We can’t see behind the bars. But we can see where they are — and why they’re there.
Inspired a bit by the work of Jeremy Keith and others, I’ve recently been playing around with some sparklines on my website. While tinkering around with things, mostly on the back end of my site, I’ve tried out several WordPress-specific plugins, both to see how they’re built and the user interfaces they provide.
There are several simple plugins for adding sparklines to WordPress websites including:
- Activity Sparks plugin by Greg Jackson which adds some configurable functionality for adding sparklines to WordPress sites including for posts and comments as well as for tracking categories/tags.
- Sparkplug by Beau Lebens has similarity to the Activity Sparks plugin (above), but with a slightly older looking and somewhat less refined output.
At present, I’m using the Activity Sparks plugin in my sidebar to display the recent activity on my site in terms of my posting frequency and the comment frequency. One chart provides the daily activity on my site over the past 3 months while the other provides the monthly activity over the past 5 years.
When on particular category pages, you can see the posting velocity for those particular categories in these respective time periods. While on the homepage and other miscellaneous pages, you can see the aggregate numbers for the website.
Generally I don’t care very much about the statistics, but in aggregate they can sometimes be fun to look at. As quick examples, I can tell roughly by looking at the 5 year time span when I added certain posting features to my website or that time my site got taken down by HackerNews.
hat tip to Khürt Williams who I needed to circle back around and finish of a small piece of this project and document it.
Resizing geographic areas by population gives more accurate view of 2012 election.
A post on the Guardian Datablog earlier today took a dataset collected by the Tweetminster folk and graphed the sorts of thing that journalists tweet about ( Journalists on Twitter: how do Britain&…
There was a striking difference in style — and substance.
An impressively telling visualization here.
The Futility Closet people recently posted “A Square Circle“, in which they showed: 49² + 73² = 7730 77² + 30² = 6829 68² + 29² = 5465 54² + 65² = 7141 71² + 41² = 6722 67² + 22² = 4973 which is a nice little result. I like this sort of recreational maths, so I spent a little time w...
An interesting cyclic structure here.
An interactive map of the geography of baseball fandom.
What does the way you speak say about where you’re from? Answer all the questions below to see your personal dialect map.
I’d love to see the data sets and sources they used for these visualizations.
How does war affect the music an orchestra plays?
The New York Philharmonic has a public dataset containing metadata for their entire performance history. I recently discovered this, and of course downloaded it and started to geek out over it. (On what was supposed to be a day off, of course!) I only explored the data for a few hours, but was able to find some really interesting things. I’m sharing them here, along with the code I used to do them (in R, using TidyVerse tools), so you can reproduce them, or dive further into other questions. (If you just want to see the results, feel free to skip over the code and just check out the visualizations and discussion below.)
Continue reading “Data mining the New York Philharmonic performance history”
A look at some of the best apps, hacks and mashups available for music streaming and scrobbling service Last.fm.
Curious about alternatives Last.fm’s broken RSS feeds and what people are doing with their listening data. Some relatively interesting ideas in here, but nothing earth shattering. One or two were focused on visualization, but otherwise nothing I felt I could use.