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lastfm-analyser.R
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lastfm-analyser.R
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get.info<-function(i.artist=NA, i.album=NA, i.track=NA, i.method="track.getInfo", i.api_key){
infotxt<-"http://ws.audioscrobbler.com/2.0/" %>%
param_set("method", i.method) %>%
param_set("api_key", i.api_key) %>%
param_set("format", "json")
if (!is.na(i.artist)) infotxt<-infotxt %>% param_set("artist", URLencode(i.artist, reserved = TRUE))
if (!is.na(i.album)) infotxt<-infotxt %>% param_set("album", URLencode(i.album, reserved = TRUE))
if (!is.na(i.track)) infotxt<-infotxt %>% param_set("track", URLencode(i.track, reserved = TRUE))
# infotxt <- substr(infotxt, 1, 999)
info <- try(fromJSON(infotxt))
if(class(info) != "try-error") return(info)
}
# get.info<-function(i.artist=NA, i.album=NA, i.track=NA, i.method="track.getInfo", i.api_key){
# infotxt<-"http://ws.audioscrobbler.com/2.0/" %>%
# param_set("method", i.method) %>%
# param_set("api_key", i.api_key) %>%
# param_set("format", "json")
# if (!is.na(i.artist)) infotxt<-infotxt %>% param_set("artist", URLencode(i.artist, reserved = TRUE))
# if (!is.na(i.album)) infotxt<-infotxt %>% param_set("album", URLencode(i.album, reserved = TRUE))
# if (!is.na(i.track)) infotxt<-infotxt %>% param_set("track", URLencode(i.track, reserved = TRUE))
# info <- content(GET(infotxt, as="parsed"))
# info
# }
# get.track.info<-function(i.track, i.artist, i.album, i.api_key){
# if (length(i.track)==0) stop("incorrect number of dimensions")
# dataunique <- data.frame(track=as.character(i.track), artist=as.character(i.artist), album=as.character(i.album), stringsAsFactors = F) %>%
# distinct() %>%
# arrange(artist, album, track)
# cat(NROW(dataunique), " tracks to fetch\n")
# res<-mapply(memoise(function(i.track, i.artist, i.album) {
# cat("Fetching ", i.track, " from ", i.artist,"/", i.album, "\n")
# info<-get.info(i.artist=i.artist, i.track=i.track, i.album=i.album, i.method="track.getInfo", i.api_key=i.api_key)
# data.frame(track=ifelse(is.null(info$track$name),"", as.character(info$track$name)),
# mbid=ifelse(is.null(info$track$mbid),"",as.character(info$track$mbid)),
# duration=ifelse(is.null(info$track$duration),"",as.character(info$track$duration)),
# artist=ifelse(is.null(info$track$artist$name),"",as.character(info$track$artist$name)),
# album=ifelse(is.null(info$track$album$title),"",as.character(info$track$album$title)),
# stringsAsFactors = F)
# }), i.track = dataunique$track, i.artist = dataunique$artist, i.album = dataunique$album)
# res=data.frame(matrix(unlist(res), ncol=5, byrow=T), stringsAsFactors = F)
# names(res)<-c("ttrack","tmbid","tduration","tartist","talbum")
# dataunique %>%
# bind_cols(res)
# }
get.track.info<-function(i.track, i.artist, i.album, i.api_key, i.slice=200, i.restart=T, i.temp=""){
if (!file.exists("temp")) dir.create("temp")
if (i.restart) do.call(file.remove, list(list.files("temp", pattern=paste0("^",i.temp,"-",i.slice,"-track_([0-9])+\\.rds$"), full.names = TRUE)))
dataoriginal<- data.frame(track=as.character(i.track), artist=as.character(i.artist), album=as.character(i.album), stringsAsFactors = F) %>%
distinct() %>%
arrange(artist, album, track) %>%
mutate(no=1:n(), slice=as.factor(1+floor((1:n()-1)/i.slice)))
dataoriginal.split<-split(dataoriginal, dataoriginal$slice)
cat(NROW(dataoriginal), " tracks to fetch\n")
for (j in 1:length(dataoriginal.split)){
cat("Processing part ", j, " of ", length(dataoriginal.split),"\n")
temp.rds<-paste0("temp/",i.temp,"-",i.slice,"-track_",j,".rds")
if (file.exists(temp.rds)){
cat("Junk already processed\n")
}else{
dataunique<-dataoriginal.split[[j]]
iteration<-1
result<-data.frame(no=1, track.title="", stringsAsFactors = F) %>%
filter(track.title!="")
while(NROW(result)<NROW(dataunique) & iteration<6){
dataunique.temp<- dataunique %>%
left_join(result, by="no") %>%
filter(track.title=="" | is.na(track.title))
cat("Iteration ",iteration, "\t", NROW(dataunique.temp), " tracks to fetch\n")
cl <- makeCluster(4)
registerDoSNOW(cl)
pb <- txtProgressBar(max = NROW(dataunique.temp), style = 3)
progress <- function(n) setTxtProgressBar(pb, n)
opts <- list(progress = progress)
result.it<-foreach(i = 1:NROW(dataunique.temp), .combine = bind_rows, .options.snow = opts,
.export = c("get.info", "%>%", "param_set", "fromJSON", "try")) %dopar% {
i.track<-dataunique.temp$track[i]
i.artist<-dataunique.temp$artist[i]
i.album<-dataunique.temp$album[i]
i.number<-dataunique.temp$no[i]
cat("Fetching ", i.track, " from ", i.artist,"/", i.album, "\n")
info<-get.info(i.artist=i.artist, i.track=i.track, i.album=i.album, i.method="track.getInfo", i.api_key=i.api_key)
data.frame(no=i.number,
track.title=ifelse(is.null(info$track$name),"", as.character(info$track$name)),
track.mbid=ifelse(is.null(info$track$mbid),"",as.character(info$track$mbid)),
