Python/R-Python-Hybrid.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using R and python together with the rpy2 package\n",
    "https://stackoverflow.com/questions/39008069/r-and-python-in-one-jupyter-notebook"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Python Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# enables the %%R magic, not necessary if you've already done this\n",
    "%load_ext rpy2.ipython\n",
    "\n",
    "import pandas as pd\n",
    "df = pd.DataFrame({\n",
    "    'cups_of_coffee': [0, 1, 2, 3, 4, 5, 6, 7, 8, 9],\n",
    "    'productivity': [2, 5, 6, 8, 9, 8, 0, 1, 0, -1]\n",
    "})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### R Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R -i df -w 5 -h 5 --units in -r 200\n",
    "# import df from global environment\n",
    "# make default figure size 5 by 5 inches with 200 dpi resolution\n",
    "\n",
    "#install.packages(\"ggplot2\", repos='http://cran.us.r-project.org', quiet=TRUE)\n",
    "library(ggplot2)\n",
    "ggplot(df, aes(x=cups_of_coffee, y=productivity)) + geom_line()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "                     mpg cyl  disp  hp drat    wt  qsec vs am gear carb\n",
       "Mazda RX4           21.0   6 160.0 110 3.90 2.620 16.46  0  1    4    4\n",
       "Mazda RX4 Wag       21.0   6 160.0 110 3.90 2.875 17.02  0  1    4    4\n",
       "Datsun 710          22.8   4 108.0  93 3.85 2.320 18.61  1  1    4    1\n",
       "Hornet 4 Drive      21.4   6 258.0 110 3.08 3.215 19.44  1  0    3    1\n",
       "Hornet Sportabout   18.7   8 360.0 175 3.15 3.440 17.02  0  0    3    2\n",
       "Valiant             18.1   6 225.0 105 2.76 3.460 20.22  1  0    3    1\n",
       "Duster 360          14.3   8 360.0 245 3.21 3.570 15.84  0  0    3    4\n",
       "Merc 240D           24.4   4 146.7  62 3.69 3.190 20.00  1  0    4    2\n",
       "Merc 230            22.8   4 140.8  95 3.92 3.150 22.90  1  0    4    2\n",
       "Merc 280            19.2   6 167.6 123 3.92 3.440 18.30  1  0    4    4\n",
       "Merc 280C           17.8   6 167.6 123 3.92 3.440 18.90  1  0    4    4\n",
       "Merc 450SE          16.4   8 275.8 180 3.07 4.070 17.40  0  0    3    3\n",
       "Merc 450SL          17.3   8 275.8 180 3.07 3.730 17.60  0  0    3    3\n",
       "Merc 450SLC         15.2   8 275.8 180 3.07 3.780 18.00  0  0    3    3\n",
       "Cadillac Fleetwood  10.4   8 472.0 205 2.93 5.250 17.98  0  0    3    4\n",
       "Lincoln Continental 10.4   8 460.0 215 3.00 5.424 17.82  0  0    3    4\n",
       "Chrysler Imperial   14.7   8 440.0 230 3.23 5.345 17.42  0  0    3    4\n",
       "Fiat 128            32.4   4  78.7  66 4.08 2.200 19.47  1  1    4    1\n",
       "Honda Civic         30.4   4  75.7  52 4.93 1.615 18.52  1  1    4    2\n",
       "Toyota Corolla      33.9   4  71.1  65 4.22 1.835 19.90  1  1    4    1\n",
       "Toyota Corona       21.5   4 120.1  97 3.70 2.465 20.01  1  0    3    1\n",
       "Dodge Challenger    15.5   8 318.0 150 2.76 3.520 16.87  0  0    3    2\n",
       "AMC Javelin         15.2   8 304.0 150 3.15 3.435 17.30  0  0    3    2\n",
       "Camaro Z28          13.3   8 350.0 245 3.73 3.840 15.41  0  0    3    4\n",
       "Pontiac Firebird    19.2   8 400.0 175 3.08 3.845 17.05  0  0    3    2\n",
       "Fiat X1-9           27.3   4  79.0  66 4.08 1.935 18.90  1  1    4    1\n",
       "Porsche 914-2       26.0   4 120.3  91 4.43 2.140 16.70  0  1    5    2\n",
       "Lotus Europa        30.4   4  95.1 113 3.77 1.513 16.90  1  1    5    2\n",
       "Ford Pantera L      15.8   8 351.0 264 4.22 3.170 14.50  0  1    5    4\n",
       "Ferrari Dino        19.7   6 145.0 175 3.62 2.770 15.50  0  1    5    6\n",
       "Maserati Bora       15.0   8 301.0 335 3.54 3.570 14.60  0  1    5    8\n",
       "Volvo 142E          21.4   4 121.0 109 4.11 2.780 18.60  1  1    4    2\n",
       "    mpg\n",
       "1  22.8\n",
       "2  24.4\n",
       "3  22.8\n",
       "4  32.4\n",
       "5  30.4\n",
       "6  33.9\n",
       "7  21.5\n",
       "8  27.3\n",
       "9  26.0\n",
       "10 30.4\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R\n",
    "library(dplyr)\n",
    "print(mtcars)\n",
    "a <- mtcars %>% select(mpg) %>% top_n(10)\n",
    "a"
   ]
  },
  {
   "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
}