library(plotly)
library(reshape2)

# Fungsi utama dengan validasi dan nested loop
compute_formula <- function(x, formula = "all") {
  valid <- c("linear", "quadratic", "cubic", "exponential", "all")
  if (!(formula %in% valid))
    stop(paste("Formula tidak valid! Pilih:", paste(valid, collapse = ", ")))

  # Definisi semua formula dalam list
  formulas <- list(
    linear      = function(x) 2 * x + 1,
    quadratic   = function(x) x^2 - 3 * x + 2,
    cubic       = function(x) x^3 - 2 * x^2 + x,
    exponential = function(x) exp(0.3 * x)
  )

  hasil <- data.frame(x = x)
  # Loop untuk hitung semua formula sekaligus
  target <- if (formula == "all") names(formulas) else formula
  for (nm in target) {
    hasil[[nm]] <- round(formulas[[nm]](x), 2)
  }
  return(hasil)
}

# Hitung untuk x = 1:20
df_formula <- compute_formula(1:20, formula = "all")
head(df_formula, 5)

# Ubah ke format panjang untuk plotting
df_long <- melt(df_formula, id.vars = "x",
                variable.name = "Formula", value.name = "Nilai")

warna <- c("linear" = "#2196F3", "quadratic" = "#4CAF50",
           "cubic" = "#FF9800", "exponential" = "#E91E63")

fig <- plot_ly()
for (fm in unique(df_long$Formula)) {
  d <- df_long[df_long$Formula == fm, ]
  fig <- fig %>%
    add_trace(x = d$x, y = d$Nilai, type = "scatter", mode = "lines+markers",
              name = fm,
              line   = list(color = warna[fm], width = 2.5),
              marker = list(color = warna[fm], size = 6),
              text   = paste0("<b>Formula:</b> ", fm,
                              "<br><b>x =</b> ", d$x,
                              "<br><b>f(x) =</b> ", d$Nilai),
              hoverinfo = "text")
}

fig %>% layout(
  title  = list(text = "<b>Perbandingan Empat Formula Matematika (x = 1 sampai 20)</b>",
                x = 0.5, font = list(size = 15)),
  xaxis  = list(title = "<b>Nilai x</b>", dtick = 2),
  yaxis  = list(title = "<b>f(x)</b>"),
  legend = list(title = list(text = "<b>Formula</b>"), orientation = "h",
                x = 0.5, xanchor = "center", y = -0.15),
  hovermode     = "closest",
  plot_bgcolor  = "white",
  paper_bgcolor = "white",
  margin = list(l = 60, r = 40, t = 70, b = 80)
)

library(dplyr)
library(knitr)
set.seed(42)

simulate_sales <- function(n_salesperson, days) {

  # Nested function 1: hitung diskon
  hitung_diskon <- function(amount) {
    if      (amount >= 900) return(0.20)
    else if (amount >= 600) return(0.15)
    else if (amount >= 300) return(0.10)
    else                    return(0.05)
  }

  hasil <- data.frame()

  # Nested loop: salesperson × hari
  for (sp in 1:n_salesperson) {
    kumulatif <- 0
    for (d in 1:days) {
      amount    <- round(runif(1, 100, 1000), 0)
      diskon    <- hitung_diskon(amount)
      net       <- round(amount * (1 - diskon), 2)
      kumulatif <- round(kumulatif + net, 2)

      hasil <- rbind(hasil, data.frame(
        sales_id      = paste0("SP-", sp),
        day           = d,
        sales_amount  = amount,
        discount_rate = paste0(diskon * 100, "%"),
        net_sales     = net,
        cumulative    = kumulatif
      ))
    }
  }
  return(hasil)
}

df_sales <- simulate_sales(n_salesperson = 5, days = 10)
head(df_sales, 10)

# Summary per salesperson
summary_sales <- df_sales %>%
  group_by(sales_id) %>%
  summarise(
    Total_Penjualan = sum(sales_amount),
    Total_Net       = sum(net_sales),
    Kumulatif_Akhir = max(cumulative),
    .groups = "drop"
  )
kable(summary_sales, caption = "Ringkasan Penjualan per Salesperson")

