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Case Study: Exploratory Data Analysis in R

基礎スキルレベル

更新日 2024/09

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

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RExploratory Data Analysis

4時間

15 ビデオ

58 演習

4,800 XP

56,672

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コース説明

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.

前提条件

Introduction to Data Visualization with ggplot2

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 Dataset

Filtering rows

Adding a year column

Adding a country column

Grouping and summarizing

Summarizing the full dataset

Summarizing by year

Summarizing by country

Sorting and filtering summarized data

Sorting by percentage of "yes" votes

Filtering summarized output

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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.

Visualization with ggplot2

Choosing an aesthetic

Plotting a line over time

Other ggplot2 layers

Visualizing by country

Summarizing by year and country

Plotting just the UK over time

Plotting multiple countries

Faceting by country

Faceting the time series

Faceting with free y-axis

Choose your own countries

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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.

Linear regression

Linear regression on the United States

Finding the slope of a linear regression

Finding the p-value of a linear regression

Tidying models with broom

Tidying a linear regression model

Combining models for multiple countries

Nesting for multiple models

Nesting a data frame

List columns

Fitting multiple models

Performing linear regression on each nested dataset

Tidy each linear regression model

Unnesting a data frame

Working with many tidy models

Filtering model terms

Filtering for significant countries

Sorting by slope

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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.

Joining datasets

Joining datasets with inner_join

Filtering the joined dataset

Visualizing colonialism votes

Tidy data observations

Using gather to tidy a dataset

Recoding the topics

Summarize by country, year, and topic

Visualizing trends in topics for one country

Tidy modeling by topic and country

Nesting by topic and country

Interpreting tidy models

Steepest trends by topic

Checking models visually

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Case Study: Exploratory Data Analysis in R

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