Efficient Workflow With Rstudio
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Efficient Workflow with RStudio
"Efficient Workflow with RStudio" "Efficient Workflow with RStudio" is a comprehensive guide designed for data professionals, analysts, and developers seeking to optimize their productivity and streamline complex analyses in RStudio. Spanning the full spectrum of modern workflow topics, the book explores the intricacies of mastering the RStudio environment, advanced project management, reproducibility, scalable data operations, and seamless pipeline orchestration. Readers will uncover best practices for customizing their workspaces, managing resources across local and remote environments, and leveraging powerful debugging and diagnostic tools to minimize bottlenecks throughout the analytical lifecycle. The book delves deeply into methods for building robust, collaborative, and reproducible projects by harnessing modern tools such as `renv`, Git integration, automated project bootstrapping, and advanced dependency management. Detailed chapters address the nuances of ingesting, cleaning, transforming, and versioning data at scale, while offering actionable strategies to ensure optimal memory utilization and performance throughout the extract-transform-load (ETL) process. As workflows grow in complexity, readers are equipped to orchestrate automation using packages like `targets` and `drake`, integrate external tools and cloud infrastructure, and enforce reliability via intelligent state management, error recovery, and comprehensive monitoring. Beyond technical best practices, "Efficient Workflow with RStudio" fosters organizational excellence through advanced programming paradigms, interactive visualization, DevOps methodologies, and collaborative documentation. With guidance on scaling analytical products to high-performance computing environments, integrating external languages, and deploying production-ready solutions underpinned by rigorous testing and continuous integration, this book enables teams to deliver transparent, reproducible, and impactful results—empowering organizations to communicate findings and drive innovation with clarity and confidence.
Efficient R Programming
Author: Colin Gillespie
language: en
Publisher: "O'Reilly Media, Inc."
Release Date: 2016-12-08
There are many excellent R resources for visualization, data science, and package development. Hundreds of scattered vignettes, web pages, and forums explain how to use R in particular domains. But little has been written on how to simply make R work effectively—until now. This hands-on book teaches novices and experienced R users how to write efficient R code. Drawing on years of experience teaching R courses, authors Colin Gillespie and Robin Lovelace provide practical advice on a range of topics—from optimizing the set-up of RStudio to leveraging C++—that make this book a useful addition to any R user’s bookshelf. Academics, business users, and programmers from a wide range of backgrounds stand to benefit from the guidance in Efficient R Programming. Get advice for setting up an R programming environment Explore general programming concepts and R coding techniques Understand the ingredients of an efficient R workflow Learn how to efficiently read and write data in R Dive into data carpentry—the vital skill for cleaning raw data Optimize your code with profiling, standard tricks, and other methods Determine your hardware capabilities for handling R computation Maximize the benefits of collaborative R programming Accelerate your transition from R hacker to R programmer
R for Data Science
Author: Hadley Wickham
language: en
Publisher: "O'Reilly Media, Inc."
Release Date: 2016-12-12
Learn how to use R to turn raw data into insight, knowledge, and understanding. This book introduces you to R, RStudio, and the tidyverse, a collection of R packages designed to work together to make data science fast, fluent, and fun. Suitable for readers with no previous programming experience, R for Data Science is designed to get you doing data science as quickly as possible. Authors Hadley Wickham and Garrett Grolemund guide you through the steps of importing, wrangling, exploring, and modeling your data and communicating the results. You'll get a complete, big-picture understanding of the data science cycle, along with basic tools you need to manage the details. Each section of the book is paired with exercises to help you practice what you've learned along the way. You'll learn how to: Wrangle—transform your datasets into a form convenient for analysis Program—learn powerful R tools for solving data problems with greater clarity and ease Explore—examine your data, generate hypotheses, and quickly test them Model—provide a low-dimensional summary that captures true "signals" in your dataset Communicate—learn R Markdown for integrating prose, code, and results