A Docker post has been on my todo list forever: Now is the time!
Tools & Tips-list on how to make your charts more accessible to people who are colourblind. You probably know more people than you think who are colourblind!
This is not a complete list, so please add ones I’ve missed in the comments!
By viewing your chart in greyscale, you can check whether elements can be differentiated based on tone or luminance alone. You can also check that it will still work if printed on a non-colour printer.
Below is an example from Adobe’s Kuler (now Adobe Color CC) where the hex values for both images below are the same, but…
It is the last day of Mental Health Awareness month so here is my contribution.
Quoting C. Lauber and co. from their 2006 research paper “Do mental health professionals stigmatize their patients?”:
“psychiatrists hold the most pronounced negative attitude whereas psychologists had the most positive ones … Psychiatrists should particularly be aware that their attitudes do not necessarily differ from those of the general public.”
Another study by Lars Hansson and co. in 2011 found similar results, concluding:
“Since patients and staff in most respects share these beliefs [negative attitudes], it is essential to develop interventions that have an…
Python / Pandas / Excel / InkScape
I recently came across London Poverty’s twitter page where they post charts about poverty and inequality in London. While scrolling I found this one on the migration of people in and out of London, with the text : “More people moved into than out of London every year since 2004/5. But the main driver of London’s population growth is its birth rate being higher than its death rate”. The original chart and the data used can be found here.
There were a couple of things I had trouble with, so thought I’d have…
My work over the last two years has consisted of investigating algorithmic accountability and transparency, including a project on Uber published in The Washington Post’s Wonkblog, and the creation of Algorithm Tips — a database where we curated algorithms used or endorsed by the US Federal Government that journalists can use to identify stories. Last year, I expanded the concept of algorithmic transparency to include editorial transparency for data journalism. We should make our process transparent so that we can be accountable to our readers and each other.
In this post, I recognise concerns journalists may have to making data…
A typical week for me can involve collecting and wrangling data, running statistics, creating visualizations, and writing articles or blogs posts. I’m continually learning to code (mostly python) with a combination of formal and informal approaches, and when I worked in the Computational Journalism Lab at the University of Maryland, I was the only coder in the office who was not the boss.
After two very different experiences where coding errors lead to a lot of hassle, I got to thinking code review would be a good idea! One, an error in syntax, was a warning message about column:row selection…
Earlier this month I presented at the first ever European Data and Computational Journalism Conference (on twitter: @DataJConf, #datajconf), hosted this time in Dublin, Ireland. Next year it will be in Cardiff, Wales, and perhaps longer than 1.5 days. Being relatively new to data journalism — and only from the US side — I am unfamiliar with what is already available in Europe as far as conferences go. But this one addresses what was apparently lacking: getting academic and industry people together!