London Underground Temperature Analysis (2013-2023)

Central and Bakerloo lines run consistently hottest • Sub-Surface lines stay coolest year-round

MakeoverMonday

2025

A MakeoverMonday data visualization analyzing 10 years of London Underground temperature data. Using unified warm color palettes, this dual-chart approach reveals how Central and Bakerloo lines consistently run 5-10°C hotter than Sub-Surface lines, with summer creating the starkest temperature differences across the network.

Author

Steven Ponce

Published

July 23, 2025

Original

The original visualization HAS YOUR TUBE LINE GOT HOTTER OVER THE PAST 10 YEARS? Average temperatures on seven London Underground lines for July-August, 2023 to 2014 (°C) comes from Transport for London (TfL)

Makeover

Figure 1: Two-panel chart showing London Underground temperature data from 2013-2023. Left panel: heatmap displaying monthly temperature patterns for 8 tube lines, with warm colors indicating higher temperatures. Right panel: ridgeline plot showing temperature distributions for each line. Both charts use unified warm color palette (dark brown for cool, golden yellow for hot temperatures). Bakerloo and Victoria lines show consistently highest temperatures (reaching 30°C+), while Sub-Surface lines remain coolest year-round (rarely exceeding 25°C). Summer months (June-August) show greatest temperature variation between lines.

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
  ggridges        # Ridgeline Plots in 'ggplot2'
  )
})

### |- figure size ----
camcorder::gg_record(
    dir    = here::here("temp_plots"),
    device = "png",
    width  =  12,
    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
#|
avg_temp_raw <- read_csv(
  here::here('data/MakeoverMonday/2025/lu-average-monthly-temperatures 2013-2024.csv')) |> 
  janitor::clean_names()
```

3. Examine the Data

Show code

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

glimpse(avg_temp_raw)
skimr::skim(avg_temp_raw)
```

4. Tidy Data

Show code

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

temp_long <- avg_temp_raw |>
  pivot_longer(cols = bakerloo:sub_surface_lines, 
               names_to = "line", 
               values_to = "temperature") |>  
  mutate(
    date = ymd(paste(year, month, "01")),
    month_num = match(month, month.name),
    season = case_when(
      month_num %in% c(12, 1, 2) ~ "Winter",
      month_num %in% c(3, 4, 5) ~ "Spring", 
      month_num %in% c(6, 7, 8) ~ "Summer",
      month_num %in% c(9, 10, 11) ~ "Autumn"
    ),
    line_clean = case_when(
      line == "waterloo_and_city" ~ "Waterloo & City",
      line == "sub_surface_lines" ~ "Sub-Surface Lines", 
      TRUE ~ str_to_title(line)
    )
  )

line_order <- temp_long |>
  group_by(line_clean) |>
  summarise(median_temp = median(temperature, na.rm = TRUE), .groups = "drop") |>
  arrange(desc(median_temp)) |>
  pull(line_clean)
```

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(NULL))

### |-  titles and caption ----
title_text <- str_glue("London Underground Temperature Analysis (2013-2023)")
subtitle_text <- str_glue("Central and Bakerloo lines run consistently hottest • Sub-Surface lines stay coolest year-round")

# Create caption
caption_text <- create_mm_caption(
  mm_year = current_year,
  mm_week = current_week,
  source_text = paste0("Transport for London (TfL)")
)

### |-  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(1.2), family = fonts$title, face = "bold",
      color = colors$title, lineheight = 1.1, hjust = 0.5,
      margin = margin(t = 5, b = 10)
    ),
    plot.subtitle = element_text(
      size = rel(0.9), hjust = 0.5, family = fonts$subtitle,
      color = alpha(colors$subtitle, 0.9), lineheight = 0.9,
      margin = margin(t = 5, b = 20)
    ),

    # Legend formatting
    legend.position = "right",
    # legend.direction = "horizontal",
    legend.box.margin = margin(b = 10),
    legend.margin = margin(b = 5),
    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)),
    axis.title.y = element_text(face = "bold", size = rel(0.85)),
    axis.text = element_text(size = rel(0.85), family = fonts$subtitle, color = colors$text),

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

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

# Set theme
theme_set(weekly_theme)

# Scale function
create_warm_color_scale <- function(legend_position = "right") {
  scale_fill_gradient2(
    name = "Temperature\n(°C)",
    low = "#2c1810",      
    mid = "#cc4125",       
    high = "#f9c74f",     
    midpoint = 22,
    breaks = seq(10, 30, 5),
    labels = paste0(seq(10, 30, 5), "°C"),
    guide = if(legend_position == "none") "none" else guide_colorbar()
  )
}
```

