What are the recently cancelled NIH research grants about?

Wed, 14 May 2025 00:00:00 +0200

Creating equal-area grids

Tue, 31 Jul 2018 00:00:00 +0200

A while ago I published a bubble-grid map of the European continent showing absolute population change over a regular grid. But I made a rookie mistake: I’ve accidentally created a grid where the grid cells have different areas, thereby compromising the bubble grid map which is supposed to show counts over equal areas. This mistake happened because I created the grid over geographic data in spherical coordinates (latitude-longitude data). What I should have done (and correctly did in a subsequent blog post) is to project the geodata to equal-area coordinates before applying a grid. Here’s a demonstration of the wrong and the correct approach…

Bubble-grid versus choropleth maps

Tue, 03 Jul 2018 00:00:00 +0200

Bubble-grid-maps

Sat, 30 Jun 2018 00:00:00 +0200

The Unknown Pleasures of Demography

Wed, 28 Jun 2017 00:00:00 +0200

My Reply to flowingdata's Causes of Death Chart

Fri, 29 Jan 2016 00:00:00 +0100

My Reply to flowingdata’s Causes of Death Chart

How I Started Learning Data Visualization

Fri, 08 Jan 2016 00:00:00 +0100

To learn the craft of data visualization one has to know where to start. The following list is comprised of items which helped me in gaining some skills, namely:

Advanced knowledge of the visualization library ggplot2,
best practices on visualizing data, and
an overview of data visualization as a scientific field.

However, while I visualize data daily as part of my scientific work I am still in the early years of my training as a vis-practitioner. The list mirrors my personal journey of getting accustomed with the field and practice of visualizing data so far…

Learning ggplot2

Learning datavis by practising it is a good idea. There are tons of tools out there which help you build visualizations. If you are comfortable programming ggplot2 is a good framework to learn first because it is widely used, flexible, follows a coherent logic of visualization and is integrated into the powerful R language for statistical computing. Note however, that it is mainly used to produce static graphs for print.

ggplot Solutions for Some Common Visualization Tasks: This is based on the R Graphics Cookbook by Winston Chang and gets you started quickly with ggplot.
Elegant Graphics for Data Analysis: The book by the author of ggplot himself and despite being quite outdated it is still a good place to learn the general idea as well as the deeper functionalities of the library. An updated version is about to get published (as of January 2016). If you have patience and some skills in LaTeX and R you can compile the new version of the book yourself. All the source files are publicly available here.
The Official and Illustrated ggplot2 Documentation: This is the technical documentation of all the functions in the ggplot2 package. It is up to date and contains many illustrated examples on how to use each function.
Tidy Data: A lot of confusion about ggplot stems from the data being in an unsuitable format. ggplot works with what Hadley Wickham calls “tidy data”. Look at the data wrangling cheat sheet to find out how to get your data into tidy format using R.
ggplot2 Cheat-Sheet: A handy reference sheet, not only compressing most of the ggplot functionality into 2 pages, but also outlining its underlying logic.
The EDSD 2015 ggplot Seminar: A five day seminar I gave last year. The lecture notes contain many examples on how to work with ggplot.
The Grammar of Graphics: ggplot is modelled after the framework for describing visualizations introduced in this book. If you are eager to learn where the underlying logic of ggplot comes from, look here.

Learning Shiny

If you already know R and want to create interactive visualizations hosted on the web Shiny gets you there really quick. Get inspired here and start learning here. This is an example of Shiny used in a professional research environment.

Learning Information Visualization

Information visualization is in the process of establishing itself as a science, meaning it generates and tests theories. These theories enable the visualization practitioner to make informed design choices.

Visualization Analysis and Design: This textbook is both comprehensive and approachable. It introduces visualization as a task driven design process as opposed to a set of ready-made techniques and teaches the knowledge necessary to design effective visualizations.
The Visual Display of Quantitative Information: In this classic the virtues of clarity, minimalism and beauty are demonstrated by example. It’s a good start to get a feeling for good design and to learn some best practices. Note however that the book covers only static visualizations.
The Syllabus of Tamara Munzners Seminar on Information Visualization: Based on the textbook Visualization Analysis and Design this syllabus covers a wide range of topics with detailed literature suggestions. Slides are provided and most of the literature is publicly available as well.
Information Visualization: Everything about human perception and cognitive processing in relation to information visualization. Other books teach you best practices and techniques, this book explains why they work.
Datavis Milestones: An illustrated history of data visualization (starting 6200 BC!).

Joining the Public Discourse

Currently vis is quite hyped and a lot of people talk about it. This is to the advantage of everyone trying to learn it: To build knowledge, just immerse yourself in the discourse.

The Data Stories Podcast: A professional quality podcast discussing trends and news in data visualization and interviewing vis-practitioners in-depth.
Twitter: A lot of infovis is discussed on twitter the moment it goes public. Search for example: #datavis, @albertocairo, @visualisingdata, @nytgraphics, @mbostock…

cc-by Jonas Schöley

The Age Pattern of Human Fetal- and Infant Mortality

Thu, 15 Oct 2015 00:00:00 +0200

Mortality Rates over Gestational Age for the US 2009 Conception Cohort

The Human Mortality Explorer

Wed, 09 Sep 2015 00:00:00 +0200

https://jschoeley.shinyapps.io/hmdexp

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