PRS/HW01/DU1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "2c0208e8",
   "metadata": {},
   "source": [
    "# Analýza demografických dat Albanie."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f9f58094",
   "metadata": {},
   "source": [
    "Naimportujem knihovny na zpracovaní a znazornění dat."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "341b181f",
   "metadata": {},
   "outputs": [],
   "source": [
    "library(eurostat)\n",
    "library(vtable)\n",
    "library(ggplot2)\n",
    "library(cowplot)\n",
    "library(dplyr)\n",
    "library(moments)\n",
    "library(scales)\n",
    "library(vioplot)\n",
    "\n",
    "options(scipen = 999)\n",
    "options(repr.plot.width = 12, repr.plot.height = 9)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "69100858",
   "metadata": {},
   "source": [
    "Příjmení reprezentanta 'Stambrouski', délka řetězce 11 a den a narození 18."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "495b57d5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<span style=white-space:pre-wrap>'Číslo zemi :  11'</span>"
      ],
      "text/latex": [
       "'Číslo zemi :  11'"
      ],
      "text/markdown": [
       "<span style=white-space:pre-wrap>'Číslo zemi :  11'</span>"
      ],
      "text/plain": [
       "[1] \"Číslo zemi :  11\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "k = 18\n",
    "l = 11\n",
    "m = (((k+l)*47)%%11) +1\n",
    "paste('Číslo zemi : ', m)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3aed99e1",
   "metadata": {},
   "source": [
    "Na  [stránce](https://en.wikipedia.org/wiki/NUTS_statistical_regions_of_Albania) jsme zjistili ID regionů Albánie. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "49674db1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A tibble: 6 × 6</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>unit</th><th scope=col>sex</th><th scope=col>age</th><th scope=col>geo</th><th scope=col>time</th><th scope=col>values</th></tr>\n",
       "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;date&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>NR</td><td>F</td><td>TOTAL</td><td>AL011</td><td>2021-01-01</td><td> 54311</td></tr>\n",
       "\t<tr><td>NR</td><td>F</td><td>TOTAL</td><td>AL012</td><td>2021-01-01</td><td>144381</td></tr>\n",
       "\t<tr><td>NR</td><td>F</td><td>TOTAL</td><td>AL013</td><td>2021-01-01</td><td> 36175</td></tr>\n",
       "\t<tr><td>NR</td><td>F</td><td>TOTAL</td><td>AL014</td><td>2021-01-01</td><td> 60852</td></tr>\n",
       "\t<tr><td>NR</td><td>F</td><td>TOTAL</td><td>AL015</td><td>2021-01-01</td><td>101080</td></tr>\n",
       "\t<tr><td>NR</td><td>F</td><td>TOTAL</td><td>AL021</td><td>2021-01-01</td><td>133744</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A tibble: 6 × 6\n",
       "\\begin{tabular}{llllll}\n",
       " unit & sex & age & geo & time & values\\\\\n",
       " <chr> & <chr> & <chr> & <chr> & <date> & <dbl>\\\\\n",
       "\\hline\n",
       "\t NR & F & TOTAL & AL011 & 2021-01-01 &  54311\\\\\n",
       "\t NR & F & TOTAL & AL012 & 2021-01-01 & 144381\\\\\n",
       "\t NR & F & TOTAL & AL013 & 2021-01-01 &  36175\\\\\n",
       "\t NR & F & TOTAL & AL014 & 2021-01-01 &  60852\\\\\n",
       "\t NR & F & TOTAL & AL015 & 2021-01-01 & 101080\\\\\n",
       "\t NR & F & TOTAL & AL021 & 2021-01-01 & 133744\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A tibble: 6 × 6\n",
       "\n",
       "| unit &lt;chr&gt; | sex &lt;chr&gt; | age &lt;chr&gt; | geo &lt;chr&gt; | time &lt;date&gt; | values &lt;dbl&gt; |\n",
       "|---|---|---|---|---|---|\n",
       "| NR | F | TOTAL | AL011 | 2021-01-01 |  54311 |\n",
       "| NR | F | TOTAL | AL012 | 2021-01-01 | 144381 |\n",
       "| NR | F | TOTAL | AL013 | 2021-01-01 |  36175 |\n",
       "| NR | F | TOTAL | AL014 | 2021-01-01 |  60852 |\n",
       "| NR | F | TOTAL | AL015 | 2021-01-01 | 101080 |\n",
       "| NR | F | TOTAL | AL021 | 2021-01-01 | 133744 |\n",
       "\n"
      ],
      "text/plain": [
       "  unit sex age   geo   time       values\n",
       "1 NR   F   TOTAL AL011 2021-01-01  54311\n",
       "2 NR   F   TOTAL AL012 2021-01-01 144381\n",
       "3 NR   F   TOTAL AL013 2021-01-01  36175\n",
       "4 NR   F   TOTAL AL014 2021-01-01  60852\n",
       "5 NR   F   TOTAL AL015 2021-01-01 101080\n",
       "6 NR   F   TOTAL AL021 2021-01-01 133744"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<style>\n",
       ".list-inline {list-style: none; margin:0; padding: 0}\n",
       ".list-inline>li {display: inline-block}\n",
       ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n",
       "</style>\n",
       "<ol class=list-inline><li>2160</li><li>6</li></ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 2160\n",
       "\\item 6\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 2160\n",
       "2. 6\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] 2160    6"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dat <- get_eurostat(id = 'demo_r_pjanaggr3')\n",
    "dat_al <- dat[dat$geo %in% c('AL011', 'AL012', 'AL013', 'AL014', 'AL015', 'AL021', 'AL022', 'AL031', 'AL032', 'AL033', 'AL034', 'AL035'),] \n",
    "head(dat_al)\n",
    "dim(dat_al)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2d1bfac2",
   "metadata": {},
   "source": [
    "Podle pokynů zadání jsme filtrovali data podle roku a pohlaví."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "173df8e8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A tibble: 6 × 6</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>unit</th><th scope=col>sex</th><th scope=col>age</th><th scope=col>geo</th><th scope=col>time</th><th scope=col>values</th></tr>\n",
       "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;date&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>NR</td><td>F</td><td>TOTAL</td><td>AL011</td><td>2021-01-01</td><td> 54311</td></tr>\n",
       "\t<tr><td>NR</td><td>F</td><td>TOTAL</td><td>AL012</td><td>2021-01-01</td><td>144381</td></tr>\n",
       "\t<tr><td>NR</td><td>F</td><td>TOTAL</td><td>AL013</td><td>2021-01-01</td><td> 36175</td></tr>\n",
       "\t<tr><td>NR</td><td>F</td><td>TOTAL</td><td>AL014</td><td>2021-01-01</td><td> 60852</td></tr>\n",
       "\t<tr><td>NR</td><td>F</td><td>TOTAL</td><td>AL015</td><td>2021-01-01</td><td>101080</td></tr>\n",
       "\t<tr><td>NR</td><td>F</td><td>TOTAL</td><td>AL021</td><td>2021-01-01</td><td>133744</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A tibble: 6 × 6\n",
       "\\begin{tabular}{llllll}\n",
       " unit & sex & age & geo & time & values\\\\\n",
       " <chr> & <chr> & <chr> & <chr> & <date> & <dbl>\\\\\n",
       "\\hline\n",
       "\t NR & F & TOTAL & AL011 & 2021-01-01 &  54311\\\\\n",
       "\t NR & F & TOTAL & AL012 & 2021-01-01 & 144381\\\\\n",
       "\t NR & F & TOTAL & AL013 & 2021-01-01 &  36175\\\\\n",
       "\t NR & F & TOTAL & AL014 & 2021-01-01 &  60852\\\\\n",
       "\t NR & F & TOTAL & AL015 & 2021-01-01 & 101080\\\\\n",
       "\t NR & F & TOTAL & AL021 & 2021-01-01 & 133744\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A tibble: 6 × 6\n",
       "\n",
       "| unit &lt;chr&gt; | sex &lt;chr&gt; | age &lt;chr&gt; | geo &lt;chr&gt; | time &lt;date&gt; | values &lt;dbl&gt; |\n",
       "|---|---|---|---|---|---|\n",
       "| NR | F | TOTAL | AL011 | 2021-01-01 |  54311 |\n",
       "| NR | F | TOTAL | AL012 | 2021-01-01 | 144381 |\n",
       "| NR | F | TOTAL | AL013 | 2021-01-01 |  36175 |\n",
       "| NR | F | TOTAL | AL014 | 2021-01-01 |  60852 |\n",
       "| NR | F | TOTAL | AL015 | 2021-01-01 | 101080 |\n",
       "| NR | F | TOTAL | AL021 | 2021-01-01 | 133744 |\n",
       "\n"
      ],
      "text/plain": [
       "  unit sex age   geo   time       values\n",
       "1 NR   F   TOTAL AL011 2021-01-01  54311\n",
       "2 NR   F   TOTAL AL012 2021-01-01 144381\n",
