davideNotebook.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Notebook Davide, overview ed elenco punti da svolgere. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Libraries and functions\n",
    "library(ggplot2)\n",
    "#la libreria plyr serve per la funzione che ho definiyo sotto che torna utile per i plot\n",
    "library(plyr)\n",
    "summarySE <- function(data=NULL, measurevar, groupvars=NULL, na.rm=FALSE,\n",
    "                      conf.interval=.95, .drop=TRUE) {\n",
    "    library(plyr)\n",
    "\n",
    "    # New version of length which can handle NA's: if na.rm==T, don't count them\n",
    "    length2 <- function (x, na.rm=FALSE) {\n",
    "        if (na.rm) sum(!is.na(x))\n",
    "        else       length(x)\n",
    "    }\n",
    "\n",
    "    # This does the summary. For each group's data frame, return a vector with\n",
    "    # N, mean, and sd\n",
    "    datac <- ddply(data, groupvars, .drop=.drop,\n",
    "      .fun = function(xx, col) {\n",
    "        c(N    = length2(xx[[col]], na.rm=na.rm),\n",
    "          mean = mean   (xx[[col]], na.rm=na.rm),\n",
    "          sd   = sd     (xx[[col]], na.rm=na.rm)\n",
    "        )\n",
    "      },\n",
    "      measurevar\n",
    "    )\n",
    "\n",
    "    # Rename the \"mean\" column    \n",
    "    datac <- rename(datac, c(\"mean\" = measurevar))\n",
    "\n",
    "    datac$se <- datac$sd / sqrt(datac$N)  # Calculate standard error of the mean\n",
    "\n",
    "    # Confidence interval multiplier for standard error\n",
    "    # Calculate t-statistic for confidence interval: \n",
    "    # e.g., if conf.interval is .95, use .975 (above/below), and use df=N-1\n",
    "    ciMult <- qt(conf.interval/2 + .5, datac$N-1)\n",
    "    datac$ci <- datac$se * ciMult\n",
    "\n",
    "    return(datac)\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Prima di tutto carico il dataset, ne eseguo un sample e ne osservo le colonne.\n",
    "Obiettivi:\n",
    "Ci si concentra sul numero medio di feriti e di vittime PER INCIDENTE in funzione di vari fattori. \n",
    "- Per prima cosa vengono mostrati due barplot in cui si osserva la frequenza del numero di vittime e del numero di feriti negli incidenti. \n",
    "    Si nota che in più del 90% dei casi di incidente grave registrati non ci sono vittime. Si osserva, inoltre, che la maggior pante degli incidenti hanno un solo ferito (sia moda che mediana).\n",
    "    Si mostrano poi il numero medio di feriti e di vittime per incidente, con le relative deviazioni standard (non deviazione della media).\n",
    "- Si analizzano poi le stesse quantità in funzione dei vari fattori. \n",
    "    Questi fattori sono:\n",
    "    - Trimestre\n",
    "    - Ora\n",
    "    - Fondo stradale\n",
    "    - Tipo di strada\n",
    "    - Pavimentazione\n",
    "    \n",
    "La logica dei confronti è la seguente:\n",
    "-> Prima viene mostrato un **barplot** la media di feriti e vittime con la relativa sd della media in funzione dei vari fattori (media e sd della media di feriti e vittime per ciascun fondo stradale, Ora, ecc..) \n",
    "-> In seguito si applica per ciascuna \"classe\" di fattori, separatamente per morti e feriti, **un test ANOVA ad una via**. Ossia un test d'ipotesi che ha come ipotesi nulla che tutte le medie siano uguali. \n",
    "   Se il test non è statisticamente significativo (confidence level del 95%), ci si ferma. Se il test invece è statisticamente significativo viene eseguito un **test della t di student fra tutte le classi**, che viene rappresentato in forma di tabella a doppia entrata. In questo modo si può tener conto dell'inflazione del p-value per confronti tra più classi, e si può osservare per quali classi si rifiuta l'ipotesi nulla (ossia che le medie siano uguali). \n",
    "   La logica dei test fatti è basata sulle ipotesi che: \n",
    "   -Gli incidenti sono eventi indipendenti aventi stessa distribuzione di probabilità\n",
    "   -Il numero di vittime e di feriti è una variabile casuale discreta, similmente agli incidenti, si ipotizza che si tratti di variabili **iid**\n",
    "Per questo motivo si sfrutta il teorema del limite centrale, che permette di applicare i test di ipotesi.\n",
    "Non è possibile applicare test di ipotesi alle quantità in sè in quanto non sono distribuite secondo una normale."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset_proj <- read.csv(\"dataset/dataset progetto.csv\", sep=';')\n",
    "SAMPLE_SIZE = 20000\n",
    "sample_rows <- sample(nrow(dataset_proj), SAMPLE_SIZE)\n",
    "sample_data = dataset_proj[sample_rows,]\n",
    "#names(sample_data)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Sistemo leggermente le principali colonne del dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "attach(sample_data)\n",
    "#giorno della settimana\n",
    "giorno <- factor(giorno)\n",
    "levels(giorno) <- c(\"Lun\", \"Mar\", \"Mer\", \"Gio\", \"Ven\", \"Sab\", \"Dom\")\n",
    "\n",
    "#pavimentazione\n",
    "pavimentazione <- factor(pavimentazione)\n",
    "levels(pavimentazione) <- c(\"good\", \"bad\", \"non-pav\")\n",
    "\n",
    "#fondo stradale\n",
    "fondo_stradale <- factor(fondo_stradale)\n",
    "levels(fondo_stradale) <- c(\"asciutto\", \"bagnato\", \"sdrucciolevole\", \"ghiacciato\", \"innevato\")\n",
    "\n",
    "#tipo di strada\n",
    "tipo_di_strada <- factor(tipo_di_strada)\n",
    "levels(tipo_di_strada) <- c(\"urbana\", \"extraurbana\", \"autostrada\", \"altro\")\n",
    "\n",
    "#trimestre\n",
    "trimestre <- factor(trimestre)\n",
    "levels(trimestre) <- c(\"Gen-Mar\", \"Apr-Giu\", \"Lug-Set\", \"Ott-Dic\")\n",
    "\n",
    "#creo una nuova colonna nel dataframe contenente la somma delle colonne \"morti_entro_24_ore\", \"morti_entro_30_giorni\"\n",
    "sample_data$morti = morti_entro_24_ore + morti_entro_30_giorni\n",
    "\n",
    "#tipo di veicolo, divido tutto in macchina, moto, veicolo pesante\n",
    "reset_veicolo <- function(val){\n",
    "    if (is.na(val)){\n",
    "        #manca il valore, pongo 0\n",
    "        return(0)\n",
    "    }\n",
    "    if (val <= 4){\n",
    "        #è una macchina\n",
    "        return(1)\n",
    "    }\n",
    "    if (val > 4 && val <= 13){\n",
    "        #è un mezzo pesante\n",
    "        return(2)\n",
    "    }\n",
    "    if (val > 4){\n",
    "        #è una moto/veicolo a 2 ruote\n",
    "        return(3)\n",
    "    }\n",
    "}\n",
    "\n",
    "coinvolta_moto <- function(val1, val2, val3){\n",
    "    if (val1 == 3 || val2 == 3 || val3 == 3){\n",
    "        return(1)\n",
    "    }else{\n",
    "        return(0)\n",
    "    }\n",
    "} \n",
    "\n",
    "coinvolta_auto <- function(val1, val2, val3){\n",
    "    if (val1 == 1 || val2 == 1 || val3 == 1){\n",
    "        return(1)\n",
    "    }else{\n",
    "        return(0)\n",
    "    }\n",
    "}\n",
    "\n",
    "coinvolto_mezzo <- function(val1, val2, val3){\n",
    "    if (val1 == 2 || val2 == 2 || val3 == 2){\n",
    "        return(1)\n",
    "    }else{\n",
    "        return(0)\n",
    "    }\n",
    "}\n",
    "\n",
    "presenza_morti <- function(val){\n",
    "    if (val == 0){\n",
    "        return(0)\n",
    "    }else{\n",
    "        return(1)\n",
    "    }\n",
    "}\n",
    "\n",
    "for (i in c(1:nrow(sample_data))){\n",
    "    sample_data$veicolo_a[i] = reset_veicolo(tipo_veicolo_a[i])\n",
    "    sample_data$veicolo_b[i] = reset_veicolo(tipo_veicoli__b_[i])\n",
    "    sample_data$veicolo_c[i] = reset_veicolo(tipo_veicolo__c_[i])\n",
    "    sample_data$presenza_morti[i] = presenza_morti(sample_data$morti[i])\n",
    "    sample_data$coinvolta_moto[i] = coinvolta_moto(sample_data$veicolo_a[i], sample_data$veicolo_b[i], sample_data$veicolo_c[i])\n",
    "    sample_data$coinvolta_auto[i] = coinvolta_auto(sample_data$veicolo_a[i], sample_data$veicolo_b[i], sample_data$veicolo_c[i])\n",
    "    sample_data$coinvolto_mezzo[i] = coinvolto_mezzo(sample_data$veicolo_a[i], sample_data$veicolo_b[i], sample_data$veicolo_c[i])\n",
    "}\n",
    "sample_data$veicolo_a <- factor(sample_data$veicolo_a)\n",
    "sample_data$veicolo_b <- factor(sample_data$veicolo_b)\n",
    "sample_data$veicolo_c <- factor(sample_data$veicolo_c)\n",
    "levels(sample_data$veicolo_a) <- c(\"Auto\", \"Mezzo pesante\", \"Motociclo\")\n",
    "levels(sample_data$veicolo_b) <- c(\"Sconosciuto\", \"Auto\", \"Mezzo pesante\", \"Motociclo\")\n",
    "levels(sample_data$veicolo_c) <- c(\"Sconosciuto\", \"Auto\", \"Mezzo pesante\", \"Motociclo\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<caption>A data.frame: 6 × 125</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>anno</th><th scope=col>provincia</th><th scope=col>comune</th><th scope=col>giorno</th><th scope=col>organo_di_rilevazione</th><th scope=col>localizzazione_incidente</th><th scope=col>denominazione_della_strada</th><th scope=col>tronco_di_strada_o_autostrada</th><th scope=col>tipo_di_strada</th><th scope=col>pavimentazione</th><th scope=col>⋯</th><th scope=col>ettometrica</th><th scope=col>trimestre</th><th scope=col>morti</th><th scope=col>veicolo_a</th><th scope=col>veicolo_b</th><th scope=col>veicolo_c</th><th scope=col>presenza_morti</th><th scope=col>coinvolta_moto</th><th scope=col>coinvolta_auto</th><th scope=col>coinvolto_mezzo</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>⋯</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><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><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><th scope=row>116257</th><td>17</td><td>23</td><td> 17</td><td>5</td><td>4</td><td>1</td><td>0 </td><td>12</td><td>2</td><td>1</td><td>⋯</td><td>0  </td><td>3</td><td>0</td><td>Auto         </td><td>Sconosciuto  </td><td>Sconosciuto  </td><td>0</td><td>0</td><td>1</td><td>0</td></tr>\n",
       "\t<tr><th scope=row>70934</th><td>17</td><td>30</td><td>122</td><td>6</td><td>2</td><td>5</td><td>13</td><td>12</td><td>2</td><td>1</td><td>⋯</td><td>0  </td><td>4</td><td>0</td><td>Auto         </td><td>Motociclo    </td><td>Sconosciuto  </td><td>0</td><td>1</td><td>1</td><td>0</td></tr>\n",
       "\t<tr><th scope=row>36576</th><td>17</td><td>12</td><td> 42</td><td>6</td><td>2</td><td>1</td><td>0 </td><td>12</td><td>3</td><td>1</td><td>⋯</td><td>0  </td><td>2</td><td>0</td><td>Auto         </td><td>Auto         </td><td>Sconosciuto  </td><td>0</td><td>0</td><td>1</td><td>0</td></tr>\n",
       "\t<tr><th scope=row>7190</th><td>17</td><td>54</td><td> 39</td><td>4</td><td>1</td><td>6</td><td>3 </td><td> 3</td><td>3</td><td>1</td><td>⋯</td><td>100</td><td>2</td><td>0</td><td>Mezzo pesante</td><td>Mezzo pesante</td><td>Mezzo pesante</td><td>0</td><td>0</td><td>0</td><td>1</td></tr>\n",
       "\t<tr><th scope=row>7431</th><td>17</td><td>56</td><td> 36</td><td>1</td><td>1</td><td>1</td><td>0 </td><td>12</td><td>2</td><td>1</td><td>⋯</td><td>0  </td><td>3</td><td>0</td><td>Motociclo    </td><td>Auto         </td><td>Sconosciuto  </td><td>0</td><td>1</td><td>1</td><td>0</td></tr>\n",
       "\t<tr><th scope=row>136922</th><td>17</td><td>36</td><td> 23</td><td>4</td><td>4</td><td>1</td><td>0 </td><td>12</td><td>2</td><td>1</td><td>⋯</td><td>0  </td><td>3</td><td>0</td><td>Auto         </td><td>Motociclo    </td><td>Sconosciuto  </td><td>0</td><td>1</td><td>1</td><td>0</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 6 × 125\n",
       "\\begin{tabular}{r|lllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllll}\n",
       "  & anno & provincia & comune & giorno & organo\\_di\\_rilevazione & localizzazione\\_incidente & denominazione\\_della\\_strada & tronco\\_di\\_strada\\_o\\_autostrada & tipo\\_di\\_strada & pavimentazione & intersezione\\_o\\_non\\_interse3 & fondo\\_stradale & segnaletica & condizioni\\_meteorologiche & natura\\_incidente & tipo\\_veicolo\\_a & tipo\\_veicoli\\_\\_b\\_ & tipo\\_veicolo\\_\\_c\\_ & veicolo\\_\\_a\\_\\_\\_\\_circostanze\\_10 & veicolo\\_\\_a\\_\\_\\_\\_circostanze\\_11 & veicolo\\_\\_b\\_\\_\\_\\_circostanze\\_13 & veicolo\\_\\_b\\_\\_\\_\\_circostanze\\_14 & veicolo\\_\\_a\\_\\_\\_anno\\_immatric18 & immatricolazione\\_veicolo\\_\\_b\\_ & immatricolazione\\_veicolo\\_\\_c\\_ & veicolo\\_\\_a\\_\\_\\_et\\_\\_conducente & veicolo\\_\\_a\\_\\_\\_sesso\\_conducente & veicolo\\_\\_a\\_\\_\\_esito\\_conducente & veicolo\\_\\_a\\_\\_\\_patente\\_condu29 & veicolo\\_\\_a\\_\\_\\_anno\\_rilascio30 & veicolo\\_\\_a\\_\\_\\_passeggeri\\_an35 & veicolo\\_\\_a\\_\\_\\_et\\_\\_passegger36 & veicolo\\_\\_a\\_\\_\\_sesso\\_passegg37 & veicolo\\_\\_a\\_\\_\\_esito\\_passegg38 & veicolo\\_\\_a\\_\\_\\_et\\_\\_passegger39 & veicolo\\_\\_a\\_\\_\\_sesso\\_passegg40 & veicolo\\_\\_a\\_\\_\\_esito\\_passegg41 & veicolo\\_\\_a\\_\\_\\_et\\_\\_passegger42 & veicolo\\_\\_a\\_\\_\\_sesso\\_passegg43 & veicolo\\_\\_a\\_\\_\\_esito\\_passegg44 & veicolo\\_\\_a\\_\\_\\_et\\_\\_passegger45 & veicolo\\_\\_a\\_\\_\\_sesso\\_passegg46 & veicolo\\_\\_a\\_\\_\\_altri\\_passegg47 & veicolo\\_\\_a\\_\\_\\_altri\\_passegg48 & veicolo\\_\\_a\\_\\_\\_altri\\_passegg49 & veicolo\\_\\_a\\_\\_\\_altri\\_passegg50 & veicolo\\_\\_b\\_\\_\\_et\\_\\_conducente & veicolo\\_\\_b\\_\\_\\_sesso\\_conducente & veicolo\\_\\_b\\_\\_\\_esito\\_conducente & veicolo\\_\\_b\\_\\_\\_patente\\_condu51 & veicolo\\_\\_b\\_\\_\\_anno\\_rilascio52 & veicolo\\_\\_b\\_\\_\\_passeggeri\\_an57 & veicolo\\_\\_b\\_\\_\\_et\\_\\_passegger58 & veicolo\\_\\_b\\_\\_\\_sesso\\_passegg59 & veicolo\\_\\_b\\_\\_\\_esito\\_passegg60 & veicolo\\_\\_b\\_\\_\\_et\\_\\_passegger61 & veicolo\\_\\_b\\_\\_\\_sesso\\_passegg62 & veicolo\\_\\_b\\_\\_\\_esito\\_passegg63 & veicolo\\_\\_b\\_\\_\\_et\\_\\_passegger64 & veicolo\\_\\_b\\_\\_\\_sesso\\_passegg65 & veicolo\\_\\_b\\_\\_\\_esito\\_passegg66 & veicolo\\_\\_b\\_\\_\\_et\\_\\_passegger67 & veicolo\\_\\_b\\_\\_\\_sesso\\_passegg68 & veicolo\\_\\_b\\_\\_\\_altri\\_passegg69 & veicolo\\_\\_b\\_\\_\\_altri\\_passegg70 & veicolo\\_\\_b\\_\\_\\_altri\\_passegg71 & veicolo\\_\\_b\\_\\_\\_altri\\_passegg72 & veicolo\\_\\_c\\_\\_\\_et\\_\\_conducente & veicolo\\_\\_c\\_\\_\\_sesso\\_conducente & veicolo\\_\\_c\\_\\_\\_esito\\_conducente & veicolo\\_\\_c\\_\\_\\_patente\\_condu73 & veicolo\\_\\_c\\_\\_\\_anno\\_rilascio74 & veicolo\\_\\_c\\_\\_\\_passeggeri\\_an79 & veicolo\\_\\_c\\_\\_\\_et\\_\\_passegger80 & veicolo\\_\\_c\\_\\_\\_sesso\\_passegg81 & veicolo\\_\\_c\\_\\_\\_esito\\_passegg82 & veicolo\\_\\_c\\_\\_\\_et\\_\\_passegger83 & veicolo\\_\\_c\\_\\_\\_sesso\\_passegg84 & veicolo\\_\\_c\\_\\_\\_esito\\_passegg85 & veicolo\\_\\_c\\_\\_\\_et\\_\\_passegger86 & veicolo\\_\\_c\\_\\_\\_sesso\\_passegg87 & veicolo\\_\\_c\\_\\_\\_esito\\_passegg88 & veicolo\\_\\_c\\_\\_\\_et\\_\\_passegger89 & veicolo\\_\\_c\\_\\_\\_sesso\\_passegg90 & veicolo\\_\\_c\\_\\_\\_altri\\_passegg91 & veicolo\\_\\_c\\_\\_\\_altri\\_passegg92 & veicolo\\_\\_c\\_\\_\\_altri\\_passegg93 & veicolo\\_\\_c\\_\\_\\_altri\\_passegg94 & pedone\\_morto\\_1\\_\\_sesso & pedone\\_morto\\_1\\_\\_et\\_ & pedone\\_ferito\\_1\\_\\_sesso & pedone\\_ferito\\_1\\_\\_et\\_ & pedone\\_morto\\_2\\_\\_sesso & pedone\\_morto\\_2\\_\\_et\\_ & pedone\\_ferito\\_2\\_\\_sesso & pedone\\_ferito\\_2\\_\\_et\\_ & pedone\\_morto\\_3\\_\\_sesso & pedone\\_morto\\_3\\_\\_et\\_ & pedone\\_ferito\\_3\\_\\_sesso & pedone\\_ferito\\_3\\_\\_et\\_ & pedone\\_morto\\_4\\_\\_sesso & pedone\\_morto\\_4\\_\\_et\\_ & pedone\\_ferito\\_4\\_\\_sesso & pedone\\_ferito\\_4\\_\\_et\\_ & altri\\_veicoli\\_coinvolti & morti\\_maschi\\_coinvolti\\_su\\_95 & morti\\_femmine\\_coinvolti\\_su96 & feriti\\_maschi\\_coinvolti\\_su97 & feriti\\_femmine\\_coinvolti\\_s98 & morti\\_entro\\_24\\_ore & morti\\_entro\\_30\\_giorni & feriti & descrizione\\_strada & Ora & chilometri & ettometrica & trimestre & morti & veicolo\\_a & veicolo\\_b & veicolo\\_c & presenza\\_morti & coinvolta\\_moto & coinvolta\\_auto & coinvolto\\_mezzo\\\\\n",
       "  & <int> & <int> & <int> & <int> & <int> & <int> & <fct> & <int> & <int> & <int> & <int> & <int> & <int> & <int> & <int> & <int> & <int> & <int> & <int> & <int> & <int> & <int> & <fct> & <fct> & <fct> & <fct> & <int> & <int> & <int> & <fct> & <int> & <fct> & <int> & <int> & <fct> & <int> & <int> & <fct> & <int> & <int> & <fct> & <int> & <int> & <int> & <int> & <int> & <fct> & <int> & <int> & <int> & <fct> & <int> & <fct> & <int> & <int> & <fct> & <int> & <int> & <fct> & <int> & <int> & <fct> & <int> & <int> & <int> & <int> & <int> & <fct> & <int> & <int> & <int> & <fct> & <int> & <fct> & <int> & <int> & <fct> & <int> & <int> & <fct> & <int> & <int> & <fct> & <int> & <int> & <int> & <int> & <int> & <int> & <fct> & <int> & <fct> & <int> & <fct> & <int> & <fct> & <lgl> & <lgl> & <int> & <fct> & <lgl> & <lgl> & <int> & <fct> & <int> & <int> & <int> & <int> & <int> & <int> & <int> & <int> & <lgl> & <int> & <fct> & <fct> & <int> & <int> & <fct> & <fct> & <fct> & <dbl> & <dbl> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\t116257 & 17 & 23 &  17 & 5 & 4 & 1 & 0  & 12 & 2 & 1 & 1 & 1 & 4 & 1 & 5 &  1 & NA & NA & 45 & NA & 53 & NA & (1997,2007{]} &             &             & 30-44 & 2 & 1 & 2 & (1987,1997{]} & NA &       & NA & NA &       & NA & NA &       & NA & NA &       & NA & 0 & 0 & 0 & 0 &       & NA & NA & NA &             & NA &       & NA & NA &       & NA & NA &       & NA & NA &       & NA & 0 & 0 & 0 & 0 &       & NA & NA & NA &             & NA &       & NA & NA &       & NA & NA &       & NA & NA &       & NA & 0 & 0 & 0 & 0 & NA &       &  4 & 65+   & NA &       & NA &       & NA & NA & NA &       & NA & NA & NA &       & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 1 & NA & 10 & 0  & 0   & 3 & 0 & Auto          & Sconosciuto   & Sconosciuto   & 0 & 0 & 1 & 0\\\\\n",
       "\t70934 & 17 & 30 & 122 & 6 & 2 & 5 & 13 & 12 & 2 & 1 & 3 & 1 & 4 & 1 & 2 &  1 & 14 & NA &  5 & NA &  1 & NA & 2010        &             &             & 45-54 & 2 & 1 & 2 & (1977,1987{]} & NA &       & NA & NA &       & NA & NA &       & NA & NA &       & NA & 0 & 0 & 0 & 0 & 55-64 &  1 &  2 &  7 &             & NA &       & NA & NA &       & NA & NA &       & NA & NA &       & NA & 0 & 0 & 0 & 0 &       & NA & NA & NA &             & NA &       & NA & NA &       & NA & NA &       & NA & NA &       & NA & 0 & 0 & 0 & 0 & NA &       & NA &       & NA &       & NA &       & NA & NA & NA &       & NA & NA & NA &       & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 1 & NA & 12 & 0  & 0   & 4 & 0 & Auto          & Motociclo     & Sconosciuto   & 0 & 1 & 1 & 0\\\\\n",
       "\t36576 & 17 & 12 &  42 & 6 & 2 & 1 & 0  & 12 & 3 & 1 & 7 & 1 & 4 & 1 & 2 &  1 &  1 & NA & 20 & NA & 35 & NA & (1997,2007{]} & 2015        &             & 30-44 & 1 & 2 & 2 & 2015        & NA &       & NA & NA &       & NA & NA &       & NA & NA &       & NA & 0 & 0 & 0 & 0 & 45-54 &  1 &  2 &  5 & 2016        & NA &       & NA & NA &       & NA & NA &       & NA & NA &       & NA & 0 & 0 & 0 & 0 &       & NA & NA & NA &             & NA &       & NA & NA &       & NA & NA &       & NA & NA &       & NA & 0 & 0 & 0 & 0 & NA &       & NA &       & NA &       & NA &       & NA & NA & NA &       & NA & NA & NA &       & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 2 & NA &  6 & 0  & 0   & 2 & 0 & Auto          & Auto          & Sconosciuto   & 0 & 0 & 1 & 0\\\\\n",
       "\t7190 & 17 & 54 &  39 & 4 & 1 & 6 & 3  &  3 & 3 & 1 & 1 & 1 & 4 & 1 & 4 & 10 &  8 &  8 & 11 & NA &  1 & NA &             &             &             & 45-54 & 1 & 1 & 3 & 2016        & NA &       & NA & NA &       & NA & NA &       & NA & NA &       & NA & 0 & 0 & 0 & 0 & 45-54 &  1 &  2 &  2 & (1997,2007{]} &  2 & 30-44 &  3 &  2 & 30-44 &  3 &  2 & 55-64 &  3 & NA &       & NA & 0 & 0 & 0 & 0 & 45-54 &  1 &  2 &  3 & 2014        & NA &       & NA & NA &       & NA & NA &       & NA & NA &       & NA & 0 & 0 & 0 & 0 & NA &       & NA &       & NA &       & NA &       & NA & NA & NA &       & NA & NA & NA &       & 1 & 0 & 0 & 0 & 0 & 0 & 0 & 5 & NA &  4 & 88 & 100 & 2 & 0 & Mezzo pesante & Mezzo pesante & Mezzo pesante & 0 & 0 & 0 & 1\\\\\n",
       "\t7431 & 17 & 56 &  36 & 1 & 1 & 1 & 0  & 12 & 2 & 1 & 3 & 1 & 4 & 1 & 2 & 16 &  1 & NA &  1 & NA &  4 & NA &             & 2016        &             & 30-44 & 1 & 2 & 2 & (1997,2007{]} & NA &       & NA & NA &       & NA & NA &       & NA & NA &       & NA & 0 & 0 & 0 & 0 & 55-64 &  2 &  1 &  2 & 2017        & NA &       & NA & NA &       & NA & NA &       & NA & NA &       & NA & 0 & 0 & 0 & 0 &       & NA & NA & NA &             & NA &       & NA & NA &       & NA & NA &       & NA & NA &       & NA & 0 & 0 & 0 & 0 & NA &       & NA &       & NA &       & NA &       & NA & NA & NA &       & NA & NA & NA &       & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 1 & NA & 20 & 0  & 0   & 3 & 0 & Motociclo     & Auto          & Sconosciuto   & 0 & 1 & 1 & 0\\\\\n",
       "\t136922 & 17 & 36 &  23 & 4 & 4 & 1 & 0  & 12 & 2 & 1 & 7 & 1 & 4 & 7 & 2 &  1 & 14 & NA & 21 & NA & 21 & NA & 2014        &             &             & 45-54 & 1 & 1 & 2 & 2017        & NA &       & NA & NA &       & NA & NA &       & NA & NA &       & NA & 0 & 0 & 0 & 0 & 65+   &  1 &  2 &  7 &             & NA &       & NA & NA &       & NA & NA &       & NA & NA &       & NA & 0 & 0 & 0 & 0 &       & NA & NA & NA &             & NA &       & NA & NA &       & NA & NA &       & NA & NA &       & NA & 0 & 0 & 0 & 0 & NA &       & NA &       & NA &       & NA &       & NA & NA & NA &       & NA & NA & NA &       & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 1 & NA &  8 & 0  & 0   & 3 & 0 & Auto          & Motociclo     & Sconosciuto   & 0 & 1 & 1 & 0\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 6 × 125\n",
       "\n",
       "| <!--/--> | anno &lt;int&gt; | provincia &lt;int&gt; | comune &lt;int&gt; | giorno &lt;int&gt; | organo_di_rilevazione &lt;int&gt; | localizzazione_incidente &lt;int&gt; | denominazione_della_strada &lt;fct&gt; | tronco_di_strada_o_autostrada &lt;int&gt; | tipo_di_strada &lt;int&gt; | pavimentazione &lt;int&gt; | ⋯ ⋯ | ettometrica &lt;fct&gt; | trimestre &lt;int&gt; | morti &lt;int&gt; | veicolo_a &lt;fct&gt; | veicolo_b &lt;fct&gt; | veicolo_c &lt;fct&gt; | presenza_morti &lt;dbl&gt; | coinvolta_moto &lt;dbl&gt; | coinvolta_auto &lt;dbl&gt; | coinvolto_mezzo &lt;dbl&gt; |\n",
       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| 116257 | 17 | 23 |  17 | 5 | 4 | 1 | 0  | 12 | 2 | 1 | ⋯ | 0   | 3 | 0 | Auto          | Sconosciuto   | Sconosciuto   | 0 | 0 | 1 | 0 |\n",
       "| 70934 | 17 | 30 | 122 | 6 | 2 | 5 | 13 | 12 | 2 | 1 | ⋯ | 0   | 4 | 0 | Auto          | Motociclo     | Sconosciuto   | 0 | 1 | 1 | 0 |\n",
       "| 36576 | 17 | 12 |  42 | 6 | 2 | 1 | 0  | 12 | 3 | 1 | ⋯ | 0   | 2 | 0 | Auto          | Auto          | Sconosciuto   | 0 | 0 | 1 | 0 |\n",
       "| 7190 | 17 | 54 |  39 | 4 | 1 | 6 | 3  |  3 | 3 | 1 | ⋯ | 100 | 2 | 0 | Mezzo pesante | Mezzo pesante | Mezzo pesante | 0 | 0 | 0 | 1 |\n",
       "| 7431 | 17 | 56 |  36 | 1 | 1 | 1 | 0  | 12 | 2 | 1 | ⋯ | 0   | 3 | 0 | Motociclo     | Auto          | Sconosciuto   | 0 | 1 | 1 | 0 |\n",
       "| 136922 | 17 | 36 |  23 | 4 | 4 | 1 | 0  | 12 | 2 | 1 | ⋯ | 0   | 3 | 0 | Auto          | Motociclo     | Sconosciuto   | 0 | 1 | 1 | 0 |\n",
       "\n"
      ],
      "text/plain": [
       "       anno provincia comune giorno organo_di_rilevazione\n",
       "116257 17   23         17    5      4                    \n",
       "70934  17   30        122    6      2                    \n",
       "36576  17   12         42    6      2                    \n",
       "7190   17   54         39    4      1                    \n",
       "7431   17   56         36    1      1                    \n",
       "136922 17   36         23    4      4                    \n",
       "       localizzazione_incidente denominazione_della_strada\n",
       "116257 1                        0                         \n",
       "70934  5                        13                        \n",
       "36576  1                        0                         \n",
       "7190   6                        3                         \n",
       "7431   1                        0                         \n",
       "136922 1                        0                         \n",
       "       tronco_di_strada_o_autostrada tipo_di_strada pavimentazione ⋯\n",
       "116257 12                            2              1              ⋯\n",
       "70934  12                            2              1              ⋯\n",
       "36576  12                            3              1              ⋯\n",
       "7190    3                            3              1              ⋯\n",
       "7431   12                            2              1              ⋯\n",
       "136922 12                            2              1              ⋯\n",
       "       ettometrica trimestre morti veicolo_a     veicolo_b     veicolo_c    \n",
       "116257 0           3         0     Auto          Sconosciuto   Sconosciuto  \n",
       "70934  0           4         0     Auto          Motociclo     Sconosciuto  \n",
       "36576  0           2         0     Auto          Auto          Sconosciuto  \n",
       "7190   100         2         0     Mezzo pesante Mezzo pesante Mezzo pesante\n",
       "7431   0           3         0     Motociclo     Auto          Sconosciuto  \n",
       "136922 0           3         0     Auto          Motociclo     Sconosciuto  \n",
       "       presenza_morti coinvolta_moto coinvolta_auto coinvolto_mezzo\n",
       "116257 0              0              1              0              \n",
       "70934  0              1              1              0              \n",
       "36576  0              0              1              0              \n",
       "7190   0              0              0              1              \n",
       "7431   0              1              1              0              \n",
       "136922 0              1              1              0              "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#visualizzo il dataset\n",
    "head(sample_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Un po' di statistica univariata"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Numero medio di feriti e morti per incidente grave"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Nel 2017 la media di feriti per incidente è stata:  1.4098 +- 0.878466 \n",
      "Nel 2017 la media di feriti per incidente è stata:  0.0199 +- 0.15035"
     ]
    }
   ],
   "source": [
    "#calcolo media e deviazione standard di feriti e morti per incidente stradale \n",
    "MEAN_INJ = mean(sample_data$feriti)\n",
    "SD_INJ = sd(sample_data$feriti)\n",
    "MEAN_DEA = mean(sample_data$morti)\n",
    "SD_DEA = sd(sample_data$morti)\n",
    "cat(\"Nel 2017 la media di feriti per incidente è stata: \", MEAN_INJ, \"+-\", SD_INJ, \"\\n\")\n",
