The Jupyter system supports over 100 programming languages (called \u201ckernels\u201d in the Jupyter ecosystem) including Python, Java, R, Julia, Matlab, Scala, and many more. You can learn these \u201ckernels\u201d in the Jupyter-kernels repository in GitHub (https:\/\/github.com\/jupyter\/jupyter\/wiki\/Jupyter-kernels<\/a>).<\/p>\n R is an interpreted programming language for statistical computing and graphics, which is widely used among statisticians and data miners for developing statistical software and data analysis.<\/p>\n There are several ways to set up Jupyter Notebook for R. We will learn two straightforward ways as follows:<\/p>\n This way allows us to run R and Python together in the same notebook.<\/p>\n The first way is to go to R web to download R installer to install it if have classical Python installed on your system. If you have Anaconda Python, you can install R by Conda as follows: Installing Jupyter notebook by: or install it using conda for Anaconda installer: After installing rpy2, we need to enable the %%R <\/strong>magic.<\/p>\n We only need to run it once for the first time. After these installations, the Jupyter Notebook now supports both Python 3 and R programming languages<\/p>\n We can install R packages directly in the Jupyter notebook. For example, we install \u2018ggplot2\u2019 by the following command:<\/p>\n Then it will ask you to select a CRAN mirror for use, you just select your nearest CRAN mirror.<\/p>\n We can also name a CRAN mirror for use in the command directly. For example, let\u2019s choose \u2018http:\/\/cran.us.r-project.org’, then we can type the following code to run:<\/p>\n After successful installation, we can load the package using the following command:<\/p>\n Let\u2019s see a concrete example in the following section.<\/p>\n In this example, we will import Python Pandas and NumPy libraries and create a pandas DataFrame.<\/p>\n After running the above code, we get something as:<\/p>\n So far, it is pure Python code. Next, we use R to make a plot using \u201cggplot2′ for the above data table, for example.<\/p>\n In the first line, we call R using \u2018%%R\u2019 magic, where<\/p>\n \u2014 <\/em><\/strong>units in <\/em>–<\/em><\/strong>r<\/em> define size unit in resolution, say 200 dpi resolution in this example. Unit can be changed to px, cm, etc<\/p>\n So the first line can be described as \u201cWe pass data df as input to R, and then make default figure size 3 by 3 inches with 200 dpi resolution.\u201d<\/em><\/p>\n The next two lines is to install package \u201cggplot2\u201d, which we have already learnt in previous section. Here just show you how to do this in one code snippet. The following line is to load the package, which we are also clear. The last few lines to make a plot using \u201cggplot2\u201d. The output looks like the following screenshot.<\/p>\n By default, it installs the kernel per-user. To install system-wide, set user to False in the installspec command:<\/p>\n Create a new notebook and select R kernel as follows.<\/p>\n Then copy the following code in the cell.<\/p>\n Run jupyter notebook, you will see the following outcome in Jupyter notebook.