       "3 NR   F   TOTAL AL013 2021-01-01  36175\n",
       "4 NR   F   TOTAL AL014 2021-01-01  60852\n",
       "5 NR   F   TOTAL AL015 2021-01-01 101080\n",
       "6 NR   F   TOTAL AL021 2021-01-01 133744"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<style>\n",
       ".list-inline {list-style: none; margin:0; padding: 0}\n",
       ".list-inline>li {display: inline-block}\n",
       ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n",
       "</style>\n",
       "<ol class=list-inline><li>120</li><li>6</li></ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 120\n",
       "\\item 6\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 120\n",
       "2. 6\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] 120   6"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dat_al <- dat_al[dat_al$time == '2021-01-01',]\n",
    "dat_al <- dat_al[dat_al$sex %in% c('F', 'M'),]\n",
    "head(dat_al)\n",
    "dim(dat_al)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2dc8b21b",
   "metadata": {},
   "source": [
    "Víme, že sloupce `time` a `unit` nejsou potřebné pro statistickou analýzu, proto je odstraníme."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "0d33a248",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "'NR'"
      ],
      "text/latex": [
       "'NR'"
      ],
      "text/markdown": [
       "'NR'"
      ],
      "text/plain": [
       "[1] \"NR\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dat_al$time <- NULL\n",
    "unique(dat_al$unit)\n",
    "dat_al$unit <- NULL"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "57cbdbd1",
   "metadata": {},
   "source": [
    "Převedeme `sex`, `age` a `geo` na kategorické proměnné.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "4faae94c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A tibble: 6 × 4</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>sex</th><th scope=col>age</th><th scope=col>geo</th><th scope=col>values</th></tr>\n",
       "\t<tr><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>F</td><td>TOTAL</td><td>AL011</td><td> 54311</td></tr>\n",
       "\t<tr><td>F</td><td>TOTAL</td><td>AL012</td><td>144381</td></tr>\n",
       "\t<tr><td>F</td><td>TOTAL</td><td>AL013</td><td> 36175</td></tr>\n",
       "\t<tr><td>F</td><td>TOTAL</td><td>AL014</td><td> 60852</td></tr>\n",
       "\t<tr><td>F</td><td>TOTAL</td><td>AL015</td><td>101080</td></tr>\n",
       "\t<tr><td>F</td><td>TOTAL</td><td>AL021</td><td>133744</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A tibble: 6 × 4\n",
       "\\begin{tabular}{llll}\n",
       " sex & age & geo & values\\\\\n",
       " <fct> & <fct> & <fct> & <dbl>\\\\\n",
       "\\hline\n",
       "\t F & TOTAL & AL011 &  54311\\\\\n",
       "\t F & TOTAL & AL012 & 144381\\\\\n",
       "\t F & TOTAL & AL013 &  36175\\\\\n",
       "\t F & TOTAL & AL014 &  60852\\\\\n",
       "\t F & TOTAL & AL015 & 101080\\\\\n",
       "\t F & TOTAL & AL021 & 133744\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A tibble: 6 × 4\n",
       "\n",
       "| sex &lt;fct&gt; | age &lt;fct&gt; | geo &lt;fct&gt; | values &lt;dbl&gt; |\n",
       "|---|---|---|---|\n",
       "| F | TOTAL | AL011 |  54311 |\n",
       "| F | TOTAL | AL012 | 144381 |\n",
       "| F | TOTAL | AL013 |  36175 |\n",
       "| F | TOTAL | AL014 |  60852 |\n",
       "| F | TOTAL | AL015 | 101080 |\n",
       "| F | TOTAL | AL021 | 133744 |\n",
       "\n"
      ],
      "text/plain": [
       "  sex age   geo   values\n",
       "1 F   TOTAL AL011  54311\n",
       "2 F   TOTAL AL012 144381\n",
       "3 F   TOTAL AL013  36175\n",
       "4 F   TOTAL AL014  60852\n",
       "5 F   TOTAL AL015 101080\n",
       "6 F   TOTAL AL021 133744"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dat_al$sex <- factor(dat_al$sex)\n",
    "dat_al$age <- factor(dat_al$age)\n",
    "dat_al$geo <- factor(dat_al$geo)\n",
    "head(dat_al)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bbd2508f",
   "metadata": {},
   "source": [
    "Při pohledu na dataset jsme si všimli, že neexistují žádné záznamy s neznámým věkem. Ověřme to."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "4d73959f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A tibble: 6 × 4</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>sex</th><th scope=col>age</th><th scope=col>geo</th><th scope=col>values</th></tr>\n",
       "\t<tr><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>F</td><td>UNK</td><td>AL011</td><td>0</td></tr>\n",
       "\t<tr><td>F</td><td>UNK</td><td>AL012</td><td>0</td></tr>\n",
       "\t<tr><td>F</td><td>UNK</td><td>AL013</td><td>0</td></tr>\n",
       "\t<tr><td>F</td><td>UNK</td><td>AL014</td><td>0</td></tr>\n",
       "\t<tr><td>F</td><td>UNK</td><td>AL015</td><td>0</td></tr>\n",
       "\t<tr><td>F</td><td>UNK</td><td>AL021</td><td>0</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A tibble: 6 × 4\n",
       "\\begin{tabular}{llll}\n",
       " sex & age & geo & values\\\\\n",
       " <fct> & <fct> & <fct> & <dbl>\\\\\n",
       "\\hline\n",
       "\t F & UNK & AL011 & 0\\\\\n",
       "\t F & UNK & AL012 & 0\\\\\n",
       "\t F & UNK & AL013 & 0\\\\\n",
       "\t F & UNK & AL014 & 0\\\\\n",
       "\t F & UNK & AL015 & 0\\\\\n",
       "\t F & UNK & AL021 & 0\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A tibble: 6 × 4\n",
       "\n",
       "| sex &lt;fct&gt; | age &lt;fct&gt; | geo &lt;fct&gt; | values &lt;dbl&gt; |\n",
       "|---|---|---|---|\n",
       "| F | UNK | AL011 | 0 |\n",
       "| F | UNK | AL012 | 0 |\n",
       "| F | UNK | AL013 | 0 |\n",
       "| F | UNK | AL014 | 0 |\n",
       "| F | UNK | AL015 | 0 |\n",
       "| F | UNK | AL021 | 0 |\n",
       "\n"
      ],
      "text/plain": [
       "  sex age geo   values\n",
       "1 F   UNK AL011 0     \n",
       "2 F   UNK AL012 0     \n",
       "3 F   UNK AL013 0     \n",
       "4 F   UNK AL014 0     \n",
       "5 F   UNK AL015 0     \n",
       "6 F   UNK AL021 0     "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<style>\n",
       ".list-inline {list-style: none; margin:0; padding: 0}\n",
       ".list-inline>li {display: inline-block}\n",
       ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n",
       "</style>\n",
       "<ol class=list-inline><li>24</li><li>4</li></ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 24\n",
       "\\item 4\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 24\n",
       "2. 4\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] 24  4"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "'Total sum = 0'"
      ],
      "text/latex": [
       "'Total sum = 0'"
      ],
      "text/markdown": [
       "'Total sum = 0'"
      ],
      "text/plain": [
       "[1] \"Total sum = 0\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "out <- dat_al[dat_al$age =='UNK',]\n",
    "head(out)\n",
    "dim(out)\n",
    "paste('Total sum =', sum(out$values), sep=' ')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fe97f982",
   "metadata": {},
   "source": [
    "Předpokládáme, že hodnoty pro age == \"TOTAL\" jsou stejné jako pro age rovné součtu \"zbytků\". Ověřme to."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "519fc70a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.frame: 6 × 4</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>sex</th><th scope=col>geo</th><th scope=col>total</th><th scope=col>dat_al_total$values</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>1</th><td>F</td><td>AL011</td><td> 54311</td><td> 54311</td></tr>\n",
       "\t<tr><th scope=row>2</th><td>F</td><td>AL012</td><td>144381</td><td>144381</td></tr>\n",
       "\t<tr><th scope=row>3</th><td>F</td><td>AL013</td><td> 36175</td><td> 36175</td></tr>\n",
       "\t<tr><th scope=row>4</th><td>F</td><td>AL014</td><td> 60852</td><td> 60852</td></tr>\n",
