final_assignment_my472.Rmd

---
title: "Final Assignment"
output: html_document
date: "2023-12-26"
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
library(rvest)
library(dplyr)
library(RSelenium)

library(spotifyr)
library("jsonlite")
library(dplyr)

library(ggplot2)
library(plotly) #making the ggplot interactive
library(knitr) #For printing table of Outlier Summary

library(tools)
library(patchwork)
```


```{r rs_ranking, include = FALSE, echo = FALSE}
#The code chunks below are commented out as requested on the Instruction of the Assignment, so that it doesn’t run but it still visible in the code appendix.

# ############### Getting Rolling Stones Ranking Info
# 
# #Retrieving the information from the Rolling Stones Ranking
# rD <- rsDriver(browser=c("firefox"), verbose = F, chromever = NULL)
# 
# driver <- rD[["client"]] 
# url <- "https://www.rollingstone.com/music/music-lists/100-greatest-artists-147446/"
# 
# #Navigating to the URL
# driver$navigate(url)
# 
# #Allowing the page to load
# Sys.sleep(2)
# 
# #Rejecting cookies
# cookie <- driver$findElement(using = 'css', value = '#onetrust-reject-all-handler')
# cookie$clickElement()
# 
# #Getting to the bottom of the page
# # loading all the dynamic html
# webElem <- driver$findElement("css", "body")
# webElem$sendKeysToElement(list(key = "end"))
# 
# #Getting the full page html
# page_source <- driver$getPageSource()[[1]]
# 
# #Extracting the rankings
# html_elements_ranking <- read_html(page_source) %>%
#   html_nodes(".c-gallery-vertical-album__number") %>% html_text()
# 
# #Extracting the Artists for each ranking
# html_elements_titles <- read_html(page_source) %>%
#   html_nodes(".c-gallery-vertical-album__title") %>% html_text()
# 
# #Finding the load_more button
# load_more <- driver$findElement(using = 'css', value = ".c-gallery-vertical__load-button")
# load_more$clickElement()
# 
# #Going to the bottom of the page to load every artist
# webElem <- driver$findElement("css", "body")
# webElem$sendKeysToElement(list(key = "end"))
# 
# #Getting the full page html
# page_source <- driver$getPageSource()[[1]]
# 
# #Extracting the rankings
# html_elements_ranking <- c(html_elements_ranking, read_html(page_source) %>%
#   html_nodes(".c-gallery-vertical-album__number") %>% html_text())
# 
# #Extracting the Artists for each ranking
# html_elements_titles <- c(html_elements_titles, read_html(page_source) %>%
#   html_nodes(".c-gallery-vertical-album__title") %>% html_text())
# 
# #Closing the port 
# driver$close()
# rD$server$stop()
# 
# # close the associated Java processes if necessary:
# system("taskkill /im java.exe /f", intern=FALSE, ignore.stdout=FALSE)
# 
# #Transforming the data into a Dataframe
# rankings_df <- data.frame(ranking = html_elements_ranking, artist = html_elements_titles)
# 
# # Save the data frame as a RDS file
# #saveRDS(rankings_df, "./data/rs_ranking.rds")
```

