Introduction to the Author

Note: If you would like to read the R Walkthrough version (with a tiny bit of Python), click here.

I’m a PhD candidate in Communication who loves live music. Still learning R so… please be gentle and send helpful comments if you so desire :)

Introduction to the Band

Vampire Weekend is a grammy-award winning band currently comprised of Ezra Koenig, Chris Tomson, and Chris Baio. They have released four albums: Vampire Weekend, Contra, Modern Vampires of the City, and most recently, Father of the Bride. Father of the Bride was released last May, and both the album itself and the tour promoting it have received critical acclaim. The album and tour have also received my own personal acclaim (“Stranger” was my Spotify song of the year in 2019 and I was lucky enough to see them perform in Philly, NYC, San Francisco and Portland last year!).

To explore the music of Vampire Weekend, we’ll first look at some of the instrumental qualities of the songs (provided via spotifyR). We’ll also examine the lyrical composition of those songs using Genius. Finally, we’ll take a deeper dive into the setlits from the FOTB tour (exluding the most recent Australia shows, since my data collection began mid-December 2019), taken from setlist.fm using repertorio. Let’s start!

Music

For our music analyses, we only want certain variables that are in our full dataset and we’ll focus specifically on songs that are on one of Vampire Weekend’s four albums. If you want to know more about the variables Spotify provides, check them out here.

First, let’s look at the estimated distributions of different variables across the four albums. In this plot, made with the ggridges package, the higher the ridge, the more songs there are with danceability scores of that value. For example, the ridge is highest around .40 for the self-titled album, which indicates that most of the songs on the album are decently easy to dance to (an inference I would agree with!).

We can also look at how danceability changes over the course of the albums. Through these plots, we can see that Contra apparently becomes harder to dance to as the album progresses. “I Think Ur A Contra” will always be one of my favorites though, regardless of how easily I can groove to it.

If we’re interested in comparing multiple musical variables at once for all of the albums, we can generate an average value for each album and use a radar plot. Surprisingly, according to Spotify’s data, the albums seem to follow similar patterns, with the self-titled having generally higher mean values on all the variables. Starting off strong in 2008!

Spotify also provides data about the key a song is played in. My musical theory knowledge is a bit weak (read: non-existent) so unfortunately it is difficult for me to confirm the accuracy of this. If you notice something funky, let me know!

Inspired by RCharlie yet again, and also the conversations I have with my close friend Emma (a die-hard VW-head) I have also created my own variable called the “Banger Index.” Yes, I really called it that. Everyone knows a banger when they hear it… but what do genuine bangers consist of? Well, my best guess is…

A nice mix of loud, energetic and high tempo music generally feels like a banger to me. And when I test it out on the data, it seems to fit pretty well, seeing as A-Punk and Cousins are right at the top of the list.

## # A tibble: 50 x 2
##    track_name        banger_index_rescale
##    <chr>                            <dbl>
##  1 A-Punk                           1    
##  2 Cousins                          0.971
##  3 Diplomat's Son                   0.961
##  4 Bryn                             0.961
##  5 Worship You                      0.909
##  6 Mansard Roof                     0.890
##  7 Holiday                          0.861
##  8 Giving Up the Gun                0.858
##  9 Don't Lie                        0.835
## 10 This Life                        0.823
## # … with 40 more rows

We’ll return to bangers later. For now, let’s move on to some lyrical analyses!

Lyrics

First, let’s see which words are most common in Vampire Weekend songs (and give the word cloud a nice tasteful color palette of the 1970s). These lyrics were retrieved using the Genius package.

Baby, baby, baby, baby right on time…

Let’s also check out what words are commonly found with other words. I choose “trigrams” (or groups of three words) for the purpose of showing more lyrical patterns, but you could choose any n-gram (bigrams, quadrigrams(?), etc). I removed the same common and custom stop words from earlier, and then filtered out extremely uncommon trigrams. These analyses are inspired by Tidy Text Mining.

I got a laugh out of the “unbearably” “white” “women” combo. We’ve also got a nice little Ya Hey circle. There are also some stragglers (“boy”, for example) that apparently occur so often next to the same word that they’re off in their own corner.

Next, I wanted to conduct some similar analyses to the music ones above, but with lyrical data instead.

I was first interested in what kind of lyrical patterns occured within each album, so I chose 3 of the variables that showcase what percentage of words in each song reflect a certain sentiment (in this case, I chose anticipation, fear and surprise.)

