Book Image

Data Analysis with R, Second Edition - Second Edition

Book Image

Data Analysis with R, Second Edition - Second Edition

Overview of this book

Frequently the tool of choice for academics, R has spread deep into the private sector and can be found in the production pipelines at some of the most advanced and successful enterprises. The power and domain-specificity of R allows the user to express complex analytics easily, quickly, and succinctly. Starting with the basics of R and statistical reasoning, this book dives into advanced predictive analytics, showing how to apply those techniques to real-world data though with real-world examples. Packed with engaging problems and exercises, this book begins with a review of R and its syntax with packages like Rcpp, ggplot2, and dplyr. From there, get to grips with the fundamentals of applied statistics and build on this knowledge to perform sophisticated and powerful analytics. Solve the difficulties relating to performing data analysis in practice and find solutions to working with messy data, large data, communicating results, and facilitating reproducibility. This book is engineered to be an invaluable resource through many stages of anyone’s career as a data analyst.
Table of Contents (24 chapters)
Title Page
Copyright and Credits
Packt Upsell
Contributors
Preface
Index

Getting help in R


Before we go further, it would serve us well to have a brief section detailing how to get help in R. Most R tutorials leave this for one of the last sections--if it is even included at all! In my own personal experience, though, getting help is going to be one of the first things you will want to do as you add more bricks to your R knowledge castle. Learning R doesn't have to be difficult; just take it slowly, ask questions, and get help early. Go you!

It is easy to get help with R right at the console. Running the help.start() function at the prompt will start a manual browser. From here, you can do anything from going over the basics of R to reading the nitty-gritty details on how R works internally.

You can get help with a particular function in R if you know its name, by supplying that name as an argument to the help function. For example, let's say you want to know more about the gsub() function that I sprang on you before. Check out the following code:

  > help("gsub") 
  > # or simply 
  > ?gsub 

This will display a manual page documenting what the function is, how to use it, and examples of its usage.

This rapid accessibility to documentation means that I'm never hopelessly lost when I encounter a function that I haven't seen before. The downside to this extraordinarily convenient help mechanism is that I rarely bother to remember the order of arguments as looking them up is just seconds away.

Occasionally, you won't quite remember the exact name of the function that you're looking for, but you'll have an idea about what the name should be. For this, you can use the help.search() function:

   > help.search("chisquare") 
   > # or simply 
   > ??chisquare 

For tougher, more semantic queries, nothing beats a good old fashioned web search engine. If you don't get relevant results the first time, try adding the term programming or statistics in there for good measure.