```{r artist_info_ranking, include = FALSE, echo = FALSE}
# ############### Getting Spotify Info for Every Artist in the RS Ranking
# 
# #Reading the client ID and Secret to access the API
# readRenviron("api.env")
# client_id <- Sys.getenv("SPOTIFY_CLIENT_ID")
# client_secret <- Sys.getenv("SPOTIFY_CLIENT_SECRET")
# 
# #Generating the access_token
# access_token <- get_spotify_access_token(client_id, client_secret)
# 
# #Retrieving the information from the Rolling Stones Ranking
# rs_ranking <- readRDS("./data/rs_ranking.rds")
# 
# #Creating a DF to store the data
# artist_id_pop <- data.frame()
# 
# # Loop through each artist in the Rolling Stones Ranking
# for (artist in rs_ranking$artist){
#   q <- artist
#   print(q)
#   # Search for the artist on Spotify
#   # and getting only the first result
#   result <- search_spotify(
#     q,
#     type = "artist",
#     market = NULL,
#     limit = 1,
#     offset = 0,
#     include_external = NULL,
#     authorization = access_token,
#     include_meta_info = FALSE
#   )
#   
#   new_row <- data.frame(result)
#   
#   # Add artist name and Spotify data to the dataframe
#   new_row <- cbind(artist_rs = q, new_row)
#   artist_id_pop <- rbind(artist_id_pop, new_row)
# }
# 
# #Save the initial result as a RDS file to avoid having to make new calls to the API
# #saveRDS(artist_id_pop, file = "./data/artist_id_pop_spotify.rds")
# 
# #Loading the data that was saved
# artist_id_pop <- readRDS("./data/artist_id_pop_spotify.rds")
# 
# # Checking for differences on the artist of the Ranking
# # and the result retrieved from the API
# for (i in 1:nrow(artist_id_pop)){
#   if (artist_id_pop$name[i] != artist_id_pop$artist_rs[i]){
#   print(paste("different name on index", i))
#   print(paste("1 -",artist_id_pop$name[i]))
#   print(paste("2 -",artist_id_pop$artist_rs[i]))
#   }
# }
# 
# #Differences are minor or non relevant (eg. &, and; upper and lower case differences)
# #Only notable differences are:
# # - Santana and Carlos Santana -> Sticking to the id provided by spotify that is the verified artist
# # - Hank Williams brought the Hank Williams Jr singer. Must be replaced by Hank Williams with the artist ID of '1FClsNYBUoNFtGgzeG74dW'
# # - Differences on line 43 "1 - George Clinton & Parliament Funkadelic" - "2 - Parliament and Funkadelic"
# 
# #Retrieving the correct information on the wrong artists
# 
# #Getting the ids of the artists that are wrong
# #Parliament and Funkadelic
# par_fun_wrong_id <- artist_id_pop %>% filter(artist_rs == "Parliament and Funkadelic") %>% pull(id)
# #Hank Williams
# hank_wil_wrong_id <- artist_id_pop %>% filter(artist_rs == "Hank Williams") %>% pull(id)
# 
# #Doing the search again to check for better results
# result <- search_spotify(
#   "Parliament Funkadelic",
#   type = "artist",
#   market = NULL,
#   limit = 20,
#   offset = 0,
#   include_external = NULL,
#   authorization = get_spotify_access_token(),
#   include_meta_info = FALSE
# )
# 
# #Selecting the lines of the band Parliament and the band Funkadelic
# result_par <- subset(result, id == "5SMVzTJyKFJ7TUb46DglcH")
# new_row_p <- cbind(artist_rs = "Parliament and Funkadelic", result_par)
# result_fun <- subset(result, id == "450o9jw6AtiQlQkHCdH6Ru")
# new_row_f <- cbind(artist_rs = "Parliament and Funkadelic", result_fun)
# #Removing the line with the wrong result
# artist_id_pop <- subset(artist_id_pop, id != par_fun_wrong_id)
# #Adding the new lines
# new_row <- rbind(new_row_p, new_row_f)
# artist_id_pop <- rbind(artist_id_pop, new_row)
# 
# #Getting Hank Williams info
# result <- search_spotify(
#   "Hank Williams",
#   type = "artist",
#   market = NULL,
#   limit = 20,
#   offset = 0,
#   include_external = NULL,
#   authorization = get_spotify_access_token(),
#   include_meta_info = FALSE
# )
# 
# #Getting the row with the correct result
# result_hank_wil <- subset(result, id == "1FClsNYBUoNFtGgzeG74dW")
# new_row <- cbind(artist_rs = "Hank Williams", result_hank_wil)
# #Removing the row that was wrong
# artist_id_pop <- subset(artist_id_pop, id != hank_wil_wrong_id)
# artist_id_pop <- rbind(artist_id_pop, new_row)
# 
# #Saving the dataset as a RDS file
# #saveRDS(artist_id_pop, file = "./data/artist_id_pop.rds")
# 
# #Retrieving the data 
# artist_id_pop <- readRDS("./data/artist_id_pop.rds")
# 
# # Merging the two dataframes, to get one dataset with
# # with the Rolling Stones Ranking and the Spotify data
# artist_info_ranking <- merge(rs_ranking, artist_id_pop,
#                              by.x = "artist", by.y = "artist_rs",
#                              all.x = TRUE)
# 
# #Changing the names of columns
# artist_info_ranking <- artist_info_ranking %>%  rename(artist_rs = artist,
#                                                        artist_spotify = name)
# 
# # Reorder columns
# artist_info_ranking <- artist_info_ranking %>%
#   select(ranking, artist_rs, artist_spotify, id, everything())
# 
# artist_info_ranking <- artist_info_ranking[order(artist_info_ranking$ranking), ]
# 
# #Correcting the name on the artist_rs column for Parliament and Funkadelic
# artist_info_ranking <- artist_info_ranking %>%
#   mutate(artist_rs = ifelse(artist_rs == "Parliament and Funkadelic",
#                             paste0("Parliament and Funkadelic (", artist_spotify, ")"),
#                             artist_rs))
# 
# # Save the merged dataframe as an RDS file
# #saveRDS(artist_info_ranking, file = "./data/artist_info_ranking.rds")
```