    "cat(\"Nel 2017 la media di feriti per incidente è stata: \", MEAN_DEA, \"+-\", SD_DEA)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Annotazioni**: non disponendo della data dei singoli incidenti non si è in grado di fornire un indice di variabilità della misura."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plotto le frequenze relative del numero di morti e feriti per incidente stradale"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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w88kMSwC8p9v4q1c+fO8owUiURGjx4d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\nmaDYAQAAyMT/AGUU0BLcL/NaAAAAAElFTkSuQmCC",
      "text/plain": [
       "Plot with title “Numero di morti per ciascun incidente stradale”"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      },
      "text/plain": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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uv79OlTHjNV7ACAKqHK3O4kKj09PT09PQiC/Pz87du3RyKRZs2aVatWZQ4oAwCU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\nAsUOAAAAgCNQ7AAAAAA44v8DmGEyoR0WWr0AAAAASUVORK5CYII=",
      "text/plain": [
       "Plot with title “Numero di feriti per ciascun incidente stradale”"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      },
      "text/plain": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#morti\n",
    "DEATHS_TABLE = table(sample_data$morti)\n",
    "barplot(DEATHS_TABLE/nrow(sample_data), \n",
    "        main = \"Numero di morti per ciascun incidente stradale\", \n",
    "        xlab = \"N° di morti\", \n",
    "        ylab = \"Frequenza relativa\", \n",
    "        ylim=c(0,1), \n",
    "        col = \"lightblue\"\n",
    "       )\n",
    "#feriti\n",
    "INJ_TABLE = table(sample_data$feriti)\n",
    "barplot(INJ_TABLE/nrow(sample_data), \n",
    "        main = \"Numero di feriti per ciascun incidente stradale\", \n",
    "        xlab = \"N° di feriti\", \n",
    "        ylab = \"Frequenza relativa\", \n",
    "        ylim=c(0,1), \n",
    "        col = \"lightblue\"\n",
    "       )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Numero di iferiti e morti diviso per trimestre e ora del giorno"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "#CI SONO DEI NAN CHE NON HO IDEA DA DOVE VENGANO E LI HO LEVATI\n",
    "MEAN_INJ_TRIM = summarySE(sample_data, measurevar = \"feriti\", groupvars = c(\"trimestre\"), na.rm = TRUE, .drop = TRUE)\n",
    "MEAN_INJ_TRIM = MEAN_INJ_TRIM[complete.cases(MEAN_INJ_TRIM), ]\n",
    "MEAN_DEA_TRIM = summarySE(sample_data, measurevar = \"morti\", groupvars = c(\"trimestre\"), na.rm = TRUE, .drop = TRUE)\n",
    "MEAN_DEA_TRIM = MEAN_DEA_TRIM[complete.cases(MEAN_DEA_TRIM), ]\n",
    "\n",
    "#rimuovo le righe dove l'orario non è noto\n",
    "MEAN_INJ_HOUR = summarySE(sample_data[Ora != 25,], measurevar = \"feriti\", groupvars = c(\"Ora\"), na.rm = FALSE, .drop = TRUE)\n",
    "MEAN_DEA_HOUR = summarySE(sample_data[Ora != 25,], measurevar = \"morti\", groupvars = c(\"Ora\"), na.rm = FALSE, .drop = TRUE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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vlnnbnLly+vrq4ODg6+/fbbPVIeAACASzXtmyeWLVt29OhRIcRrr70m3SEyaNCg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      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      },
      "text/plain": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    },
    {
     "data": {
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wAAAJzmWxdPnDx58ubNm0KIMWPGSJlM0qJFi759+woh9u/fb7Va3dXDhQsXhBB+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\nKn6Gmv9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      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      },
      "text/plain": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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WhcPl9ptDfl/Wg0GrU7Ds1msxZfK/JmkcfJ14Kme1PT07hGe1Nu3Gw2+/ul7P3M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\nVJSRkfH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      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      },
      "text/plain": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    },
    {
     "data": {
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HSCYAcAAKATBDsAAACdINgBAADoBMEOAABAJwh2AAAAOmG+3gVAz44fP56QkCBN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Ni2ITBV0gCLcCFHYAAP9DJpNFR0cLaalQ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\nAIBA/Aul+MCUVZpO0gAAAABJRU5ErkJggg==",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      },
      "text/plain": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plotto i risultati - per trimestre\n",
    "#prima i feriti\n",
    "MEAN_INJ_TRIM$trimestre <- factor(MEAN_INJ_TRIM$trimestre)\n",
    "levels(MEAN_INJ_TRIM$trimestre) <- c(\"Gen-Mar\", \"Apr-Giu\", \"Lug-Set\", \"Ott-Dic\")\n",
    "bar_inj_trim <- ggplot(MEAN_INJ_TRIM, aes(x=trimestre, y=feriti)) + \n",
    "                geom_bar(position=position_dodge(), stat=\"identity\", color=\"white\", fill=\"lightblue\") +\n",
    "                geom_errorbar(aes(ymin=feriti-se, ymax=feriti+se),\n",
    "                              width=.3,                    # Width of the error bars\n",
    "                              position=position_dodge(.9)\n",
    "                             ) + \n",
    "                labs(\n",
    "                    title = \"Media di feriti per incidente - Trimestre\",\n",
    "                    x = \"Trimestre\",\n",
    "                    y = \"Media di feriti per incidente\"\n",
    "                   ) + \n",
    "                theme(\n",
    "                     text = element_text(size=20), \n",
    "                     plot.title = element_text(size = 23, vjust = 1, margin = margin(10,0,10,0), hjust = 0.5),\n",
    "                    )\n",
    "\n",
    "#poi i morti\n",
    "MEAN_DEA_TRIM$trimestre <- factor(MEAN_DEA_TRIM$trimestre)\n",
    "levels(MEAN_DEA_TRIM$trimestre) <- c(\"Gen-Mar\", \"Apr-Giu\", \"Lug-Set\", \"Ott-Dic\")\n",
    "bar_dea_trim <- ggplot(MEAN_DEA_TRIM, aes(x=trimestre, y=morti)) + \n",
    "                geom_bar(position=position_dodge(), stat=\"identity\", color=\"white\", fill=\"lightblue\") +\n",
    "                geom_errorbar(aes(ymin=morti-se, ymax=morti+se),\n",
    "                              width=.3,                    # Width of the error bars\n",
    "                              position=position_dodge(.9)\n",
    "                             ) +\n",
    "                labs(\n",
    "                    title = \"Media di vittime per incidente - Trimestre\",\n",
    "                    x = \"Trimestre\",\n",
    "                    y = \"Media di vittime per incidente\"\n",
    "                   ) + \n",
    "                theme(\n",
    "                     text = element_text(size=20), \n",
    "                     plot.title = element_text(size = 23, vjust = 1, margin = margin(10,0,10,0), hjust = 0.5),\n",
    "                    )\n",
    "\n",
    "\n",
    "\n",
    "#plotto i risultati - per ora\n",
    "\n",
    "\n",
    "#prima i feriti\n",
    "MEAN_INJ_HOUR$Ora <- factor(MEAN_INJ_HOUR$Ora)\n",
    "levels(MEAN_INJ_HOUR$Ora) <- c(\"00\",\"1\",\"2\",\"3\",\"4\",\"5\",\"6\",\"7\",\"8\",\"9\",\"10\",\"11\",\"12\",\"13\",\"14\",\"15\",\"16\",\"17\",\"18\",\"19\",\"20\",\"21\",\"22\",\"23\")\n",
    "bar_inj_hour <- ggplot(MEAN_INJ_HOUR, aes(x=Ora, y=feriti)) + \n",
    "                geom_bar(position=position_dodge(), stat=\"identity\", color=\"white\", fill=\"lightblue\") +\n",
    "                geom_errorbar(aes(ymin=feriti-se, ymax=feriti+se),\n",
    "                              width=.3,                    # Width of the error bars\n",
    "                              position=position_dodge(.9)\n",
    "                             ) + \n",
    "                labs(\n",
    "                    title = \"Media di feriti per incidente - Ora\",\n",
    "                    x = \"Ora\",\n",
    "                    y = \"Media di feriti per incidente\"\n",
    "                   ) + \n",
    "                theme(\n",
    "                     text = element_text(size=18), \n",
    "                     plot.title = element_text(size = 23, vjust = 1, margin = margin(10,0,10,0), hjust = 0.5),\n",
    "                    )\n",
    "\n",
    "\n",
    "#poi i morti\n",
    "MEAN_DEA_HOUR$Ora <- factor(MEAN_DEA_HOUR$Ora)\n",
    "levels(MEAN_DEA_HOUR$Ora) <- c(\"00\",\"1\",\"2\",\"3\",\"4\",\"5\",\"6\",\"7\",\"8\",\"9\",\"10\",\"11\",\"12\",\"13\",\"14\",\"15\",\"16\",\"17\",\"18\",\"19\",\"20\",\"21\",\"22\",\"23\")\n",
    "bar_dea_hour <- ggplot(MEAN_DEA_HOUR, aes(x=Ora, y=morti)) + \n",
    "                geom_bar(position=position_dodge(), stat=\"identity\", color=\"white\", fill=\"lightblue\") +\n",
    "                geom_errorbar(aes(ymin=morti-se, ymax=morti+se),\n",
    "                              width=.3,                    # Width of the error bars\n",
    "                              position=position_dodge(.9)\n",
    "                             ) + \n",
    "                labs(\n",
    "                    title = \"Media di vittime per incidente - Ora\",\n",
    "                    x = \"Ora\",\n",
    "                    y = \"Media di vittime per incidente\"\n",
    "                   ) + \n",
    "                theme(\n",
    "                     text = element_text(size=18), \n",
    "                     plot.title = element_text(size = 23, vjust = 1, margin = margin(10,0,10,0), hjust = 0.5),\n",
    "                    )\n",
    "\n",
    "\n",
    "#-------------PLOTS--------------------\n",
    "bar_inj_trim  \n",
    "bar_dea_trim\n",
    "bar_inj_hour\n",
    "bar_dea_hour"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Test d'ipotesi, in ciascun trimestre, il numero di incidenti al giorno è lo stesso"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "\tOne-way analysis of means (not assuming equal variances)\n",
       "\n",
       "data:  feriti and trimestre\n",
       "F = 2.456, num df = 3, denom df = 10983, p-value = 0.06111\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "\n",
       "\tOne-way analysis of means (not assuming equal variances)\n",
       "\n",
       "data:  morti and trimestre\n",
       "F = 0.33103, num df = 3, denom df = 10910, p-value = 0.8029\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "\n",
       "\tOne-way analysis of means (not assuming equal variances)\n",
       "\n",
       "data:  feriti and Ora\n",
       "F = 6.2544, num df = 23.0, denom df = 3898.3, p-value < 2.2e-16\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " \n",
      " Nel caso dei feriti, il test è statisticamente significativo - Eseguo un t-test fra tutte le coppie"
     ]
    },
    {
     "data": {
      "text/plain": [
       "\n",
       "\tPairwise comparisons using t tests with pooled SD \n",
       "\n",
       "data:  sample_data[Ora != 25, ]$feriti and sample_data[Ora != 25, ]$Ora \n",
       "\n",
       "   1       2       3       4       5       6       7       8       9      \n",
       "2  1.00000 -       -       -       -       -       -       -       -      \n",
       "3  1.00000 0.20123 -       -       -       -       -       -       -      \n",
       "4  1.00000 0.67591 1.00000 -       -       -       -       -       -      \n",
       "5  1.00000 0.10853 1.00000 1.00000 -       -       -       -       -      \n",
       "6  0.36126 0.00705 1.00000 1.00000 1.00000 -       -       -       -      \n",
       "7  1.1e-05 3.3e-08 1.00000 1.00000 1.00000 1.00000 -       -       -      \n",
       "8  2.5e-06 4.8e-09 1.00000 1.00000 1.00000 1.00000 1.00000 -       -      \n",
       "9  3.7e-08 6.4e-11 1.00000 1.00000 1.00000 1.00000 1.00000 1.00000 -      \n",
       "10 6.5e-10 1.2e-12 0.37339 1.00000 1.00000 1.00000 1.00000 1.00000 1.00000\n",
       "11 1.1e-07 2.0e-10 1.00000 1.00000 1.00000 1.00000 1.00000 1.00000 1.00000\n",
       "12 6.6e-08 1.2e-10 1.00000 1.00000 1.00000 1.00000 1.00000 1.00000 1.00000\n",
       "13 0.00013 2.8e-07 1.00000 1.00000 1.00000 1.00000 1.00000 1.00000 1.00000\n",
       "14 1.1e-06 2.1e-09 1.00000 1.00000 1.00000 1.00000 1.00000 1.00000 1.00000\n",
       "15 0.00041 9.6e-07 1.00000 1.00000 1.00000 1.00000 1.00000 1.00000 1.00000\n",
       "16 1.7e-05 3.4e-08 1.00000 1.00000 1.00000 1.00000 1.00000 1.00000 1.00000\n",
       "17 0.00036 8.1e-07 1.00000 1.00000 1.00000 1.00000 1.00000 1.00000 1.00000\n",
       "18 1.8e-05 3.4e-08 1.00000 1.00000 1.00000 1.00000 1.00000 1.00000 1.00000\n",
       "19 0.00254 6.6e-06 1.00000 1.00000 1.00000 1.00000 1.00000 1.00000 0.21991\n",
       "20 0.00488 1.5e-05 1.00000 1.00000 1.00000 1.00000 1.00000 1.00000 0.43989\n",
       "21 0.00397 1.3e-05 1.00000 1.00000 1.00000 1.00000 1.00000 1.00000 1.00000\n",
       "22 1.00000 0.39502 1.00000 1.00000 1.00000 1.00000 0.00796 0.00542 0.00017\n",
       "23 1.00000 0.34948 1.00000 1.00000 1.00000 1.00000 0.03554 0.04161 0.00269\n",
       "24 1.00000 1.00000 1.00000 1.00000 1.00000 1.00000 0.00785 0.00767 0.00041\n",
       "   10      11      12      13      14      15      16      17      18     \n",
       "2  -       -       -       -       -       -       -       -       -      \n",
       "3  -       -       -       -       -       -       -       -       -      \n",
       "4  -       -       -       -       -       -       -       -       -      \n",
       "5  -       -       -       -       -       -       -       -       -      \n",
       "6  -       -       -       -       -       -       -       -       -      \n",
       "7  -       -       -       -       -       -       -       -       -      \n",
