Coding frequency and tools

Coding frequency

We asked respondents “In your current role, how often do you write code to complete your work objectives?”.

Coding frequency	Count
Never	6
Rarely	12
Sometimes	9
Regularly	20
All the time	29
^a Sample size = 76

What people are using code for

We asked respondents what data operations they carry out in their work, and whether they use code to do them. Please note we did not ask how much of each data operation is done with code, or how often.

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Data operation	I do some or all of this by coding	I do this without coding
Analysis	58	11
Data cleaning	56	6
Data linking	45	4
Data transfer	30	8
Data visualisation	51	15
Machine learning	17	0
Modelling	33	3
^a Sample size = 76

Access to and knowledge of programming languages

Given a list of programming tools, we asked respondents to answer “Yes”, “No” or “Don’t know” for the following statements;

This tool is available to use for my work.
I know how to program with this tool to a level suitable for my 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.

Please note that capability in programming languages is self-reported here and was not objectively defined or tested.

Access to coding tools

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Programming language	Yes	Don't Know	No
C++ / C#	9	30	37
Java / Scala	9	33	34
Javascript / Typescript	18	29	29
Matlab	1	30	45
Python	67	6	3
R	76	0	0
SAS	37	17	22
SPSS	13	23	40
SQL	62	11	3
Stata	9	36	31
VBA	51	18	7
^a Sample size = 76

Knowledge of coding tools

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Programming language	Yes	Don't Know	No
C++ / C#	6	2	68
Java / Scala	3	2	71
Javascript / Typescript	9	1	66
Matlab	19	3	54
Python	37	1	38
R	61	1	14
SAS	33	0	43
SPSS	22	1	53
SQL	57	0	19
Stata	6	2	68
VBA	23	3	50
^a Sample size = 76

Access and knowledge gaps

Using the data presented above we calculated the number of respondents with:

Access to tools they do not have the capability to use (access only),
Access to tools they are able to use (access and knowledge)
Or capability to use tools they cannot access at work (knowledge only)

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Programming language	Access only	Access and knowledge	Knowledge only
C++ / C#	7	2	4
Java / Scala	9	0	3
Javascript / Typescript	9	9	0
Matlab	1	0	19
Python	31	36	1
R	15	61	0
SAS	14	23	10
SPSS	6	7	15
SQL	9	53	4
Stata	6	3	3
VBA	29	22	1
^a Sample size = 76

Coding capability

Change in coding ability during 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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Ability Change	Count
Significantly worse	2
Slightly worse	11
No change	13
Slightly better	21
Significantly better	22
^a Sample size = 69

Where respondents first learned to code

Respondents with coding experience outside their current role were asked when they first learned to code. This output also includes analysts who code in their current role but reported no other coding experience.

These data only show where people first learned to code. They do not show all the settings in which they had learned to code, to what extent, or how long ago.

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First coding experience	Count
In current role	8
In education	43
In private sector employment	2
In public sector employment	10
Self-taught	10
Other	1
^a Sample size = 75

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.

General coding practices

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	Percent
Question	I don't understand this question	Never	Rarely	Sometimes	Regularly	All the time
I use open source software when programming	4.3	7.1	7.1	8.6	15.7	57.1
My team open sources its code	7.1	22.9	20.0	15.7	24.3	10.0
I use a source code version control system e.g. Git	0.0	2.9	10.0	11.4	11.4	64.3
Code my team writes is reviewed by a colleague	0.0	2.9	4.3	15.7	27.1	50.0
I write repetitive elements in my code as functions	0.0	5.7	4.3	20.0	34.3	35.7
I unit test my code	15.7	14.3	15.7	30.0	15.7	8.6
I collect my code and supporting material into packages	4.3	42.9	17.1	18.6	11.4	5.7
I follow a standard directory structure when programming	15.7	12.9	17.1	17.1	27.1	10.0
I follow coding guidelines or style guides when programming	1.4	7.1	14.3	22.9	32.9	21.4
I write code to automatically quality assure data	2.9	17.1	24.3	25.7	17.1	12.9
My team applies the principles set out in the Aqua book when carrying out analysis as code	27.1	10.0	11.4	14.3	25.7	11.4
^a Sample size = 70

