{
 "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 - S 2019 Pré-natal - Parte 3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Bibliotecas Utilizadas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Lendo pacotes necessários\n",
    "library(survey)\n",
    "library(ggplot2)\n",
    "library(dplyr)\n",
    "library(tictoc)\n",
    "library(foreign)\n",
    "library(forcats)\n",
    "library(tidyverse)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Carregando microdados da PNS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>293726</li><li>1087</li></ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 293726\n",
       "\\item 1087\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 293726\n",
       "2. 1087\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] 293726   1087"
      ]
     },
     "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": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "    Min.  1st Qu.   Median     Mean  3rd Qu.     Max. \n",
       " 0.00562  0.26621  0.54401  1.00000  1.12765 61.09981 "
      ]
     },
     "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>  %>% filter(V0025A==1) \n",
    "pns2019.1<-pns2019.1 %>% mutate(peso_morador_selec=((V00291*(90846/168426190))))\n",
    "pns2019.1<-pns2019.1 %>% filter(!is.na(peso_morador_selec))\n",
    "summary(pns2019.1$peso_morador_selec)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Criação de variáveis dos indicadores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>2131</dd><dt>Não</dt><dd>603</dd><dt>NA's</dt><dd>88112</dd></dl>\n"
      ],
      "text/latex": [
       "\\begin{description*}\n",
       "\\item[Sim] 2131\n",
       "\\item[Não] 603\n",
       "\\item[NA's] 88112\n",
       "\\end{description*}\n"
      ],
      "text/markdown": [
       "Sim\n",
       ":   2131Não\n",
       ":   603NA's\n",
       ":   88112\n",
       "\n"
      ],
      "text/plain": [
       "  Sim   Não  NA's \n",
       " 2131   603 88112 "
      ]
     },
     "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>2071</dd><dt>Não</dt><dd>663</dd><dt>NA's</dt><dd>88112</dd></dl>\n"
      ],
      "text/latex": [
       "\\begin{description*}\n",
       "\\item[Sim] 2071\n",
       "\\item[Não] 663\n",
       "\\item[NA's] 88112\n",
       "\\end{description*}\n"
      ],
      "text/markdown": [
       "Sim\n",
       ":   2071Não\n",
       ":   663NA's\n",
       ":   88112\n",
       "\n"
      ],
      "text/plain": [
       "  Sim   Não  NA's \n",
       " 2071   663 88112 "
      ]
     },
     "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>2246</dd><dt>Não</dt><dd>488</dd><dt>NA's</dt><dd>88112</dd></dl>\n"
      ],
      "text/latex": [
       "\\begin{description*}\n",
       "\\item[Sim] 2246\n",
       "\\item[Não] 488\n",
       "\\item[NA's] 88112\n",
       "\\end{description*}\n"
      ],
      "text/markdown": [
       "Sim\n",
       ":   2246Não\n",
       ":   488NA's\n",
       ":   88112\n",
       "\n"
      ],
      "text/plain": [
       "  Sim   Não  NA's \n",
       " 2246   488 88112 "
      ]
     },
     "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>2172</dd><dt>Não</dt><dd>562</dd><dt>NA's</dt><dd>88112</dd></dl>\n"
      ],
      "text/latex": [
       "\\begin{description*}\n",
       "\\item[Sim] 2172\n",
       "\\item[Não] 562\n",
       "\\item[NA's] 88112\n",
       "\\end{description*}\n"
      ],
      "text/markdown": [
       "Sim\n",
       ":   2172Não\n",
       ":   562NA's\n",
       ":   88112\n",
       "\n"
      ],
      "text/plain": [
       "  Sim   Não  NA's \n",
       " 2172   562 88112 "
      ]
     },
     "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>2423</dd><dt>Não</dt><dd>311</dd><dt>NA's</dt><dd>88112</dd></dl>\n"
      ],
      "text/latex": [
       "\\begin{description*}\n",
       "\\item[Sim] 2423\n",
       "\\item[Não] 311\n",
       "\\item[NA's] 88112\n",
       "\\end{description*}\n"
      ],
      "text/markdown": [
       "Sim\n",
       ":   2423Não\n",
       ":   311NA's\n",
       ":   88112\n",
       "\n"
      ],
      "text/plain": [
       "  Sim   Não  NA's \n",
       " 2423   311 88112 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Desfechos - Indicadores\n",
    "\n",
    "# 11. Proporção de mulheres que realizaram teste/exame para sífilis durante o pré-natal - S011P.\n",
