Search Results for "getting-started-with-r"

Getting Started with R

Getting Started with R

An Introduction for Biologists

  • Author: Andrew P. Beckerman,Dylan Z. Childs,Owen L. Petchey
  • Publisher: Oxford University Press
  • ISBN: 0191091936
  • Category: Science
  • Page: 240
  • View: 4504
DOWNLOAD NOW »
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.

25 Recipes for Getting Started with R

25 Recipes for Getting Started with R

  • Author: Paul Teetor
  • Publisher: "O'Reilly Media, Inc."
  • ISBN: 1449303234
  • Category: Computers
  • Page: 44
  • View: 9889
DOWNLOAD NOW »
R is a powerful tool for statistics and graphics, but getting started with this language can be frustrating. This short, concise book provides beginners with a selection of how-to recipes to solve simple problems with R. Each solution gives you just what you need to know to use R for basic statistics, graphics, and regression. You'll find recipes on reading data files, creating data frames, computing basic statistics, testing means and correlations, creating a scatter plot, performing simple linear regression, and many more. These solutions were selected from O'Reilly's R Cookbook, which contains more than 200 recipes for R that you'll find useful once you move beyond the basics.

Getting Started with RStudio

Getting Started with RStudio

An Integrated Development Environment for R

  • Author: John Verzani
  • Publisher: "O'Reilly Media, Inc."
  • ISBN: 1449317073
  • Category: Computers
  • Page: 98
  • View: 4155
DOWNLOAD NOW »
Dive into the RStudio Integrated Development Environment (IDE) for using and programming R, the popular open source software for statistical computing and graphics. This concise book provides new and experienced users with an overview of RStudio, as well as hands-on instructions for analyzing data, generating reports, and developing R software packages. The open source RStudio IDE brings many powerful coding tools together into an intuitive, easy-to-learn interface. With this guide, you’ll learn how to use its main components—including the console, source code editor, and data viewer—through descriptions and case studies. Getting Started with RStudio serves as both a reference and introduction to this unique IDE. Use RStudio to provide enhanced support for interactive R sessions Clean and format raw data quickly with several RStudio components Edit R commands with RStudio’s code editor, and combine them into functions Easily locate and use more than 3,000 add-on packages in R’s CRAN service Develop and document your own R packages with the code editor and related components Create one-click PDF reports in RStudio with a mix of text and R output

R Programming By Example

R Programming By Example

Practical, hands-on projects to help you get started with R

  • Author: Omar Trejo Navarro
  • Publisher: Packt Publishing Ltd
  • ISBN: 1788291360
  • Category: Computers
  • Page: 470
  • View: 7418
DOWNLOAD NOW »
This step-by-step guide demonstrates how to build simple-to-advanced applications through examples in R using modern tools. About This Book Get a firm hold on the fundamentals of R through practical hands-on examples Get started with good R programming fundamentals for data science Exploit the different libraries of R to build interesting applications in R Who This Book Is For This books is for aspiring data science professionals or statisticians who would like to learn about the R programming language in a practical manner. Basic programming knowledge is assumed. What You Will Learn Discover techniques to leverage R's features, and work with packages Perform a descriptive analysis and work with statistical models using R Work efficiently with objects without using loops Create diverse visualizations to gain better understanding of the data Understand ways to produce good visualizations and create reports for the results Read and write data from relational databases and REST APIs, both packaged and unpackaged Improve performance by writing better code, delegating that code to a more efficient programming language, or making it parallel In Detail R is a high-level statistical language and is widely used among statisticians and data miners to develop analytical applications. Often, data analysis people with great analytical skills lack solid programming knowledge and are unfamiliar with the correct ways to use R. Based on the version 3.4, this book will help you develop strong fundamentals when working with R by taking you through a series of full representative examples, giving you a holistic view of R. We begin with the basic installation and configuration of the R environment. As you progress through the exercises, you'll become thoroughly acquainted with R's features and its packages. With this book, you will learn about the basic concepts of R programming, work efficiently with graphs, create publication-ready and interactive 3D graphs, and gain a better understanding of the data at hand. The detailed step-by-step instructions will enable you to get a clean set of data, produce good visualizations, and create reports for the results. It also teaches you various methods to perform code profiling and performance enhancement with good programming practices, delegation, and parallelization. By the end of this book, you will know how to efficiently work with data, create quality visualizations and reports, and develop code that is modular, expressive, and maintainable. Style and Approach This is an easy-to-understand guide filled with real-world examples, giving you a holistic view of R and practical, hands-on experience.