6. Plot

Show code

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

### |-  Plot 1  heatmap ----
p1 <- temp_long |>
  mutate(line_clean = factor(line_clean, levels = line_order)) |>
  ggplot(aes(x = month_num, y = line_clean, fill = temperature)) +
  # Geoms
  geom_tile(color = "white", linewidth = 0.8) +
  # Scales
  create_warm_color_scale() +
  scale_x_continuous(
    breaks = 1:12, 
    labels = month.abb,
    expand = c(0, 0)
  ) +
  scale_y_discrete(expand = c(0, 0)) +
  # Labs
  labs(
    title = "Monthly Temperature Patterns",
    subtitle = "Heatmap reveals seasonal trends across London Underground lines",
    x = "Month",
    y = NULL
  ) +
  # Theme
  theme(
    legend.position = "right",
    legend.key.height = unit(1, "cm"),
    legend.key.width = unit(0.4, "cm"),
    axis.text.y = element_text(size = 10, hjust = 1)
  )

### |-  Plot 2  ridges plot ----
# Calculate average temperature for each line 
line_avg_temps <- temp_long |>
  group_by(line_clean) |>
  summarise(avg_temp = mean(temperature, na.rm = TRUE), .groups = "drop")

p2 <- temp_long |>
  mutate(line_clean = factor(line_clean, levels = line_order)) |>
  left_join(line_avg_temps, by = "line_clean") |>
  ggplot(aes(x = temperature, y = line_clean, fill = avg_temp)) +
  # Geoms
  geom_density_ridges(
    alpha = 0.8, 
    scale = 0.95,
    linewidth = 0.3,
    color = "white"
  ) +
  # Scales
  create_warm_color_scale(legend_position = "none") +
  scale_x_continuous(
    breaks = seq(10, 35, 5),
    labels = paste0(seq(10, 35, 5), "°C"),
    expand = c(0.01, 0)
  ) +
  scale_y_discrete(expand = c(0, 0)) +
  # Labs
  labs(
    title = "Temperature Distribution by Line",
    subtitle = "Density curves colored by each line's average temperature",
    x = "Temperature (°C)",
    y = NULL
  ) +
  # Theme
  theme(
    axis.text.y = element_blank(),
    axis.ticks.y = element_blank()
  )

### |-  combined plot ----
# Create an invisible spacer plot
spacer <- ggplot() +
  theme_void()

combined_plots <- (p1 + spacer + p2) +
  plot_layout(
    widths = c(1.2, 0.01, 1)
    )

combined_plots <- combined_plots +
  plot_annotation(
    title = title_text,
    subtitle = subtitle_text,
    caption = caption_text,
    theme = theme(
      plot.title = element_text(
        size = rel(1.85),
        family = fonts$title,
        face = "bold",
        hjust = 0.5,
        color = colors$title,
        lineheight = 1.1,
        margin = margin(t = 5, b = 5)
      ),
      plot.subtitle = element_text(
        size = rel(0.95),
        hjust = 0.5,
        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 = 12, 
  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/La_Paz
tzcode source: internal

attached base packages:
[1] stats     graphics  grDevices datasets  utils     methods   base     

other attached packages:
 [1] here_1.0.1      ggridges_0.5.6  patchwork_1.3.0 glue_1.8.0     
 [5] scales_1.3.0    showtext_0.9-7  showtextdb_3.0  sysfonts_0.8.9 
 [9] ggtext_0.1.2    lubridate_1.9.3 forcats_1.0.0   stringr_1.5.1  
[13] dplyr_1.1.4     purrr_1.0.2     readr_2.1.5     tidyr_1.3.1    
[17] tibble_3.2.1    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  tzdb_0.5.0        
 [5] yulab.utils_0.1.8  vctrs_0.6.5        tools_4.4.0        generics_0.1.3    
 [9] curl_6.0.0         parallel_4.4.0     gifski_1.32.0-1    fansi_1.0.6       
[13] pkgconfig_2.0.3    ggplotify_0.1.2    skimr_2.1.5        lifecycle_1.0.4   
[17] compiler_4.4.0     farver_2.1.2       munsell_0.5.1      repr_1.1.7        
[21] janitor_2.2.0      codetools_0.2-20   snakecase_0.11.1   htmltools_0.5.8.1 
[25] yaml_2.3.10        crayon_1.5.3       pillar_1.9.0       camcorder_0.1.0   
[29] magick_2.8.5       commonmark_1.9.2   tidyselect_1.2.1   digest_0.6.37     
[33] stringi_1.8.4      rsvg_2.6.1         rprojroot_2.0.4    fastmap_1.2.0     
[37] grid_4.4.0         colorspace_2.1-1   cli_3.6.4          magrittr_2.0.3    
[41] base64enc_0.1-3    utf8_1.2.4         withr_3.0.2        bit64_4.5.2       
[45] timechange_0.3.0   rmarkdown_2.29     bit_4.5.0          hms_1.1.3         
[49] evaluate_1.0.1     knitr_1.49         markdown_1.13      gridGraphics_0.5-1
[53] rlang_1.1.6        gridtext_0.1.5     Rcpp_1.0.13-1      xml2_1.3.6        
[57] renv_1.0.3         vroom_1.6.5        svglite_2.1.3      rstudioapi_0.17.1 
[61] jsonlite_1.8.9     R6_2.5.1           fs_1.6.5           systemfonts_1.1.0

9. GitHub Repository

Expand for GitHub Repo

The complete code for this analysis is available in mm_2025_30.qmd.

