The R Project SVN R

% File src/library/grid/vignettes/plotexample.Rnw

% Part of the R package, http://www.R-project.org

% Copyright 2001-13 Paul Murrell and the R Core Team

% Distributed under GPL 2 or later

\documentclass[a4paper]{article}

%\VignetteIndexEntry{Writing grid Code}

%\VignettePackage{grid}

\newcommand{\code}[1]{\texttt{#1}}

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\newcommand{\grid}{\pkg{grid}}

\newcommand{\grob}{\code{grob}}

\newcommand{\gTree}{\code{gTree}}

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                       \item \textsc{Aside:} \emph{#1}

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\title{Writing \grid{} Code}

\author{Paul Murrell}

\begin{document}

\maketitle

<<echo=FALSE, results=hide>>=

library(grDevices)

library(stats) # for runif()

library(grid)

ps.options(pointsize=12)

options(width=60)

@

The \grid{} system contains a degree of complexity in order

to allow things like editing graphical objects, ``packing'' graphical

objects, and so on.  This means that many of the

predefined Grid graphics functions are

relatively complicated\footnote{Although there are exceptions;  some

functions, such as \code{grid.show.viewport}, are purely for producing

illustrative diagrams and remain simple and procedural.}.

One design aim of \grid{} is to allow users to create simple graphics

simply and not to force them to use complicated concepts or write

complicated code unless they actually need to.  Along similar lines,

it is intended that people should be able to prototype even complex

graphics very simply and then refine the implementation into a more

sophisticated form if necessary.

With the predefined graphics functions being fully-developed and

complicated implementations, there is a lack of examples of simple,

prototype code.  Furthermore, given that the aim is to allow a range

of ways to produce the same graphical output, there is a need for

examples which demonstrate the various stages, from simple to complex,

that a piece of \grid{} code can go through.

This document describes the construction of a scatterplot object, like

that shown below, going from the simplest, prototype implementation to

the most complex and sophisticated.  It demonstrates that if you only

want simple graphics output then you can do it pretty simply and

quickly.  It also demonstrates how to write functions that allow your

graphics to be used by other people.  Finally, it demonstrates how to

make your graphics fully interactive (or at least as interactive as

Grid will let you make it).

@

This document should be read {\em after} the \grid{} Users'

Guide.  Here we are assuming that the reader has an understanding

of viewports, layouts, and units.  For the later sections of the

document, it will also be helpful to have an understanding of

\R{}'s \code{S3} object system.

\section*{Procedural \grid{}}

The simplest way to produce graphical output in Grid is just like

producing standard R graphical output.  You simply issue a series of

graphics commands and each command adds more ink to the plot.  The

purpose of the commands is simply to produce graphics output; in

particular, we are not concerned with any values returned by the

plotting functions.  I will call this \emph{procedural graphics}.

In order to draw a simple scatterplot, we can  issue a series

of commands which draw the various components of the plot.

Here are some random data to plot.

<<>>=

x <- runif(10)

y <- runif(10)

@

\noindent

The first step in creating the plot involves defining a ``data'' region.

This is a region which has sensible scales on the axes for plotting the

data and margins around the outside

for the axes to fit in, with a space for a title at the top.

<<datavp>>=

data.vp <- viewport(x = unit(5, "lines"),

                    y = unit(4, "lines"),

                    width = unit(1, "npc") - unit(7, "lines"),

                    height = unit(1, "npc") - unit(7, "lines"),

                    just = c("left", "bottom"),

                    xscale = range(x) + c(-0.05, 0.05)*diff(range(x)),

                    yscale = range(y) + c(-0.05, 0.05)*diff(range(y)))

@

\noindent

Now we create the data region and draw the components of the plot

relative to it: points, axes, labels, and a title.

<<procplot>>=

pushViewport(data.vp)

grid.points(x, y)

grid.rect()

grid.xaxis()

grid.yaxis()

grid.text("x axis", y = unit(-3, "lines"),

          gp = gpar(fontsize = 14))

grid.text("y axis", x = unit(-4, "lines"),

          gp = gpar(fontsize = 14), rot = 90)

grid.text("A Simple Plot",

          y = unit(1, "npc") + unit(1.5, "lines"),

          gp = gpar(fontsize = 16))

popViewport()

<<fig=TRUE, echo=FALSE, results=hide>>=

<<procplot>>

@

\section*{Facilitating Annotation}

Issuing a series of commands to produce a plot, like in the previous

section, allows the user to have a great deal of flexibility.

It is always possible to recreate viewports in order to add

further annotations.  For example, the following code

recreates the data region in order to place the date

at the bottom right corner.

