R Programming Basics

R Programming Basics

• Introduction to R

  • R is a programming language and environment for statistical computing and graphics.
  • Open-source and widely used in data analysis, statistics, and data visualization.

• Installation

  • Download R from the Comprehensive R Archive Network (CRAN).
  • Install RStudio for an integrated development environment (IDE).

• Basic Syntax

  • R is case-sensitive (e.g., Variable is different from variable).
  • Comments are indicated with #.
  • Objects can be created using <- or = (e.g., x <- 5).

• Data Types

  • Vectors: Basic data structure (e.g., c(1, 2, 3)).
  • Lists: Ordered collection of objects (e.g., list(a = 1, b = "text")).
  • Matrices: 2D collection of elements (e.g., matrix(1:6, nrow = 2)).
  • Data Frames: Table-like structure (e.g., data.frame(name = c("A", "B"), age = c(25, 30))).
  • Factors: Categorical data (e.g., factor(c("male", "female"))).

• Basic Operations

  • Arithmetic: +, -, *, /, ^.
  • Logical: & (and), | (or), ! (not).
  • Relational: ==, !=, <, >, <=, >=.

• Control Structures

  • If-Else Statements:

    if (condition) {
      # code
    } else {
      # code
    }
    

  • For Loops:

    for (i in sequence) {
      # code
    }
    

  • While Loops:

    while (condition) {
      # code
    }
    

• Functions

  • Define functions using function keyword:

    my_function <- function(arg1, arg2) {
      # code
    }
    

  • Built-in functions: mean(), sum(), sd(), etc.

• Packages

  • R has a rich ecosystem of packages (e.g., ggplot2, dplyr).
  • Install packages using install.packages("package_name").
  • Load packages with library(package_name).

• Data Import and Export

  • Read data from CSV: read.csv("file.csv").
  • Write data to CSV: write.csv(data, "file.csv").

• Basic Plotting

  • Base R plotting functions: plot(), hist(), boxplot().
  • For advanced visualization, use ggplot2.

• Help and Documentation

  • Access help using ?function_name or help(function_name).
  • Use vignette() for package-specific documentation.

These basics are foundational for further exploration and advanced techniques in R programming.

R Programming Basics

• R is a programming language specifically designed for statistical computing and graphics, making it a powerful tool for data analysis.
• It is open-source, which enhances accessibility and promotes widespread use among statisticians and data analysts globally.

Installation

• R can be downloaded from the Comprehensive R Archive Network (CRAN), ensuring users get the latest stable release.
• RStudio is recommended for an integrated development environment (IDE), providing a user-friendly interface for coding in R.

Basic Syntax

• R differentiates between uppercase and lowercase letters, so Variable and variable are recognized as distinct identifiers.
• Comments are included in the code using the # symbol, allowing users to annotate their code without affecting execution.
• Objects in R are created using assignment operators such as <- or = to store values for later use.

Introduction to R

• R is both a programming language and a software environment specifically designed for statistical computing and graphics.
• It plays a significant role in fields such as data analysis, statistical modeling, and data visualization, making it a popular choice among data scientists.

Installation

• The Comprehensive R Archive Network (CRAN) is the primary source for downloading R, ensuring access to the latest versions and packages.
• RStudio serves as a widely-used integrated development environment (IDE) that enhances the user experience and productivity in R programming.

Basic Syntax

• R is case-sensitive; this means that variable names with different cases (e.g., Variable vs. variable) are treated as distinct.
• Comments in R code can be inserted using the # symbol, allowing for clearer code documentation and explanations.
• R’s syntax supports a range of commands and functions that facilitate statistical analysis and data manipulation.