```{r spotify_toptracks, include = FALSE, echo = FALSE}
# ############### Getting Top Tracks for Every Artist in the RS Ranking
# 
# #Retrieving the necessary RDS File
# artist_info_ranking <- readRDS("./data/artist_info_ranking.rds")
# 
# #Getting the top tracks of each artist in the us
# #Setting the access_token
# access_token <- get_spotify_access_token(client_id, client_secret)
# 
# # Creating an empty dataframe to store top tracks
# top_tracks_us <- data.frame()
# 
# # Loop through each artist in the ranking dataframe
# for (i in 1:nrow(artist_info_ranking)){
#    # Get the Spotify ID of the artist
#   id <- artist_info_ranking$id[i]
#   
#   # Get the top tracks of the artist in the US
#   result <- get_artist_top_tracks(
#     id,
#     market = "US",
#     authorization = access_token,
#     include_meta_info = FALSE
#   )
#   # Print the artist's name for visibility
#   print(artist_info_ranking$artist_rs[i])
# 
#   # Assume that the main artist of the track is the one we are looking for
#   result$artists <- id
#   
#   # Include the name of the artist for better readability
#   result$artist_spotify <- artist_info_ranking$artist_spotify[i]
#   
#   # Append the new rows to the dataframe
#   new_rows <- result
#   top_tracks_us <- rbind(top_tracks_us, new_rows)
# }
# 
# # Changing column names for consistency
# for (name in c("id", "name", "popularity", "preview_url")){
#   colnames(top_tracks_us)[colnames(top_tracks_us) == name] <- paste0("track_",name)
# }
# 
# # Renaming the "artists" column to "artist_id"
# colnames(top_tracks_us)[colnames(top_tracks_us) == "artists"] <- "artist_id"
# 
# # Reordering columns for better readability
# top_tracks_us <- top_tracks_us %>%
#   select(artist_spotify, artist_id, track_name, track_popularity, track_id, everything())
# 
# # Saving the dataframe as an RDS file
# #saveRDS(top_tracks_us, file = "./data/top_tracks_us.rds")

```


```{r spotify_audio_features, include = FALSE, echo = FALSE}
# ############### Getting audio features for all the top tracks US
# 
# 
# # Setting the access_token
# access_token <- get_spotify_access_token(client_id, client_secret)
# 
# # Creating an empty dataframe to store audio features
# audio_features_us <- data.frame()
# 
# # Initializing vectors to store track information
# track_list <- c(NULL)
# artist_id <- c(NULL)
# artist_spotify <- c(NULL)
# 
# # Loop through each track in the top tracks US dataframe
# for (i in 1:nrow(top_tracks_us)){
#   # Get the track ID
#   track <- top_tracks_us$track_id[i]
#   
#   # Append track information to vectors
#   track_list <- c(track_list, track)
#   artist_id <- c(artist_id, top_tracks_us$artist_id[i])
#   artist_spotify <- c(artist_spotify, top_tracks_us$artist_spotify[i])
#   
#   # If 100 tracks are accumulated or it's the last iteration, get audio features
#   if (length(track_list) == 100 | i == nrow(top_tracks_us)){
#     result <- get_track_audio_features(track_list,
#                                        authorization = access_token)
#     
#     # Combine audio features with artist information
#     new_rows <- cbind(result, artist_id)
#     new_rows <- cbind(new_rows, artist_spotify)
#     
#     # Append the new rows to the dataframe
#     audio_features_us <- rbind(audio_features_us, new_rows)
#     
#     # Restarting the track list and related vectors
#     track_list <- c(NULL)
#     artist_id <- c(NULL)
#     artist_spotify <- c(NULL)
#   }
# }
# 
# # Renaming the "id" column to "track_id"
# colnames(audio_features_us)[colnames(audio_features_us) == "id"] <- "track_id"
# 
# # Saving the dataframe as an RDS file
# #saveRDS(audio_features_us, file = "./data/audio_features_us.rds")
```


