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What are the best ways to ensure the reliability of your data visualization code?

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Data visualization is a powerful way to communicate insights, trends, and patterns from complex data sets. However, it also requires careful attention to the quality and accuracy of your code, especially if you are using libraries, frameworks, or tools that automate some aspects of the process. In this article, you will learn some best practices to ensure the reliability of your data visualization code and avoid common pitfalls that can compromise your results.

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1 Test your code

One of the most basic and essential ways to ensure the reliability of your data visualization code is to test it regularly and thoroughly. Testing can help you catch errors, bugs, inconsistencies, or anomalies in your code before they affect your output. You can use different types of testing, such as unit testing, integration testing, or regression testing, depending on the scope and complexity of your code. You can also use tools and frameworks that facilitate testing, such as pytest, unittest, or nose for Python, or jest, mocha, or chai for JavaScript.

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Leandro Araque

Ayudo a profesionales a entender sus datos| Harvard CORe | LinkedIn Community Top Voice
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Testing is the backbone of reliable data visualization code. While the article covers the basics well, it's worth emphasizing the importance of automated testing in a CI/CD (Continuous Integration/Continuous Deployment) environment. This ensures that any changes to the code are automatically tested before they are deployed, reducing the risk of introducing errors. For complex visualizations, I recommend not just unit and integration tests but also visual regression tests. These tests capture screenshots of the visualizations and compare them to previous versions, flagging any unintended changes. Tools like Percy or Applitools can be integrated into your CI/CD pipeline for this purpose.

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2 Validate your data

Another important way to ensure the reliability of your data visualization code is to validate your data sources and inputs. Data validation can help you ensure that your data is complete, consistent, accurate, and relevant for your visualization goals. You can use different methods and techniques to validate your data, such as checking for missing values, outliers, duplicates, or errors, applying data cleaning and transformation procedures, or using data quality tools and frameworks, such as pandas, numpy, or scikit-learn for Python, or d3, lodash, or crossfilter for JavaScript.

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Leandro Araque

Ayudo a profesionales a entender sus datos| Harvard CORe | LinkedIn Community Top Voice
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One thing I'd like to add is the concept of "Data Contracts" between different stages of your data pipeline. These contracts define what kind of data is expected, its format, and other properties. Violations of the contract can be flagged automatically, making it easier to catch issues before they propagate through the system. For instance, if a data source starts sending null values for a field that should always be populated, the data contract would catch this inconsistency immediately. Tools like Great Expectations or Deequ can help enforce these contracts and maintain data quality.

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3 Document your code

A third way to ensure the reliability of your data visualization code is to document your code properly and clearly. Documentation can help you and others understand the logic, structure, and functionality of your code, as well as the assumptions, limitations, and dependencies of your data visualization project. You can use different formats and styles to document your code, such as comments, docstrings, or markdown files, depending on the conventions and preferences of your language and environment. You can also use tools and frameworks that generate documentation automatically, such as Sphinx, Doxygen, or JSDoc.

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4 Review your code

A fourth way to ensure the reliability of your data visualization code is to review your code periodically and critically. Code review can help you improve the quality, readability, and maintainability of your code, as well as identify and fix potential issues, errors, or vulnerabilities. You can use different approaches and strategies to review your code, such as self-review, peer-review, or code analysis tools, depending on the context and complexity of your code. You can also use tools and frameworks that facilitate code review, such as GitHub, GitLab, or CodeClimate.

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5 Refactor your code

A fifth way to ensure the reliability of your data visualization code is to refactor your code when necessary and appropriate. Refactoring can help you optimize the performance, efficiency, and scalability of your code, as well as eliminate redundancy, complexity, or technical debt. You can use different principles and practices to refactor your code, such as following the DRY (Don't Repeat Yourself), KISS (Keep It Simple, Stupid), or YAGNI (You Ain't Gonna Need It) rules, applying design patterns, or using code smells as indicators. You can also use tools and frameworks that support refactoring, such as PyCharm, VS Code, or WebStorm.

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6 Update your code

A sixth and final way to ensure the reliability of your data visualization code is to update your code regularly and responsibly. Updating can help you keep your code compatible, secure, and up-to-date with the latest standards, features, and best practices of your data visualization tools and libraries. You can use different methods and techniques to update your code, such as using version control systems, package managers, or dependency managers, depending on the requirements and specifications of your code. You can also use tools and frameworks that automate or simplify updating, such as pip, npm, or yarn.

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Leandro Araque

Ayudo a profesionales a entender sus datos| Harvard CORe | LinkedIn Community Top Voice
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Regularly updating your code is crucial for maintaining its reliability, but it's equally important to have a rollback strategy. In my experience, every update, no matter how minor, carries a risk of introducing new bugs or compatibility issues. Therefore, having a robust version control system and rollback strategy can be a lifesaver when an update goes awry. Automated testing should be run immediately after each update to ensure that the existing functionalities are not broken. Additionally, using containerization tools like Docker can help encapsulate the environment, making it easier to manage dependencies and roll back to previous versions if needed.

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7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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Leandro Araque

Ayudo a profesionales a entender sus datos| Harvard CORe | LinkedIn Community Top Voice
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Something else to consider is the human factor in data visualization code reliability. Code is often shared, modified, and maintained by multiple team members. Therefore, it's crucial to foster a culture of code quality within the team. Peer reviews, paired programming, and regular team meetings to discuss best practices can go a long way in ensuring code reliability. Also, don't underestimate the value of mentorship for junior team members. A well-guided team is far more likely to produce reliable, high-quality code.

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What are the best ways to ensure the reliability of your data visualization code?

1

2

3

4

5

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7

1 Test your code

2 Validate your data

3 Document your code

4 Review your code

5 Refactor your code

6 Update your code

7 Here’s what else to consider

Data Visualization

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What are the best ways to ensure the reliability of your data visualization code?

1

2

3

4

5

6

7

1 Test your code

2 Validate your data

3 Document your code

4 Review your code

5 Refactor your code

6 Update your code

7 Here’s what else to consider

Data Visualization

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