Text and figures are licensed under Creative Commons Attribution CC BY 4.0. The figures that have been reused from other sources don't fall under this license and can be recognized by a note in their caption: "Figure from ...".
+
Citation
+
For attribution, please cite this work as
+
Eckmann (2025, Dec. 11). Data Visualization | MSc CSS: Military Spending. Retrieved from https://csslab.uc3m.es/dataviz/projects/2025/100572935/
+
BibTeX citation
+
@misc{eckmann2025military,
+ author = {Eckmann, Moritz},
+ title = {Data Visualization | MSc CSS: Military Spending},
+ url = {https://csslab.uc3m.es/dataviz/projects/2025/100572935/},
+ year = {2025}
+}
+
+
+
+
+
+
+
+
+
+
+
+
From 54bb2f9417409042ca2a1b55027525060fd50ed6 Mon Sep 17 00:00:00 2001
From: MoritzUC3M <100572935@alumnos.uc3m.es>
Date: Fri, 12 Dec 2025 17:30:28 +0100
Subject: [PATCH 2/2] Updated text & Code
---
_projects/2025/100572935/100572935.Rmd | 731 ++++++++++++++++-
_projects/2025/100572935/100572935.html | 759 ++++++++++++++++-
.../2025/100572935/military-spending-gmsd.csv | 271 ++++++
_projects/2025/100572935/original_chart.png | Bin 0 -> 574510 bytes
projects/2025/100572935/index.html | 769 +++++++++++++++++-
5 files changed, 2485 insertions(+), 45 deletions(-)
create mode 100644 _projects/2025/100572935/military-spending-gmsd.csv
create mode 100644 _projects/2025/100572935/original_chart.png
diff --git a/_projects/2025/100572935/100572935.Rmd b/_projects/2025/100572935/100572935.Rmd
index 6ba749db..dd8adb52 100644
--- a/_projects/2025/100572935/100572935.Rmd
+++ b/_projects/2025/100572935/100572935.Rmd
@@ -1,6 +1,6 @@
---
-title: "Military Spending"
-description: "Comparison of military spending of 6 countries."
+title: "Military spending in six major economies"
+description: "Reproducing and improving a chart on military spending from Our World in Data."
author: "Moritz Eckmann"
date: "`r Sys.Date()`"
categories: "2025"
@@ -9,9 +9,728 @@ output:
toc: true
---
-# Introduction
+```{r setup, include=FALSE}
+knitr::opts_chunk$set(
+ out.width = "100%",
+ fig.align = "center",
+ fig.showtext = FALSE,
+ echo = TRUE,
+ message = FALSE,
+ warning = FALSE
+)
-Test
+library(tidyverse)
+library(patchwork)
-```{r}
-1 + 1
+# light grey background used in the improved chart
+bg_col <- "#F5F5F5"
+```
+
+## Introduction
+
+This project reproduces and extends a chart on military spending from
+*Our World in Data* (OWID). The original chart shows inflation–adjusted
+military expenditure for six countries (United States, Russia, China,
+India, United Kingdom, and France) from 1979 to 2023.
+
+The goals of this project are:
+
+- to **reproduce** the original visualization as closely as possible;
+- to **critically assess** its design choices; and
+- to **propose improved visualizations** that make the underlying
+ patterns easier to interpret.
+
+## Original chart
+
+{.external width="100%"}
+
+The original chart arranges the six countries in a 3x2 grid of small
+multiples. Each panel shows military spending over time, adjusted for
+inflation and expressed in US dollars. The design makes it relatively
+easy to see trends within each country, but it is harder to directly
+compare levels and growth rates across countries.
+
+In the rest of the article I first reproduce this chart using the public
+dataset provided by OWID, and then propose an alternative visualization
+that makes cross–country comparisons more straightforward. Then, I propose two further possible alternatives.
+
+## Data and preparation
+
+```{r load-data}
+ms <- readr::read_csv("military-spending-gmsd.csv",
+ show_col_types = FALSE)
+
+glimpse(ms)
+```
+
+The dataset contains one row per country and year, with military spending
+given in constant US dollars.
+
+For this project I focus on the six countries that appear in the
+original chart and convert spending to **billions of US dollars** to
+match the scale in the figure.
+
+```{r prepare-data}
+countries_focus <- c(
+ "United States", "Russia", "China",
+ "India", "United Kingdom", "France"
+)
+
+ms_clean <- ms %>%
+ filter(Entity %in% countries_focus) %>%
+ mutate(
+ country = factor(
+ Entity,
+ levels = c(
+ "United States",
+ "Russia",
+ "China",
+ "India",
+ "United Kingdom",
+ "France"
+ )
+ ),
+ spending_billion = `Military expenditure (constant US$)` / 1e9
+ )
+
+# Helper for y-axis labels: 200 -> $200B, 1200 -> $1.2T
+format_spend <- function(x) {
+ ifelse(
+ x >= 1000,
+ paste0("$", x / 1000, "T"),
+ paste0("$", x, "B")
+ )
+}
+```
+
+## Replicating the original small–multiples chart
+
+In this section I recreate the original 3×2 grid of country panels.
+The goal is to match the Our World in Data chart as closely as possible,
+using a helper function for a single country and then combining six
+panels into a grid.