track.duration=ifelse(is.null(info$track$duration),"",as.character(info$track$duration)),
track.artist=ifelse(is.null(info$track$artist$name),"",as.character(info$track$artist$name)),
track.album=ifelse(is.null(info$track$album$title),"",as.character(info$track$album$title)),
stringsAsFactors = F)
}
close(pb)
stopCluster(cl)
result <- result %>%
bind_rows(result.it) %>%
filter(track.title!="" & !is.na(track.title))
iteration<-iteration+1
}
saveRDS(result, temp.rds)
Sys.sleep(5)
}
}
result<-data.frame()
for(j in 1:length(dataoriginal.split)) result <- result %>%
bind_rows(readRDS(paste0("temp/",i.temp,"-",i.slice,"-track_",j,".rds")))
dataoriginal %>%
left_join(result, by="no") %>%
select(-no)
}
# get.artist.info<-function(i.artist){
# dataunique<- data.frame(artist=as.character(i.artist), stringsAsFactors = F) %>%
# distinct() %>%
# filter(artist!="") %>%
# arrange(artist)
# cat(NROW(dataunique), " artists to fetch\n")
# res<-mapply(memoise(function(i.artist) {
# cat("Fetching ", i.artist, "\n")
# info<-get.info(i.artist=i.artist, i.method="artist.getTopTags")
# data.frame(genre1=ifelse(NROW(info$toptags$tag) < 1,"", as.character(info$toptags$tag[1,"name"])),
# count1=ifelse(NROW(info$toptags$tag) < 1,"", as.character(info$toptags$tag[1,"count"])),
# genre2=ifelse(NROW(info$toptags$tag) < 2,"", as.character(info$toptags$tag[2,"name"])),
# count2=ifelse(NROW(info$toptags$tag) < 2,"", as.character(info$toptags$tag[2,"count"])),
# genre3=ifelse(NROW(info$toptags$tag) < 3,"", as.character(info$toptags$tag[3,"name"])),
# count3=ifelse(NROW(info$toptags$tag) < 3,"", as.character(info$toptags$tag[3,"count"])),
# stringsAsFactors = F)
# }), i.artist = dataunique$artist)
# res=data.frame(matrix(unlist(res), ncol=6, byrow=T), stringsAsFactors = F)
# names(res)<-c("artist.genre1","artist.count1","artist.genre2","artist.count2","artist.genre3","artist.count3")
# dataunique %>%
# bind_cols(res)
# }
get.artist.info<-function(i.artist, i.api_key, i.slice=200, i.restart=T, i.temp=""){
if (!file.exists("temp")) dir.create("temp")
if (i.restart) do.call(file.remove, list(list.files("temp", pattern=paste0("^",i.temp,"-",i.slice,"-artist_([0-9])+\\.rds$"), full.names = TRUE)))
dataoriginal<- data.frame(artist=as.character(i.artist), stringsAsFactors = F) %>%
distinct() %>%
filter(artist!="") %>%
arrange(artist) %>%
mutate(no=1:n(), slice=as.factor(1+floor((1:n()-1)/i.slice)))
dataoriginal.split<-split(dataoriginal, dataoriginal$slice)
cat(NROW(dataoriginal), " artists to fetch\n")
for (j in 1:length(dataoriginal.split)){
cat("Processing part ", j, " of ", length(dataoriginal.split),"\n")
temp.rds<-paste0("temp/",i.temp,"-",i.slice,"-artist_",j,".rds")
if (file.exists(temp.rds)){
cat("Junk already processed\n")
}else{
dataunique<-dataoriginal.split[[j]]
iteration<-1
result<-data.frame(no=1, artist.genre1="", stringsAsFactors = F) %>%
filter(artist.genre1!="")
while(NROW(result)<NROW(dataunique) & iteration<6){
dataunique.temp<- dataunique %>%
left_join(result, by="no") %>%
filter(artist.genre1=="" | is.na(artist.genre1))
cat("Iteration ",iteration, "\t", NROW(dataunique.temp), " artists to fetch\n")
cl <- makeCluster(4)
registerDoSNOW(cl)
pb <- txtProgressBar(max = NROW(dataunique.temp), style = 3)
progress <- function(n) setTxtProgressBar(pb, n)
opts <- list(progress = progress)
result.it<-foreach(i = 1:NROW(dataunique.temp), .combine = bind_rows, .options.snow = opts,
.export = c("get.info", "%>%", "param_set", "fromJSON", "try")) %dopar% {
i.artist<-dataunique.temp$artist[i]
i.number<-dataunique.temp$no[i]
cat("Fetching ", i.artist, "\n")
info<-get.info(i.artist=i.artist, i.method="artist.getTopTags", i.api_key=i.api_key)
data.frame(no=i.number,
artist.genre1=ifelse(NROW(info$toptags$tag) < 1,"", as.character(info$toptags$tag[1,"name"])),
artist.count1=ifelse(NROW(info$toptags$tag) < 1,"", as.character(info$toptags$tag[1,"count"])),
artist.genre2=ifelse(NROW(info$toptags$tag) < 2,"", as.character(info$toptags$tag[2,"name"])),
artist.count2=ifelse(NROW(info$toptags$tag) < 2,"", as.character(info$toptags$tag[2,"count"])),
artist.genre3=ifelse(NROW(info$toptags$tag) < 3,"", as.character(info$toptags$tag[3,"name"])),
artist.count3=ifelse(NROW(info$toptags$tag) < 3,"", as.character(info$toptags$tag[3,"count"])),
stringsAsFactors = F)
}
close(pb)
stopCluster(cl)
result <- result %>%
bind_rows(result.it) %>%
filter(artist.genre1!="" & !is.na(artist.genre1))
iteration<-iteration+1
}
saveRDS(result, temp.rds)
Sys.sleep(5)
}
}
result<-data.frame()
for(j in 1:length(dataoriginal.split)) result <- result %>%
bind_rows(readRDS(paste0("temp/",i.temp,"-",i.slice,"-artist_",j,".rds")))
dataoriginal %>%
left_join(result, by="no") %>%
select(-no)
}
