Youth unemployment in London is rising faster than the overall trend

Since late 2024, unemployment among 16–24 year-olds has risen faster than the overall 16+ total, widening the gap | 4-quarter moving average of estimated unemployed counts (thousands)

A redesign of the MakeoverMonday 2026 Week 12 chart on London unemployment. While the original showed only the overall unemployment rate, this makeover adds a youth (16–24) series to tell the article’s core story: young Londoners are being hit hardest. Built in R with ggplot2, the chart uses a ribbon gap and policy event annotations to show how the youth-overall divergence has widened since the October 2024 Budget announcement.

Original

The original visualization comes from London Unemployment Estimates

Original visualization

Makeover

Figure 1: Line chart showing estimated unemployed counts (thousands) in London from 2019 to early 2026, using 4-quarter rolling averages. Two lines are plotted: 16+ overall unemployment (dark navy) and 16–24 youth unemployment (dark red). Both lines declined through 2022–2023 before rising sharply after late 2024. The overall 16+ count reached 390k, and the youth 16–24 count reached 125.4k in the most recent period, leaving a gap of 264.5k — the largest in that period. Two shaded vertical bands mark the October 2024 Budget announcement and the April 2025 implementation of NIC and minimum wage changes, both of which coincide with the acceleration in unemployment counts.

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, ggtext, showtext, scales, 
    glue, janitor, readxl, zoo  
)
})

### |- figure size ----
camcorder::gg_record(
  dir    = here::here("temp_plots"),
  device = "png",
  width  = 10,
  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
#| 

df_raw <- readxl::read_xlsx(
  here::here("data/MakeoverMonday/2026/London Unemployment Estimates.xlsx")) |>
  clean_names()
```

3. Examine the Data

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

glimpse(df_raw)
skimr::skim_without_charts(df_raw)
```

4. Tidy Data

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

### |- filter to counts only ----
df_counts <- df_raw |>
  filter(unit == "Estimated Number of People")

### |- focal series: youth (16-24) and overall (16+) ----
df_main <- df_counts |>
  mutate(date = as.Date(period_midpoint_date)) |>
  filter(date >= as.Date("2019-01-01")) |>
  mutate(
    youth_k   = x16_24 / 1000,
    overall_k = all_aged_16_over / 1000
  ) |>
  arrange(date) |>
  select(date, period, youth_k, overall_k)

### |- 4-period rolling average ----
df_main <- df_main |>
  mutate(
    youth_smooth   = rollmean(youth_k,   k = 4, fill = NA, align = "right"),
    overall_smooth = rollmean(overall_k, k = 4, fill = NA, align = "right")
  ) |>
  filter(!is.na(youth_smooth), !is.na(overall_smooth)) |>
  mutate(
    gap_k      = overall_smooth - youth_smooth,
    gap_mid_k  = youth_smooth + gap_k / 2
  )

### |- latest values for labels ----
latest <- df_main |>
  slice_max(date, n = 1)

latest_youth   <- round(latest$youth_smooth, 1)
latest_overall <- round(latest$overall_smooth, 1)
latest_gap     <- round(latest$gap_k, 1)

### |- x positions for labels / callouts ----
x_label_end <- latest$date + 35
x_gap_label <- latest$date - 60

### |- y positions for stacked end labels ----
y_overall_lab_top <- latest$overall_smooth + 7
y_overall_lab_val <- latest$overall_smooth - 3

y_youth_lab_top   <- latest$youth_smooth + 7
y_youth_lab_val   <- latest$youth_smooth - 3

