Publications by rstats | Emily Riederer
Python Rgonomics
Interoperability was a key theme in open-source data languages in 2023. Ongoing innovations in Arrow (a language-agnostic in-memory standard for data storage), growing adoption of Quarto (the language-agnostic heir apparent to R Markdown), and even pandas creator Wes McKinney joining Posit (the language-agnostic rebranding of RStudio) all illust...
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Oh, I’m sure it’s probably nothing
Language interoperability and different ways of enabling “polyglot” workflows have seemed to take centerstage in the data world recently: Apache Arrow promises a language-independent memory format for interoperability, – RStudio its rebranding as Posit to cement their place as a leader in language-agnostic data tooling, RStudio simultaneo...
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RMarkdown Driven Development (RmdDD)
Introduction RMarkdown is an excellent platform for capturing narrative analysis and code to create reproducible reports, blogs, slides, books, and more. One benefit of RMarkdown is its abilities to keep an analyst in the “flow” of their work and to capture their thought process along the way. However, thought processes are rarely linear;...
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Rtistic: A package-by-numbers repo
Last winter, I attended a holiday party at a “paint-and-sip” venue. For those unfamiliar, “paint-and-sip” is a semi-trendy cottage industry offering evenings of music, wine, and a guided painting activity. For example, my group painted sasquatch on a snowy winter’s eve: As often happens, this completely unrelated thing set me thinking ...
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Resource Round-Up: R in Industry Edition
One of the ways that practices of reproducible research can be brought into industry is through the development of custom R packages and data tools for one’s company / organization. Not only can these tools deliver large efficiency gains and standardization, they ideally infuse corporate culture with the shared passion and mission found in open...
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RMarkdown Driven Development: the Technical Appendix
Motivation My initial post on RMarkdown Driven Development focuses on major concepts in the process of evolving a one-time, single-file analysis into a sustainable analytical tool. In the spirit of Etsy’s immutable documentation, I intentionally minimized references to specific tools or packages. After all, software is transient; principles a...
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projmgr: Managing the human dependencies of your projects
Many tools and packages aim to eliminate the pain and uncertainty of technical project management. For example, git, make, Docker, renv, and drake are just a few existing tools that enable collaboration, manage softwatre dependencies, and promote reproducibility. However, there is no analogous gold standard for managing the most time-consuming an...
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RMarkdown CSS Selector Tips
When working with R Markdown’s HTML output type, it’s possible to add a custom style to your output by passing in a CSS style sheet to the YAML header like this: output: html_document: css: "my-style-sheet.css" To use CSS effectively, it’s critical to understand how to specificy which selectors one wishes to style. This requires a mi...
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Resource Round-Up: Latent and Lasting Documentation
The importance of documentation is uncontroversial. For many data and analytical products, documentation is the user interface and key to promoting user success and future reuse. However, when project timelines get tight, too many data products are considered complete without appropriate documentation. Even when these resources initially exist, t...
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A beginner’s guide to Shiny modules
Recently, I argued the case on Twitter that Shiny modules are not an advanced topic and can actually be a great way for novice Shiny developers to start building more complex applications. My Shiny hot take is that modules are **not** an advanced topic. IMHO it’s so much easier and more natural for \#rstats users to write small, modular functi...
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