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copied!<p>I really agree with the other posters: <a href="http://www.edwardtufte.com/tufte/" rel="noreferrer">Tufte's books are fantastic</a> and well worth reading. </p> <p>First, I would point you to <a href="http://web.archive.org/web/20120510194537/http://lookingatdata.com/jsm-2009/" rel="noreferrer">a very nice tutorial on ggplot2 and ggobi</a> from "Looking at Data" earlier this year. Beyond that I would just highlight one visualization from R, and two graphics packages (which are not as widely used as base graphics, lattice, or ggplot):</p> <p><strong>Heat Maps</strong></p> <p>I really like visualizations that can handle multivariate data, especially time series data. <a href="http://en.wikipedia.org/wiki/Heat_map" rel="noreferrer">Heat maps</a> can be useful for this. One really neat one was featured by <a href="http://blog.revolution-computing.com/2009/11/charting-time-series-as-calendar-heat-maps-in-r.html" rel="noreferrer">David Smith on the Revolutions blog</a>. Here is the ggplot code courtesy of Hadley:</p> <pre><code>stock <- "MSFT" start.date <- "2006-01-12" end.date <- Sys.Date() quote <- paste("http://ichart.finance.yahoo.com/table.csv?s=", stock, "&a=", substr(start.date,6,7), "&b=", substr(start.date, 9, 10), "&c=", substr(start.date, 1,4), "&d=", substr(end.date,6,7), "&e=", substr(end.date, 9, 10), "&f=", substr(end.date, 1,4), "&g=d&ignore=.csv", sep="") stock.data <- read.csv(quote, as.is=TRUE) stock.data <- transform(stock.data, week = as.POSIXlt(Date)$yday %/% 7 + 1, wday = as.POSIXlt(Date)$wday, year = as.POSIXlt(Date)$year + 1900) library(ggplot2) ggplot(stock.data, aes(week, wday, fill = Adj.Close)) + geom_tile(colour = "white") + scale_fill_gradientn(colours = c("#D61818","#FFAE63","#FFFFBD","#B5E384")) + facet_wrap(~ year, ncol = 1) </code></pre> <p>Which ends up looking somewhat like this:</p> <p><img src="https://revolution-computing.typepad.com/.a/6a010534b1db25970b0120a63e9936970b-500wi" alt="alt text"></p> <p><strong>RGL: Interactive 3D Graphics</strong></p> <p>Another package that is well worth the effort to learn is <a href="http://rgl.neoscientists.org/" rel="noreferrer"><strong>RGL</strong></a>, which easily provides the ability to create interactive 3D graphics. There are many examples online for this (including in the rgl documentation). </p> <p><a href="http://wiki.r-project.org/rwiki/doku.php?id=graph_gallery:new-graphics" rel="noreferrer">The R-Wiki has a nice example</a> of how to plot 3D scatter plots using rgl.</p> <p><strong>GGobi</strong></p> <p>Another package that is worth knowing is <a href="http://www.ggobi.org/" rel="noreferrer">rggobi</a>. There is <a href="http://www.ggobi.org/book/" rel="noreferrer">a Springer book on the subject</a>, and lots of great documentation/examples online, including at the <a href="http://lookingatdata.com/jsm-2009/" rel="noreferrer">"Looking at Data"</a> course.</p>

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