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  3. Exploratory Data Analysis in R
  • 1

    Exploring Categorical Data

    Free

    In this chapter, you will learn how to create graphical and numerical summaries of two categorical variables.

  • 2

    Exploring Numerical Data

    In this chapter, you will learn how to graphically summarize numerical data.

  • 3

    Numerical Summaries

    Now that we've looked at exploring categorical and numerical data, you'll learn some useful statistics for describing distributions of data.

  • 4

    Case Study

    Apply what you've learned to explore and summarize a real world dataset in this case study of email spam.

Initializing

Exercise

Calculate center measures

Throughout this chapter, you will use data from gapminder, which tracks demographic data in countries of the world over time. To learn more about it, you can bring up the help file with ?gapminder.

For this exercise, focus on how the life expectancy differs from continent to continent. This requires that you conduct your analysis not at the country level, but aggregated up to the continent level. This is made possible by the one-two punch of group_by() and summarize(), a very powerful syntax for carrying out the same analysis on different subsets of the full dataset.

Instructions

100 XP
  • Create a dataset called gap2007 that contains only data from the year 2007.
  • Using gap2007, calculate the mean and median life expectancy for each continent. Don't worry about naming the new columns produced by summarize().
  • Confirm the trends that you see in the medians by generating side-by-side box plots of life expectancy for each continent.