In UBC-MDS/rlyrics: Downloads, cleans and creates wordcloud of lyrics

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rlyrics

• Authors: Abhiket Gaurav, Artan Zandian, Macy Chan, Manju Abhinandana Kumar

An R package to extract and analyze lyrics

Overview

The goal of rlyrics is to extract and analyze lyrics. It provides functions to download songs attribute datasets from Kaggle, extract lyrics, clean text and generate a word cloud.

Functions

| Function Name | Input | Output | Description | |----------------|----------------------------------------------------------|-----------|---------------------------------------------------------------------------------------------------------------------------------| | download_data | dataset, columns | Dataframe | Downloads dataset from kaggle dataset and extract columns from csv file | | extract_lyrics | song_title, artist | String | Extracts song lyrics of a song song_title by artist | | clean_text | text, bool_contra_dict | String | Cleans up the lyrics by removing special characters, html tags, #tags, contraction words and convert everything to lower case | | plot_cloud | song, max_font_size, max_words, background_color | Image | Creates a word cloud image of most occurring words of a song/songs by an artist |

Our Package in the R Ecosystem

There exist similar packages in R. However, this package is more holistic, in the sense that it downloads the lyrics through APIs, cleans the text, and then makes the word cloud. There are packages which does one of these steps. This package takes care of all the steps. Of the many other similar packages, the one that come close is: geniusr

Installation

You can install the development version of rlyrics from GitHub with:

# install.packages("devtools")
devtools::install_github("UBC-MDS/rlyrics")

Features

The rlyrics packages contains the following four functions:

1. download_data() The download data function downloads dataset from Kaggle, extracts the given columns from csv file and creates a dataframe.

2. extract_lyrics() The extract lyrics function, extracts the lyrics from API for a song title and artist and saves it as a dataframe with columns song title, artist and lyrics.

3. clean_text() The lyrics extracted from extract_lyrics() are not clean. It removes special characters, html tags, #tags, contraction words and converts everything to lower case.

4. plot_cloud() The plot cloud function creates a word cloud of most occurring words in a song/songs by an artist.

Example

Downloading and Selecting

The first function in our package is the download_data(). Here you will input your kaggle dataset and the columns to be extracted into a dataframe with columns argument.

To use the Kaggle API, sign up for a Kaggle account at Kaggle. Then go to the 'Account' tab of your user profile (https://www.kaggle.com/\/account) and select 'Create API Token'. This will trigger the download of kaggle.json, a file containing your API credentials. Place this file in the location \~/.kaggle/kaggle.json. The function will automatically read your Kaggle credentials from the above path.

For more information about API call limits and API care recommendations please visit the Kaggle API or Official Kaggle API documentation.

library(rlyrics)
# Example dataset: Spotify Song Attributes  
dataset <- "geomack/spotifyclassification"
# Extract columns 
df <- download_data(dataset, c("song_title", "artist"))

Extracting Lyrics

The extract_lyrics() function gets the song_title and artist name, checks validity and availability of the combination, and extracts the lyrics for that song in a raw string format with header, footer etc which needs to be cleaned in order to create a human-readable text.

library(rlyrics)
# extracting lyrics 
extract_lyrics( "22", "Taylor Swift")

Cleaning

The clean_text() function turns the raw lyrics into a human-readable text.

library(rlyrics)
text <- "Early optimization is the root of all evil!"
# Clean the extracted raw lyrics (text)
clean_text(text)

Creating WordCloud

WordCloud is an artistic rendering of the most frequent words in a text document. A higher occurrence for a word is translated into a larger text size. At this stage, we have helper functions to facilitate the extraction and cleaning of lyrics. The plot_cloud() function accepts a dataframe with artist and song_title data. It will then extract the lyrics for all songs and saves a WordCloud of the most occurring terms in the file_path provided by the user. The WordCloud parameters to be set are self-explanatory: max_font_size, max_word and background_color.

library(rlyrics)
song <- data.frame(song_title  = c("22", "Bohemian Rhapsody"), artist = c("Taylor Swift", "Queen"))
# plotting and saving WordCloud
plot_cloud(song, max_font_size=1.6, max_words=100, background_color="white")

knitr::include_graphics("man/figures/wordcloud.png")

Contributors

The names of core development team is listed below.

| Name | GitHub Handle | |:-----------------------:|:-----------------:| | Abhiket Gaurav | abhiket | | Artan Zandian | artanzand | | Macy Chan | macychan | | Manju Abhinandana Kumar | manju-abhinandana |

We welcome and recognize all contributions. Check out the contributing guidelines. Please note that this project is released with a Code of Conduct. By contributing to this project, you agree to abide by its terms.

License

rlyrics was created by Group 2. It is licensed under the terms of the MIT license.

UBC-MDS/rlyrics documentation built on Feb. 5, 2022, 10:35 p.m.