       "\t<tr><th scope=row>5</th><td>F</td><td>AL015</td><td>101080</td><td>101080</td></tr>\n",
       "\t<tr><th scope=row>6</th><td>F</td><td>AL021</td><td>133744</td><td>133744</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 6 × 4\n",
       "\\begin{tabular}{r|llll}\n",
       "  & sex & geo & total & dat\\_al\\_total\\$values\\\\\n",
       "  & <fct> & <fct> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\t1 & F & AL011 &  54311 &  54311\\\\\n",
       "\t2 & F & AL012 & 144381 & 144381\\\\\n",
       "\t3 & F & AL013 &  36175 &  36175\\\\\n",
       "\t4 & F & AL014 &  60852 &  60852\\\\\n",
       "\t5 & F & AL015 & 101080 & 101080\\\\\n",
       "\t6 & F & AL021 & 133744 & 133744\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 6 × 4\n",
       "\n",
       "| <!--/--> | sex &lt;fct&gt; | geo &lt;fct&gt; | total &lt;dbl&gt; | dat_al_total$values &lt;dbl&gt; |\n",
       "|---|---|---|---|---|\n",
       "| 1 | F | AL011 |  54311 |  54311 |\n",
       "| 2 | F | AL012 | 144381 | 144381 |\n",
       "| 3 | F | AL013 |  36175 |  36175 |\n",
       "| 4 | F | AL014 |  60852 |  60852 |\n",
       "| 5 | F | AL015 | 101080 | 101080 |\n",
       "| 6 | F | AL021 | 133744 | 133744 |\n",
       "\n"
      ],
      "text/plain": [
       "  sex geo   total  dat_al_total$values\n",
       "1 F   AL011  54311  54311             \n",
       "2 F   AL012 144381 144381             \n",
       "3 F   AL013  36175  36175             \n",
       "4 F   AL014  60852  60852             \n",
       "5 F   AL015 101080 101080             \n",
       "6 F   AL021 133744 133744             "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dat_al_check <- dat_al[dat_al$age != \"TOTAL\",]\n",
    "dat_al_check_grouped <- dat_al_check %>%\n",
    "group_by(sex, geo) %>%\n",
    "summarize(total = sum(values), .groups = \"drop\")\n",
    "\n",
    "dat_al_total <- dat_al[dat_al$age == \"TOTAL\",]\n",
    "dat_al_total$age = NULL\n",
    "dat_al_check_grouped <- cbind(dat_al_check_grouped, dat_al_total$values)\n",
    "head(dat_al_check_grouped)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "65754b39",
   "metadata": {},
   "source": [
    "Vidíme, že předpoklad platí, proto budeme nadále používat zkrácený dataset, kde `age` je rovno \"Total\"."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4d9654f6",
   "metadata": {},
   "source": [
    "## Úloha 1\n",
    "### Proveďte základní statistická šetření:\n",
    "> Zkoumaná data stručně popište."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "868e1ca8",
   "metadata": {},
   "source": [
    "Podivamé se na prvních a poslednich 6 záznamů a taky na rozměry dat."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "ed38767c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A tibble: 6 × 3</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>sex</th><th scope=col>geo</th><th scope=col>values</th></tr>\n",
       "\t<tr><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>F</td><td>AL011</td><td> 54311</td></tr>\n",
       "\t<tr><td>F</td><td>AL012</td><td>144381</td></tr>\n",
       "\t<tr><td>F</td><td>AL013</td><td> 36175</td></tr>\n",
       "\t<tr><td>F</td><td>AL014</td><td> 60852</td></tr>\n",
       "\t<tr><td>F</td><td>AL015</td><td>101080</td></tr>\n",
       "\t<tr><td>F</td><td>AL021</td><td>133744</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A tibble: 6 × 3\n",
       "\\begin{tabular}{lll}\n",
       " sex & geo & values\\\\\n",
       " <fct> & <fct> & <dbl>\\\\\n",
       "\\hline\n",
       "\t F & AL011 &  54311\\\\\n",
       "\t F & AL012 & 144381\\\\\n",
       "\t F & AL013 &  36175\\\\\n",
       "\t F & AL014 &  60852\\\\\n",
       "\t F & AL015 & 101080\\\\\n",
       "\t F & AL021 & 133744\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A tibble: 6 × 3\n",
       "\n",
       "| sex &lt;fct&gt; | geo &lt;fct&gt; | values &lt;dbl&gt; |\n",
       "|---|---|---|\n",
       "| F | AL011 |  54311 |\n",
       "| F | AL012 | 144381 |\n",
       "| F | AL013 |  36175 |\n",
       "| F | AL014 |  60852 |\n",
       "| F | AL015 | 101080 |\n",
       "| F | AL021 | 133744 |\n",
       "\n"
      ],
      "text/plain": [
       "  sex geo   values\n",
       "1 F   AL011  54311\n",
       "2 F   AL012 144381\n",
       "3 F   AL013  36175\n",
       "4 F   AL014  60852\n",
       "5 F   AL015 101080\n",
       "6 F   AL021 133744"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A tibble: 6 × 3</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>sex</th><th scope=col>geo</th><th scope=col>values</th></tr>\n",
       "\t<tr><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>M</td><td>AL022</td><td>449929</td></tr>\n",
       "\t<tr><td>M</td><td>AL031</td><td> 59715</td></tr>\n",
       "\t<tr><td>M</td><td>AL032</td><td>144281</td></tr>\n",
       "\t<tr><td>M</td><td>AL033</td><td> 28112</td></tr>\n",
       "\t<tr><td>M</td><td>AL034</td><td>100228</td></tr>\n",
       "\t<tr><td>M</td><td>AL035</td><td> 94060</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A tibble: 6 × 3\n",
       "\\begin{tabular}{lll}\n",
       " sex & geo & values\\\\\n",
       " <fct> & <fct> & <dbl>\\\\\n",
       "\\hline\n",
       "\t M & AL022 & 449929\\\\\n",
       "\t M & AL031 &  59715\\\\\n",
       "\t M & AL032 & 144281\\\\\n",
       "\t M & AL033 &  28112\\\\\n",
       "\t M & AL034 & 100228\\\\\n",
       "\t M & AL035 &  94060\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A tibble: 6 × 3\n",
       "\n",
       "| sex &lt;fct&gt; | geo &lt;fct&gt; | values &lt;dbl&gt; |\n",
       "|---|---|---|\n",
       "| M | AL022 | 449929 |\n",
       "| M | AL031 |  59715 |\n",
       "| M | AL032 | 144281 |\n",
       "| M | AL033 |  28112 |\n",
       "| M | AL034 | 100228 |\n",
       "| M | AL035 |  94060 |\n",
       "\n"
      ],
      "text/plain": [
       "  sex geo   values\n",
       "1 M   AL022 449929\n",
       "2 M   AL031  59715\n",
       "3 M   AL032 144281\n",
       "4 M   AL033  28112\n",
       "5 M   AL034 100228\n",
       "6 M   AL035  94060"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<style>\n",
       ".list-inline {list-style: none; margin:0; padding: 0}\n",
       ".list-inline>li {display: inline-block}\n",
       ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n",
       "</style>\n",
       "<ol class=list-inline><li>24</li><li>3</li></ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 24\n",
       "\\item 3\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 24\n",
       "2. 3\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] 24  3"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "head(dat_al_total)\n",
    "tail(dat_al_total)\n",
    "dim(dat_al_total)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e0181365",
   "metadata": {},
   "source": [
    "Budeme pracovat s datasetem, který ukazuje počet mužů a žen žijících v každém jednotlivém regionu Albánie."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "04a05792",
   "metadata": {},
   "source": [
    "> Určete nejdůležitější statistické míry, které data nejlépe charakterizují.\n",
    "\n",
    "Použijeme funkci `summary()` k získání prvního pohledu na data, se kterými budeme pracovat."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "900524e9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       " sex         geo         values      \n",
       " F:12   AL011  : 2   Min.   : 28112  \n",
       " M:12   AL012  : 2   1st Qu.: 59629  \n",
       "        AL013  : 2   Median : 95078  \n",
       "        AL014  : 2   Mean   :117906  \n",
       "        AL015  : 2   3rd Qu.:135738  \n",
       "        AL021  : 2   Max.   :462261  \n",
       "        (Other):12                   "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "summary(dat_al_total)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fc91c1f2",
