mlaloux/gist:9b509b48ee4f1d906e24

## gistfile1.py
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 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "http://stackoverflow.com/questions/23912243/ipython-notebook-and-rmagic-rpy2-cannot-find-module-ri2py-osx-10-8-5-python-2"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "http://nbviewer.ipython.org/github/ipython/ipython/blob/3607712653c66d63e0d7f13f073bde8c0f209ba8/docs/examples/notebooks/rmagic_extension.ipynb"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "load_ext rpy2.ipython"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pylab inline"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Populating the interactive namespace from numpy and matplotlib\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "WARNING: pylab import has clobbered these variables: ['less']\n",
        "`%matplotlib` prevents importing * from pylab and numpy\n"
       ]
      }
     ],
     "prompt_number": 2
    },
    {
     "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",
      "pylab.scatter(X, Y)\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 3,
       "text": [
        "<matplotlib.collections.PathCollection at 0x11083ec10>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAWgAAAEACAYAAACeQuziAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nSZIkSZIkSZKi+R/m8ZYIBCe3VAAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10260f990>"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "Rpush X Y"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "R lm(Y~X)$coef"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 5,
       "text": [
        "<FloatVector - Python:0x1108760e0 / R:0x1082e8838>\n",
        "[3.200000, 0.900000]"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "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": 6,
       "text": [
        "(3.2000000000000002, 0.90000000000000002)"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "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": 7,
       "text": [
        "<FloatVector - Python:0x1107e53b0 / R:0x10db47d90>\n",
        "[-2.500000, 0.900000]"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "for i in range(10):\n",
      "    print i"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "0\n",
        "1\n",
        "2\n",
        "3\n",
        "4\n",
        "5\n",
        "6\n",
