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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

Plotting a line over time

In the last chapter, you learned how to summarize() the votes dataset by year, particularly the percentage of votes in each year that were "yes".

You'll now use the ggplot2 package to turn your results into a visualization of the percentage of "yes" votes over time.

Instructions

100 XP

The by_year dataset has the number of votes and percentage of "yes" votes each year.

  • Load the ggplot2 package.
  • Use ggplot() with the geom_line layer to create a line plot with year on the x-axis and percent_yes on the y-axis.