Code · best-practice-and-impact · UK Public Sector GitHub Observatory

Language figures come from GitHub's own per-repository byte counts, which measure source rather than a single headline label. File counts and repository sizes come from the mirrors themselves. The two tell different stories, and the difference is the point.

The codebase

languages in use

6.9 MB of source

1.9k

files at HEAD

across every analysed repository

0 B

on disk as mirrors

full history, every branch and tag

35.6 MB

largest repository

ons-spark

What stands out

One archived repository holds 23% of all the bytes.

ons-spark is 35.6 MB of git - a 2013 static snapshot of www.gov.uk, not source code. It single-handedly makes HTML the largest language by volume (62% of all source bytes). Any size or language statistic that does not exclude it is measuring an archive, not a codebase.

Sizes here are GitHub's reported figures.

This organisation was harvested with blobless partial clones, which fetch every commit and file path but not file contents, so there is no local size to measure against. Checked elsewhere in this dataset against 1,526 fully mirrored repositories, GitHub's reported size is accurate to within about six percent.

Ruby is the house language, but its share is falling.

Ruby is the primary language of 0% of archived repositories but only 0% of live ones. Python, Go, TypeScript and HCL have taken the difference - the signature of a shift from monolithic Rails publishing apps toward data pipelines, infrastructure-as-code and platform tooling.

Most repositories are small; a handful are enormous.

47% of repositories are under a megabyte of git data. The estate is not a monolith - it is a very long tail of small services, gems and prototypes around a few large publishing applications.

Languages, two ways

By volume of source

Bytes of code. Dominated by archived static HTML.

By number of repositories

How many repositories name each language as their primary one. A better guide to what engineers actually work in.

Live estate versus archive

The same language ranking, split by whether the repository is still active. The shift is the clearest signal of technical direction in the whole dataset.

Active repositories

Archived repositories

Scale

Repository size distribution

Git data per repository, log-ish buckets.

Commits against files

Each circle is a repository, sized by number of distinct authors. Both axes are logarithmic. The diagonal band is the normal relationship between codebase size and churn; outliers above it are long-lived apps with heavy iteration, below it are dumps and imports.

The shape of a repository

Directory layout is the cheapest reliable signal of what a repository actually is. app/ with config/ and spec/ is a Rails application; src/ with test/ is Java or Node. Counted by how many repositories contain each top-level directory at HEAD.

Most common top-level directories

Most common file extensions

By total file count across the estate.

The twenty largest repositories

Repository	Language	Git size	Commits	Files	Status
ons-spark	Jupyter Notebook	35.6 MB	1,077	318	active
analysis_project_documentation	HTML	28.9 MB	251	83	active
qa-of-code-guidance	CSS	18.0 MB	926	62	active
Data-Quality-Question-Bank	HTML	7.4 MB	63	96	active
aftables	R	7.0 MB	474	104	active
CARS_dashboard	R	6.9 MB	473	14	active
govcookiecutter	Python	6.8 MB	939	115	active
afcharts	R	6.6 MB	476	115	active
CARS	R	6.2 MB	689	234	active
govcookiecutter-lite	Python	6.1 MB	489	78	active
cookiecutter-guidance	TeX	5.6 MB	192	26	archived
Quarto-QA-example	Python	4.6 MB	16	20	active
admin-data-quality-stats	JavaScript	3.5 MB	32	215	active
aqua_book_revision	TeX	3.1 MB	759	85	active
dqhub-resources	HTML	1.8 MB	15	7	active
ONS_minimum_RAP	CSS	1.5 MB	50	52	active
py-af-colours	Python	1.4 MB	144	38	archived
afcharts-py	Python	672.8 kB	603	53	active
afcolours	R	505.9 kB	52	39	archived
assumptions	Python	210.9 kB	101	40	active