{
 "metadata": {
  "name": "",
  "signature": "sha256:ae7c2c1275d37f626ca305a1544f73524af8b638ad4ef48e6e6303615c71685d"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%load_ext rpy2.ipython"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "problem rpy2 #224 : maybe a R to python3 unicode/string  problem ? "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import numpy as np\n",
      "import pylab\n",
      "X = np.array([0,1,2,3,4])\n",
      "Y = np.array([3,5,4,6,7])\n",
      "%Rpush X Y\n",
      "%R lm(Y~X)$coef\n",
      "%R d=resid(lm(Y~X)); e=coef(lm(Y~X))\n",
      "%R -o d -o e\n",
      "%Rpull e\n",
      "print (e)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Unable to unlink tempfile C:\\Users\\famille\\AppData\\Local\\Temp\\tmpslq1uzx7\n"
       ]
      },
      {
       "ename": "TypeError",
       "evalue": "sequence item 0: expected str instance, bytes found",
       "output_type": "pyerr",
       "traceback": [
        "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
        "\u001b[1;32m<ipython-input-2-9bcc6f250235>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      8\u001b[0m \u001b[0mget_ipython\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmagic\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'R -o d -o e'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      9\u001b[0m \u001b[0mget_ipython\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmagic\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'Rpull e'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 10\u001b[1;33m \u001b[0mprint\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0me\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
        "\u001b[1;32mD:\\result_tests\\WinPython-32bit-3.4.2.1_build15\\python-3.4.2rc1\\lib\\site-packages\\rpy2\\robjects\\robject.py\u001b[0m in \u001b[0;36m__str__\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m     46\u001b[0m                 \u001b[1;32mif\u001b[0m \u001b[0mos\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mexists\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtfname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     47\u001b[0m                     \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'Unable to unlink tempfile %s'\u001b[0m \u001b[1;33m%\u001b[0m \u001b[0mtfname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 48\u001b[1;33m             \u001b[0ms\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mstr\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mos\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlinesep\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0ms\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     49\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     50\u001b[0m             \u001b[0mrpy2\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrinterface\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset_writeconsole\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mwriteconsole\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
        "\u001b[1;31mTypeError\u001b[0m: sequence item 0: expected str instance, bytes found"
       ]
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "problem rpy2 #223 : only last plot is shown (but they are ok if launched one by one)"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%%R\n",
      "# ggplot2 examples\n",
      "library(ggplot2)\n",
      "\n",
      "# create factors with value labels\n",
      "mtcars$gear <- factor(mtcars$gear,levels=c(3,4,5),\n",
      "   labels=c(\"3gears\",\"4gears\",\"5gears\"))\n",
      "mtcars$am <- factor(mtcars$am,levels=c(0,1),\n",
      "   labels=c(\"Automatic\",\"Manual\"))\n",
      "mtcars$cyl <- factor(mtcars$cyl,levels=c(4,6,8),\n",
      "   labels=c(\"4cyl\",\"6cyl\",\"8cyl\"))\n",
      "\n",
      "# Kernel density plots for mpg\n",
      "# grouped by number of gears (indicated by color)\n",
      "qplot(mpg, data=mtcars, geom=\"density\", fill=gear, alpha=I(.5),\n",
      "   main=\"Distribution of Gas Milage\", xlab=\"Miles Per Gallon\",\n",
      "   ylab=\"Density\")\n",
      "\n",
      "# Scatterplot of mpg vs. hp for each combination of gears and cylinders\n",
      "# in each facet, transmittion type is represented by shape and color\n",
      "qplot(hp, mpg, data=mtcars, shape=am, color=am,\n",
      "   facets=gear~cyl, size=I(3),\n",
      "   xlab=\"Horsepower\", ylab=\"Miles per Gallon\")\n",
      "\n",
      "# Separate regressions of mpg on weight for each number of cylinders\n",
      "qplot(wt, mpg, data=mtcars, geom=c(\"point\", \"smooth\"),\n",
      "   method=\"lm\", formula=y~x, color=cyl,\n",
