Graphics Programs Reference
In-Depth Information
In 1977, statistician John Tukey published his topic Exploratory Data Analysis,
which detailed how and encouraged data professionals to analyze data through
visualization. This was during a time when most analysis was performed in the
context of hypothesis tests and statistical models, one computer filled a room,
and graphs were typically drawn by hand. For example, in his topic, Tukey
provides a tip on how to draw darker symbols with a pen instead of a pencil.
Nevertheless, although the technology was bigger and slower back then, the
driving principle is the same. You can see a lot in a picture, and what you see
can lead to answers or generate more questions you otherwise never would
have thought of.
“The greatest value of a picture is when it forces us to notice what we
never expected to see.”
—John W. Tukey, Exploratory Data Analysis (1977)
The public-facing side of visualization—the polished graphics that you see in the
news, on websites, and in topics—are ine examples of data graphics at their best,
but what is the process to get to that final picture? There is an exploration phase
that most people never see, but it can lead to visualization that is a level above
the work of those who do not look closely at their data. The better that you under-
stand what your data is about, the better you can communicate your findings.
Note:
The New York Times
and
The Washington Post
discuss the process behind their graphics at
http://
.tumblr.com/,
respectively. Work often starts with
rough sketches on paper or dry erase board and
then moves to exploration and production.
Even if you don't plan to show your results to a wide
audience, visualization as an analysis tool enables you
to explore data and find stories that you might not find
with formal statistical tests. You just need to know what
to look for and what questions to ask based on the data
that you have available.
The great thing is that tools and access to data are less of a limiting factor than
they were in Tukey's time, so you aren't stuck with just pencils, paper, and a
ruler to draw thousands of dots and lines.
PROCESS
The specific steps you take in any analysis varies by dataset and project, but
generally speaking, you should consider these four things when you explore
your data visually.
u
What data do you have?
u
What do you want to know about your data?
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