Markdown (created by John Gruber and Aaron Swartz) is an easy-to-read and easy-to-write markup language that is designed to make preparing HTML documents (web pages) easier. The Markdown syntax is inspired by how people write plain text e-mails. For example, to emphasize a word in an e-mail, constructs like *emphasized word* or _emphasized word_ are frequently used. Also, people tend to use asterisks or dashes to represent bullet lists in plain text. The idea of Markdown is to treat such constructions as actual markup commands by translating them to equivalent HTML syntax (web page). With Markdown, you can alter the appearance of text by altering its size, typeface, and more. What you cannot do with Markdown, is to alter document properties such as page size, margin sizes, and so on. If you need to control such features, you can consider switching to LaTeX (described in the following section). Alternatively, one can use Max Kuhn's odfWeave package (not supported by RStudio...
Learning RStudio for R Statistical Computing
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.
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Learning RStudio for R Statistical Computing
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Table of Contents (13 chapters)
Learning RStudio for R Statistical Computing
Credits
About the Authors
About the Reviewers
www.PacktPub.com
Preface
Free Chapter
Getting Started
Writing R Scripts and the R Console
Viewing and Plotting Data
Managing R Projects
Generating Reports
Using RStudio Effectively
Index
Customer Reviews