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

Be fruitful and dplyr

select() vs. filter()

A great selection

Mutation necessary

Lending a helper hand

Don't get me started

See how that ended up

Finding our perfect match

Contain-ted love

Looking for matches

Hold Your Selected Leaders Accountable

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 assistance

Moving with select() and everything()

Rearranging with select() and last_col()

Shifting positions with relocate()

That has crossed the line

Mutate across multiple columns

Summarize across multiple variables

Combining count() with across()

Animal crossing: new rowwise's

across() vs. c_across()

Aggregations with rowwise()

One for any, one for all

Keep Them Dogies Movin’

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 fun

Join with me

Further investigations

Lines that intersect are without parallel

Intersect vs. inner join

Speeding through the intersection

Deliver the state of the union

Save the union

Sign up for your local union, all

A little too excepting

Checking for equal sets

Checking for differences

Set Theory Claus and The North Pole

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?

Unemployment rates by region

Median unemployment rates by group

Bang-bang!!

Matching rlang operators

Bang bang into the room

Rlang-ing in your rocking chair

You are the walrus

Analyze the region results

A great ggplot twist

Plotting and scheming

The plot title thickens

Congratulations!

Speaking a New rlang-uage

International Monetary Fund (IMF)

World Bank

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!

Joining Data with dplyr

Introduction to Writing Functions in R

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

Programming with dplyr

Educator, Data Scientist, and R/Python Consultant

Course prerequisites

Course outline

The world_bank_data tibble

world_bank_data columns

Select some columns from world_bank_data

Filter rows to match continent values

Results of row filter

Mutate a new column

Results of mutate

Analyze urban percentage across regions

Let's practice!

https://s3.amazonaws.com/assets.datacamp.com/production/course_26823/subtitles/course_26823_b2b5819730bc11130a432ef30606aa18.vtt

Hold Your Selected Leaders Accountable - Be fruitful and dplyr

Be fruitful and dplyr

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