Interactive Course

Exploratory Data Analysis in R: Case Study

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

  • 4 hours
  • 15 Videos
  • 58 Exercises
  • 17,867 Participants
  • 4,800 XP

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.

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

Course Outline

  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.

  2. 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.

  3. 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.

  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.

  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.

  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.

David Robinson
David Robinson

Chief Data Scientist, DataCamp

Dave works on the data science behind DataCamp's product and curriculum development. He has worked as a data scientist at Stack Overflow and received his PhD in Quantitative and Computational Biology from Princeton University. His interests include statistics, data analysis, education, and programming in R. Follow him at @drob on Twitter or on his blog, Variance Explained.

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Course Instructor

David Robinson
David Robinson

Chief Data Scientist, DataCamp

Dave works on the data science behind DataCamp's product and curriculum development. He has worked as a data scientist at Stack Overflow and received his PhD in Quantitative and Computational Biology from Princeton University. His interests include statistics, data analysis, education, and programming in R. Follow him at @drob on Twitter or on his blog, Variance Explained.

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Collaborator(s)
  • Nick Carchedi

    Nick Carchedi

  • Tom Jeon

    Tom Jeon

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