Britain’s Power Mix: A Daily Energy Snapshot

Tracking Great Britain’s electricity demand and generation sources across the National Grid | Data from National Grid: Live

MakeoverMonday

2025

Analysis of Britain’s electricity network through small multiples visualization, showing daily patterns in energy demand, generation sources, and transfers. The visualization explores the interplay between different power sources across the National Grid from April 2024 to January 2025.

Author

Steven Ponce

Published

February 2, 2025

Original

The original visualization comes from the National Grid: Live (The National Grid is the electric power transmission network for Great Britain).

Original visualization ` Source: Elexon Insights Solution, National Grid ESO’s Data Portal, and the Carbon Intensity API

Makeover

Figure 1: Small multiples line chart showing Britain’s energy metrics from Jan 2024 to Jan 2025. Five panels display Energy (GW) trends for Demand, Transfers, Fossil Fuels, Renewables, and Other Sources. Demand shows the highest values around 30-40 GW with seasonal variation, while other metrics fluctuate between 0-20 GW. Each panel includes historical context with grey reference lines.

Steps to Create this Graphic

1. Load Packages & Setup

Show code

## 1. LOAD PACKAGES & SETUP ----
suppressPackageStartupMessages({
pacman::p_load(
tidyverse,      # Easily Install and Load the 'Tidyverse'
   tidyverse,      # Easily Install and Load the 'Tidyverse'
    ggtext,         # Improved Text Rendering Support for 'ggplot2'
    showtext,       # Using Fonts More Easily in R Graphs
    janitor,        # Simple Tools for Examining and Cleaning Dirty Data
    skimr,          # Compact and Flexible Summaries of Data
    scales,         # Scale Functions for Visualization
    glue,           # Interpreted String Literals
    here,           # A Simpler Way to Find Your Files
    lubridate,      # Make Dealing with Dates a Little Easier
    camcorder,      # Record Your Plot History 
    patchwork,      # The Composer of Plots
    gghighlight     # Highlight Lines and Points in 'ggplot2'
)
})

### |- figure size ----
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

energy_raw <- read_csv(
  here::here('data/National_Grid_Energy_Breakdown.csv')) |> 
  clean_names()

3. Examine the Data

Show code

glimpse(energy_raw)
skim(energy_raw)

4. Tidy Data

Show code

### |-  tidy data ----
energy_clean <- energy_raw |> 
  rename_with(~str_remove(., pattern = "_gw"), everything()) |> 
  mutate(date = dmy(date)) |> 
  # Pivot longer
  pivot_longer(
    cols = !date,
    names_to = "metric",
    values_to = "demand_gw"
  ) |> 
  mutate(
    metric = case_when(
      metric == "fossil_fuels" ~ "fossil fuels",
      metric == "other_sources" ~ "other sources",
      TRUE ~ metric
    ),
    metric = str_to_title(metric),
    metric = factor(metric,
                    levels = c("Demand", 
                               "Transfers", 
                               "Fossil Fuels", 
                               "Renewables", 
                               "Other Sources")
    )
  )

5. Visualization Parameters

Show code

### |-  plot aesthetics ----
# Get base colors with custom palette
colors <- get_theme_colors(palette = c("#4B79B7", "#F8F9FA", "#2C3E50", "#34495E", "#7F8C8D"))

### |-  titles and caption ----
title_text <- str_glue("Britain's Power Mix: A Daily Energy Snapshot")
subtitle_text <- str_glue("Tracking Great Britain's electricity demand and generation sources across<br>
                          the National Grid | Data from National Grid: Live")

# Create caption
caption_text <- create_mm_caption(
    mm_year = 2025,
    mm_week = 06,
    source_text = "Great Britain National Grid: Live"
)

