Event Organizer (Kelompok 2)

7 QC Tools

1 Dataset Event Organizer

library(dplyr)
library(DT)

# Set seed untuk hasil acak yang konsisten
set.seed(42)

# Vektor pilihan
request_types <- c("Vegetarian Meals", "Special Lighting", "Extra Security", "Custom Decorations", "VIP Seating")
request_statuses <- c("Fulfilled", "Not Fulfilled")

# Buat dataset
check_sheet <- data.frame(
  Event_ID = paste0("EVT", sprintf("%03d", 1:100)),
  Client_Name = paste0("Client_", 1:100),
  Request_Type = sample(request_types, 100, replace = TRUE),
  Request_Status = sample(request_statuses, 100, replace = TRUE),
  stringsAsFactors = FALSE
)

# Menambahkan kolom Problem dengan variasi berdasarkan jenis permintaan
check_sheet$Problem <- case_when(
  check_sheet$Request_Status == "Not Fulfilled" & check_sheet$Request_Type == "Vegetarian Meals" ~ "Vegetarian Meals not provided on time",
  check_sheet$Request_Status == "Not Fulfilled" & check_sheet$Request_Type == "Special Lighting" ~ "Special Lighting not set up",
  check_sheet$Request_Status == "Not Fulfilled" & check_sheet$Request_Type == "Extra Security" ~ "Lack of Extra Security",
  check_sheet$Request_Status == "Not Fulfilled" & check_sheet$Request_Type == "Custom Decorations" ~ "Decorations not delivered",
  check_sheet$Request_Status == "Not Fulfilled" & check_sheet$Request_Type == "VIP Seating" ~ "VIP seating arrangement missed",
  
  check_sheet$Request_Status == "Fulfilled" & check_sheet$Request_Type == "Vegetarian Meals" ~ "Vegetarian Meals delivered",
  check_sheet$Request_Status == "Fulfilled" & check_sheet$Request_Type == "Special Lighting" ~ "Special Lighting set up",
  check_sheet$Request_Status == "Fulfilled" & check_sheet$Request_Type == "Extra Security" ~ "Extra Security in place",
  check_sheet$Request_Status == "Fulfilled" & check_sheet$Request_Type == "Custom Decorations" ~ "Decorations in place",
  check_sheet$Request_Status == "Fulfilled" & check_sheet$Request_Type == "VIP Seating" ~ "VIP seating arranged",
  
  TRUE ~ "Other Issue"
)

# Tampilkan tabel interaktif dengan kolom Problem yang bervariasi
datatable(
  check_sheet,
  options = list(
    pageLength = 10,
    scrollCollapse = TRUE,
    autoWidth = TRUE
  ),
  rownames = FALSE,
  caption = htmltools::tags$caption(
    style = 'caption-side: top; text-align: left; 
             font-size: 18px; font-weight: bold;',
    'Check Sheet: Client Special Requests at Events with Detailed Problem Status'
  ),
  class = 'stripe hover compact'
)

2 Check Sheet

library(dplyr)
library(DT)

# Hitung frekuensi masing-masing jenis permintaan khusus klien
request_summary <- check_sheet %>%
  count(Request_Type, sort = TRUE) %>%
  rename(Frequency = n)

# Tampilkan dalam tabel interaktif
datatable(
  request_summary,
  options = list(
    scrollCollapse = TRUE,
    searching = FALSE,   # Hilangkan kotak pencarian
    paging = FALSE       # Hilangkan pagination
  ),
  rownames = FALSE,
  caption = htmltools::tags$caption(
    style = 'caption-side: top; text-align: left; 
             font-size: 18px; font-weight: bold;',
    'Check Sheet: Summary of Client Special Requests'
  ),
  class = 'stripe hover compact'
)

Visualization

library(dplyr)
library(plotly)

# Hitung frekuensi tiap jenis permintaan
request_summary <- check_sheet %>%
  count(Request_Type, sort = TRUE) %>%
  rename(Frequency = n)

