  Interactive Course

# Introduction to the Tidyverse

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.

• 4 hours
• 16 Videos
• 50 Exercises
• 71,522 Participants
• 4,150 XP

### Loved by learners at thousands of top companies:      ### Course Description

This is an introduction to the programming language R, focused on a powerful set of tools known as the "tidyverse". In the course you'll learn the intertwined processes of data manipulation and visualization through 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. This gives a taste both of the value of exploratory data analysis and the power of tidyverse tools. This is a suitable introduction for people who have no previous experience in R and are interested in learning to perform data analysis.

1. 1

#### Data wrangling

Free

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 lets you answer questions about your data.

2. #### Grouping and summarizing

So far you've been answering questions about individual country-year pairs, but we 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.

3. #### Data visualization

You've already been able to answer some questions about the data through dplyr, but you've engaged with them just as a table (such as one showing the life expectancy in the US each year). Often a better way to understand and present such data is as a graph. Here you'll learn the essential skill of data visualization, using the ggplot2 package. Visualization and manipulation are often intertwined, so you'll see how the dplyr and ggplot2 packages work closely together to create informative graphs.

4. #### Types of visualizations

You've learned to create scatter plots with ggplot2. In this chapter you'll learn to create line plots, bar plots, histograms, and boxplots. You'll see how each plot needs different kinds of data manipulation to prepare for it, and understand the different roles of each of these plot types in data analysis.

1. 1

#### Data wrangling

Free

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 lets you answer questions about your data.

2. #### Data visualization

You've already been able to answer some questions about the data through dplyr, but you've engaged with them just as a table (such as one showing the life expectancy in the US each year). Often a better way to understand and present such data is as a graph. Here you'll learn the essential skill of data visualization, using the ggplot2 package. Visualization and manipulation are often intertwined, so you'll see how the dplyr and ggplot2 packages work closely together to create informative graphs.

3. #### Grouping and summarizing

So far you've been answering questions about individual country-year pairs, but we 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.

4. #### Types of visualizations

You've learned to create scatter plots with ggplot2. In this chapter you'll learn to create line plots, bar plots, histograms, and boxplots. You'll see how each plot needs different kinds of data manipulation to prepare for it, and understand the different roles of each of these plot types in data analysis.

### What do other learners have to say? “I've used other sites, but DataCamp's been the one that I've stuck with.”

Devon Edwards Joseph

Lloyd's Banking Group “DataCamp is the top resource I recommend for learning data science.”

Louis Maiden “DataCamp is by far my favorite website to learn from.”

Ronald Bowers

Decision Science Analytics @ USAA ##### David Robinson

Chief Data Scientist, DataCamp

Dave works on the data science behind DataCamp's product and curriculum development. He has worked as a data scientist at Stack Overflow and received his PhD in Quantitative and Computational Biology from Princeton University. His interests include statistics, data analysis, education, and programming in R. Follow him at @drob on Twitter or on his blog, Variance Explained.

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##### Collaborators
• Yashas Roy

• Chester Ismay