<<ann1>>=

pushViewport(data.vp)

grid.text(date(), x = unit(1, "npc"), y = 0,

          just = c("right", "bottom"), gp = gpar(col="grey"))

popViewport()

<<fig=TRUE, echo=FALSE, results=hide>>=

<<procplot>>

<<ann1>>

@

When more complex arrangements of viewports are involved, there may be

a bewildering array of viewports created, which may make it difficult

for other users to revisit a particular region of a plot.  A

\code{lattice} plot is a good example.  In such cases, it will be more

cooperative to use \code{upViewport()} rather than

\code{popViewport()} and leave the viewports that were created during

the drawing of the plot.  Other users can then use \code{vpPath}s to

navigate to the desired region.  For example, here is a slight

modification of the original series of commands, where the original

data viewport is given a name and \code{upViewport()} is used at the

end.

<<results=hide>>=

data.vp <- viewport(name = "dataregion",

                    x = unit(5, "lines"),

                    y = unit(4, "lines"),

                    width = unit(1, "npc") - unit(7, "lines"),

                    height = unit(1, "npc") - unit(7, "lines"),

                    just = c("left", "bottom"),

                    xscale = range(x) + c(-0.05, 0.05)*diff(range(x)),

                    yscale = range(y) + c(-0.05, 0.05)*diff(range(y)))

pushViewport(data.vp)

grid.points(x, y)

grid.rect()

grid.xaxis()

grid.yaxis()

grid.text("x axis", y = unit(-3, "lines"),

          gp = gpar(fontsize = 14))

grid.text("y axis", x = unit(-4, "lines"),

          gp = gpar(fontsize = 14), rot = 90)

grid.text("A Simple Plot",

          y = unit(1, "npc") + unit(1.5, "lines"),

          gp = gpar(fontsize = 16))

upViewport()

@

The date is now added using \code{downViewport()} to get to the data

region.

<<results=hide>>=

downViewport("dataregion")

grid.text(date(), x = unit(1, "npc"), y  =  0,

          just = c("right", "bottom"), gp = gpar(col = "grey"))

upViewport()

@

\section*{Writing a \grid{} Function}

Here is the scatterplot code wrapped up as a simple function.

<<funcplot>>=

splot <- function(x = runif(10), y = runif(10), title = "A Simple Plot") {

    data.vp <- viewport(name = "dataregion",

                        x = unit(5, "lines"),

                        y = unit(4, "lines"),

                        width = unit(1, "npc") - unit(7, "lines"),

                        height = unit(1, "npc") - unit(7, "lines"),

                        just = c("left", "bottom"),

                        xscale = range(x) + c(-.05, .05)*diff(range(x)),

                        yscale = range(y) + c(-.05, .05)*diff(range(y)))

    pushViewport(data.vp)

    grid.points(x, y)

    grid.rect()

    grid.xaxis()

    grid.yaxis()

    grid.text("y axis", x = unit(-4, "lines"),

              gp = gpar(fontsize = 14), rot = 90)

    grid.text(title, y = unit(1, "npc") + unit(1.5, "lines"),

              gp = gpar(fontsize = 16))

    upViewport()

}

@

There are several advantages to creating a

function:

\begin{enumerate}

\item We get the standard advantages of a function:

we can reuse and maintain the plot code more easily.

\item We can slightly generalise the plot.  In this case, we can use it for

different data and have a different title.  We could

add more arguments to allow different margins, control over

the axis scales, and so on.

\item The plot can be embedded in other graphics output.

\end{enumerate}

Here is an example which uses the \code{splot()} function to

create a slightly modified scatterplot, embedded within

other \grid{} output.

<<embed, fig=TRUE, results=hide>>=

grid.rect(gp = gpar(fill = "grey"))

message <-

    paste("I could draw all sorts",

          "of stuff over here",

          "then create a viewport",

          "over there and stick",

          "a scatterplot in it.", sep = "\n")

grid.text(message, x = 0.25)

grid.lines(x = unit.c(unit(0.25, "npc") + 0.5*stringWidth(message) +

           unit(2, "mm"),

           unit(0.5, "npc") - unit(2, "mm")),

           y = 0.5,

           arrow = arrow(angle = 15, type = "closed"),

           gp = gpar(lwd = 3, fill = "black"))

pushViewport(viewport(x = 0.5, height = 0.5, width = 0.45, just = "left",

                      gp = gpar(cex = 0.5)))

grid.rect(gp = gpar(fill = "white"))

splot(1:10, 1:10, title = "An Embedded Plot")

upViewport()

@

It is still straightforward to annotate the scatterplot as long as

we have enough information about the viewports.  In this case,

a non-strict \code{downViewport()} will still work (though note

that \code{upViewport({\bf 0})} is required to get right back to the

top level).

