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3 Basic Data
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3.1 Getting Started
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3.1.1 Motivating Example: Flags
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3.1.2 Numbers
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3.1.3 Expressions
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3.1.4 Terminology
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3.1.5 Strings
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3.1.6 Images
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3.1.6.1 Combining Images
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3.1.6.2 Making a Flag
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3.1.7 Stepping Back: Types, Errors, and Documentation
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3.1.7.1 Types and Contracts
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3.1.7.2 Format and Notation Errors
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3.1.7.3 Finding Other
Functions: Documentation
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3.2 Naming Values
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3.2.1 The Definitions Pane
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3.2.2 Naming Values
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3.2.2.1 Names Versus Strings
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3.2.2.2 Expressions versus
Statements
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3.2.3 The Program Directory
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3.2.3.1 Understanding the Run Button
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3.2.4 Using Names to Streamline Building Images
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3.3 From Repeated Expressions to Functions
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3.3.1 Example: Similar Flags
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3.3.2 Defining Functions
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3.3.2.1 How Functions Evaluate
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3.3.2.2 Type Annotations
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3.3.2.3 Documentation
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3.3.3 Functions Practice: Moon Weight
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3.3.4 Documenting Functions with Examples
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3.3.5 Functions Practice: Cost of pens
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3.3.6 Recap: Defining Functions
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3.4 Conditionals and Booleans
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3.4.1 Motivating Example: Shipping Costs
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3.4.2 Conditionals: Computations with Decisions
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3.4.3 Booleans
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3.4.3.1 Other Boolean Operations
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3.4.3.2 Combining Booleans
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3.4.4 Asking Multiple Questions
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3.4.5 Evaluating by Reducing Expressions
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3.4.6 Composing Functions
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3.4.6.1 How Function Compositions Evaluate
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3.4.6.2 Function Composition and the Directory
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3.4.7 Nested Conditionals
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3.4.8 Recap: Booleans and Conditionals
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4 Tabular Data
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4.1 Introduction to Tabular Data
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4.1.1 Creating Tabular Data
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4.1.2 Extracting Rows and Cell Values
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4.1.3 Functions over Rows
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4.1.4 Processing Rows
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4.1.4.1 Finding Rows
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4.1.4.2 Ordering Rows
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4.1.4.3 Adding New Columns
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4.1.4.4 Calculating New Column Values
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4.1.5 Examples for Table-Producing Functions
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4.2 Processing Tables
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4.2.1 Cleaning Data Tables
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4.2.1.1 Loading Data Tables
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4.2.1.2 Dealing with Missing Entries
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4.2.1.3 Normalizing Data
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4.2.1.4 Normalization, Systematically
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4.2.1.5 Using Programs to Detect Data Errors
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4.2.2 Task Plans
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4.2.3 Preparing Data Tables
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4.2.3.1 Creating bins
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4.2.3.2 Splitting Columns
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4.2.4 Managing and Naming Data Tables
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4.2.5 Visualizations and Plots
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4.2.6 Summary: Managing a Data Analysis
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5 Lists
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5.1 From Tables to Lists
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5.1.1 Basic Statistical Questions
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5.1.2 Extracting a Column from a Table
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5.1.3 Understanding Lists
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5.1.3.1 Lists as Anonymous Data
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5.1.3.2 Creating Literal Lists
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5.1.4 Operating on Lists
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5.1.4.1 Built-In Operations on Lists of Numbers
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5.1.4.2 Built-In Operations on Lists in General
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5.1.4.3 An Aside on Naming Conventions
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5.1.4.4 Getting Elements By Position
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5.1.4.5 Transforming Lists
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5.1.4.6 Recap: Summary of List Operations
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5.1.5 Lambda: Anonymous Functions
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5.1.6 Combining Lists and Tables
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5.2 Processing Lists
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5.2.1 Making Lists and Taking Them Apart
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5.2.2 Some Example Exercises
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5.2.3 Structural Problems with Scalar Answers
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5.2.3.1 my-len: Examples
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5.2.3.2 my-sum: Examples
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5.2.3.3 From Examples to Code
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5.2.4 Structural Problems that Transform Lists
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5.2.4.1 my-doubles: Examples and Code
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5.2.4.2 my-str-len: Examples and Code
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5.2.5 Structural Problems that Select from Lists
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5.2.5.1 my-pos-nums: Examples and Code
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5.2.5.2 my-alternating:
Examples and Code
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5.2.6 Structural Problems Over Relaxed Domains
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5.2.6.1 my-max: Examples
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5.2.6.2 my-max: From Examples to Code
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5.2.7 More Structural Problems with Scalar Answers
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5.2.7.1 my-avg: Examples
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5.2.8 Structural Problems with Accumulators
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5.2.8.1 my-running-sum: First Attempt
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5.2.8.2 my-running-sum: Examples and Code
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5.2.8.3 my-alternating: Examples and Code
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5.2.9 Dealing with Multiple Answers
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5.2.9.1 uniq: Problem Setup
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5.2.9.2 uniq: Examples
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5.2.9.3 uniq: Code
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5.2.9.4 uniq: Reducing Computation
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5.2.9.5 uniq: Example and Code Variations
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5.2.9.6 uniq: Why Produce a List?
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5.2.10 Monomorphic Lists and Polymorphic Types
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5.3 Recursive Data
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5.3.1 Functions to Process Recursive Data
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5.3.2 A Template for Processing Recursive Data
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5.3.3 The Design Recipe
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6 Structured Data
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6.1 Introduction to Structured Data
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6.1.1 Understanding the Kinds of Compound Data
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6.1.1.1 A First Peek at Structured Data
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6.1.1.2 A First Peek at Conditional Data
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6.1.2 Defining and Creating Structured and Conditional Data
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6.1.2.1 Defining and Creating Structured Data
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6.1.2.2 Annotations for Structured Data
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6.1.2.3 Defining and Creating Conditional Data
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6.1.3 Programming with Structured and Conditional Data
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6.1.3.1 Extracting Fields from Structured Data
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6.1.3.2 Telling Apart Variants of Conditional Data
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6.1.3.3 Processing Fields of Variants
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6.2 Collections of Structured Data
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6.2.1 Lists as Collective Data
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6.2.2 Sets as Collective Data
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6.2.2.1 Picking Elements from Sets
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6.2.2.2 Computing with Sets
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6.2.3 Combining Structured and Collective Data
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6.2.4 Data Design Problem: Representing Quizzes
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7 Trees
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7.1 Trees
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7.1.1 Data Design Problem – Ancestry Data
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7.1.1.1 Computing Genetic Parents from an Ancestry Table
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7.1.1.2 Computing Grandparents from an Ancestry Table
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7.1.1.3 Creating a Datatype for Ancestor Trees
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7.1.2 Programs to Process Ancestor Trees
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7.1.3 Summarizing How to Approach Tree Problems
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7.1.4 Study Questions
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8 Foundations: Bonus Materials
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8.1 Functions as Data
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8.1.1 A Little Calculus
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8.1.2 A Helpful Shorthand for Anonymous Functions
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8.1.3 Streams From Functions
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8.1.4 Combining Forces: Streams of Derivatives
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8.2 Queues from Lists
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8.2.1 Using a Wrapper Datatype
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8.2.2 Combining Answers
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8.2.3 Using a Picker
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8.2.4 Using Tuples
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8.2.5 A Picker Method
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8.3 Examples, Testing, and Program Checking
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8.3.1 From Examples to Tests
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8.3.2 More Refined Comparisons
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8.3.3 When Tests Fail
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8.3.4 Oracles for Testing
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