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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 AccountableFree
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.Providing relocation assistance50 xpMoving with select() and everything()100 xpRearranging with select() and last_col()100 xpShifting positions with relocate()100 xpThat has crossed the line50 xpMutate across multiple columns100 xpSummarize across multiple variables100 xpCombining count() with across()100 xpAnimal crossing: new rowwise's50 xpacross() vs. c_across()50 xpAggregations with rowwise()100 xpOne for any, one for all100 xp
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.Join together for fun50 xpJoin with me100 xpFurther investigations100 xpLines that intersect are without parallel50 xpIntersect vs. inner join100 xpSpeeding through the intersection100 xpDeliver the state of the union50 xpSave the union100 xpSign up for your local union, all100 xpA little too excepting50 xpChecking for equal sets100 xpChecking for differences100 xp
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.What is your major mal-function?50 xpUnemployment rates by region100 xpMedian unemployment rates by group100 xpBang-bang!!50 xpMatching rlang operators100 xpBang bang into the room100 xpRlang-ing in your rocking chair50 xpYou are the walrus100 xpAnalyze the region results100 xpA great ggplot twist50 xpPlotting and scheming100 xpThe plot title thickens100 xpCongratulations!50 xp
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.