{
 "cells": [
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "To be able to run R commands in Python, install the Python package rpy2.  \n",
    "But in my case I had to install an older version:  \n",
    "pip install rpy2==3.5.17\n",
    "\n",
    "Next execute the following command:"
   ],
   "id": "f56caa6652c8de9e"
  },
  {
   "cell_type": "code",
   "id": "initial_id",
   "metadata": {
    "collapsed": true,
    "ExecuteTime": {
     "end_time": "2026-04-09T08:07:18.843766Z",
     "start_time": "2026-04-09T08:07:18.841220Z"
    }
   },
   "source": "%load_ext rpy2.ipython",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The rpy2.ipython extension is already loaded. To reload it, use:\n",
      "  %reload_ext rpy2.ipython\n"
     ]
    }
   ],
   "execution_count": 27
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "As usual, load the Python packages:",
   "id": "1a9d796bf761dccd"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-04-09T08:07:18.866415Z",
     "start_time": "2026-04-09T08:07:18.864416Z"
    }
   },
   "cell_type": "code",
   "source": "import pandas as pd",
   "id": "569322b8b13c2b09",
   "outputs": [],
   "execution_count": 28
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "As usual, load the R packages:",
   "id": "492b1f3788c7275a"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-04-09T08:07:18.879884Z",
     "start_time": "2026-04-09T08:07:18.873607Z"
    }
   },
   "cell_type": "code",
   "source": [
    "%%R\n",
    "library(tidyverse)"
   ],
   "id": "49ab6d4fa17e291c",
   "outputs": [],
   "execution_count": 29
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "Load data in Python:",
   "id": "b72458e408713fec"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-04-09T08:07:18.898184Z",
     "start_time": "2026-04-09T08:07:18.881240Z"
    }
   },
   "cell_type": "code",
   "source": [
    "py_data_toy = pd.read_csv(\"toy.csv\")\n",
    "py_data_toy[\"austrian\"] = py_data_toy[\"austrian\"].astype(bool)\n",
    "py_data_toy[\"marital_status\"] = pd.Categorical(py_data_toy[\"marital_status\"], categories=[\"single\", \"divorced\", \"married\"])\n",
    "py_data_toy[\"age_cat\"] = pd.Categorical(py_data_toy[\"age_cat\"], categories=[\"child\", \"adolescent\", \"adult\"], ordered=True)\n",
    "py_data_toy.head()"
   ],
   "id": "f68fa0777a79fb03",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "    name  austrian marital_status  year_birth     age_cat  temperature  \\\n",
       "0  Franz     False       divorced      1990.0       adult         37.0   \n",
       "1   Sepp     False         single      2009.0  adolescent         38.5   \n",
       "2  Maria      True         single      2005.0       adult         36.0   \n",
       "3  Georg      True         single      2019.0       child         35.4   \n",
       "4   Karl     False        married      1930.0       adult         39.0   \n",
       "\n",
       "   test_score  height  \n",
       "0         NaN   180.4  \n",
       "1        80.0   160.5  \n",
       "2         5.0   158.9  \n",
       "3        30.0   130.2  \n",
       "4         1.0   174.3  "
      ],
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>austrian</th>\n",
       "      <th>marital_status</th>\n",
       "      <th>year_birth</th>\n",
       "      <th>age_cat</th>\n",
       "      <th>temperature</th>\n",
       "      <th>test_score</th>\n",
       "      <th>height</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Franz</td>\n",
       "      <td>False</td>\n",
       "      <td>divorced</td>\n",
       "      <td>1990.0</td>\n",
       "      <td>adult</td>\n",
       "      <td>37.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>180.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Sepp</td>\n",
       "      <td>False</td>\n",
       "      <td>single</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>adolescent</td>\n",
       "      <td>38.5</td>\n",
       "      <td>80.0</td>\n",
       "      <td>160.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Maria</td>\n",
       "      <td>True</td>\n",
       "      <td>single</td>\n",
       "      <td>2005.0</td>\n",
       "      <td>adult</td>\n",
       "      <td>36.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>158.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Georg</td>\n",
       "      <td>True</td>\n",
       "      <td>single</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>child</td>\n",
       "      <td>35.4</td>\n",
       "      <td>30.0</td>\n",
