25 Years of Currency Shifts

Change in Big Mac purchasing power vs the US dollar, 2000–2025. Most currencies saw Big Mac prices rise relative to the dollar

MakeoverMonday
2026
A makeover of The Economist’s Big Mac Index reveals how purchasing power shifted against the US dollar from 2000 to 2025. This diverging bar chart shows 12 of 15 currencies weakened significantly—Israel, Japan, and Taiwan lost over 60 percentage points—while only Poland, Czech Republic, and Australia strengthened. Built with R and ggplot2, using semantic color choice and directional arrows for clarity.
Author

Steven Ponce

Published

February 10, 2026

Original

The original visualization comes from Global Big Mac Index

Original visualization

Makeover

Figure 1: Diverging bar chart of Big Mac Index changes vs US dollar, 2000-2025. Top 15 currency shifts from Israel (-78pp) to Poland (+33pp). Twelve currencies weakened (orange), three strengthened (blue). Arrows indicate direction: more expensive (left) vs cheaper (right).

Steps to Create this Graphic

1. Load Packages & Setup

Show code
```{r}
#| label: load
#| warning: false
#| message: false      
#| results: "hide"     

## 1. LOAD PACKAGES & SETUP ----
suppressPackageStartupMessages({
  if (!require("pacman")) install.packages("pacman")
  pacman::p_load(
    tidyverse, ggtext, showtext, scales, glue,
    janitor, lubridate
)
})

### |- figure size ----
# camcorder::gg_record(
#     dir    = here::here("temp_plots"),
#     device = "png",
#     width  = 10,
#     height = 8,
#     units  = "in",
#     dpi    = 320
# )

# Source utility functions
suppressMessages(source(here::here("R/utils/fonts.R")))
source(here::here("R/utils/social_icons.R"))
source(here::here("R/utils/image_utils.R"))
source(here::here("R/themes/base_theme.R"))
```

2. Read in the Data

Show code
```{r}
#| label: read
#| include: true
#| eval: true
#| warning: false
#| 

big_mac_index_raw <- read_csv(
  here::here("data/MakeoverMonday/2026/big-mac-full-index.csv")) |>
  clean_names()
```

3. Examine the Data

Show code
```{r}
#| label: examine
#| include: true
#| eval: true
#| results: 'hide'
#| warning: false

glimpse(big_mac_index_raw)
```

4. Tidy Data

Show code
```{r}
#| label: tidy
#| warning: false

### |-  prepare data ----
time_series_data <- big_mac_index_raw |>
  mutate(date = mdy(date))

latest_date <- max(time_series_data$date, na.rm = TRUE)

current_data <- time_series_data |>
  filter(date == latest_date) |>
  arrange(desc(usd_raw))

### |-  calculate 25-year change ----
first_obs <- time_series_data |>
  filter(year(date) == 2000) |>
  group_by(name) |>
  slice_min(date, n = 1, with_ties = FALSE) |>
  ungroup() |>
  select(name, usd_raw_start = usd_raw)

# Compute movers from 2000
currency_movers <- current_data |>
  inner_join(first_obs, by = "name") |>
  mutate(
    total_change = (usd_raw - usd_raw_start) * 100,
    direction = if_else(total_change > 0, "Strengthened", "Weakened")
  ) |>
  slice_max(order_by = abs(total_change), n = 15, with_ties = FALSE) |>
  arrange(total_change) |>
  mutate(name = fct_inorder(name))
```

5. Visualization Parameters

Show code
```{r}
#| label: params
#| include: true
#| warning: false

### |-  plot aesthetics ----
colors <- get_theme_colors(
  palette = list(
    strengthened  = "#2C5F8D",    
    weakened      = "#C97A4A",    
    neutral_dark  = "#5378A6",
    neutral_light = "gray90"
  )
)

### |-  Main titles ----
title_text <- "25 Years of Currency Shifts"

subtitle_text <- str_glue(
  "Change in Big Mac purchasing power vs the US dollar, 2000–2025. Most currencies saw Big Mac prices rise relative to the dollar"
)

caption_text <- create_mm_caption(
  mm_year = 2026, mm_week = 06,
  source_text = "Data: The Economist Big Mac Index<br>**Note:** Values show change in Big Mac price competitiveness over 25 years. Based on raw prices (not GDP-adjusted)."
)

### |-  fonts ----
setup_fonts()
fonts <- get_font_families()

### |-  plot theme ----

# Start with base theme
base_theme <- create_base_theme(colors)

# Add weekly-specific theme elements
weekly_theme <- extend_weekly_theme(
  base_theme,
  theme(
    # # Text styling
    plot.title = element_text(
      size = rel(1.3), family = fonts$title, face = "bold",
      color = colors$title, lineheight = 1.1, hjust = 0,
      margin = margin(t = 5, b = 10)
    ),
    plot.subtitle = element_text(
      size = rel(0.8), family = fonts$subtitle, face = "italic",
      color = alpha(colors$subtitle, 0.9), lineheight = 1.1,
      margin = margin(t = 0, b = 20)
    ),

    # Legend formatting
    legend.position = "plot",
    legend.justification = "right",
    legend.margin = margin(l = 12, b = 5),
    legend.key.size = unit(0.8, "cm"),
    legend.box.margin = margin(b = 10),

    # Axis formatting
    # axis.line.x = element_line(color = "#252525", linewidth = .1),
    # axis.ticks.y = element_blank(),
    axis.ticks.x = element_line(color = "gray", linewidth = 0.5),
    axis.title.x = element_text(
      face = "bold", size = rel(0.85),
      margin = margin(t = 10), family = fonts$subtitle,
      color = "gray40"
    ),
    axis.title.y = element_text(
      face = "bold", size = rel(0.85),
      margin = margin(r = 10), family = fonts$subtitle,
      color = "gray40"
    ),
    axis.text.x = element_text(
      size = rel(0.85), family = fonts$subtitle,
      color = "gray40"
    ),
    axis.text.y = element_markdown(
      size = rel(0.85), family = fonts$subtitle,
      color = "gray40"
    ),

    # Grid lines
    panel.grid.minor = element_line(color = "#ecf0f1", linewidth = 0.2),
    panel.grid.major = element_line(color = "#ecf0f1", linewidth = 0.4),

    # Margin
    plot.margin = margin(20, 20, 20, 20)
  )
)

# Set theme
theme_set(weekly_theme)
```

