EVOLUTION-MANAGER

Edit File: plotexample.Rnw

% File src/library/grid/vignettes/plotexample.Rnw
% Part of the R package, https://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}}
\newcommand{\pkg}[1]{{\normalfont\fontseries{b}\selectfont #1}}
\newcommand{\grid}{\pkg{grid}}
\newcommand{\grob}{\code{grob}}
\newcommand{\gTree}{\code{gTree}}
\newcommand{\R}{{\sffamily R}}
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\setlength{\textwidth}{140mm}
\setlength{\oddsidemargin}{10mm}

\newcommand{\aside}[1]{\begin{list}{}
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                       \item \textsc{Aside:} \emph{#1}
                       \end{list}}

\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}