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Learning RStudio for R Statistical Computing

Learning RStudio for R Statistical Computing

3.8 (16)
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Learning RStudio for R Statistical Computing

Learning RStudio for R Statistical Computing

3.8 (16)

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)
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Code chunks


In all of the markup systems supported by RStudio, chunks of R code can be embedded and executed. There are many options controlling how the code andits results are shown in the report, how resulting figures should be displayed, and so on.

Chunk syntax and options

Each markup system has its own syntax to distinguish R code from regular text, but in every system it is possible to label code chunks and to pass processing options. Both labeling and optioning are not mandatory and can be left out, so default settings will be used. The following is an overview of the code chunk denominators. You do not have to remember any of them; for each file type, the Chunks menu has the Insert chunk option.

RMarkdown: .Rmd files

Code chunks are indicated with triple backticks:

'''{r <label>, <option>=<value>,... }
# Your R code here
'''

Inline code is enclosed in single backticks:

'r <R code>'

Rhtml: .Rhtml files

Code chunks are indicated as special HTML comment sections:

&lt...
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Learning RStudio for R Statistical Computing
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