class: center, middle, inverse, title-slide # Music Data Analysis in <code>R</code> ## IV International Seminar on Statistics with <code>R</code> ### Bruna Wundervald & Julio Trecenti ### May, 2019 --- class: middle This presentation can be found at: http://brunaw.com/shortcourses/IXSER/en/pres-en.html **GitHub**: https://github.com/brunaw/SER2019 --- # Who are we .pull-left[ <img src="img/bruna.jpg" width="70%" style="display: block; margin: auto;" /> ] .pull-right[ **Bruna Wundervald** - Ph.D. Candidate in Statistics at Maynooth University. - Twitter: @bwundervald - GitHub: @brunaw ] --- # Who are we .pull-left[ <img src="img/jubs.png" width="70%" style="display: block; margin: auto;" /> ].pull-right[ **Julio Trecenti** - Ph.D. Candidate in Statistics at IME-USP - Partner at Curso-R - Twitter: @jtrecenti - GitHub: @jtrecenti ] --- # Goals - Learn how to use the packages: - `vagalumeR`: lyrics extraction - `chorrrds`: chords extraction - `Rspotify`: extract variables from the [Spotify API](https://developer.spotify.com/documentation/web-api/) - Understand how APIs work in general; - Combine data from different sources; - Understand and summarise data in various formats: - Text, - Continuous variables, - Sequences - Create a prediction model with the final data. **Not included:** audio analysis. --- # Requisites & resources - `R` beginner/intermediate - `tidyverse` - `%>%` (pipe) is essential! [**R-Music Blog**](https://r-music.rbind.io/) <img src="https://raw.githubusercontent.com/r-music/site/master/img/logo.png" style="float:left;margin-right:20px;" width=120> <h4 style="padding:0px;margin:10px;"> R for music data extraction & analysis </h4> --- # Don't get lost! - If you are ever stuck in any part of this course, don't hesitate in asking us - Have the RStudio Cheatsheets at hand at all moments: https://www.rstudio.com/resources/cheatsheets/ - If you need material in Portuguese, check the Curso-R website: https://curso-r.com/material/ --- # Loading packages Main packages: ```r library(vagalumeR) library(Rspotify) library(chorrrds) library(tidyverse) ``` --- class: bottom, center, inverse # Data extraction ## `vagalumeR`: music lyrics ## `RSpotify`: Spotify variables ## `chorrrds`: music chords --- # Data extraction - For each package, there are a few steps to be followed. - The steps involve, basically, 1. obtain the IDs of the objects for which we want to extract information (like artists, albums, songs), and 2. use those IDs inside specific functions; --- # Connecting to the APIs ## `vagalumeR` Steps: 1. Go to [`https://auth.vagalume.com.br/`](https://auth.vagalume.com.br/) and log in, 2. Go to [`https://auth.vagalume.com.br/settings/api/`](https://auth.vagalume.com.br/settings/api/) and create a new app, 3. Go to [`https://auth.vagalume.com.br/settings/api/`](https://auth.vagalume.com.br/settings/api/) again and copy the app's credential, . 4. Save that credential in an object, like: ```r key_vagalume <- "my-credential" ``` --- # Connecting to the APIs ## `Rspotify` Steps: 1. Go to [`https://developer.spotify.com/`](https://developer.spotify.com/) and log in, 2. Go to [`https://developer.spotify.com/dashboard/`](https://developer.spotify.com/dashboard/) and create a new app, 3. Save the **client ID** and the **client Secret** generated, 4. Define the redirect URL as `http://localhost:1410/`, 5. Use the `spotifyOAuth()` to authenticate: ```r library(Rspotify) key_spotify <- spotifyOAuth("app_id","client_id","client_secret") ``` > The keys will be used later to create the connection between `R` and the data extraction functions. --- ## `vagalumeR` ```r # 1. Defining the artists artist <- "chico-buarque" # 2. Look for the names and IDs of the songs songs <- artist %>% purrr::map_dfr(songNames) # 3. Map the lyrics functions in the IDs found lyrics <- songs %>% dplyr::pull(song.id) %>% purrr::map(lyrics, artist = artist, type = "id", key = key_vagalume) %>% purrr::map_dfr(data.frame) %>% dplyr::select(-song) %>% dplyr::right_join(songs %>% dplyr::select(song, song.id), by = "song.id") ``` --- ## `RSpotify` - variables - “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. - “energy” = a measure from 0.0 to 1.0 and represents a perceptual measure of intensity and activity. - “key_spotify” = estimated overall key of the track. Integers map to pitches using standard Pitch Class notation. E.g. 0 = C, 1 = C#/Db 2 = D, and so on. - “loudness” = overall loudness of a track in decibels (dB). - “mode” = indicates the modality (major or minor) of a track, the type of scale from which its melodic content is derived. - “speechiness” = detects the presence of spoken words in a track. - “acousticness” = a measure from 0.0 to 1.0 of whether the track is acoustic. - “instrumentalness” = whether a track contains no vocals. - “liveness” = detects the presence of an audience in the recording. - “valence” = measure from 0.0 to 1.0 describing the musical positiveness conveyed by a track. - “tempo” = overall estimated tempo of a track in beats per minute (BPM). - “duration_ms” = duration of the track in milliseconds. - “time_signature” = the duration of the track in milliseconds. - "popularity" = the popularity of the song --- ## `RSpotify` ```r # 1. Search the artist using the API find_artist <- searchArtist("chico buarque", token = key_spotify) # 2. Use the ID to search for album information albums <- getAlbums(find_artist$id[1], token = key_spotify) # 3. Obtain the songs of each album albums_res <- albums %>% dplyr::pull(id) %>% purrr::map_df( ~{ getAlbum(.x, token = key_spotify) %>% dplyr::select(id, name) }) %>% tidyr::unnest() ids <- albums_res %>% dplyr::pull(id) # 4. Obtain the variables for each song features <- ids %>% purrr::map_dfr(~getFeatures(.x, token = key_spotify)) %>% dplyr::left_join(albums_res, by = "id") ``` --- **Until this moment, the package does not have a simple solution to extract the popularity of the songs. How do we solve this issue? ** ```r # 5. Create a simple function to get the popularity getPop <- function(id, token){ u <- paste0("https://api.spotify.com/v1/tracks/", id) req <- httr::GET(u, httr::config(token = token)) json1 <- httr::content(req) res <- data.frame(song = json1$name, popul = json1$popularity, id = json1$id) return(res) } # 6. Map this function in the IDs found popul <- features %>% dplyr::pull(id) %>% purrr::map_dfr(~getPop(.x, token = key_spotify)) # 7. Join the popularity with the other variables features <- features %>% dplyr::right_join( popul %>% dplyr::select(-song), by = c("id" = "id")) ``` --- # Details about the APIs - **APIs can be very unstable. This means that, sometimes, even without reaching the access limit, they will fail.** How do we solve that? - Dividing the whole process into smaller batches - Using small time intervals between each access to the API, with `Sys.sleep()` for example --- # `chorrrds` ```r # 1. Searching the songs songs <- "chico-buarque" %>% chorrrds::get_songs() # 2. Mapping the chord extraction in the songs found chords <- songs %>% dplyr::pull(url) %>% purrr::map(chorrrds::get_chords) %>% purrr::map_dfr(dplyr::mutate_if, is.factor, as.character) %>% chorrrds::clean(message = FALSE) ``` --- # Combining different datasets ```r # Standardise the name of the key column and use it inside the joins chords <- chords %>% dplyr::mutate(song = stringr::str_remove(music, "chico buarque ")) %>% dplyr::select(-music) lyrics <- lyrics %>% dplyr::mutate(song = stringr::str_to_lower(song)) features <- features %>% dplyr::mutate(song = stringr::str_to_lower(name)) %>% dplyr::select(-name) all_data <- chords %>% dplyr::inner_join(lyrics, by = "song") %>% dplyr::inner_join(features, by = "song") ``` --- **What if there are a lot of unmatches?