R Programming Basic Guide

R’s visualizations are often favored in academic publications, as they can produce high-quality graphics suitable for journals and reports.

R is widely used in academia and research, particularly in statistics, bioinformatics, and social sciences, due to its strong statistical capabilities and flexibility.

R has a large and active community. Resources such as forums, mailing lists, and user groups help users find solutions and share knowledge.

R can easily integrate with languages like Python, C++, and SQL, allowing users to leverage the strengths of multiple programming environments.

R runs on various operating systems, including Windows, macOS, and Linux, making it accessible to a broad audience.

Shiny is an R package that enables users to build interactive web applications. This has made R popular for creating dashboards and data-driven web tools.

R’s visualization capabilities are highly regarded, especially with packages like ggplot2, which allows users to create complex and aesthetically pleasing visualizations using a grammar of graphics.

R is particularly strong in statistical analysis. It includes a wide range of built-in functions for statistical tests, linear and nonlinear modeling, time-series analysis, and more.

R has a vast ecosystem of packages for various purposes, including data manipulation (dplyr), visualization (ggplot2), and machine learning (caret, tidymodels).

The Comprehensive R Archive Network (CRAN) hosts thousands of R packages, making it one of the largest repositories for statistical software. It serves as a crucial resource for users to find and install additional functionality.