# Plot kumulatif interaktif
warna_sp <- c("#2196F3","#4CAF50","#FF9800","#E91E63","#9C27B0")
fig2 <- plot_ly()

for (i in seq_along(unique(df_sales$sales_id))) {
  sp  <- unique(df_sales$sales_id)[i]
  d   <- df_sales[df_sales$sales_id == sp, ]
  fig2 <- fig2 %>%
    add_trace(x = d$day, y = d$cumulative, type = "scatter",
              mode = "lines+markers", name = sp,
              line   = list(color = warna_sp[i], width = 2.5),
              marker = list(color = warna_sp[i], size = 7),
              text   = paste0("<b>", sp, "</b>",
                              "<br>Hari ke-", d$day,
                              "<br>Kumulatif: Rp ", format(d$cumulative, big.mark = ",")),
              hoverinfo = "text")
}

fig2 %>% layout(
  title  = list(text = "<b>Penjualan Kumulatif per Salesperson (10 Hari)</b>",
                x = 0.5, font = list(size = 15)),
  xaxis  = list(title = "<b>Hari ke-</b>", dtick = 1),
  yaxis  = list(title = "<b>Total Kumulatif (Rp)</b>"),
  legend = list(title = list(text = "<b>Salesperson</b>"),
                orientation = "h", x = 0.5, xanchor = "center", y = -0.18),
  hovermode     = "closest",
  plot_bgcolor  = "white",
  paper_bgcolor = "white",
  margin = list(l = 60, r = 40, t = 70, b = 90)
)

categorize_performance <- function(sales_amount) {
  kategori <- character(length(sales_amount))
  # Loop melalui setiap elemen vektor
  for (i in seq_along(sales_amount)) {
    val <- sales_amount[i]
    if      (val >= 850) kategori[i] <- "Excellent"
    else if (val >= 650) kategori[i] <- "Very Good"
    else if (val >= 450) kategori[i] <- "Good"
    else if (val >= 250) kategori[i] <- "Average"
    else                 kategori[i] <- "Poor"
  }
  return(kategori)
}

# Generate 200 data penjualan acak
set.seed(123)
sales_data    <- round(runif(200, 50, 1000), 0)
hasil_kategori <- categorize_performance(sales_data)

# Hitung distribusi dan persentase
level_order <- c("Poor","Average","Good","Very Good","Excellent")
df_kat <- as.data.frame(table(hasil_kategori))
names(df_kat) <- c("Kategori","Jumlah")
df_kat$Persentase <- round(df_kat$Jumlah / sum(df_kat$Jumlah) * 100, 1)
df_kat$Kategori   <- factor(df_kat$Kategori, levels = level_order)
df_kat <- df_kat[order(df_kat$Kategori), ]

kable(df_kat, caption = "Distribusi Kategori Kinerja dari 200 Data Penjualan")

warna_kat <- c("Poor" = "#EF5350","Average" = "#FF9800",
               "Good" = "#FFCA28","Very Good" = "#66BB6A","Excellent" = "#42A5F5")

# Bar Chart Interaktif
fig_bar <- plot_ly(df_kat, x = ~Kategori, y = ~Jumlah,
                   type = "bar",
                   marker = list(color = warna_kat[as.character(df_kat$Kategori)],
                                 line   = list(color = "white", width = 1.5)),
                   text  = ~paste0(Persentase, "%\n(n = ", Jumlah, ")"),
                   textposition = "outside",
                   hovertext = ~paste0("<b>Kategori: </b>", Kategori,
                                       "<br><b>Jumlah: </b>", Jumlah, " orang",
                                       "<br><b>Persentase: </b>", Persentase, "%"),
                   hoverinfo = "text") %>%
  layout(title       = list(text = "<b>Distribusi Kategori Kinerja — Bar Chart</b>",
                            x = 0.5, font = list(size = 14)),
         xaxis       = list(title = "<b>Kategori Kinerja</b>",
                            categoryorder = "array",
                            categoryarray = level_order),
         yaxis       = list(title = "<b>Jumlah Karyawan</b>"),
         plot_bgcolor  = "white",
         paper_bgcolor = "white",
         margin = list(l = 50, r = 30, t = 60, b = 60),
         showlegend = FALSE)