   "metadata": {},
   "source": [
    "Máme stejný počet záznamů o mužích a ženách (12), přičemž v každém regionu je jeden záznam o mužích a jeden o ženách."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "e4a0597e",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message:\n",
      "“\u001b[1m\u001b[22m`data_frame()` was deprecated in tibble 1.1.0.\n",
      "\u001b[36mℹ\u001b[39m Please use `tibble()` instead.”\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.frame: 1 × 11</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>Variable</th><th scope=col>Mean</th><th scope=col>Sd</th><th scope=col>Min</th><th scope=col>Pctile[25]</th><th scope=col>Median</th><th scope=col>Pctile[75]</th><th scope=col>Max</th><th scope=col>IQR</th><th scope=col>Skewness</th><th scope=col>Kurtosis</th></tr>\n",
       "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>dat_al_total$values</td><td>117906</td><td>111183</td><td>28112</td><td>59629</td><td>95078</td><td>135738</td><td>462261</td><td>76109</td><td>2.4</td><td>7.9</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 1 × 11\n",
       "\\begin{tabular}{lllllllllll}\n",
       " Variable & Mean & Sd & Min & Pctile{[}25{]} & Median & Pctile{[}75{]} & Max & IQR & Skewness & Kurtosis\\\\\n",
       " <chr> & <chr> & <chr> & <chr> & <chr> & <chr> & <chr> & <chr> & <chr> & <chr> & <chr>\\\\\n",
       "\\hline\n",
       "\t dat\\_al\\_total\\$values & 117906 & 111183 & 28112 & 59629 & 95078 & 135738 & 462261 & 76109 & 2.4 & 7.9\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 1 × 11\n",
       "\n",
       "| Variable &lt;chr&gt; | Mean &lt;chr&gt; | Sd &lt;chr&gt; | Min &lt;chr&gt; | Pctile[25] &lt;chr&gt; | Median &lt;chr&gt; | Pctile[75] &lt;chr&gt; | Max &lt;chr&gt; | IQR &lt;chr&gt; | Skewness &lt;chr&gt; | Kurtosis &lt;chr&gt; |\n",
       "|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| dat_al_total$values | 117906 | 111183 | 28112 | 59629 | 95078 | 135738 | 462261 | 76109 | 2.4 | 7.9 |\n",
       "\n"
      ],
      "text/plain": [
       "  Variable            Mean   Sd     Min   Pctile[25] Median Pctile[75] Max   \n",
       "1 dat_al_total$values 117906 111183 28112 59629      95078  135738     462261\n",
       "  IQR   Skewness Kurtosis\n",
       "1 76109 2.4      7.9     "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "summ <- c('mean(x)', 'sd(x)', 'min(x)', 'pctile(x)[25]', 'median(x)', 'pctile(x)[75]', \n",
    "          'max(x)', 'IQR(x)', 'skewness(x)', 'kurtosis(x)')\n",
    "df <- data_frame(dat_al_total$values)\n",
    "View(sumtable(df, out=\"return\", summ=summ, ))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6e0c3d6b",
   "metadata": {},
   "source": [
    "Vypsali jsme základní statistické míry, které nejlépe charakterizují naše data. Z těchto hodnot je patrné, že:\n",
    "\n",
    "* Median a střední hodnota se liší, přičemž střední hodnota je vyšší.\n",
    "* Na první pohled máme relativně vysokou hodnotu směrodatné odchylky.\n",
    "* Rozdělení je kladně šikmé, což znamená, že modus je menší než median.\n",
    "* Také máme vysokou hodnotu špičatosti, což indikuje větší koncentraci pravděpodobnostních dat kolem střední hodnoty."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9f8bae5b",
   "metadata": {},
   "source": [
    "Prozkoumáme data pro muže a ženy zvlášť"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "7e8d8c0e",
   "metadata": {},
   "outputs": [],
   "source": [
    "dat_al_m <- subset(dat_al_total, sex=='M', select=-sex)\n",
    "dat_al_f <- subset(dat_al_total, sex=='F', select=-sex)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b368b41f",
   "metadata": {},
   "source": [
    "Použijeme `summary()` pro zjištění základních statistických měr pro muži."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "95ebb1b6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "      geo        values      \n",
       " AL011  :1   Min.   : 28112  \n",
       " AL012  :1   1st Qu.: 59629  \n",
       " AL013  :1   Median : 95078  \n",
       " AL014  :1   Mean   :117498  \n",
       " AL015  :1   3rd Qu.:135446  \n",
       " AL021  :1   Max.   :449929  \n",
       " (Other):6                   "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "summary(dat_al_m)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0d2108a4",
   "metadata": {},
   "source": [
    "Použijeme `summary()` pro zjištění základních statistických měr pro ženy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "1db39c98",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "      geo        values      \n",
       " AL011  :1   Min.   : 29919  \n",
       " AL012  :1   1st Qu.: 58379  \n",
       " AL013  :1   Median : 97348  \n",
       " AL014  :1   Mean   :118313  \n",
       " AL015  :1   3rd Qu.:135738  \n",
       " AL021  :1   Max.   :462261  \n",
       " (Other):6                   "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "summary(dat_al_f)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5aa3bae1",
   "metadata": {},
   "source": [
    "Jak jsme zmínili výše, počet mužů a žen je skoro stejný (v průměru počet žen je trochu větší než mužů)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ebdccede",
   "metadata": {},
   "source": [
    "> Vhodně data reprezentujte pomocí vybraných grafů."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4832ec93",
   "metadata": {},
   "source": [
    "Nakreslíme box plot. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "0657b694",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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E/gAAAAAMIT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\nRK5CYII=",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 540,
       "width": 720
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "p1 <- ggplot(dat_al_total, aes(x=values, y=sex, fill=sex)) + \n",
    "        geom_boxplot(outlier.color=\"red\", show.legend=F) +\n",
    "        labs(x = \"Počet obyvatelů\", y = \"Pohlavi\", title = \"Box plot počtu mužů a žen v Albanie\")+\n",
    "        theme(plot.title = element_text(size = 20, hjust = 0.5))\n",
    "\n",
    "p1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bf612d18",
   "metadata": {},
   "source": [
    "Nakreslíme violin plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "9652d30b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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qFg8PD7UjZI+CA9oQGhr6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\npJ6CAwAAAEg9BQcAAACQegoOAAAAIPUUHAAAAEDqKTgAAACA1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      "text/plain": [
       "Plot with title “”"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 540,
       "width": 720
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "M<- dat_al_total[dat_al_total$sex == 'M', ]\n",
    "F <- dat_al_total[dat_al_total$sex == 'F', ]\n",
    "\n",
    "vioplot(as.numeric(M$values), side = \"right\", plotCentre = \"line\", col = 'blue', horizontal = TRUE,\n",
    "        xlab='Počet obyvatelů', main='Violin plot počtu mužů a žen v Albanie')      \n",
    "\n",
    "vioplot(as.numeric(F$values), side = \"left\", plotCentre = \"line\", col ='red', horizontal = TRUE, add = TRUE)       \n",
    "\n",
    "\n",
    "legend(\"topright\", legend = c(\"Male\", \"Female\"), fill = c('blue', 'red'), cex = 1.2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2cedab3b",
   "metadata": {},
   "source": [
    "* Z grafů si také můžeme všimnout, že máme jednu odlehlou hodnotu. \n",
    "* Z violin plotu lze předpokládat, že data nepocházejí z normálního rozdělení."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "32b84bf4",
   "metadata": {},
   "source": [
    "Pro jistotu si zkontrolujeme, že region \"Tirana\" je skutečně hlavním městem Albánie a že tam žije nejvíce obyvatel."