# Buat bar chart interaktif horizontal
plot_ly(request_summary,
        x = ~Frequency,
        y = ~reorder(Request_Type, Frequency),
        type = 'bar',
        orientation = 'h',
        marker = list(
          color = ~Frequency,
          colorscale = 'Viridis',
          showscale = TRUE
        )
) %>%
  layout(
    title = list(text = "Check Sheet: Frequency of Client Special Requests", font = list(size = 18)),
    xaxis = list(title = "Frequency"),
    yaxis = list(title = "Request Type"),
    margin = list(l = 120)
  )

3 Control Chart

library(plotly)
library(dplyr)

# Buat data kehadiran
set.seed(42)
attendance_data <- data.frame(
  Event = 1:30,
  Invitations = sample(90:110, 30, replace = TRUE),
  Attendees = sample(85:115, 30, replace = TRUE)
)

# Hitung rasio kehadiran (%)
attendance_data <- attendance_data %>%
  mutate(AttendanceRate = round(Attendees / Invitations * 100, 1))

# Tentukan batas kendali
CL <- 100
UCL <- 110
LCL <- 90

# Tandai outliers
attendance_data <- attendance_data %>%
  mutate(Outlier = ifelse(AttendanceRate > UCL | AttendanceRate < LCL, "Ya", "Tidak"))

# Buat control chart
plot_ly(attendance_data, x = ~Event, y = ~AttendanceRate, type = 'scatter', mode = 'lines+markers',
        line = list(color = 'blue'),
        marker = list(size = 8, color = ifelse(attendance_data$Outlier == "Ya", "red", "blue")),
        hoverinfo = 'text',
        text = ~paste("Event:", Event,
                      "<br>Undangan:", Invitations,
                      "<br>Hadir:", Attendees,
                      "<br>Rasio Kehadiran:", AttendanceRate, "%")) %>%
  add_lines(y = rep(CL, 30), name = "CL (100%)", line = list(color = 'green', dash = 'dot')) %>%
  add_lines(y = rep(UCL, 30), name = "UCL (110%)", line = list(color = 'red', dash = 'dot')) %>%
  add_lines(y = rep(LCL, 30), name = "LCL (90%)", line = list(color = 'red', dash = 'dot')) %>%
  layout(title = "Control Chart – Attendance Rate vs Invitations",
         xaxis = list(title = "Event Ke-"),
         yaxis = list(title = "Rasio Kehadiran (%)"),
         legend = list(orientation = 'h', x = 0.3, y = -0.2))

4 Fishbone Diagram

if (!requireNamespace("DiagrammeRsvg", quietly = TRUE)) {
  install.packages("DiagrammeRsvg")
}

library(DiagrammeR)
library(DiagrammeRsvg)
library(rsvg)

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

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

  # Central problem
  Problem [label='Delayed Event Start', 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='People']
  A2 [label='Process']
  A3 [label='Equipment']
  A4 [label='Venue']
  A5 [label='External Factors']
  A6 [label='Communication']

  # Reset node style for sub-categories
  node [shape=ellipse, width=2.5, height=0.6, fillcolor='#90EE90']
  A1a [label='Late arrival of key personnel']
  A1b [label='Lack of trained staff']
  A1c [label='Unprepared staff']

  A2a [label='Unclear event timeline']
  A2b [label='Inadequate event planning']
  A2c [label='Last minute changes in schedule']

  A3a [label='Equipment malfunction']
  A3b [label='Late delivery of equipment']

  A4a [label='Venue unavailability']
  A4b [label='Venue setup issues']

  A5a [label='Traffic congestion']
  A5b [label='Bad weather']

  A6a [label='Miscommunication between event planners']
  A6b [label='Inaccurate communication of event details']

  # Relationships
  A1 -> Problem
  A2 -> Problem
  A3 -> Problem
  A4 -> Problem
  A5 -> Problem
  A6 -> Problem

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

  A2a -> A2
  A2b -> A2
  A2c -> A2

  A3a -> A3
  A3b -> A3

  A4a -> A4
  A4b -> A4

  A5a -> A5
  A5b -> A5

  A6a -> A6
  A6b -> A6
}
")

