Image Redesign

Introduction

Global gold market dynamics depend on both above-ground stocks and the geographic distribution of unmined reserves. According to the 2025 U.S. Geological Survey (USGS), proven gold reserves total tens of thousands of metric tons, concentrated in a few countries.

A 2025 Visual Capitalist graphic ranks nations by unmined reserves using USGS data. While visually striking, it limits precise quantitative comparison and analytical use.

This project reconstructs the original visualization using the same USGS data, providing descriptive statistics and redesigned R Markdown visualizations for more accurate analysis.

Image Selection

This image was chosen to analyze the effectiveness of 3D volume-based encoding for univariate data. It provides a case study in how visual scaling (cubes) can potentially exaggerate or obscure the actual numerical differences between observations.

Original Design

The graphic suffers from a high “Lie Factor” because it uses 3D cubes to represent 1D data; when a value doubles, the cube’s volume increases by 2^3 (8 times), visually exaggerating the differences. It lacks a common baseline (like a Y-axis), making direct comparison impossible without reading the labels. Finally, it lacks proportional context, as it does not show how these reserves relate to the total global supply.

Redesign 1

library(tidyverse)

── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
✔ dplyr     1.2.0     ✔ readr     2.2.0
✔ forcats   1.0.1     ✔ stringr   1.6.0
✔ ggplot2   4.0.2     ✔ tibble    3.3.1
✔ lubridate 1.9.5     ✔ tidyr     1.3.2
✔ purrr     1.2.1     
── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag()    masks stats::lag()
ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors

gold_data <- read_csv("C:/Users/aryaa/Documents/GMU/R for Data Analytics - Posit Cloud_files/stat-515/data/data.csv")

Rows: 19 Columns: 4
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr (1): Country
dbl (3): Production_2023, Production_2024e, Reserves

ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

# Your code to recreate/redesign the plot goes here

reserves = gold_data %>%
  filter(
    !Country == "World total (rounded)",
    !is.na(Reserves)
  )

# Sort by Reserves
#sorted_reserves <- reserves %>%
#  arrange(desc(reserves))

#sorted_reserves

ggplot(reserves, aes(x = reorder(Country, Reserves), y = Reserves)) +
  geom_bar(stat = "identity", fill = "steelblue") +
  coord_flip() +
  labs(
    title = "Gold Reserves by Country ",
    x = "Country",
    y = "Reserves (Metric Tons)"
  ) +
  theme_minimal()

The raw dataset included a “world total” value and null entries, which would have skewed the visualization’s scale and caused errors in the mapping. Using the tidyverse library in R, I filtered out the aggregate “world total” to focus on individual country comparisons and removed NA rows to ensure data integrity. By transitioning to a ggplot2 bar graph with a common baseline, I corrected the visual distortion caused by the original 3D cubes, ensuring that the bar lengths now scale linearly with the actual metric tons.

Redesign 2

### Redesign 2: Proportional Analysis

::: {.cell}

```{.r .cell-code}
# This must be inside the curly brackets to run in the script
library(treemapify)

ggplot(reserves, aes(area = Reserves, fill = Reserves, label = Country)) +
  geom_treemap() +
  geom_treemap_text(colour = "white", place = "centre", grow = TRUE) +
  scale_fill_gradient(low = "darkblue", high = "gold") +
  labs(title = "Proportional Share of Global Gold Reserves")

:::

While the first redesign (bar chart) allowed for precise ranking, this Treemap provides the ‘part-to-whole’ context that was missing in the original image. By using 2D area encoding, we eliminate the ‘Lie Factor’ found in the original 3D cubes. In this view, it is immediately clear that most of the total unmined gold is concentrated in the top two countries (Russia and Australia), which together account for the rest of the world.

Conclusion

This project successfully transformed a visually decorative infographic into an analytically accurate statistical report. By removing the 3D “Lie Factor” and implementing a common baseline through a bar chart and a tree map, the data now adheres to Tufte’s principles of graphical integrity. The result is a clearer, more honest representation of global gold reserves.