r-solution.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "6f9874a8",
   "metadata": {},
   "source": [
    "<h5>Read data</h5>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "id": "196b5c67",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A tibble: 5 × 11</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>ID</th><th scope=col>LOW</th><th scope=col>AGE</th><th scope=col>LWT</th><th scope=col>RACE</th><th scope=col>SMOKE</th><th scope=col>PTL</th><th scope=col>HT</th><th scope=col>UI</th><th scope=col>FTV</th><th scope=col>BWT</th></tr>\n",
       "\t<tr><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>1</td><td>0</td><td>19</td><td>182</td><td>2</td><td>0</td><td>0</td><td>0</td><td>1</td><td>0</td><td>2523</td></tr>\n",
       "\t<tr><td>2</td><td>0</td><td>33</td><td>155</td><td>3</td><td>0</td><td>0</td><td>0</td><td>0</td><td>3</td><td>2551</td></tr>\n",
       "\t<tr><td>3</td><td>0</td><td>20</td><td>105</td><td>1</td><td>1</td><td>0</td><td>0</td><td>0</td><td>1</td><td>2557</td></tr>\n",
       "\t<tr><td>4</td><td>0</td><td>21</td><td>108</td><td>1</td><td>1</td><td>0</td><td>0</td><td>1</td><td>2</td><td>2594</td></tr>\n",
       "\t<tr><td>5</td><td>0</td><td>18</td><td>107</td><td>1</td><td>1</td><td>0</td><td>0</td><td>1</td><td>0</td><td>2600</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A tibble: 5 × 11\n",
       "\\begin{tabular}{lllllllllll}\n",
       " ID & LOW & AGE & LWT & RACE & SMOKE & PTL & HT & UI & FTV & BWT\\\\\n",
       " <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\t 1 & 0 & 19 & 182 & 2 & 0 & 0 & 0 & 1 & 0 & 2523\\\\\n",
       "\t 2 & 0 & 33 & 155 & 3 & 0 & 0 & 0 & 0 & 3 & 2551\\\\\n",
       "\t 3 & 0 & 20 & 105 & 1 & 1 & 0 & 0 & 0 & 1 & 2557\\\\\n",
       "\t 4 & 0 & 21 & 108 & 1 & 1 & 0 & 0 & 1 & 2 & 2594\\\\\n",
       "\t 5 & 0 & 18 & 107 & 1 & 1 & 0 & 0 & 1 & 0 & 2600\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A tibble: 5 × 11\n",
       "\n",
       "| ID &lt;dbl&gt; | LOW &lt;dbl&gt; | AGE &lt;dbl&gt; | LWT &lt;dbl&gt; | RACE &lt;dbl&gt; | SMOKE &lt;dbl&gt; | PTL &lt;dbl&gt; | HT &lt;dbl&gt; | UI &lt;dbl&gt; | FTV &lt;dbl&gt; | BWT &lt;dbl&gt; |\n",
       "|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| 1 | 0 | 19 | 182 | 2 | 0 | 0 | 0 | 1 | 0 | 2523 |\n",
       "| 2 | 0 | 33 | 155 | 3 | 0 | 0 | 0 | 0 | 3 | 2551 |\n",
       "| 3 | 0 | 20 | 105 | 1 | 1 | 0 | 0 | 0 | 1 | 2557 |\n",
       "| 4 | 0 | 21 | 108 | 1 | 1 | 0 | 0 | 1 | 2 | 2594 |\n",
       "| 5 | 0 | 18 | 107 | 1 | 1 | 0 | 0 | 1 | 0 | 2600 |\n",
       "\n"
      ],
      "text/plain": [
       "  ID LOW AGE LWT RACE SMOKE PTL HT UI FTV BWT \n",
       "1 1  0   19  182 2    0     0   0  1  0   2523\n",
       "2 2  0   33  155 3    0     0   0  0  3   2551\n",
       "3 3  0   20  105 1    1     0   0  0  1   2557\n",
       "4 4  0   21  108 1    1     0   0  1  2   2594\n",
       "5 5  0   18  107 1    1     0   0  1  0   2600"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "library(readxl)\n",
    "data <- read_excel(\"data.xls\")\n",
    "\n",
    "# first five rows\n",
    "head(data, 5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6461f9de",
   "metadata": {},
   "source": [
    "# Task 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "id": "b3455a55",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.frame: 5 × 2</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>AGE</th><th scope=col>RaceFreq</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;int&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>1</th><td>14</td><td> 9</td></tr>\n",
       "\t<tr><th scope=row>2</th><td>15</td><td> 9</td></tr>\n",
       "\t<tr><th scope=row>3</th><td>16</td><td>22</td></tr>\n",
       "\t<tr><th scope=row>4</th><td>17</td><td>37</td></tr>\n",
