if(!require(pacman)) install.packages("pacman")
if(!require(ggbump)) devtools::install_github("davidsjoberg/ggbump")
  
pacman::p_load(padr, hablar, jsonlite, ggbump, 
               httr, xml2, lubridate, tidyverse)

# Import the stream data from Spotify which are in JSON-format.
# Create a month variable by flooring the stream date column.
df <- bind_rows(
  as_tibble(fromJSON("StreamingHistory0.json")),
  as_tibble(fromJSON("StreamingHistory1.json"))
) %>% 
  mutate(month = ymd_hm(endTime) %>% as.Date() %>% floor_date("month"))

knitr::kable(head(df), "markdown")

# Choose which songs to tag
get_tags_list <- df %>% 
  group_by(artistName, trackName, month) %>% 
  summarise(n = n()) %>% 
  ungroup() %>% 
  arrange(-n) %>% 
  filter(n > 7)

# API path
path <- "HTTP://ws.audioscrobbler.com/2.0/"

# Get the data through the API
# Generates a dataframe with multiple tags for each songs
tags_df <- map2_dfr(get_tags_list$artistName, 
                    get_tags_list$trackName, 
                    function(x, y) {
  request <- GET(url = path,
                 query = list(
                   method = "track.gettoptags",
                   track = y,
                   artist = x,
                   autocorrect = 1,
                   api_key = "<your-api-key>")
  )

  # Sleep for 10 seconds to be kind to the API
  Sys.sleep(10)
  
  # Extract the top 20 tags for each song
  tags <- content(request, "text") %>% 
    as.character() %>% 
    read_xml() %>% 
    xml_find_all(".//toptags/tag/name") %>%
    xml_text() %>%
    .[1:20]

  # Create a data frame with the tags
  tibble(artist = x,
         song = y,
         tags = unique(tags))
}
)

# Do a filtering join to only include songs that were taged.
mg <- df %>% 
  semi_join(tags_df, by = c("artistName" = "artist",
                            "trackName" = "song"))

# Summarise the data to the number of streams per month, artist and song.
mg <- mg %>% 
  group_by(month, artist = artistName, song = trackName) %>% 
  summarise(n_streams = n()) %>% 
  ungroup()

# Join the tags on artists and song. 
# Makes the data frame longer since there are multiple tags per song.
mg <- mg %>% 
  left_join(tags_df, by = c("artist", "song")) %>% 
  drop_na()

# Marke the tags nice with title case
# Summarise number of stremas per tag and month. Implicitely dropping songs and artist.
mg <- mg %>% 
  mutate(tags = str_to_title(tags)) %>% 
  group_by(month, tags) %>% 
  summarise(streams_value = sum_(n_streams)) %>% 
  ungroup() %>% 
  arrange(-streams_value)

knitr::kable(head(mg), "markdown")

# Pad the tags in order to get a streams value of 0 for months when I did not listen to that tag.
rk <- mg %>%
  pad("month", end_val = max(mg$month), group = "tags") %>%
  mutate(streams_value = if_na(streams_value, 0L))

# Create one season variable in text and one in numeric to be used for a discrete x axis..
rk <- rk %>% 
  mutate(season_txt = case_when(
           month(month) %in% c(12)  ~ paste("Winter", year(month) + 1, sep = "\n"),
           month(month) %in% c(1:2) ~ paste("Winter", year(month), sep = "\n"),
           month(month) %in% c(3:5) ~ paste("Spring", year(month), sep = "\n"),
           month(month) %in% c(6:8) ~ paste("Summer", year(month), sep = "\n"),
           month(month) %in% c(9:11) ~ paste("Autumn", year(month), sep = "\n")),
         season_num = case_when(
           month(month) %in% c(12)  ~ paste0(year(month) + 1, 1),
           month(month) %in% c(1:2) ~ paste0(year(month), 1),
           month(month) %in% c(3:5) ~ paste0(year(month), 2),
           month(month) %in% c(6:8) ~ paste0(year(month), 3),
           month(month) %in% c(9:11) ~ paste0(year(month), 4)))

# Collapse data to season.
rk <- rk %>% 
  group_by(tags, season_txt, season_num) %>% 
  summarise(streams_value = sum_(streams_value)) %>% 
  ungroup()

# Create a x axis variable with the season variable from format `20191, 20192` to `1, 2` etc.
rk <- rk %>%
  left_join(rk %>%
              distinct(season_num) %>%
              mutate(order = rank(season_num)), by = "season_num")

