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.
Table of Contents (15 chapters)
RStudio for R Statistical Computing Cookbook
Credits
About the Author
About the Reviewer
www.PacktPub.com
Preface
Index

Getting a sense of your data structure with R


By following the recipes given in the previous chapter, you got your data. Everything went smoothly, and you may also already have the data as a data frame object.

However, do you know what your data looks like?

Getting to know your data structure is a crucial step within a data analysis project. It will suggest the appropriate treatment and analysis, and will help you avoid error and redundancy in the coding activity that follows.

In this recipe, we will look at a dataset structure by leveraging the describe() function from the Hmisc package. For further preliminary analysis on your data structure, you can also refer to the data visualization recipes in Chapter 3, Basic Visualization Techniques.

Getting ready

This example will be built around a dataset provided in the RStudio project related to this book.

You can download it by authenticating your account at http://packtpub.com.

This dataset is named world_gdp_data.csv and stores GDP values for 248...