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RStudio for R Statistical Computing Cookbook电子书

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14人正在读 | 0人评论 9.8

作       者:Andrea Cirillo

出  版  社:Packt Publishing

出版时间:2016-04-01

字       数:125.3万

所属分类: 进口书 > 外文原版书 > 电脑/网络

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Over 50 practical and useful recipes to help you perform data analysis with R by unleashing every native RStudio feature About This Book 54 useful and practical tasks to improve working systems Includes optimizing performance and reliability or uptime, reporting, system management tools, interfacing to standard data ports, and so on Offers 10-15 real-life, practical improvements for each user type Who This Book Is For This book is targeted at R statisticians, data scientists, and R programmers. Readers with R experience who are looking to take the plunge into statistical computing will find this Cookbook particularly indispensable. What You Will Learn Familiarize yourself with the latest advanced R console features Create advanced and interactive graphics Manage your R project and project files effectively Perform reproducible statistical analyses in your R projects Use RStudio to design predictive models for a specific domain-based application Use RStudio to effectively communicate your analyses results and even publish them to a blog Put yourself on the frontiers of data science and data monetization in R with all the tools that are needed to effectively communicate your results and even transform your work into a data product In Detail The requirement of handling complex datasets, performing unprecedented statistical analysis, and providing real-time visualizations to businesses has concerned statisticians and analysts across the globe. RStudio is a useful and powerful tool for statistical analysis that harnesses the power of R for computational statistics, visualization, and data science, in an integrated development environment. This book is a collection of recipes that will help you learn and understand RStudio features so that you can effectively perform statistical analysis and reporting, code editing, and R development. The first few chapters will teach you how to set up your own data analysis project in RStudio, acquire data from different data sources, and manipulate and clean data for analysis and visualization purposes. You'll get hands-on with various data visualization methods using ggplot2, and you will create interactive and multidimensional visualizations with D3.js. Additional recipes will help you optimize your code; implement various statistical models to manage large datasets; perform text analysis and predictive analysis; and master time series analysis, machine learning, forecasting; and so on. In the final few chapters, you'll learn how to create reports from your analytical application with the full range of static and dynamic reporting tools that are available in RStudio so that you can effectively communicate results and even transform them into interactive web applications. Style and approach RStudio is an open source Integrated Development Environment (IDE) for the R platform. The R programming language is used for statistical computing and graphics, which RStudio facilitates and enhances through its integrated environment. This Cookbook will help you learn to write better R code using the advanced features of the R programming language using RStudio. Readers will learn advanced R techniques to compute the language and control object evaluation within R functions. Some of the contents are: Accessing an API with R Substituting missing values by interpolation Performing data filtering activities R Statistical implementation for Geospatial data Developing shiny add-ins to expand RStudio functionalities Using GitHub with RStudio Modelling a recommendation engine with R Using R Markdown for static and dynamic reporting Curating a blog through RStudio Advanced statistical modelling with R and RStudio
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RStudio for R Statistical Computing Cookbook

Table of Contents

RStudio for R Statistical Computing Cookbook

Credits

About the Author

About the Reviewer

www.PacktPub.com

eBooks, discount offers, and more

Why Subscribe?

Preface

What this book covers

What you need for this book

Who this book is for

Sections

Getting ready

How to do it…

How it works…

There's more…

See also

Conventions

Reader feedback

Customer support

Downloading the example code

Downloading the color images of this book

Errata

Piracy

Questions

1. Acquiring Data for Your Project

Introduction

Acquiring data from the Web – web scraping tasks

Getting ready

How to do it...

There's more...

Accessing an API with R

Getting ready

How to do it…

How it works...

There's more...

Getting data from Twitter with the twitteR package

Getting ready

How to do it…

There's more...

Getting data from Facebook with the Rfacebook package

Getting ready

How to do it...

Getting data from Google Analytics

Getting ready

How to do it...

There's more...

Loading your data into R with rio packages

Getting ready

How to do it...

How it works...

There's more...

Converting file formats using the rio package

Getting ready

How to do it...

There's more...

2. Preparing for Analysis – Data Cleansing and Manipulation

Introduction

Getting a sense of your data structure with R

Getting ready

How to do it...

