{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](https://github.com/bigdata-icict/ETL-Dataiku-DSS/raw/master/tutoriais/pcdas_1.5.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Notebook para criação de tabela de indicadores da PNS -  módulo I 2019 Cobertura de Plano de Saúde"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Bibliotecas Utilizadas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Lendo pacotes necessários\n",
    "library(survey)\n",
    "library(ggplot2)\n",
    "library(dplyr)\n",
    "library(foreign)\n",
    "library(forcats)\n",
    "library(tidyverse)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Carregando microdados da PNS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>\n",
       ".list-inline {list-style: none; margin:0; padding: 0}\n",
       ".list-inline>li {display: inline-block}\n",
       ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n",
       "</style>\n",
       "<ol class=list-inline><li>293725</li><li>682</li></ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 293725\n",
       "\\item 682\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 293725\n",
       "2. 682\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] 293725    682"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Carregando banco de dados para R versão 3.5.0 ou superior\n",
    "load(\"<coloque aqui o caminho para o arquivo dos microdados formato RDATA PNS 2019>\")\n",
    "\n",
    "#conferindo as dimensões (número de linhas e colunas)\n",
    "dim(\"<Coloque aqui o nome do arquivo RDATA PNS 2019>\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Definição do peso e filtragem de respondentes do questionário"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "    Min.  1st Qu.   Median     Mean  3rd Qu.     Max. \n",
       " 0.01046  0.31515  0.58914  1.00000  1.16920 39.99838 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Selecionando registros válidos e calculando peso amostral - summary de verificação\n",
    "pns2019.1<- <Coloque aqui o nome do arquivo RDATA> %>% mutate(peso_moradores=((V00281*279382/209589607))) \n",
    "pns2019.1<-pns2019.1 %>% filter(!is.na(peso_moradores))\n",
    "summary(pns2019.1$peso_moradores)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Criação de variáveis dos indicadores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>\n",
       ".dl-inline {width: auto; margin:0; padding: 0}\n",
       ".dl-inline>dt, .dl-inline>dd {float: none; width: auto; display: inline-block}\n",
       ".dl-inline>dt::after {content: \":\\0020\"; padding-right: .5ex}\n",
       ".dl-inline>dt:not(:first-of-type) {padding-left: .5ex}\n",
       "</style><dl class=dl-inline><dt>Sim</dt><dd>65118</dd><dt>Não</dt><dd>214264</dd></dl>\n"
      ],
      "text/latex": [
       "\\begin{description*}\n",
       "\\item[Sim] 65118\n",
       "\\item[Não] 214264\n",
       "\\end{description*}\n"
      ],
      "text/markdown": [
       "Sim\n",
       ":   65118Não\n",
       ":   214264\n",
       "\n"
      ],
      "text/plain": [
       "   Sim    Não \n",
       " 65118 214264 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<style>\n",
       ".dl-inline {width: auto; margin:0; padding: 0}\n",
       ".dl-inline>dt, .dl-inline>dd {float: none; width: auto; display: inline-block}\n",
       ".dl-inline>dt::after {content: \":\\0020\"; padding-right: .5ex}\n",
       ".dl-inline>dt:not(:first-of-type) {padding-left: .5ex}\n",
       "</style><dl class=dl-inline><dt>Sim</dt><dd>58597</dd><dt>Não</dt><dd>220785</dd></dl>\n"
      ],
      "text/latex": [
       "\\begin{description*}\n",
       "\\item[Sim] 58597\n",
       "\\item[Não] 220785\n",
       "\\end{description*}\n"
      ],
      "text/markdown": [
       "Sim\n",
       ":   58597Não\n",
       ":   220785\n",
       "\n"
      ],
      "text/plain": [
       "   Sim    Não \n",
       " 58597 220785 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<style>\n",
       ".dl-inline {width: auto; margin:0; padding: 0}\n",
       ".dl-inline>dt, .dl-inline>dd {float: none; width: auto; display: inline-block}\n",
       ".dl-inline>dt::after {content: \":\\0020\"; padding-right: .5ex}\n",
       ".dl-inline>dt:not(:first-of-type) {padding-left: .5ex}\n",
       "</style><dl class=dl-inline><dt>Sim</dt><dd>31511</dd><dt>Não</dt><dd>247871</dd></dl>\n"
      ],
      "text/latex": [
       "\\begin{description*}\n",
       "\\item[Sim] 31511\n",
       "\\item[Não] 247871\n",
       "\\end{description*}\n"
      ],
      "text/markdown": [
       "Sim\n",
       ":   31511Não\n",
       ":   247871\n",
       "\n"
      ],
      "text/plain": [
       "   Sim    Não \n",
       " 31511 247871 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Desfechos - Indicadores\n",
    "\n",
    "#Posse de plano de saúde médico ou odontológico particular - I001P\n",
    "pns2019.1 <- pns2019.1 %>% mutate(I001P = ifelse(I00101==2 & I00102==2,2,\n",
    "                                          ifelse(I00101==1 | I00102==1,1,2)))                                         \n",
    "pns2019.1$I001P<-factor(pns2019.1$I001P, levels=c(1,2), labels=c(\"Sim\",\"Não\"))\n",
    "summary(pns2019.1$I001P)\n",
    "\n",
    "\n",
    "#Posse de plano de saúde médico particular  - I002P\n",
    "pns2019.1 <- pns2019.1 %>% mutate(I002P= ifelse(I00102==1, 1,0))\n",
    "pns2019.1$I002P<-factor(pns2019.1$I002P, levels=c(1,0), labels=c(\"Sim\", \"Não\"))\n",
    "summary(pns2019.1$I002P)\n",
    "\n",
    "#Posse de plano de saúde odontológico particular  - I003P\n",
    "pns2019.1 <- pns2019.1 %>% mutate(I003P= ifelse(I00101 ==1, 1,0))\n",
    "pns2019.1$I003P<-factor(pns2019.1$I003P, levels=c(1,0), labels=c(\"Sim\", \"Não\"))\n",
    "summary(pns2019.1$I003P)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Definições de abrangências"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Situação urbana ou rural"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>\n",
       ".dl-inline {width: auto; margin:0; padding: 0}\n",
       ".dl-inline>dt, .dl-inline>dd {float: none; width: auto; display: inline-block}\n",
       ".dl-inline>dt::after {content: \":\\0020\"; padding-right: .5ex}\n",
       ".dl-inline>dt:not(:first-of-type) {padding-left: .5ex}\n",
       "</style><dl class=dl-inline><dt>Urbano</dt><dd>212286</dd><dt>Rural</dt><dd>67096</dd></dl>\n"
      ],
      "text/latex": [
       "\\begin{description*}\n",
       "\\item[Urbano] 212286\n",
       "\\item[Rural] 67096\n",
       "\\end{description*}\n"
      ],
      "text/markdown": [
       "Urbano\n",
       ":   212286Rural\n",
       ":   67096\n",
       "\n"
      ],
      "text/plain": [
       "Urbano  Rural \n",
       "212286  67096 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Situação Urbano ou Rural\n",
    "pns2019.1 <- pns2019.1 %>% rename(Situação_Urbano_Rural=V0026)\n",
    "pns2019.1$Situação_Urbano_Rural<-factor(pns2019.1$Situação_Urbano_Rural, levels=c(1,2), labels=c(\"Urbano\", \"Rural\"))\n",
    "summary(pns2019.1$Situação_Urbano_Rural)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Sexo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>\n",
       ".dl-inline {width: auto; margin:0; padding: 0}\n",
       ".dl-inline>dt, .dl-inline>dd {float: none; width: auto; display: inline-block}\n",
       ".dl-inline>dt::after {content: \":\\0020\"; padding-right: .5ex}\n",
       ".dl-inline>dt:not(:first-of-type) {padding-left: .5ex}\n",
       "</style><dl class=dl-inline><dt>Masculino</dt><dd>134442</dd><dt>Feminino</dt><dd>144940</dd></dl>\n"
      ],
      "text/latex": [
       "\\begin{description*}\n",
       "\\item[Masculino] 134442\n",
       "\\item[Feminino] 144940\n",
       "\\end{description*}\n"
      ],
      "text/markdown": [
       "Masculino\n",
       ":   134442Feminino\n",
       ":   144940\n",
       "\n"
      ],
      "text/plain": [
       "Masculino  Feminino \n",
       "   134442    144940 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Sexo\n",
    "pns2019.1 <- pns2019.1 %>% rename(Sexo=C006)\n",
    "pns2019.1$Sexo<-factor(pns2019.1$Sexo, levels=c(1,2), labels=c(\"Masculino\", \"Feminino\"))\n",
    "summary(pns2019.1$Sexo)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### UF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>\n",
       ".dl-inline {width: auto; margin:0; padding: 0}\n",
       ".dl-inline>dt, .dl-inline>dd {float: none; width: auto; display: inline-block}\n",
       ".dl-inline>dt::after {content: \":\\0020\"; padding-right: .5ex}\n",
       ".dl-inline>dt:not(:first-of-type) {padding-left: .5ex}\n",
       "</style><dl class=dl-inline><dt>Rondônia</dt><dd>7037</dd><dt>Acre</dt><dd>7808</dd><dt>Amazonas</dt><dd>12642</dd><dt>Roraima</dt><dd>7995</dd><dt>Pará</dt><dd>13475</dd><dt>Amapá</dt><dd>6363</dd><dt>Tocantins</dt><dd>6127</dd><dt>Maranhão</dt><dd>17327</dd><dt>Piauí</dt><dd>8745</dd><dt>Ceará</dt><dd>14157</dd><dt>Rio Grande do Norte</dt><dd>9472</dd><dt>Paraíba</dt><dd>9652</dd><dt>Pernambuco</dt><dd>11934</dd><dt>Alagoas</dt><dd>9947</dd><dt>Sergipe</dt><dd>7803</dd><dt>Bahia</dt><dd>10516</dd><dt>Minas Gerais</dt><dd>14831</dd><dt>Espírito Santo</dt><dd>10078</dd><dt>Rio de Janeiro</dt><dd>13909</dd><dt>São Paulo</dt><dd>17522</dd><dt>Paraná</dt><dd>11237</dd><dt>Santa Catarina</dt><dd>10123</dd><dt>Rio Grande do Sul</dt><dd>9878</dd><dt>Mato Grosso do Sul</dt><dd>8350</dd><dt>Mato Grosso</dt><dd>7291</dd><dt>Goiás</dt><dd>8003</dd><dt>Distrito Federal</dt><dd>7160</dd></dl>\n"
      ],
      "text/latex": [
       "\\begin{description*}\n",
       "\\item[Rondônia] 7037\n",
       "\\item[Acre] 7808\n",
