R interface to UK official labour market statistics via the Nomis API. Access census data, claimant counts, employment surveys, and economic indicators from the Office for National Statistics with automatic pagination, spatial integration, and tidy output.

Installation

# CRAN
install.packages("nomisdata")
# Development
remotes::install_github("cherylisabella/nomisdata")

Quick Start

library(nomisdata)
library(ggplot2)
# Optional: Register for 100K row limit (vs 25K guest)
# Free: https://www.nomisweb.co.uk/myaccount/userjoin.asp
set_api_key("your-key", persist = TRUE)
# Download unemployment data
unemployment <- fetch_nomis(
  id = "NM_1_1",           # JSA claimant count
  time = "latest",
  geography = "TYPE480",   # UK regions
  measures = 20100,
  sex = 7
)
# Visualise it
ggplot(unemployment, aes(reorder(GEOGRAPHY_NAME, OBS_VALUE), OBS_VALUE)) +
  geom_col(fill = "#1f77b4") +
  coord_flip() +
  labs(title = "JSA Claimants by UK Region", x = NULL, y = NULL) +
  theme_minimal()

Core Functions

Function	Purpose	Example
`fetch_nomis()`	Download data	`fetch_nomis("NM_1_1", time = "latest", geography = "TYPE480")`
`fetch_spatial()`	Data + KML boundaries	`fetch_spatial("NM_1_1", time = "latest", geography = "TYPE464")`
`search_datasets()`	Search catalogue	`search_datasets(name = "employment")`
`describe_dataset()`	Dataset structure	`describe_dataset("NM_1_1")`
`get_codes()`	Dimension codes	`get_codes("NM_1_1", "geography")`
`lookup_geography()`	Find geography codes	`lookup_geography("Manchester")`
`aggregate_geography()`	Aggregate geographies	`aggregate_geography(data, to_type = "TYPE480")`
`aggregate_time()`	Aggregate time periods	`aggregate_time(monthly, period = "quarter")`
`enable_cache()`	Persistent caching	`enable_cache("~/nomis_cache")`

Full docs: ?fetch_nomis

Geography Hierarchy

Code	Level	N	Example
TYPE499	Country	7	England, Scotland, Wales, NI
TYPE480	Region	12	London, South East, North West
TYPE464	Local Authority	374	Manchester, Birmingham, Leeds
TYPE460	Constituency	650	Cities of London and Westminster
TYPE297	Ward	~9,000	Bloomsbury Ward
TYPE298	LSOA	~35,000	Lower Super Output Areas
TYPE299	MSOA	~7,000	Middle Super Output Areas

# Find codes by name
lookup_geography("Manchester")
lookup_geography("London", type = "TYPE464")  # Boroughs only
# Get all codes for a level
get_codes("NM_1_1", "geography", type = "TYPE464")

Advanced Example: Spatial Analysis

library(sf)
# Download with boundaries
spatial <- fetch_spatial(
  "NM_1_1",
  time = "latest",
  geography = "TYPE464",  # Local authorities
  measures = 20100,
  sex = 7
)
# Choropleth map
ggplot(spatial) +
  geom_sf(aes(fill = OBS_VALUE), color = NA) +
  scale_fill_viridis_c(option = "magma", name = "Claimants") +
  theme_void() +
  labs(title = "JSA Claimants by Local Authority")

Visualisations

All plots use real Nomis data (November 2025). Reproduction code in inst/plots.R.

1. National Overview

Current JSA claimants across UK constituent nations. England dominates in absolute terms (October 2025).

2. Inequality: Lorenz Curve

Geographic concentration across 317 UK local authorities. Top 10% of areas contain 71% of all claimants.

X-axis: Cumulative % of local authorities (ranked low to high)
Y-axis: Cumulative % of total unemployment
Red line: Actual distribution
Dashed line: Perfect equality

Policy implication: Targeted interventions in high-unemployment areas could reach majority of unemployed.

3. Distribution by Urban Hierarchy

Combined violin plot (density), box plot (quartiles), and individual points showing unemployment distribution across geographic classifications.

Key findings:

London boroughs and Metropolitan areas show highest variability
Small towns/rural areas mostly low but some outliers
Major cities intermediate

4. High Claimant Areas

Top 20 authorities represent over one-third of all JSA claimants while making up less than 6% of UK local authorities.

5. Gender Disparities

Male unemployment consistently exceeds female across all UK nations. Gender gaps range from 18-38% higher male unemployment.

Findings:

England: +6.2k male claimants (+20%)
Wales: +525 (+38%)
Scotland: +746 (+33%)
Northern Ireland: +145 (+19%)

6. Within-City Inequality: London

Dramatic variation across London's 33 boroughs. Colour intensity reflects claimant volume (yellow = low, red = high).

Key finding: Greater within-city inequality than between-city inequality for some pairs.

7. Temporal Dynamics

Year-on-year change (September 2025 vs 2024). Green = improvement, red = deterioration.

All UK nations show improvement ranging from -11.8% (Wales) to -66.9% (Northern Ireland).

8. Local Authority Performance

Top improving (green) and declining (red) local authorities over 12 months. Success cases provide natural experiments for policy learning.

9. Multi-Dimensional Dashboard

Four-panel dashboard: current levels, gender splits, temporal trends, and geographic concentration. Executive summary for rapid assessment.

10. Scale-Growth Dynamics

Relationship between current unemployment and year-on-year change across 405 local authorities.

How to read:

X-axis: Current JSA claimants (log scale)
Y-axis: Year-on-year % change (+ = worsening, - = improving)
Red curve: LOESS smooth showing non-linear pattern
Shaded area: 95% confidence band

Key findings:

Small areas (<1k): Highly variable (±60%), thin labour markets
Medium areas (1-5k): More stable, cluster near zero
Large areas (>10k): Variable but from structural forces

Policy implication: One-size-fits-all won't work. Small areas need diversification. Large cities need sector-specific policies. Medium towns most stable.

Common Datasets

NM_1_1 - JSA claimant count
NM_162_1 - Annual Population Survey employment
NM_17_5 - Annual Survey of Hours and Earnings
NM_2010_1 - Census 2021 (England and Wales)

Search all: search_datasets()

Related Resources

Citation

citation("nomisdata")

Disclaimer

Independent implementation unaffiliated with ONS or Durham University. Data subject to Open Government Licence v3.0.

Data from: Nomis / ONS