<<ann2, echo = FALSE, eval=FALSE>>=

downViewport("dataregion")

grid.text(date(), x = unit(1, "npc"), y  =  0,

          just = c("right", "bottom"), gp = gpar(col = "grey"))

upViewport(0)

<<echo=FALSE, results=hide>>=

<<embed>>

<<ann2>>

@

\section*{Creating \grid{} Graphical Objects}

A \grid{} function like the one in the previous section provides

output which is very flexible and can be annotated in arbitrary ways

and can be embedded within other output.  This is likely

to satisfy most uses.

However, there are some things that cannot be done (or at least would

be extremely hard to do) with such a function.  The output produced by

the function cannot be addressed as a coherent whole.  It is not

possible, for example, to to change the \code{x} and \code{y} data

used in the plot and have the points and axes update automatically.

There is no scatterplot object to save; the individual components

exist, but they are not bound together as a whole.  If/when these

sorts of issues become important, it becomes necessary to create a

\grid{} graphical object (a \grob{}) to represent the plot.

The first step is to write a function which will create a \grob{}

-- a \emph{constructor} function.  In most cases, this will involve

creating a special sort of \grob{} called a \gTree{};  this is just

a \grob{} that can have other \grob{}s as children.  Here's an example

for creating an \code{splot} \grob{}.  I have put bits of the

construction into separate functions, for reasons which will become

apparent later.

<<>>=

splot.data.vp <- function(x, y) {

  viewport(name = "dataregion",

           x = unit(5, "lines"),

           y = unit(4, "lines"),

           width = unit(1, "npc") - unit(7, "lines"),

           height = unit(1, "npc") - unit(7, "lines"),

           just = c("left", "bottom"),

           xscale = range(x) + c(-.05, .05)*diff(range(x)),

           yscale = range(y) + c(-.05, .05)*diff(range(y)))

}

splot.title <- function(title) {

      textGrob(title, name = "title",

               y = unit(1, "npc") + unit(1.5, "lines"),

               gp = gpar(fontsize = 16), vp = "dataregion")

}

splot <- function(x, y, title, name=NULL, draw=TRUE, gp=gpar(), vp=NULL) {

    spg <- gTree(x = x, y = y, title = title, name = name,

                 childrenvp  =  splot.data.vp(x, y),

                 children = gList(rectGrob(name = "border",

                                           vp = "dataregion"),

                 xaxisGrob(name = "xaxis", vp = "dataregion"),

                 yaxisGrob(name = "yaxis", vp = "dataregion"),

                 pointsGrob(x, y, name = "points", vp = "dataregion"),

                 textGrob("x axis", y = unit(-3, "lines"), name = "xlab",

                          gp = gpar(fontsize = 14), vp = "dataregion"),

                 textGrob("y axis", x = unit(-4, "lines"), name = "ylab",

                          gp = gpar(fontsize = 14), rot = 90,

                          vp = "dataregion"),

                 splot.title(title)),

                 gp = gp, vp = vp,

                 cl = "splot")

    if (draw) grid.draw(spg)

    spg

}

@

There are four important additions to the argument list compared

to the original \code{splot()} function:

\begin{enumerate}

\item The \code{name} argument allows a string identifier to be

  associated with the scatterplot object we create.  This is important

  for being able to specify the scatterplot when we try to edit it

  after drawing it and/or when it is part of a larger \grob{} (see

  later examples).

\item The \code{draw} argument makes it possible to use the function

  in a procedural manner as before:

<<splotgrob, eval=FALSE, echo=FALSE>>=

sg <- splot(1:10, 1:10, "Same as Before", name = "splot", draw = FALSE)

<<>>=

splot(1:10, 1:10, "Same as Before", name = "splot")

downViewport("dataregion")

grid.text(date(), x = unit(1, "npc"), y = 0,

          just = c("right", "bottom"), gp = gpar(col = "grey"))

upViewport(0)

@

\item The \code{gp} argument allows the user to supply \code{gpar()}

  settings for the scatterplot as a whole.

\item The \code{vp} argument allows the user to supply a viewport for

  the \code{splot} \grob{} to be drawn in.  This is especially useful

  for specifying a \code{vpPath} when the \code{splot} is used as a

  component of another \grob{} (see scatterplot matrix example below).

