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.
We asked respondents if they had any coding experience, inside or outside of work. Of 986 respondents, 95.1% had coding experience. We asked all respondents with coding experience “In your current role, how often do you write code to complete your work objectives?”
To see how this compares to other years, see the data collection page
| In your current role, how often do you write code to complete your work objectives? | Percentage |
|---|---|
| Never | 9.6% |
| Rarely | 12.7% |
| Sometimes | 18.4% |
| Regularly | 39.5% |
| Always | 19.8% |
| Sample size = 939 |
In 2026, we also asked respondents with coding experience whether they would like to use more coding in their role.
| Would you prefer to use more coding in your role? | Percentage |
|---|---|
| Yes | 62.9% |
| No | 16.4% |
| Not sure / not applicable | 20.7% |
| Sample size = 939 |
Given a list of programming tools, we asked all respondents with coding experience whether they knew how to use each tool, regardless of whether they currently use it in their work. Note this includes respondents who do not code in their current role.
Please note that capability in programming languages is self-reported here and was not objectively defined or tested.
| Programming tool | Knowledge | NA |
|---|---|---|
| Python | Yes | 51.1% |
| Python | No | 48.9% |
| R | Yes | 80.3% |
| R | No | 19.7% |
| DAX | Yes | 11% |
| DAX | No | 89% |
| Matlab | Yes | 12.5% |
| Matlab | No | 87.5% |
| SAS | Yes | 29.5% |
| SAS | No | 70.5% |
| SPSS | Yes | 30.1% |
| SPSS | No | 69.9% |
| SQL | Yes | 59.2% |
| SQL | No | 40.8% |
| Stata | Yes | 11.4% |
| Stata | No | 88.6% |
| Spark | Yes | 10.3% |
| Spark | No | 89.7% |
| VBA | Yes | 14.6% |
| VBA | No | 85.4% |
| None | Yes | 0.5% |
| None | No | 99.5% |
| Sample size = 986 |
Using the same list of programming tools, we asked respondents which tools they currently use in their role.
| Programming tool | Use |
|---|---|
| R | 64.4% |
| SQL | 40.9% |
| Python | 31.1% |
| SAS | 13.1% |
| DAX | 7.3% |
| Spark | 6.7% |
| SPSS | 5.2% |
| VBA | 3.5% |
| Stata | 0.9% |
| Matlab | 0.2% |
| Sample size = 986 |
The chart below compares the proportion of respondents with coding experience who reported knowing how to use each programming tool with the proportion who reported using it in their current role.
| Programming tool | Use | Knowledge |
|---|---|---|
| R | 64.4% | 80.3% |
| SQL | 40.9% | 59.2% |
| Python | 31.1% | 51.1% |
| SAS | 13.1% | 29.5% |
| SPSS | 5.2% | 30.1% |
| DAX | 7.3% | 11% |
| VBA | 3.5% | 14.6% |
| Spark | 6.7% | 10.3% |
| Matlab | 0.2% | 12.5% |
| Stata | 0.9% | 11.4% |
| None | 10% | 0.5% |
The proportion of respondents who report having the capability to use R and Python, is shown alongside the proportion who are able to use SAS, SPSS or Stata, for the past four years of the survey.
| Programming language type | Year | Know how to programme with these tools (percent) | Lower confidence limit (percent) | Upper confidence limit (percent) |
|---|---|---|---|---|
| 2022 | Open Source | 80.3% | 0.7802509 | 0.8231395 |
| 2023 | Open Source | 72.6% | 0.7005951 | 0.7491138 |
| 2024 | Open Source | 80.9% | 0.7869734 | 0.8296660 |
| 2026 | Open Source | 87% | 0.8477586 | 0.8897332 |
| 2022 | Proprietary | 56.3% | 0.5359005 | 0.5893030 |
| 2023 | Proprietary | 36.1% | 0.3351431 | 0.3873442 |
| 2024 | Proprietary | 40.7% | 0.3805305 | 0.4338641 |
| 2026 | Proprietary | 53.7% | 0.5053039 | 0.5674350 |
Differences in preferred languages may lead to silos between analytical professions. Here we show the percentage of respondents reporting capability in different tools, within the different analytical professions.
