main_v2.Rmd

---
title: "Frontier Walls, Labour Energetics and Qin Imperial Collapse"
author:
  - Zehao Li
  - Giacomo Fontana
  - Andrew Bevan
  - Rujin Li
date: "2024-03-01"
keep_md: true
output: 
  html_document:
    toc: true
    toc_float: true
---

# Setup

```{r}
library(knitr) # For dynamic report generation
library(tidyverse) # Comprehensive suite for data manipulation and visualization
library(sf) # Handling of spatial data and geometries
library(units) # Support for measurement units, useful when dropping geometry for spatial data
library(shotGroups) # Analysis of shot groupings, including minimum bounding box calculation
library(cluster) # Various algorithms for clustering data
library(factoextra) # Visualization tools for clustering
library(ggplot2) # Core plotting system in the tidyverse
library(ggpubr) # Enhancements for ggplot2 to easily create publication-ready plots
library(compareGroups) # Facilitates comparison of groups, mainly through plots
library(ggspatial) # Adds spatial features like scale bars to ggplot2 plots
library(ggsci) # Provides scientific journal and sci-fi themed color palettes
library(kableExtra) # Enables creation and customization of HTML or LaTeX tables
library(scales) # For scaling functions for visualizations
library(ggstatsplot) # Extension of ggplot2 for creating statistical plots easily
library(statsExpressions) # Simplifies the process of creating expressions with statistical details

# Custom theme definition for ggplot2 plots
mytheme <- theme(line = element_blank(), # Remove line elements
                 panel.grid.major = element_blank(), # No major grid lines
                 panel.grid.minor = element_blank(), # No minor grid lines
                 panel.background = element_blank(), # Transparent background
                 axis.text.x = element_blank(), # Hide x-axis text
                 axis.text.y = element_blank(), # Hide y-axis text
                 axis.title.x = element_blank(), # Hide x-axis title
                 axis.title.y = element_blank(), # Hide y-axis title
                 plot.title = element_text(size = 8), # Small plot title
                 legend.key.height = unit(1, 'cm'), # Custom legend key height
                 legend.key.width = unit(5, 'cm'), # Custom legend key width
                 legend.title = element_text(size=10), # Custom size for legend title
                 legend.text = element_text(size = 10), # Custom size for legend text
                 legend.position="bottom", # Place legend at the bottom
                 legend.box = "horizontal", # Arrange legend items horizontally
                 plot.margin = margin(t = 0,  # Top margin
                                      r = 0,  # Right margin
                                      b = 0,  # Bottom margin
                                      l = 0)) # Left margin

# Custom scale annotation for adding to plots (like scale bars)
myscale <- annotation_scale(aes(width_hint=0.1, style="ticks", location='br'), # Aesthetic settings for the scale bar
                            line_width=1.5, # Line width of the scale bar
                            height=unit(0.6, "cm"), # Height of the scale bar
                            pad_y=unit(0.9, "cm"), # Vertical padding
                            pad_x=unit(2.1, "cm"), # Horizontal padding
                            text_cex = 1.5) # Text size multiplier
```

## Load data

```{r}
#| label: Load data
#| output: false
# Load the 'wall' shapefile into R as a spatial dataframe, and then perform a series of transformations:
wall <- st_read("Data/wall.shp") %>% # Use st_read to import the shapefile
  rename(Site = Site_ID) %>% # Rename the 'Site_ID' column to 'Site' for consistency or clarity
  mutate(ID = row_number()) # Add a new column 'ID' that contains a unique identifier for each row/feature
# After loading and transforming, replace any zero values in the 'Depth' column with NA (to handle missing or undefined depths)
wall$Depth[wall$Depth == 0] <- NA

# Load the 'wallgap' shapefile as a spatial dataframe. This dataset likely represents gaps in the wall.
# Similar to the first, it renames 'Site_ID' to 'Site' for consistency across datasets.
wallgap <- st_read("Data/gap.shp") %>%
  rename(Site = Site_ID)

# Load the 'wallfilled' shapefile as a spatial dataframe. This file likely represents areas of the wall that have been filled in.
# It also renames 'Site_ID' to 'Site', maintaining consistency with the previous datasets.
wallfilled <- st_read("Data/filled.shp") %>%
  rename(Site = Site_ID)
```

# Stone-built Wall Models Generation

```{r}
#| fig-width: 15  # This chunk option sets the default figure width to 15 inches for the output.
#| fig-height: 14 # This chunk option sets the default figure height to 14 inches for the output.

# Begin creating a plot using ggplot2 package, with 'wall' data.
fig.walls <- ggplot(wall) +
  geom_sf(size = 0.1) + # Adds spatial data to the plot. 'geom_sf' is used for simple features objects, with a line size of 0.1.
  mytheme + # Adds a custom ggplot2 theme, stored in the variable 'mytheme', to style the plot.
  myscale + # Adds custom scales for the plot, stored in the variable 'myscale'. Could adjust colors, axes, etc.
  
  # Annotates the plot with text at specified locations.
  annotate("text", x = -14, y = 4, size=7.5, label = "Hayehutong") +
  annotate("text", x = -14, y = -2, size=7.5, label = "Sichenggong") +
  annotate("text", x = -14, y = -8, size=7.5, label = "Xiniwusu") +
  annotate("text", x = -14, y = -14, size=7.5, label = "Yonghegong") +
  annotate("text", x = -14, y = -20, size=7.5, label = "Bayinnuru") # Each 'annotate' function call adds a label at specified coordinates with a given text size and label.

fig.walls # This line actually generates and displays the plot stored in 'fig.walls'.

# Saves the 'fig.walls' plot to a PNG file in the specified path with given dimensions and resolution.
ggsave("fig.walls.png", path="images", width = 25, height = 15, units = "in", dpi=600)

```

## Models

```{r}
#| fig-width: 9  # Sets the default figure width to 9 inches for the output.
#| fig-height: 7 # Sets the default figure height to 7 inches for the output (corrected to fig-height).

# Set the site of interest for the visualization.
example <- "Hayehutong"

# Create a plot 'a' showing the original model for the specified site.
a <- ggplot(data = (subset(wall, wall$Site == example))) + 
  geom_sf(size = 0.1) + # Plot spatial features with a border size of 0.1.
  mytheme + # Apply custom styling from the 'mytheme' variable.
  ggtitle(paste(example, "model")) # Title the plot with the site name and "model".

# Create a plot 'b' showing the filled model for the specified site.
b <- ggplot(data = (subset(wallfilled, wallfilled$Site == example))) +
  geom_sf(size = 0.1) + # Plot spatial features with a border size of 0.1.
  mytheme + # Apply the same custom styling.
  ggtitle(paste(example, "filled model")) # Title this plot with "filled model".

# Create a plot 'c' showing the gap model for the specified site.
c <- ggplot(data = (subset(wallgap, wallgap$Site == example))) +
  geom_sf(size = 0.1) + # Plot spatial features with a border size of 0.1.
  mytheme + # Apply the same custom styling.
  ggtitle(paste(example, "gap model")) + # Title this plot with "gap model".
  annotation_scale(aes(width_hint=0.11, style="ticks", location='br'), # Add a scale annotation to the plot.
                   line_width=1.5,
                   height=unit(0.45, "cm"),
                   pad_y=unit(0, "cm"),
                   pad_x=unit(0, "cm"),
                   text_cex = 1.5)

# Arrange the three plots vertically in a single figure.
fig.models <- ggarrange(a, b, c, nrow = 3, align = "v")

fig.models # Display the arranged figure.

# Save the arranged figure to a PNG file with specified dimensions and resolution.
ggsave("fig.models.png", path="images", width = 15, height = 7, units = "in", dpi=600)

```

## Area Calculation

```{r}
#| label: Area Calculation
#| output: false
wall$Area <- st_area(wall)   # Calculates the area of each feature in the 'wall' spatial object and assigns these values to a new column 'Area' in the 'wall' dataset.

wall <- drop_units(wall)     # Removes the units from the 'Area' values calculated above, making the 'wall' dataset easier to work with for further analysis or visualization.
```

### Area plot

```{r}
#| fig-width: 9
#| fig.height: 7
# Extract the 'Area' column from 'wall' dataset.
x <- wall$Area

# Create a vector containing the minimum and maximum values of 'Area'.
ls_val <- c(min(x), max(x))

# Define a gradient fill scale for visualization, from light gray to black, based on the 'Area' values.
myfill <- scale_fill_gradient(limits = c(min(x),max(x)), low = "gray80", high = "black",
                              na.value = "white", breaks = ls_val,
                              labels=c(round(min(x), digits=2),
                                       round(max(x), digits=2)))

# Construct the plot using 'ggplot2' and 'sf' packages.
fig.area <- ggplot(wall, aes(fill = Area)) + # Set 'Area' as the fill aesthetic.
  geom_sf(size = 0.06) + # Plot the spatial data with a border size of 0.06.
  mytheme + # Apply custom theme settings.
  myscale + # Apply custom scale settings.
  myfill + # Apply the gradient fill defined earlier.
  # Adjust the colorbar guide to match specified aesthetics.
  guides(fill = guide_colourbar(barwidth = 0.5, barheight = 2, label.position = "left"), ticks = "none") +
  theme(legend.position = c(0.935, 0.25)) + # Position the legend.
  # Annotate specific locations with labels.
  annotate("text", x = -14, y = 4, size=2.5, label = "Hayehutong") +
  annotate("text", x = -14, y = -2, size=2.5, label = "Sichenggong") +
  annotate("text", x = -14, y = -8, size=2.5, label = "Xiniwusu") +
  annotate("text", x = -14, y = -14, size=2.5, label = "Yonghegong") +
  annotate("text", x = -14, y = -20, size=2.5, label = "Bayinnuru")

# Display the plot.
fig.area
```

### Calculate rectangularity using minimum bounding box

```{r}
#| output: false
#| cache: true
# Add columns mbb_Area and index
# Initialize new columns in 'wall' with NA values
wall["mbb_Area"] <- NA
wall["Rectangularity"] <- NA

# Initialize a text progress bar to monitor the loop's progress
pb <- txtProgressBar(min=1, max=nrow(wall), style=3)

# Iterate over each row in 'wall'
for(i in 1:nrow(wall)) {
  # Extract the matrix of coordinates for the i-th feature's geometry
  mat <- wall[i, ][[3]][[1]][[1]]
  
  # Calculate the minimum bounding box (mbb) for the feature
  mbb <- getMinBBox(mat)
  
  # Calculate the area of the mbb and store it in the 'mbb_Area' column
  wall[i,]$mbb_Area <- mbb$width*mbb$height
  
  # Calculate the Rectangularity index (Area of the shape / Area of the mbb) and store it
  wall[i,]$Rectangularity <- wall[i,]$Area/wall[i,]$mbb_Area
  
  # Update the progress bar
  setTxtProgressBar(pb, i)
}

# Close the progress bar after the loop completes
close(pb)

```

### Extremes in rectangularity

```{r}
#| fig-width: 9
#| fig.height: 7
#| 
# Identifies the index of the feature with the minimum rectangularity (worst fit).
n <- which.min(wall$Rectangularity)

# Extracts the coordinates matrix for this feature to plot its original geometry.
mat <- wall[n,][[3]][[1]][[1]]

# Calculates the oriented minimum bounding box (MBB) for this feature.
mbb <- getMinBBox(mat)

# Creates a plot 'a' visualizing this feature, its points, and its MBB.
a <- ggplot(data = wall[n,]) +
  geom_sf() + # Plots the feature using simple features geometry.
  geom_point(data=(as.data.frame(mat)), aes(x = V1, y = V2), size=0.6) + # Plots the original points of the feature.
  geom_polygon(data=as.data.frame(mbb$pts), aes(x = V1, y = V2), color="black", size=0.8, fill="transparent") + # Plots the MBB as a transparent polygon.
  mytheme + # Applies a custom theme for consistent styling.
  labs(caption = c(paste("Worst rectangularity fit =", round(wall[n,]$Rectangularity, digits = 2)))) # Adds a caption with the rectangularity index.

# Identifies the index of the feature with the maximum rectangularity (best fit).
n <- which.max(wall$Rectangularity)

# The next few lines repeat the process for the best fit, similar to the worst fit.
mat <- wall[n,][[3]][[1]][[1]]
mbb <- getMinBBox(mat)

# Creates a plot 'b' for the best fit.
b <- ggplot(data = wall[n,]) +
  geom_sf() +
  geom_point(data=(as.data.frame(mat)), aes(x = V1, y = V2), size=0.6) +
  geom_polygon(data=as.data.frame(mbb$pts), aes(x = V1, y = V2), color="black", size=0.8, fill="transparent") +
  mytheme +
  labs(caption = c(paste("Best rectangularity fit =", round(wall[n,]$Rectangularity, digits = 2))))

# Combines plots 'a' and 'b' side by side.
fig.extremes <- ggarrange(a, b, ncol = 2, align = "hv") +
  theme(plot.margin = unit(c(1.5,1.5,1.5,1.5),"cm"))
fig.extremes

