fifa-gabarito.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# WORKSHOP - ESTATÍSTICA BÁSICA COM R - GRUPO TURING"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Tabela de Conteúdos**\n",
    "- [Overview da programação em R para o Workshop](#-Parte-0---Overview-da-programação-em-R-para-o-Workshop)\n",
    "    - [Atribuição de valores a variáveis](#-Atribuição-de-valores-a-variáveis)\n",
    "    - [Tabelas de Dados - Dataframes](#Tabelas-de-Dados---Dataframes)\n",
    "    <br><br>\n",
    "- [📂 Importando dados em R](#-Parte-1---Importando-dados-em-R-)\n",
    "    - [Importando um csv](#Importando-um-arquivo-CSV)\n",
    "    - [Boas práticas em criação de bases de dados](#Boas-Práticas)\n",
    "    - [Importando os dados do Workshop](#Importando-o-arquivo-do-Workshop)\n",
    "    <br><br>\n",
    "- [📑 Manipulando dados em R](#-Parte-2---Manipulação-de-dados-em-R-)\n",
    "    - [Dados Faltantes](#Dados-faltantes)\n",
    "    - [Deep dive in dplyr](#Deep-dive-in-dplyr)\n",
    "        - [filter() - filtrando a base de dados](#filter---Filtrando-nossa-base-de-dados!)\n",
    "        - [Combinando os verbos](#Usando-vários-verbos-ao-mesmo-tempo)\n",
    "        <br><br>\n",
    "- [🔎 Estatística básica em R](#--Parte-3----Estatística-Básica-em-R-)\n",
    "    - [Tipos de dados](#Tipos-de-Dados)\n",
    "        - [Dados Qualitativos](#Dados-Qualitativos)\n",
    "        - [Dados Quantitativos](#Dados-Quantitativos)\n",
    "    - [Estatística Descritiva](#Estatística-Descritiva)\n",
    "        - [Medidas de tendência central](#Medidas-de-Tendência-Central)\n",
    "            - [Média](#Média)\n",
    "            - [Mediana](#Mediana)\n",
    "            - [Percentis](#Percentil)\n",
    "            - [Summary](#Summary)\n",
    "        - [Medidas de variação uni e multivariadas](#Medidas-de-Variação)\n",
    "            - [Amplitude](#Amplitude)\n",
    "            - [Amplitude interquartil](#Amplitude-Interquartil)\n",
    "            - [Variância](#Variância)\n",
    "            - [Desvio Padrão](#Desvio-Padrão)\n",
    "            - [Covariância](#Covariância)\n",
    "            - [Coeficiente de Correlação](#Coeficiente-de-Correlação)\n",
    "            <br><br>\n",
    "- [📉 Visualizando dados em R](#-Parte-4---Visualização-de-dados-em-R-com-ggplot2-)\n",
    "    - [Introdução](#Introdu%C3%A7%C3%A3o)\n",
    "        - [Vantagens](#Vantagens)\n",
    "        - [O que não fazer com ggplot2](#O-que-n%C3%A3o-fazer-com-ggplot2?)\n",
    "    - [Entendendo a lógica do ggplot2](#Entendendo-a-l%C3%B3gica-do-ggplot2)\n",
    "        - [Códigos Base](#C%C3%B3digos-base)\n",
    "    - [Exemplos](#Exemplos)\n",
    "<br><br>\n",
    "- [❓ Inferência Estatística em R](#-Parte-5---Inferência-Estatística-em-R-)\n",
    "    - [Testes de hipótese](#Testes-de-Hipótese)\n",
    "        - [Amostra vs População](#Amostra-vs-População)\n",
    "        - [Hipóteses e p-valor](#Hipóteses-e-p-valor)\n",
    "        - [Testes paramétricos VS Testes não paramétricos](#Testes-paramétricos-VS-não-paramétricos)\n",
    "        - [Teste de Kolgomorov-Smirnov](#Kolgomorov-Smirnov)\n",
    "        - Testes Paramétricos\n",
    "            - [Teste T](#Teste-T)\n",
    "            - [ANOVA de um fator](#ANOVA-de-um-fator)\n",
    "        - [Testes Não Paramétricos](#Não-paramétricos---Wilcoxon-Mann-Whitney-e-Kruskal-Wallis)\n",
    "            - [Wilconx-Mann_Whitney](#Wilcoxon-Mann-Whitney)\n",
    "            - [Kruskal-Wallys](#Kruskal-Wallys)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <span style=\"color: orange;\"> Parte 0 - Overview da programação em R para o Workshop</span>\n",
    "Nessa parte vamos passar pelo básico do básico do básico na programação R.\n",
    "### Atribuição de valores a variáveis\n",
    "Variáveis são \"nomes\" que vão representar algum valor dentro do programa, são extremamente úteis para facilitar nossa análise. <br>\n",
    "Em ```R``` além de usar o clássico ```=``` para atrbuir valores a variáveis, nós usamos uma setinha ```<-``` <br>\n",
    "O comando para \"imprimir\" alguma coisa é o ```print()```. <br>\n",
    "Caso você queira juntar/ concatenar dois textos separados por espaço, use a função ```paste()``` <br>\n",
    "Comentários são coisas escritas no código que são ignoradas pelo computador na hora de executar. No ```R``` são feitos colocando uma #hashtag na frente do texto."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Isso é uma célula\n",
    "#Você escreve códigos aqui"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Aperte na setinha à esquerda ou ```SHIFT + ENTER``` para fazer funcionar."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#isso é um comentário"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Vamos atribuir o valor 2 á uma variável \"a\"."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "a <- 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Vamos agora imprimir esse valor na tela."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] 2\n"
     ]
    }
   ],
   "source": [
    "print(a)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Vamos juntar alguns valores na hora de imprimir."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Meu nome é Rodolfo\"\n"
     ]
    }
   ],
   "source": [
    "b <- \"Meu nome é\"\n",
    "c <- \"Rodolfo\"\n",
    "d <- paste(b,c)\n",
    "print(d)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Meu nome é Rodolfo\"\n",
      "[1] \"o valor de a é: 2\"\n"
     ]
    }
   ],
   "source": [
    "print(paste(b,c))\n",
    "print(paste(\"o valor de a é:\",a))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Existem alguns tipos de valores, como textos, números, valores lógicos...vamos ver como eles funcionam em R:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"um_lógico: FALSE\"\n",
      "[1] \"um_double: 3.444\"\n",
      "[1] \"um_inteiro: 5\"\n",
      "[1] \"um_vetor:\"\n",
      "[1] 5 5 3 2\n",
      "[1] \"uma_string: Roberto\"\n"
     ]
    }
   ],
   "source": [
    "um_logico <- FALSE\n",
    "print(paste(\"um_lógico:\", um_logico))\n",
    "um_double <- 3.444\n",
    "print(paste(\"um_double:\", um_double))\n",
    "um_inteiro <- 5\n",
    "print(paste(\"um_inteiro:\", um_inteiro))\n",
    "um_vetor <- c(5,5,3,2)\n",
    "print(\"um_vetor:\")\n",
    "print(um_vetor)\n",
    "uma_string <- \"Roberto\"\n",
    "print(paste(\"uma_string:\", uma_string))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Tabelas de Dados - Dataframes\n",
    "Um tipo de variável muito importante para nós nesse workshop são os DataFrames. Os dataframes são basicamente tabelas, só que com um nome mais chique. <br>\n",
    "Para criar um dataframe, utilize a função `data.frame()` do `R`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "disciplina <- c('Matemática', 'Português', 'Física', 'Geografia', 'Biologia')\n",
    "nota <- c(7.5,5,8,7.5,6)\n",
    "notas_e_disciplinas <- data.frame(disciplina,nota)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Para visualizar nosso dataframe, podemos usar o print normalmente. O resultado será o mesmo esteja você no jupyter-notebook, no RStudio ou em qualquer outro lugar.\n",
    "<br>\n",
    "Agora, o jupyter é feito especialmente pensando-se em trabalhar com dados. Então usando a função `head()` (em que você especifica quantas linhas quer imprimir) ou simplmesmente digitando o nome do dataframe, ele será impresso de uma forma mais bonita."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  disciplina nota\n",
      "1 Matemática  7.5\n",
      "2  Português  5.0\n",
      "3     Física  8.0\n",
      "4  Geografia  7.5\n",
      "5   Biologia  6.0\n"
     ]
    }
   ],
   "source": [
    "print(notas_e_disciplinas)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th scope=col>disciplina</th><th scope=col>nota</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><td>Matemática</td><td>7.5       </td></tr>\n",
       "\t<tr><td>Português </td><td>5.0       </td></tr>\n",
       "\t<tr><td>Física    </td><td>8.0       </td></tr>\n",
       "\t<tr><td>Geografia </td><td>7.5       </td></tr>\n",
       "\t<tr><td>Biologia  </td><td>6.0       </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|ll}\n",
       " disciplina & nota\\\\\n",
       "\\hline\n",
       "\t Matemática & 7.5       \\\\\n",
       "\t Português  & 5.0       \\\\\n",
       "\t Física     & 8.0       \\\\\n",
       "\t Geografia  & 7.5       \\\\\n",
       "\t Biologia   & 6.0       \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| disciplina | nota |\n",
       "|---|---|\n",
       "| Matemática | 7.5        |\n",
       "| Português  | 5.0        |\n",
       "| Física     | 8.0        |\n",
       "| Geografia  | 7.5        |\n",
       "| Biologia   | 6.0        |\n",
       "\n"
      ],
      "text/plain": [
       "  disciplina nota\n",
       "1 Matemática 7.5 \n",
       "2 Português  5.0 \n",
       "3 Física     8.0 \n",
       "4 Geografia  7.5 \n",
       "5 Biologia   6.0 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "notas_e_disciplinas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th scope=col>disciplina</th><th scope=col>nota</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><td>Matemática</td><td>7.5       </td></tr>\n",
       "\t<tr><td>Português </td><td>5.0       </td></tr>\n",
       "\t<tr><td>Física    </td><td>8.0       </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|ll}\n",
       " disciplina & nota\\\\\n",
       "\\hline\n",
       "\t Matemática & 7.5       \\\\\n",
       "\t Português  & 5.0       \\\\\n",
       "\t Física     & 8.0       \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| disciplina | nota |\n",
       "|---|---|\n",
       "| Matemática | 7.5        |\n",
       "| Português  | 5.0        |\n",
       "| Física     | 8.0        |\n",
       "\n"
      ],
      "text/plain": [
       "  disciplina nota\n",
       "1 Matemática 7.5 \n",
       "2 Português  5.0 \n",
       "3 Física     8.0 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "head(notas_e_disciplinas, 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th></th><th scope=col>disciplina</th><th scope=col>nota</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>2</th><td>Português</td><td>5.0      </td></tr>\n",
       "\t<tr><th scope=row>3</th><td>Física   </td><td>8.0      </td></tr>\n",
       "\t<tr><th scope=row>4</th><td>Geografia</td><td>7.5      </td></tr>\n",
       "\t<tr><th scope=row>5</th><td>Biologia </td><td>6.0      </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|ll}\n",
       "  & disciplina & nota\\\\\n",
       "\\hline\n",
       "\t2 & Português & 5.0      \\\\\n",
       "\t3 & Física    & 8.0      \\\\\n",
       "\t4 & Geografia & 7.5      \\\\\n",
       "\t5 & Biologia  & 6.0      \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| <!--/--> | disciplina | nota |\n",
       "|---|---|---|\n",
       "| 2 | Português | 5.0       |\n",
       "| 3 | Física    | 8.0       |\n",
       "| 4 | Geografia | 7.5       |\n",
       "| 5 | Biologia  | 6.0       |\n",
       "\n"
      ],
      "text/plain": [
       "  disciplina nota\n",
       "2 Português  5.0 \n",
       "3 Física     8.0 \n",
       "4 Geografia  7.5 \n",
       "5 Biologia   6.0 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "notas_e_disciplinas[2:5,]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Selecionando colunas do dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>7.5</li>\n",
       "\t<li>5</li>\n",
       "\t<li>8</li>\n",
       "\t<li>7.5</li>\n",
       "\t<li>6</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 7.5\n",
       "\\item 5\n",
       "\\item 8\n",
       "\\item 7.5\n",
       "\\item 6\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 7.5\n",
       "2. 5\n",
       "3. 8\n",
       "4. 7.5\n",
       "5. 6\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] 7.5 5.0 8.0 7.5 6.0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "notas_e_disciplinas$nota"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>Matemática</li>\n",
       "\t<li>Português</li>\n",
       "\t<li>Física</li>\n",
       "\t<li>Geografia</li>\n",
       "\t<li>Biologia</li>\n",
       "</ol>\n",
       "\n",
       "<details>\n",
       "\t<summary style=display:list-item;cursor:pointer>\n",
       "\t\t<strong>Levels</strong>:\n",
       "\t</summary>\n",
       "\t<ol class=list-inline>\n",
       "\t\t<li>'Biologia'</li>\n",
       "\t\t<li>'Física'</li>\n",
       "\t\t<li>'Geografia'</li>\n",
       "\t\t<li>'Matemática'</li>\n",
       "\t\t<li>'Português'</li>\n",
       "\t</ol>\n",
       "</details>"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item Matemática\n",
       "\\item Português\n",
       "\\item Física\n",
       "\\item Geografia\n",
       "\\item Biologia\n",
       "\\end{enumerate*}\n",
       "\n",
       "\\emph{Levels}: \\begin{enumerate*}\n",
       "\\item 'Biologia'\n",
       "\\item 'Física'\n",
       "\\item 'Geografia'\n",
       "\\item 'Matemática'\n",
       "\\item 'Português'\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. Matemática\n",
       "2. Português\n",
       "3. Física\n",
       "4. Geografia\n",
       "5. Biologia\n",
       "\n",
       "\n",
       "\n",
       "**Levels**: 1. 'Biologia'\n",
       "2. 'Física'\n",
       "3. 'Geografia'\n",
       "4. 'Matemática'\n",
       "5. 'Português'\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] Matemática Português  Física     Geografia  Biologia  \n",
       "Levels: Biologia Física Geografia Matemática Português"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "notas_e_disciplinas$disciplina"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Selecionando valores específicos "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th></th><th scope=col>disciplina</th><th scope=col>nota</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>2</th><td>Português</td><td>5        </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|ll}\n",
       "  & disciplina & nota\\\\\n",
       "\\hline\n",
       "\t2 & Português & 5        \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| <!--/--> | disciplina | nota |\n",
       "|---|---|---|\n",
       "| 2 | Português | 5         |\n",
       "\n"
      ],
      "text/plain": [
       "  disciplina nota\n",
       "2 Português  5   "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "notas_e_disciplinas[notas_e_disciplinas$nota==5,]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <span style=\"color: orange;\"> Parte 1 - Importando dados em R </span>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Quando vamos fazer nossa análise de dados, comumente devemos trazer esses dados de algum outro arquivo, o que nos pede um pouco de cuidado. <br> Por exemplo, é importante sabermos qual é o tipo de arquivo que estamos importando, pois isso pode fazer nosso programa ler os dados errôneamente. <br> Um dos formatos mais comums para a análise de dados é o **CSV**, ou *Comma separeted values*."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![img](img/csv_meme.jpg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Importando um arquivo CSV "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```R\n",
    "mydata <- read.csv(\"Data.csv\", header=TRUE)\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```mydata``` é o nome da variável que atribuiremos o conteúdo do arquivo que estamos lendo <br>\n",
    "```read.csv``` lê arquivos que possuem este formato<br>\n",
    "O nome do arquivo deve vir dentro de aspas (simples ou dupla). Neste caso o arquivo está no mesmo diretório em que o nosso programa está sendo salvo, por isso não há necessidade de especificar diretórios anteriores. <br>\n",
    "```header=TRUE``` que significa que a primeira linha do nosso arquivo contém o nome das colunas."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Alguns problemas "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Caso o arquivo esteja em um diretório diferente, é necessário identificá-lo. <br>\n",
    "Além disso, caso a primeira linha já contenha dados, vc deve trocar o argumento por ```header=FALSE```. <br>\n",
    "No caso do separador não ser a vírgula, podemos usar o argumento  ```sep=\" \"``` como o separador desejado. <br>\n",
    "Vamos ver um exemplo com esses casos juntos:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```R\n",
    "mydata <- read.csv(\"C:/Usuarios/Caminho/do/arquivo/Data.csv\", header=FALSE, sep=\";\")\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```R\n",
    "names(mydata) <- c('id','name','type')\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Boas Práticas "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Caso você não queira ter problemas na hora de importar dados para fazer suas análises, são necessárias algumas boas práticas na hora de criar seu arquivo."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Neste WorkShop, usaremos o dataset Fifa, que possui dados de jogadores de futebol. Vamos visualizá-lo no Excel para entender sua estrutura:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![img2](img/excel_fifa.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```\n",
    "\"\",\"sofifa_id\",\"short_name\",\"long_name\",\"age\",\"nationality\",\"club\",\"wage_eur\",\"player_positions\",\"preferred_foot\"\n",
    "\"2600\",223152,\"S. Vilakazi\",\"Sibusiso Vilakazi\",29,\"South Africa\",\"South Africa\",0,\"CAM, CF, RW\",\"Right\"\n",
    "\"11493\",244742,\"A. Ivan\",\"Andreas Ivan\",24,\"Romania\",\"New York Red Bulls\",2000,\"RM, LM\",\"Left\"\n",
    "\"11171\",248710,\"Paulino\",\"Paulino de la Fuente GÃ³mez\",22,\"Spain\",\"Deportivo AlavÃ©s\",5000,\"RM, CAM\",\"Left\"\n",
    "\"5780\",245598,\"I. Baba\",\"Iddrisu Baba Mohammed\",23,\"Ghana\",\"RCD Mallorca\",9000,\"CDM, CM\",\"Right\"\n",
    "\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Datasets também podem ser montados num simples arquivo de texto:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Como escrever então seu arquivo de dados?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. Comece pela primeira linha e primeira coluna, ou seja, não pule células\n",
    "2. Inclua uma linha de títulos para cada coluna\n",
    "3. Tenha certeza que o número de títulos é igual ao número de colunas dos seus dados\n",
    "4. Não junte células, isso pode fazer seu programa ter problemas na hora de ler o arquivo\n",
    "5. No caso de arquivos de texto, use preferencialmente ```,``` como separador dos dados"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Importando o arquivo do Workshop "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th scope=col>X</th><th scope=col>sofifa_id</th><th scope=col>short_name</th><th scope=col>long_name</th><th scope=col>age</th><th scope=col>height_cm</th><th scope=col>weight_kg</th><th scope=col>nationality</th><th scope=col>club</th><th scope=col>wage_eur</th><th scope=col>player_positions</th><th scope=col>preferred_foot</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><td> 1813                  </td><td>224599                 </td><td>Yan Junling            </td><td>é¢œéªå‡Œ              </td><td>28                     </td><td>191                    </td><td>83                     </td><td>China PR               </td><td>Shanghai SIPG FC       </td><td>13000                  </td><td>GK                     </td><td>Right                  </td></tr>\n",
       "\t<tr><td>12287                  </td><td>243530                 </td><td>Mujaid                 </td><td>Mujaid Sadick Aliu     </td><td>19                     </td><td>180                    </td><td>75                     </td><td>Spain                  </td><td>Deportivo de La CoruÃ±a</td><td> 1000                  </td><td>CB, RB                 </td><td>Right                  </td></tr>\n",
