The World’s Fastest Cars: Some Sell Engineering, Others Sell Exclusivity

Top: deviation from the median in cost per horsepower and speed delivered per horsepower. Burgundy = worse value; steel blue = better value. Bottom: the same cars by units produced. Each filled square = one car built.

Redesigning a raw data table into a two-panel patchwork: diverging bars comparing cost vs. speed efficiency per horsepower, paired with a unit chart showing the extreme scarcity of each model.

Original

The original visualization comes from Fastest Cars in the World (2026)

Original visualization

Makeover

Figure 1: A three-panel chart about the world’s fastest cars. The top two panels are diverging bar charts showing deviation from the median in cost per horsepower (left) and speed per horsepower (right). Burgundy bars indicate worse value; steel blue indicates better value. The Bugatti Chiron SS 300+ is the most expensive per horsepower at +$1,151 above median, while the Rimac C Two and Hennessey models sit below median cost. The bottom panel is a waffle chart showing the total units produced for all eight cars. Each dark gray square represents one car built against a light gray grid of equal size, making rarity directly comparable. The Rimac C Two leads with 150 units; the Hennessey Venom GT is the rarest at just 13 units.

Steps to Create this Graphic

1. Load Packages & Setup

```{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, readxl, patchwork   
)
})

### |- figure size ----
camcorder::gg_record(
  dir    = here::here("temp_plots"),
  device = "png",
  width  = 14,
  height = 11,
  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

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

df_raw <- readxl::read_xlsx(
  here::here("data/MakeoverMonday/2026/The fastest cars.xlsx")) |>
  clean_names()
```

3. Examine the Data

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

glimpse(df_raw)
skimr::skim_without_charts(df_raw)
```

4. Tidy Data

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

df <- df_raw |>
  rename(
    top_speed_kmh = top_speed_km_h,
    hp            = horsepower_hp,
    torque        = torque_lb_ft,
    accel_sec     = x0_100_km_h_approximate_in_sec,
    units         = units_produced,
    price_per_hp  = price_per_hp_usd,
    speed_per_hp  = speed_per_hp_km_h_per_hp
  ) |>
  mutate(
    car_label = case_when(
      car == "Hennessey Venom F5"              ~ "Hennessey Venom F5",
      car == "Bugatti Chiron Super Sport 300+" ~ "Bugatti Chiron SS 300+",
      car == "SSC Tuatara"                     ~ "SSC Tuatara",
      car == "Koenigsegg Jesko"                ~ "Koenigsegg Jesko",
      car == "Koenigsegg Agera RS"             ~ "Koenigsegg Agera RS",
      car == "Hennessey Venom GT"              ~ "Hennessey Venom GT",
      car == "Bugatti Veyron 16.4 Super Sport" ~ "Bugatti Veyron SS",
      car == "Rimac C Two"                     ~ "Rimac C Two"
    ),
    car_short = case_when(
      car == "Hennessey Venom F5"              ~ "Venom F5",
      car == "Bugatti Chiron Super Sport 300+" ~ "Chiron SS 300+",
      car == "SSC Tuatara"                     ~ "Tuatara",
      car == "Koenigsegg Jesko"                ~ "Jesko",
      car == "Koenigsegg Agera RS"             ~ "Agera RS",
      car == "Hennessey Venom GT"              ~ "Venom GT",
      car == "Bugatti Veyron 16.4 Super Sport" ~ "Veyron SS",
      car == "Rimac C Two"                     ~ "C Two"
    )
  )
```

5. Visualization Parameters

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

### |- Colors ----
colors <- get_theme_colors(
  palette = list(
    burgundy      = "#6D1A36",
    steel         = "#4A6FA5",
    neutral_dark  = "#2B2B2B",
    neutral_mid   = "#6B6B6B",
    neutral_light = "#E5E5E5",
    grid_light    = "#D9D9D9",
    empty_tile    = "#F0F0F0"
  )
)

