Diesel’s Crisis Premium

Diesel prices stayed only slightly above petrol for most of the 2010s. During the Ukraine and Iran conflicts, the premium widened sharply.

This two-panel makeover reveals how the diesel premium over petrol — modest and stable for most of the 2010s — surged episodically during the Ukraine (2022) and Iran (2026) conflicts, with the 2026 spike reaching +31.9p, exceeding the prior crisis peak of +24.5p. Prices are indexed to January 2013 to enable direct shock comparison, with the bottom panel isolating the spread as a single derived variable. Built with R, ggplot2, and patchwork.

Original

The original visualization comes from UK Fuel Prices

Original visualization

Makeover

Figure 1: A two-panel line chart titled “Diesel’s Crisis Premium.” The top panel shows indexed UK fuel prices (Jan 2013 = 100) for diesel and petrol from 2013 to 2026, with both fuels moving largely in tandem before diverging sharply during crisis periods. The bottom panel isolates the diesel premium over petrol in pence per liter, revealing that during stable years, the gap stayed near the pre-2022 average of roughly +3.7p. The Ukraine conflict in 2022 pushed the premium to a peak of +24.5p; the 2026 Iran conflict drove it higher still, reaching +31.9p — the widest recorded spread in the dataset. Soft gray shading marks both crisis windows across panels. Data: RAC Fuel Watch via MakeoverMonday 2026 W20.

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, patchwork
)
})

### |- figure size ----
camcorder::gg_record(
  dir    = here::here("temp_plots"),
  device = "png",
  width  = 10,
  height = 7,
  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_excel(
  here::here("data/MakeoverMonday/2026/UK Fuel Prices (2013-2026).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

## Core series: pump prices inc VAT, proper Date column
df <- df_raw |>
  mutate(date = as.Date(date)) |>
  select(date, diesel = diesel_pump_inc_vat, petrol = unleaded_pump_inc_vat) |>
  arrange(date)

## Baseline: first observation (Jan 2013 = 100)
baseline_diesel <- df$diesel[1]
baseline_petrol <- df$petrol[1]

df_indexed <- df |>
  mutate(
    diesel_idx = diesel / baseline_diesel * 100,
    petrol_idx = petrol / baseline_petrol * 100
  )

## Panel B — diesel premium over petrol (pence)
df_spread <- df |>
  mutate(spread = diesel - petrol)

## Pre-Ukraine baseline (data before Feb 2022)
pre_ukraine_mean_spread <- df_spread |>
  filter(date < as.Date("2022-02-24")) |>
  summarise(m = mean(spread)) |>
  pull(m)

## Verify spread claims
spread_summary <- df_spread |>
  mutate(era = case_when(
    date < as.Date("2022-02-24") ~ "pre-Ukraine",
    date < as.Date("2026-02-28") ~ "Ukraine era",
    TRUE ~ "Iran era"
  )) |>
  group_by(era) |>
  summarise(
    mean_spread = mean(spread),
    median_spread = median(spread),
    min_spread = min(spread),
    max_spread = max(spread),
    .groups = "drop"
  )

## Peak values for annotations
ukraine_peak <- df_spread |>
  filter(date >= as.Date("2022-02-24"), date < as.Date("2023-06-01")) |>
  slice_max(spread, n = 1, with_ties = FALSE)

iran_peak <- df_spread |>
  filter(date >= as.Date("2026-02-28")) |>
  slice_max(spread, n = 1, with_ties = FALSE)

## Latest spread value
latest <- df_spread |> slice_max(date, n = 1, with_ties = FALSE)
latest_date <- latest$date
latest_spread <- latest$spread

