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Subjects

JAVASCRIPTHTMLCSSPYTHONSQLJAVACPPCSHARPPHPTYPESCRIPTCRUBYGOSWIFTKOTLINRUSTRBASHSCALAOBJECTIVE-CDARTPERLLUAMATLABXMLJSONMARKDOWNASSEMBLYLATEXYAMLSOLIDITYBLOCKCHAIN

Topics

Introduction to R

What is R?Installing RR IDEs (RStudio)R Syntax BasicsR Comments

R Data Types and Variables

R Numeric DataR Character DataR Logical DataR FactorsR VariablesR AssignmentsR Data Type Conversion

R Operators

R Arithmetic OperatorsR Comparison OperatorsR Logical OperatorsR Assignment Operators

R Data Structures

R VectorsR ListsR MatricesR ArraysR Data FramesR Tibbles

R Control Structures

R If-Else StatementsR For LoopsR While LoopsR Break and Next StatementsR Switch Statements

R Functions

R Function BasicsR Function ArgumentsR Return ValuesR Anonymous FunctionsR Built-in Functions

R Input and Output

R Reading Data from FilesR Writing Data to FilesR Working with CSV FilesR Working with Excel Files

R Data Manipulation

R SubsettingR Merging DataR Reshaping DataR Aggregating DataR dplyr Package

R Data Cleaning

R Handling Missing DataR Removing DuplicatesR String ManipulationR Regular Expressions in R

R Data Visualization

R Base GraphicsR ggplot2 PackageR HistogramsR Scatter PlotsR Box PlotsR Bar ChartsR Line GraphsR Heat Maps

R Statistical Analysis

R Descriptive StatisticsR Hypothesis TestingR Correlation AnalysisR Regression AnalysisR ANOVA in RR Time Series Analysis

R Programming Concepts

R VectorizationR Apply Family of FunctionsR Error HandlingR Debugging in R

R Packages

R Installing PackagesR Loading PackagesR Creating PackagesR CRAN Repository

Advanced R Topics

R Object-Oriented ProgrammingR Functional ProgrammingR Performance OptimizationR Parallel ComputingR Big Data with Spark

R for Data Science

R Data WranglingR Exploratory Data AnalysisR Machine Learning in RR Text MiningR Web Scraping

R Reporting and Presentation

R MarkdownR Shiny for Interactive AppsR Plotly for Interactive PlotsR knitr Package

R-CRAN Repository: The Heart of R Package Distribution

The Comprehensive R Archive Network (CRAN) is the central repository for R packages. It serves as a vital resource for R programmers, providing a vast collection of user-contributed packages that extend R's functionality.

What is CRAN?

CRAN is a network of servers worldwide that store identical, up-to-date versions of R packages. It's the primary source for distributing and accessing R packages, ensuring consistency and reliability across the R community.

Key Features of CRAN

  • Centralized package distribution
  • Quality control and standardization
  • Version management
  • Easy package installation
  • Documentation and vignettes

Using CRAN in R

To install packages from CRAN, use the install.packages() function. Here's a simple example:


# Install a package from CRAN
install.packages("ggplot2")

# Load the installed package
library(ggplot2)

Submitting Packages to CRAN

Developers can submit their packages to CRAN for wider distribution. The process involves:

  1. Preparing the package according to CRAN policies
  2. Testing the package thoroughly
  3. Submitting the package for review
  4. Addressing any feedback from CRAN maintainers

CRAN Task Views

CRAN Task Views provide curated lists of packages for specific domains or tasks. They're invaluable for discovering relevant packages in your area of interest.

Best Practices for Using CRAN

  • Regularly update your packages
  • Check package dependencies before installation
  • Use sessionInfo() to track package versions
  • Consider using R Installing Packages for more advanced package management

CRAN Mirrors

CRAN is mirrored on servers worldwide. Choose a nearby mirror for faster downloads:


# Set a specific CRAN mirror
options(repos = c(CRAN = "https://cloud.r-project.org"))

Conclusion

The R-CRAN Repository is an essential component of the R ecosystem. It facilitates package distribution, ensures quality, and promotes collaboration within the R community. Understanding how to effectively use CRAN is crucial for every R programmer, from beginners to advanced users.

For more information on package management in R, explore R Loading Packages and R Creating Packages.