davidshinn/rpy2.ipython.ipynb

## rpy2.ipython.ipynb
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Passing data between Python and R Using rpy2.ipython\n",
    "Reference `rpy2.ipython` docs at http://rpy.sourceforge.net/rpy2/doc-2.4/html/interactive.html#module-rpy2.ipython.rmagic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%load_ext rpy2.ipython"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot a histogram of speed from the cars dataset in R"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%%R\n",
    "library(ggplot2)\n",
    "data(cars)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'data.frame':\t50 obs. of  2 variables:\n",
       " $ speed: num  4 4 7 7 8 9 10 10 10 11 ...\n",
       " $ dist : num  2 10 4 22 16 10 18 26 34 17 ...\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R\n",
    "str(cars)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R\n",
    "qplot(cars$speed, binwidth = 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Pass the cars dataset from R to Python"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%R -n -o cars cars"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 50 entries, 0 to 49\n",
      "Data columns (total 2 columns):\n",
      "speed    50 non-null float64\n",
      "dist     50 non-null float64\n",
      "dtypes: float64(2)\n",
      "memory usage: 1.2 KB\n"
     ]
    }
   ],
   "source": [
    "cars.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f50900c2850>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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b49e//nVERJw6dSruu++++Oc//xkLFiyIH//4xzFv3rwaT1pbl9un7du3xy9/+ctoa2uL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\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f50900c2d50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cars.speed.hist(bins=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "cars['speed_over_distance'] = cars.speed / cars.dist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f5089c80210>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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98fpzX6aiz6uohmq1Wq110fbt2+PHP/5x/OxnP4uIiKeeeioaGhpixYoVhQcBgHpX6O3r\n9773vbF79+7o7++P4eHh+P3vfx933HFH2bMBQF0p9PZ1Y2NjPPbYY/HFL34xzp07F/fdd190dXWV\nPRsA1JVCb18DAOVzRy8ASEKUASAJUQaAJEqN8mjuh/3d7343lixZEt3d3bFjx44yL88YXG7vtm7d\nGh/84Aejp6cnenp64ic/+clVmJILWbNmTSxcuDDuueeeiz7Gucvrcvvn7OW1d+/eeOihh+Kuu+6K\nu+++O5555pkLPq6m81ctycjISHXx4sXVPXv2VIeHh6vd3d3VN95447zHvPjii9UvfelL1Wq1Wt2+\nfXv1/vvvL+vyjMFo9u6VV16pfvnLX75KE3Ipf/7zn6t///vfq3ffffcFf9+5y+1y++fs5TUwMFDd\nsWNHtVqtVoeGhqpLliwZc/dKe6U8mvthv/DCC7Fs2bKIiHjf+94Xx44di4MHD5Y1AgW5l/nEtmDB\ngpgyZcpFf9+5y+1y+0delUol5s6dGxERkydPjq6urhgYOP9uXrWev9KiPJr7YQ8MDMTMmTPf/vXM\nmTNj3759ZY1AQaPZu4aGhti2bVt0d3fH8uXL44033rjSY1KQczexOXsTQ39/f+zcuTPmz59/3tdr\nPX+Fbh5yIaO9H3b1//2z6GvpPtoT1Wj2YN68efHiiy9Gc3NzbNmyJb761a/Gpk2brsB0lMG5m7ic\nvfxOnDgRK1eujLVr18bkyZP/5fdrOX+lvVLu7OyMvXv3vv3rffv2RWdn53mP6ejoOO8nhAs9hitv\nNHvX2toazc3NERGxaNGiOHPmTBw5cuSKzkkxzt3E5uzldubMmVi5cmV0d3fH4sWL/+X3az1/pUV5\nNPfDvuOOO+L555+PiLf+pxZTpkyJGTNmlDUCBY1m7w4ePPj2T3uvv/56RETccMMNV3xWaufcTWzO\nXl7VajXWrl0bXV1d8fDDD1/wMbWev9Levr7Y/bB/85vfRETEgw8+GIsWLYotW7bEnXfeGc3NzbFu\n3bqyLs8YjGbvNm3aFL/+9a/juuuui+bm5njyySev8tT8r1WrVsWrr74aR44ciUWLFsXXvva1GBkZ\niQjnbiK43P45e3m99tpr0dvbG+95z3uip6cnIiK+/vWvv/3OY5Hz597XAJCEO3oBQBKiDABJiDIA\nJCHKAJCEKANAEqIMAEmIMgAkIcoAkMT/AG2MBhflVwyzAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f509c4e5e50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cars.speed_over_distance.hist(bins=30)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Pass the altered cars dataset back from Python to R "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%R -i cars"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'data.frame':\t50 obs. of  3 variables:\n",
       " $ speed              : num [1:50(1d)] 4 4 7 7 8 9 10 10 10 11 ...\n",
       " $ dist               : num [1:50(1d)] 2 10 4 22 16 10 18 26 34 17 ...\n",
       " $ speed_over_distance: num [1:50(1d)] 2 0.4 1.75 0.318 0.5 ...\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R\n",
    "str(cars)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R\n",
    "qplot(cars$speed_over_distance, binwidth = 0.05)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}