# Load libraries
library(dplyr)
library(plotly)
library(RColorBrewer)

# Create the dataset
set.seed(42)
n <- 31

dates <- seq.Date(from = as.Date("2025-05-01"), to = as.Date("2025-05-31"), by = "day")
sample_dates <- sample(dates, n, replace = TRUE)

Pengeluaran <- sample(c("Listrik", "Belanja Mingguan", "Gas LPG", "Transportasi", "Makan di luar", "Internet", "Pengeluaran darurat", "PDAM"), n, replace = TRUE)

Household <- tibble(
  Tanggal = sample_dates,
  Pengeluaran = Pengeluaran
)

# Summarize the number of delays by reason
pareto_data <- Household %>%
  count(Pengeluaran, sort = TRUE) %>%
  mutate(
    cum_freq = cumsum(n) / sum(n) * 100  # cumulative percentage
  )

# Create different colors for each Reason (adjusted for the length of reasons)
colors <- RColorBrewer::brewer.pal(n = min(12, length(pareto_data$Pengeluaran)), name = "Set3")

# Create Plotly Pareto Chart
fig <- plot_ly()

# Add Bar Chart (Count) - with different colors
fig <- fig %>% add_bars(
  x = ~reorder(pareto_data$Pengeluaran, -pareto_data$n),
  y = ~pareto_data$n,
  name = 'Number of Delays',
  marker = list(color = colors),
  yaxis = "y1"
)

# Add Cumulative Line
fig <- fig %>% add_lines(
  x = ~reorder(pareto_data$Pengeluaran, -pareto_data$n),
  y = ~pareto_data$cum_freq,
  name = 'Cumulative (%)',
  yaxis = "y2",
  line = list(color = 'red', dash = 'dash')
)

# Add Cut-off Line at 80%
fig <- fig %>% add_lines(
  x = ~reorder(pareto_data$Pengeluaran, -pareto_data$n),
  y = rep(80, length(pareto_data$Pengeluaran)),
  name = 'Cut-off 80%',
  yaxis = "y2",
  line = list(color = 'green', dash = 'dot')
)

# Adjust layout
fig <- fig %>% layout(
  title = "Pareto Chart - Delay Reasons",
  xaxis = list(
    title = "Delay Reasons",
    tickangle = -45   # tilt 45 degrees
  ),
  yaxis = list(title = "Number of Delays"),
  yaxis2 = list(
    title = "Cumulative (%)",
    overlaying = "y",
    side = "right",
    range = c(0, 100)
  ),
  legend = list(x = 0.8, y = 0.75),
  shapes = list(
    list(
      type = "line",
      x0 = -0.5,
      x1 = length(pareto_data$Pengeluaran) - 0.5,
      y0 = 80,
      y1 = 80,
      yref = "y2",
      line = list(color = "green", width = 2, dash = "dot")
    )
  )
)

# Show chart
fig

library(DiagrammeR)
library(rsvg)


grViz("
digraph fishbone {
  graph [layout = dot, rankdir = LR]

  # Default node styles
  node [fontname=Helvetica, fontsize=25, style=filled]

  # Central problem
  Problem [label='Delayed \\n Goods Delivery', shape=ellipse, fillcolor=lightcoral, width=5.0, height=1.2]

  # Category nodes (shared style)
  node [shape=diamond, width=2.5, height=1.0, fillcolor='#FFD700']
  A1 [label='Ekonomi']
  A2 [label='Komunikasi']
  A3 [label='Pembagian Tugas']
  A4 [label='Waktu']
  A5 [label='Pola asuh']
 

  # Reset node style for sub-categories
  node [shape=ellipse, width=2.5, height=0.6, fillcolor='#90EE90']
  A1a [label='Pengeluaran berlebih']
  A1b [label='Penghasilan tidak tetap']
  A1c [label='Tidak ada tabungan darurat']

  A2a [label='Kurang terbuka']
  A2b [label='Salah Paham']

  A3a [label='Tidak Adil']
  A3b [label='Tidak sesuai kesepakatan']

  A4a [label='Kurangnya waktu bersama']
  A4b [label='Terlalu sibuk bekerja']

  A5a [label='Perbedaan gaya asuh']
  A5b [label='Kesepakatan dalam disiplin']


  # Relationships
  A1 -> Problem
  A2 -> Problem
  A3 -> Problem
  A4 -> Problem
  A5 -> Problem

  A1a -> A1
  A1b -> A1
  A1c -> A1

  A2a -> A2
  A2b -> A2

  A3a -> A3
  A3b -> A3

  A4a -> A4
  A4b -> A4

  A5a -> A5
  A5b -> A5
}
")