# Pie Chart Interaktif
fig_pie <- plot_ly(df_kat, labels = ~Kategori, values = ~Jumlah,
                   type   = "pie",
                   marker = list(colors = warna_kat[as.character(df_kat$Kategori)],
                                 line   = list(color = "white", width = 2)),
                   textinfo     = "label+percent",
                   hovertext    = ~paste0("<b>", Kategori, "</b>",
                                          "<br>Jumlah: ", Jumlah, " orang",
                                          "<br>Persentase: ", Persentase, "%"),
                   hoverinfo    = "text",
                   textfont     = list(size = 13)) %>%
  layout(title         = list(text = "<b>Proporsi Kategori Kinerja — Pie Chart</b>",
                              x = 0.5, font = list(size = 14)),
         plot_bgcolor  = "white",
         paper_bgcolor = "white",
         legend        = list(orientation = "v", x = 1.0, y = 0.5),
         margin = list(l = 20, r = 20, t = 60, b = 20))

fig_bar

fig_pie

set.seed(99)

generate_company_data <- function(n_company, n_employees) {
  dept   <- c("Finance","HR","IT","Marketing","Operations")
  hasil  <- data.frame()

  # Nested loop: perusahaan × karyawan
  for (co in 1:n_company) {
    for (emp in 1:n_employees) {
      salary     <- round(runif(1, 4000, 15000), 0)
      department <- sample(dept, 1)
      perf       <- round(runif(1, 50, 100), 1)
      kpi        <- round(runif(1, 50, 100), 1)
      top        <- ifelse(kpi > 90, "Ya", "Tidak")   # conditional logic

      hasil <- rbind(hasil, data.frame(
        company_id        = paste0("Perusahaan-", co),
        employee_id       = paste0("E", co, "_", emp),
        salary            = salary,
        department        = department,
        performance_score = perf,
        KPI_score         = kpi,
        top_performer     = top
      ))
    }
  }
  return(hasil)
}

# Jalankan: 4 perusahaan × 30 karyawan
df_company <- generate_company_data(n_company = 4, n_employees = 30)

# Summary per perusahaan
summary_co <- df_company %>%
  group_by(company_id) %>%
  summarise(
    Avg_Salary      = round(mean(salary), 0),
    Avg_Performance = round(mean(performance_score), 2),
    Max_KPI         = max(KPI_score),
    Top_Performers  = sum(top_performer == "Ya"),
    .groups = "drop"
  )

kable(summary_co, caption = "Summary per Perusahaan (4 Perusahaan × 30 Karyawan)")

warna_co <- c("#2196F3","#4CAF50","#FF9800","#E91E63")

# Plot 1: Rata-rata gaji
fig_gaji <- plot_ly(summary_co, x = ~company_id, y = ~Avg_Salary,
                    type = "bar",
                    marker = list(color = warna_co,
                                  line = list(color = "white", width = 1.5)),
                    text     = ~paste0("Rp ", format(Avg_Salary, big.mark = ",")),
                    textposition = "outside",
                    hovertext = ~paste0("<b>", company_id, "</b>",
                                        "<br>Avg Salary: Rp ",
                                        format(Avg_Salary, big.mark = ",")),
                    hoverinfo = "text") %>%
  layout(title = list(text = "<b>Rata-rata Gaji per Perusahaan</b>",
                      x = 0.5, font = list(size = 13)),
         xaxis = list(title = ""), yaxis = list(title = "<b>Gaji (Rp)</b>"),
         plot_bgcolor = "white", paper_bgcolor = "white",
         showlegend = FALSE, margin = list(t = 60, b = 50))