       "8  -       -       -       -       -       -       -       -       -      \n",
       "9  -       -       -       -       -       -       -       -       -      \n",
       "10 -       -       -       -       -       -       -       -       -      \n",
       "11 1.00000 -       -       -       -       -       -       -       -      \n",
       "12 1.00000 1.00000 -       -       -       -       -       -       -      \n",
       "13 0.50550 1.00000 1.00000 -       -       -       -       -       -      \n",
       "14 1.00000 1.00000 1.00000 1.00000 -       -       -       -       -      \n",
       "15 0.21100 1.00000 1.00000 1.00000 1.00000 -       -       -       -      \n",
       "16 1.00000 1.00000 1.00000 1.00000 1.00000 1.00000 -       -       -      \n",
       "17 0.15919 1.00000 1.00000 1.00000 1.00000 1.00000 1.00000 -       -      \n",
       "18 1.00000 1.00000 1.00000 1.00000 1.00000 1.00000 1.00000 1.00000 -      \n",
       "19 0.00605 0.52054 0.34496 1.00000 1.00000 1.00000 1.00000 1.00000 1.00000\n",
       "20 0.02019 0.96225 0.65548 1.00000 1.00000 1.00000 1.00000 1.00000 1.00000\n",
       "21 0.33383 1.00000 1.00000 1.00000 1.00000 1.00000 1.00000 1.00000 1.00000\n",
       "22 4.4e-06 0.00044 0.00028 0.15495 0.00324 0.35300 0.03034 0.33383 0.03550\n",
       "23 0.00014 0.00594 0.00403 0.68237 0.03034 1.00000 0.18910 1.00000 0.23261\n",
       "24 1.9e-05 0.00096 0.00063 0.15817 0.00533 0.33383 0.03686 0.31776 0.04496\n",
       "   19      20      21      22      23     \n",
       "2  -       -       -       -       -      \n",
       "3  -       -       -       -       -      \n",
       "4  -       -       -       -       -      \n",
       "5  -       -       -       -       -      \n",
       "6  -       -       -       -       -      \n",
       "7  -       -       -       -       -      \n",
       "8  -       -       -       -       -      \n",
       "9  -       -       -       -       -      \n",
       "10 -       -       -       -       -      \n",
       "11 -       -       -       -       -      \n",
       "12 -       -       -       -       -      \n",
       "13 -       -       -       -       -      \n",
       "14 -       -       -       -       -      \n",
       "15 -       -       -       -       -      \n",
       "16 -       -       -       -       -      \n",
       "17 -       -       -       -       -      \n",
       "18 -       -       -       -       -      \n",
       "19 -       -       -       -       -      \n",
       "20 1.00000 -       -       -       -      \n",
       "21 1.00000 1.00000 -       -       -      \n",
       "22 1.00000 1.00000 1.00000 -       -      \n",
       "23 1.00000 1.00000 1.00000 1.00000 -      \n",
       "24 1.00000 1.00000 1.00000 1.00000 1.00000\n",
       "\n",
       "P value adjustment method: holm "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "\n",
       "\tOne-way analysis of means (not assuming equal variances)\n",
       "\n",
       "data:  morti and Ora\n",
       "F = 2.8837, num df = 23.0, denom df = 3867.9, p-value = 5.062e-06\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " \n",
      " Nel caso dei morti, il test è statisticamente significativo - Eseguo un t-test fra tutte le coppie"
     ]
    },
    {
     "data": {
      "text/plain": [
       "\n",
       "\tPairwise comparisons using t tests with pooled SD \n",
       "\n",
       "data:  sample_data[Ora != 25, ]$morti and sample_data[Ora != 25, ]$Ora \n",
       "\n",
       "   1       2       3       4       5       6       7       8       9      \n",
       "2  1.00000 -       -       -       -       -       -       -       -      \n",
       "3  1.00000 1.00000 -       -       -       -       -       -       -      \n",
       "4  0.20801 1.00000 1.00000 -       -       -       -       -       -      \n",
       "5  1.00000 1.00000 1.00000 1.00000 -       -       -       -       -      \n",
       "6  1.00000 1.00000 1.00000 1.00000 1.00000 -       -       -       -      \n",
       "7  1.00000 1.00000 1.00000 0.44791 1.00000 1.00000 -       -       -      \n",
       "8  1.00000 0.00032 0.01230 0.00012 0.45850 1.00000 1.00000 -       -      \n",
       "9  1.00000 1.4e-05 0.00119 1.1e-05 0.10204 1.00000 0.45068 1.00000 -      \n",
       "10 1.00000 0.00031 0.01330 0.00013 0.52503 1.00000 1.00000 1.00000 1.00000\n",
       "11 1.00000 5.3e-05 0.00337 3.1e-05 0.20670 1.00000 1.00000 1.00000 1.00000\n",
       "12 1.00000 0.00066 0.02395 0.00024 0.75947 1.00000 1.00000 1.00000 1.00000\n",
       "13 1.00000 1.8e-05 0.00144 1.3e-05 0.11634 1.00000 0.52880 1.00000 1.00000\n",
       "14 1.00000 1.4e-05 0.00119 1.1e-05 0.10226 1.00000 0.45229 1.00000 1.00000\n",
       "15 1.00000 1.1e-05 0.00095 8.8e-06 0.08722 1.00000 0.37007 1.00000 1.00000\n",
       "16 1.00000 0.00228 0.06232 0.00067 1.00000 1.00000 1.00000 1.00000 1.00000\n",
       "17 1.00000 0.00091 0.03139 0.00032 0.92609 1.00000 1.00000 1.00000 1.00000\n",
       "18 1.00000 2.4e-05 0.00201 1.8e-05 0.15630 1.00000 0.72616 1.00000 1.00000\n",
       "19 1.00000 0.00432 0.10648 0.00118 1.00000 1.00000 1.00000 1.00000 1.00000\n",
       "20 1.00000 0.00015 0.00723 6.9e-05 0.33769 1.00000 1.00000 1.00000 1.00000\n",
       "21 1.00000 0.01089 0.16982 0.00218 1.00000 1.00000 1.00000 1.00000 1.00000\n",
       "22 1.00000 0.65995 1.00000 0.09240 1.00000 1.00000 1.00000 1.00000 1.00000\n",
       "23 1.00000 1.00000 1.00000 0.66487 1.00000 1.00000 1.00000 1.00000 0.13629\n",
       "24 1.00000 1.00000 1.00000 1.00000 1.00000 1.00000 1.00000 0.32602 0.03180\n",
       "   10      11      12      13      14      15      16      17      18     \n",
       "2  -       -       -       -       -       -       -       -       -      \n",
       "3  -       -       -       -       -       -       -       -       -      \n",
       "4  -       -       -       -       -       -       -       -       -      \n",
       "5  -       -       -       -       -       -       -       -       -      \n",
       "6  -       -       -       -       -       -       -       -       -      \n",
       "7  -       -       -       -       -       -       -       -       -      \n",
       "8  -       -       -       -       -       -       -       -       -      \n",
       "9  -       -       -       -       -       -       -       -       -      \n",
       "10 -       -       -       -       -       -       -       -       -      \n",
       "11 1.00000 -       -       -       -       -       -       -       -      \n",
       "12 1.00000 1.00000 -       -       -       -       -       -       -      \n",
       "13 1.00000 1.00000 1.00000 -       -       -       -       -       -      \n",
       "14 1.00000 1.00000 1.00000 1.00000 -       -       -       -       -      \n",
       "15 1.00000 1.00000 1.00000 1.00000 1.00000 -       -       -       -      \n",
       "16 1.00000 1.00000 1.00000 1.00000 1.00000 1.00000 -       -       -      \n",
       "17 1.00000 1.00000 1.00000 1.00000 1.00000 1.00000 1.00000 -       -      \n",
       "18 1.00000 1.00000 1.00000 1.00000 1.00000 1.00000 1.00000 1.00000 -      \n",
       "19 1.00000 1.00000 1.00000 1.00000 1.00000 1.00000 1.00000 1.00000 1.00000\n",
       "20 1.00000 1.00000 1.00000 1.00000 1.00000 1.00000 1.00000 1.00000 1.00000\n",
       "21 1.00000 1.00000 1.00000 1.00000 1.00000 1.00000 1.00000 1.00000 1.00000\n",
       "22 1.00000 1.00000 1.00000 1.00000 1.00000 0.90514 1.00000 1.00000 1.00000\n",
       "23 1.00000 0.36792 1.00000 0.16398 0.13702 0.10931 1.00000 1.00000 0.22707\n",
       "24 0.35535 0.09367 0.60905 0.03877 0.03200 0.02525 1.00000 0.77934 0.05456\n",
       "   19      20      21      22      23     \n",
       "2  -       -       -       -       -      \n",
       "3  -       -       -       -       -      \n",
       "4  -       -       -       -       -      \n",
       "5  -       -       -       -       -      \n",
       "6  -       -       -       -       -      \n",
       "7  -       -       -       -       -      \n",
       "8  -       -       -       -       -      \n",
       "9  -       -       -       -       -      \n",
       "10 -       -       -       -       -      \n",
       "11 -       -       -       -       -      \n",
       "12 -       -       -       -       -      \n",
       "13 -       -       -       -       -      \n",
       "14 -       -       -       -       -      \n",
       "15 -       -       -       -       -      \n",
       "16 -       -       -       -       -      \n",
       "17 -       -       -       -       -      \n",
       "18 -       -       -       -       -      \n",
       "19 -       -       -       -       -      \n",
       "20 1.00000 -       -       -       -      \n",
       "21 1.00000 1.00000 -       -       -      \n",
       "22 1.00000 1.00000 1.00000 -       -      \n",
       "23 1.00000 0.71725 1.00000 1.00000 -      \n",
       "24 1.00000 0.19516 1.00000 1.00000 1.00000\n",
       "\n",
       "P value adjustment method: holm "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#test anova ad una via per l'uguaglianza delle medie\n",
    "test_inj_trim <- oneway.test(feriti ~ trimestre, data = sample_data)\n",
    "test_dea_trim <- oneway.test(morti ~ trimestre, data = sample_data)\n",
    "test_inj_hour <- oneway.test(feriti ~ Ora, data = sample_data[Ora != 25,])\n",
    "test_dea_hour <- oneway.test(morti ~ Ora, data = sample_data[Ora != 25,])\n",
    "test_inj_trim\n",
    "test_dea_trim\n",
    "test_inj_hour\n",
    "cat(\"\\n \\n Nel caso dei feriti, il test è statisticamente significativo - Eseguo un t-test fra tutte le coppie\")\n",
    "test_coppie_ora_inj <- pairwise.t.test(sample_data[Ora != 25,]$feriti, sample_data[Ora != 25,]$Ora)\n",
    "test_coppie_ora_inj\n",
    "test_dea_hour\n",
    "cat(\"\\n \\n Nel caso dei morti, il test è statisticamente significativo - Eseguo un t-test fra tutte le coppie\")\n",
    "test_coppie_ora_dea <- pairwise.t.test(sample_data[Ora != 25,]$morti, sample_data[Ora != 25,]$Ora)\n",
    "test_coppie_ora_dea"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Analisi fondo stradale - vedo se esiste una correlazione fra il numero di morti per incidente e il tipo di fondo stradale"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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MuXL8t/ztqbs7OzFJFzcnI8PT1r5k0dTprVCQkJV69eNfOcbw1jDtQk5jZUItgB\nsNSDGewAoA7hdicAAAAaQbADAADQCIIdAACARhDsAAAANIJgBwAAoBFcFQsAAKARHLEDAADQ\nCIIdAACARhDsAAAANIJgBwAAoBEEOwAAAI0g2AEAAGgEwQ4AAEAjCHYAAAAaQbADAADQCIId\nAACARhDsAAAANIJgBwAAoBEEOwAAAI0g2AEAAGgEwQ4AAEAjCHYAAAAaQbADAADQCIOjC6gb\nMjMzi4uLHV2FA+j1en9//8LCwuzsbEfXApMMBoOvr29+fn5ubq6ja4FJrq6u3t7eubm5+fn5\njq4FJrm7u3t6emZnZxcWFjq6Fpjk6enp7u7+wO6ahRANGjQwNYojdgAAABpBsAMAANAIgh0A\nAIBGEOwAAAA0gmAHAACgEQQ7AAAAjSDYAQAAaATBDgAAQCMIdgAAABpBsAMAANAIgh0AAIBG\nEOwAAAA0gmAHAACgEQQ7AAAAjSDYAQAAaATBDgAAQCMIdgAAABpBsAMAANAIgh0AAIBGEOwA\nAAA0gmAHAACgEQQ7AAAAjSDYAQAAaATBDgAAQCMIdgAAABpBsAMAANAIgh0AAIBGEOwAAAA0\nwuDoAgCgzsjPzy8sLLSur6ura0lJSV5eXn5+vnVTcHZ29vT0tK4vgAcEwQ4ALPWvf/0rNjbW\nUe8+YMCAtWvXOurdAdQJBDsAsFRISEj37t2t65uWlnbu3LmQkJDGjRtbN4U2bdpY1xHAg4Ng\nBwCWGj9+/Pjx463ru2vXrueff37cuHETJ060bVUAIOPiCQAAAI0g2AEAAGgEwQ4AAEAjCHYA\nAAAaQbADAADQCIIdAACARhDsAAAANIJgBwAAoBEEOwAAAI0g2AEAAGgEwQ4AAEAjCHYAAAAa\nQbADAADQCIIdAACARhDsAAAANIJgBwAAoBEEOwAAAI0g2AEAAGgEwQ4AAEAjCHYAAAAaQbAD\nAADQCIIdAACARhDsAAAANIJgBwAAoBEEOwAAAI0wOLqAusHd3d3T09PRVTiMi4uLr6+vo6uA\nSTqdTgjh6urq7Ozs6FpgkqurqxDC2dmZrak20+v1QggPDw93d3dH1wKTpMXk5eVlNBodXYsD\nlJWVmRlLsLNIQUFBcXGxo6twAL1e7+fnV1xcnJ2d7ehaYJLBYPDx8SkqKsrNzXV0LTCpsLBQ\nCFFSUpKZmenoWmCSm5ubp6dnXl5eUVGRo2uBSZ6enm5ubjk5OSUlJY6uxTHq1xmnX4oAACAA\nSURBVK9vahTBziJGo/HB/LNA+tQP7MevK1hMdQiLqa5gMdVm8tJhMVXGOXYAAAAaQbADAADQ\nCIIdAACARhDsAAAANIJgBwAAoBEEOwAAAI0g2AEAAGgEwQ4AAEAjCHYAAAAaQbADAADQCIId\nAACARhDsAAAANIJgBwAAoBEEOwAAAI0g2AEAAGgEwQ4AAEAjCHYAAAAaQbADAADQCIIdAACA\nRhDsAAAANIJgBwAAoBEEOwAAAI0g2AEAAGgEwQ4AAEAjCHYAAAAaQbADAADQCIIdAACARhDs\nAAAANIJgBwAAoBEEOwAAAI0g2AEAAGgEwQ4AAEAjCHYAAAAaQbADAADQCIIdAACARhDsAAAA\nNIJgBwAAoBEEOwAAAI0g2AEAAGgEwQ4AAEAjCHYAAAAaQbADAADQCIIdAACARhDsAAAANIJg\nBwAAoBEEOwAAAI0g2AEAAGgEwQ4AAEAjCHYAAAAaQbADAADQCIIdAACARhDsAAAANIJgBwAA\noBEEOwAAAI0g2AEAAGgEwQ4AAEAjDI4u4P+RnZ29YcOG48ePZ2RkeHl5tW/ffsSIEY0bN7ZT\n3+zs7ClTpuTl5W3cuNFGnwAAAMBhatERu/T09FdffXXnzp23b992dna+d+9eYmLi1KlTL1y4\nYKe+ixcvzsjIsN0nAAAAcKRaFOxWrFiRlpbWpEmTJUuWbNiwITY2tmPHjoWFhQsXLjQajTbv\n+9133x0/ftwOnwMAAMAxakuwy8jISEpKcnJymjNnTnBwsBAiICBg1qxZAQEBd+7c+eWXX2zb\n99q1a1988YWHh4edPg4AAEDNqy3Bbv/+/WVlZeHh4Q0bNpQHurm5Pf7440KIffv22bBvUVHR\nggULSktLX3zxRVt+BgAAAIeqLcEuOTlZCNGpU6cKw6Uhly9ftmHf1atXX79+ffTo0a1atVJX\nNQAAQC1SW66KvXfvnhCiQYMGFYZLQ6SxNun7008/7d69Ozw8/Omnn75165apaebn55e/rsLV\n1dXJycmSD6Ixer1eCKHT6R7Mj19XSEtHr9ezmGoztqY6QVpMbE21nE6nEw/wYjJ/4UFtCXZS\nivL29q4wXBpSUFBQUFDg5uamsm96evqSJUu8vb2nTZsmrRam/PTTTzNnzpT/u2zZskceeUTp\nh9IMFxcXFxcXR1eBari6urq6ujq6CpgkLR1nZ2c/Pz9H14JqeHp6enp6OroKVKPyfv8BUVpa\namZsbQl22dnZQojKVzPIQzIzM00FOwv7Go3Gjz/+ODs7+80336xfv775egICAvr06SP/t169\neoWFhZZ+GA3R6XQuLi5lZWXFxcWOrgUm6fV6Z2fn0tLSkpISR9cCk6Tv4rKysgfzy6SucHJy\nMhgMxcXFZWVljq4FJhkMBicnpwd2MRmNRjOHKmtLsPP29s7Pz8/Pz68wPC8vT3ph5o8nC/tu\n2rTpzJkzAwYM6Nq1a7X1tGnTZv78+fJ/MzMzpfj4oNHr9f7+/sXFxQ/mx68rDAaDr69vUVFR\nbm6uo2uBSUVFRUKIkpIStqbazN3d3WAwFBQUkL9rM09PT3d397y8vAf2oIOpQ12i9lw8If02\nkZOTU2G4NMTFxcXLy0tN3z/++OPrr79u2rTp+PHjbVs5AABALVFbjthJ4azycyCkIf7+/ir7\n3rp1q7S09MaNG8OGDavQrKCgYNCgQUKI559//umnn1bzKQAAAByotgS75s2b//TTT6dOnYqM\njCw//PTp00KIZs2aqexb5QnLpaWlWVlZOp3O19dXmD2wCQCoK2bMmJGYmGhdX71er9PpysrK\nqn3ikSnPPPPMG2+8YV1fQL3aEuz69OmzYcOG06dPZ2Zm+vj4SANLS0uPHDkihHjyySdV9u3S\npcuXX35ZoeOff/758ssvu7q6Vh4FAKijcnNzMzMzretbWFiYn5/v6enp7Oxs3RQqn/AN1KTa\nEuwaNGjQuXPn48ePz58//+233/bw8CgqKlqxYsWNGzcCAwM7dOggt9y2bVtRUVFISEjHjh2V\n9gUAaN7y5cut7hsbGzt79uylS5dW+AkIqCtqS7ATQsTExCQnJ587d2706NFNmza9detWUVGR\nm5vbjBkzyt9zbvPmzVlZWZGRkXKws7wvAACAhtWWq2KFEA0aNFi8ePHAgQPr169/8+ZNT0/P\nHj16LF68uGXLlnbtCwAAoA216IidEKJevXovvvjiiy++aKbNunXrrO5bQePGjXfs2KGsRAAA\ngNqqFh2xAwAAgBoEOwAAAI0g2AEAAGgEwQ4AAEAjCHYAAAAaQbADAADQCIIdAACARhDsAAAA\nNIJgBwAAoBEEOwAAAI0g2AEAAGgEwQ4AAEAjDGo6p6WlnT9/PisrKycnJzo62lY1AQAAwArW\nHLEzGo1r1qxp27ZtQEBA9+7do6KiRo4cKY3q1q3b/Pnzc3JybFokAAAAqqc42OXn5/fu3Xvs\n2LHnzp0zGo0Vxl67dm327Nm9evVKT0+3UYUAAACwiOJgN3LkyISEBCFEYGDgrFmz1q5dW35s\njx49hBBJSUlPPfWUjSoEAACARZQFu4SEhO3btwshIiIizp8//8EHHwwfPrx8g7i4uNWrVwsh\nDh8+vGfPHhsWCgAAAPOUBbulS5cKIRo3brxu3To/P78q24wbN65///5CiNjYWPX1AQAAwELK\ngt2ZM2eEEKNGjTKV6iRRUVFCiIsXL6qpDAAAAIooC3bXr18XQoSHh5tvFhAQIIS4cuWK1WUB\nAABAKWXBzsvLSwhx8+ZN881u3LghhHB3d7e6LAAAACilLNi1adNGCLFr167KNzop7+DBg0KI\n0NBQNZUBAABAEWXBbtiwYUKIQ4cOLVq0yFSbhISErVu3CiEiIyNVFgcAAADLKQt2EydODAkJ\nEUJMnz49Ojr65MmTJSUl8tiMjIyFCxdGREQIIXx9fSdNmmTbWgEAAGCGsmfFurq67ty5s3v3\n7nfu3ImPj4+Pj/f09JRGBQUFSafWCSEMBsP69evNXzkLAAAA21L85ImwsLDTp08PGTJEp9MJ\nIXJzc6XhcqoLDQ1NTEwcMGCADasEAABAtZQdsZM0atRoy5Ytly9f3rFjx7Fjx1JTU/Py8nx9\nfcPCwvr16xcZGanXK86LAAAAUMmaYCcJCQmZNm2aDUsBAACAGoovnpg4cWJhYaH5ZufPn584\nceK7775rfV0AAABQSFmwi42NjY2NLX8lbJUyMzNjY2NXrlypojAAAAAoU/1PsZWPzxUUFBgM\nJjvm5ORs2bJFCHH//n2VxQEAAMBy1Qc7Nze3CkMaNGhgyaSDg4OtqQgAAABWsePlq1xaAQAA\nUJOqP2K3cOFC+fWMGTOEEO+//76rq6uZLs7Ozl26dPn73/+uvj4AAABYqPpgN336dPm1FOxe\nffVV+YETAAAAqCWU3ccuJiZGCGHmygkAAAA4irKItnz5cjvVAQAAAJV49hcAAIBGWPOj6unT\npw8cOHDx4sWysjLzLefNm9ewYUOrCgMAAIAyioPd+++//84771Qb6STz5s1TXhIAAACsoSzY\n/fjjj2+99Zb02tPT08/Pz3z7ahsAAADAVpQFuwULFgghHn744c8///zRRx/V6XT2qQoAAACK\nKQt2586d0+v1mzdvbt26tZ0KAgAAgHWUXRWbkpISEhJCqgMAAKiFlAW7hg0bcndiAACA2klZ\nsOvdu/eFCxfu379vp2oAAABgNWXBbsaMGW5ubv/4xz/sVA0AAACspizYtW7dOi4ubtOmTaNH\nj75y5YqdagIAAIAVlJ0wt3LlSiHE2LFjly9fvnHjxvDw8BYtWjRs2NDUfU+WLFligxoBAABg\nAWXBLiYmRn5dUlJy6tSpU6dOmWlPsAMAAKgxyoJdYGCgneoAAACASsqCXUpKip3qAAAAgErK\nLp4AAABArUWwAwAA0AhVj5FIS0s7f/58VlZWTk5OdHS0rWoCAACAFawJdkaj8csvv1y4cOH5\n8+eNRqM0UAp23bp1Gzhw4OTJk728vGxZpqO5uLi4uLg4ugoHkG5kYzAYPD09HV0LTNLr9UII\nZ2dnFlNt5uzsLIRwcnJiMdVmTk5Ogq2p1pO2Jjc3twdz11xWVmZmrOJgl5+fHxkZmZCQUOXY\na9euzZ49e8uWLbt3765fv77SiddaZWVl5uejVknBzmg0lpSUOLoWmCTtisrKylhMtZn0HcLW\nVMtJRyvYmmo56UuvtLS0tLTU0bU4gHxMrUqKg93IkSOlVBcYGPjCCy+0bt36ueeek8f26NFj\n3bp1SUlJTz311KFDh6wot3YqKSkpLi52dBUOIB0KKi0tLSwsdHQtMMlgMHh4eLCYajlpD1RW\nVsZiqs2k/M3WVMsZDAYhRHFx8YO5azZP2cUTCQkJ27dvF0JEREScP3/+gw8+GD58ePkGcXFx\nq1evFkIcPnx4z549NiwUAAAA5ikLdkuXLhVCNG7ceN26dX5+flW2GTduXP/+/YUQsbGx6usD\nAACAhZQFuzNnzgghRo0aZSrVSaKiooQQFy9eVFMZAAAAFFEW7K5fvy6ECA8PN98sICBACHHl\nyhWrywIAAIBSyoKddBOTmzdvmm9248YNIYS7u7vVZQEAAEApZcGuTZs2Qohdu3aZv9T24MGD\nQojQ0FA1lQEAAEARZcFu2LBhQohDhw4tWrTIVJuEhIStW7cKISIjI1UWBwAAAMspC3YTJ04M\nCQkRQkyfPj06OvrkyZPlb+GYkZGxcOHCiIgIIYSvr++kSZNsWysAAADMUHaDYldX1507d3bv\n3v3OnTvx8fHx8fHyQ1eCgoKkU+uEEAaDYf369eavnAUAAIBtKTtiJ4QICws7ffr0kCFDpIdN\n5ebmSsPlVBcaGpqYmDhgwAAbVgkAAIBqKX6kmBCiUaNGW7ZsuXz58o4dO44dO5aampqXl+fr\n6xsWFtavX7/IyEjpOVQAAACoSdYEO0lISMi0adNsWAoAAADU4NAaAACARhDsAAAANMLcT7EP\nP/ywmkk/99xzs2fPVjMFAAAAWM5csLtw4YKaSd+5c0dNdwAAAChiLtiNGTOmyuF79+69e/eu\nEMLZ2blZs2bNmzc3GAzJyclXrlwpLCwUQjz++OMTJ05s3bq1PSoGAABAlcwFu7i4uMoD33nn\nnbt377q4uEyePHnmzJmBgYHyqIyMjI8//njRokWHDx/u2bPn888/b/t6AQAAYIKyiyd+/PHH\nefPmCSFiY2P/85//lE91Qgh/f/958+bFxcXpdLp58+bt37/flpUCAADALGXBbsmSJUajMSIi\n4tlnnzXVZujQoYMHDxZCfPLJJ2qrAwAAgMWUBbsjR44IIf7nf/7HfLMnnnhCCJGUlGR1WQAA\nAFBKWbBLT08XQri5uZlvZjAYhBD37t2zuiwAAAAopSzY+fv7CyGOHz9uvtmxY8eEEA899JDV\nZQEAAEApZcHu0UcfFUJs3rxZim5VOn78+KZNm4QQjz32mMriAAAAYDllwW7ChAlCiKKioqFD\nh+7cubNyg2+//Xbo0KFFRUVCiHHjxtmkRAAAAFjC3H3sKhs4cODw4cM3btx469atQYMGdezY\nsWvXriEhIUKI5OTkn3766eeff5Zajh49+sknn7R9vQAAADBBWbATQsTFxbm5ua1du1YIcfLk\nyZMnT1ZuM378+OXLl9ugOgAAAFhMcbBzcXH58ssvn3vuuU8++eTAgQM5OTnyKH9//759+06a\nNEm63QkA2JbBYNDrlZ1AUns4OTkJIfR6vYuLi6NrsZLRaCwuLnZ0FQDMURzsJL179+7du3dp\naekff/yRkZFhMBjq16/ftGlTnU5n2/oAQObp6ens7OzoKqwk5TmDwVCvXj1H12Kl0tJS7mMF\n1HJWBjuJk5NTixYtWrRoYatqAKBaF9NzyoxGR1eh2J/Z+UKI27mFv6VlO7oWa7Ss7+XoEgBU\nT1WwA4CadzEjp6Ss7gW7m9kFQog7eYW/pedU27gWCvHzrKu/ggMPkmqCXaNGjaQXSUlJTZo0\nmTJliqKpL1myxMq6AAAAoFA1wS41NVV6UVpaKoT49NNPFU2dYAcAAFBjlP0UGxgYaKc6AAAA\noFI1we7EiRPSC+k32ZSUFLtXBAAAAKtUE+w6depUM3UAAABAJVUXORUUFJT/b3JycmFhobp6\nAAAAYCVrgt2NGzdefPHFli1bzp49u/zwt956y8/PLzIy8sqVKzYqDwAAAJZSHOz27t37t7/9\nbdWqVZcuXao8Nj8/f9euXR06dNi9e7ctygMAAICllAW7e/fujRo1KiMjQwjx5JNPRkZGlh/7\n2muvvfLKKy4uLllZWWPHjs3KyrJlpQAAADBLWbD76KOPMjIydDpdfHz8nj17+vTpU37sI488\nsnTp0kOHDrm5ud2+fXvhwoU2LRUAAADmKAt2Bw8eFEKMGDFi+PDhptp06dJl/PjxQogjR46o\nLA4AAACWUxbsfv/9dyFEz549zTfr0qWLEOK3336zuiwAAAAopSzY5ebmCiH0+mp6lZSUCCHu\n3btndVkAAABQSlmwa9KkiRAiKSnJfDPpeRVSYwAAANQMZcGuX79+Qoh169adOnXKVJuzZ8/G\nxcUJIXr16qWyOAAAAFhOWbB79dVXXVxc8vLyBg4cGBcXJ/3kKjMajd98883AgQNzc3P1ev0r\nr7xi01IBAABgTjXPiq0gJCRk8eLFL7/88q1bt5577rk33nijXbt2QUFBrq6uf/zxx7lz565e\nvSq1fOedd9q1a2eHggEAAFA1ZcFOCBETE+Pt7T1p0qTMzMyUlJSUlJQKDZydnd9///2ZM2fa\nqEIAAABYxJpnxY4ePfrq1asLFizo0aOHl5eXNNDZ2blDhw6vv/76pUuXSHUAAAA1T/ERO4mf\nn9+MGTNmzJghhCgoKCguLvb29rZpYQAAAFDGymBXnpubm5ubm/rpAAAAQA1rfooFAABALWTN\nEbvTp08fOHDg4sWLZWVl5lvOmzevYcOGVhUGAAAAZRQHu/fff/+dd96pNtJJ5s2bp7wkAAAA\nWENZsPvxxx/feust6bWnp6efn5/59tU2AAAAgK0oC3YLFiwQQjz88MOff/75o48+qtPpbFtN\ndnb2hg0bjh8/npGR4eXl1b59+xEjRjRu3NiGfX/77bctW7bcvHnz7t27AQEBwcHBzzzzTPPm\nzW37QQAAAGqesmB37tw5vV6/efPm1q1b27yU9PT0mTNnpqWlCSE8PDzu3buXmJh49OjRuXPn\nhoWF2aTvxo0bv/rqK6PRqNfrfXx8/vzzz5s3bx45ciQmJmbAgAE2/0QAAAA1SdlVsSkpKSEh\nIfZIdUKIFStWpKWlNWnSZMmSJRs2bIiNje3YsWNhYeHChQuNRqP6vtevX//666+FEBMmTNi8\nefOXX34ZHx8fFRVlNBpXr15969Yte3woAACAGqMs2DVs2NBgsMGt7yrLyMhISkpycnKaM2dO\ncHCwECIgIGDWrFkBAQF37tz55Zdf1Pfdu3dvaWlp9+7dBw0aJH0KNze3CRMmtGjRoqio6NSp\nU/b4XAAAADVGWbDr3bv3hQsX7t+/b/M69u/fX1ZWFh4eXv72KG5ubo8//rgQYt++fer73rhx\nQwjRsWPH8n11Ol2rVq2EENevX7fZhwEAAHAEZcFuxowZbm5u//jHP2xeR3JyshCiU6dOFYZL\nQy5fvqy+b48ePSZOnNi2bdsKzbKysoQQPBINAADUdcp+V23dunVcXNzo0aNLS0vnzp1rw4tJ\n7927J4Ro0KBBheHSEGmsyr69evWq0KC0tPTQoUNHjx41GAw9evSwungAAIDaQFmwW7lypRBi\n7Nixy5cv37hxY3h4eIsWLRo2bGjqvidLliyxcMoZGRmiqsNm0pCCgoKCggJTT6S1ou/q1atP\nnDiRkZGRn58fEBAwadKkJk2alG9w4sSJxYsXy/+dOXOmnS4ZqRNcXFx8fX0dXQVMkjZAV1dX\nZ2dnR9diX05OTo4u4YGm1+s1/1UgbUSurq6a/6R1ml6vF0J4eXlVe22lJpl/SISyYBcTEyO/\nLikpOXXqlPlrDiwPdtnZ2UIIDw+PCsPlIZmZmaaCnRV9b9269eeff0qvnZ2d8/LyKvQtKCiQ\nGwghiouLH+Q9ik6ne5A/fl2h1+ttfmvJ2kbzH7CWe3C+Ch6cT1pHSV8FUrxDBcqCXWBgoJ3q\n8Pb2zs/Pz8/PrzBcjlyenp427Pv2228bjcbi4uIzZ84sXbr0ww8/nDx5cr9+/eQG3bp1O3Dg\ngPzfzMzM9PR0hZ9JC/R6vb+/f2FhoZSeUTsZDAZfX9/8/Pzc3FxH12JfPj4+mj8qWZuVlpaa\nPzFGA4qLi4UQBQUFD+Z3fl3h6enp7u6elZUlLa8HUOXTz2TKgl1KSorqYqrm5+d3586dnJyc\nCsOlIS4uLl5eXrbtq9PpXFxcOnXq9PLLL8+dO3f9+vXlgx0AAECdU1sOY0pPlZXOlitPGuLv\n76+y7/379zdu3Lh9+/bK3aVHU2RkZHBQCgAA1Gm1JdhJF9hWPmPv9OnTQohmzZqp7Ovm5vbV\nV199/vnnlU+nk/Kcs7Nz5bP0AAAA6pDaEuz69Omj1+tPnz6dmZkpDywtLT1y5IgQ4sknn1TZ\n183NrUmTJkaj8dChQxW6Hzx4UAgRHBzMqbIAAKBOq+Ycu0aNGkkvkpKSmjRpMmXKFEVTt/yq\n2AYNGnTu3Pn48ePz589/++23PTw8ioqKVqxYcePGjcDAwA4dOsgtt23bVlRUFBISIj9DwsK+\ngwYNWrp06RdffOHu7v7444/r9fqioqK9e/du3LhRCBEdHa3oowEAANQ21QS71NRU6UVpaakQ\n4tNPP1U0dcuDnRAiJiYmOTn53Llzo0ePbtq06a1bt4qKitzc3GbMmFH+HgebN2/OysqKjIws\n/3AwS/r269fvxIkTx44dW7Bgwccff+zj43Pv3j2j0ajT6Z555plHHnlE0UcDAACobWrL7U6E\nEA0aNFi8ePGGDRuOHz9+8+ZNb2/vxx57bOTIkfJRQ5V9dTrdP//5z4MHD37//fcpKSn3798P\nCgoKCgoaOnRoixYt7Pe5AAAAakY1we7EiRPSCykh2e92J5J69eq9+OKLL774opk269ats7qv\nEOKJJ5544oknrC8RAACgtqom2HXq1Klm6gAAAIBKteWqWAAAAKhEsAMAANAIgh0AAIBGEOwA\nAAA0gmAHAACgEQQ7AAAAjSDYAQAAaATBDgAAQCMUBLuDBw+OHTt27NixZ8+etV9BAAAAsI6C\nZ8VeuHBhzZo1QoiuXbu2bdvWXhUBAADAKgqO2HXs2FF6cf78efsUAwAAAOspCHadOnUaPXq0\nEOKLL764du2a3UoCAACANZRdPPHFF1+MGTMmOzv76aefPnr0qJ1qAgAAgBUUnGMnhFi/fn3v\n3r3T0tL27Nnz2GOPtWvXLjg4ODAw0NXVtcr2S5YssUWRAAAAqJ6yYPfCCy+U/++vv/7666+/\nmmlPsAMAAKgxyoJdYGCgneoAAACASsqCXUpKip3qAAAAgEo8eQIAAEAjCHYAAAAaoeyn2ArS\n0tLOnz+flZWVk5MTHR1tq5oAAABgBWuO2BmNxjVr1rRt2zYgIKB79+5RUVEjR46URnXr1m3+\n/Pk5OTk2LRIAAADVUxzs8vPze/fuPXbs2HPnzhmNxgpjr127Nnv27F69eqWnp9uoQgAAAFhE\ncbAbOXJkQkKCECIwMHDWrFlr164tP7ZHjx5CiKSkpKeeespGFQIAAMAiyoJdQkLC9u3bhRAR\nERHnz5//4IMPhg8fXr5BXFzc6tWrhRCHDx/es2ePDQsFAACAecqC3dKlS4UQjRs3XrdunZ+f\nX5Vtxo0b179/fyFEbGys+voAAABgIWXB7syZM0KIUaNGmUp1kqioKCHExYsX1VQGAAAARZQF\nu+vXrwshwsPDzTcLCAgQQly5csXqsgAAAKCUsmDn5eUlhLh586b5Zjdu3BBCuLu7W10WAAAA\nlFIW7Nq0aSOE2LVrV+UbnZR38OBBIURoaKiaygAAAKCIsmA3bNgwIcShQ4cWLVpkqk1CQsLW\nrVuFEJGRkSqLAwAAgOWUBbuJEyeGhIQIIaZPnx4dHX3y5MmSkhJ5bEZGxsKFCyMiIoQQvr6+\nkyZNsm2tAAAAMEPZs2JdXV137tzZvXv3O3fuxMfHx8fHe3p6SqOCgoKkU+uEEAaDYf369eav\nnAUAAIBtKX7yRFhY2OnTp4cMGaLT6YQQubm50nA51YWGhiYmJg4YMMCGVQIAAKBayo7YSRo1\narRly5bLly/v2LHj2LFjqampeXl5vr6+YWFh/fr1i4yM1OsV50UAAACoZE2wk4SEhEybNs2G\npQAAAEANDq0BAABohPVH7NLT0w8cOPDzzz/fvn27pKTE39+/Q4cOvXv3btq0qQ3rAwAAgIWs\nCXapqalvvPHGhg0bioqKKoxycnIaPHjwokWLiHcAAAA1TPFPsadOnWrbtu3atWvlVOfm5ubk\n5CS9Li0t/eabb9q1a/fzzz/bskwAAABUR1mwy8zMjIiISE9P1+l0Q4YM+eGHH/7888+8vLz8\n/PyLFy/GxsY2b95cCHH//v2BAwdmZmbap2YAAABUQVmwW7x4cWpqqrOz8/79+7ds2dKrV6+/\n/OUvOp3O2dm5ZcuW48ePv3jxYnR0tBAiNTV18eLF9qkZAAAAVVAW7KSHwH700Ue9evWqsoHB\nYPj8889btWolhNi2bZv6+gAAAGAhZcEuOTlZCPHMM8+YaePu7j5w4EC5MQAAAGqGsmBnNBq9\nvb0bN25svlnr1q2FEGVlZdbXBQAAAIWUBbuQkJDs7Ow//vjDfLPz588LIZo1a2Z1WQAAAFBK\nWbAbNWqUEOKzzz4z0yYrK0s6FW/o0KFqKgMAAIAiyoLd5MmTw8PDP/jgg8WLF5eWllZucO/e\nvejo6CtXrgQFBU2fPt1GRQIAAKB6yp484e7uvmvXrqFDh06dOnXJkiUvmlaCkgAAIABJREFU\nvfTSww8/HBwcXFRUdPXq1aSkpBUrVmRlZXl7e3/22WcZGRkZGRkVphAcHGy74gEAAPB/KQt2\nDz30kBCiuLhYCJGcnPz6669X2Sw7O7t///5VjjIajQorBAAAgEWUBbu0tDQ71QEAAACVlAW7\ndevW2akOAAAAqKQs2I0ePdpOdQAAAEAlZVfFAgAAoNYi2AEAAGiEsp9iH1hOTk6OLsEx9Hq9\n9K+zs7Oja4FJ0vr5ICwmnU7n6BIeaDqdTvPrGF96dYK0mB7YXbN5BDuLODs7P5gbubQfdXJy\ncnNzc3QtMEn6jjMYDJpfTNInhaPodLoHZB17ELamOs1gMAghXF1dH8yn0pu/cxzBziIFBQXS\n3fseNHq93t/fv7i4ODs729G1wCSDweDr61tUVJSbm+voWuzLx8eHbOdAZWVlmv8qKCkpEUIU\nFRVp/pPWaZ6enu7u7nl5eQ/mrlkIYeYPD74iAQAANIJgBwAAoBEEOwAAAI0g2AEAAGiEqosn\n0tLSzp8/n5WVlZOTEx0dbauaAAAAYAVrjtgZjcY1a9a0bds2ICCge/fuUVFRI0eOlEZ169Zt\n/vz5OTk5Ni0SAAAA1VMc7PLz83v37j127Nhz585VvpPKtWvXZs+e3atXr/T0dBtVCAAAAIso\nDnYjR45MSEgQQgQGBs6aNWvt2rXlx/bo0UMIkZSU9NRTT9moQgAAAFhEWbBLSEjYvn27ECIi\nIuL8+fMffPDB8OHDyzeIi4tbvXq1EOLw4cN79uyxYaEAAAAwT1mwW7p0qRCicePG69at8/Pz\nq7LNuHHj+vfvL4SIjY1VXx8AAAAspCzYnTlzRggxatQoU6lOEhUVJYS4ePGimsoAAACgiLJg\nd/36dSFEeHi4+WYBAQFCiCtXrlhdFgAAAJRSFuy8vLyEEDdv3jTf7MaNG0IId3d3q8sCAACA\nUsqCXZs2bYQQu3btqnyjk/IOHjwohAgNDVVTGQAAABRRFuyGDRsmhDh06NCiRYtMtUlISNi6\ndasQIjIyUmVxAAAAsJyyYDdx4sSQkBAhxPTp06Ojo0+ePFlSUiKPzcjIWLhwYUREhBDC19d3\n0qRJtq0VAAAAZih7Vqyrq+vOnTu7d+9+586d+Pj4+Ph4T09PaVRQUJB0ap0QwmAwrF+/3vyV\nswAAALAtxU+eCAsLO3369JAhQ3Q6nRAiNzdXGi6nutDQ0MTExAEDBtiwSgAAAFRL2RE7SaNG\njbZs2XL58uUdO3YcO3YsNTU1Ly/P19c3LCysX79+kZGRer3ivAgAAACVrAl2kpCQkGnTptmw\nFAAAAKhhfbADAKAyDw+Puvu7jcFgEEK4uLhI922ti0pLS/Pz8x1dBRyGYAcAsCVXV1cnJydH\nV2ElKZI6OTm5ubk5uhYrFRcXE+weZIqDXWFh4bJly44fP3716tXS0lLzjbds2dK0aVNrawMA\n1EklZcbEa2mOrsIav2fkCCHO3Mnyv3rX0bUoptfregU3cHQVcDBlwS4rK6tr166//fabhe15\nqhgAPICMQmQVlVTfrvYpLCkTQuSXlNbF+g16naNLgOMpC3Zz586VUp2Pj0+bNm38/f3Nt/f1\n9bW+NAAAACihLNjt3btXCPHss88uW7as7p5YCgAAoEnKgl1ycrKPj89nn31Wd88qBQAA0CrF\njxRr3rw5qQ4AAKAWUnaroXbt2v3+++9lZWV2qgYAAABWUxbsYmJisrOzP/nkEztVAwAAAKsp\n+yk2Ojp69+7dr7/+en5+/vTp011cXOxUFgAAAJRSfIPilStXHj58+M033/z3v//dtm3bBg3M\n3Qtx586dKmoDAACAAsqCXUFBQe/evZOTk4UQOTk5P/30k32qAgAAgGLKgt37779/5MgRIYSz\ns3OzZs2qvUExAAAAaoyyYBcfHy+EGDRo0KpVqwICAuxTEgAAAKyhLNhdv37dy8srLi6uXr16\ndioIAAAA1lEW7Ly8vIKDg0l1AAAAtZCy+9i1b9/+jz/+MBqNdqoGAAAAVlMW7KZMmZKRkfHV\nV1/ZqRoAAABYTVmwGzx48NSpU2NiYtasWcNxOwAAgFpF2Tl2K1eubNWqVWho6NixY999990O\nHTqYv0HxqlWr1JUHAAAASykLdjExMfLra9euXbt2zXx7gh0AAECNURbs/vrXv9qnDAAAAKil\nLNhdvXrVTnUAAABAJWUXTwAAAKDWItgBAABoBMEOAABAI6o5x65Ro0bSi6SkpCZNmkyZMkXR\n1JcsWWJlXbCd33//fc+ePdb11el0Hh4eJSUlhYWF1k3Bw8NjwoQJ1vUFAACKVBPsUlNTpRel\npaVCiE8//VTR1Al2tcHZs2fnzp3rqHevX78+wQ4AgJqh7KrYwMBAO9UB++nWrdvmzZut63v/\n/v0JEyY8/vjjr732mnVTcHFxsa7jgyY+Pt7q+z7qdDonJ6eysrKysjLrphAaGrp8+XLr+gIA\nao9qgt2JEyekF9JvsikpKXavCLYWEBAQEBBgXd/bt29LU+jevbtNi0JFd+7c+eWXXxz17jwh\nEAC0oZpg16lTp5qpA3jATZkyRek5rLLTp0/37dt38uTJc+bMsW1VAIC6RdlVsRMnTpw4cWK1\n59GfP39+4sSJ7777rvV1AQAAQCFlwS42NjY2NrakpMR8s8zMzNjY2JUrV6ooDAAAAMpUf/FE\n5eNzBQUFBoPJjjk5OVu2bBFC3L9/X2VxAAAAsFz1wc7Nza3CkAYNGlgy6eDgYGsqAgAAgFWU\n3e5EkWnTpintkp2dvWHDhuPHj2dkZHh5ebVv337EiBGNGze2Yd/09PSvv/46OTn51q1b3t7e\nQUFBffr0eeyxx5SWCgAAUNtUH+wWLlwov54xY4YQ4v3333d1dTXTxdnZuUuXLn//+98VlZKe\nnj5z5sy0tDQhhIeHx7179xITE48ePTp37tywsDCb9D158uT8+fMLCgqEED4+Punp6Xfu3Dlx\n4sQjjzzy5ptv6vU8YA0AANRh1Qe76dOny6+lYPfqq696enravJQVK1akpaU1adLkjTfeCA4O\nvnPnzrJly06ePLlw4cJVq1bpdDqVfQsLC5cuXVpQUPDoo4++8sorfn5+paWlP/7448qVK48f\nP75p06YRI0bY/EMBAADUGGXHqGJiYmJiYsxcOWG1jIyMpKQkJyenOXPmSCfnBQQEzJo1KyAg\noNobt1rYNyEh4e7duw0aNJg5c6afn58Qwun/tHfncVHV/f//38OwDAjKJqCCymLuuaBWmma4\n5laigmSmmXpp1q2u1LK6ss1KU1vUXC7NVFrMJc0lLXGp3MIVza3EfQNZZJ8Bhvn9cb7XfPgN\nMJxZYOD4uP8F77O95rzPmXnO2UatjoqK+te//iWE2LRpE89oBQAAtZplwa64uLjSZ50Iq55j\nl5CQUFJS0rZt28DAQGOjRqPp1q2bEGLXrl22T3v16lUhRI8ePUx+5OrRRx9Vq9X5+fn8rgYA\nAKjVaspz7JKTk0V5P3QhtVy8eNH2ae/du+fj4xMREWEymouLi1qtFkLIyawAAAA1Vk15jl1m\nZqYo70EqUos01MZpX3/99XInP3bsWGFhobu7u/R7uHbn7Ozs4uJSFXOuBtJdMiqVyt3d3dG1\nWE+r1XKeHQBwP6gpz7HLyMgQQnh5eZm0Sy1arVar1ZatxPZpb926tXDhQiHEoEGDSsevS5cu\nbd++3fjvgAEDGjZsKP/llObi4lIVVyVWDynPqVSqqrhdptqo1WplBzspfzs5OdXqbpJDOrgO\nR5G5jTk5OZUoeYer6dRqteLfCqTPa41GY3Jt1X2ipKTEzNCa8hy7nJwcIYSHh4dJu7ElKyur\nonBm3bR6vf7nn3+Oj4/XarXt2rWLi4srPfTq1aurV682/vvQQw+Fh4fLfzllnU/PzdYV2TIH\nh7ibkiWEyNAWJt4yd9C0xnrAz9PbzaWiLUcxpPc4JyenWn1gFTWfBQfvFf1Vqoa7f94KzD95\nTcH0er2ZoTXlOXZeXl4FBQUFBQUm7fn5+dIfZr5/WDHtkSNHvvrqq1u3bqlUqsGDB48ZM8bk\noFq7du0WL15s/DckJCQrK0v+yylNo9G4ubmlFxSm5Jme1K75MnJ1QoiCopIbOVpH12KNxvU8\nhJvIzs5W9hE7