Global Convergence in Electricity Access, 2000-2023

Evidence shows countries starting with less electricity access are improving faster

MakeoverMonday

2025

Analysis of World Bank electricity access data (2000-2023) revealing global convergence: countries starting with lowest access (<25%) achieved the most dramatic improvements (50-70+ percentage points), while high-access countries showed minimal change.

Author

Steven Ponce

Published

August 11, 2025

Original

The original visualization Access to Electricity comes from World Bank Group

Makeover

Figure 1: Two-panel chart showing global convergence in electricity access from 2000 to 2023. Top panel: The scatter plot reveals that countries with the lowest access (<25%) achieved the greatest improvements (50-70+ percentage points), while countries starting near universal access showed minimal change. Bottom panel: The line chart shows the progress of five cohorts over time, with very low starters (red line) rising from ~15% to 50% access, demonstrating a catch-up effect. Data shows evidence of global convergence as access gaps narrow.

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,  # Easily Install and Load the 'Tidyverse'
    ggtext,     # Improved Text Rendering Support for 'ggplot2'
    showtext,   # Using Fonts More Easily in R Graphs
    scales,     # Scale Functions for Visualization
    glue,       # Interpreted String Literals
    patchwork   # The Composer of Plots
  )
})

### |- figure size ----
camcorder::gg_record(
    dir    = here::here("temp_plots"),
    device = "png",
    width  =  10,
    height =  12,
    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
#|
access_electricity_raw <- read_csv(
  here::here('data/MakeoverMonday/2025/Access to Electricity.csv')) |> 
  janitor::clean_names()
```

3. Examine the Data

Show code

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

glimpse(access_electricity_raw)
skimr::skim(access_electricity_raw)
```

4. Tidy Data

Show code

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

access_electricity_tidy <- access_electricity_raw |>
  # Remove completely empty year columns (1960-1989, 2024)
  select(-c(x1960:x1989, x2024)) |>
  pivot_longer(
    cols = starts_with("x"),
    names_to = "year",
    values_to = "access_rate",
    names_prefix = "x" # Remove the "x" prefix
  ) |>
  mutate(year = as.numeric(year)) |>
  filter(!is.na(access_rate)) |>
  rename(
    country = country_name,
    code = country_code,
    indicator = indicator_name,
    indicator_id = indicator_code
  ) |>
  arrange(country, year)

# convergence analysis data
convergence_data <- access_electricity_tidy |>
  filter(year %in% c(2000, 2023)) |>
  group_by(country) |>
  filter(n() == 2) |>
  summarise(
    access_2000 = access_rate[year == 2000],
    access_2023 = access_rate[year == 2023],
    improvement = access_2023 - access_2000,
    .groups = "drop"
  ) |>
  filter(!is.na(access_2000), !is.na(access_2023)) |>
  mutate(
    cohort = case_when(
      access_2000 < 25 ~ "Very Low Start (<25%)",
      access_2000 < 50 ~ "Low Start (25-50%)",
      access_2000 < 75 ~ "Medium Start (50-75%)",
      access_2000 < 95 ~ "High Start (75-95%)",
      TRUE ~ "Universal Start (≥95%)"
    )
  )

# Countries with dramatic improvements (>= 50 percentage points)
# from Very Low Start only
top_improvers <- convergence_data |>
  filter(cohort == "Very Low Start (<25%)", improvement >= 50) |>
  arrange(desc(improvement))

# Define the logical 
cohort_levels <- c(
  "Very Low Start (<25%)",
  "Low Start (25-50%)",
  "Medium Start (50-75%)",
  "High Start (75-95%)",
  "Universal Start (≥95%)"
)