6. Plot

Show code
```{r}
#| label: plot
#| warning: false

### |- final plot ----
p <- ggplot(currency_movers, aes(x = total_change, y = name)) +
  # Annotate
  annotate(
    "rect",
    xmin = -0.75, xmax = 0.75,
    ymin = -Inf, ymax = Inf,
    fill = "gray95", alpha = 1
  ) +
  # Geoms
  geom_vline(xintercept = 0, linewidth = 0.8, color = "gray55") +
  geom_col(aes(fill = direction), width = 0.72, show.legend = FALSE) +
  geom_text(
    aes(
      label = sprintf("%+.0f", total_change),
      hjust = if_else(total_change >= 0, -0.10, 1.10)
    ),
    size = 3,
    fontface = "bold"
  ) +
  # Annotate
  annotate(
    "segment",
    x = -63, xend = -68,
    y = 14, yend = 14,
    arrow = arrow(length = unit(0.2, "cm"), type = "closed"),
    color = colors$palette$weakened,
    linewidth = 0.8
  ) +
  annotate(
    "text",
    x = -68, y = 14.5,
    label = "More expensive\nin USD",
    hjust = 0,
    vjust = 0,
    size = 2.8,
    color = colors$palette$weakened,
    fontface = "italic",
    lineheight = 0.9
  ) +
  annotate(
    "segment",
    x = 40, xend = 45,
    y = 14, yend = 14,
    arrow = arrow(length = unit(0.2, "cm"), type = "closed"),
    color = colors$palette$strengthened,
    linewidth = 0.8
  ) +
  annotate(
    "text",
    x = 40, y = 14.5,
    label = "Cheaper\nin USD",
    hjust = 0,
    vjust = 0,
    size = 2.8,
    color = colors$palette$strengthened,
    fontface = "italic",
    lineheight = 0.9
  ) +
  # Scales
  scale_fill_manual(values = c(
    "Strengthened" = colors$palette$strengthened,
    "Weakened" = colors$palette$weakened
  )) +
  scale_x_continuous(
    labels = label_number(style_positive = "plus"),
    expand = expansion(mult = c(0.10, 0.12)),
    breaks = seq(-60, 30, by = 20)
  ) +
  coord_cartesian(clip = "off") +
  # Labs
  labs(
    title = title_text,
    subtitle = subtitle_text,
    caption = caption_text,
    x = "Change in valuation since 2000 (pp, USD-based Big Mac Index)",
    y = NULL
  ) +
  # Theme
  theme(
    panel.grid.minor = element_blank(),
    panel.grid.major.y = element_blank(),
    panel.grid.major.x = element_line(color = "gray90", linewidth = 0.3),
    plot.title = element_markdown(
      size = rel(1.4),
      family = fonts$title,
      face = "bold",
      color = colors$title,
      lineheight = 1.15,
      margin = margin(t = 0, b = 5)
    ),
    plot.subtitle = element_markdown(
      size = rel(0.8),
      family = fonts$subtitle,
      face = "italic",
      color = alpha(colors$subtitle, 0.88),
      lineheight = 1.5,
      margin = margin(t = 5, b = 25)
    ),
    plot.caption = element_markdown(
      size = rel(0.5),
      family = fonts$subtitle,
      color = colors$caption,
      hjust = 0,
      lineheight = 1.4,
      margin = margin(t = 20, b = 5)
    )
  )
```

7. Save

Show code
```{r}
#| label: save
#| warning: false

### |-  plot image ----  
save_plot(
  plot = p, 
  type = "makeovermonday", 
  year = current_year,
  week = current_week,
  width = 10, 
  height = 8
  )
```

8. Session Info

R version 4.4.1 (2024-06-14 ucrt)
Platform: x86_64-w64-mingw32/x64
Running under: Windows 11 x64 (build 26100)