       "\\item[Amazonas] 12642\n",
       "\\item[Roraima] 7995\n",
       "\\item[Pará] 13475\n",
       "\\item[Amapá] 6363\n",
       "\\item[Tocantins] 6127\n",
       "\\item[Maranhão] 17327\n",
       "\\item[Piauí] 8745\n",
       "\\item[Ceará] 14157\n",
       "\\item[Rio Grande do Norte] 9472\n",
       "\\item[Paraíba] 9652\n",
       "\\item[Pernambuco] 11934\n",
       "\\item[Alagoas] 9947\n",
       "\\item[Sergipe] 7803\n",
       "\\item[Bahia] 10516\n",
       "\\item[Minas Gerais] 14831\n",
       "\\item[Espírito Santo] 10078\n",
       "\\item[Rio de Janeiro] 13909\n",
       "\\item[São Paulo] 17522\n",
       "\\item[Paraná] 11237\n",
       "\\item[Santa Catarina] 10123\n",
       "\\item[Rio Grande do Sul] 9878\n",
       "\\item[Mato Grosso do Sul] 8350\n",
       "\\item[Mato Grosso] 7291\n",
       "\\item[Goiás] 8003\n",
       "\\item[Distrito Federal] 7160\n",
       "\\end{description*}\n"
      ],
      "text/markdown": [
       "Rondônia\n",
       ":   7037Acre\n",
       ":   7808Amazonas\n",
       ":   12642Roraima\n",
       ":   7995Pará\n",
       ":   13475Amapá\n",
       ":   6363Tocantins\n",
       ":   6127Maranhão\n",
       ":   17327Piauí\n",
       ":   8745Ceará\n",
       ":   14157Rio Grande do Norte\n",
       ":   9472Paraíba\n",
       ":   9652Pernambuco\n",
       ":   11934Alagoas\n",
       ":   9947Sergipe\n",
       ":   7803Bahia\n",
       ":   10516Minas Gerais\n",
       ":   14831Espírito Santo\n",
       ":   10078Rio de Janeiro\n",
       ":   13909São Paulo\n",
       ":   17522Paraná\n",
       ":   11237Santa Catarina\n",
       ":   10123Rio Grande do Sul\n",
       ":   9878Mato Grosso do Sul\n",
       ":   8350Mato Grosso\n",
       ":   7291Goiás\n",
       ":   8003Distrito Federal\n",
       ":   7160\n",
       "\n"
      ],
      "text/plain": [
       "           Rondônia                Acre            Amazonas             Roraima \n",
       "               7037                7808               12642                7995 \n",
       "               Pará               Amapá           Tocantins            Maranhão \n",
       "              13475                6363                6127               17327 \n",
       "              Piauí               Ceará Rio Grande do Norte             Paraíba \n",
       "               8745               14157                9472                9652 \n",
       "         Pernambuco             Alagoas             Sergipe               Bahia \n",
       "              11934                9947                7803               10516 \n",
       "       Minas Gerais      Espírito Santo      Rio de Janeiro           São Paulo \n",
       "              14831               10078               13909               17522 \n",
       "             Paraná      Santa Catarina   Rio Grande do Sul  Mato Grosso do Sul \n",
       "              11237               10123                9878                8350 \n",
       "        Mato Grosso               Goiás    Distrito Federal \n",
       "               7291                8003                7160 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Estados - UFs\n",
    "pns2019.1 <- pns2019.1 %>% rename(Unidades_da_Federação=V0001)\n",
    "pns2019.1$Unidades_da_Federação<-factor(pns2019.1$Unidades_da_Federação, levels=c(11,12,13,14,15,16,17,21,22,23,24,25,26,27,28,29,31,32,33,35,41,42,43,50,51,52,53),\n",
    "                                       label=c(\"Rondônia\",\"Acre\",\"Amazonas\",\"Roraima\",\"Pará\",\"Amapá\",\"Tocantins\",\"Maranhão\",\"Piauí\",\"Ceará\",\n",
    "                                        \"Rio Grande do Norte\",\"Paraíba\",\"Pernambuco\",\"Alagoas\",\"Sergipe\",\"Bahia\",\n",
    "                                        \"Minas Gerais\",\"Espírito Santo\",\"Rio de Janeiro\",\"São Paulo\",\n",
    "                                        \"Paraná\",\"Santa Catarina\",\"Rio Grande do Sul\", \n",
    "                                        \"Mato Grosso do Sul\",\"Mato Grosso\",\"Goiás\",\"Distrito Federal\"))\n",
    "summary(pns2019.1$Unidades_da_Federação)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Grandes Regiões"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>\n",
       ".dl-inline {width: auto; margin:0; padding: 0}\n",
       ".dl-inline>dt, .dl-inline>dd {float: none; width: auto; display: inline-block}\n",
       ".dl-inline>dt::after {content: \":\\0020\"; padding-right: .5ex}\n",
       ".dl-inline>dt:not(:first-of-type) {padding-left: .5ex}\n",
       "</style><dl class=dl-inline><dt>Norte</dt><dd>61447</dd><dt>Nordeste</dt><dd>99553</dd><dt>Sudeste</dt><dd>56340</dd><dt>Sul</dt><dd>31238</dd><dt>Centro-Oeste</dt><dd>30804</dd></dl>\n"
      ],
      "text/latex": [
       "\\begin{description*}\n",
       "\\item[Norte] 61447\n",
       "\\item[Nordeste] 99553\n",
       "\\item[Sudeste] 56340\n",
       "\\item[Sul] 31238\n",
       "\\item[Centro-Oeste] 30804\n",
       "\\end{description*}\n"
      ],
      "text/markdown": [
       "Norte\n",
       ":   61447Nordeste\n",
       ":   99553Sudeste\n",
       ":   56340Sul\n",
       ":   31238Centro-Oeste\n",
       ":   30804\n",
       "\n"
      ],
      "text/plain": [
       "       Norte     Nordeste      Sudeste          Sul Centro-Oeste \n",
       "       61447        99553        56340        31238        30804 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Grandes Regiões\n",
    "pns2019.1 <- pns2019.1 %>% \n",
    "  mutate(GrandesRegioes = fct_collapse(Unidades_da_Federação, \n",
    "                                          `Norte` = c(\"Rondônia\",\"Acre\",\"Amazonas\",\"Roraima\",\"Pará\", \"Amapá\",\"Tocantins\"),\n",
    "                                          `Nordeste` = c(\"Maranhão\", \"Piauí\", \"Ceará\", \"Rio Grande do Norte\", \"Paraíba\",\"Pernambuco\", \"Alagoas\",\"Sergipe\",\"Bahia\"),\n",
    "                                          `Sudeste` = c(\"Minas Gerais\", \"Espírito Santo\",\"Rio de Janeiro\", \"São Paulo\"), \n",
    "                                          `Sul` = c(\"Paraná\", \"Santa Catarina\", \"Rio Grande do Sul\"),\n",
    "                                          `Centro-Oeste`= c(\"Mato Grosso do Sul\",\"Mato Grosso\", \"Goiás\", \"Distrito Federal\")))\n",
    "summary(pns2019.1$GrandesRegioes)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Capital"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>\n",
       ".dl-inline {width: auto; margin:0; padding: 0}\n",
       ".dl-inline>dt, .dl-inline>dd {float: none; width: auto; display: inline-block}\n",
       ".dl-inline>dt::after {content: \":\\0020\"; padding-right: .5ex}\n",
       ".dl-inline>dt:not(:first-of-type) {padding-left: .5ex}\n",
       "</style><dl class=dl-inline><dt>Porto Velho</dt><dd>7037</dd><dt>Rio Branco</dt><dd>7808</dd><dt>Manaus</dt><dd>12642</dd><dt>Boa Vista</dt><dd>7995</dd><dt>Belém</dt><dd>13475</dd><dt>Macapá</dt><dd>6363</dd><dt>Palmas</dt><dd>6127</dd><dt>São Luís</dt><dd>17327</dd><dt>Teresina</dt><dd>8745</dd><dt>Fortaleza</dt><dd>14157</dd><dt>Natal</dt><dd>9472</dd><dt>João Pessoa</dt><dd>9652</dd><dt>Recife</dt><dd>11934</dd><dt>Maceió</dt><dd>9947</dd><dt>Aracaju</dt><dd>7803</dd><dt>Salvador</dt><dd>10516</dd><dt>Belo Horizonte</dt><dd>14831</dd><dt>Vitória</dt><dd>10078</dd><dt>Rio de Janeiro</dt><dd>13909</dd><dt>São Paulo</dt><dd>17522</dd><dt>Curitiba</dt><dd>11237</dd><dt>Florianópolis</dt><dd>10123</dd><dt>Porto Alegre</dt><dd>9878</dd><dt>Campo Grande</dt><dd>8350</dd><dt>Cuiabá</dt><dd>7291</dd><dt>Goiânia</dt><dd>8003</dd><dt>Brasília</dt><dd>7160</dd></dl>\n"
      ],
      "text/latex": [
       "\\begin{description*}\n",
       "\\item[Porto Velho] 7037\n",
       "\\item[Rio Branco] 7808\n",
       "\\item[Manaus] 12642\n",
       "\\item[Boa Vista] 7995\n",
       "\\item[Belém] 13475\n",
       "\\item[Macapá] 6363\n",
       "\\item[Palmas] 6127\n",
       "\\item[São Luís] 17327\n",
       "\\item[Teresina] 8745\n",
       "\\item[Fortaleza] 14157\n",
       "\\item[Natal] 9472\n",
       "\\item[João Pessoa] 9652\n",
       "\\item[Recife] 11934\n",
       "\\item[Maceió] 9947\n",
       "\\item[Aracaju] 7803\n",
       "\\item[Salvador] 10516\n",
       "\\item[Belo Horizonte] 14831\n",
       "\\item[Vitória] 10078\n",
       "\\item[Rio de Janeiro] 13909\n",
       "\\item[São Paulo] 17522\n",
       "\\item[Curitiba] 11237\n",
       "\\item[Florianópolis] 10123\n",
       "\\item[Porto Alegre] 9878\n",
       "\\item[Campo Grande] 8350\n",
       "\\item[Cuiabá] 7291\n",
       "\\item[Goiânia] 8003\n",
       "\\item[Brasília] 7160\n",
       "\\end{description*}\n"
      ],
      "text/markdown": [
       "Porto Velho\n",
       ":   7037Rio Branco\n",
       ":   7808Manaus\n",
       ":   12642Boa Vista\n",
       ":   7995Belém\n",
       ":   13475Macapá\n",
       ":   6363Palmas\n",
       ":   6127São Luís\n",
       ":   17327Teresina\n",
       ":   8745Fortaleza\n",
       ":   14157Natal\n",
       ":   9472João Pessoa\n",
       ":   9652Recife\n",
       ":   11934Maceió\n",
       ":   9947Aracaju\n",
       ":   7803Salvador\n",
       ":   10516Belo Horizonte\n",
       ":   14831Vitória\n",
       ":   10078Rio de Janeiro\n",
       ":   13909São Paulo\n",
       ":   17522Curitiba\n",
       ":   11237Florianópolis\n",
       ":   10123Porto Alegre\n",
       ":   9878Campo Grande\n",
       ":   8350Cuiabá\n",
       ":   7291Goiânia\n",
       ":   8003Brasília\n",
       ":   7160\n",
       "\n"
      ],
      "text/plain": [
       "   Porto Velho     Rio Branco         Manaus      Boa Vista          Belém \n",
