Duration
5 days
Course Description
Learn how to use R to turn raw data into insight, knowledge, and understanding. This course introduces you to R, RStudio, and the tidyverse, a collection of R packages designed to work together to make data science fast, fluent, and fun. Suitable for participants with no previous programming experience, R for Data Science is designed to get you doing data science as quickly as possible.
Introduction
- First Steps
- Aesthetic Mappings
- Common Problems
- Facets
- Geometric Objects
- Statistical Transformations
- Position Adjustments
- Coordinate Systems
- The Layered Grammar of Graphics
Chapter 2 – Workflow: Basics
- Coding Basics
- What is in a Name?
- Calling Function
Introduction
- Filter Rows with filter()
- Arrange Rows with arrange()
- Select Columns with select()
- Add New Variables with mutate()
- Grouped Summaries with summarize()
- Group Mutates (and Filters)
Chapter 4 – Workflow: Scripts
- Running Code
- RStudio Diagnostics
Introduction
- Questions
- Variation
- Missing Values
- Covariation
- Patterns and Models
- ggplot2 Calls
- Learning More
Chapter 6 – Workflow: Projects
- What Is Real?
- Where Does Your Analysis Live?
- Paths and Directories
- RStudio Projects
Introduction
- Creating Tibbles
- Tibbles Versus data frame
- Interacting with Older Code
Introduction
- Getting Started
- Parsing a Vector
- Parsing a File
- Writing to a File
- Other Types of Data
Introduction
- Tidy Data
- Spreading and Gathering
- Separating and Pull
- Missing Values
- Case Study
- Nontidy Data
Introduction
- nycflights3
- Keys
- Mutating Joins
- Filtering Joins
- Join Problems
- Set Operations
Introduction
- String Basics
- Matching Patterns with Regular Expressions
- Tools
- Other Types of Pattern
- Other Uses of Regular Expressions
- stringi
Introduction
- Creating Factors
- General Social Survey
- Modifying Factor Order
- Modifying Factor Levels
Introduction
- Creating Date/Times
- Date-Time Components
- Time Spans
- Time Zones
Introduction
- Piping Alternatives
- When Not to Use the Pipe
- Other Tools from magrittr
Introduction
- When Should You Write a Function?
- Functions Are for Humans and Computers
- Conditional Execution
- Function Arguments
- Return Values
- Environment
Introduction
- Vector Basics
- Important Types of Atomic Vector
- Using Atomic Vectors
- Recursive Vectors (Lists)
- Attributes
- Augmented Vectors
Introduction
- For Loops
- For Loop Variations
- For Loops Versus Functionals
- The Map Functions
- Dealing with Failure
- Mapping over Multiple Arguments
- Walk
- Other Patterns of For Loops
Introduction
- A Simple Model
- Visualizing Models
- Formulas and Model Families
- Missing Values
- Other Model Families
Introduction
- Why Are Low-Quality Diamonds More Expensive?
- What Affects the Number of Daily Flights?
- Learning More About Models
Introduction
- gapminder
- List-Columns
- Creating List-Columns
- Simplifying List-Columns
- Making Tidy Data with broom
Programme scope, delivery arrangements and suitability are confirmed during enquiry.