Matrix products: default


locale:
[1] LC_COLLATE=English_United States.utf8 
[2] LC_CTYPE=English_United States.utf8   
[3] LC_MONETARY=English_United States.utf8
[4] LC_NUMERIC=C                          
[5] LC_TIME=English_United States.utf8    

time zone: America/New_York
tzcode source: internal

attached base packages:
[1] stats     graphics  grDevices datasets  utils     methods   base     

other attached packages:
 [1] here_1.0.1      janitor_2.2.0   glue_1.8.0      scales_1.4.0   
 [5] showtext_0.9-7  showtextdb_3.0  sysfonts_0.8.9  ggtext_0.1.2   
 [9] lubridate_1.9.5 forcats_1.0.1   stringr_1.6.0   dplyr_1.2.0    
[13] purrr_1.2.1     readr_2.1.6     tidyr_1.3.2     tibble_3.3.1   
[17] ggplot2_4.0.2   tidyverse_2.0.0 pacman_0.5.1   

loaded via a namespace (and not attached):
 [1] gtable_0.3.6       xfun_0.56          htmlwidgets_1.6.4  tzdb_0.5.0        
 [5] vctrs_0.7.1        tools_4.4.0        generics_0.1.3     curl_7.0.0        
 [9] parallel_4.4.0     pkgconfig_2.0.3    RColorBrewer_1.1-3 S7_0.2.1          
[13] lifecycle_1.0.5    compiler_4.4.0     farver_2.1.2       textshaping_1.0.4 
[17] codetools_0.2-20   snakecase_0.11.1   litedown_0.9       htmltools_0.5.8.1 
[21] yaml_2.3.12        pillar_1.11.1      crayon_1.5.3       magick_2.9.0      
[25] commonmark_2.0.0   tidyselect_1.2.1   digest_0.6.37      stringi_1.8.7     
[29] rprojroot_2.0.4    fastmap_1.2.0      grid_4.4.0         cli_3.6.5         
[33] magrittr_2.0.4     withr_3.0.2        bit64_4.5.2        timechange_0.4.0  
[37] rmarkdown_2.30     bit_4.5.0          ragg_1.5.0         hms_1.1.4         
[41] evaluate_1.0.5     knitr_1.51         markdown_2.0       rlang_1.1.7       
[45] gridtext_0.1.5     Rcpp_1.1.1         xml2_1.5.2         renv_1.0.3        
[49] rstudioapi_0.17.1  vroom_1.6.5        jsonlite_2.0.0     R6_2.6.1          
[53] systemfonts_1.3.1

9. GitHub Repository

The complete code for this analysis is available in mm_2026_06.qmd.

For the full repository, click here.

10. References

Primary Data (Makeover Monday):

  1. Makeover Monday 2026 Week 6: Big Mac Index
  2. Original Article: The Big Mac Index
    • Source: The Economist
    • Coverage: Big Mac prices and currency valuations, 2000–2025

Source Data:

  1. Big Mac Index Full Dataset: GitHub Repository
    • Source: The Economist
    • Data includes: Raw and GDP-adjusted indices
    • Big Mac prices from: McDonald’s directly and reporting worldwide
    • Exchange rates from: Refinitiv Datastream (July 2022 onwards)
    • GDP data from: IMF World Economic Outlook reports
    • Citation: The Economist. (2025). Big Mac Index Data. Retrieved from https://github.com/TheEconomist/big-mac-data

Methodology References:

  1. The Economist’s Big Mac Index Methodology: README Documentation
    • Explains raw vs GDP-adjusted calculations
    • Details data sources and collection methods
    • Last updated: July 2022 methodology revision

Note: This visualization uses the raw index (not GDP-adjusted) to show changes in actual purchasing power of Big Macs across currencies from 2000 to 2025.

11. Custom Functions Documentation

This analysis uses custom functions from my personal module library for efficiency and consistency across projects.

Functions Used:

  • fonts.R: setup_fonts(), get_font_families() - Font management with showtext
  • social_icons.R: create_social_caption() - Generates formatted social media captions
  • image_utils.R: save_plot() - Consistent plot saving with naming conventions
  • base_theme.R: create_base_theme(), extend_weekly_theme(), get_theme_colors() - Custom ggplot2 themes

Why custom functions?
These utilities standardize theming, fonts, and output across all my data visualizations. The core analysis (data tidying and visualization logic) uses only standard tidyverse packages.

Source Code:
View all custom functions → GitHub: R/utils

Back to top

Citation

BibTeX citation:
@online{ponce2026,
  author = {Ponce, Steven},
  title = {25 {Years} of {Currency} {Shifts}},
  date = {2026-02-10},
  url = {https://stevenponce.netlify.app/data_visualizations/MakeoverMonday/2026/mm_2026_06.html},
  langid = {en}
}
For attribution, please cite this work as:
Ponce, Steven. 2026. “25 Years of Currency Shifts.” February 10. https://stevenponce.netlify.app/data_visualizations/MakeoverMonday/2026/mm_2026_06.html.