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Case Study: Exploratory Data Analysis in R

Use data manipulation and visualization skills to explore the historical voting of the United Nations General Assembly.

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4 Hours15 Videos58 Exercises47,444 Learners4800 XPData Manipulation TrackData Scientist Track

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Course Description

Once you've started learning tools for data manipulation and visualization like dplyr and ggplot2, this course gives you a chance to use them in action on a real dataset. You'll explore the historical voting of the United Nations General Assembly, including analyzing differences in voting between countries, across time, and among international issues. In the process you'll gain more practice with the dplyr and ggplot2 packages, learn about the broom package for tidying model output, and experience the kind of start-to-finish exploratory analysis common in data science.
  1. 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.

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    The United Nations Voting Dataset
    50 xp
    Filtering rows
    100 xp
    Adding a year column
    100 xp
    Adding a country column
    100 xp
    Grouping and summarizing
    50 xp
    Summarizing the full dataset
    100 xp
    Summarizing by year
    100 xp
    Summarizing by country
    100 xp
    Sorting and filtering summarized data
    50 xp
    Sorting by percentage of "yes" votes
    100 xp
    Filtering summarized output
    100 xp
  2. 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.

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    Visualization with ggplot2
    50 xp
    Choosing an aesthetic
    50 xp
    Plotting a line over time
    100 xp
    Other ggplot2 layers
    100 xp
    Visualizing by country
    50 xp
    Summarizing by year and country
    100 xp
    Plotting just the UK over time
    100 xp
    Plotting multiple countries
    100 xp
    Faceting by country
    50 xp
    Faceting the time series
    100 xp
    Faceting with free y-axis
    100 xp
    Choose your own countries
    100 xp
  3. 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.

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    Linear regression
    50 xp
    Linear regression on the United States
    100 xp
    Finding the slope of a linear regression
    50 xp
    Finding the p-value of a linear regression
    50 xp
    Tidying models with broom
    50 xp
    Tidying a linear regression model
    100 xp
    Combining models for multiple countries
    100 xp
    Nesting for multiple models
    50 xp
    Nesting a data frame
    100 xp
    List columns
    100 xp
    Unnesting
    100 xp
    Fitting multiple models
    50 xp
    Performing linear regression on each nested dataset
    100 xp
    Tidy each linear regression model
    100 xp
    Unnesting a data frame
    100 xp
    Working with many tidy models
    50 xp
    Filtering model terms
    100 xp
    Filtering for significant countries
    100 xp
    Sorting by slope
    100 xp
  4. 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.

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    Joining datasets
    50 xp
    Joining datasets with inner_join
    100 xp
    Filtering the joined dataset
    100 xp
    Visualizing colonialism votes
    100 xp
    Tidy data
    50 xp
    Tidy data observations
    50 xp
    Using gather to tidy a dataset
    100 xp
    Recoding the topics
    100 xp
    Summarize by country, year, and topic
    100 xp
    Visualizing trends in topics for one country
    100 xp
    Tidy modeling by topic and country
    50 xp
    Nesting by topic and country
    100 xp
    Interpreting tidy models
    100 xp
    Steepest trends by topic
    50 xp
    Checking models visually
    100 xp
    Conclusion
    50 xp

In the following tracks

Data Manipulation Data Scientist

Datasets

United Nations voting datasetTopic information for each country (Descriptions)

Collaborators

n10i
Nick Carchedi
tommyjee
Tom Jeon

Prerequisites

Introduction to Data Visualization with ggplot2
David Robinson Headshot

David Robinson

Principal Data Scientist at Heap

Dave is the Principal Data Scientist at Heap. He has worked as a data scientist at DataCamp and Stack Overflow, and received his PhD in Quantitative and Computational Biology from Princeton University. Follow him at @drob on Twitter or on his blog, Variance Explained.
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