Name That Baby!!

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In 2014, when my wife and I went to the hospital to have our first child, we were all packed up and as prepared to go as we could possibly be. Living just a few blocks from the hospital, the option was available for me to swing home, with ease, if needed. But, nonetheless, the bags that would accompany us sat, packed in our spare bedroom, for the better part of two weeks. However, as prepared as we were with packing, we were equally unprepared in another major part of this whole baby having process…what the hell would we name the baby??? As there are few surprises in life, we chose not to find out the sex, though everyone assured us we were having a boy. So, needing both a girl and boy name, over several months we periodically looked up lists of baby names and talked about which ones we liked or didn’t like, but never seemed to gain much ground. Finally, the day was here and as we rushed out the door, our list was still incomplete, consisting of a single maybe for a girl name and exactly zero boy names. Well, as it turns out, we wound up having a beautiful baby girl and our maybe name, Ruby, seemed to fit her perfectly. Whew, crisis averted!!

Now, as 2017 comes to an end and we usher in 2018, we are expecting our second child in just over three weeks. And here we are sitting in the same situation. Once again, not wanting to find out the sex, this time we’ve been able to muster up one boy name, but zero girl names!! So, how does any of this pertain to Tableau and/or Data Visualization? Funny you should ask…

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Why the Viz?

After going through the same song and dance we went through in 2014, I decided to leverage my passion for Tableau and Data Viz as a new way to approach searching for baby names. Having lost track of how many times I’ve Google searched phrases including “baby names,” it seemed only right to try and make the process more simple and fun. Eventually, I landed on the Social Security Administration website, where I was able to find data on the top baby names, by decade. After narrowing down my list to go back only to the 1920s, as opposed to the 1880s, I began gathering the data.

How Can it Help?

The process of picking out baby names may be easy for some, but very difficult for others. For us, it has been the latter for a few reasons that I won’t go into. Either way, in our situation, my wife and I both tend to stay away from the ultra popular names of today, as we prefer classic names that are beginning to come back in a small way, especially for girls. This is how we landed on Ruby, which also happened to have some meaning to us. So, with these thoughts in mind, I wanted to trend the popularity of baby names over time and use that to determine if the criteria are met for a specific name.

How Does it Work?

Dating back to the 1920s, a lot of names have landed in the Top 200 most popular baby names for a given decade. So, with so many names to weed through, I needed a way to filter down the options of what was viewable at a particular time. Thus, the viz is basically useless without the first of three dashboard actions;

  1. Name Begins with Filter: Including an A to Z list on the lefthand side of the viz allows the user to filter to names that begin with a desired letter. Once a letter has been selected, the second and third dashboard actions come into play.
  2. Name Rank Trend Highlights: Hovering on a girl name will highlight the name rank trend below, while hovering on a boy name will do the same for the boy name rank trends.

Once your name is highlighted in the line chart, you will see its initial Top 200 Rank, as well as all subsequent ranks, allowing you to easily see if the name has increased or decreased in popularity. Here’s a quick example; Although the spelling is different, the name Brittany entered the Top 200 in the 1980s, ranking #21 among girl names. By the 1990s it had climbed to #7. And then in the late 1990s, Britney Spears  became a thing and by the 2000s the popularity of the name Brittany had plummeted to #189. Coincidence? You be the judge.

My hopes are that this viz can be helpful in several different ways, regardless if you like popular names, classic names or anything in between. Thank you for reading, now GO NAME THAT BABY!!

 

Viz What You Love: Part II

cmavizJust over three weeks ago, I posted a viz about Notre Dame football, supporting it with a blog post called ‘Viz What You Love,’ professing and detailing my love for the Fighting Irish football program. A few days after that post, I shared a viz outlining the history of the CMA (Country Music Association) Awards Album of the Year winners. Having grown up in the middle of nowhere, literally, in northwestern Minnesota, sports and music were two of the things that became very important to me early on in life. While, my desire to be active and competitive fire were fueled through sports, music was always there when it was time to relax, study or have fun. I love several genres of music, but where I grew up, country music was big and it has always had a place in my heart. My first ever CD was John Michael Montgomery…no seriously!! And my first ever concert was Tim McGraw, way back when his only hit was “Don’t Take the Girl.” The point is that I love country music and that one really fun way to continue improving your Tableau skills is to produce data visualizations about things you love. I like to call this “Viz What You Love.” Part II is about my CMA Awards 51 Albums of the Year viz.

When I first saw Sean Miller‘s ‘The 100 Greatest Metal Albums of All-Time’ viz, I was blown away not only by how cool it was, but also by how much information was right there at my fingertips. Now, while I’m not a huge metal-head, I’ve listened to enough to know many of the artists and albums on the list, among them Black Sabbath and Ozzy Osborne. The very first thing that caught my attention on Sean’s viz was the range of energy in Black Sabbath/Ozzy albums vs. those of Slayer, which is all energy, all the time. I hadn’t heard much Slayer before, so pulled them up on Spotify. You could say their music is…aggressive!! Anyway, I thought Sean’s viz was awesome and I wanted to try something similar from some music more familiar to me. The first step would be to find a data set…well wouldn’t you know Sean also blogged about his viz and included a sweet little trick you can do in Spotify to capture several different attributes. Thanks for sharing Sean!! Here’s the link he included in his blog that helps you sort your music, so you can then throw it into a spreadsheet and start visualizing. This process is much more seamless than I was expecting, so that was a pleasant surprise!!

As for song attributes, I chose beats per minute, energy, acoustic and popularity. Being country music was my choice, I thought valence may also be interesting, but it didn’t tell the story I was hoping for. I included all songs from each album, because I wanted to see any clustering, especially on the low and high ends of each attribute category. For instance, a majority of two-time Album of the Year award winner, Charlie Rich’s music is low energy and highly acoustic, while recent two-time winner, Chris Stapleton offers a wide variety on his albums. The extreme unpopularity of country music from the 60s through the 80s is clear, save a few notable exceptions such as Merle Haggard, Kenny Rogers and Willie Nelson. There’s a gradual increase in popularity, the newer the music is and neither of these facts are a huge surprise when you think about the demographics of Spotify listeners. I’m really going out on a limb here, but my hunch is that more millennials are using Spotify than senior citizens. I mean, my dad certainly isn’t on Spotify…can you get Spotify on a track phone??? Wait, is it track phone or TracFone? Ah, who the hell knows, the point is not many millennials are listening to Ronnie Milsap, Alabama or George Strait, but they damn well should be!! Ok, here’s what I like about the viz;

  • Like I mentioned earlier, I’m a fan of including all songs on the dot plot, as the clustering of songs within an album is interesting to see.
  • I would have never chosen these colors on my own, but a quick Google search led me to colors associated with each genre of music. So, I chose four related to country music and feel that they actually look pretty nice together, thanks in large part to the dark blue background.
  • I think the highlight actions work well, as you can hover on a song under one column and easily see where that song falls in the other categories as well.

I hope you enjoyed reading, now go out and Viz What You Love!! Thank you again for the inspiration Sean, this was a really fun project!!