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R Tools for Visual Studio (RTVS) is a plug-in for the Microsoft Visual Studio integrated development environment (IDE), used to provide support for programming in the language R.
Shiny is a web framework for developing web applications (apps), originally in R and since 2022 in python. It is free and open source. [2] It was announced by Joe Cheng, CTO of Posit, formerly RStudio, in 2012. [3] One of the uses of Shiny has been in fast prototyping. [4] In 2022, a separate implementation Shiny for Python was announced. [5]
Some of knitr's extensions include the R Markdown format [4] (used in reports published on RPubs [5]), caching, TikZ graphics and support to other languages such as Python, Perl, C++, Shell scripts and CoffeeScript, and so on. knitr is officially supported in the RStudio IDE for R, LyX, Emacs/ESS and the Architect IDE for data science.
RStudio IDE (or RStudio) is an integrated development environment for R, a programming language for statistical computing and graphics. It is available in two formats: RStudio Desktop is a regular desktop application while RStudio Server runs on a remote server and allows accessing RStudio using a web browser .
Visual Studio Code was first announced on April 29, 2015 by Microsoft at the 2015 Build conference. A preview build was released shortly thereafter. [13]On November 18, 2015, the project "Visual Studio Code — Open Source" (also known as "Code — OSS"), on which Visual Studio Code is based, was released under the open-source MIT License and made available on GitHub.
The main parts of the Jupyter Notebooks are: Metadata, Notebook format and list of cells. Metadata is a data Dictionary of definitions to set up and display the notebook. Notebook Format is a version number of the software. List of cells are different types of Cells for Markdown (display), Code (to execute), and output of the code type cells. [23]
The development team attempted to create a program which shows the programmer what the effects of their additions are in real-time, rather than requiring them to work out the effects as they write the code. [9]
It works on Linux, Windows, macOS, and is available in Python, [8] R, [9] and models built using CatBoost can be used for predictions in C++, Java, [10] C#, Rust, Core ML, ONNX, and PMML. The source code is licensed under Apache License and available on GitHub. [6] InfoWorld magazine awarded the library "The best machine learning tools" in 2017.