ggplot(day_data, aes(x = dteday, y = cnt)) +
  geom_line(color = "gray75", linewidth = 0.4, alpha = 0.9) +
  geom_smooth(method = "loess", span = 0.25,
              color = "#0077b6", fill = "#90e0ef",
              linewidth = 1.3, se = TRUE, alpha = 0.2) +
  scale_x_date(
    date_breaks = "2 months",
    date_labels = "%b\n%Y",
    expand = c(0.01, 0.01)
  ) +
  scale_y_continuous(labels = label_comma()) +
  annotate("rect",
           xmin = as.Date("2011-01-01"), xmax = as.Date("2011-12-31"),
           ymin = -Inf, ymax = Inf,
           fill = "#caf0f8", alpha = 0.10) +
  annotate("rect",
           xmin = as.Date("2012-01-01"), xmax = as.Date("2012-12-31"),
           ymin = -Inf, ymax = Inf,
           fill = "#ade8f4", alpha = 0.10) +
  annotate("text", x = as.Date("2011-07-01"), y = 8400,
           label = "2011", fontface = "bold",
           color = "#0077b6", size = 4.5, alpha = 0.5) +
  annotate("text", x = as.Date("2012-07-01"), y = 8400,
           label = "2012", fontface = "bold",
           color = "#0077b6", size = 4.5, alpha = 0.5) +
  labs(
    title    = "Tren Harian Penyewaan Sepeda (2011–2012)",
    subtitle = "Garis biru menunjukkan tren keseluruhan; garis abu-abu adalah data harian aktual",
    x        = "Tanggal",
    y        = "Jumlah Penyewaan Sepeda",
    caption  = "Sumber: UCI Machine Learning Repository — Bike Sharing Dataset"
  ) +
  theme_minimal(base_size = 13) +
  theme(
    plot.title      = element_text(face = "bold", color = "#1a1a2e", size = 15),
    plot.subtitle   = element_text(color = "gray40", size = 11),
    plot.caption    = element_text(color = "gray50", size = 9),
    panel.grid.minor = element_blank(),
    axis.text.x     = element_text(size = 9)
  )

monthly_data <- day_data |>
  group_by(year, month) |>
  summarise(total_rentals = sum(cnt), .groups = "drop")

ggplot(monthly_data, aes(x = month, y = total_rentals,
                          color = year, group = year)) +
  geom_line(linewidth = 1.3) +
  geom_point(size = 3.5, shape = 21,
             aes(fill = year), color = "white", stroke = 1.5) +
  geom_text(aes(label = label_comma()(total_rentals)),
            vjust = -1.1, size = 3, fontface = "bold") +
  scale_y_continuous(
    labels = label_comma(),
    limits = c(0, NA),
    expand = expansion(mult = c(0, 0.18))
  ) +
  scale_color_manual(values = c("2011" = "#f77f00", "2012" = "#0077b6")) +
  scale_fill_manual(values  = c("2011" = "#f77f00", "2012" = "#0077b6")) +
  labs(
    title    = "Perbandingan Pola Penyewaan Bulanan: 2011 vs 2012",
    subtitle = "Menampilkan pola musiman dan pertumbuhan tahunan",
    x        = "Bulan",
    y        = "Total Penyewaan Sepeda",
    color    = "Tahun",
    fill     = "Tahun",
    caption  = "Sumber: UCI Machine Learning Repository — Bike Sharing Dataset"
  ) +
  theme_minimal(base_size = 13) +
  theme(
    plot.title      = element_text(face = "bold", color = "#1a1a2e", size = 15),
    plot.subtitle   = element_text(color = "gray40", size = 11),
    plot.caption    = element_text(color = "gray50", size = 9),
    panel.grid.minor = element_blank(),
    legend.position = "top",
    legend.title    = element_text(face = "bold")
  )

hourly_pattern <- hour_data |>
  group_by(hr, day_type) |>
  summarise(avg_rentals = mean(cnt), .groups = "drop")

# Tandai jam puncak hari kerja
peak_workday <- hourly_pattern |>
  filter(day_type == "Hari Kerja") |>
  filter(avg_rentals == max(avg_rentals))

peak_weekend <- hourly_pattern |>
  filter(day_type == "Akhir Pekan / Libur") |>
  filter(avg_rentals == max(avg_rentals))

ggplot(hourly_pattern, aes(x = hr, y = avg_rentals,
                            color = day_type, group = day_type)) +
  geom_ribbon(
    data = hourly_pattern |> filter(day_type == "Hari Kerja"),
    aes(ymin = 0, ymax = avg_rentals),
    fill = "#90e0ef", alpha = 0.15, color = NA
  ) +
  geom_ribbon(
    data = hourly_pattern |> filter(day_type == "Akhir Pekan / Libur"),
    aes(ymin = 0, ymax = avg_rentals),
    fill = "#ffd166", alpha = 0.15, color = NA
  ) +
  geom_line(linewidth = 1.4) +
  geom_point(data = peak_workday,
             aes(x = hr, y = avg_rentals),
             color = "#0077b6", size = 4.5, shape = 18) +
  geom_point(data = peak_weekend,
             aes(x = hr, y = avg_rentals),
             color = "#f77f00", size = 4.5, shape = 18) +
  geom_text(data = peak_workday,
            aes(label = paste0("Puncak: ", round(avg_rentals, 0))),
            vjust = -1, hjust = 0.5, size = 3.3,
            color = "#0077b6", fontface = "bold") +
  geom_text(data = peak_weekend,
            aes(label = paste0("Puncak: ", round(avg_rentals, 0))),
            vjust = -1, hjust = 0.5, size = 3.3,
            color = "#f77f00", fontface = "bold") +
  scale_x_continuous(
    breaks = seq(0, 23, by = 1),
    labels = function(x) paste0(sprintf("%02d", x), ":00")
  ) +
  scale_y_continuous(labels = label_comma()) +
  scale_color_manual(
    values = c("Hari Kerja" = "#0077b6", "Akhir Pekan / Libur" = "#f77f00")
  ) +
  labs(
    title    = "Ritme Penyewaan Sepeda Dalam Sehari",
    subtitle = "Rata-rata penyewaan per jam, hari kerja vs akhir pekan/libur",
    x        = "Jam",
    y        = "Rata-rata Penyewaan Sepeda",
    color    = "Jenis Hari",
    caption  = "Sumber: UCI Machine Learning Repository — Bike Sharing Dataset"
  ) +
  theme_minimal(base_size = 13) +
  theme(
    plot.title       = element_text(face = "bold", color = "#1a1a2e", size = 15),
    plot.subtitle    = element_text(color = "gray40", size = 11),
    plot.caption     = element_text(color = "gray50", size = 9),
    panel.grid.minor = element_blank(),
    axis.text.x      = element_text(angle = 45, hjust = 1, size = 8),
    legend.position  = "top",
    legend.title     = element_text(face = "bold")
  )