The Rivalry That Became a Gap

U.S. and Saudi oil production tracked closely for decades. After 2009, U.S. production surged — creating a gap that has continued to widen.

MakeoverMonday

2026

U.S. and Saudi Arabia oil production tracked closely for decades — then shale changed everything. This gap chart uses dual-shaded ribbon fills to encode two eras: competitive parity before 2009 and accelerating U.S. dominance after. Redesigned from an Our World in Data multi-line chart to reduce cognitive load and foreground the structural shift in global oil leadership.

Author

Steven Ponce

Published

April 6, 2026

Original

The original visualization comes from Global Oil Production

Makeover

Figure 1: A gap chart showing U.S. and Saudi Arabia oil production in terawatt-hours from 1965 to 2024. The area between the two lines is shaded in two colors: warm gray before 2009, labeled “Production tracked closely,” and steel blue after 2009, labeled “U.S. pulls ahead.” A dashed vertical line marks the 2009 shale boom inflection point. The U.S. line ends at 10k TWh in 2024; Saudi Arabia at 5.9k TWh. An annotation notes that U.S. output is approximately 1.7 times that of Saudi Arabia. The chart illustrates a structural shift from decades of competitive parity to clear U.S. dominance following the shale revolution.

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
)
})

### |- figure size ----
camcorder::gg_record(
  dir    = here::here("temp_plots"),
  device = "png",
  width  = 8,
  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
#| 

df_raw <- read_csv(
  here::here("data/MakeoverMonday/2026/oil-production-by-country.csv")) |>
  clean_names()
```

3. Examine the Data

Show code

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

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

4. Tidy Data

Show code

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

df_two <- df_raw |>
  filter(
    entity %in% c("United States", "Saudi Arabia"),
    year >= 1965
  ) |>
  mutate(oil_k = oil / 1000) |>
  select(entity, year, oil_k)

df_wide <- df_two |>
  pivot_wider(names_from = entity, values_from = oil_k) |>
  rename(us = `United States`, saudi = `Saudi Arabia`) |>
  mutate(era = if_else(year < 2009, "rivalry", "gap"))

# Endpoint values for labels
us_2024    <- df_wide |> filter(year == 2024) |> pull(us)
saudi_2024 <- df_wide |> filter(year == 2024) |> pull(saudi)

# Ratio for "nearly 2×" annotation 
ratio_2024 <- round(us_2024 / saudi_2024, 1)

# Label anchor positions
us_1988    <- df_wide |> filter(year == 1988) |> pull(us)
saudi_1988 <- df_wide |> filter(year == 1988) |> pull(saudi)
rival_mid  <- (us_1988 + saudi_1988) / 2

# Gap label
us_2018    <- df_wide |> filter(year == 2018) |> pull(us)
saudi_2018 <- df_wide |> filter(year == 2018) |> pull(saudi)
gap_mid    <- (us_2018 + saudi_2018) / 2
```

5. Visualization Parameters

Show code

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

### |-  plot aesthetics ----
colors <- get_theme_colors(
  palette = list(
    us         = "#2E5B8A",
    saudi      = "#722F37",
    fill_gap   = "#C8DAE8",
    fill_rival = "#E8E4DC",
    annot      = "#5A5A5A",
    text       = "#2C2C2A"
  )
)

### |-  titles and caption ----
title_text    <- str_glue("The Rivalry That Became a Gap")

subtitle_text <- str_glue(
  "<span style='color:{colors$palette$us}'>**U.S.**</span> and ",
  "<span style='color:{colors$palette$saudi}'>**Saudi**</span> oil production ",
  "tracked closely for decades. After 2009, U.S. production surged — ",
  "creating a gap that has continued to widen."
)

caption_text <- create_mm_caption(
  mm_year = 2026,
  mm_week = 14,
  source_text = "Energy Institute — Statistical Review of World Energy (2025);<br>The Shift Data Portal (2019) via Our World in Data"
)

