R Programming Language Pdf Free Download
The R Programming Language
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Advanced R (Florian Prive)
This book aims at giving a wide understanding of many aspects of R. Combining detailed explanations with real-world examples and exercises, this book will provide you with a solid understanding of both statistics and the depth of R's functionality.
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Applied Statistics with R (David Dalpiaz)
This book provides an integrated treatment of statistical inference techniques in data science using the R Statistical Software. It provides a much-needed, easy-to-follow introduction to statistics and the R programming language.
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Advanced R, Second Edition (Hadley Wickham)
This book helps you understand how R works at a fundamental level. Designed for R programmers who want to deepen their understanding of the language, and programmers experienced in other languages to understand what makes R different and special.
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An Introduction to Statistical Learning: with Applications in R
It provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance to marketing to astrophysics in the past twenty years.
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An Introduction to Bayesian Thinking using R (Merlise Clyde)
This book provides an introduction to Bayesian inference in decision making without requiring calculus. It may be used on its own as an open-access introduction to Bayesian inference using R for anyone interested in learning about Bayesian statistics.
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Linear Regression Using R: An Introduction to Data Modeling
This book presents one of the fundamental data modeling techniques in an informal tutorial style. Learn how to predict system outputs from measured data using a detailed step-by-step process to develop, train, and test reliable regression models.
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Cookbook for R: Best R Programming TIPs (Winston Chang)
The goal of this cookbook is to provide solutions to common tasks and problems in analyzing data. Each recipe tackles a specific problem with a solution you can apply to your own project, and includes a discussion of how and why the recipe works.
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R Packages: Organize, Test, Document, and Share Your Code
Turn your R code into packages that others can easily download and use. This practical book shows you how to bundle reusable R functions, sample data, and documentation together by applying author's package development philosophy.
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R for Data Science: Visualize, Model, Transform, Tidy, & Import
This book teaches you how to do data science with R: You'll learn how to get your data into R, get it into the most useful structure, transform it, visualize it and model it, how data science can help you work with the uncertainty and capture the opportunities.
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Learning Statistics with R (Daniel Navarro)
This book 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.
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Text Mining with R: A Tidy Approach (Julia Silge, et al)
You'll explore text-mining techniques with tidytext, a package that authors developed using the tidy principles behind R packages like ggraph and dplyr. You'll learn how tidytext and other tidy tools in R can make text analysis easier and more effective.
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Advanced R Programming (Hadley Wickham)
This book presents useful tools and techniques for attacking many types of R programming problems. It not only helps current R users become R programmers but also shows existing programmers what's special about R.
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R for Beginners (Sasha D. Hafner)
The objective of this book is to introduce participants to the use of R for data manipulation and analysis. It is intented for individuals with little or no prior experience in R. The topics that are covered are the most important for getting started with R.
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The R Inferno (Patrick Burns)
This book is an essential guide to the trouble spots and oddities of R. In spite of the quirks exposed here, R is the best computing environment for most data analysis tasks. If you are using spreadsheets to understand data, switch to R.
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Exploratory Data Analysis with R (Roger D. Peng)
This book covers the essential exploratory techniques for summarizing data with R. These techniques are typically applied before formal modeling commences and can help inform the development of more complex statistical models.
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Statistical Foundations of Machine Learning using R
This book aims to present the statistical foundations of machine learning intended as the discipline which deals with the automatic design of models from data. All the examples are implemented in the statistical programming language R.
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R Succinctly (Barton Poulson)
Begin developing your mastery of the powerful R programming language. Become comfortable with the R environment and learn how to find ways for R to fulfill your data needs.
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Applied Spatial Data Analysis with R (Roger S. Bivand, et al)
It presents R packages, functions, classes and methods for handling spatial data, and showcases more specialised kinds of spatial data analysis, including spatial point pattern analysis, interpolation and geostatistics, areal data analysis and disease mapping.
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Exploring Data Science (Nina Zumel, et al)
This book introduces readers to various areas in data science and explains which methodologies work best for each, with practical examples in R, Python, and other languages.
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