# Output directory and saving
dir.create("images/event_delays", recursive = TRUE, showWarnings = FALSE)

# Menggunakan export_svg dari DiagrammeRsvg
svg_code <- DiagrammeRsvg::export_svg(graph)
rsvg_png(charToRaw(svg_code), file = "images/event_delays/fishbone_event_start_delay.png", width = 3000, height = 3000)
rsvg_pdf(charToRaw(svg_code), file = "images/event_delays/fishbone_event_start_delay.pdf")

knitr::include_graphics("images/event_delays/fishbone_event_start_delay.png")

5 Flowchart

library(DiagrammeR)

# Create the flowchart diagram using DiagrammeR's grViz function
flowchart <- grViz("digraph event_process {
  graph [layout = dot, rankdir = TB]

  # Default node style for steps
  node [fontname = Helvetica, fontsize = 16, shape = rectangle, style = filled, fillcolor = lightblue]
  
  # Define the flowchart stages with appropriate symbols
  Start [label = 'Start', shape = ellipse, fillcolor = lightcoral]
  Plan [label = 'Plan: Identify objectives, create budget, set date', shape = rectangle]
  Decision1 [label = 'Budget approved?', shape = diamond, width = 2.5]
  Prepare [label = 'Prepare: Finalize details, book vendors, send invites', shape = rectangle]
  Decision2 [label = 'Vendors booked?', shape = diamond, width = 2.5]
  Execute [label = 'Execute: Manage the event, coordinate staff', shape = rectangle]
  Decision3 [label = 'Event successful?', shape = diamond, width = 2.5]
  Evaluate [label = 'Evaluate: Collect feedback, assess outcomes, review budget', shape = rectangle]
  End [label = 'End', shape = ellipse, fillcolor = lightcoral]
  
  # Define the flow connections between the steps
  Start -> Plan
  Plan -> Decision1
  Decision1 -> Prepare [label = 'Yes']
  Decision1 -> End [label = 'No']
  Prepare -> Decision2
  Decision2 -> Execute [label = 'Yes']
  Decision2 -> End [label = 'No']
  Execute -> Decision3
  Decision3 -> Evaluate [label = 'Yes']
  Decision3 -> End [label = 'No']
  Evaluate -> End
  
  # Add arrows for flow
  edge [arrowhead = vee]
}
")

# Display the flowchart
flowchart

6 Histogram

library(plotly)

# Generate synthetic guest arrival times data
set.seed(123)
arrival_times <- rnorm(1000, mean = 60, sd = 15)  # 1000 random arrival times (mean = 60 minutes, SD = 15 minutes)

# Calculate the density of the arrival time data
density_data <- density(arrival_times)

# Create a histogram of guest arrival times using plotly
arrival_times_plot <- plot_ly(
  x = arrival_times,
  type = 'histogram',
  marker = list(color = 'lightgreen', line = list(color = 'black', width = 1)),
  name = 'Histogram of Guest Arrival Times',
  nbinsx = 30,  # Number of bins
  opacity = 0.6,
  showlegend = TRUE
) %>%
  # Add the density curve
  add_trace(
    x = density_data$x, 
    y = density_data$y * length(arrival_times) * diff(range(arrival_times)) / 30,
    type = 'scatter',
    mode = 'lines',
    name = 'Density Curve',
    line = list(color = 'black', width = 3),
    showlegend = TRUE
  ) %>%
  layout(
    title = 'Histogram of Guest Arrival Times at Several Events',
    xaxis = list(title = 'Arrival Time (minutes)', showgrid = FALSE),
    yaxis = list(title = 'Frequency / Density', showgrid = FALSE),
    bargap = 0.1,
    plot_bgcolor = 'white',
    paper_bgcolor = 'white',
    showlegend = TRUE,
    legend = list(
      orientation = 'v',    # vertical legend
      x = 0.98,             # almost at the right edge
      xanchor = 'right',
      y = 0.98,             # almost at the top
      yanchor = 'top',
      bgcolor = 'rgba(255,255,255,0.8)',  # semi-transparent background
      bordercolor = 'black',
      borderwidth = 0.3
    )
  )