       "\t<tr><td> 4257                  </td><td>202676                 </td><td>A. Taggart             </td><td>Adam Taggart           </td><td>26                     </td><td>183                    </td><td>70                     </td><td>Australia              </td><td>Suwon Samsung Bluewings</td><td> 6000                  </td><td>ST, CF                 </td><td>Right                  </td></tr>\n",
       "\t<tr><td> 8794                  </td><td>241187                 </td><td>L. Geertruida          </td><td>Lutsharel Geertruida   </td><td>18                     </td><td>180                    </td><td>75                     </td><td>Netherlands            </td><td>Feyenoord              </td><td> 1000                  </td><td>CB, CDM, RB            </td><td>Right                  </td></tr>\n",
       "\t<tr><td> 3851                  </td><td>193254                 </td><td>C. Trimmel             </td><td>Christopher Trimmel    </td><td>32                     </td><td>189                    </td><td>82                     </td><td>Austria                </td><td>1. FC Union Berlin     </td><td>24000                  </td><td>RB                     </td><td>Right                  </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|llllllllllll}\n",
       " X & sofifa\\_id & short\\_name & long\\_name & age & height\\_cm & weight\\_kg & nationality & club & wage\\_eur & player\\_positions & preferred\\_foot\\\\\n",
       "\\hline\n",
       "\t  1813                   & 224599                  & Yan Junling             & é¢œéªå‡Œ               & 28                      & 191                     & 83                      & China PR                & Shanghai SIPG FC        & 13000                   & GK                      & Right                  \\\\\n",
       "\t 12287                   & 243530                  & Mujaid                  & Mujaid Sadick Aliu      & 19                      & 180                     & 75                      & Spain                   & Deportivo de La CoruÃ±a &  1000                   & CB, RB                  & Right                  \\\\\n",
       "\t  4257                   & 202676                  & A. Taggart              & Adam Taggart            & 26                      & 183                     & 70                      & Australia               & Suwon Samsung Bluewings &  6000                   & ST, CF                  & Right                  \\\\\n",
       "\t  8794                   & 241187                  & L. Geertruida           & Lutsharel Geertruida    & 18                      & 180                     & 75                      & Netherlands             & Feyenoord               &  1000                   & CB, CDM, RB             & Right                  \\\\\n",
       "\t  3851                   & 193254                  & C. Trimmel              & Christopher Trimmel     & 32                      & 189                     & 82                      & Austria                 & 1. FC Union Berlin      & 24000                   & RB                      & Right                  \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| X | sofifa_id | short_name | long_name | age | height_cm | weight_kg | nationality | club | wage_eur | player_positions | preferred_foot |\n",
       "|---|---|---|---|---|---|---|---|---|---|---|---|\n",
       "|  1813                   | 224599                  | Yan Junling             | é¢œéªå‡Œ               | 28                      | 191                     | 83                      | China PR                | Shanghai SIPG FC        | 13000                   | GK                      | Right                   |\n",
       "| 12287                   | 243530                  | Mujaid                  | Mujaid Sadick Aliu      | 19                      | 180                     | 75                      | Spain                   | Deportivo de La CoruÃ±a |  1000                   | CB, RB                  | Right                   |\n",
       "|  4257                   | 202676                  | A. Taggart              | Adam Taggart            | 26                      | 183                     | 70                      | Australia               | Suwon Samsung Bluewings |  6000                   | ST, CF                  | Right                   |\n",
       "|  8794                   | 241187                  | L. Geertruida           | Lutsharel Geertruida    | 18                      | 180                     | 75                      | Netherlands             | Feyenoord               |  1000                   | CB, CDM, RB             | Right                   |\n",
       "|  3851                   | 193254                  | C. Trimmel              | Christopher Trimmel     | 32                      | 189                     | 82                      | Austria                 | 1. FC Union Berlin      | 24000                   | RB                      | Right                   |\n",
       "\n"
      ],
      "text/plain": [
       "  X     sofifa_id short_name    long_name            age height_cm weight_kg\n",
       "1  1813 224599    Yan Junling   é¢œéª\\217å‡Œ            28  191       83       \n",
       "2 12287 243530    Mujaid        Mujaid Sadick Aliu   19  180       75       \n",
       "3  4257 202676    A. Taggart    Adam Taggart         26  183       70       \n",
       "4  8794 241187    L. Geertruida Lutsharel Geertruida 18  180       75       \n",
       "5  3851 193254    C. Trimmel    Christopher Trimmel  32  189       82       \n",
       "  nationality club                    wage_eur player_positions preferred_foot\n",
       "1 China PR    Shanghai SIPG FC        13000    GK               Right         \n",
       "2 Spain       Deportivo de La CoruÃ±a  1000    CB, RB           Right         \n",
       "3 Australia   Suwon Samsung Bluewings  6000    ST, CF           Right         \n",
       "4 Netherlands Feyenoord                1000    CB, CDM, RB      Right         \n",
       "5 Austria     1. FC Union Berlin      24000    RB               Right         "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mydata <- read.csv(\"dados/fifa_sample_wsR.csv\", header=TRUE)\n",
    "head(mydata, 5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note que por padrão, R lê seu arquivo CSV como se fosse separado por ```,``` <br>\n",
    "O que acontece então se usarmos outro separador?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th scope=col>X.sofifa_id.short_name.long_name.age.height_cm.weight_kg.nationality.club.wage_eur.player_positions.preferred_foot</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><td>1813,224599,Yan Junling,é¢œéªå‡Œ,28,191,83,China PR,Shanghai SIPG FC,13000,GK,Right                 </td></tr>\n",
       "\t<tr><td>12287,243530,Mujaid,Mujaid Sadick Aliu,19,180,75,Spain,Deportivo de La CoruÃ±a,1000,CB, RB,Right     </td></tr>\n",
       "\t<tr><td>4257,202676,A. Taggart,Adam Taggart,26,183,70,Australia,Suwon Samsung Bluewings,6000,ST, CF,Right    </td></tr>\n",
       "\t<tr><td>8794,241187,L. Geertruida,Lutsharel Geertruida,18,180,75,Netherlands,Feyenoord,1000,CB, CDM, RB,Right</td></tr>\n",
       "\t<tr><td>3851,193254,C. Trimmel,Christopher Trimmel,32,189,82,Austria,1. FC Union Berlin,24000,RB,Right       </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|l}\n",
       " X.sofifa\\_id.short\\_name.long\\_name.age.height\\_cm.weight\\_kg.nationality.club.wage\\_eur.player\\_positions.preferred\\_foot\\\\\n",
       "\\hline\n",
       "\t 1813,224599,Yan Junling,é¢œéªå‡Œ,28,191,83,China PR,Shanghai SIPG FC,13000,GK,Right                 \\\\\n",
       "\t 12287,243530,Mujaid,Mujaid Sadick Aliu,19,180,75,Spain,Deportivo de La CoruÃ±a,1000,CB, RB,Right     \\\\\n",
       "\t 4257,202676,A. Taggart,Adam Taggart,26,183,70,Australia,Suwon Samsung Bluewings,6000,ST, CF,Right    \\\\\n",
       "\t 8794,241187,L. Geertruida,Lutsharel Geertruida,18,180,75,Netherlands,Feyenoord,1000,CB, CDM, RB,Right\\\\\n",
       "\t 3851,193254,C. Trimmel,Christopher Trimmel,32,189,82,Austria,1. FC Union Berlin,24000,RB,Right       \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| X.sofifa_id.short_name.long_name.age.height_cm.weight_kg.nationality.club.wage_eur.player_positions.preferred_foot |\n",
       "|---|\n",
       "| 1813,224599,Yan Junling,é¢œéªå‡Œ,28,191,83,China PR,Shanghai SIPG FC,13000,GK,Right                  |\n",
       "| 12287,243530,Mujaid,Mujaid Sadick Aliu,19,180,75,Spain,Deportivo de La CoruÃ±a,1000,CB, RB,Right      |\n",
       "| 4257,202676,A. Taggart,Adam Taggart,26,183,70,Australia,Suwon Samsung Bluewings,6000,ST, CF,Right     |\n",
       "| 8794,241187,L. Geertruida,Lutsharel Geertruida,18,180,75,Netherlands,Feyenoord,1000,CB, CDM, RB,Right |\n",
       "| 3851,193254,C. Trimmel,Christopher Trimmel,32,189,82,Austria,1. FC Union Berlin,24000,RB,Right        |\n",
       "\n"
      ],
      "text/plain": [
       "  X.sofifa_id.short_name.long_name.age.height_cm.weight_kg.nationality.club.wage_eur.player_positions.preferred_foot\n",
       "1 1813,224599,Yan Junling,é¢œéª\\217å‡Œ,28,191,83,China PR,Shanghai SIPG FC,13000,GK,Right                              \n",
       "2 12287,243530,Mujaid,Mujaid Sadick Aliu,19,180,75,Spain,Deportivo de La CoruÃ±a,1000,CB, RB,Right                  \n",
       "3 4257,202676,A. Taggart,Adam Taggart,26,183,70,Australia,Suwon Samsung Bluewings,6000,ST, CF,Right                 \n",
       "4 8794,241187,L. Geertruida,Lutsharel Geertruida,18,180,75,Netherlands,Feyenoord,1000,CB, CDM, RB,Right             \n",
       "5 3851,193254,C. Trimmel,Christopher Trimmel,32,189,82,Austria,1. FC Union Berlin,24000,RB,Right                    "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mydata <- read.csv(\"dados/fifa_sample_wsR.csv\", header=TRUE, sep=\";\")\n",
    "head(mydata, 5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"img/surprised_face.jpg\" width=\"300\"/>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th scope=col>X</th><th scope=col>sofifa_id</th><th scope=col>short_name</th><th scope=col>long_name</th><th scope=col>age</th><th scope=col>height_cm</th><th scope=col>weight_kg</th><th scope=col>nationality</th><th scope=col>club</th><th scope=col>wage_eur</th><th scope=col>player_positions</th><th scope=col>preferred_foot</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><td> 1813                  </td><td>224599                 </td><td>Yan Junling            </td><td>é¢œéªå‡Œ              </td><td>28                     </td><td>191                    </td><td>83                     </td><td>China PR               </td><td>Shanghai SIPG FC       </td><td>13000                  </td><td>GK                     </td><td>Right                  </td></tr>\n",
       "\t<tr><td>12287                  </td><td>243530                 </td><td>Mujaid                 </td><td>Mujaid Sadick Aliu     </td><td>19                     </td><td>180                    </td><td>75                     </td><td>Spain                  </td><td>Deportivo de La CoruÃ±a</td><td> 1000                  </td><td>CB, RB                 </td><td>Right                  </td></tr>\n",
       "\t<tr><td> 4257                  </td><td>202676                 </td><td>A. Taggart             </td><td>Adam Taggart           </td><td>26                     </td><td>183                    </td><td>70                     </td><td>Australia              </td><td>Suwon Samsung Bluewings</td><td> 6000                  </td><td>ST, CF                 </td><td>Right                  </td></tr>\n",
       "\t<tr><td> 8794                  </td><td>241187                 </td><td>L. Geertruida          </td><td>Lutsharel Geertruida   </td><td>18                     </td><td>180                    </td><td>75                     </td><td>Netherlands            </td><td>Feyenoord              </td><td> 1000                  </td><td>CB, CDM, RB            </td><td>Right                  </td></tr>\n",
       "\t<tr><td> 3851                  </td><td>193254                 </td><td>C. Trimmel             </td><td>Christopher Trimmel    </td><td>32                     </td><td>189                    </td><td>82                     </td><td>Austria                </td><td>1. FC Union Berlin     </td><td>24000                  </td><td>RB                     </td><td>Right                  </td></tr>\n",
       "\t<tr><td>15820                  </td><td>180181                 </td><td>K. Browne              </td><td>Kenny Browne           </td><td>32                     </td><td>191                    </td><td>86                     </td><td>Republic of Ireland    </td><td>Waterford FC           </td><td> 1000                  </td><td>CB                     </td><td>Left                   </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|llllllllllll}\n",
       " X & sofifa\\_id & short\\_name & long\\_name & age & height\\_cm & weight\\_kg & nationality & club & wage\\_eur & player\\_positions & preferred\\_foot\\\\\n",
       "\\hline\n",
       "\t  1813                   & 224599                  & Yan Junling             & é¢œéªå‡Œ               & 28                      & 191                     & 83                      & China PR                & Shanghai SIPG FC        & 13000                   & GK                      & Right                  \\\\\n",
       "\t 12287                   & 243530                  & Mujaid                  & Mujaid Sadick Aliu      & 19                      & 180                     & 75                      & Spain                   & Deportivo de La CoruÃ±a &  1000                   & CB, RB                  & Right                  \\\\\n",
       "\t  4257                   & 202676                  & A. Taggart              & Adam Taggart            & 26                      & 183                     & 70                      & Australia               & Suwon Samsung Bluewings &  6000                   & ST, CF                  & Right                  \\\\\n",
       "\t  8794                   & 241187                  & L. Geertruida           & Lutsharel Geertruida    & 18                      & 180                     & 75                      & Netherlands             & Feyenoord               &  1000                   & CB, CDM, RB             & Right                  \\\\\n",
       "\t  3851                   & 193254                  & C. Trimmel              & Christopher Trimmel     & 32                      & 189                     & 82                      & Austria                 & 1. FC Union Berlin      & 24000                   & RB                      & Right                  \\\\\n",
       "\t 15820                   & 180181                  & K. Browne               & Kenny Browne            & 32                      & 191                     & 86                      & Republic of Ireland     & Waterford FC            &  1000                   & CB                      & Left                   \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| X | sofifa_id | short_name | long_name | age | height_cm | weight_kg | nationality | club | wage_eur | player_positions | preferred_foot |\n",
       "|---|---|---|---|---|---|---|---|---|---|---|---|\n",
       "|  1813                   | 224599                  | Yan Junling             | é¢œéªå‡Œ               | 28                      | 191                     | 83                      | China PR                | Shanghai SIPG FC        | 13000                   | GK                      | Right                   |\n",
       "| 12287                   | 243530                  | Mujaid                  | Mujaid Sadick Aliu      | 19                      | 180                     | 75                      | Spain                   | Deportivo de La CoruÃ±a |  1000                   | CB, RB                  | Right                   |\n",
       "|  4257                   | 202676                  | A. Taggart              | Adam Taggart            | 26                      | 183                     | 70                      | Australia               | Suwon Samsung Bluewings |  6000                   | ST, CF                  | Right                   |\n",
       "|  8794                   | 241187                  | L. Geertruida           | Lutsharel Geertruida    | 18                      | 180                     | 75                      | Netherlands             | Feyenoord               |  1000                   | CB, CDM, RB             | Right                   |\n",
       "|  3851                   | 193254                  | C. Trimmel              | Christopher Trimmel     | 32                      | 189                     | 82                      | Austria                 | 1. FC Union Berlin      | 24000                   | RB                      | Right                   |\n",
       "| 15820                   | 180181                  | K. Browne               | Kenny Browne            | 32                      | 191                     | 86                      | Republic of Ireland     | Waterford FC            |  1000                   | CB                      | Left                    |\n",
       "\n"
      ],
      "text/plain": [
       "  X     sofifa_id short_name    long_name            age height_cm weight_kg\n",
       "1  1813 224599    Yan Junling   é¢œéª\\217å‡Œ            28  191       83       \n",
       "2 12287 243530    Mujaid        Mujaid Sadick Aliu   19  180       75       \n",
       "3  4257 202676    A. Taggart    Adam Taggart         26  183       70       \n",
       "4  8794 241187    L. Geertruida Lutsharel Geertruida 18  180       75       \n",
       "5  3851 193254    C. Trimmel    Christopher Trimmel  32  189       82       \n",
       "6 15820 180181    K. Browne     Kenny Browne         32  191       86       \n",
       "  nationality         club                    wage_eur player_positions\n",
       "1 China PR            Shanghai SIPG FC        13000    GK              \n",
       "2 Spain               Deportivo de La CoruÃ±a  1000    CB, RB          \n",
       "3 Australia           Suwon Samsung Bluewings  6000    ST, CF          \n",
       "4 Netherlands         Feyenoord                1000    CB, CDM, RB     \n",
       "5 Austria             1. FC Union Berlin      24000    RB              \n",
       "6 Republic of Ireland Waterford FC             1000    CB              \n",
       "  preferred_foot\n",
       "1 Right         \n",
       "2 Right         \n",
       "3 Right         \n",
       "4 Right         \n",
       "5 Right         \n",
       "6 Left          "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fifa <- read.csv(\"dados/fifa_sample_wsR.csv\", header=TRUE)\n",
    "head(fifa)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <span style=\"color: orange;\"> Parte 2 - Manipulação de dados em R </span>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A linguagem R tem como um de seus grandes destaques o pacote Tidyverse.<br>\n",
    "Para falar a verdade, o Tidyverse não é um pacote em si, mas sim um conjunto de pacotes.<br>\n",
    "Dentro dele, encontramos o `dplyr`, que é instalado automaticamente com o `install.packages(\"tidyverse\")`.\n",
    "<br>\n",
    "Aqui uma [Cheat Sheet do dplyr](https://github.com/rstudio/cheatsheets/blob/master/data-transformation.pdf)\n",
    "<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message:\n",
      "\"package 'dplyr' was built under R version 3.6.3\"\n",
      "Attaching package: 'dplyr'\n",
      "\n",
      "The following objects are masked from 'package:stats':\n",
      "\n",
      "    filter, lag\n",
      "\n",
      "The following objects are masked from 'package:base':\n",
      "\n",
      "    intersect, setdiff, setequal, union\n",
      "\n"
     ]
    }
   ],
   "source": [
    "library(dplyr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dados faltantes\n",
    "<br>\n",