```{r scraping_music_brainz_data, include = FALSE, echo = FALSE}
# ############### Scraping the music Brainz website
# ############### to get information on the end of each artist
# # 
# 
# # Reading the artist information from a saved RDS file
# artist_info_ranking <- readRDS("./data/artist_info_ranking.rds")
# 
# # Setting up a WebDriver for scraping MusicBrainz website
# rD <- rsDriver(browser=c("firefox"), verbose = F, chromever = NULL)
# driver <- rD[["client"]] 
# url <- "https://musicbrainz.org/"
# 
# # Navigating to the MusicBrainz website
# driver$navigate(url)
# 
# # Creating empty dataframes to store MusicBrainz data
# mbdata <- data.frame()
# all_results <- data.frame()
# 
# # Looping through each artist in the Rolling Stones ranking
# for (artist in artist_info_ranking$artist_spotify){
#   # Find the search field and perform the search
#       
#   # Performing a search for each artist on MusicBrainz
#   while (TRUE){
#     # printing the artist that is being searched
#     print(artist)
#     
#     # Finding the search field and performing the search
#     search_field <- driver$findElement(using = 'css', value = '#headerid-query')
#     search_field$sendKeysToElement(list(artist))
#     search_field$sendKeysToElement(list(key = "enter"))
#         
#     # Giving time for the search results to show
#     Sys.sleep(2)
#     
#     # Getting the search results
#     search_results <- try(driver$findElement(using = 'css', value = ".tbl"), silent = TRUE)
#     
#     # If there is an internal error, try the search again
#     if ("try-error" %in% class(search_results)){
#       print("Trying search again")
#       Sys.sleep(2)
#     } else {break}
#   }
#   
#   # Extracting the outer HTML of the table
#   results_html <- search_results$getElementAttribute("outerHTML")
#   
#   #Reading the HTML element of the results
#   results_html <- read_html(results_html[[1]])
#   
#   #Extracting the table of the results
#   results_table <- html_table(results_html)[[1]]
#   
#   #selecting the first row as the correct result
#   new_row <- results_table[1,]
#   new_row <- cbind(artist_spotify = artist, new_row)
#   mbdata <- rbind(mbdata, new_row)
#   
#   #Saving all other results in case there is mismatch
#   result_rows <- cbind(artist_spotify = artist, results_table)
#   all_results <- rbind(all_results, result_rows)
# }
# 
# # Closing the port 
# driver$close()
# rD$server$stop()
# 
# # close the associated Java processes if necessary:
# system("taskkill /im java.exe /f", intern=FALSE, ignore.stdout=FALSE)
# 
# # Correcting the result for The Drifters that found a Taiwanese group
# the_drifters <- all_results[(all_results$artist == "The Drifters"),]
# #The correct result is the second row
# the_drifters <- the_drifters[2,]
# 
# # replacing the row in the mbdata dataframe
# # Finding the index of the original data
# drifters_index <- which(mbdata$artist == "The Drifters")
# # Update the corresponding row in mbdata with the corrected information
# mbdata[drifters_index, ] <- the_drifters
# 
# 
# # Treating the Begin and End dates to make them in a uniform format
# # If there is only the year and month, the day 01 is assigned
# # If there is only the year, the first of july of the year is assigned
# 
# mbdata$Begin <- ifelse(nchar(mbdata$Begin) == 4, paste0(mbdata$Begin, "-07-01"), mbdata$Begin)
# mbdata$Begin <- ifelse(nchar(mbdata$Begin) == 7, paste0(mbdata$Begin, "-01"), mbdata$Begin)
# 
# mbdata$End <- ifelse(is.na(mbdata$End), "", mbdata$End)
# mbdata$End <- ifelse(nchar(mbdata$End) == 4, paste0(mbdata$End, "-07-01"), mbdata$End)
# mbdata$End <- ifelse(nchar(mbdata$End) == 7, paste0(mbdata$End, "-01"), mbdata$End)
# 
# #Saving the mbdata as a RDS file to be loaded later
# #saveRDS(mbdata, ".\data\mbdata.rds")
```