        "7\n",
        "8\n",
        "9\n"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "b = %R a=resid(lm(Y~X))\n",
      "%Rpull a\n",
      "print a\n",
      "assert id(b.data) == id(a.data)\n",
      "%R -o a"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "   1    2    3    4    5 \n",
        "-0.2  0.9 -1.0  0.1  0.2 \n",
        "\n"
       ]
      },
      {
       "ename": "AttributeError",
       "evalue": "'Array' object has no attribute 'data'",
       "output_type": "pyerr",
       "traceback": [
        "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
        "\u001b[0;32m<ipython-input-9-659a0d38ac07>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0mget_ipython\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmagic\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mu'Rpull a'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;32mprint\u001b[0m \u001b[0ma\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0;32massert\u001b[0m \u001b[0mid\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mb\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mid\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      5\u001b[0m \u001b[0mget_ipython\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmagic\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mu'R -o a'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
        "\u001b[0;31mAttributeError\u001b[0m: 'Array' object has no attribute 'data'"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "A = np.arange(20)\n",
      "%Rpull a\n",
      "print a"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "   1    2    3    4    5 \n",
        "-0.2  0.9 -1.0  0.1  0.2 \n",
        "\n"
       ]
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "http://stackoverflow.com/questions/17414349/plot-size-using-ggplot2-in-ipython-notebook-via-rmagic"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "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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3mDlzZu2HFopTxcX7wdtss03tlW///v2jXbskymy9qViJAIGsBQYMGBDPPfdcFK98O3bs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\nAAAASUVORK5CYII=\n"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "v1 is: [1] 10\n",
        "\n",
        "v2 is: [1] 10\n",
        "\n"
       ]
      }
     ],
     "prompt_number": 12
