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Collaborative Data Science Practices
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Table of contents
1
Introduction
2
Coding Principles
3
Architecture Principles
4
Prototypical R File
5
Prototypical SQL File
6
Prototypical Repository
7
Data at Rest
8
Patterns
9
Security & Private Data
10
Automation & Reproducibility
11
Scaling Up
12
Parallel Collaboration
13
Documentation
14
Style Guide
15
Publishing Results
16
Validation
17
Testing
18
Troubleshooting and Debugging
19
Workstation
20
Considerations when Selecting Tools
21
Growing a Team
22
Material for REDCap Users
23
Material for REDCap Developers
24
Material for REDCap Admins
Appendix
A
Git & GitHub
B
Regular Expressions
C
Snippets
D
Presentations
E
Scratch Pad of Loose Ideas
F
Example Dashboard
G
Example Chapter
H
Acknowledgements
I
References
View book source
3
Architecture Principles
3.1
Encapsulation
3.2
Leverage team member’s strengths & avoid weaknesses
3.2.1
Focused code files
3.2.2
Metadata for content experts
3.3
Scales
3.3.1
Single source & single analysis
3.3.2
Multiple sources & multiple analyses
3.4
Consistency
3.4.1
Across Files
3.4.2
Across Languages
3.4.3
Across Projects
2
Coding Principles
4
Prototypical R File
On this page
3
Architecture Principles
3.1
Encapsulation
3.2
Leverage team member’s strengths & avoid weaknesses
3.2.1
Focused code files
3.2.2
Metadata for content experts
3.3
Scales
3.3.1
Single source & single analysis
3.3.2
Multiple sources & multiple analyses
3.4
Consistency
3.4.1
Across Files
3.4.2
Across Languages
3.4.3
Across Projects
View source
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