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- Title: The Art of R Programming: A Tour of Statistical Software Design
- Author(s) Norman Matloff
- Publisher: No Starch Press; 1 edition (October 12, 2011); eBook (Internet Archive Edition, 2009)
- Paperback: 404 pages
- eBook: PDF
- Language: English
- ISBN-10: 1593273843
- ISBN-13: 978-1593273842
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The Art of R Programming takes you on a guided tour of software development with R, from basic types and data structures to advanced topics like closures, recursion, and anonymous functions. No statistical knowledge is required, and your programming skills can range from hobbyist to pro.
Along the way, you'll learn about functional and object-oriented programming, running mathematical simulations, and rearranging complex data into simpler, more useful formats.
Whether you're designing aircraft, forecasting the weather, or you just need to tame your data, The Art of R Programming is your guide to harnessing the power of statistical computing.
About the Authors- Norman Matloff, Ph.D., is a Professor of Computer Science at the University of California, Davis. He is the creator of several popular software packages, as well as a number of widely-used Web tutorials on computer topics.
- The R Programming Language
- Statistics, and SAS Programming
- Data Analysis and Data Mining
- Geographic Information System (GIS) and Web Mapping
- The Art of R Programming: A Tour of Statistical Software Design (Norman Matloff)
- The Mirror Site (1) - PDF (404 pages)
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R for Data Science: Visualize, Model, Transform, Tidy, Import
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Mastering Software Development in R (Roger D. Peng, et al.)
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Tidy Modeling with R: A Framework for Modeling in the Tidyverse
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Geocomputation with R (Robin Lovelace, et al.)
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Hands-On Programming with R: Functions and Simulations
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R Programming for Data Science (Roger D. Peng)
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R Graphics Cookbook: Practical Recipes for Visualizing Data
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An Introduction to R (Alex Douglas, et al.)
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Advanced R, Second Edition (Hadley Wickham)
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Advanced R Solutions (Malte Grosser, et al)
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R Packages: Organize, Test, Document, and Share Your Code
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Forecasting, Principles and Practice, Using R (R. J. Hyndman)
This textbook provides a comprehensive introduction to forecasting methods and presents enough information about each method for readers to use them sensibly. Examples use R with many data sets taken from the authors' own consulting experience.
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Introduction to Data Science: Data Analysis and Algorithms with R
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Regression Models for Data Science in R (Brian Caffo)
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Efficient R Programming: Practical Guide to Smarter Programming
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Cookbook for R: Best R Programming TIPs (Winston Chang)
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Advanced R (Florian Prive)
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R Markdown: The Definitive Guide (Yihui Xie, et al)
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Hyperparameter Tuning for Machine and Deep Learning with R
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Introduction to Statistical Learning: with Applications in R
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An Introduction to Bayesian Thinking using R
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