       "          7037           7808          12642           7995          13475 \n",
       "        Macapá         Palmas       São Luís       Teresina      Fortaleza \n",
       "          6363           6127          17327           8745          14157 \n",
       "         Natal    João Pessoa         Recife         Maceió        Aracaju \n",
       "          9472           9652          11934           9947           7803 \n",
       "      Salvador Belo Horizonte        Vitória Rio de Janeiro      São Paulo \n",
       "         10516          14831          10078          13909          17522 \n",
       "      Curitiba  Florianópolis   Porto Alegre   Campo Grande         Cuiabá \n",
       "         11237          10123           9878           8350           7291 \n",
       "       Goiânia       Brasília \n",
       "          8003           7160 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Capital\n",
    "pns2019.1<- pns2019.1 %>% mutate(Capital= fct_collapse(Unidades_da_Federação,\n",
    "                                        `Porto Velho`= \"Rondônia\", \n",
    "                                        `Boa Vista`= \"Roraima\",              \n",
    "                                        `Rio Branco`= \"Acre\", \n",
    "                                        `Manaus` = \"Amazonas\",\n",
    "                                        `Belém` = \"Pará\" ,\n",
    "                                        `Macapá`= \"Amapá\",\n",
    "                                        `Palmas` = \"Tocantins\",\n",
    "                                        `São Luís` = \"Maranhão\",\n",
    "                                        `Teresina`= \"Piauí\" ,\n",
    "                                        `Fortaleza`= \"Ceará\",\n",
    "                                        `Natal`= \"Rio Grande do Norte\",\n",
    "                                        `João Pessoa`= \"Paraíba\",\n",
    "                                        `Recife`= \"Pernambuco\",\n",
    "                                        `Maceió`= \"Alagoas\",\n",
    "                                        `Aracaju`= \"Sergipe\",\n",
    "                                        `Salvador`= \"Bahia\",\n",
    "                                        `Belo Horizonte`= \"Minas Gerais\",\n",
    "                                        `Vitória`= \"Espírito Santo\",\n",
    "                                        `Rio de Janeiro`= \"Rio de Janeiro\",\n",
    "                                        `São Paulo`= \"São Paulo\",\n",
    "                                        `Curitiba`= \"Paraná\",\n",
    "                                        `Florianópolis`= \"Santa Catarina\",\n",
    "                                        `Porto Alegre`= \"Rio Grande do Sul\",\n",
    "                                        `Campo Grande`=  \"Mato Grosso do Sul\",\n",
    "                                        `Cuiabá`= \"Mato Grosso\",\n",
    "                                        `Goiânia` = \"Goiás\",\n",
    "                                        `Brasília`= \"Distrito Federal\"))\n",
    "summary(pns2019.1$Capital)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Faixa Etária"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>\n",
       ".dl-inline {width: auto; margin:0; padding: 0}\n",
       ".dl-inline>dt, .dl-inline>dd {float: none; width: auto; display: inline-block}\n",
       ".dl-inline>dt::after {content: \":\\0020\"; padding-right: .5ex}\n",
       ".dl-inline>dt:not(:first-of-type) {padding-left: .5ex}\n",
       "</style><dl class=dl-inline><dt>Menor de 1 ano</dt><dd>3349</dd><dt>1 a 4 anos</dt><dd>14438</dd><dt>5 a 14 anos</dt><dd>40500</dd><dt>15 a 24 anos</dt><dd>43827</dd><dt>25 a 34 anos</dt><dd>39854</dd><dt>35 a 44 anos</dt><dd>41460</dd><dt>45 a 59 anos</dt><dd>52400</dd><dt>60 anos ou mais</dt><dd>43554</dd></dl>\n"
      ],
      "text/latex": [
       "\\begin{description*}\n",
       "\\item[Menor de 1 ano] 3349\n",
       "\\item[1 a 4 anos] 14438\n",
       "\\item[5 a 14 anos] 40500\n",
       "\\item[15 a 24 anos] 43827\n",
       "\\item[25 a 34 anos] 39854\n",
       "\\item[35 a 44 anos] 41460\n",
       "\\item[45 a 59 anos] 52400\n",
       "\\item[60 anos ou mais] 43554\n",
       "\\end{description*}\n"
      ],
      "text/markdown": [
       "Menor de 1 ano\n",
       ":   33491 a 4 anos\n",
       ":   144385 a 14 anos\n",
       ":   4050015 a 24 anos\n",
       ":   4382725 a 34 anos\n",
       ":   3985435 a 44 anos\n",
       ":   4146045 a 59 anos\n",
       ":   5240060 anos ou mais\n",
       ":   43554\n",
       "\n"
      ],
      "text/plain": [
       " Menor de 1 ano      1 a 4 anos     5 a 14 anos    15 a 24 anos    25 a 34 anos \n",
       "           3349           14438           40500           43827           39854 \n",
       "   35 a 44 anos    45 a 59 anos 60 anos ou mais \n",
       "          41460           52400           43554 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Faixas Etárias\n",
    "pns2019.1 <- pns2019.1 %>% mutate(faixa_idade = ifelse(C008<=0,1, \n",
    "                        ifelse(C008%in% 1:4, 2, \n",
    "                        ifelse(C008%in% 5:14, 3,\n",
    "                        ifelse(C008%in% 15:24, 4,\n",
    "                        ifelse(C008%in% 25:34, 5,\n",
    "                        ifelse(C008%in% 35:44, 6,\n",
    "                        ifelse(C008%in% 45:59, 7,8))))))))\n",
    "\n",
    "pns2019.1$faixa_idade<-factor(pns2019.1$faixa_idade, levels=c(1,2,3,4,5,6,7,8), labels=c(\"Menor de 1 ano\",\"1 a 4 anos\",\"5 a 14 anos\",\"15 a 24 anos\",\"25 a 34 anos\",\"35 a 44 anos\",\"45 a 59 anos\",\"60 anos ou mais\"))\n",
    "summary(pns2019.1$faixa_idade) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Raça"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>\n",
       ".dl-inline {width: auto; margin:0; padding: 0}\n",
       ".dl-inline>dt, .dl-inline>dd {float: none; width: auto; display: inline-block}\n",
       ".dl-inline>dt::after {content: \":\\0020\"; padding-right: .5ex}\n",
       ".dl-inline>dt:not(:first-of-type) {padding-left: .5ex}\n",
       "</style><dl class=dl-inline><dt>Branca</dt><dd>99019</dd><dt>Preta</dt><dd>28304</dd><dt>Parda</dt><dd>148273</dd><dt>NA's</dt><dd>3786</dd></dl>\n"
      ],
      "text/latex": [
       "\\begin{description*}\n",
       "\\item[Branca] 99019\n",
       "\\item[Preta] 28304\n",
       "\\item[Parda] 148273\n",
       "\\item[NA's] 3786\n",
       "\\end{description*}\n"
      ],
      "text/markdown": [
       "Branca\n",
       ":   99019Preta\n",
       ":   28304Parda\n",
       ":   148273NA's\n",
       ":   3786\n",
       "\n"
      ],
      "text/plain": [
       "Branca  Preta  Parda   NA's \n",
       " 99019  28304 148273   3786 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Raça\n",
    "pns2019.1 <- pns2019.1 %>% mutate(Raca= ifelse(C009==1, 1, \n",
    "                        ifelse(C009==2, 2, \n",
    "                            ifelse(C009==4, 3, 9))))\n",
    "\n",
    "pns2019.1$Raca<-factor(pns2019.1$Raca, levels=c(1,2,3),labels=c(\"Branca\", \"Preta\", \"Parda\"))\n",
    "\n",
    "summary(pns2019.1$Raca)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Renda per capita"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>\n",
       ".dl-inline {width: auto; margin:0; padding: 0}\n",
       ".dl-inline>dt, .dl-inline>dd {float: none; width: auto; display: inline-block}\n",
       ".dl-inline>dt::after {content: \":\\0020\"; padding-right: .5ex}\n",
       ".dl-inline>dt:not(:first-of-type) {padding-left: .5ex}\n",
       "</style><dl class=dl-inline><dt>Até 1/2 SM</dt><dd>91898</dd><dt>1/2 até 1 SM</dt><dd>81830</dd><dt>1 até 2 SM</dt><dd>61643</dd><dt>2 até 3 SM</dt><dd>19369</dd><dt>Mais de 3 SM</dt><dd>24470</dd><dt>NA's</dt><dd>172</dd></dl>\n"
      ],
      "text/latex": [
       "\\begin{description*}\n",
       "\\item[Até 1/2 SM] 91898\n",
       "\\item[1/2 até 1 SM] 81830\n",
       "\\item[1 até 2 SM] 61643\n",
       "\\item[2 até 3 SM] 19369\n",
       "\\item[Mais de 3 SM] 24470\n",
       "\\item[NA's] 172\n",
       "\\end{description*}\n"
      ],
      "text/markdown": [
       "Até 1/2 SM\n",
       ":   918981/2 até 1 SM\n",
       ":   818301 até 2 SM\n",
       ":   616432 até 3 SM\n",
       ":   19369Mais de 3 SM\n",
       ":   24470NA's\n",
       ":   172\n",
       "\n"
      ],
      "text/plain": [
       "  Até 1/2 SM 1/2 até 1 SM   1 até 2 SM   2 até 3 SM Mais de 3 SM         NA's \n",
       "       91898        81830        61643        19369        24470          172 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Rendimento domiciliar per capita\n",
    "\n",
    "pns2019.1 <- pns2019.1 %>% mutate(rend_per_capita = ifelse(VDF004 %in% 1:2, 1, \n",
    "                        ifelse(VDF004%in% 3, 2, \n",
    "                        ifelse(VDF004%in% 4, 3,\n",
    "                        ifelse(VDF004%in% 5, 4,\n",
    "                        ifelse(VDF004==9,\"NA\",5))))))\n",
    "\n",
    "pns2019.1$rend_per_capita<-factor(pns2019.1$rend_per_capita, levels=c(1,2,3,4,5), labels=c(\"Até 1/2 SM\",\"1/2 até 1 SM\",\"1 até 2 SM\",\n",
    "                                                                                   \"2 até 3 SM\",\"Mais de 3 SM\"))\n",
    "summary(pns2019.1$rend_per_capita)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Criando indicadores"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Filtrando base de indicadores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "     V0024            UPA_PNS          peso_moradores     I001P       \n",
