Case Study: Exploratory Data Analysis in R
Use data manipulation and visualization skills to explore the historical voting of the United Nations General Assembly.
Start Course for Free4 Hours15 Videos58 Exercises41,391 Learners
4800 XPCreate Your Free Account
or
By continuing, you accept our Terms of Use, our Privacy Policy and that your data is stored in the USA. You confirm you are at least 16 years old (13 if you are an authorized Classrooms user).Loved by learners at thousands of companies
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
Data cleaning and summarizing with dplyr
FreeThe 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.
Nick Carchedi
Tom JeonPrerequisites
Introduction to Data Visualization with ggplot2
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.
What do other learners have to say?
“I've used other sites—Coursera, Udacity, things like that—but DataCamp's been the one that I've stuck with.”
Devon Edwards Joseph
Lloyds Banking Group
“DataCamp is the top resource I recommend for learning data science.”
Louis Maiden
Harvard Business School
“DataCamp is by far my favorite website to learn from.”
Ronald Bowers
Decision Science Analytics, USAA
Join over 9 million learners and start Case Study: Exploratory Data Analysis in R today!
Create Your Free Account
or
By continuing, you accept our Terms of Use, our Privacy Policy and that your data is stored in the USA. You confirm you are at least 16 years old (13 if you are an authorized Classrooms user).