# Show the plot
arrival_times_plot

7 Pareto Chart

library(dplyr)
library(plotly)
library(RColorBrewer)

# Summarize the number of problems by type
pareto_data <- check_sheet %>%
  count(Problem, sort = TRUE) %>%
  mutate(
    cum_freq = cumsum(n) / sum(n) * 100  # cumulative percentage
  )

# Create different colors for each Problem
colors <- brewer.pal(n = length(pareto_data$Problem), name = "Set3")

# Create Plotly Pareto Chart
fig <- plot_ly()

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

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

# Adjust layout
fig <- fig %>% layout(
  title = "Pareto Chart - Most Common Problems During Event Execution",
  xaxis = list(
    title = "Event Problems",
    tickangle = -45   # tilt 45 degrees
  ),
  yaxis = list(title = "Number of Problems"),
  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$Problem) - 0.5,
      y0 = 80,
      y1 = 80,
      yref = "y2",
      line = list(color = "green", width = 2, dash = "dot")
    )
  )
)

# Show chart
fig

8 Scatter Diagram

library(plotly)

# Create a dataset with event size (number of guests) and staff number
set.seed(42)
event_data <- data.frame(
  Event_Size = c(50, 75, 100, 125, 150, 175, 200, 225, 250, 275, 
                 300, 325, 350, 375, 400, 425, 450, 475, 500, 525),
  Staff_Number = c(10, 14, 18, 21, 25, 28, 32, 35, 38, 41, 
                   44, 48, 51, 55, 58, 62, 65, 69, 72, 75)
)

# Introduce some noise to get a correlation of around 0.9
event_data$Staff_Number <- event_data$Staff_Number + rnorm(n = 20, mean = 0, sd = 2)

# Calculate the correlation coefficient between the event size and staff number
correlation_value <- cor(event_data$Event_Size, event_data$Staff_Number)

# Create a scatter plot using Plotly with a linear regression line
fig <- plot_ly(event_data, 
               x = ~Event_Size, 
               y = ~Staff_Number, 
               type = 'scatter', 
               mode = 'markers',
               marker = list(color = 'blue', size = 10)) %>%
  add_lines(x = event_data$Event_Size, 
            y = predict(lm(Staff_Number ~ Event_Size, data = event_data)), 
            line = list(color = 'red', dash = 'solid', width = 2)) %>%
  layout(title = paste("Scatter Plot: Relationship Between Event Size and Staff Number\nCorrelation: ", round(correlation_value, 2)),
         xaxis = list(title = "Event Size (Number of Guests)"),
         yaxis = list(title = "Staff Number"))

# Show the plot
fig
---
title: "Event Organizer (Kelompok 2)"
subtitle: "7 QC Tools"
author: 
  - "Isnaini Nur Hasanah (52240005)"
  - "Nabila Aswa (52240012)"
date:  "`r format(Sys.Date(), '%B %d, %Y')`"
output:
  rmdformats::readthedown:   # https://github.com/juba/rmdformats
    self_contained: true
    thumbnails: true
    lightbox: true
    gallery: true
    number_sections: true
    lib_dir: libs
    df_print: "paged"
    code_folding: "show"
    code_download: yes
---
<p align="center">
  <img src="C:/Users/NABILA ASWA HIDAYATI/Downloads/KEL.2.jpg" />
</p>

