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This R Shiny dashboard displays data about songs that are played via Amazon Alexa. The data is stored in Google Spreadsheets via an IFTTT application. The dashboard contains four tabs: Frequency Plot, Year to Year Popularity, Top Songs Played, and About. The user can group frequency plots based on four different time units: Day, Week, Month, and Day of Week. The Year to Year Popularity tab contains a bump chart that can be grouped based on the top five most popular Artists or Albums played. The entire dashboard can be filtered with a date range.

I played each song via the Amazon Alexa with the voice command, “Alexa, play [genre] music, or Alexa, play music by [artist].” When each song begins playing, the IFTTT application adds a new record to a google sheet using the Keep a Google spreadsheet of the songs you listen to on Alexa applet. The google sheet consists of four columns with no headers (Example below).

Example Record:

I load the google sheet into R using the Google Sheets R API then apply the following transformations to the data:

• The data frame is given column names (date_time, artist, song, album).
• Each record in the Date and Time column is converted from a character to a POSIXct date-time object.
• The artist column is split into two columns (main_artist and featuring).
• The song and album columns are cleaned to no longer have [Explicit], [Clean], (Album Version), etc tags. This allows for clean, explicit, and live versions of a song or album to be grouped together and counted as the same album.
• Date, Week_Day, Week, Month, Year, and Time columns are added to the data frame.
• Skipped songs are removed from the data. If two or more songs contain the same date-time stamp, only the last song is kept.

Resulting Record:

Two blog posts were tremendously helpful to me while I was working on this project. John Nguyen's college academic rankings bump chart provided the initial inspiration to visualize my listening history with a bump chart. I also found Jay Blanco’s project when researching for similar projects. I particularly like the crisp look of his lollipop charts. Both John and Jay's projects provided helpful code examples of charts I was interested in making for this project.

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