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	33
Rarely	35
Sometimes	44
Regularly	60
All the time	52
^a Sample size = 224

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	168	44
Data cleaning	159	24
Data linking	141	18
Data transfer	82	33
Data visualisation	138	56
Machine learning	63	5
Modelling	105	33
^a Sample size = 224

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#	19	107	98
Java / Scala	33	101	90
Javascript / Typescript	39	102	83
Matlab	11	100	113
Python	159	31	34
R	204	8	12
SAS	106	59	59
SPSS	78	74	72
SQL	160	42	22
Stata	66	85	73
VBA	160	42	22
^a Sample size = 224

Knowledge of coding tools

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Programming language	Yes	Don't Know	No
C++ / C#	31	8	185
Java / Scala	15	4	205
Javascript / Typescript	28	6	190
Matlab	49	7	168
Python	84	6	134
R	149	3	72
SAS	84	6	134
SPSS	72	5	147
SQL	154	4	66
Stata	35	8	181
VBA	92	5	127
^a Sample size = 224

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#	12	7	24
Java / Scala	27	6	9
Javascript / Typescript	20	19	9
Matlab	5	6	43
Python	87	72	12
R	60	144	5
SAS	46	60	24
SPSS	39	39	33
SQL	31	129	25
Stata	45	21	14
VBA	75	85	7
^a Sample size = 224

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	15
Slightly worse	38
No change	44
Slightly better	43
Significantly better	59
^a Sample size = 199

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	15
In education	104
In private sector employment	10
In public sector employment	43
Self-taught	35
Other	1
^a Sample size = 209

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	2.1	14.7	10.5	13.6	24.6	34.6
My team open sources its code	6.8	41.9	20.4	18.8	5.2	6.8
I use a source code version control system e.g. Git	1.6	29.3	9.4	14.7	17.3	27.7
Code my team writes is reviewed by a colleague	0.5	2.6	4.7	24.1	32.5	35.6
I write repetitive elements in my code as functions	2.6	7.3	4.7	23.6	33.5	28.3
I unit test my code	14.7	13.6	16.8	27.2	17.3	10.5
I collect my code and supporting material into packages	5.8	40.8	17.3	23.6	9.4	3.1
I follow a standard directory structure when programming	14.1	12.0	14.7	24.6	24.1	10.5
I follow coding guidelines or style guides when programming	4.2	8.9	11.0	24.6	32.5	18.8
I write code to automatically quality assure data	0.5	16.8	17.3	33.0	20.4	12.0
My team applies the principles set out in the Aqua book when carrying out analysis as code	26.7	13.1	7.9	18.8	22.5	11.0
^a Sample size = 191

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	0.0	3.1	1.6	7.3	26.2	61.8
Documentation for each function or class	5.2	18.8	11.5	19.4	31.4	13.6
README files	0.5	18.3	17.8	20.9	27.2	15.2
Desk notes	17.8	19.4	14.1	24.6	16.8	7.3
Analytical Quality Assurance (AQA) logs	6.8	22.0	14.1	26.7	20.9	9.4
Data or assumptions registers	10.5	31.4	13.6	13.1	22.0	9.4
Flow charts	1.6	24.1	18.8	37.7	14.7	3.1
^a Sample size = 191

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	53
No	87
I don't know what dependency management is	51
^a Sample size = 191

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	9
No	130
I don't know what reproducible workflows are	52
^a Sample size = 191

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	51
Heard of RAP, have not heard of RAP champions	42
Heard of RAP, does not know department champion	54
Heard of RAP champions, no champion in department	8
Knows department RAP champion	54
^a Sample size = 224

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Line manage anyone who writes codes	count
Yes	152
No	36
I don't line manage anyone	36
^a Sample size = 224

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	6.4	16.8	21.4	36.4	19.1
I feel supported to implement RAP in my work	6.9	17.9	23.1	31.8	20.2
I know where to find resources to help me implement RAP	8.7	14.5	16.8	36.4	23.7
I understand what the key components of the RAP methodology are	5.2	13.9	19.7	37.6	23.7
I think it is important to implement RAP in my work	2.9	6.9	18.5	33.5	38.2
I and/or my team are currently implementing RAP	14.5	16.2	20.8	27.2	21.4
I or my team are planning on implementing RAP in the next 12 months	12.7	15.0	22.0	28.3	22.0
^a Sample size = 173

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	64
Documentation	Basic	78
Peer review	Basic	130
Team open source code	Basic	23
Use open source software	Basic	113
Version control	Basic	86
Code packages	Advanced	24
Continuous integration	Advanced	38
Dependency management	Advanced	53
Follow code style guidelines	Advanced	98
Function documentation	Advanced	86
Functions	Advanced	118
Unit testing	Advanced	53
^a Sample size = 191

Basic RAP scores

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Basic RAP score	Count
0	15
1	39
2	43
3	36
4	35
5	17
6	6
^a Sample size = 191

Advanced RAP scores

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Advanced RAP score	Count
0	33
1	46
2	29
3	24
4	27
5	14
6	10
7	8
^a Sample size = 191

Grade profile: Grade 7 (or equivalent)

Coding frequency and tools