first_to_rpy2.ipynb

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 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import rpy2\n",
      "rpy2.__path__"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 1,
       "text": [
        "['/Users/liang/.virtualenvs/sci_sys/lib/python3.4/site-packages/rpy2-2.4.0-py3.4-macosx-10.9-x86_64.egg/rpy2']"
       ]
      }
     ],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from rpy2 import robjects"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "v = robjects.FloatVector([1.1, 2.2, 3.3, 4.4, 5.5, 6.6])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "m = robjects.r['matrix'](v, nrow = 2)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print(m)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "     [,1] [,2] [,3]\n",
        "[1,]  1.1  3.3  5.5\n",
        "[2,]  2.2  4.4  6.6\n",
        "\n"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "r = robjects.r"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "x = robjects.IntVector(range(10))\n",
      "y = r.rnorm(10)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import math, datetime\n",
      "import rpy2.robjects.lib.ggplot2 as ggplot2\n",
      "import rpy2.robjects as ro\n",
      "from rpy2.robjects.packages import importr\n",
      "base = importr('base')\n",
      "mtcars = r('mtcars')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "grdevices = importr('grDevices')\n",
      "grdevices.png(file=\"test.png\", width=512, height=512)\n",
      "pp = ggplot2.ggplot(mtcars) + \\\n",
      "     ggplot2.aes_string(x='wt', y='mpg', col='factor(cyl)') + \\\n",
      "     ggplot2.geom_point() + \\\n",
      "     ggplot2.geom_smooth(ggplot2.aes_string(group = 'cyl'),\n",
      "                         method = 'lm')\n",
      "pp.plot()\n",
      "grdevices.dev_off()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 9,
       "text": [
        "<IntVector - Python:0x10e067808 / R:0x7ffd6afd5bc8>\n",
        "[       1]"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "utils = importr('utils')\n",
      "utils.data('mtcars')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 10,
       "text": [
        "<StrVector - Python:0x110bf0ac8 / R:0x7ffd6da1b408>\n",
        "['mtcars']"
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "type(mtcars)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 11,
       "text": [
        "rpy2.robjects.vectors.DataFrame"
       ]
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print(mtcars)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "                     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",
        "\n"
       ]
      }
     ],
     "prompt_number": 12
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from rpy2.robjects.packages import importr, data\n",
      "import numpy"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 13
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "datasets = importr('datasets')\n",
      "data(datasets).fetch('occupationalStatus')['occupationalStatus']"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 14,
       "text": [
        "<Matrix - Python:0x111540588 / R:0x7ffd6bcd4630>\n",
        "[      50,       16,       12, ...,      177,       71,      106]"
       ]
      }
     ],
     "prompt_number": 14
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%load_ext rpy2.ipython"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 15
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import numpy as np\n",
      "# import matplotlib.pyplot as plt\n",
      "X = np.array([0,1,2,3,4])\n",
      "Y = np.array([3,5,4,6,7])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 16
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%Rpush X Y\n",
      "%R lm(Y~X)$coef"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 17,
       "text": [
        "<FloatVector - Python:0x111f5e0c8 / R:0x7ffd6aee1038>\n",
        "[3.200000, 0.900000]"
       ]
      }
     ],
     "prompt_number": 17
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "Xr = X - X.mean(); Yr = Y - Y.mean()\n",
      "slope = (Xr*Yr).sum() / (Xr**2).sum()\n",
      "intercept = Y.mean() - X.mean() * slope\n",
      "(intercept, slope)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 18,
       "text": [
        "(3.2000000000000002, 0.90000000000000002)"
       ]
      }
     ],
     "prompt_number": 18
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%R resid(lm(Y~X)); coef(lm(X~Y))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 19,
       "text": [
        "<FloatVector - Python:0x1115300c8 / R:0x7ffd6d4fbbc8>\n",
        "[-2.500000, 0.900000]"
       ]
      }
     ],
     "prompt_number": 19
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "v1 = %R plot(X,Y); print(summary(lm(Y~X))); vv=mean(X)*mean(Y)\n",
      "print('v1 is:', v1)\n",
      "v2 = %R mean(X)*mean(Y)\n",
      "print('v2 is:', v2)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "\n",
        "Call:\n",
        "lm(formula = Y ~ X)\n",
        "\n",
        "Residuals:\n",
        "   1    2    3    4    5 \n",
        "-0.2  0.9 -1.0  0.1  0.2 \n",
        "\n",
        "Coefficients:\n",
        "            Estimate Std. Error t value Pr(>|t|)  \n",
        "(Intercept)   3.2000     0.6164   5.191   0.0139 *\n",
        "X             0.9000     0.2517   3.576   0.0374 *\n",
        "---\n",
        "Signif. codes:  0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1\n",
        "\n",
        "Residual standard error: 0.7958 on 3 degrees of freedom\n",
        "Multiple R-squared:   0.81,\tAdjusted R-squared:  0.7467 \n",