## Key event dates
ukraine_date <- as.Date("2022-02-24")
iran_date <- as.Date("2026-02-28")
```

5. Visualization Parameters

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

### |-  plot aesthetics ----
colors <- get_theme_colors(
  palette = list(
    col_diesel  = "#9C5A2C",   
    col_petrol  = "#8B98A3",  
    col_spread  = "#426C7A",  
    col_ref     = "#B8B4AE",   
    col_text    = "#2C2C2C",
    col_sub     = "#777777",
    col_bg      = "#FAFAF7",
    col_grid    = "#E8E8E2",
    col_band    = "#426C7A",  
    col_shock   = "#D9D4CD"   
  )
)

col_diesel <- colors$palette$col_diesel
col_petrol <- colors$palette$col_petrol
col_spread <- colors$palette$col_spread
col_ref <- colors$palette$col_ref
col_text <- colors$palette$col_text
col_sub <- colors$palette$col_sub
col_bg <- colors$palette$col_bg
col_grid <- colors$palette$col_grid
col_band <- colors$palette$col_band
col_shock <- colors$palette$col_shock
  

### |-  titles and caption ----
title_text <- str_glue("Diesel's Crisis Premium")

subtitle_text <- str_glue(
  "Diesel prices stayed only slightly above petrol for most of the 2010s. ",
  "During the Ukraine and Iran conflicts,\nthe premium widened sharply."
)

caption_text <- create_mm_caption(
  mm_year = 2026,
  mm_week = 20,
  source_text = "RAC Fuel Watch | data.world/makeovermonday/2026w20-uk-fuel-prices"
)

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

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

weekly_theme <- extend_weekly_theme(
  base_theme,
  theme(
    plot.background = element_rect(fill = col_bg, color = NA),
    panel.background = element_rect(fill = col_bg, color = NA),
    panel.grid.major.x = element_blank(),
    panel.grid.minor   = element_blank(),
    panel.grid.major.y = element_line(color = col_grid, linewidth = 0.3),
    axis.text  = element_text(color = col_sub, size = 8.5),
    axis.title = element_blank(),    
    axis.title.x = element_blank(),      
    axis.title.y = element_blank(),   
    axis.ticks = element_blank(),
    plot.margin = margin(8, 16, 8, 16)
  )
)

theme_set(weekly_theme)
```

6. Plot

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

### |-  Panel A Indexed prices (context / scaffolding) ----
p_a <- ggplot(df_indexed, aes(x = date)) +

  # Rect
  annotate("rect",
    xmin = ukraine_date, xmax = as.Date("2023-09-01"),
    ymin = -Inf, ymax = Inf,
    fill = col_shock, alpha = 0.10
  ) +
  annotate("rect",
    xmin = iran_date, xmax = as.Date("2026-04-28"),
    ymin = -Inf, ymax = Inf,
    fill = col_shock, alpha = 0.10
  ) +

  # Geom
  geom_line(aes(y = petrol_idx),
    color = col_petrol,
    linewidth = 0.5, alpha = 0.5
  ) +
  geom_line(aes(y = diesel_idx),
    color = col_diesel,
    linewidth = 0.9
  ) +
  geom_hline(
    yintercept = 100, color = col_ref,
    linewidth = 0.6, linetype = "dashed"
  ) +

  # Annotate
  annotate("text",
    x = as.Date("2026-06-01"), y = 107,
    label = "Diesel", color = col_diesel,
    size = 3.0, fontface = "bold", hjust = 0
  ) +
  annotate("text",
    x = as.Date("2026-06-01"), y = 99,
    label = "Petrol", color = col_petrol,
    size = 3.0, fontface = "bold", hjust = 0
  ) +

  # Scales
  scale_x_date(
    date_breaks = "2 years", date_labels = "%Y",
    expand = expansion(mult = c(0.01, 0.10))
  ) +
  scale_y_continuous(
    breaks = seq(80, 160, by = 20),
    labels = function(x) rep("", length(x))
  ) +
  coord_cartesian(ylim = c(72, 155), clip = "off") +
  labs(subtitle = "**Indexed prices** \u2014 Jan 2013 = 100") +

  # Theme
  theme(
    panel.grid.major.y = element_blank(),
  plot.subtitle = element_markdown(
      size = 11, color = col_sub, margin = margin(b = 4), family = fonts$text
    )
  )