# Load the plotly package
library(plotly)

# Create a dataset: sleep hours vs mood level
set.seed(42)  # For reproducibility
data <- data.frame(
  Sleep_Hours = seq(4, 10, length.out = 20),  # dari 4 hingga 10 jam tidur
  Mood_Level = seq(30, 90, length.out = 20)   # asumsi mood level antara 30-90
)

# Tambahkan noise agar korelasi mendekati 0.9
data$Mood_Level <- data$Mood_Level + rnorm(n = 20, mean = 0, sd = 5)

# Hitung koefisien korelasi
correlation_value <- cor(data$Sleep_Hours, data$Mood_Level)

# Buat scatter plot menggunakan Plotly dengan garis regresi linier
fig <- plot_ly(data, 
               x = ~Sleep_Hours, 
               y = ~Mood_Level, 
               type = 'scatter', 
               mode = 'markers',
               marker = list(color = 'blue', size = 10)) %>%
  add_lines(x = data$Sleep_Hours, 
            y = predict(lm(Mood_Level ~ Sleep_Hours, data = data)), 
            line = list(color = 'red', dash = 'solid', width = 2)) %>%
  layout(title = paste("Scatter Plot: Hubungan antara Jam Tidur dan Suasana Hati\nKorelasi: ", round(correlation_value, 2)),
         xaxis = list(title = "Jam Tidur"),
         yaxis = list(title = "Suasana Hati"))

# Tampilkan plot
fig

# Load libraries
library(plotly)
library(dplyr)

# Simulasi data berat kemasan per hari (contoh)
set.seed(1)
df <- tibble(
  Hari = 1:30,
  Berat = round(rnorm(30, mean = 500, sd = 5), 1)
)

# Tentukan batas kendali (CL, UCL, LCL)
CL <- mean(df$Berat, na.rm = TRUE)
UCL <- CL + 3 * sd(df$Berat, na.rm = TRUE)
LCL <- CL - 3 * sd(df$Berat, na.rm = TRUE)

# Tandai outlier
df <- df %>%
  mutate(Outlier = ifelse(Berat > UCL | Berat < LCL, "Ya", "Tidak"))

# Buat plot
plot_ly(df, x = ~Hari, y = ~Berat, type = 'scatter', mode = 'lines+markers',
        line = list(color = 'blue'), 
        marker = list(size = 8, color = ifelse(df$Outlier == "Ya", "red", "blue")),
        hoverinfo = 'text',
        text = ~paste("Hari:", Hari, "<br>Berat:", Berat, "gram")) %>%
  add_lines(y = rep(CL, nrow(df)), name = "CL", line = list(color = 'green', dash = 'dot')) %>%
  add_lines(y = rep(UCL, nrow(df)), name = "UCL", line = list(color = 'red', dash = 'dot')) %>%
  add_lines(y = rep(LCL, nrow(df)), name = "LCL", line = list(color = 'red', dash = 'dot')) %>%
  layout(title = "Control Chart – Berat Kemasan Produk",
         xaxis = list(title = "Hari"),
         yaxis = list(title = "Berat (gram)"),
         legend = list(orientation = 'h', x = 0.3, y = -0.2))

library(DiagrammeR)

grViz("
digraph sistem_kegiatan_rumah_tangga {

  graph [layout = dot, rankdir = TB]

  node [shape = box, style = filled, fontsize = 12]

  # Node definisi
  label1 [label = 'Mulai', shape = ellipse, color = lightgreen]
  label2 [label = 'Ingin langsung\nmasak?', shape = diamond, color = orange]
  label3 [label = 'Masak', shape = box, color = pink]
  label4 [label = 'Bangun Tidur', shape = parallelogram, color = lightblue]
  label5 [label = 'Sudah selesai\nsemua?', shape = diamond, color = orange]
  label6 [label = 'Istirahat', shape = trapezium, color = thistle]
  label7 [label = 'Selesai', shape = ellipse, color = lightgreen]

  # Edge (alur)
  label1 -> label2
  label2 -> label3 [label = 'Ya']
  label2 -> label4 [label = 'Tidak']
  label3 -> label5
  label4 -> label5
  label5 -> label7 [label = 'Ya']
  label5 -> label6 [label = 'Tidak']
  label6 -> label7
}
")