      "   main=\"Regression of MPG on Weight\",\n",
      "   xlab=\"Weight\", ylab=\"Miles per Gallon\")\n",
      "\n",
      "# Boxplots of mpg by number of gears\n",
      "# observations (points) are overlayed and jittered\n",
      "qplot(gear, mpg, data=mtcars, geom=c(\"boxplot\", \"jitter\"),\n",
      "   fill=gear, main=\"Mileage by Gear Number\",\n",
      "   xlab=\"\", ylab=\"Miles per Gallon\") "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%%R\n",
      "# Separate regressions of mpg on weight for each number of cylinders\n",
      "qplot(wt, mpg, data=mtcars, geom=c(\"point\", \"smooth\"),\n",
      "   method=\"lm\", formula=y~x, color=cyl,\n",
      "   main=\"Regression of MPG on Weight\",\n",
      "   xlab=\"Weight\", ylab=\"Miles per Gallon\")\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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peGKawoQQj8fT2dlJh651dHR4PJ6zZ882Nja+8847CkyMFz+gT5w40dTU1N3d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\nIAVa0JBFlM9oooLWNFWSM7d3w9Az5cP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      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%%R\n",
      "\n",
      "# Kernel density plots for mpg\n",
      "# grouped by number of gears (indicated by color)\n",
      "qplot(mpg, data=mtcars, geom=\"density\", fill=gear, alpha=I(.5),\n",
      "   main=\"Distribution of Gas Milage\", xlab=\"Miles Per Gallon\",\n",
      "   ylab=\"Density\")\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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2MVf18oWu++Vy5vAoH82e62xjzeruLisrIzck0vlODcqR5N6+fZvu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\n"
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "problem rpy2 #212 bis : with %%R the head(hgflights) output goes to the DOS (stderr?) window instead of the ipython cell\n",
      "    "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%%R\n",
      "library(hflights)\n",
      "dim(hflights)\n",
      "head(hflights)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%R head(hflights)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>Year</th>\n",
        "      <th>Month</th>\n",
        "      <th>DayofMonth</th>\n",
        "      <th>DayOfWeek</th>\n",
        "      <th>DepTime</th>\n",
        "      <th>ArrTime</th>\n",
        "      <th>UniqueCarrier</th>\n",
        "      <th>FlightNum</th>\n",
        "      <th>TailNum</th>\n",
        "      <th>ActualElapsedTime</th>\n",
        "      <th>...</th>\n",
        "      <th>ArrDelay</th>\n",
        "      <th>DepDelay</th>\n",
        "      <th>Origin</th>\n",
        "      <th>Dest</th>\n",
        "      <th>Distance</th>\n",
        "      <th>TaxiIn</th>\n",
        "      <th>TaxiOut</th>\n",
        "      <th>Cancelled</th>\n",
        "      <th>CancellationCode</th>\n",
        "      <th>Diverted</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td> 2011</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 6</td>\n",
        "      <td> 1400</td>\n",
        "      <td> 1500</td>\n",
        "      <td> AA</td>\n",
        "      <td> 428</td>\n",
        "      <td> N576AA</td>\n",
        "      <td> 60</td>\n",
        "      <td>...</td>\n",
        "      <td>-10</td>\n",
        "      <td> 0</td>\n",
        "      <td> IAH</td>\n",
        "      <td> DFW</td>\n",
        "      <td> 224</td>\n",
        "      <td> 7</td>\n",
        "      <td> 13</td>\n",
        "      <td> 0</td>\n",
        "      <td> </td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td> 2011</td>\n",
        "      <td> 1</td>\n",
        "      <td> 2</td>\n",
        "      <td> 7</td>\n",
        "      <td> 1401</td>\n",
        "      <td> 1501</td>\n",
        "      <td> AA</td>\n",
        "      <td> 428</td>\n",
        "      <td> N557AA</td>\n",
        "      <td> 60</td>\n",
        "      <td>...</td>\n",
        "      <td> -9</td>\n",
        "      <td> 1</td>\n",
        "      <td> IAH</td>\n",
        "      <td> DFW</td>\n",
        "      <td> 224</td>\n",
        "      <td> 6</td>\n",
        "      <td>  9</td>\n",
        "      <td> 0</td>\n",
        "      <td> </td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td> 2011</td>\n",
        "      <td> 1</td>\n",
        "      <td> 3</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1352</td>\n",
        "      <td> 1502</td>\n",
        "      <td> AA</td>\n",
        "      <td> 428</td>\n",
        "      <td> N541AA</td>\n",
        "      <td> 70</td>\n",
        "      <td>...</td>\n",
        "      <td> -8</td>\n",
        "      <td>-8</td>\n",
