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About 

iPlots is a package for the R statistical environment (see www.r-project.org) which provides high interaction statistical graphics, written in Java. It offers a wide variety of plots, including histograms, barcharts, scatterplots, boxplots, fluctuation diagrams, parallel coordinates plots and spineplots. All plots support interactive features, such as querying, linked highlighting, color brushing, and interactive changing of parameters. The iPlots package was introduced at the DSC-2003 (http://www.ci.tuwien.ac.at/Conferences/DSC-2003/). The various additions of Version 2.0 were introduced at the userR!2006 Conference (http://www.r-project.org/useR-2006/).

Besides interactive plots, iPlots also provides an API for managing plots and adding user-defined objects, such as lines or polygons to a plot.

Version 2.0 not only adds new multivariate plots and interactive features throughout all plots, but also lays the foundations for interactive customizable plots (ICP).

News:

  • 2009/07/01 the current development version can be found here. The initial release is planned for September this year.

  • 2009/04/29 iplots eXtreme will be presented at the useR!2009 and the DSC2009 meetings and released soon after.

  • 2007/08/07 Released iplots_1.1-1 on CRAN. iSets and iVars are now fully transparent objects and support all usual opeartions like indexing subsetting etc. as well as sub-assignments. The latter will use hot-linking, i.e. any changes to the data are immediately reflected in the plots. See ?iset and ?ivar in R. Mac users can now use iplots in the console and R GUI (although somewhat limited) - JGR is still the recommended way.

  • 2007/04/18 Released iplots_1.0-8 on CRAN. Added initial support for (geographic) maps.

  • 2006/10/11 Released iplots_1.0-5 on CRAN. Beside several bugfixes and improved documentation it also features support for variable-length arguments (e.g. imosaic(AirBags, Cylinders, Origin) and a more sophisticated registration of iVars which prevents variable duplication.

  • 2006/10/03 Launch of new website, more documentation will be added gradually.

  • 2006/08/20 Release of iplots_1.0-3 (Version 2.0) and binaries for R 2.3.1.
    iPlots are now also officially available from CRAN.
  • 2006/05/05 Released iplots_0.2-1 (including Windows and Mac binaries for R 2.3.0) to go along with JGR. Please note that iPlots are under continuing development now and features are being added on a daily basis, so you may want to update the package evey now and then even if there is no official release. The next major release is expected for useR!2006.

  • 2005/01/25 Updated webpages; latest versions of iPlots are available from our R repositories and binaries as a part of the JGR distribution.

  • 2004/06/26 iPlots are now by default delivered with JGR (Java GUI for R). It is recommended to use iPlots with JGR, because iPlots are seamlessly integrated into JGR.
Further Reading:
 Known Bugs and Features:
  • View menus need to be cleaned up and consolodated with the context menus.
  • More plot parameters need to be exposed on the R level.
  • Some plots on some platforms do not update properly after creation.
  • ...
Contributers:
  • Simon Urbanek
  • Tobias Wichtrey
  • Alex Gouberman
  • Martin Theus

 
 Plots
(Right now, we mostly use the help-file examples - feel free to contribute!)
Mosaic Plot 
Mosaic plots in iPlots are fully interactive, i.e. they not only allow standard selection, highlighting and color brushing but also reordering of variables and the addition and exclusion of variables.
> library(MASS)
> data(Cars93)
> attach(Cars93)
> imosaic(data.frame(AirBags,Cylinders,Origin))
Here is what you will get:
Simple Mosaic Plot of Cars Data

(to make the plot more interesting, we linked all cars with more
then 200 hp. The query provides exact information for the cell)
Besides the standard mosaic plot three further variations are implemented in iPlots:
  1. Same Binsize
  2. Fluctuation Diagram
  3. Multiple Barchart
  4. (Model View)
To switch between the different representations, use the context menu. All variations have their particular strengths and weaknesses, so you will need some experience to use them most efficiently.

To rearrange the variables use the four arrow keys ... you will soon get the idea of how to manage the variables!

Since most attempts to optimally place labels on mosaic plots fail sooner or later, we use a simple linear spacing for the first variables. Any further information should be queried using <ctrl>-mouse-over.


Barcharts 
Barcharts in iPlots also feature Spineplots (use ctrl-s or the "View" menu to switch between the two representations)
 
> library(MASS)
> data(Cars93)
> attach(Cars93)
> ibar(Cylinders)
yields
Barchart

To get a spineplot via the command line, there are two alternatives:
 
# directly upon creation

> ibar(Cylinders, isSpine=T)

# or switching by changing the plot options of the barchart
# (if no plot is specified, the ibar must be the current plot)

> iplot.opt(isSpine=T)
Spineplot

Bars can be reordered either by using the options in the "View" menu or by <alt>-dragging a bar to its desired position.
iPlots respect the order of a factor, so orderings from within R can be used to order the categories in a barchart.
 
