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	16
Rarely	16
Sometimes	49
Regularly	88
All the time	61
^a Sample size = 230

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	192	30
Data cleaning	172	25
Data linking	143	15
Data transfer	81	21
Data visualisation	147	62
Machine learning	34	2
Modelling	79	19
^a Sample size = 230

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#	11	133	86
Java / Scala	11	135	84
Javascript / Typescript	21	127	82
Matlab	11	121	98
Python	144	44	42
R	202	13	15
SAS	109	75	46
SPSS	61	99	70
SQL	143	62	25
Stata	38	123	69
VBA	98	100	32
^a Sample size = 230

Knowledge of coding tools

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Programming language	Yes	Don't Know	No
C++ / C#	10	14	206
Java / Scala	8	13	209
Javascript / Typescript	14	13	203
Matlab	35	17	178
Python	105	15	110
R	158	9	63
SAS	74	8	148
SPSS	74	15	141
SQL	125	6	99
Stata	39	15	176
VBA	54	16	160
^a Sample size = 230

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#	8	3	7
Java / Scala	10	1	7
Javascript / Typescript	12	9	5
Matlab	9	2	33
Python	59	85	20
R	50	152	6
SAS	44	65	9
SPSS	34	27	47
SQL	43	100	25
Stata	20	18	21
VBA	51	47	7
^a Sample size = 230

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	7
Slightly worse	14
No change	27
Slightly better	54
Significantly better	72
^a Sample size = 174

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	56
In education	105
In private sector employment	8
In public sector employment	14
Self-taught	31
Other	6
^a Sample size = 222

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	12.6	13.6	11.2	11.7	27.1	23.8
My team open sources its code	16.8	46.7	15.4	8.9	7.5	4.7
I use a source code version control system e.g. Git	6.1	35.5	16.8	12.1	13.1	16.4
Code my team writes is reviewed by a colleague	1.4	7.5	10.7	25.7	31.3	23.4
I write repetitive elements in my code as functions	4.7	11.2	10.7	24.8	31.3	17.3
I unit test my code	38.3	19.6	9.8	16.8	10.3	5.1
I collect my code and supporting material into packages	14.0	51.9	13.6	11.7	6.1	2.8
I follow a standard directory structure when programming	24.3	15.0	7.9	19.2	24.3	9.3
I follow coding guidelines or style guides when programming	7.5	9.8	7.9	28.0	37.4	9.3
I write code to automatically quality assure data	7.9	28.0	22.0	25.2	9.8	7.0
My team applies the principles set out in the Aqua book when carrying out analysis as code	48.1	15.9	4.7	13.6	15.0	2.8
^a Sample size = 214

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	5.6	2.8	1.4	9.8	28.5	51.9
Documentation for each function or class	14.5	19.2	14.0	23.4	20.1	8.9
README files	10.3	29.4	14.0	23.4	15.4	7.5
Desk notes	25.7	19.2	7.9	21.0	19.2	7.0
Analytical Quality Assurance (AQA) logs	37.4	27.6	10.7	11.2	8.9	4.2
Data or assumptions registers	28.0	40.7	6.1	7.9	11.2	6.1
Flow charts	9.8	39.3	21.0	16.4	9.3	4.2
^a Sample size = 214

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	31
No	85
I don't know what dependency management is	98
^a Sample size = 214

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	1
No	123
I don't know what reproducible workflows are	90
^a Sample size = 214

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	80
Heard of RAP, have not heard of RAP champions	43
Heard of RAP, does not know department champion	62
Heard of RAP champions, no champion in department	5
Knows department RAP champion	31
^a Sample size = 230

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Line manage anyone who writes codes	count
Yes	24
No	74
I don't line manage anyone	132
^a Sample size = 230

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	19.3	18.0	22.7	29.3	10.7
I feel supported to implement RAP in my work	14.0	14.0	30.0	28.7	13.3
I know where to find resources to help me implement RAP	14.0	18.7	19.3	37.3	10.7
I understand what the key components of the RAP methodology are	14.0	11.3	26.0	39.3	9.3
I think it is important to implement RAP in my work	4.7	2.7	22.7	40.0	30.0
I and/or my team are currently implementing RAP	17.3	18.0	24.7	26.7	13.3
I or my team are planning on implementing RAP in the next 12 months	14.0	11.3	32.0	26.7	16.0
^a Sample size = 150

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	38
Documentation	Basic	48
Peer review	Basic	117
Team open source code	Basic	26
Use open source software	Basic	109
Version control	Basic	63
Code packages	Advanced	19
Continuous integration	Advanced	26
Dependency management	Advanced	31
Follow code style guidelines	Advanced	100
Function documentation	Advanced	62
Functions	Advanced	104
Unit testing	Advanced	33
^a Sample size = 214

Basic RAP scores

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Basic RAP score	Count
0	37
1	54
2	57
3	39
4	20
5	6
6	1
^a Sample size = 214

Advanced RAP scores

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Advanced RAP score	Count
0	47
1	62
2	47
3	31
4	16
5	5
6	5
7	1
^a Sample size = 214

Grade profile: Higher Executive Officer (or equivalent)

Coding frequency and tools