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.
We’ll use the code of the already finished map that’s hosted on our GitHub page so we don’t have to start at the very beginning again. Simply download the directory to your computer and open it in your text editor. If you’ve never created an interactive map with Leaflet.js before, please have a look at the two tutorials mentioned above before proceeding with this one.
So let’s start right away with setting up a data table with Google Spreadsheets.
Journocode turns one year old! We visualized some data that has been generated in our first year and build a website to celebrate our first anniversary.
Java Script libraries and other tools offer cool ways to visualize data, but sometimes, you may want an even more customizable way of presenting a topic on the web. Maybe you already have the perfect graphic, but it’s not interactive yet. In this tutorial, we’ll show you a way to add tooltips to your SVG graphics.
As an example, let’s start with a map of the locations of some data journalism newsrooms in the German speaking area. As always you can find all the code of this tutorial on our GitHub page.
This is what the finished map will look like:
What? Your only coding skills are a bit of R? No problemo! What if I told you there was a way to interactively show users your most interesting R-results in a fancy web app?
Shiny to the rescue
In this tutorial, we will learn step by step how to code the shiny app on Germany’s air pollutants emissions that you can see below.
Unfortunately, data comes in all shapes and sizes. Especially when analyzing data from authorities. You’ll have to be able to deal with pdfs, fused table cells and frequent changes in terms and spelling.
When I analyzed the swiss arms export data as an intern at SRF Data, we had to work with scanned copies of data sheets that weren’t machine-readable, datasets with either french, german or french and german countrynames in the same column as well as fused cells and changing spelling of the categories.
While crunching numbers, a visual analysis of your data may help you get an overview of your data or compare filtered information at a glance. Aside from the built-in graphics package, R has many additional packages to help you with that.
We want to focus on ggplot2 by Hadley Wickham, which is a very nice and quite popular graphics package.
Ggplot2 is based on a kind of statistical philosophy from a book I really recommend reading. In The Grammar of Graphics, author Leland Wilkinson goes deep into the structure of quantitative plotting. As a product, he establishes a rulebook for building charts the right way. Hadley Wickham built ggplot2 to follow these aesthetics and principles.