aePX6/VWb6W1RZ06dWrv8W8FKCkpkb5Im+fl5SWEuQdUoUoVFxfn5eU5uoqq\nJX2w5ubmmo84ClavXr2KBtWU59j5+Pikpqbm5uaatEstrq6unp6edpk2Jydn+fLl+/btE0K0\nbdt27NixzZo1KztPX1/fLl26GP/NysoqKrLyeFvtvcBOMYqKipQd7KS3NoPBYPVWWlsoux9r\nPpnbmMFgEGafPIoqdT+8FUhnYPV6veJfqRUs++47adIkIURVfGOWHiwnXS1XmtTi6+trl2nT\n0tJmzJiRmprq4+Pz0ksvderUyR61AwAA1AiWPe5kyZIlS5YsqYqz2mFhYUKIEydOmLSfPHlS\nCBEaGmr7tFqt9p133klNTW3Xrt0XX3xBqgMAAApTU34dtXfv3k5OTidPnix9kZBerz948KAQ\nol+/frZPu3v37uvXr7du3fr999/39vaukpcBAADgOJWcVDU+2u3IkSPBwcEvvfSSRXOXniQi\nh7+/f6dOnRITE2fPnv322297eHgUFhYuXbr0+vXrQUFBHTp0MI65efPmwsLCiIiIjh07WjTt\nr7/+KoTo06dP2avxJJ6enuZ/kRYAAKAmqyTY3blzR/pDujp70aJFFs1dfrATQkyaNCk5OfnM\nmTOjRo0KCQm5detWYWGhRqOZNm1a6by1YcOG7OzsgQMHGoOdnGlLSkquXbsmhPj8888rKuC/\n//1vUFCQRS8QAACg5rDsNogqzT3+/v5ffPHF2rVrExMTb9y44eXl1bVr17i4ODk/CFHptGlp\nafftTdEAAOA+UUmwO3r0qPSHlJBu375dpdXUrVt34sSJEydONDPON998Y8W0AQEBW7ZssUOJ\nAAAANVUlwS4yMrJ66gAAAICNLLsr9quvvpLz2HEAAABUP8uC3fjx44OCgsaMGbN3714eAQ8A\nAFCjWPwbEvn5+WvWrFmzZk3Tpk3HjBkzZswY808PhsPduXbl9OH91k2bl50thEi9cW3XuvIv\nbayUi6tbz6dGWDctAACwiGXBbtGiRWvXrj1w4IDBYLhy5cp77733/vvvP/bYY88999ywYcPs\n/gOysIuLp08unfmaLXO4dPa01XOo6+tHsAMAoHpYFuymTJkyZcqUGzdurFu37ocffkhMTDQY\nDPv27du3b9+UKVNiYmLGjh3bvXv3KqoV1olo237S+584aukurvb/AToAAFAui0/FCiGCg4Nf\nffXVV1999fLlyz/88MPatWuTkpJyc3NXrly5cuXK8PDwsWPHPvvss40bN7Z7ubBCUOOmQY2b\nOroKAABQ5Wz6rdjQ0NAZM2acPHny3Llz7777bsuWLYUQycnJb7/9dmhoaJ8+fexUJAAAACpn\nU7AzatGixTvvvHP27NmkpKTo6GghRElJSUJCgl1mDgAAADmsORVbrlOnTm3cuHHjxo1nzpyx\n1zwBAAAgn03BzmAwHDly5Mcff9y4cePFixeN7S4uLn379h05cqTN5QEAAEAua4JdSUnJgQMH\nNm7c+OOPP16/ft3Y7uTk1LNnz5EjRw4bNszX19d+RQIAAKBylgW7hISEjRs3btq0KSUlxdio\nUqkefvjhuLi4ESNGBAUF2btCAKgpjuz59cKJI9ZNe/PSRSHEsd92Z2ekWzeH4PAHeCokAPMs\nC3YmN7p26NBh5MiRsbGxTZo0sWtVAFATnTr4+8/frLRlDn8dPvDX4QPWTdulVz+CHQDzrDkV\n26JFi5EjR44cObJ58+Z2LwgAaqwnRj3XOaqfo5Ze19fPUYsGUFtYFuxmzJgxcuTIdu3aVVE1\nAFCTNQwNbxga7ugqAKBClgW7jz/+uIrqABzLxcVFrVY7ugorOTs7CyFUKpVGo3F0LVYyGAw6\nnc7RVQBArWe359gBtZqbm1vtTUVS5Wq12tPT09G1WIlgBwB2QbAD/s+59Fxtsd7RVVjs74xc\nIURqvu5ESpaja7FGCz9Pjdo+v4IDAPc5gh3wf25mF2QXFju6CovdytUKIbJ1xZfv5Tu6FmuE\neXsQ7ADALngzBQAAUAiCHQAAgEIQ7AAAABSCYAcAAKAQBDsAAACFINgBAAAoRCWPO2nQoIH0\nx5EjR4KDg1966SWL5r5w4UIr6wIAAICFKgl2d+7ckf7Q6/VCiEWLFlk0d4IdAABAtbHsAcVB\nQUFVVAcAAABsVEmwO3r0qPSHdE729u3bVV4RAAAArFJJsIuMjKyeOgAAAGAj7ooFAABQCIId\nAACAQpg7FduyZUtbZv3ss8++8cYbtswBAAAA8pkLdufPn7dl1qmpqbZMDgAAAIuYC3bPPPNM\nue2//PLL3bt3hRAuLi6hoaFhYWHOzs7JycmXLl3S6XRCiG7duk2YMKFVq1ZVUTEAAADKZS7Y\nxcfHl22cOXPm3bt3XV1dX3zxxenTp5d+sl1GRsann3762WefHThw4PHHHx8zZoz96wUAAEAF\nLLt54rfffps1a5YQYsWKFfPnzzd5XrGvr++sWbPi4+NVKtWsWbMSEhLsWSkAAADMsizYLVy4\n0GAwDBgwYPTo0RWNEx0d/dRTTwkhFixYYGt1AAAAkM2yYHfw4EEhRI8ePcyP1r17dyHEkSNH\nrC4LAAAAlrIs2KWnpwshNBqN+dGcnZ2FEJmZmVaXBQAAAEtZFux8fX2FEImJieZH+/PPP4UQ\n9evXt7osAAAAWMqyYPfQQw8JITZs2CBFt3IlJiauX79eCNG1a1cbiwMAAIB8lgW78ePHCyEK\nCwujo6O3bt1adoRt27ZFR0cXFhYKIcaNG2eXEgEAACCHuefYlTVo0KCYmJh169bdunVryJAh\nHTt2fPjhhyMiIoQQycnJhw8fPnbsmDTmqFGj+vXrZ/96AQAAUAHLgp0QIj4+XqPRrFmzRghx\n/Pjx48ePlx3n+eefX7JkiR2qAwAAgGyWnYoVQri6uq5evTohIWHIkCGenp6lB/n6+sbGxv7+\n++8rVqxwcXGxX5EAAAConMVH7CS9evXq1auXXq+/cuVKRkaGs7Ozn59fSEiISqWyb30AAACQ\nycpgJ1Gr1eHh4eHh4faqBgAAAFaz+FQsAAAAaiaCHQAAgEIQ7AAAABSCYAcAAKAQBDsAAACF\nINgBAAAoBMEOAABAIWx6jl1FiouLdTqdi4uLq6trVcy/+nl4eDg5WRmCrZ4Q9uLt7V3pOHST\nY6lUKh8fn0pHo5scS61Wy+kmtVpdUmKohnpQLmdnZzndVKtJbwVeXl4Gw/24pZWUlJgZWiXB\n7ptvvnnuueemTJmyaNGiqph/9cvPzy8qKrJuWg8PDw8PD/vWA4vcu3ev0p3f09NTo9FUTz0o\ny2AwZGZmVjpavXr1+LlCB9Lr9XK6ycfHR6iI4A5TXFyclZXl6CqqVp06ddzd3XNycqz+aK7t\n/P39KxpkZbDT6XRXr14t98MyJydn1apVQoiLFy9aN3PgPrR5xeL4ebNsmcO21cu3rV5u3bRh\nrdrO/fEXW5YOAKgJLA52N2/eHD169P79+yuNyREREdZWBdx3vP3rh7d+0FFLbxTG3goASmBZ\nsMvLy3v44Ydv3LhR6ZjNmzd/6623rK0KuO/0fGpEz6dGOLoKAEDtZlmwW716tZTqBgwYMHz4\n8Nzc3LfffjsrK+ujjz4KCwvLzMzcsWPHli1bfHx8fv/994CAgKqpGQAAAOWwLNitW7dOCBEZ\nGbl161bpnpR//vln4cKFLi4usbGxQohJkybNmzdv+vTpY8aM2bFjR1VUDAAAgHJZduPS9evX\nhRCxsbHGhw707t1bCJGUlGQcZ9q0aR06dNi5cyfBDgAAoDpZFuxu3bolhAgJCTG2dOjQQQhx\n4cKF0qONGzdOCLFmzRo7FAgAAAB5LAt2np6eQgitVmtsCQ4OdnNzO3/+fOlHn7Ro0UIIceDA\nATsVCQAAgMpZFuyCg4OFEGfOnDG2qFSq0NDQnJyc0k+tc3d3F0KkpqbaqUgAAABUzrJg1759\neyHE119/feXKFWNjy5YthRBbtmwxtpw9e1YI0bBhQ7uUCAAAADksC3avvPKKECI9PT0yMnLK\nlClSo3T/xNy5c6UjedevX587d6743wlZAAAAVA/Lgl27du0mTpwohMjIyFi5cqXUGBcX5+3t\nnZKS0qZNm6CgoCZNmvzzzz9CiJdfftnu5QIAAKAiFv9O89KlS//73//26dPHy8tLavHx8Vmx\nYoWzs7MQIiUlRbqL4oUXXujXr599awUAAIAZFv9WrEqlmjBhwoQJE0pKSoyNw4YNO3bs2LJl\ny65cuRIWFta/f/+BAwfatU4AAABUwuJgZ2R8RrHkwQcf/PLLL22uBwAAAFay+FQsAAAAaiaC\nHQAAgEJUciq2QYMG0h9HjhwJDg5+6aWXLJr7woULrawLAAAAFqok2N25c0f6Q6/XCyEWLVpk\n0dwJdgAAANXGspsngoKCqqgOAAAA2KiSYHf06FHpD+mc7O3bt6u8IgAAAFilkmAXGRlZPXUA\nAADARtwVCwAAoBAEOwAAAIUwdyq2ZcuWtsz62WeffeONN2yZAwAAAOQzF+zOnz9vy6xTU1Nt\nmRwAAAAWMRfsnnnmmXLbf/nll7t37wohXFxcQkNDw8LCnJ2dk5OTL126pNPphBDdunWbMGFC\nq1atqqJiAAAAlMtcsIuPjy/bOHPmzLt377q6ur744ovTp08v/WS7jIyMTz/99LPPPjtw4MDj\njz8+ZswY+9cLAACAClh288Rvv/02a9YsIcSKFSvmz59v8rxiX1/fWbNmxcfHq1SqWbNmJSQk\n2LNSAAAAmGVZsFu4cKHBYBgwYMDo0aMrGic6Ovqpp54SQixYsMDW6gAAACCbZcHu4MGDQoge\nPXqYH6179+5CiCNHjlhdFgAAACxlWbBLT08XQmg0GvOjOTs7CyEyMzOtLgsAAACWsizY+fr6\nCiESExPNj/bnn38KIerXr291WQAAALCUZcHuoYceEkJs2LBBim7lSkxMXL9+vRCia9euNhYH\nAAAA+SwLduPHjxdCFBYWRkdHb926tewI27Zti46OLiwsFEKMGzfOLiUCAABADnPPsStr0KBB\nMTEx69atu3Xr1pAhQzp27Pjwww9HREQIIZKTkw8fPnzs2DFpzFGjRvXr18/+9QIAAKAClgU7\nIUR8fLxGo1mzZo0Q4vjx48ePHy87zvPPP79kyRI7VAcAAADZLDsVK4RwdXVdvXp1QkLCkCFD\nPD09Sw/y9fWNjY39/fffV6xY4eLiYr8iAQAAUDmLj9hJevXq1atXL71ef+XKlYyMDGdnZz8/\nv5CQEJVKZd/6AAAAIJOVwU6iVqvDw8PDw8PtVQ0AAACsZlOwS0tLO3v2bHZ2dm5u7siRI+1V\nEwAAAKxg8TV2QgiDwbBq1ao2bdoEBAQ89thjgwcPjouLkwY9+uijs2fPzs3NtWuRAAAAqJzF\nwa6goKBXr17PPffcmTNnDAaDydCrV6++8cYbUVFR0o+PAQAAoNpYHOzi4uL27t0rhAgKCpox\nY4b03BOjnj17CiGOHDny5JNP2qlCAAAAyGJZsNu7d+9PP/0khBgwYMDZs2c//vjjmJiY0iPE\nx8d/9dVXQogDBw7s3LnTjoUCAADAPMuC3ZdffimEaNSo0TfffOPj41PuOOPGjevfv78QYsWK\nFbbXBwAAAJksC3anT58WQjz99NMVpTrJ4MGDhRAXLlywpTIAAABYxLJgd+3aNSFE27ZtzY8W\nEBAghLh06ZLVZQEAAMBSlgU76TfEbty4YX6069evCyHc3d2tLgsAAACWsizYtW7dWgjx888/\nl33QSWl//PGHEKJZs2a2VAYAAACLWBbshg8fLoTYv3//Z599VtE4e/fu3bRpkxBi4MCBNhYH\nAAAA+SwLdhMmTIiIiBBCTJ06deTIkcePHy8uLjYOzcjImDdv3oABA4QQ3t7eU6ZMsW+tAAAA\nMMOy34p1c3PbunXrY489lpqa+sMPP/zwww916tSRBjVu3Fi6tE4I4ezs/N1335m/cxYAAAD2\nZfEvT7Ro0eLkyZNDhw5VqVRCiLy8PKndmOqaNWu2b9++J554wo5VAgAAoFKWHbGTNGjQ4Mcf\nf7x48eKWLVv+/PPPO3fu5Ofne3t7t2jRom/fvgMHDnRysjgvAgAAwEbWBDtJRETEq6++asdS\nAAAAYAsOrQEAACiE9UfsqkJOTs7atWsTExMzMjI8PT3btWsXGxvbqFGjqpj29OnTb7311owZ\nM7p27Wq/VwAAAOAw5oJdpT8dVinpt2VlSk9Pnz59elpamhDCw8MjMzNz3759hw4d+uCDD1q0\naGH3aXft2iW/NgAAgJrPXLD766+/qq0OIcTSpUvT0tKCg4Nff/31Jk2apKamLl68+Pjx4/Pm\nzVu+fLl0E65dptVqtdu2bdu3b1/Vvh4AAIDqJetUrEajad68ufloZaOMjIwjR46o1ep33nkn\nMDBQCBEQEDBjxowXX3wxNTU1KSmpffv2tk+7Z8+en3/++dq1a1qttupeCwAAgEPICnZarTYr\nKysmJmbEiBGdOnWqijoSEhJKSkrat28vJTOJRqPp1q3bpk2bdu3aZSbYyZ/29u3bd+/edXd3\nd3d3z87O1uv1VfFaAAAAHMLcXbF//vnntGnTmjZtKoS4cuXKJ5980rlz57CwsNdff/3YsWP2\nrSM5OVkIERkZadIutVy8eNEu044aNWr1/4SGhtqjcAAAgJrC3BG7Ll26dOnSZe7cuUeOHFm/\nfv2GDRsuX758+fLlTz755JNPPgkNDZWO4ZVNVFbIzMwUQvj7+5u0Sy3S0KqYtiIZGRmlE2FI\nSIiHh4cV8xFCqNVq6yaEvbi4uBgMBvPj8FRtx1KpVC4uLnJGq4bGEXzaAAAgAElEQVRiUBG6\nqVaQ2U21mvSOzcdruWSdiu3cuXPnzp0/+eSTo0ePSgnv0qVLly9fnjNnzpw5c8LCwkaMGBET\nE9OxY0er68jIyBBCeHl5mbRLLVqtVqvVajQau09bkaSkpOnTpxv/Xbx4cZcuXSyaA2qOunXr\nOroEVK5evXqOLgGVcHJyktlN+pJKvkqh6jg7O98ne5Onp6ejS3AM8xeSWfYcu06dOnXq1GnO\nnDnHjh2TEl5ycvKlS5eMCU86hmdFwsvJyRFClD0qZmzJysqqKJzZMm1FmjRpMmbMGOO/fn5+\nBQUFFs3ByMXFxdm5Zj0v8H6j1WorPWLn6urKlz/HkrOLubm5cWzVgQwGg5w7zyx9v4V9lZSU\n6HQ6R1dRtaQPVp1OV1JS4uhaHKCkpKROnToVDbUycERGRkZGRs6ePfvEiRPr169fv379xYsX\nL126NHv27NmzZ4eHh0vH8Dp06CBzhl5eXgUFBWXf2fPz86U/zLwGW6atSFhY2EsvvWT8Nysr\nKy8vz9KZSDw8PAh2jpWXl1dpsFOpVAQ7BzIYDHJ2MWdnZ4KdA5WUlMjpJldXV6GimxxGr9db\n/YFVW9SpU8fZ2Vmr1RYVFTm6FscwE2xs3fc6dOjw0Ucf/fPPPydOnHjzzTebNWsmhEhOTp49\ne7ZFx+18fHyEELm5uSbtUourq6uZI662TAsAAKAYdvtS1b59+w8//PDo0aNTp0614iu1FM6k\nq+VKk1p8fX2raFoAAADFsE+wy83N/f7774cOHRoQEDB//nzpnLdFJ7bCwsKEECdOnDBpP3ny\npBDC/KNJbJkWAABAMWwKdvn5+evXrx8+fHhAQMDTTz+9efNmnU7n5OT02GOPLV68+NatW/Jn\n1bt3bycnp5MnT2ZlZRkb9Xr9wYMHhRD9+vWromkBAAAUw5pgp9VqN23aNHLkyICAgJiYmI0b\nNxYUFKhUqkcffXThwoU3b97ct2/f5MmTAwIC5M/T39+/U6dOhYWFs2fPlm56KCws/PLLL69f\nvx4UFFT6JozNmzevW7fu+PHjVkwLAACgYBbcranT6X755Zd169Zt2bJFesKIEEKlUj388MOx\nsbHDhw9v1KiRLaVMmjQpOTn5zJkzo0aNCgkJuXXrVmFhoUajmTZtWunHXW7YsCE7O3vgwIGl\nb86QOS0AAICCVR7sioqKdu3atW7dus2bN5c+19mlS5fY2NgRI0aEhITYpRR/f/8vvvhi7dq1\niYmJN27c8PLy6tq1a1xcXIMGDap0WgAAAGUwF+x+/fXXH374YdOmTaV/lSsyMlLKc9JvyNpX\n3bp1J06cOHHiRDPjfPPNN1ZPW9qnn35qcX0AAAA1mLlgV/q2g/bt28fGxsbExEi3oAIAAKCm\nkXWNnUajuXfv3rJly5YtW2bR3C9fvmxVVQAAALCYrGCn1WqvXLlSxZUAAADAJuaC3aOPPlpt\ndQAAAMBG5oLdH3/8UW11AAAAwEZ2+61YAAAAOBbBDgAAQCEIdgAAAApBsAMAAFAIgh0AAIBC\nEOwAAAAUgmAHAACgEAQ7AAAAhSDYAQAAKATBDgAAQCEIdgAAAApBsAMAAFAIgh0AAIBCEOwA\nAAAUgmAHAACgEAQ7AAAAhSDYAQAAKATBDgAAQCEIdgAAAApBsAMAAFAIgh0AAIBCEOwAAAAU\ngmAHAACgEAQ7AAAAhXB2dAEAANjThRNH01NuWzftlfNnhRB/nzwmVCrr5tCgcWhoqzbWTQvY\njmAHAFCUzV8tTkzYacsctq5eLlYvt27aJ0Y9N77Vh7YsHbAFwQ4AoCj948Z07BHlqKWHRDR3\n1KIBQbADAChMu26POboEwGG4eQIAAEAhCHYAAAAKQbADAABQCIIdAACAQhDsAAAAFIJgBwAA\noBAEOwAAAIUg2AEAACgEwQ4AAEAhCHYAAAAKQbADAABQCIIdAACAQhDsAAAAFIJgBwAAoBAE\nOwAAAIUg2AEAACgEwQ4AAEAhCHYAAAAKQbADAABQCIIdAACAQhDsAAAAFIJgBwAAoBDOji6g\ndtBoNBqNxrppnZ1ZyQ7m5eVlMBjMj0M3OZZKpfLy8qp0NLVaXQ3FoCJOTk5yusnJyamkkh0O\nVUitVsvpplpNesf28PAoKSlxdC0OYP4TjQ8zWQoLC/V6vXXTajQaPo0cq6CgoNJg5+HhQTc5\nkMFgKCgoqHQ0tVrt5MR5BoeR2U0uLi5CqKqhHpSrpKRETjfVau7u7mq1WqfTFRcXO7oWBzAY\nDGYONhHsZCkpKbF667k/v0/UKMXFxZUGO7rJ4eTsYpX2I6qUwWCQ200qgp3DyOymWk16x9br\n9Yp/pVbguy8AAIBCEOwAAAAUgmAHAACgEAQ7AAAAhSDYAQAAKATBDgAAQCEIdgAAAApBsAMA\nAFAIgh0AAIBCEOwAAAAUgmAHAACgEAQ7AAAAhSDYAQAAKATBDgAAQCEIdgAAAApBsAMAAFAI\ngh0AAIBCEOwAAAAUgmAHAACgEAQ7AAAAhSDYAQAAKATBDgAAQCEIdgAAAApBsAMAAFAIgh0A\nAIBCEOwAAAAUgmAHAACgEAQ7AAAAhSDYAQAAKATBDgAAQCEIdgAAAApBsAMAAFAIgh0AAIBC\nEOwAAAAUgmAHAACgEAQ7AAAAhSDYAQAAKATBDgAAQCEIdgAAAApBsAMAAFAIgh0AAIBCEOwA\nAAAUgmAHAACgEAQ7AAAAhSDYAQAAKATBDgAAQCEIdgAAAApBsAMAAFAIgh0AAIBCEOwAAAAU\ngmAHAACgEAQ7AAAAhSDYAQAAKATBDgAAQCEIdgAAAApBsAMAAFAIZ0cX8P+Tk5Ozdu3axMTE\njIwMT0/Pdu3axcbGNmrUyI7T2rIIAACAmqwGHbFLT09/+eWXt27dmpKS4uLikpmZuW/fvlde\neeX8+fP2mtaWRQAAANRwNSjYLV26NC0tLTg4eOHChWvXrl2xYkXHjh11Ot28efMMBoNdprVl\nEQAAADVcTQl2GRkZR44cUavV77zzTpMmTYQQAQEBM2bMCAgISE1NTUpKsn1aWxYBAABQ89WU\nYJeQkFBSUtK2bdvAwEBjo0aj6datmxBi165dtk9ryyIAAABqvpoS7JKTk4UQkZGRJu1Sy8WL\nF22f1pZFAAAA1Hw1JdhlZmYKIfz9/U3apRZpqI3T2rIIAACAmq+mPO4kIyNDCOHl5WXSLrVo\ntVqtVqvRaGyZ1qJF7N+/f+bMmcZx5s6d27FjR+temkqlEkI80siHuzOqn5NKJYTw9fWtdEyp\nm6Ka+tNN1U+tUgkh/Pz8Kh1T6qaBEYGVjgm7s6ib1EI8+UBQ1ReFcri4uMjpJvG/HcpRfH19\n792756ilP/nkk5s2bXLU0oUQttyyqdfrzQytKcEuJydHCOHh4WHSbmzJysqqKNjJnNaiRWg0\nmtIPt3NxcTG/Hs1wcnJSqVQqIRy5A9lA2vNr613DBoOhsn1AQjc5kFS2/G6qKScaLHefdJNa\nrRY153yQhWp1HwkhDAaDwWAoKSmRM7LUU47i7e2t0+kctXQ/Pz8H9rL8PiqX+WlrSrDz8vIq\nKCgoKCgwac/Pz5f+qFOnjo3TWrSITp06xcfHG//Nyspy4BcLB3JycvL19dXpdFIsRs3k7Ozs\n7e1dUFCQl5fn6FpQITc3Ny8vr7y8vLLvQqg53N3d69Spk5ub68DMcZ9ITEy0eto6deq4u7tn\nZWUVFRVZPZP09HSrp3W4steVGdWU71Q+Pj5CiNzcXJN2qcXV1dXT09PGaW1ZBAAAQM1Xs4Kd\ndBlcaVKL+WukZE5ryyIAAABqvpoS7MLCwoQQJ06cMGk/efKkECI0NNT2aW1ZBAAAQM1XU4Jd\n7969nZycTp48mZWVZWzU6/UHDx4UQvTr18/2aW1ZBAAAQM1XU4Kdv79/p06dCgsLZ8+eLd3N\nUFhY+OWXX16/fj0oKKhDhw7GMTdv3rxu3brjx49bOq38RQAAANRGNeWuWCHEpEmTkpOTz5w5\nM2rUqJCQkFu3bhUWFmo0mmnTppV+1s6GDRuys7MHDhxY+sFyMqeVORoAAEBtVFOO2Akh/P39\nv/jii0GDBvn5+d24caNOnTo9e/b84osvHnjgAXtNa8siAAAAajhV7X0MY3Wy8WE5tRfPsasV\neI5drcBz7GoF6Tl2OTk5PMeuJrPLc+xqtVrwHDsAAADYiGAHAACgEAQ7AAAAhSDYAQAAKATB\nDgAAQCEIdgAAAApBsAMAAFAIgh0AAIBCEOwAAAAUgmAHAACgEAQ7AAAAhSDYAQAAKATBDgAA\nQCEIdgAAAApBsAMAAFAIgh0AAIBCEOwAAAAUgmAHAACgEAQ7AAAAhSDYAQAAKATBDgAAQCGc\nHV1A7VCvXj1Hl+AYaWlpUVFR3bt3f++99xxdCyp09uzZ6OjoYcOGTZkyxdG1oEK//fbbe++9\nN2nSpJiYGEfXggqtX79+yZIlM2fO7Nmzp6NrQYUWL168YcOGhQsXtm7d2tG11DgcsYM5JSUl\n2dnZBQUFji4E5uj1+uzsbJ1O5+hCYE5RUVF2dnZhYaGjC4E5hYWF2dnZRUVFji4E5uh0uuzs\nbL1e7+hCaiKCHQAAgEIQ7AAAABSCa+xgjkaj6d27d5s2bRxdCMypV69e7969mzVr5uhCYE5g\nYGDv3r2bNGni6EJgTuPGjXv37h0YGOjoQmDOAw880Lt37/v28nfzVAaDwdE1AAAAwA44FQsA\nAKAQBDsAAACFINgBdvPuu+8OGTLk+PHjji7k/pWamjpkyJDx48c7uhA72LNnz5AhQxYtWnTf\nFiCEOHjw4JAhQ+bMmePAGoDahWCHCq1YsWLIkCHPPPOMowsBajd2parm2DV8f/bv/fmqawWC\nHexv+PDhQ4YMMXnCZ7mNqIHoKaB6sK/VKIrpDh53ggp17NixTp06bm5uji4EqN3YlaqaY9fw\n/dm/9+errhUIdqhQx44dO3bs6OgqAPsoLCw8ffq0p6dn8+bNq3nR7EpVzbFr+P7s3/vzVdcK\nBLva5+jRo9u3b79+/XpmZqaXl1dgYGCvXr2ioqKcnf+vN/Pz87ds2ZKYmHjr1i1nZ+egoKBe\nvXr17dtXrVaXntXVq1c3bdqUnJyckpISGBjYrl27mJiYunXrSkMPHjw4e/bsbt26vf7660KI\nzZs3r1y5cujQoc8991zpmWzdunX58uXSFetz5sw5cOCA1D5s2DAhxJo1a5YtW1a20dvbW2q5\nePHizz//fOnSpVu3bgUFBYWGhvbp06e2PxL5n3/+Wbt27fnz5w0GQ3h4eKdOnYYMGaJSqUqP\nI6cfhRB//fXXjh07Lly4kJOTExERERMT06BBg/Hjx7dr1+6DDz4Q/+um2NjYuLi4zZs37969\nOyUlxdvbu1WrVk8//XRQUJD8hZbbfQ7pKZkb8OHDh/fs2fPPP//k5+eHhoY++eST4eHhpUeQ\nVk5cXFyfPn1mzJiRmprauXPnt99+W87GbGw0v5vIGcdkV5JYtz7T09O3bNny999/X7582d3d\nPTw8fPDgwe3atZOGLl68eOfOnT179nz11VdNJpwxY8bZs2fHjBkj9WwVFWCpSjdvo6Kiog0b\nNuzbty8tLc3Hx6dly5ajRo0ybt7lrmGZu1gV9W9t2desZvKqZb4RWfR+ZcetXfHdURrBrpbZ\nsGHDmjVrhBCurq716tXLyck5d+7cuXPn/vrrL+PGnZOTM3Xq1Dt37qhUqnr16hUVFf39999/\n//33qVOnSr/v7NmzZ/HixYWFhU5OTnXr1r169erVq1f37dv3+eef+/v7W1deq1atXFxcfvvt\nN4PB0KNHDycnJ1dX13IbpfF37969YMEC6SnZ9erVu3r16pUrV/bu3Vt6h6x1Tp48uXXrVr1e\n7+HhodVqk5KSkpKSjh49+sYbb3h4eEjjyOlHIcS2bdu++uorvV6vVqs9PDxOnz595syZJ554\nouxC9Xr9Rx99dO3ata5du9apU+fQoUP79u07ceLEggULfHx8ZC60hvSUzA14+fLlW7duFUKo\nVCoPD4+zZ8+ePXu2X79+ZWdYVFQ0e/bs1NRUtVptfLOWSc5uYsWuZN36PH/+/Lvvvpufny9N\nde/evcTExMTExOjo6LFjxwohunfvvnPnzsTERGmbMU6Ynp5+7tw5lUrVs2fPKi3AIvI3b51O\nN3PmzDNnzvj5+QUFBd24cSM1NfXkyZMLFy6sqENl7mJV1L+1ZV+zOzlvRDJHs+PWfr91B8Gu\nNklPT4+Pj1epVJMnT+7Tp49arTYYDPv37//ss8/27dtn/Lrz3Xff3blzp3Xr1v/+978DAgKE\nEMeOHfv4448PHDhw7ty5li1bCiFu3769cOHCkpKS0aNHDx48WKPRpKWlffbZZ6dPn/7iiy9M\nvivLN3jwYCHEwYMHCwsLX375ZRcXl4oahRCZmZlLly41GAx9+/Z99tln