Distance from Normal

Most countries sit near the global norm. A small group sits far above. Each dot is one country. Deviation from global median (482 gCO₂/kWh).

Ordered dot plot showing each country’s electricity carbon intensity relative to the global median (482 gCO₂/kWh). The distribution is right-skewed — most countries cluster near the norm, while a small group of coal-dependent systems drives a long upper tail. Built in R/ggplot2 using Our World in Data’s energy dataset, styled in the FlowingData editorial tradition: warm gray base, single muted burgundy accent, annotation-driven narrative.

Figure 1: Ordered dot plot titled “Distance from Normal.” Each dot represents one country, positioned horizontally by its deviation from the global median electricity carbon intensity of 482 gCO₂/kWh. The distribution is right-skewed: most countries cluster near zero, while a small group of coal-dependent systems — led by Turkmenistan (+825), Uzbekistan (+639), and Falkland Islands (+518) — extends far into the right tail. Two countries with hydro-heavy grids, Nepal and Lesotho, anchor the left tail near −460 gCO₂/kWh. Data from Our World in Data Energy Dataset (Ember / Energy Institute).

Steps to Create this Graphic

1. Load Packages & Setup

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

## 1. LOAD PACKAGES & SETUP ----
suppressPackageStartupMessages({
pacman::p_load(
  tidyverse, ggtext, showtext,  
  janitor, scales, glue, ggrepel
  )
})

### |- 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

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

owid_energy_raw <- read_csv(
  here::here("data/30DayChartChallenge/2026/owid-energy-data.csv"),
  show_col_types = FALSE
  ) |>
  clean_names() 
```

3. Examine the Data

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

glimpse(owid_energy_raw)
```

4. Tidy Data

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

### |- exclude aggregates (OWID uses these non-country entities) ----
non_countries <- c(
  "World", "Africa", "Asia", "Europe", "North America", "South America",
  "Oceania", "Antarctica", "European Union (27)",
  "High-income countries", "Low-income countries",
  "Lower-middle-income countries", "Upper-middle-income countries",
  "OECD (Edelstein)", "Non-OECD (Edelstein)",
  "Asia Pacific (Ember)", "Europe (Ember)", "Middle East (Ember)",
  "Africa (Ember)", "Americas (Ember)"
)

### |- most recent non-NA carbon intensity per country ----
df_ci <- owid_energy_raw |>
  filter(
    !country %in% non_countries,
    year <= 2023,
    !is.na(carbon_intensity_elec),
    carbon_intensity_elec > 0
  ) |>
  group_by(country) |>
  slice_max(year, n = 1, with_ties = FALSE) |>
  ungroup() |>
  select(country, year, carbon_intensity_elec)

### |- compute global median + deviation ----
global_median <- median(df_ci$carbon_intensity_elec, na.rm = TRUE)

df_dev <- df_ci |>
  mutate(
    deviation    = carbon_intensity_elec - global_median,
    direction    = if_else(deviation >= 0, "above", "below"),
    rank_dev     = rank(deviation, ties.method = "first"),
    country_fct  = fct_reorder(country, deviation)
  )

### |- identify highlight countries ----
top_above <- df_dev |>
  filter(direction == "above") |>
  arrange(desc(deviation)) |>
  slice_head(n = 3) |>
  pull(country)

top_below <- df_dev |>
  filter(direction == "below") |>
  arrange(deviation) |>
  slice_head(n = 2) |>
  pull(country)

highlight_countries <- c(top_above, top_below)

df_dev <- df_dev |>
  mutate(
    highlight = country %in% highlight_countries,
    highlight_type = case_when(
      country %in% top_above ~ "above",
      country %in% top_below ~ "below",
      TRUE ~ "none"
    )
  )