### |- Titles and caption ----
title_text <- "The World's Fastest Cars: Some Sell Engineering, Others Sell Exclusivity"

subtitle_text <- glue(
  "Top: deviation from the median in <b>cost per horsepower</b> and ",
  "<b>speed delivered per horsepower</b>. ",
  "<span style='color:{colors$palette$burgundy}'>Burgundy</span> = worse value; ",
  "<span style='color:{colors$palette$steel}'>steel blue</span> = better value.<br>",
  "Bottom: the same cars by <b>units produced</b>. Each filled square = one car built."
)

caption_text <- create_mm_caption(
  mm_year     = 2026,
  mm_week     = 11,
  source_text = "RankRed.com · data.world/makeovermonday"
)

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

### |- plot theme ----
base_theme <- create_base_theme(colors)

weekly_theme <- extend_weekly_theme(
  base_theme,
  theme(
    panel.grid.major.x = element_line(color = colors$palette$grid_light, linewidth = 0.3),
    panel.grid.major.y = element_blank(),
    axis.ticks         = element_blank(),
    axis.text.x        = element_text(size = 9, color = colors$palette$neutral_mid),
    axis.text.y        = element_text(size = 10, color = colors$palette$neutral_mid),
    axis.title         = element_blank(),
    plot.title = element_text(
      size = rel(1.25), family = fonts$title, face = "bold",
      color = colors$palette$neutral_dark, lineheight = 1.05,
      margin = margin(t = 3, b = 4)
    ),
    plot.subtitle = element_text(
      size = rel(0.8), family = fonts$subtitle,
      color = colors$palette$neutral_mid, lineheight = 1.1,
      margin = margin(t = 0, b = 8)
    )
  )
)

