Heart Disease Analysis with R.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loading Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Registered S3 methods overwritten by 'ggplot2':\n",
      "  method         from \n",
      "  [.quosures     rlang\n",
      "  c.quosures     rlang\n",
      "  print.quosures rlang\n",
      "Registered S3 method overwritten by 'rvest':\n",
      "  method            from\n",
      "  read_xml.response xml2\n",
      "-- Attaching packages --------------------------------------- tidyverse 1.2.1 --\n",
      "v ggplot2 3.1.1       v purrr   0.3.2  \n",
      "v tibble  2.1.1       v dplyr   0.8.0.1\n",
      "v tidyr   0.8.3       v stringr 1.4.0  \n",
      "v readr   1.3.1       v forcats 0.4.0  \n",
      "-- Conflicts ------------------------------------------ tidyverse_conflicts() --\n",
      "x dplyr::filter() masks stats::filter()\n",
      "x dplyr::lag()    masks stats::lag()\n"
     ]
    }
   ],
   "source": [
    "library(tidyverse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th scope=col>ï..age</th><th scope=col>sex</th><th scope=col>cp</th><th scope=col>trestbps</th><th scope=col>chol</th><th scope=col>fbs</th><th scope=col>restecg</th><th scope=col>thalach</th><th scope=col>exang</th><th scope=col>oldpeak</th><th scope=col>slope</th><th scope=col>ca</th><th scope=col>thal</th><th scope=col>target</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><td>63 </td><td>1  </td><td>3  </td><td>145</td><td>233</td><td>1  </td><td>0  </td><td>150</td><td>0  </td><td>2.3</td><td>0  </td><td>0  </td><td>1  </td><td>1  </td></tr>\n",
       "\t<tr><td>37 </td><td>1  </td><td>2  </td><td>130</td><td>250</td><td>0  </td><td>1  </td><td>187</td><td>0  </td><td>3.5</td><td>0  </td><td>0  </td><td>2  </td><td>1  </td></tr>\n",
       "\t<tr><td>41 </td><td>0  </td><td>1  </td><td>130</td><td>204</td><td>0  </td><td>0  </td><td>172</td><td>0  </td><td>1.4</td><td>2  </td><td>0  </td><td>2  </td><td>1  </td></tr>\n",
       "\t<tr><td>56 </td><td>1  </td><td>1  </td><td>120</td><td>236</td><td>0  </td><td>1  </td><td>178</td><td>0  </td><td>0.8</td><td>2  </td><td>0  </td><td>2  </td><td>1  </td></tr>\n",
       "\t<tr><td>57 </td><td>0  </td><td>0  </td><td>120</td><td>354</td><td>0  </td><td>1  </td><td>163</td><td>1  </td><td>0.6</td><td>2  </td><td>0  </td><td>2  </td><td>1  </td></tr>\n",
       "\t<tr><td>57 </td><td>1  </td><td>0  </td><td>140</td><td>192</td><td>0  </td><td>1  </td><td>148</td><td>0  </td><td>0.4</td><td>1  </td><td>0  </td><td>1  </td><td>1  </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|llllllllllllll}\n",
       " ï..age & sex & cp & trestbps & chol & fbs & restecg & thalach & exang & oldpeak & slope & ca & thal & target\\\\\n",
       "\\hline\n",
       "\t 63  & 1   & 3   & 145 & 233 & 1   & 0   & 150 & 0   & 2.3 & 0   & 0   & 1   & 1  \\\\\n",
       "\t 37  & 1   & 2   & 130 & 250 & 0   & 1   & 187 & 0   & 3.5 & 0   & 0   & 2   & 1  \\\\\n",
       "\t 41  & 0   & 1   & 130 & 204 & 0   & 0   & 172 & 0   & 1.4 & 2   & 0   & 2   & 1  \\\\\n",
       "\t 56  & 1   & 1   & 120 & 236 & 0   & 1   & 178 & 0   & 0.8 & 2   & 0   & 2   & 1  \\\\\n",
       "\t 57  & 0   & 0   & 120 & 354 & 0   & 1   & 163 & 1   & 0.6 & 2   & 0   & 2   & 1  \\\\\n",
       "\t 57  & 1   & 0   & 140 & 192 & 0   & 1   & 148 & 0   & 0.4 & 1   & 0   & 1   & 1  \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| ï..age | sex | cp | trestbps | chol | fbs | restecg | thalach | exang | oldpeak | slope | ca | thal | target |\n",
       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| 63  | 1   | 3   | 145 | 233 | 1   | 0   | 150 | 0   | 2.3 | 0   | 0   | 1   | 1   |\n",
       "| 37  | 1   | 2   | 130 | 250 | 0   | 1   | 187 | 0   | 3.5 | 0   | 0   | 2   | 1   |\n",