```

### Visualizing rectangularity

```{r}
#| fig-width: 9
#| fig.height: 7

# Extract the 'Rectangularity' column from the 'wall' dataset.
x <- wall$Rectangularity

# Determine the minimum and maximum values of rectangularity for setting the scale.
ls_val <- c(min(x), max(x))

# Create a gradient fill scale for coloring the features based on their rectangularity values.
myfill <- scale_fill_gradient(limits = c(min(x),max(x)), low = "gray90", high = "gray20",
                              na.value = "white", breaks = ls_val,
                              labels=c(round(min(x), digits=2),
                                       round(max(x), digits=2)))

# Construct the plot
fig.rectangularity <- ggplot(wall, aes(fill = Rectangularity)) + # Base plot with 'Rectangularity' as fill
  geom_sf(size = 0.06) + # Draw the features with a border size of 0.06
  mytheme + # Apply custom theme settings
  myscale + # Apply custom scale settings
  myfill + # Apply the gradient fill defined earlier
  # Adjust the colorbar guide to match specified aesthetics
  guides(fill = guide_colourbar(barwidth = 0.5, barheight = 2, label.position = "left"), ticks = "none") +
  theme(legend.position = c(0.935, 0.25)) + # Position the legend
  # Annotate specific locations with labels
  annotate("text", x = -14, y = 4, size=4.5, label = "Hayehutong") +
  annotate("text", x = -14, y = -2, size=4.5, label = "Sichenggong") +
  annotate("text", x = -14, y = -8, size=4.5, label = "Xiniwusu") +
  annotate("text", x = -14, y = -14, size=4.5, label = "Yonghegong") +
  annotate("text", x = -14, y = -20, size=4.5, label = "Bayinnuru")

# Display the plot
fig.rectangularity
```

## Gap Calculation

```{r}
#| label: Variable gap calculation 
#| output: false
#| cache: true

# Create a temporary copy of the 'wall' dataset.
tmp <- wall

# Generate a buffer around each feature in 'wall' with a specified distance.
tmp$buffer <- st_buffer(wall, dist = 0.01)

# Determine which buffered features are entirely within the 'wallfilled' dataset.
# 'st_covered_by' checks if the buffer is within 'wallfilled'; 'lengths > 0' ensures there is an overlap.
tmp$covered <- st_covered_by(tmp$buffer, wallfilled) %>% lengths > 0

# Filter 'tmp' to keep only features not at the edge, i.e., those covered by 'wallfilled'.
tmp <- subset(tmp, tmp$covered[{TRUE}])

# Initialize a 'Gap' column in 'tmp' to store calculated gap areas.
tmp["Gap"] <- NA

# Initialize a text progress bar for loop monitoring.
pb <- txtProgressBar(min=1, max=nrow(wallgap), style=3)

# Loop through each feature in 'tmp'.
for(i in 1:nrow(tmp)) {
  # Identify gaps touching the current feature using 'st_touches' and extract them.
  tmp1 <- st_touches(tmp, wallgap)[[i]]
  tmp2 <- st_sf(wallgap$geometry[tmp1])

  # Calculate the area of each identified gap.
  tmp2$area <- st_area(tmp2)

  # Sum the areas of all gaps touching the current feature and store in 'Gap'.
  tmp[i,]$Gap <- sum(tmp2$area)

  # Update progress bar.
  setTxtProgressBar(pb, i)
}

# Close the progress bar after completing the loop.
close(pb)
```

#Join gap area to data frame dataframe

```{r}
# Join the gap area information from 'tmp' back to the original 'wall' dataset.
wall <- left_join(wall, 
                  # Remove the geometry column from 'tmp' to prepare for the join.
                  st_drop_geometry(tmp) %>%
                  # Select only the 'ID' and 'Gap' columns from 'tmp' for the join.
                  dplyr::select(ID, Gap), 
                  # Specify the column ('ID') to use for matching rows between the two datasets.
                  by = "ID")

```

#Plot gap area

```{r}
#| fig-width: 7
#| fig.height: 3

# Subset the 'wall' dataset to include only rows where 'Gap' is not NA, then extract 'Gap' values.
x <- subset(wall, !is.na(Gap))$Gap

# Determine the minimum and maximum values of 'Gap' for use in the color gradient.
ls_val <- c(min(x), max(x))

# Define a color gradient for the 'Gap' values from light gray to dark gray.
myfill <- scale_fill_gradient(limits = c(min(x),max(x)), low = "gray90", high = "gray20",
                              na.value = "white", breaks = ls_val,
                              labels=c(round(min(x), digits=2),
                                       round(max(x), digits=2)))

# Construct the plot.
fig.gap <- ggplot(wall, aes(fill = Gap)) + # Base plot with 'Gap' determining the fill color.
  geom_sf(size = 0.06) + # Draw the spatial features with a border size of 0.06.
  mytheme + # Apply custom theme settings for consistent styling.
  myscale + # Apply custom scale settings for consistent scaling.
  myfill + # Apply the defined color gradient to the plot.
  # Define the appearance of the colorbar legend.
  guides(fill = guide_colourbar(barwidth = 0.5, barheight = 2, label.position = "left"), ticks = "none") +
  theme(legend.position = c(0.935, 0.25)) + # Position the legend.
  # Annotate specific locations on the map for additional context.
  annotate("text", x = -14, y = 4, size=2.5, label = "Hayehutong") +
  annotate("text", x = -14, y = -2, size=2.5, label = "Sichenggong") +
  annotate("text", x = -14, y = -8, size=2.5, label = "Xiniwusu") +
  annotate("text", x = -14, y = -14, size=2.5, label = "Yonghegong") +
  annotate("text", x = -14, y = -20, size=2.5, label = "Bayinnuru")

# Display the plot.
fig.gap


```

## General statistics

```{r}
#| label: General statistics 
#| output: false
#| cache: true 

# Prepare the dataset for total statistics by duplicating 'wall' and setting a uniform site label.
wallstats <- wall
wallstats$Site <- "Total sample" # Assign a common site label for aggregate statistics.

# Group the data by 'Site' after removing geometric data, then calculate various statistics.
wallstats <- group_by(st_drop_geometry(wallstats), Site) %>%
summarise(
  count = n(), # Count of features.
  "Area_mean" = mean(Area, na.rm = TRUE), # Mean of the Area column, excluding NA values.
  "Area_sd" = sd(Area, na.rm = TRUE), # Standard deviation of the Area column, excluding NA values.
  "Area_IQR" = IQR(Area, na.rm = TRUE), # Interquartile range of the Area column, excluding NA values.
  "Rectangularity_mean" = mean(Rectangularity, na.rm = TRUE), # Mean of Rectangularity, excluding NA values.
  "Rectangularity_sd" = sd(Rectangularity, na.rm = TRUE), # Standard deviation of Rectangularity, excluding NA values.
  "Rectangularity_IQR" = IQR(Rectangularity, na.rm = TRUE), # Interquartile range of Rectangularity, excluding NA values.
  "Gap_mean" = mean(Gap, na.rm = TRUE), # Mean of Gap areas, excluding NA values.
  "Gap_sd" = sd(Gap, na.rm = TRUE), # Standard deviation of Gap areas, excluding NA values.
  "Gap_IQR" = IQR(Gap, na.rm = TRUE), # Interquartile range of Gap areas, excluding NA values.
  "Depth_mean" = mean(Depth, na.rm = TRUE), # Mean of Depth, excluding NA values.
  "Depth_sd" = sd(Depth, na.rm = TRUE), # Standard deviation of Depth, excluding NA values.
  "Depth_IQR" = IQR(Depth, na.rm = TRUE) # Interquartile range of Depth, excluding NA values.
)

# Calculate statistics for each site separately by grouping the data without geometries.
sitestats <- group_by(st_drop_geometry(wall), Site) %>%
summarise(
  # Repeat the same set of statistical calculations for each site.
  count = n(),
  "Area_mean" = mean(Area, na.rm = TRUE),
  "Area_sd" = sd(Area, na.rm = TRUE),
  "Area_IQR" = IQR(Area, na.rm = TRUE),
  "Rectangularity_mean" = mean(Rectangularity, na.rm = TRUE),
  "Rectangularity_sd" = sd(Rectangularity, na.rm = TRUE),
  "Rectangularity_IQR" = IQR(Rectangularity, na.rm = TRUE),
  "Gap_mean" = mean(Gap, na.rm = TRUE),
  "Gap_sd" = sd(Gap, na.rm = TRUE),
  "Gap_IQR" = IQR(Gap, na.rm = TRUE),
  "Depth_mean" = mean(Depth, na.rm = TRUE),
  "Depth_sd" = sd(Depth, na.rm = TRUE),
  "Depth_IQR" = IQR(Depth, na.rm = TRUE)
)

# Combine the site-specific statistics and the overall statistics into a single data frame.
statistics <- sitestats
statistics[nrow(statistics) + 1,] = wallstats # Append the total statistics row to the 'statistics' data frame.
```

## Figure of stone blocks variability across the different sites.

```{r}
#| label: fig-variablesDistribution 
#| fig-cap: Stone blocks variability across the different sites. 
#| fig-width: 14
#| fig.height: 10
# Create a density plot for the 'Area' variable, colored by 'Site'.
a <- ggplot(wall, aes(x=Area, color=Site)) +
  geom_density() + # Adds a density plot
  scale_color_simpsons() + # Applies a color scale based on 'The Simpsons' (assumes a custom scale or package)
  theme_minimal() + # Uses a minimal theme for aesthetics
  xlab("Area") + # Label for the x-axis
  ylab("Density") # Label for the y-axis

# Create a density plot for the 'Rectangularity' variable, colored by 'Site'.
b <- ggplot(wall, aes(x=Rectangularity, color=Site)) +
  geom_density() + # Adds a density plot
  scale_color_simpsons() + # Applies the same Simpsons color scale
  theme_minimal() + # Uses the same minimal theme
  ylab(NULL) # Removes the y-axis label

# Create a density plot for the 'Gap' variable (excluding NA values), colored by 'Site'.
c <- ggplot(data=subset(wall, !is.na(Gap)), aes(x=Gap, color=Site)) +
  geom_density() + # Adds a density plot
  scale_color_simpsons() + # Applies the same Simpsons color scale
  theme_minimal() + # Uses the same minimal theme
  ylab(NULL) # Removes the y-axis label

# Arrange the three plots side by side with a common legend at the bottom.
fig.variablesDistribution <- ggarrange(a, b, c, ncol = 3, common.legend = TRUE, legend ="bottom")

# Display the arranged plots.
fig.variablesDistribution

