||: Paper Back
|Number of Pages
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
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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