## ----chunk-options, include=FALSE---------------------------------------------
library(taylor)

if (identical(Sys.getenv("IN_PKGDOWN"), "true")) {
  tiny_width <- small_width <- med_width <- 7
  large_width <- 9
} else {
  tiny_width <- small_width <- med_width <- 5
  large_width <- 5.5
}

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.asp = 0.618,
  fig.width = small_width,
  fig.align = "center",
  out.width = "90%"
)

if (capabilities("cairo") && Sys.info()[['sysname']] != "Darwin") {
  knitr::opts_chunk$set(
    dev = "png",
    dev.args = list(type = "cairo")
  )
}

## ----setup--------------------------------------------------------------------
library(taylor)

## -----------------------------------------------------------------------------
taylor_all_songs

## -----------------------------------------------------------------------------
taylor_albums

## -----------------------------------------------------------------------------
eras_tour_surprise

## -----------------------------------------------------------------------------
album_palettes$lover

## -----------------------------------------------------------------------------
album_compare

## ----eras-plot, eval = FALSE--------------------------------------------------
# library(dplyr)
# library(tidyr)
# library(ggplot2)
# 
# leg_labels <- unique(eras_tour_surprise$leg)
# leg_labels <- gsub("South America", "South\nAmerica", leg_labels)
# 
# surprise_song_count <- eras_tour_surprise |>
#   nest(dat = -c(leg, date, city, night)) |>
#   arrange(date) |>
#   mutate(
#     leg = factor(
#       leg,
#       levels = unique(eras_tour_surprise$leg),
#       labels = leg_labels
#     )
#   ) |>
#   mutate(show_number = seq_len(n()), .after = night) |>
#   unnest(dat) |>
#   left_join(
#     distinct(taylor_album_songs, track_name, album_name),
#     join_by(song == track_name),
#     relationship = "many-to-one"
#   ) |>
#   count(leg, date, city, night, show_number, album_name) |>
#   complete(nesting(leg, date, city, night, show_number), album_name) |>
#   mutate(n = replace_na(n, 0)) |>
#   arrange(album_name, date, night) |>
#   mutate(surprise_count = cumsum(n), .by = album_name) |>
#   left_join(
#     select(taylor_albums, album_name, album_release),
#     by = "album_name"
#   ) |>
#   mutate(
#     surprise_count = case_when(
#       album_name == "THE TORTURED POETS DEPARTMENT" &
#         date < album_release ~ NA_integer_,
#       .default = surprise_count
#     )
#   ) |>
#   add_row(
#     leg = factor("Europe"),
#     album_name = "THE TORTURED POETS DEPARTMENT",
#     show_number = 83.5,
#     surprise_count = 0L
#   ) |>
#   mutate(
#     album_name = gsub(" \\(Taylor's Version\\)", "", album_name),
#     album_name = replace_na(album_name, "Other"),
#     album_group = album_name,
#     album_name = factor(
#       album_name,
#       c(album_levels, "Other"),
#       labels = c(
#         gsub("POETS DEPARTMENT", "POETS\nDEPARTMENT", album_levels),
#         "Other"
#       )
#     )
#   )
# 
# ggplot(surprise_song_count) +
#   facet_wrap(~album_name, ncol = 3) +
#   geom_line(
#     data = ~ select(.x, -album_name),
#     aes(x = show_number, y = surprise_count, group = album_group),
#     color = "grey80",
#     na.rm = TRUE
#   ) +
#   geom_line(
#     aes(x = show_number, y = surprise_count, color = album_group),
#     show.legend = FALSE,
#     linewidth = 2,
#     na.rm = TRUE
#   ) +
#   scale_color_albums(na.value = "grey80") +
#   scale_x_continuous(breaks = c(1, seq(20, 500, 20))) +
#   labs(x = "Show", y = "Songs Played") +
#   theme_minimal() +
#   theme(
#     strip.text.x = element_text(hjust = 0, size = 10),
#     axis.title = element_text(size = 9)
#   )

