Plot Digitizer vs R
Side-by-side comparison for macOS
Plot Digitizer
8.0Digitize scanned plots of functional data
R
8.0Environment for statistical computing and graphics
| Metric | Plot Digitizer | R |
|---|---|---|
| Category | Science | Developer Tools |
| AI Score | 8.0 | 8.0 |
| 30-day Installs | 8 | 1.1K |
| 90-day Installs | 10 | 3.0K |
| 365-day Installs | 84 | 11.7K |
| Version | 2.6.12 | 4.6.0,sonoma |
| Auto-updates | No | No |
| Deprecated | Yes | No |
| GitHub Stars | 162 | — |
| GitHub Forks | 26 | — |
| Open Issues | 11 | — |
| License | GPL-3.0 | — |
| Language | Python | — |
| Last GitHub Commit | 1y ago | — |
| First Seen | Nov 19, 2015 | Mar 2, 2019 |
Reviews
Plot Digitizer
Plot Digitizer is a unique tool for extracting functional data from scanned plots. It allows users to import images, set axes, and export data points, making it invaluable for researchers and engineers needing to analyze graphical data.
Converts scanned plots into digital data points for analysis.
Pros
- + Open-source under GPL-3.0, promoting transparency and customization.
- + Cross-platform support for macOS, Windows, and Linux.
- + Supports multiple export formats for data flexibility.
Cons
- - No auto-update feature, requiring manual checks for updates.
- - User interface may be less intuitive for some users.
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