       "      <td>130.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Karl</td>\n",
       "      <td>False</td>\n",
       "      <td>married</td>\n",
       "      <td>1930.0</td>\n",
       "      <td>adult</td>\n",
       "      <td>39.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>174.3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 30
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "Do something in python ...",
   "id": "8ede481d09bc7f86"
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "Use the following command to import the pandas data frame py_data_toy to R:",
   "id": "68b194059304aa8c"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-04-09T08:07:18.907604Z",
     "start_time": "2026-04-09T08:07:18.899322Z"
    }
   },
   "cell_type": "code",
   "source": "%R -i py_data_toy",
   "id": "bb6c267f03a703d",
   "outputs": [],
   "execution_count": 31
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "In R the variable has the name py_data_toy, so we copy that to r_data_toy and delete py_data_toy in R only:",
   "id": "14328d0de20fb4a8"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-04-09T08:07:18.934580Z",
     "start_time": "2026-04-09T08:07:18.908892Z"
    }
   },
   "cell_type": "code",
   "source": [
    "%%R\n",
    "r_data_toy <- py_data_toy\n",
    "rm(py_data_toy)\n",
    "r_data_toy |> head()"
   ],
   "id": "6db5d1307193a9b0",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    name austrian marital_status year_birth    age_cat temperature test_score\n",
      "0  Franz    FALSE       divorced       1990      adult        37.0        NaN\n",
      "1   Sepp    FALSE         single       2009 adolescent        38.5         80\n",
      "2  Maria     TRUE         single       2005      adult        36.0          5\n",
      "3  Georg     TRUE         single       2019      child        35.4         30\n",
      "4   Karl    FALSE        married       1930      adult        39.0          1\n",
      "5 Ulrike     TRUE        married       1980      adult        38.0          9\n",
      "  height\n",
      "0  180.4\n",
      "1  160.5\n",
      "2  158.9\n",
      "3  130.2\n",
      "4  174.3\n",
      "5  172.1\n"
     ]
    }
   ],
   "execution_count": 32
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "In Python py_data_toy is still available:",
   "id": "1e15c0576440d86a"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-04-09T08:07:18.942441Z",
     "start_time": "2026-04-09T08:07:18.935618Z"
    }
   },
   "cell_type": "code",
   "source": "py_data_toy.head()",
   "id": "9bb7f3c93cce781c",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "    name  austrian marital_status  year_birth     age_cat  temperature  \\\n",
       "0  Franz     False       divorced      1990.0       adult         37.0   \n",
       "1   Sepp     False         single      2009.0  adolescent         38.5   \n",
       "2  Maria      True         single      2005.0       adult         36.0   \n",
       "3  Georg      True         single      2019.0       child         35.4   \n",
       "4   Karl     False        married      1930.0       adult         39.0   \n",
       "\n",
       "   test_score  height  \n",
       "0         NaN   180.4  \n",
       "1        80.0   160.5  \n",
       "2         5.0   158.9  \n",
       "3        30.0   130.2  \n",
       "4         1.0   174.3  "
      ],
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>austrian</th>\n",
       "      <th>marital_status</th>\n",
       "      <th>year_birth</th>\n",
       "      <th>age_cat</th>\n",
       "      <th>temperature</th>\n",
       "      <th>test_score</th>\n",
       "      <th>height</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Franz</td>\n",
       "      <td>False</td>\n",
       "      <td>divorced</td>\n",
       "      <td>1990.0</td>\n",
       "      <td>adult</td>\n",
       "      <td>37.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>180.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Sepp</td>\n",
       "      <td>False</td>\n",
       "      <td>single</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>adolescent</td>\n",
       "      <td>38.5</td>\n",
       "      <td>80.0</td>\n",
       "      <td>160.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Maria</td>\n",
       "      <td>True</td>\n",
       "      <td>single</td>\n",
       "      <td>2005.0</td>\n",
       "      <td>adult</td>\n",
