R Data Science Training Course

Day One

I. EXPLORE

1 Data Visualization with ggplot2

First Steps

• Aesthetic Mappings
• Common Problems
• Facets
• Geometric Objects
• Statistical Transformations
• Postition Adjustments
• Coordinate systems
• The layered Grammar of Graphics

2 Workflow :Basics

• Coding Basics
• What’s in a Name?
• Calling Functions

3 Data Transformation with dplyr

Introduction

• Prerequisites
• nycflights13
• dplyr Basics

Filter Rows with filter()

• Comparisons
• Logical Operators
• Missing Values

Arrange Rows with arrange ()

Select Columns with select ()

Add new Variables with mutate ()

Grouped Summaries with summarize ()

• Combining Multiple Operations with the Pipe
• Values
• Counts
• Useful Summary Functions
• Grouping by Multiple Variables
• Ungrouping

Grouped Mutates (and Filters)

4 Workflow:Scripts

Running Code

RStudion Diagnostics

Day Two

1. Exploratory Data Analysis

Variation

• Visualizing Distributions
• Typical Values
• Unusual Values

Missing Values

Covariation

• A Categorical and Continuous Variable
• Two Categorical Variables
• Two Continuous Variables

Patterns and Models

ggplot2 Calls

Learning More

2.Workflow; Projects

• What Is Real
• Where Does Your Analysis Live?
• Paths and Directories
• RStudio Projects

II WRANGLE

3.Tibbles with tibble

• Creating Tibbles
• Tibbles versus data.frame

Printing

Subsetting

• Interacting with Older Code

4. Data Import with readr

Getting Started

• Compared to Base R

Parsing a Vector

• Numbers
• Strings
• Factors
• Dates, Date-Times, and Times

Parsing a File

• Strategy
• Problems
• Other Strategies

Writing to a File

Other Types of Data

5.Tidy Data with tidyr

Tidy Data

Spreading and Gathering

• Gathering
• Spreading

Separating and Pull

• Seperate
• Unite

Missing Values

Case Study

Nontidy Data

Day Three

1. Relational Data with dplyr

nycflights13

Keys

Mutating Joins

• Understanding Joins
• Inner Join
• Outer Join
• Duplicate Keys
• Defining the key Columns

Filtering Joins

Join Problems

Set Operations

2. Strings with stringr>

String Basics

• String Length
• Combining Strings
• Subsetting Strings
• Locales

Matching Patterns with Regular Expressions

• Basic Matches
• Excercises
• Anchors
• Excercises
• Character Classes and Alternatives
• Excercises
• Repetition
• Grouping and backreferences

Tools

• Detect Matches
• Exercises
• Extract Matches
• Exercises
• Grouped Matches
• Exercises
• Replacing Matches
• Exercises
• Splitting
• Exercises
• Find Matches

Other types of pattern

Other Uses of Regular Expressions

stringi

3. Factors with forcats

• Creating factors
• General Social Survey
• Modifying Factor Order
• Modifying Factor Levels

4. Dates and Times with lubridate

Creating Date/Times

• From Strings
• From Individual Components
• From Other Types

Date-Time Components

• Getting Components
• Rounding
• Setting Components

Time Spans

• Durations
• Periods
• Intervals

Time Zones

III PROGRAM

Pipes with magrittr

Piping Alternatives

• Intermediate Steps
• Overwrite the Original
• Function Composition
• Use the Pipe

When Not to Use the Pipe

Other Tools from magrittr

Day Four

1. Functions

When Should You Write a Function? Functions Are for Humans and Computers Conditional Execution

• Conditions
• Multiple Conditions
• Code Style

Function Arguments

• Choosing Names
• Checking Values
• Dot-Dot-Dot (…)
• Lazy Evaluation

Return Values

• Explicit Return Statements
• Writing Pipeable Functions

Environment

2. Vectors

Vector Basics Important Types of Atomic Vector

• Logical
• Numeric
• Character
• Missing Values

Using Atomic Vectors

• Coercion
• Test Functions
• Scalars and Recycling Rules
• Naming Vectors
• Subsetting

Recursive Vectors (Lists)

• Visualizing Lists
• Subsetting
• Lists of Condiments

Attributes Augmented Vectors

• Factors
• Dates and Date-Times
• Tibbles

3. iteration with purrr

For Loops For Loop Variations

• Modifying an Existing Object
• Looping Patterns
• Unknown Output Length
• Unknown Sequence Length

For Loops Versus Functionals The Map Functions

• Shortcuts
• Base R

Dealing with Failure Mapping over Multiple Arguments

• Invoking Different Functions

Walk Other Patterns of For Loops

• Predicate Functions
• Reduce and Accumulate

IV MODEL

4. Model Basics with modelr

A Simple Model Visualizing Models

• Predictions
• Residuals

Formulas and Model Families

• Categorical Variables
• Interactions (Continuous and Categorical)
• Interactions (Two Continuous)
• Transformations

Missing Values Other Model Families

5. Model Building Why Are Low-Quality Diamonds More Expensive?

• Price and Carat
• A More Complicated Model

What Affects the Number of Daily Flights?

• Day of Week
• Seasonal Saturday Effect
• Computed Variables
• Time of Year: An Alternative Approach

Learning More About Models

Day Five

1.Many Models with purr and broom

gapminder

• Nested Data
• List-Columns
• Unnesting
• Model Quality

List-Columns Creating List-Columns

• With Nesting
• From Vectorized Functions
• From Multivalued Summaries
• From a Named List

Simplifying List-Columns

• List to Vector
• Unnesting

Making Tidy Data with broom

V. COMMUNICATE

2. R Markdown

R Markdown Basics Text Formatting with Markdown Code Chunks

• Chunk Name
• Chunk Options
• Table
• Caching
• Global Options
• Inline Code

Troubleshooting YAML Header

• Parameters
• Bibliographies and Citations

Learning More

3. Graphics for Communication with ggplot2

Label Annotations Scales

• Axis Ticks and Legend Keys
• Legend Layout
• Replacing a Scale

Exercises Zooming Themes Saving Your Plots

• Figure Sizing
• Other Important Options

Learning More

4. R Markdown Formats

Output Options Documents Notebooks Presentations Dashboards Interactivity

• htmlwidgets
• Shiny

Websites Other Formats

5. R Markdown Workflow