Programming with dplyr

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

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!

• 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.
• 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.
• 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.
• 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.