Get started with your first joining verb: inner-join! You'll learn to join tables together to answer questions about the LEGO dataset, which contains information across many tables about the sets, parts, themes, and colors of LEGOs over time.  


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?

Joining Tables

Learn two more mutating joins, the left and right join, which are mirror images of each other! You'll learn use cases for each type of join as you explore parts and colors of LEGO themes. Then, you'll explore how to join tables to themselves to understand the hierarchy of LEGO themes in the data. 

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

Left and Right Joins

In this chapter, you'll cover three more joining verbs: full-join, semi-join, and anti-join. You'll then use these verbs to answer questions about the similarities and differences between a variety of LEGO sets. 

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_join 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

Full, Semi, and Anti Joins

Put together all the types of join you learned in this course to analyze a new dataset: Stack Overflow questions, answers, and tags. This includes calculating and visualizing trends for some notable tags like dplyr and ggplot2. You'll also master one more method for combining tables, the bind_rows verb, which stacks tables on top of each other.

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!

Case Study: Joins on Stack Overflow Data

sets

themes

parts

part_categories

inventories

inventory_parts

colors

questions

question_tags

answers

Often in data science, you'll encounter fascinating data that is spread across multiple tables. This course will teach you the skills you'll need to join multiple tables together to analyze them in combination. You'll practice your skills using a fun dataset about LEGOs from the Rebrickable website. The dataset contains information about the sets, parts, themes, and colors of LEGOs, but is spread across many tables. You'll work with the data throughout the course as you learn a total of six different joins! You'll learn four mutating joins: inner join, left join, right join, and full join, and two filtering joins: semi join and anti join. In the final chapter, you'll apply your new skills to Stack Overflow data, containing each of the almost 300,000 Stack Oveflow questions that are tagged with R, including information about their answers, the date they were asked, and their score. Get ready to take your dplyr skills to the next level!

Data Manipulation with dplyr 

Learn to combine data across multiple tables to answer complex questions with dplyr. You'll learn 6 different joins including inner, full, anti, and more.

Joining Data with dplyr

Learn to combine data across multiple tables to answer more complex questions with dplyr.

Associate Data Scientist in R

Datenanalyst  in R

Datenbearbeitung  in R

Data Scientist

Joining sets and themes

The inventories table

Joining sets and inventories

Filtering the joined table

Parts and pieces

The inventory parts

Let's practice!

https://s3.amazonaws.com/assets.datacamp.com/production/course_21394/subtitles/course_21394_5e3451fc08370f16fd00f5a6150df6d7.vtt

Joining Tables - Joining with a one-to-many relationship

Joining with a one-to-many relationship

Create Your Free Account