Tag: Flourish

Miles Davis Song Plots

In this blog we’re going to use Flourish with more MusicBrainz data to plot the length of Miles Davis songs on a range of vinyl releases. This type of data often suggests the use of a scatter plot with an x-y axis to best visualize the information. For instance, we could place record labels on the x-axis, and the length of each song (in seconds) on the y-axis. However, with record labels being a categorical variable (i.e.- discrete values such as Sony, Columbia, etc.) there are better options for understanding the data versus a true scatter plot.

The first of these is a boxplot, which provides the ability to see the distribution of data (song lengths) by record label. Let’s take a look at this data in Flourish:

Here we have limited the data display to a single label (showing all was quite messy!). Select CBS or Columbia to see labels with many Miles Davis releases. We now see the median length of a recording, as well as the 25th percentile (bottom of the box) and the 75th percentile (top of the box). It’s also easy to see individual songs that lie below or above the typical range; in statistical terms, these are called outliers. On our plot, they represent songs that are either much shorter than normal (below the extended line) or longer than normal (above the extended line).

This is all useful information, but presents some limitations. Boxplots are very good at doing the aggregations for us while obscuring the individual data values, especially values that lie inside the box. To improve our ability to see those values we turn to a violin plot, which excels at showing the shape of a distribution, rather than the fixed shape provided by the boxplot. We have also combined a beeswarm plot with the violin plot so we can see every individual value:

Again, select CBS or Columbia to view a label with many releases/songs to understand why we elected to use this approach. Hover over individual points to learn more about an individual song – it’s length, release, artist, label, and song title. For me, this approach is best if I’m trying to explore the data; the boxplot is great when I’m interested in overall patterns. Both are powerful tools suited to their individual strengths.

I’ll be using Flourish to interrogate the MusicBrainz data further in future posts, but that’s it for now. Thanks for reading!

Miles Davis Sunburst Visualization

With the Christmas holiday chaos (somewhat literally this year) in the rearview, I’ve been playing a bit with the MusicBrainz data and the Flourish visualization library. First up was using some repurposed code to visualize Miles Davis recordings. I thought a sunburst diagram might be an interesting way to show album releases and the songs on each release. Turns out it wasn’t quite as simple as I thought…it never is!

After multiple query tweaks and iterations, I’ve got something fun and interesting. Miles produced so much music, with much of it re-released in multiple formats (think vinyl vs. cd) and in various collections, factors that wound up influencing my query and chart logic. As is the case for many jazz artists, multiple labels are an issue, so why not create a filter to view releases for each label (Columbia, Blue Note, etc.)? And many songs turn up on multiple releases (studio, live, collections), so we need to account for that as well.

So my thought with using a sunburst was to group songs and releases together, and allow filtering by label. Mind you, it took multiple attempts to get the data in the best format, but we eventually wound up with something workable to feed the sunburst chart.

If you aren’t familiar with the sunburst chart, here’s a quick primer. The goal of a sunburst chart is to display hierarchical information in a circular layout with 2 or 3 levels (typically). The outer layer has more surface area to work with, and successive inner layers each have less visual space to use. For this reason, I wound up using individual songs in the outermost layer, with their respective albums as the inner layer. With an average of perhaps 5-10 songs per album, this takes advantage of the sunburst hierarchy framework.

Here’s what the code eventually became, after multiple iterations:

SELECT distinct ac.name AS artist, l.label_code, l.name AS label_name, r.name AS release, mf.name AS format, t.name AS id, t.name AS label, t.name AS name,
r.name AS recording,
CASE WHEN t.length < 180000 THEN ‘< 3 Minutes’ WHEN t.length < 300000 THEN ‘3-5 Minutes’ WHEN t.length < 420000 THEN ‘5-7 Minutes’ WHEN t.length < 600000 THEN ‘7-10 Minutes’ WHEN t.length > 600000 THEN ’10+ Minutes’
ELSE ‘No Length’ END category

FROM public.release r
INNER JOIN public.artist_credit ac
ON r.artist_credit = ac.id
INNER JOIN public.medium m
ON r.id = m.release
INNER JOIN public.medium_format mf
ON m.format = mf.id
INNER JOIN public.release_label rl
ON r.id = rl.release
INNER JOIN public.label l
ON rl.label = l.id
INNER JOIN public.track t
ON m.id = t.medium
INNER JOIN public.recording re
ON t.recording = re.id

