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R for Cloud Computing

R for Cloud Computing

A Ohri

Springer-Verlag New York Inc.
2014
nidottu
R for Cloud Computing looks at some of the tasks performed by business analysts on the desktop (PC era) and helps the user navigate the wealth of information in R and its 4000 packages as well as transition the same analytics using the cloud. With this information the reader can select both cloud vendors and the sometimes confusing cloud ecosystem as well as the R packages that can help process the analytical tasks with minimum effort, cost and maximum usefulness and customization. The use of Graphical User Interfaces (GUI) and Step by Step screenshot tutorials is emphasized in this book to lessen the famous learning curve in learning R and some of the needless confusion created in cloud computing that hinders its widespread adoption. This will help you kick-start analytics on the cloud including chapters on both cloud computing, R, common tasks performed in analytics including the current focus and scrutiny of Big Data Analytics, setting up and navigating cloud providers.Readers are exposed to a breadth of cloud computing choices and analytics topics without being buried in needless depth. The included references and links allow the reader to pursue business analytics on the cloud easily. It is aimed at practical analytics and is easy to transition from existing analytical set up to the cloud on an open source system based primarily on R.This book is aimed at industry practitioners with basic programming skills and students who want to enter analytics as a profession. Note the scope of the book is neither statistical theory nor graduate level research for statistics, but rather it is for business analytics practitioners. It will also help researchers and academics but at a practical rather than conceptual level.The R statistical software is the fastest growing analytics platform in the world, and is established in both academia and corporations for robustness, reliability and accuracy. The cloud computing paradigm is firmly established as the next generation of computing from microprocessors to desktop PCs to cloud.
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R for SAS and SPSS Users

R for SAS and SPSS Users

Robert A. Muenchen

Springer-Verlag New York Inc.
2016
nidottu
R is a powerful and free software system for data analysis and graphics, with over 5,000 add-on packages available. This book introduces R using SAS and SPSS terms with which you are already familiar. It demonstrates which of the add-on packages are most like SAS and SPSS and compares them to R's built-in functions. It steps through over 30 programs written in all three packages, comparing and contrasting the packages' differing approaches. The programs and practice datasets are available for download.The glossary defines over 50 R terms using SAS/SPSS jargon and again using R jargon. The table of contents and the index allow you to find equivalent R functions by looking up both SAS statements and SPSS commands. When finished, you will be able to import data, manage and transform it, create publication quality graphics, and perform basic statistical analyses. This new edition has updated programming, an expanded index, and even more statistical methods covered in over 25 new sections.
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R for Business Analytics

R for Business Analytics

A Ohri

Springer-Verlag New York Inc.
2016
nidottu
R for Business Analytics looks at some of the most common tasks performed by business analysts and helps the user navigate the wealth of information in R and its 4000 packages. With this information the reader can select the packages that can help process the analytical tasks with minimum effort and maximum usefulness. The use of Graphical User Interfaces (GUI) is emphasized in this book to further cut down and bend the famous learning curve in learning R. This book is aimed to help you kick-start with analytics including chapters on data visualization, code examples on web analytics and social media analytics, clustering, regression models, text mining, data mining models and forecasting. The book tries to expose the reader to a breadth of business analytics topics without burying the user in needless depth. The included references and links allow the reader to pursue business analytics topics. This book is aimed at business analysts with basic programming skills for using R for Business Analytics. Note the scope of the book is neither statistical theory nor graduate level research for statistics, but rather it is for business analytics practitioners. Business analytics (BA) refers to the field of exploration and investigation of data generated by businesses. Business Intelligence (BI) is the seamless dissemination of information through the organization, which primarily involves business metrics both past and current for the use of decision support in businesses. Data Mining (DM) is the process of discovering new patterns from large data using algorithms and statistical methods. To differentiate between the three, BI is mostly current reports, BA is models to predict and strategize and DM matches patterns in big data. The R statistical software is the fastest growing analytics platform in the world, and is established in both academia and corporations for robustness, reliability and accuracy. The book utilizes Albert Einstein’s famous remarks on making things as simple as possible, but no simpler. This book will blow the last remaining doubts in your mind about using R in your business environment. Even non-technical users will enjoy the easy-to-use examples. The interviews with creators and corporate users of R make the book very readable. The author firmly believes Isaac Asimov was a better writer in spreading science than any textbook or journal author.
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R for Programmers

