Case Study: Exploratory Data Analysis in R Course

Name: Case Study: Exploratory Data Analysis in R
Rating: 4.933333333333334 (45 reviews)

Case Study: Exploratory Data Analysis in R

BasicSkill Level

4.9+

45 reviews

Updated 09/2024

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

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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

Introduction to Data Visualization with ggplot2

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

50 XP

Filtering rows

100 XP

Adding a year column

100 XP

Adding a country column

100 XP

Grouping and summarizing

50 XP

Summarizing the full dataset

100 XP

Summarizing by year

100 XP

Summarizing by country

100 XP

Sorting and filtering summarized data

50 XP

Sorting by percentage of "yes" votes

100 XP

Filtering summarized output

100 XP

Start Chapter

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

50 XP

Choosing an aesthetic

50 XP

Plotting a line over time

100 XP

Other ggplot2 layers

100 XP

Visualizing by country

50 XP

Summarizing by year and country

100 XP

Plotting just the UK over time

100 XP

Plotting multiple countries

100 XP

Faceting by country

50 XP

Faceting the time series

100 XP

Faceting with free y-axis

100 XP

Choose your own countries

100 XP

Start Chapter

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

50 XP

Linear regression on the United States

100 XP

Finding the slope of a linear regression

50 XP

Finding the p-value of a linear regression

50 XP

Tidying models with broom

50 XP

Tidying a linear regression model

100 XP

Combining models for multiple countries

100 XP

Nesting for multiple models

50 XP

Nesting a data frame

100 XP

List columns

100 XP

Unnesting

100 XP

Fitting multiple models

50 XP

Performing linear regression on each nested dataset

100 XP

Tidy each linear regression model

100 XP

Unnesting a data frame

100 XP

Working with many tidy models

50 XP

Filtering model terms

100 XP

Filtering for significant countries

100 XP

Sorting by slope

100 XP

Start Chapter

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

50 XP

Joining datasets with inner_join

100 XP

Filtering the joined dataset

100 XP

Visualizing colonialism votes

100 XP

Tidy data

50 XP

Tidy data observations

50 XP

Using gather to tidy a dataset

100 XP

Recoding the topics

100 XP

Summarize by country, year, and topic

100 XP

Visualizing trends in topics for one country

100 XP

Tidy modeling by topic and country

50 XP

Nesting by topic and country

100 XP

Interpreting tidy models

100 XP

Steepest trends by topic

50 XP

Checking models visually

100 XP

Conclusion

50 XP

Start Chapter

Case Study: Exploratory Data Analysis in R

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Don’t just take our word for it

*4.9

from 45 reviews

93%

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julio

7 days ago

Damla

3 weeks ago

Phyllis

last month

Course is excellent in providing practice of R skills. However, the workspace fails miserably - continually providing error messages for errors that don't exist. To proceed beyond such a screen means having to manipulate, retype, recopy and every other necessary tactic that finally proves acceptable the script which was originally entered. Huge time waster that needs fixing.

Puyan

2 months ago

Olena

2 months ago

Delruba Mahmud

2 months ago

julio

Puyan

Delruba Mahmud

FAQs

What real-world dataset does this case study explore?

You analyze the historical voting record of the United Nations General Assembly, examining how countries vote differently across time and across international issues.

Which R packages will I practice using in this course?

You work primarily with dplyr for data wrangling, ggplot2 for visualization, and the broom package for tidying statistical model output. Tidyr and purrr also appear.

Is this course appropriate for R beginners?

Yes. It is beginner level, requiring only Introduction to the Tidyverse and Introduction to Data Visualization with ggplot2 as prerequisites.

Does this course include statistical modeling or is it only visualization?

It includes both. Chapter 3 teaches you to fit linear models to each country using broom and purrr, then compare and interpret the outputs alongside your visualizations.

How is the course structured across its four chapters?

You progress from cleaning and summarizing data with dplyr, to visualizing trends with ggplot2, to tidy modeling with broom, and finally to joining and reshaping datasets for deeper analysis.

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Case Study: Exploratory Data Analysis in R

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Course Description

Prerequisites

Data cleaning and summarizing with dplyr

Data visualization with ggplot2

Tidy modeling with broom

Joining and tidying

Earn Statement of Accomplishment

Don’t just take our word for it

FAQs

What real-world dataset does this case study explore?

Which R packages will I practice using in this course?

Is this course appropriate for R beginners?

Does this course include statistical modeling or is it only visualization?

How is the course structured across its four chapters?

Join over 19 million learners and start Case Study: Exploratory Data Analysis in R today!

Create Your Free Account

Grow your data skills with DataCamp for Mobile

Course Description

Earn Statement of Accomplishment

Don’t just take our word for it

FAQs

Which R packages will I practice using in this course?

Is this course appropriate for R beginners?

Does this course include statistical modeling or is it only visualization?

How is the course structured across its four chapters?

Join over .css-nklxlk{color:var(--wf-brand--main, #03EF62);}19 million learners and start Case Study: Exploratory Data Analysis in R today!

Create Your Free Account

Grow your data skills with DataCamp for Mobile

Join over 19 million learners and start Case Study: Exploratory Data Analysis in R today!