    "Dados faltantes podem ser um grande problema quando estamos lidando com uma base de dados. Assim, uma abordagem geralmente aplicada é simplesmente removê-los da sua base de dados. No R, muitas funções (a grande maioria delas) exibe um parâmetro `na.rm` que abrevia \"remover NAs\" ou *not available* ."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] NA\n"
     ]
    }
   ],
   "source": [
    "#definimos aqui um vetor, vamos ver o que acontece quando aplicamos a função mean()  a ele\n",
    "vetor <- c(2, 3, 4, 5, NA, 7)\n",
    "print(mean(vetor))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] 4.2\n"
     ]
    }
   ],
   "source": [
    "#Agora com o na.rm = TRUE\n",
    "print(mean(vetor, na.rm=TRUE))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Outra coisa que podemos querer fazer é inputar algum dado padrão aos dados faltantes, isso pode ser feito da seguinte maneira:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] 2 3 4 5 0 7\n"
     ]
    }
   ],
   "source": [
    "vetor[is.na(vetor)] <- 0\n",
    "print(vetor)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] 2.0 3.0 4.0 5.0 4.2 7.0\n"
     ]
    }
   ],
   "source": [
    "vetor <- c(2, 3, 4, 5, NA, 7)\n",
    "vetor[is.na(vetor)] <- mean(vetor, na.rm=TRUE)\n",
    "print(vetor)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Vamos dar uma olhada na nossa tabela da Fifa, vamos ver se ela tem algum dado faltante."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "0"
      ],
      "text/latex": [
       "0"
      ],
      "text/markdown": [
       "0"
      ],
      "text/plain": [
       "[1] 0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Calculando a quantidade de dados faltantes\n",
    "sum(is.na(fifa))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Como não há dados faltantes, não precisamos nos preocupar."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Deep dive in dplyr\n",
    "Vamos agora nos aprofundar na biblioteca `dplyr`. <br>\n",
    "Essa biblioteca é boa porque deixa o nosso código extremamente legível para um ser humano.<br>\n",
    "Isso ocorre porque ela usa uma estrutura de \"verbos\" que regem o que será feito com a base de dados.<br>\n",
    "Os verbos existentes na biblioteca são os seguintes:\n",
    "1. `mutate()` - que adiciona novas variáveis (colunas) ao dataset a partir de operações/ aplicação de funções nas variáveis existentes \n",
    "2. `select()` - seleciona as colunas com base em critérios de nome\n",
    "3. `filter()` - filtra os dados com base em valores.\n",
    "4. `summarise()` - reduz um grande número de valores para valores resumo (como média, contagem, desvio, etc). Para quem mexe com excel, aqui seria algo para criar uma tabela dinâmica (pivot table). Em geral é usado com `group_by()`\n",
    "5. `arrange()`- muda a ordenação das linhas com base em algum critério\n",
    "<br>\n",
    "<br>\n",
    "Todos esses verbos podem ser performados tendo como critério um grupo, usando o `group_by()` , mas é mais comum usar isso no `summarise()`. <br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A estrutura de utilização dos verbos é bem simples: <br>\n",
    "`base de dados %>% verbo` <br>\n",
    "Podemos aplicar vários verbos em seguida também, com a seguinte estutura (a ordem de execução será a ordem desses verbos): <br>\n",
    "`base de dados %>% verbo1 %>% verbo2 %>% verbo3`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Nesse Workshop, não usaremos todas essas funções, assim vamos explicar a que mais iremos usar e depois mostraremos um exemplo com todas as funções juntas para entender seu uso."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### `filter` - Filtrando nossa base de dados!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Vamos supor que você quer analisar sua base de dados e segmentar essa análise com base em algum critério. Isso é só um dos cenários em que pode ser muito importante você usar filtros!<br>\n",
    "Vamos aqui filtrar a base para ver jogadores com idade acima de 30 anos."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th scope=col>X</th><th scope=col>sofifa_id</th><th scope=col>short_name</th><th scope=col>long_name</th><th scope=col>age</th><th scope=col>height_cm</th><th scope=col>weight_kg</th><th scope=col>nationality</th><th scope=col>club</th><th scope=col>wage_eur</th><th scope=col>player_positions</th><th scope=col>preferred_foot</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><td> 3851              </td><td>193254             </td><td>C. Trimmel         </td><td>Christopher Trimmel</td><td>32                 </td><td>189                </td><td>82                 </td><td>Austria            </td><td>1. FC Union Berlin </td><td>24000              </td><td>RB                 </td><td>Right              </td></tr>\n",
       "\t<tr><td>15820              </td><td>180181             </td><td>K. Browne          </td><td>Kenny Browne       </td><td>32                 </td><td>191                </td><td>86                 </td><td>Republic of Ireland</td><td>Waterford FC       </td><td> 1000              </td><td>CB                 </td><td>Left               </td></tr>\n",
       "\t<tr><td> 5415              </td><td>192594             </td><td>G. Kagelmacher     </td><td>Gary Kagelmacher   </td><td>31                 </td><td>183                </td><td>78                 </td><td>Uruguay            </td><td>KV Kortrijk        </td><td> 8000              </td><td>CB, RB             </td><td>Right              </td></tr>\n",
       "\t<tr><td>14560              </td><td>200703             </td><td>R. Siemaszko       </td><td>RafaÅ‚ Siemaszko   </td><td>32                 </td><td>170                </td><td>68                 </td><td>Poland             </td><td>Arka Gdynia        </td><td> 1000              </td><td>ST                 </td><td>Right              </td></tr>\n",
       "\t<tr><td> 4595              </td><td>198611             </td><td>N. ÄurÄ‘iÄ‡       </td><td>Nikola ÄurÄ‘iÄ‡   </td><td>33                 </td><td>184                </td><td>77                 </td><td>Serbia             </td><td>Hammarby IF        </td><td> 5000              </td><td>ST, CAM            </td><td>Right              </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|llllllllllll}\n",
       " X & sofifa\\_id & short\\_name & long\\_name & age & height\\_cm & weight\\_kg & nationality & club & wage\\_eur & player\\_positions & preferred\\_foot\\\\\n",
       "\\hline\n",
       "\t  3851               & 193254              & C. Trimmel          & Christopher Trimmel & 32                  & 189                 & 82                  & Austria             & 1. FC Union Berlin  & 24000               & RB                  & Right              \\\\\n",
       "\t 15820               & 180181              & K. Browne           & Kenny Browne        & 32                  & 191                 & 86                  & Republic of Ireland & Waterford FC        &  1000               & CB                  & Left               \\\\\n",
       "\t  5415               & 192594              & G. Kagelmacher      & Gary Kagelmacher    & 31                  & 183                 & 78                  & Uruguay             & KV Kortrijk         &  8000               & CB, RB              & Right              \\\\\n",
       "\t 14560               & 200703              & R. Siemaszko        & RafaÅ‚ Siemaszko    & 32                  & 170                 & 68                  & Poland              & Arka Gdynia         &  1000               & ST                  & Right              \\\\\n",
       "\t  4595               & 198611              & N. ÄurÄ‘iÄ‡        & Nikola ÄurÄ‘iÄ‡    & 33                  & 184                 & 77                  & Serbia              & Hammarby IF         &  5000               & ST, CAM             & Right              \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| X | sofifa_id | short_name | long_name | age | height_cm | weight_kg | nationality | club | wage_eur | player_positions | preferred_foot |\n",
       "|---|---|---|---|---|---|---|---|---|---|---|---|\n",
       "|  3851               | 193254              | C. Trimmel          | Christopher Trimmel | 32                  | 189                 | 82                  | Austria             | 1. FC Union Berlin  | 24000               | RB                  | Right               |\n",
       "| 15820               | 180181              | K. Browne           | Kenny Browne        | 32                  | 191                 | 86                  | Republic of Ireland | Waterford FC        |  1000               | CB                  | Left                |\n",
       "|  5415               | 192594              | G. Kagelmacher      | Gary Kagelmacher    | 31                  | 183                 | 78                  | Uruguay             | KV Kortrijk         |  8000               | CB, RB              | Right               |\n",
       "| 14560               | 200703              | R. Siemaszko        | RafaÅ‚ Siemaszko    | 32                  | 170                 | 68                  | Poland              | Arka Gdynia         |  1000               | ST                  | Right               |\n",
       "|  4595               | 198611              | N. ÄurÄ‘iÄ‡        | Nikola ÄurÄ‘iÄ‡    | 33                  | 184                 | 77                  | Serbia              | Hammarby IF         |  5000               | ST, CAM             | Right               |\n",
       "\n"
      ],
      "text/plain": [
       "  X     sofifa_id short_name     long_name           age height_cm weight_kg\n",
       "1  3851 193254    C. Trimmel     Christopher Trimmel 32  189       82       \n",
       "2 15820 180181    K. Browne      Kenny Browne        32  191       86       \n",
       "3  5415 192594    G. Kagelmacher Gary Kagelmacher    31  183       78       \n",
       "4 14560 200703    R. Siemaszko   RafaÅ‚ Siemaszko    32  170       68       \n",
       "5  4595 198611    N. Ä\\220urÄ‘iÄ‡   Nikola Ä\\220urÄ‘iÄ‡    33  184       77       \n",
       "  nationality         club               wage_eur player_positions\n",
       "1 Austria             1. FC Union Berlin 24000    RB              \n",
       "2 Republic of Ireland Waterford FC        1000    CB              \n",
       "3 Uruguay             KV Kortrijk         8000    CB, RB          \n",
       "4 Poland              Arka Gdynia         1000    ST              \n",
       "5 Serbia              Hammarby IF         5000    ST, CAM         \n",
       "  preferred_foot\n",
       "1 Right         \n",
       "2 Left          \n",
       "3 Right         \n",
       "4 Right         \n",
       "5 Right         "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "filtro_idade <- fifa %>% filter(age > 30)\n",
    "filtro_idade[1:5,]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Usando vários verbos ao mesmo tempo\n",
    "\n",
    "Uma coisa legal do `dplyr` é que a gente pode usar vários verbos ao mesmo tempo"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Aqui vamos pegar uma base apenas com aqueles com **idade abaixo de 37 anos**, depois vamos avaliar as **combinações de nacionalidade e pé preferido** possíves, para cada uma dando sua **máxima idade**. Finalmente, vamos fazer com que os resultados sejam **ordenados da maior idade máxima para a menor**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "`summarise()` regrouping output by 'nationality' (override with `.groups` argument)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th scope=col>nationality</th><th scope=col>preferred_foot</th><th scope=col>idade_maxima</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><td>Austria            </td><td>Left               </td><td>36                 </td></tr>\n",
       "\t<tr><td>England            </td><td>Right              </td><td>36                 </td></tr>\n",
       "\t<tr><td>Italy              </td><td>Right              </td><td>36                 </td></tr>\n",
       "\t<tr><td>Senegal            </td><td>Right              </td><td>36                 </td></tr>\n",
       "\t<tr><td>Brazil             </td><td>Left               </td><td>35                 </td></tr>\n",
       "\t<tr><td>Brazil             </td><td>Right              </td><td>35                 </td></tr>\n",
       "\t<tr><td>Germany            </td><td>Right              </td><td>35                 </td></tr>\n",
       "\t<tr><td>Switzerland        </td><td>Right              </td><td>35                 </td></tr>\n",
       "\t<tr><td>Togo               </td><td>Right              </td><td>35                 </td></tr>\n",
       "\t<tr><td>England            </td><td>Left               </td><td>34                 </td></tr>\n",
       "\t<tr><td>Japan              </td><td>Right              </td><td>34                 </td></tr>\n",
       "\t<tr><td>Scotland           </td><td>Right              </td><td>34                 </td></tr>\n",
       "\t<tr><td>Spain              </td><td>Right              </td><td>34                 </td></tr>\n",
       "\t<tr><td>Azerbaijan         </td><td>Right              </td><td>33                 </td></tr>\n",
       "\t<tr><td>China PR           </td><td>Left               </td><td>33                 </td></tr>\n",
       "\t<tr><td>China PR           </td><td>Right              </td><td>33                 </td></tr>\n",
       "\t<tr><td>Iceland            </td><td>Right              </td><td>33                 </td></tr>\n",
       "\t<tr><td>Korea Republic     </td><td>Right              </td><td>33                 </td></tr>\n",
       "\t<tr><td>Serbia             </td><td>Right              </td><td>33                 </td></tr>\n",
       "\t<tr><td>Sweden             </td><td>Right              </td><td>33                 </td></tr>\n",
       "\t<tr><td>Switzerland        </td><td>Left               </td><td>33                 </td></tr>\n",
       "\t<tr><td>Austria            </td><td>Right              </td><td>32                 </td></tr>\n",
       "\t<tr><td>Colombia           </td><td>Right              </td><td>32                 </td></tr>\n",
       "\t<tr><td>Croatia            </td><td>Right              </td><td>32                 </td></tr>\n",
       "\t<tr><td>Italy              </td><td>Left               </td><td>32                 </td></tr>\n",
       "\t<tr><td>Poland             </td><td>Right              </td><td>32                 </td></tr>\n",
       "\t<tr><td>Republic of Ireland</td><td>Left               </td><td>32                 </td></tr>\n",
       "\t<tr><td>Romania            </td><td>Left               </td><td>32                 </td></tr>\n",
       "\t<tr><td>South Africa       </td><td>Right              </td><td>32                 </td></tr>\n",
       "\t<tr><td>Cameroon           </td><td>Right              </td><td>31                 </td></tr>\n",
       "\t<tr><td>...</td><td>...</td><td>...</td></tr>\n",
       "\t<tr><td>Guinea Bissau       </td><td>Right               </td><td>25                  </td></tr>\n",
       "\t<tr><td>Ivory Coast         </td><td>Left                </td><td>25                  </td></tr>\n",
       "\t<tr><td>Australia           </td><td>Left                </td><td>24                  </td></tr>\n",
       "\t<tr><td>Ghana               </td><td>Right               </td><td>24                  </td></tr>\n",
       "\t<tr><td>Mexico              </td><td>Left                </td><td>24                  </td></tr>\n",
       "\t<tr><td>Northern Ireland    </td><td>Right               </td><td>24                  </td></tr>\n",
       "\t<tr><td>Russia              </td><td>Right               </td><td>24                  </td></tr>\n",
       "\t<tr><td>Albania             </td><td>Left                </td><td>23                  </td></tr>\n",
       "\t<tr><td>Bolivia             </td><td>Right               </td><td>23                  </td></tr>\n",
       "\t<tr><td>Paraguay            </td><td>Right               </td><td>23                  </td></tr>\n",
       "\t<tr><td>Sierra Leone        </td><td>Left                </td><td>23                  </td></tr>\n",
       "\t<tr><td>Georgia             </td><td>Left                </td><td>22                  </td></tr>\n",
       "\t<tr><td>Iran                </td><td>Left                </td><td>22                  </td></tr>\n",
       "\t<tr><td>New Zealand         </td><td>Left                </td><td>22                  </td></tr>\n",
       "\t<tr><td>Northern Ireland    </td><td>Left                </td><td>22                  </td></tr>\n",
       "\t<tr><td>Belgium             </td><td>Left                </td><td>21                  </td></tr>\n",
       "\t<tr><td>DR Congo            </td><td>Right               </td><td>21                  </td></tr>\n",
       "\t<tr><td>Ecuador             </td><td>Left                </td><td>21                  </td></tr>\n",
       "\t<tr><td>Mali                </td><td>Right               </td><td>21                  </td></tr>\n",
       "\t<tr><td>Mauritania          </td><td>Left                </td><td>21                  </td></tr>\n",
       "\t<tr><td>United Arab Emirates</td><td>Right               </td><td>21                  </td></tr>\n",
       "\t<tr><td>Wales               </td><td>Right               </td><td>21                  </td></tr>\n",
       "\t<tr><td>Albania             </td><td>Right               </td><td>20                  </td></tr>\n",
       "\t<tr><td>Finland             </td><td>Right               </td><td>20                  </td></tr>\n",
       "\t<tr><td>New Zealand         </td><td>Right               </td><td>20                  </td></tr>\n",
       "\t<tr><td>Slovenia            </td><td>Right               </td><td>20                  </td></tr>\n",
       "\t<tr><td>Cuba                </td><td>Right               </td><td>19                  </td></tr>\n",
       "\t<tr><td>Luxembourg          </td><td>Right               </td><td>19                  </td></tr>\n",
       "\t<tr><td>Sierra Leone        </td><td>Right               </td><td>19                  </td></tr>\n",
       "\t<tr><td>Greece              </td><td>Right               </td><td>18                  </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|lll}\n",
       " nationality & preferred\\_foot & idade\\_maxima\\\\\n",
       "\\hline\n",
       "\t Austria             & Left                & 36                 \\\\\n",
       "\t England             & Right               & 36                 \\\\\n",
       "\t Italy               & Right               & 36                 \\\\\n",
       "\t Senegal             & Right               & 36                 \\\\\n",
       "\t Brazil              & Left                & 35                 \\\\\n",
       "\t Brazil              & Right               & 35                 \\\\\n",
       "\t Germany             & Right               & 35                 \\\\\n",
       "\t Switzerland         & Right               & 35                 \\\\\n",
       "\t Togo                & Right               & 35                 \\\\\n",
       "\t England             & Left                & 34                 \\\\\n",
       "\t Japan               & Right               & 34                 \\\\\n",
       "\t Scotland            & Right               & 34                 \\\\\n",
       "\t Spain               & Right               & 34                 \\\\\n",
       "\t Azerbaijan          & Right               & 33                 \\\\\n",
       "\t China PR            & Left                & 33                 \\\\\n",
       "\t China PR            & Right               & 33                 \\\\\n",
       "\t Iceland             & Right               & 33                 \\\\\n",
       "\t Korea Republic      & Right               & 33                 \\\\\n",
       "\t Serbia              & Right               & 33                 \\\\\n",
