The Endgame, or Putting It All Together - Data Science and Big Data Analytics - page 523

visualization and have precise control over the output. D3.js is a JavaScript library
for manipulating data and creating web-based visualization with standards, such
as Hypertext Markup Language (HTML), Scalable Vector Graphics (SVG), and
Cascading Style Sheets (CSS). For more examples of using open source
visualization tools, refer to Nathan Yau's website, flowingdata.com [1], or his
book Visualize This [2], which discusses additional methods for creating data
representations with open source tools.
Regarding the commercial tools shown in Table 12.2 , Tableau, Spotfire (by
TIBCO), and QlikView function as data visualization tools and as interactive
business intelligence (BI) tools. Due to the growth of data in the past few years,
organizations for the first time are beginning to place more emphasis on ease of
use and visualization in BI over more traditional BI tools and databases. These
tools make visualization easy and have user interfaces that are cleaner and simpler
to navigate than their predecessors. Although not traditionally considered a data
visualization tool, Adobe Illustrator is listed in Table 12.2 because some
professionals use it to enhance visualization made in other tools. For example,
some users develop a simple data visualization in R, save the image as a PDF
or JPEG, and then use a tool such as Illustrator to enhance the quality of the
graphic or stitch multiple visualization work into an infographic. Inkscape is an
open source tool used for similar use cases, with much of Illustrator's functionality.
12.3.1 Key Points Supported with Data
It is more difficult to observe key insights when data is in tables instead of in charts.
To underscore this point, in Say it with Charts, Gene Zelazny [3] mentions that
to highlight data, it is best to create a visual representation out of it, such as a chart,
graph, or other data visualization. The opposite is also true. Suppose an analyst
chooses to downplay the data. Sharing it in a table draws less attention to it and
makes it more difficult for people to digest.
The way one chooses to organize the visual in terms of the color scheme, labels, and
sequence of information also influences how the viewer processes the information
and what he perceives as the key message from the chart. The table shown in Figure
12.16 contains many data points. Given the layout of the information, it is difficult
to identify the key points at a glance. Looking at 45 years of store opening data can
be challenging, as shown in Figure 12.16 .