### |- policy event windows ----
announce_start  <- as.Date("2024-10-15")
announce_end    <- as.Date("2024-11-15")
implement_start <- as.Date("2025-03-15")
implement_end   <- as.Date("2025-05-15")
```

5. Visualization Parameters

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

### |- Colors ----
colors <- get_theme_colors(
  palette = list(
    youth   = "#7B1E3A",
    overall = "#2C3E50",
    gap     = "#F8E6E3",
    zone    = "#D5DBDB",
    gap_txt = "#A93226"
  )
)

### |- Titles and caption ----
title_text <- "Youth unemployment in London is rising faster than the overall trend"

subtitle_text <- paste0(
  "Since late 2024, unemployment among 16–24 year-olds has risen faster than the overall 16+ total, widening the gap.<br>",
  "<span style='font-size:9pt;'>4-quarter moving average of estimated unemployed counts (thousands)</span>"
)

caption_text <- create_mm_caption(
  mm_year     = 2026,
  mm_week     = 12,
  source_text = "GLA Economics, ONS Quarterly Labour Force Survey<br>
**Note:** Counts shown are estimated unemployed people, not unemployment rates.<br>
Policy zones mark the Oct 2024 Budget announcement and Apr 2025 NIC & minimum wage implementation."
)

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

### |- plot theme ----
base_theme   <- create_base_theme(colors)
weekly_theme <- extend_weekly_theme(
  base_theme,
  theme(
    axis.title         = element_blank(),
    axis.text.x        = element_text(size = 9, color = "gray40"),
    axis.text.y        = element_text(size = 9, color = "gray40"),
    axis.line.x        = element_line(color = "gray82", linewidth = 0.4),
    
    panel.grid.major.y = element_line(color = "gray90", linewidth = 0.35),
    panel.grid.major.x = element_blank(),
    panel.grid.minor   = element_blank(),
    
    legend.position    = "none",
    
    plot.margin = margin(t = 16, r = 55, b = 12, l = 16),
    
    plot.title = element_text(
      size        = rel(1.3),
      family      = fonts$title,
      face        = "bold",
      color       = colors$title,
      lineheight  = 1.05,
      hjust       = 0,
      margin      = margin(t = 5, b = 10)
    ),
    plot.subtitle = element_markdown(
      size        = rel(0.75),
      family      = fonts$subtitle,
      face        = "italic",
      color       = alpha(colors$subtitle, 0.92),
      lineheight  = 1.08,
      margin      = margin(t = 0, b = 20)
    ),
    plot.caption = element_markdown(
      size        = rel(0.5),
      family      = fonts$subtitle,
      color       = colors$caption,
      hjust       = 0,
      lineheight  = 1.35,
      margin      = margin(t = 20, b = 5)
    )
  )
)

theme_set(weekly_theme)
```

6. Plot

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

### |- main plot ----

p <- ggplot(df_main, aes(x = date)) +
  
  # Annotate
  annotate(
    "rect",
    xmin  = announce_start, xmax = announce_end,
    ymin  = -Inf, ymax = Inf,
    fill  = colors$palette$zone,
    alpha = 0.28
  ) +
  annotate(
    "rect",
    xmin  = implement_start, xmax = implement_end,
    ymin  = -Inf, ymax = Inf,
    fill  = colors$palette$zone,
    alpha = 0.28
  ) +
  # Geoms
  geom_ribbon(
    aes(
      ymin = pmin(youth_smooth, overall_smooth),
      ymax = pmax(youth_smooth, overall_smooth)
    ),
    fill  = colors$palette$gap,
    alpha = 0.45
  ) +
  geom_line(
    aes(y = overall_smooth),
    color     = colors$palette$overall,
    linewidth = 0.95,
    lineend   = "round"
  ) +
  geom_line(
    aes(y = youth_smooth),
    color     = colors$palette$youth,
    linewidth = 1.25,
    lineend   = "round"
  ) +
  geom_point(
    data  = latest,
    aes(y = overall_smooth),
    color = colors$palette$overall,
    size  = 3.6
  ) +
  geom_point(
    data  = latest,
    aes(y = youth_smooth),
    color = colors$palette$youth,
    size  = 3.6
  ) +
  