When we overlay these 3 variables, we can look at patterns in comparison to each other. For example, the percentage of surprise words per song in Father of the Bride stay relatively constant, but percentages of fear words change more frequently. Obvious Bicycle (one of my all time favorite VW songs) has a much greater percentage of anticipation words (e.g “wait”) than all of the other songs. This is also indicative of how much I anticipate hearing it relative to other songs - I’ve also requested it at two shows… because I really like it.

Moving on to a final radar plot, we can again see some surprising results. I’ve always thought of MVOTC as a more contemplative and moody album, but apparently it has the highest average percentage of positive and joy words, whereas Father of the Bride has the highest average percentage of negative words. How my mental tables have turned.

‘Father of the Bride’ Tour Setlists

Now for the moment we’ve all been waiting for/the original reason I embarked on this project! If you’ve stuck with me this far, thank you.

Let’s see what the setlist data has to say (after we add the rescaled variables to the dataset like we did before… can’t forget about the banger index!)

First, let’s look at which songs were the most common “openers” or first songs of the shows. We can see quite a bit of variety here, with Bambina, Sympathy, Sunflower and Harmony Hall being the most common. Gotta love a Sympathy opener!

Next, let’s look at which songs were the most common “closers” or final songs on the setlists. For anyone who has seen Vampire Weekend on this tour, “Walcott” and “Ya Hey” clearly dominate the end of the set. Personally, I prefer the coveted Worship You -> Ya Hey -> Walcott, but you can’t always get what you want.

Now let’s focus on patterns throughout the setlists. First we’ll look at how the loudness metric tends to change over the course of the show. This plot shows us that the first half of a VW show is pretty loud, with a slight dip after the 75% mark. Our ears all need a good rest sometimes!

We can also look at how the show tends changes lyrically. Unlike danceability, which takes a bit of a hit later in the show, positive lyrical vibes tend to fluctuate throughout the whole show.

As a self-proclaimed Phish head, I also had to know the answer to the question of how similar shows are to one another. One way to calculate this is through cosine similarity, which you can read about here if you are interested. Essentially, the higher the cosine similarity, the more similar two setlists are to one another. With 92 shows in our dataset, that’s a lot of comparisons! However, we can just look at the mean value, as well as the range of values.

## [1] 0.8300675
## [1] 0.04686676 1.00000000

The mean value is .83, with a range of .05 to 1.0. This provides evidence that most shows are fairly similar, but remember that comparing a show to itself results in a value of 1. Therefore this mean value might be a bit of an overestimation. Vampire Weekend certainly switches up their setlist enough that I always have something unexpected to look forward to :)

Last but not least, let us not forget the infamous Banger Index. This plot shows that Vampire Weekend likes to pull out their biggest bangers in the “third quarter” (between the 50% - 75% marks) and then give us a bit of a break afterwards. Personally, I’m not surprised at all by these results - my feet are rarely both on the ground during a VW show. #bangersonly

Who amongst us was lucky enough to be at the show with the highest average Banger Index? If you were at the 5/25/2019 show at Stewart Park, consider yourself blessed to hear an 8-song heater.

## # A tibble: 90 x 2
##    eventDate  mean_banger
##    <chr>            <dbl>
##  1 25-05-2019       0.779
##  2 05-12-2019       0.735
##  3 04-12-2019       0.720
##  4 03-07-2019       0.692
##  5 17-05-2019       0.690
##  6 22-09-2019       0.688
##  7 15-09-2019       0.684
##  8 19-10-2019       0.684
##  9 18-10-2019       0.678
## 10 05-11-2019       0.671
## # … with 80 more rows
## # A tibble: 8 x 5
## # Groups:   show_num [1]
##   show_num eventDate  venue.name   song_num track_name  
##      <dbl> <chr>      <chr>           <dbl> <chr>       
## 1       75 25-05-2019 Stewart Park        1 Harmony Hall
## 2       75 25-05-2019 Stewart Park        2 Sunflower   
## 3       75 25-05-2019 Stewart Park        3 Holiday     
## 4       75 25-05-2019 Stewart Park        4 Diane Young 
## 5       75 25-05-2019 Stewart Park        5 Cousins     
## 6       75 25-05-2019 Stewart Park        6 A-Punk      
## 7       75 25-05-2019 Stewart Park        7 This Life   
## 8       75 25-05-2019 Stewart Park        8 Unbelievers

That’s all for now, folks :) Hope you enjoyed this data-driven deep dive into Vampire Weekend’s music, lyrics and setlists from this past tour. If you have any comments, questions, or suggestions, feel free to toss ’em my way @hnm1231 on Twitter. Hope to see you at a show!