       " Min.   :1110011   Min.   :110000016   Min.   : 0.01046   Sim: 65118  \n",
       " 1st Qu.:2152011   1st Qu.:210048674   1st Qu.: 0.31515   Não:214264  \n",
       " Median :2752010   Median :270027654   Median : 0.58914               \n",
       " Mean   :2916319   Mean   :288513887   Mean   : 1.00000               \n",
       " 3rd Qu.:3522011   3rd Qu.:350367169   3rd Qu.: 1.16920               \n",
       " Max.   :5310220   Max.   :530051067   Max.   :39.99838               \n",
       "                                                                      \n",
       " I002P        I003P            V0031            C009           VDF004     \n",
       " Sim: 58597   Sim: 31511   Min.   :1.000   Min.   :1.000   Min.   :1.000  \n",
       " Não:220785   Não:247871   1st Qu.:1.000   1st Qu.:1.000   1st Qu.:2.000  \n",
       "                           Median :2.000   Median :4.000   Median :3.000  \n",
       "                           Mean   :2.608   Mean   :2.736   Mean   :3.197  \n",
       "                           3rd Qu.:4.000   3rd Qu.:4.000   3rd Qu.:4.000  \n",
       "                           Max.   :4.000   Max.   :9.000   Max.   :7.000  \n",
       "                                                           NA's   :172    \n",
       " Situação_Urbano_Rural        Sexo           Unidades_da_Federação\n",
       " Urbano:212286         Masculino:134442   São Paulo     : 17522   \n",
       " Rural : 67096         Feminino :144940   Maranhão      : 17327   \n",
       "                                          Minas Gerais  : 14831   \n",
       "                                          Ceará         : 14157   \n",
       "                                          Rio de Janeiro: 13909   \n",
       "                                          Pará          : 13475   \n",
       "                                          (Other)       :188161   \n",
       "      GrandesRegioes            Capital                faixa_idade   \n",
       " Norte       :61447   São Paulo     : 17522   45 a 59 anos   :52400  \n",
       " Nordeste    :99553   São Luís      : 17327   15 a 24 anos   :43827  \n",
       " Sudeste     :56340   Belo Horizonte: 14831   60 anos ou mais:43554  \n",
       " Sul         :31238   Fortaleza     : 14157   35 a 44 anos   :41460  \n",
       " Centro-Oeste:30804   Rio de Janeiro: 13909   5 a 14 anos    :40500  \n",
       "                      Belém         : 13475   25 a 34 anos   :39854  \n",
       "                      (Other)       :188161   (Other)        :17787  \n",
       "     Raca            rend_per_capita \n",
       " Branca: 99019   Até 1/2 SM  :91898  \n",
       " Preta : 28304   1/2 até 1 SM:81830  \n",
       " Parda :148273   1 até 2 SM  :61643  \n",
       " NA's  :  3786   2 até 3 SM  :19369  \n",
       "                 Mais de 3 SM:24470  \n",
       "                 NA's        :  172  \n",
       "                                     "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Selecionando variáveis para cálculo de indicadores no survey\n",
    "pns2019survey<- pns2019.1 %>% select(\"V0024\",\"UPA_PNS\",\"peso_moradores\", \"I001P\",\"I002P\",\"I003P\", \"V0031\",\"C009\",\"VDF004\",\n",
    "                                     \"Situação_Urbano_Rural\",\"Sexo\",\"Unidades_da_Federação\", \"GrandesRegioes\",\n",
    "                                     \"Capital\",\"faixa_idade\", \"Raca\",\"rend_per_capita\")\n",
    "summary(pns2019survey)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exporta tabela filtrada com os dados específicos - Módulo I 2019"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Salvando csv para cálculo de indicadores no survey\n",
    "diretorio_saida <- \"<coloque aqui o diretório>\"\n",
    "write.csv(pns2019survey, file.path(diretorio_saida, \"pns2019Isurvey.csv\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cria plano amostral complexo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "#survey design\n",
    "desPNSI=svydesign(id=~UPA_PNS, strat=~V0024, weight=~peso_moradores, nest=TRUE, data=pns2019survey)\n",
    "desPNSIC=subset(desPNSI, V0031==1)\n",
    "desPNSIR=subset(desPNSI, C009!=9)\n",
    "desPNSIRE=subset(desPNSI, VDF004!=9)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Criação da tabela de indicadores\n",
    "Essa tabela é responsável por unir os indicadores no formato do painel de indicadores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "matrizIndicadores = data.frame()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Definição de variáveis para iteração dos indicadores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "ListaIndicadores = c(~I001P,~I002P,~I003P)\n",
    "ListaIndicadoresTexto = c(\"I001P\",\"I002P\",\"I003P\")\n",
    "ListaDominios = c(~Sexo,~Raca,~rend_per_capita,~faixa_idade,~Situação_Urbano_Rural,~Unidades_da_Federação,~GrandesRegioes,~Capital)\n",
    "ListaDominiosTexto = c(\"Sexo\",\"raça\",\"rend_per_capita\",\"fx_idade\",\"urb_rur\",\"uf\",\"região\",\"capital\")\n",
    "ListaTotais = c('Brasil','Capital')\n",
    "Ano <- \"2019\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Preenchendo a tabela de indicadores\n",
    "Essas iterações rodam por indicador, abrangência e por design"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Cálculo dos indicadores usando o pacote survey \n",
    "i <- 0\n",
    "#Para cada indicador\n",
    "for( indicador in ListaIndicadores){\n",
    "    i <- i + 1\n",
    "    j <- 1\n",
    "for (dominio in ListaDominios){\n",
    "               #design especifico para capital que é subconjunto do dataframe total\n",
    "               if (ListaDominiosTexto[j]==\"capital\"){\n",
    "                  dataframe_indicador<-svyby(indicador , dominio , desPNSIC , svymean,vartype= c(\"ci\",\"cv\"))\n",
    "                   }\n",
    "               #Uso design do subconjunto para raça/cor que inclui preta,branca e parda as outras \n",
    "               #não possuiam dados suficientes para os dominios dos indicadores\n",
    "               else if (ListaDominiosTexto[j]==\"raça\"){\n",
    "                dataframe_indicador<-svyby(indicador , dominio , desPNSIR , svymean,vartype= c(\"ci\",\"cv\"))\n",
    "                   }\n",
    "               #Uso design do subconjunto para renda que exclui valores NULOS \n",
    "               else if (ListaDominiosTexto[j]==\"rend_per_capita\"){\n",
    "                dataframe_indicador<-svyby(indicador , dominio , desPNSIRE , svymean,vartype= c(\"ci\",\"cv\"))\n",
    "                   }\n",
    "               #design geral para o subconjunto maior que 18 anos    \n",
    "               else {\n",
    "                   dataframe_indicador<-svyby(indicador , dominio , desPNSI , svymean,vartype= c(\"ci\",\"cv\"))\n",
    "                 \n",
    "               }\n",
    "               #União do dataframe de indicadores no formato do painel disponibilizado para PNS\n",
    "               dataframe_indicador<-data.frame(dataframe_indicador)\n",
    "               colnames(dataframe_indicador) <- c(\"abr_nome\",\"Sim\",\"Não\",\"LowerS\",\"LowerN\",\"UpperS\",\"UpperN\",\"cvS\",\"cvN\")\n",
    "               dataframe_indicador$Indicador <- ListaIndicadoresTexto[i]\n",
    "               dataframe_indicador$abr_tipo <- ListaDominiosTexto[j]\n",
    "               dataframe_indicador$Ano <- Ano\n",
    "               dataframe_indicador <- dataframe_indicador %>% select(\"abr_tipo\",\"abr_nome\",\"Ano\",\"Indicador\",\"Sim\",\"LowerS\",\"UpperS\",\"cvS\")\n",
    "               matrizIndicadores <-rbind(matrizIndicadores,dataframe_indicador)\n",
    "               j <- j + 1\n",
    "               \n",
    "    }\n",
    "\n",
    "}\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Criando a tabela pela abrangência total"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "matriz_totais <- data.frame()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Preenchendo a tabela com as abrangencia Brasil e total das capitais"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "i=0\n",
    "for(indicador in ListaIndicadores){\n",
    "    i <- i+1\n",
    "    for(total in ListaTotais){\n",
    "        dataframe_indicador <- data.frame()\n",
    "        dataframe_indicador_S <- data.frame()\n",
    "        if(total == 'Capital'){\n",
    "                       dataframe_indicador <- svymean(indicador,desPNSIC)\n",
    "             }else{\n",
    "                       dataframe_indicador <- svymean(indicador,desPNSI)\n",
    "        }\n",
    "        intervalo_confianca <- confint(dataframe_indicador)\n",
    "        coeficiente_variacao <- cv(dataframe_indicador)\n",
    "        dataframe_indicador <- cbind(data.frame(dataframe_indicador),data.frame(intervalo_confianca))\n",
    "        dataframe_indicador <- cbind(data.frame(dataframe_indicador),data.frame(coeficiente_variacao))\n",
    "        \n",
    "        dataframe_indicador <- dataframe_indicador %>% \n",
    "                               select('mean','X2.5..','X97.5..',coeficiente_variacao) \n",
    "        dataframe_indicador_S <- dataframe_indicador %>% \n",
    "                                 slice(1)\n",
    "                \n",
    "        colnames(dataframe_indicador_S) <- c('Sim','LowerS','UpperS', 'cvS')\n",
    "        dataframe_indicador_S$Indicador <- ListaIndicadoresTexto[i]\n",
    "        dataframe_indicador_S$abr_tipo <- \"total\"\n",
    "        dataframe_indicador_S$abr_nome <- total\n",
    "        dataframe_indicador_S$Ano <- Ano \n",
    "        dataframe_indicador_S <- dataframe_indicador_S %>% \n",
