expertise – paulvanderlaken.com

Past months, multiple companies shared their approaches to data visualization and their lessons learned.

Click the companies in the list below to jump to their respective section

The Financial Times
The Britisch Broadcast Corporation
The Economist
FiveThirtyEight

Financial Times

The Financial Times (FT) released a searchable database of the many data visualizations they produced over the years. Some lovely examples include:

Graphic showing what May needs to happen to get her deal over the line when MPs vote on Friday — Data visualization belonging to a recent Brexit piece by the FT, viahttps://www.ft.com/graphics

Dutch housing graphic — Searching the FT database for *European House Prices* via https://www.ft.com/graphics returns this map of the Netherlands.

BBC

The BBC released a free cookbook for data visualization using R programming. Here is the associated Medium post announcing the book.

The BBC data team developed an R package (bbplot) which makes the process of creating publication-ready graphics in their in-house style using R’s ggplot2 library a more reproducible process, as well as making it easier for people new to R to create graphics.

Apart from sharing several best practices related to data visualization, they walk you through the steps and R code to create graphs such as the below:

One of the graphs the BBC cookbook will help you create, via https://bbc.github.io/rcookbook/

Ultimately, you should be able to reproduce these BBC style graphics, viahttps://medium.com/bbc-visual-and-data-journalism/how-the-bbc-visual-and-data-journalism-team-works-with-graphics-in-r-ed0b35693535

Economist

The data team at the Economist also felt a need to share their lessons learned via Medium. They show some of their most misleading, confusing, and failing graphics of the past years, and share the following mistakes and their remedies:

Truncating the scale (image #1 below)
Forcing a relationship by cherry-picking scales
Choosing the wrong visualisation method (image #2 below)
Taking the “mind-stretch” a little too far (image #3 below)
Confusing use of colour (image #4 below)
Including too much detail
Lots of data, not enough space

Moreover, they share the data behind these failing and repaired data visualizations:

Via https://medium.economist.com/mistakes-weve-drawn-a-few-8cdd8a42d368

FiveThirtyEight

I could not resist including this (older) overview of the 52 best charts FiveThirtyEight claimed they made.

All 538’s data visualizations are just stunningly beautiful and often very
ingenious, using new chart formats to display complex patterns. Moreover, the range of topics they cover is huge. Anything ranging from their traditional background — politics — to great cover stories on sumo wrestling and pricy wine.

Viahttps://fivethirtyeight.com/features/the-52-best-and-weirdest-charts-we-made-in-2016/

Via https://fivethirtyeight.com/features/the-52-best-and-weirdest-charts-we-made-in-2016/ You should definitely check out the original cover story via https://projects.fivethirtyeight.com/sumo/

A regular expression (regex or regexp for short) is a special text string for describing a search pattern. You can think of regular expressions as wildcards on steroids. You are probably familiar with wildcard notations such as *.txt to find all text files in a file manager. The regex equivalent is .*\.txt$.

Last week I posted a first tutorial on Regular Expressions in R and I am working its sequels. You may find additional resources on Regular Expressions in the learning overviews (R, Python, Data Science).

Today I came across this website of Regular Expression Crosswords, which proves a great resource to playfully master regular expression. All puzzles are validated live using the JavaScript regex engine. The figure below explains how it works