```{r most_popular_info, include=FALSE, echo=FALSE}
# ############### Retrieving information of the Most Popular Artists today
# ############### Retrieving their top tracks and audio features on Spotify
# 
# 
# # Setting the URL for the webpage with Spotify's most popular artists
# url <- "https://chartmasters.org/spotify-most-popular-artists/"
# 
# # Reading HTML content from the webpage
# html_content <- read_html(url)
# 
# # Extracting tabular data from the HTML content
# tab <- html_table(html_content, fill = TRUE)
# popular_artists <- tab[[1]]
# 
# # Renaming columns for better readability
# colnames(popular_artists) <- c("rank",
#                                "artist_img",
#                                "artist_cm",
#                                "popularity",
#                                "weekly +/-",
#                                "daily Streams")
# 
# # Removing unnecessary columns
# popular_artists <- popular_artists %>% select(-artist_img, -"weekly +/-", - popularity)
# 
# ### Getting the Spotify id for these artists
# 
# # Getting the info to access the API
# readRenviron("api.env")
# client_id <- Sys.getenv("SPOTIFY_CLIENT_ID")
# client_secret <- Sys.getenv("SPOTIFY_CLIENT_SECRET")
# access_token <- get_spotify_access_token(client_id, client_secret)
# 
# # Creating dataframe to store information
# spotify_id <- data.frame()
# # Looping through each artist in the popular_artists dataframe
# for (artist in popular_artists$artist_cm){
#   q <- artist
#   print(q)
#   
#   result <- search_spotify(
#     q,
#     type = "artist",
#     market = NULL,
#     limit = 1,
#     offset = 0,
#     include_external = NULL,
#     authorization = access_token,
#     include_meta_info = FALSE
#   )
#   
#   # Creating a new row with the obtained result
#   new_row <- data.frame(result)
#   new_row <- cbind(artist_cm = q, new_row)
#   spotify_id <- rbind(spotify_id, new_row)
# }
# 
# # Selecting the relevant information
# spotify_id <- spotify_id %>% select(artist_cm, artist_spotify = name, artist_id = id, genres, popularity)
# 
# # Checking for different names
# indices <- which(spotify_id$artist_spotify != spotify_id$artist_cm)
# 
# if (length(indices) > 0) {
#   print(paste("Different name on indices:", indices))
#   print(paste("1 -", spotify_id$artist_spotify[indices]))
#   print(paste("2 -", spotify_id$artist_cm[indices]))
# }
# 
# # The differences don't seem relevant, no other measures needed
# 
# # Merging the two datasets (most popular artists and spotify IDs)
# spotify_id_subset <- spotify_id %>% select(artist_cm, artist_spotify, artist_id, artist_popularity = popularity)
# 
# popular_artists <- merge(popular_artists, spotify_id_subset, by = "artist_cm")
# 
# #Rearranging the columns
# popular_artists <- popular_artists %>% select(rank,
#                                                 artist_cm,
#                                                 artist_spotify,
#                                                 everything()) %>% arrange(rank)
# 
# # Saving the merged dataframe as an RDS file
# #saveRDS(popular_artists, "./data/popular_artists_cm.rds")
# 
# #loading the information
# popular_artists <- readRDS("./data/popular_artists_cm.rds")
# 
# #Getting the top tracks of each artist in the us
# #Setting the access_token
# access_token <- get_spotify_access_token(client_id, client_secret)
# 
# pop_top_tracks_us <- data.frame()
# # Looping through each artist in the popular_artists dataframe
# for (i in 1:nrow(popular_artists)){
#   id <- popular_artists$artist_id[i]
#   
#   # Getting the top tracks of the artist in the US
#   result <- get_artist_top_tracks(
#     id,
#     market = "US",
#     authorization = access_token,
#     include_meta_info = FALSE
#   )
#   print(popular_artists$artist_spotify[i])
#   
#   # Assuming that the main artist of the track is the one that we are looking for
#   result$artists <- id
#   
#   # Including the name of the artist for better readability
#   result$artist_spotify <- popular_artists$artist_spotify[i]
#   
#   # Appending the new rows to the dataframe
#   new_rows <- result
#   pop_top_tracks_us <- rbind(pop_top_tracks_us, new_rows)
# }
# 
# #selecting the relevant columns
# pop_top_tracks_us <- pop_top_tracks_us %>% select(artist_spotify,
#                                                   artist_id = artists,
#                                                   track_id = id,
#                                                   track_name = name,
#                                                   track_popularity = popularity,
#                                                   album.release_date,
#                                                   album.release_date_precision,
#                                                   )
# 
# # Getting audio features for all the top tracks in the US
# pop_audio_features_us <- data.frame()
# track_list <- c(NULL)
# artist_id_list <- c(NULL)
# artist_list <- c(NULL)
# 
# # Looping through each track in the pop_top_tracks_us dataframe
# for (i in 1:nrow(pop_top_tracks_us)){
# 
#   track <- pop_top_tracks_us$track_id[i]
#   track_list <- c(track_list, track)
#   artist_id_list <- c(artist_id_list, pop_top_tracks_us$artist_id[i])
#   artist_list <- c(artist_list, pop_top_tracks_us$artist_spotify[i])
# 
#   # If 100 tracks are accumulated or it's the last iteration, get audio features
#   if (length(track_list) == 100 | i == nrow(pop_top_tracks_us)){
#     result <- get_track_audio_features(track_list,
#                                        authorization = access_token)
# 
#     # Combining audio features with artist information
#     new_rows <- cbind(result, artist_id = artist_id_list)
#     new_rows <- cbind(new_rows, artist_spotify = artist_list)
# 
#     # Appending the new rows to the dataframe
#     pop_audio_features_us <- rbind(pop_audio_features_us, new_rows)
# 
#     # Restarting the track list and related vectors
#     track_list <- c(NULL)
#     artist_id_list <- c(NULL)
#     artist_list <- c(NULL)
#   }
# }
# # Renaming the "id" column to "track_id"
# colnames(pop_audio_features_us)[colnames(pop_audio_features_us) == "id"] <- "track_id"
# pop_audio_features_us_subset <- pop_audio_features_us %>% select (-artist_spotify)
# 
# # Merging the top tracks and the audio features
# pop_audio_features_us <- merge(pop_top_tracks_us,
#                                pop_audio_features_us_subset,
#                                by.x = c("track_id", "artist_id"),
#                                by.y = c("track_id", "artist_id"))
# 
# 
# # Saving the merged dataframe as an RDS file
# #saveRDS(pop_audio_features_us, "./data/popular_artists_audio_features.rds")
```


