UK Unemployment: Five Decades of Volatility and Range

A comprehensive view of when unemployment changed most dramatically and which decades were most stable

MakeoverMonday Week 34: Transforming UK unemployment data into volatility and range analysis. Reveals when unemployment changed most dramatically and compares decade stability using bar charts and dumbbell plots.

Author

Steven Ponce

Published

August 19, 2025

Original

The original visualization Unemployment Rate comes from UK Office for National Statistics

Makeover

Figure 1: Two-panel chart showing UK unemployment volatility from 1975-2025. Top panel: Bar chart of year-over-year unemployment changes by decade, with orange bars showing rising unemployment and blue bars showing falling unemployment. The largest spikes occur during the 1980s Thatcher Era and the 2008 financial crisis. Bottom panel: Dumbbell chart showing unemployment ranges by decade, with the 1980s having the highest volatility (5.7% to 11.9% range) and the 1970s the most stable (3.4% to 5.7% range).

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 = 10,
    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
#|
uk_unemployment_raw <- read_csv(
  here::here('data/MakeoverMonday/2025/UK_unemployment rate_adjusted.csv'),
             skip = 7,
             col_names = c("date", "percent"),
             show_col_types = FALSE) |>
    slice(-1)
```

3. Examine the Data

Show code

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

glimpse(uk_unemployment_raw)
skimr::skim(uk_unemployment_raw)
```

4. Tidy Data

Show code

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

# helper function for date parsing
parse_uk_dates <- function(date_str) {
  result <- rep(as.Date(NA), length(date_str))
  
  # Case 1: Year only (e.g., "1971")
  year_only <- str_detect(date_str, "^\\d{4}$")
  if(any(year_only, na.rm = TRUE)) {
    result[year_only] <- as.Date(paste0(date_str[year_only], "-01-01"))
  }
  
  # Case 2: Quarter format (e.g., "1971 Q1")
  quarter_format <- str_detect(date_str, "^\\d{4}\\s+Q[1-4]$")
  if(any(quarter_format, na.rm = TRUE)) {
    for(i in which(quarter_format)) {
      year <- str_extract(date_str[i], "^\\d{4}")
      quarter_num <- as.numeric(str_extract(date_str[i], "(?<=Q)\\d"))
      month <- (quarter_num - 1) * 3 + 1
      result[i] <- as.Date(paste0(year, "-", sprintf("%02d", month), "-01"))
    }
  }
  
  # Case 3: Month-Year formats (various separators)
  month_year <- str_detect(date_str, "[A-Z]{3}")
  if(any(month_year, na.rm = TRUE)) {
    for(i in which(month_year)) {
      if(str_detect(date_str[i], "^[A-Z]{3}-\\d{4}$")) {
        # Format: JAN-1971
        parts <- str_split(date_str[i], "-")[[1]]
        month_abbr <- parts[1]
        year <- parts[2]
      } else if(str_detect(date_str[i], "^\\d{4}-[A-Z]{3}$")) {
        # Format: 1971-JAN  
        parts <- str_split(date_str[i], "-")[[1]]
        year <- parts[1]
        month_abbr <- parts[2]
      } else if(str_detect(date_str[i], "^[A-Z]{3}\\s+\\d{4}$")) {
        # Format: JAN 1971
        parts <- str_split(date_str[i], "\\s+")[[1]]
        month_abbr <- parts[1]
        year <- parts[2]
      } else if(str_detect(date_str[i], "^\\d{4}\\s+[A-Z]{3}$")) {
        # Format: 1971 JAN
        parts <- str_split(date_str[i], "\\s+")[[1]]
        year <- parts[1]
        month_abbr <- parts[2]
      } else {
        next
      }
      
      month_num <- match(month_abbr, toupper(month.abb))
      if(!is.na(month_num)) {
        result[i] <- as.Date(paste0(year, "-", sprintf("%02d", month_num), "-01"))
      }
    }
  }
  
  return(result)
}

# Tidy
uk_unemployment_tidy <- uk_unemployment_raw |>
  mutate(
    # Parse dates and create time variables
    date = parse_uk_dates(date),
    year = year(date),
    month = month(date, label = TRUE, abbr = TRUE),
    quarter = paste0("Q", quarter(date, with_year = FALSE)),
    decade = paste0(floor(year / 10) * 10, "s"),