+
+### Step 1: Define the helper function:
+
+- `bg_rep`, the white background color used for all panels, and
+- `make_country_plot()`, a function that:
+ - filters the data to one country,
+ - draws the time series of `spending_billion` over `Year`, and
+ - sets axes and theme to match the original chart.
+
+```{r replication-setup}
+# White background for the replicated chart
+bg_rep <- "white"
+
+# Helper function: create the panel for a single country
+make_country_plot <- function(ctry) {
+ ggplot(
+ filter(ms_clean, country == ctry),
+ aes(x = Year, y = spending_billion)
+ ) +
+ geom_line(color = "steelblue", linewidth = 0.8) +
+ scale_x_continuous(
+ breaks = c(1979, 1990, 2000, 2010, 2023),
+ limits = c(1979, 2023),
+ expand = expansion(mult = c(0, 0))
+ ) +
+ scale_y_continuous(
+ limits = c(0, NA),
+ labels = format_spend,
+ expand = expansion(mult = c(0, 0.05))
+ ) +
+ labs(
+ tag = ctry,
+ x = NULL,
+ y = NULL
+ ) +
+ theme_minimal(base_size = 12) +
+ theme(
+ panel.background = element_rect(fill = bg_rep, colour = NA),
+ plot.background = element_rect(fill = bg_rep, colour = NA),
+ panel.grid.major.x = element_blank(),
+ panel.grid.minor.x = element_blank(),
+ panel.grid.major.y = element_line(
+ linetype = "dashed",
+ colour = "grey80"
+ ),
+ panel.grid.minor.y = element_blank(),
+ axis.text.x = element_text(colour = "grey30"),
+ axis.text.y = element_text(colour = "grey30"),
+ # move country label up and slightly right
+ plot.tag = element_text(
+ hjust = 0,
+ face = "bold",
+ size = 12,
+ family = "sans"
+ ),
+ plot.tag.position = c(0.03, 1.10),
+ # a bit more top margin to give room above the panel
+ plot.margin = margin(t = 30, r = 5, b = 10, l = 45)
+ )
+}
+```
+
+### Step 2: Create one panel per country
+Here I call `make_country_plot()` once for each country in the original
+figure. This reuses exactly the same plotting logic six times, which
+keeps the code short and ensures that all panels share identical scales
+and styling.
+
+```{r replication-single-panels}
+p_us <- make_country_plot("United States")
+p_ru <- make_country_plot("Russia")
+p_cn <- make_country_plot("China")
+p_in <- make_country_plot("India")
+p_uk <- make_country_plot("United Kingdom")
+p_fr <- make_country_plot("France")
+```
+
+### Step 3: Arrange panels in a 3×2 grid
+In this chunk I combine the six panels into a 3×2 grid using the
+`patchwork` operators:
+
+- `|` puts two panels side by side (columns),
+- `/` stacks rows on top of each other.
+
+The result is the same small–multiples layout as in the Our World in
+Data chart.
+
+```{r replication-grid, fig.width=10, fig.height=7}
+p_countries <- (p_us | p_ru) /
+ (p_cn | p_in) /
+ (p_uk | p_fr)
+
+p_countries
+```
+
+### Step 4: Add shared title and subtitle
+Finally, I add a shared title and subtitle using `plot_annotation()`.
+The additional `theme()` call customises the title and subtitle
+appearance and makes sure the overall background is also white. The last
+line prints `p_replication`, so the completed replication plot appears
+in the knitted document.
+
+```{r replication-final, fig.width=10, fig.height=7}
+p_replication <- p_countries +
+ plot_annotation(
+ title = "Military spending",
+ subtitle = "This data is expressed in US dollars and adjusted for inflation."
+ ) &
+ theme(
+ plot.background = element_rect(fill = bg_rep, colour = NA),
+ plot.title = element_text(
+ hjust = 0,
+ colour = "grey20",
+ face = "bold",
+ size = 20,
+ family = "serif"
+ ),
+ plot.subtitle = element_text(
+ hjust = 0,
+ colour = "grey40",
+ size = 12,
+ family = "sans"
+ )
+ )
+
+p_replication
+```
+
+
+## Improved visualization: all countries in one chart
+
+The small–multiples design makes it easy to focus on one country at a
+time, but it is harder to compare levels and growth rates across
+countries. As an alternative, I plot all six countries in a single chart
+and enrich the context with a few key historical events.
+
+### Step 1: Palette and overall y-axis
+
+In this first chunk I define:
+
+- `okabe_ito`, a colourblind-friendly palette that assigns one colour to
+ each country; and
+- `y_max`, the rounded maximum of `spending_billion`, used to set a
+ common y-axis limit for all countries in the combined chart.
+
+```{r improved-setup}
+# Colorblind-friendly palette (Okabe–Ito)
+okabe_ito <- c(
+ "United States" = "#E69F00",
+ "Russia" = "#56B4E9",
+ "China" = "#009E73",
+ "India" = "#CC79A7",
+ "United Kingdom" = "#0072B2",
+ "France" = "#D55E00"
+)
+
+# Maximum y value (rounded) for the plot
+y_max <- ceiling(
+ max(ms_clean$spending_billion, na.rm = TRUE) / 100
+) * 100
+```
+
+### Step 2: Line-end labels
+
+This chunk prepares the labels that appear at the **end of each line**:
+
+- `labels_raw` keeps only the last year available for each country.
