Rpy2 and R plots in a Jupyter Notebook!.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## How to Call R from Python: Plotting \n",
    "\n",
    "This is a code example in which we use the Python package rpy2 to create R plots. We use the r-packages 'graphics' and 'ggplot2' and display them here in this Jupyter Notebook.\n",
    "\n",
    "This Notebook is for the [YouTube Video about using Rpy2 and plotting in Jupyter notebooks](https://youtu.be/RK-n78ZOXUg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import rpy2.robjects as robjects\n",
    "from rpy2.robjects.lib import grdevices\n",
    "from rpy2.robjects.vectors import IntVector\n",
    "from IPython.display import Image, display\n",
    "\n",
    "graphics = robjects.packages.importr('graphics')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "with grdevices.render_to_bytesio(grdevices.jpeg, width=1024, height=896, res=150) as img:\n",
    "    graphics.barplot(IntVector((1,3,2,5,4)), ylab=\"Value\")\n",
    "    \n",
    "display(Image(data=img.getvalue(), format='jpeg', embed=True))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import rpy2.robjects.lib.ggplot2 as ggplot2\n",
    "\n",
    "datasets = robjects.packages.importr('datasets')\n",
    "mtcars = robjects.packages.data(datasets).fetch('mtcars')['mtcars']\n",
    "\n",
    "with grdevices.render_to_bytesio(grdevices.png, width=1024, height=896, res=150) as img:\n",
    "    gp = ggplot2.ggplot(mtcars)\n",
    "    pp = (\n",
    "        gp\n",
    "        + ggplot2.aes_string(x='wt', y='mpg')\n",
    "        + ggplot2.geom_point(ggplot2.aes_string(colour='qsec'))\n",
    "        + ggplot2.scale_colour_gradient(low='yellow', high='red')\n",
    "        + ggplot2.labs(title='Rpy2 ggplot2 MTCARS')\n",
    "        + ggplot2.theme_bw()\n",
    "        + ggplot2.theme(**{'panel.grid.major': ggplot2.element_blank(),\n",
    "                           'panel.grid.minor': ggplot2.element_blank()\n",
    "            \n",
    "        })\n",
    "        \n",
    "    )\n",
    "    \n",
    "    pp.plot()\n",
    "    \n",
    "display(Image(data=img.getvalue(), format='png', embed=True))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}