For the full repository, click here.

10. References

Expand for References

Data:

Makeover Monday 2025 Week 30: London Underground Average Monthly Temperatures

Article

Daily Mail Online: Is your commute a scorcher?

--- title: "London Underground Temperature Analysis (2013-2023)" subtitle: "Central and Bakerloo lines run consistently hottest • Sub-Surface lines stay coolest year-round" description: "A MakeoverMonday data visualization analyzing 10 years of London Underground temperature data. Using unified warm color palettes, this dual-chart approach reveals how Central and Bakerloo lines consistently run 5-10°C hotter than Sub-Surface lines, with summer creating the starkest temperature differences across the network." author: "Steven Ponce" date: "2025-07-23" categories: ["MakeoverMonday", "2025"] tags: [ "london-underground", "temperature-analysis", "heatmap", "ridgeline-plot", "ggplot2", "ggridges", "patchwork", "warm-colors", "public-transport", "seasonal-patterns", "tfl-data", "unified-color-palette", "horizontal-scanning", "data-storytelling" ] image: "thumbnails/mm_2025_30.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 <- 30 project_file <- "mm_2025_30.qmd" project_image <- "mm_2025_30.png" ## Data Sources data_main <- "https://data.world/makeovermonday/2025w30-london-underground-average-monthly-temperatures/settings?tab=access" data_secondary <- "https://www.dailymail.co.uk/sciencetech/article-13739705/london-underground-hottest-line.html" ## 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_30/original_chart.png" ## Organization/Platform Links org_primary <- "https://data.london.gov.uk/dataset/london-underground-average-monthly-temperatures/" org_secondary <- "https://data.world/makeovermonday" # 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 **HAS YOUR TUBE LINE GOT HOTTER OVER THE PAST 10 YEARS? Average temperatures on seven London Underground lines for July-August, 2023 to 2014 (°C)** comes from `r create_link("Transport for London (TfL)", data_secondary)` ![Original visualization](%60r%20chart_original%60) ### Makeover ![Two-panel chart showing London Underground temperature data from 2013-2023. Left panel: heatmap displaying monthly temperature patterns for 8 tube lines, with warm colors indicating higher temperatures. Right panel: ridgeline plot showing temperature distributions for each line. Both charts use unified warm color palette (dark brown for cool, golden yellow for hot temperatures). Bakerloo and Victoria lines show consistently highest temperatures (reaching 30°C+), while Sub-Surface lines remain coolest year-round (rarely exceeding 25°C). Summer months (June-August) show greatest temperature variation between lines.](%60r%20project_image%60){#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 ggridges # Ridgeline Plots in 'ggplot2' ) }) ### |- figure size ---- camcorder::gg_record( dir = here::here("temp_plots"), device = "png", width = 12, 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 #| avg_temp_raw <- read_csv( here::here('data/MakeoverMonday/2025/lu-average-monthly-temperatures 2013-2024.csv')) |> janitor::clean_names() ``` #### 3. Examine the Data ```{r} #| label: examine #| include: true #| eval: true #| results: 'hide' #| warning: false glimpse(avg_temp_raw) skimr::skim(avg_temp_raw) ``` #### 4. Tidy Data ```{r} #| label: tidy #| warning: false temp_long <- avg_temp_raw |> pivot_longer(cols = bakerloo:sub_surface_lines, names_to = "line", values_to = "temperature") |> mutate( date = ymd(paste(year, month, "01")), month_num = match(month, month.name), season = case_when( month_num %in% c(12, 1, 2) ~ "Winter", month_num %in% c(3, 4, 5) ~ "Spring", month_num %in% c(6, 7, 8) ~ "Summer", month_num %in% c(9, 10, 11) ~ "Autumn" ), line_clean = case_when( line == "waterloo_and_city" ~ "Waterloo & City", line == "sub_surface_lines" ~ "Sub-Surface Lines", TRUE ~ str_to_title(line) ) ) line_order <- temp_long |> group_by(line_clean) |> summarise(median_temp = median(temperature, na.rm = TRUE), .groups = "drop") |> arrange(desc(median_temp)) |> pull(line_clean) ``` #### 5. Visualization Parameters ```{r} #| label: params #| include: true #| warning: false ### |- plot aesthetics ---- # Get base colors with custom palette colors <- get_theme_colors(palette = list(NULL)) ### |- titles and caption ---- title_text <- str_glue("London Underground Temperature Analysis (2013-2023)") subtitle_text <- str_glue("Central and Bakerloo lines run consistently hottest • Sub-Surface lines stay coolest year-round") # Create caption caption_text <- create_mm_caption( mm_year = current_year, mm_week = current_week, source_text = paste0("Transport for London (TfL)") ) ### |- 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(1.2), family = fonts$title, face = "bold", color = colors$title, lineheight = 1.1, hjust = 0.5, margin = margin(t = 5, b = 10) ), plot.subtitle = element_text( size = rel(0.9), hjust = 0.5, family = fonts$subtitle, color = alpha(colors$subtitle, 0.9), lineheight = 0.9, margin = margin(t = 5, b = 20) ), # Legend formatting legend.position = "right", # legend.direction = "horizontal", legend.box.margin = margin(b = 10), legend.margin = margin(b = 5), 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)), axis.title.y = element_text(face = "bold", size = rel(0.85)), axis.text = element_text(size = rel(0.85), family = fonts$subtitle, color = colors$text), # Grid lines panel.grid.major = element_line(color = "#ecf0f1", linewidth = 0.4), panel.grid.minor = element_blank(), # Margin plot.margin = margin(20, 20, 20, 20) ) ) # Set theme theme_set(weekly_theme) # Scale function create_warm_color_scale <- function(legend_position = "right") { scale_fill_gradient2( name = "Temperature\n(°C)", low = "#2c1810", mid = "#cc4125", high = "#f9c74f", midpoint = 22, breaks = seq(10, 30, 5), labels = paste0(seq(10, 30, 5), "°C"), guide = if(legend_position == "none") "none" else guide_colorbar() ) } ``` #### 6. Plot ```{r} #| label: plot #| warning: false ### |- Plot 1 heatmap ---- p1 <- temp_long |> mutate(line_clean = factor(line_clean, levels = line_order)) |> ggplot(aes(x = month_num, y = line_clean, fill = temperature)) + # Geoms geom_tile(color = "white", linewidth = 0.8) + # Scales create_warm_color_scale() + scale_x_continuous( breaks = 1:12, labels = month.abb, expand = c(0, 0) ) + scale_y_discrete(expand = c(0, 0)) + # Labs labs( title = "Monthly Temperature Patterns", subtitle = "Heatmap reveals seasonal trends across London Underground lines", x = "Month", y = NULL ) + # Theme theme( legend.position = "right", legend.key.height = unit(1, "cm"), legend.key.width = unit(0.4, "cm"), axis.text.y = element_text(size = 10, hjust = 1) ) ### |- Plot 2 ridges plot ---- # Calculate average temperature for each line line_avg_temps <- temp_long |> group_by(line_clean) |> summarise(avg_temp = mean(temperature, na.rm = TRUE), .groups = "drop") p2 <- temp_long |> mutate(line_clean = factor(line_clean, levels = line_order)) |> left_join(line_avg_temps, by = "line_clean") |> ggplot(aes(x = temperature, y = line_clean, fill = avg_temp)) + # Geoms geom_density_ridges( alpha = 0.8, scale = 0.95, linewidth = 0.3, color = "white" ) + # Scales create_warm_color_scale(legend_position = "none") + scale_x_continuous( breaks = seq(10, 35, 5), labels = paste0(seq(10, 35, 5), "°C"), expand = c(0.01, 0) ) + scale_y_discrete(expand = c(0, 0)) + # Labs labs( title = "Temperature Distribution by Line", subtitle = "Density curves colored by each line's average temperature", x = "Temperature (°C)", y = NULL ) + # Theme theme( axis.text.y = element_blank(), axis.ticks.y = element_blank() ) ### |- combined plot ---- # Create an invisible spacer plot spacer <- ggplot() + theme_void() combined_plots <- (p1 + spacer + p2) + plot_layout( widths = c(1.2, 0.01, 1) ) combined_plots <- combined_plots + plot_annotation( title = title_text, subtitle = subtitle_text, caption = caption_text, theme = theme( plot.title = element_text( size = rel(1.85), family = fonts$title, face = "bold", hjust = 0.5, color = colors$title, lineheight = 1.1, margin = margin(t = 5, b = 5) ), plot.subtitle = element_text( size = rel(0.95), hjust = 0.5, 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 = 12, 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 `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("London Underground Average Monthly Temperatures", data_main)` 2. Article - `r create_link("Daily Mail Online: Is your commute a scorcher?", data_secondary)` :::