```{r cleaning_environment, include=FALSE, echo = FALSE}
# # All the data has been retrieved and processed
# # Whenever needed the data will be directly loaded
# # Cleaning the environment for better performance
# 
# rm(list = ls())

```

**Github repository:** 'https://github.com/editor210/MY_472_Final_Assignment'

#### **1. Introduction**
This research endeavors to explore how the music of the ["100 Greatest Musical Artists of All Time"](https://www.rollingstone.com/music/music-lists/100-greatest-artists-147446/), according to Rolling Stones (RS) magazine, endured at the close of 2023.The analysis comprises three stages, analysing Spotify popularity, examining the impact of the time of the releases on popularity and evaluating the tracks audio features and their current popularity.

#### **2. Data**
Various data sources were used to explore this topic:

1. Data retrieved via webscraping
  - ["100 Greatest Musical Artists of All Time"](https://www.rollingstone.com/music/music-lists/100-greatest-artists-147446/) - Artists and ranking positions
  - [MusicBrainz](https://musicbrainz.org/) - Life Span of each of the Ranked Artists
  - [ChartMaster](https://chartmasters.org/spotify-most-popular-artists/) - List of today's most popular artists.
2. Other Sources
  - [Spotify API](https://developer.spotify.com/documentation/web-api): Utilized the [spotifyr](https://www.rdocumentation.org/packages/spotifyr/versions/2.2.4) package for artist searches, top tracks retrieval, and audio features extraction.

The data processing involved retrieving the data, treating the datasets maintaining either the artists id, the track id or a combination of both as a primary key. After the processing of the information the data was saved as a RDS file.

Key decisions during this stage included treating Parliament and Funkadelic as separate bands due to their recent merger, resulting in a final ranking of 101 artists. Additionally, the decision to use an RDS file, as a more efficient choice, instead of a relational database due to the complexity of flattening variables within dataframes from the Spotify API.

#### **3. Analysis**
##### **3.1 Popularity Analysis**
Spotify's popularity index will be used to assess the endurance of the RS ranked artists' music. This index for an artist is based on each artists' track popularity, which, according to the Spotify documentation, has a high weight on the current popularity, since "songs that are being played a lot now will have a higher popularity than songs that were played a lot in the past". Given the range of 0 to 100, and assuming a normal distribution of popularity among Spotify artists, this study will classify those with a popularity index over 50 as "Popular", while those below are "Unpopular".

The interactive plot below shows each artist's RS Ranking position versus their current Spotify popularity. Dot colors indicate popularity.

```{r warning=FALSE, include = FALSE, echo = FALSE}
#loading the needed datasets
artist_info_ranking <- readRDS("./data/artist_info_ranking.rds")

# Define artist popularity based on a threshold of 50
artist_info_ranking$is_popular <- ifelse(artist_info_ranking$popularity > 50,
                                         "Popular",
                                         "Not Popular")

# Create a new dataset to use in the plot
# with additional columns and arrange the data
plot_one_data <- artist_info_ranking %>%
  # New Column with followers in thousands and round to 2 decimal places
  mutate(followers_k = round(followers.total/1000, 2)) %>%
  
  # Convert artist_spotify to a factor
  mutate(artist_spotify = factor(artist_spotify, artist_spotify)) %>%
  
  # Prepare text for tooltip in interactive plot
  mutate(text = paste("Artist: ", artist_spotify,
                      "\nPopularity on Spotify: ", popularity,
                      "\nRanking Rolling Stones: ", ranking,
                      "\nFollowers (1,000): ", followers_k, sep=""))

# Create a ggplot scatter plot with a smooth line and additional formatting
p1 <- plot_one_data %>%
  ggplot() +
  geom_point(aes(x = ranking, y = popularity, color = is_popular, text = text), size = 3, alpha = 0.5) +
  theme_minimal() +
  labs(title = "Rolling Stones Ranking x Spotify Popularity",
       color = NULL,
       x = "Rolling Stones Ranking",
       y = "Spotify Popularity") +
  ylim(0, 100) +
  xlim(0, 100) +
  geom_vline(xintercept = 0, color = "black", alpha = 0.1) +  # Add vertical line at x = 0
  geom_hline(yintercept = 0, color = "black", alpha = 0.1) +  # Add horizontal line at y = 0
  theme(legend.position = "bottom",
        panel.grid = element_line(color = alpha("gray", 0.2), linetype = "dashed"),  # Adjust legend position
        axis.text = element_text(size = 8),  # Adjust axis text size
        axis.title = element_text(size = 10),
        plot.title = element_text(hjust = 0.5))  # Adjust axis title size

# Convert ggplot plot to plotly for interactivity with tooltips
p1_int <- ggplotly(p1, tooltip = "text")
```


```{css, echo=FALSE}
/* Centering the Plotly interactive plot*/

/*Source: https://stackoverflow.com/questions/47193192/r-markdown-and-plotly-fig-align-not-working-with-html-output*/

.center {
  display: table;
  margin-right: auto;
  margin-left: auto;
}
```

<div class = 'center'>
```{r, include = TRUE, echo = FALSE, fig.width = 6, fig.height = 3, fig.align='center'}
#Plotting the graph
p1_int
```
</div>

From the plot above, it is clear that, the vast majority of artists in the RS ranking and, consequently theirs songs, have endured and can be considered popular by the end of 2023.

##### **3.2 Time and Artist's Status Impact on Popularity**

The artist's current popularity can be influenced by the song's release date and the artist's status. Older songs may lose relevance, and the artist's ongoing activity can impact popularity.

Combining Spotify API album release dates with artist lifespan data from [MusicBrainz.org](https://musicbrainz.org/), the graph below plots top tracks in the United States market for RS artists as "Regular Release" if the artist was active and "Releases of Non-Active Artist" otherwise, while comparing the tracks's popularity versus the date of the release.