+- For countries with similar spending (France, UK, India, Russia),
+ I slightly **stagger** the y-positions (`label_y`) using `min_small`
+ and `step_small` so their labels do not overlap.
+- `label_text` shortens “United Kingdom” to “UK” to keep the label
+ compact.
+
+The resulting data frame `labels_df` is later used in `geom_text()` to
+write country names directly next to the lines instead of using a legend.
+
+```{r improved-labels}
+# Labels at the end of each line
+labels_raw <- ms_clean %>%
+ dplyr::group_by(country) %>%
+ dplyr::filter(Year == max(Year)) %>%
+ dplyr::ungroup()
+
+small_countries <- c("France", "United Kingdom", "India", "Russia")
+
+min_small <- min(
+ labels_raw$spending_billion[labels_raw$country %in% small_countries]
+) - 20
+step_small <- 28
+
+labels_df <- labels_raw %>%
+ dplyr::mutate(
+ label_text = dplyr::if_else(
+ country == "United Kingdom",
+ "UK",
+ as.character(country)
+ ),
+ label_y = dplyr::case_when(
+ country == "France" ~ min_small + 0 * step_small,
+ country == "United Kingdom" ~ min_small + 1 * step_small,
+ country == "India" ~ min_small + 2 * step_small,
+ country == "Russia" ~ min_small + 3 * step_small,
+ TRUE ~ spending_billion
+ )
+ )
+```
+
+### Step 3: Annotating key historical events
+
+Here I define `events_df`, a small tibble with:
+
+- `year`: the x-position of the event (vertical dashed line), and
+- `label`: short descriptions that will be written near the top of the
+ plot.
+
+These events provide context and help explain some of the changes in
+military spending over time.
+
+```{r improved-events}
+# Some key events for reference
+events_df <- tibble::tibble(
+ year = c(1990, 2001, 2017),
+ label = c(
+ "End of Cold War",
+ "9/11 attacks",
+ "Trump 1st presidency"
+ )
+)
+```
+
+### Step 4: Putting everything together in one chart
+
+The final chunk builds the actual chart:
+
+- `ggplot(ms_clean, aes(...)) + geom_line()` draws the six time series
+ of spending in a single plot, coloured by country using `okabe_ito`.
+- `geom_segment()` and the first `geom_text()` layer use `events_df` to
+ draw vertical dashed lines and their textual labels for key events.
+- The second `geom_text()` layer uses `labels_df` to place country
+ labels at the end of each line, removing the need for a legend.
+- `scale_x_continuous()` and `scale_y_continuous()` control the axes and
+ use `format_spend` to format dollar amounts.
+- `scale_colour_manual(values = okabe_ito)` enforces the chosen palette.
+- The `labs()` and `theme()` calls set the title, subtitle and visual
+ style, including a light grey background (`bg_col`) and centred,
+ bold title.
+
+```{r improved-all-countries, fig.width=10, fig.height=6, preview=TRUE}
+ggplot(
+ ms_clean,
+ aes(x = Year, y = spending_billion, colour = country)
+) +
+ geom_line(linewidth = 0.9) +
+ # event lines
+ geom_segment(
+ data = events_df,
+ aes(
+ x = year, xend = year,
+ y = 0, yend = y_max * 0.96
+ ),
+ inherit.aes = FALSE,
+ linetype = "dashed",
+ colour = "grey60"
+ ) +
+ geom_text(
+ data = events_df,
+ aes(
+ x = year,
+ y = y_max * 0.99,
+ label = label
+ ),
+ inherit.aes = FALSE,
+ vjust = 1.0,
+ size = 3.4,
+ family = "sans",
+ fontface = "bold",
+ colour = "grey40"
+ ) +
+ # line-end labels
+ geom_text(
+ data = labels_df,
+ aes(
+ x = Year,
+ y = label_y,
+ label = label_text
+ ),
+ hjust = 0,
+ nudge_x = 0.5,
+ size = 3.4,
+ show.legend = FALSE
+ ) +
+ scale_x_continuous(
+ breaks = c(1979, 1990, 2000, 2010, 2023),
+ limits = c(1979, 2025),
+ expand = expansion(mult = c(0, 0))
+ ) +
+ scale_y_continuous(
+ limits = c(0, y_max),
+ breaks = seq(0, y_max, by = 200),
+ labels = format_spend,
+ expand = expansion(mult = c(0, 0.05))
+ ) +
+ scale_colour_manual(values = okabe_ito, name = NULL) +
+ coord_cartesian(clip = "off") +
+ labs(
+ title = "Military spending",
+ subtitle = paste(
+ "Six countries, constant 2022 US dollars.",
+ "Lines show annual military expenditure;",
+ "vertical dashed lines mark key geopolitical events."
