Difference between R and RStudio – R VS RStudio – Techbytes
R vs RStudio: What is the Difference Between R and RStudio?
Hearing about R and RStudio for the first time can easily make you assume the two are either the same or related in some way. For starters, R can sound like a distinct version of RStudio. This confusion is understandable because, besides sounding similar, installing R on your machine creates the need for RStudio. It is possible to use R without RStudio, but you can’t run RStudio without R installed in your system.
These two aren't necessarily the same, here are the major differences between R and RStudio.
1. R is a programming language; RStudio is an IDE
Yes, R lets you run computations while RStudio provides the interface. R is the programming language that will help you to run all the statistical computations you need. RStudio, on another hand, is an IDE (integrated development environment) that gives you the interface you need. Basically, if R were the engine of the car, then RStudio would be the dashboard – you only use RStudio to work with what R avails for you.
This means RStudio is more elaborate compared to R. As it is traditional with IDEs, you will find yourself navigating between RStudio's source code editor, debugger, and build automation tools. On another hand, R, a programming language, is focused on nothing but what your code can and can't do.
2. R is independent; RStudio can only work with R
You must not use RStudio IDE with R. The programmer can choose to run R in the console and without RStudio. Several other options can substitute RStudio on R:
- RIDE - an R-Brain IDE (or simply as RIDE) for both R and Python. It comes with a highly flexible layout and support for multiple languages – https://r-brain.io/
- Jupyter notebook - the popular Jupyter Notebook App can be described as a server-client application designed to allow editing and running some notebook documents through a web browser. The best thing is that Jupyter Notebook App permits execution on your local desktop – http://jupyter.org/
- Jupyter lab - Jupyter lab is slightly different from Jupyter notebook, but they are related in some way. It is an extensible environment designed to support reproducible and interactive computing tasks based on Architecture (more on this later) and Jupyter Notebook –https://github.com/jupyterlab/jupyterlab/
- R Tools for Visual Studio (RTVS) - this is an open-source and free extension meant for Visual Studio 2017. RTVS is currently supported in Visual Studio running on Windows but entirely unsupported on Visual Studio for Mac OS –https://www.visualstudio.com/vs/features/rtvs/
- Radiant – this is a platform-independent and open-source browser-based interface made for business analytics application in R. It is based on the Shiny package. You can choose to run it locally or opt for the server.
- Architect - this is a lightweight IDE designed to focus specifically on the basic needs of any data scientist – both beginner and advanced. All data science jobs from analyzing data sets to writing detailed reports can be easily performed in one environment with a single logic – https://www.getarchitect.io/
- displayr - this simple yet powerful all-in-one statistical data analysis and reporting tool is designed to survey data. It helps you achieve automation by code or menu. Elegant visualizations produced by displayr can be useful in your projects as well. If you are looking for an RStudio substitute that supports, instant publishing, reproducibility, collaboration, auto-updating, and secure cloud platform, consider choosing displayr – https://www.displayr.com/features/
- Tinn-R - the Tinn-R Editor is basically a GUI for R programming language and environment
- RKWard - just the right option if you are looking for a tool that is easy to use and one that comes with an easily extensible GUI/IDE for R programming.
- Rbox - this package consists of a collection of multiple other packages that can be used to run R through Atom editor – shttps://atom.io/packages/rbox
- R AnalyticFlow - this is a data analysis software designed to utilize the R environment and support statistical computing.
- Nvim-R - the Vim plugin is meant for editing R code.
- Rattle - this is a Graphical User Interface mainly used in data mining applications in R.
- Rgedit - just another simple text-editor plugin.
However, RStudio is just the right IDE you need for R and it goes an extra length to make things a whole lot easier for you. RStudio is still the most powerful IDE you can ever use with R, thanks partly to its emphasis on data-friendly environments.
3. In Mac, R comes with .pkg extension while RStudio features .tgz extension
When dealing with Windows OS, we are used to just one file extension for all executable files – the .exe extension. So, this means both RStudio and R for windows come with the .exe extension. However, things are a little different when trying to install these two on Mac OS.
If you are running on macOS, you will download R from www.r-project.org IN the .pkg extension. .Pkg is very much like .zip because it is used on packages used to hold compressed installer files although it has come to be synonymous with applications meant for Mac OS X platforms. The RStudio for Mac OS comes with the .tgz extension instead of .pkg even though it will need to run together with R on Mac OS. The benefit of a TGZ file is that it is a result of applying the GNU Zip (or gzip) compression on your TAR archive, meaning it can be used across any LINUX-based platform, even Android. TAR archives usually don't support compression the same way most other formats do. So, after a few of the parts in the file have been archived into one TAR file, gzip will be applied to reduce the overall file size and ease the process of distribution. This means you can download one RStudio installation file with this extension and install it on any machine running it on a LINUX-based OS.
In a nutshell
R is a programming language while RStudio is its IDE. This makes the relationship between these two to be close to that of the engine and the dashboard of your car. You can use R without RStudio, but you can't use RStudio without R. There are a lot of substitute IDEs you can use in place of RStudio. Finally, and more importantly, these two come with two different file formats if you intend to install them on Mac OS.
