Data Science Desktop Survival Guide by Graham Williams
Desktop Survival Project Home Preface Data Science Introducing R R Constructs R Tasks R Strings R Read, Write, and Create Data Template Data Exploration Data Wrangling Data Visualisation Statistics ML Template ML Scenarios ML Activities ML Applications ML Algorithms Cluster Analysis Decision Trees Computer Vision Graph Data Privacy Literate Data Science Coding with Style Resources Bibliography Index

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Getting Help

REVIEW A key skill of any programmer, including those programming over data, is the ability to identify how to access the full power of our tools. The breadth and depth of the capabilities of R means that there is much to learn around both the basics of R programming and the multitude of packages that support the data scientist. R provides extensive in-built documentation with many introductory manuals and resources accessible through the Help tab. These are a good adjunct to our very brief introduction here to R. Further we can use 's search facility for documentation on any action and we will find manual pages that provide an understanding of the purpose, arguments, and return value of functions, commands, and operators. We can also ask for help using the utils::? operator as in:

# Ask for documentation on using the library command.

? library

The utils package which provides this operator is another package that is attached by default into R. Thus we can drop its prefix as we did here when we run the command in the R Console. provides a search box which can be used to find specific topics within the vast collection of R documentation. Now would be a good time to check the documentation for the base::library() command. Beginning with this chapter we will also list at the conclusion of each chapter the functions, commands, and operators introduced within the chapter together with a brief synopsis of the purpose. The following section reviews all of the functions introduced so far.