```{r, include = FALSE, echo = FALSE}
artist_info_ranking <- readRDS("./data/artist_info_ranking.rds")
top_tracks_us <- readRDS("./data/top_tracks_us.rds")
mbdata <- readRDS("./data/mbdata.rds")

#Selecting the necessary variables from each of the datasets
subset_mbdata <- mbdata %>% select(artist_spotify, Begin, End)
subset_artist_info_ranking <- artist_info_ranking %>% select(artist_spotify, artist_id = id, popularity)
subset_top_tracks_us <- top_tracks_us %>% select(artist_spotify, artist_id, track_name, track_id, track_popularity, album.release_date)

merge_data_p3 <- merge(subset_mbdata, subset_artist_info_ranking, by = "artist_spotify")

merge_data_p3 <- merge(merge_data_p3, subset_top_tracks_us, by = "artist_id")

# Convert dates to Date objects
merge_data_p3$Begin <- as.Date(merge_data_p3$Begin)
merge_data_p3$End <- as.Date(merge_data_p3$End)
merge_data_p3$album.release_date <- as.Date(merge_data_p3$album.release_date)

merge_data_p3$band_ended <- ifelse(merge_data_p3$End < merge_data_p3$album.release_date, "Release of Non Active Artist", "Regular Release")

merge_data_p3$band_ended[is.na(merge_data_p3$band_ended)] <- "Regular Release"

merge_data_p3$is_pop <- ifelse(merge_data_p3$popularity > 50, "Popular Artist", "Unpopular Artist")

average_popularity <- merge_data_p3 %>%
  group_by(band_ended) %>%
  summarise(avg_popularity = mean(popularity))

plot_data <- merge_data_p3 %>% filter(!is.na(album.release_date))
```

```{r fig.width=11, message=FALSE, warning=FALSE, echo = FALSE, include = TRUE}
# Create the plot for band_ended facet
p2 <- ggplot(plot_data %>% filter(is_pop == "Popular Artist"), aes(x = album.release_date, y = track_popularity, color = band_ended)) +
  geom_point(size = 3, alpha = 0.7) +
  scale_x_date(date_breaks = "5 years", date_labels = "%Y", limits = c(as.Date("1958-01-01"), as.Date("2023-12-12") )) +
  labs(title = "Track Popularity Over Album Release Dates",
       subtitle = "'Popular' Artists",
       x = "Album Release Date",
       y = "Track Popularity",
       color = "") +
  theme_minimal() +
  ylim(0,100) +
  geom_smooth(method = "lm", se = FALSE) +
  theme(legend.position = "bottom",
        axis.text.x = element_text(angle = 45),
        panel.grid.minor.x = element_blank(),
        panel.grid.major.x = element_line(linetype = "dashed"),
        panel.grid.major.y = element_line(linetype = "dashed"),
        plot.title = element_text(size = 16, hjust = 0.5, color = "black", face = "bold"),
        plot.subtitle = element_text(size = 12, hjust = 0.5, color = "black")) +
  geom_hline(aes(yintercept = 0), linetype = "solid", color = "black", alpha = 0.5)

p3 <- ggplot(plot_data %>% filter(is_pop != "Popular Artist"), aes(x = album.release_date, y = track_popularity, color = band_ended)) +
  geom_point(size = 3, alpha = 0.7) +
  scale_x_date(date_breaks = "5 years", date_labels = "%Y", limits = c(as.Date("1958-01-01"), as.Date("2023-12-12") )) +
  labs(title = "Track Popularity Over Album Release Dates",
       subtitle = "'Unpopular' Artists",
       x = "Album Release Date",
       y = "Track Popularity",
       color = "") +
  theme_minimal() +
  ylim(0,100) +
  #xlim(as.Date("1960-01-01"), NA) +
  geom_smooth(method = "lm", se = FALSE)+
  theme(legend.position = "bottom",
        axis.text.x = element_text(angle = 45),
        panel.grid.minor.x = element_blank(),
        panel.grid.major.x = element_line(linetype = "dashed"),
        panel.grid.major.y = element_line(linetype = "dashed"),
        plot.title = element_text(size = 16, hjust = 0.5, color = "black", face = "bold"),
        plot.subtitle = element_text(size = 12, hjust = 0.5, color = "black")) +
  geom_hline(aes(yintercept = 0), linetype = "solid", color = "black", alpha = 0.5)

