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Programming with dplyr

Learn how to perform advanced dplyr transformations and incorporate dplyr and ggplot2 code in functions.

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4 Hours15 Videos47 Exercises
3850 XP

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Course Description

The tidyverse includes a tremendous set of packages that make working with data simple and fast. But have you ever tried to put dplyr functions inside functions and been stuck with strange errors or unexpected results? Those errors were likely due to tidy evaluation, which requires a little extra work to handle. In Programming with dplyr, you’ll be equipped with strategies for solving these errors via the rlang package. You’ll also learn other techniques for programming with dplyr using data from the World Bank and International Monetary Fund to analyze worldwide trends throughout. You’ll be a tidyverse function writing ninja by the end of the course!

  1. 1

    Hold Your Selected Leaders Accountable

    Free

    In this chapter, you'll revisit dplyr pipelines and enhance your column selection skills with helper functions and regular expressions.

    Play Chapter Now
    Be fruitful and dplyr
    50 xp
    select() vs. filter()
    50 xp
    A great selection
    100 xp
    Mutation necessary
    100 xp
    Lending a helper hand
    50 xp
    Don't get me started
    100 xp
    See how that ended up
    100 xp
    Finding our perfect match
    50 xp
    Contain-ted love
    100 xp
    Looking for matches
    100 xp
  2. 2

    Keep Them Dogies Movin’

    Here, you'll learn how to move columns around in your data and perform the same transformation across multiple data columns. You'll also choose rows that match any or all column criteria.

    Play Chapter Now
    Providing relocation assistance
    50 xp
    Moving with select() and everything()
    100 xp
    Rearranging with select() and last_col()
    100 xp
    Shifting positions with relocate()
    100 xp
    That has crossed the line
    50 xp
    Mutate across multiple columns
    100 xp
    Summarize across multiple variables
    100 xp
    Combining count() with across()
    100 xp
    Animal crossing: new rowwise's
    50 xp
    across() vs. c_across()
    50 xp
    Aggregations with rowwise()
    100 xp
    One for any, one for all
    100 xp
  3. 3

    Set Theory Claus and The North Pole

    For this section, you'll revisit dplyr joins. You'll then take this further by using set theory clauses to examine overlaps and differences between datasets.

    Play Chapter Now
    Join together for fun
    50 xp
    Join with me
    100 xp
    Further investigations
    100 xp
    Lines that intersect are without parallel
    50 xp
    Intersect vs. inner join
    100 xp
    Speeding through the intersection
    100 xp
    Deliver the state of the union
    50 xp
    Save the union
    100 xp
    Sign up for your local union, all
    100 xp
    A little too excepting
    50 xp
    Checking for equal sets
    100 xp
    Checking for differences
    100 xp
  4. 4

    Speaking a New rlang-uage

    In this final part of the course, you'll use rlang operators to turn arguments into variables and create functions that incorporate dplyr and ggplot2 code.

    Play Chapter Now
    What is your major mal-function?
    50 xp
    Unemployment rates by region
    100 xp
    Median unemployment rates by group
    100 xp
    Bang-bang!!
    50 xp
    Matching rlang operators
    100 xp
    Bang bang into the room
    100 xp
    Rlang-ing in your rocking chair
    50 xp
    You are the walrus
    100 xp
    Analyze the region results
    100 xp
    A great ggplot twist
    50 xp
    Plotting and scheming
    100 xp
    The plot title thickens
    100 xp
    Congratulations!
    50 xp

Datasets

International Monetary Fund (IMF)World Bank

Collaborators

Maggie MatsuiJames ChapmanAmy Peterson

Prerequisites

Joining Data with dplyrIntroduction to Writing Functions in R
Dr. Chester Ismay Headshot

Dr. Chester Ismay

Educator, Data Scientist, and R/Python Consultant

Chester enjoys helping others get into data science, figuring out how to best practice and improve on their skills, and working as a part-time consultant on R and Python programming projects. He is co-author of "Statistical Inference via Data Science: A ModernDive into R and the Tidyverse" available at moderndive.com and for purchase from CRC Press. He likes leading education and data science teams with the goal of improving best practices based on data from the learning sciences.
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