# Plot 2: Jumlah top performer
fig_top <- plot_ly(summary_co, x = ~company_id, y = ~Top_Performers,
                   type = "bar",
                   marker = list(color = warna_co,
                                 line = list(color = "white", width = 1.5)),
                   text     = ~paste0(Top_Performers, " orang"),
                   textposition = "outside",
                   hovertext = ~paste0("<b>", company_id, "</b>",
                                       "<br>Top Performer (KPI > 90): ",
                                       Top_Performers, " orang"),
                   hoverinfo = "text") %>%
  layout(title = list(text = "<b>Jumlah Top Performer (KPI > 90)</b>",
                      x = 0.5, font = list(size = 13)),
         xaxis = list(title = ""), yaxis = list(title = "<b>Jumlah Karyawan</b>"),
         plot_bgcolor = "white", paper_bgcolor = "white",
         showlegend = FALSE, margin = list(t = 60, b = 50))

fig_gaji

fig_top

set.seed(2024)

monte_carlo_pi <- function(n_points) {
  x <- runif(n_points, -1, 1)
  y <- runif(n_points, -1, 1)

  dalam_lingkaran <- logical(n_points)
  dalam_subkotak  <- logical(n_points)

  # Loop untuk cek setiap titik
  for (i in 1:n_points) {
    dalam_lingkaran[i] <- (x[i]^2 + y[i]^2) <= 1
    dalam_subkotak[i]  <- (x[i] >= 0 & x[i] <= 0.5 &
                            y[i] >= 0 & y[i] <= 0.5)
  }

  list(
    pi_estimasi   = round(4 * sum(dalam_lingkaran) / n_points, 6),
    prob_subkotak = round(sum(dalam_subkotak) / n_points, 4),
    df_plot       = data.frame(
      x      = x, y = y,
      status = ifelse(dalam_lingkaran, "Dalam Lingkaran", "Luar Lingkaran")
    )
  )
}

hasil_mc <- monte_carlo_pi(5000)
cat("=== Hasil Monte Carlo Simulation (n = 5.000 titik) ===\n")

cat("Estimasi π         :", hasil_mc$pi_estimasi, "\n")

cat("Nilai π sebenarnya :", round(pi, 6), "\n")

cat("Selisih            :", round(abs(pi - hasil_mc$pi_estimasi), 6), "\n")

cat("Prob. sub-kotak    :", hasil_mc$prob_subkotak,
    "(teoritis: 0.0625)\n")

# Ambil 2000 titik untuk plot agar tidak terlalu berat
df_plot <- hasil_mc$df_plot[sample(nrow(hasil_mc$df_plot), 2000), ]

fig_mc <- plot_ly(df_plot, x = ~x, y = ~y, color = ~status,
                  colors = c("Dalam Lingkaran" = "#4CAF50",
                             "Luar Lingkaran"  = "#EF5350"),
                  type = "scatter", mode = "markers",
                  marker = list(size = 3, opacity = 0.5),
                  text  = ~paste0("<b>Status:</b> ", status,
                                  "<br>x = ", round(x, 3),
                                  "<br>y = ", round(y, 3)),
                  hoverinfo = "text") %>%
  layout(
    title  = list(
      text = paste0("<b>Monte Carlo — Estimasi π ≈ ",
                    hasil_mc$pi_estimasi, "</b>"),
      x = 0.5, font = list(size = 14)),
    xaxis  = list(title = "<b>x</b>", range = c(-1.1, 1.1), dtick = 0.5,
                  zeroline = TRUE, zerolinecolor = "#888"),
    yaxis  = list(title = "<b>y</b>", range = c(-1.1, 1.1), dtick = 0.5,
                  zeroline = TRUE, zerolinecolor = "#888",
                  scaleanchor = "x", scaleratio = 1),
    legend = list(title  = list(text = "<b>Status Titik</b>"),
                  orientation = "h", x = 0.5, xanchor = "center", y = -0.18),
    annotations = list(
      list(x = 0.25, y = 0.55, text = "<b>Sub-kotak</b>",
           showarrow = FALSE, font = list(size = 12, color = "#7B1FA2")),
      list(x = 0.25, y = -1.0,
           text = paste0("n = 5.000 | π ≈ ", hasil_mc$pi_estimasi,
                         " | Prob sub-kotak = ", hasil_mc$prob_subkotak),
           showarrow = FALSE, font = list(size = 11, color = "#555"))
    ),
    shapes = list(
      # Lingkaran
      list(type = "circle", x0 = -1, y0 = -1, x1 = 1, y1 = 1,
           line = list(color = "#1565C0", width = 2.5)),
      # Sub-kotak
      list(type = "rect", x0 = 0, y0 = 0, x1 = 0.5, y1 = 0.5,
           line = list(color = "#7B1FA2", width = 2, dash = "dash"))
    ),
    plot_bgcolor  = "white",
    paper_bgcolor = "white",
    margin = list(l = 60, r = 40, t = 60, b = 90)
  )

