HTML, CSS & JavaScript: The Basics (Part I)

HTML, CSS & JavaScript: The Basics (Part I)

Part II

Becoming a proficient web developer is hard — but understanding the basics isn’t. So this is what we’ll do today. By the end of this tutorial, you should have an idea of what people mean when they talk about HTML, CSS and JavaScript.

In this part, we’ll talk about the purpose of those three and learn a bit of basic HTML. In part two, we’ll learn a little more about CSS and JavaScript, especially about the use of JavaScript libraries, and how to combine all three to build a website. So let’s do this!

R: Your first web application with shiny

R: Your first web application with shiny

Data driven journalism doesn’t necessarily involve user interaction. The analysis and its results may be enough to write a dashing article without ever mentioning a number. But let’s face it: We love to interact with data visualizations! To build those, some basic knowledge of JavaScript and HTML is usually required.
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

Shiny is a highly customizable web application framework that turns your analysis into an interactive web app. No HTML, no JavaScript, no CSS required — although you can use it to expand your app. Also, the layout is responsive (although it’s not perfect for every phone).

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.

Similarity and distance in data: Part 1

Similarity and distance in data: Part 1

Part 2

In your work, you might encounter a situation where you want to analyze how similar your data points are to each other. Depending on the structure of your data though, “similar” may mean very different things. For example, if you’re working with records containing real-valued vectors, the notion of similarity has to be different than, say, for character strings or even whole documents. That’s why there’s a small collection of similarity measures to choose from, each tailored to different types of data and different purposes.

R: plotting with the ggplot2 package

R: plotting with the ggplot2 package

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.

Project: Visualizing WhatsApp chat logs –
Part 1: Cleaning the data

Project: Visualizing WhatsApp chat logs – <br>Part 1: Cleaning the data

Part 2 | Code

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.

  1. Cleaning the data: That’s what this part is for. We’ll get the .txt file ready to be properly evaluated.
  2. Visualizing the data: That’s what we’ll talk about in part two — creating some interesting visuals for our chat logs.

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.

R crash course: Basic data structures

R crash course: Basic data structures

 

„To understand computations in R, two slogans are helpful: Everything that exists is an object. Everything that happens is a function call.“John M. Chambers

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.