Whose Grid Got Cleaner?

Carbon intensity of electricity generation (gCO₂/kWh), 2020 to 2024. Highlighted lines = the 3 largest absolute declines in carbon intensity. Lower values mean cleaner electricity.

Slope chart comparing the carbon intensity of electricity generation (gCO₂/kWh) across 20 countries between 2020 and 2024. Chile, Poland, and Australia recorded the three largest absolute declines. A reference line marks the 100 gCO₂/kWh clean grid benchmark. Built with R and ggplot2. as part of the #30DayChartChallenge 2026 — Day 4: Comparisons | Slope.

Figure 1: Slope chart showing carbon intensity of electricity generation (gCO₂/kWh) for 20 countries, comparing 2020 to 2024. Three countries are highlighted in green as the largest absolute improvers: Chile (−172 g), Poland (−126 g), and Australia (−88 g). A dashed blue line marks the 100 gCO₂/kWh clean grid benchmark. France, Sweden, Norway, and Brazil already fall below this threshold in both years. All other countries are shown in gray and show modest or no improvement over the period.

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  = 6,
  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 data ----
# Download: https://raw.githubusercontent.com/owid/energy-data/master/owid-energy-data.csv
energy_raw <- read_csv(
  here::here("data/30DayChartChallenge/2026/owid-energy-data.csv")
)
```

3. Examine the Data

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

glimpse(energy_raw)
```

4. Tidy Data

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

### |- define curated country list ----
countries_keep <- c(
  "United Kingdom", "Germany", "Estonia", "Poland",
  "South Africa", "Australia", "United States", "Canada",
  "France", "Sweden", "Norway", "Brazil",
  "India", "China", "Vietnam", "Indonesia",
  "Chile", "Mexico", "Japan", "South Korea"
)

### |- filter to 2020 and 2024 ----
slope_data <- energy_raw |>
  filter(
    country %in% countries_keep,
    year %in% c(2020, 2024),
    !is.na(carbon_intensity_elec)
  ) |>
  select(country, year, carbon_intensity_elec) |>
  group_by(country) |>
  filter(n() == 2) |>
  ungroup()

### |- compute delta ----
country_delta <- slope_data |>
  pivot_wider(
    names_from   = year,
    values_from  = carbon_intensity_elec,
    names_prefix = "yr_"
  ) |>
  mutate(
    delta     = yr_2024 - yr_2020,
    delta_pct = (yr_2024 - yr_2020) / yr_2020 * 100
  ) |>
  arrange(delta)

### |- identify top 3 improvers (largest absolute drop) ----
top_improvers <- country_delta |>
  slice_min(order_by = delta, n = 3) |>
  pull(country)

### |- join highlight flag back to slope data ----
slope_data <- slope_data |>
  left_join(
    country_delta |> select(country, delta, delta_pct),
    by = "country"
  ) |>
  mutate(
    highlight = country %in% top_improvers
  )

### |- endpoint data for geom_point ----
endpoints <- slope_data |>
  filter(year %in% c(2020, 2024))

### |- pre-build label data frames ----
labels_highlighted <- slope_data |> filter(highlight, year == 2024)
labels_other <- slope_data |> filter(!highlight, year == 2024)
```

5. Visualization Parameters

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

### |- plot aesthetics ----
colors <- get_theme_colors(
  palette = c(
    "TRUE"  = "#007F5F",
    "FALSE" = "gray80"
  )
)

col_ref_line <- "#457B9D"
col_bg       <- colors$background

### |- titles and caption ----
title_text <- str_glue("Whose Grid Got Cleaner?")

subtitle_text <- str_glue(
  "Carbon intensity of electricity generation (gCO\u2082/kWh), <b>2020 \u2192 2024</b>.<br>",
  "Highlighted lines = the <b>3 largest absolute declines</b> in carbon intensity.<br>",
  "Lower values mean cleaner electricity."
)

caption_text <- create_dcc_caption(
  dcc_year    = 2026,
  dcc_day     = 04,
  source_text = "Our World in Data \u2014 Energy Dataset"
)

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

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

weekly_theme <- extend_weekly_theme(
  base_theme,
  theme(
    # Canvas
    plot.background = element_rect(fill = col_bg, color = NA),
    panel.background = element_rect(fill = col_bg, color = NA),
    #
    # Grid
    panel.grid.major.y = element_line(color = "gray92", linewidth = 0.3),
    panel.grid.major.x = element_blank(),
    panel.grid.minor = element_blank(),

    # Axes
    axis.ticks = element_blank(),
    axis.text.x = element_text(
      size = 11, face = "bold", color = "gray30",
      family = fonts$text, margin = margin(t = 6)
    ),
    axis.text.y = element_text(size = 9, color = "gray50", family = fonts$text),
    axis.title = element_blank(),

    # Text hierarchy
    plot.title = element_text(
      size = 25, face = "bold", family = fonts$title,
      color = "gray10", margin = margin(b = 6)
    ),
    plot.subtitle = element_markdown(
      size = 10, family = "sans", color = "gray35",
      lineheight = 1.4, margin = margin(b = 20)
    ),
    plot.caption = element_markdown(
      size = 7, color = "gray55", family = fonts$caption,
      hjust = 0, margin = margin(t = 16), lineheight = 1.3
    ),
    plot.margin = margin(t = 24, r = 25, b = 16, l = 24)
  )
)

theme_set(weekly_theme)
```

