7 days of r/dataisbeautiful: a visualization that shows that data is beautiful
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Originally posted on January 29th, 2015.
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.
“Citi”
One thing that jumps out immediately is the high occurrence of the word “Citi.”
“Citi” is presumably a reference to New York’s bike sharing service that is sponsored by Citibank.
It turns out that there were 4 data visualization posts 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
How we talk about data
The word cloud suggests some interesting patterns about how data visualizations 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.
r/dataisbeautiful
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 visualize 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 visualization.
Originally posted on January 29th, 2015.
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.
“Citi”
One thing that jumps out immediately is the high occurrence of the word “Citi.”
“Citi” is presumably a reference to New York’s bike sharing service that is sponsored by Citibank.
It turns out that there were 4 data visualization posts 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
How we talk about data
The word cloud suggests some interesting patterns about how data visualizations 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.
r/dataisbeautiful
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 visualize 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 visualization.