### |-  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(
    # Weekly-specific modifications
    axis.line.x           = element_line(color = "#252525", linewidth = .2),
    
    panel.spacing.x       = unit(2, 'lines'),
    panel.spacing.y       = unit(1, 'lines'),
    panel.grid.major.x    = element_blank(),
    panel.grid.major.y    = element_line(color = alpha(colors[5], 0.2), linewidth = 0.2),
    panel.grid.minor      = element_blank(),
    
    strip.text          = element_textbox(
      size              = rel(0.9),
      face              = 'bold',
      color             = colors$palette[3],
      fill              = alpha(colors$palette[1], 0.1),
      box.color         = alpha(colors[1], 0.5),
      halign            = 0.5,
      linetype          = 1,
      r                 = unit(3, "pt"),
      width             = unit(1, "npc"),
      padding           = margin(5, 10, 5, 10),
      margin            = margin(b = 10)
    ),
  )
)

# Set theme
theme_set(weekly_theme)

6. Plot

Show code

### |-  Plot  ----
p <- ggplot(energy_clean,
       aes(x = date, y = demand_gw, group = metric)) +
  # Geoms
  geom_line(linewidth = 0.3, alpha = 0.2) +
  gghighlight(
    use_direct_label = FALSE,
    unhighlighted_params = list(
      linewidth = 0.3,
      alpha = 0.45,
      color = 'gray20'
    )
  ) +
  geom_line(color = colors$palette[1], linewidth = 1.2) +
  
  # Scales
  scale_x_date(
    breaks = "3 month",               
    labels = label_date_short(),
    limits = c(min(energy_clean$date), max(energy_clean$date))  
  ) +
  scale_y_continuous(breaks = pretty_breaks()) +
  coord_cartesian(clip = 'off') +
  
  # Labs
  labs(
    x = NULL,
    y = "Energy (GW)",
    title = title_text,
    subtitle = subtitle_text,
    caption = caption_text,
  ) +
  
  # Facet
  facet_wrap(~metric, ncol = 2) +
  
  # Theme
  theme(
    plot.title = element_text(
      size   = rel(2),
      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$subtitle,
      color  = colors$subtitle,
      lineheight = 1.2,
      margin = margin(t = 5, b = 15)
    ),
    plot.caption = element_markdown(
      size   = rel(0.6),
      family = fonts$caption,
      color  = colors$caption,
      hjust  = 0.5,
      margin = margin(t = 10)
    )
  )

7. Save

Show code

### |-  plot image ----  
save_plot(
  p, 
  type = "makeovermonday", 
  year = 2025,
  week = 06,
  width = 8, 
  height = 8
  )

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] gghighlight_0.4.1 patchwork_1.3.0   camcorder_0.1.0   here_1.0.1       
 [5] glue_1.8.0        scales_1.3.0      skimr_2.1.5       janitor_2.2.0    
 [9] showtext_0.9-7    showtextdb_3.0    sysfonts_0.8.9    ggtext_0.1.2     
[13] lubridate_1.9.3   forcats_1.0.0     stringr_1.5.1     dplyr_1.1.4      
[17] purrr_1.0.2       readr_2.1.5       tidyr_1.3.1       tibble_3.2.1     
[21] ggplot2_3.5.1     tidyverse_2.0.0  

loaded via a namespace (and not attached):
 [1] gtable_0.3.6      xfun_0.49         htmlwidgets_1.6.4 tzdb_0.4.0       
 [5] vctrs_0.6.5       tools_4.4.0       generics_0.1.3    curl_6.0.0       
 [9] parallel_4.4.0    gifski_1.32.0-1   fansi_1.0.6       pacman_0.5.1     
[13] pkgconfig_2.0.3   lifecycle_1.0.4   compiler_4.4.0    farver_2.1.2     
[17] textshaping_0.4.0 munsell_0.5.1     repr_1.1.7        codetools_0.2-20 
[21] snakecase_0.11.1  htmltools_0.5.8.1 yaml_2.3.10       crayon_1.5.3     
[25] pillar_1.9.0      magick_2.8.5      commonmark_1.9.2  tidyselect_1.2.1 
[29] digest_0.6.37     stringi_1.8.4     rsvg_2.6.1        rprojroot_2.0.4  
[33] fastmap_1.2.0     grid_4.4.0        colorspace_2.1-1  cli_3.6.3        
[37] magrittr_2.0.3    base64enc_0.1-3   utf8_1.2.4        withr_3.0.2      
[41] bit64_4.5.2       timechange_0.3.0  rmarkdown_2.29    bit_4.5.0        
[45] ragg_1.3.3        hms_1.1.3         evaluate_1.0.1    knitr_1.49       
[49] markdown_1.13     rlang_1.1.4       gridtext_0.1.5    Rcpp_1.0.13-1    
[53] xml2_1.3.6        renv_1.0.3        vroom_1.6.5       svglite_2.1.3    
[57] rstudioapi_0.17.1 jsonlite_1.8.9    R6_2.5.1          systemfonts_1.1.0