### |-  Panel B: Diesel premium over petrol (payoff panel) ----
latest_label   <- paste0("+", round(latest_spread, 1), "p")
ukraine_label  <- paste0("2022 peak: +", round(ukraine_peak$spread, 1), "p")

p_b <- ggplot(df_spread, aes(x = date, y = spread)) +

  # Rect
  annotate("rect",
    xmin = ukraine_date, xmax = as.Date("2023-09-01"),
    ymin = -Inf, ymax = Inf,
    fill = col_shock, alpha = 0.10
  ) +
  annotate("rect",
    xmin = iran_date, xmax = as.Date("2026-04-28"),
    ymin = -Inf, ymax = Inf,
    fill = col_shock, alpha = 0.10
  ) +
  annotate("rect",
    xmin = min(df_spread$date), xmax = max(df_spread$date),
    ymin = 0, ymax = pre_ukraine_mean_spread,
    fill = col_band, alpha = 0.06
  ) +

  # Geoms
  geom_hline(
    yintercept = pre_ukraine_mean_spread,
    color = col_ref, linewidth = 0.5, linetype = "dashed"
  ) +
  geom_area(fill = col_spread, alpha = 0.07) +
  geom_line(color = col_spread, linewidth = 1.4) +

  # Annotate
  annotate("text",
    x = ukraine_peak$date - 60,
    y = ukraine_peak$spread + 1.8,
    label = ukraine_label,
    size = 3.0, color = col_sub, hjust = 1, fontface = "italic"
  ) +
  geom_point(
    data = ukraine_peak, aes(x = date, y = spread),
    color = col_sub, size = 2.0, shape = 21,
    fill = col_bg, stroke = 1.2
  ) +

  # Annotate
  annotate("text",
    x = latest_date - 50,
    y = latest_spread + 2.2,
    label = latest_label,
    size = 4.4, color = col_diesel, hjust = 1, fontface = "bold"
  ) +
  geom_point(
    data = latest, aes(x = date, y = spread),
    color = col_spread, size = 3.0
  ) +
  annotate("text",
    x = ukraine_peak$date + 20,
    y = ukraine_peak$spread + 5.0,
    label = "Ukraine\nconflict",
    size = 2.6, color = col_sub, hjust = 0.5, lineheight = 0.97
  ) +
  # Scales
  scale_x_date(
    date_breaks = "2 years", date_labels = "%Y",
    expand = expansion(mult = c(0.01, 0.05))
  ) +
  scale_y_continuous(
    labels = function(x) paste0(ifelse(x >= 0, "+", ""), x, "p"),
    breaks = seq(-5, 40, by = 5)
  ) +
  coord_cartesian(clip = "off") +
  # Labs
  labs(subtitle = "**Diesel premium over petrol**") +

  # Theme
  theme(
    plot.subtitle = element_markdown(
      size = 11, color = col_sub, margin = margin(b = 4), family = fonts$text
    )
  )

### |-  Combine plots ----
p_combined <- (p_a / p_b) +
    plot_layout(heights = c(0.85, 1.15)) +
    plot_annotation(
      title = title_text,
      subtitle = subtitle_text,
      caption  = caption_text,
      theme = theme(
        plot.background = element_rect(fill = col_bg, color = NA),
        plot.title = element_text(
          face = "bold", size = 30, color = col_text,
          margin = margin(t = 12, b = 4), family = fonts$title
        ),
        plot.subtitle = element_text(
          size = 12, color = col_sub, lineheight = 1.2,
          margin = margin(b = 14), family = fonts$text
        ),
        plot.caption    = element_markdown(
          size = 8, color = col_sub, hjust = 0,
          margin = margin(t = 10), family = fonts$caption
        ),
        plot.margin = margin(16, 24, 12, 24)
      )
    )
```

7. Save

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

### |-  plot image ----  
save_plot_patchwork(
  plot = p_combined, 
  type = "makeovermonday", 
  year = current_year,
  week = current_week,
  width = 10, 
  height = 7
  )
```

8. Session Info

TipExpand for Session Info

R version 4.5.3 (2026-03-11 ucrt)
Platform: x86_64-w64-mingw32/x64
Running under: Windows 11 x64 (build 26100)

