Robert I. Kabacoff - R in action

16.4.3 latticist

The latticist package lets you explore a data set using lattice displays. It provides a graphic user interface to the graphs described in section 16.2, but it can also be used to create displays from the vcd package (see chapter 11, section 11.4). If desired, latticist and can also be integrated with playwith. For example, executing the following code

library(latticist)

mtcars$cyl <- factor(mtcars$cyl) mtcars$gear <- factor(mtcars$gear) latticist(mtcars, use.playwith=TRUE)

will bring up the interface in figure 16.13.

In addition to having the playwith functionality (point identification, annotation, zooming, panning, styles), the user can now create lattice graphs by selecting from drop-down menus and buttons. To learn more about the latticist package, see http://code.google.com/p/latticist/.

Figure 16.13 playwith window with latticist functionality. The user can create lattice and vcd graphs interactively.

A similar interface is available for ggplot2 graphs, through Plot Builder, a plug-in for Deducer, a popular GUI for R (see appendix A). Because it can’t be run from the R console, we won’t discuss it here. If you’re interested, visit the Deducer website at www. deducer.org.

16.4.4Interactive graphics with the iplots package

Whereas playwith and latticist allow you to interact with a single graph, the iplots package takes interaction in a different direction. This package provides interactive mosaic plots, bar plots, box plots, par-

Listing 16.6 iplots demonstration

library(iplots)

attach(mtcars)

cylinders <- factor(cyl) gears <- factor(gear) transmission <- factor(am) ihist(mpg)

ibar(gears) iplot(mpg, wt)

ibox(mtcars[c("mpg", "wt", "qsec", "disp", "hp")]) ipcp(mtcars[c("mpg", "wt", "qsec", "disp", "hp")]) imosaic(transmission, cylinders)

detach(mtcars)

Six windows containing graphs will open. Rearrange them on the desktop so that each is visible (each can be resized if necessary). A portion of the display is provided in figure 16.14.

Now try the following:

■Click on the three-gear bar in the Barchart (gears) window. The bar will turn red. In addition, all cars with three-gear engines will be highlighted in the other graphic windows.

■Mouse down and drag to select a rectangular region of points in the Scatter plot (wt vs mpg) window. These points will be highlighted and the corresponding observations in every other graphics window will also turn red.

■Hold down the Ctrl key and move the mouse pointer over a point, bar, box plot, or line in one of the graphs. Details about that object will appear in a pop-up window.

■Right-click on any object and note the options that are offered in the context menu. For example, you can right-click on the Boxplot (mpg) window and change the graph to a parallel coordinates plot (PCP).

■You can drag to select more than one object (point, bar, and so on) or use Shiftclick to select noncontiguous objects. Try selecting both the threeand five-gear bars in the Barchart (gears) window.

The functions in the iplots package allow you to explore the variable distributions and relationships among variables in subgroups of observations that you select interactively. This can provide insights that would be difficult and time-consuming to obtain in other ways. For more information on the iplots package, visit the project website at http://rosuda.org/iplots/.

Figure 16.14 An iplots demonstration created by listing 16.6. Only four of the six windows are displayed to save room. In these graphs, the user has clicked

on the three-gear bar in the bar chart window.

afterword: Into the rabbit hole

We’ve covered a broad range of topics in the book, including major ones like the R development environment, data management, traditional statistical models, and statistical graphics. We’ve also covered hidden gems like resampling statistics, missing values imputation, and interactive graphics. The great (or perhaps infuriating) thing about R is that there’s always more to learn.

R is a large, robust, and evolving statistical platform and programming language. With so many new packages, frequent updates, and new directions, how can a user stay current? Happily, many websites support this active community and provide coverage of platform and package changes, new methodologies, and a wealth of tutorials. I’ve listed some of my favorite sites below.

The R Project (http://www.r-project.org/)

The official R website and your first stop for all things R. The site includes extensive documentation, including An Introduction to R, The R Language Definition, Writing R Extensions, R Data Import/Export, R Installation and Administration, and The R FAQ.

The R Journal (http://journal.r-project.org/)

A freely accessible refereed journal containing articles on the R project and contributed packages.

R Bloggers (http://www.r-bloggers.com/)

A central hub (blog aggregator) collecting content from bloggers writing about R. Contains new articles daily. I’m addicted to it.

Planet R (http://planetr.stderr.org)

Another good site-aggregator, including information from a wide range of sources. Updated daily.

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appendix A Graphic user interfaces

You turned here first, didn’t you? By default, R provides a simple command-line interface (CLI). The user enters statements at a command-line prompt (> by default) and each command is executed one at a time. For many data analysts, the CLI is one of R’s most significant limitations.

There have been a number of attempts to create more graphical interfaces, ranging from code editors that interact with R (such as RStudio), to GUIs for specific functions or packages (such as BiplotGUI), to full-blown GUIs that allow the user to construct analyses through interactions with menus and dialog boxes (such as R Commander).

Several of the more useful code editorsare listed in table A.1.

Table A.1 Integrated development environments and syntax editors

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