A Beginner's Guide to R

A Beginner's Guide to R

  • Author: Alain Zuur,Elena N. Ieno,Erik Meesters
  • Publisher: Springer Science & Business Media
  • ISBN: 9780387938370
  • Category: Computers
  • Page: 220
  • View: 6962
DOWNLOAD NOW »
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.

Beginning R

Beginning R

The Statistical Programming Language

  • Author: Mark Gardener
  • Publisher: John Wiley & Sons
  • ISBN: 1118239377
  • Category: Computers
  • Page: 504
  • View: 3296
DOWNLOAD NOW »
Conquer the complexities of this open source statisticallanguage R is fast becoming the de facto standard for statisticalcomputing and analysis in science, business, engineering, andrelated fields. This book examines this complex language usingsimple statistical examples, showing how R operates in auser-friendly context. Both students and workers in fields thatrequire extensive statistical analysis will find this book helpfulas they learn to use R for simple summary statistics, hypothesistesting, creating graphs, regression, and much more. It coversformula notation, complex statistics, manipulating data andextracting components, and rudimentary programming. R, the open source statistical language increasingly used tohandle statistics and produces publication-quality graphs, isnotoriously complex This book makes R easier to understand through the use ofsimple statistical examples, teaching the necessary elements in thecontext in which R is actually used Covers getting started with R and using it for simple summarystatistics, hypothesis testing, and graphs Shows how to use R for formula notation, complex statistics,manipulating data, extracting components, and regression Provides beginning programming instruction for those who wantto write their own scripts Beginning R offers anyone who needs to performstatistical analysis the information necessary to use R withconfidence.

Graphing Data with R

Graphing Data with R

An Introduction

  • Author: John Jay Hilfiger
  • Publisher: "O'Reilly Media, Inc."
  • ISBN: 1491922583
  • Category: COMPUTERS
  • Page: 336
  • View: 4288
DOWNLOAD NOW »
It’s much easier to grasp complex data relationships with a graph than by scanning numbers in a spreadsheet. This introductory guide shows you how to use the R language to create a variety of useful graphs for visualizing and analyzing complex data for science, business, media, and many other fields. You’ll learn methods for highlighting important relationships and trends, reducing data to simpler forms, and emphasizing key numbers at a glance. Anyone who wants to analyze data will find something useful here—even if you don’t have a background in mathematics, statistics, or computer programming. If you want to examine data related to your work, this book is the ideal way to start. Get started with R by learning basic commands Build single variable graphs, such as dot and pie charts, box plots, and histograms Explore the relationship between two quantitative variables with scatter plots, high-density plots, and other techniques Use scatterplot matrices, 3D plots, clustering, heat maps, and other graphs to visualize relationships among three or more variables

Making Your Case

Making Your Case

Using R for Program Evaluation

  • Author: Charles Auerbach,Wendy Zeitlin
  • Publisher: OUP Us
  • ISBN: 0190228083
  • Category: Evaluation research (Social action programs)
  • Page: 295
  • View: 2652
DOWNLOAD NOW »
There is a growing need for research within practice settings. Increasing competition for funding requires organizations to demonstrate that the funding they are seeking is going towards effective programming. Additionally, the evidence-based practice movement is generally pushing organizations towards research activities, both as producers and consumers.There have been many books written about research methodology and data analysis in the helping professions, and many books have been written about using R to analyze and present data; however, this book specifically addresses using R to evaluate programs in organizational settings. This book is divided into three sections. The first section addresses background information that is helpful in conducting practice-based research. The second section of the book provides necessary background to begin working with R. Topics include how to download R and RStudio, navigation, R packages, basic R functions, and importing data. This section also introduces The Clinical Record, a freely available database program to help organizations record and track client information. The remainder of the book uses case studies to illustrate how to use R to conduct program evaluations. Techniques include data description and visualization, bivariate analysis, simple and multiple regression, and logistic regression. The final chapter illustrates a comprehensive summary of the skills demonstrated throughout the book using The Clinical Record as a data repository.

