If you followed part one of this project, you should now have a clean data set that you can work with. Now, we’ll create some pretty visualizations with it. This is how it will look:
A few weeks ago, we discovered it’s possible to export WhatsApp conversation logs as a .txt file. It’s quite an interesting piece of data, so we figured, why not analyze it? So here we go: A code-along R project in two steps.
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.
Data structures in R are quite different from most programming languages. Understanding them is a necessity, because they define the way you’ll work with your data. Problems in understanding data structures will probably also produce problems in your code.
As you know by now, R is all about functions. In the event that there isn’t one for the exact thing you want to do, you can even write your own! Writing your own functions is a very useful way to automate your work. Once defined, it’s easy to call new functions as often as you need. It’s a good habit to get into when programming with R — and with lots of other languages as well.
In this crash course section, we’ll talk about importing all sorts of data into R and installing fancy new packages. Also, we’ll learn to know our way around the workspace.
Your workspace in R is like the desk you work at. It’s where all the data, defined variables and other objects you’re currently working with are stored. Like with a desk, you might want to clean it every once in a while and throw out stuff you don’t need any more. There’s a few useful commands to help you do that. Take a look and try them out:
Now that you installed RStudio, learned about assignments and wrote some basic code, there’s nothing stopping you from becoming a journocoder!
To get a deeper understanding of how R stores your data, we’re now going to take a closer look at data structures in R, starting with a central concept: Vectors.
At journocode, we’re starting out with an intro to the tool we rely on most right now: The statistical programming language R. “R: A Language for Data Analysis and Graphics” is mostly used in statistics, but is very useful for journalists working with data as well.
— Timo Grossenbacher (@grssnbchr) 22. Oktober 2015