cap13/CH13EJ6-Creacion-de-arbol-de-decision-y-mejora-con-PCA.ipynb

{
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"width: 100%; clear: both;\">\n",
    "<div style=\"float: left; width: 50%;\">\n",
    "<img src=\"http://www.uoc.edu/portal/_resources/common/imatges/marca_UOC/UOC_Masterbrand.jpg\" align=\"left\">\n",
    "</div>\n",
    "<div style=\"float: right; width: 50%;\">\n",
    "<p style=\"margin: 0; padding-top: 22px; text-align:right;\"><a href=\"http://www.editorialuoc.cat/mineria-de-datos\" target=\"_blank\">Minería de datos: Modelos y algoritmos</a></p>\n",
    "<p style=\"margin: 0; text-align:right;\">Estudios de Informática, Multimedia y Telecomunicación</p>\n",
    "<p style=\"margin: 0; text-align:right; padding-button: 100px;\">Universitat Oberta de Catalunya (<b>UOC</b>)</p>\n",
    "</div>\n",
    "</div>\n",
    "<div style=\"width:100%;\">&nbsp;</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# CH13EJ6 Creación de arbol de decisión y mejora con PCA"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "En este ejemplo se muestra como usar PCA para mejorar la clasificación de un conjunto de datos [Breast Cancer Wisconsin (Diagnostic) Data Set](https://archive.ics.uci.edu/ml/datasets/Breast+Cancer+Wisconsin+(Diagnostic))."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Instalar los packages necesarios "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loading required package: car\n",
      "\n",
      "Warning message:\n",
      "“package ‘car’ was built under R version 3.5.2”\n",
      "Loading required package: carData\n",
      "\n",
      "Warning message:\n",
      "“package ‘carData’ was built under R version 3.5.2”\n",
      "Loading required package: C50\n",
      "\n",
      "Loading required package: gmodels\n",
      "\n"
     ]
    }
   ],
   "source": [
    "if(!require(car)){\n",
    "    install.packages('car',repos='http://cran.es.r-project.org')\n",
    "    require(car)\n",
    "}\n",
    "if(!require(C50)){\n",
    "    install.packages('C50',repos='http://cran.es.r-project.org')\n",
    "    require(C50)\n",
    "}\n",
    "if(!require(gmodels)){\n",
    "    install.packages('gmodels',repos='http://cran.es.r-project.org')\n",
    "    require(gmodels)\n",
    "}\n",
    "set.seed(606);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Carga del dataset\n",
    "\n",
    "Leer el conjunto de datos original desde su URL en el repositorio **UCI Machine Learning**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "wdbc=read.table(\n",
    "    url(\"https://archive.ics.uci.edu/ml/machine-learning-databases/breast-cancer-wisconsin/breast-cancer-wisconsin.data\"), \n",
    "    sep=\",\", header=FALSE)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Obtener las dimensiones (N y d) del conjunto de datos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table>\n",
       "<thead><tr><th></th><th scope=col>V1</th><th scope=col>V2</th><th scope=col>V3</th><th scope=col>V4</th><th scope=col>V5</th><th scope=col>V6</th><th scope=col>V7</th><th scope=col>V8</th><th scope=col>V9</th><th scope=col>V10</th><th scope=col>V11</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>1</th><td>1000025</td><td>5      </td><td>1      </td><td>1      </td><td>1      </td><td>2      </td><td>1      </td><td>3      </td><td>1      </td><td>1      </td><td>2      </td></tr>\n",
       "\t<tr><th scope=row>2</th><td>1002945</td><td>5      </td><td>4      </td><td>4      </td><td>5      </td><td>7      </td><td>10     </td><td>3      </td><td>2      </td><td>1      </td><td>2      </td></tr>\n",
       "\t<tr><th scope=row>3</th><td>1015425</td><td>3      </td><td>1      </td><td>1      </td><td>1      </td><td>2      </td><td>2      </td><td>3      </td><td>1      </td><td>1      </td><td>2      </td></tr>\n",
       "\t<tr><th scope=row>4</th><td>1016277</td><td>6      </td><td>8      </td><td>8      </td><td>1      </td><td>3      </td><td>4      </td><td>3      </td><td>7      </td><td>1      </td><td>2      </td></tr>\n",
       "\t<tr><th scope=row>5</th><td>1017023</td><td>4      </td><td>1      </td><td>1      </td><td>3      </td><td>2      </td><td>1      </td><td>3      </td><td>1      </td><td>1      </td><td>2      </td></tr>\n",
       "\t<tr><th scope=row>6</th><td>1017122</td><td>8      </td><td>10     </td><td>10     </td><td>8      </td><td>7      </td><td>10     </td><td>9      </td><td>7      </td><td>1      </td><td>4      </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|lllllllllll}\n",
       "  & V1 & V2 & V3 & V4 & V5 & V6 & V7 & V8 & V9 & V10 & V11\\\\\n",
       "\\hline\n",
