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RStudio vs DAML: What are the differences?

RStudio: Open source and enterprise-ready professional software for the R community. An integrated development environment for R, with a console, syntax-highlighting editor that supports direct code execution Publish and distribute data products across your organization. One button deployment of Shiny applications, R Markdown reports, Jupyter Notebooks, and more. Collections of R functions, data, and compiled code in a well-defined format. You can expand the types of analyses you do by adding packages.; DAML: Open-source programming language for writing distributed applications. It is an open-source programming language for writing distributed applications quickly, concisely, and correctly. It runs on the leading blockchain platforms which means that you can build your application now and pick which platform works best for you later.

RStudio belongs to "Text Editor" category of the tech stack, while DAML can be primarily classified under "Languages".

Some of the features offered by RStudio are:

  • Enhanced Security and Authentication
  • Administrative Tools
  • Metrics and Monitoring

On the other hand, DAML provides the following key features:

  • Supports existing platforms integration like Hyperledger Fabric, R3’s Corda and Amazon Aurora
  • Scenario-based Testing
  • Fine-grained Permissions

RStudio is an open source tool with 3.07K GitHub stars and 764 GitHub forks. Here's a link to RStudio's open source repository on GitHub.

- No public GitHub repository available -

What is DAML?

It is an open-source programming language for writing distributed applications quickly, concisely, and correctly. It runs on the leading blockchain platforms which means that you can build your application now and pick which platform works best for you later.

What is RStudio?

An integrated development environment for R, with a console, syntax-highlighting editor that supports direct code execution. Publish and distribute data products across your organization. One button deployment of Shiny applications, R Markdown reports, Jupyter Notebooks, and more. Collections of R functions, data, and compiled code in a well-defined format. You can expand the types of analyses you do by adding packages.
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