       "| 41  | 0   | 1   | 130 | 204 | 0   | 0   | 172 | 0   | 1.4 | 2   | 0   | 2   | 1   |\n",
       "| 56  | 1   | 1   | 120 | 236 | 0   | 1   | 178 | 0   | 0.8 | 2   | 0   | 2   | 1   |\n",
       "| 57  | 0   | 0   | 120 | 354 | 0   | 1   | 163 | 1   | 0.6 | 2   | 0   | 2   | 1   |\n",
       "| 57  | 1   | 0   | 140 | 192 | 0   | 1   | 148 | 0   | 0.4 | 1   | 0   | 1   | 1   |\n",
       "\n"
      ],
      "text/plain": [
       "  ï..age sex cp trestbps chol fbs restecg thalach exang oldpeak slope ca thal\n",
       "1 63     1   3  145      233  1   0       150     0     2.3     0     0  1   \n",
       "2 37     1   2  130      250  0   1       187     0     3.5     0     0  2   \n",
       "3 41     0   1  130      204  0   0       172     0     1.4     2     0  2   \n",
       "4 56     1   1  120      236  0   1       178     0     0.8     2     0  2   \n",
       "5 57     0   0  120      354  0   1       163     1     0.6     2     0  2   \n",
       "6 57     1   0  140      192  0   1       148     0     0.4     1     0  1   \n",
       "  target\n",
       "1 1     \n",
       "2 1     \n",
       "3 1     \n",
       "4 1     \n",
       "5 1     \n",
       "6 1     "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data <- read.csv(\"heart.csv\")\n",
    "head(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th></th><th scope=col>ï..age</th><th scope=col>sex</th><th scope=col>cp</th><th scope=col>trestbps</th><th scope=col>chol</th><th scope=col>fbs</th><th scope=col>restecg</th><th scope=col>thalach</th><th scope=col>exang</th><th scope=col>oldpeak</th><th scope=col>slope</th><th scope=col>ca</th><th scope=col>thal</th><th scope=col>target</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>298</th><td>59 </td><td>1  </td><td>0  </td><td>164</td><td>176</td><td>1  </td><td>0  </td><td> 90</td><td>0  </td><td>1.0</td><td>1  </td><td>2  </td><td>1  </td><td>0  </td></tr>\n",
       "\t<tr><th scope=row>299</th><td>57 </td><td>0  </td><td>0  </td><td>140</td><td>241</td><td>0  </td><td>1  </td><td>123</td><td>1  </td><td>0.2</td><td>1  </td><td>0  </td><td>3  </td><td>0  </td></tr>\n",
       "\t<tr><th scope=row>300</th><td>45 </td><td>1  </td><td>3  </td><td>110</td><td>264</td><td>0  </td><td>1  </td><td>132</td><td>0  </td><td>1.2</td><td>1  </td><td>0  </td><td>3  </td><td>0  </td></tr>\n",
       "\t<tr><th scope=row>301</th><td>68 </td><td>1  </td><td>0  </td><td>144</td><td>193</td><td>1  </td><td>1  </td><td>141</td><td>0  </td><td>3.4</td><td>1  </td><td>2  </td><td>3  </td><td>0  </td></tr>\n",
       "\t<tr><th scope=row>302</th><td>57 </td><td>1  </td><td>0  </td><td>130</td><td>131</td><td>0  </td><td>1  </td><td>115</td><td>1  </td><td>1.2</td><td>1  </td><td>1  </td><td>3  </td><td>0  </td></tr>\n",
       "\t<tr><th scope=row>303</th><td>57 </td><td>0  </td><td>1  </td><td>130</td><td>236</td><td>0  </td><td>0  </td><td>174</td><td>0  </td><td>0.0</td><td>1  </td><td>1  </td><td>2  </td><td>0  </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|llllllllllllll}\n",
       "  & ï..age & sex & cp & trestbps & chol & fbs & restecg & thalach & exang & oldpeak & slope & ca & thal & target\\\\\n",
       "\\hline\n",
       "\t298 & 59  & 1   & 0   & 164 & 176 & 1   & 0   &  90 & 0   & 1.0 & 1   & 2   & 1   & 0  \\\\\n",
       "\t299 & 57  & 0   & 0   & 140 & 241 & 0   & 1   & 123 & 1   & 0.2 & 1   & 0   & 3   & 0  \\\\\n",
       "\t300 & 45  & 1   & 3   & 110 & 264 & 0   & 1   & 132 & 0   & 1.2 & 1   & 0   & 3   & 0  \\\\\n",
       "\t301 & 68  & 1   & 0   & 144 & 193 & 1   & 1   & 141 & 0   & 3.4 & 1   & 2   & 3   & 0  \\\\\n",
       "\t302 & 57  & 1   & 0   & 130 & 131 & 0   & 1   & 115 & 1   & 1.2 & 1   & 1   & 3   & 0  \\\\\n",