WHERE r.artist_credit = 1954
and mf.name = ’12” Vinyl’

ORDER BY l.name, r.name

What we’re doing here, in a nutshell, is retrieving all the information for Miles Davis’ 12″ vinyl releases; many of these recordings were eventually released on CD, so we’re attempting to avoid duplication here. The ‘r.artist_credit = 1954’ line refers to Miles Davis and his MusicBrainz artist ID, while the medium_format name field is set to grab just 12″ vinyl releases.

Enough of the technical details – let’s view some results:

Here’s a look at the dropdown filter we created using labels:

Miles Davis sunburst labels filter

Note that we ordered our query by both label name and release name; this translates to an alpha sorted dropdown on labels, making it much more intuitive to select a specific label. We can choose to display all labels, but that gets rather messy for an artist like Miles who recorded for or was re-released by many companies. Let’s filter it down to Columbia, a major label who Miles recorded for many times:

Miles Davis Columbia releases

The inner circle displays individual releases, of which there are many, while the outer ring displays the songs on each release. The Flourish sunburst charts are interactive, but it’s a challenge to see what’s going on in our static image. Let’s move to the Blue Note label, a major force in jazz, but one where Miles was not a major player:

Miles Davis Blue Note releases

Now we can see the layout, with album releases surrounded by individual songs. We can go a step further by clicking on the Miles Davis, Volume 1 layer, which reveals the following:

Miles Davis Blue Note drilldown

Now we are focused strictly on that release and can easily view the songs on that album. Hope you get the general idea for how the sunburst charts work. Now have a go at it yourself with the live version:

I’ll have more of these to come, as it feels like a great way to capture a lot of information in a fun, interactive layout. See you soon, and thanks for reading!

Mapping Venues with Flourish

I recently shared some venue maps created in Carto using MusicBrainz data where we can see all the musical venues (with lat/lng coordinates) listed in the database. Now that I am exploring Flourish as a visualization tool, it is time to test the mapping capabilities to see if it provides a viable alternative for geographic visualization.

As with the Carto map, the first step is to retrieve the data with a query from my local MusicBrainz database:

select p.”name” , p.address , p.coordinates , split_part(trim(p.coordinates ::text, ‘()’), ‘,’, 1)::float AS lat,
split_part(trim(p.coordinates ::text, ‘()’), ‘,’, 2)::float AS lng,
pt.”name” as type, ar.name as locale
from place p
inner join place_type pt
on p.”type” = pt.id
inner join area ar
on p.id = ar.id

where p.coordinates is not null

The results are exported to a .csv file for ingestion by the Flourish map template. In this case we are using a point map to display venues using the latitude (lat) and longitude (lng) data we just created. Before uploading our data, we can see that Flourish provides an easy to use template populated with sample data, allowing us to see the data format we need to deliver:

Sample data for a Flourish point map

Here we can see a name field (City) and latitude and longitude fields for positioning points on a map. Other attributes provide data that might be used for sizing, coloring, or context.

In our case, we can upload our venues .csv file in this general format, and then tell Flourish which columns to use. Here’s what our data looks like after uploading:

Data for venue mapping

Now that the data has been uploaded we can specify which columns to use. Rather than using column names (Name, Address, etc.), Flourish works off the actual column locations (A, B, C, etc.). These are the values we need to input in the options tab. We can use A, B, and F for describing the data with labels and/or popups, use D for latitude and E for longitude, and switch from the Date to the Preview pane.

When we complete that task, Flourish gives us an attractive map, with many templates to choose from, and dozens of options for styling the map and surrounding area:

Venues map zoomed in to Italy

Here’s a quick view of the many available categories for styling your map:

Template options for mapping with Flourish

We’ll skip the details for now, but it is obvious that we could do an amazing number of things to customize our map.

To answer my earlier question, it appears that Flourish will be more than capable for mapping a variety of data, and I’m looking forward to testing it with much larger data sets. See you soon, and thanks for reading!