<\/p>\n In this article, two methods of running R in Jupyter notebook. One method is to install Python rpy2 library, and another method is to install R kernel for Jupyter Notebook. The differences between these methods are that we can run Python code and R in the same Jupyter notebook for the first methods, while we can only run R code in a separate Jupyter notebook for the second method.<\/p>\n Besides, you can watch a video version of this paper as follows. Please subscribe for more tips by click: https:\/\/www.youtube.com\/channel\/UCQpUJn9GfUjKkFP2X-1pjoQ?sub_confirmation=1<\/a><\/p>\n If you are interested in learning Jupyter notebook systematically, welcome to my following tutorial and online course:<\/p>\n (1) 22 Pretty Handy Features of Jupyter Notebook: <\/em><\/strong>https:\/\/www.youtube.com\/watch?v=xEI3065M-3g&t=3s<\/a><\/p>\nR Language<\/h3>\n
Use Python\u00a0Kernel<\/h3>\n
(1) Installation Requirements<\/h4>\n
\n
(2) Two Ways to Install\u00a0R<\/h4>\n
\nconda install -c r r-essentials<\/code><\/p>\n(3) Install Jupyter\u00a0Notebook<\/h4>\n
\npip install notebook<\/code><\/p>\n
\nconda install notebook<\/code><\/p>\n(4) Install rpy2\u00a0library<\/h4>\n
\n
\npip install numpy<\/code><\/li>\n
\npip install pandas<\/code><\/li>\n
\npip install rpy2<\/code> or
\nconda install rpy2<\/code><\/li>\n<\/ul>\n%load_ext<\/strong> rpy2.ipython<\/pre>\n
(3) Install R\u00a0packages<\/h4>\n
\r\n
%%<\/code><\/strong>R\r\ninstall.packages('ggplot2')<\/code><\/pre>\n%%<\/strong>R\r\ninstall.packages('ggplot2',repos=<\/strong>'http:\/\/cran.us.r-project.org', quiet=TRUE<\/strong>)<\/code><\/pre>\n%%<\/strong>R\r\nlibrary(ggplot2)<\/code><\/pre>\n(4) Example<\/h4>\n
import<\/strong> pandas as<\/strong> pd\r\n\r\ndf =<\/strong> pd.<\/strong>DataFrame({\r\n 'x_var': [0, 1, 2, 3, 4, 5, 6, 7, 8, 9],\r\n 'y_var': [3, 5, 7, 6, 9, 8, 10, 12, 13, 11]\r\n})<\/code><\/pre>\ndf<\/code><\/pre>\n![\"\"](\"https:\/\/academy.deepsim.cawp-content\/uploads\/2022\/08\/1LovTEKQ8ceYhu0BhndLHIw.png\")
%%<\/strong>R -<\/strong>i df -<\/strong>w 3 -<\/strong>h 3 --<\/strong>units in -<\/strong>r 200<\/code><\/pre>\ninstall.packages('ggplot2',repos=<\/strong>'http:\/\/cran.us.r-project.org', quiet=TRUE<\/strong>)<\/code><\/pre>\nlibrary(ggplot2)\r\n\r\nggplot(data =<\/strong> df, aes(x =<\/strong> x_var, y =<\/strong> y_var)) +<\/strong>\r\n geom_point(color =<\/strong> 'red', size =<\/strong> 4) +<\/strong>\r\n stat_smooth()<\/code><\/pre>\n\n
![](\"https:\/\/miro.medium.com\/max\/875\/1*TBmGa2-s0YJtEqZtEa0ZPA.png\")
Use R\u00a0kernel<\/h3>\n
(1) Install R kernel for Jupyter\u00a0Notebook<\/h4>\n
IRkernel<\/code> can be installed via the Comprehensive R Archive Network [CRAN].<\/p>\n%%<\/strong>R \r\ninstall.packages('IRkernel')<\/code><\/pre>\n(2) Making the kernel available to\u00a0Jupyter<\/h4>\n
IRkernel::installspec()<\/code> will install a kernel with the name \u201cir\u201d and a display name of \u201cR\u201d.<\/p>\n%%<\/strong>R\r\nIRkernel::<\/strong>installspec(user =<\/strong> FALSE<\/strong>) # to register the kernel in the current R installation<\/em><\/code><\/pre>\n(3) Create R Jupyter\u00a0Notebook<\/h4>\n
![](\"https:\/\/miro.medium.com\/max\/875\/1*inH8O8s4HGCHEEtdpa2-0g.png\")
n <-<\/strong> floor(rnorm(10000, mean =<\/strong> 200, sd =<\/strong> 50))\r\nt <-<\/strong> table(n)\r\nbarplot(t)<\/code><\/pre>\n![](\"https:\/\/miro.medium.com\/max\/809\/1*Ce9-gooEgzyG8EkGJKI9PA.png\")
Conclusion<\/h3>\n