R for Programmers

Dan Zhang

Productivity Press
2016
nidottu
Unlike other books about R, written from the perspective of statistics, R for Programmers: Mastering the Tools is written from the perspective of programmers, providing a channel for programmers with expertise in other programming languages to quickly understand R. The contents are divided into four sections: The first section consists of the basics of R, which explains the advantages of using R, the installation of different versions of R, and the 12 frequently used packages of R. This will help you understand the tool packages, time series packages, and performance monitoring packages of R quickly.The second section discusses the server of R, which examines the communication between R and other programming languages and the application of R as servers. This will help you integrate R with other programming languages and implement the server application of R. The third section discusses databases and big data, which covers the communication between R and various databases, as well as R’s integration with Hadoop. This will help you integrate R with the underlying level of other databases and implement the processing of big data by R, based on Hadoop.The fourth section comprises the appendices, which introduce the installation of Java, various databases, and Hadoop. Because this is a reference book, there is no special sequence for reading all the chapters. You can choose the chapters in which you have an interest. If you are new to R, and you wish to master R comprehensively, simply follow the chapters in sequence.
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R for Programmers

R for Programmers

Dan Zhang

Productivity Press
2017
nidottu
This book discusses advanced topics such as R core programing, object oriented R programing, parallel computing with R, and spatial data types. The author leads readers to merge mature and effective methdologies in traditional programing to R programing. It shows how to interface R with C, Java, and other popular programing laguages and platforms.
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R for Programmers

R for Programmers

Dan Zhang

Productivity Press
2018
nidottu
After the fundamental volume and the advanced technique volume, this volume focuses on R applications in the quantitative investment area. Quantitative investment has been hot for some years, and there are more and more startups working on it, combined with many other internet communities and business models. R is widely used in this area, and can be a very powerful tool. The author introduces R applications with cases from his own startup, covering topics like portfolio optimization and risk management.
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R for Beginners: Become R Expert in 5 Days

R for Beginners: Become R Expert in 5 Days

Kamakshaiah Musunuru

Independently Published
2020
nidottu
R is a language of statistics. R is not only a programming language for statistical computing but a platform for research management. R supports all activities spanning from data collection to reports and dashboards. At first this may sound weird, but it is true. People only know R as a programming language meant for statistical analysis. Today, R is also used for full stack applications due to the advent of few packages like Shiny, Plumber and many more. However, much of the community does not know about various opportunities for web-development available through R. These server applications are useful for creating online survey forms, which assists in collecting data. Packages like Shiny could be helpful for assisting in analyzing such collected data. This book is a sleek and slender manual for beginners. The primary aim of this book is not hard-core programming, but to provide a comprehensive manual for practice of R. There is sufficient description on programming concepts such as control flow, loops, functions etc. While coming to contents; this text has 5 chapters and each chapter represents a unique concept related to practice of code in R. The idea behind 5 chapters is that the learner may be able to pick R with in few weeks. This book might be much helpful for practitioners, academics, scholars and graduating students to acquire knowledge on R in a very short time.
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R for the Rest of Us

R for the Rest of Us

David Keyes

NO STARCH PRESS,US
2024
nidottu
For statisticians, R is the go-to programming language for complex numerical analysis - but it comes in handy for a lot more than that. In R Without Statistics you'll discover ways that R can be used by the rest of us! Packed with real-world examples and easy-to-follow coding instructions, it introduces R's application in a wide range of non-statistical tasks, from data visualization and interpreting survey results, to map plotting and automating workloads. Each chapter features an actual R programmer who achieved something novel using the language, and then covers the case study and code samples demonstrating exactly how they did it. Whether it's creating visualizations for Scientific American, applying a consistent theme to BBC graphics, organizing professional government reports, or effectively mapping the spread of COVID-19, R offers a unique way to transform your work.
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R for Data Science Cookbook

R for Data Science Cookbook

Yu-Wei Chiu)

Packt Publishing Limited
2016
nidottu
Over 100 hands-on recipes to effectively solve real-world data problems using the most popular R packages and techniques About This Book • Gain insight into how data scientists collect, process, analyze, and visualize data using some of the most popular R packages • Understand how to apply useful data analysis techniques in R for real-world applications • An easy-to-follow guide to make the life of data scientist easier with the problems faced while performing data analysis Who This Book Is For This book is for those who are already familiar with the basic operation of R, but want to learn how to efficiently and effectively analyze real-world data problems using practical R packages. What You Will Learn • Get to know the functional characteristics of R language • Extract, transform, and load data from heterogeneous sources • Understand how easily R can confront probability and statistics problems • Get simple R instructions to quickly organize and manipulate large datasets • Create professional data visualizations and interactive reports • Predict user purchase behavior by adopting a classification approach • Implement data mining techniques to discover items that are frequently purchased together • Group similar text documents by using various clustering methods In Detail This cookbook offers a range of data analysis samples in simple and straightforward R code, providing step-by-step resources and time-saving methods to help you solve data problems efficiently. The first section deals with how to create R functions to avoid the unnecessary duplication of code. You will learn how to prepare, process, and perform sophisticated ETL for heterogeneous data sources with R packages. An example of data manipulation is provided, illustrating how to use the “dplyr” and “data.table” packages to efficiently process larger data structures. We also focus on “ggplot2” and show you how to create advanced figures for data exploration. In addition, you will learn how to build an interactive report using the “ggvis” package. Later chapters offer insight into time series analysis on financial data, while there is detailed information on the hot topic of machine learning, including data classification, regression, clustering, association rule mining, and dimension reduction. By the end of this book, you will understand how to resolve issues and will be able to comfortably offer solutions to problems encountered while performing data analysis. Style and approach This easy-to-follow guide is full of hands-on examples of data analysis with R. Each topic is fully explained beginning with the core concept, followed by step-by-step practical examples, and concluding with detailed explanations of each concept used.
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R for Data Science

