Working with Data in the Tidyverse

Learn to work with data using tools from the tidyverse, and master the important skills of taming and tidying your data.

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4 Hours17 Videos56 Exercises24,208 Learners
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Course Description

In this course, you'll learn to work with data using tools from the tidyverse in R. By data, we mean your own data, other people's data, messy data, big data, small data - any data with rows and columns that comes your way! By work, we mean doing most of the things that sound hard to do with R, and that need to happen before you can analyze or visualize your data. But work doesn't mean that it is not fun - you will see why so many people love working in the tidyverse as you learn how to explore, tame, tidy, and transform your data. Throughout this course, you'll work with data from a popular television baking competition called "The Great British Bake Off."

  1. 1

    Explore your data

    Free

    You will start this course by learning how to read data into R. We'll begin with the readr package, and use it to read in data files organized in rows and columns. In the rest of the chapter, you'll learn how to explore your data using tools to help you view, summarize, and count values effectively. You'll see how each of these steps gives you more insights into your data.

    Play Chapter Now
    Import your data
    50 xp
    Read a CSV file
    100 xp
    Assign missing values
    100 xp
    Know your data
    50 xp
    Arrange and glimpse
    50 xp
    Summarize your data
    100 xp
    Know your variable types
    50 xp
    Count with your data
    50 xp
    Distinct and count
    100 xp
    Count episodes
    100 xp
    Count bakers
    100 xp
    Plot counts
    100 xp
  2. 2

    Tame your data

    In this chapter, you will learn some basics of data taming, like how to tame your variable types, names, and values.

    Play Chapter Now
    Cast column types
    50 xp
    Cast a column to a date
    100 xp
    Cast a column to a number
    100 xp
    Cast a column as a factor
    100 xp
    Recode values
    50 xp
    Recode a character variable
    100 xp
    Recode a numeric variable
    100 xp
    Select variables
    50 xp
    Combine functions with select
    50 xp
    Recode factor to plot
    100 xp
    Select and reorder variables
    100 xp
    Tame variable names
    50 xp
    Reformat variables
    100 xp
    Rename and subset variables
    100 xp
    Rename and reorder variables
    100 xp
  3. 3

    Tidy your data

    Now that your data has been tamed, it is time to get tidy. In this chapter, you will get hands-on experience tidying data and combining multiple tidying functions together in a chain using the pipe operator.

    Play Chapter Now
    Introduction to Tidy Data
    50 xp
    Tidy line-up
    50 xp
    Plot untidy data
    100 xp
    Gather
    50 xp
    Gather by hand
    50 xp
    Gather & plot
    100 xp
    Gather & plot non-sequential columns
    100 xp
    separate()
    50 xp
    Separate a column
    100 xp
    Unite columns
    100 xp
    spread()
    50 xp
    Spread rows into columns
    100 xp
    Tidy multiple sets of columns
    50 xp
    Masterclass: Tidy I
    100 xp
    Masterclass: Tidy II
    100 xp
    Masterclass: Tidy III
    100 xp
  4. 4

    Transform your data

    In this chapter, you will learn how to tame specific types of variables that are known to be tricky to work with, such as dates, strings, and factors.

    Play Chapter Now
    Complex recoding with case_when
    50 xp
    Combine two variables
    100 xp
    Add another bin
    100 xp
    Factors
    50 xp
    Cast a factor and examine levels
    100 xp
    Plot factor counts
    100 xp
    Dates
    50 xp
    Cast characters as dates
    100 xp
    Calculate timespans
    100 xp
    Strings
    50 xp
    Wrangle a character variable
    100 xp
    Detect a string pattern
    100 xp
    Final thoughts
    50 xp

In the following tracks

Importing & Cleaning Data

Datasets

BakeoffBakersDessertsRatings

Collaborators

Yashas RoyChester IsmayBenjamin Feder

Prerequisites

Introduction to Data Visualization with ggplot2
Alison Hill Headshot

Alison Hill

Professor and Data Scientist

Alison is an Associate Professor of Pediatrics at Oregon Health & Science University (OHSU) in Portland, Oregon, and the Assistant Director of OHSU’s Center for Spoken Language Understanding, home to the Computer Science graduate education program. She has studied health-related applications of Natural Language Processing-based methods, with a focus on pediatric populations with developmental disabilities like Autism Spectrum Disorders. Alison is also an experienced educator, with peer- and student-nominated awards for teaching. She teaches graduate-level data science courses on Statistics and Data Visualization using R.
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