        "F-statistic: 12.79 on 1 and 3 DF,  p-value: 0.03739\n",
        "\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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V8cILL8Tzzz8fb7/9dowePbpZCsWzzuKx9b6K2957771m7cedCBAgsKECzzzzTLzyyitx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\nAAAAAElFTkSuQmCC\n"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "v1 is: [1] 10\n",
        "\n",
        "v2 is: [1] 10\n",
        "\n"
       ]
      }
     ],
     "prompt_number": 20
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "mtcars_df = %Rget mtcars"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 21
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "type(mtcars_df)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 22,
       "text": [
        "pandas.core.frame.DataFrame"
       ]
      }
     ],
     "prompt_number": 22
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "mtcars_df.head(10)"
     ],
     "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>mpg</th>\n",
        "      <th>cyl</th>\n",
        "      <th>disp</th>\n",
        "      <th>hp</th>\n",
        "      <th>drat</th>\n",
        "      <th>wt</th>\n",
        "      <th>qsec</th>\n",
        "      <th>vs</th>\n",
        "      <th>am</th>\n",
        "      <th>gear</th>\n",
        "      <th>carb</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td> 21.0</td>\n",
        "      <td> 6</td>\n",
        "      <td> 160.0</td>\n",
        "      <td> 110</td>\n",
        "      <td> 3.90</td>\n",
        "      <td> 2.620</td>\n",
        "      <td> 16.46</td>\n",
        "      <td> 0</td>\n",
        "      <td> 1</td>\n",
        "      <td> 4</td>\n",
        "      <td> 4</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td> 21.0</td>\n",
        "      <td> 6</td>\n",
        "      <td> 160.0</td>\n",
        "      <td> 110</td>\n",
        "      <td> 3.90</td>\n",
        "      <td> 2.875</td>\n",
        "      <td> 17.02</td>\n",
        "      <td> 0</td>\n",
        "      <td> 1</td>\n",
        "      <td> 4</td>\n",
        "      <td> 4</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td> 22.8</td>\n",
        "      <td> 4</td>\n",
        "      <td> 108.0</td>\n",
        "      <td>  93</td>\n",
        "      <td> 3.85</td>\n",
        "      <td> 2.320</td>\n",
        "      <td> 18.61</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 4</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td> 21.4</td>\n",
        "      <td> 6</td>\n",
        "      <td> 258.0</td>\n",
        "      <td> 110</td>\n",
        "      <td> 3.08</td>\n",
        "      <td> 3.215</td>\n",
        "      <td> 19.44</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td> 3</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td> 18.7</td>\n",
        "      <td> 8</td>\n",
        "      <td> 360.0</td>\n",
        "      <td> 175</td>\n",
        "      <td> 3.15</td>\n",
        "      <td> 3.440</td>\n",
        "      <td> 17.02</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 3</td>\n",
        "      <td> 2</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5</th>\n",
        "      <td> 18.1</td>\n",
        "      <td> 6</td>\n",
        "      <td> 225.0</td>\n",
        "      <td> 105</td>\n",
        "      <td> 2.76</td>\n",
        "      <td> 3.460</td>\n",
        "      <td> 20.22</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td> 3</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6</th>\n",
        "      <td> 14.3</td>\n",
        "      <td> 8</td>\n",
        "      <td> 360.0</td>\n",
        "      <td> 245</td>\n",
        "      <td> 3.21</td>\n",
        "      <td> 3.570</td>\n",
        "      <td> 15.84</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 3</td>\n",
        "      <td> 4</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>7</th>\n",
        "      <td> 24.4</td>\n",
        "      <td> 4</td>\n",
        "      <td> 146.7</td>\n",
        "      <td>  62</td>\n",
        "      <td> 3.69</td>\n",
        "      <td> 3.190</td>\n",
        "      <td> 20.00</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td> 4</td>\n",
        "      <td> 2</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>8</th>\n",
        "      <td> 22.8</td>\n",
        "      <td> 4</td>\n",
        "      <td> 140.8</td>\n",
        "      <td>  95</td>\n",
        "      <td> 3.92</td>\n",
        "      <td> 3.150</td>\n",
        "      <td> 22.90</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td> 4</td>\n",
        "      <td> 2</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9</th>\n",
        "      <td> 19.2</td>\n",
        "      <td> 6</td>\n",
        "      <td> 167.6</td>\n",
        "      <td> 123</td>\n",
        "      <td> 3.92</td>\n",
        "      <td> 3.440</td>\n",
        "      <td> 18.30</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td> 4</td>\n",
        "      <td> 4</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>10 rows \u00d7 11 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 23,
       "text": [
        "    mpg  cyl   disp   hp  drat     wt   qsec  vs  am  gear  carb\n",
        "0  21.0    6  160.0  110  3.90  2.620  16.46   0   1     4     4\n",
        "1  21.0    6  160.0  110  3.90  2.875  17.02   0   1     4     4\n",
        "2  22.8    4  108.0   93  3.85  2.320  18.61   1   1     4     1\n",
        "3  21.4    6  258.0  110  3.08  3.215  19.44   1   0     3     1\n",
        "4  18.7    8  360.0  175  3.15  3.440  17.02   0   0     3     2\n",