Please note that respondents might be members of more than one profession, and may report capability in more than one tool.
| Profession | R | Python | SQL | SPSS | Stata | SAS | VBA | Matlab | Spark | DAX |
|---|---|---|---|---|---|---|---|---|---|---|
| Government Operational Research Service | 93.7 | 68.4 | 70.5 | 10.5 | 6.3 | 33.7 | 24.2 | 24.2 | 7.4 | 14.7 |
| Data Scientists | 90.1 | 84.7 | 84.7 | 23.4 | 6.3 | 17.1 | 13.5 | 24.3 | 30.6 | 10.8 |
| Government Science & Engineering | 90 | 65 | 55 | 10 | 5 | 20 | 35 | 15 | 10 | 0 |
| Data Engineers | 66.7 | 88.9 | 83.3 | 33.3 | 11.1 | 16.7 | 16.7 | 16.7 | 27.8 | 27.8 |
| Government Statistician Group | 86.4 | 50.3 | 61.8 | 34.9 | 10.7 | 36.8 | 13.8 | 14.4 | 11.3 | 7 |
| Government Economic Service | 77.1 | 38.6 | 34.3 | 7.1 | 40 | 21.4 | 7.1 | 4.3 | 1.4 | 1.4 |
| Government Digital and Data | 75.9 | 68.5 | 66.7 | 14.8 | 5.6 | 22.2 | 11.1 | 13 | 16.7 | 16.7 |
| Government Geography Profession | 73.7 | 73.7 | 52.6 | 31.6 | 5.3 | 0 | 15.8 | 31.6 | 10.5 | 5.3 |
| Government Social Research | 63.8 | 33 | 36.2 | 61.7 | 14.9 | 19.1 | 5.3 | 3.2 | 12.8 | 6.4 |
We asked respondents to answer “Yes”, “No” for the following question:
Please note these outputs include people with coding experience who do not code in their current role.
| Do you know how to use Git? | Percentage |
|---|---|
| Yes, I use it in my current role | 57.2% |
| Yes, but I don't use it in my current role | 17.1% |
| No | 25.7% |
| Sample size = 938 |
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.
| How many years of professional coding experience do you have? | Percentage |
|---|---|
| None | 3.2% |
| Less than 1 year | 12.7% |
| Between 1 and 3 years | 21.3% |
| Between 3 and 5 years | 20% |
| Over 5 years | 42.8% |
| Sample size = 939 |
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.
| How has your coding ability changed during your current role? | Percentage |
|---|---|
| It has become significantly better | 34% |
| It has become slightly better | 36.4% |
| It has stayed the same | 16.2% |
| It has become slightly worse | 9.7% |
| It has become significantly worse | 3.7% |
| Sample size = 938 |
We asked respondents if they’d feel confident managing a coding project.
| Would you feel confident managing a coding project? | Percentage |
|---|---|
| Yes | 6.2% |
| No | 72.9% |
| Not sure | 20.8% |
| Sample size = 48 |
Respondents who do not currently code in their role were asked whether they would like to use coding as part of their work.
| Would you like to use coding in your current role? | Percentage |
|---|---|
| Yes | 47.9% |
| No | 20.8% |
| Not sure / not applicable | 31.2% |
| Sample size = 48 |
We asked respondents about any barriers they face in learning to code in their current role. The most commonly cited barrier was a lack of opportunity.
| Barrier | Yes(%) |
|---|---|
| Lack of opportunity | 49.3% |
| Availability of tools | 22.8% |
| Suitability of resources | 20.8% |
| Availability of resources | 19.3% |
| No barriers/not applicable | 16.8% |
| Lack of peer support | 14.1% |
| Lack of management support | 12.9% |
| Sample size = 986 |
We also asked respondents about any barriers to improving their coding skills in their current role.
| Barrier | Yes(%) |
|---|---|
| Lack of opportunity | 52.1% |
| Availability of resources | 27.1% |
| No barriers/not applicable | 27.1% |
| Suitability of resources | 25% |
| Availability of tools | 16.7% |
| Lack of peer support | 10.4% |
| Lack of management support | 8.3% |
| Sample size = 48 |
We asked all respondents who use code in their role, it they use AI tools when coding at work.
