Resources
This carefully curated collection of resources will help you find packages and learning resources to help you on your R journey.
Chat with Large Language Models • {ellmer}
The 'ellmer' package facilitates the use of large language models (LLMs) directly from R. It provides access to multiple LLM providers and features like streaming outputs and structured data extraction. 'ellmer' supports models such as Anthropic's Claude, AWS Bedrock, and OpenAI's GPT, among others. The package offers interactive and programmatic ways to converse with models, maintaining the conversation state, which is useful for building on previous interactions. 'ellmer' is practical for both organizational and personal use, accommodating various IT restrictions and preferences.
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Data Science for the Biomedical Sciences
Data Science for the Biomedical Sciences is a book that provides an introduction to data science concepts and tools specifically tailored for the biomedical sciences. It covers topics such as spreadsheets, R and RStudio, data loading, descriptive calculations, data cleaning, visualization, analysis, working with multiple datasets, APIs, functions, survival analysis, machine learning, and more.
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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 Major League Teams use R to Analyze Baseball Data
Keith Woolner, on September 27, 2023, delivers a presentation showcasing how Major League Baseball teams utilize the R programming language to perform data analysis on baseball statistics. The video, available on YouTube, dives into methodologies and tools used within the industry to crunch numbers and derive insights that can potentially give teams a competitive edge. It touches upon predictive modeling, player performance evaluation, and related statistical techniques, evidencing R's pivotal role in sports analytics and data-driven decision-making in professional baseball.
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Posit AI Newsletter
Posit's blog for August 29, 2025, announces the publication of an AI newsletter curated by Sara Altman and Simon Couch, previously internal, now available biweekly. The newsletter discusses significant AI developments including environmental reports on LLMs by Mistral AI and Google, and introduces Positron Assistant and Databot for R/Python coding and data analysis. It raises awareness about the energy demands of AI during training and inference stages, emphasizes responsible AI tool usage, and shares external insights and resources on AI advancements and security vulnerabilities with the data science community.
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resouRces
This content encompasses a comprehensive list of R-related educational materials, packages, tutorials, and datasets with projected dates ranging up to the year 2025. It includes various titles that focus on learning R programming, data analysis, data visualization, geospatial mapping, and statistical methods. Significant emphasis is placed on resources for learning R, such as introductions to R, books, courses, and video tutorials. Additionally, specific packages for data wrangling, statistical modeling, and visualization are mentioned, indicating the evolution and specialization of R's ecosystem to cater to diverse data science needs.
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Welcome to ModernDive (v2) | Statistical Inference via Data Science
ModernDive (v2) is the website for 'Statistical Inference via Data Science: A ModernDive into R and the Tidyverse (Second Edition)'. It showcases updates from the first edition, which is available online and for purchase. The book, authored by Chester Ismay, Albert Y. Kim, and Arturo Valdivia, teaches R and data science concepts. It's scheduled for print by CRC Press in 2025 and is licensed under Creative Commons. Readers can contribute on GitHub and anticipate a resource-rich approach to stats with a focus on tidyverse tools for data analysis.
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