fig_mc

# Fungsi normalisasi min-max (loop-based)
normalize_columns <- function(df) {
  df_norm  <- df
  num_cols <- sapply(df, is.numeric)
  for (col in names(df)[num_cols]) {
    mn <- min(df[[col]], na.rm = TRUE)
    mx <- max(df[[col]], na.rm = TRUE)
    df_norm[[col]] <- round((df[[col]] - mn) / (mx - mn), 4)
  }
  return(df_norm)
}

# Fungsi z-score standardisasi (loop-based)
z_score <- function(df) {
  df_z     <- df
  num_cols <- sapply(df, is.numeric)
  for (col in names(df)[num_cols]) {
    m <- mean(df[[col]], na.rm = TRUE)
    s <- sd(df[[col]],   na.rm = TRUE)
    df_z[[col]] <- round((df[[col]] - m) / s, 4)
  }
  return(df_z)
}

# Terapkan ke kolom numerik df_company
df_num  <- df_company[, c("salary", "performance_score", "KPI_score")]
df_norm <- normalize_columns(df_num)
df_z    <- z_score(df_num)

# Tambah fitur baru
df_company$performance_category <- categorize_performance(df_company$performance_score)
df_company$salary_bracket <- cut(
  df_company$salary,
  breaks = c(0, 6000, 9000, 12000, Inf),
  labels = c("Rendah (<6K)","Sedang (6K-9K)","Tinggi (9K-12K)","Sangat Tinggi (>12K)")
)

cat("=== Perbandingan Statistik Salary ===\n")

cat("Sebelum   → Min:", min(df_num$salary), "| Max:", max(df_num$salary),
    "| Mean:", round(mean(df_num$salary), 0), "\n")

cat("Min-Max   → Min:", min(df_norm$salary), "| Max:", max(df_norm$salary),
    "| Mean:", round(mean(df_norm$salary), 4), "\n")

cat("Z-Score   → Min:", round(min(df_z$salary), 3),
    "| Max:", round(max(df_z$salary), 3),
    "| Mean:", round(mean(df_z$salary), 6), "\n")

library(reshape2)

# Siapkan data gabungan untuk 3 kondisi
df_plot6 <- data.frame(
  Nilai   = c(df_num$salary, df_norm$salary, df_z$salary),
  Kondisi = rep(c("Sebelum (Asli)", "Min-Max [0,1]", "Z-Score [μ=0]"),
                each = nrow(df_num))
)

fig6 <- plot_ly(df_plot6, x = ~Nilai, color = ~Kondisi,
                type = "histogram",
                nbinsx = 20,
                opacity = 0.8,
                colors  = c("Sebelum (Asli)" = "#2196F3",
                             "Min-Max [0,1]"  = "#4CAF50",
                             "Z-Score [μ=0]"  = "#FF9800")) %>%
  layout(
    barmode = "overlay",
    title   = list(text = "<b>Distribusi Salary: Sebelum vs Sesudah Transformasi</b>",
                   x = 0.5, font = list(size = 14)),
    xaxis   = list(title = "<b>Nilai</b>"),
    yaxis   = list(title = "<b>Frekuensi</b>"),
    legend  = list(title = list(text = "<b>Kondisi</b>"),
                   orientation = "h", x = 0.5, xanchor = "center", y = -0.18),
    plot_bgcolor  = "white",
    paper_bgcolor = "white",
    margin = list(l = 60, r = 40, t = 70, b = 90)
  )

fig6

# Boxplot KPI berdasarkan salary bracket
fig6b <- plot_ly(df_company, x = ~salary_bracket, y = ~KPI_score,
                 type   = "box",
                 color  = ~salary_bracket,
                 colors = c("#42A5F5","#66BB6A","#FF9800","#EF5350"),
                 text   = ~paste0("<b>Bracket:</b> ", salary_bracket,
                                  "<br><b>KPI:</b> ", KPI_score),
                 hoverinfo = "text") %>%
  layout(
    title  = list(text = "<b>Distribusi KPI Score berdasarkan Salary Bracket</b>",
                  x = 0.5, font = list(size = 14)),
    xaxis  = list(title = "<b>Salary Bracket</b>"),
    yaxis  = list(title = "<b>KPI Score</b>"),
    legend = list(title = list(text = "<b>Bracket</b>")),
    plot_bgcolor  = "white",
    paper_bgcolor = "white",
    margin = list(l = 60, r = 40, t = 70, b = 80),
    showlegend = FALSE
  )

fig6b

set.seed(777)
df_dash <- generate_company_data(n_company = 6, n_employees = 60)