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "id": "580476a8",
   "metadata": {},
   "source": [
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "46760b98",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "'max M : 449929'"
      ],
      "text/latex": [
       "'max M : 449929'"
      ],
      "text/markdown": [
       "'max M : 449929'"
      ],
      "text/plain": [
       "[1] \"max M : 449929\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "'max F : 462261'"
      ],
      "text/latex": [
       "'max F : 462261'"
      ],
      "text/markdown": [
       "'max F : 462261'"
      ],
      "text/plain": [
       "[1] \"max F : 462261\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A tibble: 2 × 3</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>sex</th><th scope=col>geo</th><th scope=col>values</th></tr>\n",
       "\t<tr><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>F</td><td>AL022</td><td>462261</td></tr>\n",
       "\t<tr><td>M</td><td>AL022</td><td>449929</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A tibble: 2 × 3\n",
       "\\begin{tabular}{lll}\n",
       " sex & geo & values\\\\\n",
       " <fct> & <fct> & <dbl>\\\\\n",
       "\\hline\n",
       "\t F & AL022 & 462261\\\\\n",
       "\t M & AL022 & 449929\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A tibble: 2 × 3\n",
       "\n",
       "| sex &lt;fct&gt; | geo &lt;fct&gt; | values &lt;dbl&gt; |\n",
       "|---|---|---|\n",
       "| F | AL022 | 462261 |\n",
       "| M | AL022 | 449929 |\n",
       "\n"
      ],
      "text/plain": [
       "  sex geo   values\n",
       "1 F   AL022 462261\n",
       "2 M   AL022 449929"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "paste('max M :', max(dat_al_total[dat_al_total$sex=='M',]$values))\n",
    "paste('max F :', max(dat_al_total[dat_al_total$sex=='F',]$values))\n",
    "dat_al_total[dat_al_total$geo == 'AL022',]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6ca1c725",
   "metadata": {},
   "source": [
    "## ÚLOHA 2"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1af01832",
   "metadata": {},
   "source": [
    "### Test 1. Test normality dat."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "20e068ff",
   "metadata": {},
   "source": [
    "Nejprve si nakreslíme Q-Q plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "bf6cdfa3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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99/+OmnaTcA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\nc6eWl2dduzZ+zjm+0tLwzTeLTgQAAMzJXlOjFRTIhw5FBgzwV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      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 540,
       "width": 720
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "qq_m <- ggplot(dat_al_m, aes(sample=values)) +\n",
    "            stat_qq(distribution=qnorm) +\n",
    "            stat_qq_line(distribution=qnorm, col='red')+\n",
    "            labs(title = \"Q-Q plot mužů v Albanie\")+\n",
    "            theme(plot.title = element_text(size = 15, hjust = 0.5))\n",
    "\n",
    "qq_f <- ggplot(dat_al_f, aes(sample=values)) +\n",
    "            stat_qq(distribution=qnorm) +\n",
    "            stat_qq_line(distribution=qnorm, col='red')+\n",
    "            labs(title = \"Q-Q plot žen v Albanie\")+\n",
    "            theme(plot.title = element_text(size = 15, hjust = 0.5))\n",
    "plot_grid(qq_m, qq_f)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "27d71a51",
   "metadata": {},
   "source": [
    "Z Q-Q plotu také předpokládáme, že data nejsou normálně rozdělena."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "931f4baa",
   "metadata": {},
   "source": [
    "Pro ověření našého předpokládu použijeme **Shapirův-Wilkův** test, který je vhodný pro malé a středně velké vzorky a je výkonnější než test Kolmogorova-Smirnova.\n",
    "Proto provedeme **Shapirův-Wilkův** test na hladině významnosti 5 % pro testování hypotézy, zda naše data pocházejí z normálního rozdělení.\n",
    " \n",
    "\n",
    "Tento test normality používáme k ověření, zda můžeme nadále používat statistické metody, které předpokládají normální rozdělení dat.\n",
    "\n",
    "- $H_0$: $X_1,\\ldots, X_n$ je výběr z normálního rozdělení, kde $X_1,\\ldots, X_n$ = výběr mužů\n",
    "- $H_A$: neplatí $H_0$\n",
    "\n",
    "- $H_0$: $X_1,\\ldots, X_n$ je výběr z normálního rozdělení, kde $X_1,\\ldots, X_n$ = výběr žen\n",
    "- $H_A$: neplatí $H_0$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "0a51bb2d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "\tShapiro-Wilk normality test\n",
       "\n",
       "data:  dat_al_total[dat_al_total$sex == \"F\", ]$values\n",
       "W = 0.65229, p-value = 0.0003037\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "shapiro.test(dat_al_total[dat_al_total$sex == 'F',]$values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "052d49de",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "\tShapiro-Wilk normality test\n",
       "\n",
       "data:  dat_al_total[dat_al_total$sex == \"M\", ]$values\n",
       "W = 0.66353, p-value = 0.0003837\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "shapiro.test(dat_al_total[dat_al_total$sex == 'M',]$values)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "be30e7bb",
   "metadata": {},
   "source": [
    "Závěr pro test pro muži:\n",
    "* P-hodnota je menší než 5%, **zamítáme** nulovou hypotézu.\n",
    "* Nemůžeme předpokládat, že naše data pocházejí z normálního rozdělení. Proto nemůžeme v budoucnu používat testy, které vyžadují tento předpoklad.\n",
    "\n",
    "Závěr pro test pro ženy:\n",
    "* P-hodnota je menší než 5%, **zamítáme** nulovou hypotézu.\n",
    "* Nemůžeme předpokládat, že naše data pocházejí z normálního rozdělení. Proto nemůžeme v budoucnu používat testy, které vyžadují tento předpoklad."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "26360ed8",
   "metadata": {},
   "source": [
    "### Test 2. Test shody distibucí."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "48084231",
   "metadata": {},
   "source": [
    "Z grafů jsme si všimli, že muži a ženy mají skoro stejné rozdělení, nyní to otestujeme na hladině významnosti 5 %. Pro tento účel jsme zvolili **Wilcoxon signed-rank test**. Zvolili jsme tento test, protože máme párové výběry, u kterých jsme na základě provedeného výše testu předpokládali, že nejsou normálně rozdělené.\n",
    "\n",
    "Předpokládáme, že oba výběry pocházejí ze stejného spojitého rozdělení. Testujeme hypotézu.\n",
    "\n",
    "- $H_0$: Mediánový rozdíl mezi párovými vzorky je nulový. To znamená, že rozdíly mezi páry jsou rovnoměrně rozloženy kolem nuly.\n",
    "- $H_A$: neplatí $H_0$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "b9466a4c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "\tWilcoxon signed rank exact test\n",
       "\n",
       "data:  M$values and F$values\n",
       "V = 37, p-value = 0.9097\n",