## ----eras-plot, echo = FALSE, message = FALSE, warning = FALSE----------------
library(dplyr)
library(tidyr)
library(ggplot2)

leg_labels <- unique(eras_tour_surprise$leg)
leg_labels <- gsub("South America", "South\nAmerica", leg_labels)

surprise_song_count <- eras_tour_surprise |>
  nest(dat = -c(leg, date, city, night)) |>
  arrange(date) |>
  mutate(
    leg = factor(
      leg,
      levels = unique(eras_tour_surprise$leg),
      labels = leg_labels
    )
  ) |>
  mutate(show_number = seq_len(n()), .after = night) |>
  unnest(dat) |>
  left_join(
    distinct(taylor_album_songs, track_name, album_name),
    join_by(song == track_name),
    relationship = "many-to-one"
  ) |>
  count(leg, date, city, night, show_number, album_name) |>
  complete(nesting(leg, date, city, night, show_number), album_name) |>
  mutate(n = replace_na(n, 0)) |>
  arrange(album_name, date, night) |>
  mutate(surprise_count = cumsum(n), .by = album_name) |>
  left_join(
    select(taylor_albums, album_name, album_release),
    by = "album_name"
  ) |>
  mutate(
    surprise_count = case_when(
      album_name == "THE TORTURED POETS DEPARTMENT" &
        date < album_release ~ NA_integer_,
      .default = surprise_count
    )
  ) |>
  add_row(
    leg = factor("Europe"),
    album_name = "THE TORTURED POETS DEPARTMENT",
    show_number = 83.5,
    surprise_count = 0L
  ) |>
  mutate(
    album_name = gsub(" \\(Taylor's Version\\)", "", album_name),
    album_name = replace_na(album_name, "Other"),
    album_group = album_name,
    album_name = factor(
      album_name,
      c(album_levels, "Other"),
      labels = c(
        gsub("POETS DEPARTMENT", "POETS\nDEPARTMENT", album_levels),
        "Other"
      )
    )
  )

ggplot(surprise_song_count) +
  facet_wrap(~album_name, ncol = 3) +
  geom_line(
    data = ~ select(.x, -album_name),
    aes(x = show_number, y = surprise_count, group = album_group),
    color = "grey80",
    na.rm = TRUE
  ) +
  geom_line(
    aes(x = show_number, y = surprise_count, color = album_group),
    show.legend = FALSE,
    linewidth = 2,
    na.rm = TRUE
  ) +
  scale_color_albums(na.value = "grey80") +
  scale_x_continuous(breaks = c(1, seq(20, 500, 20))) +
  labs(x = "Show", y = "Songs Played") +
  theme_minimal() +
  theme(
    strip.text.x = element_text(hjust = 0, size = 10),
    axis.title = element_text(size = 9)
  )

## ----eras-1989, eval = FALSE--------------------------------------------------
# library(patchwork)
# 
# missing_firsts <- tibble(
#   date = as.Date(c("2023-11-01", "2024-02-01", "2024-05-01", "2024-10-01"))
# )
# day_ones <- surprise_song_count |>
#   slice_min(date, by = c(leg, album_name)) |>
#   select(leg, date, album_name) |>
#   mutate(date = date - 1)
# 
# surprise_dat <- surprise_song_count |>
#   bind_rows(missing_firsts) |>
#   arrange(date) |>
#   fill(leg, .direction = "up") |>
#   bind_rows(day_ones) |>
#   arrange(album_name, date) |>
#   group_by(album_name) |>
#   fill(surprise_count, .direction = "down")
# 
# tour1 <- surprise_dat |>
#   filter(leg %in% c("North America (Leg 1)", "South\nAmerica")) |>
#   ggplot() +
#   facet_grid(cols = vars(leg), scales = "free_x", space = "free_x") +
#   geom_line(
#     aes(x = date, y = surprise_count, group = album_name),
#     color = "grey80",
#     na.rm = TRUE
#   ) +
#   geom_line(
#     data = ~ filter(.x, album_name == "1989"),
#     aes(x = date, y = surprise_count),
#     color = album_compare["1989_tv"],
#     show.legend = FALSE,
#     linewidth = 2,
#     na.rm = TRUE
#   ) +
#   scale_color_albums() +
#   scale_x_date(breaks = "month", date_labels = "%b\n%Y", expand = c(.02, .02)) +
#   expand_limits(y = c(0, 37)) +
#   labs(x = NULL, y = "Songs Played") +
#   theme_minimal() +
#   theme(
#     strip.text.x = element_text(hjust = 0, size = 10),
#     axis.title = element_text(size = 9)
#   )
# 
# tour2 <- surprise_dat |>
#   filter(!leg %in% c("North America (Leg 1)", "South\nAmerica")) |>
#   ggplot() +
#   facet_grid(cols = vars(leg), scales = "free_x", space = "free_x") +
#   geom_line(
#     aes(x = date, y = surprise_count, group = album_name),
#     color = "grey80",
#     na.rm = TRUE
#   ) +
#   geom_line(
#     data = ~ filter(.x, album_name == "1989"),
#     aes(x = date, y = surprise_count),
#     color = album_compare["1989_tv"],
#     show.legend = FALSE,
#     linewidth = 2,
#     na.rm = TRUE
#   ) +
#   scale_color_albums() +
#   scale_x_date(breaks = "month", date_labels = "%b\n%Y", expand = c(.02, .02)) +
#   expand_limits(y = c(0, 37)) +
#   labs(x = NULL, y = "Songs Played") +
#   theme_minimal() +
#   theme(
#     strip.text.x = element_text(hjust = 0, size = 10),
#     axis.title = element_text(size = 9)
#   )
# 
# tour1 / tour2 + plot_layout(axes = "collect")

