Download or read book A Beginner s Guide to R written by Alain Zuur and published by Springer Science & Business Media. This book was released on 2009-06-24 with total page 228 pages. Available in PDF, EPUB and Kindle. Book excerpt: Based on their extensive experience with teaching R and statistics to applied scientists, the authors provide a beginner's guide to R. To avoid the difficulty of teaching R and statistics at the same time, statistical methods are kept to a minimum. The text covers how to download and install R, import and manage data, elementary plotting, an introduction to functions, advanced plotting, and common beginner mistakes. This book contains everything you need to know to get started with R.
Download or read book An Introductory Guide to R written by Eric L. Einspruch and published by Guilford Publications. This book was released on 2022-07-01 with total page 196 pages. Available in PDF, EPUB and Kindle. Book excerpt: A friendly, straightforward guide that does not assume knowledge of programming, this book helps new R users hit the ground running. Eric L. Einspruch provides an overview of the software and shows how to download and install R, RStudio, and R packages. Featuring example code, screenshots, tips, learning exercises, and worked-through examples of statistical techniques, the book demonstrates the capabilities and nuances of these powerful free statistical analysis and data visualization tools. Fundamental aspects of data wrangling, analysis, visualization, and reporting are introduced, using both Base R and Tidyverse approaches. Einspruch emphasizes processes that support research reproducibility, such as use of comments to document R code and use of R Markdown capabilities. The book also helps readers navigate the vast array of R resources available to further develop their skills.
Download or read book Getting Started with SAP R 3 written by Dennis L. Prince and published by Prima Lifestyles. This book was released on 1998 with total page 312 pages. Available in PDF, EPUB and Kindle. Book excerpt: Taking a direct results-oriented approach, this book effectively teaches new users how to work within the R/3 application and how to use SAP functions and when and why to use them.
Download or read book A Survivor s Guide to R written by Kurt Taylor Gaubatz and published by SAGE Publications. This book was released on 2014-04-22 with total page 489 pages. Available in PDF, EPUB and Kindle. Book excerpt: Focusing on developing practical R skills rather than teaching pure statistics, Dr. Kurt Taylor Gaubatz’s A Survivor’s Guide to R provides a gentle yet thorough introduction to R. The book is structured around critical R tasks, and focuses on applied knowledge, rather than abstract concepts. Gaubatz’s easy-to-read approach helps students with little or no background in statistics or programming to develop real-world R skills through straightforward coverage of R objects and functions. Focusing on real-world data, the challenges of dataset construction, and the use of R’s powerful graphing tools, the guide is written in an accessible, sympathetic, even humorous style that ensures students acquire functional R skills they can use in their own projects and carry into their work beyond the classroom.
Download or read book The Book of R written by Tilman M. Davies and published by No Starch Press. This book was released on 2016-07-16 with total page 833 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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 your data and performing statistical tests and modeling. You’ll even learn how to create impressive data visualizations with R’s basic graphics tools and contributed packages, like ggplot2 and ggvis, as well as interactive 3D visualizations using the rgl package. Dozens of hands-on exercises (with downloadable solutions) take you from theory to practice, as you learn: –The fundamentals of programming in R, including how to write data frames, create functions, and use variables, statements, and loops –Statistical concepts like exploratory data analysis, probabilities, hypothesis tests, and regression modeling, and how to execute them in R –How to access R’s thousands of functions, libraries, and data sets –How to draw valid and useful conclusions from your data –How to create publication-quality graphics of your results 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. Make The Book of R your doorway into the growing world of data analysis.
Download or read book R for Data Science written by Hadley Wickham and published by "O'Reilly Media, Inc.". This book was released on 2016-12-12 with total page 521 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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
Download or read book Getting Started with R written by Andrew P. Beckerman and published by Oxford University Press. This book was released on 2017 with total page 251 pages. Available in PDF, EPUB and Kindle. Book excerpt: R is rapidly becoming the standard software for statistical analyses, graphical presentation of data, and programming in the natural, physical, social, and engineering sciences. Getting Started with R is now the go-to introductory guide for biologists wanting to learn how to use R in their research. It teaches readers how to import, explore, graph, and analyse data, while keeping them focused on their ultimate goals: clearly communicating their data in oral presentations, posters, papers, and reports. It provides a consistent workflow for using R that is simple, efficient, reliable, and reproducible. This second edition has been updated and expanded while retaining the concise and engaging nature of its predecessor, offering an accessible and fun introduction to the packages dplyr and ggplot2 for data manipulation and graphing. It expands the set of basic statistics considered in the first edition to include new examples of a simple regression, a one-way and a two-way ANOVA. Finally, it introduces a new chapter on the generalised linear model. Getting Started with R is suitable for undergraduates, graduate students, professional researchers, and practitioners in the biological sciences.
