课程

Case Study: Exploratory Data Analysis in R

基础技能水平

更新时间 2024年9月

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

免费开始课程

RExploratory Data Analysis

4小时

15 视频

58 道练习

4,800 XP

56,667

成就证明

深受数千家公司学习者的喜爱

需要团队培训？

企业版试用

课程描述

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.

先决条件

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

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

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

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

Case Study: Exploratory Data Analysis in R

课程完成

获得成就证明

将此证书添加到您的 LinkedIn 档案、简历或履历中
在社交媒体和绩效评估中分享立即注册

加入超过19百万学习者，今天就开始Case Study: Exploratory Data Analysis in R！

通过 DataCamp for Mobile 提升您的数据技能

随时随地通过我们的移动课程和每日 5 分钟编程挑战提升技能。