** ```r nrow(chords) - nrow(all_data) #> 8973 ``` -- - Solving those cases manually can take a lot of time. - One simple way is to use string similarity to find similar titles between the songs. - We can do that by calculating the distances between the titles and verifying how many "letters of difference" they have. For example: ```r nome1 <- "Geni e o Zepelim" nome2 <- "Geni e o Zepelin" # Finding the distance RecordLinkage::levenshteinDist(nome1, nome2) ``` ``` ## [1] 1 ``` ```r # Finding the similarity = 1 - dist / str_length(biggest string) RecordLinkage::levenshteinSim(nome1, nome2) ``` ``` ## [1] 0.9375 ``` - When the titles are different, we should take the most similar ones and merge. - Usually, there's no clear cut point for the similarity, so we define it arbitrarily. --- # Fixing the titles ```r # Let's find the string distances between the titles and use this # information to fix them in the dataset # 1. Which ones are in the chords data but not in the lyrics one? anti_chords_lyrics <- chords %>% dplyr::anti_join(lyrics, by = "song") # 2. Saving the titles to fix names_to_fix <- anti_chords_lyrics %>% dplyr::distinct(song) %>% dplyr::pull(song) # 3. Calculating the distances between the titles of the # lyrics dataset and the unmatched ones from the chords dataset dists <- lyrics$song %>% purrr::map(RecordLinkage::levenshteinSim, str1 = names_to_fix) ``` --- ```r # 4. Retrieving the most similar titles in the two datasets ordered_dists <- dists %>% purrr::map_dbl(max) max_dists <- dists %>% purrr::map_dbl(which.max) # 5. Filtering the one that have similarity > 0.70 indexes_min_dist <- which(ordered_dists > 0.70) songs_min_dist <- lyrics$song[indexes_min_dist] index_lyrics <- max_dists[which(ordered_dists > 0.70)] # 6. Saving the similar ones in a data.frame results_dist_lyrics <- data.frame(from_chords = names_to_fix[index_lyrics], from_lyrics = songs_min_dist) ``` **Examples of similar cases found:** *a bela a fera* **and** *a bela e a fera*, *logo eu* **and** *logo eu?*, *não fala de maria* **and** *não fala de maria *, ... -- Now we have fewer problems! Let's fix them manually. --- ## Fixing manually ```r chords <- chords %>% dplyr::mutate( song = dplyr::case_when( song == 'a bela a fera' ~ 'a bela e a fera', song == 'a historia de lily braun' ~ 'a história de lily braun', song == 'a moca do sonho' ~ 'a moça do sonho', song == 'a ostra o vento' ~ 'a ostra e o vento', song == 'a televisao' ~ 'a televisão', song == 'a valsa dos clows' ~ 'a valsa dos clowns', song == 'a voz do dono o dono da voz' ~ 'a voz do dono e o dono da voz', song == 'agora falando serio' ~ 'agora falando sério', TRUE ~ song)) ``` -- Tip: the previous sintaxe can be easily created with: ```r cat( paste0("song == ", "'", results_dist_lyrics$from_chords, "' ~ '", results_dist_lyrics$from_lyrics, "', "), collapse = "") ``` > Link for the data: https://github.com/brunaw/SER2019/tree/master/shortcourse/data/all_data.txt --- # Redoing the `joins` ```r all_data <- chords %>% dplyr::inner_join(lyrics, by = "song") %>% dplyr::inner_join(features, by = "song") # Finally saving the complete data! write.table(all_data, "all_data.txt") ``` --- class: bottom, center, inverse # Exploratory Analysis --- # Part 1: lyrics Extra packages: - `tm`: text analysis in general - `tidytext`: `tidy` text analysis - `lexiconPT`: sentiment dictionary for portuguese --- # n-grams **n-grams**: the words and its "past" - Useful to analyze more complex expressions, keep more complex expressions or sequences ```r nome1 <- "Geni e o Zepelim" tokenizers::tokenize_ngrams(nome1, n = 1) ``` ``` ## [[1]] ## [1] "geni" "e" "o" "zepelim" ``` ```r tokenizers::tokenize_ngrams(nome1, n = 2) ``` ``` ## [[1]] ## [1] "geni e" "e o" "o zepelim" ``` ```r tokenizers::tokenize_ngrams(nome1, n = 3) ``` ``` ## [[1]] ## [1] "geni e o" "e o zepelim" ``` --- # n-grams - the `unnest_tokens()` separates the n-grams of each lyric. ```r library(tidytext) library(wordcloud) # List of portuguese stopwords: stopwords_pt <- data.frame(word = tm::stopwords("portuguese")) # Breaking the phrases into single words with 1-gram unnested <- all_data %>% select(text) %>% unnest_tokens(word, text, token = "ngrams", n = 1) %>% # Removing stopwords dplyr::anti_join(stopwords_pt, by = c("word" = "word")) ``` *stopwords*: very frequent words of a language, which might not be essential for the overall meaning of a sentence --- # Part 1: lyrics Counting each word that appeared in the songs: ```r unnested %>% dplyr::count(word) %>% arrange(desc(n)) %>% slice(1:10) ``` ``` ## # A tibble: 10 x 2 ## word n ## <chr> <int> ## 1 é 40064 ## 2 pra 24276 ## 3 iá 23832 ## 4 amor 18460 ## 5 diz 16505 ## 6 chocalho 15888 ## 7 vai 14603 ## 8 morena 11148 ## 9 esperando 10987 ## 10 dia 10642 ``` --- # 1-grams ```r unnested %>% dplyr::count(word) %>% # removing words that barely appeared dplyr::filter(n < quantile(n, 0.999)) %>% dplyr::top_n(n = 30) %>% ggplot(aes(reorder(word, n), n)) + geom_linerange(aes(ymin = min(n), ymax = n, x = reorder(word, n)), position = position_dodge(width = 0.2), size = 1, colour = 'darksalmon') + geom_point(colour = 'dodgerblue4', size = 3, alpha = 0.9) + coord_flip() + labs(x = 'Top 30 most common words', y = 'Count') + theme_bw(14) ``` --- <img src="pres-en_files/figure-html/unnamed-chunk-23-1.png" style="display: block; margin: auto;" /> --- # In a `wordcloud` format ```r unnested %>% count(word) %>% with(wordcloud(word, n, family = "serif", random.order = FALSE, max.words = 30, colors = c("darksalmon", "dodgerblue4"))) ``` <img src="pres-en_files/figure-html/unnamed-chunk-24-1.png" style="display: block; margin: auto;" /> --- # 2-grams ```r all_data %>% select(text) %>% unnest_tokens(bigram, text, token = "ngrams", n = 2) %>% separate(bigram, c("word1", "word2"), sep = " ") %>% filter(!word1 %in% stopwords_pt$word, !is.na(word1), !is.na(word2), !word2 %in% stopwords_pt$word) %>% count(word1, word2, sort = TRUE) %>% mutate(word = paste(word1, word2)) %>% filter(n < quantile(n, 0.999)) %>% arrange(desc(n)) %>% slice(1:30) %>% ggplot(aes(reorder(word, n), n)) + geom_linerange(aes(ymin = min(n), ymax = n, x = reorder(word, n)), position = position_dodge(width = 0.2), size = 1, colour = 'darksalmon') + geom_point(colour = 'dodgerblue4', size = 3, alpha = 0.9) + coord_flip() + labs(x = 'Top 30 most common 2-grams', y = 'Count') + theme_bw(18) ``` --- <img src="pres-en_files/figure-html/unnamed-chunk-26-1.png" style="display: block; margin: auto;" /> --- # Sentiment analysis ```r # devtools::install_github("sillasgonzaga/lexiconPT") # Retrieving the sentiments of portuguese words from the lexiconPT package sentiments_pt <- lexiconPT::oplexicon_v2.1 %>% mutate(word = term) %>% select(word, polarity) # Joining the sentiments with the words from the songs add_sentiments <- all_data %>% select(text, song) %>% group_by_all() %>% slice(1) %>% ungroup() %>% unnest_tokens(word, text) %>% dplyr::anti_join(stopwords_pt, by = c("word" = "word")) %>% dplyr::inner_join(sentiments_pt, by = c("word" = "word")) ``` --- ```r add_sentiments %>% group_by(polarity) %>% count(word) %>% filter(n < quantile(n, 0.999)) %>% top_n(n = 15) %>% ggplot(aes(reorder(word, n), n)) + geom_linerange(aes(ymin = min(n), ymax = n, x = reorder(word, n)), position = position_dodge(width = 0.2), size = 1, colour = 'darksalmon') + geom_point(colour = 'dodgerblue4', size = 3, alpha = 0.9) + facet_wrap(~polarity, scales = "free") + coord_flip() + labs(x = 'Top 15 most common words', y = 'Counts', title = 'Sentiments') + theme_bw(14) ``` --- class: center <img src="pres-en_files/figure-html/unnamed-chunk-29-1.png" style="display: block; margin: auto;" /> --- # Which are the most positive and most negative songs? ```r summ <- add_sentiments %>% group_by(song) %>% summarise(mean_pol = mean(polarity)) # 15 most positive and most negative songs summ %>% arrange(desc(mean_pol)) %>% slice(c(1:15, 