# Dataset Event Organizer

```{r, echo= TRUE, warning=FALSE, message=FALSE}
library(dplyr)
library(DT)

# Set seed untuk hasil acak yang konsisten
set.seed(42)

# Vektor pilihan
request_types <- c("Vegetarian Meals", "Special Lighting", "Extra Security", "Custom Decorations", "VIP Seating")
request_statuses <- c("Fulfilled", "Not Fulfilled")

# Buat dataset
check_sheet <- data.frame(
  Event_ID = paste0("EVT", sprintf("%03d", 1:100)),
  Client_Name = paste0("Client_", 1:100),
  Request_Type = sample(request_types, 100, replace = TRUE),
  Request_Status = sample(request_statuses, 100, replace = TRUE),
  stringsAsFactors = FALSE
)

# Menambahkan kolom Problem dengan variasi berdasarkan jenis permintaan
check_sheet$Problem <- case_when(
  check_sheet$Request_Status == "Not Fulfilled" & check_sheet$Request_Type == "Vegetarian Meals" ~ "Vegetarian Meals not provided on time",
  check_sheet$Request_Status == "Not Fulfilled" & check_sheet$Request_Type == "Special Lighting" ~ "Special Lighting not set up",
  check_sheet$Request_Status == "Not Fulfilled" & check_sheet$Request_Type == "Extra Security" ~ "Lack of Extra Security",
  check_sheet$Request_Status == "Not Fulfilled" & check_sheet$Request_Type == "Custom Decorations" ~ "Decorations not delivered",
  check_sheet$Request_Status == "Not Fulfilled" & check_sheet$Request_Type == "VIP Seating" ~ "VIP seating arrangement missed",
  
  check_sheet$Request_Status == "Fulfilled" & check_sheet$Request_Type == "Vegetarian Meals" ~ "Vegetarian Meals delivered",
  check_sheet$Request_Status == "Fulfilled" & check_sheet$Request_Type == "Special Lighting" ~ "Special Lighting set up",
  check_sheet$Request_Status == "Fulfilled" & check_sheet$Request_Type == "Extra Security" ~ "Extra Security in place",
  check_sheet$Request_Status == "Fulfilled" & check_sheet$Request_Type == "Custom Decorations" ~ "Decorations in place",
  check_sheet$Request_Status == "Fulfilled" & check_sheet$Request_Type == "VIP Seating" ~ "VIP seating arranged",
  
  TRUE ~ "Other Issue"
)

# Tampilkan tabel interaktif dengan kolom Problem yang bervariasi
datatable(
  check_sheet,
  options = list(
    pageLength = 10,
    scrollCollapse = TRUE,
    autoWidth = TRUE
  ),
  rownames = FALSE,
  caption = htmltools::tags$caption(
    style = 'caption-side: top; text-align: left; 
             font-size: 18px; font-weight: bold;',
    'Check Sheet: Client Special Requests at Events with Detailed Problem Status'
  ),
  class = 'stripe hover compact'
)
```

# Check Sheet

```{r,echo= TRUE, warning=FALSE, message=FALSE}
library(dplyr)
library(DT)

# Hitung frekuensi masing-masing jenis permintaan khusus klien
request_summary <- check_sheet %>%
  count(Request_Type, sort = TRUE) %>%
  rename(Frequency = n)

# Tampilkan dalam tabel interaktif
datatable(
  request_summary,
  options = list(
    scrollCollapse = TRUE,
    searching = FALSE,   # Hilangkan kotak pencarian
    paging = FALSE       # Hilangkan pagination
  ),
  rownames = FALSE,
  caption = htmltools::tags$caption(
    style = 'caption-side: top; text-align: left; 
             font-size: 18px; font-weight: bold;',
    'Check Sheet: Summary of Client Special Requests'
  ),
  class = 'stripe hover compact'
)
```

**Visualization**

```{r, echo= TRUE, warning=FALSE, message=FALSE}
library(dplyr)
library(plotly)

# Hitung frekuensi tiap jenis permintaan
request_summary <- check_sheet %>%
  count(Request_Type, sort = TRUE) %>%
  rename(Frequency = n)

# Buat bar chart interaktif horizontal
plot_ly(request_summary,
        x = ~Frequency,
        y = ~reorder(Request_Type, Frequency),
        type = 'bar',
        orientation = 'h',
        marker = list(
          color = ~Frequency,
          colorscale = 'Viridis',
          showscale = TRUE
        )
) %>%
  layout(
    title = list(text = "Check Sheet: Frequency of Client Special Requests", font = list(size = 18)),
    xaxis = list(title = "Frequency"),
    yaxis = list(title = "Request Type"),
    margin = list(l = 120)
  )
```