       "alternative hypothesis: true location shift is not equal to 0\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "wilcox.test(M$values, F$values, paired=TRUE)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e24daf2e",
   "metadata": {},
   "source": [
    "Závěr:\n",
    "* P-hodnota je větši než 5%, proto **nezamitáme** nulovou hypotézu.\n",
    "* Můžeme předpokládat, že oba výběrý pocházejí ze stejného spojitého rozdělení."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6b8ea4d9",
   "metadata": {},
   "source": [
    "### Test 3. Test shody medianu s medianem NUTS3 Evropské unie. \n",
    "Zajímá nás, zda je medián počtu obyvatel regionů v Albánii stejný jako medián počtu obyvatel mezi všemi regiony celé Evropské unie. \n",
    " \n",
    "Pro tento účel použijeme jednovýběrový Wilcoxonův znaménkový test na hladině významnosti 5%. Zvolili jsme tento test, protože je speciálně určen pro porovnávání, zda medián našeho výběru se liší od mediánu všech regionů EU.\n",
    "\n",
    "Budeme testovat hypotézu, kde $x_a$ je medián počtu obyvatel regionů v Albánii a $x_c$ je medián počtu obyvatel mezi všemi regiony EU.\n",
    "\n",
    "- $H_0$: $x_a = x_c$\n",
    "- $H_A$: $x_a \\ne x_c$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "313b076e",
   "metadata": {},
   "source": [
    "Najdeme median v roku '2021-01-01' pro muže a ženy a pro věk = \"Total\" mezi všemi regiony celoevropské unie."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "b7dde0d7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<span style=white-space:pre-wrap>'Median z celoevropského rámce :  130096'</span>"
      ],
      "text/latex": [
       "'Median z celoevropského rámce :  130096'"
      ],
      "text/markdown": [
       "<span style=white-space:pre-wrap>'Median z celoevropského rámce :  130096'</span>"
      ],
      "text/plain": [
       "[1] \"Median z celoevropského rámce :  130096\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dat_eu <- dat[dat$time == \"2021-01-01\" & nchar(dat$geo) == 5 & dat$sex %in% c(\"F\", \"M\") & dat$age == \"TOTAL\",]\n",
    "dat_eu$sex <- factor(dat_eu$sex)\n",
    "dat_eu$geo <- factor(dat_eu$geo)\n",
    "dat_eu$unit <- NULL\n",
    "dat_eu$time <- NULL\n",
    "dat_eu$age <- NULL\n",
    "eu_median <- median(dat_eu$values)\n",
    "paste(\"Median z celoevropského rámce : \", eu_median)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "d91cac32",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "\tWilcoxon signed rank exact test\n",
       "\n",
       "data:  dat_al_total$values\n",
       "V = 68, p-value = 0.01787\n",
       "alternative hypothesis: true location is not equal to 130096\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "wilcox.test(x=dat_al_total$values, mu=eu_median)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "27d60981",
   "metadata": {},
   "source": [
    "P-hodnota je menší než 5%, proto **zamitáme** nulovou hypotézu.\n",
    "Předpokladáme, že medián našeho výběru je významně odlišný od mediánu všech regionů Evropské unie."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2fe8dad9",
   "metadata": {},
   "source": [
    "## ÚLOHA 3\n",
    "> Sestavte kontingenční tabulku počtů obyvatel podle pohlaví a NUTS 3 oblastí. Tuto tabulku interpretujte, navrhněte hypotézy o jejích vlastnostech a proveďte příslušný test. Interpretujte jeho výsledky.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d3c18226",
   "metadata": {},
   "source": [
    "Sestavíme kontingenční tabulku a matici pravděpodobnosti (*regiony* x *pohlaví*), které popisují vztah dvou příznaků rozdělených do kategorií."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "64539149",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A table: 3 × 13 of type dbl</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>AL011</th><th scope=col>AL012</th><th scope=col>AL013</th><th scope=col>AL014</th><th scope=col>AL015</th><th scope=col>AL021</th><th scope=col>AL022</th><th scope=col>AL031</th><th scope=col>AL032</th><th scope=col>AL033</th><th scope=col>AL034</th><th scope=col>AL035</th><th scope=col>Sum</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>F</th><td> 54311</td><td>144381</td><td>36175</td><td> 60852</td><td>101080</td><td>133744</td><td>462261</td><td> 59735</td><td>141721</td><td>29919</td><td>101965</td><td> 93615</td><td>1419759</td></tr>\n",
       "\t<tr><th scope=row>M</th><td> 59372</td><td>147648</td><td>38213</td><td> 59826</td><td> 96097</td><td>132501</td><td>449929</td><td> 59715</td><td>144281</td><td>28112</td><td>100228</td><td> 94060</td><td>1409982</td></tr>\n",
       "\t<tr><th scope=row>Sum</th><td>113683</td><td>292029</td><td>74388</td><td>120678</td><td>197177</td><td>266245</td><td>912190</td><td>119450</td><td>286002</td><td>58031</td><td>202193</td><td>187675</td><td>2829741</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A table: 3 × 13 of type dbl\n",
       "\\begin{tabular}{r|lllllllllllll}\n",
       "  & AL011 & AL012 & AL013 & AL014 & AL015 & AL021 & AL022 & AL031 & AL032 & AL033 & AL034 & AL035 & Sum\\\\\n",
       "\\hline\n",
       "\tF &  54311 & 144381 & 36175 &  60852 & 101080 & 133744 & 462261 &  59735 & 141721 & 29919 & 101965 &  93615 & 1419759\\\\\n",
       "\tM &  59372 & 147648 & 38213 &  59826 &  96097 & 132501 & 449929 &  59715 & 144281 & 28112 & 100228 &  94060 & 1409982\\\\\n",
       "\tSum & 113683 & 292029 & 74388 & 120678 & 197177 & 266245 & 912190 & 119450 & 286002 & 58031 & 202193 & 187675 & 2829741\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A table: 3 × 13 of type dbl\n",
       "\n",
       "| <!--/--> | AL011 | AL012 | AL013 | AL014 | AL015 | AL021 | AL022 | AL031 | AL032 | AL033 | AL034 | AL035 | Sum |\n",
       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| F |  54311 | 144381 | 36175 |  60852 | 101080 | 133744 | 462261 |  59735 | 141721 | 29919 | 101965 |  93615 | 1419759 |\n",
       "| M |  59372 | 147648 | 38213 |  59826 |  96097 | 132501 | 449929 |  59715 | 144281 | 28112 | 100228 |  94060 | 1409982 |\n",
       "| Sum | 113683 | 292029 | 74388 | 120678 | 197177 | 266245 | 912190 | 119450 | 286002 | 58031 | 202193 | 187675 | 2829741 |\n",
       "\n"
      ],
      "text/plain": [
       "     geo\n",
       "sex   AL011  AL012  AL013 AL014  AL015  AL021  AL022  AL031  AL032  AL033\n",
       "  F    54311 144381 36175  60852 101080 133744 462261  59735 141721 29919\n",
       "  M    59372 147648 38213  59826  96097 132501 449929  59715 144281 28112\n",
       "  Sum 113683 292029 74388 120678 197177 266245 912190 119450 286002 58031\n",
       "     geo\n",
       "sex   AL034  AL035  Sum    \n",
       "  F   101965  93615 1419759\n",
       "  M   100228  94060 1409982\n",
