+
+
+
+
+
+
+wheat
+Fouad Gasmi
+2025-02-18
+ +# --------------------------------------------------------------------
+# Setup chunk for R Markdown
+# --------------------------------------------------------------------
+knitr::opts_chunk$set(echo = TRUE, warning = FALSE, message = FALSE)# --------------------------------------------------------------------
+# Check if packages are installed; if not, install them
+# --------------------------------------------------------------------
+required_packages <- c("ggplot2", "dplyr")
+for(p in required_packages){
+ if(!requireNamespace(p, quietly = TRUE)){
+ install.packages(p)
+ }
+}
+
+library(ggplot2)
+library(dplyr)# --------------------------------------------------------------------
+# Load data
+# --------------------------------------------------------------------
+df <- read.csv("data/Wheat.csv", header = TRUE)
+
+# Inspect structure
+str(df)## 'data.frame': 53 obs. of 4 variables:
+## $ rownames: int 1 2 3 4 5 6 7 8 9 10 ...
+## $ Year : int 1565 1570 1575 1580 1585 1590 1595 1600 1605 1610 ...
+## $ Wheat : num 41 45 42 49 41.5 47 64 27 33 32 ...
+## $ Wages : num 5 5.05 5.08 5.12 5.15 5.25 5.54 5.61 5.69 5.78 ...# --------------------------------------------------------------------
+# Create a data frame of monarch reigns (for background shading)
+# --------------------------------------------------------------------
+monarch_df <- data.frame(
+ name = c("Elizabeth", "James I", "Charles I", "Cromwell",
+ "Charles II", "James II", "W&M", "Anne",
+ "George I", "George II", "George III", "George IV"),
+ start = c(1565, 1603, 1625, 1649,
+ 1660, 1685, 1689, 1702,
+ 1714, 1727, 1760, 1820),
+ end = c(1603, 1625, 1649, 1660,
+ 1685, 1689, 1702, 1714,
+ 1727, 1760, 1820, 1821)
+)
+
+monarch_df$fill_color <- ifelse(
+ seq_len(nrow(monarch_df)) %% 2 == 1,
+ "white",
+ "gray90"
+)# --------------------------------------------------------------------
+# Minimalistic theme
+# --------------------------------------------------------------------
+theme_playfair <- theme_minimal(base_size = 12) +
+ theme(
+ panel.grid.major = element_line(color = "grey80"),
+ panel.grid.minor = element_line(color = "grey90")
+ )# --------------------------------------------------------------------
+# First Plot (Bars for Wheat, Area & Line for Wages)
+# --------------------------------------------------------------------
+g1 <- ggplot(df, aes(x = Year)) +
+ # Bars for Wheat prices
+ geom_bar(
+ aes(y = Wheat),
+ stat = "identity",
+ fill = "grey70",
+ width = 4,
+ alpha = 0.8
+ ) +
+ # Area for Wages
+ geom_area(aes(y = Wages), fill = "lightblue", alpha = 0.6) +
+ # Red line on top of the area for Wages
+ geom_line(aes(y = Wages), color = "red", linewidth = 1) +
+ labs(
+ x = "Year",
+ y = "Shillings (Combined Wheat & Wages)",
+ title = "Graph Replicating William Playfair's Idea",
+ subtitle = "Wheat price (bars) and wages (area/line) on the same scale"
+ ) +
+ theme_playfair
+
+# Display first version
+print(g1)
# Add monarch backgrounds and labels
+g1 <- g1 +
+ geom_rect(
+ data = monarch_df,
+ aes(
+ xmin = start,
+ xmax = end,
+ ymin = -Inf,