```

## Results of the Shapiro-Wilk normality test

```{r}
#| label: tbl-normalDistribution 
#| tbl-cap: Results of the Shapiro-Wilk normality test. 
# Perform Shapiro-Wilk normality tests for 'Area' at each site.
a <- shapiro.test(wall$Area[wall$Site == "Hayehutong"])
b <- shapiro.test(wall$Area[wall$Site == "Sichenggong"])
c <- shapiro.test(wall$Area[wall$Site == "Yonghegong"])
d <- shapiro.test(wall$Area[wall$Site == "Xiniwusu"])
e <- shapiro.test(wall$Area[wall$Site == "Bayinnuru"])

# Extract and combine the test statistics (W) into a vector.
tmp1 <- unname(c(a$statistic,b$statistic,c$statistic,d$statistic,e$statistic))

# Extract and combine the p-values into a vector.
tmp2 <- unname(c(a$p.value,b$p.value,c$p.value,d$p.value,e$p.value))

# Create a data frame with the test statistics and p-values.
results <- data.frame(tmp1, tmp2)
colnames(results) <- c("w","p-value") # Rename columns for clarity.

# Set row names to correspond to the sites tested.
rownames(results) <- c("Hayehutong","Sichenggong","Yonghegong","Xiniwusu","Bayinnuru")

# Format the p-values for readability.
results$`p-value` <- format(results$`p-value`, digits = 3)

# Use 'kable' to create a formatted table and 'kable_styling' to apply styling options.
kable(results) %>%
kable_styling(bootstrap_options = c("condensed"),
              full_width = T,
              position = "left")
```

## Results of the Fligner-Killeen test of homogeneity of variances

```{r}
#| label: tbl-homogeneity
#| tbl-cap: Results of the Fligner-Killeen test of homogeneity of variances.

# Perform Fligner-Killeen test to check homogeneity of variances for 'Area' across 'Site'.
a <- fligner.test(Area ~ Site, data = wall)

# Perform Fligner-Killeen test for 'Rectangularity' across 'Site'.
b <- fligner.test(Rectangularity ~ Site, data = wall)

# Perform Fligner-Killeen test for 'Gap' across 'Site'.
c <- fligner.test(Gap ~ Site, data = wall)

# Combine the median chi-squared test statistics from each test into a vector.
tmp1 <- unname(c(a$statistic, b$statistic, c$statistic))

# Combine the degrees of freedom (df) from each test into a vector.
tmp2 <- unname(c(a$parameter, b$parameter, c$parameter))

# Combine the p-values from each test into a vector.
tmp3 <- unname(c(a$p.value, b$p.value, c$p.value))
            
# Create a data frame from the combined test statistics, degrees of freedom, and p-values.
results <- data.frame(tmp1, tmp2, tmp3)
# Rename the columns for better readability.
colnames(results) <- c("med chi-squared", "df", "p-value")
# Assign row names corresponding to the variables tested.
rownames(results) <- c("Area", "Rectangularity", "Gap")

# Format the p-values in the results for better readability.
results$`p-value` <- format(results$`p-value`, digits = 3)

# Create a formatted table of the results using 'kable' from 'knitr' and style it with 'kable_styling' from 'kableExtra'.
kable(results) %>%
  kable_styling(bootstrap_options = c("condensed"),
                full_width = T,
                position = "left")

```

## Results of the Kruskal-Wallis test and Dunn pairwise comparison

```{r}
#| label: fig-statistics
#| fig-cap: Results of the Kruskal-Wallis test and Dunn pairwise comparison.
#| fig-width: 16
#| fig.height: 8
# Drop geometry information from 'wall' to work with statistical data only.
dat <- st_drop_geometry(wall)

# Generate a box plot for 'Area' across different 'Site', including statistical analysis.
dat_res <- ggbetweenstats(data = dat, x = Site, y = Area,
                          plot.type = "box",
                          type = "nonparametric",
                          pairwise.comparisons = TRUE,  # Perform post-hoc tests if more than 2 groups
                          pairwise.display = "significant",  # Display only significant differences
                          ggsignif.args = list(textsize = 4, tip_length = 0.01),  # Customize significance labels
                          bf.message = FALSE,  # Disable Bayes Factor message
                          centrality.plotting = FALSE,  # Disable plotting of central measure
                          package = "ggsci",  # Use 'ggsci' package for aesthetics
                          palette = "springfield_simpsons",  # Simpsons theme palette
                          xlab = "")  # Remove x-axis label
a <- dat_res +
  labs(title = dat_res[["labels"]][["subtitle"]][[2]],  # Use generated subtitle as title
       subtitle = dat_res[["labels"]][["subtitle"]][[3]]) +  # Use generated subtitle
  theme_minimal() +  # Use minimal theme for plot
  theme(plot.title = element_text(size = 10), legend.position = "none")  # Customize plot title and remove legend

# For 'Rectangularity'
dat_res <- ggbetweenstats(data = dat, x = Site, y = Rectangularity,
                          plot.type = "box",
                          type = "nonparametric",
                          pairwise.comparisons = TRUE,  # Perform post-hoc tests if more than 2 groups
                          pairwise.display = "significant",  # Display only significant differences
                          ggsignif.args = list(textsize = 4, tip_length = 0.01),  # Customize significance labels
                          bf.message = FALSE,  # Disable Bayes Factor message
                          centrality.plotting = FALSE,  # Disable plotting of central measure
                          package = "ggsci",  # Use 'ggsci' package for aesthetics
                          palette = "springfield_simpsons",  # Simpsons theme palette
                          xlab = "")  # Remove x-axis label
b <- dat_res +
  labs(title = dat_res[["labels"]][["subtitle"]][[2]],  # Use generated subtitle as title
       subtitle = dat_res[["labels"]][["subtitle"]][[3]]) +  # Use generated subtitle
  theme_minimal() +  # Use minimal theme for plot
  theme(plot.title = element_text(size = 10), legend.position = "none")  # Customize plot title and remove legend

# For 'Gap', making sure to handle any NAs in 'Gap' similarly
dat_res <- ggbetweenstats(data = dat, x = Site, y = Gap,
                          plot.type = "box",
                          type = "nonparametric",
                          pairwise.comparisons = TRUE,  # Perform post-hoc tests if more than 2 groups
                          pairwise.display = "significant",  # Display only significant differences
                          ggsignif.args = list(textsize = 4, tip_length = 0.01),  # Customize significance labels
                          bf.message = FALSE,  # Disable Bayes Factor message
                          centrality.plotting = FALSE,  # Disable plotting of central measure
                          package = "ggsci",  # Use 'ggsci' package for aesthetics
                          palette = "springfield_simpsons",  # Simpsons theme palette
                          xlab = "")  # Remove x-axis label
c <- dat_res +
  labs(title = dat_res[["labels"]][["subtitle"]][[2]],  # Use generated subtitle as title
       subtitle = dat_res[["labels"]][["subtitle"]][[3]]) +  # Use generated subtitle
  theme_minimal() +  # Use minimal theme for plot
  theme(plot.title = element_text(size = 10), legend.position = "none")  # Customize plot title and remove legend
```

## Cluster calculation

```{r}
#| label: cluster calculation
#| output: false
# Create a subset of 'wall' with only ID, Area, and Rectangularity columns.
subset_wall <- wall[c("ID", "Area", "Rectangularity")]
# Remove rows with missing values in any of the specified columns.
subset_wall <- na.omit(subset_wall)
# Remove spatial geometry and units from the subset for analysis.
subset_wall <- drop_units(st_drop_geometry(subset_wall))

# Scale 'Area' and 'Rectangularity' to have zero mean and unit variance.
subset_wall <- subset_wall %>%
  mutate(Area = scale(Area)) %>%
  mutate(Rectangularity = scale(Rectangularity))

# Determine the optimal number of clusters using the silhouette method.
clust <- fviz_nbclust(subset_wall[c('Area', 'Rectangularity')], kmeans, method = "silhouette", linecolor = "gray30")
# Extract the data from the fviz_nbclust result to find the maximum silhouette score.
n_clust <- clust$data
max_cluster <- as.numeric(n_clust$clusters[which.max(n_clust$y)])

# Perform k-means clustering with the optimal number of clusters found.
Cluster <- kmeans(subset_wall[c('Area', 'Rectangularity')], centers = max_cluster, nstart = 25)

# Append the cluster assignments to the subset.
subset_wall <- subset_wall %>%
  mutate(Cluster = Cluster$cluster)

# Join the cluster assignments back to the original 'wall' dataset using 'ID' as the key.
wall <- left_join(wall, subset_wall %>%
  dplyr::select(ID, Cluster), by = "ID")
# Convert the 'Cluster' column to a factor for potential categorical analysis.
wall$Cluster = as.factor(wall$Cluster)
```

### Optimal number of clusters (a) and scatter plot of the consequent division in clusters (b)

```{r}
#| label: fig-clusterGraph
#| fig-cap: Results of the silhouette method with indicated the optimal number of clusters (a) and scatter plot of the consequent division in clusters (b).
#| fig-width: 14
#| fig.height: 10
# Define custom colors for different clusters using a manual color scale.
myfill <- scale_color_manual(values = c("#D2af81ff", "#b7bbbfff", "#1a9993ff"))

# Assuming 'clust' is a ggplot object created earlier, apply a minimal theme and title.
a <- clust +
  theme_minimal() +
  ggtitle("a") # Note: Replace "a" with a more descriptive title if necessary.

# Create a scatter plot of 'Area' vs. 'Rectangularity', colored by 'Cluster'.
b <- ggplot(wall, aes(x=Area, y=Rectangularity, color=Cluster)) +
  geom_point() + # Plot points
  myfill + # Apply the defined custom colors for clusters
  theme_minimal() + # Use a minimal theme for a clean look
  ggtitle("b") # Note: Replace "b" with a more descriptive title if necessary.

# Arrange the two plots vertically in a single figure.
fig.clusterGraph <- ggarrange(a, b, nrow = 2)

# Display the arranged figure.
fig.clusterGraph

```

```{r}
#| label: cluster statistics
#| output: false
# Prepare the dataset for total statistics by copying 'wall' and assigning a uniform label "Total sample" to all observations.
wallstats <- wall
wallstats$Cluster <- "Total sample"

# Calculate overall statistics after dropping geometry data, grouping by the newly assigned label.
wallstats <- group_by(st_drop_geometry(wallstats), Cluster) %>%
  summarise(
    count = n(),  # Calculate the total number of observations
    "Area_mean" = mean(Area, na.rm = TRUE),  # Calculate the mean of 'Area', excluding missing values
    "rectangularity_mean" = mean(Rectangularity, na.rm = TRUE),  # Calculate the mean of 'Rectangularity', excluding missing values
    "Depth_mean" = mean(Depth, na.rm = TRUE)  # Calculate the mean of 'Depth', excluding missing values
  )

# Calculate statistics for each existing cluster within the dataset.
clusterstats <- group_by(st_drop_geometry(wall), Cluster) %>%
  summarise(
    count = n(),  # Count of observations within each cluster
    "Area_mean" = mean(Area, na.rm = TRUE),  # Mean of 'Area' by cluster, excluding NA
    "rectangularity_mean" = mean(Rectangularity, na.rm = TRUE),  # Mean of 'Rectangularity' by cluster, excluding NA
    "Depth_mean" = mean(Depth, na.rm = TRUE)  # Mean of 'Depth' by cluster, excluding NA
  )

# Ensure the 'Cluster' column in 'clusterstats' is treated as character for consistent data manipulation.
clusterstats$Cluster <- as.character(clusterstats$Cluster)

# Initialize 'clusterstatistics' with the cluster-specific stats.
clusterstatistics <- clusterstats

# Append the overall statistics to the end of 'clusterstatistics', creating a comprehensive summary.
clusterstatistics[nrow(clusterstatistics) + 1,] = wallstats


