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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

Filtering summarized output

In the last exercise, you may have noticed that the country that voted least frequently, Zanzibar, had only 2 votes in the entire dataset. You certainly can't make any substantial conclusions based on that data!

Typically in a progressive analysis, when you find that a few of your observations have very little data while others have plenty, you set some threshold to filter them out.

Instructions

100 XP

Use filter() to remove from the sorted data countries that have fewer than 100 votes.