       "\t<tr><th scope=row>5</th><td>18</td><td>33</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 5 × 2\n",
       "\\begin{tabular}{r|ll}\n",
       "  & AGE & RaceFreq\\\\\n",
       "  & <fct> & <int>\\\\\n",
       "\\hline\n",
       "\t1 & 14 &  9\\\\\n",
       "\t2 & 15 &  9\\\\\n",
       "\t3 & 16 & 22\\\\\n",
       "\t4 & 17 & 37\\\\\n",
       "\t5 & 18 & 33\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 5 × 2\n",
       "\n",
       "| <!--/--> | AGE &lt;fct&gt; | RaceFreq &lt;int&gt; |\n",
       "|---|---|---|\n",
       "| 1 | 14 |  9 |\n",
       "| 2 | 15 |  9 |\n",
       "| 3 | 16 | 22 |\n",
       "| 4 | 17 | 37 |\n",
       "| 5 | 18 | 33 |\n",
       "\n"
      ],
      "text/plain": [
       "  AGE RaceFreq\n",
       "1 14   9      \n",
       "2 15   9      \n",
       "3 16  22      \n",
       "4 17  37      \n",
       "5 18  33      "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Need to establish a relationship between Age of Mother in years and Race\n",
    "age_freq <- as.data.frame(table(data$AGE))\n",
    "colnames(age_freq) <- c(\"AGE\", \"RaceFreq\")\n",
    "\n",
    "# view table first five rows\n",
    "head(age_freq, 5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3a45aa8a",
   "metadata": {},
   "source": [
    "<h5>Graphing</h5>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "id": "7452c975",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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xMdn/34449Pnjz597//fe7cufZUJ4Ro167dF198UVhYuHLlyuquWOkIdgAAwEXr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\n+Aul7wGUIThzSAAAAABJRU5ErkJggg==",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Line graph of frequency (y) against age(x)\n",
    "library(ggplot2)\n",
    "\n",
    "# Create a column graph of AGE gainst Frequency\n",
    "ggplot(age_freq, aes(x = AGE, y = RaceFreq, color = AGE)) +\n",
    "  geom_col() + ggtitle(\"Age of Mother in years against Total Race\") +\n",
    "  labs(x = \"Age of Mother(years)\", y = \"Race Frequency\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ac3c47d3",
   "metadata": {},
   "source": [
    "<h6>Comments on the results</h6>\n",
    "<small><strong>\"</strong>Observe and the graphs' nature and say something about what you see.<strong>\"</strong></small>\n",
    "\n",
    "NB: You can choose to draw age against a specific race to hav a greater scope to analyse"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5ac6c35c",
   "metadata": {},
   "source": [
    "# Task 2 a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "id": "a0eff76f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A tibble: 3 × 3</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>RACE</th><th scope=col>mean</th><th scope=col>sd</th></tr>\n",
       "\t<tr><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>1</td><td>3080.358</td><td>749.0213</td></tr>\n",
       "\t<tr><td>2</td><td>2685.912</td><td>642.0547</td></tr>\n",
       "\t<tr><td>3</td><td>2749.674</td><td>733.5639</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A tibble: 3 × 3\n",
       "\\begin{tabular}{lll}\n",
       " RACE & mean & sd\\\\\n",
       " <dbl> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\t 1 & 3080.358 & 749.0213\\\\\n",
       "\t 2 & 2685.912 & 642.0547\\\\\n",
       "\t 3 & 2749.674 & 733.5639\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A tibble: 3 × 3\n",
       "\n",
       "| RACE &lt;dbl&gt; | mean &lt;dbl&gt; | sd &lt;dbl&gt; |\n",
       "|---|---|---|\n",
       "| 1 | 3080.358 | 749.0213 |\n",
       "| 2 | 2685.912 | 642.0547 |\n",
       "| 3 | 2749.674 | 733.5639 |\n",
       "\n"
      ],
      "text/plain": [
       "  RACE mean     sd      \n",
       "1 1    3080.358 749.0213\n",
       "2 2    2685.912 642.0547\n",