+ ),
+ x = NULL,
+ y = NULL
+ ) +
+ theme_minimal(base_size = 12) +
+ theme(
+ panel.background = element_rect(fill = bg_col, colour = NA),
+ plot.background = element_rect(fill = bg_col, colour = NA),
+ panel.grid.major.x = element_blank(),
+ panel.grid.minor.x = element_blank(),
+ panel.grid.major.y = element_line(
+ linetype = "dashed",
+ colour = "grey80"
+ ),
+ panel.grid.minor.y = element_blank(),
+ axis.text = element_text(colour = "grey30"),
+ legend.position = "none",
+ plot.margin = margin(t = 10, r = 60, b = 10, l = 10),
+ plot.title = element_text(
+ hjust = 0,
+ colour = "grey20",
+ face = "bold",
+ size = 18,
+ family = "serif"
+ ),
+ plot.subtitle = element_text(
+ hjust = 0,
+ colour = "grey40",
+ size = 11,
+ family = "sans"
+ )
+ )
+```
+
+Because all countries share the same axes, it becomes easier to compare
+both **absolute levels** and **trends** across the six countries than in
+the original small-multiples design.
+
+## Alternatives
+The Graph still has weaknesses. Firstly, it gets difficult to read the development from Russia, India, UK and France. So an alternative to the absolut y-axis might make sense. Secondly, it fails to depict the relative development. Therefore, two alternatives are introduced.
+
+### Alternative 1: Logarithmic y-axis
+
+The absolute plot with all six countries in one panel makes it easy to
+compare spending levels, but the United States dominates the scale.
+Spending in China and India is much smaller in absolute terms, even
+though their **growth rates** are very large.
+
+A common way to emphasise *proportional* changes is to use a logarithmic
+y-axis. On a log scale, equal vertical distances correspond to equal
+percentage changes. This makes it easier to compare growth patterns
+across countries, at the cost of making absolute differences in levels
+less intuitive.
+
+```{r log-scale-plot, fig.width=10, fig.height=6}
+# smallest positive value for log scale
+min_pos <- min(
+ ms_clean$spending_billion[ms_clean$spending_billion > 0],
+ na.rm = TRUE
+)
+
+# nice log breaks in billions
+log_breaks <- c(10, 20, 50, 100, 200, 500, 1000)
+log_breaks <- log_breaks[log_breaks >= min_pos & log_breaks <= y_max]
+
+ggplot(
+ ms_clean,
+ aes(x = Year, y = spending_billion, colour = country)
+) +
+ geom_line(linewidth = 0.9) +
+ # event lines (reuse events_df from the absolute plot)
+ geom_segment(
+ data = events_df,
+ aes(
+ x = year, xend = year,
+ y = min_pos, yend = y_max * 0.96
+ ),
+ inherit.aes = FALSE,
+ linetype = "dashed",
+ colour = "grey60"
+ ) +
+ geom_text(
+ data = events_df,
+ aes(
+ x = year,
+ y = y_max * 0.99,
+ label = label
+ ),
+ inherit.aes = FALSE,
+ vjust = 1.0,
+ size = 3.4,
+ family = "sans",
+ fontface = "bold",
+ colour = "grey40"
+ ) +
+ # line-end labels (reuse labels_df)
+ geom_text(
+ data = labels_df,
+ aes(x = Year, y = label_y, label = label_text),
+ hjust = 0,
+ nudge_x = 0.5,
+ size = 3.4,
+ show.legend = FALSE
+ ) +
+ scale_x_continuous(
+ breaks = c(1979, 1990, 2000, 2010, 2023),
+ limits = c(1979, 2025),
+ expand = expansion(mult = c(0, 0))
+ ) +
+ scale_y_log10(
+ limits = c(min_pos, y_max),
+ breaks = log_breaks,
+ labels = format_spend,
+ expand = expansion(mult = c(0, 0.05))
+ ) +
+ scale_colour_manual(values = okabe_ito, name = NULL) +
+ coord_cartesian(clip = "off") +
+ labs(
+ title = "Military spending (log scale)",
+ subtitle = paste(
+ "Same axis scale for all six countries,",
+ "logarithmic y-axis (constant 2022 US dollars)."
+ ),
+ x = NULL,
+ y = NULL
+ ) +
+ theme_minimal(base_size = 12) +
+ theme(
+ panel.background = element_rect(fill = bg_col, colour = NA),
+ plot.background = element_rect(fill = bg_col, colour = NA),
+ panel.grid.major.x = element_blank(),
+ panel.grid.minor.x = element_blank(),
+ panel.grid.major.y = element_line(
+ linetype = "dashed",
+ colour = "grey80"
+ ),
+ panel.grid.minor.y = element_blank(),
+ axis.text = element_text(colour = "grey30"),
+ legend.position = "none",
+ plot.margin = margin(t = 10, r = 60, b = 10, l = 10),
+ plot.title = element_text(
+ hjust = 0,
+ colour = "grey20",
+ face = "bold",
+ size = 18,
+ family = "serif"
+ ),
+ plot.subtitle = element_text(
+ hjust = 0,
+ colour = "grey40",
+ size = 11,
+ family = "sans"
+ )
+ )
+```
+
+The log-scale version is less insightful because the non-linear y-axis is harder to interpret and visually compresses differences between countries. It highlights proportional growth, but the distorted axis can easily confuse readers who want to compare actual spending levels.
+
+### Alternative 2: Relative change since 1979 (index plot)
+
+Another way to emphasise relative change is to normalise each country’s
+spending to a common starting value. Here I set spending in 1979 to
+**100** for each country and plot an index over time. A value of 200
+means that spending has doubled relative to 1979.