    # Clean unemployment rate
    unemployment_rate = percent,

    # Add data frequency indicator
    data_frequency = case_when(
      str_detect(uk_unemployment_raw$date, "^\\d{4}$") ~ "Annual",
      str_detect(uk_unemployment_raw$date, "Q") ~ "Quarterly",
      str_detect(uk_unemployment_raw$date, "[A-Z]{3}") ~ "Monthly",
      TRUE ~ "Other"
    ),

    # Add economic periods for context
    economic_period = case_when(
      year >= 1971 & year <= 1979 ~ "1970s Oil Crisis Era",
      year >= 1980 & year <= 1989 ~ "1980s Recession & Recovery",
      year >= 1990 & year <= 1999 ~ "1990s Recession & Growth",
      year >= 2000 & year <= 2007 ~ "2000s Pre-Financial Crisis",
      year >= 2008 & year <= 2015 ~ "2008 Financial Crisis & Recovery",
      year >= 2016 & year <= 2019 ~ "Post-Brexit Vote Era",
      year >= 2020 ~ "COVID-19 Era",
      TRUE ~ "Other"
    ),

    # Flag recession periods (approximate UK recessions)
    recession_period = case_when(
      (year >= 1974 & year <= 1975) ~ "1974-75 Recession",
      (year >= 1980 & year <= 1981) ~ "1980-81 Recession",
      (year >= 1990 & year <= 1991) ~ "1990-91 Recession",
      (year >= 2008 & year <= 2009) ~ "2008-09 Financial Crisis",
      (year >= 2020 & year <= 2021) ~ "2020-21 COVID Recession",
      TRUE ~ "Non-recession"
    )
  ) |>
  # Keep only successfully parsed dates
  filter(!is.na(date)) |>
  arrange(date) |>
  # Keep only essential columns
  select(
    date, year, month, quarter, decade, unemployment_rate,
    data_frequency, economic_period, recession_period
  )

# Year-over-year data
yoy_data <- uk_unemployment_tidy |>
  arrange(date) |>
  mutate(
    yoy_change = unemployment_rate - lag(unemployment_rate, 12),
    change_direction = ifelse(yoy_change > 0, "Rising Unemployment", "Falling Unemployment")
  ) |>
  filter(!is.na(yoy_change), year >= 1975) |>
  mutate(
    decade_group = paste0(floor(year / 10) * 10, "s"),
    # Create better decade labels
    decade_label = case_when(
      decade_group == "1970s" ~ "1970s\nOil Crisis",
      decade_group == "1980s" ~ "1980s\nThatcher Era",
      decade_group == "1990s" ~ "1990s\nPost-Recession",
      decade_group == "2000s" ~ "2000s\nPre-Crisis",
      decade_group == "2010s" ~ "2010s\nAusterity",
      decade_group == "2020s" ~ "2020s\nCOVID Era",
      TRUE ~ decade_group
    ),
    # Flag extreme changes
    extreme_change = abs(yoy_change) > 2
  )

# Data for annotations (YoY)
annotation_data <- yoy_data |>
  group_by(decade_label) |>
  summarise(
    date = min(date),
    .groups = "drop"
  ) |>
  mutate(
    date = if_else(str_detect(decade_label, "2020s"), date + months(12), date)
  )

