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Apply data-wrangling and visualization tools from the tidyverse to musical data. Find the most common chords and chord progressions in a sample of pop/rock music from the 1950s-1990s, and compare the styles of different artists. This project assumes familiarity with standard TidyVerse tools for R, in particular the `tibble` data structure and the `dplyr` and `ggplot2` packages. No specific musical knowledge is required, though it may give you ideas for further exploration of the dataset after completing the project. This project uses a parsed and cleaned version of the [McGill Billboard Dataset](http://ddmal.music.mcgill.ca/research/billboard), version 2.0 (CC0 license).
- 2The most common chords
- 3Visualizing the most common chords
- 4Chord "bigrams"
- 5Visualizing the most common chord progressions
- 6Finding the most common artists
- 7Tagging the corpus
- 8Comparing chords in piano-driven and guitar-driven songs
- 9Comparing chord bigrams in piano-driven and guitar-driven songs
Data scientist and instructional technology specialist
Kris Shaffer, Ph.D., is a data scientist and instructional technology specialist at the University of Mary Washington. He also does freelance work in web intelligence and analytics, and has an academic background in music theory and the digital humanities. You can find him on the web at pushpullfork.com.
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