Reshaping and Joining Data Frames
In this lesson, you will learn to:
Reshape your dataset by pivoting longer or wider
Use mutating joins and filtering joins to combine different datasets with related information.
Decompose a complicated problem into small, manageable steps.
Time Estimates: 
Videos: 20 min 
Readings: 20-60 min 
Activities: 60 min 
Check-ins: 3 Reshaping Data
Decomposition
Required Reading: Computational Thinking
Please read the Decomposition and Hidden Assumptions sections only - stop when you reach the beginning of the Turn it into a recipe (let’s make an algorithm) section.
Tidy Data and Reshaping
Required Video: Reshaping Data (Pivoting)
Recommended Reading: R4DS 12.1-12.3: Tidy Data
Optional Reading: R4DS 12.4-12.5: Separate/Unite and Missing Values
(A few more tricks for data cleaning/wrangling, if you’re interested.)
Check-In 1: Pivoting
Question 1: Create a new dataset called cereals_3, that has three columns:
The name of the cereal
A column called “Nutrient” with values
protein,fat, orfiber.A column called “Amount” with the corresponding amount of the nutrient.
Question 2: Why didn’t we have to add a rowid to pivot wider in this case?
Check-In 2: Decomposition
Cereals in this dataset are placed on shelf 1, 2, or 3. We would like to know if these cereal placements correspond to different nutritional values; for example, perhaps sugary cereals made for children are on a lower shelf.
Create a new dataset called cereals_4, that has four columns:
The name of the manufacturer
The mean amount of sugar in cereals on shelf 1.
The mean amount of sugar in cereals on shelf 2.
The mean amount of sugar in cereals on shelf 3.