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R Programming
1. Introduction to R
2. Setting Up the R Environment
3. R Fundamentals
4. R Data Types
5. R Data Structures
6. Data Import and Export
7. Data Manipulation with Base R
8. Data Manipulation with Tidyverse
9. Programming Constructs in R
10. Data Visualization
11. Statistical Analysis in R
12. Reproducible Research and Reporting
13. Package Management and Development
14. Advanced R Programming
R Data Types
Understanding R's Type System
Atomic Types vs. Composite Types
Type Hierarchy
Dynamic Typing Nature
Atomic Vector Types
Numeric Types
Integer Type
Double (Floating Point) Type
Numeric Precision
Scientific Notation
Character Type
String Creation
String Literals
Escape Sequences
Unicode Support
Logical Type
TRUE and FALSE Values
Logical Operations
Boolean Algebra
Complex Type
Complex Number Creation
Real and Imaginary Parts
Complex Arithmetic
Raw Type
Byte-level Data
Raw Data Operations
Special Values and Constants
Missing Values (`NA`)
Different Types of NA
Testing for Missing Values
Handling Missing Data
Not a Number (`NaN`)
Mathematical Undefined Results
Testing for NaN
Infinity Values
Positive Infinity (`Inf`)
Negative Infinity (`-Inf`)
Testing for Infinity
NULL Values
Representing Absence
NULL vs. NA Differences
Testing for NULL
Type Checking and Conversion
Type Inspection Functions
`class()` Function
`typeof()` Function
`mode()` Function
`storage.mode()` Function
Type Testing Functions
`is.numeric()`
`is.character()`
`is.logical()`
`is.complex()`
`is.raw()`
`is.na()`
`is.null()`
`is.infinite()`
`is.nan()`
Type Coercion
Implicit Coercion Rules
Coercion Hierarchy
Explicit Coercion Functions
`as.numeric()`
`as.character()`
`as.logical()`
`as.complex()`
`as.raw()`
Handling Coercion Warnings
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5. R Data Structures