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

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

weekly_theme <- extend_weekly_theme(
  base_theme,
  theme(
    axis.title        = element_blank(),
    axis.text.x       = element_text(size = 9, color = "gray45"),
    axis.text.y       = element_text(size = 9, color = "gray45"),
    axis.line.x       = element_line(color = "gray80", linewidth = 0.3),
    axis.ticks.x      = element_line(color = "gray80", linewidth = 0.3),
    axis.ticks.length = unit(3, "pt"),
    
    panel.grid.major.y = element_line(color = "gray92", linewidth = 0.3),
    panel.grid.major.x = element_blank(),
    panel.grid.minor   = element_blank(),
    
    plot.margin = margin(t = 20, r = 90, b = 10, l = 20)
  )
)

theme_set(weekly_theme)
```

6. Plot

Show code

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

### |-  main plot ----
p <- ggplot(df_wide, aes(x = year)) +
  
  # Geoms
  geom_ribbon(
    data    = df_wide |> filter(era == "rivalry"),
    mapping = aes(ymin = saudi, ymax = us),
    fill    = colors$palette$fill_rival,
    alpha   = 0.9
  ) +
  geom_ribbon(
    data    = df_wide |> filter(era == "gap"),
    mapping = aes(ymin = saudi, ymax = us),
    fill    = colors$palette$fill_gap,
    alpha   = 0.85
  ) +
  annotate(
    "segment",
    x = 2009, xend = 2009,
    y = 0, yend = 10,
    color = "gray70",
    linewidth = 0.35,
    linetype = "dashed"
  ) +
  geom_line(
    aes(y = saudi),
    color = colors$palette$saudi,
    linewidth = 1.1,
    lineend = "round"
  ) +
  geom_line(
    aes(y = us),
    color = colors$palette$us,
    linewidth = 1.4,
    lineend = "round"
  ) +
  
  # Annotate
  annotate(
    "text",
    x = 2024.5,
    y = us_2024,
    label = glue("U.S.\n{round(us_2024, 1)}k TWh"),
    hjust = 0,
    size = 3.1,
    fontface = "bold",
    color = colors$palette$us,
    lineheight = 1.2
  ) +
  annotate(
    "text",
    x = 2024.5,
    y = saudi_2024,
    label = glue("Saudi\n{round(saudi_2024, 1)}k TWh"),
    hjust = 0,
    size = 3.1,
    fontface = "bold",
    color = colors$palette$saudi,
    lineheight = 1.2
  ) +
  annotate(
    "text",
    x        = 2024.5,
    y        = us_2024 - 1.2,
    label    = glue("\u007e{ratio_2024}\u00d7 Saudi Arabia"),
    hjust    = 0,
    size     = 2.7,
    color    = "gray50",
    fontface = "italic"
  ) +
  annotate(
    "text",
    x          = 1988,
    y          = rival_mid,
    label      = "Production\ntracked closely",
    hjust      = 0.5,
    size       = 2.8,
    color      = "gray55",
    fontface   = "italic",
    lineheight = 1.3
  ) +
  annotate(
    "text",
    x          = 2017,
    y          = gap_mid + 0.8,
    label      = "U.S. pulls ahead",
    hjust      = 0.5,
    size       = 2.8,
    color      = colors$palette$us,
    fontface   = "italic"
  ) +
  annotate(
    "text",
    x          = 2009,
    y          = 9.7,
    label      = "Shale boom\nbegins",
    hjust      = 0.5,
    size       = 2.7,
    color      = "gray50",
    lineheight = 1.2
  ) +
  
  # Scales
  scale_x_continuous(
    breaks = seq(1970, 2020, by = 10),
    expand = expansion(mult = c(0.01, 0.16))
  ) +
  scale_y_continuous(
    labels = label_number(suffix = "k TWh", accuracy = 1),
    breaks = seq(0, 10, by = 2),
    limits = c(0, 10.5),
    expand = expansion(mult = c(0, 0.02))
  ) +
  
  # Labs
  labs(
    title    = title_text,
    subtitle = subtitle_text,
    caption  = caption_text
  ) +
  
  # Theme
  theme(
    plot.title = element_text(
      size = 24, face = "bold", color = colors$palette$text,
      margin = margin(b = 6), family = fonts$title
    ),
    plot.subtitle = element_textbox_simple(
      size = 11, color = "gray35",
      margin = margin(b = 16), family = fonts$subtitle,
      lineheight = 1.4
    ),
    plot.caption = element_textbox_simple(
      size = 8, color = "gray55", family = fonts$caption,
      margin = margin(t = 12),
      lineheight = 1.3
    )
  )
```

