Database Reference
In-Depth Information
.style("fill","#CCCCCC")
.style("stroke", "#FFF");
</script>
</body>
</html>
These examples merely scratch the surface of what can be created with D3.js. Data
visualization experts have used it to build an amazing variety of compelling narratives.
Although newcomers to Web-based data visualization may find the learning curve
of D3.js to be daunting, it's currently an important technology for those interested in
communicating interactive data insights to online users.
A primary goal of the visual representation of data is to communicate abstract ideas
about numeric concepts into spatial representations that viewers can easily understand.
Not only does this field require analytical expertise, but notions of aesthetics are
important as well.
Although the tools for working with larger datasets have become more accessible,
the ability of humans to comprehend information presented in spatial formats has not.
Therefore, a common challenge for creating data visualizations is to understand how
to best produce subsets of data to expose to users. Data-transformation processes are
often required to cluster, or aggregate, data from very large datasets. Visualizations are
no longer bound to the medium of paper. Digital publishing provides data scientists
with the opportunity to produce interactive visualizations that enable users to select
their own slices of data to explore.
A very useful feature of the statistics language R is its feature-rich plotting and
graphics capability. Many scientific publications currently publish data visualizations
generated directly using R, and the additional ggplot2 library is very popular as well.
The Python programming language is also growing in use by the data community.
matplotlib is a popular 2-D plotting and charts library for Python and is often used in
conjunction with other Python data analysis libraries such as SciPy and Pandas. mat-
plotlib is also well integrated with the iPython interactive shell, making it easy to col-
laborate and share data analysis and visualizations with other researchers.
The Internet has become an amazing medium for publishing interactive data visual-
izations, especially for growing fields such as data-driven journalism. One of the most
popular and useful tools for building Web-based visualizations is the D3.js library,
which provides an interface for manipulating and transforming SVG graphics files.
D3.js is very well designed and feature complete, and a large number of open-source
plug-ins extends its utility.
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