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Reshaping Data with tidyr

Transform almost any dataset into a tidy format to make analysis easier.

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4 Hours15 Videos54 Exercises10,280 Learners4650 XPData Manipulation with R TrackImporting & Cleaning Data with R TrackTidyverse Fundamentals with R Track

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Course Description

Data in the wild can be scary—when confronted with a complicated and messy dataset you may find yourself wondering, where do I even start? The tidyr package allows you to wrangle such beasts into nice and tidy datasets. Inaccessible values stored in column names will be put into rows, JSON files will become data frames, and missing values will never go missing again. You'll practice these techniques on a wide range of messy datasets, learning along the way how many dogs the Soviet Union sent into space and what bird is most popular in New Zealand. With the tidyr package in your tidyverse toolkit, you'll be able to transform almost any dataset in a tidy format which will pay-off during the rest of your analysis.
  1. 1

    Tidy Data

    Free

    You'll be introduced to the concept of tidy data which is central to this course. In the first two lessons, you'll jump straight into the action by separating messy character columns into tidy variables and observations ready for analysis. In the final lesson, you'll learn how to overwrite and remove missing values.

    Play Chapter Now
    What is tidy data?
    50 xp
    Tidy data structure
    100 xp
    Multiple variables per column
    100 xp
    Columns with multiple values
    50 xp
    International phone numbers
    100 xp
    Extracting observations from values
    100 xp
    Separating into columns and rows
    100 xp
    Missing values
    50 xp
    And the Oscar for best director goes to ... <NA>
    100 xp
    Imputing sales data
    100 xp
    Nuclear bombs per continent
    100 xp
  2. 2

    From Wide to Long and Back

    This chapter is all about pivoting data from a wide to long format and back again using the pivot_longer() and pivot_wider() functions. You'll need these functions when variables are hidden in messy column names or when variables are stored in rows instead of columns. You'll learn about space dogs, nuclear bombs, and planet temperatures along the way.

    Play Chapter Now
    From wide to long data
    50 xp
    Nuclear bombs per country
    100 xp
    WHO obesity per country
    100 xp
    Bond... James Bond
    100 xp
    Deriving variables from column headers
    50 xp
    New-Zealand's bird of the year
    100 xp
    Big tech stock prices
    100 xp
    Deriving variables from complex column headers
    50 xp
    Soviet space dogs, the dog perspective
    100 xp
    WHO obesity vs. life expectancy
    100 xp
    Uncounting observations
    100 xp
    From long to wide data
    50 xp
    Soviet space dogs, the flight perspective
    100 xp
    Planet temperature & distance to the Sun
    100 xp
    Transposing planet data
    100 xp
  3. 3

    Expanding Data

    Values can often be missing in your data, and sometimes entire observations are absent too. In this chapter, you'll learn how to complete your dataset with these missing observations. You'll add observations with zero values to counted data, expand time series to a full sequence of intervals, and more!

    Play Chapter Now
    Creating unique combinations of vectors
    50 xp
    Letters of the genetic code
    100 xp
    When did humans replace dogs in space?
    100 xp
    Finding missing observations
    100 xp
    Completing data with all value combinations
    50 xp
    Completing the Solar System
    100 xp
    Zero Olympic medals
    100 xp
    Creating a sequence with full_seq()
    100 xp
    The Cold War's hottest year
    100 xp
    Advanced completions
    50 xp
    Olympic medals per continent
    100 xp
    Tracking a virus outbreak
    100 xp
    Counting office occupants
    100 xp
  4. 4

    Rectangling Data

    In the final chapter, you'll learn how to turn nested data structures such as JSON and XML files into tidy, rectangular data. This skill will enable you to process data from web APIs. You'll also learn how nested data structures can be used to write elegant modeling pipelines that produce tidy outputs.

    Play Chapter Now
    Intro to non-rectangular data
    50 xp
    Rectangular vs. non-rectangular files
    100 xp
    Rectangling Star Wars movies
    100 xp
    From nested values to observations
    50 xp
    Unnesting wide or long
    100 xp
    Rectangling Star Wars planets
    100 xp
    The Solar System's biggest moons
    100 xp
    Selecting nested variables
    50 xp
    Hoisting Star Wars films
    100 xp
    Hoisting movie ratings
    100 xp
    Nesting data for modeling
    50 xp
    Tidy model outputs with broom
    100 xp
    Nesting tibbles
    100 xp
    Modeling on nested data frames
    100 xp
    Congratulations!
    50 xp

In the following tracks

Data Manipulation with RImporting & Cleaning Data with RTidyverse Fundamentals with R

Datasets

Nuclear explosions dataPlanet dataStar Wars dataNetflix dataANSUR II dataOlympic medals data

Collaborators

Amy Peterson
Maggie Matsui

Prerequisites

Data Manipulation with dplyr
Jeroen Boeye Headshot

Jeroen Boeye

Machine Learning Engineer @ Faktion

Jeroen is a machine learning engineer working at Faktion, an AI company from Belgium. He uses both R and Python for his analyses and has a PhD background in computational biology. His experience mostly lies in working with structured data, produced by sensors or digital processes.
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