Book Image

RStudio for R Statistical Computing Cookbook

By : Andrea Cirillo
Book Image

RStudio for R Statistical Computing Cookbook

By: Andrea Cirillo

Overview of this book

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.

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RStudio for R Statistical Computing Cookbook
Andrea Cirillo
Apr, 2016 | 246 pages
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Table of Contents (15 chapters)
RStudio for R Statistical Computing Cookbook
RStudio for R Statistical Computing Cookbook
Credits
Credits
About the Author
About the Author
About the Reviewer
About the Reviewer
www.PacktPub.com
www.PacktPub.com
Preface
Preface
Free Chapter
1
Acquiring Data for Your Project
Acquiring Data for Your Project
Introduction
Acquiring data from the Web – web scraping tasks
Accessing an API with R
Getting data from Twitter with the twitteR package
Getting data from Facebook with the Rfacebook package
Getting data from Google Analytics
Loading your data into R with rio packages
Converting file formats using the rio package
2
Preparing for Analysis – Data Cleansing and Manipulation
Preparing for Analysis – Data Cleansing and Manipulation
Introduction
Getting a sense of your data structure with R
Preparing your data for analysis with the tidyr package
Detecting and removing missing values
Substituting missing values using the mice package
Detecting and removing outliers
Performing data filtering activities
3
Basic Visualization Techniques
Basic Visualization Techniques
Introduction
Looking at your data using the plot() function
Using pairs.panel() to look at (visualize) correlations between variables
Adding text to a ggplot2 plot at a custom location
Changing axes appearance to ggplot2 plot (continous axes)
Producing a matrix of graphs with ggplot2
Drawing a route on a map with ggmap
Making use of the igraph package to draw a network
Showing communities in a network with the linkcomm package
4
Advanced and Interactive Visualization
Advanced and Interactive Visualization
Introduction
Producing a Sankey diagram with the networkD3 package
Creating a dynamic force network with the visNetwork package
Building a rotating 3D graph and exporting it as a GIF
Using the DiagrammeR package to produce a process flow diagram in RStudio
5
Power Programming with R
Power Programming with R
Introduction
Writing modular code in RStudio
Implementing parallel computation in R
Creating custom objects and methods in R using the S3 system
Evaluating your code performance using the profvis package
Comparing an alternative function's performance using the microbenchmarking package
Using GitHub with RStudio
6
Domain-specific Applications
Domain-specific Applications
Introduction
Dealing with regular expressions
Analyzing PDF reports in a folder with the tm package
Creating word clouds with the wordcloud package
Performing a Twitter sentiment analysis
Detecting fraud in e-commerce orders with Benford's law
Measuring customer retention using cohort analysis in R
Making a recommendation engine
Performing time series decomposition using the stl() function
Exploring time series forecasting with forecast()
Tracking stock movements using the quantmod package
Optimizing portfolio composition and maximising returns with the Portfolio Analytics package
Forecasting the stock market
7
Developing Static Reports
Developing Static Reports
Introduction
Using one markup language for all types of documents – rmarkdown
Writing and styling PDF documents with RStudio
Writing wonderful tufte handouts with the tufte package and rmarkdown
Sharing your code and plots with slides
Curating a blog through RStudio
8
Dynamic Reporting and Web Application Development
Dynamic Reporting and Web Application Development
Introduction
Generating dynamic parametrized reports with R Markdown
Developing a single-file Shiny app
Changing a Shiny app UI based on user input
Creating an interactive report with Shiny
Constructing RStudio add-ins
Sharing your work on RPubs
Deploying your app on Amazon AWS with ramazon
Index
Index

Introduction

The American statistician Edward Deming once said:

"Without data you are just another man with an opinion."

I think this great quote is enough to highlight the importance of the data acquisition phase of every data analysis project. This phase is exactly where we are going to start from. This chapter will give you tools for scraping the Web, accessing data via web APIs, and importing nearly every kind of file you will probably have to work with quickly, thanks to the magic package rio.

All the recipes in this book are based on the great and popular packages developed and maintained by the members of the R community.

After reading this section, you will be able to get all your data into R to start your data analysis project, no matter where it comes from.

Before starting the data acquisition process, you should gain a clear understanding of your data needs. In other words, what data do you need in order to get solutions to your problems?

A rule of thumb to solve this problem is to look at the process that you are investigating—from input to output—and outline all the data that will go in and out during its development.

In this data, you will surely have that chunk of data that is needed to solve your problem.

In particular, for each type of data you are going to acquire, you should define the following:

  • The source: This is where data is stored

  • The required authorizations: This refers to any form of authorization/authentication that is needed in order to get the data you need

  • The data format: This is the format in which data is made available

  • The data license: This is to check whether there is any license covering data utilization/distribution or whether there is any need for ethics/privacy considerations

After covering these points for each set of data, you will have a clear vision of future data acquisition activities. This will let you plan ahead the activities needed to clearly define resources, steps, and expected results.

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