R for Data Science

Dan Toomey

Packt Publishing Limited
2014
nidottu
If you are a data analyst who has a firm grip on some advanced data analysis techniques and wants to learn how to leverage the features of R, this is the book for you. You should have some basic knowledge of the R language and should know about some data science topics.
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R For Healthcare Research - Volume I: Basic Statistical Methods

R For Healthcare Research - Volume I: Basic Statistical Methods

Jason Oke; Mei-man Lee

World Scientific Europe Ltd
2025
sidottu
R for Healthcare Research is intended to show healthcare professionals, researchers and students of healthcare research how to use the open-source statistical software R. It is intended both for novices and experienced users of R, guiding readers from the fundamentals of installing the software through to a careful and thorough coverage of the most widely used techniques in healthcare research and evidence-based medicine.This textbook is structured to provide readers with both a conceptual understanding of the theories, analyses and models found in healthcare research as well as to act as a practical guide for how to programme these concepts in R. Volume I covers the basics of statistics, including data types, probability and sampling, before moving on to survey common measures of disease and the most appropriate ways of displaying data. Readers will gain a solid grounding of how to conduct group comparisons, evaluate the strength of associations, and check the accuracy of their tests.Worked examples are consistently provided throughout, and each chapter concludes with exercises to familiarise readers with the topics covered. An R package hosting all of the referenced datasets accompanies this textbook.
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R For Healthcare Research - Volume I: Basic Statistical Methods

R For Healthcare Research - Volume I: Basic Statistical Methods

Jason Oke; Mei-man Lee

World Scientific Europe Ltd
2025
nidottu
R for Healthcare Research is intended to show healthcare professionals, researchers and students of healthcare research how to use the open-source statistical software R. It is intended both for novices and experienced users of R, guiding readers from the fundamentals of installing the software through to a careful and thorough coverage of the most widely used techniques in healthcare research and evidence-based medicine.This textbook is structured to provide readers with both a conceptual understanding of the theories, analyses and models found in healthcare research as well as to act as a practical guide for how to programme these concepts in R. Volume I covers the basics of statistics, including data types, probability and sampling, before moving on to survey common measures of disease and the most appropriate ways of displaying data. Readers will gain a solid grounding of how to conduct group comparisons, evaluate the strength of associations, and check the accuracy of their tests.Worked examples are consistently provided throughout, and each chapter concludes with exercises to familiarise readers with the topics covered. An R package hosting all of the referenced datasets accompanies this textbook.
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R for Data Analysis in easy steps

R for Data Analysis in easy steps

Mike McGrath

In Easy Steps Limited
2023
nidottu
The R language is widely used by statisticians for data analysis, and the popularity of R programming has therefore increased substantially in recent years. The emerging Internet of Things (IoT) gathers increasing amounts of data that can be analyzed to gain useful insights into trends. R for Data Analysis in easy steps, 2nd edition has an easy-to-follow style that will appeal to anyone who wants to produce graphic visualizations to gain insights from gathered data. The book begins by explaining core programming principles of the R programming language, which stores data in "vectors" from which simple graphs can be plotted. Next, it describes how to create "matrices" to store and manipulate data from which graphs can be plotted to provide better insights. This book then demonstrates how to create "data frames" from imported data sets, and how to employ the "Grammar of Graphics" to produce advanced visualizations that can best illustrate useful insights from your data. R for Data Analysis in easy steps, 2nd edition contains separate chapters on the major features of the R programming language. There are complete example programs that demonstrate how to create Line graphs, Bar charts, Histograms, Scatter graphs, Box plots, and more. The code for each R script is listed, together with screenshots that illustrate the actual output when that script has been executed. The free, downloadable example R code is provided for clearer understanding. By the end of this book you will have gained a sound understanding of R programming, and be able to write your own scripts that can be executed to produce graphic visualizations for data analysis. You need have no previous knowledge of any programming language, so it's ideal for the newcomer to computer programming. Updated for the latest version of R.
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R For Marketing Research and Analytics

