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 Exercises41,961 Learners
4800 XP

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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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In the following tracks

Data Analyst Data Manipulation Data Scientist

Collaborators

Nick CarchediTom Jeon
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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