### |- label data (only highlighted countries) ----
df_labels <- df_dev |>
  filter(highlight) |>
  mutate(
    label = case_when(
      deviation >= 0 ~ glue("{country}\n+{round(deviation)} gCO₂/kWh"),
      TRUE ~ glue("{country}\n{round(deviation)} gCO₂/kWh")
    )
  )
```

5. Visualization Parameters

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

### |- plot aesthetics ----
colors <- get_theme_colors(
  palette = list(
    col_accent_above = "#7a3b3b",   
    col_accent_below = "#6b7c85",   
    col_dot_base     = "#c9c6c1",   
    col_zero         = "#aaaaaa",  
    col_grid         = "#eeeeee",
    col_text_dark    = "#4a4a4a",
    col_text_mid     = "#6a6a6a",
    col_text_mute    = "#aaaaaa",
    col_bg           = "#ffffff"
  )
)

# extract scalars
col_above <- colors$palette$col_accent_above
col_below <- colors$palette$col_accent_below
col_base  <- colors$palette$col_dot_base

### |- titles and caption ----
title_text    <- "Distance from Normal"

subtitle_text <- glue(
  "Most countries sit near the global norm. A small group sits far above.<br>",
  "Each dot is one country. Deviation from global median ({round(global_median)} gCO₂/kWh)."
)

caption_text <- create_dcc_caption(
  dcc_year    = 2026,
  dcc_day     = 12,
  source_text = "Our World in Data — Energy Dataset (Ember / Energy Institute)"
)

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

# FlowingData-specific font additions 
font_add_google("Special Elite", "special_elite")
font_add_google("Source Sans 3", "source_sans")
showtext_auto(enable = TRUE)

### |- theme (FlowingData-inspired) ----
theme_fd <- theme_minimal(base_size = 11) +
  theme(
    plot.background = element_rect(fill = colors$palette$col_bg, color = NA),
    panel.background = element_rect(fill = colors$palette$col_bg, color = NA),

    # very faint horizontal reference only
    panel.grid.major.x = element_line(color = colors$palette$col_grid, linewidth = 0.3),
    panel.grid.major.y = element_blank(),
    panel.grid.minor = element_blank(),
    axis.line = element_blank(),
    axis.ticks = element_blank(),

    # y-axis: no country labels 
    axis.text.y = element_blank(),
    axis.text.x = element_text(
      family = "source_sans", size = 8,
      color = colors$palette$col_text_mute,
      margin = margin(t = 3)
    ),
    axis.title.y = element_blank(),
    axis.title.x = element_text(
      family = "source_sans", size = 9,
      color = colors$palette$col_text_mid,
      margin = margin(t = 6)
    ),
    legend.position = "none",
    plot.title = element_text(
      family = "special_elite",
      size  = 22,
      color = colors$palette$col_text_dark,
      hjust = 0,
      face = "bold",
      margin = margin(b = 6)
    ),
    plot.subtitle = element_textbox_simple(
      family = "source_sans",
      size = 10,
      color = colors$palette$col_text_mid,
      hjust = 0,
      lineheight = 1.4,
      margin = margin(b = 20)
    ),
    plot.caption = element_textbox_simple(
      family = "source_sans",
      size = 7,
      color = colors$palette$col_text_mute,
      hjust = 0,
      margin = margin(t = 14)
    ),
    plot.margin = margin(20, 20, 14, 20)
  )
```

6. Plot

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

### |- main plot ----
p <- df_dev |>
  ggplot(aes(x = deviation, y = rank_dev)) +
  
  # Geoms
  geom_vline(
    xintercept = 0,
    color = colors$palette$col_zero,
    linewidth  = 0.5,
    linetype = "solid"
  ) +
  geom_point(
    data = filter(df_dev, !highlight),
    color = col_base,
    size = 1.6,
    alpha = 0.90,
    shape = 16
  ) +
  geom_point(
    data = filter(df_dev, highlight_type == "above"),
    color = col_above,
    size = 2.8,
    shape = 16
  ) +
  geom_point(
    data = filter(df_dev, highlight_type == "below"),
    color = col_below,
    size = 1.8,    
    shape = 16
  ) +
  geom_text_repel(
    data = filter(df_labels, highlight_type == "above"),
    aes(label = label),
    family = "source_sans",
    size = 2.6,
    color = col_above,
    fontface = "bold",
    hjust = 0,
    direction = "y",
    nudge_x = 60,
    force = 2,
    force_pull = 0.5,
    segment.color = col_above,
    segment.size = 0.3,
    segment.alpha = 0.4,
    box.padding = 0.6,
    min.segment.length = 0.2,
    seed = 42
  ) +
  geom_text_repel(
    data = filter(df_labels, highlight_type == "below"),
    aes(label = label),
    family = "source_sans",
    size = 2.4,
    color = colors$palette$col_text_mid,   
    fontface = "plain",
    hjust = 1,
    direction = "y",
    nudge_x = -40,
    segment.color = colors$palette$col_text_mute,
    segment.size  = 0.25,
    segment.alpha = 0.4,
    box.padding   = 0.4,
    min.segment.length = 0.2
  ) +
  