+
+This design deliberately hides information about absolute levels and
+focuses instead on the question: *who has grown the most since 1979?*
+It highlights that countries like China and India have experienced
+much faster proportional increases than more established military powers.
+
+```{r index-1979-plot, fig.width=10, fig.height=6}
+# build index data: 1979 value = 100 for each country
+ms_index <- ms_clean %>%
+ dplyr::filter(!is.na(spending_billion)) %>%
+ dplyr::group_by(country) %>%
+ dplyr::mutate(
+ base_1979 = spending_billion[Year == 1979][1]
+ ) %>%
+ dplyr::filter(!is.na(base_1979) & base_1979 > 0) %>%
+ dplyr::mutate(
+ spending_index = spending_billion / base_1979 * 100
+ ) %>%
+ dplyr::ungroup() %>%
+ dplyr::filter(!is.na(spending_index))
+
+# y-axis max for the index plot (rounded up nicely)
+y_max_idx <- ceiling(
+ max(ms_index$spending_index, na.rm = TRUE) / 50
+) * 50
+
+# line-end labels for the index plot
+labels_idx <- ms_index %>%
+ dplyr::group_by(country) %>%
+ dplyr::filter(Year == max(Year)) %>%
+ dplyr::ungroup() %>%
+ dplyr::mutate(
+ label_text = dplyr::if_else(
+ country == "United Kingdom",
+ "UK",
+ as.character(country)
+ )
+ )
+
+# key events (redefined here so the chunk is self-contained)
+events_idx <- tibble::tibble(
+ year = c(1990, 2001, 2017),
+ label = c(
+ "End of Cold War",
+ "9/11 attacks",
+ "Trump 1st presidency"
+ )
+)
+
+ggplot(
+ ms_index,
+ aes(x = Year, y = spending_index, colour = country)
+) +
+ geom_line(linewidth = 0.9) +
+ # event lines
+ geom_segment(
+ data = events_idx,
+ aes(
+ x = year, xend = year,
+ y = 0, yend = y_max_idx * 0.96
+ ),
+ inherit.aes = FALSE,
+ linetype = "dashed",
+ colour = "grey60"
+ ) +
+ geom_text(
+ data = events_idx,
+ aes(
+ x = year,
+ y = y_max_idx * 0.99,
+ label = label
+ ),
+ inherit.aes = FALSE,
+ vjust = 1.0,
+ size = 3.4,
+ family = "sans",
+ fontface = "bold",
+ colour = "grey40"
+ ) +
+ # line-end labels
+ geom_text(
+ data = labels_idx,
+ aes(
+ x = Year,
+ y = spending_index,
+ label = label_text
+ ),
+ hjust = 0,
+ nudge_x = 0.5,
+ size = 3.4,
+ show.legend = FALSE
+ ) +
+ scale_x_continuous(
+ breaks = c(1979, 1990, 2000, 2010, 2023),
+ limits = c(1979, 2025),
+ expand = expansion(mult = c(0, 0))
+ ) +
+ scale_y_continuous(
+ limits = c(0, y_max_idx),
+ breaks = seq(0, y_max_idx, by = 50),
+ labels = function(x) paste0(round(x), " (1979 = 100)"),
+ expand = expansion(mult = c(0, 0.05))
+ ) +
+ scale_colour_manual(values = okabe_ito, name = NULL) +
+ coord_cartesian(clip = "off") +
+ labs(
+ title = "Military spending indexed to 1979 = 100",
+ subtitle = paste(
+ "Relative change in military spending.",
+ "Each country starts at 100 in 1979."
+ ),
+ x = NULL,
+ y = NULL
+ ) +
+ theme_minimal(base_size = 12) +
+ theme(
+ panel.background = element_rect(fill = bg_col, colour = NA),
+ plot.background = element_rect(fill = bg_col, colour = NA),
+ panel.grid.major.x = element_blank(),
+ panel.grid.minor.x = element_blank(),
+ panel.grid.major.y = element_line(
+ linetype = "dashed",
+ colour = "grey80"
+ ),
+ panel.grid.minor.y = element_blank(),
+ axis.text = element_text(colour = "grey30"),
+ legend.position = "none",
+ plot.margin = margin(t = 10, r = 60, b = 10, l = 10),
+ plot.title = element_text(
+ hjust = 0,
+ colour = "grey20",
+ face = "bold",
+ size = 18,
+ family = "serif"
+ ),
+ plot.subtitle = element_text(
+ hjust = 0,
+ colour = "grey40",
+ size = 11,
+ family = "sans"
+ )
+ )
+```
+
+The indexed chart clearly shows relative growth, but it hides all information about the actual size of military budgets. This makes it good for ranking growth rates, but less suitable than the main improved chart if we care about both growth **and** the absolute scale of spending.
+
+## Conclusions and further work
+
+Replicating the original chart shows that small multiples work well for seeing each country separately, but make cross-country comparisons harder. The improved combined chart, with a shared scale, line-end labels and event annotations, makes it much easier to compare both levels and trends across all six countries at once.
