
y_local <- rnorm(100)
angle <- (atan2(y_local, x_global) / (2*pi)) + 0.5
color <- hsv(h = angle, v = 0.75)
doRays <- function(x, y, col) {
segments(0, 0, x, y, col = col)
}
playwith(plot(x_global, y_local, pch = 8, col = color,
panel.first = doRays(x_global, y_local, color)),
...)
}
doLocalStuff(title = "locals only") ## eval.args = NA is default
## List objects that have been copied and stored:
## Note: if you rm(x_global) now, redraws will fail.
ls(playDevCur()$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:
## Big data example, do not try to guess labels or time.mode.
gc()
bigobj <- rpois(5000000, 1)
print(object.size(bigobj), units = "Mb")
gc()
playwith(qqmath(~ bigobj, f.value = ppoints(500)),
data.points = NA, labels = NA, time.mode = FALSE)
playDevOff()
gc()
## or generate the trellis object first:
trel <- qqmath(~ bigobj, f.value = ppoints(500))
playwith(trel)
rm(trel)
## in this case, it is much better to compute the sample first:
subobj <- quantile(bigobj, ppoints(500), na.rm = TRUE)
playwith(qqmath(~ subobj))
rm(subobj)
rm(bigobj)
## End(Not run)
## See demo(package = "playwith") for examples of new tools.
}
|
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