# Rank the streams value per. 
# Ranks above 5 will be set to 6 and tags the never reached top 5 are removed.
rk <- rk %>%
  group_by(order) %>%
  mutate(rank = rank(-streams_value, ties.method = "random")) %>%
  ungroup() %>%
  group_by(tags) %>%
  mutate(any_top_5 = any(rank <= 5)) %>% 
  ungroup() %>%
  mutate(rank = if_else(rank > 5,
                        6L,
                        rank)) %>% 
  filter(any_top_5 == TRUE)

# Find first and last season for each tag. Remove observations if outside that span.
rk <- rk %>%
  group_by(tags) %>%
  mutate(first_top5 = min_(order[rank <= 5]),
         last_top5 = max_(order[rank <= 5]),
         d_first_top5 = if_else(order == first_top5,
                                1,
                                0)) %>%
  filter(!is.na(first_top5),
         order >= first_top5,
         order <= last_top5) %>%
  ungroup()

# Create groups for the "active" top 5 tags. 
# This is needed to supress geom_bump to draw lines more than to the next season.
rk <- rk %>% 
  arrange(tags, order) %>% 
  group_by(tags) %>% 
  mutate(lag_zero = if_else(lag(rank) %in% c(6, NA) & rank <= 5, 1, 0, 0)) %>% 
  ungroup() %>% 
  mutate(group = cumsum(lag_zero))

# Select the columns needed for the plot
rk <- rk %>% 
  select(tags, season_txt, order, rank, first_top5, last_top5, d_first_top5, group)

knitr::kable(head(rk %>% mutate(season_txt = str_replace(season_txt, "\n", " "))), "markdown")

# Make a palette by drawing pseudo random colors from `RColorBrewer`
set.seed(42)
custom_palette <- c(RColorBrewer::brewer.pal(9, "Set1"),
                    RColorBrewer::brewer.pal(5, "Dark2")) %>% 
  sample(n_distinct(rk$tags))

p <- rk %>% 
  ggplot(aes(order, rank, color = tags, group = tags)) +
  geom_bump(smooth = 15, size = 2, alpha = 0.2) +
  scale_y_reverse() 

p

p <- p +
  geom_bump(data = rk %>% filter(rank <= 5), 
            aes(order, rank, group = group, color = tags), 
            smooth = 15, size = 2, inherit.aes = F)

p

p <- p + 
  geom_point(data = rk %>% filter(d_first_top5 == 1),
             aes(x = order - .2),
             size = 5) +
    geom_segment(data = rk %>% filter(rank <=5),
               aes(x = order - .2, xend = order + .2, y = rank, yend = rank),
               size = 2,
               lineend = "round")
    
p

p +
  scale_x_continuous(breaks = rk$order %>% unique() %>% sort(),
                     labels = rk %>% distinct(order, season_txt) %>% arrange(order) %>% pull(season_txt), 
                     expand = expand_scale(mult = .1)) +
  geom_text(data = rk %>% filter(d_first_top5 == 1),
            aes(label = tags, x = order-.2),
            color = "white",
            nudge_y = .43,
            nudge_x = -.05,
            size = 3.5,
            fontface = 2,
            hjust = 0) +
  geom_text(data = rk %>% filter(order == max(order)),
            aes(label = tags),
            color = "gray70",
            nudge_x = .31,
            hjust = 0,
            size = 3,
            fontface = 2) +
  cowplot::theme_minimal_hgrid(font_size = 14) +
  theme(legend.position = "none",
        panel.grid = element_blank(),
        plot.title = element_text(hjust = .5, color = "white"),
        plot.caption = element_text(hjust = 1, color = "white", size = 8),
        plot.subtitle = element_text(hjust = .5, color = "white", size = 10),
        axis.line = element_blank(),
        axis.ticks = element_blank(),
        axis.text.y = element_blank(),
        axis.title.y = element_blank(),
        axis.text.x = element_text(face = 2, color = "white"),
        panel.background = element_rect(fill = "black"),
        plot.background = element_rect(fill = "black")) +
  labs(x = NULL,
       title ="My year on Spotify",
       subtitle ="Top 5 genre tags spanning the seasons",
       caption = "\nSource:\nPersonal Spotify data\nLast.fm API") +
  scale_colour_manual(values = custom_palette) +
  geom_point(data = tibble(x = 0.55, y = 1:5), aes(x = x, y = y), 
            inherit.aes = F,
            color = "white",
            size = 10,
            pch = 21) +
  geom_text(data = tibble(x = .55, y = 1:5), aes(x = x, y = y, label = y), 
             inherit.aes = F,
            color = "white")