R For Marketing Research and Analytics

Chris Chapman; Elea McDonnell Feit

Springer Nature Switzerland AG
2019
nidottu
The 2nd edition of R for Marketing Research and Analytics continues to be the best place to learn R for marketing research. This book is a complete introduction to the power of R for marketing research practitioners. The text describes statistical models from a conceptual point of view with a minimal amount of mathematics, presuming only an introductory knowledge of statistics. Hands-on chapters accelerate the learning curve by asking readers to interact with R from the beginning. Core topics include the R language, basic statistics, linear modeling, and data visualization, which is presented throughout as an integral part of analysis.Later chapters cover more advanced topics yet are intended to be approachable for all analysts. These sections examine logistic regression, customer segmentation, hierarchical linear modeling, market basket analysis, structural equation modeling, and conjoint analysis in R. The text uniquely presents Bayesian models with a minimally complex approach, demonstrating and explaining Bayesian methods alongside traditional analyses for analysis of variance, linear models, and metric and choice-based conjoint analysis. With its emphasis on data visualization, model assessment, and development of statistical intuition, this book provides guidance for any analyst looking to develop or improve skills in R for marketing applications.The 2nd edition increases the book’s utility for students and instructors with the inclusion of exercises and classroom slides. At the same time, it retains all of the features that make it a vital resource for practitioners: non-mathematical exposition, examples modeled on real world marketing problems, intuitive guidance on research methods, and immediately applicable code.
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R for Political Science Research

R for Political Science Research

Jane L. Sumner

Springer International Publishing AG
2025
sidottu
This text teaches basic R skills to political science students with no programming background. Intended specifically for the students who need to learn R for a class and who have no interest in R or may even be afraid of or hostile to it, this text builds an awareness of basics, confidence, and a skill set necessary to transition into more advanced texts. To that end, in addition to standard topics, this book includes three chapters specific to the new or reluctant learner. The Introduction explicitly sets expectations for how to use the book and discusses fixed and growth mentalities, and why a growth mentality is crucial for learning R. Chapter 1 includes some basic information on programming, R, and their place in political science research. Chapter 2 explicitly discusses errors, warnings, and methods of debugging. Further chapters build on this by including new errors or warnings that students may encounter as they progress. In service of the aim to give students a solid foundation in R and awareness of what it is and can do, this book teaches and uses both tidyverse and base R frameworks throughout. After completing the book, students should be prepared to learn more advanced materials.
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R for Marketing Research and Analytics

R for Marketing Research and Analytics

Chris Chapman; Elea McDonnell Feit

Springer International Publishing AG
2015
nidottu
This book is a complete introduction to the power of R for marketing research practitioners. The text describes statistical models from a conceptual point of view with a minimal amount of mathematics, presuming only an introductory knowledge of statistics. Hands-on chapters accelerate the learning curve by asking readers to interact with R from the beginning. Core topics include the R language, basic statistics, linear modeling, and data visualization, which is presented throughout as an integral part of analysis.Later chapters cover more advanced topics yet are intended to be approachable for all analysts. These sections examine logistic regression, customer segmentation, hierarchical linear modeling, market basket analysis, structural equation modeling, and conjoint analysis in R. The text uniquely presents Bayesian models with a minimally complex approach, demonstrating and explaining Bayesian methods alongside traditional analyses for analysis of variance, linear models, and metric and choice-based conjoint analysis.With its emphasis on data visualization, model assessment, and development of statistical intuition, this book provides guidance for any analyst looking to develop or improve skills in R for marketing applications.
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R für Dummies

R für Dummies

Andrie de Vries; Joris Meys

Wiley-VCH Verlag GmbH
2021
nidottu
Wollen Sie auch die umfangreichen Möglichkeiten von R nutzen, um Ihre Daten zu analysieren, sind sich aber nicht sicher, ob Sie mit der Programmiersprache wirklich zurechtkommen? Keine Sorge - dieses Buch zeigt Ihnen, wie es geht - selbst wenn Sie keine Vorkenntnisse in der Programmierung oder Statistik haben. Andrie de Vries und Joris Meys zeigen Ihnen Schritt für Schritt und anhand zahlreicher Beispiele, was Sie alles mit R machen können und vor allem wie Sie es machen können. Von den Grundlagen und den ersten Skripten bis hin zu komplexen statistischen Analysen und der Erstellung aussagekräftiger Grafiken. Auch fortgeschrittenere Nutzer finden in diesem Buch viele Tipps und Tricks, die Ihnen die Datenauswertung erleichtern.
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