How to encrypt your emails with PGP keys

by Moritz Zajonz 1 Comment
How to encrypt your emails with PGP keys

Important Notice: Considering the recent disclosure of vulnerabilities in popular e-mail clients like Mozilla Thunderbird, we decided to delete this post. The current PGP implementation in email clients has vulnerabilities, that haven’t been fixed for now and will take time to get fixed. For more information about the technical side visit efail.de and for a detailed explanation, read the post by the Electronic Frontier Foundation. Thanks for your interest in this topic! We will update this post when new info is available.

 {Credit for the awesome featured image goes to Phil Ninh}

JavaScript: Coding marker maps with Leaflet.js

JavaScript: Coding marker maps with Leaflet.js

Showing locations on a map can be pretty cool to provide some context for your story or to give your reader an overview of where the story takes place. A good way to build a simple, yet responsive and professional looking map is to use the JavaScript library Leaflet.

In an earlier post, “Your first choropleth map“, we used Leaflet as well, but coded the map using the Leaflet R package, which works like a wrapper to translate the more common Leaflet functions into R syntax. It’s very useful if you’re more used to R syntax and don’t want to learn JavaScript anytime soon. But using the original JS library and coding the map with JavaScript will give us way more freedom when customizing the map, which is why we’ll try it that way today.

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:

HTML, CSS & a little JavaScript: The Basics (Part II)

HTML, CSS & a little JavaScript: The Basics (Part II)

Part I

This is part two of our tutorial on HTML, CSS and a little bit of JavaScript. In the last part, we learned about the basic functions of those three languages and have gotten to know a few useful HTML commands. If you’ve already read part one or you know all of that stuff anyway, this is the perfect spot for you to continue – by learning about CSS and how to implement JavaScript libraries into your webpage. Let’s get right to it!

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.