Complete Data Analysis Using R

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R For Data Science

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Author : Hadley Wickham
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2016-12-12

R For Data Science written by Hadley Wickham and has been published by "O'Reilly Media, Inc." this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-12-12 with Computers categories.

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

Complete Data Analysis Using R

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Author : Marco Lehmann
language : en
Publisher: SAGE
Release Date : 2022-11-10

Complete Data Analysis Using R written by Marco Lehmann and has been published by SAGE this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-11-10 with Mathematics categories.

This book gets you up and running with using R in your research project, focusing on data analysis.

Data Analysis Using R Low Priced Edition A Primer For Data Scientist

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Author : DR DHAVAL MAHETA.
language : en
Publisher: Notion Press
Release Date : 2021-11-22

Data Analysis Using R Low Priced Edition A Primer For Data Scientist written by DR DHAVAL MAHETA. and has been published by Notion Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-11-22 with Education categories.

The objective of this book is to address the needs of students in using software for data science. The pace and content of this book is set in such a way so that it gives user friendly approach to students and other learners. The key features of this book are more than 60 packages have been discussed with relevant examples. The author has also incorporated the data analysis using R-Commander which is Graphic User Interface (GUI) of R programming. This book is a primer for upcoming fields such as Data Science and Machine Learning.

Graphical Data Analysis With R

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Author : Antony Unwin
language : en
Publisher: CRC Press
Release Date : 2015-03-25

Graphical Data Analysis With R written by Antony Unwin and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-03-25 with Mathematics categories.

See How Graphics Reveal Information Graphical Data Analysis with R shows you what information you can gain from graphical displays. The book focuses on why you draw graphics to display data and which graphics to draw (and uses R to do so). All the datasets are available in R or one of its packages and the R code is available at rosuda.org/GDA. Graphical data analysis is useful for data cleaning, exploring data structure, detecting outliers and unusual groups, identifying trends and clusters, spotting local patterns, evaluating modelling output, and presenting results. This book guides you in choosing graphics and understanding what information you can glean from them. It can be used as a primary text in a graphical data analysis course or as a supplement in a statistics course. Colour graphics are used throughout.

Complete Data Analysis Using R

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Author : Marco Lehmann
language : en
Publisher: SAGE
Release Date : 2022-11-10

Complete Data Analysis Using R written by Marco Lehmann and has been published by SAGE this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-11-10 with Mathematics categories.

This step-by-step guide shows you how to use R to get data analysis right. The book explores the entire process of analysis, covering key steps from preparing your data to putting your analysis together and writing up your findings. It helps you get to grips with doing different statistical techniques in R and: Equips you with practical data visualisation tools to create graphs and tables. Shows you how to prepare and present your research for assessment, publication and dissemination. Covers key issues facing today’s social scientists, such as making research reproducible. Features include an introduction to each chapter, and end-of-chapter exercises to check your understanding of the material. The online resources for this text include data sets that you can perform your own analysis on, and links to publications that are relevant to programming with R. A good starting point for any postgraduate student conducting a research project, this book will help you develop your statistics and programming knowledge and get quickly up to speed.

Comparative Approaches To Using R And Python For Statistical Data Analysis

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Author : Sarmento, Rui
language : en
Publisher: IGI Global
Release Date : 2017-01-06

Comparative Approaches To Using R And Python For Statistical Data Analysis written by Sarmento, Rui and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-01-06 with Business & Economics categories.

The application of statistics has proliferated in recent years and has become increasingly relevant across numerous fields of study. With the advent of new technologies, its availability has opened into a wider range of users. Comparative Approaches to Using R and Python for Statistical Data Analysis is a comprehensive source of emerging research and perspectives on the latest computer software and available languages for the visualization of statistical data. By providing insights on relevant topics, such as inference, factor analysis, and linear regression, this publication is ideally designed for professionals, researchers, academics, graduate students, and practitioners interested in the optimization of statistical data analysis.

Exploratory Data Analysis Using R

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Author : Ronald K. Pearson
language : en
Publisher: CRC Press
Release Date : 2018-05-04

Exploratory Data Analysis Using R written by Ronald K. Pearson and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-05-04 with Business & Economics categories.