       "\t303 & 57  & 0   & 1   & 130 & 236 & 0   & 0   & 174 & 0   & 0.0 & 1   & 1   & 2   & 0  \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| <!--/--> | ï..age | sex | cp | trestbps | chol | fbs | restecg | thalach | exang | oldpeak | slope | ca | thal | target |\n",
       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| 298 | 59  | 1   | 0   | 164 | 176 | 1   | 0   |  90 | 0   | 1.0 | 1   | 2   | 1   | 0   |\n",
       "| 299 | 57  | 0   | 0   | 140 | 241 | 0   | 1   | 123 | 1   | 0.2 | 1   | 0   | 3   | 0   |\n",
       "| 300 | 45  | 1   | 3   | 110 | 264 | 0   | 1   | 132 | 0   | 1.2 | 1   | 0   | 3   | 0   |\n",
       "| 301 | 68  | 1   | 0   | 144 | 193 | 1   | 1   | 141 | 0   | 3.4 | 1   | 2   | 3   | 0   |\n",
       "| 302 | 57  | 1   | 0   | 130 | 131 | 0   | 1   | 115 | 1   | 1.2 | 1   | 1   | 3   | 0   |\n",
       "| 303 | 57  | 0   | 1   | 130 | 236 | 0   | 0   | 174 | 0   | 0.0 | 1   | 1   | 2   | 0   |\n",
       "\n"
      ],
      "text/plain": [
       "    ï..age sex cp trestbps chol fbs restecg thalach exang oldpeak slope ca thal\n",
       "298 59     1   0  164      176  1   0        90     0     1.0     1     2  1   \n",
       "299 57     0   0  140      241  0   1       123     1     0.2     1     0  3   \n",
       "300 45     1   3  110      264  0   1       132     0     1.2     1     0  3   \n",
       "301 68     1   0  144      193  1   1       141     0     3.4     1     2  3   \n",
       "302 57     1   0  130      131  0   1       115     1     1.2     1     1  3   \n",
       "303 57     0   1  130      236  0   0       174     0     0.0     1     1  2   \n",
       "    target\n",
       "298 0     \n",
       "299 0     \n",
       "300 0     \n",
       "301 0     \n",
       "302 0     \n",
       "303 0     "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tail(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Observations: 303\n",
      "Variables: 14\n",
      "$ ï..age   <int> 63, 37, 41, 56, 57, 57, 56, 44, 52, 57, 54, 48, 49, 64, 58...\n",
      "$ sex      <int> 1, 1, 0, 1, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 0, 0, 0, 0, 1, 0...\n",
      "$ cp       <int> 3, 2, 1, 1, 0, 0, 1, 1, 2, 2, 0, 2, 1, 3, 3, 2, 2, 3, 0, 3...\n",
      "$ trestbps <int> 145, 130, 130, 120, 120, 140, 140, 120, 172, 150, 140, 130...\n",
      "$ chol     <int> 233, 250, 204, 236, 354, 192, 294, 263, 199, 168, 239, 275...\n",
      "$ fbs      <int> 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0...\n",
      "$ restecg  <int> 0, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1...\n",
      "$ thalach  <int> 150, 187, 172, 178, 163, 148, 153, 173, 162, 174, 160, 139...\n",
      "$ exang    <int> 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0...\n",
      "$ oldpeak  <dbl> 2.3, 3.5, 1.4, 0.8, 0.6, 0.4, 1.3, 0.0, 0.5, 1.6, 1.2, 0.2...\n",
      "$ slope    <int> 0, 0, 2, 2, 2, 1, 1, 2, 2, 2, 2, 2, 2, 1, 2, 1, 2, 0, 2, 2...\n",
      "$ ca       <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2...\n",
      "$ thal     <int> 1, 2, 2, 2, 2, 1, 2, 3, 3, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2...\n",
      "$ target   <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1...\n"
     ]
    }
   ],
   "source": [
    "glimpse(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "14"
      ],
      "text/latex": [
       "14"
      ],
      "text/markdown": [
       "14"
      ],
      "text/plain": [
       "[1] 14"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ncol(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "303"
      ],
      "text/latex": [
       "303"
      ],
      "text/markdown": [
       "303"
      ],
      "text/plain": [
       "[1] 303"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "nrow(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>'ï..age'</li>\n",
       "\t<li>'sex'</li>\n",