+
+The log-scale and indexed alternatives are useful for exploring specific questions about proportional growth, but their axes are less intuitive and hide absolute spending levels, so they work better as complementary views than as the main figure. As next steps, one could normalise spending by GDP or population, add more countries or alliances, or build an interactive version that lets readers switch between these different perspectives.
diff --git a/_projects/2025/100572935/100572935.html b/_projects/2025/100572935/100572935.html
index 045de043..069d6fff 100644
--- a/_projects/2025/100572935/100572935.html
+++ b/_projects/2025/100572935/100572935.html
@@ -89,32 +89,32 @@
- Military Spending
+ Military spending in six major economies
-
+
-
-
+
+
-
+
-
+
-
-
+
+
@@ -2362,7 +2362,7 @@
This article is in review.
@@ -2372,21 +2372,21 @@
This article is in review.
-
Military Spending
+
Military spending in six major economies
2025
-
Comparison of military spending of 6 countries.
+
Reproducing and improving a chart on military spending from Our World in Data.
This project reproduces and extends a chart on military spending from
+Our World in Data (OWID). The original chart shows inflation–adjusted
+military expenditure for six countries (United States, Russia, China,
+India, United Kingdom, and France) from 1979 to 2023.
+
The goals of this project are:
+
+
to reproduce the original visualization as closely as possible;
+
to critically assess its design choices; and
+
to propose improved visualizations that make the underlying
+patterns easier to interpret.
+
+
Original chart
+
+
+Original chart. Source: Barnum et al., Global Military Spending Dataset (2025), Our World in Data.
+
+
The original chart arranges the six countries in a 3x2 grid of small
+multiples. Each panel shows military spending over time, adjusted for
+inflation and expressed in US dollars. The design makes it relatively
+easy to see trends within each country, but it is harder to directly
+compare levels and growth rates across countries.
+
In the rest of the article I first reproduce this chart using the public
+dataset provided by OWID, and then propose an alternative visualization
+that makes cross–country comparisons more straightforward. Then, I propose two further possible alternatives.
Rows: 270
+Columns: 6
+$ Entity <chr> "China", "China", "Chi…
+$ Code <chr> "CHN", "CHN", "CHN", "…
+$ Year <dbl> 1979, 1980, 1981, 1982…
+$ `Military expenditure (constant US$)` <dbl> 86628520000, 875424400…
+$ `World regions according to OWID` <chr> "Asia", "Asia", "Asia"…
+$ time <dbl> 2023, 2023, 2023, 2023…
+
+
The dataset contains one row per country and year, with military spending
+given in constant US dollars.
+
For this project I focus on the six countries that appear in the
+original chart and convert spending to billions of US dollars to
+match the scale in the figure.
In this section I recreate the original 3×2 grid of country panels.
+The goal is to match the Our World in Data chart as closely as possible,
+using a helper function for a single country and then combining six
+panels into a grid.
+
Step 1: Define the helper function:
+
+
bg_rep, the white background color used for all panels, and
+
+
make_country_plot(), a function that:
+
+
filters the data to one country,
+
+
draws the time series of spending_billion over Year, and
+
+
sets axes and theme to match the original chart.
+
+
+
+
+
# White background for the replicated chart
+bg_rep<-"white"
+
+# Helper function: create the panel for a single country
+make_country_plot<-function(ctry){
+ggplot(
+filter(ms_clean, country==ctry),
+aes(x =Year, y =spending_billion)
+)+
+geom_line(color ="steelblue", linewidth =0.8)+
+scale_x_continuous(
+ breaks =c(1979, 1990, 2000, 2010, 2023),
+ limits =c(1979, 2023),
+ expand =expansion(mult =c(0, 0))
+)+
+scale_y_continuous(
+ limits =c(0, NA),
+ labels =format_spend,
+ expand =expansion(mult =c(0, 0.05))
+)+
+labs(
+ tag =ctry,
+ x =NULL,
+ y =NULL
+)+
+theme_minimal(base_size =12)+
+theme(
+ panel.background =element_rect(fill =bg_rep, colour =NA),
+ plot.background =element_rect(fill =bg_rep, colour =NA),
+ panel.grid.major.x =element_blank(),
+ panel.grid.minor.x =element_blank(),
+ panel.grid.major.y =element_line(
+ linetype ="dashed",
+ colour ="grey80"
+),
+ panel.grid.minor.y =element_blank(),
+ axis.text.x =element_text(colour ="grey30"),
+ axis.text.y =element_text(colour ="grey30"),
+# move country label up and slightly right
+ plot.tag =element_text(
+ hjust =0,
+ face ="bold",
+ size =12,
+ family ="sans"
+),
+ plot.tag.position =c(0.03, 1.10),
+# a bit more top margin to give room above the panel
+ plot.margin =margin(t =30, r =5, b =10, l =45)
+)
+}
+
+
+
Step 2: Create one panel per country
+
Here I call make_country_plot() once for each country in the original
+figure. This reuses exactly the same plotting logic six times, which
+keeps the code short and ensures that all panels share identical scales
+and styling.
Finally, I add a shared title and subtitle using plot_annotation().
+The additional theme() call customises the title and subtitle
+appearance and makes sure the overall background is also white. The last
+line prints p_replication, so the completed replication plot appears
+in the knitted document.
+
+
+
p_replication<-p_countries+
+plot_annotation(
+ title ="Military spending",
+ subtitle ="This data is expressed in US dollars and adjusted for inflation."
