Introduction to rlyrics

This document introduces you to the rlyrics package and all the functions inside it. Here to explore the package we have used data fetched from kaggle dataset as an example. Once you have installed the package read vignette("Intro to rlyrics") to learn more about it.

Set up

To get access to 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/<username>/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.

Overview

The goal of rlyrics is to extract and analyze lyrics. It provides functions to download songs attribute, extract lyrics, clean text and generate a word cloud. It makes all the steps seamless and fast.Using this package a user can analyse any (English) lyrics from any artist

The package is an assimilation of four independent functions:

download_data(): The download data function downloads dataset from Kaggle, extracts the given columns from csv file and creates a dataframe.
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.
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.
plot_cloud(): The plot cloud function creates a word cloud of most occurring words in a song/songs by an artist.

Load the library

library(rlyrics)

1. Downloading and Selecting

The first function in our library is the rlyrics::download_data(). This functions downloads the data and extracts the columns into a dataframe.

dataset <- "geomack/spotifyclassification"
df <- rlyrics::download_data(dataset, c("song_title", "artist"))
#> New names:
#> * `` -> ...1
#> Rows: 1969 Columns: 17
#> ── Column specification ────────────────────────────────────────────────────────
#> Delimiter: ","
#> chr  (2): song_title, artist
#> dbl (15): ...1, acousticness, danceability, duration_ms, energy, instrumenta...
#> 
#> ℹ Use `spec()` to retrieve the full column specification for this data.
#> ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
head(df)
#> # A tibble: 6 × 2
#>   song_title     artist          
#>   <chr>          <chr>           
#> 1 Mask Off       Future          
#> 2 Redbone        Childish Gambino
#> 3 Xanny Family   Future          
#> 4 Master Of None Beach House     
#> 5 Parallel Lines Junior Boys     
#> 6 Sneakin’       Drake

2. Extracting Lyrics

The second function in our library is the rlyrics::extract_lyrics(). This 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.

lyrics <- rlyrics::extract_lyrics( "22", "Taylor Swift")

3. Cleaning the text

As we see the raw lyrics need to be cleaned, that’s where our third function rlyrics::clean_text() comes to play. This function turns the raw lyrics into a human-readable text, by removing all the HTML Tags, hash tags, contraction words, special characters. It also coverts everything to lower case

clean_text <- rlyrics::clean_text(lyrics)

4. Creating WordCloud

In the last and final function we are using Word Cloud. 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 rlyrics::plot_cloud() function accepts a dataframe with multiple artists and multiple 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.

song <-  data.frame(song_title  = c("22", "Bohemian Rhapsody"), 
                    artist = c("Taylor Swift", "Queen"))

# plotting and saving WordCloud
word_cloud <- rlyrics::plot_cloud(song, max_font_size=1.6, max_words=100, background_color="white")

Abhiket Gaurav, Artan Zandian, Macy Chan, Manju Abhinandana Kumar