# Display the plots side by side
library(gridExtra)
grid.arrange(p2, p3, ncol = 2)

```

Notably, pre-1995 releases dominate, which is expected given the historical figures in the RS ranking.

The first plot lack significant correlation while the second graph suggests a connection between release time and popularity. Interestingly, recent releases boost non-active artists’ popularity but reduce it for active artists. Without making any causality claims, this suggests that an artist’s end may enhance long-term popularity, by having multiple hits while active, or by the relaunching of old hits during inactive periods.


##### **3.3 Audio Feature Analysis**

Next, this study explores audio features in the US top tracks of today's  [50 most popular artists](https://chartmasters.org/spotify-most-popular-artists/) versus RS ranked popular and unpopular artists. Plots below display density distributions of tracks' Audio Features, colored by artists' popularity.

```{r, fig.height=20, include=FALSE, echo=FALSE}
# Merge the dataframes based on artist_spotify
artist_info_ranking <- readRDS("./data/artist_info_ranking.rds")
top_tracks_us <- readRDS("./data/top_tracks_us.rds")
audio_features_us <- readRDS("./data/audio_features_us.rds")

#First we need to merge the artist_info_ranking and the audio_feature_us to determine the popular and non popular artists
artist_info_ranking_subset <- artist_info_ranking %>% select(artist_id = id, popularity)

audio_features_us_merged <- merge(audio_features_us, artist_info_ranking_subset, by = "artist_id") 

#Creating the column is_pop in the audio_features_us_merged
audio_features_us_merged$is_pop <- ifelse(audio_features_us_merged$popularity > 50, "Popular", "Unpopular")

#Importing the data from the popular artists of 2023 audio features
popular_art_aud_feat <- readRDS("./data/popular_artists_audio_features.rds")
#Creating a column to indicate that these are the most popular artists
popular_art_aud_feat$is_pop <- "Most Popular Today"

#Selecting the columns that will be analised
subset_cols <- c("artist_spotify",
                 "track_id",
                 "artist_id",
                 "is_pop",
                 "danceability",
                 "energy",
                 "loudness",
                 "mode",
                 "speechiness",
                 "acousticness",
                 "instrumentalness",
                 "liveness",
                 "valence",
                 "tempo")

#Creating the subsets that will be merged to be plotted
audio_features_us_subset <- audio_features_us_merged %>% select(all_of(subset_cols))
popular_art_aud_feat_subset <- popular_art_aud_feat %>% select(all_of(subset_cols))

#The dfs have the same columns, so we will just rbind them
audio_feature_plot_data <- rbind(audio_features_us_subset, popular_art_aud_feat_subset)

# Select relevant columns for comparison
comparison_cols <- c("danceability",
                 "energy",
                 "loudness",
                 "speechiness",
                 "acousticness",
                 "liveness",
                 "valence",
                 "tempo")
```

```{r fig.height=10, fig.width = 10, include=TRUE, echo=FALSE}
custom_colors <- c("#1F78B4", "#FF7F00", "#33A02C")

#Plotting based on artist popularity
plots <- lapply(comparison_cols, function(col) {
  ggplot(audio_feature_plot_data, aes(x = get(col), fill = is_pop)) +
    geom_density(alpha = 0.3) +
    scale_fill_manual(values = custom_colors) +
    labs(title = paste("Density Plot of", toTitleCase(as.character(col))),
         x = col,
         fill = "Artist Popularity") +
    theme_minimal()+
    theme(legend.position = "bottom",
          plot.title = element_text(hjust = 0.5))
})
# Arrange plots in a grid with 3 columns
grid_plots <- wrap_plots(plots, ncol = 2)

# Display the grid of plots
print(grid_plots)
```

Due to word limit constraints, the discussion will focus on features with significant differences, not mentioning features’ [definitions](https://developer.spotify.com/documentation/web-api/reference/get-audio-features).

In summary, today's most successful music exhibits higher danceability (around 0.75 energy level), is quieter, less acoustic, released in the studio version (lower liveness), and with lower valence. Notably, Popular Top 100 artists align more with today's most popular artists in energy, loudness, acousticness, and liveness, suggesting audio features may impact track and artist endurance.

The difference in the Valence feature is also interesting. According to Spotify, this feature reflects overall positiveness of the song, whereas low valence indicates a more negative tone (e.g. sad, depressed, angry). Further analysis could investigate the relationship of this change with broader societal issues over time.


##### **3.4 Conclusion**

This study analyzed how the music from the artists of the top 100 Rolling Stones has endured. Results show that the majority of the artists and their songs, have succeeded against time. The number of hits, relaunching old successes, and song audio features may impact current popularity. Further studies may explore causality and test these findings globally.


## Appendix: All code in this assignment

```{r ref.label=knitr::all_labels(), echo=TRUE, eval=FALSE} 
# this chunk generates the complete code appendix. 
# eval=FALSE tells R not to run (``evaluate'') the code here (it was already run before).
```