Documentation

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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	Percent
Question	I don't understand this question	Never	Rarely	Sometimes	Regularly	All the time
Code comments	1.4	1.4	2.9	8.6	32.9	52.9
Documentation for each function or class	4.3	14.3	11.4	25.7	24.3	20.0
README files	1.4	7.1	15.7	12.9	31.4	31.4
Desk notes	24.3	15.7	15.7	18.6	17.1	8.6
Analytical Quality Assurance (AQA) logs	11.4	18.6	11.4	31.4	22.9	4.3
Data or assumptions registers	8.6	24.3	8.6	14.3	30.0	14.3
Flow charts	1.4	15.7	28.6	38.6	14.3	1.4
^a Sample size = 70

Dependency management

Respondents who currently use code in their work were asked whether they use any tools for dependency management. We provided examples of tools that may be used for dependency management:

Requirements files, e.g. python requirements.txt or R DESCRIPTION files
Virtual environments (e.g. venv or renv) or virtual machines
Containers e.g. Docker

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Use dependency management software	Count
Yes	39
No	11
I don't know what dependency management is	20
^a Sample size = 70

Reproducible workflow packages

Respondents were asked whether they use continuous integration technologies. As above, respondents were provided with examples of what those might be:

GitHub actions
Jenkins
Travis

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Use reproducible workflow packages	Count
Yes	3
No	41
I don't know what reproducible workflows are	26
^a Sample size = 70

RAP knowledge and opinions

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.

Knowledge of RAP

We asked our respondents whether they had heard of RAP and what their knowledge is of their own department RAP champion.

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RAP champion knowledge	Count
Have not heard of RAP	6
Heard of RAP, have not heard of RAP champions	15
Heard of RAP, does not know department champion	27
Heard of RAP champions, no champion in department	1
Knows department RAP champion	23
^a Sample size = 76

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Line manage anyone who writes codes	count
Yes	34
No	7
I don't line manage anyone	35
^a Sample size = 76

Opinions on RAP

We asked respondents who had heard of RAP whether they agreed with a series of statements.

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	Percent
Question	Strongly disagree	Disagree	Neutral	Agree	Strongly agree
I feel confident implementing RAP in my work	11.4	21.4	11.4	32.9	22.9
I feel supported to implement RAP in my work	5.7	15.7	22.9	32.9	22.9
I know where to find resources to help me implement RAP	7.1	10.0	15.7	45.7	21.4
I understand what the key components of the RAP methodology are	12.9	12.9	17.1	32.9	24.3
I think it is important to implement RAP in my work	7.1	7.1	14.3	34.3	37.1
I and/or my team are currently implementing RAP	20.0	15.7	14.3	24.3	25.7
I or my team are planning on implementing RAP in the next 12 months	18.6	12.9	12.9	30.0	25.7
^a Sample size = 70

RAP scores

RAP component scores “regularly” or “all the time” to the relevant questions. For documentation, this includes both code comments and README files. For the continuous integration and dependency management components we only collected “yes”, “no” or “I don’t understand the question” responses. As such, we gave “yes” responses a score of 1. The sum total of each respondent’s scores is presented here as “RAP scores”.

A score of one for each RAP component is derived where respondents answered “regularly” or “all the time” to the relevant questions. For documentation, this includes both code comments and README files. For the continuous integration and dependency management components we only collected “yes”, “no” or “I don’t understand the question” responses. As such, we gave “yes” responses a score of 1. The sum total of each respondent’s scores is presented here as “RAP scores”. “Basic components” are the components which make up the RAP MVP. “Advanced components” are components that help improve reproducibility, but were are considered part of the minimum standard.

RAP components

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Component	Type	Count
AQUA book guidance	Basic	26
Documentation	Basic	40
Peer review	Basic	54
Team open source code	Basic	24
Use open source software	Basic	51
Version control	Basic	53
Code packages	Advanced	12
Continuous integration	Advanced	27
Dependency management	Advanced	39
Follow code style guidelines	Advanced	38
Function documentation	Advanced	31
Functions	Advanced	49
Unit testing	Advanced	17
^a Sample size = 70

Basic RAP scores

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Basic RAP score	Count
0	5
1	7
2	2
3	17
4	17
5	14
6	8
^a Sample size = 70

Advanced RAP scores

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Advanced RAP score	Count
0	9
1	10
2	14
3	8
4	11
5	6
6	7
7	5
^a Sample size = 70

Department profile: Ministry of Justice (excl. agencies)

Coding frequency and tools