Learning Social Media Analytics with R

Learning Social Media Analytics with R

  • Author: Raghav Bali,Dipanjan Sarkar,Tushar Sharma
  • Publisher: Packt Publishing Ltd
  • ISBN: 1787125467
  • Category: Computers
  • Page: 394
  • View: 8985
DOWNLOAD NOW »
Tap into the realm of social media and unleash the power of analytics for data-driven insights using R About This Book A practical guide written to help leverage the power of the R eco-system to extract, process, analyze, visualize and model social media data Learn about data access, retrieval, cleaning, and curation methods for data originating from various social media platforms. Visualize and analyze data from social media platforms to understand and model complex relationships using various concepts and techniques such as Sentiment Analysis, Topic Modeling, Text Summarization, Recommendation Systems, Social Network Analysis, Classification, and Clustering. Who This Book Is For It is targeted at IT professionals, Data Scientists, Analysts, Developers, Machine Learning Enthusiasts, social media marketers and anyone with a keen interest in data, analytics, and generating insights from social data. Some background experience in R would be helpful, but not necessary, since this book is written keeping in mind, that readers can have varying levels of expertise. What You Will Learn Learn how to tap into data from diverse social media platforms using the R ecosystem Use social media data to formulate and solve real-world problems Analyze user social networks and communities using concepts from graph theory and network analysis Learn to detect opinion and sentiment, extract themes, topics, and trends from unstructured noisy text data from diverse social media channels Understand the art of representing actionable insights with effective visualizations Analyze data from major social media channels such as Twitter, Facebook, Flickr, Foursquare, Github, StackExchange, and so on Learn to leverage popular R packages such as ggplot2, topicmodels, caret, e1071, tm, wordcloud, twittR, Rfacebook, dplyr, reshape2, and many more In Detail The Internet has truly become humongous, especially with the rise of various forms of social media in the last decade, which give users a platform to express themselves and also communicate and collaborate with each other. This book will help the reader to understand the current social media landscape and to learn how analytics can be leveraged to derive insights from it. This data can be analyzed to gain valuable insights into the behavior and engagement of users, organizations, businesses, and brands. It will help readers frame business problems and solve them using social data. The book will also cover several practical real-world use cases on social media using R and its advanced packages to utilize data science methodologies such as sentiment analysis, topic modeling, text summarization, recommendation systems, social network analysis, classification, and clustering. This will enable readers to learn different hands-on approaches to obtain data from diverse social media sources such as Twitter and Facebook. It will also show readers how to establish detailed workflows to process, visualize, and analyze data to transform social data into actionable insights. Style and approach This book follows a step-by-step approach with detailed strategies for understanding, extracting, analyzing, visualizing, and modeling data from several major social network platforms such as Facebook, Twitter, Foursquare, Flickr, Github, and StackExchange. The chapters cover several real-world use cases and leverage data science, machine learning, network analysis, and graph theory concepts along with the R ecosystem, including popular packages such as ggplot2, caret,dplyr, topicmodels, tm, and so on.

Report Writing for Data Science in R

Report Writing for Data Science in R

  • Author: Roger Peng
  • Publisher: Lulu.com
  • ISBN: 1329733649
  • Category: Computers
  • Page: 128
  • View: 4684
DOWNLOAD NOW »
This book teaches the concepts and tools behind reporting modern data analyses in a reproducible manner. Reproducibility is the idea that data analyses should be published or made available with their data and software code so that others may verify the findings and build upon them. The need for reproducible report writing is increasing dramatically as data analyses become more complex, involving larger datasets and more sophisticated computations. Reproducibility allows for people to focus on the actual content of a data analysis, rather than on superficial details reported in a written summary. In addition, reproducibility makes an analysis more useful to others because the data and code that actually conducted the analysis are available. This book will focus on literate statistical analysis tools which allow one to publish data analyses in a single document that allows others to easily execute the same analysis to obtain the same results.