Richard Cotton is a Senior Data Evangelist with 15 years of experience turning statistical rigor and software craftsmanship into accessible learning for data practitioners. Based in the NYC area, he leads content and training at DataCamp—producing the DataFramed podcast, webinars, cheat sheets, and curriculum that scale practical R and SQL skills across thousands of learners. A prolific open-source contributor, Richard has improved core R tooling such as sparklyr, ggplot2 tests, and knitr hooks, blending backend development with test automation to strengthen data workflows. His background spans academia, consulting, and biotech proteomics, where he built reproducible pipelines and taught R to researchers—an uncommon mix that informs his pragmatic approach to pedagogy. Known for making complex analyses approachable, he quietly combines deep domain knowledge with a talent for clear technical communication.
15 years of coding experience
12 years of employment as a software developer
Master of Research (MRes) Environmental Mathematics, Master of Research (MRes) Environmental Mathematics at University of York
Master of Mathematics (MMath) Mathematics, Master of Mathematics (MMath) Mathematics at University of Warwick
Contributions:7 commits, 3 comments, 4 issues in 1 year 1 month
Contributions summary:Richard primarily contributed to the testing aspect of the `ggplot2` library. Their commits focused on creating and modifying unit tests for the `sanitise_dim` function, ensuring the function behaved as expected across various inputs, including edge cases and non-numeric values. The user's work improved the robustness and reliability of the `ggplot2` package by adding comprehensive test coverage. They also formatted test files to adhere to the project's style guide.
A general-purpose tool for dynamic report generation in R
Role in this project:
Back-end Developer
Contributions:10 commits in 6 months
Contributions summary:Richard contributed to the `knitr` package by implementing and refining hooks for different output formats, including AsciiDoc and Textile. They introduced new patterns and customized code rendering, such as adding inline code and chunk handling. These changes involved modifying existing functions and structures within the package's core functionalities to support dynamic report generation in various formats. They also updated existing hooks with fixes.
literate-programmingr-packagereportknitrrstats
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