How Is R Studio Different From R 3.6.1

R Studio and R 3.6.1 are both popular tools used for statistical analysis and data visualization in the field of data science. However, they have some key differences that make them unique in their own ways.


As a data scientist, I have had the opportunity to work extensively with both R Studio and R 3.6.1. In this article, I will share my insights and personal experiences to highlight the differences between these two tools.

Background on R Studio

R Studio is an integrated development environment (IDE) specifically designed for working with the R programming language. It provides a user-friendly interface and a wide range of powerful features that enhance the productivity of R users. With its intuitive layout, R Studio allows data scientists like myself to write and execute R code efficiently.

One of the key advantages of R Studio is its seamless integration with various R packages. These packages extend the functionality of R and provide specialized tools for data analysis, machine learning, and visualization. R Studio makes it easy to install and manage these packages, allowing users to quickly access a vast ecosystem of innovative tools.

The Power of R 3.6.1

R 3.6.1, on the other hand, is the latest version of the R programming language itself. It is a comprehensive statistical programming language that offers a wide range of functions and methods for data analysis. R 3.6.1 comes with numerous built-in packages that cover various statistical techniques and algorithms, making it a powerful tool for exploratory data analysis and modeling.

Unlike R Studio, which focuses on providing an enhanced development environment, R 3.6.1 is primarily a programming language. This means that it can be run from the command line or integrated into other software systems, making it more versatile in certain scenarios.

Differences in Functionality

While both R Studio and R 3.6.1 share the same underlying language, they offer different functionalities and user experiences. Here are some key differences:

  • Interface: R Studio provides a user-friendly interface with a built-in code editor, console, and data viewer, making it easy to write, debug, and test R code. R 3.6.1, on the other hand, relies on a command-line interface, which may require some learning curve for beginners.
  • Workflow: R Studio offers a seamless workflow with features like project management, version control, and interactive debugging. These features streamline the development process and enhance collaboration among team members. R 3.6.1, being a language, does not provide these integrated workflow features.
  • Package Management: R Studio simplifies the installation and management of R packages through its graphical user interface. It provides a package manager that allows users to search, install, update, and remove packages effortlessly. R 3.6.1, on the other hand, requires users to manually install and update packages from the command line.
  • Data Visualization: R Studio excels in data visualization with its built-in graphics capabilities and support for popular visualization libraries like ggplot2. It provides a visual interface for creating interactive plots and dashboards. While R 3.6.1 also offers data visualization capabilities, it requires users to write code to generate plots.


In conclusion, both R Studio and R 3.6.1 have their own strengths and are valuable tools in the field of data science. R Studio provides a user-friendly environment with advanced features for code development, package management, and data visualization. On the other hand, R 3.6.1 is a versatile programming language that can be integrated into various software systems and offers a vast ecosystem of built-in packages.

As a data scientist, it is essential to have a good understanding of both R Studio and R 3.6.1, as they complement each other and cater to different aspects of the data science workflow. Whether you prefer the convenience of an integrated development environment or the flexibility of a programming language, both tools have their place in the data scientist’s toolkit.