The term ‘AI’ was not defined in this question.
| Do you use any AI assistants when coding at work? | Percentage |
|---|---|
| Yes | 70% |
| No | 30% |
| Sample size = 848 |
Overall, 70% of coders reported using AI tools when coding at work. The chart below breaks this down by analytical profession, showing the proportion of coders who use AI assistants.
| Profession | Yes |
|---|---|
| Government Digital and Data | 81.6% |
| Government Economic Service | 79.6% |
| Data Engineers | 77.8% |
| Government Operational Research Service | 76.7% |
| Data Scientists | 74.8% |
| Government Science & Engineering | 73.7% |
| Government Statistician Group | 68.5% |
| Government Social Research | 60.3% |
| Government Geography Profession | 60% |
We asked all respondents who use AI tools when coding at work, what they use these tools for.
| AI use | Never | Rarely | Sometimes | Regularly | Always |
|---|---|---|---|---|---|
| Debugging code | 7.1% | 10.1% | 29.5% | 40.6% | 12.8% |
| Documenting code | 41.9% | 23.6% | 16.8% | 12.6% | 5.1% |
| Getting answers to a coding question | 1.7% | 7.2% | 27.9% | 50.3% | 12.8% |
| Learning about new concepts | 12.5% | 18.7% | 32.2% | 27.6% | 9.1% |
| Testing code | 52.4% | 21.2% | 14.6% | 7.7% | 4% |
| Writing code | 4% | 17% | 36.2% | 33% | 9.8% |
| Sample size = 594 |
Respondents were asked if they use any cloud data platforms in their current role.
| Data Platform | Yes(%) |
|---|---|
| Microsoft (e.g. Azure) | 19.5% |
| Amazon (e.g. AWS) | 18.7% |
| Databricks | 14.2% |
| Cloudera | 5.8% |
| Google (e.g. GCP) | 3.7% |
| Sample size = 986 |
RAP refers to the use of good software engineering practices to make analysis pipelines more reproducible. This approach aims to use automation to improve the quality and efficiency of analytical processes.
The following links contain more resources on RAP:
We asked respondents to what extent they agreed with the statement: “I am currently implementing RAP in my work.”
| To what extent to you agree with the following statement: 'I am currently implementing RAP in my work'? | Percentage |
|---|---|
| Strongly Disagree | 6.3% |
| Disagree | 13% |
| Neutral | 19.4% |
| Agree | 34.3% |
| Strongly Agree | 27% |
| Sample size = 938 |
We also asked respondents about the main barriers to implementing RAP in their current role. The most commonly reported barrier was a lack of time.
| Barrier | Yes(%) |
|---|---|
| Not enough time | 37.7% |
| Work not suited to RAP | 32.2% |
| Current skills | 28.6% |
| Lack of guidance | 20.8% |
| Availability of tools | 16% |
| Lack of peer support | 14.2% |
| No barriers | 13.6% |
| Lack of management support | 11.1% |
| Other | 5.7% |
| Sample size = 986 |
We asked respondents who reported writing code at work about the good practices they apply when writing code at work. These questions cover many of the coding practices recommended in the quality assurance of code for analysis and research guidance, as well as the minimum RAP standards set by the cross-government RAP champions network.
Coding practices have been classified as either ‘Basic’ or ‘Advanced’. Basic practices are those that make up the minimum RAP standards, while Advanced practices help improve reproducibility. The percentage of respondents who reported applying these practices either ‘Regularly’ or ‘All the time’ is shown below.
Open sourcing was defined as ‘making code freely available to be modified and redistributed’
| RAP component | Type | Percentage of analysts who code in their work |
|---|---|---|
| Use open source software | Basic | 75.8% |
| Work to quality standards | Basic | 64.5% |
| Version control | Basic | 53.5% |
| Peer review | Basic | 52.8% |
| Create project README | Basic | 41.3% |
| Open source own code | Basic | 14.7% |
| Manually test code | Advanced | 74% |
| Write functions | Advanced | 56% |
| Use configuration files | Advanced | 55.5% |
| Follow code style guidelines | Advanced | 53% |
| Use control flow | Advanced | 52.8% |
| Document dependencies | Advanced | 32.3% |
| Document functions | Advanced | 28.7% |
| Write automated tests | Advanced | 14.3% |
| Sample size = 896 |
The table below shows RAP component use over time for 2023, 2024 and 2026. Empty cells indicate that a component was not asked in that year.
| RAP component | 2023 | 2024 | 2026 |
|---|---|---|---|
| Use open source software | 71.2 | 79.9 | 75.8 |
| Manually test code | 79.3 | 74 | |
| Work to quality standards | 69 | 64.5 | |
| Write functions | 55.9 | 56.2 | 56 |
| Use configuration files | 53.9 | 55.5 | |
| Version control | 44.7 | 56.5 | 53.5 |
| Follow code style guidelines | 49.3 | 56.9 | 53 |
| Peer review | 53.6 | 58.7 | 52.8 |
| Use control flow | 58.4 | 52.8 | |
| Create project README | 29.4 | 43.6 | 41.3 |
| Document dependencies | 29.4 | 34.6 | 32.3 |
| Document functions | 31.6 | 31.8 | 28.7 |
| Open source own code | 12.5 | 15.5 | 14.7 |
| Write automated tests | 16.6 | 14.3 |
We asked respondents who reported writing code at work how frequently they apply good coding practices when writing code at work.