+)&
+theme(
+ plot.background =element_rect(fill =bg_rep, colour =NA),
+ plot.title =element_text(
+ hjust =0,
+ colour ="grey20",
+ face ="bold",
+ size =20,
+ family ="serif"
+),
+ plot.subtitle =element_text(
+ hjust =0,
+ colour ="grey40",
+ size =12,
+ family ="sans"
+)
+)
+
+p_replication
+
+
+
+
Improved visualization: all countries in one chart
+
The small–multiples design makes it easy to focus on one country at a
+time, but it is harder to compare levels and growth rates across
+countries. As an alternative, I plot all six countries in a single chart
+and enrich the context with a few key historical events.
+
Step 1: Palette and overall y-axis
+
In this first chunk I define:
+
+
okabe_ito, a colourblind-friendly palette that assigns one colour to
+each country; and
+
+
y_max, the rounded maximum of spending_billion, used to set a
+common y-axis limit for all countries in the combined chart.
+
+
+
+
# Colorblind-friendly palette (Okabe–Ito)
+okabe_ito<-c(
+"United States"="#E69F00",
+"Russia"="#56B4E9",
+"China"="#009E73",
+"India"="#CC79A7",
+"United Kingdom"="#0072B2",
+"France"="#D55E00"
+)
+
+# Maximum y value (rounded) for the plot
+y_max<-ceiling(
+max(ms_clean$spending_billion, na.rm =TRUE)/100
+)*100
+
+
+
Step 2: Line-end labels
+
This chunk prepares the labels that appear at the end of each line:
+
+
labels_raw keeps only the last year available for each country.
+
+
For countries with similar spending (France, UK, India, Russia),
+I slightly stagger the y-positions (label_y) using min_small
+and step_small so their labels do not overlap.
+
+
label_text shortens “United Kingdom” to “UK” to keep the label
+compact.
+
+
The resulting data frame labels_df is later used in geom_text() to
+write country names directly next to the lines instead of using a legend.
+
+
+
# Labels at the end of each line
+labels_raw<-ms_clean%>%
+dplyr::group_by(country)%>%
+dplyr::filter(Year==max(Year))%>%
+dplyr::ungroup()
+
+small_countries<-c("France", "United Kingdom", "India", "Russia")
+
+min_small<-min(
+labels_raw$spending_billion[labels_raw$country%in%small_countries]
+)-20
+step_small<-28
+
+labels_df<-labels_raw%>%
+dplyr::mutate(
+ label_text =dplyr::if_else(
+country=="United Kingdom",
+"UK",
+as.character(country)
+),
+ label_y =dplyr::case_when(
+country=="France"~min_small+0*step_small,
+country=="United Kingdom"~min_small+1*step_small,
+country=="India"~min_small+2*step_small,
+country=="Russia"~min_small+3*step_small,
+TRUE~spending_billion
+)
+)
+
+
+
Step 3: Annotating key historical events
+
Here I define events_df, a small tibble with:
+
+
year: the x-position of the event (vertical dashed line), and
+
+
label: short descriptions that will be written near the top of the
+plot.
+
+
These events provide context and help explain some of the changes in
+military spending over time.
+
+
+
# Some key events for reference
+events_df<-tibble::tibble(
+ year =c(1990, 2001, 2017),
+ label =c(
+"End of Cold War",
+"9/11 attacks",
+"Trump 1st presidency"
+)
+)
+
+
+
Step 4: Putting everything together in one chart
+
The final chunk builds the actual chart:
+
+
ggplot(ms_clean, aes(...)) + geom_line() draws the six time series
+of spending in a single plot, coloured by country using okabe_ito.
+
geom_segment() and the first geom_text() layer use events_df to
+draw vertical dashed lines and their textual labels for key events.
+
The second geom_text() layer uses labels_df to place country
+labels at the end of each line, removing the need for a legend.
+
scale_x_continuous() and scale_y_continuous() control the axes and
+use format_spend to format dollar amounts.
+
scale_colour_manual(values = okabe_ito) enforces the chosen palette.
+
The labs() and theme() calls set the title, subtitle and visual
+style, including a light grey background (bg_col) and centred,
+bold title.
Because all countries share the same axes, it becomes easier to compare
+both absolute levels and trends across the six countries than in
+the original small-multiples design.
+
Alternatives
+
The Graph still has weaknesses. Firstly, it gets difficult to read the development from Russia, India, UK and France. So an alternative to the absolut y-axis might make sense. Secondly, it fails to depict the relative development. Therefore, two alternatives are introduced.
+
Alternative 1: Logarithmic y-axis
+
The absolute plot with all six countries in one panel makes it easy to
+compare spending levels, but the United States dominates the scale.
+Spending in China and India is much smaller in absolute terms, even
+though their growth rates are very large.
+
A common way to emphasise proportional changes is to use a logarithmic
+y-axis. On a log scale, equal vertical distances correspond to equal
+percentage changes. This makes it easier to compare growth patterns
+across countries, at the cost of making absolute differences in levels
+less intuitive.
The log-scale version is less insightful because the non-linear y-axis is harder to interpret and visually compresses differences between countries. It highlights proportional growth, but the distorted axis can easily confuse readers who want to compare actual spending levels.