```

### Plots of the identified clusters on the wall stretches

```{r}
#| label: fig-clusterPlot
#| fig-cap: Plotting of the identified clusters on the wall stretches.
#| fig-width: 14
#| fig.height: 8
# Define custom fill colors for different clusters.
myfill <- scale_fill_manual(values = c("#D2af81ff", "#b7bbbfff", "#1a9993ff"))

# Create a spatial plot with 'Cluster' determining the fill color.
fig.clusterPlot <- ggplot(wall, aes(fill = Cluster)) +
  geom_sf(size = 0.06) + # Add spatial data with specified border size.
  mytheme + # Apply a custom theme for styling.
  myscale + # Apply custom scaling settings.
  myfill + # Apply the defined custom fill colors for clusters.
  theme(legend.position = c(0.935, 0.25)) + # Set legend position.
  # Add annotations for specific locations on the map for context.
  annotate("text", x = -14, y = 4, size=7.5, label = "Hayehutong") +
  annotate("text", x = -14, y = -2, size=7.5, label = "Sichenggong") +
  annotate("text", x = -14, y = -8, size=7.5, label = "Xiniwusu") +
  annotate("text", x = -14, y = -14, size=7.5, label = "Yonghegong") +
  annotate("text", x = -14, y = -20, size=7.5, label = "Bayinnuru")

# Display the plot.
fig.clusterPlot

# Save the plot as a PNG file with specified dimensions and resolution.
ggsave("fig.clusterplot.png", path="images", width = 25, height = 15, units = "in", dpi=600)
```

### Scatter plot of depth and area distribution by site type (a) and depth distribution by cluster (b)

```{r}
#| label: fig-depth 
#| fig-cap: Scatter plot of depth and area distribution by site type (a) and depth distribution by cluster (b).
#| fig-width: 10
#| fig.height: 4.5
# Remove 'Gap' column from the dataset to avoid analysis on this variable.
wall_noNull <- wall
wall_noNull$Gap <- NULL

# Define custom fill colors for clusters and custom color scales for sites.
myfill <- scale_fill_manual(values = c("#D2af81ff", "#b7bbbfff", "#1a9993ff", "#c6fe39", "#3963fe"))
mycol <- scale_color_manual(values = c("#FED439FF", "#709AE1FF", "#8A9197FF", "#ff9993", "#9993ff"))

# Plot 'a': Scatter plot of Area vs. Depth, colored by 'Site'.
a <- ggplot(na.omit(wall_noNull), aes(x=Area, y=Depth)) +
  geom_point(aes(color=Site)) +  # Points colored by 'Site'.
  geom_smooth(method=lm, color="black") +  # Linear regression line across the scatter plot.
  theme(legend.position = "none") +  # Remove legend for cleaner presentation.
  mycol +  # Apply custom color scale for sites.
  theme_minimal() +  # Use a minimal theme for a clean look.
  ggtitle("Area vs. Depth by Site")  # Title for the plot.

# Plot 'b': Boxplot of Depth by 'Cluster'.
b <- ggplot(na.omit(wall_noNull), aes(x=Cluster, y=Depth, fill=Cluster)) +
  geom_boxplot() +  # Boxplot showing distribution of 'Depth' by 'Cluster'.
  theme(legend.position = "none") +  # Remove legend.
  myfill +  # Apply custom fill colors for clusters.
  theme_minimal() +  # Use a minimal theme.
  ylab(NULL) +  # Remove y-axis label for a cleaner look.
  ggtitle("Depth Distribution by Cluster") +  # Title for the plot.
  annotate("text",
           x = 1:length(table(na.omit(wall_noNull)$Cluster)),  # X positions for annotations.
           y = aggregate(Depth ~ Cluster, na.omit(wall_noNull), median)[ , 2],  # Y positions for annotations.
           label = paste("n=", table(na.omit(wall_noNull)$Cluster)),  # Annotation text showing counts per cluster.
           vjust = -0.5, cex = 2.5)  # Adjust vertical position and size of annotation text.

# Combine the two plots side by side.
fig.depth <- ggarrange(a, b, ncol = 2)

# Display the combined figure.
fig.depth


```

### Volume by cluster

```{r}
#| label: tbl-sideSize 
#| tbl-cap: Properties of the average stone block by cluster.
# Calculate volumes using cluster-specific depth means.
wall <- wall %>%
  mutate(clustdepth = case_when(
    Cluster == 1 ~ clusterstatistics$Depth_mean[1],  # Assign mean depth for Cluster 1.
    Cluster == 2 ~ clusterstatistics$Depth_mean[2],  # Assign mean depth for Cluster 2.
    Cluster == 3 ~ clusterstatistics$Depth_mean[3]   # Assign mean depth for Cluster 3.
  ))
wall$volume <- wall$Area * wall$clustdepth  # Compute volume as Area * depth.

# Define a function to calculate the cuberoot, considering negative values.
cuberoot = function(x) {
  if(x < 0) { - (-x)^(1/3) }  # Correct cuberoot for negative numbers.
  else { x^(1/3) }  # Standard cuberoot for positive numbers.
}

# Calculate overall statistics, treating the dataset as a single "cluster".
wallstats <- wall
wallstats$Cluster <- "Total sample"
wallstats <- group_by(st_drop_geometry(wallstats), Cluster) %>%
  summarise(
    Count = n(),
    "Mean volume (m^3)" = mean(volume, na.rm = TRUE),
    "SD volume (m^3)" = sd(volume, na.rm = TRUE),
    "IQR volume (m^3)" = IQR(volume, na.rm = TRUE),
    "Average stone Side (m)" = cuberoot(mean(volume, na.rm = TRUE))  # Calculate average side length from volume.
  )

# Calculate statistics for each cluster separately.
clusterstats <- group_by(st_drop_geometry(wall), Cluster) %>%
  summarise(
    Count = n(),
    "Mean volume (m^3)" = mean(volume, na.rm = TRUE),
    "SD volume (m^3)" = sd(volume, na.rm = TRUE),
    "IQR volume (m^3)" = IQR(volume, na.rm = TRUE),
    "Average stone side (m)" = cuberoot(mean(volume, na.rm = TRUE))  # Calculate average side length from volume.
  )

# Combine the statistics for individual clusters with the overall statistics.
clusterstats$Cluster <- as.character(clusterstats$Cluster)  # Ensure 'Cluster' is character for consistency.
clusterstatistics_volume <- clusterstats
clusterstatistics_volume[nrow(clusterstatistics_volume) + 1,] = wallstats  # Append overall stats to cluster-specific stats.

# Display the combined statistics in a table with custom formatting.
kable(clusterstatistics_volume, digits = 4) %>%
  kable_styling(bootstrap_options = c("condensed"),
                full_width = T,
                position = "left")


```

## Facade volume analysis and composition by wall type

```{r}
#| label: tbl-percentage 
#| tbl-cap: Percentage of facade composition divided by blocks below x, between x and y, above y and gap area of walls of Type-A, Type-B and Type-C.
# Calculate the area of wallgap and wallfilled using sf functions and remove the units for simplicity
wallgap$Area_wallgap <- drop_units(st_area(wallgap))
wallfilled$Area_wallfilled <- drop_units(st_area(wallfilled))

# Calculate volumes for different types of wall sections, excluding specific sites for Type A
## Type-A: Exclude 'Hayehutong' and 'Yonghegong' sites
tA_wall <- subset(wall, wall$Site != "Hayehutong" & wall$Site != "Yonghegong")
tA_wallgap <- subset(wallgap, wallgap$Site != "Hayehutong" & wallgap$Site != "Yonghegong")
tA_wallfilled <- subset(wallfilled, wallfilled$Site != "Hayehutong" & wallfilled$Site != "Yonghegong")
# Calculate the percentage of gap area relative to the filled area and total volume for type A
tA_volperc_gap <- sum(tA_wallgap$Area_wallgap) * 100 / (sum(tA_wallfilled$Area_wallfilled))
tA_volgap <- sum(tA_wall$volume) * tA_volperc_gap / 100
tA_voltotal <- sum(tA_wall$volume) + tA_volgap

## Type-B: Specific calculations for 'Hayehutong' site
tB_wall <- subset(wall, wall$Site == "Hayehutong")
tB_wallgap <- subset(wallgap, wallgap$Site == "Hayehutong")
tB_wallfilled <- subset(wallfilled, wallfilled$Site == "Hayehutong")
# Calculate the percentage of gap area and total volume for type B
tB_volperc_gap <- sum(tB_wallgap$Area_wallgap) * 100 / (sum(tB_wallfilled$Area_wallfilled))
tB_volgap <- sum(tB_wall$volume) * tB_volperc_gap / 100
tB_voltotal <- sum(tB_wall$volume) + tB_volgap

## Type-C: Specific calculations for 'Yonghegong' site
tC_wall <- subset(wall, wall$Site == "Yonghegong")
tC_wallgap <- subset(wallgap, wallgap$Site == "Yonghegong")
tC_wallfilled <- subset(wallfilled, wallfilled$Site == "Yonghegong")
# Calculate the percentage of gap area and total volume for type C
tC_volperc_gap <- sum(tC_wallgap$Area_wallgap) * 100 / (sum(tC_wallfilled$Area_wallfilled))
tC_volgap <- sum(tC_wall$volume) * tC_volperc_gap / 100
tC_voltotal <- sum(tC_wall$volume) + tC_volgap

##Calculation by linear m
### type-A
tA_volperc_upto0.02 <- sum(tA_wall$volume[tA_wall$volume<=0.02])*100/tA_voltotal
tA_volperc_0.02to0.05 <- sum(tA_wall$volume[tA_wall$volume>0.02 & tA_wall$volume<=0.05])*100/tA_voltotal
tA_volperc_over0.05 <- sum(tA_wall$volume[tA_wall$volume>0.05])*100/tA_voltotal
### type-B
tB_volperc_upto0.02 <- sum(tB_wall$volume[tB_wall$volume<=0.02])*100/tB_voltotal
tB_volperc_0.02to0.05 <- sum(tB_wall$volume[tB_wall$volume>0.02 & tB_wall$volume<=0.05])*100/tB_voltotal
tB_volperc_over0.05 <- sum(tB_wall$volume[tB_wall$volume>0.05])*100/tB_voltotal
### type-C
tC_volperc_upto0.02 <- sum(tC_wall$volume[tC_wall$volume<=0.02])*100/tC_voltotal
tC_volperc_0.02to0.05 <- sum(tC_wall$volume[tC_wall$volume>0.02 & tC_wall$volume<=0.05])*100/tC_voltotal
tC_volperc_over0.05 <- sum(tC_wall$volume[tC_wall$volume>0.05])*100/tC_voltotal
### fa?ade composition matrix
composition <- matrix(1:12, nrow = 4, ncol = 3)
composition <- rbind(c(tA_volperc_upto0.02,tB_volperc_upto0.02,tC_volperc_upto0.02),
c(tA_volperc_0.02to0.05,tB_volperc_0.02to0.05,tC_volperc_0.02to0.05),
c(tA_volperc_over0.05,tB_volperc_over0.05,tC_volperc_over0.05),
c(tA_volperc_gap,tB_volperc_gap,tC_volperc_gap))
rownames(composition) <- c("upto0.02","0.02to0.05","over0.05", "gap")
colnames(composition) <- c("tA","tB","tC")
### facade composition table
compositiontab <- composition
rownames(compositiontab) <- c("Percentage of blocks up to 0.02m^3","Percentage of blocks between 0.02 and 0.05m^3", "Percentage of blocks over 0.05m^3","Gaps")
colnames(compositiontab) <- c("Type-A","Type-B","Type-C")
kable(compositiontab, digits = 4) %>%
kable_styling(bootstrap_options = c("condensed"),
full_width = T,
position = "left")
```