# Dumbbell data (min/max)
dumbbell_data <- uk_unemployment_tidy |>
  group_by(decade) |>
  summarise(
    min_rate = min(unemployment_rate),
    max_rate = max(unemployment_rate),
    range_width = max_rate - min_rate,
    .groups = "drop"
  ) |>
  # Better decade labels 
  mutate(
    decade_label = case_when(
      decade == "1970s" ~ "1970s",
      decade == "1980s" ~ "1980s",
      decade == "1990s" ~ "1990s",
      decade == "2000s" ~ "2000s",
      decade == "2010s" ~ "2010s",
      decade == "2020s" ~ "2020s*",
      TRUE ~ decade
    )
  ) |>
  filter(decade != "2020s") # Remove incomplete decade
```

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(
   "#00a8f3", "#061c2c", "#2d2d2d","#6b6b6b"
))

### |-  titles and caption ----
title_text <- str_glue("UK Unemployment: Five Decades of Volatility and Range")

subtitle_text <- str_glue(
  "A comprehensive view of when unemployment changed most dramatically and which decades were most stable"
)

# Create caption
caption_text <- create_mm_caption(
  mm_year = current_year,
  mm_week = current_week,
  source_text = paste0("UK Office for National Statistics")
)

### |-  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_markdown(
      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 = "plot",
    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  YoY bar chart ----
p1 <- ggplot(yoy_data, aes(x = date, y = yoy_change)) +
  # Geoms
  geom_text(
    data = annotation_data, # annotations for the decade labels
    aes(x = date, label = decade_label),
    y = 5,
    hjust = 0,
    vjust = 0,
    size = 3.1,
    family = fonts$text,
    color = colors$palette[3],
    fontface = "bold",
    lineheight = 1.2
  ) +
  geom_hline(yintercept = 0, color = 'gray', linetype = "solid", size = 0.4) +
  geom_col(aes(fill = change_direction), alpha = 0.85, width = 30, position = "identity") +
  geom_point( # Highlight extreme changes
    data = yoy_data |> filter(extreme_change),
    aes(color = change_direction), 
    size = 1.2, 
    alpha = 0.9
  ) +
  # Scales
  scale_fill_manual(
    values = c(
      "Rising Unemployment" = colors$palette[2],
      "Falling Unemployment" = colors$palette[1]
    ),
    name = "Unemployment",
    labels = c("Falling", "Rising")
  ) +
  scale_color_manual(
    values = c(
      "Rising Unemployment" = colors$palette[2],
      "Falling Unemployment" = colors$palette[1]
    ),
    guide = "none"
  ) +
  scale_y_continuous(
    labels = function(x) paste0(ifelse(x >= 0, "+", ""), round(x, 1), "pp"),
    breaks = seq(-2,3, by = 1),
  ) +
  scale_x_date(
    breaks = scales::date_breaks("5 years"),
    labels = scales::date_format("%Y")
  ) +
  # Labs
  labs(
    title = "Unemployment Volatility Across Five Decades",
    subtitle = str_glue(
    "Year-over-year changes reveal when unemployment shifted most dramatically<br>
    <span style='color:{colors$palette[2]}'>**Rising unemployment**</span> periods vs 
    <span style='color:{colors$palette[1]}'>**falling unemployment**</span> periods • 
    Dots show changes >2 percentage points"
    ),
    x = NULL,
    y = "Annual Change (percentage points)",
  )

### |- P2  dumbbell chart----
p2 <- ggplot(dumbbell_data, aes(y = fct_reorder(decade_label, range_width))) +
  # Geoms
  geom_segment(
    aes(x = min_rate, xend = max_rate, yend = decade_label),
    color = 'gray', 
    linewidth = 0.35,
    alpha = 0.6
  ) +
  geom_point( # Minimum points
    aes(x = min_rate), 
    color = colors$palette[1], 
    size = 2,
    alpha = 0.9
  ) +
  geom_point( # Maximum points
    aes(x = max_rate), 
    color = colors$palette[2], 
    size = 2,
    alpha = 0.9
  ) +
  geom_text(
    aes(x = min_rate, label = paste0(round(min_rate, 1), "%")),
    nudge_x = -0.45, 
    size = 3.8, 
    color = colors$palette[1], 
    fontface = "bold",
    family = fonts$text
  ) +
  geom_text(
    aes(x = max_rate, label = paste0(round(max_rate, 1), "%")),
    nudge_x = 0.45, 
    size = 3.8, 
    color = colors$palette[2], 
    fontface = "bold",
    family = fonts$text
  ) +
  geom_text(  # Range labels
    aes(x = (min_rate + max_rate)/2, 
        label = paste0("Range: ", round(range_width, 1), "pp")),
    nudge_y = 0.2,
    size = 3.3,
    color = colors$palette[3],
    fontface = "italic",
    family = fonts$text
  ) +
  # Scales
  scale_x_continuous(
    labels = percent_format(scale = 1),
    breaks = seq(0, 12, 2),
    limits = c(0, 13)
  ) +
  # Labs
  labs(
    title = "Unemployment Range by Decade: Stability vs Volatility",
    subtitle = str_glue(
    "Each decade's <span style='color:{colors$palette[1]}'>**lowest**</span> and 
     <span style='color:{colors$palette[2]}'>**highest**</span> unemployment rates<br>
     The 1980s and 1990s show the greatest volatility with ranges >5 percentage points"
    ),
    x = "Unemployment Rate",
    y = NULL,
  ) +
  # Theme
  theme(
    axis.line.y = element_blank(),
    panel.grid.major.x = element_blank()
  )

### |-  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 = 10
  )
```