theme_set(weekly_theme)
```

6. Plot

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

### |- PANEL A: Diverging Bars — Luxury Premium vs. Engineering Efficiency ----

# Derived Data ----
med_price_hp <- median(df$price_per_hp)
med_speed_hp <- median(df$speed_per_hp)

plot_df <- df |>
  mutate(
    price_hp_dev = price_per_hp - med_price_hp,
    speed_hp_dev = speed_per_hp - med_speed_hp,
    cost_flag    = if_else(price_hp_dev > 0, "Above median cost", "Below median cost"),
    eff_flag     = if_else(speed_hp_dev > 0, "Above median efficiency", "Below median efficiency")
  ) |>
  arrange(desc(price_hp_dev)) |>
  mutate(car_order = factor(car_short, levels = rev(car_short)))

### |-  left panel: cost per HP ----
p_cost <- plot_df |>
  ggplot(aes(x = price_hp_dev, y = car_order, fill = cost_flag)) +
  geom_col(width = 0.62) +
  geom_vline(xintercept = 0, linewidth = 0.6, color = colors$palette$neutral_dark) +
  geom_text(
    aes(
      label = if_else(price_hp_dev > 0,
                      paste0("+", dollar(round(price_hp_dev))),
                      dollar(round(price_hp_dev))),
      x = if_else(price_hp_dev > 0, price_hp_dev + 28, price_hp_dev - 28)
    ),
    size = 3,
    color = colors$palette$neutral_dark,
    fontface = "bold",
    hjust = if_else(plot_df$price_hp_dev > 0, 0, 1)
  ) +
  annotate(
    "segment",
    x = 0, xend = 0, y = 0.55, yend = 1.15,
    color = colors$palette$neutral_mid, linewidth = 0.35, linetype = 2
  ) +
  annotate(
    "text",
    x = 20, y = 0.95,
    label = glue("median = {dollar(round(med_price_hp))}/HP"),
    hjust = 0, size = 2.7,
    color = colors$palette$neutral_mid, fontface = "italic"
  ) +
  scale_fill_manual(
    values = c(
      "Above median cost" = colors$palette$burgundy,
      "Below median cost" = colors$palette$steel
    ),
    guide = "none"
  ) +
  scale_x_continuous(
    labels = label_dollar(),
    limits = c(-520, 1325),
    expand = expansion(mult = c(0, 0))
  ) +
  labs(
    title    = "Cost per horsepower",
    subtitle = "Deviation from median ($/HP) · sorted most to least expensive"
  ) +
  theme(
    plot.title    = element_text(size = rel(1.0), face = "bold"),
    plot.subtitle = element_text(size = rel(0.76), margin = margin(b = 8)),
    axis.text.y   = element_text(color = colors$palette$neutral_mid)
  )

### |-  right panel: speed per HP ----
p_speed <- plot_df |>
  ggplot(aes(x = speed_hp_dev, y = car_order, fill = eff_flag)) +
  geom_col(width = 0.62) +
  geom_vline(xintercept = 0, linewidth = 0.6, color = colors$palette$neutral_dark) +
  geom_text(
    aes(
      label = if_else(speed_hp_dev > 0,
                      paste0("+", number(speed_hp_dev, accuracy = 0.001)),
                      number(speed_hp_dev, accuracy = 0.001)),
      x = if_else(speed_hp_dev > 0, speed_hp_dev + 0.004, speed_hp_dev - 0.004)
    ),
    size = 3,
    color = colors$palette$neutral_dark,
    fontface = "bold",
    hjust = if_else(plot_df$speed_hp_dev > 0, 0, 1)
  ) +
  annotate(
    "segment",
    x = 0, xend = 0, y = 0.55, yend = 1.15,
    color = colors$palette$neutral_mid, linewidth = 0.35, linetype = 2
  ) +
  annotate(
    "text",
    x = 0.002, y = 0.95,
    label = glue("median = {number(med_speed_hp, accuracy = 0.001)} km/h/HP"),
    hjust = 0, size = 2.7,
    color = colors$palette$neutral_mid, fontface = "italic"
  ) +
  scale_fill_manual(
    values = c(
      "Above median efficiency" = colors$palette$steel,
      "Below median efficiency" = colors$palette$burgundy
    ),
    guide = "none"
  ) +
  scale_x_continuous(
    labels = label_number(accuracy = 0.01),
    limits = c(-0.10, 0.07),
    expand = expansion(mult = c(0, 0))
  ) +
  labs(
    title    = "Speed per horsepower",
    subtitle = "Deviation from median (km/h per HP) · same car order as left panel"
  ) +
  theme(
    plot.title    = element_text(size = rel(1.0), face = "bold"),
    plot.subtitle = element_text(size = rel(0.76), margin = margin(b = 8)),
    axis.text.y   = element_blank()
  )

### |- PANEL B: Unit Chart — How Many Were Ever Built? ----

# Derived Data ----
rarity_base <- df |>
  transmute(
    car_short,
    units,
    facet_label = glue("{car_short}\n{units} units")
  ) |>
  arrange(desc(units)) |>
  mutate(facet_label = factor(facet_label, levels = facet_label))

max_units <- max(rarity_base$units)
n_cols    <- 15
n_rows    <- ceiling(max_units / n_cols)

rarity_full <- rarity_base |>
  rowwise() |>
  reframe(
    car_short   = car_short,
    facet_label = facet_label,
    units       = units,
    unit_id     = seq_len(n_cols * n_rows)
  ) |>
  mutate(
    filled = unit_id <= units,
    col    = (unit_id - 1) %% n_cols + 1,
    row    = (unit_id - 1) %/% n_cols + 1
  )

### |-  unit chart ----
p_rarity <- rarity_full |>
  ggplot(aes(x = col, y = -row, fill = filled)) +
  geom_tile(width = 0.8, height = 0.8, color = "white", linewidth = 0.45) +
  facet_wrap(~facet_label, ncol = 4) +
  scale_fill_manual(
    values = c("TRUE" = "#4A4A4A", "FALSE" = "#E8E8E8"),
    guide  = "none"   # filled vs. empty is self-evident; no legend needed
  ) +
  labs(
    title    = "How many were ever built?",
    subtitle = "Each square represents one car. Common empty grid makes rarity comparable across models."
  ) +
  coord_equal() +
  theme(
    axis.text.x        = element_blank(),
    axis.text.y        = element_blank(),
    axis.line          = element_blank(),
    panel.grid         = element_blank(),
    panel.grid.major   = element_blank(),
    panel.grid.minor   = element_blank(),
    panel.grid.major.x = element_blank(),
    strip.text = element_text(
      size = rel(0.8), face = "bold",
      color = colors$palette$neutral_dark,
      lineheight = 1.3
    ),
    panel.spacing   = unit(1.1, "lines"),
    legend.position = "none"
  )

### |-  Combined Plots ----
p_top <- p_cost + p_speed + plot_layout(widths = c(1.15, 1))

combined_plots <- p_top / p_rarity +
  plot_layout(heights = c(1.05, 1)) +
  plot_annotation(
    title    = title_text,
    subtitle = subtitle_text,
    caption  = caption_text,
    theme = theme(
      plot.title = element_text(
        size = rel(1.42), face = "bold",
        color = colors$palette$neutral_dark,
        margin = margin(b = 5)
      ),
      plot.subtitle = element_markdown(
        size = rel(0.88),
        color = colors$palette$neutral_mid,
        lineheight = 1.15,
        margin = margin(b = 12)
      ),
      plot.caption = element_markdown(
        size = rel(0.70),
        color = colors$palette$neutral_mid,
        hjust = 0
      ),
      plot.margin = margin(t = 14, r = 16, b = 12, l = 16)
    )
  )
```