> levels(AirBags)
[1] "Driver & Passenger" "Driver only"    "None"         

> AirBagsO <- ordered(AirBags, 
              c("None", "Driver only", "Driver & Passenger"))

> levels(AirBagsO)
[1] "None"               "Driver only"    "Driver & Passenger"

> ibar(AirBags)
ID:9 Name: "Barchart (AirBags.5)"

> ibar(AirBagsO)
ID:10 Name: "Barchart (AirBagsO.6)"
Original Order       Ordered Factor


Maps 
Interactive maps - which use the data-format of the maptools package - will be availabe soon!


Parallel
Plots 
There is a whole family of parallel plots in iPlots.

I. Parallel Coordinate Plot

A parallel coordinate plot connects all cases by lines.
 
# Make a PCP for all continuous variables ...

> ipcp(Cars93[c(4:8,12:15,17,19:25)])
The default plot will scale all variables individually between min and max.
Parallel Coordinate Plot

There are various options in the "View" menu including
  • Common or individual scale
  • Show or hide dots or lines (so a parallel dotplot can be drawn)
  • Show only selected cases
Most other options are general options and will be discussed in the conventions section.

II. Parallel Boxplot

Parallel boxplots are quite similar to PCPs but feature statistics like the median and the hinges, which makes them more useful for comparing distributions of variables.
 
# Make a parallel boxplot for all price variables ...

> ibox(Cars93[4:6])
Boxplot
 Boxplot Common Scale
 Boxplot with PCP only highlight

Parallel boxplot for the three prices in the dataset. Each variable uses the full range.

Using a common scale in the parallel boxplot gives sensible results:
max price > price > min price
Showing only selected cases with the corresponding lines.

All options can be found in the "View" menu of the plot.
(Note, that the scale is only displayed when all variables share the same scale!)

III. Boxplot y by x

Boxplots y by x are quite different from the two other parallel plots, as they are conditional plots,  showing boxplots by group. If ibox() is called with a continuous variable and a factor, a boxplot y by x is created
 
# split the boxplot for horsepower by number of cylinders

> ibox(Horsepower, Cylinders)
Boxplot y by x

This example shows nicely that all cars having more than 200hp (this is the selected group) have either 6 or more cylinders or a rotary engine. Obviously, a boxplot y by x always uses the same scale for all boxplots for a proper comparison.


Scatterplots 
The scatterplot is probably the most frequently used plot of all statistical graphics. Scatterplots in iPlots can be used in the same way as the standard scatterplot in R.
 
> iplot(Horsepower, MPG.city)
Scatterplot

Looking at the above example, a natural step would be to add a scatterplot smoother to the plot.
 
# create a default lowess smoother

> l <- lowess(Horsepower, MPG.city)

# use ilines() to add the smoother to the iplot()

> ilines(l)
PlotPolygon(coord=1:1,dc=PlotColor(black),fc=none,points=93,...) 

# we like to have it a bit rougher, so we remove the first smooth
# and rerun the lowess and plot it again.

> iobj.rm()

> l <- lowess(Horsepower, MPG.city, f=0.5)

> ilines(l)
PlotPolygon(coord=1:1,dc=PlotColor(black),fc=none,points=93,...)
Lowess smoother

We can also add some additional information to the plot:
 
# set point size to 5 and color the points by number of cylinders

> iplot.opt(ptDiam=5, col=unclass(Cylinders))
is denn scho Weihnachten?

Many other options for the scatterplot within iPlots can be found in the "View" menu.


Histograms 
Histograms in iPlots are fully interactive and can also be switched to the spineogram view.
 
> ihist(Horsepower)
Histogram

Binwith and anchorpoint can either be changed interactively by <alt>-dragging the leftmost interval border (anchorpoint) or any other bin break (binwidth), or by explicitly specifying the two parameters via iplot.opt() or when creating the plot.
 
> iplot.opt(anchor=25, binw=25)
iopt

Histograms rescale by default when their parameters are changed, which can be controlled by two flags (see the help pages for details).
 
Selections 
Selection in all iPlots can be done via selecting single objects like a point, a bar/rectangle or a line, or by creating a drag-box, which selects all points within the rectangle.

Via the iset-functions, the selection state of an iplot session can be queried and modified.
 
# get selected indices

> iset.selected()
 [1] 76 63 59 52 30 10  5  2 50 57 48 11 28 19

# what proportion of the dataset is selcted?

> sum(sign(iset.selected()))/length(Horsepower)
[1] 0.1505376

# select all cases for cars with more than 150hp

> iset.select(Horsepower >= 150)
bla bla

All plots, which share the same iset are updated immediately.

