Profession summary: Government Science & Engineering

Note

Please note, these are the initial summary statistics for CARS 2026 and further analysis will follow. We advise linking directly to this document when distributing to ensure the most up to date information.

How to use this research

Responding to CARS is voluntary. The results presented here are from a self-selecting sample of government analysts. Because respondents are self-selecting, the results we present reflect the views of the analysts who participated.

For more detail, see the data collection page.

Coding frequency and tools

How often analysts are using code at work

We asked all respondents if they had any coding experience, inside or outside of work. Of 20 respondents, 100% reported having coding experience. We asked respondents with coding experience “In your current role, how often do you write code to complete your work objectives?”

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In your current role, how often do you write code to complete your work objectives? Percentage
Never 5%
Rarely 15%
Sometimes 35%
Regularly 30%
Always 15%
Sample size = 20

Access to and knowledge of programming languages

Given a list of programming tools, we asked all respondents if the tool was available to use for their work.

Access to tools does not necessarily refer to official policy. Some analysts may have access to tools others cannot access within the same organisation.

Access to coding tools

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Programming language Use
R 60%
Python 45%
SQL 45%
SAS 10%
VBA 10%
DAX 0%
Matlab 0%
SPSS 0%
Stata 0%
Spark 0%
Sample size = 20

Given the same list of programming tools, all respondents were asked if they knew how to program with the tool to a level suitable for their work, answering “Yes”, “No” or “Not required for my work”.

Please note that capability in programming languages is self-reported here and was not objectively defined or tested. The statement “not required for my work” was similarly not defined.

Knowledge of coding tools

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Programming language Knowledge
R 90%
Python 65%
SQL 55%
VBA 35%
SAS 20%
Matlab 15%
SPSS 10%
Spark 10%
Stata 5%
DAX 0%
Sample size = 20

Access to and knowledge of git

We asked respondents to answer “Yes” or “No” for the following question:

  • Do you know how to use git to version-control your work?

Please note these outputs include people who do not code at work.

Knowledge of git

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Do you know how to use Git? Percentage
Yes, I use it in my current role 45%
Yes, but I don't use it in my current role 25%
No 30%
Sample size = 20

Coding capability and change

Coding experience

We asked respondents with coding experience how many years of experience they had in a coding role, excluding any years in education.

This data includes any experience in other roles and sectors, but does not define the type of experience.

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How many years of professional coding experience do you have? Percentage
None 10%
Less than 1 year 0%
Between 1 and 3 years 15%
Between 3 and 5 years 20%
Over 5 years 55%
Sample size = 20

Change in coding ability during current role

We asked “Has your coding ability changed during your current role?”

This question was only asked of respondents with coding experience outside of their current role. This means analysts who first learned to code in their current role are not included in the data.

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How has your coding ability changed during your current role? Percentage
It has become significantly better 15%
It has become slightly better 40%
It has stayed the same 20%
It has become slightly worse 20%
It has become significantly worse 5%
Sample size = 20

Coding practices

We asked respondents who said they currently use code in their work, how often they carry out various coding practices. For more information on the practices presented below, please read our guidance on Quality Assurance of Code for Analysis and Research

Open sourcing was defined as ‘making code freely available to be modified and redistributed’

Consistency of good coding practices

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Statement I don't understand this question (%) Never (%) Rarely (%) Sometimes (%) Regularly (%) All the time (%)
Have code reviewed 0% 10.5% 10.5% 31.6% 21.1% 26.3%
Manually test code 0% 0% 5.3% 15.8% 36.8% 42.1%
Separate settings from code 5.3% 5.3% 5.3% 15.8% 36.8% 31.6%
Use a standard code style 5.3% 10.5% 5.3% 21.1% 47.4% 10.5%
Use control flow 0% 5.3% 10.5% 15.8% 47.4% 21.1%
Use open source software 0% 5.3% 10.5% 10.5% 21.1% 52.6%
Write automated tests 5.3% 21.1% 10.5% 42.1% 10.5% 10.5%
Write functions 0% 5.3% 0% 36.8% 21.1% 36.8%
Sample size = 19

Documentation

We asked respondents who reported writing code at work how frequently they write different forms of documentation when programming in their current role.

Embedded documentation is one of the components which make up a RAP minimum viable product. Documentation is important to help others be clear on how to use the product and what the code is intended to do.

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Statement I don't understand this question (%) Never (%) Rarely (%) Sometimes (%) Regularly (%) Always (%)
Create README files 0% 10.5% 10.5% 26.3% 26.3% 26.3%
Document dependencies 0% 22.2% 5.6% 22.2% 11.1% 38.9%
Document functions 0% 6.2% 18.8% 43.8% 18.8% 12.5%
Document manual QA steps 0% 5.9% 23.5% 23.5% 17.6% 29.4%
Document pipeline design 0% 16.7% 0% 44.4% 11.1% 27.8%
Sample size = 19

Working practices

We asked respondents who reported writing code at work how frequently they use good working practices in the coding projects they work on.

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Statement I don't understand this question (%) Never (%) Rarely (%) Sometimes (%) Regularly (%) Always (%)
Have a succession plan 5.3% 5.3% 5.3% 26.3% 15.8% 42.1%
Publish code in the open 15.8% 36.8% 10.5% 31.6% 5.3% 0%
Understand project aims 0% 0% 0% 21.1% 36.8% 42.1%
Understand project roles 21.1% 0% 5.3% 21.1% 42.1% 10.5%
Use version control 10.5% 21.1% 15.8% 10.5% 15.8% 26.3%
Work to quality standards 10.5% 5.3% 0% 21.1% 36.8% 26.3%
Sample size = 19

Reproducible analytical pipelines (RAP)

We asked respondents about their knowledge of and opinions on reproducible analytical pipelines (RAP). RAP refers to the use of practices from software engineering to make analysis more reproducible. These practices build on the advantages of writing analysis as code by ensuring increased quality, trust, efficiency, business continuity and knowledge management.

The RAP champions are a network of analysts across government who promote and support RAP development in their departments. Please contact the analysis standards and pipelines team for any enquiries about RAP or the champions network.

The Analysis Function RAP strategy was released in June 2022 and sets out plans for adopting RAP across government.

RAP scores

In this section we present RAP components and RAP scores.

For each RAP component a percent positive was calculated. Positive responses were recorded where an answer of “regularly” or “all the time” was given. For documentation, a positive response was recorded if both code comments and README files questions received positive responses. For the continuous integration and dependency management components, responses of “yes” were recorded as positive.

“Basic” components are the components which make up the RAP MVP. “Advanced” components are components which help improve reproducibility, but are not considered part of the minimum standard.

RAP components

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RAP component Type Percentage of analysts who code in their work
Use open source software Basic 73.7%
Work to quality standards Basic 63.2%
Create project README Basic 52.6%
Peer review Basic 47.4%
Version control Basic 42.1%
Open source own code Basic 5.3%
Manually test code Advanced 78.9%
Use control flow Advanced 68.4%
Use configuration files Advanced 68.4%
Write functions Advanced 57.9%
Follow code style guidelines Advanced 57.9%
Document dependencies Advanced 47.4%
Document functions Advanced 26.3%
Write automated tests Advanced 21.1%
Sample size = 19