       "\t<li>'cp'</li>\n",
       "\t<li>'trestbps'</li>\n",
       "\t<li>'chol'</li>\n",
       "\t<li>'fbs'</li>\n",
       "\t<li>'restecg'</li>\n",
       "\t<li>'thalach'</li>\n",
       "\t<li>'exang'</li>\n",
       "\t<li>'oldpeak'</li>\n",
       "\t<li>'slope'</li>\n",
       "\t<li>'ca'</li>\n",
       "\t<li>'thal'</li>\n",
       "\t<li>'target'</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 'ï..age'\n",
       "\\item 'sex'\n",
       "\\item 'cp'\n",
       "\\item 'trestbps'\n",
       "\\item 'chol'\n",
       "\\item 'fbs'\n",
       "\\item 'restecg'\n",
       "\\item 'thalach'\n",
       "\\item 'exang'\n",
       "\\item 'oldpeak'\n",
       "\\item 'slope'\n",
       "\\item 'ca'\n",
       "\\item 'thal'\n",
       "\\item 'target'\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 'ï..age'\n",
       "2. 'sex'\n",
       "3. 'cp'\n",
       "4. 'trestbps'\n",
       "5. 'chol'\n",
       "6. 'fbs'\n",
       "7. 'restecg'\n",
       "8. 'thalach'\n",
       "9. 'exang'\n",
       "10. 'oldpeak'\n",
       "11. 'slope'\n",
       "12. 'ca'\n",
       "13. 'thal'\n",
       "14. 'target'\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       " [1] \"ï..age\"   \"sex\"      \"cp\"       \"trestbps\" \"chol\"     \"fbs\"     \n",
       " [7] \"restecg\"  \"thalach\"  \"exang\"    \"oldpeak\"  \"slope\"    \"ca\"      \n",
       "[13] \"thal\"     \"target\"  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "colnames(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "     ï..age           sex               cp           trestbps    \n",
       " Min.   :29.00   Min.   :0.0000   Min.   :0.000   Min.   : 94.0  \n",
       " 1st Qu.:47.50   1st Qu.:0.0000   1st Qu.:0.000   1st Qu.:120.0  \n",
       " Median :55.00   Median :1.0000   Median :1.000   Median :130.0  \n",
       " Mean   :54.37   Mean   :0.6832   Mean   :0.967   Mean   :131.6  \n",
       " 3rd Qu.:61.00   3rd Qu.:1.0000   3rd Qu.:2.000   3rd Qu.:140.0  \n",
       " Max.   :77.00   Max.   :1.0000   Max.   :3.000   Max.   :200.0  \n",
       "      chol            fbs            restecg          thalach     \n",
       " Min.   :126.0   Min.   :0.0000   Min.   :0.0000   Min.   : 71.0  \n",
       " 1st Qu.:211.0   1st Qu.:0.0000   1st Qu.:0.0000   1st Qu.:133.5  \n",
       " Median :240.0   Median :0.0000   Median :1.0000   Median :153.0  \n",
       " Mean   :246.3   Mean   :0.1485   Mean   :0.5281   Mean   :149.6  \n",
       " 3rd Qu.:274.5   3rd Qu.:0.0000   3rd Qu.:1.0000   3rd Qu.:166.0  \n",
       " Max.   :564.0   Max.   :1.0000   Max.   :2.0000   Max.   :202.0  \n",
       "     exang           oldpeak         slope             ca        \n",
       " Min.   :0.0000   Min.   :0.00   Min.   :0.000   Min.   :0.0000  \n",
       " 1st Qu.:0.0000   1st Qu.:0.00   1st Qu.:1.000   1st Qu.:0.0000  \n",
       " Median :0.0000   Median :0.80   Median :1.000   Median :0.0000  \n",
       " Mean   :0.3267   Mean   :1.04   Mean   :1.399   Mean   :0.7294  \n",
       " 3rd Qu.:1.0000   3rd Qu.:1.60   3rd Qu.:2.000   3rd Qu.:1.0000  \n",
       " Max.   :1.0000   Max.   :6.20   Max.   :2.000   Max.   :4.0000  \n",
       "      thal           target      \n",
       " Min.   :0.000   Min.   :0.0000  \n",
       " 1st Qu.:2.000   1st Qu.:0.0000  \n",
       " Median :2.000   Median :1.0000  \n",
       " Mean   :2.314   Mean   :0.5446  \n",
       " 3rd Qu.:3.000   3rd Qu.:1.0000  \n",
       " Max.   :3.000   Max.   :1.0000  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "summary(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Transformation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "data2 <- data %>%\n",
    "  mutate(sex = if_else(sex == 1, \"MALE\", \"FEMALE\"),\n",
