R Data Analysis Frameworks
A comprehensive list of data analysis and visualization frameworks available for the R language.
R Language Data Analysis Ecosystem
R is a programming language specialized in statistical analysis and data visualization, widely used from academic research to business analytics. The unified ecosystem centered around tidyverse enables a consistent workflow from data acquisition through preprocessing, analysis, to visualization.
Key Features
- Rich Statistical Functionality: Comprehensive implementation of statistical methods with rapid availability of cutting-edge statistical theory
- Superior Visualization Capabilities: Easy creation of publication-quality graphics, led by ggplot2
- Tidyverse Ecosystem: Unified grammar for data manipulation with intuitive code writing using pipe operators
- Active Academic Community: High adoption in universities and research institutions with continuous development of latest analytical methods
Data Processing Characteristics
- In-memory Processing: Data is typically loaded into memory for processing, requiring special consideration for large datasets
- Vectorized Operations: Achieves high-speed processing by utilizing vector operations instead of loops
- Extensive Packages: Choose from over 20,000 packages registered on CRAN for required functionality
Framework Selection Guidelines
R language frameworks are optimal when prioritizing statistical analysis and exploratory data analysis. They particularly excel in creating figures for research papers, statistical modeling, and building interactive dashboards. Consider integration with data.table or arrow for large-scale data processing, and utilize Shiny for web application development.
GitHub Star Comparison
| No | Name | GitHub Stars | Description | Trend | License | Official Site |
|---|---|---|---|---|---|---|
| 1 | ggplot2 | ⭐ 6.7k | Powerful data visualization package based on Grammar of Graphics. Builds complex graphs incrementally through a layer system. Creates beautiful and insightful visualizations with rich geoms, statistical functions, and theme systems. | Reigns as the absolute standard for data visualization in R in 2025. Widely used from academic papers to business reports. Integration with plotly and enhanced animation features enable interactive visualizations. | MIT | Official |
| 2 | Shiny | ⭐ 5.5k | Framework for building web applications with R. Creates interactive dashboards and data analysis apps easily through reactive programming model. Enables web app development without requiring HTML, CSS, or JavaScript knowledge. | Maintains important position as BI dashboard and prototyping tool in 2025. Combined with enterprise frameworks like Golem and Rhino, enables full-scale web application development. | GPL-3 | Official |
| 3 | dplyr | ⭐ 4.9k | Core package of the tidyverse. Provides a grammar of data manipulation with verbs like filter, select, mutate, summarize, and arrange for intuitive data operations. Enables concise expression of complex data transformations when combined with pipe operators. | Maintains unshakeable position as the standard for data manipulation in R in 2025. Serves as the center of the tidyverse ecosystem, widely used from data science introduction to practice. Integration with new backends (data.table, arrow, duckdb) enables large-scale data processing. | MIT | Official |
| 4 | data.table | ⭐ 3.8k | Package for high-speed in-memory data processing. Specialized for large dataset operations, achieving higher performance than dplyr. Efficiently executes filtering, grouping, and aggregation with its unique syntax. | Continues as R's mainstay for big data processing in 2025. Expanding adoption in performance-critical projects and financial/pharmaceutical industries. Supported by speed-focused users in comparison with dplyr. | MPL-2.0 | Official |
| 5 | tidyr | ⭐ 1.4k | Package for data reshaping and tidying. Provides essential data cleaning functions including wide-to-long format conversion, nested data manipulation, and missing value handling. | Continues as important part of tidyverse ecosystem in 2025. Introduction of pivot functions makes data transformation more intuitive. Established as indispensable tool in data science preprocessing workflows. | MIT | Official |