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python/r_magic_command.ipynb

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+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "name": "r-magic-command.ipynb",
+      "provenance": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "EnyONbNhCqSK",
+        "colab_type": "text"
+      },
+      "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": []
+    }
+  ]
+}