121:135)) %>% mutate(situation = rep(c('+positive', '+negative'), each = 15)) %>% ggplot(aes(reorder(song, mean_pol), mean_pol)) + geom_linerange(aes(ymin = min(mean_pol), ymax = mean_pol, x = reorder(song, mean_pol)), position = position_dodge(width = 0.2), size = 1, colour = 'darksalmon') + geom_point(colour = 'dodgerblue4', size = 3, alpha = 0.9) + facet_wrap(~situation, scales = "free") + coord_flip() + labs(x = 'Songs', y = 'Polarities') + theme_bw(14) ``` --- --- <img src="pres-en_files/figure-html/unnamed-chunk-31-1.png" style="display: block; margin: auto;" /> --- class: middle ## What do we know so far? - The most common words and bi-grams - The are more positive than negative words in the lyrics - In which songs the most positive or negative feelings are --- # Part 2. Chords Extra packages: - `ggridges`: density plots - `chorddiag`: chords diagrams ```r # Removing enarmonies chords <- all_data %>% select(chord, song) %>% dplyr::mutate(chord = case_when( chord == "Gb" ~ "F#", chord == "C#" ~ "Db", chord == "G#" ~ "Ab", chord == "A#" ~ "Bb", chord == "D#" ~ "Eb", chord == "E#" ~ "F", chord == "B#" ~ "C", TRUE ~ chord)) ``` --- # Part 2. Chords ```r # Top 20 songs with more distinct chords chords %>% dplyr::group_by(song, chord) %>% dplyr::summarise(distintos = n_distinct(chord)) %>% dplyr::summarise(cont = n()) %>% dplyr::mutate(song = fct_reorder(song, cont)) %>% top_n(n = 20) %>% ggplot(aes(y = cont, x = song)) + geom_bar(colour = 'dodgerblue4', fill = 'darksalmon', size = 0.5, alpha = 0.6, stat = "identity") + labs(x = 'Songs', y = 'Counts') + coord_flip() + theme_bw(14) ``` --- <img src="pres-en_files/figure-html/unnamed-chunk-34-1.png" style="display: block; margin: auto;" /> --- # Extracting variables - The chords data are, in fact, just pieces of text. - Text in a raw state is not very informative. Let's use the `feature_extraction()` function to extract covariables related to the chords that have a clear interpretation: - minor - diminished - augmented - sus - chords with the 7th - chords with the major 7th - chords with the 6th - chords with the 4th - chords with the augmented 5th - chords with the diminished 5th - chords with the 9th - chords with varying bass --- # Extracting variables ```r feat_chords <- all_data %>% select(chord, song) %>% chorrrds::feature_extraction() %>% select(-chord) %>% group_by(song) %>% summarise_all(mean) dt <- feat_chords %>% tidyr::gather(group, vars, minor, seventh, seventh_M, sixth, fifth_dim, fifth_aug, fourth, ninth, bass, dimi, augm) ``` --- # Extracting variables ```r dplyr::glimpse(feat_chords) ``` ``` ## Observations: 135 ## Variables: 13 ## $ song <chr> "a banda", "a bela e a fera", "a cidade ideal", "a gal… ## $ minor <dbl> 0.28282828, 0.43939394, 0.15294118, 0.07317073, 0.0000… ## $ dimi <dbl> 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.0000… ## $ augm <dbl> 0.00000000, 0.00000000, 0.02352941, 0.00000000, 0.0000… ## $ sus <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, … ## $ seventh <dbl> 0.7878788, 0.9090909, 0.5294118, 0.4390244, 1.0000000,… ## $ seventh_M <dbl> 0.04040404, 0.00000000, 0.02352941, 0.00000000, 0.0000… ## $ sixth <dbl> 0.17171717, 0.12121212, 0.00000000, 0.00000000, 0.2673… ## $ fourth <dbl> 0.00000000, 0.34848485, 0.00000000, 0.00000000, 0.1386… ## $ fifth_aug <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, … ## $ fifth_dim <dbl> 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.0000… ## $ ninth <dbl> 0.31313131, 0.50000000, 0.00000000, 0.00000000, 0.7871… ## $ bass <dbl> 0.10101010, 0.03030303, 0.07058824, 0.00000000, 0.1386… ``` --- # Visualizing it ```r library(ggridges) # Renaming current levels dt$group <- forcats::lvls_revalue( dt$group, c("Augmented", "Bass", "Diminished", "Augm. Fifth", "Dimi. Fifth", "Fourth", "Minor", "Ninth", "Seventh", "Major Seventh", "Sixth")) # Plotting densities of the extracted features dt %>% ggplot(aes(vars, group, fill = group)) + geom_density_ridges(alpha = 0.6) + scale_fill_cyclical(values = c("dodgerblue4", "darksalmon")) + guides(fill = FALSE) + xlim(0, 1) + labs(x = "Densities", y = "extracted features") + theme_bw(14) ``` --- <img src="pres-en_files/figure-html/unnamed-chunk-38-1.png" style="display: block; margin: auto;" /> --- # Chord diagrams using the chords The chords transitions are an important element of the harmonic structure of songs. Let's check how those transitions are happening in this case. ```r # devtools::install_github("mattflor/chorddiag") # Counting the transitions between the chords comp <- chords %>% dplyr::mutate( # Cleaning the chords to the base form chord_clean = stringr::str_extract(chord, pattern = "^([A-G]#?b?)"), seq = lead(chord_clean)) %>% dplyr::filter(chord_clean != seq) %>% dplyr::group_by(chord_clean, seq) %>% dplyr::summarise(n = n()) mat <- tidyr::spread(comp, key = chord_clean, value = n, fill = 0) mm <- as.matrix(mat[, -1]) # Building the chord diagram chorddiag::chorddiag(mm, showTicks = FALSE, palette = "Blues") ``` --- # Regular expressions (regex) - Mini-language used to represent text - If you're working with text, you need to know regex - In `R`, regex can be used with the `stringr` package - To know more about regex: - [Slides](https://ctlente.com/pt/teaching/stringr-lubridate/) - [Online material](https://www.curso-r.com/material/stringr/) - [Cheat Sheet](https://github.com/rstudio/cheatsheets/raw/master/strings.pdf) --- # Chord diagram <div id="htmlwidget-1575c4f6f4f27bba38f1" style="width:100%;height:504px;" class="chorddiag html-widget"></div> <script type="application/json" data-for="htmlwidget-1575c4f6f4f27bba38f1">{"x":{"matrix":[[0,1,8,82,83,133,49,124,10,0,694,25,66,50,105,14,2],[21,0,0,6,3,0,2,0,0,0,9,0,2,2,1,2,0],[32,0,0,6,6,32,0,10,0,6,4,55,23,3,25,0,0],[164,9,10,0,28,98,26,64,2,3,91,0,43,342,48,13,5],[16,0,16,42,0,31,4,58,1,5,28,18,130,9,41,4,4],[70,6,26,50,46,0,25,36,3,30,100,21,98,16,447,2,1],[32,0,1,28,11,49,0,47,0,1,70,3,1,26,24,110,0],[549,1,7,32,43,60,15,0,12,1,59,94,69,44,117,12,1],[3,3,0,2,1,4,4,30,0,0,13,0,0,3,1,0,0],[2,0,18,1,3,5,0,11,0,0,3,5,0,1,10,2,2],[173,15,2,569,20,74,22,79,11,3,0,15,133,31,112,18,0],[23,0,16,7,76,27,0,56,0,2,15,0,17,6,28,2,0],[104,0,10,7,32,305,30,62,1,1,46,15,0,30,61,6,10],[81,11,2,32,3,37,186,41,1,0,61,0,29,0,117,13,2],[103,2,79,54,44,117,22,489,3,3,58,23,99,38,0,32,5],[72,0,2,28,8,1,18,10,20,0,26,1,1,15,28,0,0],[0,0,5,0,0,4,0,0,0,8,0,0,9,0,6,0,0]],"options":{"type":"directional","width":null,"height":null,"margin":100,"showGroupnames":true,"groupNames":["A","A#","Ab","B","Bb","C","C#","D","D#","Db","E","Eb","F","F#","G","G#","Gb"],"groupColors":["#F7FBFF","#DEEBF7","#C6DBEF","#9ECAE1","#6BAED6","#4292C6","#2171B5","#08519C","#08306B"],"groupThickness":0.1,"groupPadding":0.0349065850398866,"groupnamePadding":[30,30,30,30,30,30,30,30,30,30,30,30,30,30,30,30,30],"groupnameFontsize":18,"groupedgeColor":null,"chordedgeColor":"#808080","categoryNames":null,"categorynamePadding":100,"categorynameFontsize":28,"showTicks":false,"tickInterval":10,"ticklabelFontsize":10,"fadeLevel":0.1,"showTooltips":true,"showZeroTooltips":true,"tooltipNames":["A","A#","Ab","B","Bb","C","C#","D","D#","Db","E","Eb","F","F#","G","G#","Gb"],"tooltipFontsize":12,"tooltipUnit":"","tooltipGroupConnector":" ▶ ","precision":"null","clickAction":null,"clickGroupAction":null}},"evals":[],"jsHooks":[]}</script> --- # The circle of fifths - Allows us to understand the most probable harmonic fields <img src="img/fifths.jpg" style="display: block; margin: auto;" /> --- class: middle ## What do we know so far? - Some songs are harmonically more "complex" than others: - number of distinct chords - extracted variables - The most common or rare chords transitions --- # Part 3. Spotify Variables ## Exploring the variables ```r spot <- all_data %>% group_by(song) %>% slice(1) %>% ungroup() # Density