# cohort analysis data
cohort_analysis <- access_electricity_tidy |>
  filter(year == 2000) |>
  mutate(
    starting_cohort = case_when(
      access_rate < 25 ~ "Very Low Start (<25%)",
      access_rate < 50 ~ "Low Start (25-50%)",
      access_rate < 75 ~ "Medium Start (50-75%)",
      access_rate < 95 ~ "High Start (75-95%)",
      TRUE ~ "Universal Start (≥95%)"
    ),
    # Order factor levels for meaningful legend ordering (low to high)
    starting_cohort = factor(starting_cohort, levels = cohort_levels)
  ) |>
  select(country, starting_cohort) |>
  inner_join(access_electricity_tidy, by = "country") |>
  filter(year >= 2000) |>
  group_by(starting_cohort, year) |>
  summarise(
    median_access = median(access_rate, na.rm = TRUE),
    q25 = as.numeric(quantile(access_rate, 0.25, na.rm = TRUE)),  
    q75 = as.numeric(quantile(access_rate, 0.75, na.rm = TRUE)), 
    countries = n(),
    .groups = "drop"
  ) |>
  filter(countries >= 3)
```

5. Visualization Parameters

Show code

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

### |-  plot aesthetics ----
# Get base colors with custom palette
colors <- get_theme_colors(palette = list(
  "Very Low Start (<25%)" = "#8B0000",    
  "Low Start (25-50%)" = "#FF8C00",       
  "Medium Start (50-75%)" = "#006400",   
  "High Start (75-95%)" = "#1E90FF",      
  "Universal Start (≥95%)" = "#4B0082"    
))

### |-  titles and caption ----
title_text <- str_glue("Global convergence in electricity access, 2000-2023")

subtitle_text <- str_glue(
  "Evidence shows countries starting with less electricity access are improving faster"
)

# Create caption
caption_text <- create_mm_caption(
  mm_year = current_year,
  mm_week = current_week,
  source_text = paste0("World Bank Group")
)

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

### |-  plot theme ----

# Start with base theme
base_theme <- create_base_theme(colors)

# Add weekly-specific theme elements
weekly_theme <- extend_weekly_theme(
  base_theme,
  theme(
    plot.title = element_text(
      size = rel(1.4), family = fonts$title, face = "bold",
      color = colors$title, lineheight = 1.1, hjust = 0,
      margin = margin(t = 5, b = 10)
    ),
    plot.subtitle = element_text(
      size = rel(0.9), family = fonts$subtitle, face = "italic",
      color = alpha(colors$subtitle, 0.9), lineheight = 1.1,
      margin = margin(t = 0, b = 20)
    ),

    # Legend formatting
    legend.position = "right",
    legend.justification = "top",
    legend.margin = margin(l = 12, b = 5),
    legend.key.size = unit(0.8, "cm"),
    legend.box.margin = margin(b = 10),
    legend.title = element_text(face = "bold"),

    # Axis formatting
    axis.ticks.y = element_blank(),
    axis.ticks.x = element_line(color = "gray", linewidth = 0.5),
    axis.ticks.length = unit(0.2, "cm"),
    axis.title.x = element_text(
      face = "bold", size = rel(0.85),
      margin = margin(t = 10)
    ),
    axis.title.y = element_text(
      face = "bold", size = rel(0.85),
      margin = margin(r = 10)
    ),
    axis.text.x = element_text(
      size = rel(0.85), family = fonts$subtitle,
      color = colors$text
    ),
    axis.text.y = element_text(
      size = rel(0.85), family = fonts$subtitle,
      color = colors$text
    ),

    # Grid lines
    panel.grid.minor = element_line(color = "#ecf0f1", linewidth = 0.2),
    panel.grid.major = element_line(color = "#ecf0f1", linewidth = 0.4),

    # Margin
    plot.margin = margin(20, 20, 20, 20)
  )
)

# Set theme
theme_set(weekly_theme)
```

