There has been some debate about the jittery gauge chart we used in our live election forecast. Rather than replying to dozens of tweets I decided to wrap this up in an old-fashioned blog post™. Feel free to add your thoughts to the comments.
Great Britain has voted to leave the E.U., and election result cartograms are all over the internet. However, for our map we decided to stick with a simple map instead.
This is a transcript of my lightning talk at NICAR 2015 yesterday.
Please give the animated GIFs some time to load :)
Charting tools are great.
They let us create charts and visualizations without writing code, and in a fraction of the time it would take to do in tools like Adobe Illustrator.
It’s ok to use word clouds if your goal is to encourage reading of a large set of otherwise unrelated words that are connected to one or two interesting values (and word count in a text doesn’t qualify as interesting).
— gregor aisch (@driven_by_data) January 29, 2015
This I tweeted yesterday and now I feel that if I encourage the (dangerous) use of word clouds, I have to explain this exception in a little more detail. Why is it sometimes ok to use a widely rejected visualization method, and most times not?
Using open source tools it is now super easy to make your own map tiles, and with a little extra work you can render them in whatever map projection you want. No more excuses to use Mercator! For example, here is a map we published today at The Upshot. It shows where prime-age women are working more or less then average, and includes data from county-level in the overview map down to every census tract once you zoom in. And all is nicely projected in Albers Equal-Area Conic, a projection widely adopted as standard for U.S. maps.
Never trust a statistic that you
haven’t visualized yourself.
It’s election time in Germany and, as usual, there are tons of opinion polls telling us who is going to win the election anyway. It is debatable whether or not pre-election polls are healthy for our democracy in general, but at least everybody agrees that the polls should be kind of neutral. And if they are not, the institutes publishing the polls should be blamed publicly.
But how do we know if an institute publishes ‘biased’ polls? You guessed it: with data. More precisely: with data and the unique power of data visualization.
Let’s face it. Relational databases, such as MySQL, SQLite and PostgreSQL, are pretty cool – but nobody actually uses them. At least not in the day-to-day work with small to medium scale datasets. But why is that? Why do we see an awful lot of data stored in static files in CSV or JSON format, even though
You can do this using the
colorRampPalette() function that comes with the grDevices package. Calling this function will return another function that you can call to generate the color palette.