Using R and RStudio for Data Management, Statistical Analysis, and Graphics PDF Download

Author: Nicholas J. Horton
Publisher: CRC Press
ISBN: 1482237377
Category : Mathematics
Languages : en
Pages : 313

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Book Description
Improve Your Analytical SkillsIncorporating the latest R packages as well as new case studies and applications, Using R and RStudio for Data Management, Statistical Analysis, and Graphics, Second Edition covers the aspects of R most often used by statistical analysts. New users of R will find the book's simple approach easy to understand while more

Author: Martin Elff
Publisher: Sage Publications Limited
ISBN: 9781526459961
Category : Social Science
Languages : en
Pages : 256

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Book Description
An invaluable step-by-step, pedagogically engaging guide to data management in R for social science researchers, this book shows students how to recode and document data, as well as how to combine data from different sources or import from statistical packages other than R.

Author: Ken Kleinman
Publisher: CRC Press
ISBN: 1466584491
Category : Mathematics
Languages : en
Pages : 473

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Book Description
An Up-to-Date, All-in-One Resource for Using SAS and R to Perform Frequent Tasks The first edition of this popular guide provided a path between SAS and R using an easy-to-understand, dictionary-like approach. Retaining the same accessible format, SAS and R: Data Management, Statistical Analysis, and Graphics, Second Edition explains how to easily perform an analytical task in both SAS and R, without having to navigate through the extensive, idiosyncratic, and sometimes unwieldy software documentation. The book covers many common tasks, such as data management, descriptive summaries, inferential procedures, regression analysis, and graphics, along with more complex applications. New to the Second Edition This edition now covers RStudio, a powerful and easy-to-use interface for R. It incorporates a number of additional topics, including using application program interfaces (APIs), accessing data through database management systems, using reproducible analysis tools, and statistical analysis with Markov chain Monte Carlo (MCMC) methods and finite mixture models. It also includes extended examples of simulations and many new examples. Enables Easy Mobility between the Two Systems Through the extensive indexing and cross-referencing, users can directly find and implement the material they need. SAS users can look up tasks in the SAS index and then find the associated R code while R users can benefit from the R index in a similar manner. Numerous example analyses demonstrate the code in action and facilitate further exploration. The datasets and code are available for download on the book’s website.

Author: Martin Elff
Publisher: SAGE
ISBN: 1529737664
Category : Social Science
Languages : en
Pages : 410

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Book Description
An invaluable, step-by-step guide to data management in R for social science researchers. This book will show you how to recode data, combine data from different sources, document data, and import data from statistical packages other than R. It explores both qualitative and quantitative data and is packed with a range of supportive learning features such as code examples, overview boxes, images, tables, and diagrams.

Author: Ken Kleinman
Publisher: CRC Press
ISBN: 1420070592
Category : Mathematics
Languages : en
Pages : 325

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Book Description
An All-in-One Resource for Using SAS and R to Carry out Common TasksProvides a path between languages that is easier than reading complete documentationSAS and R: Data Management, Statistical Analysis, and Graphics presents an easy way to learn how to perform an analytical task in both SAS and R, without having to navigate through the extensive, id

Author: Hadley Wickham
Publisher: "O'Reilly Media, Inc."
ISBN: 1491910364
Category : Computers
Languages : en
Pages : 521

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Book Description
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

Author: Bradley C. Boehmke, Ph.D.
Publisher: Springer
ISBN: 3319455990
Category : Computers
Languages : en
Pages : 238

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Book Description
This guide for practicing statisticians, data scientists, and R users and programmers will teach the essentials of preprocessing: data leveraging the R programming language to easily and quickly turn noisy data into usable pieces of information. Data wrangling, which is also commonly referred to as data munging, transformation, manipulation, janitor work, etc., can be a painstakingly laborious process. Roughly 80% of data analysis is spent on cleaning and preparing data; however, being a prerequisite to the rest of the data analysis workflow (visualization, analysis, reporting), it is essential that one become fluent and efficient in data wrangling techniques. This book will guide the user through the data wrangling process via a step-by-step tutorial approach and provide a solid foundation for working with data in R. The author's goal is to teach the user how to easily wrangle data in order to spend more time on understanding the content of the data. By the end of the book, the user will have learned: How to work with different types of data such as numerics, characters, regular expressions, factors, and dates The difference between different data structures and how to create, add additional components to, and subset each data structure How to acquire and parse data from locations previously inaccessible How to develop functions and use loop control structures to reduce code redundancy How to use pipe operators to simplify code and make it more readable How to reshape the layout of data and manipulate, summarize, and join data sets

Author: Nils B. Weidmann
Publisher: Cambridge University Press
ISBN: 1108997465
Category : Social Science
Languages : en
Pages : 243

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Book Description
The 'data revolution' offers many new opportunities for research in the social sciences. Increasingly, social and political interactions can be recorded digitally, leading to vast amounts of new data available for research. This poses new challenges for organizing and processing research data. This comprehensive introduction covers the entire range of data management techniques, from flat files to database management systems. It demonstrates how established techniques and technologies from computer science can be applied in social science projects, drawing on a wide range of different applied examples. This book covers simple tools such as spreadsheets and file-based data storage and processing, as well as more powerful data management software like relational databases. It goes on to address advanced topics such as spatial data, text as data, and network data. This book is one of the first to discuss questions of practical data management specifically for social science projects. This title is also available as Open Access on Cambridge Core.

Author: Torsten Hothorn
Publisher: CRC Press
ISBN: 1420079336
Category : Mathematics
Languages : en
Pages : 383

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Book Description
A Proven Guide for Easily Using R to Effectively Analyze Data Like its bestselling predecessor, A Handbook of Statistical Analyses Using R, Second Edition provides a guide to data analysis using the R system for statistical computing. Each chapter includes a brief account of the relevant statistical background, along with appropriate references. New to the Second Edition New chapters on graphical displays, generalized additive models, and simultaneous inference A new section on generalized linear mixed models that completes the discussion on the analysis of longitudinal data where the response variable does not have a normal distribution New examples and additional exercises in several chapters A new version of the HSAUR package (HSAUR2), which is available from CRAN This edition continues to offer straightforward descriptions of how to conduct a range of statistical analyses using R, from simple inference to recursive partitioning to cluster analysis. Focusing on how to use R and interpret the results, it provides students and researchers in many disciplines with a self-contained means of using R to analyze their data.

Author: Mete Sünsüli
Publisher: Saygın Kantara
ISBN:
Category : Computers
Languages : en
Pages : 120

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Book Description
R programming language is one of the unique tools of data mining and data analysis, which is increasingly important in the world. This book reports the functions of R programming language related to spatial data as a quick start guide. In the Book, it is referred to basic R functions starting with installation of RStudio and R platform. Spatial Data Library termed as “Geospatial Data Abstraction Library” for raster objects and basic functions belonging to “OpenGIS Simple Features Reference” library for vector objects were tested and presented in R environment. The code snippets and commands used in this book were listed at the end of the book.