6. Plot

Show code

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

### |- P1  scatter plot----
p1 <- ggplot(convergence_data, aes(x = access_2000, y = improvement)) +
  # Geoms
  geom_vline(
    xintercept = 25,
    linetype = "dashed",
    color = "#E8516F",
    alpha = 0.6,
    linewidth = 0.5
  ) +
  geom_point(
    aes(color = cohort),
    size = 2.8,
    alpha = 0.85,
    stroke = 0
  ) +
  ggrepel::geom_text_repel(
    data = top_improvers,
    aes(label = country),
    vjust = 1.3,
    hjust = 0.5,
    size = 3,
    family = "sans",
    color = alpha(colors$text, 0.9),
    fontface = "bold",
    show.legend = FALSE,
    box.padding = 0.5,        
    point.padding = 0.3,      
    segment.color = alpha(colors$text, 0.6),  
    segment.size = 0.3,       
    max.overlaps = Inf,      
    min.segment.length = 0.1, 
    seed = 42                
  ) +
  # Scales
  scale_x_continuous(
    labels = percent_format(scale = 1),
    name = "Access Rate in 2000",
    breaks = seq(0, 100, 20),
    limits = c(-5, 100),
    expand = expansion(mult = c(0.02, 0.02))
  ) +
  scale_y_continuous(
    name = "Improvement (percentage points)",
    breaks = seq(-30, 100, 20),
    limits = c(-30, 100),
    expand = expansion(mult = c(0.02, 0.02))
  ) +
  scale_color_manual(
    values = colors$palette,
    name = "Starting Level\n(2000)"
  ) +
  # Labs
  labs(
    title = "Convergence in electricity access",
    subtitle = "Lower starting access rates correlate with greater improvements (2000-2023)"
  ) +
  # Theme
  theme(
    legend.position = "none"
  )

### |- P2  line chart----
p2 <- ggplot(cohort_analysis, aes(x = year, y = median_access, color = starting_cohort)) +
  # Geoms
  geom_ribbon(
    aes(ymin = q25, ymax = q75, fill = starting_cohort),
    alpha = 0.15,
    color = NA
  ) +
  geom_line(
    linewidth = 1.2,
    alpha = 0.9
  ) +
  geom_hline(
    yintercept = c(50, 75, 90),
    linetype = "dotted",
    color = "#bdc3c7",
    alpha = 0.7,
    linewidth = 0.4
  ) +
  # Scales
  scale_x_continuous(
    name = "Year",
    breaks = seq(2000, 2025, 5),
    expand = expansion(mult = c(0.01, 0.02))
  ) +
  scale_y_continuous(
    labels = percent_format(scale = 1),
    name = "Median Access Rate",
    breaks = seq(0, 100, 25),
    expand = expansion(mult = c(0.01, 0.02))
  ) +
  scale_color_manual(
    values = unlist(colors$palette),
    name = "Starting Level\n(2000)"
  ) +
  scale_fill_manual(
    values = colors$palette,
    name = "Starting Level\n(2000)"
  ) +
  # Labs
  labs(
    title = "Progress by starting access level",
    subtitle = "Median access rates over time, grouped by 2000 baseline levels"
  ) +
  # Theme
  theme(
    legend.position = "right",
    legend.justification = "top"
  ) +
  guides(
    color = guide_legend(
      title.position = "top",
      title.hjust = 0.5,
      override.aes = list(linewidth = 2, alpha = 1)
    ),
    fill = "none"
  )

### |-  combined plot ----
combined_plots <- p1 / p2 +
  plot_layout(heights = c(1, 1)) 

combined_plots <- combined_plots +
  plot_annotation(
    title = title_text,
    subtitle = subtitle_text,
    caption = caption_text,
    theme = theme(
      plot.title = element_text(
        size = rel(1.7),
        family = fonts$title,
        face = "bold",
        color = colors$title,
        lineheight = 1.1,
        margin = margin(t = 5, b = 5)
      ),
      plot.subtitle = element_markdown(
        size = rel(0.95),
        family = fonts$subtitle,
        color = alpha(colors$subtitle, 0.9),
        lineheight = 0.9,
        margin = margin(t = 5, b = 0)
      ),
      plot.caption = element_markdown(
        size = rel(0.65),
        family = fonts$caption,
        color = colors$caption,
        hjust = 0.5,
        margin = margin(t = 10)
      )
    )
  )
```

7. Save

Show code

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

### |-  plot image ----  
save_plot_patchwork(
  plot = combined_plots, 
  type = "makeovermonday", 
  year = current_year,
  week = current_week,
  width = 10, 
  height = 12
  )
```