+ ymax = Inf,
+ fill = fill_color
+ ),
+ inherit.aes = FALSE,
+ alpha = 0.5,
+ color = NA
+ ) +
+ scale_fill_identity() +
+ geom_text(
+ data = monarch_df,
+ aes(
+ x = (start + end)/2,
+ y = Inf,
+ label = name
+ ),
+ inherit.aes = FALSE,
+ vjust = 1.2,
+ size = 3,
+ color = "black",
+ fontface = "bold"
+ )
+
+# Display updated plot
+print(g1)
# --------------------------------------------------------------------
+# Second Plot with Two Axes (Wheat vs. Wages)
+# --------------------------------------------------------------------
+
+# Calculate scaling factor (to align Wages visually to Wheat scale for the same y-axis range)
+scale_factor <- max(df$Wheat, na.rm = TRUE) / max(df$Wages, na.rm = TRUE)
+
+g2 <- ggplot(df, aes(x = Year)) +
+ geom_line(aes(y = Wheat), color = "blue", size = 1) +
+ geom_line(aes(y = Wages * scale_factor), color = "red", size = 1) +
+ scale_y_continuous(
+ name = "Wheat Price (shillings per quarter bushel)",
+ sec.axis = sec_axis(
+ trans = ~ . / scale_factor,
+ name = "Weekly Wages (shillings per week)"
+ )
+ ) +
+ labs(
+ x = "Year",
+ title = "Wheat Price and Wages on Two Axis",
+ subtitle = "Left Axis: shillings/quarter bushel, Right Axis: shillings/week"
+ ) +
+ theme_playfair
+
+print(g2)
# Add monarch backgrounds and labels
+g2 <- g2 +
+ geom_rect(
+ data = monarch_df,
+ aes(xmin = start, xmax = end, ymin = -Inf, ymax = Inf, fill = fill_color),
+ alpha = 0.5,
+ inherit.aes = FALSE
+ ) +
+ scale_fill_identity() +
+ geom_text(
+ data = monarch_df,
+ aes(x = (start + end)/2, y = Inf, label = name),
+ inherit.aes = FALSE,
+ vjust = 1.2,
+ size = 3
+ )
+
+print(g2)
# --------------------------------------------------------------------
+# Third Plot: Bars for Wheat, Line for Wages on Two Axes
+# --------------------------------------------------------------------
+
+scale_factor <- max(df$Wheat, na.rm = TRUE) / max(df$Wages, na.rm = TRUE)
+
+g2b <- ggplot(df, aes(x = Year)) +
+ # Bars for Wheat
+ geom_bar(
+ aes(y = Wheat),
+ stat = "identity",
+ width = 4,
+ fill = "grey70",
+ alpha = 0.8
+ ) +
+ # Red line for Wages (scaled)
+ geom_line(
+ aes(y = Wages * scale_factor),
+ color = "red",
+ size = 1
+ ) +
+ scale_y_continuous(
+ name = "Wheat Price (shillings per quarter bushel)",
+ sec.axis = sec_axis(
+ trans = ~ . / scale_factor,
+ name = "Weekly Wages (shillings per week)"
+ )
+ ) +
+ labs(
+ x = "Year",
+ title = "Wheat Price (bars) and Wages (line) on Two Axes",
+ subtitle = "Main Axis: Wheat, Secondary Axis: Wages"
+ ) +
+ theme_playfair
+
+print(g2b)
# Add monarch backgrounds and labels
+g2b <- g2b +
+ geom_rect(
+ data = monarch_df,
+ aes(
+ xmin = start,
+ xmax = end,
+ ymin = -Inf,
+ ymax = Inf,
+ fill = fill_color
+ ),
+ inherit.aes = FALSE,
+ alpha = 0.5
+ ) +
+ scale_fill_identity() +
+ geom_text(
+ data = monarch_df,
+ aes(
+ x = (start + end)/2,
+ y = Inf,
+ label = name
+ ),
+ inherit.aes = FALSE,
+ vjust = 1.2,
+ size = 3,
+ color = "black"
+ )
+
+print(g2b)
# --------------------------------------------------------------------
+# Fourth Plot: Purchasing Power = Wages / Wheat
