The R system is designed to be a powerful and flexible statistical computing environment. It is based on the S language, which was developed at Bell Labs in the 1970s. R is a free and open-source software environment, which means that it is available to everyone to use, modify, and redistribute.
R system consists of a number of components
- A programming language: R is a general-purpose programming language that is specifically designed for statistical computing. It is a high-level language, which means that it is easy to read and write. R also has a large number of built-in functions for statistical analysis.
- A data handling system: R includes a number of functions for importing, exporting, and manipulating data. This makes it easy to work with data from a variety of sources, including spreadsheets, databases, and statistical packages.
- A graphics system: R includes a powerful graphics system that allows users to create high-quality visualizations of data. The graphics system is based on the PostScript language, which is a standard for document printing and graphics.
- A package system: R includes a package system that allows users to share and install additional functionality. There are thousands of R packages available, which provide a wide range of statistical, graphical, and mathematical functionality.
Design principles that were used in the development of the R system
- Simplicity: R is designed to be a simple and easy-to-use language. The syntax is designed to be similar to other programming languages, such as C and Java.
- Flexibility: R is designed to be a flexible language that can be used for a wide range of statistical computing tasks. It includes a wide range of built-in functions and a package system that allows users to add additional functionality.
- Community: R is a community-driven project. The R Development Core Team is responsible for the core language, but the majority of the functionality in R is provided by packages that are written by the R community.
- Reproducibility: R is designed to be a reproducible language. This means that it is possible to create scripts that can be used to reproduce the results of statistical analysis. This is important for scientific research, as it allows other researchers to verify the results of published studies.
The R system is divided into two conceptual parts: the base R system and everything else. The base R system is the core of R and contains the most fundamental functions. Everything else is in the form of packages, which can be installed from the Comprehensive R Archive Network (CRAN) or from other sources.
The base R system includes the following packages:
- base: This package contains the most fundamental functions in R.
- utils: This package provides miscellaneous utility functions.
- stats: This package provides statistical functions.
- datasets: This package contains example datasets.
- graphics: This package provides graphics functions.
- grDevices: This package provides graphics device drivers.
- grid: This package provides a high-level graphics system.
- methods: This package provides methods for classes.
- tools: This package provides tools for working with R packages.
- parallel: This package provides parallel computing functionality.
- compiler: This package provides compiler support for R.
- splines: This package provides spline functions.
- tcltk: This package provides Tcl/Tk bindings for R.
- stats4: This package provides a more object-oriented approach to statistics.
In addition to the base R system, there are many other packages available on CRAN. These packages can be used to extend the functionality of R and to perform a wide variety of tasks. Some of the most popular CRAN packages include:
- boot: This package provides functions for bootstrapping.
- class: This package provides functions for classification and clustering.
- cluster: This package provides functions for clustering.
- codetools: This package provides functions for checking R code.
- foreign: This package provides functions for reading and writing data from other statistical software packages.
- KernSmooth: This package provides functions for kernel smoothing.
- lattice: This package provides a powerful lattice graphics system.
- mgcv: This package provides functions for generalized additive models.
- nlme: This package provides functions for nonlinear mixed-effects models.
- rpart: This package provides functions for recursive partitioning.
- survival: This package provides functions for survival analysis.
- MASS: This package provides a collection of statistical functions.
- spatial: This package provides functions for spatial data analysis.
- nnet: This package provides functions for neural networks.
- Matrix: This package provides functions for working with matrices.
In addition to the CRAN repository, there are many other places where R packages can be found. One popular source for R packages is the Bioconductor project. Bioconductor is a open-source software project that provides tools for bioinformatics research. Bioconductor packages are designed to work with high-throughput biological data.
R packages can also be found on personal websites and on other websites. There is no central repository for R packages, so it can be difficult to find all of the available packages. However, there are a number of websites that list R packages, such as the RSeek website.
The R system is a powerful tool for statistical computing and graphics. The base R system provides a solid foundation for statistical analysis, and the many available packages extend the functionality of R and make it possible to perform a wide variety of tasks.