8. Session Info

Expand for Session Info

R version 4.4.1 (2024-06-14 ucrt)
Platform: x86_64-w64-mingw32/x64
Running under: Windows 11 x64 (build 22631)

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 datasets  utils     methods   base     

other attached packages:
 [1] here_1.0.1      patchwork_1.3.0 glue_1.8.0      scales_1.3.0   
 [5] showtext_0.9-7  showtextdb_3.0  sysfonts_0.8.9  ggtext_0.1.2   
 [9] lubridate_1.9.3 forcats_1.0.0   stringr_1.5.1   dplyr_1.1.4    
[13] purrr_1.0.2     readr_2.1.5     tidyr_1.3.1     tibble_3.2.1   
[17] ggplot2_3.5.1   tidyverse_2.0.0 pacman_0.5.1   

loaded via a namespace (and not attached):
 [1] gtable_0.3.6       xfun_0.49          htmlwidgets_1.6.4  ggrepel_0.9.6     
 [5] tzdb_0.5.0         yulab.utils_0.1.8  vctrs_0.6.5        tools_4.4.0       
 [9] generics_0.1.3     curl_6.0.0         parallel_4.4.0     gifski_1.32.0-1   
[13] fansi_1.0.6        pkgconfig_2.0.3    ggplotify_0.1.2    skimr_2.1.5       
[17] lifecycle_1.0.4    compiler_4.4.0     farver_2.1.2       munsell_0.5.1     
[21] repr_1.1.7         janitor_2.2.0      codetools_0.2-20   snakecase_0.11.1  
[25] htmltools_0.5.8.1  yaml_2.3.10        crayon_1.5.3       pillar_1.9.0      
[29] camcorder_0.1.0    magick_2.8.5       commonmark_1.9.2   tidyselect_1.2.1  
[33] digest_0.6.37      stringi_1.8.4      rsvg_2.6.1         rprojroot_2.0.4   
[37] fastmap_1.2.0      grid_4.4.0         colorspace_2.1-1   cli_3.6.4         
[41] magrittr_2.0.3     base64enc_0.1-3    utf8_1.2.4         withr_3.0.2       
[45] bit64_4.5.2        timechange_0.3.0   rmarkdown_2.29     bit_4.5.0         
[49] hms_1.1.3          evaluate_1.0.1     knitr_1.49         markdown_1.13     
[53] gridGraphics_0.5-1 rlang_1.1.6        gridtext_0.1.5     Rcpp_1.0.13-1     
[57] xml2_1.3.6         renv_1.0.3         svglite_2.1.3      rstudioapi_0.17.1 
[61] vroom_1.6.5        jsonlite_1.8.9     R6_2.5.1           fs_1.6.5          
[65] systemfonts_1.1.0

9. GitHub Repository

Expand for GitHub Repo

The complete code for this analysis is available in mm_2025_33.qmd.

For the full repository, click here.

10. References

Expand for References

Data:

