python/r_magic_command.ipynb

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  "metadata": {
    "colab": {
      "name": "r-magic-command.ipynb",
      "provenance": []
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    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EnyONbNhCqSK",
        "colab_type": "text"
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      "source": [
        "# **Using R and Python in the Same Notebook**\n",
        "\n",
        "Chanin Nantasenamat\n",
        "\n",
        "[*'Data Professor' YouTube channel*](http://youtube.com/dataprofessor)\n",
        "\n",
        "In this Jupyter notebook, I will show you how to use R and Python in the same notebook.\n",
        "\n",
        "---"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2h-2I4CviFCR",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# activate R magic\n",
        "%load_ext rpy2.ipython"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FftFvPLNiZME",
        "colab_type": "text"
      },
      "source": [
        "## Python"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3hPnRI2piJM3",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import pandas as pd"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yNKM70-ZiPcg",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "x <- 42\n",
        "print(x)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dtkChhxpiWEd",
        "colab_type": "text"
      },
      "source": [
        "## R"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ozqbZ3lviTPj",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "%%R\n",
        "x <- 42\n",
        "print(x)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "napTAYyXiU8r",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "%%R\n",
        "install.packages('caret')\n",
        "install.packages('mlbench')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4eB_IbK4kztb",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "%%R\n",
        "install.packages('mlbench')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Bl0feNEUi-Jk",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "%%R\n",
        "library(caret)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zY7WFnrSj4Mr",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "%%R\n",
        "############################################\n",
        "# Data Professor                           #\n",
        "# http://youtube.com/dataprofessor         #\n",
        "# http://github.com/dataprofessor          #\n",
        "# http://facebook.com/dataprofessor        #\n",
        "# https://www.instagram.com/data.professor #\n",
        "############################################\n",
        "\n",
        "# Importing libraries\n",
        "library(mlbench) # Contains several benchmark data sets (especially the Boston Housing dataset)\n",
        "library(caret) # Package for machine learning algorithms / CARET stands for Classification And REgression Training\n",
        "\n",
        "# Importing the Boston Housing data set\n",
        "data(BostonHousing)\n",
        "\n",
        "head(BostonHousing)\n",
        "\n",
        "# Check to see if there are missing data?\n",
        "sum(is.na(BostonHousing))\n",
        "\n",
        "# To achieve reproducible model; set the random seed number\n",
        "set.seed(100)\n",
        "\n",
        "# Performs stratified random split of the data set\n",
        "TrainingIndex <- createDataPartition(BostonHousing$medv, p=0.8, list = FALSE)\n",
        "TrainingSet <- BostonHousing[TrainingIndex,] # Training Set\n",
        "TestingSet <- BostonHousing[-TrainingIndex,] # Test Set\n",
        "\n",
        "\n",
        "###############################\n",
        "\n",
        "# Build Training model\n",
        "Model <- train(medv ~ ., data = TrainingSet,\n",
        "               method = \"lm\",\n",
        "               na.action = na.omit,\n",
        "               preProcess=c(\"scale\",\"center\"),\n",
        "               trControl= trainControl(method=\"none\")\n",
        ")\n",
        "\n",
        "# Apply model for prediction\n",
        "Model.training <-predict(Model, TrainingSet) # Apply model to make prediction on Training set\n",
        "Model.testing <-predict(Model, TestingSet) # Apply model to make prediction on Testing set\n",
        "\n",
        "# Model performance (Displays scatter plot and performance metrics)\n",
        "  # Scatter plot of Training set\n",
        "plot(TrainingSet$medv,Model.training, col = \"blue\" )\n",
        "plot(TestingSet$medv,Model.testing, col = \"blue\" )"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Q6A7bOvbll8D",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}