### Wall volumes by block

```{r}
#| label: tbl-volume
#| tbl-cap: Type-A, Type-B and Type-C wall volumes divided by block composition.
# For Type-A: Calculate average volumes and total facade volume based on block size categories and overall area
tA_avervol_upto0.02 <- tA_voltotal / sum(tA_wallfilled$Area) * composition["upto0.02", "tA"] / 100
tA_avervol_0.02to0.05 <- tA_voltotal / sum(tA_wallfilled$Area) * composition["0.02to0.05", "tA"] / 100
tA_avervol_over0.05 <- tA_voltotal / sum(tA_wallfilled$Area) * composition["over0.05", "tA"] / 100
tA_facadevol <- tA_avervol_upto0.02 + tA_avervol_0.02to0.05 + tA_avervol_over0.05

# For Type-B: Repeat the calculation for blocks and total facade volume
tB_avervol_upto0.02 <- tB_voltotal / sum(tB_wallfilled$Area) * composition["upto0.02", "tB"] / 100
tB_avervol_0.02to0.05 <- tB_voltotal / sum(tB_wallfilled$Area) * composition["0.02to0.05", "tB"] / 100
tB_avervol_over0.05 <- tB_voltotal / sum(tB_wallfilled$Area) * composition["over0.05", "tB"] / 100
tB_facadevol <- tB_avervol_upto0.02 + tB_avervol_0.02to0.05 + tB_avervol_over0.05

# For Type-C: Repeat the calculation for blocks and total facade volume
tC_avervol_upto0.02 <- tC_voltotal / sum(tC_wallfilled$Area) * composition["upto0.02", "tC"] / 100
tC_avervol_0.02to0.05 <- tC_voltotal / sum(tC_wallfilled$Area) * composition["0.02to0.05", "tC"] / 100
tC_avervol_over0.05 <- tC_voltotal / sum(tC_wallfilled$Area) * composition["over0.05", "tC"] / 100
tC_facadevol <- tC_avervol_upto0.02 + tC_avervol_0.02to0.05 + tC_avervol_over0.05

# Create a matrix to store the calculated volumes for each type and category
stonevolume <- rbind(
  c(tA_avervol_upto0.02, tB_avervol_upto0.02, tC_avervol_upto0.02),
  c(tA_avervol_0.02to0.05, tB_avervol_0.02to0.05, tC_avervol_0.02to0.05),
  c(tA_avervol_over0.05, tB_avervol_over0.05, tC_avervol_over0.05),
  c(tA_facadevol, tB_facadevol, tC_facadevol)
)
rownames(stonevolume) <- c("upto0.02", "0.01to0.05", "over0.05", "total")
colnames(stonevolume) <- c("tA", "tB", "tC")

# Convert the matrix into a table and format it for presentation
stonevolumetab <- stonevolume
rownames(stonevolumetab) <- c("Volume of blocks up to 0.02 (m^3)", "Volume of blocks between 0.02 and 0.05 (m^3)", "Volume of blocks over 0.05 (m^3)", "Total (m^3)")
colnames(stonevolumetab) <- c("Type-A", "Type-B", "Type-C")
kable(stonevolumetab, digits = 4) %>%
  kable_styling(bootstrap_options = c("condensed"), full_width = T, position = "left")
```

### Total volume of stones and rubble for a wall section by type

```{r}
#| label: tbl-total 
#| tbl-cap: Total volume of stones and rubble for a wall section 2.2 m deep and 1 m high of Type-A, Type-B and Type-C walls.
# Define fixed dimensions for the wall section
sectiondepth <- 2.2 # Depth of the wall section in meters
sectionheight <- 1 # Height of the wall section in meters
volsection <- 1 * sectiondepth * sectionheight # Calculate volume of the section

# Calculate volumes of stones and rubble for Type-A
tA_volstones <- tA_facadevol * 2 * sectionheight # Calculate stone volume for Type-A
tA_volrubble <- volsection - tA_volstones # Calculate remaining rubble volume for Type-A

# Calculate volumes of stones and rubble for Type-B
tB_volstones <- tB_facadevol * 2 * sectionheight # Calculate stone volume for Type-B
tB_volrubble <- volsection - tB_volstones # Calculate remaining rubble volume for Type-B

# Calculate volumes of stones and rubble for Type-C
tC_volstones <- tC_facadevol * 2 * sectionheight # Calculate stone volume for Type-C
tC_volrubble <- volsection - tC_volstones # Calculate remaining rubble volume for Type-C

# Prepare matrix for the volumes of stones and rubble for each type
stonevolume <- rbind(
  c(tA_volstones, tB_volstones, tC_volstones), # Row for stone volumes
  c(tA_volrubble, tB_volrubble, tC_volrubble)  # Row for rubble volumes
)
rownames(stonevolume) <- c("volstones", "volrubble") # Assign row names
colnames(stonevolume) <- c("tA", "tB", "tC") # Assign column names for each type

# Convert the matrix to a table and apply formatting for presentation
stonevolumetab <- stonevolume # Create a table from the matrix
rownames(stonevolumetab) <- c("Stone volume (m^3)", "Rubble volume (m^3)") # Set descriptive row names
colnames(stonevolumetab) <- c("Type-A", "Type-B", "Type-C") # Set column names to indicate wall types

# Display the table using kable from knitr and apply kableExtra styling
kable(stonevolumetab, digits = 4) %>%
  kable_styling(bootstrap_options = c("condensed"),
                full_width = T,
                position = "left")


```

### Table of independent rates

```{r}
#| label: tbl-independentrates 
#| tbl-cap: Energetics rates employed in the analysis independent of wall height.
# Define the energetics index for each activity in person-hours per cubic meter (ph/m³)
in_quarrywall <- 0.09 # For quarrying wall stones
in_quarryrubble <- 0.5 # For quarrying rubble
in_leveling <- 0.3 # For leveling activities
in_buildrubbleearth <- 0.375 # For assembling rubble and earth fill

in_quarrylost <- 0.15 # 15% increase of the effective volume due to loss, not directly added to the matrix

# Construct the energetics matrix with the defined indices
energetics <- matrix(1:4, nrow = 4, ncol = 1) # Initial placeholder matrix creation, later overwritten
energetics <- rbind(
  in_quarrywall,
  in_quarryrubble,
  in_leveling,
  in_buildrubbleearth
)

# Assign meaningful row names to the energetics matrix
rownames(energetics) <- c(
  "Stone quarry (m^3/ph)",
  "Rubble quarry (m^3/ph)",
  "Leveling (m^3/ph)",
  "Assembly rubble and earth fill (m^3/ph)"
)

# Define column name for the matrix
colnames(energetics) <- "Rates"

# Display the energetics matrix as a formatted table using kable and kable_styling for aesthetics
kable(energetics, digits = 4) %>% # Use 4 decimal places for precision
  kable_styling(
    bootstrap_options = c("condensed"), # Use a condensed style for the table
    full_width = T, # Extend the table to the full width of the container
    position = "left" # Align the table to the left
  )

```

### Table of dependent rates

```{r}
#| label: tbl-dependentrates
#| tbl-cap: Energetics rates employed in the analysis dependent on wall height.
# Define the energetics index for assembling stone blocks of various sizes
a <- 4
b <- 0.3
h <- 3
w <- 2.9
in_buildstone0.02to0.05 <- 1 / (6 * (a/2 + 0.2*a*(h-1) + b/(2*w)))  # For blocks between 0.02 and 0.05 m³
in_buildstoneupto0.02 <- 1.42 * in_buildstone0.02to0.05 # For blocks up to 0.02 m³
in_buildstoneover0.05 <- 0.79 * in_buildstone0.02to0.05 # For blocks above 0.05 m³

# Create a matrix to summarize the energetics indices
energetics <- matrix(1:3, nrow = 3, ncol = 1) # Placeholder matrix, to be overwritten
energetics <- rbind(
  c(in_buildstoneupto0.02),
  c(in_buildstone0.02to0.05),
  c(in_buildstoneover0.05)
)

# Assign meaningful row names to the matrix to describe each energetics index
rownames(energetics) <- c(
  "Assembly up to 0.02 m^3 (m^3/ph)",
  "Assembly between 0.02 and 0.05 m^3 (m^3/ph)",
  "Assembly above 0.05 m^3 (m^3/ph)"
)

# Define a column name for the matrix to indicate the measurement unit
colnames(energetics) <- c("Rates (m³/ph)")

# Display the energetics matrix as a formatted table using the kable function and apply styling for aesthetics
kable(energetics, digits = 4) %>% # Use 4 decimal places for precision in displaying rates
  kable_styling(
    bootstrap_options = c("condensed"), # Use a condensed styling option for the table
    full_width = T, # Extend the table to the full width of the container
    position = "left" # Align the table to the left
  )

```

## Table of costs by type

```{r}
#| label: tbl-costs 
#| tbl-cap: Costs estimated for building wall Type-A, Type-B and Type-C expressed as a linear rate of ph per m. 
# Set wall dimensions used in calculations
wallheight <- 3
wallwidth <- 2.9

## Material acquisition calculations for each wall type, incorporating quarry loss
### Type-A
tA_materialstone <- (tA_volstones + tA_volstones * in_quarrylost) * wallheight / in_quarrywall
tA_materialrubble <- tA_volrubble * wallheight / in_quarryrubble
tA_materialtotal <- tA_materialstone + tA_materialrubble

### Type-B
tB_materialstone <- (tB_volstones + tB_volstones * in_quarrylost) * wallheight / in_quarrywall
tB_materialrubble <- tB_volrubble * wallheight / in_quarryrubble
tB_materialtotal <- tB_materialstone + tB_materialrubble

### Type-C
tC_materialstone <- (tC_volstones + tC_volstones * in_quarrylost) * wallheight / in_quarrywall
tC_materialrubble <- tC_volrubble * wallheight / in_quarryrubble
tC_materialtotal <- tC_materialstone + tC_materialrubble

## Leveling the terrain calculations for each type, using a fixed energetics index
tA_leveling <- wallwidth / in_leveling
tB_leveling <- wallwidth / in_leveling
tC_leveling <- wallwidth / in_leveling

## Wall assembly calculations, including facade and fill, for each wall type
### Type-A
tA_assemblyfacade <- (((tA_facadevol * (composition["upto0.02", "tA"] / 100)) / in_buildstoneupto0.02) +
((tA_facadevol * (composition["0.02to0.05", "tA"] / 100)) / in_buildstone0.02to0.05) +
((tA_facadevol * (composition["over0.05", "tA"] / 100)) / in_buildstoneover0.05)) * wallheight * 2
tA_assemblyfill <- tA_volrubble * wallheight / in_buildrubbleearth
tA_assemplytotal <- tA_assemblyfacade + tA_assemblyfill

### Type-B
tB_assemblyfacade <- (((tB_facadevol * (composition["upto0.02", "tB"] / 100)) / in_buildstoneupto0.02) +
((tB_facadevol * (composition["0.02to0.05", "tB"] / 100)) / in_buildstone0.02to0.05) +
((tB_facadevol * (composition["over0.05", "tB"] / 100)) / in_buildstoneover0.05)) * wallheight * 2
tB_assemblyfill <- tB_volrubble * wallheight / in_buildrubbleearth
tB_assemplytotal <- tB_assemblyfacade + tB_assemblyfill

### Type-C
tC_assemblyfacade <- (((tC_facadevol * (composition["upto0.02", "tC"] / 100)) / in_buildstoneupto0.02) +
((tC_facadevol * (composition["0.02to0.05", "tC"] / 100)) / in_buildstone0.02to0.05) +
((tC_facadevol * (composition["over0.05", "tC"] / 100)) / in_buildstoneover0.05)) * wallheight * 2
tC_assemblyfill <- tC_volrubble * wallheight / in_buildrubbleearth
tC_assemplytotal <- tC_assemblyfacade + tC_assemblyfill

## Total construction effort calculations for each type
tA_total <- tA_materialtotal + tA_leveling + tA_assemplytotal
tB_total <- tB_materialtotal + tB_leveling + tB_assemplytotal
tC_total <- tC_materialtotal + tC_leveling + tC_assemplytotal

## Calculate mean values for comparison across types
meanmaterialstone <- mean(c(tA_materialstone, tB_materialstone, tC_materialstone))
meanmaterialrubble <- mean(c(tA_materialrubble, tB_materialrubble, tC_materialrubble))
meanleveling <- mean(c(tA_leveling, tB_leveling, tC_leveling))
meanassemblyfacade <- mean(c(tA_assemblyfacade, tB_assemblyfacade, tC_assemblyfacade))
meanassemblyfill <- mean(c(tA_assemblyfill, tB_assemblyfill, tC_assemblyfill))
meantotal <- mean(c(tA_total, tB_total, tC_total))

## Construct and display an energetic matrix summarizing the efforts for material acquisition, leveling, and assembly
estimates <- rbind(
  c(tA_materialstone, tB_materialstone, tC_materialstone, meanmaterialstone),
  c(tA_materialrubble, tB_materialrubble, tC_materialrubble, meanmaterialrubble),
  c(tA_leveling, tB_leveling, tC_leveling, meanleveling),
  c(tA_assemblyfacade, tB_assemblyfacade, tC_assemblyfacade, meanassemblyfacade),
  c(tA_assemblyfill, tB_assemblyfill, tC_assemblyfill, meanassemblyfill),
  c(tA_total, tB_total, tC_total, meantotal)
)
rownames(estimates) <- c("Stone quarry (ph/m)", "Rubble quarry (ph/m)", "Leveling (ph/m)", "Assembly facade (ph/m)", "Assembly fill (ph/m)", "Total wall construction (ph/m)")
colnames(estimates) <- c("Type-A", "Type-B", "Type-C", "Average")

# Display the estimates in a styled table
kable(estimates, digits = 4) %>%
  kable_styling(bootstrap_options = c("condensed"), full_width = T, position = "left")

