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Programming with dplyr

IntermediateSkill Level
4.7+
45 reviews
Updated 04/2024
Learn how to perform advanced dplyr transformations and incorporate dplyr and ggplot2 code in functions.
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RData Manipulation4 hr15 videos47 Exercises3,850 XP3,330Statement of Accomplishment

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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!

Prerequisites

Joining Data with dplyrIntroduction to Writing Functions in R
1

Hold Your Selected Leaders Accountable

In this chapter, you'll revisit dplyr pipelines and enhance your column selection skills with helper functions and regular expressions.
Start Chapter
2

Keep Them Dogies Movin’

3

Set Theory Claus and The North Pole

4

Speaking a New rlang-uage

Programming with dplyr
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*4.7
from 45 reviews
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  • MARK
    3 weeks ago

  • Tung
    last month

    .

  • Vitalii
    3 months ago

  • Henrique
    3 months ago

    Awesome, some new tricks on my sleeve

  • Domenico
    3 months ago

  • Pau
    3 months ago

MARK

Vitalii

"Awesome, some new tricks on my sleeve"

Henrique

FAQs

What is tidy evaluation and why does this course cover it?

Tidy evaluation is the system dplyr uses to interpret variable names, which causes errors when you put dplyr code inside custom functions. This course teaches you to handle those errors using the rlang package.

Will I learn to write functions that use dplyr and ggplot2 internally?

Yes, the final chapter teaches you to use rlang operators to turn function arguments into variables, so you can create reusable functions that wrap dplyr and ggplot2 code.

What datasets are used throughout the course?

The course uses data from the World Bank and International Monetary Fund to analyze worldwide trends while you practice programming with dplyr.

Is experience with dplyr joins required before taking this course?

Yes, Joining Data with dplyr is a prerequisite. Chapter 3 revisits joins and extends them with set theory operations to examine overlaps and differences between datasets.

Does this course cover applying transformations across multiple columns at once?

Yes, Chapter 2 teaches you how to perform the same transformation across multiple data columns and how to select rows that match criteria across any or all columns.

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