    "                             select(\"abr_tipo\",\"abr_nome\",\"Ano\",\"Indicador\",\"Sim\",\"LowerS\",\"UpperS\",'cvS')\n",
    "        \n",
    "        matriz_totais <-rbind(matriz_totais,dataframe_indicador_S)\n",
    "        \n",
    "    }\n",
    "}\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Unindo tabela de indicadores e de totais"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "matrizIndicadores<-rbind(matrizIndicadores,matriz_totais)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Visualizando tabela de indicadores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<caption>A data.frame: 243 × 8</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>abr_tipo</th><th scope=col>abr_nome</th><th scope=col>Ano</th><th scope=col>Indicador</th><th scope=col>Sim</th><th scope=col>LowerS</th><th scope=col>UpperS</th><th scope=col>cvS</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>Masculino</th><td>Sexo           </td><td>Masculino      </td><td>2019</td><td>I001P</td><td>0.27388650</td><td>0.26625307</td><td>0.28151992</td><td>0.014220036</td></tr>\n",
       "\t<tr><th scope=row>Feminino</th><td>Sexo           </td><td>Feminino       </td><td>2019</td><td>I001P</td><td>0.29508554</td><td>0.28761689</td><td>0.30255420</td><td>0.012913572</td></tr>\n",
       "\t<tr><th scope=row>Branca</th><td>raça           </td><td>Branca         </td><td>2019</td><td>I001P</td><td>0.38802339</td><td>0.37699291</td><td>0.39905386</td><td>0.014504020</td></tr>\n",
       "\t<tr><th scope=row>Preta</th><td>raça           </td><td>Preta          </td><td>2019</td><td>I001P</td><td>0.21423604</td><td>0.20290490</td><td>0.22556718</td><td>0.026985658</td></tr>\n",
       "\t<tr><th scope=row>Parda</th><td>raça           </td><td>Parda          </td><td>2019</td><td>I001P</td><td>0.20099362</td><td>0.19472077</td><td>0.20726647</td><td>0.015923352</td></tr>\n",
       "\t<tr><th scope=row>Até 1/2 SM</th><td>rend_per_capita</td><td>Até 1/2 SM     </td><td>2019</td><td>I001P</td><td>0.05825318</td><td>0.05366052</td><td>0.06284585</td><td>0.040225086</td></tr>\n",
       "\t<tr><th scope=row>1/2 até 1 SM</th><td>rend_per_capita</td><td>1/2 até 1 SM   </td><td>2019</td><td>I001P</td><td>0.17653124</td><td>0.16848817</td><td>0.18457431</td><td>0.023246217</td></tr>\n",
       "\t<tr><th scope=row>1 até 2 SM</th><td>rend_per_capita</td><td>1 até 2 SM     </td><td>2019</td><td>I001P</td><td>0.35748528</td><td>0.34693500</td><td>0.36803556</td><td>0.015057667</td></tr>\n",
       "\t<tr><th scope=row>2 até 3 SM</th><td>rend_per_capita</td><td>2 até 3 SM     </td><td>2019</td><td>I001P</td><td>0.54822804</td><td>0.52978537</td><td>0.56667072</td><td>0.017163845</td></tr>\n",
       "\t<tr><th scope=row>Mais de 3 SM</th><td>rend_per_capita</td><td>Mais de 3 SM   </td><td>2019</td><td>I001P</td><td>0.79230690</td><td>0.77994087</td><td>0.80467292</td><td>0.007963219</td></tr>\n",
       "\t<tr><th scope=row>Menor de 1 ano</th><td>fx_idade       </td><td>Menor de 1 ano </td><td>2019</td><td>I001P</td><td>0.27324375</td><td>0.24541975</td><td>0.30106776</td><td>0.051954282</td></tr>\n",
       "\t<tr><th scope=row>1 a 4 anos</th><td>fx_idade       </td><td>1 a 4 anos     </td><td>2019</td><td>I001P</td><td>0.28148882</td><td>0.26626871</td><td>0.29670892</td><td>0.027587250</td></tr>\n",
       "\t<tr><th scope=row>5 a 14 anos</th><td>fx_idade       </td><td>5 a 14 anos    </td><td>2019</td><td>I001P</td><td>0.25153348</td><td>0.24067604</td><td>0.26239092</td><td>0.022023359</td></tr>\n",
       "\t<tr><th scope=row>15 a 24 anos</th><td>fx_idade       </td><td>15 a 24 anos   </td><td>2019</td><td>I001P</td><td>0.23409422</td><td>0.22506088</td><td>0.24312756</td><td>0.019688363</td></tr>\n",
       "\t<tr><th scope=row>25 a 34 anos</th><td>fx_idade       </td><td>25 a 34 anos   </td><td>2019</td><td>I001P</td><td>0.29703277</td><td>0.28601233</td><td>0.30805321</td><td>0.018929825</td></tr>\n",
       "\t<tr><th scope=row>35 a 44 anos</th><td>fx_idade       </td><td>35 a 44 anos   </td><td>2019</td><td>I001P</td><td>0.32592919</td><td>0.31400964</td><td>0.33784875</td><td>0.018659011</td></tr>\n",
       "\t<tr><th scope=row>45 a 59 anos</th><td>fx_idade       </td><td>45 a 59 anos   </td><td>2019</td><td>I001P</td><td>0.29365128</td><td>0.28305072</td><td>0.30425183</td><td>0.018418260</td></tr>\n",
       "\t<tr><th scope=row>60 anos ou mais</th><td>fx_idade       </td><td>60 anos ou mais</td><td>2019</td><td>I001P</td><td>0.30214641</td><td>0.29153927</td><td>0.31275355</td><td>0.017911533</td></tr>\n",
       "\t<tr><th scope=row>Urbano</th><td>urb_rur        </td><td>Urbano         </td><td>2019</td><td>I001P</td><td>0.32162083</td><td>0.31325373</td><td>0.32998794</td><td>0.013273418</td></tr>\n",
       "\t<tr><th scope=row>Rural</th><td>urb_rur        </td><td>Rural          </td><td>2019</td><td>I001P</td><td>0.07013919</td><td>0.06309588</td><td>0.07718250</td><td>0.051235138</td></tr>\n",
       "\t<tr><th scope=row>Rondônia</th><td>uf             </td><td>Rondônia       </td><td>2019</td><td>I001P</td><td>0.13765773</td><td>0.11780679</td><td>0.15750867</td><td>0.073575358</td></tr>\n",
       "\t<tr><th scope=row>Acre</th><td>uf             </td><td>Acre           </td><td>2019</td><td>I001P</td><td>0.09564527</td><td>0.08104591</td><td>0.11024462</td><td>0.077879315</td></tr>\n",
       "\t<tr><th scope=row>Amazonas</th><td>uf             </td><td>Amazonas       </td><td>2019</td><td>I001P</td><td>0.16262579</td><td>0.14226485</td><td>0.18298673</td><td>0.063879330</td></tr>\n",
       "\t<tr><th scope=row>Roraima</th><td>uf             </td><td>Roraima        </td><td>2019</td><td>I001P</td><td>0.08270107</td><td>0.06120669</td><td>0.10419544</td><td>0.132606751</td></tr>\n",
       "\t<tr><th scope=row>Pará</th><td>uf             </td><td>Pará           </td><td>2019</td><td>I001P</td><td>0.15734934</td><td>0.13529986</td><td>0.17939882</td><td>0.071496582</td></tr>\n",
       "\t<tr><th scope=row>Amapá</th><td>uf             </td><td>Amapá          </td><td>2019</td><td>I001P</td><td>0.12025701</td><td>0.09769034</td><td>0.14282369</td><td>0.095743452</td></tr>\n",
       "\t<tr><th scope=row>Tocantins</th><td>uf             </td><td>Tocantins      </td><td>2019</td><td>I001P</td><td>0.13093836</td><td>0.10401968</td><td>0.15785704</td><td>0.104891131</td></tr>\n",
       "\t<tr><th scope=row>Maranhão</th><td>uf             </td><td>Maranhão       </td><td>2019</td><td>I001P</td><td>0.06474890</td><td>0.05637050</td><td>0.07312730</td><td>0.066020806</td></tr>\n",
       "\t<tr><th scope=row>Piauí</th><td>uf             </td><td>Piauí          </td><td>2019</td><td>I001P</td><td>0.16703997</td><td>0.14137165</td><td>0.19270830</td><td>0.078402336</td></tr>\n",
       "\t<tr><th scope=row>Ceará</th><td>uf             </td><td>Ceará          </td><td>2019</td><td>I001P</td><td>0.18305157</td><td>0.16819794</td><td>0.19790520</td><td>0.041401017</td></tr>\n",
       "\t<tr><th scope=row>⋮</th><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td></tr>\n",
       "\t<tr><th scope=row>Boa Vista2</th><td>capital</td><td>Boa Vista     </td><td>2019</td><td>I003P</td><td>0.0517498</td><td>0.03507424</td><td>0.06842536</td><td>0.16440829</td></tr>\n",
       "\t<tr><th scope=row>Belém2</th><td>capital</td><td>Belém         </td><td>2019</td><td>I003P</td><td>0.1957352</td><td>0.15697972</td><td>0.23449069</td><td>0.10102204</td></tr>\n",
       "\t<tr><th scope=row>Macapá2</th><td>capital</td><td>Macapá        </td><td>2019</td><td>I003P</td><td>0.1030244</td><td>0.07468566</td><td>0.13136324</td><td>0.14034369</td></tr>\n",
       "\t<tr><th scope=row>Palmas2</th><td>capital</td><td>Palmas        </td><td>2019</td><td>I003P</td><td>0.1174263</td><td>0.08227911</td><td>0.15257354</td><td>0.15271348</td></tr>\n",
       "\t<tr><th scope=row>São Luís2</th><td>capital</td><td>São Luís      </td><td>2019</td><td>I003P</td><td>0.1162225</td><td>0.09210003</td><td>0.14034504</td><td>0.10589709</td></tr>\n",
       "\t<tr><th scope=row>Teresina2</th><td>capital</td><td>Teresina      </td><td>2019</td><td>I003P</td><td>0.1244709</td><td>0.10360634</td><td>0.14533540</td><td>0.08552493</td></tr>\n",
       "\t<tr><th scope=row>Fortaleza2</th><td>capital</td><td>Fortaleza     </td><td>2019</td><td>I003P</td><td>0.2386610</td><td>0.21231893</td><td>0.26500307</td><td>0.05631451</td></tr>\n",
       "\t<tr><th scope=row>Natal2</th><td>capital</td><td>Natal         </td><td>2019</td><td>I003P</td><td>0.2055142</td><td>0.17330238</td><td>0.23772606</td><td>0.07996972</td></tr>\n",
       "\t<tr><th scope=row>João Pessoa2</th><td>capital</td><td>João Pessoa   </td><td>2019</td><td>I003P</td><td>0.2426274</td><td>0.20603639</td><td>0.27921836</td><td>0.07694603</td></tr>\n",
       "\t<tr><th scope=row>Recife2</th><td>capital</td><td>Recife        </td><td>2019</td><td>I003P</td><td>0.2525145</td><td>0.21182256</td><td>0.29320634</td><td>0.08221925</td></tr>\n",
       "\t<tr><th scope=row>Maceió2</th><td>capital</td><td>Maceió        </td><td>2019</td><td>I003P</td><td>0.2105149</td><td>0.18016714</td><td>0.24086274</td><td>0.07355229</td></tr>\n",
       "\t<tr><th scope=row>Aracaju2</th><td>capital</td><td>Aracaju       </td><td>2019</td><td>I003P</td><td>0.2508889</td><td>0.20907376</td><td>0.29270401</td><td>0.08503620</td></tr>\n",
       "\t<tr><th scope=row>Salvador2</th><td>capital</td><td>Salvador      </td><td>2019</td><td>I003P</td><td>0.2518312</td><td>0.21763440</td><td>0.28602800</td><td>0.06928319</td></tr>\n",