# Control Chart

```{r, echo=TRUE, warning=FALSE, message=FALSE}

library(plotly)
library(dplyr)

# Buat data kehadiran
set.seed(42)
attendance_data <- data.frame(
  Event = 1:30,
  Invitations = sample(90:110, 30, replace = TRUE),
  Attendees = sample(85:115, 30, replace = TRUE)
)

# Hitung rasio kehadiran (%)
attendance_data <- attendance_data %>%
  mutate(AttendanceRate = round(Attendees / Invitations * 100, 1))

# Tentukan batas kendali
CL <- 100
UCL <- 110
LCL <- 90

# Tandai outliers
attendance_data <- attendance_data %>%
  mutate(Outlier = ifelse(AttendanceRate > UCL | AttendanceRate < LCL, "Ya", "Tidak"))

# Buat control chart
plot_ly(attendance_data, x = ~Event, y = ~AttendanceRate, type = 'scatter', mode = 'lines+markers',
        line = list(color = 'blue'),
        marker = list(size = 8, color = ifelse(attendance_data$Outlier == "Ya", "red", "blue")),
        hoverinfo = 'text',
        text = ~paste("Event:", Event,
                      "<br>Undangan:", Invitations,
                      "<br>Hadir:", Attendees,
                      "<br>Rasio Kehadiran:", AttendanceRate, "%")) %>%
  add_lines(y = rep(CL, 30), name = "CL (100%)", line = list(color = 'green', dash = 'dot')) %>%
  add_lines(y = rep(UCL, 30), name = "UCL (110%)", line = list(color = 'red', dash = 'dot')) %>%
  add_lines(y = rep(LCL, 30), name = "LCL (90%)", line = list(color = 'red', dash = 'dot')) %>%
  layout(title = "Control Chart – Attendance Rate vs Invitations",
         xaxis = list(title = "Event Ke-"),
         yaxis = list(title = "Rasio Kehadiran (%)"),
         legend = list(orientation = 'h', x = 0.3, y = -0.2))
```

# Fishbone Diagram

```{r, echo=TRUE, warning=FALSE, message=FALSE}

if (!requireNamespace("DiagrammeRsvg", quietly = TRUE)) {
  install.packages("DiagrammeRsvg")
}

library(DiagrammeR)
library(DiagrammeRsvg)
library(rsvg)

graph <- grViz("
digraph fishbone {
  graph [layout = dot, rankdir = LR]

  # Default node styles
  node [fontname=Helvetica, fontsize=25, style=filled]

  # Central problem
  Problem [label='Delayed Event Start', 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='People']
  A2 [label='Process']
  A3 [label='Equipment']
  A4 [label='Venue']
  A5 [label='External Factors']
  A6 [label='Communication']

  # Reset node style for sub-categories
  node [shape=ellipse, width=2.5, height=0.6, fillcolor='#90EE90']
  A1a [label='Late arrival of key personnel']
  A1b [label='Lack of trained staff']
  A1c [label='Unprepared staff']

  A2a [label='Unclear event timeline']
  A2b [label='Inadequate event planning']
  A2c [label='Last minute changes in schedule']

  A3a [label='Equipment malfunction']
  A3b [label='Late delivery of equipment']

  A4a [label='Venue unavailability']
  A4b [label='Venue setup issues']

  A5a [label='Traffic congestion']
  A5b [label='Bad weather']

  A6a [label='Miscommunication between event planners']
  A6b [label='Inaccurate communication of event details']

  # Relationships
  A1 -> Problem
  A2 -> Problem
  A3 -> Problem
  A4 -> Problem
  A5 -> Problem
  A6 -> Problem

  A1a -> A1
  A1b -> A1
  A1c -> A1

  A2a -> A2
  A2b -> A2
  A2c -> A2

  A3a -> A3
  A3b -> A3

  A4a -> A4
  A4b -> A4

  A5a -> A5
  A5b -> A5

  A6a -> A6
  A6b -> A6
}
")

# Output directory and saving
dir.create("images/event_delays", recursive = TRUE, showWarnings = FALSE)

# Menggunakan export_svg dari DiagrammeRsvg
svg_code <- DiagrammeRsvg::export_svg(graph)
rsvg_png(charToRaw(svg_code), file = "images/event_delays/fishbone_event_start_delay.png", width = 3000, height = 3000)
rsvg_pdf(charToRaw(svg_code), file = "images/event_delays/fishbone_event_start_delay.pdf")

knitr::include_graphics("images/event_delays/fishbone_event_start_delay.png")
```

