Python_loves_R.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Calling R in a Python Notebook"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load and setup rpy2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import rpy2.rinterface\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext rpy2.ipython"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Running R in a cell"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R\n",
    "X_vals <- c(1,2,3,4,5)\n",
    "y_vals <- c(1,2,4,8,16)\n",
    "\n",
    "plot(X_vals, y_vals, col='blue', pch=3, main='R plot in Python')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Moving objects from Python to R"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We create the pandas dataframe to be loaded into R"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[4 2 1 4 3 3 4 4 1 0]\n",
      "[10.56  2.43  2.61  4.28  8.93  3.79  6.81  6.16  0.91  0.  ]\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(666)\n",
    "A = np.random.randint(5, size=10)\n",
    "mod = np.random.normal(size=10) + 2\n",
    "B = A * mod\n",
    "print(A)\n",
    "print(B.round(2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   A          B\n",
      "0  4  10.562293\n",
      "1  2   2.427114\n",
      "2  1   2.608870\n",
      "3  4   4.275953\n",
      "4  3   8.934667\n",
      "5  3   3.789246\n",
      "6  4   6.805070\n",
      "7  4   6.157651\n",
      "8  1   0.911207\n",
      "9  0   0.000000\n"
     ]
    }
   ],
   "source": [
    "python_df = pd.DataFrame({'A': A,\n",
    "                          'B': B})\n",
    "print(python_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To use the dataframe in R, we have to specify the object to use in the header."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.6/dist-packages/rpy2/robjects/pandas2ri.py:191: FutureWarning: from_items is deprecated. Please use DataFrame.from_dict(dict(items), ...) instead. DataFrame.from_dict(OrderedDict(items)) may be used to preserve the key order.\n",
      "  res = PandasDataFrame.from_items(items)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R -i python_df\n",
    "\n",
    "# Use sink to suppress messages\n",
    "plot(python_df, \n",
    "     main='Plotting a Pandas Dataframe in R', \n",
    "     col='blue', \n",
    "     pch=9)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Moving objects from R to Python"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "One way for us to be able to use R objects in Python is to declare so in the cell header."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%R -o model -o coefficients\n",
    "\n",
    "model <- lm(B ~ A, data=python_df)\n",
    "coefficients <- model$coefficients"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After doing so, we are able to simply call those objects in Python as we would with regular python objects. Notice the datatype."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Call:\n",
      "lm(formula = B ~ A, data = python_df)\n",
      "\n",
      "Coefficients:\n",
      "(Intercept)            A  \n",
      "   -0.05861      1.80993  \n",
      "\n",
      "\n",
      "<class 'rpy2.robjects.vectors.ListVector'>\n",
      "\n",
      "---\n",
      "\n",
      "(Intercept)           A \n",
      "-0.05860516  1.80992769 \n",
      "\n",
      "<class 'rpy2.robjects.vectors.FloatVector'>\n"
     ]
    }
   ],
   "source": [
    "print(model)\n",
    "print(type(model))\n",
    "print('\\n---\\n')\n",
    "print(coefficients)\n",
    "print(type(coefficients))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Some objects, such as dataframes, are automatically transformed into their Python equivalent. Here, we load a dataframe in R and then use seaborn to visualize it, as we would with a regular pandas dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.6/dist-packages/rpy2/robjects/pandas2ri.py:191: FutureWarning: from_items is deprecated. Please use DataFrame.from_dict(dict(items), ...) instead. DataFrame.from_dict(OrderedDict(items)) may be used to preserve the key order.\n",
      "  res = PandasDataFrame.from_items(items)\n"
     ]
    }
   ],
   "source": [
    "%%R -o cars_df\n",
    "\n",
    "library(datasets)\n",
    "cars_df <- cars"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   speed  dist\n",
      "0    4.0   2.0\n",
      "1    4.0  10.0\n",
      "2    7.0   4.0\n",
      "3    7.0  22.0\n",
      "4    8.0  16.0\n"
     ]
    }
   ],
   "source": [
    "print(cars_df.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.6/dist-packages/seaborn/axisgrid.py:2065: UserWarning: The `size` parameter has been renamed to `height`; pleaes update your code.\n",
      "  warnings.warn(msg, UserWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(x_vars=['speed'], \n",
    "             y_vars=['dist'], \n",
    "             data=cars_df,\n",
    "             size=5)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Other options for sharing objects between R and Python"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can still use R objects in Python if we do not declare so in the header my manually calling them using %Rget"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1] 1 2 3 4 5\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R\n",
    "\n",
    "my_var = c(1:5)\n",
    "my_var"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] 1 2 3 4 5\n",
      "\n",
      "<class 'rpy2.robjects.vectors.IntVector'>\n",
      "\n",
      "---\n",
      "\n",
      "[1, 2, 3, 4, 5]\n"
     ]
    }
   ],
   "source": [
    "my_python_var = %Rget my_var\n",
    "\n",
    "print(my_python_var)\n",
    "print(type(my_python_var))\n",
    "print('\\n---\\n')\n",
    "print(list(my_python_var))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, we can push objects from Python to R using Rpush."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "my_python_var2 = np.array([1, 2, 3, 4, 5])\n",
    "my_python_var3 = np.array([1, 2, 4, 8, 16])\n",
    "\n",
    "%Rpush my_python_var2 my_python_var3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R\n",
    "plot(my_python_var2, \n",
    "     my_python_var3,\n",
    "     main='Python objects pushed to R', \n",
    "     col='red', \n",
    "     pch=16)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}