Choosing Tools to Visualize Data - Data Points: Visualization That Means Something - page 71

useFuL hTmL, JaVascrIpT, and css resources

a

jQuery ( http://jquery.com/ )—A JavaScript library that makes coding

in the language much more efficient and makes your finished
product easier to read.
jQuery Sparklines (

a

http://omnipotent.net/jquery.sparkline/ )—
Make static and animated sparklines in JavaScript.
Protovis (

a

http://vis.stanford.edu/protovis/ )—A visualization-
specific JavaScript library designed to learn by example.
JavaScript InfoVis Toolkit (

a

http://datafl.ws/15f )—Another
visualization library, although not quite as developed as Protovis.
Google Charts API (

a

http://code.google.com/apis/chart/ )—Build
traditional charts on-the-fly, simply by modifying a URL.

r
If you read FlowingData, you probably know that my favorite software for
data graphics is R. It's free and open-source statistical computing soft-
ware, which also has good statistical graphics functionality. It is also most
statisticians' analysis software of choice. There are paid alternatives such
as S-plus and SAS, but it's hard to beat the price of free and an active
development community.

One of the advantages that R has over the previously mentioned software
is that it was specifically designed to analyze data. HTML was designed to
make web pages, and Flash is used for tons of other things, such as video
and animated advertisements. R, on the other hand, was built and is main-
tained by statisticians for statisticians, which can be good and bad depend-
ing on what angle you're looking from.

There are lots of R packages that enable you to make data graphics with
just a few lines of code. Load your data into R, and you can have a graphic
with even just one line of code. For example, you can quickly make a
treemap using the Portfolio package, as shown in Figure 3-19.

Just as easily, you can build a heatmap, as shown in Figure 3-20.

And of course, you can also make more traditional statistical graphics,
such as scatterplots and time series charts, which are discussed in Chap-
ter 4, “Visualizing Patterns over Time.”