```

## Table of total cost in person-hour and in days

```{r}
#| label: tbl-results
#| tbl-cap: Total costs estimated in person-hour and in days, the latter considering a hypothetical 5 h a day. 
# Calculate the average total labour cost needed for wall construction in person-hours per meter
averagewalltotal <- mean(c(tA_total, tB_total, tC_total))

# Define the lengths of different wall sections in meters
qin_stonelength <- 345053
han_n_stonelength <- 8533
han_s_stonelength <- 10012

# Specify the daily working hours
workinghour <- 5

# Calculate total effort in person-hours and convert to person-days for each wall section
qin_stonetotal <- averagewalltotal * qin_stonelength / workinghour
han_n_stonetotal <- averagewalltotal * han_n_stonelength / workinghour
han_s_stonetotal <- averagewalltotal * han_s_stonelength / workinghour

# Organize results into a matrix and label appropriately for clarity
results <- matrix(1:6, nrow = 3, ncol = 2) # Initial placeholder matrix, to be overwritten
results <- rbind(
  c(averagewalltotal * qin_stonelength, qin_stonetotal),
  c(averagewalltotal * han_n_stonelength, han_n_stonetotal),
  c(averagewalltotal * han_s_stonelength, han_s_stonetotal)
)
rownames(results) <- c("Qin Wall (Stone section)", "Han north wall (Stone Section)", "Han south wall (Stone Section)")
colnames(results) <- c("Person-hour", "Person-days")

# Display the calculated results in a formatted table using kable and kableExtra for styling
kable(results, digits = 0) %>%
  kable_styling(bootstrap_options = c("condensed"), full_width = T, position = "left")

```

# Rammed Earth Wall

```{r}
#| label: tbl-earth 
# Sum the confirmed and uncertain lengths for earth sections at each site
qin_earth <- 79481
han_n_earth <- 468398
han_s_earth <- 398739
qin_unsure <- 43212
han_n_unsure <- 26544
han_s_unsure <- 0
qin_earthlength <- qin_earth + qin_unsure
han_n_earthlength <- han_n_earth + han_n_unsure
han_s_earthlength <- han_s_earth + han_s_unsure

# Calculate the volume of the wall per meter using trapezoidal formula
upper <- 1.5 # Upper width of the wall in meters
under <- 2.9 # Bottom width of the wall in meters
height <- 3 # Height of the wall in meters
wallvolume <- 1 * ((upper + under) / 2) * height # Volume formula for trapezoidal wall section

# Define the rate of earth construction in cubic meters per person-day based on experimental data
earthratehour <- 0.62 # Work rate in m^3 per person-hour, calculated from Xie et al 2021
earthrate <- earthratehour * workinghour # Convert to m^3 per person-day based on working hours

# Calculate the total volume for earth sections at each site
qin_earthvolume <- qin_earthlength * wallvolume
han_n_earthvolume <- han_n_earthlength * wallvolume
han_s_earthvolume <- han_s_earthlength * wallvolume

# Calculate the total person-days required for constructing the earth sections at each site
qin_earthtotal <- qin_earthvolume / earthrate
han_n_earthtotal <- han_n_earthvolume / earthrate
han_s_earthtotal <- han_s_earthvolume / earthrate

# Organize the results into a matrix and label appropriately for clarity
results <- matrix(1:3, nrow = 3, ncol = 1) # Initial placeholder matrix, to be overwritten
results <- rbind(
  c(qin_earthtotal),
  c(han_n_earthtotal),
  c(han_s_earthtotal)
)
rownames(results) <- c("Qin Wall (Earth section)", "Han north wall (Earth Section)", "Han south wall (Earth Section)")
colnames(results) <- c("Person-days")

# Display the calculated results in a formatted table using kable and apply styling for aesthetics
kable(results, digits = 0) %>%
  kable_styling(bootstrap_options = c("condensed"), full_width = T, position = "left")

```

# Mixed Wall

```{r}
#| label: tbl-mixed 
# Define the total lengths of mixed wall sections at each site in meters
qinstoneearthlength <- 6521
han_n_stoneearthlength <- 14822
han_s_stoneearthlength <- 110676

# Calculate the total person-days required for each mixed wall section
# The calculations incorporate average rates for material acquisition (stone), leveling, assembly (facade), and earthwork
# Dividing by working hours converts the effort into person-days
qinstoneearthtotal <- ((meanmaterialstone + meanleveling + meanassemblyfacade) * qinstoneearthlength / workinghour + (wallvolume / earthrate) * qinstoneearthlength)
han_n_stoneearthtotal <- ((meanmaterialstone + meanleveling + meanassemblyfacade) * han_n_stoneearthlength / workinghour + (wallvolume / earthrate) * han_n_stoneearthlength)
han_s_stoneearthtotal <- ((meanmaterialstone + meanleveling + meanassemblyfacade) * han_s_stoneearthlength / workinghour + (wallvolume / earthrate) * han_s_stoneearthlength)

# Organize the calculated total person-days for each mixed wall section into a matrix
results <- matrix(1:3, nrow = 3, ncol = 1) # Initial placeholder matrix, to be overwritten
results <- rbind(
  c(qinstoneearthtotal),
  c(han_n_stoneearthtotal),
  c(han_s_stoneearthtotal)
)
rownames(results) <- c("Qin Wall (mixed wall)", "Han north wall (mixed wall)", "Han south wall (mixed wall)")
colnames(results) <- c("Person-days")

# Display the results in a formatted table using kable and apply styling for better readability
kable(results, digits = 0) %>%
  kable_styling(bootstrap_options = c("condensed"), full_width = T, position = "left")

```

## Table of total labour cost of wall

```{r}
#| label: tbl-sumup
# Calculate total labour required for each wall by adding up the person-days for stone, earth, and mixed sections.
qinlabour <- qin_stonetotal + qin_earthtotal + qinstoneearthtotal
han_n_labour <- han_n_stonetotal + han_n_earthtotal + han_n_stoneearthtotal
han_s_labour <- han_s_stonetotal + han_s_earthtotal + han_s_stoneearthtotal

# Organize the labour calculations into a matrix for display. Each row represents a different wall (Qin, Han North, Han South),
# and each column represents a different type of construction within that wall (Stone wall, Earth wall, Mixed wall, and the Total labour).
results <- matrix(1:12, nrow = 3, ncol = 4) # Initial placeholder matrix, to be overwritten
results <- rbind(
  c(qin_stonetotal, qin_earthtotal, qinstoneearthtotal, qinlabour),
  c(han_n_stonetotal, han_n_earthtotal, han_n_stoneearthtotal, han_n_labour),
  c(han_s_stonetotal, han_s_earthtotal, han_s_stoneearthtotal, han_s_labour)
)
rownames(results) <- c("Qin Wall (Person days)", "Han north wall (Person days)", "Han south wall (Person days)")
colnames(results) <- c("Stone wall", "Earth wall", "Stone Earth wall", "Total")

# Display the results in a formatted table using the `kable` function.
kable(results, digits = 0) %>% # Use zero digits after the decimal point for cleaner number formatting
  kable_styling(bootstrap_options = c("condensed"), # Use a condensed table style for a more compact display
                full_width = T, # Extend the table to the full width of its container
                position = "left") # Align the table to the left side of its container

```

# Workforce on the Wall

# Beacons

```{r}
# Define the number of beacons made of stone, earth, and a combination of earth and stone for the Qin wall
qinbeacon_stone <- 353
qinbeacon_earth <- 438
qinbeacon_earthstone <- 37 + qinbeacon_stone

# Define the number of beacons for the Han north wall
han_n_beacon_stone <- 5
han_n_beacon_earth <- 2
han_n_beacon_earthstone <- 4 + han_n_beacon_stone

# Define the number of beacons for the Han south wall
han_s_beacon_stone <- 37
han_s_beacon_earth <- 56
han_s_beacon_earthstone <- 5 + han_s_beacon_stone

# Calculate the total number of beacons for each wall by adding the earth beacons and mixed (earth+stone) beacons
qinbeacon_total <- qinbeacon_earth + qinbeacon_earthstone
han_n_beacon_total <- han_n_beacon_earth + han_n_beacon_earthstone
han_s_beacon_total <- han_s_beacon_earth + han_s_beacon_earthstone

# Organize the beacon counts into a matrix for display
results <- matrix(1:9, nrow = 3, ncol = 3) # Initial placeholder matrix, to be overwritten
results <- rbind(
  c(qinbeacon_earth, qinbeacon_earthstone, qinbeacon_total),
  c(han_n_beacon_earth, han_n_beacon_earthstone, han_n_beacon_total),
  c(han_s_beacon_earth, han_s_beacon_earthstone, han_s_beacon_total)
)
rownames(results) <- c("Qin Wall Beacon", "Han north wall Beacon", "Han south wall Beacon")
colnames(results) <- c("Earth", "Stone Earth", "Total")

# Display the results in a formatted table using the `kable` function from the knitr package and apply styling with `kable_styling` from the kableExtra package
kable(results, digits = 4) %>%
  kable_styling(bootstrap_options = c("condensed"), full_width = T, position = "left")

```

## Stone composition of beacon yard wall per 0.5 m\^3

```{r}
# Calculate the mean facade volume. The stones used for beacons are similar with for beacons
mean_facadevol <- (tA_facadevol + tB_facadevol + tC_facadevol) / 3

# Yard wall section dimensions and volumes
yardsectiondepth <- 0.5
yardsectionheight <- 1
yardvolsection <- 1 * yardsectiondepth * yardsectionheight # Volume of the section

yard_volstones <- mean_facadevol * 2 * yardsectionheight # Calculated stone volume for yard wall
yard_volrubble <- yardvolsection - yard_volstones # Remaining volume is considered as rubble

# Prepare matrix to store the volumes of stones and rubble for the yard wall
stonevolume <- rbind(
  c(yard_volstones),
  c(yard_volrubble)
)
rownames(stonevolume) <- c("volstones", "volrubble")
colnames(stonevolume) <- c("Yard wall")

# Convert the matrix into a table and apply formatting for presentation
stonevolumetab <- stonevolume
rownames(stonevolumetab) <- c("Stone volume (m^3)", "Rubble volume (m^3)")
colnames(stonevolumetab) <- c("Yard wall")

# Display the stone and rubble volume calculations for the yard wall in a formatted table
kable(stonevolumetab, digits = 4) %>%
  kable_styling(bootstrap_options = c("condensed"), full_width = T, position = "left")
```