       "\t<tr><th scope=row>Belo Horizonte2</th><td>capital</td><td>Belo Horizonte</td><td>2019</td><td>I003P</td><td>0.2232149</td><td>0.19464782</td><td>0.25178191</td><td>0.06529713</td></tr>\n",
       "\t<tr><th scope=row>Vitória2</th><td>capital</td><td>Vitória       </td><td>2019</td><td>I003P</td><td>0.2442612</td><td>0.21145848</td><td>0.27706401</td><td>0.06851849</td></tr>\n",
       "\t<tr><th scope=row>Rio de Janeiro5</th><td>capital</td><td>Rio de Janeiro</td><td>2019</td><td>I003P</td><td>0.2424249</td><td>0.21767184</td><td>0.26717790</td><td>0.05209585</td></tr>\n",
       "\t<tr><th scope=row>São Paulo4</th><td>capital</td><td>São Paulo     </td><td>2019</td><td>I003P</td><td>0.1725544</td><td>0.15170250</td><td>0.19340637</td><td>0.06165555</td></tr>\n",
       "\t<tr><th scope=row>Curitiba2</th><td>capital</td><td>Curitiba      </td><td>2019</td><td>I003P</td><td>0.2715516</td><td>0.23207858</td><td>0.31102456</td><td>0.07416511</td></tr>\n",
       "\t<tr><th scope=row>Florianópolis2</th><td>capital</td><td>Florianópolis </td><td>2019</td><td>I003P</td><td>0.2169666</td><td>0.18424574</td><td>0.24968754</td><td>0.07694567</td></tr>\n",
       "\t<tr><th scope=row>Porto Alegre2</th><td>capital</td><td>Porto Alegre  </td><td>2019</td><td>I003P</td><td>0.2402589</td><td>0.19989965</td><td>0.28061813</td><td>0.08570683</td></tr>\n",
       "\t<tr><th scope=row>Campo Grande2</th><td>capital</td><td>Campo Grande  </td><td>2019</td><td>I003P</td><td>0.2287930</td><td>0.19856658</td><td>0.25901934</td><td>0.06740551</td></tr>\n",
       "\t<tr><th scope=row>Cuiabá2</th><td>capital</td><td>Cuiabá        </td><td>2019</td><td>I003P</td><td>0.1168575</td><td>0.09457907</td><td>0.13913590</td><td>0.09727016</td></tr>\n",
       "\t<tr><th scope=row>Goiânia2</th><td>capital</td><td>Goiânia       </td><td>2019</td><td>I003P</td><td>0.1862813</td><td>0.15699138</td><td>0.21557120</td><td>0.08022334</td></tr>\n",
       "\t<tr><th scope=row>Brasília2</th><td>capital</td><td>Brasília      </td><td>2019</td><td>I003P</td><td>0.2269430</td><td>0.19472756</td><td>0.25915846</td><td>0.07242679</td></tr>\n",
       "\t<tr><th scope=row>I001PSim</th><td>total  </td><td>Brasil        </td><td>2019</td><td>I001P</td><td>0.2849550</td><td>0.27772857</td><td>0.29218143</td><td>0.01293896</td></tr>\n",
       "\t<tr><th scope=row>I001PSim1</th><td>total  </td><td>Capital       </td><td>2019</td><td>I001P</td><td>0.4238657</td><td>0.41115798</td><td>0.43657347</td><td>0.01529650</td></tr>\n",
       "\t<tr><th scope=row>I002PSim</th><td>total  </td><td>Brasil        </td><td>2019</td><td>I002P</td><td>0.2604517</td><td>0.25325236</td><td>0.26765108</td><td>0.01410323</td></tr>\n",
       "\t<tr><th scope=row>I002PSim1</th><td>total  </td><td>Capital       </td><td>2019</td><td>I002P</td><td>0.3933567</td><td>0.38041498</td><td>0.40629835</td><td>0.01678635</td></tr>\n",
       "\t<tr><th scope=row>I003PSim</th><td>total  </td><td>Brasil        </td><td>2019</td><td>I003P</td><td>0.1272771</td><td>0.12280493</td><td>0.13174926</td><td>0.01792748</td></tr>\n",
       "\t<tr><th scope=row>I003PSim1</th><td>total  </td><td>Capital       </td><td>2019</td><td>I003P</td><td>0.2048613</td><td>0.19700024</td><td>0.21272238</td><td>0.01957825</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 243 × 8\n",
       "\\begin{tabular}{r|llllllll}\n",
       "  & abr\\_tipo & abr\\_nome & Ano & Indicador & Sim & LowerS & UpperS & cvS\\\\\n",
       "  & <chr> & <fct> & <chr> & <chr> & <dbl> & <dbl> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\tMasculino & Sexo            & Masculino       & 2019 & I001P & 0.27388650 & 0.26625307 & 0.28151992 & 0.014220036\\\\\n",
       "\tFeminino & Sexo            & Feminino        & 2019 & I001P & 0.29508554 & 0.28761689 & 0.30255420 & 0.012913572\\\\\n",
       "\tBranca & raça            & Branca          & 2019 & I001P & 0.38802339 & 0.37699291 & 0.39905386 & 0.014504020\\\\\n",
       "\tPreta & raça            & Preta           & 2019 & I001P & 0.21423604 & 0.20290490 & 0.22556718 & 0.026985658\\\\\n",
       "\tParda & raça            & Parda           & 2019 & I001P & 0.20099362 & 0.19472077 & 0.20726647 & 0.015923352\\\\\n",
       "\tAté 1/2 SM & rend\\_per\\_capita & Até 1/2 SM      & 2019 & I001P & 0.05825318 & 0.05366052 & 0.06284585 & 0.040225086\\\\\n",
       "\t1/2 até 1 SM & rend\\_per\\_capita & 1/2 até 1 SM    & 2019 & I001P & 0.17653124 & 0.16848817 & 0.18457431 & 0.023246217\\\\\n",
       "\t1 até 2 SM & rend\\_per\\_capita & 1 até 2 SM      & 2019 & I001P & 0.35748528 & 0.34693500 & 0.36803556 & 0.015057667\\\\\n",
       "\t2 até 3 SM & rend\\_per\\_capita & 2 até 3 SM      & 2019 & I001P & 0.54822804 & 0.52978537 & 0.56667072 & 0.017163845\\\\\n",
       "\tMais de 3 SM & rend\\_per\\_capita & Mais de 3 SM    & 2019 & I001P & 0.79230690 & 0.77994087 & 0.80467292 & 0.007963219\\\\\n",
       "\tMenor de 1 ano & fx\\_idade        & Menor de 1 ano  & 2019 & I001P & 0.27324375 & 0.24541975 & 0.30106776 & 0.051954282\\\\\n",
       "\t1 a 4 anos & fx\\_idade        & 1 a 4 anos      & 2019 & I001P & 0.28148882 & 0.26626871 & 0.29670892 & 0.027587250\\\\\n",
       "\t5 a 14 anos & fx\\_idade        & 5 a 14 anos     & 2019 & I001P & 0.25153348 & 0.24067604 & 0.26239092 & 0.022023359\\\\\n",
       "\t15 a 24 anos & fx\\_idade        & 15 a 24 anos    & 2019 & I001P & 0.23409422 & 0.22506088 & 0.24312756 & 0.019688363\\\\\n",
       "\t25 a 34 anos & fx\\_idade        & 25 a 34 anos    & 2019 & I001P & 0.29703277 & 0.28601233 & 0.30805321 & 0.018929825\\\\\n",
       "\t35 a 44 anos & fx\\_idade        & 35 a 44 anos    & 2019 & I001P & 0.32592919 & 0.31400964 & 0.33784875 & 0.018659011\\\\\n",
       "\t45 a 59 anos & fx\\_idade        & 45 a 59 anos    & 2019 & I001P & 0.29365128 & 0.28305072 & 0.30425183 & 0.018418260\\\\\n",
       "\t60 anos ou mais & fx\\_idade        & 60 anos ou mais & 2019 & I001P & 0.30214641 & 0.29153927 & 0.31275355 & 0.017911533\\\\\n",
       "\tUrbano & urb\\_rur         & Urbano          & 2019 & I001P & 0.32162083 & 0.31325373 & 0.32998794 & 0.013273418\\\\\n",
       "\tRural & urb\\_rur         & Rural           & 2019 & I001P & 0.07013919 & 0.06309588 & 0.07718250 & 0.051235138\\\\\n",
       "\tRondônia & uf              & Rondônia        & 2019 & I001P & 0.13765773 & 0.11780679 & 0.15750867 & 0.073575358\\\\\n",
       "\tAcre & uf              & Acre            & 2019 & I001P & 0.09564527 & 0.08104591 & 0.11024462 & 0.077879315\\\\\n",
       "\tAmazonas & uf              & Amazonas        & 2019 & I001P & 0.16262579 & 0.14226485 & 0.18298673 & 0.063879330\\\\\n",
       "\tRoraima & uf              & Roraima         & 2019 & I001P & 0.08270107 & 0.06120669 & 0.10419544 & 0.132606751\\\\\n",
       "\tPará & uf              & Pará            & 2019 & I001P & 0.15734934 & 0.13529986 & 0.17939882 & 0.071496582\\\\\n",
       "\tAmapá & uf              & Amapá           & 2019 & I001P & 0.12025701 & 0.09769034 & 0.14282369 & 0.095743452\\\\\n",
       "\tTocantins & uf              & Tocantins       & 2019 & I001P & 0.13093836 & 0.10401968 & 0.15785704 & 0.104891131\\\\\n",
       "\tMaranhão & uf              & Maranhão        & 2019 & I001P & 0.06474890 & 0.05637050 & 0.07312730 & 0.066020806\\\\\n",
       "\tPiauí & uf              & Piauí           & 2019 & I001P & 0.16703997 & 0.14137165 & 0.19270830 & 0.078402336\\\\\n",
       "\tCeará & uf              & Ceará           & 2019 & I001P & 0.18305157 & 0.16819794 & 0.19790520 & 0.041401017\\\\\n",
       "\t⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮\\\\\n",
       "\tBoa Vista2 & capital & Boa Vista      & 2019 & I003P & 0.0517498 & 0.03507424 & 0.06842536 & 0.16440829\\\\\n",
       "\tBelém2 & capital & Belém          & 2019 & I003P & 0.1957352 & 0.15697972 & 0.23449069 & 0.10102204\\\\\n",
       "\tMacapá2 & capital & Macapá         & 2019 & I003P & 0.1030244 & 0.07468566 & 0.13136324 & 0.14034369\\\\\n",
       "\tPalmas2 & capital & Palmas         & 2019 & I003P & 0.1174263 & 0.08227911 & 0.15257354 & 0.15271348\\\\\n",
       "\tSão Luís2 & capital & São Luís       & 2019 & I003P & 0.1162225 & 0.09210003 & 0.14034504 & 0.10589709\\\\\n",
       "\tTeresina2 & capital & Teresina       & 2019 & I003P & 0.1244709 & 0.10360634 & 0.14533540 & 0.08552493\\\\\n",
       "\tFortaleza2 & capital & Fortaleza      & 2019 & I003P & 0.2386610 & 0.21231893 & 0.26500307 & 0.05631451\\\\\n",
       "\tNatal2 & capital & Natal          & 2019 & I003P & 0.2055142 & 0.17330238 & 0.23772606 & 0.07996972\\\\\n",
       "\tJoão Pessoa2 & capital & João Pessoa    & 2019 & I003P & 0.2426274 & 0.20603639 & 0.27921836 & 0.07694603\\\\\n",
       "\tRecife2 & capital & Recife         & 2019 & I003P & 0.2525145 & 0.21182256 & 0.29320634 & 0.08221925\\\\\n",
       "\tMaceió2 & capital & Maceió         & 2019 & I003P & 0.2105149 & 0.18016714 & 0.24086274 & 0.07355229\\\\\n",
       "\tAracaju2 & capital & Aracaju        & 2019 & I003P & 0.2508889 & 0.20907376 & 0.29270401 & 0.08503620\\\\\n",
       "\tSalvador2 & capital & Salvador       & 2019 & I003P & 0.2518312 & 0.21763440 & 0.28602800 & 0.06928319\\\\\n",
       "\tBelo Horizonte2 & capital & Belo Horizonte & 2019 & I003P & 0.2232149 & 0.19464782 & 0.25178191 & 0.06529713\\\\\n",
       "\tVitória2 & capital & Vitória        & 2019 & I003P & 0.2442612 & 0.21145848 & 0.27706401 & 0.06851849\\\\\n",
       "\tRio de Janeiro5 & capital & Rio de Janeiro & 2019 & I003P & 0.2424249 & 0.21767184 & 0.26717790 & 0.05209585\\\\\n",
       "\tSão Paulo4 & capital & São Paulo      & 2019 & I003P & 0.1725544 & 0.15170250 & 0.19340637 & 0.06165555\\\\\n",