7. Save

Show code

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

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

8. Session Info

Expand 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      janitor_2.2.1   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_2.2.0     tidyr_1.3.2     tibble_3.2.1   
[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_4.3.1        generics_0.1.4     curl_7.0.0        
 [9] parallel_4.3.1     gifski_1.32.0-2    pkgconfig_2.0.3    skimr_2.2.2       
[13] RColorBrewer_1.1-3 S7_0.2.0           lifecycle_1.0.5    compiler_4.3.1    
[17] farver_2.1.2       textshaping_1.0.4  repr_1.1.7         codetools_0.2-19  
[21] snakecase_0.11.1   litedown_0.9       htmltools_0.5.9    yaml_2.3.12       
[25] pillar_1.11.1      crayon_1.5.3       camcorder_0.1.0    magick_2.8.6      
[29] commonmark_2.0.0   tidyselect_1.2.1   digest_0.6.39      stringi_1.8.7     
[33] rsvg_2.6.2         rprojroot_2.1.1    fastmap_1.2.0      grid_4.3.1        
[37] cli_3.6.5          magrittr_2.0.3     base64enc_0.1-6    withr_3.0.2       
[41] bit64_4.6.0-1      timechange_0.4.0   rmarkdown_2.30     bit_4.6.0         
[45] otel_0.2.0         ragg_1.5.0         hms_1.1.4          evaluate_1.0.5    
[49] knitr_1.51         markdown_2.0       rlang_1.1.7        gridtext_0.1.6    
[53] Rcpp_1.1.1         xml2_1.5.2         svglite_2.1.3      rstudioapi_0.18.0 
[57] vroom_1.7.0        jsonlite_2.0.0     R6_2.6.1           systemfonts_1.3.2

9. GitHub Repository

Expand for GitHub Repo

The complete code for this analysis is available in mm_2026_14.qmd.

For the full repository, click here.

10. References

Expand for References

Primary Data (Makeover Monday): 1. Makeover Monday 2026 Week 14: Global Oil Production 2. Original Chart: Oil Production by Country — Our World in Data - Source: Our World in Data interactive explorer (CC BY 4.0) - Authors: Hannah Ritchie, Pablo Rosado, and Max Roser (2023)

Source Data: 3. Energy Institute — Statistical Review of World Energy (2025) - Coverage: Oil production by country, 1965–2024 - Unit: Terawatt-hours (converted from exajoules by Our World in Data)

The Shift Data Portal — Energy Production from Fossil Fuels (2019)
- Coverage: Historical production series, 1900–1964 (pre-Energy Institute period)
- Note: Not used in this chart — visualization begins at 1965

Full citation (per Our World in Data): > Energy Institute - Statistical Review of World Energy (2025); The Shift Data Portal (2019) – with major processing by Our World in Data. “Oil production” [dataset]. Retrieved from https://ourworldindata.org/grapher/oil-production-by-country

Note: This redesign focuses on the United States and Saudi Arabia only (1965–2024), where the rivalry-to-gap narrative is most legible. The five remaining countries in the dataset (Iraq, UAE, Norway, Qatar, Oman) are excluded from the final chart; at this scale they compress to near-zero and add no analytical value to the central story. Production values are reported in terawatt-hours as supplied by Our World in Data; no additional unit conversion was applied.

11. Custom Functions Documentation

📦 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

Citation

BibTeX citation:

@online{ponce2026,
  author = {Ponce, Steven},
  title = {The {Rivalry} {That} {Became} a {Gap}},
  date = {2026-04-06},
  url = {https://stevenponce.netlify.app/data_visualizations/MakeoverMonday/2026/mm_2026_14.html},
  langid = {en}
}

For attribution, please cite this work as:

Ponce, Steven. 2026. “The Rivalry That Became a Gap.” April 6. https://stevenponce.netlify.app/data_visualizations/MakeoverMonday/2026/mm_2026_14.html.