### Labour rates for beacon yard wall

```{r}
#| label: tbl-costs-yardwalls
# Define yard wall dimensions and rates
yardwallheight <- 2
yardwallwidth <- 0.5
yardwalllength <- 65.25
in_buildyard <- 1 / (6 * (4/2 + 0.2*4*(2-1) + 0.3/(2*0.5))) 

# Material acquisition calculations
yard_materialstone <- (yard_volstones + yard_volstones * in_quarrylost) * yardwallheight / in_quarrywall
yard_materialrubble <- yard_volrubble * yardwallheight / in_quarryrubble
yard_materialtotal <- yard_materialstone + yard_materialrubble

# Leveling the terrain calculations
yard_leveling <- yardwallwidth / in_leveling

# Wall assembly calculations
yard_assemblyfacade <- (mean_facadevol / in_buildyard) * yardwallheight * 2
yard_assemblyfill <- yard_volrubble * yardwallheight / in_buildrubbleearth
yard_assemplytotal <- yard_assemblyfacade + yard_assemblyfill

# Total construction effort calculation
yard_total <- yard_materialtotal + yard_leveling + yard_assemplytotal

# Energetic matrix for yard wall construction
estimates <- matrix(1:7, nrow = 7, ncol = 1)
estimates <- rbind(
  c(yard_materialstone),
  c(yard_materialrubble),
  c(yard_leveling),
  c(yard_assemblyfacade),
  c(yard_assemblyfill),
  c(yard_total)
)
rownames(estimates) <- c("Stone quarry (ph/m)", "Rubble quarry (ph/m)", "Leveling (ph/m)", "Assembly facade (ph/m)", "Assembly fill (ph/m)", "Total wall construction (ph/m)")
colnames(estimates) <- c("Yard wall")

# Displaying the energetic matrix in a table
kable(estimates, digits = 4) %>%
  kable_styling(bootstrap_options = c("condensed"), full_width = T, position = "left")

```

### Beacon yard wall labour cost in person hour and person day

```{r}
#| label: qin stone beacon yard labour cost
qinbeacon_yardcost <- qinbeacon_total * yardwalllength * yard_total
qinbeacon_yardcost
#Results
results <- matrix(1:2, nrow = 1, ncol = 2)
results <- rbind(c(qinbeacon_yardcost,qinbeacon_yardcost/workinghour))
rownames(results) <- c("Qin Wall")
colnames(results) <- c("Person-hour","Person-day")
kable(results, digits = 4) %>%
kable_styling(bootstrap_options = c("condensed"),
full_width = T,
position = "left")
```

## Rammed earth beaon labour cost

```{r}
#| label: Earth beaon labour cost
# Calculate beacon volumes
# Qin Wall beacon volume using the formula for a truncated cone
qinbeaconr1 <- 3
qinbeaconr2 <- 5
qinbeaconheight <- 4
qin_beaconvolume <- (1/3) * pi * qinbeaconheight * (qinbeaconr1^2 + qinbeaconr2^2 + (qinbeaconr1 * qinbeaconr2))

# Han Walls beacon volumes using the formula for a rectangular prism
han_n_beaconvolume <- 7.3 * 6.5 * 3 # Dimensions provided for a single beacon
han_s_beaconvolume <- 7.3 * 6.5 * 3 # Same dimensions assumed for simplicity

# Calculate the labour required for constructing beacon rammed earth platforms
# Qin beacon labour calculation, corrected to include the earthrate for conversion to person-days
qinbeacon_earthcost <- qin_beaconvolume / earthrate * qinbeacon_total

# Han beacon labour calculations, assuming the `han_n_beacon_total` and `han_s_beacon_total` represent the total number of beacons
han_n_beacon_earthcost <- han_n_beaconvolume / earthrate * han_n_beacon_total
han_s_beacon_earthcost <- han_s_beaconvolume / earthrate * han_s_beacon_total

# Prepare the results for presentation
results <- matrix(1:3, nrow = 3, ncol = 1) # Initial placeholder matrix, to be overwritten
results <- rbind(
  c(qinbeacon_earthcost),
  c(han_n_beacon_earthcost),
  c(han_s_beacon_earthcost)
)
rownames(results) <- c("Qin Wall", "Han north wall", "Han south wall")
colnames(results) <- c("Beacon rammed earth platform (Person-days)")

# Display the calculated results in a formatted table using kable and apply styling for aesthetics
kable(results, digits = 0) %>%
  kable_styling(bootstrap_options = c("condensed"), full_width = T, position = "left")

```

## Stone composition of beacon beacon facade per 0.3 m\^3

```{r}
#| label: qin earth beacon stone cover labour cost
# Calculate the slant height of the truncated cone (beacon tower)
l <- sqrt((qinbeaconr1 - qinbeaconr2)^2 + qinbeaconheight^2)

# Calculate the surface area (s) to be covered with stones, excluding the base
s <- pi * (qinbeaconr1^2 + qinbeaconr2^2 + qinbeaconr1 * l + qinbeaconr2 * l) - pi * qinbeaconr1^2

# Determine the labour intensity for building the beacon's stone cover
in_buildqinbeacon <- 1 / (6 * (4/2 + 0.2*4*(l-1) + 0.3/(2*0.3)))

# Calculate facade lengths and volumes for stone and rubble construction
qinbeaconfacadelength <- s / l
qinbeaconfacadedepth <- 0.3
qinbeaconfacadesectionheight <- 1
qinbeaconfacadevolsection <- 1 * qinbeaconfacadedepth * qinbeaconfacadesectionheight
qinbeaconfacade_volstones <- mean_facadevol * 1 * qinbeaconfacadesectionheight
qinbeaconfacade_volrubble <- qinbeaconfacadevolsection - qinbeaconfacade_volstones

# Prepare matrix to store volumes for stones and rubble
materialsvolume <- matrix(1:2, nrow = 2, ncol = 1)
stonevolume <- rbind(
  c(qinbeaconfacade_volstones),
  c(qinbeaconfacade_volrubble)
)
rownames(stonevolume) <- c("volstones", "volrubble")
colnames(stonevolume) <- c("Yard wall")

# Convert the matrix into a table and format for presentation
stonevolumetab <- stonevolume
rownames(stonevolumetab) <- c("Stone volume (m^3)", "Rubble volume (m^3)")
colnames(stonevolumetab) <- c("Qin Beacon Facade")

# Display the stone and rubble volume calculations in a formatted table
kable(stonevolumetab, digits = 4) %>%
  kable_styling(bootstrap_options = c("condensed"), full_width = T, position = "left")

```

### Qin earth beacon stone facade labour rate

```{r}
# Define beacon facade wall dimensions and construction rate
qinbeaconfacadewallheight <- l
qinbeaconfacadewallwidth <- 0.3

# Material acquisition calculations
qinbeaconfacade_materialstone <- (qinbeaconfacade_volstones + qinbeaconfacade_volstones * in_quarrylost) * qinbeaconfacadewallheight / in_quarrywall
qinbeaconfacade_materialrubble <- qinbeaconfacade_volrubble * qinbeaconfacadewallheight / in_quarryrubble
qinbeaconfacade_materialtotal <- qinbeaconfacade_materialstone + qinbeaconfacade_materialrubble

# Leveling the terrain labour calculation
qinbeaconfacade_leveling <- qinbeaconfacadewallwidth / in_leveling

# Wall assembly labour calculation
qinbeaconfacade_assemblyfacade <- (mean_facadevol / in_buildqinbeacon) * qinbeaconfacadewallheight * 1
qinbeaconfacade_assemblyfill <- qinbeaconfacade_volrubble * qinbeaconfacadewallheight / in_buildrubbleearth
qinbeaconfacade_assemplytotal <- qinbeaconfacade_assemblyfacade + qinbeaconfacade_assemblyfill

# Calculate the total labour required for the beacon facade wall construction
qinbeaconfacade_total <- qinbeaconfacade_materialtotal + qinbeaconfacade_leveling + qinbeaconfacade_assemplytotal

# Organize and display the construction labour estimates in a table
estimates <- matrix(1:7, nrow = 7, ncol = 1)
estimates <- rbind(
  c(qinbeaconfacade_materialstone),
  c(qinbeaconfacade_materialrubble),
  c(qinbeaconfacade_leveling),
  c(qinbeaconfacade_assemblyfacade),
  c(qinbeaconfacade_assemblyfill),
  c(qinbeaconfacade_total)
)
rownames(estimates) <- c("Stone quarry (ph/m)", "Rubble quarry (ph/m)", "Leveling (ph/m)", "Assembly facade (ph/m)", "Assembly fill (ph/m)", "Total wall construction (ph/m)")
colnames(estimates) <- c("Beacon facade wall")

# Present the table with kable styling
kable(estimates, digits = 4) %>%
  kable_styling(bootstrap_options = c("condensed"), full_width = T, position = "left")

```

### Qin beacon stone facade in person-hour

```{r}
#| label:Qin beacon stone cover tbl-costs
# Calculate the total facade length for all Qin beacons
qin_beaconfacade_length <- qinbeaconfacadelength * qinbeacon_earthstone

# Calculate the total labor in person-days for building the facade wall across all Qin beacons
qinbeacon_beaconfacade <- qinbeacon_earthstone * qin_beaconfacade_length * qinbeaconfacade_total

# Display the result in a formatted table
results <- matrix(c(qinbeacon_beaconfacade), nrow = 1, ncol = 1)
rownames(results) <- c("Qin beacon stone facade")
colnames(results) <- c("Person-hours")

kable(results, digits = 4) %>%
  kable_styling(bootstrap_options = c("condensed"), full_width = T, position = "left")
```

## Stone composition for Han beacon facade per 1.1 m\^3

```{r}
#| label: tbl-beacon facade unit
# Define section dimensions for the Han beacon facade
hanbeaconfacadesectiondepth <- 1.1
hanbeaconfacadesectionheight <- 1
hanbeaconfacadevolsection <- 1 * hanbeaconfacadesectiondepth * hanbeaconfacadesectionheight

# Calculate volumes for stones and rubble based on the mean facade volume
hanbeaconfacade_volstones <- mean_facadevol * 1 * hanbeaconfacadesectionheight
hanbeaconfacade_volrubble <- hanbeaconfacadevolsection - hanbeaconfacade_volstones

# Organize volumes into a matrix
materialsvolume <- matrix(1:2, nrow = 2, ncol = 1)
stonevolume <- rbind(
  c(hanbeaconfacade_volstones),
  c(hanbeaconfacade_volrubble)
)
rownames(stonevolume) <- c("volstones", "volrubble")
colnames(stonevolume) <- c("Han Beacon Facade")

# Convert the matrix into a table and apply formatting for presentation
stonevolumetab <- stonevolume
rownames(stonevolumetab) <- c("Stone volume (m^3)", "Rubble volume (m^3)")
colnames(stonevolumetab) <- c("Han beacon facade")

# Display the table using kable and apply styling
kable(stonevolumetab, digits = 4) %>%
  kable_styling(bootstrap_options = c("condensed"), full_width = T, position = "left")

```

### Costs estimated for Han beacon facade wall

```{r}
# Define facade wall dimensions and volume for the Han beacon
hanbeaconfacadewallheight <- 3
hanbeaconfacadewallwidth <- 1.1
in_buildhanbeacon <- 1 / (6 * (4/2 + 0.2*4*(3-1) + 0.3/(2*1.1)))
han_beaconvolume <- 7.5 * 6.5 * 3  # Predefined total volume

# Material acquisition for stone and rubble
hanbeaconfacade_materialstone <- (hanbeaconfacade_volstones + hanbeaconfacade_volstones * in_quarrylost) * hanbeaconfacadewallheight / in_quarrywall
hanbeaconfacade_materialrubble <- hanbeaconfacade_volrubble * hanbeaconfacadewallheight / in_quarryrubble
hanbeaconfacade_materialtotal <- hanbeaconfacade_materialstone + hanbeaconfacade_materialrubble

# Leveling the terrain
hanbeaconfacade_leveling <- hanbeaconfacadewallwidth / in_leveling

# Wall assembly
hanbeaconfacade_assemblyfacade <- (mean_facadevol / in_buildhanbeacon) * hanbeaconfacadewallheight
hanbeaconfacade_assemblyfill <- hanbeaconfacade_volrubble * hanbeaconfacadewallheight / in_buildrubbleearth
hanbeaconfacade_assemplytotal <- hanbeaconfacade_assemblyfacade + hanbeaconfacade_assemblyfill

# Calculate the total labor required
hanbeaconfacade_total <- hanbeaconfacade_materialtotal + hanbeaconfacade_leveling + hanbeaconfacade_assemplytotal

# Organize and display the construction labor estimates in a table
estimates <- matrix(1:7, nrow = 7, ncol = 1)
estimates <- rbind(
  c(hanbeaconfacade_materialstone),
  c(hanbeaconfacade_materialrubble),
  c(hanbeaconfacade_leveling),
  c(hanbeaconfacade_assemblyfacade),
  c(hanbeaconfacade_assemblyfill),
  c(hanbeaconfacade_total)
)
rownames(estimates) <- c("Stone quarry (ph/m)", "Rubble quarry (ph/m)", "Leveling (ph/m)", "Assembly facade (ph/m)", "Assembly fill (ph/m)", "Total wall construction (ph/m)")
colnames(estimates) <- c("Han beacon facade wall")

# Present the table with kable styling
kable(estimates, digits = 4) %>%
  kable_styling(bootstrap_options = c("condensed"), full_width = T, position = "left")