       "\t Sweden              & Right               & 33                 \\\\\n",
       "\t Switzerland         & Left                & 33                 \\\\\n",
       "\t Austria             & Right               & 32                 \\\\\n",
       "\t Colombia            & Right               & 32                 \\\\\n",
       "\t Croatia             & Right               & 32                 \\\\\n",
       "\t Italy               & Left                & 32                 \\\\\n",
       "\t Poland              & Right               & 32                 \\\\\n",
       "\t Republic of Ireland & Left                & 32                 \\\\\n",
       "\t Romania             & Left                & 32                 \\\\\n",
       "\t South Africa        & Right               & 32                 \\\\\n",
       "\t Cameroon            & Right               & 31                 \\\\\n",
       "\t ... & ... & ...\\\\\n",
       "\t Guinea Bissau        & Right                & 25                  \\\\\n",
       "\t Ivory Coast          & Left                 & 25                  \\\\\n",
       "\t Australia            & Left                 & 24                  \\\\\n",
       "\t Ghana                & Right                & 24                  \\\\\n",
       "\t Mexico               & Left                 & 24                  \\\\\n",
       "\t Northern Ireland     & Right                & 24                  \\\\\n",
       "\t Russia               & Right                & 24                  \\\\\n",
       "\t Albania              & Left                 & 23                  \\\\\n",
       "\t Bolivia              & Right                & 23                  \\\\\n",
       "\t Paraguay             & Right                & 23                  \\\\\n",
       "\t Sierra Leone         & Left                 & 23                  \\\\\n",
       "\t Georgia              & Left                 & 22                  \\\\\n",
       "\t Iran                 & Left                 & 22                  \\\\\n",
       "\t New Zealand          & Left                 & 22                  \\\\\n",
       "\t Northern Ireland     & Left                 & 22                  \\\\\n",
       "\t Belgium              & Left                 & 21                  \\\\\n",
       "\t DR Congo             & Right                & 21                  \\\\\n",
       "\t Ecuador              & Left                 & 21                  \\\\\n",
       "\t Mali                 & Right                & 21                  \\\\\n",
       "\t Mauritania           & Left                 & 21                  \\\\\n",
       "\t United Arab Emirates & Right                & 21                  \\\\\n",
       "\t Wales                & Right                & 21                  \\\\\n",
       "\t Albania              & Right                & 20                  \\\\\n",
       "\t Finland              & Right                & 20                  \\\\\n",
       "\t New Zealand          & Right                & 20                  \\\\\n",
       "\t Slovenia             & Right                & 20                  \\\\\n",
       "\t Cuba                 & Right                & 19                  \\\\\n",
       "\t Luxembourg           & Right                & 19                  \\\\\n",
       "\t Sierra Leone         & Right                & 19                  \\\\\n",
       "\t Greece               & Right                & 18                  \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| nationality | preferred_foot | idade_maxima |\n",
       "|---|---|---|\n",
       "| Austria             | Left                | 36                  |\n",
       "| England             | Right               | 36                  |\n",
       "| Italy               | Right               | 36                  |\n",
       "| Senegal             | Right               | 36                  |\n",
       "| Brazil              | Left                | 35                  |\n",
       "| Brazil              | Right               | 35                  |\n",
       "| Germany             | Right               | 35                  |\n",
       "| Switzerland         | Right               | 35                  |\n",
       "| Togo                | Right               | 35                  |\n",
       "| England             | Left                | 34                  |\n",
       "| Japan               | Right               | 34                  |\n",
       "| Scotland            | Right               | 34                  |\n",
       "| Spain               | Right               | 34                  |\n",
       "| Azerbaijan          | Right               | 33                  |\n",
       "| China PR            | Left                | 33                  |\n",
       "| China PR            | Right               | 33                  |\n",
       "| Iceland             | Right               | 33                  |\n",
       "| Korea Republic      | Right               | 33                  |\n",
       "| Serbia              | Right               | 33                  |\n",
       "| Sweden              | Right               | 33                  |\n",
       "| Switzerland         | Left                | 33                  |\n",
       "| Austria             | Right               | 32                  |\n",
       "| Colombia            | Right               | 32                  |\n",
       "| Croatia             | Right               | 32                  |\n",
       "| Italy               | Left                | 32                  |\n",
       "| Poland              | Right               | 32                  |\n",
       "| Republic of Ireland | Left                | 32                  |\n",
       "| Romania             | Left                | 32                  |\n",
       "| South Africa        | Right               | 32                  |\n",
       "| Cameroon            | Right               | 31                  |\n",
       "| ... | ... | ... |\n",
       "| Guinea Bissau        | Right                | 25                   |\n",
       "| Ivory Coast          | Left                 | 25                   |\n",
       "| Australia            | Left                 | 24                   |\n",
       "| Ghana                | Right                | 24                   |\n",
       "| Mexico               | Left                 | 24                   |\n",
       "| Northern Ireland     | Right                | 24                   |\n",
       "| Russia               | Right                | 24                   |\n",
       "| Albania              | Left                 | 23                   |\n",
       "| Bolivia              | Right                | 23                   |\n",
       "| Paraguay             | Right                | 23                   |\n",
       "| Sierra Leone         | Left                 | 23                   |\n",
       "| Georgia              | Left                 | 22                   |\n",
       "| Iran                 | Left                 | 22                   |\n",
       "| New Zealand          | Left                 | 22                   |\n",
       "| Northern Ireland     | Left                 | 22                   |\n",
       "| Belgium              | Left                 | 21                   |\n",
       "| DR Congo             | Right                | 21                   |\n",
       "| Ecuador              | Left                 | 21                   |\n",
       "| Mali                 | Right                | 21                   |\n",
       "| Mauritania           | Left                 | 21                   |\n",
       "| United Arab Emirates | Right                | 21                   |\n",
       "| Wales                | Right                | 21                   |\n",
       "| Albania              | Right                | 20                   |\n",
       "| Finland              | Right                | 20                   |\n",
       "| New Zealand          | Right                | 20                   |\n",
       "| Slovenia             | Right                | 20                   |\n",
       "| Cuba                 | Right                | 19                   |\n",
       "| Luxembourg           | Right                | 19                   |\n",
       "| Sierra Leone         | Right                | 19                   |\n",
       "| Greece               | Right                | 18                   |\n",
       "\n"
      ],
      "text/plain": [
       "    nationality          preferred_foot idade_maxima\n",
       "1   Austria              Left           36          \n",
       "2   England              Right          36          \n",
       "3   Italy                Right          36          \n",
       "4   Senegal              Right          36          \n",
       "5   Brazil               Left           35          \n",
       "6   Brazil               Right          35          \n",
       "7   Germany              Right          35          \n",
       "8   Switzerland          Right          35          \n",
       "9   Togo                 Right          35          \n",
       "10  England              Left           34          \n",
       "11  Japan                Right          34          \n",
       "12  Scotland             Right          34          \n",
       "13  Spain                Right          34          \n",
       "14  Azerbaijan           Right          33          \n",
       "15  China PR             Left           33          \n",
       "16  China PR             Right          33          \n",
       "17  Iceland              Right          33          \n",
       "18  Korea Republic       Right          33          \n",
       "19  Serbia               Right          33          \n",
       "20  Sweden               Right          33          \n",
       "21  Switzerland          Left           33          \n",
       "22  Austria              Right          32          \n",
       "23  Colombia             Right          32          \n",
       "24  Croatia              Right          32          \n",
       "25  Italy                Left           32          \n",
       "26  Poland               Right          32          \n",
       "27  Republic of Ireland  Left           32          \n",
       "28  Romania              Left           32          \n",
       "29  South Africa         Right          32          \n",
       "30  Cameroon             Right          31          \n",
       "... ...                  ...            ...         \n",
       "83  Guinea Bissau        Right          25          \n",
       "84  Ivory Coast          Left           25          \n",
       "85  Australia            Left           24          \n",
       "86  Ghana                Right          24          \n",
       "87  Mexico               Left           24          \n",
       "88  Northern Ireland     Right          24          \n",
       "89  Russia               Right          24          \n",
       "90  Albania              Left           23          \n",
       "91  Bolivia              Right          23          \n",
       "92  Paraguay             Right          23          \n",
       "93  Sierra Leone         Left           23          \n",
       "94  Georgia              Left           22          \n",
       "95  Iran                 Left           22          \n",
       "96  New Zealand          Left           22          \n",
       "97  Northern Ireland     Left           22          \n",
       "98  Belgium              Left           21          \n",
       "99  DR Congo             Right          21          \n",
       "100 Ecuador              Left           21          \n",
       "101 Mali                 Right          21          \n",
       "102 Mauritania           Left           21          \n",
       "103 United Arab Emirates Right          21          \n",
       "104 Wales                Right          21          \n",
       "105 Albania              Right          20          \n",
       "106 Finland              Right          20          \n",
       "107 New Zealand          Right          20          \n",
       "108 Slovenia             Right          20          \n",
       "109 Cuba                 Right          19          \n",
       "110 Luxembourg           Right          19          \n",
       "111 Sierra Leone         Right          19          \n",
       "112 Greece               Right          18          "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fifa %>%\n",
    "    filter(age < 37) %>%\n",
    "    group_by(nationality, preferred_foot) %>% \n",
    "    summarise(idade_maxima=max(age)) %>%\n",
    "    arrange(desc(idade_maxima))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th scope=col>X</th><th scope=col>sofifa_id</th><th scope=col>short_name</th><th scope=col>long_name</th><th scope=col>age</th><th scope=col>height_cm</th><th scope=col>weight_kg</th><th scope=col>nationality</th><th scope=col>club</th><th scope=col>wage_eur</th><th scope=col>player_positions</th><th scope=col>preferred_foot</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><td> 1813                  </td><td>224599                 </td><td>Yan Junling            </td><td>é¢œéªå‡Œ              </td><td>28                     </td><td>191                    </td><td>83                     </td><td>China PR               </td><td>Shanghai SIPG FC       </td><td>13000                  </td><td>GK                     </td><td>Right                  </td></tr>\n",
       "\t<tr><td>12287                  </td><td>243530                 </td><td>Mujaid                 </td><td>Mujaid Sadick Aliu     </td><td>19                     </td><td>180                    </td><td>75                     </td><td>Spain                  </td><td>Deportivo de La CoruÃ±a</td><td> 1000                  </td><td>CB, RB                 </td><td>Right                  </td></tr>\n",
       "\t<tr><td> 4257                  </td><td>202676                 </td><td>A. Taggart             </td><td>Adam Taggart           </td><td>26                     </td><td>183                    </td><td>70                     </td><td>Australia              </td><td>Suwon Samsung Bluewings</td><td> 6000                  </td><td>ST, CF                 </td><td>Right                  </td></tr>\n",
       "\t<tr><td> 8794                  </td><td>241187                 </td><td>L. Geertruida          </td><td>Lutsharel Geertruida   </td><td>18                     </td><td>180                    </td><td>75                     </td><td>Netherlands            </td><td>Feyenoord              </td><td> 1000                  </td><td>CB, CDM, RB            </td><td>Right                  </td></tr>\n",
       "\t<tr><td> 3851                  </td><td>193254                 </td><td>C. Trimmel             </td><td>Christopher Trimmel    </td><td>32                     </td><td>189                    </td><td>82                     </td><td>Austria                </td><td>1. FC Union Berlin     </td><td>24000                  </td><td>RB                     </td><td>Right                  </td></tr>\n",
       "\t<tr><td>15820                  </td><td>180181                 </td><td>K. Browne              </td><td>Kenny Browne           </td><td>32                     </td><td>191                    </td><td>86                     </td><td>Republic of Ireland    </td><td>Waterford FC           </td><td> 1000                  </td><td>CB                     </td><td>Left                   </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|llllllllllll}\n",
       " X & sofifa\\_id & short\\_name & long\\_name & age & height\\_cm & weight\\_kg & nationality & club & wage\\_eur & player\\_positions & preferred\\_foot\\\\\n",
       "\\hline\n",
       "\t  1813                   & 224599                  & Yan Junling             & é¢œéªå‡Œ               & 28                      & 191                     & 83                      & China PR                & Shanghai SIPG FC        & 13000                   & GK                      & Right                  \\\\\n",
       "\t 12287                   & 243530                  & Mujaid                  & Mujaid Sadick Aliu      & 19                      & 180                     & 75                      & Spain                   & Deportivo de La CoruÃ±a &  1000                   & CB, RB                  & Right                  \\\\\n",
       "\t  4257                   & 202676                  & A. Taggart              & Adam Taggart            & 26                      & 183                     & 70                      & Australia               & Suwon Samsung Bluewings &  6000                   & ST, CF                  & Right                  \\\\\n",
       "\t  8794                   & 241187                  & L. Geertruida           & Lutsharel Geertruida    & 18                      & 180                     & 75                      & Netherlands             & Feyenoord               &  1000                   & CB, CDM, RB             & Right                  \\\\\n",
       "\t  3851                   & 193254                  & C. Trimmel              & Christopher Trimmel     & 32                      & 189                     & 82                      & Austria                 & 1. FC Union Berlin      & 24000                   & RB                      & Right                  \\\\\n",
       "\t 15820                   & 180181                  & K. Browne               & Kenny Browne            & 32                      & 191                     & 86                      & Republic of Ireland     & Waterford FC            &  1000                   & CB                      & Left                   \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| X | sofifa_id | short_name | long_name | age | height_cm | weight_kg | nationality | club | wage_eur | player_positions | preferred_foot |\n",
       "|---|---|---|---|---|---|---|---|---|---|---|---|\n",
       "|  1813                   | 224599                  | Yan Junling             | é¢œéªå‡Œ               | 28                      | 191                     | 83                      | China PR                | Shanghai SIPG FC        | 13000                   | GK                      | Right                   |\n",
       "| 12287                   | 243530                  | Mujaid                  | Mujaid Sadick Aliu      | 19                      | 180                     | 75                      | Spain                   | Deportivo de La CoruÃ±a |  1000                   | CB, RB                  | Right                   |\n",
       "|  4257                   | 202676                  | A. Taggart              | Adam Taggart            | 26                      | 183                     | 70                      | Australia               | Suwon Samsung Bluewings |  6000                   | ST, CF                  | Right                   |\n",
       "|  8794                   | 241187                  | L. Geertruida           | Lutsharel Geertruida    | 18                      | 180                     | 75                      | Netherlands             | Feyenoord               |  1000                   | CB, CDM, RB             | Right                   |\n",
       "|  3851                   | 193254                  | C. Trimmel              | Christopher Trimmel     | 32                      | 189                     | 82                      | Austria                 | 1. FC Union Berlin      | 24000                   | RB                      | Right                   |\n",
