Book Image

Learning RStudio for R Statistical Computing

Book Image

Learning RStudio for R Statistical Computing

Overview of this book

Data is coming at us faster, dirtier, and at an ever increasing rate. The necessity to handle many, complex statistical analysis projects is hitting statisticians and analysts across the globe. This book will show you how to deal with it like never before, thus providing an edge and improving productivity. "Learning RStudio for R Statistical Computing" will teach you how to quickly and efficiently create and manage statistical analysis projects, import data, develop R scripts, and generate reports and graphics. R developers will learn about package development, coding principles, and version control with RStudio. This book will help you to learn and understand RStudio features to effectively perform statistical analysis and reporting, code editing, and R development. The book starts with a quick introduction where you will learn to load data, perform simple analysis, plot a graph, and generate automatic reports. You will then be able to explore the available features for effective coding, graphical analysis, R project management, report generation, and even project management. "Learning RStudio for R Statistical Computing" is stuffed with feature-rich and easy-to-understand examples, through step-by-step instructions helping you to quickly master the most popular IDE for R development.
Table of Contents (13 chapters)

Interactive plotting with the manipulate package


During exploratory data analysis, it is often useful to play with the parameters of a graphic. This can be done, of course, in the R console by repeatedly executing the same command and changing the graphical parameters. RStudio includes the manipulate package, that facilitates altering parameters of the current plot.

The manipulate function

The most important function of the manipulate package is manipulate. The value of the first argument of the manipulate function must be an expression or function that generates a plot. Various arguments can be added to define custom sliders, buttons, checkboxes, or pickers (drop-down menus) that are to be used in a small user interface (a manipulator) to manipulate a graphic. The following is an example of a manipulator:

library(manipulate)

manipulate(
 plot( Length ~ Rings, data=abalone
 	, axes = axes
 	, cex = cex
 	, pch = if(pch) 19 else 1
 	)
 , axes = checkbox(TRUE, "Show axes")
 , cex = slider...