       "3 3    2749.674 733.5639"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "library(dplyr)\n",
    "\n",
    "analysis <- data %>%\n",
    "  group_by(RACE) %>%\n",
    "  summarize(mean = mean(BWT), sd = sd(BWT))\n",
    "\n",
    "# where 1 -White, 2 - Black and 3 -Other\n",
    "analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b1c069cb",
   "metadata": {},
   "source": [
    "\n",
    "\n",
    "<h5>Comments on the Results</h5>\n",
    "\n",
    "<small>Basing on Mean</small>\n",
    "\n",
    "*Other Race Babies are more likely to die than Black Babies than other White Babies in that order, since from the background information we are told that babies with less weight usually die earlier than heavier ones. And the mean weights BWT show that\n",
    "\n",
    "*White Babies (3080.358) > Black Babies (2685.912) > Other Race Babies (2749.674)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3ef59e8c",
   "metadata": {},
   "source": [
    "# Task 2 b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "id": "bfbe2359",
   "metadata": {},
   "outputs": [],
   "source": [
    "library(dplyr)\n",
    "\n",
    "bwt_white <- data %>%\n",
    "  filter(RACE == 1) %>%\n",
    "  select(BWT)\n",
    "\n",
    "bwt_black <- data %>%\n",
    "  filter(RACE == 2) %>%\n",
    "  select(BWT)\n",
    "\n",
    "bwt_other <- data %>%\n",
    "  filter(RACE == 3) %>%\n",
    "  select(BWT)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "id": "4aca24b9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "'White Race and Black Race'"
      ],
      "text/latex": [
       "'White Race and Black Race'"
      ],
      "text/markdown": [
       "'White Race and Black Race'"
      ],
      "text/plain": [
       "[1] \"White Race and Black Race\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "'==========================='"
      ],
      "text/latex": [
       "'==========================='"
      ],
      "text/markdown": [
       "'==========================='"
      ],
      "text/plain": [
       "[1] \"===========================\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "'Standard Error is =&gt; 83.7296367613615'"
      ],
      "text/latex": [
       "'Standard Error is => 83.7296367613615'"
      ],
      "text/markdown": [
       "'Standard Error is =&gt; 83.7296367613615'"
      ],
      "text/plain": [
       "[1] \"Standard Error is => 83.7296367613615\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "'Confidence Interval =&gt; 228.921474624525 up to 559.968757163554'"
      ],
      "text/latex": [
       "'Confidence Interval => 228.921474624525 up to 559.968757163554'"
      ],
      "text/markdown": [
       "'Confidence Interval =&gt; 228.921474624525 up to 559.968757163554'"
      ],
      "text/plain": [
       "[1] \"Confidence Interval => 228.921474624525 up to 559.968757163554\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "'P-Value is =&gt; 5.83164557610555e-06'"
      ],
      "text/latex": [
       "'P-Value is => 5.83164557610555e-06'"
      ],
      "text/markdown": [
       "'P-Value is =&gt; 5.83164557610555e-06'"
      ],
      "text/plain": [
       "[1] \"P-Value is => 5.83164557610555e-06\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## COMPARISONS in THE RACES\n",
    "# Between White Race and Black Race basing on the BWT (WB)\n",
    "results1 <- t.test(bwt_white, bwt_black)\n",
    "paste(\"White Race and Black Race\")\n",
    "paste(\"===========================\")\n",
    "paste(\"Standard Error is =>\", results1$stderr)\n",
    "paste(\"Confidence Interval =>\", results1$conf.int[1], \"up to\", results1$conf.int[2])\n",
    "paste(\"P-Value is =>\", results1$p.value)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "id": "e7f37b1e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "'White Race and Other Race'"