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  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] codetools_0.2-20   htmltools_0.5.8.1  yaml_2.3.10        crayon_1.5.3      
[25] pillar_1.9.0       camcorder_0.1.0    magick_2.8.5       commonmark_1.9.2  
[29] tidyselect_1.2.1   digest_0.6.37      stringi_1.8.4      rsvg_2.6.1        
[33] rprojroot_2.0.4    fastmap_1.2.0      grid_4.4.0         colorspace_2.1-1  
[37] cli_3.6.4          magrittr_2.0.3     base64enc_0.1-3    utf8_1.2.4        
[41] withr_3.0.2        bit64_4.5.2        timechange_0.3.0   rmarkdown_2.29    
[45] bit_4.5.0          hms_1.1.3          evaluate_1.0.1     knitr_1.49        
[49] markdown_1.13      gridGraphics_0.5-1 rlang_1.1.6        gridtext_0.1.5    
[53] Rcpp_1.0.13-1      xml2_1.3.6         renv_1.0.3         svglite_2.1.3     
[57] rstudioapi_0.17.1  vroom_1.6.5        jsonlite_1.8.9     R6_2.5.1          
[61] 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_34.qmd.

For the full repository, click here.

10. References

Expand for References

Data:

Makeover Monday 2025 Week 34: UK Unemployment Rate

Article

UK Unemployment Rate

--- title: "UK Unemployment: Five Decades of Volatility and Range" subtitle: "A comprehensive view of when unemployment changed most dramatically and which decades were most stable" description: "MakeoverMonday Week 34: Transforming UK unemployment data into volatility and range analysis. Reveals when unemployment changed most dramatically and compares decade stability using bar charts and dumbbell plots." author: "Steven Ponce" date: "2025-08-19" categories: ["MakeoverMonday", "Data Visualization", "R Programming", "2025"] tags: [ "unemployment", "economic-data", "time-series", "volatility-analysis", "recession-periods", "ggplot2", "data-tidying", "faceted-charts", "dumbbell-chart", "bar-chart", "uk-statistics", "labor-market", "economic-trends", "1980s-recession", "financial-crisis" ] image: "thumbnails/mm_2025_34.png" format: html: toc: true toc-depth: 5 code-link: true code-fold: true code-tools: true code-summary: "Show code" self-contained: true theme: light: [flatly, assets/styling/custom_styles.scss] dark: [darkly, assets/styling/custom_styles_dark.scss] 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 <- 34 project_file <- "mm_2025_34.qmd" project_image <- "mm_2025_34.png" ## Data Sources data_main <- "https://data.world/makeovermonday/2025-week-34-unemployment-rate" data_secondary <- "https://www.ons.gov.uk/employmentandlabourmarket/peoplenotinwork/unemployment/timeseries/mgsx/lms?utm_source=chatgpt.com" ## 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_34/original_chart.png" ## Organization/Platform Links org_primary <- "https://www.ons.gov.uk/employmentandlabourmarket/peoplenotinwork/unemployment/timeseries/mgsx/lms?utm_source=chatgpt.com" org_secondary <- "https://www.ons.gov.uk/" # 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 **Unemployment Rate** comes from `r create_link("UK Office for National Statistics", data_secondary)`  ![Original visualization](https://raw.githubusercontent.com/poncest/MakeoverMonday/refs/heads/master/2025/Week_34/original_chart.png) ### Makeover ![Two-panel chart showing UK unemployment volatility from 1975-2025. Top panel: Bar chart of year-over-year unemployment changes