R notes provide a thorough guide to mastering R programming for data analysis and statistical computing. Covering core topics such as data manipulation, statistical modeling, and data visualization with ggplot2, these notes are perfect for both beginners and advanced users. They also explore advanc...
R Programming Language Overview
1. Introduction to R
History of R: R was developed by Ross Ihaka and Robert Gentleman in 1993 as a programming
language and environment for statistical computing and graphics.
Characteristics of R: R is widely used in data analysis, statistical modeling, and data visualization,
offering a rich ecosystem of packages and libraries.
R vs Python: R is known for its strength in statistical analysis and visualization, while Python is more
versatile for general programming and machine learning tasks.
2. Data Types in R
Basic Data Types: R supports basic data types like numeric, character, logical (boolean), and factor
for categorical data.
Vectors: Vectors are the most basic data structures in R, representing ordered collections of data
elements of the same type.
Data Frames: Data frames are two-dimensional structures used for storing tabular data, with rows
and columns similar to a table or spreadsheet.
3. Control Flow in R
If-Else Statements: R supports conditional statements using if, else if, and else for decision-making.
Loops: R provides for loops and while loops for iterative execution of code blocks.
Apply Functions: R includes the apply family of functions (apply, lapply, sapply) for applying a
function over a collection of data elements.
4. Functions in R
Function Declaration: Functions in R are declared using the function keyword, and can return values
or perform operations on data.
Default Arguments: R allows for setting default values for function arguments, making functions
more flexible and reusable.
Anonymous Functions: R supports anonymous (lambda) functions, which can be passed as
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