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  3. Case Study: Exploratory Data Analysis in R
  • 1

    Data cleaning and summarizing with dplyr

    Free

    The best way to learn data wrangling skills is to apply them to a specific case study. Here you'll learn how to clean and filter the United Nations voting dataset using the dplyr package, and how to summarize it into smaller, interpretable units.

  • 2

    Data visualization with ggplot2

    Once you've cleaned and summarized data, you'll want to visualize them to understand trends and extract insights. Here you'll use the ggplot2 package to explore trends in United Nations voting within each country over time.

  • 3

    Tidy modeling with broom

    While visualization helps you understand one country at a time, statistical modeling lets you quantify trends across many countries and interpret them together. Here you'll learn to use the tidyr, purrr, and broom packages to fit linear models to each country, and understand and compare their outputs.

  • 4

    Joining and tidying

    In this chapter, you'll learn to combine multiple related datasets, such as incorporating information about each resolution's topic into your vote analysis. You'll also learn how to turn untidy data into tidy data, and see how tidy data can guide your exploration of topics and countries over time.

Initializing

Exercise

Faceting with free y-axis

In the previous plot, all six graphs had the same axis limits. This made the changes over time hard to examine for plots with relatively little change.

Instead, you may want to let the plot choose a different y-axis for each facet.

Instructions

100 XP

Change the faceted plot so that the y-axis is freely chosen for each facet, rather than being the same for all six.