       "\t1 & 1000025 & 5       & 1       & 1       & 1       & 2       & 1       & 3       & 1       & 1       & 2      \\\\\n",
       "\t2 & 1002945 & 5       & 4       & 4       & 5       & 7       & 10      & 3       & 2       & 1       & 2      \\\\\n",
       "\t3 & 1015425 & 3       & 1       & 1       & 1       & 2       & 2       & 3       & 1       & 1       & 2      \\\\\n",
       "\t4 & 1016277 & 6       & 8       & 8       & 1       & 3       & 4       & 3       & 7       & 1       & 2      \\\\\n",
       "\t5 & 1017023 & 4       & 1       & 1       & 3       & 2       & 1       & 3       & 1       & 1       & 2      \\\\\n",
       "\t6 & 1017122 & 8       & 10      & 10      & 8       & 7       & 10      & 9       & 7       & 1       & 4      \\\\\n",
       "\\end{tabular}\n"
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      "text/plain": [
       "       V1 V2 V3 V4 V5 V6 V7 V8 V9 V10 V11\n",
       "1 1000025  5  1  1  1  2  1  3  1   1   2\n",
       "2 1002945  5  4  4  5  7 10  3  2   1   2\n",
       "3 1015425  3  1  1  1  2  2  3  1   1   2\n",
       "4 1016277  6  8  8  1  3  4  3  7   1   2\n",
       "5 1017023  4  1  1  3  2  1  3  1   1   2\n",
       "6 1017122  8 10 10  8  7 10  9  7   1   4"
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     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>699</li>\n",
       "\t<li>11</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 699\n",
       "\\item 11\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 699\n",
       "2. 11\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] 699  11"
      ]
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     "metadata": {},
     "output_type": "display_data"
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   ],
   "source": [
    "head(wdbc)\n",
    "dim(wdbc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Manipulación de datos\n",
    "\n",
    "Correcciones que se aplicarán a los datos:\n",
    "- Recodificar los '?' por NA \n",
    "- Filtrar los valores ausentes, i.e. quedarse solo con los registros completos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "wdbc$V7=as.numeric(recode(wdbc$V7, \"'?'=NA\"))\n",
    "\n",
    "x=wdbc[complete.cases(wdbc),]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Partición del dataset\n",
    "\n",
    "Particionar el conjunto de entrada en dos (training y test set), según la regla del 2/3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "N=dim(x)[1]\n",
    "all=seq(1,N)\n",
    "\n",
    "# seleccionar 2/3 al azar\n",
    "train=sort(sample(N,N*2/3.0))\n",
    "test=setdiff(all,train)\n",
    "\n",
    "xtrain=x[train,]\n",
    "xtest=x[test,]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "Revisar que haya una representación parecida en la variable clasificadora del conjunto de entreno y test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "        2         4 \n",
       "0.6571429 0.3428571 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "\n",
       "        2         4 \n",
       "0.6359649 0.3640351 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "prop.table(table(xtrain$V11))\n",
    "prop.table(table(xtest$V11))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Creación del primer árbol\n",
    "\n",
    "Crear un primer árbol usando todos los parámetros por defecto y dibujarlo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "Call:\n",
       "C5.0.default(x = xtrain[, c(2:10)], y = as.factor(xtrain$V11))\n",
       "\n",
       "Classification Tree\n",
       "Number of samples: 455 \n",
       "Number of predictors: 9 \n",
       "\n",
       "Tree size: 6 \n",
       "\n",
       "Non-standard options: attempt to group attributes\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "\n",
       "Call:\n",
       "C5.0.default(x = xtrain[, c(2:10)], y = as.factor(xtrain$V11))\n",
       "\n",
       "\n",
       "C5.0 [Release 2.07 GPL Edition]  \tTue Oct 20 17:07:08 2020\n",
       "-------------------------------\n",
       "\n",
       "Class specified by attribute `outcome'\n",
       "\n",
       "Read 455 cases (10 attributes) from undefined.data\n",
       "\n",
       "Decision tree:\n",
       "\n",
       "V3 <= 2:\n",
       ":...V7 <= 1: 2 (255/1)\n",
       ":   V7 > 1:\n",
       ":   :...V2 <= 6: 2 (25/2)\n",
       ":       V2 > 6: 4 (3)\n",
       "V3 > 2:\n",
       ":...V7 > 1: 4 (149/8)\n",
       "    V7 <= 1:\n",
       "    :...V3 <= 3: 2 (12)\n",
       "        V3 > 3: 4 (11/2)\n",
       "\n",
       "\n",
       "Evaluation on training data (455 cases):\n",
       "\n",