| Statement | I don't understand this question (%) | Never (%) | Rarely (%) | Sometimes (%) | Regularly (%) | Always (%) |
|---|---|---|---|---|---|---|
| Have code reviewed | 2.7% | 5.1% | 12.6% | 23.8% | 26.9% | 28.9% |
| Manually test code | 3.3% | 2.9% | 3.9% | 11.7% | 33% | 45.2% |
| Separate settings from code | 8.5% | 7.1% | 8.4% | 17.5% | 29.2% | 29.4% |
| Use a standard code style | 12.7% | 12% | 6.1% | 13.1% | 28.2% | 27.8% |
| Use control flow | 7% | 9.2% | 9% | 19.1% | 28.8% | 27% |
| Use open source software | 2% | 4.6% | 4.1% | 9.2% | 21.6% | 58.5% |
| Write automated tests | 14.2% | 28.5% | 23.9% | 18.3% | 9.9% | 5.2% |
| Write functions | 4% | 6.2% | 10% | 20.5% | 35.1% | 24.1% |
| Sample size = 848 |
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.
| Statement | I don't understand this question (%) | Never (%) | Rarely (%) | Sometimes (%) | Regularly (%) | Always (%) |
|---|---|---|---|---|---|---|
| Create README files | 0% | 17.1% | 13.2% | 22.5% | 23.3% | 23.8% |
| Document dependencies | 0% | 25.5% | 17.9% | 18.2% | 18.3% | 20.1% |
| Document functions | 0% | 32.8% | 15.6% | 15.9% | 18.1% | 17.7% |
| Document manual QA steps | 0% | 9.7% | 15.1% | 25.9% | 29.7% | 19.6% |
| Document pipeline design | 0% | 21.3% | 18.8% | 26.5% | 21.3% | 12.2% |
| Sample size = 785 |
We asked respondents who reported writing code at work how frequently they use good working practices in the coding projects they work on.
| Statement | I don't understand this question (%) | Never (%) | Rarely (%) | Sometimes (%) | Regularly (%) | Always (%) |
|---|---|---|---|---|---|---|
| Have a succession plan | 5.9% | 3.4% | 4% | 15.1% | 35.8% | 35.7% |
| Publish code in the open | 13.8% | 41.7% | 14.9% | 14% | 9.1% | 6.5% |
| Understand project aims | 3.7% | 0.4% | 1.2% | 7.8% | 38.6% | 48.5% |
| Understand project roles | 9.2% | 0.8% | 2.6% | 14.6% | 36.6% | 36.2% |
| Use version control | 6.2% | 16.4% | 9.6% | 11.3% | 23.1% | 33.4% |
| Work to quality standards | 9.3% | 2.4% | 4% | 16.2% | 38.3% | 29.8% |
| Sample size = 848 |
Respondents were asked whether they follow any analysis or coding quality standards, whether that be either internal standards, external guidance, or a combination of both.
| Do you work to any analysis or coding quality standards? | Percentage |
|---|---|
| Yes, internal to my organisation | 23.8% |
| Yes, Civil Service or other external standards | 16.6% |
| Yes, both internal and external standards | 26.8% |
| No, I am not aware of any | 19.7% |
| Not sure | 13.1% |
| Sample size = 938 |
Respondents were asked whether they were aware of the Duck Book.
The Duck Book is guidance produced by the ONS Analysis Standards and Pipelines Hub. It outlines software engineering best practices tailored to analysts and researchers who work with data using code.
| Are you aware of the 'Duck Book' (Quality Assurance of Code for Analysis and Research)? | Percentage |
|---|---|
| Yes, I use it | 29.5% |
| Yes, but I don't use it | 29.2% |
| No | 41.3% |
| Sample size = 349 |
In 2026, we asked respondents whether they or their team use cross-government or department-specific analysis packages in their work.
See the list of packages reported by respondents on the useful pages page.
| Do you or your team use any cross-government or department-specific analysis packages in your work? | Percentage |
|---|---|
| Yes | 37.2% |
| No | 39.1% |
| Not sure or not applicable | 23.7% |
| Sample size = 938 |