DataGraph vs R
Side-by-side comparison for macOS
DataGraph
6.0Scientific/statistical graphing software
R
8.0Environment for statistical computing and graphics
| Metric | DataGraph | R |
|---|---|---|
| Category | Science | Developer Tools |
| AI Score | 6.0 | 8.0 |
| 30-day Installs | 3 | 1.1K |
| 90-day Installs | 16 | 3.0K |
| 365-day Installs | 74 | 11.7K |
| Version | 5.5 | 4.6.0,sonoma |
| Auto-updates | No | No |
| Deprecated | No | No |
| GitHub Stars | 126 | — |
| GitHub Forks | 5 | — |
| Open Issues | - | — |
| License | Apache-2.0 | — |
| Language | Haskell | — |
| Last GitHub Commit | 10y ago | — |
| First Seen | Jul 27, 2014 | Mar 2, 2019 |
Reviews
DataGraph
DataGraph is a powerful tool for creating and analyzing scientific and statistical graphs. Its key features include advanced graphing capabilities and a user-friendly interface, making it ideal for researchers and data analysts.
DataGraph is a statistical graphing software that allows users to create and analyze various types of graphs and charts.
Pros
- + Advanced graphing and statistical analysis capabilities
- + User-friendly interface for creating and analyzing graphs
- + Open-source with an Apache-2.0 license
Cons
- - No updates since 2016, suggesting limited ongoing development
- - Limited community support and discussion
R
R is a powerful environment for statistical computing and graphics, widely used in academia and data science. It offers extensive packages and tools for data analysis, visualization, and machine learning, making it indispensable for researchers and statisticians.
R provides an environment for statistical analysis, data visualization, and programming.
Pros
- + Extensive statistical and graphical capabilities
- + Large ecosystem of packages and tools
- + Active and supportive community
Cons
- - Steep learning curve for beginners
- - Some enterprise features require additional tools