of the popularity of the songs spot %>% ggplot(aes(popul)) + geom_density(colour = 'dodgerblue4', fill = "darksalmon", alpha = 0.8) + labs(y = 'Density', x = 'Popularity') + theme_bw(14) ``` --- <img src="pres-en_files/figure-html/unnamed-chunk-43-1.png" style="display: block; margin: auto;" /> It varies a lot! --- # Most popular and least popular songs ```r spot %>% arrange(desc(popul)) %>% slice(c(1:15, 121:135)) %>% mutate(situation = rep(c('+popul', '-popul'), each = 15)) %>% select(popul, situation, song) %>% ggplot(aes(reorder(song, popul), popul, group = 1)) + geom_bar(colour = 'dodgerblue4', fill = "darksalmon", size = 0.3, alpha = 0.6, stat = "identity") + facet_wrap(~situation, scales = "free") + coord_flip() + labs(x = 'Songs', y = 'Popularity') + theme_bw(14) ``` --- <img src="pres-en_files/figure-html/unnamed-chunk-45-1.png" style="display: block; margin: auto;" /> --- # Danceability `x` variables ```r dt <- spot %>% select(energy, loudness, speechiness, liveness, duration_ms, acousticness) %>% tidyr::gather(group, vars) dt$danceability <- spot$danceability dt %>% ggplot(aes(danceability, vars)) + geom_point(colour = "darksalmon") + geom_smooth(method = "lm", colour = "dodgerblue4") + labs(x = "Danceability", y = "Variables") + facet_wrap(~group, scales = "free") + theme_bw(14) ``` --- class: center <img src="pres-en_files/figure-html/unnamed-chunk-47-1.png" style="display: block; margin: auto;" /> --- class: middle ## What do we know so far? - How the popularity varies for this dataset - What are the least and most popular songs - How is the relationship between the danceability and the other variables --- class: bottom, center, inverse # Modeling --- # Modeling Let's consider now that we have an especial interest in the popularity of the songs. Which variables would be most associated with higher or smaller levels of popularity? To start with, let's transform the popularity into a class variable: ```r library(randomForest) spot <- spot %>% mutate(pop_class = ifelse( popul < quantile(popul, 0.25), "unpopular", ifelse(popul < quantile(popul, 0.55), "neutral", "popular"))) spot %>% janitor::tabyl(pop_class) ``` ``` ## pop_class n percent ## neutral 38 0.2814815 ## popular 63 0.4666667 ## unpopular 34 0.2518519 ``` --- **Wrangling the data to make it ready for modeling** ```r # Combining the previous datasets and wrangling set.seed(1) model_data <- feat_chords %>% right_join(spot, by = c("song" = "song")) %>% right_join(summ, by = c("song" = "song")) %>% select(-analysis_url, -uri, -id.x, -id.y, -song, -name, -text, -lang, -chord, -long_str, -key.x, -song.id, -sus, -popul) %>% mutate(pop_class = as.factor(pop_class)) %>% # Separating into train and test set mutate(part = ifelse(runif(n()) > 0.25, "train", "test")) model_data %>% janitor::tabyl(part) ``` ``` ## part n percent ## test 30 0.2222222 ## train 105 0.7777778 ``` --- Separating in train set (75%) and test set (25%): ```r train <- model_data %>% filter(part == "train") %>% select(-part) test <- model_data %>% filter(part == "test") %>% select(-part) ``` The model will be like: ` pop_class ~ minor + dimi + augm + seventh + seventh_M + sixth + fourth + fifth_aug + fifth_dim + ninth + bass + danceability + energy + key.y + loudness + mode + speechiness + acousticness + instrumentalness + liveness + valence + tempo + duration_ms + time_signature + mean_pol ` --- ```r m0 <- randomForest(pop_class ~ ., data = train, ntree = 1000) m0 ``` ``` ## ## Call: ## randomForest(formula = pop_class ~ ., data = train, ntree = 1000) ## Type of random forest: classification ## Number of trees: 1000 ## No. of variables tried at each split: 5 ## ## OOB estimate of error rate: 33.33% ## Confusion matrix: ## neutral popular unpopular class.error ## neutral 21 12 1 0.3823529 ## popular 7 41 0 0.1458333 ## unpopular 7 8 8 0.6521739 ``` --- **Visualizing the variable importance:** ```r