  # Annotate
  annotate(
    "text",
    x        = x_label_end,
    y        = y_overall_lab_top,
    label    = "16+ overall",
    color    = colors$palette$overall,
    hjust    = 0,
    vjust    = 0.5,
    size     = 2.8,
    family   = fonts$text
  ) +
  annotate(
    "text",
    x        = x_label_end,
    y        = y_overall_lab_val,
    label    = glue("{latest_overall}k"),
    color    = colors$palette$overall,
    hjust    = 0,
    vjust    = 0.5,
    size     = 3.8,
    fontface = "bold",
    family   = fonts$text
  ) +
  annotate(
    "text",
    x        = x_label_end,
    y        = y_youth_lab_top,
    label    = "16–24 youth",
    color    = colors$palette$youth,
    hjust    = 0,
    vjust    = 0.5,
    size     = 2.8,
    family   = fonts$text
  ) +
  annotate(
    "text",
    x        = x_label_end,
    y        = y_youth_lab_val,
    label    = glue("{latest_youth}k"),
    color    = colors$palette$youth,
    hjust    = 0,
    vjust    = 0.5,
    size     = 3.8,
    fontface = "bold",
    family   = fonts$text
  ) +
  annotate(
    "text",
    x      = announce_start,
    y      = Inf,
    label  = "Budget\n(Oct 2024)",
    color  = "gray48",
    hjust  = 0.5,
    vjust  = 1.35,
    size   = 2.7,
    family = fonts$text
  ) +
  annotate(
    "text",
    x      = implement_start,
    y      = Inf,
    label  = "Implementation\n(Apr 2025)",
    color  = "gray48",
    hjust  = 0.5,
    vjust  = 3.7,
    size   = 2.7,
    family = fonts$text
  ) +
  annotate(
    "curve",
    x     = x_gap_label + 12,
    xend  = latest$date - 5,
    y     = latest$gap_mid_k + 10,
    yend  = latest$gap_mid_k + 2,
    curvature = -0.2,
    linewidth = 0.35,
    color = alpha(colors$palette$gap_txt, 0.8)
  ) +
  annotate(
    "text",
    x      = x_gap_label,
    y      = latest$gap_mid_k + 12,
    label  = glue("+{latest_gap}k gap"),
    color  = colors$palette$gap_txt,
    hjust  = 0,
    vjust  = 0.5,
    size   = 3.1,
    fontface = "bold",
    family = fonts$text
  ) +
  annotate(
    "text",
    x      = as.Date("2025-08-01"),
    y      = latest$gap_mid_k - 12,
    label  = "Largest gap in the recent period",
    color  = alpha(colors$palette$gap_txt, 0.9),
    hjust  = 0,
    vjust  = 0.5,
    size   = 2.7,
    family = fonts$text
  ) +
  
  # Scales
  scale_x_date(
    date_breaks = "1 year",
    date_labels = "Q4 %Y",
    expand      = expansion(mult = c(0.01, 0.13))
  ) +
  scale_y_continuous(
    labels = label_number(suffix = "k", accuracy = 1),
    breaks = c(100, 200, 300, 400),
    expand = expansion(mult = c(0.06, 0.12))
  ) +
  
  # Labs
  labs(
    title    = title_text,
    subtitle = subtitle_text,
    caption  = caption_text
  )
```

7. Save

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

### |-  plot image ----  
save_plot(
  plot = p, 
  type = "makeovermonday", 
  year = current_year,
  week = current_week,
  width = 10, 
  height = 8
  )
```

8. Session Info

TipExpand for Session Info

R version 4.3.1 (2023-06-16 ucrt)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 11 x64 (build 26100)

Matrix products: default


locale:
[1] LC_COLLATE=English_United States.utf8 
[2] LC_CTYPE=English_United States.utf8   
[3] LC_MONETARY=English_United States.utf8
[4] LC_NUMERIC=C                          
[5] LC_TIME=English_United States.utf8    

time zone: America/New_York
tzcode source: internal

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

other attached packages:
 [1] here_1.0.2      zoo_1.8-15      readxl_1.4.5    janitor_2.2.1  
 [5] glue_1.8.0      scales_1.4.0    showtext_0.9-7  showtextdb_3.0 
 [9] sysfonts_0.8.9  ggtext_0.1.2    lubridate_1.9.5 forcats_1.0.1  
[13] stringr_1.6.0   dplyr_1.2.0     purrr_1.2.1     readr_2.2.0    
[17] tidyr_1.3.2     tibble_3.2.1    ggplot2_4.0.2   tidyverse_2.0.0
[21] pacman_0.5.1   