# Kategorisasi KPI tier dengan loop
kpi_tier <- character(nrow(df_dash))
for (i in 1:nrow(df_dash)) {
  kpi <- df_dash$KPI_score[i]
  if      (kpi >= 90) kpi_tier[i] <- "Platinum"
  else if (kpi >= 75) kpi_tier[i] <- "Gold"
  else if (kpi >= 60) kpi_tier[i] <- "Silver"
  else                kpi_tier[i] <- "Bronze"
}
df_dash$KPI_tier <- factor(kpi_tier,
                            levels = c("Bronze","Silver","Gold","Platinum"))

# Summary per perusahaan
summary_dash <- df_dash %>%
  group_by(company_id) %>%
  summarise(
    Avg_Salary     = round(mean(salary), 0),
    Avg_KPI        = round(mean(KPI_score), 2),
    Top_Performers = sum(top_performer == "Ya"),
    Total          = n(),
    .groups = "drop"
  ) %>%
  mutate(Pct_Top = paste0(round(Top_Performers / Total * 100, 1), "%"))

kable(summary_dash, caption = "Dashboard Summary — 6 Perusahaan × 60 Karyawan")

# Hitung distribusi KPI tier per perusahaan
tier_df <- df_dash %>%
  group_by(company_id, KPI_tier) %>%
  summarise(jumlah = n(), .groups = "drop")

warna_tier <- c("Bronze" = "#CD7F32","Silver" = "#A8A9AD",
                "Gold"   = "#FFD700","Platinum" = "#42A5F5")

fig7a <- plot_ly()
for (tier in c("Bronze","Silver","Gold","Platinum")) {
  d <- tier_df[tier_df$KPI_tier == tier, ]
  fig7a <- fig7a %>%
    add_trace(x = d$company_id, y = d$jumlah,
              type = "bar", name = tier,
              marker = list(color = warna_tier[tier]),
              text  = d$jumlah, textposition = "inside",
              hovertext = paste0("<b>", d$company_id, "</b>",
                                 "<br>Tier: ", tier,
                                 "<br>Jumlah: ", d$jumlah, " orang"),
              hoverinfo = "text")
}

fig7a %>% layout(
  barmode = "group",
  title   = list(text = "<b>Distribusi KPI Tier per Perusahaan</b>",
                 x = 0.5, font = list(size = 14)),
  xaxis   = list(title = ""),
  yaxis   = list(title = "<b>Jumlah Karyawan</b>"),
  legend  = list(title = list(text = "<b>KPI Tier</b>"),
                 orientation = "h", x = 0.5, xanchor = "center", y = -0.18),
  plot_bgcolor  = "white",
  paper_bgcolor = "white",
  margin = list(l = 60, r = 40, t = 70, b = 90)
)

# Scatter plot: salary vs KPI dengan regression line per perusahaan
warna_co6 <- c("#2196F3","#4CAF50","#FF9800","#E91E63","#9C27B0","#00BCD4")
perusahaan_list <- unique(df_dash$company_id)

fig7b <- plot_ly()
for (i in seq_along(perusahaan_list)) {
  co   <- perusahaan_list[i]
  d    <- df_dash[df_dash$company_id == co, ]
  fit  <- lm(KPI_score ~ salary, data = d)
  x_rng <- seq(min(d$salary), max(d$salary), length.out = 50)
  y_pred <- predict(fit, newdata = data.frame(salary = x_rng))