# get.album.info<-function(i.artist, i.album, i.api_key){
# dataunique<- data.frame(artist=as.character(i.artist), album=as.character(i.album), stringsAsFactors = F) %>%
# distinct() %>%
# filter(artist!="" & album!="") %>%
# arrange(artist, album)
# cat(NROW(dataunique), " albums to fetch\n")
# res<-mapply(memoise(function(i.artist, i.album) {
# cat("Fetching ", i.album, "/", i.artist, "\n")
# info1<-get.info(i.artist=i.artist, i.album=i.album, i.method="album.getInfo", i.api_key=i.api_key)
# info2<-get.info(i.artist=i.artist, i.album=i.album, i.method="album.getTopTags", i.api_key=i.api_key)
# data.frame(album=ifelse(is.null(info1$album$name),"", as.character(info1$album$name)),
# artist=ifelse(is.null(info1$album$artist),"",as.character(info1$album$artist)),
# published=ifelse(is.null(info1$album$wiki$published),"",as.character(info1$album$wiki$published)),
# genre1=ifelse(NROW(info2$toptags$tag) < 1,"", as.character(info2$toptags$tag[1,"name"])),
# count1=ifelse(NROW(info2$toptags$tag) < 1,"", as.character(info2$toptags$tag[1,"count"])),
# genre2=ifelse(NROW(info2$toptags$tag) < 2,"", as.character(info2$toptags$tag[2,"name"])),
# count2=ifelse(NROW(info2$toptags$tag) < 2,"", as.character(info2$toptags$tag[2,"count"])),
# genre3=ifelse(NROW(info2$toptags$tag) < 3,"", as.character(info2$toptags$tag[3,"name"])),
# count3=ifelse(NROW(info2$toptags$tag) < 3,"", as.character(info2$toptags$tag[3,"count"])),
# stringsAsFactors = F)
# }), i.artist = dataunique$artist, i.album = dataunique$album)
# res=data.frame(matrix(unlist(res), ncol=9, byrow=T), stringsAsFactors = F)
# names(res)<-c("lalbum","lartist","lpublished","album.genre1","album.count1","album.genre2","album.count2","album.genre3","album.count3")
# dataunique %>%
# bind_cols(res)
# }
get.album.info<-function(i.artist, i.album, i.api_key, i.slice=200, i.restart=T, i.temp=""){
if (!file.exists("temp")) dir.create("temp")
if (i.restart) do.call(file.remove, list(list.files("temp", pattern=paste0("^",i.temp,"-",i.slice,"-album_([0-9])+\\.rds$"), full.names = TRUE)))
dataoriginal<- data.frame(artist=as.character(i.artist), album=as.character(i.album), stringsAsFactors = F) %>%
distinct() %>%
filter(artist!="" & album!="") %>%
arrange(artist, album) %>%
mutate(no=1:n(), slice=as.factor(1+floor((1:n()-1)/i.slice)))
dataoriginal.split<-split(dataoriginal, dataoriginal$slice)
cat(NROW(dataoriginal), " albums to fetch\n")
for (j in 1:length(dataoriginal.split)){
cat("Processing part ", j, " of ", length(dataoriginal.split),"\n")
temp.rds<-paste0("temp/",i.temp,"-",i.slice,"-album_",j,".rds")
if (file.exists(temp.rds)){
cat("Junk already processed\n")
}else{
dataunique<-dataoriginal.split[[j]]
iteration<-1
result<-data.frame(no=1, album.title="", stringsAsFactors = F) %>%
filter(album.title!="")
while(NROW(result)<NROW(dataunique) & iteration<6){
dataunique.temp<- dataunique %>%
left_join(result, by="no") %>%
filter(album.title=="" | is.na(album.title))
cat("Iteration ",iteration, "\t", NROW(dataunique.temp), " albums to fetch\n")
cl <- makeCluster(4)
registerDoSNOW(cl)
pb <- txtProgressBar(max = NROW(dataunique.temp), style = 3)
progress <- function(n) setTxtProgressBar(pb, n)
opts <- list(progress = progress)
result.it<-foreach(i = 1:NROW(dataunique.temp), .combine = bind_rows, .options.snow = opts,
.export = c("get.info", "%>%", "param_set", "fromJSON", "try")) %dopar% {
i.artist<-dataunique.temp$artist[i]
i.album<-dataunique.temp$album[i]
i.number<-dataunique.temp$no[i]
cat("Fetching ", i.album, "/", i.artist, "\n")
info1<-get.info(i.artist=i.artist, i.album=i.album, i.method="album.getInfo", i.api_key=i.api_key)
info2<-get.info(i.artist=i.artist, i.album=i.album, i.method="album.getTopTags", i.api_key=i.api_key)
data.frame(no=i.number,
album.title=ifelse(is.null(info1$album$name),"", as.character(info1$album$name)),
album.artist=ifelse(is.null(info1$album$artist),"",as.character(info1$album$artist)),
album.published=ifelse(is.null(info1$album$wiki$published),"",as.character(info1$album$wiki$published)),
album.genre1=ifelse(NROW(info2$toptags$tag) < 1,"", as.character(info2$toptags$tag[1,"name"])),
album.count1=ifelse(NROW(info2$toptags$tag) < 1,"", as.character(info2$toptags$tag[1,"count"])),
album.genre2=ifelse(NROW(info2$toptags$tag) < 2,"", as.character(info2$toptags$tag[2,"name"])),
album.count2=ifelse(NROW(info2$toptags$tag) < 2,"", as.character(info2$toptags$tag[2,"count"])),
album.genre3=ifelse(NROW(info2$toptags$tag) < 3,"", as.character(info2$toptags$tag[3,"name"])),
album.count3=ifelse(NROW(info2$toptags$tag) < 3,"", as.character(info2$toptags$tag[3,"count"])),
stringsAsFactors = F)
}
close(pb)
stopCluster(cl)
result <- result %>%
bind_rows(result.it) %>%
filter(album.title!="" & !is.na(album.title))
iteration<-iteration+1
}
saveRDS(result, temp.rds)
Sys.sleep(5)
}
}
result<-data.frame()
for(j in 1:length(dataoriginal.split)) result <- result %>%
bind_rows(readRDS(paste0("temp/",i.temp,"-",i.slice,"-album_",j,".rds")))
dataoriginal %>%
left_join(result, by="no") %>%
select(-no)
}
coalesceplus<-function (...)
{
if (missing(..1)) {
abort("At least one argument must be supplied")
}
values <- rlang::dots_list(...)