--- title: "The Rivalry That Became a Gap" subtitle: "U.S. and Saudi oil production tracked closely for decades. After 2009, U.S. production surged — creating a gap that has continued to widen." description: "U.S. and Saudi Arabia oil production tracked closely for decades — then shale changed everything. This gap chart uses dual-shaded ribbon fills to encode two eras: competitive parity before 2009 and accelerating U.S. dominance after. Redesigned from an Our World in Data multi-line chart to reduce cognitive load and foreground the structural shift in global oil leadership." date: "2026-04-06" author: - name: "Steven Ponce" url: "https://stevenponce.netlify.app" citation: url: "https://stevenponce.netlify.app/data_visualizations/MakeoverMonday/2026/mm_2026_14.html" categories: ["MakeoverMonday", "2026"] tags: [ "makeover-monday", "data-visualization", "ggplot2", "energy", "oil-production", "geopolitics", "ribbon-chart", "time-series", "United-States", "Saudi-Arabia", "shale-revolution", "two-country-comparison" ] image: "thumbnails/mm_2026_14.png" format: html: toc: true toc-depth: 5 code-link: true code-fold: true code-tools: true code-summary: "Show code" editor_options: chunk_output_type: inline execute: freeze: true cache: true error: false message: false warning: false eval: true editor: markdown: wrap: 72 --- ```{r} #| label: setup-links #| include: false # CENTRALIZED LINK MANAGEMENT ## Project-specific info current_year <- 2026 current_week <- 14 project_file <- "mm_2026_14.qmd" project_image <- "mm_2026_14.png" ## Data Sources data_main <- "https://data.world/makeovermonday/2026w14" data_secondary <- "https://data.world/makeovermonday/2026w14" ## Repository Links repo_main <- "https://github.com/poncest/personal-website/" repo_file <- paste0("https://github.com/poncest/personal-website/blob/master/data_visualizations/MakeoverMonday/", current_year, "/", project_file) ## External Resources/Images chart_original <- "https://raw.githubusercontent.com/poncest/MakeoverMonday/refs/heads/master/2026/Week_14/original_chart.png" ## Organization/Platform Links org_primary <- "https://ourworldindata.org/grapher/oil-production-by-country" org_secondary <- "https://ourworldindata.org/grapher/oil-production-by-country" # Helper function to create markdown links create_link <- function(text, url) { paste0("[", text, "](", url, ")") } # Helper function for citation-style links create_citation_link <- function(text, url, title = NULL) { if (is.null(title)) { paste0("[", text, "](", url, ")") } else { paste0("[", text, "](", url, ' "', title, '")') } } ``` ### Original The original visualization comes from `r create_link("Global Oil Production", data_secondary)` ![Original visualization](https://raw.githubusercontent.com/poncest/MakeoverMonday/refs/heads/master/2026/Week_14/original_chart.png) ### Makeover ![A gap chart showing U.S. and Saudi Arabia oil production in terawatt-hours from 1965 to 2024. The area between the two lines is shaded in two colors: warm gray before 2009, labeled "Production tracked closely," and steel blue after 2009, labeled "U.S. pulls ahead." A dashed vertical line marks the 2009 shale boom inflection point. The U.S. line ends at 10k TWh in 2024; Saudi Arabia at 5.9k TWh. An annotation notes that U.S. output is approximately 1.7 times that of Saudi Arabia. The chart illustrates a structural shift from decades of competitive parity to clear U.S. dominance following the shale revolution.](mm_2026_14.png){#fig-1} ### [**Steps to Create this Graphic**]{.mark} #### [1. Load Packages & Setup]{.smallcaps} ```{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 ) }) ### |- figure size ---- camcorder::gg_record( dir = here::here("temp_plots"), device = "png", width = 8, 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]{.smallcaps} ```{r} #| label: read #| include: true #| eval: true #| warning: false #| df_raw <- read_csv( here::here("data/MakeoverMonday/2026/oil-production-by-country.csv")) |> clean_names() ``` #### [3. Examine the Data]{.smallcaps} ```{r} #| label: examine #| include: true #| eval: true #| results: 'hide' #| warning: false glimpse(df_raw) skimr::skim_without_charts(df_raw) ``` #### [4. Tidy Data]{.smallcaps} ```{r} #| label: tidy #| warning: false df_two <- df_raw |> filter( entity %in% c("United States", "Saudi Arabia"), year >= 1965 ) |> mutate(oil_k = oil / 1000) |> select(entity, year, oil_k) df_wide <- df_two |> pivot_wider(names_from = entity, values_from = oil_k) |> rename(us = `United States`, saudi = `Saudi Arabia`) |> mutate(era = if_else(year < 2009, "rivalry", "gap")) # Endpoint values for labels us_2024 <- df_wide |> filter(year == 2024) |> pull(us) saudi_2024 <- df_wide |> filter(year == 2024) |> pull(saudi) # Ratio for "nearly 2×" annotation ratio_2024 <- round(us_2024 / saudi_2024, 1) # Label anchor positions us_1988 <- df_wide |> filter(year == 1988) |> pull(us) saudi_1988 <- df_wide |> filter(year == 1988) |> pull(saudi) rival_mid <- (us_1988 + saudi_1988) / 2 # Gap label us_2018 <- df_wide |> filter(year == 2018) |> pull(us) saudi_2018 <- df_wide |> filter(year == 2018) |> pull(saudi) gap_mid <- (us_2018 + saudi_2018) / 2 ``` #### [5. Visualization Parameters]{.smallcaps} ```{r} #| label: params #| include: true #| warning: false ### |- plot aesthetics ---- colors <- get_theme_colors( palette = list( us = "#2E5B8A", saudi = "#722F37", fill_gap = "#C8DAE8", fill_rival = "#E8E4DC", annot = "#5A5A5A", text = "#2C2C2A" ) ) ### |- titles and caption ---- title_text <- str_glue("The Rivalry That Became a Gap") subtitle_text <- str_glue( "**U.S.** and ", "**Saudi** oil production ", "tracked closely for decades. After 2009, U.S. production surged — ", "creating a gap that has continued to widen." ) caption_text <- create_mm_caption( mm_year = 2026, mm_week = 14, source_text = "Energy Institute — Statistical Review of World Energy (2025); The Shift Data Portal (2019) via Our World in Data" ) ### |- fonts ---- setup_fonts() fonts <- get_font_families() ### |- plot theme ---- base_theme <- create_base_theme(colors) weekly_theme <- extend_weekly_theme( base_theme, theme( axis.title = element_blank(), axis.text.x = element_text(size = 9, color = "gray45"), axis.text.y = element_text(size = 9, color = "gray45"), axis.line.x = element_line(color = "gray80", linewidth = 0.3), axis.ticks.x = element_line(color = "gray80", linewidth = 0.3), axis.ticks.length = unit(3, "pt"), panel.grid.major.y = element_line(color = "gray92", linewidth = 0.3), panel.grid.major.x = element_blank(), panel.grid.minor = element_blank(), plot.margin = margin(t = 20, r = 90, b = 10, l = 20) ) ) theme_set(weekly_theme) ``` #### [6. Plot]{.smallcaps} ```{r} #| label: plot #| warning: false ### |- main plot ---- p <- ggplot(df_wide, aes(x = year)) + # Geoms geom_ribbon( data = df_wide |> filter(era == "rivalry"), mapping = aes(ymin = saudi, ymax = us), fill = colors$palette$fill_rival, alpha = 0.9 ) + geom_ribbon( data = df_wide |> filter(era == "gap"), mapping = aes(ymin = saudi, ymax = us), fill = colors$palette$fill_gap, alpha = 0.85 ) + annotate( "segment", x = 2009, xend = 2009, y = 0, yend = 10, color = "gray70", linewidth = 0.35, linetype = "dashed" ) + geom_line( aes(y = saudi), color = colors$palette$saudi, linewidth = 1.1, lineend = "round" ) + geom_line( aes(y = us), color = colors$palette$us, linewidth = 1.4, lineend = "round" ) + # Annotate annotate( "text", x = 2024.5, y = us_2024, label = glue("U.S.