How to Run R in Jupyter Notebook Using Anaconda | Full Setup Guide

Want to use the **R programming language inside Jupyter Notebook**? In this step-by-step tutorial, you'll learn exactly **how to run R in Jupyter Notebook using Anaconda**. This guide is perfect for data scientists, analysts, or students who work in both Python and R environments and want a unified workflow in Jupyter.

With this setup, you can leverage the power of R for statistics and data analysis while taking advantage of Jupyter's interactive features — all from within the Anaconda environment.

In this video, you’ll learn:

* How to install the R language on your system
* How to install and set up Jupyter with Anaconda
* How to install the R kernel for Jupyter
* How to launch Jupyter Notebook and run R code
* How to verify and switch between Python and R kernels

Prerequisites:

* Anaconda (Python distribution) installed
* Internet access to install required packages

Step-by-Step Instructions:

1. **Install R**

* Go to [https://cran.r-project.org](https://cran.r-project.org)
* Download and install R for your operating system (Windows/Mac/Linux)

2. **Install IRKernel (R Kernel for Jupyter)**
Open R or RStudio and run:

```r
install.packages('IRkernel')
IRkernel::installspec()
```

3. **Launch Anaconda Navigator or Terminal**

* Open Jupyter Notebook from Anaconda Navigator
* Or run `jupyter notebook` in your terminal/command prompt

4. **Create or Open a Notebook**

* Click **New - R** to open an R notebook
* Type and run any R code (e.g., `summary(cars)` or `plot(1:10)`)

Bonus Tips:

* You can run both **Python and R notebooks** in the same Jupyter environment
* Use R magic commands in Python notebooks with `rpy2`
* Customize your kernel name with `IRkernel::installspec(name = "ir", displayname = "R")`
* For advanced visualization, install `ggplot2`, `dplyr`, and `tidyverse` in R

Ideal for:

* Data scientists working across multiple languages
* Students taking data analysis or machine learning courses
* Researchers combining statistical models with Python code

If this helped you run R in Jupyter, don’t forget to like, share, and subscribe for more data science tutorials. Let us know in the comments if you want a guide on using R and Python together in one notebook!

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