       "\t    Decision Tree   \n",
       "\t  ----------------  \n",
       "\t  Size      Errors  \n",
       "\n",
       "\t     6   13( 2.9%)   <<\n",
       "\n",
       "\n",
       "\t   (a)   (b)    <-classified as\n",
       "\t  ----  ----\n",
       "\t   289    10    (a): class 2\n",
       "\t     3   153    (b): class 4\n",
       "\n",
       "\n",
       "\tAttribute usage:\n",
       "\n",
       "\t100.00%\tV3\n",
       "\t100.00%\tV7\n",
       "\t  6.15%\tV2\n",
       "\n",
       "\n",
       "Time: 0.0 secs\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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nnttdekd+/ekiZNGiuKsu01L1++LO+++66899570qBBAxk1apRkzZrVtvX1pWKe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XxLM49v+eoWfBhhAYNGiQHothTMZAAqYR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\n17Zt26Rfv37O6Oj54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kSXLPPffoaW5HHHGEKGfMcuutt0qpUqUK5NqzZ0+9nlM5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\nRU5ErkJggg==",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 300,
       "width": 600
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "model1 <- C50::C5.0(xtrain[,c(2:10)],as.factor(xtrain$V11))\n",
    "model1\n",
    "summary(model1)\n",
    "options(repr.plot.width=10, repr.plot.height=5)\n",
    "plot(model1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Evaluar el árbol creado mediante la matriz de confusión para cada subconjunto"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " \n",
      "   Cell Contents\n",
      "|-------------------------|\n",
      "|                       N |\n",
      "|         N / Table Total |\n",
      "|-------------------------|\n",
      "\n",
      " \n",
      "Total Observations in Table:  228 \n",
      "\n",
      " \n",
      "             | predicted V11 \n",
      "  actual V11 |         2 |         4 | Row Total | \n",
      "-------------|-----------|-----------|-----------|\n",
      "           2 |       136 |         9 |       145 | \n",
      "             |     0.596 |     0.039 |           | \n",
      "-------------|-----------|-----------|-----------|\n",
      "           4 |         3 |        80 |        83 | \n",
      "             |     0.013 |     0.351 |           | \n",
      "-------------|-----------|-----------|-----------|\n",
      "Column Total |       139 |        89 |       228 | \n",
      "-------------|-----------|-----------|-----------|\n",
      "\n",
      " \n"
     ]
    }
   ],
   "source": [
    "V11_pred <- predict(model1, xtest)\n",
    "CrossTable(xtest$V11,V11_pred,prop.chisq=FALSE,prop.c=FALSE,prop.r=FALSE,dnn = c('actual V11','predicted V11'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "El **error del árbol es del 0,07**. Sólo 16 casos clasifican incorrectamente. \n",
    "\n",
    "Veamos si es mejorable con la técnica de PCA."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. PCA\n",
    "\n",
    "Utilizar PCA para extraer características, analizando el número de componentes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Importance of components:\n",
       "                          Comp.1     Comp.2     Comp.3    Comp.4     Comp.5\n",
       "Standard deviation     6.4690496 2.07938452 1.96040844 1.7081162 1.66343185\n",
       "Proportion of Variance 0.6713021 0.06935961 0.06164959 0.0468028 0.04438611\n",
       "Cumulative Proportion  0.6713021 0.74066173 0.80231132 0.8491141 0.89350023\n",
       "                           Comp.6     Comp.7     Comp.8     Comp.9\n",
       "Standard deviation     1.61979548 1.31447411 1.19395660 0.92845281\n",
       "Proportion of Variance 0.04208792 0.02771667 0.02286726 0.01382792\n",
       "Cumulative Proportion  0.93558815 0.96330482 0.98617208 1.00000000"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "TipoCancer <- xtrain[,11]\n",
    "VarCancer <- xtrain[,2:10]\n",
    "\n",
    "pcaVarCancer <- princomp(VarCancer)\n",
    "summary(pcaVarCancer) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Examinada la desviación estándard, la proporción de la varianza y varianza acumulada escogemos el primer componente para intentar mejorar nuestro modelo."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Añadir una nueva variable C1 a los datos usando el primer componente calculado"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "xtrain$C1=predict(pcaVarCancer,xtrain[,2:11])[,1]\n",
    "xtest$C1=predict(pcaVarCancer,xtest[,2:11])[,1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. Creación del segundo árbol \n",
    "\n",