       "| 15820                   | 180181                  | K. Browne               | Kenny Browne            | 32                      | 191                     | 86                      | Republic of Ireland     | Waterford FC            |  1000                   | CB                      | Left                    |\n",
       "\n"
      ],
      "text/plain": [
       "  X     sofifa_id short_name    long_name            age height_cm weight_kg\n",
       "1  1813 224599    Yan Junling   é¢œéª\\217å‡Œ            28  191       83       \n",
       "2 12287 243530    Mujaid        Mujaid Sadick Aliu   19  180       75       \n",
       "3  4257 202676    A. Taggart    Adam Taggart         26  183       70       \n",
       "4  8794 241187    L. Geertruida Lutsharel Geertruida 18  180       75       \n",
       "5  3851 193254    C. Trimmel    Christopher Trimmel  32  189       82       \n",
       "6 15820 180181    K. Browne     Kenny Browne         32  191       86       \n",
       "  nationality         club                    wage_eur player_positions\n",
       "1 China PR            Shanghai SIPG FC        13000    GK              \n",
       "2 Spain               Deportivo de La CoruÃ±a  1000    CB, RB          \n",
       "3 Australia           Suwon Samsung Bluewings  6000    ST, CF          \n",
       "4 Netherlands         Feyenoord                1000    CB, CDM, RB     \n",
       "5 Austria             1. FC Union Berlin      24000    RB              \n",
       "6 Republic of Ireland Waterford FC             1000    CB              \n",
       "  preferred_foot\n",
       "1 Right         \n",
       "2 Right         \n",
       "3 Right         \n",
       "4 Right         \n",
       "5 Right         \n",
       "6 Left          "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "head(fifa)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <span style=\"color: orange;\">  Parte 3 - Estatística Básica em R </span>\n",
    "\n",
    "Agora que já vimos o básico de importação de dados em **R**, estamos prontos para entender como essa linguagem é utilizada efetivamente para fazer análises estatísticas!\n",
    "\n",
    "Mas não se preocupe, vamos explorando os diversos temas de estatística enquanto aprendemos mais sobre **R**."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Tipos de Dados\n",
    "\n",
    "Um dos pontos base da Estatística consiste em entender nossas informações e como elas elas são organizadas: os **dados**. Esses dados podem ser divididos em dois tipos diferentes: dados **qualitativos** e dados **quantitativos**.\n",
    "\n",
    "### Dados Qualitativos\n",
    "\n",
    "Esses dados representam **categorias** do nosso espaço, ou seja, eles dividem os indivíduos em diversas classes que não se intersectam. Por exemplo, a cor de uma flor é um tipo de dado qualitativo, já que as categoriza em classes como: *'amarelo'*, *'rosa'*, *'vermelho'*, *'azul'*, etc. Esses dados são bem úteis para representar características que não são facilmente descritas por números, como qualidades (daí vem o nome)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th scope=col>Flor</th><th scope=col>Cor</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><td>Margarida</td><td>Amarelo  </td></tr>\n",
       "\t<tr><td>Hortênsia</td><td>Azul     </td></tr>\n",
       "\t<tr><td>Violeta  </td><td>Azul     </td></tr>\n",
       "\t<tr><td>Rosa     </td><td>Vermelho </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|ll}\n",
       " Flor & Cor\\\\\n",
       "\\hline\n",
       "\t Margarida & Amarelo  \\\\\n",
       "\t Hortênsia & Azul     \\\\\n",
       "\t Violeta   & Azul     \\\\\n",
       "\t Rosa      & Vermelho \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| Flor | Cor |\n",
       "|---|---|\n",
       "| Margarida | Amarelo   |\n",
       "| Hortênsia | Azul      |\n",
       "| Violeta   | Azul      |\n",
       "| Rosa      | Vermelho  |\n",
       "\n"
      ],
      "text/plain": [
       "  Flor      Cor     \n",
       "1 Margarida Amarelo \n",
       "2 Hortênsia Azul    \n",
       "3 Violeta   Azul    \n",
       "4 Rosa      Vermelho"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Montando um Dataframe de Cores de Flores\n",
    "\n",
    "Flor = c(\"Margarida\", \"Hortênsia\", \"Violeta\", \"Rosa\")\n",
    "Cor = c(\"Amarelo\", \"Azul\", \"Azul\", \"Vermelho\")\n",
    "flores = data.frame(Flor, Cor)\n",
    "flores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th scope=col>Flor</th><th scope=col>Cor</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><td>Hortênsia</td><td>Azul     </td></tr>\n",
       "\t<tr><td>Violeta  </td><td>Azul     </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|ll}\n",
       " Flor & Cor\\\\\n",
       "\\hline\n",
       "\t Hortênsia & Azul     \\\\\n",
       "\t Violeta   & Azul     \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| Flor | Cor |\n",
       "|---|---|\n",
       "| Hortênsia | Azul      |\n",
       "| Violeta   | Azul      |\n",
       "\n"
      ],
      "text/plain": [
       "  Flor      Cor \n",
       "1 Hortênsia Azul\n",
       "2 Violeta   Azul"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Mostrando as flores de cor Azul\n",
    "\n",
    "flores %>% filter(Cor == \"Azul\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dados Quantitativos\n",
    "\n",
    "São tipos de dados **numéricos**, ou seja, são mensuráveis e podem ser manipulados matematicamente. Essa categoria engloba os dados representáveis por números, como a altura de uma pessoa ou a área de um terreno. Esses dados são mais versáteis, já que conseguimos fazer comparações, médias e outros tipos de operações aritméticas que não são possíveis com os dados qualitativos."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th scope=col>Nome</th><th scope=col>Altura</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><td>Marcela  </td><td>161      </td></tr>\n",
       "\t<tr><td>Alexandre</td><td>184      </td></tr>\n",
       "\t<tr><td>Rafaela  </td><td>172      </td></tr>\n",
       "\t<tr><td>Mariana  </td><td>174      </td></tr>\n",
       "\t<tr><td>Victor   </td><td>169      </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|ll}\n",
       " Nome & Altura\\\\\n",
       "\\hline\n",
       "\t Marcela   & 161      \\\\\n",
       "\t Alexandre & 184      \\\\\n",
       "\t Rafaela   & 172      \\\\\n",
       "\t Mariana   & 174      \\\\\n",
       "\t Victor    & 169      \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| Nome | Altura |\n",
       "|---|---|\n",
       "| Marcela   | 161       |\n",
       "| Alexandre | 184       |\n",
       "| Rafaela   | 172       |\n",
       "| Mariana   | 174       |\n",
       "| Victor    | 169       |\n",
       "\n"
      ],
      "text/plain": [
       "  Nome      Altura\n",
       "1 Marcela   161   \n",
       "2 Alexandre 184   \n",
       "3 Rafaela   172   \n",
       "4 Mariana   174   \n",
       "5 Victor    169   "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Montando um Dataframe de Altura de Pessoas\n",
    "\n",
    "Nome = c(\"Marcela\", \"Alexandre\", \"Rafaela\", \"Mariana\", \"Victor\")\n",
    "Altura = c(161, 184, 172, 174, 169)\n",
    "alturas = data.frame(Nome, Altura)\n",
    "alturas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th scope=col>Nome</th><th scope=col>Altura</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><td>Alexandre</td><td>184      </td></tr>\n",
       "\t<tr><td>Rafaela  </td><td>172      </td></tr>\n",
       "\t<tr><td>Mariana  </td><td>174      </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|ll}\n",
       " Nome & Altura\\\\\n",
       "\\hline\n",
       "\t Alexandre & 184      \\\\\n",
       "\t Rafaela   & 172      \\\\\n",
       "\t Mariana   & 174      \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| Nome | Altura |\n",
       "|---|---|\n",
       "| Alexandre | 184       |\n",
       "| Rafaela   | 172       |\n",
       "| Mariana   | 174       |\n",
       "\n"
      ],
      "text/plain": [
       "  Nome      Altura\n",
       "1 Alexandre 184   \n",
       "2 Rafaela   172   \n",
       "3 Mariana   174   "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Selecionando as pessoas com altura maior que 170 cm\n",
    "\n",
    "alturas %>% filter(Altura > 170)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Estatística Descritiva\n",
    "\n",
    "O tipo de análise estatística mais básico é a **Estatística Descritiva**, que busca simplesmente resumir e descrever os nossos dados da melhor maneira possível. Esse ramo utiliza de diversas medidas para expressar características diferentes dos nossos dados, como veremos a seguir."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Medidas de Tendência Central\n",
    "\n",
    "As **Medidas de Tendência Central** procuram representrar um valor central em torno do qual os dados se agrupam. Você provavelmente já ouviu falar de alguns exemplos desse tipo de medida, como a **média** e a **mediana**.\n",
    "\n",
    "#### Média\n",
    "\n",
    "A **Média** é a medida numérica que representa a posição central dos nossos dados. Seu valor é obtido ao dividir a soma de todos os nossos dados pela sua quantidade.\n",
    "\n",
    "$$\\large \\bar x ={\\frac {1}{N}}\\sum _{i=1}^{N}x_{i}$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "8396.9298245614"
      ],
      "text/latex": [
       "8396.9298245614"
      ],
      "text/markdown": [
       "8396.9298245614"
      ],
      "text/plain": [
       "[1] 8396.93"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Média de salário dos jogadores\n",
    "\n",
    "mean(fifa$wage_eur)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "`summarise()` ungrouping output (override with `.groups` argument)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th scope=col>preferred_foot</th><th scope=col>mean_wage</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><td>Left    </td><td>9844.262</td></tr>\n",
       "\t<tr><td>Right   </td><td>7868.263</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|ll}\n",
       " preferred\\_foot & mean\\_wage\\\\\n",
       "\\hline\n",
       "\t Left     & 9844.262\\\\\n",
       "\t Right    & 7868.263\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| preferred_foot | mean_wage |\n",
       "|---|---|\n",
       "| Left     | 9844.262 |\n",
       "| Right    | 7868.263 |\n",
       "\n"
      ],
      "text/plain": [
       "  preferred_foot mean_wage\n",
       "1 Left           9844.262 \n",
       "2 Right          7868.263 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Média de salário por pé utilizado\n",
    "\n",
    "fifa %>% group_by(preferred_foot) %>%\n",
    "     summarise(mean_wage = mean(wage_eur))\n",
    "\n",
    "# A média dos canhotos é maior que a dos destros\n",
    "# Isso significa que os canhotos ganham mais que os destros?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message in mean.default(fifa$preferred_foot):\n",
      "\"argument is not numeric or logical: returning NA\""
     ]
    },
    {
     "data": {
      "text/html": [
       "&lt;NA&gt;"
      ],
      "text/latex": [
       "<NA>"
      ],
      "text/markdown": [
       "&lt;NA&gt;"
      ],
      "text/plain": [
       "[1] NA"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Não é possível calcular a média de dados qualitativos\n",
    "\n",
    "mean(fifa$preferred_foot)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Mediana\n",
    "\n",
    "A **Mediana** é a medida numérica que representa o dado que se encontra na metade da nossa distribuição quanto esta se encontra ordenada. Ou seja, a mediana é o valor que divide os nossos dados de tal forma que metade deles sejam maiores que a mediana e a outra metade seja menor."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "25"
      ],
      "text/latex": [
       "25"
      ],
      "text/markdown": [
       "25"
      ],
      "text/plain": [
       "[1] 25"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Mediana da idade dos jogadores\n",
    "\n",
    "median(fifa$age)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "ename": "ERROR",
     "evalue": "Error in median.default(fifa$nationality): need numeric data\n",
     "output_type": "error",
     "traceback": [
      "Error in median.default(fifa$nationality): need numeric data\nTraceback:\n",
      "1. median(fifa$nationality)",
      "2. median.default(fifa$nationality)",
      "3. stop(\"need numeric data\")"
     ]
    }
   ],
   "source": [
    "# Não é possível calcular a mediana de dados qualitativos nominais (não ordinais)\n",
    "\n",
    "median(fifa$nationality)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Percentil\n",
    "\n",
    "O **Percentil** não é uma medida de tendência central, mas se relaciona bastante com a Mediana. Os percentis são medidas que dividem as nossas amostras tal que **p%** delas sejam menores que o percentil. O **50º percentil**, portanto, é a nossa já conhecida mediana, enquanto o **25º percentil** é denominado primeiro quartil."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th scope=col>0%</th><th scope=col>25%</th><th scope=col>50%</th><th scope=col>75%</th><th scope=col>100%</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><td>0     </td><td>1000  </td><td>3000  </td><td>8000  </td><td>225000</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{lllll}\n",
       " 0\\% & 25\\% & 50\\% & 75\\% & 100\\%\\\\\n",
       "\\hline\n",
       "\t 0      & 1000   & 3000   & 8000   & 225000\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| 0% | 25% | 50% | 75% | 100% |\n",
       "|---|---|---|---|---|\n",
       "| 0      | 1000   | 3000   | 8000   | 225000 |\n",
       "\n"
      ],
      "text/plain": [
       "     0% 25%  50%  75%  100%  \n",
       "[1,] 0  1000 3000 8000 225000"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Obtendo os quartis do salário dos jogadores\n",
    "\n",
    "quartis = quantile(fifa$wage_eur)\n",
    "\n",
    "# Transpondo o resultado para ficar mais visualizável\n",
    "t(quartis)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th scope=col>30%</th><th scope=col>60%</th><th scope=col>90%</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><td>2000 </td><td>4000 </td><td>19000</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{lll}\n",
       " 30\\% & 60\\% & 90\\%\\\\\n",
       "\\hline\n",
       "\t 2000  & 4000  & 19000\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| 30% | 60% | 90% |\n",
       "|---|---|---|\n",
       "| 2000  | 4000  | 19000 |\n",
       "\n"
      ],
      "text/plain": [
       "     30%  60%  90%  \n",
       "[1,] 2000 4000 19000"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Obtendo os percentis 30%, 60% e 90% do salário dos jogadores\n",
    "\n",
    "percentis = quantile(fifa$wage_eur, c(.30, .60, .90))\n",
    "\n",
    "# Transpondo o resultado para ficar mais visualizável\n",
    "t(percentis)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Summary\n",
    "\n",
    "Uma maneira bem simples de mostrar vários dos valores que vimos até agora é utilizando a função ```summary()```. Ela nos apresenta o *mínimo*, o *1º quartil*, a *mediana*, a *média*, o *3º quartil* e o *máximo* da variável em questão, da seguinte forma:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. \n",
       "      0    1000    3000    8397    8000  225000 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Resumo do salário dos jogadores\n",
    "\n",
    "summary(fifa$wage_eur)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Medidas de Variação\n",
    "\n",
    "As **Medidas de Variação** medem o *grau de afastamento* dos dados em torno de um valor central. Elas são importantes para medir a homogeneidade dos dados, indicando se nossas amostras estão próximas entre si ou muito afastadas.\n",
    "\n",
    "#### Amplitude\n",
    "\n",
    "A medida mais simples de variação é a **Amplitude**, que mede simplesmente a diferença entre a maior e a menor amostra. Entretanto, essa medida é bem sensível outliers em suas extremidades.\n",
    "\n",
    "$$ \\large A =  x_{max} - x_{min} $$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "225000"
      ],
      "text/latex": [
       "225000"
      ],
      "text/markdown": [
       "225000"
      ],
      "text/plain": [
       "[1] 225000"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Calculando a amplitude do salário dos jogadores\n",
    "\n",
    "amplitude = max(fifa$wage_eur) - min(fifa$wage_eur)\n",
    "\n",
    "amplitude"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Amplitude Interquartil\n",
    "\n",
    "Uma outra medida de variação mais precisa é a **Amplitude Interquartil**, definida como a diferença entre o 3º e o 1º quartil. Por desprezar os valores mais extremos, essa medida é menos sujeita a flutuações que a amplitude normal."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th scope=col>0%</th><th scope=col>25%</th><th scope=col>50%</th><th scope=col>75%</th><th scope=col>100%</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><td>0     </td><td>1000  </td><td>3000  </td><td>8000  </td><td>225000</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{lllll}\n",
       " 0\\% & 25\\% & 50\\% & 75\\% & 100\\%\\\\\n",
       "\\hline\n",
       "\t 0      & 1000   & 3000   & 8000   & 225000\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| 0% | 25% | 50% | 75% | 100% |\n",
       "|---|---|---|---|---|\n",
       "| 0      | 1000   | 3000   | 8000   | 225000 |\n",
       "\n"
      ],
      "text/plain": [
       "     0% 25%  50%  75%  100%  \n",
       "[1,] 0  1000 3000 8000 225000"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Revendo os quartis do salário dos jogadores\n",
    "\n",
    "quartis = quantile(fifa$wage_eur)\n",
    "t(quartis)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "7000"
      ],
      "text/latex": [
       "7000"
      ],
      "text/markdown": [
       "7000"
      ],
      "text/plain": [
       "[1] 7000"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Calculando a amplitude interquartil do salário dos jogadores\n",
    "\n",
    "IQR(fifa$wage_eur)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Variância\n",
    "\n",
    "A **Variância** é uma medida de variação que mensura a dispersão das amostras com relação à média.\n",
    "\n",
    "Seu valor é obtido a partir do cálculo da média do quadrado das diferenças entre os dados e a média, da seguinte forma:\n",
    "<br>\n",
    "<br>\n",
    "\n",
    "$$\\large s^2 = {\\frac{\\sum_{i=1}^{N}(x_{i} -\\bar{x})^2}{N-1}}$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "374015726.817043"
      ],
      "text/latex": [
       "374015726.817043"
      ],
      "text/markdown": [
       "374015726.817043"
      ],
      "text/plain": [
       "[1] 374015727"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Calculando a variância do salário dos jogadores\n",
    "\n",
    "var(fifa$wage_eur)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Desvio Padrão\n",
    "\n",
    "O **Desvio Padrão** é uma outra medida de variação obtida simplesmente pela raiz quadrada da variância. Essa raiz é feita para que a unidade de medida seja a mesma dos nossos dados.\n",
    "<br>\n",
    "<br>\n",
    "\n",
    "$$\\large s = \\sqrt{\\frac{\\sum_{i=1}^{N}(x_{i} -\\bar{x})^2}{N-1}}$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "19339.4862087141"
      ],
      "text/latex": [
       "19339.4862087141"
      ],
      "text/markdown": [
       "19339.4862087141"
      ],
      "text/plain": [
       "[1] 19339.49"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Calculando o desvio padrão do salário dos jogadores\n",
    "\n",
    "sd(fifa$wage_eur)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Covariância\n",
    "\n",
    "A **Covariância** é a medida da *relação linear* entre duas variáveis: seu valor representa o quanto elas \"caminham juntas\". Se a covarância é positiva, significa que as duas crescem juntas; se a covariância é negativa, significa que conforme uma cresce, a outra decresce.\n",
