You can find the data story here.

Through our data story we studied how music can serve as a way to improve or not improve common mental health disorders such as Depression and Anxiety. We transformed this dataset, filtered, and created pivot tables to gather more of our findings. Our embedded visualizations were made on Flourish to help provide more insight on our data if mental health was changed after listening to music. 

Origin of our dataset

While the data used was found on Kaggle.com, it comes from Catherine Rasgaitis that further reports the data of music and reported mental health issues. 

Spreadsheet

The original spreadsheet contained 737 rows that contain timestamps of when these surveys were taken. There are 29 columns which indicated the characteristics that were pulled from the survey to make a dataset. 

The first column gave us the age of the respondent. The second column told us what primary streaming service they use. The third column gave the knowledge of how many hours a day the respondent listens to music. The fourth is whether they listen to music while working or not. The fifth and sixth columns tell us if they are an instrumentalist or a composer. The seventh column informed us what their top genre is. The eighth column was if they are exploratory. The ninth column is if they speak a foreign language. The tenth is Beats Per Minute (BPM). The eleventh column is how often they listen to classical music. The twelfth through the twenty sixth column told us how often the respondents listen to the different genres such as hip-hop, jazz, country, edm, latin, lofi, rap, rock, etc., and is rated from rarely to very frequently listened to. The twenty seventh column informed us if they had anxiety. The twenty eighth told us if they had depression. The last one, the twenty ninth, told us if music improved their anxiety or depression. 

Transforming the Data

While our dataset was already organized well there was not much to change. Since we wanted to focus on music and mental health there were some characteristics that we chose to remove. Time data was recorded: while time can be an important factor it was not needed for explaining our dataset. Overall we had to expand our data set in order to see all of the information that was given to us and rename some of the characteristics to make them more concise and easier to read. We also chose to remove data about Insommona and OCD as we wanted to focus on 2 of the more common mental health disorders, in this case Anxiety and Depression. 

Analyze

 For our dataset we used the filtering and sorting method. From this method we were able to see clearly the ages of respondents (A to Z) and from there find how many were in each age group. When making the visualizations we decided to make them as easy to read and pleasing to the eye as possible. We started with the survey results of listening to music while working or not and chose the “survey results”graphic in Flourish. Some of the other graphics that we made included graphs on the amount of people that use certain music genres and another graphic that showed which music streaming platform was the most used. As for our insights we chose to use Flourish to help bring our dataset findings into a visual perspective. Overall, we can all relate to struggling with mental health but our dataset looks to strive for a new way to help decrease amounts of Anxiety and Depression. 

List of Credits

Changes Made to Excel Sheet: Gabby O’Connell 

Choosing Data for Visualizations: Annie Hilgartner

Creation of Visualizations: Annie Hilgartner and Gabby O’Connell

About this Project: Annie Hilgartner and Gabby O’Connell 

Data Story: Gabby O’Connell