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   "source": [
    "# Calling R Functions in IPython\n",
    "\n",
    "# Installation Instructions on Windows\n",
    "%rmagic can be difficult to install/setup.  These are the steps that worked for me.  Please feel free to\n",
    "suggest additions, subtractions and edits that make the process easier.  I advise you take the steps below in order if you can.  \n",
    "There can be some discrepencies when the environment variables do not exist when *rpy2* is installed.\n",
    "\n",
    "# 1.  Install R\n",
    "[http://www.r-project.org/](The R Project for Statistical Computing)\n",
    "\n",
    "# 2.  Set Environment Variables\n",
    "* Add the path to R.dll to the environment variable PATH (C:\\Program Files\\R\\R-3.0.3\\bin\\i386)\n",
    "* Add an environment variable R_HOME (C:\\Program Files\\R\\R-3.0.3 in my case)\n",
    "* Add an environment variable R_USER (simply my username in Windows)\n",
    "\n",
    "# Python Modules\n",
    "You can use pip or easy_install for many of these packages, however on Windows, I suggest you visit the Unofficial \n",
    "Windows Binaries for Python Extension Packages created by Christoph Gohlke at UC Irvine: \n",
    "[http://www.lfd.uci.edu/~gohlke/pythonlibs/](http://www.lfd.uci.edu/~gohlke/pythonlibs/)\n",
    "* pyreadline\n",
    "* pyzmq\n",
    "* tornado\n",
    "* jinja2\n",
    "* ipython\n",
    "* rpy2\n",
    "* Python for Windows Extensions [http://sourceforge.net/projects/pywin32/](http://sourceforge.net/projects/pywin32/)\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import Modules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import numpy as NUM\n",
    "import pylab as PYLAB\n",
    "import arcpy as ARCPY\n",
    "import numpy as NUM\n",
    "import SSDataObject as SSDO\n",
    "import scipy as SCIPY\n",
    "import pandas as PANDAS"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Initialize Data Object, Select Fields and Obtain Data\n",
    "\n",
    "### Use Case - Using Regression Trees to Analyze 2008 Presidential Vote in California Counties"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "inputFC = r'../data/CA_Polygons.shp'\n",
    "ssdo = SSDO.SSDataObject(inputFC)\n",
    "ssdo.obtainData(ssdo.oidName, ['PCR2008', 'POPDEN08', 'PERCNOHS', 'MAJORO'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Make Use of PANDAS Data Frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "ids = [ssdo.order2Master[i] for i in range(ssdo.numObs)]\n",
    "convertDictDF = {}\n",
    "for fieldName in ssdo.fields.keys():\n",
    "    convertDictDF[fieldName] = ssdo.fields[fieldName].data\n",
    "df = PANDAS.DataFrame(convertDictDF, index = ids)\n",
    "print(df[0:5])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Push PANDAS Data Frame to R Data Frame - Use the -i flag"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%load_ext rpy2.ipython\n",
    "#%reload_ext rpy2.ipython\n",
    "\n",
    "%R -i df "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Analyze in R"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%R library(rms)\n",
    "%R logit = lrm(MAJORO ~ PCR2008 + POPDEN08 + PERCNOHS, data = df, x = TRUE, y = TRUE)\n",
    "%R z_scores = logit$coefficients / sqrt(diag(logit$var))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Pull Results Back to Python - Use the -o flag"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%R -o logit_coef logit_coef = logit$coefficients\n",
    "%R -o p_values p_values = pnorm(abs(z_scores), lower.tail = FALSE) * 2.0\n",
    "\n",
    "print(\"Coefficients\")\n",
    "py_coef = NUM.array(logit_coef)\n",
    "print(py_coef)\n",
    "\n",
    "print(\"p_values\")\n",
    "py_pvalues = NUM.array(p_values)\n",
    "print(py_pvalues)\n"
   ]
  }
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