+# --------------------------------------------------------------------
+
+df$PurchasingPower <- df$Wages / df$Wheat
+
+g3 <- ggplot(df, aes(x = Year, y = PurchasingPower)) +
+ geom_line(color = "darkgreen", size = 1) +
+ geom_point(color = "darkgreen", size = 2) +
+ labs(
+ x = "Year",
+ y = "Quarters of Bushels of Wheat per Weekly Wage",
+ title = "Evolution of Workers' Purchasing Power (in Wheat Volume)",
+ subtitle = "Inspired by Playfair's demonstration of rising purchasing power over time"
+ ) +
+ theme_playfair
+
+print(g3)
# Add monarch backgrounds and labels
+g3 <- g3 +
+ geom_rect(
+ data = monarch_df,
+ aes(xmin = start, xmax = end, ymin = -Inf, ymax = Inf, fill = fill_color),
+ alpha = 0.5,
+ inherit.aes = FALSE
+ ) +
+ scale_fill_identity() +
+ geom_text(
+ data = monarch_df,
+ aes(x = (start + end)/2, y = Inf, label = name),
+ inherit.aes = FALSE,
+ vjust = 1.2,
+ size = 3
+ )
+
+print(g3)
# --------------------------------------------------------------------
+# Fifth Plot: Scatter/Path of Wheat vs. Wages (Time as a Color Gradient)
+# --------------------------------------------------------------------
+
+# Make sure df is sorted by Year
+df <- df[order(df$Year), ]
+
+g4 <- ggplot(df, aes(x = Wheat, y = Wages)) +
+ geom_path(
+ aes(color = Year),
+ arrow = arrow(type = "open", length = unit(0.15, "inches")),
+ size = 1
+ ) +
+ geom_point(aes(color = Year), size = 2) +
+ # Color gradient from oldest (blue) to newest (red)
+ scale_color_gradient(low = "blue", high = "red") +
+ labs(
+ x = "Wheat Price (shillings/quarter bushel)",
+ y = "Weekly Wages (shillings/week)",
+ color = "Year",
+ title = "Relationship Between Wheat Price and Weekly Wages (No Direct Time Axis)",
+ subtitle = "Color and arrow indicate chronological progression"
+ ) +
+ theme_minimal(base_size = 12)
+
+print(g4)
# --------------------------------------------------------------------
+# Summarize by Decade, Then Plot (Path + Arrow + Labels)
+# --------------------------------------------------------------------
+df_decade <- df %>%
+ mutate(decade = floor(Year / 10) * 10) %>%
+ group_by(decade) %>%
+ summarize(
+ Wheat = mean(Wheat, na.rm = TRUE),
+ Wages = mean(Wages, na.rm = TRUE)
+ ) %>%
+ ungroup()
+
+g_better <- ggplot(df_decade, aes(x = Wheat, y = Wages)) +
+ geom_path(
+ arrow = arrow(length = unit(0.15, "inches"), type = "open"),
+ color = "darkblue",
+ size = 1
+ ) +
+ geom_point(color = "darkblue", size = 3) +
+ geom_text(
+ aes(label = decade),
+ hjust = -0.1,
+ vjust = -0.5,
+ color = "black",
+ size = 3
+ ) +
+ labs(
+ x = "Wheat Price (shillings/quarter bushel)",
+ y = "Weekly Wages (shillings/week)",
+ title = "Wheat Price vs. Wages (Aggregated by Decade)",
+ subtitle = "Arrows and labels indicate progression over time (no direct time axis)"
+ ) +
+ theme_minimal(base_size = 12)
+
+print(g_better)
I find g3 does a better job of illustrating purchasing power. g4 and +g_better become too cluttered between 1560 and 1700, making it difficult +to interpret the data at a glance. Even when the data is aggregated by +decade (g4 vs. g_better), it remains challenging to grasp the +information quickly.
+ + + + +