That’s a word cloud. It took me about 30 seconds to build.
It shows the relative word frequencies in the titles of posts to r/dataisbeautiful over the last 7 days.
One thing that jumps out immediately is the high occurrence of the word “Citi”.
It turns out that there were 4 data visualisations about Citi Bikes last week,
- Snowstorm shutters 20% of NYC Citi Bike system
- NYC Citi Bike fleet size drops to record low as Operation Overhaul begins
- This winter, NYC Citi Bike riders are taking shorter trips
- Remarkable how NYC Citi Bike penalty fees are such a steady contributor to bottom line
And they were all submitted by the same Reddit user: wefollocitibike
How we talk about data
The word cloud suggests some interesting patterns about how data visualisations are talked about.
But it is difficult to draw clear conclusions from a word cloud because it relies on the viewer being able to compare different font sizes – which is a hard thing to do.
To make it easier for you, here is a bar chart of the top 10 words ranked by relative frequency.
Build a word cloud in 30 seconds
I was inspired to give this a go after reading today’s tutorial that shows how to use Import.io Magic to extract written content from a blog and then visualise the results in a word cloud, in the shape of a logo.
It was very quick. It took longer to write this post than it did to create the visualisation.