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%%R -i X,Y -o XYcoef\n",
      "XYlm = lm(Y~X)\n",
      "XYcoef = coef(XYlm)\n",
      "print(summary(XYlm))\n",
      "par(mfrow=c(2,2))\n",
      "plot(XYlm)\n"
     ],
     "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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ISJkzZ+ZlfvXr1+e19liqly9fPl+y8CsNgqkg9rCQIGB3BK4rw1l4h8MvCG3E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\nEAQSg8D/A9fSI5zFufU1AAAAAElFTkSuQmCC\n"
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%%R -w 480 -h 480 -u px\n",
      "library(ggplot2)\n",
      "dat <- data.frame(x = rnorm(10), y = rnorm(10), \n",
      "                  lab = sample(c('A', 'B'), 10, replace = TRUE))\n",
      "x <- ggplot(dat, aes(x = x, y = y, color = lab)) + geom_point()\n",
      "print(x)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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a5cUrz5Px4mXo5uJV0elM+jGXw8PD01m14XUI4IYJ2QACCCCAgCmBpL605vLS\nc6mPpUvQ+lJme3u7PProo6WHAv2fAA6Um8YQQAABBEwK/PCHPxT9CcM0vXP2MPScPiCAAAIIIBBh\nAQI4woNH1xFAAAEEoitAAEd37Og5AggggECEBXgNOMKDR9cRQACBuAvoB3jMmTMnlmUSwLEcVopC\nAAEE4iHgxwd4hEWGS9BhGQn6gQACCCBglQABbNVwUywCCCCAQFgECOCwjAT9QAABBBCwSoAAtmq4\nKRYBBBBAICwCBHBYRoJ+IIAAAghYJUAAWzXcFIsAAgggEBYBAjgsI0E/EEAAAQSsEiCArRpuikUA\nAQQQCIsAARyWkaAfCCCAAAJWCRDAVg03xSKAAAIIhEWAAA7LSNAPBBBAAAGrBAhgq4abYhFAAAEE\nwiJAAIdlJOgHAggggIBVAgSwVcNNsQgggAACYREggMMyEvQDAQQQQMAqAQLYquGmWAQQQKA+geSJ\nE5J54Z8lfbi3vhVZuqZAuuYSLIAAAgggYKVAqr9f5m79O0mMjzv1D998i4x+bLWVFn4UzRmwH6ps\nEwEEEIiBQPP+N8rhq+W07N0Tg6rCUwIBHJ6xoCcIIIBAqATGO7sq+vOn9yse5E7dAlyCrpuMFRBA\nAAE7BMYu+Iic/MubpOWNvTLe1S1nrr7GjsIDqpIADgiaZhBAAIEoCuQuWyv6w+S9AJegvTdliwgg\ngAACCNQUIIBrErEAAggggAAC3gsQwN6bskUEEEAAAQRqChDANYlYAAEEEEAAAe8FCGDvTdkiAggg\ngAACNQUI4JpELIAAAggggID3AgSw96ZsEQEEEEAAgZoCBHBNIhZAAAEEEEDAewEC2HtTtogAAggg\ngEBNAQK4JhELIIAAAggg4L0AAey9KVtEAAEEEECgpgABXJOIBRBAAAEEEPBegAD23pQtIoAAAggg\nUFOAAK5JxAIIIIAAAgh4L0AAe2/KFhFAAAEEEKgpQADXJGIBBBBAAAEEvBcggL03ZYsIIIAAAgjU\nFCCAaxKxAAIIIIAAAt4LEMDem7JFBBBAAAEEagoQwDWJWAABBBBAAAHvBQhg703ZIgIIIIAAAjUF\n0jWX8HCBXbt2yZ49e2T27NmyceNGyWQyFVt/8MEHZXBw0Jm3YsUKWb9+fcXj3EEAAQQQQCAuAoEF\ncE9Pjxw6dEg2b94szz//vOzcuVM2bNhQdhwdHZWBgQG5/fbbnXnJJCfnZRxuIIAAAgjETiCwAD54\n8KCsXr1aUqmUrF27VrZu3VqBefToUZk1a5YTzgsXLpSLL7644vGXXnpJDh8+XJ63bNkyufDCC8v3\na93Qs+2JiQmn/VrLxvlx9c/n83Eu8Zy1JRIJ0Z/29vZzLhf3B3U/aGtri3uZ56yvpaVFmpqaJJ0O\n7DB4zv6YelBPdvTYyBS8QGB7nl5a7urqcipsbW2VkZGRimpPnTol+oTo7OyUl19+WfSM+YYbbigv\no+toQJcmDdR6gkR3sEKhUNc6pbbi8n8pfOpxi0vtpTrUQCebDbR+dbDdQI8Helyw2YHngz4bzE2+\nBvCWLVskl8vJqlWrRANUb+s0NjZWEaY6b+XKlc6P3l6yZIncc889FQGs29Cfs6fe3t6z757zdunM\nb2ho6JzLxflBfbI1NzeXxyHOtU5Vm+4H+h4Em/cDtdFfYLPZ7FRMVszXM189Fg0PD1tR72RF6jFB\nrwLoS4B+T+edd57fTURu+74G8B133FEGee2115zXgNesWeOc3ZbOhksLvPLKK6Jnwddee6309/eL\nXoZmQgABBBBAIK4Cvgbw2Wh69rpv3z7Ztm2b8xvnpk2bnIe3b98u+o5nPQN+6KGH5MCBA3L8+HG5\n9dZbz16d2wgggAACCMRKIFF8HaQQZEV6qUMvg0416WVqfS3YzVTPJWh9042+1mPzpUcuQYvzJrzu\n7m7p6+tzs4vFdhkuQYt0dHRwCTrAS9BLly6N7fNpuoUF/rc+5wpfLcJt+E63YNZDAAEEEEAgDAKB\nB3AYiqYPCCCAAAIImBYggE2PAO0jgAACCFgpQABbOewUjQACCCBgWoAANj0CtI8AAgggYKUAAWzl\nsFM0AggggIBpAQLY9AjQPgIIIICAlQIEsJXDTtEIIIAAAqYFCGDTI0D7CCCAAAJWChDAVg47RSOA\nAAIImBYggE2PAO0jgAACCFgpQABbOewUjQACCCBgWoAANj0CtI8AAlYKJN9/XwIg9g0AAA6YSURB\nVJr/5TVJDg5YWT9FiwT2dYRgI4AAAgh8IJA+0idz/uHvJVH8hrZCU5MM/s03JN/ZBY9lApwBWzbg\nlIsAAuYFWl77vRO+2pPE2Ji0FM+EmewTIIDtG3MqRgABwwL59o6KHkwUv5uYyT4BLkHbN+ZUjAAC\nhgWyV1wpqeEhaertkdELPiLZS9ca7hHNmxAggE2o0yYCCNgtkErJyF/cYLcB1QuXoNkJEEAAAQQQ\nMCBAABtAp0kEEEAAAQQIYPYBBBBAAAEEDAgQwAbQaRIBBBBAAAECmH0AAQQQQAABAwIEsAF0mkQA\nAQQQQIAAZh9AAAEEEEDAgAABbACdJhFAAAEEECCA2QcQQAABBBAwIEAAG0CnSQQQQAABBAhg9gEE\nEEAAAQQMCBDABtBpEgEEEEAAAQKYfQABBBBAAAEDAnwbkgF0mvRHYLxQkGcGhyQ7UZD1c2fLjOI3\nzjAhgAACYRUggMM6MvSrboG/PfIH+e3wKWe9XwwMyj9csFSSiUTd22EFBBBAIAgBLkEHoUwbvgvo\n2e+zH4avNrY/m5Ojo2O+t0sDCCCAwHQFCODpyrFeqATSxTPdj2Rayn06r3j5eX4TF3jKINxAAIHQ\nCXCECt2Q0KHpCvynxYvkwXdPSK4wIV+Z1yEtSX6/nK4l6yGAgP8CBLD/xrQQkEBnc5P87aLOgFqj\nGQQQQKAxAU4RGvNjbQQQQAABBKYlQABPi42VEEAAAQQQaEyAAG7Mj7URQAABBBCYlgABPC02VkIA\nAQQQQKAxAd6E1ZgfayOAAAKuBBKnT0vb//5fkhoclOyfXSGjFy13tR4LxVeAM+D4ji2VIYBAiARm\n/vJJad39sjQfeEtmPfaoJN9/L0S9oysmBAhgE+q0iQAC1gmkjh0r15yYmJBUf3/5PjfsFCCA7Rx3\nqkYAgYAFch9bXW4xP3u2jC9ZWr7PDTsFeA3YznGnagQQCFjgzJ9/Wsa7u4uvAQ9JbsUlUshkAu4B\nzYVNgAAO24jQHwQQiK3A2EcvEr4iJLbDW3dhXIKum4wVEEAAAQQQaFyAAG7ckC0ggAACCCBQtwAB\nXDcZKyCAAAIIINC4QGRfA54ovo0/U8ebGNLptCSK3xlbzzqN84ZvC6ni9+Sqg61TsvgVhewHIvp8\n4LmQkkKhgEPxmKDPC78ntbb52DOZb2QDWHeYbDY7WU2Tzmtra5N8Pl/XOpNuKMIzdedvbm6WXC4X\n4Soa67r+AqIHgnr2ncZaDOfaGr62G8yYMUPGx8etdtBjQlNTk4yOjvq+oxK+1cT+/9pT3SZzEEAA\nAQQQsF6AALZ+FwAAAQQQQMCEAAFsQp02EUAAAQSsFyCArd8FAEAAAQQQMCFAAJtQp00EEEAAAesF\nCGDrdwEAEEAAAQRMCBDAJtRpEwEEEEDAegEC2PpdAAAEEEAAARMCBLAJddpEAAEEELBegAC2fhcA\nAAEEEEDAhAABbEKdNhFAAAEErBcggK3fBQBAAAEEEDAhQACbUKdNBBBAAAHrBQhg63cBABBAAAEE\nTAgQwCbUaRMBBBBAwHoBAtj6XQAABBBAAAETAgSwCXXaRAABBBCwXoAAtn4XAAABBBBAwIQAAWxC\nnTYRQAABBKwXIICt3wUAQAABBBAwIUAAm1CnTQQQQAAB6wUIYOt3AQAQQAABBEwIEMAm1GkTAQQQ\nQMB6AQLY+l0AAAQQQAABEwIEsAl12kQAAQQQsF6AALZ+FwAAAQQQQMCEAAFsQp02EUAAAQSsFyCA\nrd8FAEAAAQQQMCFAAJtQp00EEEAAAesFCGDrdwEAEEAAAQRMCBDAJtRpEwEEEEDAegEC2PpdAAAE\nEEAAARMCBLAJddpEAAEEELBegAC2fhcAAAEEEEDAhAABbEKdNhFAAAEErBcggK3fBQBAAAEEEDAh\nQACbUKdNBBBAAAHrBQhg63cBABBAAAEETAgQwCbUaRMBBBBAwHoBAtj6XQAABBBAAAETAgSwCXXa\nRAABBBCwXoAAtn4XAAABBBBAwIQAAWxCnTYRQAABBKwXIICt3wUAQAABBBAwIUAAm1CnTQQQQAAB\n6wUIYOt3AQAQQAABBEwIEMAm1GkTAQQQQMB6AQLY+l0AAAQQQAABEwIEsAl12kQAAQQQsF6AALZ+\nFwAAAQQQQMCEAAFsQp02EUAAAQSsF0gHLbBnzx7p7e2VDRs2VDW9a9cu0cdnz54tGzdulEwmU7UM\nMxBAAAEEEIiDQKBnwDt37pQdO3ZINputsuvp6ZFDhw7J5s2bZdmyZaLLMiGAAAIIIBBXgUDPgGfM\nmCG33HKL7N27t8rz4MGDsnr1akmlUrJ27VrZunVrxTL79++Xd999tzxv4cKF0t7eXr5f60Zzc7NM\nTEzIrFmzai0a68fT6bSoha1TIpEQ/WE/SEtTU5Otu4FTt9afTCat3xf0mNvS0mL1vmCq+EADeN26\ndbJv375Jax0cHJSuri7nsdbWVhkZGalYrr+/XzSkS5M+cTo7O0t3a/6vO5keeG3e0bR+dVMLWyc1\n0Mnm/UDr133A5v2gZMAx4YNfSPXkhCl4AV8DeMuWLZLL5WTVqlVy0003nbM6DV1dVqexsbGq30qv\nvvpq0Z+zJ30t2e2kZ8v5fF6GhobcrhK75fRgo2e/JefYFeiiIA0d3ddOnDjhYun4LqLvr5jspaD4\nVlxdWUdHh3OsGR4ern7Qkjl6TNArAaOjo75XPHPmTN/biFoDvgbwHXfc4dpj0aJFzmvAa9asEX09\nuHQ27HoDLIgAAggggECEBHwNYDcO27dvlxUrVjhnyXp5etu2baK/kW7atMnN6iyDAAIIIIBAJAUS\nheIUpp7rpRC3bxLiEnR9I8cl6A9e++zu7pa+vr768GK2NJegRbgELc77YoK6BL106dKYPYsaLyfQ\nP0Ny01234etmWyyDAAIIIIBAWAVCF8BhhaJfCCCAAAIIeClAAHupybYQQAABBBBwKUAAu4RiMQQQ\nQAABBLwUIIC91GRbCCCAAAIIuBQggF1CsRgCCCCAAAJeChDAXmqyLQQQQAABBFwKGP8gDpf9ZDEE\nEGhQIHHqlGT+724p6Nd8rqv8WNcGN83qCCAwDQECeBporIJA5ATGx2Xu32+T1MD7TtfH+w5L9ot/\nFbky6DACcRLgEnScRpNaEJhCIFX88olS+OoiqTf+3xRLMhsBBIISIICDkqYdBAwK5IvfBjbR1lbu\nQX7xkvJtbiCAgBkBLkGbcadVBIIVKH4N5dBff01aX/hnKbRkJH/9Z4Ntn9YQQKBKgACuImEGAvEU\nyJ+/QE79my84xemXMRS/EDiehVIVAhER4BJ0RAaKbiKAAAIIxEuAAI7XeFINAggggEBEBAjgiAwU\n3UQAAQQQiJcAARyv8aQaBBBAAIGICBDAERkouokAAgggEC8BAjhe40k1CCCAAAIRESCAIzJQdBMB\nBBBAIF4CBHC8xpNqEEAAAQQiIkAAR2Sg6CYCCCCAQLwECOB4jSfVIIAAAghERIAAjshA0U0EEEAA\ngXgJEMDxGk+qQQABBBCIiAABHJGBopsIIIAAAvESIIDjNZ5UgwACCCAQEQECOCIDRTcRQAABBOIl\nQADHazypBgEEEEAgIgKJQnGKSF8b6uYTTzwhc+fOlU9/+tMNbYeVoy0wPDwsd999t9x1113RLoTe\nNyzw+OOPy8KFC+Waa65peFtsAIHpCFhzBjw+Pi76w2S3gP6+mc1m7UagekdgbGyMYwL7glEBawLY\nqDKNI4AAAggg8CcCBPCfgHAXAQQQQACBIASseQ346NGj0tTUJPPnzw/ClTZCKqCXHd9880255JJL\nQtpDuhWUwNtvvy2ZTEbmzZsXVJO0g0CFgDUBXFE1dxBAAAEEEDAswCVowwNA8wgggAACdgoQwHaO\nO1UjgAACCBgWsC6An3rqKXn99dcNs9O8CQH9M7Tt27fLvffeK7/+9a9NdIE2QySwZ88e+cUvfhGi\nHtEV2wSsCeBcLiePPPKIvPjii/ztn217+Yf1/uY3v3E+eOGb3/ymHD9+XPbt22epBGXv3LlTduzY\nwd+EsysYFbAmgEdGRmTt2rVy5ZVXGgWncXMCBw8elEsvvVRSqZSsWbPGeTe0ud7QskmBGTNmyC23\n3GKyC7SNgFgTwO3t7bJixQqG3GKBwcFBaWtrcwT0f/2ljMlOgXXr1kkyac3hz85BjkDV6Qj0cVpd\nfOedd+QnP/mJs+6NN97onPlMa0OsFBuB1tZW0Zci9O/BR0dHZebMmbGpjUIQQCB6ArEN4M7OTvne\n974XvRGhx74JLFq0SHp7e2XlypXS09Mj3d3dvrXFhhFAAIFaArEN4FqF87h9Atddd53zxpvnnnvO\nOQvWKyNMCCCAgCkBPgnLlDztGhPQy8/Nzc3G2qdhBBBAQAUIYPYDBBBAAAEEDAjwNkAD6DSJAAII\nIIAAAcw+gAACCCCAgAEBAtgAOk0igAACCCBAALMPIGBYoFAoyM033ywPPPCA05MjR444n9SlfyrF\nhAAC8RXgTVjxHVsqi5DA888/Lxs2bJD9+/fL1772NVm+fLls2bIlQhXQVQQQqFeAAK5XjOUR8Eng\nG9/4huzevdv5iMzXXntNMpmMTy2xWQQQCIMAH8QRhlGgDwgUBe6880654IIL5L777iN82SMQsECA\nM2ALBpkSoyFw2223OZegDx8+LHv37pU5c+ZEo+P0EgEEpiXAm7CmxcZKCHgroN9P+/TTT8uvfvUr\nufrqq+Xb3/62tw2wNQQQCJ0AZ8ChGxI6ZJuAfi2ifkHED37wA9m4caMcO3bM+epM/cL4T33qU7Zx\nUC8C1ggQwNYMNYUigAACCIRJgEvQYRoN+oIAAgggYI0AAWzNUFMoAggggECYBAjgMI0GfUEAAQQQ\nsEaAALZmqCkUAQQQQCBMAgRwmEaDviCAAAIIWCNAAFsz1BSKAAIIIBAmAQI4TKNBXxBAAAEErBEg\ngK0ZagpFAAEEEAiTAAEcptGgLwgggAAC1gj8f54S0U5AkapsAAAAAElFTkSuQmCC\n"
      }
     ],
     "prompt_number": 14
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%%R -w 50 -h 25 -u cm\n",
      "library(ggplot2)\n",
      "dat <- data.frame(x = rnorm(10), y = rnorm(10), \n",
      "                  lab = sample(c('A', 'B'), 10, replace = TRUE))\n",
      "x <- ggplot(dat, aes(x = x, y = y, color = lab)) + geom_point()\n",
      "print(x)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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AAQIECBAgUIWAkLgKZecgQIAAAQIECBAgQIAAAQIECBAgQIBAnwoIifu0MaZF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\nIECAAAECBAgQIECgBgL/BwyAUyuTgRwZAAAAAElFTkSuQmCC\n"
      }
     ],
     "prompt_number": 15
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%%R \n",
      "require(ggplot2)\n",
      "library(gstats)\n",
      "Rdevice svg\n",
      "import pandas as pd\n",
      "data = pd.DataFrame({\"x\":[1,2,3], \"y\": [3,2,1]})\n",
      "%%R -i data\n",
      "plot = ggplot(data) + geom_line(aes(x=x, y=y))\n",
      "print(plot)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "ERROR: Cell magic `%%R` not found.\n"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}