For this week’s #MakeoverMonday we’re looking at how many visits abroad people from the UK are making on a monthly basis.
Fos this week’s #MakeoverMonday challenge, Charlie picked a chart about Amazon’s revenue vs profit. Line charts are a great way to capture time-series data at different levels of granularity.
In #MakeoverMonday we naturally mix heavier topics with some lighter and easier ones. We also aim to have a good variety of datasets. For this week I chose soccer in the US as a topic.
For his first dataset, Charlie picked the topic of how popular different American sports are with spectators.
In this blog I describe my Makeover approach for the original viz.
week 49, Andy picked a dataset about union membership, from an article by Mona Chalabi, who writes for the Guardian and is a recognised journalist who uses data visualisation and data art to make information more accessible.
Given the buzz of #data19 last week, we’re working with a small and simple dataset for week 47, so we can all have some extra time to catch up on the things we missed at home.
For week 29, Andy picked a topic that is likely to cause a few people to blush. It’s always interesting
It’s Pride Month which offers a great opportunity to remind people that we’re not done yet when it comes to equality.
So it’s important to use our #MakeoverMonday platform also to highlight inequality, attitudes, opinions and perceptions.
For week 19 Andy picked baseball data and I’ll be the first to admit that I expected it to not be fun, because it’s a topic I know nothing about and I wasn’t quite ready to spend hours learning about baseball. Until I did.
For this week’s MakeoverMonday challenge, we look at data about bicycle imports to the UK from 2010 – 2016.