by decade, with orange bars showing rising unemployment and blue bars showing falling unemployment. The largest spikes occur during the 1980s Thatcher Era and the 2008 financial crisis. Bottom panel: Dumbbell chart showing unemployment ranges by decade, with the 1980s having the highest volatility (5.7% to 11.9% range) and the 1970s the most stable (3.4% to 5.7% range).](mm_2025_34.png){#fig-1} ### **Steps to Create this Graphic** #### 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 = 10, 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 #| uk_unemployment_raw <- read_csv( here::here('data/MakeoverMonday/2025/UK_unemployment rate_adjusted.csv'), skip = 7, col_names = c("date", "percent"), show_col_types = FALSE) |> slice(-1) ``` #### 3. Examine the Data ```{r} #| label: examine #| include: true #| eval: true #| results: 'hide' #| warning: false glimpse(uk_unemployment_raw) skimr::skim(uk_unemployment_raw) ``` #### 4. Tidy Data ```{r} #| label: tidy #| warning: false # helper function for date parsing parse_uk_dates <- function(date_str) { result <- rep(as.Date(NA), length(date_str)) # Case 1: Year only (e.g., "1971") year_only <- str_detect(date_str, "^\\d{4}$") if(any(year_only, na.rm = TRUE)) { result[year_only] <- as.Date(paste0(date_str[year_only], "-01-01")) } # Case 2: Quarter format (e.g., "1971 Q1") quarter_format <- str_detect(date_str, "^\\d{4}\\s+Q[1-4]$") if(any(quarter_format, na.rm = TRUE)) { for(i in which(quarter_format)) { year <- str_extract(date_str[i], "^\\d{4}") quarter_num <- as.numeric(str_extract(date_str[i], "(?<=Q)\\d")) month <- (quarter_num - 1) * 3 + 1 result[i] <- as.Date(paste0(year, "-", sprintf("%02d", month), "-01")) } } # Case 3: Month-Year formats (various separators) month_year <- str_detect(date_str, "[A-Z]{3}") if(any(month_year, na.rm = TRUE)) { for(i in which(month_year)) { if(str_detect(date_str[i], "^[A-Z]{3}-\\d{4}$")) { # Format: JAN-1971 parts <- str_split(date_str[i], "-")[[1]] month_abbr <- parts[1] year <- parts[2] } else if(str_detect(date_str[i], "^\\d{4}-[A-Z]{3}$")) { # Format: 1971-JAN parts <- str_split(date_str[i], "-")[[1]] year <- parts[1] month_abbr <- parts[2] } else if(str_detect(date_str[i], "^[A-Z]{3}\\s+\\d{4}$")) { # Format: JAN 1971 parts <- str_split(date_str[i], "\\s+")[[1]] month_abbr <- parts[1] year <- parts[2] } else if(str_detect(date_str[i], "^\\d{4}\\s+[A-Z]{3}$")) { # Format: 1971 JAN parts <- str_split(date_str[i], "\\s+")[[1]] year <- parts[1] month_abbr <- parts[2] } else { next } month_num <- match(month_abbr, toupper(month.abb)) if(!is.na(month_num)) { result[i] <- as.Date(paste0(year, "-", sprintf("%02d", month_num), "-01")) } } } return(result) } # Tidy uk_unemployment_tidy <- uk_unemployment_raw |> mutate( # Parse dates and create time variables date = parse_uk_dates(date), year = year(date), month = month(date, label = TRUE, abbr = TRUE), quarter = paste0("Q", quarter(date, with_year = FALSE)), decade = paste0(floor(year / 10) * 10, "s"), # Clean unemployment rate unemployment_rate = percent, # Add data frequency indicator data_frequency = case_when( str_detect(uk_unemployment_raw$date, "^\\d{4}$") ~ "Annual", str_detect(uk_unemployment_raw$date, "Q") ~ "Quarterly", str_detect(uk_unemployment_raw$date, "[A-Z]{3}") ~ "Monthly", TRUE ~ "Other" ), # Add economic periods