\n{round(us_2024, 1)}k TWh"), hjust = 0, size = 3.1, fontface = "bold", color = colors$palette$us, lineheight = 1.2 ) + annotate( "text", x = 2024.5, y = saudi_2024, label = glue("Saudi\n{round(saudi_2024, 1)}k TWh"), hjust = 0, size = 3.1, fontface = "bold", color = colors$palette$saudi, lineheight = 1.2 ) + annotate( "text", x = 2024.5, y = us_2024 - 1.2, label = glue("\u007e{ratio_2024}\u00d7 Saudi Arabia"), hjust = 0, size = 2.7, color = "gray50", fontface = "italic" ) + annotate( "text", x = 1988, y = rival_mid, label = "Production\ntracked closely", hjust = 0.5, size = 2.8, color = "gray55", fontface = "italic", lineheight = 1.3 ) + annotate( "text", x = 2017, y = gap_mid + 0.8, label = "U.S. pulls ahead", hjust = 0.5, size = 2.8, color = colors$palette$us, fontface = "italic" ) + annotate( "text", x = 2009, y = 9.7, label = "Shale boom\nbegins", hjust = 0.5, size = 2.7, color = "gray50", lineheight = 1.2 ) + # Scales scale_x_continuous( breaks = seq(1970, 2020, by = 10), expand = expansion(mult = c(0.01, 0.16)) ) + scale_y_continuous( labels = label_number(suffix = "k TWh", accuracy = 1), breaks = seq(0, 10, by = 2), limits = c(0, 10.5), expand = expansion(mult = c(0, 0.02)) ) + # Labs labs( title = title_text, subtitle = subtitle_text, caption = caption_text ) + # Theme theme( plot.title = element_text( size = 24, face = "bold", color = colors$palette$text, margin = margin(b = 6), family = fonts$title ), plot.subtitle = element_textbox_simple( size = 11, color = "gray35", margin = margin(b = 16), family = fonts$subtitle, lineheight = 1.4 ), plot.caption = element_textbox_simple( size = 8, color = "gray55", family = fonts$caption, margin = margin(t = 12), lineheight = 1.3 ) ) ``` #### [7. Save]{.smallcaps} ```{r} #| label: save #| warning: false ### |- plot image ---- save_plot( plot = p, type = "makeovermonday", year = current_year, week = current_week, width = 8, height = 8 ) ``` #### [8. Session Info]{.smallcaps} ::: {.callout-tip collapse="true"} ##### Expand for Session Info ```{r, echo = FALSE} #| eval: true #| warning: false sessionInfo() ``` ::: #### [9. GitHub Repository]{.smallcaps} ::: {.callout-tip collapse="true"} ##### Expand for GitHub Repo The complete code for this analysis is available in `r create_link(project_file, repo_file)`. For the full repository, `r create_link("click here", repo_main)`. ::: #### [10. References]{.smallcaps} ::: {.callout-tip collapse="true"} ##### Expand for References **Primary Data (Makeover Monday):** 1. Makeover Monday `r current_year` Week `r current_week`: `r create_link("Global Oil Production", data_main)` 2. Original Chart: `r create_link("Oil Production by Country — Our World in Data", "https://ourworldindata.org/grapher/oil-production-by-country")` - Source: Our World in Data interactive explorer (CC BY 4.0) - Authors: Hannah Ritchie, Pablo Rosado, and Max Roser (2023) **Source Data:** 3. `r create_link("Energy Institute — Statistical Review of World Energy (2025)", "https://www.energyinst.org/statistical-review/")` - Coverage: Oil production by country, 1965–2024 - Unit: Terawatt-hours (converted from exajoules by Our World in Data) 4. `r create_link("The Shift Data Portal — Energy Production from Fossil Fuels (2019)", "https://www.theshiftdataportal.org/")` - Coverage: Historical production series, 1900–1964 (pre-Energy Institute period) - Note: Not used in this chart — visualization begins at 1965 **Full citation (per Our World in Data):** > Energy Institute - Statistical Review of World Energy (2025); The Shift Data Portal (2019) – with major processing by Our World in Data. "Oil production" [dataset]. Retrieved from https://ourworldindata.org/grapher/oil-production-by-country **Note:** This redesign focuses on the United States and Saudi Arabia only (1965–2024), where the rivalry-to-gap narrative is most legible. The five remaining countries in the dataset (Iraq, UAE, Norway, Qatar, Oman) are excluded from the final chart; at this scale they compress to near-zero and add no analytical value to the central story. Production values are reported in terawatt-hours as supplied by Our World in Data; no additional unit conversion was applied. ::: #### [11. Custom Functions Documentation]{.smallcaps} ::: {.callout-note collapse="true"} ##### 📦 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](https://github.com/poncest/personal-website/tree/master/R) :::