    "         fbs = if_else(fbs == 1, \">120\", \"<=120\"),\n",
    "         exang = if_else(exang == 1, \"YES\" ,\"NO\"),\n",
    "         cp = if_else(cp == 1, \"ATYPICAL ANGINA\",\n",
    "                      if_else(cp == 2, \"NON-ANGINAL PAIN\", \"ASYMPTOMATIC\")),\n",
    "         restecg = if_else(restecg == 0, \"NORMAL\",\n",
    "                           if_else(restecg == 1, \"ABNORMALITY\", \"PROBABLE OR DEFINITE\")),\n",
    "         slope = as.factor(slope),\n",
    "         ca = as.factor(ca),\n",
    "         thal = as.factor(thal),\n",
    "         target = if_else(target == 1, \"YES\", \"NO\")\n",
    "         ) %>% \n",
    "  mutate_if(is.character, as.factor) %>% \n",
    "  dplyr::select(target, sex, fbs, exang, cp, restecg, slope, ca, thal, everything())\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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6SxRwqEZCEkjZACIVkISSOkQEgWQtIIKRCShZA0QgqEZCEkjZACIVkISSOkQEgW\nQtIIKRCShZA0QgqEZCEkjZACIVkISSOkQEgWQtIIKRCShZA0QgqEZCEkjZACIVkISSOkQEgW\nQtIIKRCShZA0QgqEZCEkjZACIVkISSOkQEgWQtIIKRCShZA0QgqEZCEkjZACIVkISSOkQEgW\nQtIIKRCShZA0QgqEZCEkjZACIVkISSOkQEgWQtIIKRCShZA0QgqEZCEkjZACIVkISSOkQEgW\nQtIIKRCShZA0QgqEZCEkjZACIVkISSOkQEgWQtIIKRCShZA0QgqEZCEkjZACIVkISSOkQEgW\nQtIIKRCShZA0QgqEZCEkjZACIVkISSOkQEgWQtIIKRCShZA0QgqEZCEkjZACIVkISSOkQEgW\nQtIIKRCShZA0QgqEZCEkjZACIVkISSOkQEgWQtIIKRCShZA0QgqEZCEkjZACIVkISSOkQEgW\nQtIIKRCShZA0QgqEZCEkjZACIVkISSOkQEgWQtIIKRCShZA0QgqEZCEkjZACIVkISSOkQEgW\nQtIIKRCShZA0QgqEZCEkjZACIVkISSOkQEgWQtIIKRCShZA0QgqEZCEkjZACIVkISSOkQEgW\nQtIIKRCShZA0QgqEZCEkjZACIVkISSOkQEgWQtIIKRCShZA0QgqEZCEkjZACIVkISSOkQEgW\nQtIIKRCShZA0QgqEZCEkjZACIVkISSOkQEgWQtIIKRCShZA0QgqEZCEkjZACIVkISSOkQEgW\nQtIIKRCShZA0QgqEZCEkjZACIVkISSOkQEgWQtIIKRCShZA0QgqEZCEkjZACIVkISSOkQEgW\nQtIIKRCShZA0QgqEZCEkbckh9Vunc4RkISRtwSH1xw87hGQhJI2QAiFZCEkjpEBIFkLSCGnr\nv0H24wFm6X57pFqzeaD3NpuFSRz6Fghp4WazMIlD3wIhLdxsFiZx6FsgpIWbzcIkDn0LhLRw\ns1mYxKFv4X7vbKg1mwd6b7NZmMShb+F+77WrNZsHem+zWZiUcW+HkBZuNguTMu7tENLCzWZh\nUsa9HUJauNksTMq4t0NICzebhUkZ93YIaeFmszAp494OIS3cbBYmZdzbIaSFm83CpIx7O4S0\ncLNZmJRxb4eQFm42C5My7u0Q0sLNZmFSxr0dQlq42SxMyri3Q0gLN5uFSRn3dghp4WazMCnj\n3g4hLdxsFiZl3NshpIWbzcKkjHs7hLRws1mYlHFvh5AWbjYLkzLu7RDSws1mYVLGvR1CWrjZ\nLEzKuLdDSAs3m4VJGfd2CGnhZrMwKePeDiEt3GwWJmXc2yGkhZvNwqSMezuEtHCzWZiUcW+H\nkBZuNguTMu7tENLCzcaxMHsAAAKvSURBVGZhUsa9nVtDmgv+14JXsDA5CGnhWJgchLRwLEwO\nQlo4FibHUkICmiIkIAEhAQkICUhASEACQgIS/O6Q+vGn8/8d+2L1x8/9we4ci3ObXx7SIaHj\nSabl+G3llFRcxtrc4JeHdJqQs53TsvWbsyXZsDgJlhJSf7po8a6GhH/320OKiSGkkf7yqRyv\nkG5GSEt0OvpySoiWbvLrQzpUREhj/ejjl4vxL35/SPsnMoQ0RkjplhISB6bGLkNicW62gJD2\nPzzi50gjX0Li50i3WkJIvLPh0teDDSzOjX53SMCdEBKQgJCABIQEJCAkIAEhAQkICUhASEAC\nQgISENINuu7yhPRyfM9At9OvP/6+EeaFv8wbmCGdru3C6183wrzwl3mDfwhp+Pjx1PWfDR8W\nChDSDc5D+nzquqddIG+Pw/O33eXv/cOwB7rY4Kl7Ppx57rvVy0Zvra7ED0VINzgPpB+KWW1P\nvO6fvq2Hyx+6JxHSe/ewP7Pe3fJFby2uxE9FSDfoTra7j/3sb+d+1f0ZWumGG+z3S6cNRif2\n/31s3rr+2tZfrsRPRUg3OAtptVvK7nH4+PH6/BApfBtS3z297i5QW8sr8TMR0g0uuoikNg9x\nan+Db0J63T5pW+1j+7q1uhI/FH85N7gS0lO3enn9+C6kt2Hvsj/zvur6N721uBI/FX85NzgL\naXWey+d3IT0Or3fizMu1rcWV+Kn4K7rBWUjr4YjAn/3huLfN58P1kIafI23iNdLb5n04p7YW\nV+KnIqQbnIX0uTtG3b3HYetxSP3xdgdvm/Hh72e9tbgSPxUh3eD850PbHU33MBSy2Z04hfRy\nEdJq/XncaN13/fNGb62uxA9FSEACQgISEBKQgJCABIQEJCAkIAEhAQkICUhASEACQgISEBKQ\ngJCABP8HsWxYwmMmCLgAAAAASUVORK5CYII=",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Bar Plot for target (heart disease)\n",
    "\n",
    "ggplot(data2, aes(x=data2$target, fill=data2$target))+\n",
    "   geom_bar()+\n",
    "   xlab(\"Heart Disease\")+\n",
    "   ylab(\"count\")+\n",
    "   ggtitle(\"Presence & Absence of Heart Disease\")+\n",
    "   scale_fill_discrete(name= 'Heart Disease', labels =c(\"Absence\", \"Presence\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "       NO       YES \n",
