An R package for downloading data from the Office for National Statistics.
The Office for National Statistics is the UK’s national statistical authority. It produces the headline economic data that drives policy and markets — GDP, inflation, unemployment, wages, trade, house prices, retail sales, and population estimates. Its releases move sterling, set the agenda for the Bank of England’s Monetary Policy Committee, and underpin economic research across the UK.
The ONS publishes thousands of time series through its website, each identified by a four-character CDID code. The data is freely available, but using it programmatically requires knowing these codes, constructing download URLs with topic paths and dataset identifiers, parsing CSV files with metadata headers, and handling mixed-frequency data where annual, quarterly, and monthly observations appear in a single file.
This package replaces all of that with named functions that return clean data frames.
The onsr
package on CRAN wraps the ONS Beta API — but that API does not host most
headline economic time series. GDP, CPI, unemployment, wages, and trade
data are not available through it. onsr is useful for
Census data and regional breakdowns, but cannot access the series most
people actually want. It was last actively developed in 2021.
This package takes a different approach. It fetches data directly
from the ONS website’s CSV time series endpoint, which covers every
published CDID. You get named functions — ons_gdp(),
ons_cpi(), ons_unemployment() — that handle
series codes, URL construction, frequency filtering, and caching
internally.
The data is freely available, but using it programmatically requires
knowing obscure CDID codes (GDP growth is IHYQ, the CPI
annual rate is D7G7), constructing URLs with topic paths
and dataset identifiers, parsing CSV files that mix metadata headers
with data rows, and filtering out duplicate observations where annual,
quarterly, and monthly data appear in the same file. Every analyst who
works with ONS data writes the same boilerplate.
# Without this package
url <- paste0(
"https://www.ons.gov.uk/generator?format=csv",
"&uri=/economy/grossdomesticproductgdp/timeseries/ihyq/pn2"
)
raw <- readLines(url)
# ... skip metadata rows, parse periods, filter frequency, handle errors ...
# With this package
library(ons)
ons_gdp(from = "2020-01-01")Install the development version from GitHub:
# install.packages("remotes")
remotes::install_github("charlescoverdale/ons")| Function | Description | From |
|---|---|---|
ons_gdp() |
Quarterly GDP (growth, YoY, level, nominal) | 1948 |
ons_monthly_gdp() |
Monthly GDP index | 1997 |
ons_cpi() |
Consumer price inflation (CPI, CPIH, RPI; rate or index) | 1987 |
ons_unemployment() |
Unemployment rate (16+, seasonally adjusted) | 1971 |
ons_employment() |
Employment rate (total, male, female) | 1971 |
ons_inactivity() |
Economic inactivity (thousands) | 1971 |
ons_wages() |
Average weekly earnings (level, growth, real) | 2000 |
ons_trade() |
Trade balance, exports, imports (GBP millions) | 1948 |
ons_retail_sales() |
Retail sales index (volume or value) | 1988 |
ons_house_prices() |
UK average house price (GBP) | 1968 |
ons_productivity() |
Output per hour or per worker (index) | 1971 |
ons_population() |
Mid-year population estimates | 1971 |
ons_public_finances() |
Public sector net borrowing (GBP millions) | 1993 |
ons_get() |
Fetch any series by CDID code | Any |
ons_search() |
