Resources
This carefully curated collection of resources will help you find packages and learning resources to help you on your R journey.
Book announcement R 4 Social Network Analysis
The blog post 'R 4 Social Network Analysis' announces an in-progress book aimed at introducing social network analysis (SNA) in R to practitioners. Authored by schochastics and Termeh Shafie, both of whom have extensive experience in SNA and R package development, the book will cover key SNA topics and demonstrate how to manage network analytical tasks in R. It addresses the scarcity and dispersal of current SNA learning materials and seeks to provide a central, up-to-date source. The book's practical focus is on applying R tools rather than delving into theory, making it suitable for those ready to apply SNA techniques. It is openly written on GitHub using quarto, inviting community feedback through issues.
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Easily download files from the Open Science Framework with Papercheck
The 20% Statistician is a blog focusing on statistics, research methods, and open science. It aims to help researchers understand crucial statistical concepts, claiming that grasping 20% of statistics can improve 80% of inferences. A recent post highlights the challenge of downloading files from the Open Science Framework (OSF). The authors, DeBruine and Lakens, introduced 'Papercheck,' an R package with a function 'osf_file_download' that simplifies this process. Papercheck recreates OSF's folder structure within a local directory, making it user-friendly to access project files for review or reuse.
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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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Modern Data Science with R
Modern Data Science with R is a comprehensive data science textbook that incorporates statistical and computational thinking to solve real-world problems with data. It covers topics such as data wrangling, data visualization, inferential statistics, and more. The book is currently in its 3rd edition and includes updates and changes to reflect the evolving R ecosystem. It also provides instructor resources, reviews, and errata on its website.
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R Workshop: Handling Uncertainty in your Data
This R Workshop titled 'Handling Uncertainty in your Data' is designed to educate participants on managing data uncertainty. Organized by Dr. Mario Reutter and Juli Nagel, and sponsored by IGOR, the workshop spans two afternoons with sessions on R basics, measurement precision theory, and practical techniques for computing confidence intervals and uncertainty visualization in R. Additionally, it provides a general intro to R, data wrangling, and visualization, with a spotlight on translating measurement precision into visual representations.
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Recreate Some SAS® Procedures in R Using {procs}
The R package 'procs' replicates commonly used SAS procedures, targeting functions like PROC FREQ, PROC MEANS, PROC TTEST, and PROC REG. It simplifies the transition for SAS users to R by providing familiar functionality and outputs. This includes rich reporting outputs similar to SAS, pre-validated results to ensure fidelity with SAS outputs, ease of adoption for existing SAS users, and stability to maintain backward compatibility. The package includes data manipulation functions and aims to help save time in statistical results comparison and reporting.
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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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