To perform more complex selections, pressing the <shift>-modifier will select data in XOR mode.


Color Brush

Selections are a transient attribute of the data. Whenever a new selection is defined, the old selection state is overwritten. A persistent color attribute can be defined using color brushing within iPlots.
Color brushing can either be applied via the col=... option in iplot.opt() or any other iPlot graphics, or via the "View > Set Colors (CB)" menu command.
Color Brush 1  Color Brush 2

The same could be done from within R (though using the standard R colors) with:
 
# set colors according to Cylinders (using standard R colors)

> iplot.opt(col=unclass(Cylinders))
A second option for using color brushing is to apply rainbow colors over the range of a continuous variable. The menu command "View > Set Colors (rainbow)" will set the color scheme.
Color Brush 4  Color Brush 3


iObjects 
WIth iObjects you can annotate plots with further graphical elements. iObjects are
  • ilines()
  • itext()
  • iabline()
Here is a simple example
 
# create the scatterplot

iplot(EngineSize, Horsepower)

# select all but the outliers, save the selection subset for 
# the regression

> subs <- iset.selected()

# add the linear regression to the scatterplot

> iabline(lm(Horsepower ~ EngineSize, subset=subs))
iabline

Not really a good model, but at least we got rid of the outliers very easily.

There are some housekeeping functions for iObjects (please refer to the help files for details).
  • iobj.cur()
  • iobj.get()
  • iobj.list()
  • iobj.next()
  • iobj.opt()
  • iobj.prev()
  • iobj.rm()
  • iobj.set()
 
 Conventions 
Interactive graphics benefits strongly from an interface which is highly consistent and has a flat learning curve. The most important functionality within iPlots are the keyboard shortcuts and modifier keys.
  • <shift>-selection is always in XOR-mode
  • to query an object or plot canvas, mouse-over while <ctrl> is pressed
  • <command>-drag-box (MAC), middle mouse button zooms in and out
    (to zoom out, just click, i.e. a zero size drag-box)
  • <command>-r / <ctrl>-r rotates plots

Many other common functions can be found in the "View" menu.

The handling of iPlots is done via a set of maintenance functions (see help files for details). The basic concept follows the handling of devices within R. It is, however, advisable to use plot objects directly, especially in scripts.
  • iplot.cur()
  • iplot.data()
  • iplot.list()
  • iplot.new()
  • iplot.next()
  • iplot.off()
  • iplot.opt()
  • iplot.prev()
  • iplot.set()
The same principles are used for maintaining the so called isets, which are the datasets defining case linking in iPlots.
  • iset.brush()
  • iset.col()
  • iset.cur()
  • iset.df()
  • iset.list()
  • iset.new()
  • iset.next()
  • iset.prev()
  • iset.sel.changed()
  • iset.select()
  • iset.selectAll()
  • iset.selected()
  • iset.selectNone()
  • iset.set()


α-Channel 
The α-channel can be used to specify the transparency of a painted object. This is useful, when plotting very many objects , which would otherwise result in heavy overplotting. Thus the density of objects can be easily displayed.

All glyph-based plots in iPlots offer α-channel transparency to handle large data and avoid overplotting.

Alpha

(α-transparency in parallel coordinate plots)

Alpha in scatterplots

(α-transparency in a scatterplot  -  you recognise the dataset?!)

Use the arrow keys (left and right) to interactively increase or decrease the transparency.


FAQ

Where can I download iPlots?
  • The current version of iPlots for R can be downloaded from CRAN. Use either the package manager from your favorite GUI or type: install.packages("iplots",dep=TRUE)
  • The latest source packages can be found in our R repositories:

Is there anything else I need to install in order to use iPlots?
iPlots needs SUN's JRE to run. For Windows and OS X it is recommended to use at least Sun Java 1.4 or higher.
For other UNIX operating systems (in particular Linux) Sun Java 1.5 or higher is highly recommended (64-bit Linux requires at least Java 1.6 beta). For the best user-experience, iPlots can be run from within JGR.


rJava fails to load on Unix, what can I do?
If you get a message similar to the following: 
Error in dyn.load(x, as.logical(local), as.logical(now)) :
        unable to load shared library '/usr/lib/R/library/rJava/libs/rJava.so':
  libjvm.so: cannot open shared object file: No such file or directory
then your R was configured without Java support. To enable Java support in R, make sure you have installed R 2.4.0 or higher, Java works (e.g. java -version) and run as root: 
R CMD javareconf
Other Problems
Please post questions and bug reports to the mailing list stats-rosuda-devel.

Issues of general interest will be posted on this page.

Last update: Martin Theus, 04/29/2009