Makeover Monday 2025 Week 33: Access to Electricity

Article

Access to electricity

--- title: "Global Convergence in Electricity Access, 2000-2023" subtitle: "Evidence shows countries starting with less electricity access are improving faster" description: "Analysis of World Bank electricity access data (2000-2023) revealing global convergence: countries starting with lowest access (<25%) achieved the most dramatic improvements (50-70+ percentage points), while high-access countries showed minimal change." author: "Steven Ponce" date: "2025-08-11" categories: ["MakeoverMonday", "2025"] tags: [ "electricity-access", "world-bank-data", "convergence-analysis", "development-economics", "scatter-plot", "time-series", "cohort-analysis", "global-development", "energy-access", "data-transformation", "ggplot2", "sustainable-development", "makeover", "economic-convergence", "patchwork" ] image: "thumbnails/mm_2025_33.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 --- ```{r} #| label: setup-links #| include: false # CENTRALIZED LINK MANAGEMENT ## Project-specific info current_year <- 2025 current_week <- 33 project_file <- "mm_2025_33.qmd" project_image <- "mm_2025_33.png" ## Data Sources data_main <- "https://data.world/makeovermonday/2025w33-access-to-electricity-of-population" data_secondary <- "https://data.worldbank.org/indicator/EG.ELC.ACCS.ZS?view=map" ## 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/2025/Week_33/original_chart.png" ## Organization/Platform Links org_primary <- "https://data.worldbank.org/indicator/EG.ELC.ACCS.ZS?view=map" org_secondary <- "https://data.worldbank.org/" # 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 **Access to Electricity** comes from `r create_link("World Bank Group", data_secondary)`  ![Original visualization](https://raw.githubusercontent.com/poncest/MakeoverMonday/refs/heads/master/2025/Week_33/original_chart.png) ### Makeover ![Two-panel chart showing global convergence in electricity access from 2000 to 2023. Top panel: The scatter plot reveals that countries with the lowest access (\<25%) achieved the greatest improvements (50-70+ percentage points), while countries starting near universal access showed minimal change. Bottom panel: The line chart shows the progress of five cohorts over time, with very low starters (red line) rising from \~15% to 50% access, demonstrating a catch-up effect. Data shows evidence of global convergence as access gaps narrow.](mm_2025_33.png){#fig-1} ### <mark> **Steps to Create this Graphic** </mark> #### 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, # Easily Install and Load the 'Tidyverse' ggtext, # Improved Text Rendering Support for 'ggplot2' showtext, # Using Fonts More Easily in R Graphs scales, # Scale Functions for Visualization glue, # Interpreted String Literals patchwork # The Composer of Plots ) }) ### |- figure size ---- camcorder::gg_record( dir = here::here("temp_plots"), device = "png", width = 10, height = 12, 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 #| access_electricity_raw <- read_csv( here::here('data/MakeoverMonday/2025/Access to Electricity.csv')) |> janitor::clean_names() ``` #### 3. Examine the Data ```{r} #| label: examine #| include: true #| eval: true #| results: 'hide' #| warning: false glimpse(access_electricity_raw) skimr::skim(access_electricity_raw) ``` #### 4. Tidy Data ```{r} #| label: tidy #| warning: false access_electricity_tidy <- access_electricity_raw |> # Remove completely empty year columns (1960-1989, 2024) select(-c(x1960:x1989, x2024)) |> pivot_longer( cols = starts_with("x"), names_to = "year", values_to = "access_rate", names_prefix = "x" # Remove the "x" prefix ) |> mutate(year = as.numeric(year)) |> filter(!is.na(access_rate)) |> rename( country = country_name, code = country_code, indicator = indicator_name, indicator_id = indicator_code ) |> arrange(country, year) # convergence analysis data convergence_data <- access_electricity_tidy |> filter(year %in% c(2000, 2023)) |> group_by(country) |> filter(n() == 2) |> summarise( access_2000 = access_rate[year == 2000], access_2023 = access_rate[year == 2023], improvement = access_2023 - access_2000, .groups = "drop" ) |> filter(!is.na(access_2000), !is.na(access_2023)) |> mutate( cohort = case_when( access_2000 < 25 ~ "Very Low Start (<25%)", access_2000 < 50 ~ "Low Start (25-50%)", access_2000 < 75 ~ "Medium Start (50-75%)", access_2000 < 95 ~ "High Start (75-95%)", TRUE ~ "Universal Start (≥95%)" ) ) # Countries with dramatic improvements (>= 50 percentage points) # from Very Low Start only top_improvers <- convergence_data |> filter(cohort == "Very Low Start (<25%)", improvement >= 50) |> arrange(desc(improvement)) # Define the logical cohort_levels <- c( "Very Low Start (<25%)", "Low Start (25-50%)", "Medium Start (50-75%)", "High Start (75-95%)", "Universal Start (≥95%)" ) # cohort analysis data cohort_analysis <- access_electricity_tidy |> filter(year == 2000) |> mutate( starting_cohort = case_when( access_rate < 25 ~ "Very Low Start (<25%)", access_rate < 50 ~ "Low Start (25-50%)", access_rate < 75 ~ "Medium Start (50-75%)", access_rate < 95 ~ "High Start (75-95%)", TRUE ~ "Universal Start (≥95%)" ), # Order factor levels for meaningful legend ordering (low to high) starting_cohort = factor(starting_cohort, levels = cohort_levels) ) |> select(country, starting_cohort) |> inner_join(access_electricity_tidy, by = "country") |> filter(year >= 2000) |> group_by(starting_cohort, year) |> summarise( median_access = median(access_rate, na.rm = TRUE), q25 = as.numeric(quantile(access_rate, 0.25, na.rm = TRUE)), q75 = as.numeric(quantile(access_rate, 0.75, na.rm = TRUE)), countries = n(), .groups = "drop" ) |> filter(countries >= 3) ``` #### 5. Visualization Parameters ```{r} #| label: params #| include: true #| warning: false ### |- plot aesthetics ---- # Get base colors with custom palette colors <- get_theme_colors(palette = list( "Very Low Start (<25%)" = "#8B0000", "Low Start (25-50%)" = "#FF8C00", "Medium Start (50-75%)" = "#006400", "High Start (75-95%)" = "#1E90FF", "Universal Start (≥95%)" = "#4B0082" )) ### |- titles and caption ---- title_text <- str_glue("Global convergence in electricity access, 2000-2023") subtitle_text <- str_glue( "Evidence shows countries starting with less electricity access are improving faster" ) # Create caption caption_text <- create_mm_caption( mm_year = current_year, mm_week = current_week, source_text = paste0("World Bank Group") ) ### |- fonts ---- setup_fonts() fonts <- get_font_families() ### |- plot theme ---- # Start with base theme base_theme <- create_base_theme(colors) # Add weekly-specific theme elements weekly_theme <- extend_weekly_theme( base_theme, theme( plot.title = element_text( size = rel(1.4), family = fonts$title, face = "bold", color = colors$title, lineheight = 1.1, hjust = 0, margin = margin(t = 5, b = 10) ), plot.subtitle = element_text( size = rel(0.9), family = fonts$subtitle, face = "italic", color = alpha(colors$subtitle, 0.9), lineheight = 1.1, margin = margin(t = 0, b = 20) ), # Legend formatting legend.position = "right", legend.justification = "top", legend.margin = margin(l = 12, b = 5), legend.key.size = unit(0.8, "cm"), legend.box.margin = margin(b = 10), legend.title = element_text(face = "bold"), # Axis formatting axis.ticks.y = element_blank(), axis.ticks.x = element_line(color = "gray", linewidth = 0.5), axis.ticks.length = unit(0.2, "cm"), axis.title.x = element_text( face = "bold", size = rel(0.85), margin = margin(t = 10) ), axis.title.y = element_text( face = "bold", size = rel(0.85), margin = margin(r = 10) ), axis.text.x = element_text( size = rel(0.85), family = fonts$subtitle, color = colors$text ), axis.text.y = element_text( size = rel(0.85), family = fonts$subtitle, color = colors$text ), # Grid lines panel.grid.minor = element_line(color = "#ecf0f1", linewidth = 0.2), panel.grid.major = element_line(color = "#ecf0f1", linewidth = 0.4), # Margin plot.margin = margin(20, 20, 20, 20) ) ) # Set theme theme_set(weekly_theme) ``` #### 6. Plot ```{r} #| label: plot #| warning: false ### |- P1 scatter plot---- p1 <- ggplot(convergence_data, aes(x = access_2000, y = improvement)) + # Geoms geom_vline( xintercept = 25, linetype = "dashed", color = "#E8516F", alpha = 0.6, linewidth = 0.5 ) + geom_point( aes(color = cohort), size = 2.8, alpha = 0.85, stroke = 0 ) + ggrepel::geom_text_repel( data = top_improvers, aes(label = country), vjust = 1.3, hjust = 0.5, size = 3, family = "sans", color = alpha(colors$text, 0.9), fontface = "bold", show.legend = FALSE, box.padding = 0.5, point.padding = 0.3, segment.color = alpha(colors$text, 0.6), segment.size = 0.3, max.overlaps = Inf, min.segment.length = 0.1, seed = 42 ) + # Scales scale_x_continuous( labels = percent_format(scale = 1), name = "Access Rate in 2000", breaks = seq(0, 100, 20), limits = c(-5, 100), expand = expansion(mult = c(0.02, 0.02)) ) + scale_y_continuous( name = "Improvement (percentage points)", breaks = seq(-30, 100, 20), limits = c(-30, 100), expand = expansion(mult = c(0.02, 0.02)) ) + scale_color_manual( values = colors$palette, name = "Starting Level\n(2000)" ) + # Labs labs( title = "Convergence in electricity access", subtitle = "Lower starting access rates correlate with greater improvements (2000-2023)" ) + # Theme theme( legend.position = "none" ) ### |- P2 line chart---- p2 <- ggplot(cohort_analysis, aes(x = year, y = median_access, color = starting_cohort)) + # Geoms geom_ribbon( aes(ymin = q25, ymax = q75, fill = starting_cohort), alpha = 0.15, color = NA ) + geom_line( linewidth = 1.2, alpha = 0.9 ) + geom_hline( yintercept = c(50, 75, 90), linetype = "dotted", color = "#bdc3c7", alpha = 0.7, linewidth = 0.4 ) + # Scales scale_x_continuous( name = "Year", breaks = seq(2000, 2025, 5), expand = expansion(mult = c(0.01, 0.02)) ) + scale_y_continuous( labels = percent_format(scale = 1), name = "Median Access Rate", breaks = seq(0, 100, 25), expand = expansion(mult = c(0.01, 0.02)) ) + scale_color_manual( values = unlist(colors$palette), name = "Starting Level\n(2000)" ) + scale_fill_manual( values = colors$palette, name = "Starting Level\n(2000)" ) + # Labs labs( title = "Progress by starting access level", subtitle = "Median access rates over time, grouped by 2000 baseline levels" ) + # Theme theme( legend.position = "right", legend.justification = "top" ) + guides( color = guide_legend( title.position = "top", title.hjust = 0.5, override.aes = list(linewidth = 2, alpha = 1) ), fill = "none" ) ### |- combined plot ---- combined_plots <- p1 / p2 + plot_layout(heights = c(1, 1)) combined_plots <- combined_plots + plot_annotation( title = title_text, subtitle = subtitle_text, caption = caption_text, theme = theme( plot.title = element_text( size = rel(1.7), family = fonts$title, face = "bold", color = colors$title, lineheight = 1.1, margin = margin(t = 5, b = 5) ), plot.subtitle = element_markdown( size = rel(0.95), family = fonts$subtitle, color = alpha(colors$subtitle, 0.9), lineheight = 0.9, margin = margin(t = 5, b = 0) ), plot.caption = element_markdown( size = rel(0.65), family = fonts$caption, color = colors$caption, hjust = 0.5, margin = margin(t = 10) ) ) ) ``` #### 7. Save ```{r} #| label: save #| warning: false ### |- plot image ---- save_plot_patchwork( plot = combined_plots, type = "makeovermonday", year = current_year, week = current_week, width = 10, height = 12 ) ``` #### 8. Session Info ::: {.callout-tip collapse="true"} ##### Expand for Session Info ```{r, echo = FALSE} #| eval: true #| warning: false sessionInfo() ``` ::: #### 9. GitHub Repository ::: {.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 ::: {.callout-tip collapse="true"} ##### Expand for References 1. Data: - Makeover Monday `r current_year` Week `r current_week`: `r create_link("Access to Electricity", data_main)` 2. Article - `r create_link("Access to electricity", data_secondary)` :::