       "\tCuritiba2 & capital & Curitiba       & 2019 & I003P & 0.2715516 & 0.23207858 & 0.31102456 & 0.07416511\\\\\n",
       "\tFlorianópolis2 & capital & Florianópolis  & 2019 & I003P & 0.2169666 & 0.18424574 & 0.24968754 & 0.07694567\\\\\n",
       "\tPorto Alegre2 & capital & Porto Alegre   & 2019 & I003P & 0.2402589 & 0.19989965 & 0.28061813 & 0.08570683\\\\\n",
       "\tCampo Grande2 & capital & Campo Grande   & 2019 & I003P & 0.2287930 & 0.19856658 & 0.25901934 & 0.06740551\\\\\n",
       "\tCuiabá2 & capital & Cuiabá         & 2019 & I003P & 0.1168575 & 0.09457907 & 0.13913590 & 0.09727016\\\\\n",
       "\tGoiânia2 & capital & Goiânia        & 2019 & I003P & 0.1862813 & 0.15699138 & 0.21557120 & 0.08022334\\\\\n",
       "\tBrasília2 & capital & Brasília       & 2019 & I003P & 0.2269430 & 0.19472756 & 0.25915846 & 0.07242679\\\\\n",
       "\tI001PSim & total   & Brasil         & 2019 & I001P & 0.2849550 & 0.27772857 & 0.29218143 & 0.01293896\\\\\n",
       "\tI001PSim1 & total   & Capital        & 2019 & I001P & 0.4238657 & 0.41115798 & 0.43657347 & 0.01529650\\\\\n",
       "\tI002PSim & total   & Brasil         & 2019 & I002P & 0.2604517 & 0.25325236 & 0.26765108 & 0.01410323\\\\\n",
       "\tI002PSim1 & total   & Capital        & 2019 & I002P & 0.3933567 & 0.38041498 & 0.40629835 & 0.01678635\\\\\n",
       "\tI003PSim & total   & Brasil         & 2019 & I003P & 0.1272771 & 0.12280493 & 0.13174926 & 0.01792748\\\\\n",
       "\tI003PSim1 & total   & Capital        & 2019 & I003P & 0.2048613 & 0.19700024 & 0.21272238 & 0.01957825\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 243 × 8\n",
       "\n",
       "| <!--/--> | abr_tipo &lt;chr&gt; | abr_nome &lt;fct&gt; | Ano &lt;chr&gt; | Indicador &lt;chr&gt; | Sim &lt;dbl&gt; | LowerS &lt;dbl&gt; | UpperS &lt;dbl&gt; | cvS &lt;dbl&gt; |\n",
       "|---|---|---|---|---|---|---|---|---|\n",
       "| Masculino | Sexo            | Masculino       | 2019 | I001P | 0.27388650 | 0.26625307 | 0.28151992 | 0.014220036 |\n",
       "| Feminino | Sexo            | Feminino        | 2019 | I001P | 0.29508554 | 0.28761689 | 0.30255420 | 0.012913572 |\n",
       "| Branca | raça            | Branca          | 2019 | I001P | 0.38802339 | 0.37699291 | 0.39905386 | 0.014504020 |\n",
       "| Preta | raça            | Preta           | 2019 | I001P | 0.21423604 | 0.20290490 | 0.22556718 | 0.026985658 |\n",
       "| Parda | raça            | Parda           | 2019 | I001P | 0.20099362 | 0.19472077 | 0.20726647 | 0.015923352 |\n",
       "| Até 1/2 SM | rend_per_capita | Até 1/2 SM      | 2019 | I001P | 0.05825318 | 0.05366052 | 0.06284585 | 0.040225086 |\n",
       "| 1/2 até 1 SM | rend_per_capita | 1/2 até 1 SM    | 2019 | I001P | 0.17653124 | 0.16848817 | 0.18457431 | 0.023246217 |\n",
       "| 1 até 2 SM | rend_per_capita | 1 até 2 SM      | 2019 | I001P | 0.35748528 | 0.34693500 | 0.36803556 | 0.015057667 |\n",
       "| 2 até 3 SM | rend_per_capita | 2 até 3 SM      | 2019 | I001P | 0.54822804 | 0.52978537 | 0.56667072 | 0.017163845 |\n",
       "| Mais de 3 SM | rend_per_capita | Mais de 3 SM    | 2019 | I001P | 0.79230690 | 0.77994087 | 0.80467292 | 0.007963219 |\n",
       "| Menor de 1 ano | fx_idade        | Menor de 1 ano  | 2019 | I001P | 0.27324375 | 0.24541975 | 0.30106776 | 0.051954282 |\n",
       "| 1 a 4 anos | fx_idade        | 1 a 4 anos      | 2019 | I001P | 0.28148882 | 0.26626871 | 0.29670892 | 0.027587250 |\n",
       "| 5 a 14 anos | fx_idade        | 5 a 14 anos     | 2019 | I001P | 0.25153348 | 0.24067604 | 0.26239092 | 0.022023359 |\n",
       "| 15 a 24 anos | fx_idade        | 15 a 24 anos    | 2019 | I001P | 0.23409422 | 0.22506088 | 0.24312756 | 0.019688363 |\n",
       "| 25 a 34 anos | fx_idade        | 25 a 34 anos    | 2019 | I001P | 0.29703277 | 0.28601233 | 0.30805321 | 0.018929825 |\n",
       "| 35 a 44 anos | fx_idade        | 35 a 44 anos    | 2019 | I001P | 0.32592919 | 0.31400964 | 0.33784875 | 0.018659011 |\n",
       "| 45 a 59 anos | fx_idade        | 45 a 59 anos    | 2019 | I001P | 0.29365128 | 0.28305072 | 0.30425183 | 0.018418260 |\n",
       "| 60 anos ou mais | fx_idade        | 60 anos ou mais | 2019 | I001P | 0.30214641 | 0.29153927 | 0.31275355 | 0.017911533 |\n",
       "| Urbano | urb_rur         | Urbano          | 2019 | I001P | 0.32162083 | 0.31325373 | 0.32998794 | 0.013273418 |\n",
       "| Rural | urb_rur         | Rural           | 2019 | I001P | 0.07013919 | 0.06309588 | 0.07718250 | 0.051235138 |\n",
       "| Rondônia | uf              | Rondônia        | 2019 | I001P | 0.13765773 | 0.11780679 | 0.15750867 | 0.073575358 |\n",
       "| Acre | uf              | Acre            | 2019 | I001P | 0.09564527 | 0.08104591 | 0.11024462 | 0.077879315 |\n",
       "| Amazonas | uf              | Amazonas        | 2019 | I001P | 0.16262579 | 0.14226485 | 0.18298673 | 0.063879330 |\n",
       "| Roraima | uf              | Roraima         | 2019 | I001P | 0.08270107 | 0.06120669 | 0.10419544 | 0.132606751 |\n",
       "| Pará | uf              | Pará            | 2019 | I001P | 0.15734934 | 0.13529986 | 0.17939882 | 0.071496582 |\n",
       "| Amapá | uf              | Amapá           | 2019 | I001P | 0.12025701 | 0.09769034 | 0.14282369 | 0.095743452 |\n",
       "| Tocantins | uf              | Tocantins       | 2019 | I001P | 0.13093836 | 0.10401968 | 0.15785704 | 0.104891131 |\n",
       "| Maranhão | uf              | Maranhão        | 2019 | I001P | 0.06474890 | 0.05637050 | 0.07312730 | 0.066020806 |\n",
       "| Piauí | uf              | Piauí           | 2019 | I001P | 0.16703997 | 0.14137165 | 0.19270830 | 0.078402336 |\n",
       "| Ceará | uf              | Ceará           | 2019 | I001P | 0.18305157 | 0.16819794 | 0.19790520 | 0.041401017 |\n",
       "| ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ |\n",
       "| Boa Vista2 | capital | Boa Vista      | 2019 | I003P | 0.0517498 | 0.03507424 | 0.06842536 | 0.16440829 |\n",
       "| Belém2 | capital | Belém          | 2019 | I003P | 0.1957352 | 0.15697972 | 0.23449069 | 0.10102204 |\n",
       "| Macapá2 | capital | Macapá         | 2019 | I003P | 0.1030244 | 0.07468566 | 0.13136324 | 0.14034369 |\n",
       "| Palmas2 | capital | Palmas         | 2019 | I003P | 0.1174263 | 0.08227911 | 0.15257354 | 0.15271348 |\n",
       "| São Luís2 | capital | São Luís       | 2019 | I003P | 0.1162225 | 0.09210003 | 0.14034504 | 0.10589709 |\n",
       "| Teresina2 | capital | Teresina       | 2019 | I003P | 0.1244709 | 0.10360634 | 0.14533540 | 0.08552493 |\n",
       "| Fortaleza2 | capital | Fortaleza      | 2019 | I003P | 0.2386610 | 0.21231893 | 0.26500307 | 0.05631451 |\n",
       "| Natal2 | capital | Natal          | 2019 | I003P | 0.2055142 | 0.17330238 | 0.23772606 | 0.07996972 |\n",
       "| João Pessoa2 | capital | João Pessoa    | 2019 | I003P | 0.2426274 | 0.20603639 | 0.27921836 | 0.07694603 |\n",
       "| Recife2 | capital | Recife         | 2019 | I003P | 0.2525145 | 0.21182256 | 0.29320634 | 0.08221925 |\n",
       "| Maceió2 | capital | Maceió         | 2019 | I003P | 0.2105149 | 0.18016714 | 0.24086274 | 0.07355229 |\n",
       "| Aracaju2 | capital | Aracaju        | 2019 | I003P | 0.2508889 | 0.20907376 | 0.29270401 | 0.08503620 |\n",
       "| Salvador2 | capital | Salvador       | 2019 | I003P | 0.2518312 | 0.21763440 | 0.28602800 | 0.06928319 |\n",
       "| Belo Horizonte2 | capital | Belo Horizonte | 2019 | I003P | 0.2232149 | 0.19464782 | 0.25178191 | 0.06529713 |\n",
       "| Vitória2 | capital | Vitória        | 2019 | I003P | 0.2442612 | 0.21145848 | 0.27706401 | 0.06851849 |\n",
       "| Rio de Janeiro5 | capital | Rio de Janeiro | 2019 | I003P | 0.2424249 | 0.21767184 | 0.26717790 | 0.05209585 |\n",
       "| São Paulo4 | capital | São Paulo      | 2019 | I003P | 0.1725544 | 0.15170250 | 0.19340637 | 0.06165555 |\n",
       "| Curitiba2 | capital | Curitiba       | 2019 | I003P | 0.2715516 | 0.23207858 | 0.31102456 | 0.07416511 |\n",
       "| Florianópolis2 | capital | Florianópolis  | 2019 | I003P | 0.2169666 | 0.18424574 | 0.24968754 | 0.07694567 |\n",
       "| Porto Alegre2 | capital | Porto Alegre   | 2019 | I003P | 0.2402589 | 0.19989965 | 0.28061813 | 0.08570683 |\n",
       "| Campo Grande2 | capital | Campo Grande   | 2019 | I003P | 0.2287930 | 0.19856658 | 0.25901934 | 0.06740551 |\n",
       "| Cuiabá2 | capital | Cuiabá         | 2019 | I003P | 0.1168575 | 0.09457907 | 0.13913590 | 0.09727016 |\n",
       "| Goiânia2 | capital | Goiânia        | 2019 | I003P | 0.1862813 | 0.15699138 | 0.21557120 | 0.08022334 |\n",
       "| Brasília2 | capital | Brasília       | 2019 | I003P | 0.2269430 | 0.19472756 | 0.25915846 | 0.07242679 |\n",
       "| I001PSim | total   | Brasil         | 2019 | I001P | 0.2849550 | 0.27772857 | 0.29218143 | 0.01293896 |\n",
       "| I001PSim1 | total   | Capital        | 2019 | I001P | 0.4238657 | 0.41115798 | 0.43657347 | 0.01529650 |\n",
       "| I002PSim | total   | Brasil         | 2019 | I002P | 0.2604517 | 0.25325236 | 0.26765108 | 0.01410323 |\n",
       "| I002PSim1 | total   | Capital        | 2019 | I002P | 0.3933567 | 0.38041498 | 0.40629835 | 0.01678635 |\n",
       "| I003PSim | total   | Brasil         | 2019 | I003P | 0.1272771 | 0.12280493 | 0.13174926 | 0.01792748 |\n",
       "| I003PSim1 | total   | Capital        | 2019 | I003P | 0.2048613 | 0.19700024 | 0.21272238 | 0.01957825 |\n",
       "\n"
      ],
      "text/plain": [
       "                abr_tipo        abr_nome        Ano  Indicador Sim       \n",
       "Masculino       Sexo            Masculino       2019 I001P     0.27388650\n",
       "Feminino        Sexo            Feminino        2019 I001P     0.29508554\n",