    "\n",
    "Seu valor é obtido da seguinte forma:\n",
    "<br>\n",
    "\n",
    "$$\\large s_{x,y}=\\frac{\\sum_{i=1}^{N}(x_{i}-\\bar{x})(y_{i}-\\bar{y})}{N-1}$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "9212.19876614614"
      ],
      "text/latex": [
       "9212.19876614614"
      ],
      "text/markdown": [
       "9212.19876614614"
      ],
      "text/plain": [
       "[1] 9212.199"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Calculando a covarância entre a idade e o salário dos jogadores\n",
    "# Unidade em anos euro\n",
    "\n",
    "cov(fifa$age, fifa$wage_eur)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A covariância positiva significaria que quanto maior a idade do jogador, maior é a chance dele ganhar mais."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Coeficiente de Correlação\n",
    "\n",
    "O **Coeficiente de Correlação** também mede a relação linear entre duas variáveis, mas é obtida a partir da divisão da covariância pelos desvios padrões individuais de cada variável. Dessa forma, encontramos uma medida **normalizada** da relação entre as duas. \n",
    "<br>\n",
    "<br>\n",
    "\n",
    "$$\\large r_{x,y}= \\frac{s_{x,y}}{s_{x} s_{y}}$$\n",
    "\n",
    "Quanto mais próximo de 1, maior a _relação linear positiva_ entre as duas variáveis, e seu gráfico se aproxima de uma linha reta _crescente_. Quanto mais próximo de -1, maior a _relação linear negativa_ entre as duas variáveis, e seu gráfico se aproxima de uma linha reta _decrescente_. Quando seu valor é próximo de 0, não existe uma relação muito grande entre as duas variáveis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "0.103072200550895"
      ],
      "text/latex": [
       "0.103072200550895"
      ],
      "text/markdown": [
       "0.103072200550895"
      ],
      "text/plain": [
       "[1] 0.1030722"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Calculando o coeficiente de correlação entre a idade e o salário dos jogadores\n",
    "\n",
    "cor(fifa$age, fifa$wage_eur)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A idade dos jogadores tem uma relação linear positiva pequena com seu salário."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <span style=\"color: orange;\"> Parte 4 - Visualização de dados em R com ggplot2 </span>\n",
    "\n",
    "## Introdução\n",
    "\n",
    "__ggplot2__ foi desenvolvido para apresentações gráficas de qualidade em segundos. \n",
    "\n",
    "Este pacote é baseado em **gramática de gráficos**, a ideia é que você possa construir qualquer gráfico combinando adequadamente elementos da gramática, do mesmo modo que é feito na língua portuguesa.\n",
    "\n",
    "### Vantagens\n",
    "\n",
    "- Baseado em __gramática de gráficos__;\n",
    "- Alto nível de abstração em plots;\n",
    "- Flexibilidade;\n",
    "- Sistema de temas para melhorar aparência de gráficos;\n",
    "- Muitos usuários ativos na comunidade open source;\n",
    "\n",
    "### O que não fazer com ggplot2?\n",
    "\n",
    "- Gráficos de 3 dimensões (vide biblioteca [rgl](https://www.rdocumentation.org/packages/rgl/versions/0.100.50));\n",
    "- Gráficos iterativos (vide biblioteca [ggvis](https://ggvis.rstudio.com/));\n",
    "- Gráficos de teoria dos grafos (vide biblioteca [igraph](https://igraph.org/r/));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "-- \u001b[1mAttaching packages\u001b[22m --------------------------------------- tidyverse 1.2.1 --\n",
      "\n",
      "\u001b[32mv\u001b[39m \u001b[34mggplot2\u001b[39m 3.3.2     \u001b[32mv\u001b[39m \u001b[34mpurrr  \u001b[39m 0.3.4\n",
      "\u001b[32mv\u001b[39m \u001b[34mtibble \u001b[39m 3.0.3     \u001b[32mv\u001b[39m \u001b[34mdplyr  \u001b[39m 1.0.0\n",
      "\u001b[32mv\u001b[39m \u001b[34mtidyr  \u001b[39m 1.1.0     \u001b[32mv\u001b[39m \u001b[34mstringr\u001b[39m 1.4.0\n",
      "\u001b[32mv\u001b[39m \u001b[34mreadr  \u001b[39m 1.3.1     \u001b[32mv\u001b[39m \u001b[34mforcats\u001b[39m 0.4.0\n",
      "\n",
      "Warning message:\n",
      "\"package 'ggplot2' was built under R version 3.6.3\"\n",
      "Warning message:\n",
      "\"package 'tibble' was built under R version 3.6.3\"\n",
      "Warning message:\n",
      "\"package 'tidyr' was built under R version 3.6.3\"\n",
      "Warning message:\n",
      "\"package 'purrr' was built under R version 3.6.3\"\n",
      "Warning message:\n",
      "\"package 'dplyr' was built under R version 3.6.3\"\n",
      "-- \u001b[1mConflicts\u001b[22m ------------------------------------------ tidyverse_conflicts() --\n",
      "\u001b[31mx\u001b[39m \u001b[34mdplyr\u001b[39m::\u001b[32mfilter()\u001b[39m masks \u001b[34mstats\u001b[39m::filter()\n",
      "\u001b[31mx\u001b[39m \u001b[34mdplyr\u001b[39m::\u001b[32mlag()\u001b[39m    masks \u001b[34mstats\u001b[39m::lag()\n",
      "\n"
     ]
    }
   ],
   "source": [
    "library(tidyverse) # pacote que vamos usar em quase todas as análises de dados"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Esse warning mostra as funções do tidyverse que conflitam com as funções do R base. Se precisar explicitar de onde a função vem, basta usar a notação ```package::function()```:\n",
    "```R\n",
    "# exemplo\n",
    "\n",
    "ggplot2::ggplot()\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Relembrando os dados que vamos usar"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.frame: 6 × 12</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>X</th><th scope=col>sofifa_id</th><th scope=col>short_name</th><th scope=col>long_name</th><th scope=col>age</th><th scope=col>height_cm</th><th scope=col>weight_kg</th><th scope=col>nationality</th><th scope=col>club</th><th scope=col>wage_eur</th><th scope=col>player_positions</th><th scope=col>preferred_foot</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;fct&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>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>1</th><td> 1813</td><td>224599</td><td>Yan Junling  </td><td>é¢œéªå‡Œ           </td><td>28</td><td>191</td><td>83</td><td>China PR           </td><td>Shanghai SIPG FC       </td><td>13000</td><td>GK         </td><td>Right</td></tr>\n",
       "\t<tr><th scope=row>2</th><td>12287</td><td>243530</td><td>Mujaid       </td><td>Mujaid Sadick Aliu  </td><td>19</td><td>180</td><td>75</td><td>Spain              </td><td>Deportivo de La CoruÃ±a</td><td> 1000</td><td>CB, RB     </td><td>Right</td></tr>\n",
       "\t<tr><th scope=row>3</th><td> 4257</td><td>202676</td><td>A. Taggart   </td><td>Adam Taggart        </td><td>26</td><td>183</td><td>70</td><td>Australia          </td><td>Suwon Samsung Bluewings</td><td> 6000</td><td>ST, CF     </td><td>Right</td></tr>\n",
       "\t<tr><th scope=row>4</th><td> 8794</td><td>241187</td><td>L. Geertruida</td><td>Lutsharel Geertruida</td><td>18</td><td>180</td><td>75</td><td>Netherlands        </td><td>Feyenoord              </td><td> 1000</td><td>CB, CDM, RB</td><td>Right</td></tr>\n",
       "\t<tr><th scope=row>5</th><td> 3851</td><td>193254</td><td>C. Trimmel   </td><td>Christopher Trimmel </td><td>32</td><td>189</td><td>82</td><td>Austria            </td><td>1. FC Union Berlin     </td><td>24000</td><td>RB         </td><td>Right</td></tr>\n",
       "\t<tr><th scope=row>6</th><td>15820</td><td>180181</td><td>K. Browne    </td><td>Kenny Browne        </td><td>32</td><td>191</td><td>86</td><td>Republic of Ireland</td><td>Waterford FC           </td><td> 1000</td><td>CB         </td><td>Left </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 6 × 12\n",
       "\\begin{tabular}{r|llllllllllll}\n",
       "  & X & sofifa\\_id & short\\_name & long\\_name & age & height\\_cm & weight\\_kg & nationality & club & wage\\_eur & player\\_positions & preferred\\_foot\\\\\n",
       "  & <int> & <int> & <fct> & <fct> & <int> & <int> & <int> & <fct> & <fct> & <int> & <fct> & <fct>\\\\\n",
       "\\hline\n",
       "\t1 &  1813 & 224599 & Yan Junling   & é¢œéªå‡Œ            & 28 & 191 & 83 & China PR            & Shanghai SIPG FC        & 13000 & GK          & Right\\\\\n",
       "\t2 & 12287 & 243530 & Mujaid        & Mujaid Sadick Aliu   & 19 & 180 & 75 & Spain               & Deportivo de La CoruÃ±a &  1000 & CB, RB      & Right\\\\\n",
       "\t3 &  4257 & 202676 & A. Taggart    & Adam Taggart         & 26 & 183 & 70 & Australia           & Suwon Samsung Bluewings &  6000 & ST, CF      & Right\\\\\n",
       "\t4 &  8794 & 241187 & L. Geertruida & Lutsharel Geertruida & 18 & 180 & 75 & Netherlands         & Feyenoord               &  1000 & CB, CDM, RB & Right\\\\\n",
       "\t5 &  3851 & 193254 & C. Trimmel    & Christopher Trimmel  & 32 & 189 & 82 & Austria             & 1. FC Union Berlin      & 24000 & RB          & Right\\\\\n",
       "\t6 & 15820 & 180181 & K. Browne     & Kenny Browne         & 32 & 191 & 86 & Republic of Ireland & Waterford FC            &  1000 & CB          & Left \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 6 × 12\n",
       "\n",
       "| <!--/--> | X &lt;int&gt; | sofifa_id &lt;int&gt; | short_name &lt;fct&gt; | long_name &lt;fct&gt; | age &lt;int&gt; | height_cm &lt;int&gt; | weight_kg &lt;int&gt; | nationality &lt;fct&gt; | club &lt;fct&gt; | wage_eur &lt;int&gt; | player_positions &lt;fct&gt; | preferred_foot &lt;fct&gt; |\n",
       "|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| 1 |  1813 | 224599 | Yan Junling   | é¢œéªå‡Œ            | 28 | 191 | 83 | China PR            | Shanghai SIPG FC        | 13000 | GK          | Right |\n",
       "| 2 | 12287 | 243530 | Mujaid        | Mujaid Sadick Aliu   | 19 | 180 | 75 | Spain               | Deportivo de La CoruÃ±a |  1000 | CB, RB      | Right |\n",
       "| 3 |  4257 | 202676 | A. Taggart    | Adam Taggart         | 26 | 183 | 70 | Australia           | Suwon Samsung Bluewings |  6000 | ST, CF      | Right |\n",
       "| 4 |  8794 | 241187 | L. Geertruida | Lutsharel Geertruida | 18 | 180 | 75 | Netherlands         | Feyenoord               |  1000 | CB, CDM, RB | Right |\n",
       "| 5 |  3851 | 193254 | C. Trimmel    | Christopher Trimmel  | 32 | 189 | 82 | Austria             | 1. FC Union Berlin      | 24000 | RB          | Right |\n",
       "| 6 | 15820 | 180181 | K. Browne     | Kenny Browne         | 32 | 191 | 86 | Republic of Ireland | Waterford FC            |  1000 | CB          | Left  |\n",
       "\n"
      ],
      "text/plain": [
       "  X     sofifa_id short_name    long_name            age height_cm weight_kg\n",
       "1  1813 224599    Yan Junling   é¢œéª\\217å‡Œ            28  191       83       \n",
       "2 12287 243530    Mujaid        Mujaid Sadick Aliu   19  180       75       \n",
       "3  4257 202676    A. Taggart    Adam Taggart         26  183       70       \n",
       "4  8794 241187    L. Geertruida Lutsharel Geertruida 18  180       75       \n",
       "5  3851 193254    C. Trimmel    Christopher Trimmel  32  189       82       \n",
       "6 15820 180181    K. Browne     Kenny Browne         32  191       86       \n",
       "  nationality         club                    wage_eur player_positions\n",
       "1 China PR            Shanghai SIPG FC        13000    GK              \n",
       "2 Spain               Deportivo de La CoruÃ±a  1000    CB, RB          \n",
       "3 Australia           Suwon Samsung Bluewings  6000    ST, CF          \n",
       "4 Netherlands         Feyenoord                1000    CB, CDM, RB     \n",
       "5 Austria             1. FC Union Berlin      24000    RB              \n",
       "6 Republic of Ireland Waterford FC             1000    CB              \n",
       "  preferred_foot\n",
       "1 Right         \n",
       "2 Right         \n",
       "3 Right         \n",
       "4 Right         \n",
       "5 Right         \n",
       "6 Left          "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fifa = read.csv(\"dados/fifa_sample_wsR.csv\", header=TRUE)\n",
    "head(fifa)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Entendendo a lógica do ggplot2\n",
    "\n",
    "\n",
    "### Códigos base\n",
    "\n",
    "#### [Inicializando objeto](https://ggplot2.tidyverse.org/reference/ggplot.html)\n",
    "\n",
    "```R\n",
    "# exemplo\n",
    "ggplot(data = fifa)\n",
    "```\n",
    "\n",
    "- dados\n",
    "- mapeamento estético (opcional)\n",
    "\n",
    "#### [Mapeamento Estético](https://ggplot2.tidyverse.org/reference/aes.html)\n",
    "\n",
    "```R\n",
    "# exemplos\n",
    "aes(x = age, y = wage_eur)\n",
    "aes(x = age)\n",
    "```\n",
    "\n",
    "- atribui variáveis aos eixos;\n",
    "- adicionar detalhes (cores, contorno, preenchimento, ...)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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o4WniYk2o4WniYk2o4WniYk2o4WniYk\n2o4WniYk2o4WniYk2o4WniYk2o4WniYk2o4WniYk2o4WniYk2o4WniYk2o4WniYk2o4WniYk\n2o4WniYk2o4WniYk2o4WniYk2o4WniYk2o4WniYk2o4WniYk2o4WniYk2o4WniYk2o4WniYk\n2o4WniYk2o4WniYk2o4WniYk2o4WniYk2o4WniYk2o4WniYk2o4WniYk2o4WniYk2o4WniYk\n2o4WniYk2o4WniYk2o4WniYk2o4WniYk2o4WniYk2o4WniYk2o4WniYk2o4WniYk2o4WniYk\n2o4WniYk2o4WniYk2o4WniYk2o4WniYk2o4WniYk2o4WniYk2o4WniYk2o4WniYk2o4WniYk\n2o4WniYk2o4WniYk2o4WniYk2o4WniYk2o4WniYk2o4WniYk2o4WniYk2o4WniYk2o4WniYk\n2o4WniYk2o4WniYk2o4WniYk2o4WniYk2o4WniYk2o4WniYk2o4WniYk2o4WniYk2o4WniYk\n2o4WniYk2o4WniYk2o4WniYk2o4WniYk2o4WniYk2o4WniYk2o4WniYk2o4WniYk2o4WniYk\n2o4WniYk2o4WniYk2o4WniYk2o4WniYk2o4WniYk2o4WniYk2o4WniYk2o4WniYk2o4WniYk\n2o4WniYk2o4WniYk2o4WniYk2o4WniYk2o4WniYk2o4WniYk2o4WniYk2o4WniYk2o4WniYk\n2o4WniYk2o4WniYk2o4WniYk2o4WniYk2o4WniYk2o4WniYk2o4WniYk2o4WniYk2o4WniYk\n2o4WniYk2o4WniYk2o4WniYk2o4WniYk2o4WniYk2o4WniYk2o4WniYk2o4WniYk2o4WniYk\n2o4WniYk2o4WniYk2o4WniYk2o4WniYk2o4WniYk2o4WniYk2o4WniYk2o4WniYk2o4WniYk\n2o4WniYk2o4WniYk2o4WniYk2o4WniYk2o4WniYk2o4WniYk2o4WniYk2o4WniYk2o4WniYk\n2o4WniYk2o4WniYk2o4WniYk2o4WniYk2o4WniYk2o4WniYk2o4WniYk2o4WniYk2o4WniYk\n2o4WniYk2o4WniYk2o4WniYk2o4WniYk2o4WniYk2o4WniYk2o4WniYk2o4WniYk2o4WniYk\n2o4WniYk2o4WniYk2o4WniYk2o4WniYk2o4WniYk2o4WniYk2o4WniYk2o4WniYk2o4WniYk\n2o4WniYk2o4WniYk2o4WniYk2o4WniYk2o4WniYk2o4WniYk2o4WniYk2o4WniYk2o4WniYk\n2o4WniYk2o4WniYk2o4WniYk2o4WniYk2o4WniYk2o4WniYk2o4WniYk2o4WniYk2o4WniYk\n2o4WniYk2o4WniYk2o4WniYk2o4WniYk2o4WniYk2o4WniYk2o4WniYk2o4WniYk2o4WniYk\n2o4WniYk2o4WniYk2o4WniYk2o4WniYk2o4WniYk2o4WniYk2o4WniYk2o4WniYk2o4WniYk\n2o4WniYk2o4WniYk2o4WniYk2o4WniYk2o4WniYk2o4WniYk2o4WniYk2o4WniYk2o4WniYk\n2o4WniYk2o4WniYk2o4WniYk2o4Wnvb2kB7vP3fZC0+z7W0XnvbabUI65Gx704WnCekqZ9ub\nLjxNSFc529504Wn/NiTn3BtOSM4dcEJy7oATknMHnJCcO+CE5NwB9zyk8+O3l7vOmL+7b4t6\ny/68rTYu/NzKj+3uKYVXTXsW0tOv6v22zk/fnL//oHPfN8V2XS783MqP7e4phdc9th8hne+E\n9Por/4kIP7fyY/uWwltDSj7wH9f8A/F45+ashws/t+pjO98dElLzTWv5D8TDtuhjKz+36mM7\nKKRX/fKfdq//nf286247v+HN/k+67rTz3SEh/eVV5apP/XLn/3sRuvbfSH/6vnF//vTtVwup\n/pflX191TkivvPP56f3mr/jW7tnXE2vTwtsGvmoXnPZwB32OVPudnZ99F96WnNb80/p8WmvZ\n4x3x1q73dZRvf9cGp6W3rfzLhmtveeFev82/tXPugBOScweckJw74ITk3AEnJOcOOCE5d8AJ\nybkDTkjOHXBCcu6AE5JzB5yQnDvghOTcASek5H3+7XQ6v7+8+nJ7uvl0unyYvr47nd59vfIw\n9zcnpOLdl3O5+5K+nh9f3v/kw6uba09zL5+Qindz+nh398cln99Pt3dfbx9f3Xf1/vTh2tvc\niyek5n359PtDPjenL/c/eHx1+fnTb9de5l48ISXv9tsbutNjPg+vvr/Jc73zcSneu9PNh09f\nhDR0Pi7Fe/wq3Utv7Vz0fHSKdzp9fvoSw/vT7cMbvcur93d3Hy8/dMETUvHef38b9+PL30+v\n/rj2NvfiCSl5706n288Pb/Au/0H24+Orh5+89jL38glp4U7F/2WVe35Cat/ls6X7d3rvrr3D\n/cMJqX1Pny19ufYO9w8npPh9uDmd3ukof0Jy7oATknMHnJCcO+CE5NwBJyTnDjghOXfACcm5\nA05Izh1w/wM32MYPwD+W/gAAAABJRU5ErkJggg==",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ggplot(data = fifa)+ aes(x = age)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### [Geometrias](https://ggplot2.tidyverse.org/reference/index.html#section-layer-geoms)\n",
    "\n",
    "- Gráfico de barras - geom_bar()\n",
    "- Histograma - geom_histogram()\n",
    "- Dispersão - geom_point() ou geom_jitter()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Gráficos de uma variável"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Histogramas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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UQCuGJ0a4gEcMXo1hAJ4IrRrSESwBWjW0MkgCtGt4ZIAFeMbg2RAK4Y3RoiAVwx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\nyJoMWgAAAABJRU5ErkJggg==",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ggplot(fifa)+aes(height_cm)+geom_histogram(bins=15)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Ajustando detalhes do histograma"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## A componente que muda a cor dos histogramas se chama \"fill\" e o contorno de \"color\"."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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P56x9FfMUh/POr+tpVtV226lb1m4csr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",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ggplot(fifa)+aes(height_cm)+geom_histogram(bins=15, fill=\"#00AFBB\",color=\"black\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Possibilidades de cores: #d11141, darkgreen, #00b159, #00aedb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loading required package: gridExtra\n",
      "\n",
      "\n",
      "Attaching package: 'gridExtra'\n",
      "\n",
      "\n",
      "The following object is masked from 'package:dplyr':\n",
      "\n",
      "    combine\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "require(gridExtra)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "grafico1=ggplot(fifa)+aes(height_cm)+geom_histogram(bins=15, fill=\"#d11141\",color=\"black\")\n",
    "grafico2=ggplot(fifa)+aes(height_cm)+geom_histogram(bins=15, fill=\"darkgreen\",color=\"black\")\n",
    "grafico3=ggplot(fifa)+aes(height_cm)+geom_histogram(bins=15, fill=\"#00b159\",color=\"black\")\n",
    "grafico4=ggplot(fifa)+aes(height_cm)+geom_histogram(bins=15, fill=\"#00aedb\",color=\"black\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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fnLv3gBkgoktR0BaQHS43T+R2PP99OjxpKA9DVqOwLSAqT3J/lUnuwD0teo7QhI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\nQmCC",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "grid.arrange(grafico1, grafico2, grafico3, grafico4,nrow=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## O fundo de qualquer gráfico pode ser alterado através do \"theme\"\n",
    "#### [Themes](https://ggplot2.tidyverse.org/reference/ggtheme.html)\n",
    "- theme_classic()\n",
    "- theme_light()\n",
    "- theme_bw()\n",
    "- theme_test()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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+If2t0S0lvCK6/HUyaf7n+y4ctx/f3P7v7g+rch3f8X/v3x/YT0dvDK6/GUyeNP\neP/735/+/m4cx0+/R+jm+8+XE9Ibwyuvx1Mmtzcfj+P6nx9/dn1cffk5pM8/h3T7791fsT7e\nENKbwyvvxsFvU10RQtqH4/jn4ZdcYT0IaR8+Pfwt0s3L7z+Op58mBxO8/UZ8vnr4G6AXENIC\n8PYAAggJQAAhAQggJAABhAQggJAABBASgABCAhDwPz0r8a2EO0SWAAAAAElFTkSuQmCC",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ggplot(fifa)+aes(height_cm)+geom_histogram(bins=15, fill=\"#00aedb\",color=\"black\")+theme_test()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### [O CheatSheet do ggplot2](https://github.com/rstudio/cheatsheets/blob/master/data-visualization-2.1.pdf)\n",
    "\n",
    "### Boas Práticas de Visualização de Dados\n",
    "\n",
    "- Títulos\n",
    "- Nome dos Eixos\n",
    "- Unidades dos eixos\n",
    "\n",
    "```R\n",
    "# inserindo textos nos graficos\n",
    "labs(\n",
    "    x = \"Idade\", y = \"Salário (Euros)\",\n",
    "    title = \"Idade X Salário\"\n",
    ")\n",
    "```\n",
    "\n",
    "- Cores\n",
    "\n",
    "```R\n",
    "aes(\n",
    "    #color = preferred_foot,\n",
    "    color = \"red\",\n",
    ")\n",
    "ou\n",
    "aes(\n",
    "    #color = preferred_foot,\n",
    "    fill = \"red\",\n",
    ")\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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BJCKuoxB94pf7zX7m09zLfvX0I6BHSM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\nAABJRU5ErkJggg==",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "(ggplot(fifa)+aes(height_cm)+geom_histogram(bins=15, fill=\"#00aedb\",color=\"black\")\n",