Matrix products: default
  LAPACK version 3.12.1

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 janitor_2.2.1   glue_1.8.0     
 [5] scales_1.4.0    showtext_0.9-8  showtextdb_3.0  sysfonts_0.8.9 
 [9] ggtext_0.1.2    lubridate_1.9.5 forcats_1.0.1   stringr_1.6.0  
[13] dplyr_1.2.1     purrr_1.2.2     readr_2.2.0     tidyr_1.3.2    
[17] tibble_3.3.1    ggplot2_4.0.3   tidyverse_2.0.0 pacman_0.5.1   

loaded via a namespace (and not attached):
 [1] gtable_0.3.6       xfun_0.57          htmlwidgets_1.6.4  tzdb_0.5.0        
 [5] yulab.utils_0.2.4  vctrs_0.7.3        tools_4.5.3        generics_0.1.4    
 [9] curl_7.0.0         gifski_1.32.0-2    pkgconfig_2.0.3    ggplotify_0.1.3   
[13] RColorBrewer_1.1-3 skimr_2.2.2        S7_0.2.1           readxl_1.4.5      
[17] lifecycle_1.0.5    compiler_4.5.3     farver_2.1.2       textshaping_1.0.5 
[21] repr_1.1.7         codetools_0.2-20   snakecase_0.11.1   litedown_0.9      
[25] htmltools_0.5.9    yaml_2.3.12        pillar_1.11.1      camcorder_0.1.0   
[29] magick_2.9.1       commonmark_2.0.0   tidyselect_1.2.1   digest_0.6.39     
[33] stringi_1.8.7      rsvg_2.7.0         rprojroot_2.1.1    fastmap_1.2.0     
[37] grid_4.5.3         cli_3.6.6          magrittr_2.0.5     base64enc_0.1-6   
[41] withr_3.0.2        rappdirs_0.3.4     timechange_0.4.0   rmarkdown_2.31    
[45] otel_0.2.0         cellranger_1.1.0   hms_1.1.4          evaluate_1.0.5    
[49] haven_2.5.5        knitr_1.51         markdown_2.0       gridGraphics_0.5-1
[53] rlang_1.2.0        gridtext_0.1.6     Rcpp_1.1.1         xml2_1.5.2        
[57] svglite_2.2.2      rstudioapi_0.18.0  jsonlite_2.0.0     R6_2.6.1          
[61] fs_2.0.1           systemfonts_1.3.2 

9. GitHub Repository

TipExpand for GitHub Repo

The complete code for this analysis is available in mm_2026_20.qmd.

For the full repository, click here.

10. References

TipExpand for References

Primary Data (Makeover Monday): 1. Makeover Monday 2026 Week 20: UK Fuel Prices 2. Original Chart: RAC Fuel Watch — Petrol and Diesel Prices - Source: RAC Fuel Watch; average UK pump prices for unleaded petrol and diesel, 2013–2026 - Coverage: 318 bi-weekly observations from January 2013 to April 2026

Source Data: 3. RAC Fuel Watch - Coverage: Average UK pump prices in pence per litre; unleaded and diesel reported separately - Unit: Pence per litre (p/L); VAT-inclusive and VAT-exclusive prices provided; wholesale delivery prices also included 4. MakeoverMonday 2026 W20 — data.world - Excel file: UK Fuel Prices (2013–2026).xlsx; 318 rows × 7 columns

Note: Analysis uses VAT-inclusive pump prices (unleaded_pump_inc_vat, diesel_pump_inc_vat) as the primary series, reflecting lived consumer experience at the forecourt. Prices indexed to the first observation (January 2, 2013 = 100) to enable direct shock comparison across fuels and crisis periods. The diesel premium (Panel B) is derived as diesel_pump_inc_vat − unleaded_pump_inc_vat in pence per litre. The pre-2022 mean premium (~+3.7p) is computed from all observations before February 24, 2022, and serves as the stable-regime reference line. Peak spread values for Ukraine (2022) and Iran (2026) crisis windows are computed dynamically from the data.

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