       "Branca          raça            Branca          2019 I001P     0.38802339\n",
       "Preta           raça            Preta           2019 I001P     0.21423604\n",
       "Parda           raça            Parda           2019 I001P     0.20099362\n",
       "Até 1/2 SM      rend_per_capita Até 1/2 SM      2019 I001P     0.05825318\n",
       "1/2 até 1 SM    rend_per_capita 1/2 até 1 SM    2019 I001P     0.17653124\n",
       "1 até 2 SM      rend_per_capita 1 até 2 SM      2019 I001P     0.35748528\n",
       "2 até 3 SM      rend_per_capita 2 até 3 SM      2019 I001P     0.54822804\n",
       "Mais de 3 SM    rend_per_capita Mais de 3 SM    2019 I001P     0.79230690\n",
       "Menor de 1 ano  fx_idade        Menor de 1 ano  2019 I001P     0.27324375\n",
       "1 a 4 anos      fx_idade        1 a 4 anos      2019 I001P     0.28148882\n",
       "5 a 14 anos     fx_idade        5 a 14 anos     2019 I001P     0.25153348\n",
       "15 a 24 anos    fx_idade        15 a 24 anos    2019 I001P     0.23409422\n",
       "25 a 34 anos    fx_idade        25 a 34 anos    2019 I001P     0.29703277\n",
       "35 a 44 anos    fx_idade        35 a 44 anos    2019 I001P     0.32592919\n",
       "45 a 59 anos    fx_idade        45 a 59 anos    2019 I001P     0.29365128\n",
       "60 anos ou mais fx_idade        60 anos ou mais 2019 I001P     0.30214641\n",
       "Urbano          urb_rur         Urbano          2019 I001P     0.32162083\n",
       "Rural           urb_rur         Rural           2019 I001P     0.07013919\n",
       "Rondônia        uf              Rondônia        2019 I001P     0.13765773\n",
       "Acre            uf              Acre            2019 I001P     0.09564527\n",
       "Amazonas        uf              Amazonas        2019 I001P     0.16262579\n",
       "Roraima         uf              Roraima         2019 I001P     0.08270107\n",
       "Pará            uf              Pará            2019 I001P     0.15734934\n",
       "Amapá           uf              Amapá           2019 I001P     0.12025701\n",
       "Tocantins       uf              Tocantins       2019 I001P     0.13093836\n",
       "Maranhão        uf              Maranhão        2019 I001P     0.06474890\n",
       "Piauí           uf              Piauí           2019 I001P     0.16703997\n",
       "Ceará           uf              Ceará           2019 I001P     0.18305157\n",
       "⋮               ⋮               ⋮               ⋮    ⋮         ⋮         \n",
       "Boa Vista2      capital         Boa Vista       2019 I003P     0.0517498 \n",
       "Belém2          capital         Belém           2019 I003P     0.1957352 \n",
       "Macapá2         capital         Macapá          2019 I003P     0.1030244 \n",
       "Palmas2         capital         Palmas          2019 I003P     0.1174263 \n",
       "São Luís2       capital         São Luís        2019 I003P     0.1162225 \n",
       "Teresina2       capital         Teresina        2019 I003P     0.1244709 \n",
       "Fortaleza2      capital         Fortaleza       2019 I003P     0.2386610 \n",
       "Natal2          capital         Natal           2019 I003P     0.2055142 \n",
       "João Pessoa2    capital         João Pessoa     2019 I003P     0.2426274 \n",
       "Recife2         capital         Recife          2019 I003P     0.2525145 \n",
       "Maceió2         capital         Maceió          2019 I003P     0.2105149 \n",
       "Aracaju2        capital         Aracaju         2019 I003P     0.2508889 \n",
       "Salvador2       capital         Salvador        2019 I003P     0.2518312 \n",
       "Belo Horizonte2 capital         Belo Horizonte  2019 I003P     0.2232149 \n",
       "Vitória2        capital         Vitória         2019 I003P     0.2442612 \n",
       "Rio de Janeiro5 capital         Rio de Janeiro  2019 I003P     0.2424249 \n",
       "São Paulo4      capital         São Paulo       2019 I003P     0.1725544 \n",
       "Curitiba2       capital         Curitiba        2019 I003P     0.2715516 \n",
       "Florianópolis2  capital         Florianópolis   2019 I003P     0.2169666 \n",
       "Porto Alegre2   capital         Porto Alegre    2019 I003P     0.2402589 \n",
       "Campo Grande2   capital         Campo Grande    2019 I003P     0.2287930 \n",
       "Cuiabá2         capital         Cuiabá          2019 I003P     0.1168575 \n",
       "Goiânia2        capital         Goiânia         2019 I003P     0.1862813 \n",
       "Brasília2       capital         Brasília        2019 I003P     0.2269430 \n",
       "I001PSim        total           Brasil          2019 I001P     0.2849550 \n",
       "I001PSim1       total           Capital         2019 I001P     0.4238657 \n",
       "I002PSim        total           Brasil          2019 I002P     0.2604517 \n",
       "I002PSim1       total           Capital         2019 I002P     0.3933567 \n",
       "I003PSim        total           Brasil          2019 I003P     0.1272771 \n",
       "I003PSim1       total           Capital         2019 I003P     0.2048613 \n",
       "                LowerS     UpperS     cvS        \n",
       "Masculino       0.26625307 0.28151992 0.014220036\n",
       "Feminino        0.28761689 0.30255420 0.012913572\n",
       "Branca          0.37699291 0.39905386 0.014504020\n",
       "Preta           0.20290490 0.22556718 0.026985658\n",
       "Parda           0.19472077 0.20726647 0.015923352\n",
       "Até 1/2 SM      0.05366052 0.06284585 0.040225086\n",
       "1/2 até 1 SM    0.16848817 0.18457431 0.023246217\n",
       "1 até 2 SM      0.34693500 0.36803556 0.015057667\n",
       "2 até 3 SM      0.52978537 0.56667072 0.017163845\n",
       "Mais de 3 SM    0.77994087 0.80467292 0.007963219\n",
       "Menor de 1 ano  0.24541975 0.30106776 0.051954282\n",
       "1 a 4 anos      0.26626871 0.29670892 0.027587250\n",
       "5 a 14 anos     0.24067604 0.26239092 0.022023359\n",
       "15 a 24 anos    0.22506088 0.24312756 0.019688363\n",
       "25 a 34 anos    0.28601233 0.30805321 0.018929825\n",
       "35 a 44 anos    0.31400964 0.33784875 0.018659011\n",
       "45 a 59 anos    0.28305072 0.30425183 0.018418260\n",
       "60 anos ou mais 0.29153927 0.31275355 0.017911533\n",
       "Urbano          0.31325373 0.32998794 0.013273418\n",
       "Rural           0.06309588 0.07718250 0.051235138\n",
       "Rondônia        0.11780679 0.15750867 0.073575358\n",
       "Acre            0.08104591 0.11024462 0.077879315\n",
       "Amazonas        0.14226485 0.18298673 0.063879330\n",
       "Roraima         0.06120669 0.10419544 0.132606751\n",
       "Pará            0.13529986 0.17939882 0.071496582\n",
       "Amapá           0.09769034 0.14282369 0.095743452\n",
       "Tocantins       0.10401968 0.15785704 0.104891131\n",
       "Maranhão        0.05637050 0.07312730 0.066020806\n",
       "Piauí           0.14137165 0.19270830 0.078402336\n",
       "Ceará           0.16819794 0.19790520 0.041401017\n",
       "⋮               ⋮          ⋮          ⋮          \n",
       "Boa Vista2      0.03507424 0.06842536 0.16440829 \n",
       "Belém2          0.15697972 0.23449069 0.10102204 \n",
       "Macapá2         0.07468566 0.13136324 0.14034369 \n",
       "Palmas2         0.08227911 0.15257354 0.15271348 \n",
       "São Luís2       0.09210003 0.14034504 0.10589709 \n",
       "Teresina2       0.10360634 0.14533540 0.08552493 \n",
       "Fortaleza2      0.21231893 0.26500307 0.05631451 \n",
       "Natal2          0.17330238 0.23772606 0.07996972 \n",
       "João Pessoa2    0.20603639 0.27921836 0.07694603 \n",
       "Recife2         0.21182256 0.29320634 0.08221925 \n",
       "Maceió2         0.18016714 0.24086274 0.07355229 \n",
       "Aracaju2        0.20907376 0.29270401 0.08503620 \n",
       "Salvador2       0.21763440 0.28602800 0.06928319 \n",
       "Belo Horizonte2 0.19464782 0.25178191 0.06529713 \n",
       "Vitória2        0.21145848 0.27706401 0.06851849 \n",
       "Rio de Janeiro5 0.21767184 0.26717790 0.05209585 \n",
       "São Paulo4      0.15170250 0.19340637 0.06165555 \n",
       "Curitiba2       0.23207858 0.31102456 0.07416511 \n",
       "Florianópolis2  0.18424574 0.24968754 0.07694567 \n",
       "Porto Alegre2   0.19989965 0.28061813 0.08570683 \n",
       "Campo Grande2   0.19856658 0.25901934 0.06740551 \n",
       "Cuiabá2         0.09457907 0.13913590 0.09727016 \n",
       "Goiânia2        0.15699138 0.21557120 0.08022334 \n",
       "Brasília2       0.19472756 0.25915846 0.07242679 \n",
       "I001PSim        0.27772857 0.29218143 0.01293896 \n",
       "I001PSim1       0.41115798 0.43657347 0.01529650 \n",
       "I002PSim        0.25325236 0.26765108 0.01410323 \n",
       "I002PSim1       0.38041498 0.40629835 0.01678635 \n",
       "I003PSim        0.12280493 0.13174926 0.01792748 \n",
       "I003PSim1       0.19700024 0.21272238 0.01957825 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "matrizIndicadores"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Exportando tabela de indicadores calculados - Módulo I 2019"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "diretorio_saida <- \"<coloque aqui o diretório>\"\n",
    "write.table(matrizIndicadores,file=paste0(diretorio_saida,\"Indicadores_2019I_R.csv\"),sep = \";\",dec = \",\",row.names = FALSE)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "R",
   "language": "R",
   "name": "ir"
  },
  "language_info": {
   "codemirror_mode": "r",
   "file_extension": ".r",
   "mimetype": "text/x-r-source",
   "name": "R",
   "pygments_lexer": "r",
   "version": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}