After doing these courses, I feel confident creating professional visualizations and dashboards
Shoebur RahmanProcurement Specialist, Giz Bangladesh
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.
Prerequisites
Curriculum
Course outline
1
Data cleaning and summarizing with dplyr
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.
- The United Nations Voting Dataset50 XP
- Filtering rows100 XP
- Adding a year column100 XP
- Adding a country column100 XP
- Grouping and summarizing50 XP
- Summarizing the full dataset100 XP
- Summarizing by year100 XP
- Summarizing by country100 XP
- Sorting and filtering summarized data50 XP
- Sorting by percentage of "yes" votes100 XP
- Filtering summarized output100 XP
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.
R
Case Study: Exploratory Data Analysis in R
Course
Complete