7. Save

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

### |-  plot image ----  
save_plot_patchwork(
  plot = combined_plots, 
  type = "makeovermonday", 
  year = current_year,
  week = current_week,
  width = 14, 
  height = 11
  )
```

8. Session Info

TipExpand for Session Info

R version 4.3.1 (2023-06-16 ucrt)
Platform: x86_64-w64-mingw32/x64 (64-bit)
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 utils     datasets  methods   base     

other attached packages:
 [1] here_1.0.2      patchwork_1.3.2 readxl_1.4.5    janitor_2.2.1  
 [5] glue_1.8.0      scales_1.4.0    showtext_0.9-7  showtextdb_3.0 
 [9] sysfonts_0.8.9  ggtext_0.1.2    lubridate_1.9.5 forcats_1.0.1  
[13] stringr_1.6.0   dplyr_1.2.0     purrr_1.2.1     readr_2.2.0    
[17] tidyr_1.3.2     tibble_3.2.1    ggplot2_4.0.2   tidyverse_2.0.0
[21] 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_4.3.1        generics_0.1.4     yulab.utils_0.2.4 
 [9] curl_7.0.0         pkgconfig_2.0.3    ggplotify_0.1.3    RColorBrewer_1.1-3
[13] S7_0.2.0           lifecycle_1.0.5    compiler_4.3.1     farver_2.1.2      
[17] codetools_0.2-19   snakecase_0.11.1   litedown_0.9       htmltools_0.5.9   
[21] yaml_2.3.10        pillar_1.10.2      magick_2.8.6       commonmark_2.0.0  
[25] tidyselect_1.2.1   digest_0.6.37      stringi_1.8.7      labeling_0.4.3    
[29] rprojroot_2.1.1    fastmap_1.2.0      grid_4.3.1         cli_3.6.4         
[33] magrittr_2.0.3     withr_3.0.2        rappdirs_0.3.4     timechange_0.4.0  
[37] rmarkdown_2.30     otel_0.2.0         cellranger_1.1.0   hms_1.1.4         
[41] evaluate_1.0.5     knitr_1.51         markdown_2.0       gridGraphics_0.5-1
[45] rlang_1.1.7        gridtext_0.1.6     Rcpp_1.1.1         xml2_1.5.2        
[49] rstudioapi_0.18.0  jsonlite_2.0.0     R6_2.6.1           fs_1.6.6          

9. GitHub Repository

TipExpand for GitHub Repo

The complete code for this analysis is available in mm_2026_11.qmd.

For the full repository, click here.

10. References

TipExpand for References

Primary Data (Makeover Monday): 1. Makeover Monday 2026 Week 11: 8 Fastest Cars in the World (2026) 2. Original Article: Fastest Cars in the World - Source: RankRed - Coverage: Top 8 fastest production cars as of 2026, including speed, power, price, and production figures

Source Data: 3. Dataset: 2026 Week 11 — 8 Fastest Cars in the World - Source: RankRed via data.world/makeovermonday - Data includes: Car name, manufacturer, country, engine type, top speed (mph and km/h), horsepower, torque, 0–100 km/h time, price (USD), units produced, price per HP, and speed per HP - Scope: 8 production cars ranked by verified top speed as of 2026

Note: All values are reported directly from the source. Deviation from median was computed for price per HP and speed per HP to enable the diverging bar comparison. No external data sources, price deflators, or population adjustments were applied.

11. Custom Functions Documentation

Note📦 Custom Helper Functions

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