    "+theme_test()+labs(y = \"Quantidade\", x = \"Altura (cm)\", title = \"Altura dos jogadores\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Box plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "(ggplot(data = fifa2) + aes( age) +\n",
    " geom_boxplot(fill=\"#00AFBB\")+\n",
    " theme_minimal() + coord_flip() +\n",
    " labs(y=\"\",  x=\"Idade\")\n",
    " )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Função densidade de probabilidade dos dados"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message:\n",
      "\"Ignoring unknown aesthetics: fill\"\n",
      "`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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CLViEQkcSQi1YhEJHEkItWIRCRxJCLV\niEQkcSQi1YhEJHEkItWIRCRxJCLViEQkcSQi1YhEJHEkItWIRCRxJCLViEQkcSQi1YhEJHEk\nItWIRCRxJCLViEQkcSQi1YicjMz7S9Ocj99SfAs1QCTJVIocjczvof/biE3zJcS3UANEkkyl\nyNHIvDani0Xnj+YoxLdQA0SSTKXI0ch0f6n3/k8kFmqASJKpFIlI4khEqhHp37Q7Na9CfAs1\nQCTJVIoc72xomz7tjxDfQg0QSTKVIicj83ZomsPpV4pvoQaIJJlKkRyQFUciUo1IRBJHIlKN\nSLeqjRshvoUaIJJkKkUikjgSkWpEjkfm5fhzPv8cX6T4FmpgRaQGkfQiRyPz0lz31zVSJlmo\ngXqRmrxtBUTaEOk5s+GSXzbt8iMsUuOJWEe019IOcjQmx+a6acc7Un4kRfJZFC1TqCPZwKho\nH56VkKPy/XBmQ3EERZrxKGLqhzoi0b9wtA/PSshx+X5P3ZkNb5zZkB8xkZ7GuJB4lRBpQyQH\nZMWRQiJNLXpAIlVCpA2RiCSOlBHJY5ELiVEJkTZEjst34qNnaUSOpXo1GkIWTUKkDZGj8p2W\nX+fSYqEG6kQKaTSGLIwVIm2IHJWvbb6Pzc/vkYuf5Kd4ogY18kDmTEKkDZHTA7Jvzef5l4uf\n5Kd0os545IHMmIRIGyKnIn0271yzoSRlE/UmRjwkvHmHSBsiJ+faffw0h/MXIuWnaKLerUiA\nBE1CpA2Ro/J1Bh27YeHiJ9kpmagPJ1IgIZMQaUPkuHyfh+5SQs1Jim+hBnpEegqRBvGbhEgb\nIjkgK47Mn6hNrkh+kxBpQyQiiSOzJ6prQyrEZxIibYjkq+biyNyJOqh7MsQzaoi0IRKRxJGZ\nE3VY9nTIdNgQaUMk12wQR+ZN1JEHGZCJSYi0IZJrNogjsybq2IIcSCwDkbhmgwWkhAMiNiLS\nhkiu2SCOlNgqE9k+RKQNkVyzQRwpsZ9AZI8FIm2I5JoN4kiJPdci+9ARaUMkB2TFkRLHUkWO\n6iLShkhEEkdmiVQK8eEQaUPk8ICsc0xWiG+hBn8rkr/apaeQzzEQCZEsIDNO3C6F+JGItCGS\nTTtxZNJEDb1qFX87cIaBSIhkAZn+nbxCiB+LSJsiOWlVHJksUikkhEWkDZGIJI5MvdxCKcT7\ncETaGMmmnTgy8QJApZDA47sg0nZIRBJHSlySDpGsIdn9LY5ME6kUEuxF0CREWgOJSOLI2Ik6\nW2ZEMoZk004cmSJSMWSmGyGTEGkNJCKJIyMn6vzbvsBsD5mESGsg+RqFOFKVSB4OIq2B5It9\n4si4ibrwOVRitiPShki+ai6OjJqoSzt0xESaghBpDSQXPxFHRotUDFnsiNckRFoDOSofl+Mq\nT8xEXTzAgEjGkOPycYHI4igSyWsSIq2B5KRVcWTERF0usJBI/xBpIyQiiSOXJ2pEgaVE8piE\nSGsgOSArjowTqRgS2ZHpxh0irYFEJHHk4kSNecMXE2n6loRIayDH5TuxaVeapYkaVV45kSYm\nIdIayFH5TnxGKk6MSMWQhI4g0hbIUfna5vvY/Pwemy8hvoUabCtS3KuUoEhjkxBpDeT0zIa3\n5vP82xyF+BZqUINIDg+R1kBORfps3h+nCnVpLxnfbm833Pv8sVCDTUWK3GyWFOkfIq2PnJwi\n9PHTHM5fz8FuH/85t8e/h2OhBluKFPv5U1SkoUmItAZyVL7OoGNX9td7CyKlZlGkYkhyRwYb\nd4i0BnJcvs/D+fzaNKdHg0+k1nNfIBZqsKFI0ftDZUX6h0hrIxfL5xXp/hHJua/rHOkSmqjd\nXV1KIVkdcUySQJNrSkUa/ZyJhReT7d6R4g/QCb8jxYiUsaq+aB+elZCL12zwfka6/UQkX8IT\nNeFAt7RIjkmItAZy8ZoNiJQaRJKOBeTiNRvYtEtNcKKmnHklLtLTJERaA7l4zYaQSJ6dDb5Y\nqMFGIiWdwohIxpDL12x4nM3g3h79DMdCDTYUqRhS0JG7SYi0BpJrNogjZ0QqhxR0BJHWRPJV\nc3GkVpHuJiHSGkhEEkf6J2piTdcSqUGkdZB81VwcqVakf4i0HhKRxJHeiZr6Jr+KSFeTEGkN\npP/MhhN/jSI/vomavLWMSMaQi2c2FMZCDTYSqRhSLpL/GsaIJJBR+V7vZza8+hdPjoUarC9S\n+u4bRDKG9J/ZcGavXX40i+S7hDEiSQSRxJGhjyaFEERSjWTTThypWqSwSYhUFHY2iCNDh2/K\nIIikG8nub3GkbpGCJiFSUTggK44MneJWBEEk5cjxF/ukPhvdY6EGdYkUMgmRijK59rcw30IN\n1hXp8e2FEoi0SL5d9En9C0f78KyEHJXv+3iS2s1wjYUaIBIiFWdyHImvUZQm9A3vAoi8SJ59\n9En9C0f78KyERCRxJCJJxwKSvXbiyNBVsPIhwiL5N+4QqSiIJI5EJOlYQHoPyB7fxPgWarCi\nSM6MVSSS1yREKgqnCIkj/0Ckxh9E2g4ZutIqJ61mx5mh7nxdVaSQMaFmj0mIVJTFK60WxkIN\nEAmRihO60ip/jDk7zwk6mK2qRPKYhEhFGZfv9fjdbdod+YyUHUSSjgVk8ICs0EFZCzVYS6Th\nXNUl0vTUVUQqCiKJIxFJOhaQHJAVRz72dw+nqjKRJiYhUlEQSRyJSNKxgEQkcaTIRF1fpDLR\nw9E+PCshEUkciUjSsYBEJHGkyIf5DUQq2hkSjvbhWQmJSBHIwKlss7MakeRiAYlIEciMWV16\nCs4WIpUcMA5nFyOenkn53l8ur7Td6Q0ysVADREKk4ozK93voN1ma5kuIb6EGK4hU/DWFTUQq\nOKk2nF2MeHom1/4+dWeAf3DSqhtESsguRjw9nq9R3P+JxEIN5EXyfatOo0j5XzwMZxcjnh5E\nikAiUkJ2MeLp8W/anfiGrJvUWe3zSKdI2RdnCWcXI56e8c4GrtngCSIlZBcjnp5J+d74sy6T\nJM5qr0dKRcq9gGU4uxjx9HBANgKJSAnZxYinB5EikOkixS8t85T5ImVe5D+cXYx4etzySX87\ntouFGvypSKHz+BDJFhKRIpBJszqkQZIDQQlSIJHoW38Z8aKMy/dyvUDkixTfQg0QCZGKE7qu\nnZRJFmogKlJwu0ytSFl/LDqcXYx4ejxnNpy50uooiJSQXYx4ekble1z7m3ckJwmzOryjQK9I\nV5MQqSij8vHXKHxBpITsYsTTMy5f//eRDm+c2eAmUSQBB7YV6R/7aYvDAdkIZPysljoEhEjW\nkIgUgdy9SP8QqTSIFIGMntViJyX8hUhCU2EXI54eRIpAIlJCdjHi6UGkCGTsrL5OR4siCZq0\nixFPDyJFIBEpIbsY8fQgUgQyRSSZb0BsLpLc7oZdjHh6JsXjApHTRM7qxrpIIibtYsTTM75m\nAxeI9ASRErKLEU+P/ypCXCBykLhZfZ+KNkUSM2kXI54ez9nfXNduHERKyC5GPD2IFIGMmtWP\niYhI0rGA9G/acYHIQeoRScCkXYx4esY7G/gahScxs/o5DRFJOhaQk8pxgchp6hDpLGPSLkY8\nPRyQjUBGzGpnEiKSdCwgESkCWYlIZ0TKz2Qy3CPEt1ADAZHcktkWqXjgdzHi6UGkCCQiJWQX\nI56ecd24QKQni7N6MAPtiiRi0i5GPD2jsnGBSF8QKSG7GPH0eM5sOHOByFGiRIpfOootAMlA\nC5i0ixFPz6hqXCDSl6VZ3SDSM7sY8fSMqsYFIn2pRyQBk3Yx4ukZF40LRHqyMKtHkw+RpGMB\nyQHZCGRFIpWbtIsRTw8iRSAjRIpfOpItAEGkDZHjmp04IDtN2tSzLVKxSbsY8fSMSvbwCJGc\nIFJCdjHi6RmVrG2+j83P75GLn7hJ+3huXKRSk3Yx4umZHpB9az7Pv1z8xA0iJWQXI56eqUif\nzfvjDIfyWKhBuUjxS0ezBSC5aETKyORcu4+f5nD+QiQ3aecC7EKk/PHfxYinZ1SwzqBjV0cu\nfuIEkRKyixFPz7hgn4fuUkLNKYf1314TmnrdXV2il05gC0Cy0Z6VIt6ERZKOhReTknckz6u3\n+XeksrekXYx4ehApArkgUvzSCWwBSD66xKRdjHh6JuXjgOw0aV/ORiTpWEAiUgSyOpFKTNrF\niKdnXC2u2eBJcOp55xsiSccCcnIciWs2TFOfSAUm7WLE0zM9s6EL12wYJDT1/LMNkaRjATkq\nFtds8KVCkfJN2sWIp2dUK67Z4MucSPFLp7EFIIi0IXJcK67Z4EmNImWbtIsRTw8HZCOQgXka\nmGmIJB0LSESKQFYpUu63KXYx4ukZHpB1jskK8S3UIE+kYJX2JFLGNNjFiKcHkSKQiJSQXYx4\neti0i0D6pl54mu1FpEyTdjHi6UGkCCQiJWQXI56ecaHe2+5rsu2bFN9CDbJFil861QEVIuXt\nbtjFiKdnVKj3S+X6g7JSJlmoQY5IM6/W+xIp2aRdjHh6RnU6NF+Xf+/fTSvEt1CDjUQKpHS2\nh58SkTZETk9a/WwOj5NXy2OhBhkize3ZTJy/pbM9/JTl6ByTdjHi6RmVqW1+Xpvv7lOSEN9C\nDRAphEak6IzK9NadsNoVMOsyQp5YqEG6SNdtsv2LlLO7YRcjnp5xmU5N+3l5Y5LyyEQNECmI\nznhL2sWIp4fjSBHI8dS77SRAJF92MeLpQaQIZL0iZZi0ixFPz2TTruVcu0lGU+++1xqRfNnF\niKdnVCT+0JgvFYuUvrthFyOensnu73dZvoUaJIr0OIxajUhJJu1ixNMzPSArGws1QKQ5NCJF\nZVSj+3XtxGKhBmkiPc/rqUKkZJN2MeLpmVxF6Ch1/aBrLNTAkkgpJ+wh0oZI30WY2NkwykSk\n2am3qkgpzTIipZq0ixFPDyJFIN2p1yBSUS1zYgHJAdkIZN0iJZq0ixFPDyJFIJ2p534iQaSM\nWubEAnJSoPeX7u8xf0vxLdQAkfrmmfVPMWkXI56eUX1+D33NmuZLiG+hBqkiLUw9RJKOBeSo\nPq/NqTso+9EchfgWapAgUlOlSEkm7WLE0+M5s+H+TyQWaoBIffNcARBpKYgUgXyqMzj4WY9I\nKSbtYsTT49+0OzWvQnwLNUCkvnm2Aoi0kPHOBv7QmCcDkRan3h5FSvg2xS5GPD2T6rx1f2js\nxB8ac/PY371rkQK5rVS0SbsY8fRwQDYCWYdIgebbSiHSfBApAnk/JWh0nnVNIsWbtIsRT8+w\nNr9vx0u5XgS/JWuhBog013xfK0SazaA2n/crn7RSJzaYqEGKSL455l96nWmd0iwqUrRJuxjx\n9Lil+Wma1+4ku6+XRuyLshZqECnS+A0JkXJqmRMLSLc0z6NHr1yy2A0i9esVZ9IuRjw9bmXa\n5n706Kf7ixQisVCDOJEmHiFSTi1zYgHpVqZpfDfLYqEGiDTX7KxYlEm7GPH0IFIEsptjU48Q\nKaeWObGARKQIJCLd1izGpF2MeHoQKQLZeD1CpJxa5sQCclKn4SlWxbFQA0Saax6sWsTM2MWI\npweRIpCN1yNEyqllTiwgOdcuAolIj3VDpEAQKQLZeD2qVaSFKbOLEU8PIkUgEWlQiaJa5sQC\nEpEikH6PahQpwqRdjHh6ECkCiUjDUpTUMicWkIgUgfR7VKVIy7sbdjHi6UGkZWTgDalekWYn\nzR5GPCOItIxEpHEx8muZFQtIRFpEhjyqU6RFk3Yw4jlBpEUkIk3LkVvLvFhAItISMuhRpSIt\n7W6wP+JZQaQlJCL5CpJXy8xYQCLSAjLsESKl1jI3FpCItIBEJH9JcmqZGwtIRJpHBk6zC82x\n/iEi81egGZE2RCLSPBKRAkXJqGV2LCARaRZ5ffFFpGlV0muZHwtIRJpFIpJ3JedMMj7iuUGk\nOeTt0wAieeqSWsuCWEAi0hwSkQIrOWOS7RHPDiLNIW/zBZEChUmpZUEsIBFpBtkgUnAlgyaZ\nHvH8INIMEpEQKTaIFEY+Djwikr84CbUsigUkIoWRiPQPkWKDSEHk80wYRAqUJ7qWZbGARKQg\nEpH65lDNEGkQRAoinzMFkebrs1zLslhAIlII2SBS3xwsGiK5QaQQEpGuzeGq+U2yO+JFQaQA\n0v3SDSL5VxSRnkGkABKRbs0zZfOaZHbEy4JIfuTgW6CIFFhTRHoEkfxIRLo3z9XNZ5LVES/M\nskjtJePb7s829MA+FmoQEun5S9IcQyTpWEAuitQ+/nvefrTNS9TFQg08yOEMQaRQPCYZHfHS\nIJIXieRB0ngAABCWSURBVEiP5tnCIdI9WSLdf1n2yEQNpsjR/ECkYKYm2Rzx4pSJNPyI9N9u\n0k0P99fAHAs8WGT+CjTLoJMqVVsERGpHvwdi4cVkghy/zPKOFM7kLcnkiJenSKThDW8s1ACR\n5poXSodI1+SK1A4XCMZCDcbIydxApJmMq2VxxAWSKVI7uS8QCzVApLnmpdohUp88kcafk8Kx\nUIMRcrojCpHmMqqXwRGXSPyZDe3zdts6N+YfbaEGiDTXvFi8ZlgwgyMuEc61GyM9xxgRaS6I\n1AWRxkhEGjYvV29okr0RFwkijZC+8zCrFikQd4Wd7xIvL102PFJBJESKbl71GQdr/Cha/tta\n5PBIBZFWRnpfPhFp2jxYY0RCpBESkSKbh6uMSIg0QPq35xFp2jxc5XvdEGmtWKgBImU0D1cZ\nkRDJRQZ2MCHStHm0zrfKIdJasVADRMpoHq0zIkkDR7FQgwcydMQDkabN45W+1g6R1oqFGiBS\nRvN4pRFp3ViowR0ZPASPSNPmyVoj0qqxUANEymierHVfPURaKxZqcEOGzwkLTY9A1py/Kc2b\ninRGpFVjoQZX5MzJlWrU0CzSee5FpHx4hINIq4oUuFuNGogkFURCpOjmjUU6I9KKsVCDHjn3\ntRk1aiCSVBAJkaKbtxYpbBIiFcdCDTrk7Pc41aiBSFJBJESKbt5cpKBJiFQcCzW4IOcvLKBG\nDUSSCiIhUnTz9iKFTEKk4liowfyehjMi+ZrDhYxfOjYWJhEidUEkQZE8j0Ck4iitQeg0ucDS\na87I9Zr/SqTpQxCpOEprMBzrx+gjUnRzoFKItFKU1mAw1s/BR6To5lClvCYhUnGU1gCRSptD\nlUKkdaK0BhORFqbH9hNVoPlPRPKahEjFUVoDd6SdgUek6OZgpRBplSitASKVNocr5TEJkYqj\ntAbOQLvDjkjRzeFKIdIaUVqD50A3iJTVPFOpqUmIVBylNRiKFDU9Np6oAs2ItCGydpGGY45I\n0c1zlZqYhEjFUVoDRCptnqsUIslHaQ0eu+mGIx46BW/NGble85+JNC4rIpVHaQ1CIq059TZv\n/luRmuWlY6N0Eg1St0iTV841p97mzX8n0uQFqmy0lE6iQaoWabotv+bU27z5D0WK++wZG6WT\naJDqRdpu6m3ejEgbImsWyXMEfs2pt3nzX4oUdXwuNkon0SCItNnU27wZkTZEViyS7yzlNafe\n5s1/KlLMOYyxUTqJBkGkzabe5s1/L1Izv3RslE6iQeoVyftNzjWn3ubNfytSxNdTYqN0Eg2C\nSJtNvc2b/1ik5W8ex0bpJBqkWpH8V7tZc+pt3oxIGyIRabOpt3nzX4v0rDEiFUdpDQKnoqpx\nYD8iNTNLx0bpJBoEkTabeps3/7lI/xBJLEprEPhuhBoHdiLS3SREKo7SGiBSaXOgruPFroVG\npOLorEHoy3pqHNiLSP8QSSg6a4BIxc2Bwk4X7EqNSMVRWYPgt8fVOIBIblROolFqFCl8GQY1\nDuxHpOvHpLIB0ziJxqlVpDXnmJZmRNoQWaFIhq8LZFGkf4gkEYU1QCSJ5kBtvcuWmqRwEk1S\nn0j9sKqZ7LWIlDLPPBcWFNg+HAaRECm6WYtIqW9JaU+ZF0QqRV4HVc1kr0KkRJMQyRNtIjWI\nhEhnRCpF3re11Uz2ekSKn2mI5Aki/V0zIoWDSEXIx4CqmeyViHROMQmRPFEl0vOFUc1kR6QE\niGAQqQDZIJJcc6DEwaUTTEIkT9SJdLu55hzT0oxI4SBSPtL9xKtmslcjUoJJiOSJHpEGe47U\nTPa6RIqbbYjkCSL9XbMukeJNQiRP1Ig0HEY1kx2R0iBSQaQSkZzf1pxjWpqViRRtEiJ5okWk\n0Riqmew1iRRrEiJ5gkh/16xTpOUZh0ieKBFpPIBqJntVIkW+JSGSJzpEmgygmslel0hxJiGS\nJ3pEGjasOce0NCsUKcokRPJEhUjTwVMz2REpAyIQREKk6GaNIsWYhEieaBDJM3RqJnt1IkWY\nhEieKBDJN3JqJjsiZUGKU5VITSAZlEnjmnNMS7NOkZZNQiRPikQSKSgirdJcMGRLJlUv0n/C\nCRU0DdIlGr2r5lWfsWDIGv+YyI77NllHJK9cBY+VeGUKvPqpmexmRSoZsoW3pOrfkXz5Y5FC\nH6rUTPYqRVowCZE80SBSAnpXzXpFmjcJkTz5W5GC46VmsiPSGuO+HERKKWh4uNRM9kpFmjUJ\nkTxBpL9r1izS3EWFEMmTvxRp5lVPzWSvVqQZkxDJkz8UKWfrYVfN+kVK21yIHPeoIFJ0QWdP\nJ1Iz2esVKfyWhEie/JlI86flqZnsZkUKJLB0cIjExz0yiBRZ0IXTW9VMdrMipTWHx0h43GOD\nSHEFXTpNXNEcW69ZTUeCG4KIFJ2/EWnx6xaK5th6zWo6EhyytPO3YuZMbBApXqQc9K6a1XRk\nViTPwdo0SFYQKaagy1//UzTH1mtW05HwkCV9WSxmzsQGkWJFykPvqllNRxZEir0OQMyciQ0i\nRRR02SNE2rh5dqxiL/EUM2dig0jLBV3esEOkrZsXRmv0VfRkSHoQKU6kXPSumtV0ZHbIpiYh\nkifbixTjESJt3Lw0YFF/KiRmzsQGkZYKGrNhh0hbNycNGSJ5sp1IobO/ktC7albTkeCZee7Q\n5Y57XhApLNJjxCKWVjTH1mtW05HFN6qhSYjkybYiec5ARiTFzY/hGJiESJ5sKpLHI0TS3Pwc\nj8GW3tLSAkGkGZF8HiGS5mZnQByTEMmTzXc2xC2taDKt16ymIzE78xyTEMmTrUWKXFrRZFqv\nWU1HovaKP01CJE82EyngESJpbp6OYPK4ZwaRAgUNeYRImps9Y5g47rlBJH9Bgx4hkuZm3ygi\nkjfbiBT2CJE0N3uHEZF82USkGY8QSXOzfyARyZMtRMoov6LJtF6zmo5Ei5TxkpgZREreIPBH\nZB4ob1bTkXiR0jfSM4NI/n09Mx9RJQbcZrOajiSIlLzbKDOINC7o4zjezuaYQLOajqSIlHog\nIzOINCro8vFwiQG32aymI0kiJR5azwwiDQsacYaWxIDbbFbTkTSR0k72ygwiDQrqeIRICp5R\nUqTJYxApN4vldz1CJAXPKCRSyhdiMoNITkEHHiGSgmeUEinhK5qZQaRnQYceIZKCZxQTKf6i\nAZlBpEdBRx4hkoJnlBPJYxIi5Wa2/GOPEEnBMwqKNDUJkXIzV/6JR4ik4BklRZqYhEi5mSn/\n1CNEUvCMqc3zJ0KOTEKk3IRF8niESAqeUbh5aBIi5SYoks8jRFLwjNLNg3coRMrN/AZB7NKi\nI2uqWU1H8ptdkxApN4Hp4fcIkRQ84wrNT5MQKTfeOgc0QiQNz7hK810lRMqNp85BjRBJwzOu\n03wzCZFyM6lzaKvOv/TcYCmYHus3q+lIafPsuOelapEaRKp1JcVNqlmkhW3lWudYuFlNRwSa\npU2qWKTH7htEimxW0xGZtRFVqV6RFneDVjzHKllJSZOqFck9MDd/hlbUqCiaHus1q+mIpEhC\nKlUq0vBUEYFRUTQ91mtW0xGhZv85ltvPS28siDR6y2GORTar6YiYSGexN6UKRZpsuTHHIpvV\ndEROJLE3pfpEmn4AYo5FNqvpiKRIQm9KtYnk24/AHItsVtMRUZFkVKpMJO/uOOZYZLOajgiL\ndDepZO7WJZJ/tzZzLLJZTUekRRJ4U6pJpNDRIeZYZLOajsiLVKxSPSKFj7IyxyKb1XRkDZEK\nVapFpLmTFZhjkc1qOrKOSEUfleoQKeekn7rnmJJn3FakEpWMi9QE4l1mzb+YrGh6rNespiNC\nzUkRn5cL2VikQImcJZx1YI6VNavpyKor6d98SRQp8IwJE12VSKOXAuZYWbOajmwiUpN0ofA9\nizR5S2WOlTWr6cjKK+lTqVaRvFumzLGyZjUdWX0lpypVKpL/Ax5zrKxZTUc2WMnxFl6NIgV3\nkzDHyprVdGSblUza4bY7kebWnTlW1qymI1utZIJJuxNp7pv4zLGyZjUd2XAlY1Xaq0j+vm1W\n/n02q+nItisZpdL+RPoXXmHmWFmzmo5svpLDj0u+6bVDkcIdZo6VNavpyF+s5IJLiMQci25W\n05G/WEnP+XPn8El1IUh8EGm/zWo68icinb3apD1jwkRHpP02q+nIX4l0n0eLKiFSbrOaeYBI\nhc1xU2rhrQmRcpvVzANEKmyOn1Izn40QKbdZzTxApMLmpCkV2teASLnNauYBIhU2p06poEse\nqRApu/y7albTEU0i3e+P8UlWpPaS8e3xz3AQ6e+a1XREoUi3pTYUqX3897w9/jkTRPq7ZjUd\nUSvSfeHABh4iLTarmQeIVNgsPqUQKaVZzTxApMLmVafUkhwueWmBBJH+W8zyRzxC9GRxQq8j\nki/iV7QECVIjEpFAghQIIoEEKRBEAglSIIgEEqRA4s9saN3bmWc2iAQkSH1IlX8fCSRIa0hE\nAglSIIgEEqRAEAkkSIEgEkiQAkEkkCAFgkggQQoEkUCCFAgigQQpEEQCCVIgiAQSpEAQCSRI\ngSASSJACQSSQIAWCSCBBCgSRQIIUCCKBBCkQRAIJUiCIBBKkQBAJJEiBIBJIkAJBJJAgBYJI\nIEEKBJFAghQIIoEEKRBEAglSIIgEEqRA1haJkCqCSIQIBJEIEQgiESIQRCJEIIhEiEAQiRCB\nIBIhAkEkQgSCSIQIxJRI8X9M/Q/Ttrfuqe6ljVoa6WUXSyK1t//axy9qo76XNmppo5d9EGmN\ntGftvbRRSxu97GNOpLOFsiKSTGz0so8tkW4fPs5n3WVth/9pjI0paqOXfUyJdLNIfVkNiHT/\n+K67l9d9Ntp72ceUSLf/1JfVgEhGatnZrr+XXRBJPu3zf7W9tFLLs5VeIpJ8EEkqNnrZB5Hk\ng0hSsdHLPpZEsnKc+9Y33b20UUsbvexiSiRCtAaRCBEIIhEiEEQiRCCIRIhAEIkQgSASIQJB\nJEIEgkiECASRdKe55vU79gGfr5fHjBtfP2V7RSZBJN1p7ok06as9e0Q6t1/C/SKjIJLu3KQ4\nNce45dt3r0jvys9Usx9E0p27FP3P39fLRt5v9+tb2xzeuxs/XdPPY/FTe1/21Fw2536OzeGz\n/7U9bdzx2oJIujMQqe228Q7nzpIuF5N++6b297b0b3O6LXvqbl3vvXn16+UToSCS7lxFurwV\nvV7ehjo5Tk2/8fZz/mra2ybfsbm/3bx1b0PdY07NW//r8fx77BGffQNZLYikO4+dDZfNt0M/\nWM1L99Z02w936Np/+nepLi/dr5fH9Nt1j3u7h/10DyPrBZF052pR2+/+vjt1eX+5bLMdrs6c\nz87ehfuv/Wbf8F7PHggiGMqrO+78f4p0Pn8fmm6XdkCkt7YZa4ZI64by6o47/w+DwXrv7hpt\n2j2c+ei35JxNO0RaOZRXd9z53++J++h2L7TN1/nbs7Ph5fFGdGw+HvfyGWmDIJLuuCLd9mZ/\n33d/v012fz/22nWa/bq7v9lrt3IQSXcGW2Td0ddjf7LPqW3at3vT84Ds8zjSxanX6wHZD44j\nbRFE2lVOnvFs+rMdOLNh3SDSvtK+O780l49SF7deOddu/SDSvvLlGnN6HMvl7O+1g0g7y+er\n88v74foJiu8jrR5EIkQgiESIQBCJEIEgEiECQSRCBIJIhAgEkQgRCCIRIpD/AeKrMBRGrtFX\nAAAAAElFTkSuQmCC",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Distribuição do salário dos jogadores\n",
    "ggplot(fifa) +aes( weight_kg) + \n",
    "    geom_histogram(aes(y =..density..),fill=\"#00AFBB\",color=\"black\")+\n",
    "  stat_function(fun = dnorm, args = list( mean(fifa$weight_kg), sd(fifa$weight_kg)),aes(fill=\"black\"),size=1.2)+\n",
    "  theme_minimal() +\n",
    "  labs(y = \"Densidade de probabilidade\", x = \"Peso (kg)\", \n",
    "  title = \"Densidade de probabilidade da massa corporal dos jogadores\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Trabalhando com variáveis categóricas"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Gráfico de barras"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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FI5axOSREipnLU/eNPuav/t/FeaBCGl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\nSSMAAAAASUVORK5CYII=",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "(ggplot(fifa)+aes(preferred_foot)+geom_bar(fill=\"#00aedb\")+theme_bw()+\n",
    "labs(y = \"Quantidade\", x = \"Pé Preferido\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Gráfico de pizza"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.frame: 2 × 2</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>quant</th><th scope=col>grupo</th></tr>\n",
       "\t<tr><th scope=col>&lt;int&gt;</th><th scope=col>&lt;fct&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>122</td><td>Right</td></tr>\n",
       "\t<tr><td>334</td><td>Left </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 2 × 2\n",
       "\\begin{tabular}{ll}\n",
       " quant & grupo\\\\\n",
       " <int> & <fct>\\\\\n",
       "\\hline\n",
       "\t 122 & Right\\\\\n",
       "\t 334 & Left \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 2 × 2\n",
       "\n",
       "| quant &lt;int&gt; | grupo &lt;fct&gt; |\n",
       "|---|---|\n",
       "| 122 | Right |\n",
       "| 334 | Left  |\n",
       "\n"
      ],
      "text/plain": [
       "  quant grupo\n",
       "1 122   Right\n",
       "2 334   Left "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df=data.frame(quant=as.vector(table(fifa$preferred_foot)), grupo=unique(fifa$preferred_foot))\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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DQkKskXJBwNAWkISMsBKTQgDQFpOSCF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\nA2b9wubYLQzjAAAAAElFTkSuQmCC",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "(ggplot(df, aes(x = \"\", y = quant, fill = grupo)) +\n",
    "  geom_col(color = \"black\") +\n",
    "  geom_label(aes(label = quant),\n",
    "             position = position_stack(vjust = 0.5),\n",
    "             show.legend = FALSE) +\n",
    "  coord_polar(theta = \"y\")+theme_bw())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Gráficos utilizando duas variáveis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "fifa2 <- fifa %>% \n",
    "    mutate(player_positions = strsplit(as.character(player_positions), \", \")) %>% \n",
    "    unnest(player_positions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "(ggplot(data = fifa2) + aes(x = player_positions) +\n",
    " geom_bar(aes(fill = preferred_foot), position = \"stack\") + \n",
    " theme_minimal() + coord_flip() +\n",
    " labs(x = \"Posição do jogador\", y = \"Contagem\", fill = \"Pé preferencial\",\n",
    "     title = \"Contagem de Pessoas por Posição de Jogador e pé preferencial\")\n",
    " )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Box plot com variável categórica"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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rZYAkWiF7TFEigSvaAtlkCR6AVtsQSKRC9oiyVQJHpBWyyBItEL2mIJFIle0BZL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\nAElFTkSuQmCC",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "(ggplot(data = fifa2) + aes(preferred_foot, age) +\n",
    " geom_boxplot(aes(fill=preferred_foot))+\n",
    " theme_minimal() + coord_flip() +\n",
    " labs(\n",
    "     x = \"Pé preferencial\", y = \"Salário dos jogadores\", fill = \"Pé preferencial\")\n",
    " )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Gráfico de dispersão"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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xMu0gW3Gp3Au95FpHzEAN8MIiES8A5wREIk4B3g4SJxjgS8DzxdJK7aAe8CjxdJ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\n",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "(ggplot(data = fifa) + aes(x = height_cm, y = weight_kg) + geom_point()\n",
    "+labs(\"Peso\",\"Altura\")+theme_minimal())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Correlação entre as duas variaveis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "0.791050698718556"
      ],
      "text/latex": [
       "0.791050698718556"
      ],
      "text/markdown": [
       "0.791050698718556"
      ],
      "text/plain": [
       "[1] 0.7910507"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cor(fifa$weight_kg, fifa$height_cm)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Escrevendo texto no gráfico"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "(ggplot(fifa), mapping = aes(x = height_cm, y = weight_kg)) + geom_point()+labs(\"Peso\",\"Altura\")\n",
    "+theme_minimal()\n",
    "+annotate(\"text\", x = 175, y = 95, label = paste(\"Correlação = \",round(cor(fifa$weight_kg, fifa$height_cm),2)))\n",
    "+theme_test()\n",
    "+labs(x=\"Altura\",y=\"Peso\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Ajuste de uma regressão linear\n",
    " #### [Visualização e ajuste de modelos estatísticos](https://ggplot2.tidyverse.org/reference/geom_smooth.html)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "`geom_smooth()` using formula 'y ~ x'\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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Jt+DtOzdZeMCGmJXbu9An+oooeFvmAc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\nFBASoOD/W9Hh/1q/bb0AAAAASUVORK5CYII=",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "(ggplot(data = fifa) + aes(x = height_cm, y = weight_kg) + geom_point()+labs(\"Peso\",\"Altura\")\n",
    "+theme_test()\n",
    "+annotate(\"text\", x = 175, y = 95, label = paste(\"Correlação = \",round(cor(fifa$weight_kg, fifa$height_cm),2)))\n",
    "+labs(x=\"Altura\",y=\"Peso\")+ geom_smooth(method = lm, se = TRUE))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Separando os dados pela cor dos pontos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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4+oIk2bbLjA2aoFDZNcHjls3HnE3dkw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\n",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "(ggplot(data = fifa)+  aes(x = age, y = wage_eur) + \n",
    "  geom_point(aes(color = preferred_foot)) +\n",
    "  labs(\n",
    "    x = \"Idade\", y = \"Salário (euros))\",\n",
