From the data to the story: A typical ddj workflow in R

by Marie-Louise Timcke 2 Comments
From the data to the story: A typical ddj workflow in R

R is getting more and more popular among Data Journalists worldwide, as Timo Grossenbacher from SRF Data pointed out recently in a talk at useR!2017 conference in Brussels. Working as a data trainee at Berliner Morgenpost’s Interactive Team, I can confirm that R indeed played an important role in many of our lately published projects, for example when we identified the strongholds of german parties. While we also use the software for more complex statistics from time to time, something that R helps us with on a near-daily basis is the act of cleaning, joining and¬†superficially analyzing data. Sometimes it’s just to briefly check if there is a story hiding in the data. But sometimes, the steps you will learn in this tutorial are just the first part of a bigger, deeper data analysis.

Your first interactive choropleth map with R

Your first interactive choropleth map with R

When it comes to data journalism, visualizing your data isn’t what it’s all about. Getting and cleaning your data, analyzing and verifying your findings is way more important.

Still, an interactive eye-catcher holding interesting information will definitely not hurt your data story. Plus, you can use graphics for a visual analysis, too.

Here, we’ll show you how to build a choropleth map, where your data is visualized as colored polygon areas like countries and states.
We will code a multilayer map on Dortmunds students as an example. You’ll be able to switch between layered data from different years. The popups hold additional information on Dortmunds districts.