for context economic_period = case_when( year >= 1971 & year <= 1979 ~ "1970s Oil Crisis Era", year >= 1980 & year <= 1989 ~ "1980s Recession & Recovery", year >= 1990 & year <= 1999 ~ "1990s Recession & Growth", year >= 2000 & year <= 2007 ~ "2000s Pre-Financial Crisis", year >= 2008 & year <= 2015 ~ "2008 Financial Crisis & Recovery", year >= 2016 & year <= 2019 ~ "Post-Brexit Vote Era", year >= 2020 ~ "COVID-19 Era", TRUE ~ "Other" ), # Flag recession periods (approximate UK recessions) recession_period = case_when( (year >= 1974 & year <= 1975) ~ "1974-75 Recession", (year >= 1980 & year <= 1981) ~ "1980-81 Recession", (year >= 1990 & year <= 1991) ~ "1990-91 Recession", (year >= 2008 & year <= 2009) ~ "2008-09 Financial Crisis", (year >= 2020 & year <= 2021) ~ "2020-21 COVID Recession", TRUE ~ "Non-recession" ) ) |> # Keep only successfully parsed dates filter(!is.na(date)) |> arrange(date) |> # Keep only essential columns select( date, year, month, quarter, decade, unemployment_rate, data_frequency, economic_period, recession_period ) # Year-over-year data yoy_data <- uk_unemployment_tidy |> arrange(date) |> mutate( yoy_change = unemployment_rate - lag(unemployment_rate, 12), change_direction = ifelse(yoy_change > 0, "Rising Unemployment", "Falling Unemployment") ) |> filter(!is.na(yoy_change), year >= 1975) |> mutate( decade_group = paste0(floor(year / 10) * 10, "s"), # Create better decade labels decade_label = case_when( decade_group == "1970s" ~ "1970s\nOil Crisis", decade_group == "1980s" ~ "1980s\nThatcher Era", decade_group == "1990s" ~ "1990s\nPost-Recession", decade_group == "2000s" ~ "2000s\nPre-Crisis", decade_group == "2010s" ~ "2010s\nAusterity", decade_group == "2020s" ~ "2020s\nCOVID Era", TRUE ~ decade_group ), # Flag extreme changes extreme_change = abs(yoy_change) > 2 ) # Data for annotations (YoY) annotation_data <- yoy_data |> group_by(decade_label) |> summarise( date = min(date), .groups = "drop" ) |> mutate( date = if_else(str_detect(decade_label, "2020s"), date + months(12), date) ) # Dumbbell data (min/max) dumbbell_data <- uk_unemployment_tidy |> group_by(decade) |> summarise( min_rate = min(unemployment_rate), max_rate = max(unemployment_rate), range_width = max_rate - min_rate, .groups = "drop" ) |> # Better decade labels mutate( decade_label = case_when( decade == "1970s" ~ "1970s", decade == "1980s" ~ "1980s", decade == "1990s" ~ "1990s", decade == "2000s" ~ "2000s", decade == "2010s" ~ "2010s", decade == "2020s" ~ "2020s*", TRUE ~ decade ) ) |> filter(decade != "2020s") # Remove incomplete decade ``` #### 5. Visualization Parameters ```{r} #| label: params #| include: true #| warning: false ### |- plot aesthetics ---- # Get base colors with custom palette colors <- get_theme_colors(palette = list( "#00a8f3", "#061c2c", "#2d2d2d","#6b6b6b" )) ### |- titles and caption ---- title_text <- str_glue("UK Unemployment: Five Decades of Volatility and Range") subtitle_text <- str_glue( "A comprehensive view of when unemployment changed most dramatically and which decades were most stable" ) # Create caption caption_text <- create_mm_caption( mm_year = current_year, mm_week = current_week, source_text = paste0("UK Office for National Statistics") ) ### |- 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_markdown( 