    "    title = \"Idade X Salário\", color = \"Pé preferencial\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Separando gráficos por alguma variável\n",
    "#### [Facetas](https://ggplot2.tidyverse.org/reference/facet_grid.html)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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0+FZHdMMFIkGIX1E858RTp9TjG7Y4KRIsEorJ9wFIlFigSjsH7CUSQWKRKMwvoJ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\nwXNU/wAAAABJRU5ErkJggg==",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# exemplo teste com nomes dos eixos e cores\n",
    "ggplot(fifa)+  aes(x = age, y = wage_eur) + \n",
    "  geom_point(aes(color=preferred_foot)) +\n",
    "  labs(\n",
    "    x = \"Idade\", y = \"Salário (euros)\",\n",
    "    title = \"Idade X Salário\", color = \"Pé preferencial\"\n",
    ")+facet_grid(cols=vars(preferred_foot))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <span style=\"color: orange;\"> Parte 5 - Inferência Estatística em R </span>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Testes de Hipótese\n",
    "Muitas vezes, quando estamos fazendo alguma análise, queremos verificar se **duas médias são diferentes**. <br>\n",
    "Por exemplo, temos dois grupos de pessoas, um que tomou vacina da covid e outro que tomou um placebo, e queremos saber se existe diferença na prevenção/redução de sintomas para as pessoas que tomaram a vacina em comparação as pessoas que tomaram somente o placebo. \n",
    "<br>\n",
    "<br>\n",
    "Esse é o típico cenário em que devemos aplicar um **teste de hipótese**.\n",
    "<br>\n",
    "#### Amostra vs População\n",
    "![censovssample](img/census-vs-sample.jpg)\n",
    "Os testes de hipótese precisam ser feitos porque, muitas vezes, nós não temos a totalidade dos dados e sim uma **amostra**.\n",
    "<br> <br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Hipóteses e p-valor\n",
    "\n",
    "Existem teoremas matemáticos que garantem que as médias tiradas de amostras aleatorias de uma população convergem para a própria média da população.\n",
    "Mas pode ser que, por acaso, vc tenha pegado uma amostra extrema que não representa essa população - **os testes de hipótese são uma forma de verificar qual é essa probabilidade e se ela é considerável**.\n",
    "Os **testes de hipóteses** têm uma estrutura comum. Eles assumem uma *hipótese nula* e uma *hipótese alternativa*.<br>\n",
    "Em geral: <br>\n",
    "$H_0 =$ (hipótese nula) As médias são iguais/Se determinada amostra segue alguma distribuição específica <br>\n",
    "$H_A =$ (hipótese alternativa) O oposto da hipótese nula\n",
    "<br>\n",
    "O que o teste de hipótese nos retorna é o **p-valor**, ele nos dá **a probabilidade de encontrar uma amostra igual ou mais extrema do que a que obtemos dada a hipótese nula** <br>\n",
    "Em geral, quando o $valor-p \\leq 0.05$ não rejeitamos $H_A$\n",
    "\n",
    "\\begin{equation}\n",
    "valor-p = \\mathbb{P}_{H_0}(estatistica-teste_{teorica} \\geq estatística-teste_{observada})\n",
    "\\end{equation}\n",
    "<br>\n",
    "\n",
    "![erros](img/pvalue.png)\n",
    "<br>\n",
    "Quando o nosso $p-valor \\leq \\alpha$ dizemos que há **significância estatística** para rejeitar a hipótese nula, em outras palavras: é pouco provável que o valor encontrado seja fruto apenas de variações aleatórias da amostra"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Testes paramétricos VS não-paramétricos\n",
    "![distribuicaonormal](img/normal.jpeg)\n",
    "Os testes de hipótese podem ser divididos entre paramétricos e não paramétricos.\n",
    "<br>\n",
    "Os testes **paramétricos** assumem a **alguma distribuição de probabilidades especificada**, enquanto os **não paramétricos** não possuem tal premissa.<br>\n",
    "\n",
    "## Imagine que estamos interessados em responder as seguintes perguntas:\n",
    "- Existe diferença entre a altura de pessoas destras e canhotas?\n",
    "- Existe diferença entre o salario de pessoas destras e canhotas?\n",
    "\n",
    "#### Kolgomorov-Smirnov\n",
    "Para saber se devemos ou não aplicar um teste paramétrico ou não paramétrico, precisamos saber se nossos dados seguem ou não uma distribuição normal. Para isso usamos o teste de *Kolgomorov-Smirnov* comparando a nossa amostra com uma distribuição normal.\n",
    "Nesse teste, as hipóteses são as seguintes:<br>\n",
    "$H_0 = $ a distribuição dos dados é igual da distribuição especificada <br>\n",
    "$H_A = $ a distribuição dos dados é diferente da distribuição especificada\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "destros = fifa %>% filter(preferred_foot == 'Right')\n",
    "canhotos = fifa %>% filter(preferred_foot == 'Left')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Vamos analisar a primeira pergunta de interesse: Existe diferença entre a altura de pessoas destras e canhotas?\n",
    "### Será que a distribuiçao de probabilidade da altura dos jogadores canhotos e destros pode ser considerada normal?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message in ks.test(destros$height_cm, \"pnorm\", mean(destros$height_cm), :\n",
      "\"ties should not be present for the Kolmogorov-Smirnov test\"\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "\n",
       "\tOne-sample Kolmogorov-Smirnov test\n",
       "\n",
       "data:  destros$height_cm\n",
       "D = 0.058247, p-value = 0.2072\n",
       "alternative hypothesis: two-sided\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ks.test(destros$height_cm,\"pnorm\",mean(destros$height_cm),sd(destros$height_cm))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message:\n",
      "\"Ignoring unknown aesthetics: fill\"\n"
     ]
    },
    {
     "data": {
      "image/png": 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UFK0o/s0dlSAUiDtu79jS3SIIC0Ex2wSaoRJPw1ioVmPbLbNBWCFLBJqhGk4e8j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\nAAmCGASQIIhBAAmCGASQIIhBAAmCGPQ/aEqddHnoK4kAAAAASUVORK5CYII=",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Distribuição do salário dos jogadores\n",
    "ggplot(destros) +aes( height_cm) + \n",
    "    geom_histogram(bins=15, aes(y =..density..),fill=\"#00AFBB\",color=\"black\")+\n",
    "  stat_function(fun = dnorm, args = list( mean(destros$height_cm), sd(destros$height_cm)),aes(fill=\"black\"),size=1.2)+\n",
    "  theme_minimal() +\n",
    "  labs(y = \"Densidade de probabilidade\", x = \"Peso (kg)\", \n",
    "  title = \"Densidade de probabilidade da massa corporal dos jogadores\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message in ks.test(canhotos$height_cm, \"pnorm\", mean(canhotos$height_cm), :\n",
      "\"ties should not be present for the Kolmogorov-Smirnov test\"\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "\n",
       "\tOne-sample Kolmogorov-Smirnov test\n",
       "\n",
       "data:  canhotos$height_cm\n",
       "D = 0.066124, p-value = 0.6603\n",
       "alternative hypothesis: two-sided\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ks.test(canhotos$height_cm,\"pnorm\",mean(canhotos$height_cm),sd(canhotos$height_cm))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message:\n",
      "\"Ignoring unknown aesthetics: fill\"\n"
     ]
    },
    {
     "data": {
      "image/png": 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9oxbjgghfpxXzu7ACk4ehYOSMGSC5ITR75/7juoHaOGZwdpOUUFIPXiBpFjeYIU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\nAAAAAElFTkSuQmCC",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Distribuição do salário dos jogadores\n",
    "ggplot(canhotos) +aes( height_cm) + \n",
    "    geom_histogram(bins=15, aes(y =..density..),fill=\"#00AFBB\",color=\"black\")+\n",
    "  stat_function(fun = dnorm, args = list( mean(canhotos$height_cm), sd(canhotos$height_cm)),aes(fill=\"black\"),size=1.2)+\n",
    "  theme_minimal() +\n",
    "  labs(y = \"Densidade de probabilidade\", x = \"Peso (kg)\", \n",
    "  title = \"Densidade de probabilidade da massa corporal dos jogadores\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Temos evidências para acreditar que a altura dos jogadores canhotos e destros pode ser considerada com distribuição normal!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Teste T\n",
    "O teste T pode ser utilizado quando queremos verificar se a media teórica é igual/maior/menor a um determinado valor ou para verificar se a média de uma população é igual/maior/menor que a média de outra população.\n",
    "\n",
    "<br>\n",
    "Vamos aqui aplicar o teste T com o objetivo de comparar as médias de duas populações diferentes. A hipótese nula é de que as médias são iguais e a alternativa é de que são diferentes. <br>\n",
    "Esse teste precisa saber se as variâncias são iguais ou diferentes. Pra isso vamos aplicar um teste chamado teste F. <br>\n",
    "Para o teste  F, a hipótese nula é de que as variâncias são diferentes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "\tF test to compare two variances\n",
       "\n",
       "data:  destros$height_cm and canhotos$height_cm\n",
       "F = 1.2329, num df = 333, denom df = 121, p-value = 0.1782\n",
       "alternative hypothesis: true ratio of variances is not equal to 1\n",
       "95 percent confidence interval:\n",
       " 0.9082437 1.6395094\n",
       "sample estimates:\n",
       "ratio of variances \n",
       "          1.232911 \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "var.test(destros$height_cm, canhotos$height_cm)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Não rejeitamos que as variâncias são diferentes, vamos prosseguir assumindo homocedasticidade (variâncias iguais)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "\tTwo Sample t-test\n",
       "\n",
       "data:  destros$height_cm and canhotos$height_cm\n",
       "t = 0.68135, df = 454, p-value = 0.496\n",
       "alternative hypothesis: true difference in means is not equal to 0\n",
       "95 percent confidence interval:\n",
       " -0.9339099  1.9251733\n",
       "sample estimates:\n",
       "mean of x mean of y \n",
       " 181.3563  180.8607 \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "t.test(destros$height_cm, canhotos$height_cm, var.equal = TRUE)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Temos evidências estatisticas para considerar que as alturas são iguais entre os destros e os canhotos"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Vamos analisar a segunda pergunta de interesse: Existe diferença entre o salário de pessoas destras e canhotas?\n",
    "### Será que a distribuiçao de probabilidade do salário dos jogadores destros e canhotos pode ser considerada normal?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message in ks.test(canhotos$wage_eur, \"pnorm\", mean(canhotos$wage_eur), :\n",
      "\"ties should not be present for the Kolmogorov-Smirnov test\"\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "\n",
       "\tOne-sample Kolmogorov-Smirnov test\n",
       "\n",
       "data:  canhotos$wage_eur\n",
       "D = 0.34092, p-value = 9.656e-13\n",
       "alternative hypothesis: two-sided\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ks.test(canhotos$wage_eur,\"pnorm\",mean(canhotos$wage_eur),sd(canhotos$wage_eur))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message:\n",
      "\"Ignoring unknown aesthetics: fill\"\n",
      "`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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UcI0BJPNoOkIwRoiSebQdIRArTEk80g\n6QgBWuLJZpB0hAAt8WQzSDpCgJZ4shkkHSFASzzZDJKOEKAlnmwGSUcI0BJPNoOkIwRoiSeb\nQdIRArTEk80g6QgBWuLJZpB0hAAt8WQzSDpCgJZ4shkkHSFASzzZDJKOEKAlnmwGSUcI0BJP\nNoOkIwRoiSebQdIRArTEk80g6QgBWuLJZpB0hAAt8WQzSDpCgJZ4shkkHSFASzzZDJKOEKAl\nnmwGSUcI0BJPNoOkIwRoiSebQdIRArTEk80g6QgBWuLJZpB0hAAt8WQzSDpCgJZ4shkkHSFA\nSzzZDJKOEKAlnmwGSUcI0BJPNoOkIwRoiSebQdIRArTEk80g6QgBWuLJZpB0hAAt8WQzSDpC\ngJZ4shkkHSFASzzZDJKOEKAlnmwGSUcI0BJPNoOkIwRoiSebQdIRArTEk80g6QgBWuLJZpB0\nhAAt8WQzSDpCgJZ4shkkHSFASzzZDJKOEKAlnmwGSUcI0BJPNoOkIwRoiSebQdIRArTEk60z\nfT/fiuK4/Zlr51GTBilGCNAST7bW9D1silMdi+J7vqPBJg1SjBCgJZ5sren7XuxOFB2/iu18\nR4NNGqQYIUBLPNla0/cE0e1frjJIQUKAlniyGSQdIUBLPNn6D+12xft8R4NNGqQYIUBLPNna\nJxvKoq7yd76jwSYNUowQoCWebJ3p+7Epis3uMNfOoyYNUowQoCWebL4gqyMEaIknm0HSEQK0\nxJMtnb5FWouY6m/SIMUIAVriyWaQdIQALfFka0/ft+3v8fi7fZvt50GTBilGCNAST7bW9H0r\nzufriowkGaQgIUBLPNl67mw41cGHdoRCgJZ4srWm77Y4H9p5j0QoBGiJJ1tr+v76zgZeIUBL\nPNna0/ewq+5s+PCdDYRCgJZ4svmCrI4QoCWebAZJRwjQEk+29vTd+YIsrRCgJZ5srem7850N\nvEKAlniytaZvWfxsi9/D1g8/IRQCtMSTrXtB9qPYHw9++AmhEKAlnmxdkPbFp5/ZQCkEaIkn\nW+deu6/fYnP8NkiEQoCWeLK1pm9F0LY61+CHn/AJAVriydaevvtN9SihYjfbz4MmDVKMEKAl\nnmy+IKsjBGiJJ5tB0hECtMSTzb9qriMEaIknm0HSEQK0xJPNz2zQEQK0xJPNz2zQEQK0xJPN\nz2zQEQK0xJPNz2zQEQK0xJPNz2zQEQK0xJPNz2zQEQK0xJPNF2R1hAAt8WQbPX3L7pJTXb92\n1z5o0iDFCAFa4snWvCB7HLwg20WlvP43BaK6GYMUIwRoiSfbSJDKLi6Lg9RfI0Wj+3ENQoCW\neLKNm6jllZvkIK5MV0xqcgCkWfup6H5cgxCgJZ5sk0C67YSOyTetj0j/ntY0kJ7ruVxR1Q/S\n4CFVeXwEUrpwTHmPFCQEaIkn2xiQ7p+G0r1Pl6qxTRqkGCFASzzZxkzU8gpQef/eIK1PCNAS\nT7ZJ15HK9gIf2q1JCNAST7ax15Eef0aadObOIAUJAVriyTYJpMbp78adDZOaNEgxQoCWeLIB\n3WtnkDILAVriyWaQdIQALfFkA/o1CoOUWQjQEk82oF/sM0iZhQAt8WQD+lVzg5RZCNASTzag\nh58YpMxCgJZ4sgE9jssgZRYCtMSTDegBkQYpsxCgJZ5sQI8sNkiZhQAt8WQzSDpCgJZ4svmC\nrI4QoCWebAZJRwjQEk+29kTd+dCOVgjQEk+21kTd+TMSrxCgJZ5s7cduFT/b4vewLb7nOxps\n0iDFCAFa4snWvbPho9gfD8V2vqPBJg1SjBCgJZ5sXZD2xeftVqEsZZCChAAt8WTr3CL09Vts\njt8GiVAI0BJPttZErQjaVuca3uc7GmzSIMUIAVriydaeqPvN8fheFLvZfh40aZBihAAt8WTz\nBVkdIUBLPNkMko4QoCWebH5mg44QoCWebH5mg44QoCWebH5mg44QoCWebH5mg44QoCWebH5m\ng44QoCWebH5mg44QoCWebP5Vcx0hQEs82QySjhCgJZ5sviCrIwRoiSebQdIRArTEk63/zoad\n72wgFAK0xJPNdzboCAFa4snWmqjv1zsb/PtIfEKAlniy9d/Z4F81ZxQCtMSTzSDpCAFa4snm\nQzsdIUBLPNl8skFHCNASTzaf/tYRArTEk80XZHWEAC3xZGv/Yl/Gz0a3Jg1SjBCgJZ5snWd/\nz3fytEmDFCMEaIknW2ui/mx3GU8zXJo0SDFCgJZ4snWuI/nXKGiFAC3xZDNIOkKAlniy+ayd\njhCgJZ5sBklHCNAST7beC7Lbj9l2HjVpkGKEAC3xZPMtQjpCgJZ4sg09adU3rfIJAVriyeYn\nreoIAVriyTb0pFX/MWY+IUBLPNnaE/V9+1Md2m39GYlPCNAST7bBC7L5LsoapCAhQEs82QyS\njhCgJZ5sviCrIwRoiSebQdIRArTEk80g6QgBWuLJZpB0hAAt8WQzSDpCgJZ4shkkHSFASzzZ\nDJKOEKAlnmydifr5VhTH6vaGbGWQgoQALfFka03Uw6a+EFsU3/MdDTZpkGKEAC3xZOs8+3tX\n3QH+5ZtWCYUALfFk6/k1iuu/XGWQgoQALfFkM0g6QoCWeLL1H9rt/BuyhEKAlniytU82+JkN\nvEKAlniydSbqh/+sC6sQoCWebL4gqyMEaIknm0HSEQK0xJMtnah/8NuxdTMGKUYI0BJPNoOk\nIwRoiSdbe6K+nR8Q+Tbbz4MmDVKMEKAlnmxDz7XLSJJBChICtMSTzU9a1RECtMSTbejZ394j\n8QkBWuLJ5r9GoSMEaIknW+/fR9p8+M4GQiFASzzZfEFWRwjQEk82g6QjBGiJJ5tB0hECtMST\nzSDpCAFa4slmkHSEAC3xZDNIOkKAlniyGSQdIUBLPNn8gEgdIUBLPNn8gEgdIUBLPNn8gEgd\nIUBLPNn8XDsdIUBLPNkMko4QoCWebH5ApI4QoCWebH5ApI4QoCWebIs/IPLf05oG0nM9lyuq\nHoCUv7xHChICtMSTzSDpCAFa4snm59rpCAFa4slmkHSEAC3xZPMDInWEAC3xZPMDInWEAC3x\nZPMDInWEAC3xZPMDInWEAC3xZPMDInWEAC3xZPMDInWEAC3xZPMFWR0hQEs82QySjhCgJZ5s\n7Ym68wVZWiFASzzZWhN15zsbeIUALfFka03UsvjZFr+HrR9+QigEaIknW/eC7EexPx788BNC\nIUBLPNm6IO2LTz+zgVII0BJPts69dl+/xeb4bZAIhQAt8WRrTdSKoG11rsEPP+ETArTEk609\nUfeb6lFCxW62nwdNGqQYIUBLPNl8QVZHCNASTzaDpCMEaIknm3/VXEcI0BJPNoOkIwRoiSeb\nn9mgIwRoiSebn9mgIwRoiSebn9mgIwRoiSebn9mgIwRoiSebn9mgIwRoiSebn9mgIwRoiSeb\nL8jqCAFa4slmkHSEAC3xZGtekD36giyxEKAlnmwGSUcI0BJPNh/a6QgBWuLJZpB0hAAt8WRr\nT9TPsvo12fJjtp8HTRqkGCFASzzZWhP18/ThqL4om5EkgxQkBGiJJ1trom6K79O/z5+inO9o\nsEmDFCMEaIknW9/juDZ+HBelEKAlnmydJ63+vhc/1aek+Y4GmzRIMUKAlniytSbqR3XDarVD\nyvgYIYMUJARoiSdb969RlPvTjsmP4yIUArTEk83XkXSEAC3xZDNIOkKAlniydQ7tSt9rxyoE\naIknm//QmI4QoCWebJ3T35/zrTxr0iDFCAFa4snW/xShrGWQgoQALfFkG3iuXc4ySEFCgJZ4\nsnWeIrTN+PygS5MGKUYI0BJPts6hnU820AoBWuLJZpB0hAAt8WTzBVkdIUBLPNkMko4QoCWe\nbJ2J+vlW/T3mn7l2HjVpkGKEAC3xZGtN1MOm/nxUFN/zHQ02aZBihAAt8WRrTdT3YlddlP0q\ntvMdDTZpkGKEAC3xZOu5s+H6L1cZpCAhQEs82QySjhCgJZ5s/Yd2u+J9vqPBJg1SjBCgJZ5s\n7ZMN/kNjvEKAlniydSbqR/WHxnb+Q2OEQoCWeLL5gqyOEKAlnmwGSUcI0BJPtuZEPXxsTx+Q\n3vL+lqxBChICtMSTrTFR99cnn5QZb2wwSFFCgJZ4sqUT9bco3qub7L7fipy/KGuQgoQALfFk\nSyfq/erRux9ZTCgEaIknWzpRy+J69ei3+osUucogBQkBWuLJ1vpjzD0vFy+DFCQEaIknm0HS\nEQK0xJPNIOkIAVriyWaQdIQALfFka4JU+ClCxEKAlniyGSQdIUBLPNl8r52OEKAlnmwGSUcI\n0BJPNoOkIwRoiSebQdIRArTEk80g6QgBWuLJZpB0hAAt8WQzSDpCgJZ4shkkHSFASzzZDJKO\nEKAlnmwGSUcI0BJPNoOkIwRoiSebQdIRArTEk80g6QgBWuLJZpB0hAAt8WQzSDpCgJZ4shkk\nHSFASzzZDJKOEKAlnmwGSUcI0BJPNoOkIwRoiSebQdIRArTEk23cRC1PNbiw7F37oEmDFCME\naIkn26iJWt7+61s4BaK6SYMUIwRoiSebQdIRArTEk238Z6SameQg7grSVI4MUpQQoCWebJNA\nauyZbiA1PyL9e1rTQHqu53JF1Qsg3Q7iOiClC8eU90hBQoCWeLJNAynd+3SpGtukQYoRArTE\nk23sRE2xOdNkkNYmBGiJJ9vIiVom/6eLfGi3IiFASzzZRl6QTb70fUaadObOIAUJAVriyTbu\nOtL1s1HjBF16Z8OkJg1SjBCgJZ5svtdORwjQEk82g6QjBGiJJ5tB0hECtMSTzSDpCAFa4slm\nkHSEAC3xZDNIOkKAlniyGSQdIUBLPNkMko4QoCWebAZJRwjQEk82g6QjBGiJJ5tB0hECtMST\nzSDpCAFa4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+W++lqnqrvj1tUx/X3dZW\nm/1Wm7lylUFCrzNFZX36+8rUaf9yOmbbnJk5HpOzC9dv68O+5tqeMxCuxcqdi17p/L+DdDz+\nbIrqlPYASB9l0cbMIOUsdy56pfN/0xiuz2pV69DuxsxXfSSXHNoZpKzlzkWvdP7XZ+K+qtML\nZfF9/Ok52fB22xFti6/bWn9Gyl4GCb1SkC5ns3+up78/Oqe/b2ftKswO6elvn7XLWgYJvRpH\nZNXV1219s8+uLMqP66L7Bdn7daQTU+/nC7Jfvo6UvwwSWe16RrSo73bwnQ05yyCxVfmZfFOc\nPkqd2Hr3vXa5yyCx1XdKzO52Ldd3f+ctg0RX+/fkm8/N+ROUfx8pcxkkl2uBMkgu1wJlkFyu\nBcoguVwLlEFyuRYog+RyLVAGyeVaoAySy7VA/Q9UuiHty7OdZQAAAABJRU5ErkJggg==",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Distribuição do salário dos jogadores\n",
    "ggplot(canhotos) +aes( wage_eur) + \n",
    "    geom_histogram( aes(y =..density..),fill=\"#00AFBB\",color=\"black\")+\n",
    "  stat_function(fun = dnorm, args = list( mean(canhotos$wage_eur), sd(canhotos$wage_eur)),aes(fill=\"black\"),size=1.2)+\n",
    "  theme_minimal() +\n",
    "  labs(y = \"Densidade de probabilidade\", x = \"Peso (kg)\", \n",
    "  title = \"Densidade de probabilidade da massa corporal dos jogadores\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message in ks.test(destros$wage_eur, \"pnorm\", mean(destros$wage_eur), sd(destros$wage_eur)):\n",
      "\"ties should not be present for the Kolmogorov-Smirnov test\"\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "\n",
       "\tOne-sample Kolmogorov-Smirnov test\n",
       "\n",
       "data:  destros$wage_eur\n",
       "D = 0.33699, p-value < 2.2e-16\n",
       "alternative hypothesis: two-sided\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ks.test(destros$wage_eur,\"pnorm\",mean(destros$wage_eur),sd(destros$wage_eur))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message:\n",
      "\"Ignoring unknown aesthetics: fill\"\n",
      "`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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FzBAskUpW1h/DABBVJVP/cBcQULJFOU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",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Distribuição do salário dos jogadores\n",
    "ggplot(destros) +aes( wage_eur) + \n",
    "    geom_histogram( aes(y =..density..),fill=\"#00AFBB\",color=\"black\")+\n",
    "  stat_function(fun = dnorm, args = list( mean(destros$wage_eur), sd(destros$wage_eur)),aes(fill=\"black\"),size=1.2)+\n",
    "  theme_minimal() +\n",
    "  labs(y = \"Densidade de probabilidade\", x = \"Peso (kg)\", \n",
    "  title = \"Densidade de probabilidade da massa corporal dos jogadores\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Não paramétricos - Wilcoxon-Mann-Whitney \n",
    "Técnicas não paramétricas fazem poucas suposições sobre as distribuições originais.\n",
    "#### Wilcoxon-Mann-Whitney\n",
    "O teste U de Mann-Whitney (também conhecido por teste da soma dos postos de Wilcoxon, teste de Wilcoxon-Mann-Whitney ou teste de Mann-Whitney) é a versão da rotina de teste não-paramétrico de t de Student."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "\tWilcoxon rank sum test with continuity correction\n",
       "\n",
       "data:  destros$wage_eur and canhotos$wage_eur\n",
       "W = 18692, p-value = 0.1708\n",
       "alternative hypothesis: true location shift is not equal to 0\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "wilcox.test(destros$wage_eur, canhotos$wage_eur)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Temos evidências para acreditar que não que existe diferença entre ganho de capital entre destros e canhotos"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "R",
   "language": "R",
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