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Beginning R: The Statistical Programming Language by Mark Gardener

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Chapter 4

Data: Descriptive Statistics and Tabulation

What you will learn in this chapter:

  • How to summarize data samples
  • How to use cumulative statistics
  • How to create summary tables
  • How to cross-tabulate
  • How to test for different object types

Important elements in data analysis include summary and descriptive statistics. These provide a shorthand way of describing and summarizing your data, which is important in pointing you towards the correct analytical procedure and helping you understand your data. There are three main ways you can describe or summarize your data:

  • Summary statistics
  • Tabulation
  • Graphical

In this chapter you will learn about using summary statistics to provide a shorthand way of describing your data as opposed to merely listing the contents. You will also look at tabulation as a method to create summaries. Tables can split your data into manageable chunks that show you patterns that you would otherwise miss. Producing a graphical summary of your data is also important because a visual impression can convey more to a reader than numerical values; these are the subjects of Chapter 5.

Summary Commands

An essential starting point with any set of data is to get an overview of what you are dealing with. There are a few ways to go about doing this. You might start by using the ls() command to see what named objects you have. You can then type the name of one of the objects to view its contents. However, if the object contains a lot of data, the display may be quite ...

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