# Flowchart

```{r, echo=TRUE, warning=FALSE, message=FALSE}

library(DiagrammeR)

# Create the flowchart diagram using DiagrammeR's grViz function
flowchart <- grViz("digraph event_process {
  graph [layout = dot, rankdir = TB]

  # Default node style for steps
  node [fontname = Helvetica, fontsize = 16, shape = rectangle, style = filled, fillcolor = lightblue]
  
  # Define the flowchart stages with appropriate symbols
  Start [label = 'Start', shape = ellipse, fillcolor = lightcoral]
  Plan [label = 'Plan: Identify objectives, create budget, set date', shape = rectangle]
  Decision1 [label = 'Budget approved?', shape = diamond, width = 2.5]
  Prepare [label = 'Prepare: Finalize details, book vendors, send invites', shape = rectangle]
  Decision2 [label = 'Vendors booked?', shape = diamond, width = 2.5]
  Execute [label = 'Execute: Manage the event, coordinate staff', shape = rectangle]
  Decision3 [label = 'Event successful?', shape = diamond, width = 2.5]
  Evaluate [label = 'Evaluate: Collect feedback, assess outcomes, review budget', shape = rectangle]
  End [label = 'End', shape = ellipse, fillcolor = lightcoral]
  
  # Define the flow connections between the steps
  Start -> Plan
  Plan -> Decision1
  Decision1 -> Prepare [label = 'Yes']
  Decision1 -> End [label = 'No']
  Prepare -> Decision2
  Decision2 -> Execute [label = 'Yes']
  Decision2 -> End [label = 'No']
  Execute -> Decision3
  Decision3 -> Evaluate [label = 'Yes']
  Decision3 -> End [label = 'No']
  Evaluate -> End
  
  # Add arrows for flow
  edge [arrowhead = vee]
}
")

# Display the flowchart
flowchart
```

# Histogram

```{r, echo=TRUE, warning=FALSE, message=FALSE}

library(plotly)

# Generate synthetic guest arrival times data
set.seed(123)
arrival_times <- rnorm(1000, mean = 60, sd = 15)  # 1000 random arrival times (mean = 60 minutes, SD = 15 minutes)

# Calculate the density of the arrival time data
density_data <- density(arrival_times)

# Create a histogram of guest arrival times using plotly
arrival_times_plot <- plot_ly(
  x = arrival_times,
  type = 'histogram',
  marker = list(color = 'lightgreen', line = list(color = 'black', width = 1)),
  name = 'Histogram of Guest Arrival Times',
  nbinsx = 30,  # Number of bins
  opacity = 0.6,
  showlegend = TRUE
) %>%
  # Add the density curve
  add_trace(
    x = density_data$x, 
    y = density_data$y * length(arrival_times) * diff(range(arrival_times)) / 30,
    type = 'scatter',
    mode = 'lines',
    name = 'Density Curve',
    line = list(color = 'black', width = 3),
    showlegend = TRUE
  ) %>%
  layout(
    title = 'Histogram of Guest Arrival Times at Several Events',
    xaxis = list(title = 'Arrival Time (minutes)', showgrid = FALSE),
    yaxis = list(title = 'Frequency / Density', showgrid = FALSE),
    bargap = 0.1,
    plot_bgcolor = 'white',
    paper_bgcolor = 'white',
    showlegend = TRUE,
    legend = list(
      orientation = 'v',    # vertical legend
      x = 0.98,             # almost at the right edge
      xanchor = 'right',
      y = 0.98,             # almost at the top
      yanchor = 'top',
      bgcolor = 'rgba(255,255,255,0.8)',  # semi-transparent background
      bordercolor = 'black',
      borderwidth = 0.3
    )
  )

# Show the plot
arrival_times_plot
```