      ],
      "text/latex": [
       "'White Race and Other Race'"
      ],
      "text/markdown": [
       "'White Race and Other Race'"
      ],
      "text/plain": [
       "[1] \"White Race and Other Race\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "'==========================='"
      ],
      "text/latex": [
       "'==========================='"
      ],
      "text/markdown": [
       "'==========================='"
      ],
      "text/plain": [
       "[1] \"===========================\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "'Standard Error is =&gt; 65.7734525860129'"
      ],
      "text/latex": [
       "'Standard Error is => 65.7734525860129'"
      ],
      "text/markdown": [
       "'Standard Error is =&gt; 65.7734525860129'"
      ],
      "text/plain": [
       "[1] \"Standard Error is => 65.7734525860129\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "'Confidence Interval =&gt; 201.439191492672 up to 459.927416442196'"
      ],
      "text/latex": [
       "'Confidence Interval => 201.439191492672 up to 459.927416442196'"
      ],
      "text/markdown": [
       "'Confidence Interval =&gt; 201.439191492672 up to 459.927416442196'"
      ],
      "text/plain": [
       "[1] \"Confidence Interval => 201.439191492672 up to 459.927416442196\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "'P-Value is =&gt; 7.05848336257112e-07'"
      ],
      "text/latex": [
       "'P-Value is => 7.05848336257112e-07'"
      ],
      "text/markdown": [
       "'P-Value is =&gt; 7.05848336257112e-07'"
      ],
      "text/plain": [
       "[1] \"P-Value is => 7.05848336257112e-07\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Between White Race and Other Race basing on the BWT (WO)\n",
    "results2 <- t.test(bwt_white, bwt_other)\n",
    "paste(\"White Race and Other Race\")\n",
    "paste(\"===========================\")\n",
    "paste(\"Standard Error is =>\", results2$stderr)\n",
    "paste(\"Confidence Interval =>\", results2$conf.int[1], \"up to\", results2$conf.int[2])\n",
    "paste(\"P-Value is =>\", results2$p.value)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "id": "2a39c58d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "'Black Race and Other Race'"
      ],
      "text/latex": [
       "'Black Race and Other Race'"
      ],
      "text/markdown": [
       "'Black Race and Other Race'"
      ],
      "text/plain": [
       "[1] \"Black Race and Other Race\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "'==========================='"
      ],
      "text/latex": [
       "'==========================='"
      ],
      "text/markdown": [
       "'==========================='"
      ],
      "text/plain": [
       "[1] \"===========================\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "'Standard Error is =&gt; 87.300341041151'"
      ],
      "text/latex": [
       "'Standard Error is => 87.300341041151'"
      ],
      "text/markdown": [
       "'Standard Error is =&gt; 87.300341041151'"
      ],
      "text/plain": [
       "[1] \"Standard Error is => 87.300341041151\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "'Confidence Interval =&gt; -236.17558030463 up to 108.651956451419'"
      ],
      "text/latex": [
       "'Confidence Interval => -236.17558030463 up to 108.651956451419'"
      ],
      "text/markdown": [
       "'Confidence Interval =&gt; -236.17558030463 up to 108.651956451419'"
      ],
      "text/plain": [
       "[1] \"Confidence Interval => -236.17558030463 up to 108.651956451419\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "'P-Value is =&gt; 0.466233698748352'"
      ],
      "text/latex": [
       "'P-Value is => 0.466233698748352'"
      ],
      "text/markdown": [
