Ch. 1 - Joining Tables

The inner_join verb

What columns would you join on?

Joining parts and part categories

Joining with a one-to-many relationship

Joining parts and inventories

Joining in either direction

Joining three or more tables

Joining three tables

What’s the most common color?


Ch. 2 - Left and Right Joins

The left_join verb

Left joining two sets by part and color

Left joining two sets by color

Finding an observation that doesn’t have a match

The right-join verb

Which join is best?

Counting part colors

Cleaning up your count

Joining tables to themselves

Joining themes to their children

Joining themes to their grandchildren

Left-joining a table to itself


Ch. 3 - Full, Semi, and Anti Joins

The full_join verb

Differences between Batman and Star Wars

Aggregating each theme

Full-joining Batman and Star Wars LEGO parts

Comparing Batman and Star Wars LEGO parts

The semi- and anti-join verbs

Select the join

Something within one set but not another

What colors are included in at least one set?

Which set is missing version 1?

Visualizing set differences

Aggregating sets to look at their differences

Combining sets

Visualizing the difference: Batman and Star Wars


Ch. 4 - Case Study: Joins on Stack Overflow Data

Stack Overflow questions

Left-joining questions and tags

Comparing scores across tags

What tags never appear on R questions?

Joining questions and answers

Finding gaps between questions and answers

Joining question and answer counts

Joining questions, answers, and tags

Average answers by question

The bind_rows verb

Joining questions and answers with tags

Binding and counting posts with tags

Visualizing questions and answers in tags

Congratulations!


About Michael Mallari

Michael is a hybrid thinker and doer—a byproduct of being a StrengthsFinder “Learner” over time. With nearly 20 years of engineering, design, and product experience, he helps organizations identify market needs, mobilize internal and external resources, and deliver delightful digital customer experiences that align with business goals. He has been entrusted with problem-solving for brands—ranging from Fortune 500 companies to early-stage startups to not-for-profit organizations.

Michael earned his BS in Computer Science from New York Institute of Technology and his MBA from the University of Maryland, College Park. He is also a candidate to receive his MS in Applied Analytics from Columbia University.

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