\end{enumerate}

The important parts of the \gTree{}  definition are:

\begin{enumerate}

\item The \code{children} argument provides a list of \grob{}s which

  are part of the scatterplot.  When the scatterplot is drawn, all

  children will be drawn.  Notice that instead of the procedural

  \code{grid.*()} functions we use \code{*Grob()} functions which just

  produce \grob{}s and do not perform any drawing.  Also notice that I

  have given each of the children a name; this will make it possible

  to access the components of the scatterplot (see later examples).

\item The \code{childrenvp} argument provides a viewport (or

  \code{vpStack}, \code{vpList}, or \code{vpTree}) which will be

  pushed before the children are drawn.  The difference between this

  argument and the \code{vp} argument common to all \grob{}s is that

  the \code{vp} is pushed before drawing the children and then popped

  after, whereas the \code{childrenvp} gets pushed \emph{and} then a

  call to \code{upViewport()} is made before the children are drawn.

  This allows the children to simply specify the viewport they should

  be drawn in by way of a \code{vpPath} in their \code{vp} argument.

  In this way, viewports remain available for further annotation such

  as we have already seen in procedural code.

\item The \code{gp} and \code{vp} arguments are automatically handled

  by the \gTree{} drawing methods so that \code{gpar()} settings will

  be enforced and the viewport will be pushed when the \code{splot} is

  drawn.

\item The \code{cl} argument means that the \grob{} created is a

  special sort of \grob{} called \code{splot}.  This will allow us to

  write methods specifically for our scatterplot (see later examples).

\end{enumerate}

@

Now that we have a \grob{}, there are some more interesting things

that we can do with it.  First of all, the \code{splot} \grob{}

provides a container for the \grob{}s which make up the scatterplot.

If we modify the \code{splot} \grob{}, it affects all of the children.

<<results=hide>>=

splot(1:10, 1:10, "Same as Before", name = "splot")

grid.edit("splot", gp = gpar(cex=0.5))

<<fig=TRUE, echo=FALSE, results=hide>>=

<<splotgrob>>

sg <- editGrob(sg, gp = gpar(cex = 0.5))

grid.draw(sg)

@

We can access elements of the \code{splot} \grob{} to edit them

individually.

<<results=hide>>=

splot(1:10, 1:10, "Same as Before", name = "splot")

grid.edit(gPath("splot", "points"), gp = gpar(col = 1:10))

<<fig=TRUE, echo=FALSE, results=hide>>=

<<splotgrob>>

sg <- editGrob(sg, gPath = "points", gp = gpar(col = 1:10))

grid.draw(sg)

@

With a little more work we can make the scatterplot a bit more dynamic.

The following describes a \code{editDetails()} method for the

\code{splot} \grob{}.  This will be called whenever a scatterplot

is edited and will update the components of the scatterplot.

<<>>=

editDetails.splot <- function(x, specs) {

    if (any(c("x", "y") %in% names(specs))) {

        if (is.null(specs$x)) xx <- x$x else xx <- specs$x

        if (is.null(specs$y)) yy <- x$y else yy <- specs$y

        x$childrenvp <- splot.data.vp(xx, yy)

        x <- addGrob(x, pointsGrob(xx, yy, name = "points",

                                   vp = "dataregion"))

    }

  x

}

splot(1:10, 1:10, "Same as Before", name = "splot")

grid.edit("splot", x = 1:100, y = (1:100)^2)

<<fig=TRUE, echo=FALSE, results=hide>>=

<<splotgrob>>

sg <- editGrob(sg, x = 1:100, y = (1:100)^2)

grid.draw(sg)

@

The \code{splot} \grob{} can also be used in the construction of other

\grob{}s.  Here's a simple scatterplot matrix \grob{}\footnote{{\bf

    Warning:} As the number of \grob{}s in a \gTree{} gets larger the

  construction of the \gTree{} will get slow.  If this happens, the

  best solution is to just use a \grid{} function rather than a

  \gTree{}, and wait for me to implement some ideas for speeding

  things up!}.