## ----eras-1989, echo = FALSE, message = FALSE, warning = FALSE----------------
library(patchwork)

missing_firsts <- tibble(
  date = as.Date(c("2023-11-01", "2024-02-01", "2024-05-01", "2024-10-01"))
)
day_ones <- surprise_song_count |>
  slice_min(date, by = c(leg, album_name)) |>
  select(leg, date, album_name) |>
  mutate(date = date - 1)

surprise_dat <- surprise_song_count |>
  bind_rows(missing_firsts) |>
  arrange(date) |>
  fill(leg, .direction = "up") |>
  bind_rows(day_ones) |>
  arrange(album_name, date) |>
  group_by(album_name) |>
  fill(surprise_count, .direction = "down")

tour1 <- surprise_dat |>
  filter(leg %in% c("North America (Leg 1)", "South\nAmerica")) |>
  ggplot() +
  facet_grid(cols = vars(leg), scales = "free_x", space = "free_x") +
  geom_line(
    aes(x = date, y = surprise_count, group = album_name),
    color = "grey80",
    na.rm = TRUE
  ) +
  geom_line(
    data = ~ filter(.x, album_name == "1989"),
    aes(x = date, y = surprise_count),
    color = album_compare["1989_tv"],
    show.legend = FALSE,
    linewidth = 2,
    na.rm = TRUE
  ) +
  scale_color_albums() +
  scale_x_date(breaks = "month", date_labels = "%b\n%Y", expand = c(.02, .02)) +
  expand_limits(y = c(0, 37)) +
  labs(x = NULL, y = "Songs Played") +
  theme_minimal() +
  theme(
    strip.text.x = element_text(hjust = 0, size = 10),
    axis.title = element_text(size = 9)
  )

tour2 <- surprise_dat |>
  filter(!leg %in% c("North America (Leg 1)", "South\nAmerica")) |>
  ggplot() +
  facet_grid(cols = vars(leg), scales = "free_x", space = "free_x") +
  geom_line(
    aes(x = date, y = surprise_count, group = album_name),
    color = "grey80",
    na.rm = TRUE
  ) +
  geom_line(
    data = ~ filter(.x, album_name == "1989"),
    aes(x = date, y = surprise_count),
    color = album_compare["1989_tv"],
    show.legend = FALSE,
    linewidth = 2,
    na.rm = TRUE
  ) +
  scale_color_albums() +
  scale_x_date(breaks = "month", date_labels = "%b\n%Y", expand = c(.02, .02)) +
  expand_limits(y = c(0, 37)) +
  labs(x = NULL, y = "Songs Played") +
  theme_minimal() +
  theme(
    strip.text.x = element_text(hjust = 0, size = 10),
    axis.title = element_text(size = 9)
  )

tour1 / tour2 + plot_layout(axes = "collect")

## ----examples, echo = FALSE, results = "asis", eval = identical(Sys.getenv("IN_PKGDOWN"), "true")----
# examples <- read.csv("data/example-uses.csv")
# cells <- paste(
#   "<td>",
#   paste0("  <a href=\"", examples$href, "\">"),
#   paste0(
#     "    <img src=\"",
#     examples$preview,
#     "\" ",
#     "alt=\"",
#     examples$description,
#     "\" width=\"100%\"/>"
#   ),
#   "  </a>",
#   "</td>",
#   sep = "\n"
# )
# 
# needed_rows <- ceiling(length(cells) / 3)
# rows <- vapply(
#   seq_len(needed_rows),
#   function(x) {
#     paste(
#       "<tr>",
#       paste(cells[((x * 3) - 2):(x * 3)], collapse = "\n"),
#       "</tr>",
#       sep = "\n"
#     )
#   },
#   character(1)
# )
# 
# tab <- paste(
#   "<table class=\"taylor-examples\" width=\"100%\">",
#   paste(rows, collapse = "\n"),
#   "</table>",
#   sep = "\n"
# )
# 
# cat(tab)