        "5  18.1    6  225.0  105  2.76  3.460  20.22   1   0     3     1\n",
        "6  14.3    8  360.0  245  3.21  3.570  15.84   0   0     3     4\n",
        "7  24.4    4  146.7   62  3.69  3.190  20.00   1   0     4     2\n",
        "8  22.8    4  140.8   95  3.92  3.150  22.90   1   0     4     2\n",
        "9  19.2    6  167.6  123  3.92  3.440  18.30   1   0     4     4\n",
        "\n",
        "[10 rows x 11 columns]"
       ]
      }
     ],
     "prompt_number": 23
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%%R\n",
      "head(mtcars)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "                   mpg cyl disp  hp drat    wt  qsec vs am gear carb\n",
        "Mazda RX4         21.0   6  160 110 3.90 2.620 16.46  0  1    4    4\n",
        "Mazda RX4 Wag     21.0   6  160 110 3.90 2.875 17.02  0  1    4    4\n",
        "Datsun 710        22.8   4  108  93 3.85 2.320 18.61  1  1    4    1\n",
        "Hornet 4 Drive    21.4   6  258 110 3.08 3.215 19.44  1  0    3    1\n",
        "Hornet Sportabout 18.7   8  360 175 3.15 3.440 17.02  0  0    3    2\n",
        "Valiant           18.1   6  225 105 2.76 3.460 20.22  1  0    3    1\n"
       ]
      }
     ],
     "prompt_number": 24
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "sv = ro.StrVector('ababbc')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 25
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print(sv)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[1] \"a\" \"b\" \"a\" \"b\" \"b\" \"c\"\n",
        "\n"
       ]
      }
     ],
     "prompt_number": 26
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "sv.r_repr()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 27,
       "text": [
        "'c(\"a\", \"b\", \"a\", \"b\", \"b\", \"c\")'"
       ]
      }
     ],
     "prompt_number": 27
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fac = ro.FactorVector(sv)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 28
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print(fac.r_repr())\n",
      "print(fac)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "structure(c(1L, 2L, 1L, 2L, 2L, 3L), .Label = c(\"a\", \"b\", \"c\"\n",
        "), class = \"factor\")\n",
        "[1] a b a b b c\n",
        "Levels: a b c\n",
        "\n"
       ]
      }
     ],
     "prompt_number": 29
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "x = robjects.r['seq'](1, 5)\n",
      "# robjects.r.seq(1,5)\n",
      "x.names = robjects.StrVector('abcde')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 30
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print(x)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "a b c d e \n",
        "1 2 3 4 5 \n",
        "\n"
       ]
      }
     ],
     "prompt_number": 31
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "x[0]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 32,
       "text": [
        "1"
       ]
      }
     ],
     "prompt_number": 32
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "i = robjects.IntVector((1, 3))\n",
      "print(x.rx(i))\n",
      "\n",
      "b = robjects.BoolVector((False, True, False, True, True))\n",
      "print(x.rx(b))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "a c \n",
        "1 3 \n",
        "\n",
        "b d e \n",
        "2 4 5 \n",
        "\n"
       ]
      }
     ],
     "prompt_number": 33
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "i = - robjects.IntVector((1, 3)).ro\n",
      "print(x.rx(i))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "b d e \n",
        "2 4 5 \n",
        "\n"
       ]
      }
     ],
     "prompt_number": 34
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%%time\n",
      "big_list = [i for i in range(10**7)]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "CPU times: user 695 ms, sys: 273 ms, total: 968 ms\n",
        "Wall time: 971 ms\n"
       ]
      }
     ],
     "prompt_number": 35
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%%time\n",
      "big_int_vec = robjects.IntVector(big_list)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "CPU times: user 699 ms, sys: 52.6 ms, total: 751 ms\n",
        "Wall time: 748 ms\n"
       ]
      }
     ],
     "prompt_number": 36
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%%R\n",
      "gp = ggplot(mtcars)\n",
      "pp = gp + aes_string(x='wt', y='mpg') + geom_point()\n",
      "show(pp)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "NULL\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
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2bZJVq1aZY62TJk2S008/PZlYXZmmveTbvIJkp2s+D88RQAABBBBIVuDkm4LJltDOdCtX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\n"
      }
     ],
     "prompt_number": 37
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%Rpush mtcars_df"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 38
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%%R\n",
      "print(class(mtcars_df))\n",
      "head(mtcars_df)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "[1] \"data.frame\"\n",
        "   mpg cyl disp  hp drat    wt  qsec vs am gear carb\n",
        "1 21.0   6  160 110 3.90 2.620 16.46  0  1    4    4\n",
        "2 21.0   6  160 110 3.90 2.875 17.02  0  1    4    4\n",
        "3 22.8   4  108  93 3.85 2.320 18.61  1  1    4    1\n",
        "4 21.4   6  258 110 3.08 3.215 19.44  1  0    3    1\n",
        "5 18.7   8  360 175 3.15 3.440 17.02  0  0    3    2\n",
        "6 18.1   6  225 105 2.76 3.460 20.22  1  0    3    1\n"
       ]
      }
     ],
     "prompt_number": 44
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}