  # Annotate
  annotate(
    "text",
    x = 0,
    y = nrow(df_dev) * 0.50,
    label = "Most countries\ncluster here",
    family = "source_sans",
    size = 3.0,
    color = colors$palette$col_text_mid,
    hjust = 0.5,
    lineheight = 1.2
  ) +
  annotate(
    "text",
    x = 230,
    y = nrow(df_dev) * 0.76,
    label = "Long right tail driven\nby coal-dependent systems",
    family = "source_sans",
    size = 2.9,
    color = col_above,
    hjust = 0,
    lineheight = 1.2
  ) +
  annotate(
    "text",
    x = -330,
    y = nrow(df_dev) * 0.14,
    label = "A small group of low-carbon\nsystems sits well below the norm",
    family = "source_sans",
    size = 2.8,
    color = colors$palette$col_text_mid,
    hjust = 0.5,
    lineheight = 1.2
  ) +
  annotate(
    "text",
    x = 4,
    y = nrow(df_dev) + 2,
    label = "Global median\n(typical country)",
    family = "source_sans",
    size = 2.7,
    color = colors$palette$col_zero,
    hjust = 0,
    vjust = 1,
    lineheight = 1.2
  ) +
  annotate(
    "text",
    x = min(df_dev$deviation) * 0.95,
    y = -3,
    label = "\u2190 cleaner",
    family = "source_sans",
    size = 2.7,
    color = colors$palette$col_text_mute,
    hjust = 0,
    fontface = "italic"
  ) +
  annotate(
    "text",
    x = max(df_dev$deviation) * 0.82,   
    y = -3,
    label = "dirtier \u2192",
    family = "source_sans",
    size = 2.7,
    color = col_above,
    hjust = 1,
    fontface = "italic"
  ) +
  
  # Scales
  scale_x_continuous(
    labels = function(x) ifelse(x > 0, glue("+{x}"), as.character(x)),
    breaks = c(-600, -400, -200, 0, 200, 400, 600, 800)
  ) +
  
  # Labs
  labs(
    title = title_text,
    subtitle = subtitle_text,
    caption  = caption_text,
    x  = "Deviation from global median carbon intensity (gCO₂/kWh)"
  ) +
  
  # Theme
  theme_fd
```

7. Save

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

### |-  plot image ----  
save_plot(
  p, 
  type = "30daychartchallenge", 
  year = 2026, 
  day = 12, 
  width = 10, 
  height = 8
  )
```

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      ggrepel_0.9.8   glue_1.8.0      scales_1.4.0   
 [5] janitor_2.2.1   showtext_0.9-7  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_1.2.0     purrr_1.2.1     readr_2.2.0     tidyr_1.3.2    
[17] tibble_3.2.1    ggplot2_4.0.2   tidyverse_2.0.0

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    pacman_0.5.1       pkgconfig_2.0.3   
[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  codetools_0.2-19   snakecase_0.11.1  
[21] litedown_0.9       htmltools_0.5.9    yaml_2.3.12        pillar_1.11.1     
[25] crayon_1.5.3       camcorder_0.1.0    magick_2.8.6       commonmark_2.0.0  
[29] tidyselect_1.2.1   digest_0.6.39      stringi_1.8.7      labeling_0.4.3    
[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     withr_3.0.2        bit64_4.6.0-1     
[41] timechange_0.4.0   rmarkdown_2.30     bit_4.6.0          otel_0.2.0        
[45] ragg_1.5.0         hms_1.1.4          evaluate_1.0.5     knitr_1.51        
[49] markdown_2.0       rlang_1.1.7        gridtext_0.1.6     Rcpp_1.1.1        
[53] xml2_1.5.2         svglite_2.1.3      rstudioapi_0.18.0  vroom_1.7.0       
[57] jsonlite_2.0.0     R6_2.6.1           systemfonts_1.3.2 

9. GitHub Repository

TipExpand for GitHub Repo

The complete code for this analysis is available in 30dcc_2026_12.qmd.

For the full repository, click here.

10. References

TipExpand for References

1. Data Sources:
   • Renewable Energy Statistics Team. (2024). Our World in Data Energy Dataset [Dataset]. Our World in Data (based on Ember and Energy Institute data). https://raw.githubusercontent.com/owid/energy-data/master/owid-energy-data.csv

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