+
Alternative 2: Relative change since 1979 (index plot)
+
Another way to emphasise relative change is to normalise each country’s
+spending to a common starting value. Here I set spending in 1979 to
+100 for each country and plot an index over time. A value of 200
+means that spending has doubled relative to 1979.
+
This design deliberately hides information about absolute levels and
+focuses instead on the question: who has grown the most since 1979?
+It highlights that countries like China and India have experienced
+much faster proportional increases than more established military powers.
-
1+1
+
# build index data: 1979 value = 100 for each country
+ms_index<-ms_clean%>%
+dplyr::filter(!is.na(spending_billion))%>%
+dplyr::group_by(country)%>%
+dplyr::mutate(
+ base_1979 =spending_billion[Year==1979][1]
+)%>%
+dplyr::filter(!is.na(base_1979)&base_1979>0)%>%
+dplyr::mutate(
+ spending_index =spending_billion/base_1979*100
+)%>%
+dplyr::ungroup()%>%
+dplyr::filter(!is.na(spending_index))
+
+# y-axis max for the index plot (rounded up nicely)
+y_max_idx<-ceiling(
+max(ms_index$spending_index, na.rm =TRUE)/50
+)*50
+
+# line-end labels for the index plot
+labels_idx<-ms_index%>%
+dplyr::group_by(country)%>%
+dplyr::filter(Year==max(Year))%>%
+dplyr::ungroup()%>%
+dplyr::mutate(
+ label_text =dplyr::if_else(
+country=="United Kingdom",
+"UK",
+as.character(country)
+)
+)
+
+# key events (redefined here so the chunk is self-contained)
+events_idx<-tibble::tibble(
+ year =c(1990, 2001, 2017),
+ label =c(
+"End of Cold War",
+"9/11 attacks",
+"Trump 1st presidency"
+)
+)
+
+ggplot(
+ms_index,
+aes(x =Year, y =spending_index, colour =country)
+)+
+geom_line(linewidth =0.9)+
+# event lines
+geom_segment(
+ data =events_idx,
+aes(
+ x =year, xend =year,
+ y =0, yend =y_max_idx*0.96
+),
+ inherit.aes =FALSE,
+ linetype ="dashed",
+ colour ="grey60"
+)+
+geom_text(
+ data =events_idx,
+aes(
+ x =year,
+ y =y_max_idx*0.99,
+ label =label
+),
+ inherit.aes =FALSE,
+ vjust =1.0,
+ size =3.4,
+ family ="sans",
+ fontface ="bold",
+ colour ="grey40"
+)+
+# line-end labels
+geom_text(
+ data =labels_idx,
+aes(
+ x =Year,
+ y =spending_index,
+ label =label_text
+),
+ hjust =0,
+ nudge_x =0.5,
+ size =3.4,
+ show.legend =FALSE
+)+
+scale_x_continuous(
+ breaks =c(1979, 1990, 2000, 2010, 2023),
+ limits =c(1979, 2025),
+ expand =expansion(mult =c(0, 0))
+)+
+scale_y_continuous(
+ limits =c(0, y_max_idx),
+ breaks =seq(0, y_max_idx, by =50),
+ labels =function(x)paste0(round(x), " (1979 = 100)"),
+ expand =expansion(mult =c(0, 0.05))
+)+
+scale_colour_manual(values =okabe_ito, name =NULL)+
+coord_cartesian(clip ="off")+
+labs(
+ title ="Military spending indexed to 1979 = 100",
+ subtitle =paste(
+"Relative change in military spending.",
+"Each country starts at 100 in 1979."
+),
+ x =NULL,
+ y =NULL
+)+
+theme_minimal(base_size =12)+
+theme(
+ panel.background =element_rect(fill =bg_col, colour =NA),
+ plot.background =element_rect(fill =bg_col, colour =NA),
+ panel.grid.major.x =element_blank(),
+ panel.grid.minor.x =element_blank(),
+ panel.grid.major.y =element_line(
+ linetype ="dashed",
+ colour ="grey80"
+),
+ panel.grid.minor.y =element_blank(),
+ axis.text =element_text(colour ="grey30"),
+ legend.position ="none",
+ plot.margin =margin(t =10, r =60, b =10, l =10),
+ plot.title =element_text(
+ hjust =0,
+ colour ="grey20",
+ face ="bold",
+ size =18,
+ family ="serif"
+),
+ plot.subtitle =element_text(
+ hjust =0,
+ colour ="grey40",
+ size =11,
+ family ="sans"
+)
+)
-
[1] 2
+
+
The indexed chart clearly shows relative growth, but it hides all information about the actual size of military budgets. This makes it good for ranking growth rates, but less suitable than the main improved chart if we care about both growth and the absolute scale of spending.
+
Conclusions and further work
+
Replicating the original chart shows that small multiples work well for seeing each country separately, but make cross-country comparisons harder. The improved combined chart, with a shared scale, line-end labels and event annotations, makes it much easier to compare both levels and trends across all six countries at once.
+
The log-scale and indexed alternatives are useful for exploring specific questions about proportional growth, but their axes are less intuitive and hide absolute spending levels, so they work better as complementary views than as the main figure. As next steps, one could normalise spending by GDP or population, add more countries or alliances, or build an interactive version that lets readers switch between these different perspectives.
+
+