```

### Labour cost for Han beacon facade

```{r}
#| label: tbl-costs-hanfacade
#| tbl-cap: Costs estimated for building beacon facade wall expressed as person-day. 
# Define the number of beacon stones and facade length for Han north wall and south wall
han_n_beaconfacade_length <- 27 * han_n_beacon_earthstone
han_s_beaconfacade_length <- 27 * han_s_beacon_earthstone

# Calculate the labor required for constructing the facade of the beacon towers
han_n_beacon_beaconfacade <- han_n_beacon_earthstone * han_n_beaconfacade_length * hanbeaconfacade_total
han_s_beacon_beaconfacade <- han_s_beacon_earthstone * han_s_beaconfacade_length * hanbeaconfacade_total

# Organize the labor required into a matrix and calculate person-days for Qin, Han North, and Han South walls
qinbeacon_facadelabour <- qinbeacon_beaconfacade / workinghour
han_n_beacon_facadelabour <- han_n_beacon_beaconfacade / workinghour
han_s_beacon_facadelabour <- han_s_beacon_beaconfacade / workinghour

results <- matrix(1:3, nrow = 3, ncol = 1) # Initial placeholder matrix, to be overwritten
results <- rbind(
  c(qinbeacon_facadelabour),
  c(han_n_beacon_facadelabour),
  c(han_s_beacon_facadelabour)
)
rownames(results) <- c("Qin wall", "Han north wall", "Han south wall")
colnames(results) <- c("Beacon facade(Person-days)")

# Display the calculated results in a formatted table using kable and apply styling for aesthetics
kable(results, digits = 0) %>%
  kable_styling(bootstrap_options = c("condensed"), full_width = T, position = "left")


```

## Total labour cost for beacon

```{r}
#| label: Total person-days for beacon construction
# Calculate total person-days for beacon construction, including earthwork and facade labour costs
qinbeacon_construction <- qinbeacon_earthcost + (qinbeacon_beaconfacade + qinbeacon_yardcost) / workinghour 
han_n_beacon_construction <- han_n_beacon_earthcost + (han_n_beacon_beaconfacade / workinghour)
han_s_beacon_construction <- han_s_beacon_earthcost + (han_s_beacon_beaconfacade / workinghour)

# Organize the calculated total labor for each wall's beacon construction into a matrix
results <- matrix(1:3, nrow = 3, ncol = 1)  # Initial placeholder matrix, to be overwritten
results <- rbind(
    c(qinbeacon_construction),
    c(han_n_beacon_construction),
    c(han_s_beacon_construction)
)
rownames(results) <- c("Qin wall labour cost", "Han north wall labour cost", "Han south wall labour cost")
colnames(results) <- c("Beacon Construction (Person-days)")

# Present the table with kable styling for a clear and concise display of the labor costs
kable(results, digits = 0) %>%
    kable_styling(bootstrap_options = c("condensed"), full_width = T, position = "left")
```

### Total labour cost for wall and beacon construction

```{r}
#| label: tbl-results-cost-final
# Initialize the matrix with appropriate dimensions
results <- matrix(1:9, nrow = 3, ncol = 3)

# Calculate the total labor for wall and beacon construction, and their sum
qinbeacon_construction <- qinbeacon_earthcost + (qinbeacon_beaconfacade + qinbeacon_yardcost) / workinghour
han_n_beacon_construction <- han_n_beacon_earthcost + (han_n_beacon_beaconfacade) / workinghour
han_s_beacon_construction <- han_s_beacon_earthcost + (han_s_beacon_beaconfacade) / workinghour

qintotal <-  qinlabour + qinbeacon_construction
han_n_total <- han_n_labour + han_n_beacon_construction
han_s_total <- han_s_labour + han_s_beacon_construction

#| label: combine the ratio of the total Qin wall
# Define total lengths of the walls (not directly used in these calculations)
qinlength <- 474267
han_n_length <- 518297
han_s_length <- 519427

# Calculate construction time and labour force required for the Qin wall, adjusting for efficiency and terrain with qinratio

qinfort <- 44*10*10*1800/3.1
qinratio <- qintotal/(qintotal+qinfort) # The ratio is set from a calculation of the construction of 44 forts on the frontier.
qinconstructtime <- 2 * 180 * qinratio  # Adjusted construction time for the Qin wall
qinlabourforce <- qinlabour / qinconstructtime  # Labour force calculation for Qin wall

# Set construction times for Han North and South walls and calculate respective labour forces
han_n_constructtime <- 180  # Preset construction time for Han North wall
han_n_labourforce <- han_n_labour / han_n_constructtime  # Labour force calculation for Han North wall

han_s_constructtime <- 180  # Preset construction time for Han South wall
han_s_labourforce <- han_s_labour / han_s_constructtime  # Labour force calculation for Han South wall

# Populate the matrix with calculated values
results <- rbind(
  c(qinlabour, qinbeacon_construction, qintotal),
  c(han_n_labour, han_n_beacon_construction, han_n_total),
  c(han_s_labour, han_s_beacon_construction, han_s_total)
)
rownames(results) <- c("Qin wall labour cost", "Han north wall labour cost", "Han south wall labour cost")
colnames(results) <- c("Wall Construction (Person-days)", "Beacon Construction(Person-days)", "Total(Person-days)")

# Display the results in a styled table
kable(results, digits = 0) %>%
  kable_styling(bootstrap_options = c("condensed"), full_width = T, position = "left")

```

# Total Workforce with Supervisory

```{r}
#| label: tbl-results-withsupervisory
# Initialize the matrix for the results
results <- matrix(1:9, nrow = 3, ncol = 3)

# Calculate the total labor force
qin_labourforce_total <- (qinlabour + qinbeacon_construction) / qinconstructtime
han_n_labourforce_total <- (han_n_labour + han_n_beacon_construction) / han_n_constructtime
han_s_labourforce_total <- (han_s_labour + han_s_beacon_construction) / han_s_constructtime

# Populate the matrix with calculated values
results <- rbind(
  c(qinconstructtime, qin_labourforce_total, qin_labourforce_total * 1.16),
  c(han_n_constructtime, han_n_labourforce_total, han_n_labourforce_total * 1.16),
  c(han_s_constructtime, han_s_labourforce_total, han_s_labourforce_total * 1.16)
)
rownames(results) <- c("Qin wall labour cost", "Han north wall labour cost", "Han south wall labour cost")
colnames(results) <- c("Construct time (days)", "Workforce", "Workforce with supervision")

# Display the results in a styled table
kable(results, digits = 0) %>%
  kable_styling(bootstrap_options = c("condensed"), full_width = T, position = "left")

```

# Food Supply for Wall Construction Only

```{r}
#| label: tbl-results-remote
# Calculate the food cost for the Qin wall construction labor, adjusting by the same cost factor of 0.83
qin_foodcost <- qintotal * 0.83
# Repeat the calculation for the Han North wall
han_n_foodcost <- han_n_total * 0.83
# And for the Han South wall
han_s_foodcost <- han_s_total * 0.83

# Determine the number of cart transport trips needed for the Qin wall using the calculated food cost
qin_cart <- qin_foodcost / 250
# And for the Han North wall
han_n_cart <- han_n_foodcost / 250
# And for the Han South wall
han_s_cart <- han_s_foodcost / 250

# Calculate the manpower needed for cart transportation for the Qin wall, multiplying by 6 men per cart
qin_cartmen <- qin_cart * 6
# Repeat for the Han North wall
han_n_cartmen <- han_n_cart * 6
# And for the Han South wall
han_s_cartmen <- han_s_cart * 6

# Prepare the results matrix for display
results <- matrix(1:9, nrow = 3, ncol = 3)
# Populate the matrix with calculated values
results <- rbind(
  c(qin_foodcost, qin_cart, qin_cartmen),
  c(han_n_foodcost, han_n_cart, han_n_cartmen),
  c(han_s_foodcost, han_s_cart, han_s_cartmen)
)
# Set the row and column names for clarity
rownames(results) <- c("Qin wall", "Han north wall", "Han south wall")
colnames(results) <- c("Food cost (Dou)", "cart transport trips", "Person on cart (6 men per cart)")

# Display the table with kable styling for readability
kable(results, digits = 0) %>%
  kable_styling(bootstrap_options = c("condensed"), full_width = T, position = "left")


```

## Considering food loss during trips

```{r}
# Considering food loss during trips, unit: Dou
qin_foodcost_withloss <- qin_foodcost/0.39
han_foodcost_withloss <- (han_n_foodcost + han_s_foodcost)/0.923

qin_cart_trips <- qin_foodcost_withloss/250
han_cart_trips <- han_foodcost_withloss/250

qin_cart_hauler_wall <- qin_cart_trips*6
han_cart_hauler_wall <- han_cart_trips*6

# qin supply distance: 800 km. Round trip doubles the distance. Speed: 25 km/day.
# han supply distance: 100 km
qin_days <- 800*2/25
han_days <- 100*2/25

qin_carthauler_personday_withloss <- qin_cart_hauler_wall * qin_days
han_carthauler_personday_withloss <- han_cart_hauler_wall * han_days

# Prepare the results matrix for display
results <- matrix(1:2, nrow = 2, ncol = 2)
# Populate the matrix with calculated values
results <- rbind(
  c(qin_carthauler_personday_withloss, qin_cart_hauler_wall),
  c(han_carthauler_personday_withloss, han_cart_hauler_wall)
)

# Set the row and column names for clarity
rownames(results) <- c("Qin logistical supply (not including immigration)", "Han logistical supply")
colnames(results) <- c("Person-days","Logistic Personel")

# Display the table with kable styling for readability
kable(results, digits = 0) %>%
  kable_styling(bootstrap_options = c("condensed"), full_width = T, position = "left")

```

# Combination of the Qin Immigration and Qin Wall Project

```{r}
# Set the number of immigrants
immigration <- 350000
# Calculate the total annual food cost in Dou for immigrants, assuming 2 years at a cost adjusted by 0.83 as food cost per person-day
immigration_foodcost <- immigration * 360 * 2 * 0.83
# Total food cost considering the loss rate
totalfoodcost <- immigration_foodcost + qin_foodcost
# Considering the loss during the transportation
totalfoodcost_withloss <- totalfoodcost/0.39
# Cart trips
total_cart_withloss <- totalfoodcost_withloss / 250
# Total cart haulers
total_cart_hauler <- total_cart_withloss * 6
# Total person-days
total_persondays <- total_cart_hauler * qin_days

# Prepare the results matrix for display
results <- matrix(1:2, nrow = 1, ncol = 2)
# Populate the matrix with calculated values
results <- rbind(
  c(total_persondays, total_cart_hauler)
)

# Set the row and column names for clarity
rownames(results) <- c("Qin logistical supply (including immigration)")
colnames(results) <- c("Person-days","Logistic Personel")

# Display the table with kable styling for readability
kable(results, digits = 0) %>%
  kable_styling(bootstrap_options = c("condensed"), full_width = T, position = "left")

```