New Data Visualization post up on Win Vector Blog

Beyondmeans

I have a new post up on the Win-Vector blog, exploring a couple of data visualization tasks, and touching on the difference between graphing for data exploration and graphing for the communication of results.

Visualization is a useful tool for data exploration and statistical analysis, and it’s an important method for communicating your discoveries to others. While those two uses of visualization are related, they aren’t identical.

One of the reasons that I like ggplot so much is that it excels at layering together multiple views and summaries of data in ways that improve both data exploration and communication. Of course, getting at the right graph can be a bit of work, and often I will stop when I get to a visualization that tells me what I need to know — even if no one can read that graph but me. In this post I’ll look at a couple of ggplot graphs that take the extra step: communicating effectively to others.

The post concerns itself mostly with the ggplot code to generate the graphs, but there is a bigger-picture point, too. Data visualization is a bit like the drafts of a piece of writing: early graphs are rough, sometimes ugly, and highly detailed. By the time you get to the point of presenting the results — of articulating the “story” that is found in the data — you might want to use graphs that abstract away some of that detail, so that your viewers more clearly see the point you are trying to make, or the key insight that you are trying to convey.

You can read the post here.

About nzumel
I dance. I'm a data scientist. I'm a dancing data scientist. In my spare time, I like to read folklore (and research about folklore), ghost stories, random cognitive science papers, and to sometimes blog about it all.

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