       "  Sum 202193 187675 2829741"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A table: 3 × 13 of type dbl</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>AL011</th><th scope=col>AL012</th><th scope=col>AL013</th><th scope=col>AL014</th><th scope=col>AL015</th><th scope=col>AL021</th><th scope=col>AL022</th><th scope=col>AL031</th><th scope=col>AL032</th><th scope=col>AL033</th><th scope=col>AL034</th><th scope=col>AL035</th><th scope=col>Sum</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>F</th><td>0.01919292</td><td>0.05102269</td><td>0.01278386</td><td>0.02150444</td><td>0.03572058</td><td>0.04726369</td><td>0.1633581</td><td>0.02110971</td><td>0.05008268</td><td>0.010573052</td><td>0.03603333</td><td>0.03308253</td><td>0.5017275</td></tr>\n",
       "\t<tr><th scope=row>M</th><td>0.02098143</td><td>0.05217721</td><td>0.01350406</td><td>0.02114186</td><td>0.03395965</td><td>0.04682443</td><td>0.1590001</td><td>0.02110264</td><td>0.05098735</td><td>0.009934478</td><td>0.03541950</td><td>0.03323979</td><td>0.4982725</td></tr>\n",
       "\t<tr><th scope=row>Sum</th><td>0.04017435</td><td>0.10319990</td><td>0.02628792</td><td>0.04264631</td><td>0.06968023</td><td>0.09408812</td><td>0.3223581</td><td>0.04221234</td><td>0.10107003</td><td>0.020507531</td><td>0.07145283</td><td>0.06632232</td><td>1.0000000</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A table: 3 × 13 of type dbl\n",
       "\\begin{tabular}{r|lllllllllllll}\n",
       "  & AL011 & AL012 & AL013 & AL014 & AL015 & AL021 & AL022 & AL031 & AL032 & AL033 & AL034 & AL035 & Sum\\\\\n",
       "\\hline\n",
       "\tF & 0.01919292 & 0.05102269 & 0.01278386 & 0.02150444 & 0.03572058 & 0.04726369 & 0.1633581 & 0.02110971 & 0.05008268 & 0.010573052 & 0.03603333 & 0.03308253 & 0.5017275\\\\\n",
       "\tM & 0.02098143 & 0.05217721 & 0.01350406 & 0.02114186 & 0.03395965 & 0.04682443 & 0.1590001 & 0.02110264 & 0.05098735 & 0.009934478 & 0.03541950 & 0.03323979 & 0.4982725\\\\\n",
       "\tSum & 0.04017435 & 0.10319990 & 0.02628792 & 0.04264631 & 0.06968023 & 0.09408812 & 0.3223581 & 0.04221234 & 0.10107003 & 0.020507531 & 0.07145283 & 0.06632232 & 1.0000000\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A table: 3 × 13 of type dbl\n",
       "\n",
       "| <!--/--> | AL011 | AL012 | AL013 | AL014 | AL015 | AL021 | AL022 | AL031 | AL032 | AL033 | AL034 | AL035 | Sum |\n",
       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| F | 0.01919292 | 0.05102269 | 0.01278386 | 0.02150444 | 0.03572058 | 0.04726369 | 0.1633581 | 0.02110971 | 0.05008268 | 0.010573052 | 0.03603333 | 0.03308253 | 0.5017275 |\n",
       "| M | 0.02098143 | 0.05217721 | 0.01350406 | 0.02114186 | 0.03395965 | 0.04682443 | 0.1590001 | 0.02110264 | 0.05098735 | 0.009934478 | 0.03541950 | 0.03323979 | 0.4982725 |\n",
       "| Sum | 0.04017435 | 0.10319990 | 0.02628792 | 0.04264631 | 0.06968023 | 0.09408812 | 0.3223581 | 0.04221234 | 0.10107003 | 0.020507531 | 0.07145283 | 0.06632232 | 1.0000000 |\n",
       "\n"
      ],
      "text/plain": [
       "     geo\n",
       "sex   AL011      AL012      AL013      AL014      AL015      AL021     \n",
       "  F   0.01919292 0.05102269 0.01278386 0.02150444 0.03572058 0.04726369\n",
       "  M   0.02098143 0.05217721 0.01350406 0.02114186 0.03395965 0.04682443\n",
       "  Sum 0.04017435 0.10319990 0.02628792 0.04264631 0.06968023 0.09408812\n",
       "     geo\n",
       "sex   AL022     AL031      AL032      AL033       AL034      AL035     \n",
       "  F   0.1633581 0.02110971 0.05008268 0.010573052 0.03603333 0.03308253\n",
       "  M   0.1590001 0.02110264 0.05098735 0.009934478 0.03541950 0.03323979\n",
       "  Sum 0.3223581 0.04221234 0.10107003 0.020507531 0.07145283 0.06632232\n",
       "     geo\n",
       "sex   Sum      \n",
       "  F   0.5017275\n",
       "  M   0.4982725\n",
       "  Sum 1.0000000"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dat_al_table <- xtabs(formula = values ~ sex + geo, data=dat_al_total)\n",
    "addmargins(dat_al_table)\n",
    "\n",
    "dat_al_proptable <- prop.table(dat_al_table)\n",
    "addmargins(dat_al_proptable)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a3f04eaa",
   "metadata": {},
   "source": [
    "Pro snadné pochopení kontingenční tabulky si nakreslíme mosaic plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "eaa59f69",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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IGuItBBoFsT6CyBriLQQaBbE+gsga4i0EGgWxPoLIGuItBBoFsT6CyBriLQQaBb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\ngg==",
      "text/plain": [
       "Plot with title “Kontingenční tabulka počtů obyvatel podle pohlaví a oblastí”"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 540,
       "width": 720
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "mosaicplot(t(dat_al_table), color=T, main = \"Kontingenční tabulka počtů obyvatel podle pohlaví a oblastí\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b3ad5d0a",
   "metadata": {},
   "source": [
    "Z tohoto grafu je také vidět, že máme stejné poměry mužů a žen v každém regionu Albánie. Očekávaně má AL022 (region hlavního města) největší počet obyvatel."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "25f79840",
   "metadata": {},
   "source": [
    "### Pearsonův $\\chi^2$ test nezávislosti\n",
    "\n",
    "Testujeme, zda pravděpodobnosti $p_{i1},\\ldots,p_{ic}$ jsou **nezávislé** na řádkovém indexu na hladině významnosti 5%. Pro náš dataset platí, že pro všechna (𝑖,𝑗) jsou teoretické četnosti $\\frac{n_{i\\cdot} n_{\\cdot j}}{n}\\geq 5$.\n",
    "\n",
    "- $H_0$:  $p_{ij} = p_{i\\cdot} p_{\\cdot j}, \\qquad i=1,\\ldots,r; j=1,\\ldots, c$\n",
    "- $H_A$: neplatí $H_0$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "47f2605a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "\tPearson's Chi-squared test\n",
       "\n",
       "data:  dat_al_table\n",
       "X-squared = 686.26, df = 11, p-value < 0.00000000000000022\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pears <- chisq.test (dat_al_table)\n",
    "pears\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "224534ab",
   "metadata": {},
   "source": [
    "P-hodnota je menší než 5%, proto **zamitáme** nulovou hypotézu.\n",
    "Předpokladáme, že existuje statisticky významná vazba mezi pohlaví a regiony."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "93bbf656",
   "metadata": {},
   "source": [
    "Můžeme snadno spočítat, jaké byly očekávané četnosti v případě nezávislosti. Pozorované četnosti jsou:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "77ea36e0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A matrix: 3 × 13 of type dbl</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>AL011</th><th scope=col>AL012</th><th scope=col>AL013</th><th scope=col>AL014</th><th scope=col>AL015</th><th scope=col>AL021</th><th scope=col>AL022</th><th scope=col>AL031</th><th scope=col>AL032</th><th scope=col>AL033</th><th scope=col>AL034</th><th scope=col>AL035</th><th scope=col>Sum</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>F</th><td> 57037.89</td><td>146519</td><td>37322.51</td><td> 60547.48</td><td> 98929.13</td><td>133582.4</td><td>457670.8</td><td> 59931.36</td><td>143495.1</td><td>29115.75</td><td>101445.8</td><td> 94161.72</td><td>1419759</td></tr>\n",
