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Course

Introduction to the Tidyverse

BasicSkill Level

4.8+

Updated 12/2025

Get started on the path to exploring and visualizing your own data with the tidyverse, a powerful and popular collection of data science tools within R.

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RProgramming4 hr16 videos50 Exercises4,150 XP380K+Statement of Accomplishment

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Course Description

This is an introduction to the programming language R, focused on a powerful set of tools known as the Tidyverse. You'll learn the intertwined processes of data manipulation and visualization using the tools dplyr and ggplot2. You'll learn to manipulate data by filtering, sorting, and summarizing a real dataset of historical country data in order to answer exploratory questions. You'll then learn to turn this processed data into informative line plots, bar plots, histograms, and more with the ggplot2 package. You’ll get a taste of the value of exploratory data analysis and the power of Tidyverse tools. This is a suitable introduction for those who have no previous experience in R and are interested in performing data analysis.The videos contain live transcripts you can reveal by clicking "Show transcript" at the bottom left of the videos. The course glossary can be found on the right in the resources section. To obtain CPE credits you need to complete the course and reach a score of 70% on the qualified assessment. You can navigate to the assessment by clicking on the CPE credits callout on the right.

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What you'll learn

Construct effective scatterplots and apply additional aesthetics to explore relationships in data
Explore and transform data using dplyr verbs to prepare datasets for analysis
Produce a variety of visualizations (line, bar, histogram, boxplot) to communicate distributional and temporal patterns
Summarize and aggregate data using group_by() and summarize() to reveal patterns across groups

Prerequisites

There are no prerequisites for this course

1

Data wrangling

In this chapter, you'll learn to do three things with a table: filter for particular observations, arrange the observations in a desired order, and mutate to add or change a column. You'll see how each of these steps allows you to answer questions about your data.

The gapminder dataset

Loading the gapminder and dplyr packages

Understanding a data frame

The filter verb

Filtering for one year

Filtering for one country and one year

The arrange verb

Arranging observations by life expectancy

Filtering and arranging

The mutate verb

Using mutate to change or create a column

Combining filter, mutate, and arrange

2

Data visualization

Often a better way to understand and present data as a graph. In this chapter, you'll learn the essential skills of data visualization using the ggplot2 package, and you'll see how the dplyr and ggplot2 packages work closely together to create informative graphs.

Visualizing with ggplot2

Variable assignment

Comparing population and GDP per capita

Comparing population and life expectancy

Putting the x-axis on a log scale

Putting the x- and y- axes on a log scale

Additional aesthetics

Adding color to a scatter plot

Adding size and color to a plot

Creating a subgraph for each continent

Faceting by year

3

Grouping and summarizing

So far you've been answering questions about individual country-year pairs, but you may be interested in aggregations of the data, such as the average life expectancy of all countries within each year. Here you'll learn to use the group by and summarize verbs, which collapse large datasets into manageable summaries.

The summarize verb

Summarizing the median life expectancy

Summarizing the median life expectancy in 1957

Summarizing multiple variables in 1957

The group_by verb

Summarizing by year

Summarizing by continent

Summarizing by continent and year

Visualizing summarized data

Visualizing median life expectancy over time

Visualizing median GDP per capita per continent over time

Comparing median life expectancy and median GDP per continent in 2007

4

Types of visualizations

In this chapter, you'll learn how to create line plots, bar plots, histograms, and boxplots. You'll see how each plot requires different methods of data manipulation and preparation, and you’ll understand how each of these plot types plays a different role in data analysis.

Visualizing median GDP per capita over time

Visualizing median GDP per capita by continent over time

Visualizing median GDP per capita by continent

Visualizing GDP per capita by country in Oceania

Visualizing population

Visualizing population with x-axis on a log scale

Comparing GDP per capita across continents

Adding a title to your graph

Introduction to the Tidyverse

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FAQs

Is this course suitable for beginners?

Yes, this course is suitable for beginners. It introduces the programming language R and the Tidyverse tools for data manipulation and visualization.

Will I receive a certificate at the end of the course?

Yes, if you complete the course successfully, you will receive a certificate at the end of the course.

Who will benefit from this course?

This course is beneficial for jobs that require data analysis and visualization skills, such as data scientists, statisticians, and business analysts.

What is the Tidyverse?

The Tidyverse is a powerful collection of R packages for data manipulation and visualization. It is designed for data scientists, statisticians, and business analyticsto quickly and easily explore, transform, and visualize data.

What skills will I learn?

You will learn how to manipulate data using filtering, sorting, and summarizing; how to create line plots, bar plots, histograms, and boxplots; and how to use the ggplot2 and dplyr packages and the Tidyverse tools to analyze data.

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