A First Course in Statistical Programming with R

• Author : W. John Braun,Duncan J. Murdoch
• Publisher : Cambridge University Press
• Release Date : 2007-12-13
• ISBN : 9781139466530

This is the only introduction you'll need to start programming in R, the open-source language that is free to download, and lets you adapt the source code for your own requirements. Co-written by one of the R Core Development Team, and by an established R author, this book comes with real R code that complies with the standards of the language. Unlike other introductory books on the ground-breaking R system, this book emphasizes programming, including the principles that apply to

• Author : W. Braun
• Publisher : Unknown
• Release Date : 2007
• ISBN : OCLC:1137354181

This is the only introduction you'll need to start programming in R, the open-source language that is free to download, and lets you adapt the source code for your own requirements. Co-written by one of the R Core Development Team, and by an established R author, this book comes with real R code that complies with the standards of the language. Unlike other introductory books on the ground-breaking R system, this book emphasizes programming, including the principles that apply to

• Author : Tilman M. Davies
• Publisher : No Starch Press
• Release Date : 2016-07-16
• ISBN : 9781593277796

The Book of R is a comprehensive, beginner-friendly guide to R, the world’s most popular programming language for statistical analysis. Even if you have no programming experience and little more than a grounding in the basics of mathematics, you’ll find everything you need to begin using R effectively for statistical analysis. You’ll start with the basics, like how to handle data and write simple programs, before moving on to more advanced topics, like producing statistical summaries of

• Author : W. John Braun,Duncan J. Murdoch
• Publisher : Cambridge University Press
• Release Date : 2021-05-20
• ISBN : 9781108995146

Get started computing with data. Learn general principles while learning R - now including the tidyverse.

• Author : Norman Matloff
• Publisher : No Starch Press
• Release Date : 2011-10-11
• ISBN : 9781593273842

R is the world's most popular language for developing statistical software: Archaeologists use it to track the spread of ancient civilizations, drug companies use it to discover which medications are safe and effective, and actuaries use it to assess financial risks and keep economies running smoothly. 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

• Author : Mark Gardener
• Publisher : John Wiley & Sons
• Release Date : 2012-05-24
• ISBN : 9781118239377

Conquer the complexities of this open source statistical language R is fast becoming the de facto standard for statistical computing and analysis in science, business, engineering, and related fields. This book examines this complex language using simple statistical examples, showing how R operates in a user-friendly context. Both students and workers in fields that require extensive statistical analysis will find this book helpful as they learn to use R for simple summary statistics, hypothesis testing, creating graphs, regression, and much

• Author : Hadley Wickham,Garrett Grolemund
• Publisher : "O'Reilly Media, Inc."
• Release Date : 2016-12-12
• ISBN : 9781491910368

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,

• Author : Michael J. Crawley
• Publisher : John Wiley & Sons
• Release Date : 2007-06-13
• ISBN : 0470515066

The high-level language of R is recognized as one of the most powerful and flexible statistical software environments, and is rapidly becoming the standard setting for quantitative analysis, statistics and graphics. R provides free access to unrivalled coverage and cutting-edge applications, enabling the user to apply numerous statistical methods ranging from simple regression to time series or multivariate analysis. Building on the success of the author’s bestselling Statistics: An Introduction using R, The R Book is packed with worked

• Author : Daniel Navarro
• Publisher : Lulu.com
• Release Date : 2022-05-29
• ISBN : 9781326189723

• Author : Gareth James,Daniela Witten,Trevor Hastie,Robert Tibshirani
• Publisher : Springer Science & Business Media
• Release Date : 2013-06-24
• ISBN : 9781461471387

An Introduction to Statistical Learning 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. This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, and more.

• Author : Benjamin S. Baumer,Daniel T. Kaplan,Nicholas J. Horton
• Publisher : CRC Press
• Release Date : 2021-03-31
• ISBN : 9780429575396

From a review of the first edition: "Modern Data Science with R... is rich with examples and is guided by a strong narrative voice. What’s more, it presents an organizing framework that makes a convincing argument that data science is a course distinct from applied statistics" (The American Statistician). Modern Data Science with R is a comprehensive data science textbook for undergraduates that incorporates statistical and computational thinking to solve real-world data problems. Rather than focus exclusively on case

• Author : Darrin Speegle,Bryan Clair
• Publisher : CRC Press
• Release Date : 2021-11-26
• ISBN : 9781000504514

This book is a fresh approach to a calculus based, first course in probability and statistics, using R throughout to give a central role to data and simulation. The book introduces probability with Monte Carlo simulation as an essential tool. Simulation makes challenging probability questions quickly accessible and easily understandable. Mathematical approaches are included, using calculus when appropriate, but are always connected to experimental computations. Using R and simulation gives a nuanced understanding of statistical inference. The impact of departure

• Author : Quan Li
• Publisher : Oxford University Press
• Release Date : 2018-05-09
• ISBN : 9780190656232

Statistical analysis is common in the social sciences, and among the more popular programs is R. This book provides a foundation for undergraduate and graduate students in the social sciences on how to use R to manage, visualize, and analyze data. The focus is on how to address substantive questions with data analysis and replicate published findings. Using R for Data Analysis in Social Sciences adopts a minimalist approach and covers only the most important functions and skills in R

• Author : Robert Knell
• Publisher : Robert Knell
• Release Date : 2014-05-14
• ISBN : 9780957597112

R is now the most widely used statistical software in academic science and it is rapidly expanding into other fields such as finance. R is almost limitlessly flexible and powerful, hence its appeal, but can be very difficult for the novice user. There are no easy pull-down menus, error messages are often cryptic and simple tasks like importing your data or exporting a graph can be difficult and frustrating. Introductory R is written for the novice user who knows a

• Author : Maria L. Rizzo
• Publisher : CRC Press
• Release Date : 2007-11-15
• ISBN : 9781420010718

Computational statistics and statistical computing are two areas that employ computational, graphical, and numerical approaches to solve statistical problems, making the versatile R language an ideal computing environment for these fields. One of the first books on these topics to feature R, Statistical Computing with R covers the traditiona