Download or read book Bare Bones R written by Thomas P. Hogan and published by SAGE. This book was released on 2010 with total page 97 pages. Available in PDF, EPUB and Kindle. Book excerpt: A practical, hands-on introduction to the elements of R and R Commander.
Download or read book Introductory R A Beginner s Guide to Data Visualisation Statistical Analysis and Programming in R written by Robert J. Knell and published by Robert Knell. This book was released on 2014-05-14 with total page 531 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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 little about statistics but who hasn't yet got to grips with the ways of R. This new edition is completely revised and greatly expanded with new chapters on the basics of descriptive statistics and statistical testing, considerably more information on statistics and six new chapters on programming in R. Topics covered include: A walkthrough of the basics of R's command line interface Data structures including vectors, matrices and data frames R functions and how to use them Expanding your analysis and plotting capacities with add-in R packages A set of simple rules to follow to make sure you import your data properly An introduction to the script editor and advice on workflow A detailed introduction to drawing publication-standard graphs in R How to understand the help files and how to deal with some of the most common errors that you might encounter. Basic descriptive statistics The theory behind statistical testing and how to interpret the output of statistical tests Thorough coverage of the basics of data analysis in R with chapters on using chi-squared tests, t-tests, correlation analysis, regression, ANOVA and general linear models What the assumptions behind the analyses mean and how to test them using diagnostic plots Explanations of the summary tables produced for statistical analyses such as regression and ANOVA Writing your own functions in R Using table operations to manipulate matrices and data frames Using conditional statements and loops in R programmes. Writing longer R programmes. The techniques of statistical analysis in R are illustrated by a series of chapters where experimental and survey data are analysed. There is a strong emphasis on using real data from real scientific research, with all the problems and uncertainty that implies, rather than well-behaved made-up data that give ideal and easy to analyse results.
Download or read book Sustainable Development written by David Reid and published by Routledge. This book was released on 2013-11-05 with total page 284 pages. Available in PDF, EPUB and Kindle. Book excerpt: 'Sustainable development' is the catchphrase of the 1990s. Governments around the world, international institutions, local organizations and NGOs have committed themselves to its principles and have adopted policies to promote it. But 'sustainable development' is difficult to define - let alone implement - and its proponents and advocates may all interpret it in very different ways. This introductory guide provides a clear and accurate account of what sustainable development actually is. David Reid gives an overview of the history of the concept and how it has evolved in recent years, describes the obstacles to achieving sustainable development, and looks at recent progress towards implementing it - and at how much we have still to do.
Download or read book An Introductory Guide to SPSS for Windows written by Eric L. Einspruch and published by SAGE. This book was released on 2005-03-10 with total page 160 pages. Available in PDF, EPUB and Kindle. Book excerpt: 'An Introductory Guide to SPSS for Windows' develops SPSS skills through the use of sample programs that illustrate how to conduct the analyses typically found in an introductory statistics course.
Download or read book An Introduction to Statistical Learning written by Gareth James and published by Springer Nature. This book was released on 2023-08-01 with total page 617 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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, marketing, and 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, deep learning, survival analysis, multiple testing, and more. Color graphics and real-world examples are used to illustrate the methods presented. This book is targeted at statisticians and non-statisticians alike, who wish to use cutting-edge statistical learning techniques to analyze their data. Four of the authors co-wrote An Introduction to Statistical Learning, With Applications in R (ISLR), which has become a mainstay of undergraduate and graduate classrooms worldwide, as well as an important reference book for data scientists. One of the keys to its success was that each chapter contains a tutorial on implementing the analyses and methods presented in the R scientific computing environment. However, in recent years Python has become a popular language for data science, and there has been increasing demand for a Python-based alternative to ISLR. Hence, this book (ISLP) covers the same materials as ISLR but with labs implemented in Python. These labs will be useful both for Python novices, as well as experienced users.
