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A must-have resource to get you primed for testing R packages interfacing with the web, how ggblend taps into new compositing functionality for clearer plots, and how R stacks up with Excel in handling dates.
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Introducing the new ggflowchart package, how a dockerized development environment is another win for reproducibility, and our take on Colin Fay's keynote from the Appsilon Shiny Conference.
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Why effective code reviews can bring many benefits to data science teams, the origin story of the sketch package to transpile R code to JavaScript, and a primer on error handling in both R and Python.
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A few strict checks offered in R 4.3.0, measuring and writing performant code in the Tidyverse, and a please for indenting your code with (more) spaces.
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Using development containers to bootstrap a reproducible R and Quarto environment, a comprehensive approach to extending the data frame class, and plotting your own universe of labels with ggsolar.
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A data-driven look at package loading annotations in R scripts, a fit-for-purpose package that makes a large contribution to the global R ecosystem, and a collection of amazing showcases of webR in action that is paving the way for continued innovation.
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Ten unique ways to create your own Web APIs in R, and how you can import local and remote data files in CSV and (yes) Excel formats with a selection of innovative R packages.
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A new perspective on the value of base R functions, enhancing the capabilities of gpttools with vector databases, and three ways you can add alt text to your R-based visualizations.
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The future of running R in your web browser is here with webR 0.1, a demonstration of integrating Quarto and webR with immense potential in the space of reproducible analysis, and two fundamental techniques from the world of software development tailored to non-programmers.
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A episode full of discovery in this week's edition of R-Weekly Highlights! How you can parse your own R code with parse and getParseData, a closer look at the search capabilities in R-Universe, and a look back at the key milestones in the history of the R language.
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