<<fig=TRUE>>=

cellname <- function(i, j) paste("cell", i, j, sep = "")

splom.vpTree <- function(n) {

    vplist <- vector("list", n^2)

    for (i in 1:n)

        for (j in 1:n)

            vplist[[(i - 1)*n + j]] <-

              viewport(layout.pos.row = i, layout.pos.col = j,

                       name = cellname(i, j))

    vpTree(viewport(layout = grid.layout(n, n), name = "cellgrid"),

    do.call("vpList", vplist))

}

cellpath <- function(i, j) vpPath("cellgrid", cellname(i, j))

splom <- function(df, name = NULL, draw = TRUE) {

    n <- dim(df)[2]

    glist <- vector("list", n*n)

    for (i in 1:n)

        for (j in 1:n) {

            glist[[(i - 1)*n + j]] <-if (i == j)

                textGrob(paste("diag", i, sep = ""),

                         gp = gpar(col = "grey"), vp = cellpath(i, j))

            else if (j > i)

                textGrob(cellname(i, j),

                         name = cellname(i, j),

                         gp = gpar(col = "grey"), vp = cellpath(i, j))

            else

                splot(df[,j], df[,i], "",

                      name = paste("plot", i, j, sep = ""),

                      vp = cellpath(i, j),

                      gp = gpar(cex = 0.5), draw = FALSE)

        }

    smg <- gTree(name = name, childrenvp = splom.vpTree(n),

                 children = do.call("gList", glist))

    if (draw) grid.draw(smg)

    smg

}

df <- data.frame(x = rnorm(10), y = rnorm(10), z = rnorm(10))

splom(df)

@

This \grob{} can be edited as usual:

<<>>=

splom(df)

grid.edit("plot21::xlab", label = "", redraw = FALSE)

grid.edit("plot32::ylab", label = "", redraw = FALSE)

grid.edit("plot21::xaxis", label = FALSE, redraw = FALSE)

grid.edit("plot32::yaxis", label = FALSE)

<<splomgrob, eval=FALSE, echo=FALSE>>=

smg <- splom(df, draw = FALSE)

<<fig=TRUE, echo=FALSE, results=hide>>=

<<splomgrob>>

smg <- editGrob(smg, gPath = "plot21::xaxis", label = FALSE)

smg <- editGrob(smg, gPath = "plot21::xlab", label = "")

smg <- editGrob(smg, gPath = "plot32::yaxis", label = FALSE)

smg <- editGrob(smg, gPath = "plot32::ylab", label = "")

grid.draw(smg)

@

But of more interest, because this is a \grob{}, is the

\emph{programmatic} interface.  With a \grob{} (as opposed to a

function) it is possible to modify the description of what is being

drawn via an API (as opposed to having to edit the original code).  In

the following, we remove one of the ``spare'' cell labels and put in

its place the current date.

<<>>=

splom(df, name = "splom")

grid.remove("cell12")

grid.add("splom", textGrob(date(), name = "date",

                           gp = gpar(fontface = "italic"),

                           vp = "cellgrid::cell12"))

<<fig=TRUE, echo=FALSE, results=hide>>=

<<splomgrob>>

smg <- removeGrob(smg, "cell12")

smg <- addGrob(smg, textGrob(date(), name = "date",

                             gp = gpar(fontface = "italic"),

                             vp = "cellgrid::cell12"))

grid.draw(smg)

@

With the date added as a component of the scatterplot matrix, it is

saved as part of the matrix.  The next sequence saves the scatterplot

matrix, loads it again, extracts the bottom-left plot and the date

and just draws those two objects together.

<<>>=

splom(df, name = "splom")

grid.remove("cell12")

grid.add("splom", textGrob(date(), name = "date",

                           gp = gpar(fontface = "italic"),

                           vp = "cellgrid::cell12"))

smg <- grid.get("splom")

save(smg, file = "splom.RData")

load("splom.RData")

plot <- getGrob(smg, "plot31")

date <- getGrob(smg, "date")

plot <- editGrob(plot, vp = NULL, gp = gpar(cex = 1))

date <- editGrob(date, y = unit(1, "npc") - unit(1, "lines"), vp = NULL)

grid.newpage()

grid.draw(plot)

grid.draw(date)

<<fig=TRUE, echo=FALSE, results=hide>>=

<<splomgrob>>

smg <- removeGrob(smg, "cell12")

smg <- addGrob(smg, textGrob(date(), name = "date",

                             gp = gpar(fontface = "italic"),

                             vp = "cellgrid::cell12"))

save(smg, file = "splom.RData")

load("splom.RData")

plot <- getGrob(smg, "plot31")

date <- getGrob(smg, "date")

plot <- editGrob(plot, vp = NULL, gp = gpar(cex = 1))

date <- editGrob(date, y = unit(1, "npc") - unit(1, "lines"), vp = NULL)

grid.draw(plot)

grid.draw(date)

@

All of this may seem a bit irrelevant to interactive use, but it does

provide a basis for creating an editable plot interface as used in

M.~Kondrin's \pkg{Rgrace} package (available on CRAN 2005--7).

\end{document}