Resources
This carefully curated collection of resources will help you find packages and learning resources to help you on your R journey.
Creating template files with R
Nicola Rennie's blog post teaches readers how to save time when dealing with repetitive tasks by creating template files with R. The post explains fine-tuning R scripts for tasks like #TidyTuesday, where similar sections are involved each week. Instead of copying and pasting scripts and GitHub README files weekly and updating parts manually, Rennie introduces a method for generating template files and folders based on a date argument. This process includes creating organized directories and template files, replete with content placeholders, which can then be customized for the specific week's work.
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Duplicating Quarto elements with code templates to reduce copy and paste errors
This blog post from the Water Data For The Nation Blog demonstrates how to use Quarto code templates to create reproducible Quarto documents, such as reports and slideshows, with fewer errors. Using custom templates allows for the easy replication of code chunks, such as those producing statistical summaries or visualizations for different datasets. The example used is USGS streamgage data, with a focus on automating the creation of slideshows in Quarto's markdown format. Advanced topics like adding columns, tables, and speaker notes to PowerPoint slides via Quarto are also covered. Methods for iterating over data in a more efficient and less error-prone way than traditional copy and paste techniques are highlighted.
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Efficiency and Consistency: Automate Subset Graphics with ggplot2 and purrr - Cédric Scherer
Efficiency and Consistency: Automate Subset Graphics with ggplot2 and purrr is a blog post by Cédric Scherer that discusses automated plot generation with ggplot2. The post explains how to create a set of explorative or explanatory charts for different variables or categories of a dataset using a functional programming approach. It provides examples and tips for working with variables and explores data sets visually.
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Four ways to write assertion checks in R
This content provides a personal narrative detailing the importance of writing assertion checks in R, particularly when dealing with data that can change structure over time. The author shares a transformation from a confident young analyst to one who has learned to be cautious and employ defensive programming techniques. The focus is on the 'identifier' function, illustrating the need for assertions with 'stopifnot()' to handle unexpected and incorrect inputs. The article emphasizes rigorous validation of assumptions to prevent silent errors in code.
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Helpers for Automatic Translation of Markdown-based Content • babeldown
Helpers for Automatic Translation of Markdown-based Content
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How (and Why) I came to Use R for Data Analysis and Evaluation
Alberto Espinoza recounts his journey with R for data analysis and evaluation, marking his 10-year experience since first encountering R during his graduate assistantship. Initially clueless about R, he was tasked with assisting and leading statistics labs using R. Despite early challenges and a steep learning curve, he recognized R's power over software like SPSS or Excel. His continued use of R spanned graduate projects, market research, data preparation for Tableau, and Survey Monkey analysis. Espinoza outlines R's advantages: reproducibility, efficiency, clarity, and an extensive package ecosystem, underlining R's significance in his professional growth.
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How I’m using Claude Code to write R code
Simon P. Couch discusses his experiences using Claude Code to write R code, including initial impressions, subsequent reduced usage, and recent enhancements leading to more effective use. He highlights the importance of providing the model with adequate context to understand the project, through techniques such as creating a CLAUDE.md file with instructions and project-specific information. Couch details how incorporating the Model Context Protocol, slash commands, and external documentation improved his workflow. By facilitating model access to R package documentation and setting up an MCP server, he can now use Claude Code more efficiently for coding assistance.
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How to Build and Automate Serverless Dashboards with R or Python
Albert Rapp walks through how to build serverless dashboards in R or Python and then automate their deployment using GitHub Actions. The video covers how to set up a dashboard that runs without a server, and then how to configure a GitHub Actions workflow so the dashboard redeploys automatically whenever you push changes to your repository. It’s a practical guide for anyone who wants to get a Quarto dashboard online without the overhead of managing a server—and without having to remember to redeploy manually every time something changes.
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How to make your data analysis life easier using Positron, Raycast, and Espanso
Andrew Heiss discusses the Positron settings and tools he leverages to streamline his data analysis workflow. He shares a highlight of his favorite Positron extensions, such as Air, Better Comments, and Rainbow CSV, and delves into his custom settings for Positron, including editor preferences like font and whitespace handling, version control with Git signing, and various settings to improve the user interface and functionality. Andrew's tips aim to help others in the data science field optimize their setup for efficiency and ease of use.
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How to open files, folders, websites in R
Maëlle's R Blog post discusses best practices for using R functions and tools to open files, folders, and websites efficiently. It emphasizes streamlining the process to quickly begin work on projects. The post describes functions like utils::file.edit(), usethis::use_r(), and usethis::use_test(), and suggests keyboard-based workflows in RStudio IDE and Positron to navigate projects effectively. It acknowledges inputs and reviews from Hannah Frick and discusses using the command line and configuring PATH for project launching.
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I wrote this talk with an LLM
In this keynote presentation from useR! 2025, Hadley Wickham explores how AI can augment rather than replace data science workflows. As the Chief Scientist at Posit and creator of the tidyverse, Wickham shares insights on the evolving relationship between humans and AI in data analysis. The talk demonstrates practical ways that large language models can enhance productivity while maintaining the critical thinking and domain expertise that data scientists bring to their work.
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Introducing Databot: An AI assistant for exploratory data analysis
Databot is an AI-powered assistant developed by Posit to augment the exploratory data analysis (EDA) capabilities of data scientists who use Python or R. This ambitious application of large language models (LLMs) aims to fast-track the EDA process, which conventionally takes hours, down to just minutes. Unlike autonomous or sandbox-constrained AI agents, Databot works interactively in a highly collaborative 'pair programming' style, engaging the user with rapid code-writing, execution, and analysis. It employs a cycle termed the 'WEAR loop' to ensure insights are reliable, serendipitous, and transparent. Databot remains a research preview exclusively available for Positron users.
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