    "Crear un segundo árbol con la nueva variable usando todos los parámetros por defecto y dibujarlo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "Call:\n",
       "C5.0.default(x = xtrain[, c(2:10, 12)], y = as.factor(xtrain$V11))\n",
       "\n",
       "Classification Tree\n",
       "Number of samples: 455 \n",
       "Number of predictors: 10 \n",
       "\n",
       "Tree size: 5 \n",
       "\n",
       "Non-standard options: attempt to group attributes\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "\n",
       "Call:\n",
       "C5.0.default(x = xtrain[, c(2:10, 12)], y = as.factor(xtrain$V11))\n",
       "\n",
       "\n",
       "C5.0 [Release 2.07 GPL Edition]  \tTue Oct 20 17:08:45 2020\n",
       "-------------------------------\n",
       "\n",
       "Class specified by attribute `outcome'\n",
       "\n",
       "Read 455 cases (11 attributes) from undefined.data\n",
       "\n",
       "Decision tree:\n",
       "\n",
       "C1 <= -1.060818: 2 (287/1)\n",
       "C1 > -1.060818:\n",
       ":...C1 > 3.516298: 4 (127/2)\n",
       "    C1 <= 3.516298:\n",
       "    :...V9 <= 4: 4 (31/4)\n",
       "        V9 > 4:\n",
       "        :...V2 <= 7: 2 (7)\n",
       "            V2 > 7: 4 (3)\n",
       "\n",
       "\n",
       "Evaluation on training data (455 cases):\n",
       "\n",
       "\t    Decision Tree   \n",
       "\t  ----------------  \n",
       "\t  Size      Errors  \n",
       "\n",
       "\t     5    7( 1.5%)   <<\n",
       "\n",
       "\n",
       "\t   (a)   (b)    <-classified as\n",
       "\t  ----  ----\n",
       "\t   293     6    (a): class 2\n",
       "\t     1   155    (b): class 4\n",
       "\n",
       "\n",
       "\tAttribute usage:\n",
       "\n",
       "\t100.00%\tC1\n",
       "\t  9.01%\tV9\n",
       "\t  2.20%\tV2\n",
       "\n",
       "\n",
       "Time: 0.0 secs\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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WbsGTKRi7B3RsHKi65hyEsvvbQTz2/KsNHUNaoPAoceeqjGT2bxk1UyBpyynicQ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\nvfdePftFOQsN48GDB+t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H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\nIT04ILEAAAAASUVORK5CYII=",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 180,
       "width": 240
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "model2 <- C50::C5.0(xtrain[,c(2:10,12)],as.factor(xtrain$V11))\n",
    "model2\n",
    "summary(model2)\n",
    "options(repr.plot.width=4, repr.plot.height=3)\n",
    "plot(model2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Observar que para la creación el árbol solo se ha usado la variable que corresponde con el componente 1. El resto de variables se ha descartado."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Evaluar el árbol creado mediante la matriz de confusión para cada subconjunto"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " \n",
      "   Cell Contents\n",
      "|-------------------------|\n",
      "|                       N |\n",
      "|         N / Table Total |\n",
      "|-------------------------|\n",
      "\n",
      " \n",
      "Total Observations in Table:  228 \n",
      "\n",
      " \n",
      "             | predicted V11 \n",
      "  actual V11 |         2 |         4 | Row Total | \n",
      "-------------|-----------|-----------|-----------|\n",
      "           2 |       141 |         4 |       145 | \n",
      "             |     0.618 |     0.018 |           | \n",
      "-------------|-----------|-----------|-----------|\n",
      "           4 |         4 |        79 |        83 | \n",
      "             |     0.018 |     0.346 |           | \n",
      "-------------|-----------|-----------|-----------|\n",
      "Column Total |       145 |        83 |       228 | \n",
      "-------------|-----------|-----------|-----------|\n",
      "\n",
      " \n"
     ]
    }
   ],
   "source": [
    "V11_pred <- predict(model2, xtest)\n",
    "CrossTable(xtest$V11,V11_pred,prop.chisq=FALSE,prop.c=FALSE,prop.r=FALSE,dnn = c('actual V11','predicted V11'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Tenemos una tasa del **0,039 de error en clasificación**. Hemos logrado reducir las clasificaciones erroneas de tumor maligno a 0 manteniendose la misma tasa de falsos positivos."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "R",
   "language": "R",
   "name": "ir"
  },
  "language_info": {
   "codemirror_mode": "r",
   "file_extension": ".r",
   "mimetype": "text/x-r-source",
   "name": "R",
   "pygments_lexer": "r",
   "version": "3.5.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}