x <- values[[1]]
values <- values[-1]
for (i in seq_along(values)) {
x <- dplyr:::replace_with(x, is.na(x) | x=="", values[[i]], glue("Argument {i + 1}"),
glue("length of {fmt_args(~x)}"))
}
x
}
full_seq_yearmon <- function(x) {
as.yearmon(full_seq(as.numeric(x), 1/12, 0.1))
}
lastfm_export_multi<-function(i.user){
out.export<-lastfm_export(i.user[1]) %>%
select(-time) %>%
mutate(user=i.user[1])
if (length(i.user)>1) for (i in 2:length(i.user)) out.export <- out.export %>%
rbind(lastfm_export(i.user[i]) %>%
select(-time) %>%
mutate(user=i.user[i])) %>%
distinct()
out.export
}
import.lastfm.user <- function(i.user, i.restart=F){
cat("import.profile> start\n")
cat("import.profile> importing user(s): ", paste(i.user, collapse = "+"), "\n")
o.filename<-paste0("processed/",paste(i.user, collapse = "+"),"-lastfm.rds")
if (i.restart & file.exists(o.filename)){
cat("import.profile> removing existing files\n")
file.remove(o.filename)
}
if (file.exists(o.filename)){
cat("import.profile> found existing file: ",o.filename,"\n")
o.data<-readRDS(o.filename)
}else{
cat("import.profile> fetching scrobbling list from lastfm\n")
o.data <- lastfm_export_multi(i.user) %>%
mutate(artist=as.character(artist),
track=as.character(track),
album=as.character(album),
weekday=as.character(weekday)) %>%
filter(track!="")
cat("import.profile> saving data\n")
saveRDS(o.data, o.filename)
}
cat("import.profile> end\n")
o.data
}
import.lastfm.data <- function(i.data, i.user, i.restart=F, i.api_key, i.slice=500){
cat("import.data> start\n")
o.filename<-paste0("processed/",paste(i.user, collapse = "+"),"-lastfm.info.rds")
o.filename.tracks<-paste0("processed/",paste(i.user, collapse = "+"),"-tracks.info.rds")
o.filename.albums<-paste0("processed/",paste(i.user, collapse = "+"),"-albums.info.rds")
o.filename.artists<-paste0("processed/",paste(i.user, collapse = "+"),"-artists.info.rds")
if (i.restart){
cat("import.data> removing existing files\n")
if (file.exists(o.filename)) file.remove(o.filename)
if (file.exists(o.filename.tracks)) file.remove(o.filename.tracks)
if (file.exists(o.filename.albums)) file.remove(o.filename.albums)
if (file.exists(o.filename.artists)) file.remove(o.filename.artists)
}
# Tracks information
cat("import.data> tracks information\n")
if (file.exists(o.filename.tracks)){
cat("import.data> found existing file: ",o.filename.tracks,"\n")
o.data.tracks<-readRDS(o.filename.tracks)
}else{
cat("import.data> fetching tracks data from lastfm\n")
o.data.tracks<-get.track.info(i.data$track, i.data$artist, i.data$album, i.api_key=i.api_key, i.slice=i.slice, i.restart=i.restart, i.temp=paste(i.user, collapse = "+"))
saveRDS(o.data.tracks, o.filename.tracks)
}
# Albums information
cat("import.data> albums information\n")
if (file.exists(o.filename.albums)){
cat("import.data> found existing file: ",o.filename.albums,"\n")
o.data.albums<-readRDS(o.filename.albums)
}else{
cat("import.data> fetching albums data from lastfm\n")
o.data.albums<-get.album.info(i.data$artist, i.data$album, i.api_key=i.api_key, i.slice=i.slice, i.restart=i.restart, i.temp=paste(i.user, collapse = "+"))
saveRDS(o.data.albums, o.filename.albums)
}
# Artists information
cat("import.data> artists information\n")
if (file.exists(o.filename.artists)){
cat("import.data> found existing file: ",o.filename.artists,"\n")
o.data.artists<-readRDS(o.filename.artists)
}else{
cat("import.data> fetching artists data from lastfm\n")
o.data.artists<-get.artist.info(i.data$artist, i.api_key=i.api_key, i.slice=i.slice, i.restart=i.restart, i.temp=paste(i.user, collapse = "+"))
saveRDS(o.data.artists, o.filename.artists)
}
cat("import.data> creating output file\n")
o.data <- i.data %>%
left_join(o.data.tracks %>%
select(-slice), by=c("track", "artist", "album")) %>%
left_join(o.data.artists %>%
select(-slice), by=c("artist")) %>%
left_join(o.data.albums %>%
select(-slice) %>%
mutate(
album.genre1=if_else(tolower(album.genre1)=="spanish","",album.genre1),
album.genre2=if_else(tolower(album.genre2)=="spanish","",album.genre2),
album.genre3=if_else(tolower(album.genre3)=="spanish","",album.genre3)
) %>%
mutate(
album.genre1=if_else(tolower(album.genre1)=="albums i own","",album.genre1),
album.genre2=if_else(tolower(album.genre2)=="albums i own","",album.genre2),
album.genre3=if_else(tolower(album.genre3)=="albums i own","",album.genre3)
) %>%
mutate(
album.genre1=if_else(grepl("^\\d{4}$",album.genre1),"",album.genre1),
album.genre2=if_else(grepl("^\\d{4}$",album.genre2),"",album.genre2),
album.genre3=if_else(grepl("^\\d{4}$",album.genre3),"",album.genre3)
), by=c("artist", "album")) %>%
mutate(dummy=1) %>%
left_join(o.data.tracks %>%
mutate(track.duration=as.numeric(track.duration)) %>%
filter(track.duration>0 & !is.na(track.duration)) %>%
summarise(gduration=round(mean(track.duration),0)) %>%
mutate(dummy=1), by="dummy") %>%
mutate(
published=album.published,
track=coalesceplus(track, track.title),
album=coalesceplus(album, track.album, album.title),
artist=coalesceplus(artist, track.artist, album.artist),
album.genre1=coalesceplus(album.genre1, artist.genre1),
album.count1=coalesceplus(album.count1, artist.count1),
album.genre2=coalesceplus(album.genre2, artist.genre2),
album.count2=coalesceplus(album.count2, artist.count2),
album.genre3=coalesceplus(album.genre3, artist.genre3),
album.count3=coalesceplus(album.count3, artist.count3),
# duration=if_else(track.duration=="0" | track.duration=="" | is.na(track.duration), gduration, as.numeric(track.duration))
duration=as.numeric(if_else(track.duration=="0" | track.duration=="" | is.na(track.duration), "", track.duration))
) %>%
select(-album.published, -track.title, -track.mbid, -track.artist, -track.album, -album.title, -album.artist, -track.duration, -gduration, -dummy) %>%
arrange(fulldate) %>%
mutate(artist=stri_trans_totitle(artist),
track=stri_trans_totitle(track),
album=stri_trans_totitle(album),
artist.genre1=stri_trans_totitle(artist.genre1),
artist.genre2=stri_trans_totitle(artist.genre2),
artist.genre3=stri_trans_totitle(artist.genre3),
album.genre1=stri_trans_totitle(album.genre1),
album.genre2=stri_trans_totitle(album.genre2),
album.genre3=stri_trans_totitle(album.genre3),
yrmth = zoo::as.yearmon(fulldate), year = year(fulldate))
saveRDS(o.data, o.filename)
cat("import.data> end\n")
o.data
}
#' Find tickmarks for a given range of the y-axis that best fit an optimal number of tickmarks
#' you decide. f.i: what if i want to have a graph with 8 tickmarks in a range of 34 to 345
# i.min=50
# i.max=max(data.label$value)
# i.number.ticks=3
# i.include.min=T
# i.include.max=T
# i.valid.ticks=apply(expand.grid(c(1,2,2.5,5), 10^(1:3)), 1, FUN = function(x) {x[1] * x[2]})
optimal.tickmarks<-function(i.min,i.max,i.number.ticks=10,
i.valid.ticks=apply(expand.grid(c(1,2,2.5,5), 10^(-10:10)), 1, FUN = function(x) {x[1] * x[2]}),