       "0.4554455 0.5445545 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "prop.table(table(data2$target))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# count the frequency of the values of age\n",
    "\n",
    "data2 %>%\n",
    "  group_by(ï..age) %>%\n",
    "  count() %>%\n",
    "  filter(n>10) %>%\n",
    "  ggplot()+\n",
    "  geom_col(aes(ï..age, n), fill = 'green')+\n",
    "  ggtitle(\"Age Analysis\")+\n",
    "  xlab(\"Age\")+\n",
    "  ylab(\"Agecount\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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L9NsoFn3iPtTr9H2v7fK0zojy33SO6R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",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# comapre blood pressure across the chest pain\n",
    "\n",
    "data2 %>%\n",
    "  ggplot(aes(x=sex, y=trestbps))+\n",
    "  geom_boxplot(fill ='purple')+\n",
    "  xlab('sex')+\n",
    "  ylab('BP')+\n",
    "  facet_grid(~cp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message:\n",
      "\"Continuous x aesthetic -- did you forget aes(group=...)?\""
     ]
    },
    {
     "data": {
      "image/png": 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uebDdFLBGkuB+m5Kf9kw9P/zV4iSHM5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\nhun3xDMAAAAASUVORK5CYII=",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data %>%\n",
    "  ggplot(aes(x=sex, y=trestbps))+\n",
    "  geom_boxplot(fill ='purple')+\n",
    "  xlab('sex')+\n",
    "  ylab('BP')+\n",
    "  facet_grid(~cp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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jma8u+Jiw0vzSgBZ6R3dkZy+fvkEIlI\nH/4FWSIRiUghhUQi0h9E+rb+CL+0SiQiva1I3z7Gb38TiUhvK9J69b9TDWpmSCQiLfOCLJGO\nhkhE+tMf7U6/8F3MkEhEeuOLDV8+n/xSbDFDIhHJXzUPKSQSkYg0sEY3ASJ5QTaksJsAkYgU\nUthNgEhvKtJf67u3N1//IdLRdBMg0luK9Hm1+qXQevWNSMfSTYBIbyjS36v19193btarv4l0\nJN0EiPSGIn1efb+/9331mUhH0k2ASG96+fvQXSIdTjcBIi0k0ppIR9JNgEjX1wONh0X6vHr4\n9aB//bt2R9NN4MOLdH09YdJhkf561Ofr47MlIj2XbgIfXaTr6xGTDot0u159ufl5c/Nl9elU\njwoZDhV2EyDSW4r08I9xnfGvcRUyHCrsJkCktxTp9vbvLz81+nLyi0iVDIcKuwl8dJHe9DnS\nizJwTEQi0ru6akekc9JNgEh++zuksJsAkYgUUthNgEhECinsJkAkIoUUdhMgEpFCCrsJEIlI\nIYXdBIhEpJDCbgJEIlJIYTcBIhEppLCbAJGIFFLYTYBIRAop7CZAJCKFFHYTgIBIIYXdBCAg\nUkhhNwEIiBRS2E0AAn8fKaSwm8CHR+BvyKYUdhP46Aje+t9sINLJ6Sbw0REQaSQffIqmGosR\nEGkkH3yKphqbEXiOlFLYTQACV+1CCrsJQOB1pJDCbgIQECmksJsABEQKKewmAAGRQgq7CUBA\npJDCbgIQzBIg0ovTTQACIoUUdhOAgEghhd0EICBSSGE3AQiIFFLYTQACIoUUdhOAgEghhd0E\nICBSSGE3AQiIFFLYTQACIoUUdhOAgEghhd0EICBSSGE3AQiIFFLYTQACIoUUdhOAgEghhd0E\nICBSSGE3AQiIFFLYTQACIoUUdhOAYHmR1j9z6PYIw+s3ycDB/zBFQ43dCJYWaX3/Zvf2qEj/\n9wYh0lQgINLrY4oguMxzJCLtpXuKIAgS6deTlmcf8jYinbbdYxlaZi7LbwiC2cJTRPp9ccEZ\naTvHCZwRZ6SPfEYiUu8UQUCkgZgiCFy1G4gpgoBIAzFFEPT8ZgORTg+RPoBIf87z+yTS6SES\nkZ7dJ5FOD5GI9Ow+iXR6iESkZ/dJpNNDJCI9u08inR4iEenZfRLJX8k6I0R6bp9EguCMEOm5\nfZoiCM4IkZ7bpymC4IwQ6bl9miIIzgiRntunKYLgjBDpuX2aIgjOCJGe26cpguCMEOm5fZoi\nCM4IkZ7bpymC4MO8JE2kF4dIp4j0UQgQ6cUhEpGINLAGkYhEpIE1iEQkIg2sQSQiEWlgDSIR\niUgDaxCJSEQaWINIRCLSwBpEIhKRBtYgEpGINLAGkYhEpIE1iEQkIg2sQSQiEWlgDSIRiUgD\naxCJSEQaWINIRCLSwBpEIhKRBtYgEpHeqUjL/r1mIhHpvYq0aCORiESkgUYiEYlIA41EIhKR\nBhqJRCQiDTQSiUhEGmgkEpGINNBIJCIRaaCRSEQi0kAjkYhEpIFGIhGJSAONRCISkQYaiUQk\nIg00EolIRBpoJBKRiDTQSCQiEWmgMVGkZf9GFpGINNAYKdKyhYlDcHqIlNFIJCIRaaCRSEQi\n0kAjkYhEpIFGIhGJSAONRCISkQYaiUQkIg00EolIRBpoJBKRiDTQSCQiEWmgkUiniLTs7yyd\nFSJlNBKJSEQaaCQSkZYRaXGGRMoTadkNnRUiPdu46HeNSEQi0sB3jUiRIi07douItDzDRRuJ\nlCnSoo1Een0jkYhEpIFGIhGJSAONRCLSpEjP5m2OKKsxDQECF0PgjPTSxuMEnJE+zhAQ6cWN\nRCISkQYaiUQkIg00niLSoi8OLs/cEBDp9Y1EIhKRBhqJRCQiDTSeItKiG1q+0BAQ6fWNRCIS\nkQYaiUQkIg00EolIRBpoJBKRiDTQSCQiEWmgkUhEItJAI5GIRKSBRiIR6b2KtOgvEhCJSEQi\n0kRh5BAs2kgkIg0URg7Boo3vVKRFG4lEJCINNBKJSEQaaCQSkYg00Bgp0qLPEolEpIFGIhGJ\nSAONRCISkQYaI0VattAQEOn1jUQiEpEGGolEJCINNBIpU6RFnyYS6fWNRCISkQYaiUQkIg00\nEilTpEUbifT6RiIRiUgDjUQiEpEGGolEJCINNBKJSMuItOjlkwsyTNnQ8oVEWkKkM/JHMmcs\ncxmGKRtavpBIRBpkmLKh5QuJRKRBhikbWr6QSEQaZJiyoeULiUSkQYYpG1q+kEhEGmSYsqHl\nC4lEpEGGKRtavpBIRBpkmLKh5QuJRKRBhikbWr6QSEQaZJiyoeULiUSkQYYpG1q+kEhEGmSY\nsqHlC4lEpEGGKRtavpBIRBpkmLKh5QuJRKRBhikbWr6QSEQaZJiyoR+L/x0wIhFpkGHKhs6E\nNbAGkYg0yDBlQ2fCGliDSEQaZJiyoTNhDaxBJCINMkzZ0JmwBtYgEpEGGaZs6ExYA2sQiUiD\nDFM2dCasgTWIRKRBhikbOhPWwBoniLTs9fjFG4n0+rkl0ikITs/QcS1bSKTXzy2RiESkgbkl\nEpGINDC3RCISkQbmlkhEItLA3BKJSEQamFsiEem9inSZlxD+sCEinR4ivT7dDP9QQ6TT0z0E\nRHpxThFp0VPkeSESkTIKuwlAQKSQwm4CEBAppLCbAARECinsJgDB8iKtf+bQLYbFBCBYXKT1\n/ZvdWwybCUBApJDCbgIQXOY5EpH20k0AgiCRfr08eNrj/5yRRaILj2X5DUEwW3iaSOtbZ6Td\ndBOA4CJnJCLtp5sABJcQab35hki/000AgguItN6yiUi/000Aggu8ILtxQ6THdBOAYPnXkdb3\nv8rgNxu20k0AAr9rF1LYTQACIoUUdhOAgEghhd0EICBSSGE3AQiIFFLYTQACIoUUdhOAgEgh\nhd0EICBSSGE3AQiIFFLYTQACIoUUdhOAgEghhd0EICBSSGE3AQiIFFLYTQACIoUUdhOAgEgh\nhd0EICBSSGE3AQiIFFLYTQACIoUUdhOAgEghhd0EICBSSGE3AQiIFFLYTQACIoUUdhOAgEgh\nhd0EICBSSGE3AQiIFFLYTQACIoUUdhOAgEghhd0EICBSSGE3AQiIFFLYTQACIoUUdhOAgEgh\nhd0EICBSSGE3AQiIFFLYTQACIoUUdhOAgEghhd0EICBSSGE3AQiIFFLYTQACIoUUdhOAgEgh\nhd0EICBSSGE3AQiIFFLYTQACIoUUdhOAgEghhd0EIPgxsWsivT7dBCAgUkhhNwEIiBRS2E0A\nAiKFFHYTgIBIIYXdBCAgUkhhNwEIiBRS2E0AAiKFFHYTgIBIIYXdBCAgUkhhNwEIiBRS2E0A\nAiKFFI4SGFnkrMQhINLrQyQiEWkgRCISkQZCJCIRaSBEIhKRBkKk5UUaHaOBtYg0ECIRiUgD\nIRKRiDQQIhGJSAMhEpGINBAiEYlIAyESkYg0ECIRiUgDIRKRiDQQIhFp8SG4vh5oJNLrQ6Rq\nka6vJ0waFGki1+++UI5m2e/J9e/MLRhxRlr857Ez0kc/I92L9NpliPT6dBMgEpEGQqSPLlLe\nc6SBYyISkVy1e/1miESkH15HGtgNkYhEpIHdEIlIRBrYDZGIRKSB3RCJSEQa2A2RiESkgd0Q\niUhEGtgNkYhEpIHdEIlIRBrYDZGIdAEERHp9iEQkIg2ESEQi0kCIRCQiDYRIRCLSQIhEJCIN\nhEhEItJAiEQkIg2ESEQi0kCIRCQiDYRIRCLSQIhEJCINhEhEItJAiEQkIg2ESEQiUkhhNwEI\niBRS2E0AAiKFFHYTgIBIIYXdBCAgUkhhNwEIiBRS2E0AAv83ipDCbgIfHoH/P1JKYTeBj47A\n/7FvJB98iqYaixEQaSQffIqmGosREGkkH3yKphqbEXiOlFLYTQACV+1CCrsJQOB1pJDCbgIQ\nECmksJsABEQKKewmAAGRQgq7CUBApJDCbgIQECmksJsABEQKKewmAAGRQgq7CUBApJDCbgIQ\nECmksJsABEQKKewmAAGRQgq7CUBApJDCbgIQECmksJsABEQKKewmAAGRQgq7CUBApJDCbgIQ\nECmksJsABEQKKewmAAGRQgq7CUBApJDCbgIQECmksJsABEQKKewmAAGRQgq7CUBApJDCbgIQ\nECmksJsABEQKKewmAAGRQgq7CUBApJDCbgIQpIk0ket3X3gsy28IgtlCIkWESO1DQKSIEKl9\nCIgUESK1D0GESCLtIZLIQIgkMhAiiQyESCIDIZLIQBYTaf07j3fu7j18au/t5lcNVC/Uc2wb\nlyMAwZv3LCfS3p3b+53/vlnfrjc/O/ltXarn2Db27iy3MwjeuodIi4VI73kILirSr/uPB/V2\nDBfqObqNvc7FdgbBW/ekiLT53/ZXTVQv03N0G3udi+0MgrfuWfhiw+Od388z/3Bso88zl+k5\nto3LEYDgzXsue0b6fWDrhw+uN45ttnqRnuPb2O1cbGcQvHVPikiHr4aOVS/Sc3wbu52L7QyC\nt+5JEenpA7tfNVL9wX8cQ/Dez0j3/z0d0RsxXKTnhG3sdC62MwjeuucCIm2dWfeO7f6T0y9q\nL9FzwjZ+31mcAARv3uN37UQGQiSRgRBJZCBEEhkIkUQGQiSRgRBJZCBEEhkIkUQGQiSRgRBJ\nZCBEEhkIkUQGQqS+/G+9+vTX3Z3/vq5WX/+7vf2y+uf29p/V50tv7COHSHX5trrLnUnruzuf\nfgp19+bznU1yqRCpLqvVv7c3q/XPM9Pq251Wf93d+/733TtysRCpLuvV1++/7nz69c1bfbn9\n/RflLrknIVJdvv/8E92nf2/vTk2/8/Pu36vV35fe18cOkQrzz6fV+oZIUSFSZf660+fT0zdv\n/emTP9pdNESqy3p1c/vP3cWGb3fXF/6+u+r9v9X376v/XXpjHzpEqsvvy98/tfnv1+Xv1T+/\nL39/Wv136Z195BCpL9/Wq/Wv08+/X1erzzePL8h+ufTGPnKIJDIQIokMhEgiAyGSyECIJDIQ\nIokMhEgiAyGSyECIJDIQIokMhEgiAyGSyED+H7pEOf810bzrAAAAAElFTkSuQmCC",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data2 %>%\n",
    "  ggplot(aes(x=sex, y=chol))+\n",
    "  geom_boxplot(fill ='orange')+\n",
    "  xlab('sex')+\n",
    "  ylab('Chol')+\n",
    "  facet_grid(~cp)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Correlation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "package 'corrplot' successfully unpacked and MD5 sums checked\n",
      "\n",
      "The downloaded binary packages are in\n",
      "\tC:\\Users\\HP\\AppData\\Local\\Temp\\RtmpwNj0dO\\downloaded_packages\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message:\n",
      "\"package 'ggplot2' is in use and will not be installed\""
     ]
    }
   ],
   "source": [
    "install.packages(\"corrplot\")\n",
    "install.packages(\"ggplot2\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message:\n",