  # Titik scatter
  fig7b <- fig7b %>%
    add_trace(x = d$salary, y = d$KPI_score,
              type = "scatter", mode = "markers",
              name = co, legendgroup = co,
              marker = list(color = warna_co6[i], size = 6, opacity = 0.7),
              text  = paste0("<b>", co, "</b>",
                             "<br>Salary: Rp ", format(d$salary, big.mark = ","),
                             "<br>KPI: ", d$KPI_score),
              hoverinfo = "text", showlegend = TRUE)

  # Garis regresi
  fig7b <- fig7b %>%
    add_trace(x = x_rng, y = y_pred,
              type = "scatter", mode = "lines",
              name = paste0(co, " (regresi)"), legendgroup = co,
              line = list(color = warna_co6[i], width = 2, dash = "dash"),
              hoverinfo = "none", showlegend = FALSE)
}

fig7b %>% layout(
  title  = list(text = "<b>Salary vs KPI Score per Perusahaan (+ Regression Line)</b>",
                x = 0.5, font = list(size = 14)),
  xaxis  = list(title = "<b>Salary (Rp)</b>"),
  yaxis  = list(title = "<b>KPI Score</b>"),
  legend = list(title = list(text = "<b>Perusahaan</b>"),
                orientation = "h", x = 0.5, xanchor = "center", y = -0.2),
  hovermode     = "closest",
  plot_bgcolor  = "white",
  paper_bgcolor = "white",
  margin = list(l = 60, r = 40, t = 70, b = 100)
)

# Analisis departemen: rata-rata salary
dept_sum <- df_dash %>%
  group_by(department) %>%
  summarise(Avg_Salary = round(mean(salary), 0),
            Jumlah     = n(), .groups = "drop") %>%
  arrange(desc(Avg_Salary))

fig7c <- plot_ly(dept_sum,
                 x = ~reorder(department, -Avg_Salary),
                 y = ~Avg_Salary,
                 type = "bar",
                 marker = list(color = "#3F51B5",
                               line  = list(color = "white", width = 1.5)),
                 text     = ~paste0("Rp ", format(Avg_Salary, big.mark = ","),
                                    "\n(", Jumlah, " org)"),
                 textposition = "outside",
                 hovertext = ~paste0("<b>", department, "</b>",
                                     "<br>Avg Salary: Rp ",
                                     format(Avg_Salary, big.mark = ","),
                                     "<br>Jumlah: ", Jumlah, " karyawan"),
                 hoverinfo = "text") %>%
  layout(
    title  = list(text = "<b>Rata-rata Salary per Departemen (Semua Perusahaan)</b>",
                  x = 0.5, font = list(size = 14)),
    xaxis  = list(title = "<b>Departemen</b>"),
    yaxis  = list(title = "<b>Avg Salary (Rp)</b>"),
    plot_bgcolor  = "white",
    paper_bgcolor = "white",
    showlegend = FALSE,
    margin = list(l = 60, r = 40, t = 70, b = 60)
  )

fig7c

# Fungsi generate report untuk satu perusahaan
generate_report <- function(df, co_id) {
  d <- df[df$company_id == co_id, ]
  list(
    company        = co_id,
    total_karyawan = nrow(d),
    avg_salary     = round(mean(d$salary), 0),
    avg_kpi        = round(mean(d$KPI_score), 2),
    avg_perf       = round(mean(d$performance_score), 2),
    top_performers = sum(d$top_performer == "Ya"),
    dept_terbanyak = names(sort(table(d$department), decreasing = TRUE))[1],
    kpi_max        = max(d$KPI_score),
    kpi_min        = min(d$KPI_score)
  )
}

# Loop otomatis untuk semua perusahaan
co_list      <- unique(df_dash$company_id)
semua_laporan <- list()

for (co in co_list) {
  semua_laporan[[co]] <- generate_report(df_dash, co)
}

# Gabungkan ke satu data frame
df_laporan <- do.call(rbind, lapply(semua_laporan, function(x) {
  data.frame(
    Perusahaan     = x$company,
    Total_Karyawan = x$total_karyawan,
    Avg_Salary     = x$avg_salary,
    Avg_KPI        = x$avg_kpi,
    Top_Performers = x$top_performers,
    Dept_Terbanyak = x$dept_terbanyak,
    KPI_Max        = x$kpi_max,
    KPI_Min        = x$kpi_min,
    stringsAsFactors = FALSE
  )
}))

kable(df_laporan, caption = "Laporan Otomatis — 6 Perusahaan (Auto-Generated)")