       "'P-Value is =&gt; 0.466233698748352'"
      ],
      "text/plain": [
       "[1] \"P-Value is => 0.466233698748352\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Between Black Race and Other Race basing on the BWT (BO)\n",
    "results3 <- t.test(bwt_black, bwt_other)\n",
    "paste(\"Black Race and Other Race\")\n",
    "paste(\"===========================\")\n",
    "paste(\"Standard Error is =>\", results3$stderr)\n",
    "paste(\"Confidence Interval =>\", results3$conf.int[1], \"up to\", results3$conf.int[2])\n",
    "paste(\"P-Value is =>\", results3$p.value)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eb1fe3ff",
   "metadata": {},
   "source": [
    "# Task 3 a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "id": "09ac3346",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<span style=white-space:pre-wrap>'There are  600  Individuals'</span>"
      ],
      "text/latex": [
       "'There are  600  Individuals'"
      ],
      "text/markdown": [
       "<span style=white-space:pre-wrap>'There are  600  Individuals'</span>"
      ],
      "text/plain": [
       "[1] \"There are  600  Individuals\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.frame: 4 × 4</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>Hypertension</th><th scope=col>Smoking</th><th scope=col>Individuals</th><th scope=col>Percent</th></tr>\n",
       "\t<tr><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>0</td><td>0</td><td>342</td><td>57.000000</td></tr>\n",
       "\t<tr><td>1</td><td>0</td><td>218</td><td>36.333333</td></tr>\n",
       "\t<tr><td>0</td><td>1</td><td> 23</td><td> 3.833333</td></tr>\n",
       "\t<tr><td>1</td><td>1</td><td> 17</td><td> 2.833333</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 4 × 4\n",
       "\\begin{tabular}{llll}\n",
       " Hypertension & Smoking & Individuals & Percent\\\\\n",
       " <fct> & <fct> & <int> & <dbl>\\\\\n",
       "\\hline\n",
       "\t 0 & 0 & 342 & 57.000000\\\\\n",
       "\t 1 & 0 & 218 & 36.333333\\\\\n",
       "\t 0 & 1 &  23 &  3.833333\\\\\n",
       "\t 1 & 1 &  17 &  2.833333\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 4 × 4\n",
       "\n",
       "| Hypertension &lt;fct&gt; | Smoking &lt;fct&gt; | Individuals &lt;int&gt; | Percent &lt;dbl&gt; |\n",
       "|---|---|---|---|\n",
       "| 0 | 0 | 342 | 57.000000 |\n",
       "| 1 | 0 | 218 | 36.333333 |\n",
       "| 0 | 1 |  23 |  3.833333 |\n",
       "| 1 | 1 |  17 |  2.833333 |\n",
       "\n"
      ],
      "text/plain": [
       "  Hypertension Smoking Individuals Percent  \n",
       "1 0            0       342         57.000000\n",
       "2 1            0       218         36.333333\n",
       "3 0            1        23          3.833333\n",
       "4 1            1        17          2.833333"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ht_smoke <- as.data.frame(table(data$SMOKE,data$HT))\n",
    "colnames(ht_smoke) <- c(\"Hypertension\",\"Smoking\",\"Individuals\")\n",
    "\n",
    "ht_smoke$Percent <- ht_smoke$Individuals / sum(ht_smoke$Individuals) * 100\n",
    "paste(\"There are \", sum(ht_smoke$Individuals), \" Individuals\")\n",
    "\n",
    "# where 1 -Yes and 0 is No\n",
    "ht_smoke"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "49d072b7",
   "metadata": {},
   "source": [
    "<h4>Comments on the relationship btn Smoking and HyperTension</h4>\n",
    "\n",
    "From a Contingency Table (since our data is binary)\n",
    "\n",
    "- We see that for a person not smoking there's 57% chance that that individual won't suffer hypertension\n",
    "\n",
    "Other methods of seeking relationship for binary data is determining the chi-squre test and using a logistic regression model"
   ]
  }
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