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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.

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4 Hours16 Videos50 Exercises237,255 Learners4150 XPData Analyst TrackData Scientist TrackR Programmer TrackTidyverse Fundamentals Track

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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.

  1. 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.

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    The gapminder dataset
    50 xp
    Loading the gapminder and dplyr packages
    100 xp
    Understanding a data frame
    50 xp
    The filter verb
    50 xp
    Filtering for one year
    100 xp
    Filtering for one country and one year
    100 xp
    The arrange verb
    50 xp
    Arranging observations by life expectancy
    100 xp
    Filtering and arranging
    100 xp
    The mutate verb
    50 xp
    Using mutate to change or create a column
    100 xp
    Combining filter, mutate, and arrange
    100 xp
  2. 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.

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  3. 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.

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  4. 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.

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In the following tracks

Data Analyst Data ScientistR ProgrammerTidyverse Fundamentals




yashasYashas RoychesterChester Ismay
David Robinson Headshot

David Robinson

Principal Data Scientist at Heap

Dave is the Principal Data Scientist at Heap. He has worked as a data scientist at DataCamp and Stack Overflow, and received his PhD in Quantitative and Computational Biology from Princeton University. Follow him at @drob on Twitter or on his blog, Variance Explained.
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