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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 Exercises3850 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.

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    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

Datasets

International Monetary Fund (IMF)World Bank

Collaborators

maggiematsui
Maggie Matsui
james-datacamp
James Chapman
amy-4121b590-cc52-442a-9779-03eb58089e08
Amy Peterson
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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