# Visualisasi KPI Max vs Min vs Average per perusahaan
df_kpi_vis <- df_laporan[, c("Perusahaan","KPI_Max","KPI_Min","Avg_KPI")]

fig8 <- plot_ly()

# Area antara min dan max (shading)
for (i in 1:nrow(df_kpi_vis)) {
  fig8 <- fig8 %>%
    add_trace(x = c(df_kpi_vis$Perusahaan[i], df_kpi_vis$Perusahaan[i]),
              y = c(df_kpi_vis$KPI_Min[i], df_kpi_vis$KPI_Max[i]),
              type = "scatter", mode = "lines",
              line = list(color = "#B0BEC5", width = 8),
              hoverinfo = "none", showlegend = FALSE)
}

# KPI Max
fig8 <- fig8 %>%
  add_trace(x = df_kpi_vis$Perusahaan, y = df_kpi_vis$KPI_Max,
            type = "scatter", mode = "lines+markers",
            name = "KPI Tertinggi",
            line   = list(color = "#4CAF50", width = 2.5),
            marker = list(color = "#4CAF50", size = 10, symbol = "triangle-up"),
            text   = paste0("<b>KPI Tertinggi</b>: ", df_kpi_vis$KPI_Max),
            hoverinfo = "text")

# KPI Min
fig8 <- fig8 %>%
  add_trace(x = df_kpi_vis$Perusahaan, y = df_kpi_vis$KPI_Min,
            type = "scatter", mode = "lines+markers",
            name = "KPI Terendah",
            line   = list(color = "#EF5350", width = 2.5),
            marker = list(color = "#EF5350", size = 10, symbol = "triangle-down"),
            text   = paste0("<b>KPI Terendah</b>: ", df_kpi_vis$KPI_Min),
            hoverinfo = "text")

# KPI Average
fig8 <- fig8 %>%
  add_trace(x = df_kpi_vis$Perusahaan, y = df_kpi_vis$Avg_KPI,
            type = "scatter", mode = "lines+markers",
            name = "KPI Rata-rata",
            line   = list(color = "#2196F3", width = 2.5, dash = "dot"),
            marker = list(color = "#2196F3", size = 9, symbol = "diamond"),
            text   = paste0("<b>KPI Rata-rata</b>: ", df_kpi_vis$Avg_KPI),
            hoverinfo = "text")

fig8 %>% layout(
  title  = list(text = "<b>Rentang KPI Score per Perusahaan — Laporan Otomatis</b>",
                x = 0.5, font = list(size = 14)),
  xaxis  = list(title = "<b>Perusahaan</b>"),
  yaxis  = list(title = "<b>KPI Score</b>", range = c(45, 105)),
  legend = list(title = list(text = "<b>Metrik</b>"),
                orientation = "h", x = 0.5, xanchor = "center", y = -0.18),
  hovermode     = "closest",
  plot_bgcolor  = "white",
  paper_bgcolor = "white",
  margin = list(l = 60, r = 40, t = 70, b = 90)
)

# Ekspor laporan ke CSV
write.csv(df_laporan, "laporan_otomatis_perusahaan.csv", row.names = FALSE)

cat("✅ File 'laporan_otomatis_perusahaan.csv' berhasil diekspor!\n\n")

cat("=== Ringkasan Eksekusi Automated Report ===\n")

cat("Total perusahaan diproses  :", nrow(df_laporan), "\n")

cat("Total karyawan seluruh co. :", sum(df_laporan$Total_Karyawan), "\n")

cat("Rata-rata top performer/co.:", round(mean(df_laporan$Top_Performers), 1), "orang\n")

cat("Avg KPI tertinggi (co.)    :", df_laporan$Perusahaan[which.max(df_laporan$Avg_KPI)],
    "→", max(df_laporan$Avg_KPI), "\n")

cat("Avg KPI terendah (co.)     :", df_laporan$Perusahaan[which.min(df_laporan$Avg_KPI)],
    "→", min(df_laporan$Avg_KPI), "\n")