      "\"package 'corrplot' was built under R version 3.6.3\"corrplot 0.84 loaded\n"
     ]
    }
   ],
   "source": [
    "library(corrplot)\n",
    "library(ggplot2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th></th><th scope=col>ï..age</th><th scope=col>trestbps</th><th scope=col>chol</th><th scope=col>thalach</th><th scope=col>oldpeak</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>ï..age</th><td> 1.0000000  </td><td> 0.27935091 </td><td> 0.213677957</td><td>-0.398521938</td><td> 0.21001257 </td></tr>\n",
       "\t<tr><th scope=row>trestbps</th><td> 0.2793509  </td><td> 1.00000000 </td><td> 0.123174207</td><td>-0.046697728</td><td> 0.19321647 </td></tr>\n",
       "\t<tr><th scope=row>chol</th><td> 0.2136780  </td><td> 0.12317421 </td><td> 1.000000000</td><td>-0.009939839</td><td> 0.05395192 </td></tr>\n",
       "\t<tr><th scope=row>thalach</th><td>-0.3985219  </td><td>-0.04669773 </td><td>-0.009939839</td><td> 1.000000000</td><td>-0.34418695 </td></tr>\n",
       "\t<tr><th scope=row>oldpeak</th><td> 0.2100126  </td><td> 0.19321647 </td><td> 0.053951920</td><td>-0.344186948</td><td> 1.00000000 </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|lllll}\n",
       "  & ï..age & trestbps & chol & thalach & oldpeak\\\\\n",
       "\\hline\n",
       "\tï..age &  1.0000000   &  0.27935091  &  0.213677957 & -0.398521938 &  0.21001257 \\\\\n",
       "\ttrestbps &  0.2793509   &  1.00000000  &  0.123174207 & -0.046697728 &  0.19321647 \\\\\n",
       "\tchol &  0.2136780   &  0.12317421  &  1.000000000 & -0.009939839 &  0.05395192 \\\\\n",
       "\tthalach & -0.3985219   & -0.04669773  & -0.009939839 &  1.000000000 & -0.34418695 \\\\\n",
       "\toldpeak &  0.2100126   &  0.19321647  &  0.053951920 & -0.344186948 &  1.00000000 \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| <!--/--> | ï..age | trestbps | chol | thalach | oldpeak |\n",
       "|---|---|---|---|---|---|\n",
       "| ï..age |  1.0000000   |  0.27935091  |  0.213677957 | -0.398521938 |  0.21001257  |\n",
       "| trestbps |  0.2793509   |  1.00000000  |  0.123174207 | -0.046697728 |  0.19321647  |\n",
       "| chol |  0.2136780   |  0.12317421  |  1.000000000 | -0.009939839 |  0.05395192  |\n",
       "| thalach | -0.3985219   | -0.04669773  | -0.009939839 |  1.000000000 | -0.34418695  |\n",
       "| oldpeak |  0.2100126   |  0.19321647  |  0.053951920 | -0.344186948 |  1.00000000  |\n",
       "\n"
      ],
      "text/plain": [
       "         ï..age     trestbps    chol         thalach      oldpeak    \n",
       "ï..age    1.0000000  0.27935091  0.213677957 -0.398521938  0.21001257\n",
       "trestbps  0.2793509  1.00000000  0.123174207 -0.046697728  0.19321647\n",
       "chol      0.2136780  0.12317421  1.000000000 -0.009939839  0.05395192\n",
       "thalach  -0.3985219 -0.04669773 -0.009939839  1.000000000 -0.34418695\n",
       "oldpeak   0.2100126  0.19321647  0.053951920 -0.344186948  1.00000000"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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4ex2YRESJHPDmOz3esLhERIMc0OY7MJ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",
      "text/plain": [
       "Plot with title \"\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cor_heart <- cor(data2[, 10:14])\n",
    "cor_heart\n",
    "\n",
    "corrplot(cor_heart, method ='square', type='upper')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "??method"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "R [conda env:R]",
   "language": "R",
   "name": "conda-env-R-r"
  },
  "language_info": {
   "codemirror_mode": "r",
   "file_extension": ".r",
   "mimetype": "text/x-r-source",
   "name": "R",
   "pygments_lexer": "r",
   "version": "3.6.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}