        "      <td> IAH</td>\n",
        "      <td> DFW</td>\n",
        "      <td> 224</td>\n",
        "      <td> 5</td>\n",
        "      <td> 17</td>\n",
        "      <td> 0</td>\n",
        "      <td> </td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td> 2011</td>\n",
        "      <td> 1</td>\n",
        "      <td> 4</td>\n",
        "      <td> 2</td>\n",
        "      <td> 1403</td>\n",
        "      <td> 1513</td>\n",
        "      <td> AA</td>\n",
        "      <td> 428</td>\n",
        "      <td> N403AA</td>\n",
        "      <td> 70</td>\n",
        "      <td>...</td>\n",
        "      <td>  3</td>\n",
        "      <td> 3</td>\n",
        "      <td> IAH</td>\n",
        "      <td> DFW</td>\n",
        "      <td> 224</td>\n",
        "      <td> 9</td>\n",
        "      <td> 22</td>\n",
        "      <td> 0</td>\n",
        "      <td> </td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td> 2011</td>\n",
        "      <td> 1</td>\n",
        "      <td> 5</td>\n",
        "      <td> 3</td>\n",
        "      <td> 1405</td>\n",
        "      <td> 1507</td>\n",
        "      <td> AA</td>\n",
        "      <td> 428</td>\n",
        "      <td> N492AA</td>\n",
        "      <td> 62</td>\n",
        "      <td>...</td>\n",
        "      <td> -3</td>\n",
        "      <td> 5</td>\n",
        "      <td> IAH</td>\n",
        "      <td> DFW</td>\n",
        "      <td> 224</td>\n",
        "      <td> 9</td>\n",
        "      <td>  9</td>\n",
        "      <td> 0</td>\n",
        "      <td> </td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5</th>\n",
        "      <td> 2011</td>\n",
        "      <td> 1</td>\n",
        "      <td> 6</td>\n",
        "      <td> 4</td>\n",
        "      <td> 1359</td>\n",
        "      <td> 1503</td>\n",
        "      <td> AA</td>\n",
        "      <td> 428</td>\n",
        "      <td> N262AA</td>\n",
        "      <td> 64</td>\n",
        "      <td>...</td>\n",
        "      <td> -7</td>\n",
        "      <td>-1</td>\n",
        "      <td> IAH</td>\n",
        "      <td> DFW</td>\n",
        "      <td> 224</td>\n",
        "      <td> 6</td>\n",
        "      <td> 13</td>\n",
        "      <td> 0</td>\n",
        "      <td> </td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>6 rows \u00d7 21 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 7,
       "text": [
        "   Year  Month  DayofMonth  DayOfWeek  DepTime  ArrTime UniqueCarrier  \\\n",
        "0  2011      1           1          6     1400     1500            AA   \n",
        "1  2011      1           2          7     1401     1501            AA   \n",
        "2  2011      1           3          1     1352     1502            AA   \n",
        "3  2011      1           4          2     1403     1513            AA   \n",
        "4  2011      1           5          3     1405     1507            AA   \n",
        "5  2011      1           6          4     1359     1503            AA   \n",
        "\n",
        "   FlightNum TailNum  ActualElapsedTime        ...          ArrDelay  \\\n",
        "0        428  N576AA                 60        ...               -10   \n",
        "1        428  N557AA                 60        ...                -9   \n",
        "2        428  N541AA                 70        ...                -8   \n",
        "3        428  N403AA                 70        ...                 3   \n",
        "4        428  N492AA                 62        ...                -3   \n",
        "5        428  N262AA                 64        ...                -7   \n",
        "\n",
        "   DepDelay  Origin Dest Distance  TaxiIn  TaxiOut  Cancelled  \\\n",
        "0         0     IAH  DFW      224       7       13          0   \n",
        "1         1     IAH  DFW      224       6        9          0   \n",
        "2        -8     IAH  DFW      224       5       17          0   \n",
        "3         3     IAH  DFW      224       9       22          0   \n",
        "4         5     IAH  DFW      224       9        9          0   \n",
        "5        -1     IAH  DFW      224       6       13          0   \n",
        "\n",
        "   CancellationCode Diverted  \n",
        "0                          0  \n",
        "1                          0  \n",
        "2                          0  \n",
        "3                          0  \n",
        "4                          0  \n",
        "5                          0  \n",
        "\n",
        "[6 rows x 21 columns]"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 7
    }
   ],
   "metadata": {}
  }
 ]
}