69atm5eXt379+h9/\n/DE+Pr5Lly4hISH2WG3VbfPmzaGhodOmTQsJCdFqtXv37l2+fHlSUtLGjRtHjx4tZPfjpUuX\nli9frlKpnn/++f79+7u5uV27dm3evHnbt28vu9Bdu3YFBwd/8cUX7u7uQojhw4e/+eabZ86c\n2blzZ1xcnMyF1pCekrMBHzx4cOvWrSqVauTIkYMHD/b09Lx58+aCBQt++eWXsjPcvn27RqN5\n8803O3XqZHK0xjw5u4kVu5J167OoqOjTTz/Nz8/v2bPnc8895+PjU1hYuGvXrlWrVv34449h\nYWE9evRo06aNj49PZmZmUlJS6dNkBw4cMBgMHTp08PPzq9IC5K9bizbvo0ePent7f/zxx61b\ntxZCXL169T//+U9WVlZCQsLw4cPLji9zF6ui/q1F+5rdVfpGJHM0O27t92F3cFdsbXLu3DmD\nwRAZGdm/f3/pC4pKperevbt0TPjq1avSaOfPnxdCxMbGSh+KQojIyMh+/fqFhYWlpaVJLfHx\n8Xq9ftCgQSNGjNBoNEIIf3//119/3cXF5dSpU9KXpKr2/fff63S6zp07v/jii9JJjTp16owd\nO7Z3794lJSXffPNNNdRQFdzc3N5//31ph9doNE888cQLL7wghNiyZUtWVpaQ3Y/ffPONwWAY\nNmzYk08+KV2h3Lhx4w8++MD4VbK07OzsSZMmSe+S0gylb6vXrl2TWmQutFzV3FOVbsAGgyE+\nPl4IMWzYsLi4OE9PTyFEo0aNPvjgg3KPoBQUFEydOvXhhx+2KNUJebuJFbuSdetz+/btd+7c\nadeu3b///W/pqIarq+vAgQMnTJgghPjuu++EECqVqmvXrkKIw4cPl552//79QoioqKiqLkA+\nizZvIcSLL74opTohRJMmTaRP4uvXr5c7ssytvYr6txbta3ZX6RuRzNHsuLXfh91BsKtNunXr\ntnnz5rffftukXafTCSH0er30r/Qu+eeff5a+bXvChAmff/559+7dpX+PHDki/ncszahu3brj\nx4+PiYnJy8urshfxf5KSkoQQ0dHRJu1PPvmkcWhtFBUVZfLT1FFRUfXr19fpdMnJyUJePxYX\nFx8/flylUg0ZMqT0OPXq1Sv3uEjDhg1Nfl1eOkVl3CpkbjzlquaeqnQDTk9Pv3nzpouLi0lJ\nrq6ugwYNKjvDoKCgBx980IpK5OwmVuxK1q1PaUFlL9bs1auXq6vrrVu3pNQrraLDhw8bfwf8\n7t27Fy5c8PDweOSRR6qhADks3bz9/Py6dOlSuqVRo0bSfMqdv8ytvYr6txbta3ZX6RuRzNHs\nuLXfh93BqdjaRKVSSVt5Tk7OzZs3b9++ffXq1ZMnT166dKn0aE888cTZs2e3b9/+22+/de/e\nvW3btq1atfL19TWOkJGRodPpNBqNyZWq0rTV8EKEEHq9PjU1VQgRGhpqMigkJEStVufn52dn\nZ5tcwlwrlH1FTk5O4eHhd+/evX37tpDXjykpKSUlJT4+PiYZUQjRtGnTsgsNDAw0X5XMjaes\n6u+pSjfgmzdvCiGCgoKkY3Wlmdw8Ialfv74VZcjZTazYlaxen9KrTkhIOHjwoMkgZ2fnwsLC\ntLQ0f3//li1b+vv7p6WlnT9/XjptLZ2Z6tatm3QwrKoLKPdVm7B085ZiXGkmn/dlh1a6tVdR\n/8pcerkU8K5Y6RuRzNHstbWL+7I7CHa1zKFDh9avX5+cnCx9QdFoNBEREQEBAdL2J3nsscc8\nPDy+/fbbS5cu7dixY8eOHSqVqlmzZk899dSjjz4qhJBGLn0pqy2MX5Uscu/ePb1e7+rqaryf\nwMjJycnb2zs9Pf3u3bs1cJ+pVLkrVsol0qlYIaMfzfRRuY3GkxpmyNl4yqr+nqp0A7537574\n3yo1IV1VY8LLy0vmoktvzHJ2Eyt2JevWZ0lJSWZmphDi0KFDFc1ZOi2oUqm6dev2008/HTp0\nSPqo++OPP0SpM1NVXYAclm7eZRN8pWzZxeTUaePSy6WAd0U5b0SVjmbHrV1yv3UHwa422bNn\nz+eff65Wq7t3796hQ4dmzZqFhISoVKo5c+aYbKCdO3fu3Lnz3bt3T548efbs2aNHj/7999+f\nfPJJampqdHS0dNA7JyfHLlVZNx9vb2+1Wl1YWJifn2+y2xgMBikAlfvJXfNlZGSUbZROHEgX\njcnpR+mzREowJozp0CLyNx4TDukp8xuwdFio3PUsfR6YMH90p7TSG7Oc3cSKXcm69Sl9kGRm\nZq5YscJ46WFFunfvLn3UjRs3LiUl5Z9//gkKCmrVqlW1FVApSzdv+T0okbO1V1H/ylx6RYtT\n6ruiRey4tYv7sjsIdrXJ+vXrhRBTpkzp3bt36fbSX5T1en16erpKpapfv379+vX79OnTp0+f\n4uLijRs3fvvtt1u3bo2Ojg4ICFCr1bm5uZmZmSZfRhMSEpKTk6Oiopo1a1ZuDWWPz0nHzC2l\nVqvr169/586dq1evSt+0Ss+wuLhYo9HY65hiNSt7hF+v10uNDRs2FPL6sUGDBiqVKjMzMycn\nx+SAU0UXjJsnZ6HlquaekrMBN2jQQAhx586dvLy8OnXqlJ78ypUr8pdlfmOWs5uEh4dbuitZ\nvT4bNmyYmZl57dq1sh910ueTsf2BBx4ICAhISUm5dOnSiRMnhBCPP/64MRtVQwGVsvvmbULO\n1l5F/Stz6eVS8Luipey1tYv7sju4eaI2kY76GG8Nk2RnZ587d874b0FBwfjx459//vn09HRj\no7Ozs3QOS6vVCiGcnJykLzTSY8CMioqK4uPjpWdDlF26dD9RSkpK6caMjIzExETrXk7btm2F\nEJs3bzZp//HHH4UQNfnxj+bt3bvX5LjR7t2709LSfH19IyIihLx+dHNza968ucFg2LZtW+nR\n8vLy9u3bZ0VVchZakersKTkbsK+vb5MmTYqKikxK0uv1W7ZskbMUORuznN3Eul3JuvUpPZf1\np59+Mmk/duzY+PHjP/7449KN0uo6fPjw/v37VSqVyZmpaijAPLtv3ibkbO1V17+1ZV+ryey4\ntd+H3UGwq02kJ2js2bPHeKTh4sWLM2fOlD7tbt26JYTw9PSUDgv997//NZ5ByMzMlB7PaNy4\nx44dq1KpNm3atGXLlsLCQiFEfn7+woULMzMzIyIiyn02T+PGjYUQf/7559GjR6WWtLS0jz76\nqKJqyz0pVroxLi7O1dX10KFDy5Ytk24uKygoWLNmTUJCgpOT05gxYyxaOTVHUVHRW2+9dfny\nZSGETqfbuXPn0qVLhRCxsbHS9bxy+lEIIa2BdevW/fzzz9L9oSkpKe+88450J6CTk2U7r8yF\nGjmqp2RuwM8884wQYt26devXry8oKBBC3L179913301JSZGzZmRuzHJ2Eyt2JevW51NPPeXj\n45OUlLRgwQLjmklKSvr8889Fmds2pbsFf/nll+Tk5FatWplcq14NBVTKvpu3CZlbexX1b23Z\n12oyO27t92F3cCq2Nhk5cuSsWbN++OGH3bt3+/v7S1dutmjR4umnn/7uu+/WrFlz4sSJDz/8\ncPLkyTNnzjx06FBiYqKfn59er8/IyDAYDD4+PhMnTpRm1axZs7Fjx65Zs2bFihVff/21r69v\nRkaGXq+vW7futGnTyl16mzZtWrRocf78+ffff79Ro0Zqtfr69eteXl7/+te/Fi5cWHpMb2/v\n1NTUqVOnBgYGzpw5U7q2tGyjv7//hAkTFi9evH379p9//ll61KTBYHB2dn7++edNboavRUaP\nHh0fH//yyy97enoWFBRIt9P37du3b9++0ggy+7F169ZxcXFr165dunTp8uXLvby87t27Jz1D\na9asWZZesStzoaIG9JScDfihhx56/PHH9+7dGx8f/80339StW1e65CUuLm7nzp2VLkLmxixn\nN7FiV7JufWo0mmnTps2dOzchISEhIcHPz0+n0+Xm5gohBg8ebHKUIjw8vGHDhtKHVq9evaq/\ngErZd/M2IXNrr6L+rUX7Wo1lx639PuwO9bvvvuvoGiBXo0aNWrVqlZ6enpGRkZOTExoaOmjQ\noBdeeKFNmzYajaa4uDgiIqJDhw5BQUEPPfRQXl5eYWFhRkaGk5NTSEhI//79X3nlldJXerZs\n2bJDhw5arbaoqOjevXvBwcE9evR4/fXXjQ8suH79+v79+xs3biwd6HZycuratavBYMjOzr57\n967BYOjatetrr73m6+ubl5fXunVr43MKGjVqdPHixczMzOLi4gEDBkiPJSu3MSIiIjIyUoo+\nGRkZjRo16tChw8svv2zyzKraIiMjw9vbOy4url27dunp6enp6e7u7g8++ODIkSNjYmKMByFk\n9qMQom3bts2aNSsoKMjJydHr9V26dHnttdf0ev2uXbvat28vPWzdpJuMbt68eejQoSZNmnTr\n1s2ihTq8p2RuwA8//LB0sZ1Wq83Pzw8PDx83btzAgQNTU1MbN25sfuXI35gr3U3kjFO2DDnr\n8/Lly4cPHw4PDzc2BgYGRkVFFRYWqlSqtLQ0T0/Pli1bTs6lb2oAAA03SURBVJ48ecCAAWVX\nY1ZW1pkzZ9zc3Eo/Sd+oGgqolC2bt9Ru3LxNRpO/tVdF/9aifc0WJq9a5huRzNGE/bb2+6Q7\nSlNZ96wKAI7y22+/zZ8/f+zYsWUfmwnUdmzegI24xg6ooT766KPx48efPn3apD0hIUEIUWPP\nAgBysHkDVYRgB9RQTZo0SU1NXb16tfHaYYPBsH79+qSkJD8/P+muMaCWYvMGqginYoEaSqfT\nvfHGGxcvXpSeDeHk5HTt2rWMjAxnZ+c333yzU6dOji4QsB6bN1BFCHZAzaXT6Xbs2PH777+n\npKTo9foGDRqEhIQMGTJEeh4eUKuxeQNVgWAHAACgEFxjBwAAoBAEOwAAAIUg2AEAACgEwQ4A\nAEAhCHYAAAAKQbADAABQCIIdADv46aefVLJ9++23jq73/xk9erRKpWrZsqWjC6lB7LVO3N3d\nVSrV22+/bZeqAMhEsAMAAFAIZ0cXAEBR+vbtGxUVZX6c9u3bV08xtcv8+fOnTZsmhNBqtW5u\nbo4uB0CtRLADYE89evR4/fXXHV0FANynOBULAACgEByxA4Da5MSJE0ePHm3RokX37t0dXQuA\nGocjdgAc6caNG6+99lq7du28vb3d3d2bNm06atSoXbt2lTtygwYNVCrVsmXLhBBnz54dP358\naGiou7t7eHj44MGDf//994qWsmPHjmHDhjVq1Eij0QQHB48YMeK3336zY2FmaLXaBQsW9O3b\nNywszMPD44EHHujfv/+6desMBoNxnMmTJ6tUKukCOyGERqNRqVQTJkwo/apXr15dVFQ0evTo\njh07Tpw4ceXKlaWXcvr06eeffz4yMjIgIMDDw6NZs2ZRUVFLlizJy8uz4zqxYinlKiwsXLJk\nSVRUVGBgoLu7e4sWLWJiYipdOgBZDABgs82bN0tvKbNmzZI/1erVq93d3ct9axo+fHhBQYHJ\n+EFBQUKIpUuXbtq0ycPDQ3oqR7NmzZyc/t931A8//NBkEp1ON3LkyLLzV6lUM2fOfOaZZ4QQ\nLVq0sLGwipw6dapBgwblzqd79+46nU4abdKkSWVHGD9+fOlXvWrVqtGjRxuHjh07VhpaUlJS\n7uSS4ODgGzdu2L5OrFiKRqMRQvznP/8xab9w4cIDDzxQ7nyeeuqp/Px8mesWQLkIdgDswIpg\nJz36zviJPnfu3K+++uqFF17w9/eXGqOjo00mkSLO5MmT3d3dR40alZGRIbX/9ddf0nPX1Gr1\nyZMnS0/y7LPPSnNzdXUdOXLkZ5999vHHH/fv319qrF+/ftkQY0Vh5bp3715oaKgQwsnJKTo6\nevHixd9///3s2bONsWbq1KnSmFqtNjc398MPP5Ta09LScnNztVpt6Vct1ezp6fnSSy8tWbLk\n1KlT0tDly5dLU/n5+U2dOnXlypXffffdnDlzOnbsKLVHRUWZFGbFOrFiKeUGu9u3bwcGBkqT\ndOvWbdasWatXr3711VcbNmxonE9JSYmc1QugXAQ7AHZgDHb9+/efV7Evv/xSGr+goED6gNdo\nNNJ5SaOrV6926NBBmtuWLVtKD5IijhAiLi7OpIBDhw5Jg+bPn29s3L17t9TYoEGDQ4cOlR5/\n1apVrq6u0tDSIca6wsq1bt06aeRFixaVbs/JyQkLCxNChISElG6fN2+eNL4x0pm86o4dO165\ncsVkKX369BFC+Pr6Xrp0qXS7Xq8fMGCAEMLFxaX0DK1YJ1YsxVBBsBs2bJg0/3nz5pUOcBkZ\nGU888YQ0aNWqVQYA1iLYAbADY7Azz9/fXxp/9erVUsu0adPKzi0pKUk6ZtanT5/S7VLEcXZ2\nvnjxoskkJSUlderUEUJMnDjR2Pjkk09KS/npp5/KLuXVV18tG2KsK6xcr732mjSrtLQ0k0GL\nFi0aNGjQoEGD8vLyjI2VBruDBw+azEev19evX1+j0bzwwgtlCzBeh3fhwgVjoxXrxIqlGMoL\ndhcvXpROmsfExJSdT0ZGhhSpW7duXXYoAJm4eQKAA0h3IajV6unTp5cd+uCDD0rHb37//ffC\nwkKToW3atAkPDzdpVKlUPj4+QoiSkhKpRa/X79mzRwjRoUOHIUOGlF3K9OnTXVxc7FiYCT8/\nP+mP77//3mTQlClTtm7dunXrVg8PD/MzMWrbtu0jjzxi0ujk5JSamlpQUPDll1+WnSQ3N1f6\nw/C/GzWsWyeWLqUiW7ZskXqn3Ccd+vj4jB07Vghx9uzZ9PR087MCUBGCHQB7Mn+N3d27d6XR\nTp06JYQIDg4OCAgodz7SxVs6ne7ChQsmgyIiIuRUcuXKlZycHCFE2TwkCQoKkq6BK82WwkwM\nHjxYOmr10ksvtWrVau7cuWfPnq00/VSkadOmckYzGAw3btzYsmXLyy+/XPZ3Wq1bJ5YupSKH\nDx8WQnh4eDRv3lxXnlatWkkzP3PmjMx5AjDBc+wAOEBGRoYQwkyGMOaYsgdvjFffy1mEMBsE\nw8LC/v77b3sVZqJly5bffvvt5MmTU1NTz50799prr7322mv16tV79NFHn3zyyejoaOMhPTmM\nJ2TLyszMXLt2bUJCwrlz5y5fvqzVaisa07p1YulSKpKSkiKEyM/P9/T0ND+mlD4BWIFgB8AB\nKj1wZTwhWFxcbDLIeMuqec7Olb+/lR3HlsLKio6O7t2797fffrtt27Y9e/ZotdqsrKzt27dv\n3759+vTpn3zyycSJEyudiaSiX4/98ccfx48fn5mZKf3r5eXVpk2b5s2bt2/fvqio6M033yw9\nsnXrxNKlVET+4+4IdoDVCHYAHMDX1/fmzZuXL1+uaIRLly5Jf1h0WKs044TJyckVjVN2kN0L\nq1u37uTJkydPnqzT6Y4ePfrHH398//33p06dysrKmjRpUtOmTfv27StnPuU6depUbGxscXFx\nQEDA9OnTBw8e/MADDxiD7w8//GAyvnXrxNKlVMTb21sIERIScu3aNZmTALAU19gBcIAHH3xQ\nCHHjxo20tLRyRzhx4oQQwsXFpXnz5tYtIiQkxNfXVwjx559/ljtCZmamMaVVRWEpKSkpKSn3\n7t2T/nVzc+vWrduMGTOSkpJ++eUXJycng8FQ9r4Kiyxbtkw6cLh79+5p06Y1b9689OFM4wE2\nI+vWiaVLqUiLFi2EENevX5c/CQBLEewAOID0XDS9Xj9//vyyQ//666+tW7cKIbp16yb/vlET\nKpWqd+/eQoijR49u27at7Ajz58/X6XRVV9gjjzwSFBT06KOPlh3Ut29f6eYMG2//vHLlihAi\nMDCwTZs2ZYfu3LnTpMW6dWLpUioirVshhMnvoRk9++yzISEh5a4xADIR7AA4QGxsrJRsFixY\n8NNPP5UedOPGjTFjxkjXur388su2LGXKlCnSH5MmTTpy5EjpQZs2bfrss8+qtDDp/tkzZ878\n+uuvJoN27959584dUcHdqcYHiFRKetBxamrq9evXTQZ9+umnxvpLz9CKdWLFUso1cOBA6Tk1\nb7/99r59+0yGrlq1Kj4+/saNGwMHDjQ/HwBmcI0dAAfQaDTLli0bOnRofn7+0KFDR4wY0a1b\nNx8fn+PHj3/77bfSU1Gio6OfeuopW5bSo0ePcePGrVy58ubNm927dx8xYkSXLl30ev0ff/yx\nadMmg8HQtWvXgwcPVlFh48aN27hxoxBi6NChzz777COPPOLl5ZWWlvb777+vX79eCOHn51f6\nN1uNt2XMmTNn6NCh3t7e0u+kmdG/f3/pZy169+49ffr0Vq1aabXa8+fPr1y58tixYz4+PtJJ\nz2nTpo0dO3bUqFHOzs5WrBMrllJutWq1+uuvv46KiiooKHj88ceHDRvWvXv34ODg1NTUrVu3\n7tixQwgRGRn54osvVrpuAVSoqp58DOB+YsVvxRoMhq+//rqimz1HjBhRUFBgMr70yI8XX3yx\n3LkFBwcLIcaPH1+6sbCwsNwfvBdCvPnmm6tWrRJlfj7LisIq8tZbb1V0D6+/v//+/ftLj2wS\np4wvxPyrnjx5crnz79ev3507d0pfCJibm2v1OrFiKeX+pJjBYNi+fXu9evXKnVvXrl3v3r0r\nc90CKBfBDoAdWBfsDAbD1atXp06d2qZNm7p167q5uYWEhMTFxf3666/ljmxFsJP8/PPPw4YN\na9iwoaura2Bg4KBBg3bt2mUwGBITE1955ZWPPvrIxsLMSEpKGjNmTMeOHQMCAtzd3Zs1a9an\nT5+FCxeW/jExo2XLljVr1szNzS0wMHDmzJlyXrXBYNi+fXu/fv2aNm3q5uYWHBwcExOzfft2\nadDNmzdfeOGFfv36vfDCCzqdzpZ1YulSKgp2BoMhLS3tvffe69y5s4+Pj7u7e6tWrYYOHWqc\nGwBbqAzWPgYdAGoUnU6XmZmpUqlkPsEYAJSHYAcAAKAQ3BULAACgEAQ7AAAAhSDYAQAAKATB\nDgAAQCEIdgAAAApBsAMAAFAIgh0AAIBCEOwAAAAUgmAHAACgEAQ7AAAAhSDYAQAAKATBDgAA\nQCEIdgAAAApBsAMAAFAIgh0AAIBC/H/Yhy1wkMW7tQAAAABJRU5ErkJggg==",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      },
      "text/plain": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "\n",
       "\tOne-way analysis of means (not assuming equal variances)\n",
       "\n",
       "data:  morti and fondo_stradale\n",
       "F = 0.41087, num df = 3.00, denom df = 270.27, p-value = 0.7453\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Nel caso dei morti, il test non è statisticamente significativo - Mi fermo qui"
     ]
    },
    {
     "data": {
      "text/plain": [
       "\n",
       "\tOne-way analysis of means (not assuming equal variances)\n",
       "\n",
       "data:  feriti and fondo_stradale\n",
       "F = 4.1502, num df = 4.00, denom df = 129.46, p-value = 0.003391\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " \n",
      " Nel caso dei feriti, il test è statisticamente significativo - Eseguo un t-test fra tutte le coppie"
     ]
    },
    {
     "data": {
      "text/plain": [
       "\n",
       "\tPairwise comparisons using t tests with pooled SD \n",
       "\n",
       "data:  feriti and fondo_stradale \n",
       "\n",
       "               asciutto bagnato sdrucciolevole ghiacciato\n",
       "bagnato        0.5162   -       -              -         \n",
       "sdrucciolevole 0.1594   0.1059  -              -         \n",
       "ghiacciato     0.5162   0.5162  0.1403         -         \n",
       "innevato       0.0189   0.0283  0.0026         0.1594    \n",
       "\n",
       "P value adjustment method: holm "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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Li0tDQnJ8fTtXhSWFhYbU/xUywAAIBOEOwAAAB0gmAHAACgEwQ7AAAAnbjdL54A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\n4vL+++8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      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      },
      "text/plain": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#comandi per il plot - morti\n",
    "SUM_FONDO = summarySE(sample_data, measurevar = \"morti\", groupvars = c(\"fondo_stradale\"))\n",
    "SUM_FONDO$fondo_stradale <- factor(SUM_FONDO$fondo_stradale)\n",
    "levels(SUM_FONDO$fondo_stradale) <- c(\"asciutto\", \"bagnato\", \"sdrucciolevole\", \"ghiacciato\", \"innevato\")\n",
    "bar_inj_fondo <- ggplot(SUM_FONDO, aes(x=fondo_stradale, y=morti)) + \n",
    "                 geom_bar(position=position_dodge(), stat=\"identity\", color=\"white\", fill=\"lightblue\") +\n",
    "                 geom_errorbar(aes(ymin=morti-se, ymax=morti+se),\n",
    "                               width=.3,                    # Width of the error bars\n",
    "                               position=position_dodge(.9)\n",
    "                              ) +\n",
    "                 labs(\n",
    "                        title = \"Media di vittime per incidente - Fondo Stradale\",\n",
    "                        x = \"Fondo stradale\",\n",
    "                        y = \"Media di vittime per incidente\"\n",
    "                    ) + \n",
    "                 theme(\n",
    "                        text = element_text(size=18), \n",
    "                        plot.title = element_text(size = 22, vjust = 1, margin = margin(10,0,10,0), hjust = 0.5),\n",
    "                    )\n",
    "\n",
    "\n",
    "#test - morti\n",
    "#escludo il suolo innevato per mancanza di dati\n",
    "sample_data_no_snow = sample_data[fondo_stradale != \"innevato\",]\n",
    "test_fondo_morti <- oneway.test(morti ~ fondo_stradale, data = sample_data_no_snow)\n",
    "\n",
    "\n",
    "#comandi per il plot - feriti\n",
    "SUM_FONDO_INJ = summarySE(sample_data, measurevar = \"feriti\", groupvars = c(\"fondo_stradale\"))\n",
    "SUM_FONDO_INJ$fondo_stradale <- factor(SUM_FONDO_INJ$fondo_stradale)\n",
    "levels(SUM_FONDO_INJ$fondo_stradale) <- c(\"asciutto\", \"bagnato\", \"sdrucciolevole\", \"ghiacciato\", \"innevato\")\n",
    "bar_dea_fondo <- ggplot(SUM_FONDO_INJ, aes(x=fondo_stradale, y=feriti)) + \n",
    "                 geom_bar(position=position_dodge(), stat=\"identity\", color=\"white\", fill=\"lightblue\") +\n",
    "                 geom_errorbar(aes(ymin=feriti-se, ymax=feriti+se),\n",
