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Big Data Test Infrastructure (BDTI)

VS Code - R

The VS Code R extension enhances Visual Studio Code (VS Code) with robust support for R programming, making it a powerful tool for data analysis, statistical computing, and graphical visualisation. This extension, developed and maintained by the open-source community, provides features that streamline R development workflows.

VSCode-R

VS Code R Components

  • R language support: Provides syntax highlighting, IntelliSense, and code snippets for R.
  • R debugger: Integrated debugging support for R, allowing breakpoints, variable inspection, and step-through code execution.
  • Linting and formatting: Tools to analyse R code for potential errors and style issues using linters like lintr.
  • R markdown integration: Support for creating and rendering R Markdown documents directly within VSCode.
  • Interactive R Console: An interactive R console integrated into VSCode for running R scripts and commands.
  • Environment and package management: Simplified management of R environments and package dependencies.
  • Plot viewer: Integrated plot viewer for displaying graphical outputs from R scripts.
  • Notebook interface: Support for running R scripts within Jupyter Notebooks in VSCode.

Features

  • Rich IntelliSense: Autocomplete, code suggestions, and parameter info for R code.
  • Advanced debugging: Breakpoints, call stack, watch expressions, and interactive debugging for efficient problem-solving.
  • Integrated R markdown: Full support for creating, editing, and rendering R Markdown documents within VSCode.
  • Seamless linting and formatting: Continuous code quality checks and automated formatting to adhere to coding standards.
  • Interactive development: Interactive R console and plot viewer for rapid prototyping and visualisation.
  • Environment and package management: Integrated tools for managing R environments and package dependencies.
  • Notebook integration: Support for running and editing R scripts within Jupyter Notebooks in VSCode.
  • Customisation and extensibility: Highly customisable settings and the ability to extend functionality through additional extensions.

Use cases

  • Data analysis: Efficient analysis of datasets using R's rich set of statistical and graphical techniques.
  • Statistical computing: Developing and running statistical models and simulations.
  • Report generation: Creating dynamic reports and documents with R Markdown.
  • Data visualisation: Generating and customising plots and graphs for data visualisation.
  • Machine learning: Building and evaluating machine learning models using R packages.
  • Educational use: Ideal environment for teaching and learning R programming and statistical concepts.
  • Bioinformatics: Analysing biological data and conducting genomic studies with specialised R packages.

Resources