Running elementary.os on a MacBook, what works well, and what does not.

After being disappointed with the direction in which macOS is heading, especially the continuous push into iCloud features that I refuse to use1, I decided to say good-bye to macOS and try elementary.os.

After testing it on a separate desktop computer for a couple of months I finally installed it on my MacBook2. In this post I am documenting my experience for those who might be interested in switching as well.

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  1. most annoyingly the iCloud synchronization of sensitive files that is turned-on by default
  2. 13-inch MacBook Pro from 2015

In Defense of Interactive Graphics

No, interactive graphics are not dead.

It is also not true that “85% of the Times‘ page visitors online simply ignore interactive infographics altogether“.

But since I sort of helped creating1 this confusion, I think it’s time to set this straight:

Interactive graphics are still great, and there are a lot of good reasons to make them!

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  1. The number comes from me measuring the percent of readers who clicked on a prominent button in a couple of graphics we published in 2015. I mentioned the number at a conference, from where it ended up in another talk, then on Medium and finally on FastCo.Design. However, the 85% of readers didn’t ignore the graphic, they just didn’t click the button.

Look, Ma, No More Mercator Tiles

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.

here’s how you do it


Analyzing bias in opinion polls with R

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.
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Start using databases, today!

This post is written to welcome dataset, a new library to simplify working with databases in Python.

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

  • they are hard to query (you need to write a custom script every time)
  • they are messy, as they cannot store meta data such as data types
  • it is a pain to update them incrementally, say if some record has changed

click to read the answer :)