9. GitHub Repository

Expand for GitHub Repo

The complete code for this analysis is available in mm_2025_06.qmd.

For the full repository, click here.

10. References

Expand for References

Article:
- National Grid - Live: The National Grid is the electric power transmission network for Great Britain
Data:

Makeover Monday 2025 Week 06: National Grid: Live

--- title: "Britain's Power Mix: A Daily Energy Snapshot" subtitle: "Tracking Great Britain's electricity demand and generation sources across the National Grid | Data from National Grid: Live" description: "Analysis of Britain's electricity network through small multiples visualization, showing daily patterns in energy demand, generation sources, and transfers. The visualization explores the interplay between different power sources across the National Grid from April 2024 to January 2025." author: "Steven Ponce" date: "2025-02-02" categories: ["MakeoverMonday", "2025"] tags: [ "ggplot2", "tidyverse", "energy-data", "time-series", "small-multiples", "national-grid", "electricity", "britain", "power-generation", "renewable-energy", "fossil-fuels", "data-journalism", "energy-analytics" ] image: "thumbnails/mm_2025_06.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 # filters: # - social-share # share: # permalink: "https://stevenponce.netlify.app/data_visualizations/MakeoverMonday/2025/mm_2025_06.html" # description: "Exploring Britain's daily energy patterns: A visualization of power demand, renewable and fossil fuel generation, and grid transfers across the National Grid system #DataViz #MakeoverMonday #EnergyData" # twitter: true # linkedin: true # email: true # facebook: false # reddit: false # stumble: false # tumblr: false # mastodon: true # bsky: true --- ### Original The original visualization comes from the National Grid: Live (The National Grid is the electric power transmission network for Great Britain). ![Original visualization](https://raw.githubusercontent.com/poncest/MakeoverMonday/master/2025/Week_06/original_chart.png) ` Source: [Elexon Insights Solution, National Grid ESO’s Data Portal, and the Carbon Intensity API](https://grid.iamkate.com/) ### Makeover ![Small multiples line chart showing Britain's energy metrics from Jan 2024 to Jan 2025. Five panels display Energy (GW) trends for Demand, Transfers, Fossil Fuels, Renewables, and Other Sources. Demand shows the highest values around 30-40 GW with seasonal variation, while other metrics fluctuate between 0-20 GW. Each panel includes historical context with grey reference lines.](mm_2025_06.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({ pacman::p_load( tidyverse, # Easily Install and Load the 'Tidyverse' tidyverse, # Easily Install and Load the 'Tidyverse' ggtext, # Improved Text Rendering Support for 'ggplot2' showtext, # Using Fonts More Easily in R Graphs janitor, # Simple Tools for Examining and Cleaning Dirty Data skimr, # Compact and Flexible Summaries of Data scales, # Scale Functions for Visualization glue, # Interpreted String Literals here, # A Simpler Way to Find Your Files lubridate, # Make Dealing with Dates a Little Easier camcorder, # Record Your Plot History patchwork, # The Composer of Plots gghighlight # Highlight Lines and Points in 'ggplot2' ) }) ### |- figure size ---- 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 ```{r} #| label: read #| include: true #| eval: true #| warning: false energy_raw <- read_csv( here::here('data/National_Grid_Energy_Breakdown.csv')) |> clean_names() ``` #### 3. Examine the Data ```{r} #| label: examine #| include: true #| eval: true #| results: 'hide' #| warning: false glimpse(energy_raw) skim(energy_raw) ``` #### 4. Tidy Data ```{r} #| label: tidy #| warning: false ### |- tidy data ---- energy_clean <- energy_raw |> rename_with(~str_remove(., pattern = "_gw"), everything()) |> mutate(date = dmy(date)) |> # Pivot longer pivot_longer( cols = !date, names_to = "metric", values_to = "demand_gw" ) |> mutate( metric = case_when( metric == "fossil_fuels" ~ "fossil fuels", metric == "other_sources" ~ "other sources", TRUE ~ metric ), metric = str_to_title(metric), metric = factor(metric, levels = c("Demand", "Transfers", "Fossil Fuels", "Renewables", "Other Sources") ) ) ``` #### 5. Visualization Parameters ```{r} #| label: params #| include: true #| warning: false ### |- plot aesthetics ---- # Get base colors with custom palette colors <- get_theme_colors(palette = c("#4B79B7", "#F8F9FA", "#2C3E50", "#34495E", "#7F8C8D")) ### |- titles and caption ---- title_text <- str_glue("Britain's Power Mix: A Daily Energy Snapshot") subtitle_text <- str_glue("Tracking Great Britain's electricity demand and generation sources across<br> the National Grid | Data from National Grid: Live") # Create caption caption_text <- create_mm_caption( mm_year = 2025, mm_week = 06, source_text = "Great Britain National Grid: Live" ) ### |- 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( # Weekly-specific modifications axis.line.x = element_line(color = "#252525", linewidth = .2), panel.spacing.x = unit(2, 'lines'), panel.spacing.y = unit(1, 'lines'), panel.grid.major.x = element_blank(), panel.grid.major.y = element_line(color = alpha(colors[5], 0.2), linewidth = 0.2), panel.grid.minor = element_blank(), strip.text = element_textbox( size = rel(0.9), face = 'bold', color = colors$palette[3], fill = alpha(colors$palette[1], 0.1), box.color = alpha(colors[1], 0.5), halign = 0.5, linetype = 1, r = unit(3, "pt"), width = unit(1, "npc"), padding = margin(5, 10, 5, 10), margin = margin(b = 10) ), ) ) # Set theme theme_set(weekly_theme) ``` #### 6. Plot ```{r} #| label: plot #| warning: false ### |- Plot ---- p <- ggplot(energy_clean, aes(x = date, y = demand_gw, group = metric)) + # Geoms geom_line(linewidth = 0.3, alpha = 0.2) + gghighlight( use_direct_label = FALSE, unhighlighted_params = list( linewidth = 0.3, alpha = 0.45, color = 'gray20' ) ) + geom_line(color = colors$palette[1], linewidth = 1.2) + # Scales scale_x_date( breaks = "3 month", labels = label_date_short(), limits = c(min(energy_clean$date), max(energy_clean$date)) ) + scale_y_continuous(breaks = pretty_breaks()) + coord_cartesian(clip = 'off') + # Labs labs( x = NULL, y = "Energy (GW)", title = title_text, subtitle = subtitle_text, caption = caption_text, ) + # Facet facet_wrap(~metric, ncol = 2) + # Theme theme( plot.title = element_text( size = rel(2), family = fonts$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 = colors$subtitle, lineheight = 1.2, margin = margin(t = 5, b = 15) ), plot.caption = element_markdown( size = rel(0.6), family = fonts$caption, color = colors$caption, hjust = 0.5, margin = margin(t = 10) ) ) ``` #### 7. Save ```{r} #| label: save #| warning: false ### |- plot image ---- save_plot( p, type = "makeovermonday", year = 2025, week = 06, width = 8, height = 8 ) ``` #### 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 [`mm_2025_06.qmd`](https://github.com/poncest/personal-website/blob/master/data_visualizations/MakeoverMonday/2025/mm_2025_06.qmd). For the full repository, [click here](https://github.com/poncest/personal-website/). ::: #### 10. References ::: {.callout-tip collapse="true"} ##### Expand for References 1. Article: - National Grid - Live: [The National Grid is the electric power transmission network for Great Britain](https://grid.iamkate.com/) 2. Data: - Makeover Monday 2025 Week 06: [National Grid: Live](https://data.world/makeovermonday/2025w6-britains-national-grid-energy-sources) :::