{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The ipython notebook environment is a superb environment for empirical research.  Sometimes, though, you would like to access the capabilities of other software.  This post shows how to incorporate R and Matlab into ipython notebooks.\n",
    "\n",
    "<!--TEASER_END-->"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Starting MATLAB on ZMQ socket ipc:///tmp/pymatbridge\n",
      "Send 'exit' command to kill the server\n",
      ".MATLAB started and connected!\n"
     ]
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "%load_ext rmagic \n",
    "%load_ext pymatbridge"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/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>x</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td> 0.376486</td>\n",
       "      <td> 0.383480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td> 0.717613</td>\n",
       "      <td> 0.810156</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td> 0.158627</td>\n",
       "      <td> 0.334782</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td> 0.899574</td>\n",
       "      <td> 0.005522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td> 0.092767</td>\n",
       "      <td> 0.055730</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          x         y\n",
       "0  0.376486  0.383480\n",
       "1  0.717613  0.810156\n",
       "2  0.158627  0.334782\n",
       "3  0.899574  0.005522\n",
       "4  0.092767  0.055730"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import statsmodels.api as sm\n",
    "\n",
    "data = pd.DataFrame(np.random.rand(100,2),columns=['x','y'])\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes.AxesSubplot at 0x732f150>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2dc4f90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot the data in a x,y scatter\n",
    "data.plot(kind='scatter', x='x', y='y')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "-------------------------Summary of Regression Analysis-------------------------\n",
       "\n",
       "Formula: Y ~ <x> + <intercept>\n",
       "\n",
       "Number of Observations:         100\n",
       "Number of Degrees of Freedom:   2\n",
       "\n",
       "R-squared:         0.0002\n",
       "Adj R-squared:    -0.0100\n",
       "\n",
       "Rmse:              0.2858\n",
       "\n",
       "F-stat (1, 98):     0.0172, p-value:     0.8960\n",
       "\n",
       "Degrees of Freedom: model 1, resid 98\n",
       "\n",
       "-----------------------Summary of Estimated Coefficients------------------------\n",
       "      Variable       Coef    Std Err     t-stat    p-value    CI 2.5%   CI 97.5%\n",
       "--------------------------------------------------------------------------------\n",
       "             x    -0.0126     0.0961      -0.13     0.8960    -0.2009     0.1758\n",
       "     intercept     0.5508     0.0567       9.71     0.0000     0.4397     0.6619\n",
       "---------------------------------End of Summary---------------------------------"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# run a regression model\n",
    "results = pd.ols(y=data['y'], x=data[['x']])\n",
    "results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#Running the same analysis and plot in R and viewing in the Ipython Notebook"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# push x,y to R\n",
    "x=data.x\n",
    "y=data.y\n",
    "%Rpush x y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "Call:\n",
       "lm(formula = y ~ x)\n",
       "\n",
       "Residuals:\n",
       "    Min      1Q  Median      3Q     Max \n",
       "-0.5351 -0.2245  0.0055  0.2533  0.4468 \n",
       "\n",
       "Coefficients:\n",
       "            Estimate Std. Error t value Pr(>|t|)    \n",
       "(Intercept)   0.5508     0.0567   9.715 5.04e-16 ***\n",
       "x            -0.0126     0.0961  -0.131    0.896    \n",
       "---\n",
       "Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n",
       "\n",
       "Residual standard error: 0.2858 on 98 degrees of freedom\n",
       "Multiple R-squared:  0.0001753,\tAdjusted R-squared:  -0.01003 \n",
       "F-statistic: 0.01719 on 1 and 98 DF,  p-value: 0.896\n",
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R\n",
    "fit <- lm(y ~ x)  # Least-squares regression\n",
    "summary(fit)  # Display the coefficients of the fit."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "EAoFDQBAKBQ0AAChUNAAAIRCQQMAEAoFDQBAKBQ0AAChUNAAAIRCQQMAEAoFDQBAKBQ0AACh/gcv\n",
       "r7bRjStACAAAAABJRU5ErkJggg==\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R\n",
    "plot(x,y)  # Plot the data points."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To accomplish this, we needed to install the \"R magic\" python package called **rmagic**.  There is a similar package for matlab called **python-matlab-bridge** which has a magic called **pymatbridge**."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#Running the same analysis in Matlab and viewing in the Ipython Notebook"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#pymatbridge requires x,y to be lists for passing:\n",
    "x = data.x.values.tolist()\n",
    "y = data.y.values.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ans =\n",
       "    0.5508\n",
       "   -0.0126\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%matlab -i x,y\n",
    "x = horzcat(ones(rows(x'),1),x');\n",
    "y=y';                 \n",
    "ols(y,x)\n",
    "scatter(x(:,2),y)"
   ]
  }
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