Title: R Programming Language
Author: Kriko
Published: Jun 8, 2023
Last modified: Jul 7, 2026

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# R Programming Language

**R** is an open-source programming language and software environment widely used
for statistical computing, [data analysis](https://kriko.io/services/data-analysis-insight)
and graphics. It was initially developed in the early 1990s by Ross Ihaka and Robert
Gentleman at the University of Auckland. Today, its development is supported by 
the R Core Team and contributors from around the world.

R is frequently used by statisticians, researchers, [data](https://kriko.io/services/data-analysis-insight)
analysts and [data](https://kriko.io/services/data-analysis-insight) scientists.
It supports a broad range of methods, including linear and nonlinear modelling, 
classification, clustering, time-series analysis and machine learning. It is also
particularly effective for **[data](https://kriko.io/services/data-analysis-insight)
visualisation**, statistical analysis and [data](https://kriko.io/services/data-analysis-insight)
manipulation.

One of R's main strengths is its extensive ecosystem of libraries and packages. 
Users can expand the language's capabilities by installing packages designed for
specific analytical methods, industries and research areas. Many of these packages
are distributed through the Comprehensive R Archive Network, commonly known as CRAN.

R can be used through a command-line interface as well as integrated development
environments, or IDEs. These environments make it easier to write, organise, test
and debug R code while managing analytical projects more efficiently.

A typical [data analysis](https://kriko.io/services/data-analysis-insight) workflow
in R may include the following stages:

**[Data](https://kriko.io/services/data-analysis-insight) Import:** [Data](https://kriko.io/services/data-analysis-insight)
is collected from sources such as databases, spreadsheets, files, APIs or analytical
platforms and transferred into the R environment.

**[Data](https://kriko.io/services/data-analysis-insight) Transformation:** Raw 
[data](https://kriko.io/services/data-analysis-insight) is cleaned, reorganised 
and converted into a structure suitable for analysis. This may include filtering
records, correcting values, combining datasets and creating new variables.

**[Data](https://kriko.io/services/data-analysis-insight) Exploration:** The [data](https://kriko.io/services/data-analysis-insight)
is examined to identify patterns, relationships, anomalies and possible areas of
interest. Visualisations and summary statistics are frequently used during this 
stage.

**Statistical Modelling:** Appropriate statistical or machine learning models are
selected, developed and evaluated according to the analytical objective.

**Communication:** Findings are presented through charts, reports, dashboards or
reproducible documents so that insights can be understood and used by relevant stakeholders.

R supports **reproducible analysis** by allowing code, calculations, visualisations
and analytical outputs to be documented within the same workflow. This makes it 
easier to review, repeat and share analytical processes.

With its extensive package ecosystem, strong statistical capabilities and flexible
visualisation tools, R remains an important environment for academic research, data
science and advanced analytics. The official R Project describes it as a language
and environment developed specifically for **statistical computing** and graphics.

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