6. Plot

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

### |- main plot ----
p <-
  slope_data |>
  # Geoms
  ggplot(aes(
    x = year,
    y = carbon_intensity_elec,
    group = country,
    color = as.character(highlight)
  )) +
  geom_hline(
    yintercept = 100,
    linetype = "dashed",
    color = col_ref_line,
    linewidth  = 0.5,
    alpha = 0.7
  ) +
  annotate(
    "text",
    x = 2019,
    y = 110,
    label = "~100 gCO\u2082/kWh clean grid benchmark",
    color = col_ref_line,
    size = 2.6,
    fontface = "italic",
    family = fonts$text,
    hjust = 0,
    vjust = 0
  ) +
  geom_line(
    data = slope_data |> filter(!highlight),
    linewidth = 0.45,
    alpha = 0.50
  ) +
  geom_line(
    data = slope_data |> filter(highlight),
    linewidth = 1.55,
    alpha = 0.98,
    color = colors$palette["TRUE"]
  ) +
  geom_point(
    data = endpoints |> filter(!highlight),
    size  = 1.5,
    alpha = 0.45
  ) +
  geom_point(
    data = endpoints |> filter(highlight),
    size  = 3.2,
    alpha = 0.98,
    color = colors$palette["TRUE"]
  ) +
  geom_text_repel(
    data = labels_highlighted,
    aes(label = glue("{country} (-{round(abs(delta), 0)} g)")),
    hjust = 0,
    nudge_x = 0.55,
    direction = "y",
    segment.size = 0.3,
    segment.color = "gray70",
    size = 3.5,
    lineheight = 1.15,
    family = fonts$text,
    fontface = "bold",
    color = colors$palette["TRUE"],
    box.padding = 0.30,
    point.padding = 0.20,
    min.segment.length = 0
  ) +
  geom_text_repel(
    data = labels_other,
    aes(label = country),
    hjust = 0,
    nudge_x = 0.45,
    direction = "y",
    segment.size = 0.12,
    segment.color = "gray88",
    size = 1.8,
    color = "gray70",
    family = fonts$text,
    box.padding = 0.07,
    point.padding = 0.05,
    min.segment.length = 0,
    force = 0.8,
    seed = 42
  ) +

  # Scales
  scale_color_manual(
    values = c("TRUE" = colors$palette["TRUE"], "FALSE" = colors$palette["FALSE"])
  ) +
  scale_x_continuous(
    breaks = c(2020, 2024),
    labels = c("2020", "2024"),
    limits = c(2019, 2026),
    expand = c(0, 0)
  ) +
  scale_y_continuous(
    labels = label_number(suffix = " g"),
    breaks = seq(0, 800, by = 200),
    limits = c(0, NA),
    expand = expansion(mult = c(0.02, 0.05))
  ) +
  coord_cartesian(clip = "off") +

  # Labs
  labs(
    title = title_text,
    subtitle = subtitle_text,
    caption = caption_text,
    x = NULL,
    y = NULL
  ) +
  guides(color = "none")
```

7. Save

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

### |-  plot image ----  
save_plot(
  p, 
  type = "30daychartchallenge", 
  year = 2026, 
  day = 04, 
  width = 6, 
  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      rsvg_2.6.2        
[33] rprojroot_2.1.1    fastmap_1.2.0      grid_4.3.1         cli_3.6.5         
[37] magrittr_2.0.3     withr_3.0.2        bit64_4.6.0-1      timechange_0.4.0  
[41] rmarkdown_2.30     bit_4.6.0          otel_0.2.0         ragg_1.5.0        
[45] hms_1.1.4          evaluate_1.0.5     knitr_1.51         markdown_2.0      
[49] rlang_1.1.7        gridtext_0.1.6     Rcpp_1.1.1         xml2_1.5.2        
[53] svglite_2.1.3      rstudioapi_0.18.0  vroom_1.7.0        jsonlite_2.0.0    
[57] 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_04.qmd.

For the full repository, click here.

10. References

TipExpand for References

1. Data Sources:

   • Ritchie, H., Rosado, P., & Roser, M. (2024). Our World in Data — Energy Dataset. Based on data from Ember and the Energy Institute Statistical Review of World Energy. Retrieved April 2026 from https://raw.githubusercontent.com/owid/energy-data/master/owid-energy-data.csv

   • Ember. (2024). Yearly Electricity Data. Retrieved from https://ember-energy.org

   • Energy Institute. (2024). Statistical Review of World Energy. Retrieved from https://www.energyinst.org/statistical-review

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