i.include.min=F,i.include.max=F){
# Y ahora calculo el tickmark que m?s se acerca a esos 10 tickmarks objetivo.
# Option 1: free, I can put tickmarks outside c(i.min,i.max)
if (i.include.min) dif0<-i.min else dif0<-0
i.min=i.min-dif0
i.max=i.max-dif0
ticks.min<-floor(i.min/i.valid.ticks)
ticks.max<-ceiling(i.max/i.valid.ticks)
ticks.maxmin<-ticks.max-ticks.min+1
n.valid.ticks<-length(i.valid.ticks)
posicion.ticks<-(1:n.valid.ticks)[min(abs(ticks.maxmin-i.number.ticks))==abs(ticks.maxmin-i.number.ticks)][1]
ini<-(ticks.min*i.valid.ticks)[posicion.ticks]+dif0
fin<-(ticks.max*i.valid.ticks)[posicion.ticks]+dif0
salto<-i.valid.ticks[posicion.ticks]
# Tickmarks
tickmarks<-seq(ini,fin,salto)
# Number of ticks
numero.ticks<-length(tickmarks)
if (i.include.max) {
fin<-i.max
tickmarks[numero.ticks]<-i.max
}
# Rank
range.y<-c(ini,fin)
# Returning
return(list(by=salto,number=numero.ticks,range=range.y,tickmarks=tickmarks))
}
# i<-1
# i.profile.name=profile.names.list[[i]]
# i.restart=restart.tasks
# i.api_key=api_key
# i.slice=500
lastfm.analiser <- function(i.profile.name, i.restart=F, i.api_key, i.slice=500){
if (!dir.exists("output")) dir.create("output")
if (!dir.exists("processed")) dir.create("processed")
if (!dir.exists("temp")) dir.create("temp")
lastfm.data <- import.lastfm.user(i.user=i.profile.name$lastfm.users, i.restart=i.restart) %>%
import.lastfm.data(i.user=i.profile.name$lastfm.users, i.restart=i.restart, i.api_key=i.api_key, i.slice=i.slice) %>%
mutate(weekday=as.numeric(strftime(fulldate,'%u')))
plot.index<-0
do.call(file.remove, list(list.files("output", pattern=paste0("^",i.profile.name$name,"_p[0-9]{2}_.*$"), full.names = TRUE)))
# Process data
# Most listened albums and month it was most listened
# Scrobbles per album
temp1 <- lastfm.data %>%
group_by(artist, album, album.genre1) %>%
summarise(n=n(), startdate=min(fulldate), enddate=max(fulldate)) %>%
mutate(fullalbum=paste0(artist, "\n", album)) %>%
ungroup() %>%
arrange(desc(n), fullalbum) %>%
mutate(rank = row_number(-n))
# Most heard month per album
temp2 <- lastfm.data %>%
group_by(artist, album, yrmth) %>%
summarise(n=n()) %>%
group_by(artist, album) %>%
arrange(artist, album, desc(n), yrmth) %>%
slice(1) %>%
select(-n) %>%
ungroup()
albums.summary <- temp1 %>%
inner_join(temp2, by=c("artist", "album")) %>%
arrange(artist, album, yrmth) %>%
rename(yrmthmostheared=yrmth) %>%
arrange(artist, album)
rm(temp1, temp2)
# Most listened albums and month it was most listened year by year
# Scrobbles per album
temp1 <- lastfm.data %>%
group_by(artist, album, year) %>%
summarise(n=n()) %>%
mutate(fullalbum=paste0(artist, "\n", album)) %>%
group_by(year) %>%
arrange(year, desc(n), fullalbum) %>%
mutate(rank = row_number(-n)) %>%
ungroup()
# Most heard month per album
temp2 <- lastfm.data %>%
group_by(artist, album, year, yrmth) %>%
summarise(n=n()) %>%
group_by(artist, album, year) %>%
arrange(artist, album, year, desc(n), yrmth) %>%
slice(1) %>%
select(-n) %>%
ungroup()
albums.summary.per.year <- temp1 %>%
inner_join(temp2, by=c("artist", "album", "year")) %>%
arrange(artist, album, year, yrmth) %>%
rename(yrmthmosthearedinyear=yrmth) %>%
arrange(year, artist, album)
rm(temp1, temp2)
# General graphs - Scrobbles
# Plot: Serie scrobbles per month
n.top.albums.per.year<-5
temp1 <- albums.summary.per.year %>%
group_by(year) %>%
arrange(year, desc(n), artist, album) %>%
slice(1:n.top.albums.per.year) %>%
arrange(year, desc(n)) %>%
mutate(rank = row_number(-n)) %>%
ungroup()
temp2 <- lastfm.data %>%
group_by(yrmth) %>%
summarise(n=n()) %>%
tidyr::complete(yrmth = full_seq_yearmon(yrmth))
ggplot(temp2, aes(x=yrmth, y=n)) +
geom_bar(stat="identity", fill="steelblue") +
zoo::scale_x_yearmon() +
# geom_line() +
# geom_point() +
geom_label_repel(data=temp1, aes(yrmthmosthearedinyear, n, label = fullalbum), size=2) +
labs(x = "Month", y = "Plays", title = "Scrobbling frequency over time",
subtitle = "Number of scrobbles per month and top scrobbled albums",
caption = paste0("source: last.fm scrobbles\nUser: ", i.profile.name$name)) +
theme_minimal()
plot.index <- plot.index + 1
plot.index.text <- stringi::stri_pad(trimws(as.character(plot.index)), width = 2, side = "left", pad = "0")
ggsave(paste0("output/",i.profile.name$name,"_p",plot.index.text,"_scrobbles_serie.png"), width = 12, height = 9)
rm("temp1", "temp2")
# Plot: Scrobbles per month
ggplot(lastfm.data, aes(x=factor(month(fulldate), levels=1:12, labels=c("Enero", "Febrero", "Marzo", "Abril", "Mayo", "Junio", "Julio", "Agosto", "Septiembre", "Octubre", "Noviembre", "Diciembre")))) +
geom_bar(stat = "count", width=0.7, fill="steelblue") +
labs(x = "Week day", y = "Listens", title = "Scrobbling per week day",
subtitle = "Number of scrobbles per month",
caption = paste0("source: last.fm scrobbles\nUser: ", i.profile.name$name)) +
theme_minimal()
plot.index <- plot.index + 1
