![]() ![]() danceability: Danceability describes how suitable a track is for dancing based on a combination of musical elements including tempo, rhythm stability, beat strength, and overall regularity.1.0 represents high confidence the track is acoustic. acousticness: A confidence measure from 0.0 to 1.0 of whether the track is acoustic.We choose certain features and the definitions are showing below: ![]() ![]() In Spotify API, it provides many audio features. Based on our experience, we believe that tempo, keys and audio features are decisive factors for recognizing music genres, and they should be what we are going to visualize. Spotify provides thorough explanation to its API, and it is really critical for a developer or analyst to read it before starting to do anything. You will be able to build up your own music analytical tool and have more insights with Spotify API after the reading.įirst of all, we need to understand our data and what information we have. The first section is about the steps to build up the visualization application in the second part, we can share how it could be helpful for us to determine different music genres. Otherwise, if you have installed R, the following command can run it on your own environment. If you cannot connect to it properly, please try it next month. However there are some limitations for shinyapp.io - 25 active hours per month for free. ![]() The website is accessible through the link here. In this article, we use R Shiny as an interface to communicate with Spotify API and host it on shinyapps.io. We can easily classify a song as Blues, Jazz or Country music, so what is the implication behind it? Can we tell, or see, the difference of music with data? Thanks to the open API from Spotify, we can understand different aspects of music, including tempo, key and audio features, and build up web applications to answer our questions efficiently. Everyone has his/her favorite musicians and those musicians usually are good at certain types of music. ![]()
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