# Buat data check sheet
check_sheet <- data.frame(
  No = 1:10,
  Hari = c("Senin", "Senin", "Selasa", "Rabu", "Rabu", "Kamis", "Jumat", "Sabtu", "Sabtu", "Minggu"),
  Nama_Pasien = c("Pasien A", "Pasien B", "Pasien C", "Pasien D", "Pasien E", 
                  "Pasien F", "Pasien G", "Pasien H", "Pasien I", "Pasien J"),
  Jenis_Keluhan = c("Kebersihan ruang tunggu",
                    "Waktu tunggu terlalu lama",
                    "Keterlambatan penanganan IGD",
                    "Petugas kurang ramah",
                    "Kurangnya informasi prosedur",
                    "Tidak tersedia obat",
                    "Kesalahan administrasi",
                    "Kamar mandi tidak bersih",
                    "Antrian tidak tertib",
                    "Peralatan medis tidak lengkap")
)

# Tampilkan tabel
print(check_sheet)

# Load library

library(qcc)

# Data suhu tubuh pasien per jam
suhu <- c(36.7, 37.1, 37.5, 38.2, 37.8, 36.9, 37.0, 38.5)

# Buat data dalam format matrix (subgroup size = 1)
data_matrix <- matrix(suhu, ncol = 1)

# Buat Control Chart
qcc(data_matrix, 
    type = "xbar.one", 
    title = "Control Chart Suhu Tubuh Pasien per Jam di IGD", 
    xlab = "Jam ke-", 
    ylab = "Suhu Tubuh (°C)",
    center = 37.3,               # CL (Center Line)
    std.dev = (38.5 - 36.1)/6)   # Estimasi standar deviasi dari UCL dan LCL (±3σ)

# Load Library
library(DiagrammeR)

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

  node [shape=plaintext, fontname = Helvetica]

  # Efek utama
  effect [label='Keterlambatan Pelayanan Pasien', fontsize=16, fontcolor=black]

  # Kategori utama
  Man       -> effect
  Method    -> effect
  Machine   -> effect
  Material  -> effect
  Measurement -> effect
  Environment -> effect

  # Sub-kategori
  Man1 -> Man; Man2 -> Man; Man3 -> Man
  Method1 -> Method; Method2 -> Method; Method3 -> Method
  Machine1 -> Machine; Machine2 -> Machine; Machine3 -> Machine
  Material1 -> Material; Material2 -> Material; Material3 -> Material
  Measurement1 -> Measurement; Measurement2 -> Measurement; Measurement3 -> Measurement
  Environment1 -> Environment; Environment2 -> Environment; Environment3 -> Environment

  # Label sub-kategori
  Man1 [label='Staf medis tidak cukup']
  Man2 [label='Perawat kurang terlatih']
  Man3 [label='Komunikasi antar petugas lambat']

  Method1 [label='Prosedur administrasi panjang']
  Method2 [label='Tidak ada sistem triase otomatis']
  Method3 [label='Jadwal dokter tidak sinkron']

  Machine1 [label='Komputer pendaftaran lambat']
  Machine2 [label='Alat medis rusak']
  Machine3 [label='Tidak ada sistem antrian digital']

  Material1 [label='Formulir manual habis']
  Material2 [label='Label pasien tidak tersedia']
  Material3 [label='Kekurangan perlengkapan medis']

  Measurement1 [label='Tidak ada monitoring waktu tunggu']
  Measurement2 [label='Indikator performa tidak tersedia']
  Measurement3 [label='Data pasien tidak di-update']

  Environment1 [label='Ruang tunggu sesak']
  Environment2 [label='Ventilasi buruk']
  Environment3 [label='Kebisingan menghambat komunikasi']
}
")

library(DiagrammeR)

grViz("
digraph RS_Flowchart {
  graph [layout = dot, rankdir = TB]

  node [shape = box, style = filled, fillcolor = grey, fontname = Helvetica]

  // Tahapan utama
  A [label = 'Pasien Datang']
  B [label = 'Pendaftaran']
  C [label = 'Pemeriksaan Awal']
  D [label = 'Pemeriksaan Dokter']
  E [label = 'Pemeriksaan Penunjang\\n(Lab, Radiologi, dll)']
  F [label = 'Diagnosis & Rencana Tindakan']
  DEC [label = 'Rawat Jalan atau Rawat Inap?', shape = diamond, fillcolor = lightblue]

  // Jalur rawat jalan
  G1 [label = 'Pemberian Obat / Resep']
  H1 [label = 'Evaluasi Akhir oleh Dokter']
  I1 [label = 'Administrasi & Pembayaran']
  
  // Jalur rawat inap
  G2 [label = 'Tindakan Medis / Terapi']
  H2 [label = 'Observasi / Rawat Inap']
  I2 [label = 'Evaluasi Akhir oleh Dokter']
  J2 [label = 'Administrasi & Pembayaran']
  
  // Tujuan akhir
  Z [label = 'Pasien Pulang / Keluar RS']

  // Alur utama ke keputusan
  A -> B -> C -> D -> E -> F -> DEC

  // Rawat jalan
  DEC -> G1 [label = 'Rawat Jalan']
  G1 -> H1 -> I1 -> Z

  // Rawat inap
  DEC -> G2 [label = 'Rawat Inap']
  G2 -> H2 -> I2 -> J2 -> Z
}
")

# Simulasi data waktu tunggu pasien (dalam menit)
set.seed(123)  # agar hasil konsisten
waktu_tunggu <- round(rexp(10, rate = 1/5))  # rerata ~5 menit, 10 pasien

# Buat histogram waktu tunggu
hist(waktu_tunggu,
     main = "Histogram Waktu Tunggu Pasien di UGD",
     xlab = "Waktu Tunggu (menit)",
     ylab = "Jumlah Pasien",
     col = "grey",
     border = "white",
     breaks = 6)

# Tambahkan garis rata-rata
abline(v = mean(waktu_tunggu), col = "black", lwd = 2, lty = 2)
text(mean(waktu_tunggu), max(table(cut(waktu_tunggu, breaks = 6))) * 0.8,
     labels = paste("Rata-rata =", round(mean(waktu_tunggu), 1), "menit"),
     pos = 4, col = "black")

library(qcc)

# Data: penyakit dan frekuensinya
penyakit <- c("Demam", "Cedera", "Diare", "Asma", "Infeksi Saluran Pernapasan", "Hipertensi", "Nyeri Dada")
frekuensi <- c(25, 18, 10, 8, 7, 5, 4)

# Buat Pareto Chart dengan warna abu-abu
pareto.chart(frekuensi,
             names = penyakit,
             main = "Pareto Chart: Penyakit Paling Umum di UGD",
             ylab = "Frekuensi Kasus",
             col = rep("gray", length(frekuensi)))  # semua batang abu-abu

library(ggplot2)

# Data
waktu_pelayanan <- c(12, 18, 22, 27, 31, 36, 40, 44, 47, 50)
kepuasan_pasien <- c(9, 8, 7, 6, 6, 5, 4, 3, 2, 1)

# Buat data frame
data <- data.frame(WaktuPelayanan = waktu_pelayanan,
                   KepuasanPasien = kepuasan_pasien)

# Plot
ggplot(data, aes(x = WaktuPelayanan, y = KepuasanPasien)) +
  geom_point(color = "gray", size = 3) +
  labs(title = "Scatter Plot: Waktu Pelayanan vs Kepuasan Pasien",
       x = "Waktu Pelayanan (menit)",
       y = "Skor Kepuasan Pasien") +
  theme_minimal()