plot.index.text <- stringi::stri_pad(trimws(as.character(plot.index)), width = 2, side = "left", pad = "0")
ggsave(paste0("output/",i.profile.name$name,"_p",plot.index.text,"_scrobbles_month.png"), width = 8, height = 6)
# Plot: Scrobbles per weekday
ggplot(lastfm.data, aes(x=factor(weekday, levels=1:7, labels=c("lunes", "martes", "miércoles", "jueves", "viernes", "sábado", "domingo")))) +
geom_bar(stat = "count", width=0.7, fill="steelblue") +
labs(x = "Week day", y = "Listens", title = "Scrobbling per week day",
subtitle = "Number of scrobbles per week day",
caption = paste0("source: last.fm scrobbles\nUser: ", i.profile.name$name)) +
theme_minimal()
plot.index <- plot.index + 1
plot.index.text <- stringi::stri_pad(trimws(as.character(plot.index)), width = 2, side = "left", pad = "0")
ggsave(paste0("output/",i.profile.name$name,"_p",plot.index.text,"_scrobbles_weekday.png"), width = 8, height = 6)
# Plot: Listening clock
temp1 <- lastfm.data %>%
mutate(hour=hour(fulldate)) %>%
group_by(hour) %>%
summarise(value=n()) %>%
ungroup() %>%
full_join(data.frame(hour=0:23), by="hour") %>%
arrange(hour) %>%
mutate(id=1:n(), hour=stringi::stri_pad(trimws(as.character(hour)), width = 2, side = "left", pad = "0"),
value=ifelse(is.na(value), 0, value),
group="scrobblings")
temp2<-optimal.tickmarks(0, max(temp1$value), i.number.ticks=5, i.include.min=F, i.include.max=F)
temp3 <- temp1 %>%
bind_rows(temp1 %>% mutate(group="nonscronnlings", value=temp2$range[2]-value))
temp4<-data.frame(xstart=1, xend=24, y=temp2$tickmarks[c(-1,-temp2$number)])
ggplot() +
# Note that id is a factor. If x is numeric, there is some space between the first bar
# bar plot
geom_bar(data=temp3, aes(x=as.factor(id), y=value, fill=group), stat="identity", alpha=1) +
# bar colors, from brewer, in blues
scale_fill_manual(values = c("#CDE1FF", "#0066FF")) +
# polar coordinates, to make it look like a clock
# horizontal/circular lines that gives the scale of the y axis
geom_segment(data=temp4, mapping=aes(x=xstart, y=y, xend=xend, yend=y), colour = "grey", alpha=1, size=0.3 , inherit.aes = FALSE ) +
# i put again the bars to be on top of the circular lines
geom_bar(data=temp3, aes(x=as.factor(id), y=value, fill=group), stat="identity", alpha=1) +
# the scale numbers, I place it at 24.5
annotate("text", x = rep(24.5,temp2$number-2), y = temp4$y, label = temp4$y , color="grey", size=3 , angle=0, fontface="bold", hjust=0.5) +
ylim(-temp2$range[2]*2/3,y=10/8*temp2$range[2]) +
theme_minimal() +
scale_x_discrete() +
theme(
legend.position = "none",
axis.text = element_blank(),
#axis.title = element_blank(),
panel.grid = element_blank()
# , plot.margin = unit(rep(-1,4), "cm")
) +
coord_polar() +
labs(x = "Time", y = "Scrobblings", title = "Hours of day scrobbling",
subtitle = "Scrobbling frequency per hour of day",
caption=paste0("source: last.fm scrobbles\nUser: ", i.profile.name$name)) +
#geom_text(data=temp1, aes(x=id-0.5, y=9/8*temp2$range[2], label=hour, hjust=0.5), color="black", fontface="bold",alpha=0.6, size=5, inherit.aes = FALSE )
geom_text(data=temp1 %>% filter(hour %in% c('00','06','12','18')), aes(x=id-0.5, y=-1.5/8*temp2$range[2], label=hour, hjust=0.5), color="black", fontface="bold",alpha=0.6, size=5.5, inherit.aes = FALSE )
plot.index <- plot.index + 1
plot.index.text <- stringi::stri_pad(trimws(as.character(plot.index)), width = 2, side = "left", pad = "0")
ggsave(paste0("output/",i.profile.name$name,"_p",plot.index.text,"_scrobbles_clock.png"), width = 12, height = 9)
rm("temp1", "temp2", "temp3", "temp4")
# Artists
# Plot: top artists
n.top.artists<-25
top.artists <- lastfm.data %>%
group_by(artist, artist.genre1) %>%
summarise(n=n()) %>%
ungroup() %>%
arrange(desc(n)) %>%
slice(1:n.top.artists) %>%
ungroup()
ggplot(top.artists, aes(reorder(artist, n), n, color = artist.genre1)) +
geom_segment(aes(xend = reorder(artist, n)), yend = 0, color = 'grey50') +
geom_point(size = 3) +
coord_flip() +
labs(y = "Plays", x = "", title = "Favourite Artists", subtitle = "Top artist by scrobbles", caption = paste0("source: last.fm scrobbles\nUser: ", i.profile.name$name)) +
theme_minimal() +
scale_color_viridis(discrete = T, name = "Genre") +
theme(panel.grid.major.x = element_blank(),
panel.grid.minor.x = element_blank(),
panel.grid.major.y = element_line(color = 'grey60', linetype = 'dashed'))
plot.index <- plot.index + 1
plot.index.text <- stringi::stri_pad(trimws(as.character(plot.index)), width = 2, side = "left", pad = "0")
ggsave(paste0("output/",i.profile.name$name,"_p",plot.index.text,"_artists_top.png"), width = 8, height = 6)
# Plot: Preferencences over time
n.top.artists <- 25
minmaxrank.artists <- lastfm.data %>%
select(year, artist) %>%
distinct() %>%
group_by(year) %>%
summarise(n=n()) %>%
ungroup() %>%
summarise(maxrank=max(n), minrank=min(n), percentage.over.top=sum(n>n.top.artists)/n())
if (minmaxrank.artists$percentage.over.top<0.80) n.top.artists <- minmaxrank.artists$minrank