Download or read book Advanced R written by Hadley Wickham and published by CRC Press. This book was released on 2015-09-15 with total page 669 pages. Available in PDF, EPUB and Kindle. Book excerpt: An Essential Reference for Intermediate and Advanced R Programmers Advanced R presents useful tools and techniques for attacking many types of R programming problems, helping you avoid mistakes and dead ends. With more than ten years of experience programming in R, the author illustrates the elegance, beauty, and flexibility at the heart of R. The book develops the necessary skills to produce quality code that can be used in a variety of circumstances. You will learn: The fundamentals of R, including standard data types and functions Functional programming as a useful framework for solving wide classes of problems The positives and negatives of metaprogramming How to write fast, memory-efficient code This book not only helps current R users become R programmers but also shows existing programmers what’s special about R. Intermediate R programmers can dive deeper into R and learn new strategies for solving diverse problems while programmers from other languages can learn the details of R and understand why R works the way it does.
Download or read book Learning Statistics with R written by Daniel Navarro and published by Lulu.com. This book was released on 2013-01-13 with total page 617 pages. Available in PDF, EPUB and Kindle. Book excerpt: "Learning Statistics with R" covers the contents of an introductory statistics class, as typically taught to undergraduate psychology students, focusing on the use of the R statistical software and adopting a light, conversational style throughout. The book discusses how to get started in R, and gives an introduction to data manipulation and writing scripts. From a statistical perspective, the book discusses descriptive statistics and graphing first, followed by chapters on probability theory, sampling and estimation, and null hypothesis testing. After introducing the theory, the book covers the analysis of contingency tables, t-tests, ANOVAs and regression. Bayesian statistics are covered at the end of the book. For more information (and the opportunity to check the book out before you buy!) visit http://ua.edu.au/ccs/teaching/lsr or http://learningstatisticswithr.com
Download or read book Biostatistics with R written by Jan Lepš and published by Cambridge University Press. This book was released on 2020-07-30 with total page 385 pages. Available in PDF, EPUB and Kindle. Book excerpt: A straightforward introduction to a wide range of statistical methods for field biologists, using thoroughly explained R code.
Download or read book Doing Meta Analysis with R written by Mathias Harrer and published by CRC Press. This book was released on 2021-09-15 with total page 500 pages. Available in PDF, EPUB and Kindle. Book excerpt: Doing Meta-Analysis with R: A Hands-On Guide serves as an accessible introduction on how meta-analyses can be conducted in R. Essential steps for meta-analysis are covered, including calculation and pooling of outcome measures, forest plots, heterogeneity diagnostics, subgroup analyses, meta-regression, methods to control for publication bias, risk of bias assessments and plotting tools. Advanced but highly relevant topics such as network meta-analysis, multi-three-level meta-analyses, Bayesian meta-analysis approaches and SEM meta-analysis are also covered. A companion R package, dmetar, is introduced at the beginning of the guide. It contains data sets and several helper functions for the meta and metafor package used in the guide. The programming and statistical background covered in the book are kept at a non-expert level, making the book widely accessible. Features • Contains two introductory chapters on how to set up an R environment and do basic imports/manipulations of meta-analysis data, including exercises • Describes statistical concepts clearly and concisely before applying them in R • Includes step-by-step guidance through the coding required to perform meta-analyses, and a companion R package for the book
Download or read book Introduction to Deep Learning Using R written by Taweh Beysolow II and published by Apress. This book was released on 2017-07-19 with total page 240 pages. Available in PDF, EPUB and Kindle. Book excerpt: Understand deep learning, the nuances of its different models, and where these models can be applied. The abundance of data and demand for superior products/services have driven the development of advanced computer science techniques, among them image and speech recognition. Introduction to Deep Learning Using R provides a theoretical and practical understanding of the models that perform these tasks by building upon the fundamentals of data science through machine learning and deep learning. This step-by-step guide will help you understand the disciplines so that you can apply the methodology in a variety of contexts. All examples are taught in the R statistical language, allowing students and professionals to implement these techniques using open source tools. What You'll Learn Understand the intuition and mathematics that power deep learning models Utilize various algorithms using the R programming language and its packages Use best practices for experimental design and variable selection Practice the methodology to approach and effectively solve problems as a data scientist Evaluate the effectiveness of algorithmic solutions and enhance their predictive power Who This Book Is For Students, researchers, and data scientists who are familiar with programming using R. This book also is also of use for those who wish to learn how to appropriately deploy these algorithms in applications where they would be most useful.
