R is rich with facilities for creating and developing interesting graphics.
Base R contains functionality for many plot types including coplots,
mosaic plots, biplots, and the list goes on. There are devices such as
postscript, png, jpeg and pdf for outputting graphics as well as device
drivers for all platforms running R.
lattice
and
grid are supplied with R's recommended packages and are
included in every binary distribution.
lattice
is an R
implementation of William Cleveland's trellis graphics, while grid
defines a much more flexible graphics environment than the base R graphics.
R's base graphics are implemented in the same way as in the S3
system developed by Becker, Chambers, and Wilks. There is a static
device, which is treated as a static canvas and objects are drawn on
the device through R plotting commands. The device has a set of global
parameters such as margins and layouts which can be manipulated by the
user using
par()
commands. The R graphics engine does not maintain a
user visible graphics list, and there is no system of double
buffering, so objects cannot be easily edited without redrawing a whole
plot. This situation may change in R 2.7.x, where developers are working on double
buffering for R devices. Even so, the base R graphics can produce many plots with
extremely fine graphics in many specialized instances.
One can quickly run into trouble with R's base graphic system if
one wants to design complex layouts where scaling is maintained
properly on resizing, nested graphs are desired or more interactivity
is needed. grid was designed by Paul Murrell to overcome
some of these limitations and as a result packages like
lattice,
ggplot2,
vcd
or
hexbin
use
grid for the underlying primitives. When using plots designed with
grid one needs to keep in mind that grid is based on a system of
viewports and graphic objects. To add objects one needs to use grid
commands, e.g.,
grid.polygon()
rather than
polygon(). Also grid
maintains a stack of viewports from the device and one needs to make
sure the desired viewport is at the top of the stack. There is a great
deal of explanatory documentation included with grid as
vignettes.
The graphics packages in R can be organized roughly into the following
topics, which range from the more user oriented at the top to the more developer
oriented at the bottom. The categories are not mutually exclusive but are for the
convenience of presentation:

Plotting
:
Enhancements for specialized plots can be found in
plotrix,
for polar plotting,
vcd
for categorical data,
hexbin
for hexagon binning,
gclus
for ordering plots and
gplots
for some plotting enhancements. Some specialized graphs, like Chernoff faces
are implemented in
aplpack, which also has a nice implementation
of Tukey's bag plot.
For 3D plots
lattice,
scatterplot3d
and
misc3d
provide a selection of plots for
different kinds of 3D plotting.
scatterplot3d
is based on R's base
graphics system, while
misc3d
is based on
rgl. The package
onion
for visualizing
quaternions and octonions is well suited to display 3D graphics
based on derived meshes.

Graphic Applications
: This area is not much different from the plotting
section except that these packages have tools that may not for display, but can
aid in creating effective displays. Also included are packages with more esoteric
plotting methods. For specific subject areas, like maps, or clustering the excellent
task views contributed by other dedicated useRs is an excellent place to start.

Effect ordering
:
The
gclus
package focuses on the ordering of graphs to accentuate cluster
structure or natural ordering in the data. While not for graphics directly
cba
and
seriation
have functions for creating 1 dimensional orderings from higher dimensional criteria.
For ordering an array of displays,
biclust
can be useful.

Large Data Sets
:
Large data sets can present very different challenges from moderate and small
datasets. Aside from overplotting, rendering 1,000,000 points can tax even modern
GPU's. For bivariate data
ash
can produce a bivariate smoothed histogram very
quickly, and
hexbin
can bin bivariate
data onto a hexagonal lattice, the advantage being that the irregular lines and
orientation of hexagons do not create linear artifacts. For multivariate data,
hexbin
can be used to create a scatterplot matrix, combined with
lattice.
An alternative is to use
scagnostics
to produce a scatterplot matrix
of "data about the data", and look for interesting combinations of variables.

Trees and Graphs
:
ape
and
ade4
have functions for plotting
phylogenetic trees, which can be used for plotting dendrograms from
clustering procedures. While these packages produce decent graphics, they
do not use sophisticated algorithms for node placement, so may not be useful
for very large trees.
igraph
has the TilfordRheingold algorithm
implemented and is useful for plotting larger trees.
diagram
has
facilities for flow diagrams and simple graphs. For more sophisticated graphs
Rgraphviz
and
igraph
have functions for plotting and
layout, especially useful for representing large networks.

Graphics Systems
:
lattice
is built on top of the grid
graphics system and is an R implementation of William Cleveland's trellis
system for SPLUS.
lattice
allows for building many types of plots
with sophisticated layouts based on conditioning.
ggplot2
is an R
implementation of the system described in "A Grammar of Graphics" by Leland
Wilkinson. Like
lattice,
ggplot2
(also built on top of grid)
assists in trellislike graphics, but allows for much more. Since it is built on
the idea of a semantics for graphics there is much more emphasis on reshaping
data, transformation, and assembling the elements of a plot.

Devices
: Whereas grid is built on top of
the R graphics engine, many in the R community have found the R
graphics engine somewhat inflexible and have written separate device
drivers that either emphasize interactivity or plotting in various
graphics formats. R base supplies devices for PostScript, PDF, JPEG
and other formats. Devices on CRAN include
cairoDevice
which
is a device based libcairo, which can actually render to many device
types. The cairo device is designed to work with
RGtk2,
which is an interface to the Gimp Tool Kit, similar to pyGTK2.
rgl
provides a device driver
based on OpenGL, and is good for 3D and interactive
development. Lastly, the Augsburg group supplies a set of packages
that includes a Javabased device,
JavaGD.

Colors
: The package
colorspace
provides a set of functions for
transforming between color spaces and
mixcolor()
for mixing colors
within a color space.
Based on the HCL colors provided in
colorspace,
vcd
provides a set of functions for choosing color palettes suitable for
coding categorical variables (
rainbow_hcl()) and numerical
information (
sequential_hcl(),
diverge_hcl()). Similar
types of palettes are provided in
RColorBrewer.
dichromat
is focused on palettes for colorimpaired viewers.

Interactive Graphics
: There are several efforts to
implement interactive graphics systems that interface well with R. In an interactive
system the user can interactively query the graphics on the screen with the mouse,
or a moveable brush to zoom, pan and query on the device as well as link with
other views of the data.
The RoSuDA repository maintained and developed by the
University of Augsburg group has two packages,
iplots
and iwidgets as
well as their Java development environment including a Java
device,
JavaGD. Their interactive graphics tools contain functions for
alpha blending, which produces darker shading around areas with more
data. This is exceptionally useful for parallel coordinate plots where
many lines can quickly obscure patterns. Lastly, the
rgl
package has mechanisms for
interactive manipulation of plots, especially 3D rotations and surfaces.

Development
: For development of specialized
graphics packages in R, grid should probably be the first
consideration for any new plot type.
rgl
has better tools for
3D graphics, since the device is interactive, though it can be slow.
An alternative is to use Java and
the Java device in the RoSuDA packages, though Java has its own
drawbacks. For porting plotting code to grid, using the
package
gridBase
presents a nice intermediate step to embed
base graphics in grid graphics and vice versa.