Rprog: R Programming for Data Science

Course Information

Overview

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.

Course Outline

Introduction
First Steps
Aesthetic Mappings
Common Problems
Facets
Geometric Objects
Statistical Transformations
Position Adjustments
Coordinate Systems
The Layered Grammar of Graphics

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)

Running Code
RStudio Diagnostics

Introduction
Questions
Variation
Missing Values
Covariation
Patterns and Models
ggplot2 Calls
Learning More

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

Overview

Audience Profile

Prerequisities

At Course Completion

Course Outline

Introduction
First Steps
Aesthetic Mappings
Common Problems
Facets
Geometric Objects
Statistical Transformations
Position Adjustments
Coordinate Systems
The Layered Grammar of Graphics

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)

Running Code
RStudio Diagnostics

Introduction
Questions
Variation
Missing Values
Covariation
Patterns and Models
ggplot2 Calls
Learning More

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

Course Price

RM4,000.00 exc. 8% tax

Rprog: R Programming for Data Science

Rprog: R Programming for Data Science

Course Information

Course Name

Duration

Overview

Course Outline

Overview

Overview

Audience Profile

Prerequisities

At Course Completion

Course Outline

Course Outline

Course Price

Training Dates

Fill up the form for enquiry

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Rprog: R Programming for Data Science

Rprog: R Programming for Data Science

Course Information

Course Name

Duration

Overview

Overview

Overview

Audience Profile

Prerequisities

At Course Completion

Course Outline

Course Outline

Course Price

Training Dates

Fill up the form for enquiry