loaded via a namespace (and not attached):
 [1] gtable_0.3.6       xfun_0.56          htmlwidgets_1.6.4  lattice_0.21-8    
 [5] tzdb_0.5.0         vctrs_0.7.1        tools_4.3.1        generics_0.1.4    
 [9] curl_7.0.0         gifski_1.32.0-2    pkgconfig_2.0.3    RColorBrewer_1.1-3
[13] skimr_2.2.2        S7_0.2.0           lifecycle_1.0.5    compiler_4.3.1    
[17] farver_2.1.2       textshaping_1.0.4  repr_1.1.7         codetools_0.2-19  
[21] snakecase_0.11.1   litedown_0.9       htmltools_0.5.9    yaml_2.3.12       
[25] pillar_1.11.1      camcorder_0.1.0    magick_2.8.6       commonmark_2.0.0  
[29] tidyselect_1.2.1   digest_0.6.39      stringi_1.8.7      rsvg_2.6.2        
[33] rprojroot_2.1.1    fastmap_1.2.0      grid_4.3.1         cli_3.6.4         
[37] magrittr_2.0.3     base64enc_0.1-6    withr_3.0.2        timechange_0.4.0  
[41] rmarkdown_2.30     otel_0.2.0         cellranger_1.1.0   ragg_1.5.0        
[45] hms_1.1.4          evaluate_1.0.5     haven_2.5.5        knitr_1.51        
[49] markdown_2.0       rlang_1.1.7        gridtext_0.1.6     Rcpp_1.1.1        
[53] xml2_1.5.2         svglite_2.1.3      rstudioapi_0.18.0  jsonlite_2.0.0    
[57] R6_2.6.1           systemfonts_1.3.2 

9. GitHub Repository

TipExpand for GitHub Repo

The complete code for this analysis is available in mm_2026_12.qmd.

For the full repository, click here.

10. References

TipExpand for References

Primary Data (Makeover Monday): 1. Makeover Monday 2026 Week 12: London Unemployment Estimates 2. Original Article: Unemployment in key charts: Young Londoners hit hardest by labour market slowdown - Source: GLA Economics (Greater London Authority) - Coverage: London unemployment trends by age group, with policy event context

Source Data: 3. Dataset: 2026 Week 12 — London Unemployment Estimates - Source: ONS Quarterly Labour Force Survey via data.world/makeovermonday - Data includes: Rolling 3-month period estimates of unemployed counts by age group (16–17, 18–24, 16–24, 25–34, 35–49, 50–64, 16–64, 65+) for London, Jun 2001–Jan 2026 - Scope: 294 rolling periods; counts are estimated numbers of unemployed people, not labor-force-denominated rates

4. ONS Regional Unemployment by Age: Dataset x02
   • Source: Office for National Statistics
   • Coverage: Regional unemployment by age, used as original source context

Note: Counts shown are estimated unemployed people (thousands), not unemployment rates. The smoothed series uses a 4-period rolling average to match the original chart’s approach. Policy zones mark the October 2024 Autumn Budget announcement (NIC increase from 13.8% to 15%) and the April 2025 implementation of NIC and minimum wage changes for 18–20 year-olds. No population denominators or external adjustment data were applied.

11. Custom Functions Documentation

Note📦 Custom Helper Functions

This analysis uses custom functions from my personal module library for efficiency and consistency across projects.

Functions Used:

• fonts.R: setup_fonts(), get_font_families() - Font management with showtext
• social_icons.R: create_social_caption() - Generates formatted social media captions
• image_utils.R: save_plot() - Consistent plot saving with naming conventions
• base_theme.R: create_base_theme(), extend_weekly_theme(), get_theme_colors() - Custom ggplot2 themes

Why custom functions?
These utilities standardize theming, fonts, and output across all my data visualizations. The core analysis (data tidying and visualization logic) uses only standard tidyverse packages.

Source Code:
View all custom functions → GitHub: R/utils