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 = "plot", 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 YoY bar chart ---- p1 <- ggplot(yoy_data, aes(x = date, y = yoy_change)) + # Geoms geom_text( data = annotation_data, # annotations for the decade labels aes(x = date, label = decade_label), y = 5, hjust = 0, vjust = 0, size = 3.1, family = fonts$text, color = colors$palette[3], fontface = "bold", lineheight = 1.2 ) + geom_hline(yintercept = 0, color = 'gray', linetype = "solid", size = 0.4) + geom_col(aes(fill = change_direction), alpha = 0.85, width = 30, position = "identity") + geom_point( # Highlight extreme changes data = yoy_data |> filter(extreme_change), aes(color = change_direction), size = 1.2, alpha = 0.9 ) + # Scales scale_fill_manual( values = c( "Rising Unemployment" = colors$palette[2], "Falling Unemployment" = colors$palette[1] ), name = "Unemployment", labels = c("Falling", "Rising") ) + scale_color_manual( values = c( "Rising Unemployment" = colors$palette[2], "Falling Unemployment" = colors$palette[1] ), guide = "none" ) + scale_y_continuous( labels = function(x) paste0(ifelse(x >= 0, "+", ""), round(x, 1), "pp"), breaks = seq(-2,3, by = 1), ) + scale_x_date( breaks = scales::date_breaks("5 years"), labels = scales::date_format("%Y") ) + # Labs labs( title = "Unemployment Volatility Across Five Decades", subtitle = str_glue( "Year-over-year changes reveal when unemployment shifted most dramatically **Rising unemployment** periods vs **falling unemployment** periods • Dots show changes >2 percentage points" ), x = NULL, y = "Annual Change (percentage points)", ) ### |- P2 dumbbell chart---- p2 <- ggplot(dumbbell_data, aes(y = fct_reorder(decade_label, range_width))) + # Geoms geom_segment( aes(x = min_rate, xend = max_rate, yend = decade_label), color = 'gray', linewidth = 0.35, alpha = 0.6 ) + geom_point( # Minimum points aes(x = min_rate), color = colors$palette[1], size = 2, alpha = 0.9 ) + geom_point( # Maximum points aes(x = max_rate), color = colors$palette[2], size = 2, alpha = 0.9 ) + geom_text( aes(x = min_rate, label = paste0(round(min_rate, 1), "%")), nudge_x = -0.45, size = 3.8, color = colors$palette[1], fontface = "bold", family = fonts$text ) + geom_text( aes(x = max_rate, label = paste0(round(max_rate, 1), "%")), nudge_x = 0.45, size = 3.8, color = colors$palette[2], fontface = "bold", family = fonts$text ) + geom_text( # Range labels aes(x = (min_rate + max_rate)/2, label = paste0("Range: ", round(range_width, 1), "pp")), nudge_y = 0.2, size = 3.3, color = colors$palette[3], fontface = "italic", family = fonts$text ) + # Scales scale_x_continuous( labels = percent_format(scale = 1), breaks = seq(0, 12, 2), limits = c(0, 13) ) + # Labs labs( title = "Unemployment Range by Decade: Stability vs Volatility", subtitle = str_glue( "Each decade's **lowest** and **highest** unemployment rates The 1980s and 1990s show the greatest volatility with ranges >5 percentage points" ), x = "Unemployment Rate", y = NULL, ) + # Theme theme( axis.line.y = element_blank(), panel.grid.major.x = element_blank() ) ### |- 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 = 10 ) ``` #### 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("UK Unemployment Rate", data_main)` 2. Article - `r create_link("UK Unemployment Rate", data_secondary)` :::