Stumbled upon a pretty fantastic group of Airbnb datasets for Amsterdam, Barcelona, London, NYC, Paris, Portland, San Francisco, and Sydney. You can find them here. Looks like things are spread across a few different tables so some join/blend action will probably be necessary. But on first glance, they look pretty robust. Enjoy!
Great news, everyone! All of the session from Tableau Conference 2014 in Seattle are now online for your viewing pleasure, including my super fun Pimp My Viz presentation. Check it out, learn some cool tips, and get thinking of ideas of vizzes to submit for *hopefully* Pimp My Viz 2015!
Last year, I made a series of visualizations based on playlist data from my favorite local radio station: KEXP. Well, it’s a new year and there’s new data, so here I go again! First, let’s start with the classic viz I made last year. This view shows the top artists, albums, and tracks of the year. The color denotes which DJ played them. You can use the dropdowns to view specific DJs or toggle between artist, album, and track.
Our big winner here was Spoon, not surprising as they released one of the best new albums of 2014. However, DJs must’ve been drawing from their back catalog a bit to get them up to all those plays because their new album was in 5th place when it comes to top albums. The top spot actually went to what was probably my favorite album of the year, “Sun Structures” by Temples.
I mean…that hair + that awesome psychedelic sound….what’s not to love?
By the way, if you look at the top albums and tracks, you’ll often see an asterisk for artist. This is because the DJ assistants at KEXP who input all the playlist data can’t agree if it’s Alt-J or alt-J. Capitalization irregularities actually plagued this whole dataset, so some of the numbers may look a little lower than they should be. Another issue is when bands have song titles with the same name. For example, a track named “Feel” appears to be the second most played song, when in actuality that’s a combination of plays for songs by Bombay Bicycle Club, Ty Segall, Big Star, and a few others. Unfortunately, you can’t use combined fields in parameters in Tableau, so there was no way for me to fix it on this particular dashboard.
The number one song is “Red Eyes” by The War on Drugs. I was especially tickled by the high placement of the song “Queen” by local hero Perfume Genius. This song is amazing and deserves the number spot for the line “No family is safe when I sashay” alone. The video is pretty weird and rad:
The whole album is amazing and I couldn’t help but use intoxicants and lay on the floor and listen to it when I put the vinyl on for the first time.
I wanted a couple dashboards that people could go into and make insights about their favorite bands and DJs. First, an artist explorer. You can use the dropdown to choose a couple different artists to compare. There’s a text search, if you don’t want to read through the whole big list. As a starting point, I compare my two favorite Seattle bands, La Luz and TacocaT. They seem to be pretty even in plays.
Next, here’s a dashboard that looks at what DJs like to play. I’ve started out with infamous morning DJ and tastemaker John Richards. Man, that dude loves Strands of Oaks’ newest album. Especially on Tuesday mornings at 6 am. In fact, he actually said himself it was his favorite album of the year. It’s pretty fun to click on one of the artists on the right and see what time they are played the most.
This year, I really wanted to do something with the top 90.3 albums of the year. KEXP had a form up on their website in December for their listeners to vote for their five favorite 2014 album releases of the year. They tallied up the votes and ranked the albums and did a fun countdown at the end of December. I wanted to compare the ranks by listeners to how often those albums were actually played. That’s how I got this nifty quadrant chart:
I’ve divided the chart into 4 sections. “High Listener Rank, High DJ Plays” means that the listeners and DJs were in agreement that these albums fucking rocked and should be considered to top honors. The listeners and DJs are also in agreement in the “Low Listener Rank, Low DJ Plays” quadrant. The interesting stuff is really in the other two quadrants. I noticed that in “Low Listener Rank, High DJ Plays” there are a few more local bands and just generally less known bands. This section is showing our taste-makers at work. I’d bet that a lot of people voted for these albums after hearing them first on KEXP. The “High Listener Rank, Low DJ Plays” section is interesting because it’s filled with indie favorites: The Afghan Whigs, Jenny Lewis, Aphex Twin… these are some heavy-hitters. These are all pretty well-known artists outside the college rock scene, which is maybe why KEXP plays them a little less than some other bands.
Last up, I just wanted to make some quick points of things I thought would be interesting to know. For example, did you know that the artist with the most distinct albums played on KEXP was Johnny Cash? It probably helps that he made a buttload of them. Click through these story points if you want to see more stuff like that.
I think the third story point is especially interesting. I filtered the list to only show artists that only have one song played on KEXP and than sorted it by number of plays. So you can think of it as the ultimate 1-hit wonders on KEXP in 2014.The song that ended up in the top spot on that list was actually pretty cool:
Dig into those dashboards if you feel so inclined. And be sure to tweet me and interesting tidbits you find in there!
I hope your year is already off to a fabulous start! I’ve been thinking, I’d really like to set some goals for myself this year, so I’m making my DATA RESOLUTIONS. And I’m declaring them in front of all you people on the internet so that I’ll feel at least slightly accountable. Ready…. let’s go!
Resolution 1: Learn Python
I love using tools like import.io for web scraping, but sometimes it isn’t enough. This year I want to learn enough Python to be able to do some web scraping and data formatting jobs.
People all over the web are using D3, including for some nifty Tableau applications. So, I want to as well!
Resolution 3: Grow the /r/tableau community
I’ve been neglecting my poor baby, the /r/tableau subreddit. This year, I want to get more happening with it including contests, discussions, IamA’s with Tableau Employees and more!
That’s enough for now, since these are all pretty big undertakings. What are your data resolutions?
This is a fantastic book from Christian Rudder, co-founder of OkCupid. Fans of the OkTrends blog (my favorite data blog of all time) will love this. Tons of hilarious and poignant insights based on data from online dating profiles.
Give your data lover a little piece of data history. This is a great collection of antique graphs and maps I found on Etsy. There’s plenty of other sellers of these kinds of prints, too, if you just search for “antique graphs”.
I’ve noticed that there’s a lot of us out there in the dataviz world that are also musicians. I’m a sax and bass player. I know many of you out there are also into playing music. I’ve always wondered what it was that draws musical people to dataviz (or vice versa).
My theory is that both dataviz and music engage both the analytical, mathematical side and artistic creative sides of your brain. I was recently sent this TED-Ed video that alludes to the same idea. Check it out. It’s a party in our brains!
Tomorrow is the first month of November so that means people everywhere will put down their razors for the month and see what happens in support of men’s health. It’s Movember! If you haven’t heard, Matt Francis is organizing a Tableau Team. Unfortunately (or probably fortunately, really…) I can’t grow a mustache. Luckily, Tableau can grow one for me!
A few other Tableau celebrities got the mustache treatment, as well. Check it out in my viz!