How it works...

Preparing your data for analysis with the tidyr package

Getting ready

How to do it...

How it works...

There's more...

Detecting and removing missing values

Getting ready

How to do it...

How it works...

There's more...

Substituting missing values using the mice package

Getting ready

How to do it...

How it works...

There's more...

Detecting and removing outliers

How to do it...

How it works...

Performing data filtering activities

Getting ready

How to do it…

How it works...

3. Basic Visualization Techniques

Introduction

Looking at your data using the plot() function

Getting ready

How to do it...

How it works...

Using pairs.panel() to look at (visualize) correlations between variables

Getting ready

How to do it...

How it works…

There's more…

Adding text to a ggplot2 plot at a custom location

Getting ready

How to do it...

How it works…

There's more…

Changing axes appearance to ggplot2 plot (continous axes)

Getting ready

How to do it...

Producing a matrix of graphs with ggplot2

Getting ready

How to do it...

How it works…

Drawing a route on a map with ggmap

Getting ready

How to do it...

How it works…

See also

Making use of the igraph package to draw a network

Getting ready

How to do it...

How it works…

Showing communities in a network with the linkcomm package

Getting ready

How to do it…

How it works…

4. Advanced and Interactive Visualization

Introduction

Producing a Sankey diagram with the networkD3 package

Getting ready

How to do it...

How it works...

Creating a dynamic force network with the visNetwork package

Getting ready

How to do it...

How it works...

There's more...

Building a rotating 3D graph and exporting it as a GIF

Getting ready

How to do it...

Using the DiagrammeR package to produce a process flow diagram in RStudio

Getting ready

How to do it...

5. Power Programming with R

Introduction

Writing modular code in RStudio

Getting ready

How to do it...

How it works...

Implementing parallel computation in R

Getting ready

How to do it...

How it works...

There's more...

Creating custom objects and methods in R using the S3 system

How to do it...

How it works...

Evaluating your code performance using the profvis package

Getting ready

How to do it...

Comparing an alternative function's performance using the microbenchmarking package

Getting ready

How to do it...

Using GitHub with RStudio

Getting ready

How to do it...

There's more...

6. Domain-specific Applications

Introduction

Dealing with regular expressions

How to do it...

Analyzing PDF reports in a folder with the tm package

Getting ready

How to do it...

How it works...

Creating word clouds with the wordcloud package

Getting ready

How to do it...

How it works...

Performing a Twitter sentiment analysis

Getting ready

How to do it...

How it works...

Detecting fraud in e-commerce orders with Benford's law

Getting ready

How to do it...

How it works...

Measuring customer retention using cohort analysis in R

Getting ready

How to do it...

How it works...

Making a recommendation engine

Getting ready

How to do it...

Performing time series decomposition using the stl() function

Getting ready

How to do it...

Exploring time series forecasting with forecast()

Getting ready

How to do it...

Tracking stock movements using the quantmod package

Getting ready

How to do it...

Optimizing portfolio composition and maximising returns with the Portfolio Analytics package

Getting ready

How to do it...

Forecasting the stock market

Getting ready

How to do it...

7. Developing Static Reports

Introduction

Using one markup language for all types of documents – rmarkdown

Getting ready

How to do it...

How it works...

There’s more...

Writing and styling PDF documents with RStudio

Getting ready

How to do it...

There’s more...

Writing wonderful tufte handouts with the tufte package and rmarkdown

Getting ready

How to do it...

There’s more...

Sharing your code and plots with slides

How to do it...

Curating a blog through RStudio

Getting ready

How to do it...

8. Dynamic Reporting and Web Application Development

Introduction

Generating dynamic parametrized reports with R Markdown

Getting ready

How to do it...

How it works…

There's more…

Developing a single-file Shiny app

Getting ready

How to do it…

How it works…

See also

Changing a Shiny app UI based on user input

Getting ready

How to do it...

See also

Creating an interactive report with Shiny

How to do it…

How it works...

See also

Constructing RStudio add-ins

Getting ready

How to do it...

There's more…

Sharing your work on RPubs

Getting ready

How to do it...

There's more…

Deploying your app on Amazon AWS with ramazon

Getting ready

How to do it...

Index

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