With less than a month left to apply for this year’s Data Journalism Awards, here are some inside tips from the jury that may help you win one of the twelve prizes up for grabs.
A guest post by Marianne Bouchart, manager of the Data Journalism Awards
What do you need to be the perfect data journalist? Here is an extensive collection of skills you need to become the human Swiss Army knife for data. If you are willing to work 48 hours a day, you’ll get the knack in no time.
A guest post by Steffen Kühne
Web scraping is one of the most useful and least understood methods for journalists to gather data. It’s the thing that helps you when, in your online research, you come across information that qualifies as data, but does not have a handy “Download” button. Here’s your guide on how to get started — without any coding necessary.
Note: This tutorial was first published in our data-driven Advent calendar 2018 behind door number 13. You can check it out here.
Soon it’s Christmas! After two data-driven Advent calendars (2016 & 2017), this is our third edition. This year we’ve also come up with something special: We’ll have texts by awesome guest authors for you every day! The best data-driven journalism projects of the year, interviews and “Tutorial Thursdays” with tips and tricks from us squirrels! 🐿☃🎁
Go to ProjectOn March 24th, we hosted the JournoCon 2018. A data journalism bootcamp-slash-conference for students, freelance journalists and anyone wanting to learn more about the field. To those who were there: Thank you for making it an amazing day! To those who weren’t: Here’s a recap.
by Sophie Rotgeri, Moritz Zajonz and Elena Erdmann
One of the most important skills for data journalists is scraping. It allows us to download any data that is openly available online as part of a website, even when it’s not supposed to be downloaded: may it be information about the members of parliament or – as in our christmas-themed example – a list of christmas markets in Germany.
A guest post by Hans Hack
When working on map related projects, I often need specific geographical data from OpenStreetMap (OSM) from a certain area. For a recent project of mine, I needed all the roads in Germany in a useful format so I can work with them in a GIS program. So how do I do I get the data to work with? With a useful little program called Osmfilter.
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.
In our previous posts about Leaflet.js, we coded an interactive marker map and learned how to update our data with a google spreadsheet. In this short tutorial, we will show you how to make your map searchable so users can find a specific marker.
In our post Coding marker maps with Leaflet.js we filled a map visualization with information given directly in the code, but we also learned how to loop through data in a JSON file to create markers and pop-ups. This time, we will loop through an public Google Spreadsheet. We will do this with the same example map as in the previous post. But this little trick will not only help you with building marker maps but can come in handy for any JavaScript application you need to automate the handling of your data.
So let’s start right away with setting up a data table with Google Spreadsheets.