imp0 <- randomForest::importance(m0) imp0 <- data.frame(var = dimnames(imp0)[[1]], value = c(imp0)) imp0 %>% arrange(var, value) %>% mutate(var = fct_reorder(factor(var), value, min)) %>% ggplot(aes(var, value)) + geom_point(size = 3.5, colour = "darksalmon") + coord_flip() + labs(x = "Variables", y = "Decrease in Gini criteria") + theme_bw(14) ``` --- **Visualizing the variable importance:** <img src="pres-en_files/figure-html/unnamed-chunk-53-1.png" style="display: block; margin: auto;" /> --- ```r corrplot::corrplot(cor(train %>% select_if(is.numeric), method = "spearman")) ``` <img src="pres-en_files/figure-html/unnamed-chunk-54-1.png" style="display: block; margin: auto;" /> --- **Redoing the model with the best variables** ```r vars <- imp0 %>% arrange(desc(value)) %>% slice(1:10) %>% pull(var) form <- paste0("pop_class ~ ", paste0(vars, collapse = '+')) %>% as.formula() m1 <- randomForest(form, data = train, ntree = 1000, mtry = 5) m1 ``` ``` ## ## Call: ## randomForest(formula = form, data = train, ntree = 1000, mtry = 5) ## Type of random forest: classification ## Number of trees: 1000 ## No. of variables tried at each split: 5 ## ## OOB estimate of error rate: 25.71% ## Confusion matrix: ## neutral popular unpopular class.error ## neutral 26 7 1 0.2352941 ## popular 7 41 0 0.1458333 ## unpopular 5 7 11 0.5217391 ``` --- # Measuring the accuracy in the test set ```r pred <- predict(m0, test) sum(pred == test$pop_class)/nrow(test) ``` ``` ## [1] 0.5333333 ``` ```r mean(m0$err.rate[,1]) ``` ``` ## [1] 0.3409731 ``` --- **How could we improve this model?** - More data! - Evaluate better the correlation between the variables - Remove noisy predictors - Engineer new features --- class: middle # Citation ``` @misc{musicdatainR, author = {Wundervald, Bruna and Trecenti, Julio}, title = {Music Data Analysis in R}, url = {https://github.com/brunaw/SER2019}, year = {2019} } ``` --- class: center, middle ## Acknowledgments This work was supported by a Science Foundation Ireland Career Development Award grant number: 17/CDA/4695 <img src="img/SFI_logo.jpg" width="50%" height="40%" style="display: block; margin: auto;" /> --- # Some references <p><cite>Feinerer, I, K. Hornik, and D. Meyer (2008). &ldquo;Text Mining Infrastructure in R&rdquo;. In: <em>Journal of Statistical Software</em> 25.5, pp. 1&ndash;54. URL: <a href="http://www.jstatsoft.org/v25/i05/">http://www.jstatsoft.org/v25/i05/</a>.</cite></p> <p><cite>Silge, J., D. Robinson, and J. Hester (2016). <em>tidytext: Text mining using dplyr, ggplot2, and other tidy tools</em>. DOI: <a href="https://doi.org/10.5281/zenodo.56714">10.5281/zenodo.56714</a>. URL: <a href="http://dx.doi.org/10.5281/zenodo.56714">http://dx.doi.org/10.5281/zenodo.56714</a>.</cite></p> <p><cite>Wundervald, B. (2018a). <em>R-Music: Introduction to the vagalumeR package</em>. URL: <a href="https://r-music.rbind.io/posts/2018-11-22-introduction-to-the-vagalumer-package/">https://r-music.rbind.io/posts/2018-11-22-introduction-to-the-vagalumer-package/</a>.</cite></p> <p><cite>&mdash; (2018b). <em>R-Music: Introduction to the vagalumeR package</em>. URL: <a href="https://r-music.rbind.io/posts/2018-11-22-introduction-to-the-vagalumer-package/">https://r-music.rbind.io/posts/2018-11-22-introduction-to-the-vagalumer-package/</a>.</cite></p> <p><cite>Wundervald, B. and T. M. Dantas (2018). <em>R-Music: Rspotify</em>. URL: <a href="https://r-music.rbind.io/posts/2018-10-01-rspotify/">https://r-music.rbind.io/posts/2018-10-01-rspotify/</a>.</cite></p> <p><cite>Wundervald, B. and J. Trecenti (2018). <em>R-Music: Introduction to the chorrrds package</em>. URL: <a href="https://r-music.rbind.io/posts/2018-08-19-chords-analysis-with-the-chorrrds-package/">https://r-music.rbind.io/posts/2018-08-19-chords-analysis-with-the-chorrrds-package/</a>.</cite></p> --- class: bottom, center, inverse <font size="30">Thank you! </font>