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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

Boxplots and density plots

The mileage of a car tends to be associated with the size of its engine (as measured by the number of cylinders). To explore the relationship between these two variables, you could stick to using histograms, but in this exercise you'll try your hand at two alternatives: the box plot and the density plot.

Instructions

100 XP

A quick look at unique(cars$ncyl) shows that there are more possible levels of ncyl than you might think. Here, restrict your attention to the most common levels.

  • Filter cars to include only cars with 4, 6, or 8 cylinders and save the result as common_cyl. The %in% operator may prove useful here.
  • Create side-by-side box plots of city_mpg separated out by ncyl.
  • Create overlaid density plots of city_mpg colored by ncyl.