    "                          width=.3,                    # Width of the error bars\n",
    "                          position=position_dodge(.9)\n",
    "                         ) + \n",
    "                 labs(\n",
    "                        title = \"Media di feriti per incidente - Fondo Stradale\",\n",
    "                        x = \"Fondo stradale\",\n",
    "                        y = \"Media di feriti per incidente\"\n",
    "                    ) + \n",
    "                 theme(\n",
    "                        text = element_text(size=18), \n",
    "                        plot.title = element_text(size = 22, vjust = 1, margin = margin(10,0,10,0), hjust = 0.5),\n",
    "                    )\n",
    "\n",
    "\n",
    "\n",
    "#test - feriti\n",
    "test_fondo_feriti <- oneway.test(feriti ~ fondo_stradale, data = sample_data)\n",
    "test_coppie_fondo_feriti <- pairwise.t.test(feriti, fondo_stradale)\n",
    "\n",
    "\n",
    "#------------------PLOTS--------------------\n",
    "bar_inj_fondo\n",
    "bar_dea_fondo\n",
    "\n",
    "#------------------test---------------------\n",
    "test_fondo_morti\n",
    "cat(\"Nel caso dei morti, il test non è statisticamente significativo - Mi fermo qui\")\n",
    "\n",
    "test_fondo_feriti\n",
    "cat(\"\\n \\n Nel caso dei feriti, il test è statisticamente significativo - Eseguo un t-test fra tutte le coppie\")\n",
    "test_coppie_fondo_feriti"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### La mortalità degli incidenti non dipende dal tipo di strada -> Vedi sopra"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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MzMU6dOabXa999/v2nTpkKIwMDAefPmBQYGpqWlnT9/3vJhExIS7t+/HxAQMGfO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\n/BV3xx1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      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      },
      "text/plain": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "\n",
       "\tOne-way analysis of means (not assuming equal variances)\n",
       "\n",
       "data:  morti and tipo_di_strada\n",
       "F = 15.619, num df = 2.0, denom df = 5888.9, p-value = 1.716e-07\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " \n",
      " Nel caso dei morti, il test è statisticamente significativo - Eseguo un t-test fra tutte le coppie"
     ]
    },
    {
     "data": {
      "text/plain": [
       "\n",
       "\tPairwise comparisons using t tests with pooled SD \n",
       "\n",
       "data:  sample_data_filt$morti and sample_data_filt$tipo_di_strada \n",
       "\n",
       "  1       2   \n",
       "2 4.9e-05 -   \n",
       "3 0.01    0.75\n",
       "\n",
       "P value adjustment method: holm "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "\n",
       "\tOne-way analysis of means (not assuming equal variances)\n",
       "\n",
       "data:  feriti and tipo_di_strada\n",
       "F = 19.812, num df = 2.0, denom df = 5111.4, p-value = 2.684e-09\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " \n",
      " Nel caso dei feriti, il test è statisticamente significativo - Eseguo un t-test fra tutte le coppie"
     ]
    },
    {
     "data": {
      "text/plain": [
       "\n",
       "\tPairwise comparisons using t tests with pooled SD \n",
       "\n",
       "data:  sample_data_filt$feriti and sample_data_filt$tipo_di_strada \n",
       "\n",
       "  1       2      \n",
       "2 0.0052  -      \n",
       "3 7.5e-11 5.8e-08\n",
       "\n",
       "P value adjustment method: holm "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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vXGr1XTQrB76KGHHn/88ZCQkMjISKVP06ZNZ82a5ezsHBsb+/nnn5cMdk2bNp02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      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
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       "height": 420,
       "width": 420
      },
      "text/plain": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#escludo le righe dove non è noto il tipo di strada\n",
    "sample_data_filt = sample_data[tipo_di_strada != \"altro\",]\n",
    "\n",
    "#comandi per il plot - morti\n",
    "SUM_STRADA = summarySE(sample_data_filt, measurevar = \"morti\", groupvars = c(\"tipo_di_strada\"))\n",
    "SUM_STRADA$tipo_di_strada <- factor(SUM_STRADA$tipo_di_strada)\n",
    "levels(SUM_STRADA$tipo_di_strada) <- c(\"urbana\", \"extra-urbana\", \"autostrada\")\n",
    "bar_dea_strada <- ggplot(SUM_STRADA, aes(x=tipo_di_strada, y=morti)) + \n",
    "              geom_bar(position=position_dodge(), stat=\"identity\", color=\"white\", fill=\"lightblue\") +\n",
    "              geom_errorbar(aes(ymin=morti-se, ymax=morti+se),\n",
    "                            width=.3,                    # Width of the error bars\n",
    "                            position=position_dodge(.9)\n",
    "                           ) + \n",
    "              labs(\n",
    "                    title = \"Media di vittime per incidente - Tipo di strada\",\n",
    "                    x = \"Tipo di strada\",\n",
    "                    y = \"Media di vittime per incidente\"\n",
    "                   ) + \n",
    "              theme(\n",
    "                    text = element_text(size=18), \n",
    "                    plot.title = element_text(size = 21, vjust = 1, margin = margin(10,0,10,0), hjust = 0.5),\n",
    "                    )\n",
    "\n",
    "\n",
    "#test - morti\n",
    "test_strada_morti <- oneway.test(morti ~ tipo_di_strada, data = sample_data_filt)\n",
    "test_coppie_strada_morti <- pairwise.t.test(sample_data_filt$morti, sample_data_filt$tipo_di_strada)\n",
    "\n",
    "\n",
    "#comandi per il plot - feriti\n",
    "SUM_STRADA = summarySE(sample_data_filt, measurevar = \"feriti\", groupvars = c(\"tipo_di_strada\"))\n",
    "SUM_STRADA$tipo_di_strada <- factor(SUM_STRADA$tipo_di_strada)\n",
    "levels(SUM_STRADA$tipo_di_strada) <- c(\"urbana\", \"extra-urbana\", \"autostrada\")\n",
    "bar_inj_strada <- ggplot(SUM_STRADA, aes(x=tipo_di_strada, y=feriti)) + \n",
    "                  geom_bar(position=position_dodge(), stat=\"identity\", color=\"white\", fill=\"lightblue\") +\n",
    "                  geom_errorbar(aes(ymin=feriti-se, ymax=feriti+se),\n",
    "                                width=.3,                    # Width of the error bars\n",
    "                                position=position_dodge(.9)\n",
    "                               ) + \n",
    "                  labs(\n",
    "                        title = \"Media di feriti per incidente - Tipo di strada\",\n",
    "                        x = \"Tipo di strada\",\n",
    "                        y = \"Media di feriti per incidente\"\n",
    "                       ) + \n",
    "                  theme(\n",
    "                        text = element_text(size=18), \n",
    "                        plot.title = element_text(size = 21, vjust = 1, margin = margin(10,0,10,0), hjust = 0.5),\n",
    "                        )\n",
    "\n",
    "\n",
    "#test - feriti\n",
    "test_strada_feriti <- oneway.test(feriti ~ tipo_di_strada, data = sample_data_filt)\n",
    "test_coppie_strada_feriti <- pairwise.t.test(sample_data_filt$feriti, sample_data_filt$tipo_di_strada)\n",
    "\n",
    "\n",
    "#-------------------PLOT--------------\n",
    "bar_dea_strada\n",
    "bar_inj_strada\n",
    "\n",
    "#-------------------TEST--------------\n",
    "test_strada_morti\n",
    "cat(\"\\n \\n Nel caso dei morti, il test è statisticamente significativo - Eseguo un t-test fra tutte le coppie\")\n",
    "test_coppie_strada_morti\n",
    "\n",
    "test_strada_feriti\n",
    "cat(\"\\n \\n Nel caso dei feriti, il test è statisticamente significativo - Eseguo un t-test fra tutte le coppie\")\n",
    "test_coppie_strada_feriti"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### La mortalità degli incidenti non dipende dal tipo di pavimentazione -> Vedi sopra"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "\tOne-way analysis of means (not assuming equal variances)\n",
       "\n",
       "data:  morti and pavimentazione\n",
       "F = 2.298, num df = 2.000, denom df = 98.168, p-value = 0.1058\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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eQRT09P69YPAADQ4OzlVOy+ffuqqqrat28vpTqJVqt9/PHHhcGzq5LMzMzbt2+7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PLy+vNm3atGjR4rHHHisuLn7nnXfKLKRJkyZdusnAJ84AAAajSURBVHQ5evToli1b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\nAACAkyDYAQAAOIn/B4i3duZnE8m7AAAAAElFTkSuQmCC",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      },
      "text/plain": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "\n",
       "\tOne-way analysis of means (not assuming equal variances)\n",
       "\n",
       "data:  feriti and pavimentazione\n",
       "F = 4.4458, num df = 2.000, denom df = 98.907, p-value = 0.01416\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " \n",
      " Nel caso dei feriti, il test è statisticamente significativo - Eseguo un t-test fra tutte le coppie"
     ]
    },
    {
     "data": {
      "text/plain": [
       "\n",
       "\tPairwise comparisons using t tests with pooled SD \n",
       "\n",
       "data:  sample_data$feriti and sample_data$pavimentazione \n",
       "\n",
       "  1    2   \n",
       "2 0.78 -   \n",
       "3 0.10 0.23\n",
       "\n",
       "P value adjustment method: holm "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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Z3Dt25dp51gV1ZWNmTIkCFDhgQGBt66dWvHjh2HDx+OjY1t2bLlo48+qjQ+ffr0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      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      },
      "text/plain": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#escludo le righe dove non è noto il tipo di strada\n",
    "#sample_data_filt = sample_data[tipo_di_strada != \"altro\",]\n",
    "\n",
    "#comandi per il plot - morti\n",
    "SUM_PAV = summarySE(sample_data, measurevar = \"morti\", groupvars = c(\"pavimentazione\"))\n",
    "SUM_PAV$pavimentazione <- factor(SUM_PAV$pavimentazione)\n",
    "levels(SUM_PAV$pavimentazione) <- c(\"Buona\", \"Scarsa\", \"Non Pavimentata\")\n",
    "bar_pavimentazione_morti <- ggplot(SUM_PAV, aes(x=pavimentazione, y=morti)) + \n",
    "                            geom_bar(position=position_dodge(), stat=\"identity\", color=\"white\", fill=\"lightblue\") +\n",
    "                            geom_errorbar(aes(ymin=morti-se, ymax=morti+se),\n",
    "                          width=.3,                    # Width of the error bars\n",
    "                          position=position_dodge(.9)) + \n",
    "                            labs(\n",
    "                            title = \"Media di vittime per incidente - Pavimentazione\",\n",
    "                                x = \"Tipo di pavimentazione\",\n",
    "                                y = \"Media di vittime per incidente\"\n",
    "                               ) + \n",
    "                            theme(\n",
    "                                    text = element_text(size=18), \n",
    "                                    plot.title = element_text(size = 21, vjust = 1, margin = margin(10,0,10,0), hjust = 0.5),\n",
    "                                 )\n",
    "\n",
    "\n",
    "bar_pavimentazione_morti\n",
    "\n",
    "#test - morti\n",
    "test_pavimentazione_morti <- oneway.test(morti ~ pavimentazione, data = sample_data)\n",
    "test_pavimentazione_morti\n",
    "\n",
    "#comandi per il plot - feriti\n",
    "SUM_PAV = summarySE(sample_data, measurevar = \"feriti\", groupvars = c(\"pavimentazione\"))\n",
    "SUM_PAV$pavimentazione <- factor(SUM_PAV$pavimentazione)\n",
    "levels(SUM_PAV$pavimentazione) <- c(\"Buona\", \"Scarsa\", \"Non Pavimentata\")\n",
    "bar_pavimentazione_feriti <- ggplot(SUM_PAV, aes(x=pavimentazione, y=feriti)) + \n",
    "                             geom_bar(position=position_dodge(), stat=\"identity\", color=\"white\", fill=\"lightblue\") +\n",
    "                             geom_errorbar(aes(ymin=feriti-se, ymax=feriti+se),\n",
    "                                          width=.3,                    # Width of the error bars\n",
    "                                          position=position_dodge(.9)) + \n",
    "                             labs(\n",
    "                             title = \"Media di feriti per incidente - Pavimentazione\",\n",
    "                                 x = \"Tipo di pavimentazione\",\n",
    "                                 y = \"Media di feriti per incidente\"\n",
    "                                  ) + \n",
    "                            theme(\n",
    "                                    text = element_text(size=18), \n",
    "                                    plot.title = element_text(size = 21, vjust = 1, margin = margin(10,0,10,0), hjust = 0.5),\n",
    "                                 )\n",
    "\n",
    "bar_pavimentazione_feriti\n",
    "\n",
    "#test - feriti\n",
    "test_pavimentazione_feriti <- oneway.test(feriti ~ pavimentazione, data = sample_data)\n",
    "test_pavimentazione_feriti\n",
    "cat(\"\\n \\n Nel caso dei feriti, il test è statisticamente significativo - Eseguo un t-test fra tutte le coppie\")\n",
    "test_coppie_pavimetazione_feriti <- pairwise.t.test(sample_data$feriti, sample_data$pavimentazione)\n",
    "test_coppie_pavimetazione_feriti"
   ]
  }
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