       "      <td>36.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>158.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Georg</td>\n",
       "      <td>True</td>\n",
       "      <td>single</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>child</td>\n",
       "      <td>35.4</td>\n",
       "      <td>30.0</td>\n",
       "      <td>130.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Karl</td>\n",
       "      <td>False</td>\n",
       "      <td>married</td>\n",
       "      <td>1930.0</td>\n",
       "      <td>adult</td>\n",
       "      <td>39.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>174.3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 33
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "And next we can use ggplot:",
   "id": "6b7861477a8ee1e2"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-04-09T08:07:19.057250Z",
     "start_time": "2026-04-09T08:07:18.943171Z"
    }
   },
   "cell_type": "code",
   "source": [
    "%%R\n",
    "r_data_toy |> # data\n",
    "  ggplot(aes(y = year_birth)) + # aesthetics (define what is on y axis) is used for all geom objects\n",
    "  geom_boxplot() # define geom object (plot type here boxplot)"
   ],
   "id": "eedd17d046491ebf",
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "R[write to console]: In addition: \n",
      "R[write to console]: Warning message:\n",
      "\n",
      "R[write to console]: Removed 1 row containing non-finite outside the scale range (`stat_boxplot()`). \n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 34
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "Do something in R:",
   "id": "6710311463983dc0"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-04-09T08:07:19.071132Z",
     "start_time": "2026-04-09T08:07:19.058264Z"
    }
   },
   "cell_type": "code",
   "source": [
    "%%R\n",
    "r_summary <- r_data_toy |> \n",
    "  group_by(age_cat) |> \n",
    "  summarise(\n",
    "    mean_height = mean(height),\n",
    "    mean_temperature = mean(temperature),\n",
    "    .groups = \"drop\" # not necessary here\n",
    "  )\n",
    "\n",
    "r_summary"
   ],
   "id": "b59606473a3ab1c",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# A tibble: 4 × 3\n",
      "  age_cat    mean_height mean_temperature\n",
      "  <ord>            <dbl>            <dbl>\n",
      "1 child             130.             35.4\n",
      "2 adolescent        160.             38.5\n",
      "3 adult             NaN              37.4\n",
      "4 <NA>              NaN              39.5\n"
     ]
    }
   ],
   "execution_count": 35
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "And with the next code we can use r_summary in Python.\n",
    "But we use the name py_summary for the object and delete the name r_summary."
   ],
   "id": "32e92fb64f162275"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-04-09T08:07:19.083864Z",
     "start_time": "2026-04-09T08:07:19.071827Z"
    }
   },
   "cell_type": "code",
   "source": [
    "r_summary = None  # only to make PyCharm happy (it woks without that but Pycharm thinks there is an error)\n",
    "%R -o r_summary\n",
    "py_summary = r_summary\n",
    "del r_summary\n",
    "type(py_summary)"
   ],
   "id": "997d2cc6b06e1e50",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 36
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-04-09T08:07:19.094274Z",
     "start_time": "2026-04-09T08:07:19.084429Z"
    }
   },
   "cell_type": "code",
   "source": "py_summary",
   "id": "2d4f1e2a719b85f",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "      age_cat  mean_height  mean_temperature\n",
       "1       child        130.2         35.400000\n",
       "2  adolescent        160.5         38.500000\n",
       "3       adult          NaN         37.370588\n",
       "4         NaN          NaN         39.500000"
      ],
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age_cat</th>\n",
       "      <th>mean_height</th>\n",
       "      <th>mean_temperature</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>child</td>\n",
       "      <td>130.2</td>\n",
       "      <td>35.400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>adolescent</td>\n",
       "      <td>160.5</td>\n",
       "      <td>38.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>adult</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.370588</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>39.500000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 37
  }
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