       "\t<tr><th scope=row>M</th><td> 56645.11</td><td>145510</td><td>37065.49</td><td> 60130.52</td><td> 98247.87</td><td>132662.6</td><td>454519.2</td><td> 59518.64</td><td>142506.9</td><td>28915.25</td><td>100747.2</td><td> 93513.28</td><td>1409982</td></tr>\n",
       "\t<tr><th scope=row>Sum</th><td>113683.00</td><td>292029</td><td>74388.00</td><td>120678.00</td><td>197177.00</td><td>266245.0</td><td>912190.0</td><td>119450.00</td><td>286002.0</td><td>58031.00</td><td>202193.0</td><td>187675.00</td><td>2829741</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A matrix: 3 × 13 of type dbl\n",
       "\\begin{tabular}{r|lllllllllllll}\n",
       "  & AL011 & AL012 & AL013 & AL014 & AL015 & AL021 & AL022 & AL031 & AL032 & AL033 & AL034 & AL035 & Sum\\\\\n",
       "\\hline\n",
       "\tF &  57037.89 & 146519 & 37322.51 &  60547.48 &  98929.13 & 133582.4 & 457670.8 &  59931.36 & 143495.1 & 29115.75 & 101445.8 &  94161.72 & 1419759\\\\\n",
       "\tM &  56645.11 & 145510 & 37065.49 &  60130.52 &  98247.87 & 132662.6 & 454519.2 &  59518.64 & 142506.9 & 28915.25 & 100747.2 &  93513.28 & 1409982\\\\\n",
       "\tSum & 113683.00 & 292029 & 74388.00 & 120678.00 & 197177.00 & 266245.0 & 912190.0 & 119450.00 & 286002.0 & 58031.00 & 202193.0 & 187675.00 & 2829741\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A matrix: 3 × 13 of type dbl\n",
       "\n",
       "| <!--/--> | AL011 | AL012 | AL013 | AL014 | AL015 | AL021 | AL022 | AL031 | AL032 | AL033 | AL034 | AL035 | Sum |\n",
       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| F |  57037.89 | 146519 | 37322.51 |  60547.48 |  98929.13 | 133582.4 | 457670.8 |  59931.36 | 143495.1 | 29115.75 | 101445.8 |  94161.72 | 1419759 |\n",
       "| M |  56645.11 | 145510 | 37065.49 |  60130.52 |  98247.87 | 132662.6 | 454519.2 |  59518.64 | 142506.9 | 28915.25 | 100747.2 |  93513.28 | 1409982 |\n",
       "| Sum | 113683.00 | 292029 | 74388.00 | 120678.00 | 197177.00 | 266245.0 | 912190.0 | 119450.00 | 286002.0 | 58031.00 | 202193.0 | 187675.00 | 2829741 |\n",
       "\n"
      ],
      "text/plain": [
       "     geo\n",
       "sex   AL011     AL012  AL013    AL014     AL015     AL021    AL022    AL031    \n",
       "  F    57037.89 146519 37322.51  60547.48  98929.13 133582.4 457670.8  59931.36\n",
       "  M    56645.11 145510 37065.49  60130.52  98247.87 132662.6 454519.2  59518.64\n",
       "  Sum 113683.00 292029 74388.00 120678.00 197177.00 266245.0 912190.0 119450.00\n",
       "     geo\n",
       "sex   AL032    AL033    AL034    AL035     Sum    \n",
       "  F   143495.1 29115.75 101445.8  94161.72 1419759\n",
       "  M   142506.9 28915.25 100747.2  93513.28 1409982\n",
       "  Sum 286002.0 58031.00 202193.0 187675.00 2829741"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "addmargins(pears$expected)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6a3f1046",
   "metadata": {},
   "source": [
    "Spočítáme rozdíl mezi očekávanými a skutečnými hodnotami."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "b03b79e4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A table: 3 × 13 of type dbl</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>AL011</th><th scope=col>AL012</th><th scope=col>AL013</th><th scope=col>AL014</th><th scope=col>AL015</th><th scope=col>AL021</th><th scope=col>AL022</th><th scope=col>AL031</th><th scope=col>AL032</th><th scope=col>AL033</th><th scope=col>AL034</th><th scope=col>AL035</th><th scope=col>Sum</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>F</th><td> 2726.892</td><td> 2137.993</td><td> 1147.508</td><td>-304.5235</td><td>-2150.868</td><td>-161.5502</td><td>-4590.152</td><td> 196.355</td><td> 1774.081</td><td>-803.2489</td><td>-519.2028</td><td> 546.7167</td><td> 0.000000000007275958</td></tr>\n",
       "\t<tr><th scope=row>M</th><td>-2726.892</td><td>-2137.993</td><td>-1147.508</td><td> 304.5235</td><td> 2150.868</td><td> 161.5502</td><td> 4590.152</td><td>-196.355</td><td>-1774.081</td><td> 803.2489</td><td> 519.2028</td><td>-546.7167</td><td>-0.000000000007275958</td></tr>\n",
       "\t<tr><th scope=row>Sum</th><td>    0.000</td><td>    0.000</td><td>    0.000</td><td>   0.0000</td><td>    0.000</td><td>   0.0000</td><td>    0.000</td><td>   0.000</td><td>    0.000</td><td>   0.0000</td><td>   0.0000</td><td>   0.0000</td><td> 0.000000000000000000</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A table: 3 × 13 of type dbl\n",
       "\\begin{tabular}{r|lllllllllllll}\n",
       "  & AL011 & AL012 & AL013 & AL014 & AL015 & AL021 & AL022 & AL031 & AL032 & AL033 & AL034 & AL035 & Sum\\\\\n",
       "\\hline\n",
       "\tF &  2726.892 &  2137.993 &  1147.508 & -304.5235 & -2150.868 & -161.5502 & -4590.152 &  196.355 &  1774.081 & -803.2489 & -519.2028 &  546.7167 &  0.000000000007275958\\\\\n",
       "\tM & -2726.892 & -2137.993 & -1147.508 &  304.5235 &  2150.868 &  161.5502 &  4590.152 & -196.355 & -1774.081 &  803.2489 &  519.2028 & -546.7167 & -0.000000000007275958\\\\\n",
       "\tSum &     0.000 &     0.000 &     0.000 &    0.0000 &     0.000 &    0.0000 &     0.000 &    0.000 &     0.000 &    0.0000 &    0.0000 &    0.0000 &  0.000000000000000000\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A table: 3 × 13 of type dbl\n",
       "\n",
       "| <!--/--> | AL011 | AL012 | AL013 | AL014 | AL015 | AL021 | AL022 | AL031 | AL032 | AL033 | AL034 | AL035 | Sum |\n",
       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| F |  2726.892 |  2137.993 |  1147.508 | -304.5235 | -2150.868 | -161.5502 | -4590.152 |  196.355 |  1774.081 | -803.2489 | -519.2028 |  546.7167 |  0.000000000007275958 |\n",
       "| M | -2726.892 | -2137.993 | -1147.508 |  304.5235 |  2150.868 |  161.5502 |  4590.152 | -196.355 | -1774.081 |  803.2489 |  519.2028 | -546.7167 | -0.000000000007275958 |\n",
       "| Sum |     0.000 |     0.000 |     0.000 |    0.0000 |     0.000 |    0.0000 |     0.000 |    0.000 |     0.000 |    0.0000 |    0.0000 |    0.0000 |  0.000000000000000000 |\n",
       "\n"
      ],
      "text/plain": [
       "     geo\n",
       "sex   AL011     AL012     AL013     AL014     AL015     AL021     AL022    \n",
       "  F    2726.892  2137.993  1147.508 -304.5235 -2150.868 -161.5502 -4590.152\n",
       "  M   -2726.892 -2137.993 -1147.508  304.5235  2150.868  161.5502  4590.152\n",
       "  Sum     0.000     0.000     0.000    0.0000     0.000    0.0000     0.000\n",
       "     geo\n",
       "sex   AL031    AL032     AL033     AL034     AL035     Sum                  \n",
       "  F    196.355  1774.081 -803.2489 -519.2028  546.7167  0.000000000007275958\n",
       "  M   -196.355 -1774.081  803.2489  519.2028 -546.7167 -0.000000000007275958\n",
       "  Sum    0.000     0.000    0.0000    0.0000    0.0000  0.000000000000000000"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "addmargins(pears$expected - pears$observed)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9430c3cd",
   "metadata": {},
   "source": [
    "Je to vše :)"
   ]
  }
 ],
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