artist.summary.per.year <- albums.summary.per.year %>%
group_by(year, artist) %>%
summarise(n=sum(n)) %>%
ungroup() %>%
group_by(year) %>%
arrange(year, desc(n), artist) %>%
slice(1:n.top.artists) %>%
mutate(rank = row_number(-n)) %>%
ungroup()
enough.artist.information<-artist.summary.per.year %>%
group_by(year) %>%
summarise(n=n()) %>%
ungroup() %>%
filter(n==n.top.artists) %>%
summarise(minyear=min(year), maxyear=max(year))
artist.summary.per.year <- artist.summary.per.year %>%
filter(year>=enough.artist.information$minyear & year<=enough.artist.information$maxyear)
artist_end_tags <- artist.summary.per.year %>%
ungroup() %>%
filter(year == enough.artist.information$maxyear) %>%
mutate(year = enough.artist.information$maxyear+(enough.artist.information$maxyear-enough.artist.information$minyear)/6)
artist_start_tags <- artist.summary.per.year %>%
ungroup() %>%
filter(year == enough.artist.information$minyear) %>%
mutate(year = enough.artist.information$minyear-(enough.artist.information$maxyear-enough.artist.information$minyear)/6)
colors.n<-n.top.artists
colors <- colorRampPalette(RColorBrewer::brewer.pal(max(3,min(8,colors.n)),"Accent"))(colors.n)
names(colors)<-lastfm.data %>%
group_by(artist) %>%
summarise(n=n()) %>%
arrange(desc(n)) %>%
slice(1:colors.n) %>%
pull(artist)
othertags <- artist.summary.per.year %>% distinct(artist) %>% filter(!artist %in% names(colors)) %>% .$artist
colors <- c(colors, setNames(rep("gray", length(othertags)), othertags))
highlights <- filter(artist.summary.per.year, artist %in% names(colors)[colors != "gray"])
ggplot(data = artist.summary.per.year, aes(year, rank, color = artist, group = artist, label = artist)) +
geom_line(size = 1.7, alpha = 0.25) +
geom_line(size = 2.5, data = highlights) +
geom_point(size = 4, alpha = 0.25) +
geom_point(size = 4, data = highlights) +
geom_point(size = 1.75, color = "white") +
geom_text(data = artist_start_tags, x = enough.artist.information$minyear-(enough.artist.information$maxyear-enough.artist.information$minyear)/6, size = 4.5) +
geom_text(data = artist_end_tags, x = enough.artist.information$maxyear+(enough.artist.information$maxyear-enough.artist.information$minyear)/6, size = 4.5) +
scale_y_reverse(breaks = 1:n.top.artists) +
scale_x_continuous(
breaks = seq(min(artist.summary.per.year$year), max(artist.summary.per.year$year)),
limits = c(min(artist.summary.per.year$year) - 1.5, max(artist.summary.per.year$year) + 1.6)) +
scale_color_manual(values = colors) +
theme_minimal() +
theme(
legend.position = "",
panel.grid.major.y = element_blank(),
panel.grid.minor.y = element_blank(),
panel.grid.minor.x = element_blank(),
panel.grid.major.x = element_line(color = 'grey60', linetype = 'dashed')) +
labs(x = "Year", y = "Rank", title = "Artist preference over time",
subtitle = "Top artists per year by scrobbles",
caption=paste0("source: last.fm scrobbles\nUser: ", i.profile.name$name))
plot.index <- plot.index + 1
plot.index.text <- stringi::stri_pad(trimws(as.character(plot.index)), width = 2, side = "left", pad = "0")
ggsave(paste0("output/",i.profile.name$name,"_p",plot.index.text,"_artists_preferences.png"), width = 12, height = 9)
# Plot 5: distribution over time
n.top.artists<-10
top.artists <- lastfm.data %>%
group_by(artist) %>%
summarise(n=n()) %>%
ungroup() %>%
arrange(desc(n), artist) %>%
mutate(rank=row_number(-n)) %>%
ungroup() %>%
filter(rank<=n.top.artists)
data <- lastfm.data %>%
inner_join(
top.artists %>%
select(artist), by="artist") %>%
mutate(genre=as.factor(artist), year=as.factor(year)) %>%
group_by(genre, year) %>%
summarise(value=10*log1p(n())) %>%
arrange(year, genre, value) %>%
ungroup()
# Set a number of 'empty bar' to add at the end of each group
empty_bar=3
to_add = data.frame( matrix(NA, empty_bar*nlevels(data$year), ncol(data)) )
colnames(to_add) = colnames(data)
to_add$year=rep(levels(data$year), each=empty_bar)
data=rbind(data, to_add)
data=data %>% arrange(year)
data$id=seq(1, nrow(data))
# Get the name and the y position of each label
label_data=data
number_of_bar=nrow(label_data)
angle= 90 - 360 * (label_data$id-0.5) /number_of_bar # I substract 0.5 because the letter must have the angle of the center of the bars. Not extreme right(1) or extreme left (0)
label_data$hjust<-ifelse( angle < -90, 1, 0)
label_data$angle<-ifelse(angle < -90, angle+180, angle)
# prepare a data frame for base lines
base_data=data %>%
group_by(year) %>%
summarize(start=min(id), end=max(id) - empty_bar) %>%
rowwise() %>%
mutate(title=mean(c(start, end)))
# prepare a data frame for grid (scales)
grid_data = base_data
grid_data$end = grid_data$end[ c( nrow(grid_data), 1:nrow(grid_data)-1)] + 1
grid_data$start = grid_data$start - 1
grid_data=grid_data[-1,]
# Make the plot
ggplot(data, aes(x=as.factor(id), y=value, fill=year)) + # Note that id is a factor. If x is numeric, there is some space between the first bar
