After being disappointed with the direction in which macOS is heading, especially the continuous push into iCloud features that I refuse to use, 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 MacBook. In this post I am documenting my experience for those who might be interested in switching as well.
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 creating 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!
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.
tl;dr: Here’s a demo with source code.
D3 is nice, but it also makes some simple things look really complicated. One of them is making a simple HTML table. Let’s say you got a simple dataset, stored as array of objects just as you would get from d3.csv:
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.
I want to see the features! →
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
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.
Probably one of the most useful things about Cynthia Brewers color advice for cartography are the multihue color schemes. This post explains how you can create your own, using two new features of chroma.js: Bezier interpolation and automatic lightness correction.
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 :)
Currently I’m taking the wonderful course Computing for Data Analysis on Coursera, and in this weeks lecture I learned about how to define custom color palettes in R.
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.