Search the ONS catalogue by keyword | — |
clear_cache() |
Delete locally cached data files | — |
library(ons)
# Quarterly GDP growth
gdp <- ons_gdp(from = "2000-01-01")
tail(gdp, 6)
#> date value
#> 2024-07-01 0.2
#> 2024-10-01 0.3
#> 2025-01-01 0.7
#> 2025-04-01 0.2
#> 2025-07-01 0.1
#> 2025-10-01 0.1# CPI annual rate
cpi <- ons_cpi(from = "2024-01-01")
tail(cpi, 6)
#> date value
#> 2025-08-01 3.8
#> 2025-09-01 3.8
#> 2025-10-01 3.6
#> 2025-11-01 3.2
#> 2025-12-01 3.4
#> 2026-01-01 3.0
# CPIH or RPI
ons_cpi(measure = "cpih", from = "2024-01-01")
ons_cpi(measure = "rpi", type = "index", from = "2024-01-01")# Unemployment rate (16+, SA)
unemp <- ons_unemployment(from = "2020-01-01")
tail(unemp, 6)
#> date value
#> 2025-06-01 4.7
#> 2025-07-01 4.8
#> 2025-08-01 5.0
#> 2025-09-01 5.1
#> 2025-10-01 5.1
#> 2025-11-01 5.2# Average weekly earnings (GBP)
wages <- ons_wages(from = "2024-01-01")
tail(wages, 6)
#> date value
#> 2025-07-01 728
#> 2025-08-01 733
#> 2025-09-01 734
#> 2025-10-01 740
#> 2025-11-01 740
#> 2025-12-01 735
# Year-on-year growth or real terms
ons_wages(measure = "growth", from = "2024-01-01")
ons_wages(measure = "real", from = "2024-01-01")# UK average house price
hp <- ons_house_prices(from = "2024-01-01")
tail(hp, 6)
#> date average_price_gbp annual_change_pct
#> 2025-07-01 270432 3.0
#> 2025-08-01 271808 2.5
#> 2025-09-01 270827 2.3
#> 2025-10-01 270548 2.0
#> 2025-11-01 272043 2.8
#> 2025-12-01 270259 2.4# Trade balance (GBP millions, negative = deficit)
trade <- ons_trade(from = "2024-01-01")
tail(trade, 6)
#> date value
#> 2025-07-01 -2152
#> 2025-08-01 -2151
#> 2025-09-01 -4530
#> 2025-10-01 -5790
#> 2025-11-01 -5564
#> 2025-12-01 -4340
# Exports or imports separately
ons_trade(measure = "exports", from = "2024-01-01")# Public sector net borrowing (GBP millions)
pf <- ons_public_finances(from = "2024-01-01")
tail(pf, 6)
#> date value
#> 2025-08-01 15239
#> 2025-09-01 22471
#> 2025-10-01 16183
#> 2025-11-01 10380
#> 2025-12-01 13355
#> 2026-01-01 -30366# If you know the ONS CDID code, use ons_get() directly
# Find codes at https://www.ons.gov.uk/timeseriestool
# GDP growth vs CPI inflation
ons_get(c("IHYQ", "D7G7"), from = "2024-01-01", to = "2024-06-30")
#> date cdid value
#> 2024-01-01 IHYQ 0.8
#> 2024-04-01 IHYQ 0.6
#> 2024-01-01 D7G7 4.0
#> 2024-02-01 D7G7 3.4
#> 2024-03-01 D7G7 3.2
#> 2024-04-01 D7G7 2.3
#> 2024-05-01 D7G7 2.0
#> 2024-06-01 D7G7 2.0
# Search for series by keyword
ons_search("GDP growth")All downloads are cached locally in your user cache directory. Subsequent calls return the cached copy instantly — no network request is made.
# Force a fresh download
ons_gdp(from = "2020-01-01", cache = FALSE)
# Remove files older than 7 days
clear_cache(max_age_days = 7)
# Remove all cached files
clear_cache()This package is part of a family of R packages for UK economic and fiscal data. They share a consistent interface — named functions, tidy data frames, local caching — and are designed to work together. All outputs join cleanly on date columns.
| Package | What it covers |
|---|---|
boe |
Bank of England data (Bank Rate, SONIA, gilt yields, exchange rates, mortgage rates) |
hmrc |
HMRC tax receipts, corporation tax, stamp duty, R&D credits, and tax gap data |
obr |
OBR fiscal forecasts and the Public Finances Databank |
inflateR |
Adjust values for inflation using CPI or GDP deflator data |
Please report bugs or requests at https://github.com/charlescoverdale/ons/issues.