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Learn R For Data Science Theory and Application

Download R For Data Science free in PDF. In this Notes you learn how to use R to turn raw data into insight, knowledge and understanding. This notes is very useful, It introduce R, RStudio, and the tidyverse, a collection of R packages design to work together to make data science fast, fluent and fun. It is suitable for readers.

 

You Learn These Topic From This Notes:

1. Data virtualization with ggplot2

  • Introduction
  • First Steps
  • Common Problems
  • Facets

2. Workflow: Basics

  • Coding Basics
  • Calling Function

3. Data Transformation with dplyr

  • Introduction
  • Filter Rows With filter()
  • Arrange Rows With arrange()
  • Add New Variable With mutate()

4. Workflow: Scripts

  • Running Codes
  • RStudio Diagnostics

5. Exploratory Data Analysis

  • Questions
  • Verification
  • Missing Values
  • Patterns and Models

6. Workflow: Projects

  • What is Real?
  • Paths and Directories
  • RStudio Projects

7. Tibbles With Tibble

  • Creating Tibbles
  • Tibbles Versus Data. Frame
  • Interacting With older Code

8. Data Import With Readr

  • Getting Started
  • Parsing a Vector
  • Parsing a File
  • Writing To a File

9. Tidy Data With Tidyr

  • Tidy data
  • Spreading and Gathering
  • Separating and Pull
  • Missing Values

10. Relational Data With dplyr

  • Mutating Joins
  • Filtering Joins
  • Join Problems
  • Set Operations

11. String With Stringr

  • String Basics
  • Matching Patterns With Regular Expressions
  • Tools
  • Other Types of Patterns

12. Factor With Forcats

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

13. Dates and Times With Lubridates

  • Creating Date/Time
  • Date-Time Components
  • Time Spans
  • Time Zones

14. Pipes With Magrittr

  • Introduction
  • Piping Alternatives
  • Other Tools From magrittr

15. Functions

  • Function Are For Human and Computers
  • Conditional Execution
  • Function Arguments
  • Return Values

16. Vectors 

  • Vector Basics
  • Important Types Of Atomic Vectors
  • Using Atomic Vectors
  • Attributes

17. Iteration With Purrr

  • For Loop
  • For Loop Variation
  • For Loop Versus Functional
  • The Map Functions

18. Model Basics With Modelr

  • A simple Model
  • Visualization Models
  • Formulas and Model Families
  • Missing Values

19. Model Building

  • Why are Low Quality Diamonds More Expensive?
  • What affect The Number Of Daily  Flights?
  • Learning More About Models

20. Many Models With Purrr and Broom

  • Gapminder
  • List- Columns
  • Creating List-Columns
  • Simplify List-Columns

21. R Markdown

  • R Markdown Basics
  • Text Formatting With Markdown
  • Code Chunks
  • Troubleshooting
  • YAML Header

22. Graphics For Communication With ggplot2

  • Label
  • Annotations
  • Scales
  • Zooming

23. R Markdown Formats

  • Output Options
  • Documents
  • Notebooks
  • Presentations

24. R Markdown Workflow




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