# Pareto Chart

```{r, echo=TRUE, warning=FALSE, message=FALSE}

library(dplyr)
library(plotly)
library(RColorBrewer)

# Summarize the number of problems by type
pareto_data <- check_sheet %>%
  count(Problem, sort = TRUE) %>%
  mutate(
    cum_freq = cumsum(n) / sum(n) * 100  # cumulative percentage
  )

# Create different colors for each Problem
colors <- brewer.pal(n = length(pareto_data$Problem), name = "Set3")

# Create Plotly Pareto Chart
fig <- plot_ly()

# Add Bar Chart (Count) - with different colors
fig <- fig %>% add_bars(
  x = ~reorder(pareto_data$Problem, -pareto_data$n),
  y = ~pareto_data$n,
  name = 'Number of Problems',
  marker = list(color = colors),
  yaxis = "y1"
)

# Add Cumulative Line
fig <- fig %>% add_lines(
  x = ~reorder(pareto_data$Problem, -pareto_data$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$Problem, -pareto_data$n),
  y = rep(80, length(pareto_data$Problem)),
  name = 'Cut-off 80%',
  yaxis = "y2",
  line = list(color = 'green', dash = 'dot')
)

# Adjust layout
fig <- fig %>% layout(
  title = "Pareto Chart - Most Common Problems During Event Execution",
  xaxis = list(
    title = "Event Problems",
    tickangle = -45   # tilt 45 degrees
  ),
  yaxis = list(title = "Number of Problems"),
  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$Problem) - 0.5,
      y0 = 80,
      y1 = 80,
      yref = "y2",
      line = list(color = "green", width = 2, dash = "dot")
    )
  )
)

# Show chart
fig
```

# Scatter Diagram

```{r, echo=TRUE, warning=FALSE, message=FALSE}

library(plotly)

# Create a dataset with event size (number of guests) and staff number
set.seed(42)
event_data <- data.frame(
  Event_Size = c(50, 75, 100, 125, 150, 175, 200, 225, 250, 275, 
                 300, 325, 350, 375, 400, 425, 450, 475, 500, 525),
  Staff_Number = c(10, 14, 18, 21, 25, 28, 32, 35, 38, 41, 
                   44, 48, 51, 55, 58, 62, 65, 69, 72, 75)
)

# Introduce some noise to get a correlation of around 0.9
event_data$Staff_Number <- event_data$Staff_Number + rnorm(n = 20, mean = 0, sd = 2)

# Calculate the correlation coefficient between the event size and staff number
correlation_value <- cor(event_data$Event_Size, event_data$Staff_Number)

# Create a scatter plot using Plotly with a linear regression line
fig <- plot_ly(event_data, 
               x = ~Event_Size, 
               y = ~Staff_Number, 
               type = 'scatter', 
               mode = 'markers',
               marker = list(color = 'blue', size = 10)) %>%
  add_lines(x = event_data$Event_Size, 
            y = predict(lm(Staff_Number ~ Event_Size, data = event_data)), 
            line = list(color = 'red', dash = 'solid', width = 2)) %>%
  layout(title = paste("Scatter Plot: Relationship Between Event Size and Staff Number\nCorrelation: ", round(correlation_value, 2)),
         xaxis = list(title = "Event Size (Number of Guests)"),
         yaxis = list(title = "Staff Number"))

# Show the plot
fig
```
