example_polyglot_python_r.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src='./img/opengeohub_logo.png' alt='OpenGeoHub Logo' align='right' width='15%'></img>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Polyglot programming example - Combining Python and R in Jupyter notebooks"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Example outline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The example shows how you can visualize a data frame that was created with Python's `pandas` library with R's visualisation package `ggplot`. Example taken from [here](https://stackoverflow.com/questions/39008069/r-and-python-in-one-jupyter-notebook)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Install rpy2 library"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> `conda install -c r rpy2`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load `rpy2` with the magic function `%load_ext`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext rpy2.ipython"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Define a `pandas` dataframe in Python"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame({\n",
    "    'cups_of_coffee': [0, 1, 2, 3, 4, 5, 6, 7, 8, 9],\n",
    "    'productivity': [2, 5, 6, 8, 9, 8, 0, 1, 0, -1]\n",
    "})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot the pandas dataframe with R's `ggplot`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With the command `%%R`you indicate that this cell shall execute R code. The additional options mean the following:\n",
    "* `-i` - the global variable you want to bring to the cell, e.g. the defined dataframe\n",
    "* `-w`, `-h` - Width of the ouput plot\n",
    "* `-units` - Resolution of the output plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R -i df -w 10 -h 5 --units in -r 100\n",
    "\n",
    "#install.packages(\"ggplot2\", repos='http://cran.us.r-project.org', quiet=TRUE)\n",
    "#library(ggplot2)\n",
    "ggplot(df, aes(x=cups_of_coffee, y=productivity)) + geom_line()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr>\n",
    "&copy; 2020 | Julia Wagemann\n",
    "<a rel=\"license\" href=\"http://creativecommons.org/licenses/by/4.0/\"><img style=\"float: right\" alt=\"Creative Commons Lizenzvertrag\" style=\"border-width:0\" src=\"https://i.creativecommons.org/l/by/4.0/88x31.png\" /></a>"
   ]
  }
 ],
 "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": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}