Exploratory Data Analysis Using R provides a classroom-tested introduction to exploratory data analysis (EDA) and introduces the range of "interesting" – good, bad, and ugly – features that can be found in data, and why it is important to find them. It also introduces the mechanics of using R to explore and explain data. The book begins with a detailed overview of data, exploratory analysis, and R, as well as graphics in R. It then explores working with external data, linear regression models, and crafting data stories. The second part of the book focuses on developing R programs, including good programming practices and examples, working with text data, and general predictive models. The book ends with a chapter on "keeping it all together" that includes managing the R installation, managing files, documenting, and an introduction to reproducible computing. The book is designed for both advanced undergraduate, entry-level graduate students, and working professionals with little to no prior exposure to data analysis, modeling, statistics, or programming. it keeps the treatment relatively non-mathematical, even though data analysis is an inherently mathematical subject. Exercises are included at the end of most chapters, and an instructor's solution manual is available. About the Author: Ronald K. Pearson holds the position of Senior Data Scientist with GeoVera, a property insurance company in Fairfield, California, and he has previously held similar positions in a variety of application areas, including software development, drug safety data analysis, and the analysis of industrial process data. He holds a PhD in Electrical Engineering and Computer Science from the Massachusetts Institute of Technology and has published conference and journal papers on topics ranging from nonlinear dynamic model structure selection to the problems of disguised missing data in predictive modeling. Dr. Pearson has authored or co-authored books including Exploring Data in Engineering, the Sciences, and Medicine (Oxford University Press, 2011) and Nonlinear Digital Filtering with Python. He is also the developer of the DataCamp course on base R graphics and is an author of the datarobot and GoodmanKruskal R packages available from CRAN (the Comprehensive R Archive Network).

Data Analysis With R Second Edition

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Author : Tony Fischetti
language : en
Publisher: Packt Publishing
Release Date : 2018-03-28

Data Analysis With R Second Edition written by Tony Fischetti and has been published by Packt Publishing this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-03-28 with Computers categories.

Frequently the tool of choice for academics, R has spread deep into the private sector and can be found in the production pipelines at some of the most advanced and successful enterprises. The power and domain-specificity of R allows the user to express complex analytics easily, quickly, and succinctly. Starting with the basics of R and statistical reasoning, this book dives into advanced predictive analytics, showing how to apply those techniques to real-world data though with real-world examples. Packed with engaging problems and exercises, this book begins with a review of R and its syntax with packages like Rcpp, ggplot2, and dplyr. From there, get to grips with the fundamentals of applied statistics and build on this knowledge to perform sophisticated and powerful analytics. Solve the difficulties relating to performing data analysis in practice and find solutions to working with messy data, large data, communicating results, and facilitating reproducibility. This book is engineered to be an invaluable resource through many stages of anyone's career as a data analyst.

Hands On Exploratory Data Analysis With R

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Author : Radhika Datar
language : en
Publisher: Packt Publishing Ltd
Release Date : 2019-05-31

Hands On Exploratory Data Analysis With R written by Radhika Datar and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-05-31 with Computers categories.

Learn exploratory data analysis concepts using powerful R packages to enhance your R data analysis skills Key FeaturesSpeed up your data analysis projects using powerful R packages and techniquesCreate multiple hands-on data analysis projects using real-world dataDiscover and practice graphical exploratory analysis techniques across domainsBook Description Hands-On Exploratory Data Analysis with R will help you build not just a foundation but also expertise in the elementary ways to analyze data. You will learn how to understand your data and summarize its main characteristics. You'll also uncover the structure of your data, and you'll learn graphical and numerical techniques using the R language. This book covers the entire exploratory data analysis (EDA) process—data collection, generating statistics, distribution, and invalidating the hypothesis. As you progress through the book, you will learn how to set up a data analysis environment with tools such as ggplot2, knitr, and R Markdown, using tools such as DOE Scatter Plot and SML2010 for multifactor, optimization, and regression data problems. By the end of this book, you will be able to successfully carry out a preliminary investigation on any dataset, identify hidden insights, and present your results in a business context. What you will learnLearn powerful R techniques to speed up your data analysis projectsImport, clean, and explore data using powerful R packagesPractice graphical exploratory analysis techniquesCreate informative data analysis reports using ggplot2Identify and clean missing and erroneous dataExplore data analysis techniques to analyze multi-factor datasetsWho this book is for Hands-On Exploratory Data Analysis with R is for data enthusiasts who want to build a strong foundation for data analysis. If you are a data analyst, data engineer, software engineer, or product manager, this book will sharpen your skills in the complete workflow of exploratory data analysis.

Mastering Shiny

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Author : Hadley Wickham
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2021-04-29

Mastering Shiny written by Hadley Wickham and has been published by "O'Reilly Media, Inc." this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-04-29 with Computers categories.

Master the Shiny web framework—and take your R skills to a whole new level. By letting you move beyond static reports, Shiny helps you create fully interactive web apps for data analyses. Users will be able to jump between datasets, explore different subsets or facets of the data, run models with parameter values of their choosing, customize visualizations, and much more. Hadley Wickham from RStudio shows data scientists, data analysts, statisticians, and scientific researchers with no knowledge of HTML, CSS, or JavaScript how to create rich web apps from R. This in-depth guide provides a learning path that you can follow with confidence, as you go from a Shiny beginner to an expert developer who can write large, complex apps that are maintainable and performant. Get started: Discover how the major pieces of a Shiny app fit together Put Shiny in action: Explore Shiny functionality with a focus on code samples, example apps, and useful techniques Master reactivity: Go deep into the theory and practice of reactive programming and examine reactive graph components Apply best practices: Examine useful techniques for making your Shiny apps work well in production