For this data visualization, I used my group’s data about the history of the NY Philharmonic. There is a lot going on in this dataset, so I wanted to create something that would help me grasp some trends in the data. I chose to look at the all composers whose pieces were part of a NY Philharmonic program over the 69 seasons that are contained within the dataset. I thought it would be interesting to see if certain composers were really popular at different times, or only at certain times, or whether there were a variety of composers that were used throughout the history.
I created a line graph to help illustrate change over time. Each line in the graph corresponds to a composer that appeared in at least one program over the 69 seasons. I think it does a good job of showing which composers were more popular over others during a specific season and it also shows which composers were consistently popular. However, it is not particularly good at comparing popularity between seasons as not all seasons had the same number of programs or the same number of pieces in each program. This being said, the graph makes certain composers look extremely popular because there were many pieces by the composer performed in that season, however in these cases there were also a large number of pieces performed during that season in general. For example, comparing the 1894-985 season with the 1900-01 season, it appears that Wagner had an insane increase in popularity in 1900-01 compared with 1894-95, however, one must take into account the total number of pieces performed (of which there were significantly more in 1900-01). Between specific seasons, it would be more accurate to compare each composer’s number of pieces to the total number performed.
This data visualization is definitely helpful in showing popularity, which would be difficult to see in the data when it is in a table form.
Your visualization is colorful and sophisticated, at least to me. You definitely raise the right questions which cannot find its answer in the spreadsheet but it can be visualized in the chart. You know exactly your expectation of the visualizations too that’s why I like your objective analysis. Any visualization has its explicit agenda as well as hidden drawbacks. The visualization indeed does a good job in comparing your chosen topic about the popularity as you point out. But is there any way to solve the problems hidden in the chart? Perhaps you can narrow down your subjects for more detailed examinations.
Wow, we REALLY see the rise of Wagner, don’t we? Good point about it being hard to judge popularity when you don’t see how many pieces were performed, total. Perhaps you can find a way to express the prevalence of each composer as a percentage of the total.
Your data visualization is really easy to understand and interpret, as was your analysis. I grew up in a household with classical music playing nonstop, so I found it really interesting to follow along the graph to see which composers were commonly played and when they were so common. I actually used the editing tool on the visualization to “exclude” some of the names I had never heard of in order to more clearly see the trends of the most well known composers, like Mozart and Schubert. I also thought your insight about how one would need to make sense a composer’s popularity relative to the total number of pieces performed was really perceptive.
Jennifer, this is amazing! I really love how I can interact with the data visualization by selecting to focus on certain composers. I could clearly see which composers were more or less prevalent at different times. In order to resolve the issue with varying numbers of programs/songs per season, I would suggest dividing the total number of songs by each composer by the total number of performances that season. That way the number of songs is more relative and comparable throughout the span of time.
Thanks for sharing such an amazing visualization! You utilized the full strengths of tableau to create the line graph, which turns out to be the fittest approach in displaying this dataset. I enjoy interacting with your visualization and reading through your analysis. As for the potential problem you mentioned, I think it can be solved by adding a new row in the dataset which calculates the percentage of the works of each composer played every season. Create another chart based on the popularity percentage, and it will make a great complement to the existing visualization.