geom_bar(aes(x=as.factor(id), y=value, fill=year), stat="identity", alpha=0.5) +
# Add a val=100/75/50/25 lines. I do it at the beginning to make sur barplots are OVER it.
geom_segment(data=grid_data, aes(x = end, y = 80, xend = start, yend = 80), colour = "grey", alpha=1, size=0.3 , inherit.aes = FALSE ) +
geom_segment(data=grid_data, aes(x = end, y = 60, xend = start, yend = 60), colour = "grey", alpha=1, size=0.3 , inherit.aes = FALSE ) +
geom_segment(data=grid_data, aes(x = end, y = 40, xend = start, yend = 40), colour = "grey", alpha=1, size=0.3 , inherit.aes = FALSE ) +
geom_segment(data=grid_data, aes(x = end, y = 20, xend = start, yend = 20), colour = "grey", alpha=1, size=0.3 , inherit.aes = FALSE ) +
# Add text showing the value of each 100/75/50/25 lines
annotate("text", x = rep(max(data$id),4), y = c(20, 40, 60, 80), label = c("20", "40", "60", "80") , color="grey", size=3 , angle=0, fontface="bold", hjust=1) +
geom_bar(aes(x=as.factor(id), y=value, fill=year), stat="identity", alpha=0.5) +
ylim(-100,120) +
theme_minimal() +
theme(
legend.position = "none",
axis.text = element_blank(),
#axis.title = element_blank(),
panel.grid = element_blank()
# , plot.margin = unit(rep(-1,4), "cm")
) +
labs(x = "Year", y = "Rank", title = "Artists preferences over time",
subtitle = "Scrobbling frequency of artists by year",
caption=paste0("source: last.fm scrobbles\nUser: ", i.profile.name$name)) +
coord_polar() +
geom_text(data=label_data, aes(x=id, y=value+10, label=genre, hjust=hjust), color="black", fontface="bold",alpha=0.6, size=2.5, angle= label_data$angle, inherit.aes = FALSE ) +
# Add base line information
geom_segment(data=base_data, aes(x = start, y = -5, xend = end, yend = -5), colour = "black", alpha=0.8, size=0.6 , inherit.aes = FALSE ) +
geom_text(data=base_data, aes(x = title, y = -18, label=year),
hjust=0.5,
# hjust=c(1,1,0,0),
colour = "black", alpha=0.8, size=4, fontface="bold", inherit.aes = FALSE)
plot.index <- plot.index + 1
plot.index.text <- stringi::stri_pad(trimws(as.character(plot.index)), width = 2, side = "left", pad = "0")
ggsave(paste0("output/",i.profile.name$name,"_p",plot.index.text,"_artists_distribution.png"), width = 12, height = 9)
# Artist discovery
artist.first.time <- lastfm.data %>%
group_by(artist) %>%
summarise(yrfirst=min(year), n=n()) %>%
group_by(yrfirst) %>%
arrange(yrfirst, desc(n)) %>%
mutate(rank = row_number(-n))
artist.first.time.data <- artist.first.time %>%
group_by(yrfirst) %>%
summarise(n=n())
artist.first.time.labels <- artist.first.time %>%
group_by(yrfirst) %>%
summarise(n=n()) %>%
inner_join(artist.first.time %>% filter(rank<=3), by="yrfirst") %>%
group_by(yrfirst) %>%
arrange(n.y) %>%
mutate(freq = n.y / sum(n.y, na.rm=T), freqcum=cumsum(freq), position=n.x*if_else(rank==3,freq/2,lag(freqcum)+freq/2))
ggplot(artist.first.time.data, aes(x=as.factor(yrfirst), y=n)) +
geom_bar(stat = "identity", width=0.7, fill="steelblue") +
geom_label(data=artist.first.time.labels, aes(as.factor(yrfirst), position, label = artist), size=3) +
labs(x = "Year first listened", y = "Number of artists", title = "Artist discovery",
subtitle = "New artists discovered per year",
caption = paste0("source: last.fm scrobbles\nUser: ", i.profile.name$name)) +
theme_minimal()
plot.index <- plot.index + 1
plot.index.text <- stringi::stri_pad(trimws(as.character(plot.index)), width = 2, side = "left", pad = "0")
ggsave(paste0("output/",i.profile.name$name,"_p",plot.index.text,"_artists_discovered.png"), width = 8, height = 6)
# Albums
# Plot: top albums
n.top.albums<-25
top.albums <- albums.summary %>%
filter(rank<=n.top.albums)
# group_by(fullalbum, album.genre1) %>%
# summarise(n=n()) %>%
# ungroup() %>%
# arrange(desc(n)) %>%
# slice(1:n.top.albums) %>%
# ungroup()
ggplot(top.albums, aes(reorder(fullalbum, n), n, color = album.genre1)) +
geom_segment(aes(xend = reorder(fullalbum, n)), yend = 0, color = 'grey50') +
geom_point(size = 3) +
coord_flip() +
labs(y = "Plays", x = "", title = "Favourite Albums", subtitle = "Top albums by scrobbles", caption = paste0("source: last.fm scrobbles\nUser: ", i.profile.name$name)) +
theme_minimal() +
scale_color_viridis(discrete = T, name = "Genre") +
theme(panel.grid.major.x = element_blank(),
panel.grid.minor.x = element_blank(),
panel.grid.major.y = element_line(color = 'grey60', linetype = 'dashed'))
plot.index <- plot.index + 1
plot.index.text <- stringi::stri_pad(trimws(as.character(plot.index)), width = 2, side = "left", pad = "0")
ggsave(paste0("output/",i.profile.name$name,"_p",plot.index.text,"_albums_top.png"), width = 8, height = 6)
# Plot: distribution
n.top.albums<-10
ds <- lastfm.data %>%
inner_join(albums.summary %>%
filter(rank<=n.top.albums) %>%
select(artist, album, fullalbum), by=c("artist", "album")) %>%
mutate(date=as.Date(format(fulldate,"%Y%m%d"), format="%Y%m%d")) %>%
select(fullalbum, date)
ggplot(ds, aes(date, fill=fullalbum)) +
geom_density(alpha=0.55) +
scale_x_date() +
theme_minimal() +
theme(
panel.grid.major.y = element_blank(),
panel.grid.minor.y = element_blank(),
panel.grid.minor.x = element_blank(),
panel.grid.major.x = element_line(color = 'grey60', linetype = 'dashed')) +
scale_fill_brewer(name="Albums", palette="Spectral") +
labs(x = "Year", y = "Rank", title = "Albums scrobbled distribution",
subtitle = "Distribution of the top albums scrobbling frequency",
caption=paste0("source: last.fm scrobbles\nUser: ", i.profile.name$name))
plot.index <- plot.index + 1
plot.index.text <- stringi::stri_pad(trimws(as.character(plot.index)), width = 2, side = "left", pad = "0")
ggsave(paste0("output/",i.profile.name$name,"_p",plot.index.text,"_albums_distribution.png"), width = 12, height = 9)
# Plot: time listening
n.top.albums<-25