For the praxis visualization assignment, I explored a rock music list a friend gave me. It was shared as an excel file, so exporting as a CSV for Tableau and Palladio was made simple. The list includes music artists, specific song(s) from that artist, the genre(s) for the song, and the year the song was released.

Palladio

After loading the data in, I could experiment with the map, graph, table, and gallery views. Map view wasn’t useful as I do not have location data in this data set and table view mirrors what I have already done with this list in Notion. The graph view was the most useful for seeing the data in a different light. I used artist as the source and genre, song, and year as the targets. Each time, the changes in relations between the information surprised me. The first time I used this list, I just started listening based on the order in the excel file. With the graph view, I was experiencing the data as a journey; listening to a particular song by an artist and then another because of the relation they share by genre or year released. Overall, this was the most appealing visualization between the two platforms I applied my dataset to.

Tableau

Previously, I tried this dataset out last week by attempting to add country tags for each artist and create a map with it. I found that the list skewed heavily towards the UK and the USA. I didn’t continue down this route for that reason and there are like 300 or 400 entries that would need to have a location added. Even though it didn’t work out with making a map, I wanted to try the dataset again to see if I could still get some insight (and get more experience with Tableau). I watched some tutorials to better understand how adding dimensions and measures would affect a visualization.

I made three worksheets. The first is a look at the artist by year. This view didn’t tell me much and because there was an abundance of artists included, it just felt like an endless scroll. The second worksheet was a look at entry count by year. This highlighted a heavy concentration in the 1960-1980s. The third view was the frequency that an artist appeared in the list overall. I think the last two views were the most illuminating.

https://public.tableau.com/app/profile/maci.morris/vizzes

Using the dataset in Tableau was a more outwardly analytical experience. I am looking forward to discussing the list with my friend. Some questions I have are: what was his methodology for compiling this list, and did he realize that there was a favoring of Western countries? I was wondering about the term “Intro” in this context. He named the list “Intro to Rock” and I wonder if his interpretation of intro means that it should start at what we believe to be the origins in time for rock. “Difficult Heritage and the Complexities of Indigenous Data” and “A Review of ‘Two Plantations” were most impactful in giving me examples of colonial impact in data and data representation, and what it could look to collect and present data more ethically. As I was working on this, I noticed more immediately than I would have previously that the list is more male centric and favors Western artists. As a list between friends I don’t feel it’s a huge issue, but I am reminded of the numerous times I’ve encountered recommendation list for popular media. The lists often reflect this same issue of representation, where cultural “best of“ is concerned.