    "\n",
    "pns2019.1$S011P <- NA\n",
    "pns2019.1$S011P[pns2019.1$C006==2 & pns2019.1$C008>=18 & pns2019.1$S068==1] <- 2\n",
    "pns2019.1$S011P[pns2019.1$C006==2 & pns2019.1$C008>=18 & pns2019.1$S068==1 & pns2019.1$S080==1] <- 1\n",
    "pns2019.1$S011P<-factor(pns2019.1$S011P, levels=c(1,2), labels=c(\"Sim\",\"Não\"))\n",
    "summary(pns2019.1$S011P)\n",
    "\n",
    "# 12. Proporção de mulheres que realizaram teste/exame para sífilis durante o pré-natal e receberam o resultado antes do parto - S012P.\n",
    "\n",
    "pns2019.1$S012P <- NA\n",
    "pns2019.1$S012P[pns2019.1$C006==2 & pns2019.1$C008>=18 & pns2019.1$S068==1] <- 2\n",
    "pns2019.1$S012P[pns2019.1$C006==2 & pns2019.1$C008>=18 & pns2019.1$S068==1 & pns2019.1$S081==1] <- 1\n",
    "pns2019.1$S012P<-factor(pns2019.1$S012P, levels=c(1,2), labels=c(\"Sim\",\"Não\"))\n",
    "summary(pns2019.1$S012P)\n",
    "\n",
    "# 13. Proporção de mulheres que realizaram teste/exame para hepatite B durante o pré-natal - S013P.\n",
    "\n",
    "pns2019.1$S013P <- NA\n",
    "pns2019.1$S013P[pns2019.1$C006==2 & pns2019.1$C008>=18 & pns2019.1$S068==1] <- 2\n",
    "pns2019.1$S013P[pns2019.1$C006==2 & pns2019.1$C008>=18 & pns2019.1$S068==1 & pns2019.1$S088==1] <- 1\n",
    "pns2019.1$S013P<-factor(pns2019.1$S013P, levels=c(1,2), labels=c(\"Sim\",\"Não\"))\n",
    "summary(pns2019.1$S013P)\n",
    "\n",
    "# 14. Proporção de mulheres que realizaram teste/exame para hepatite B durante o pré-natal e receberam o resultado antes do parto - S014P.\n",
    "\n",
    "pns2019.1$S014P <- NA\n",
    "pns2019.1$S014P[pns2019.1$C006==2 & pns2019.1$C008>=18 & pns2019.1$S068==1] <- 2\n",
    "pns2019.1$S014P[pns2019.1$C006==2 & pns2019.1$C008>=18 & pns2019.1$S068==1 & pns2019.1$S089==1] <- 1\n",
    "pns2019.1$S014P<-factor(pns2019.1$S014P, levels=c(1,2), labels=c(\"Sim\",\"Não\"))\n",
    "summary(pns2019.1$S014P)\n",
    "\n",
    "# 15. Proporção de mulheres que tiveram solicitação de teste/exame para HIV/AIDS durante o pré-natal - S015P.\n",
    "\n",
    "pns2019.1$S015P <- NA\n",
    "pns2019.1$S015P[pns2019.1$C006==2 & pns2019.1$C008>=18 & pns2019.1$S068==1] <- 2\n",
    "pns2019.1$S015P[pns2019.1$C006==2 & pns2019.1$C008>=18 & pns2019.1$S068==1 & pns2019.1$S090==1] <- 1\n",
    "pns2019.1$S015P<-factor(pns2019.1$S015P, levels=c(1,2), labels=c(\"Sim\",\"Não\"))\n",
    "summary(pns2019.1$S015P)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Definições de abrangências"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Situação urbana ou rural"
   ]
  },
  {
   "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>urbano</dt><dd>69873</dd><dt>rural</dt><dd>20973</dd></dl>\n"
      ],
      "text/latex": [
       "\\begin{description*}\n",
       "\\item[urbano] 69873\n",
       "\\item[rural] 20973\n",
       "\\end{description*}\n"
      ],
      "text/markdown": [
       "urbano\n",
       ":   69873rural\n",
       ":   20973\n",
       "\n"
      ],
      "text/plain": [
       "urbano  rural \n",
       " 69873  20973 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Situação Urbano ou Rural\n",
    "pns2019.1 <- pns2019.1 %>% rename(Sit_Urbano_Rural=V0026)\n",
    "pns2019.1$Sit_Urbano_Rural<-factor(pns2019.1$Sit_Urbano_Rural, levels=c(1,2), labels=c(\"urbano\", \"rural\"))\n",
    "summary(pns2019.1$Sit_Urbano_Rural)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### UF"
   ]
  },
  {
   "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>Rondônia</dt><dd>2176</dd><dt>Acre</dt><dd>2380</dd><dt>Amazonas</dt><dd>3479</dd><dt>Roraima</dt><dd>2238</dd><dt>Pará</dt><dd>3853</dd><dt>Amapá</dt><dd>1554</dd><dt>Tocantins</dt><dd>1922</dd><dt>Maranhão</dt><dd>5080</dd><dt>Piauí</dt><dd>2740</dd><dt>Ceará</dt><dd>4265</dd><dt>Rio Grande do Norte</dt><dd>2962</dd><dt>Paraíba</dt><dd>3158</dd><dt>Pernambuco</dt><dd>4083</dd><dt>Alagoas</dt><dd>2987</dd><dt>Sergipe</dt><dd>2610</dd><dt>Bahia</dt><dd>3659</dd><dt>Minas Gerais</dt><dd>5209</dd><dt>Espírito Santo</dt><dd>3541</dd><dt>Rio de Janeiro</dt><dd>4966</dd><dt>São Paulo</dt><dd>6114</dd><dt>Paraná</dt><dd>3967</dd><dt>Santa Catarina</dt><dd>3738</dd><dt>Rio Grande do Sul</dt><dd>3767</dd><dt>Mato Grosso do Sul</dt><dd>2863</dd><dt>Mato Grosso</dt><dd>2468</dd><dt>Goiás</dt><dd>2702</dd><dt>Distrito Federal</dt><dd>2365</dd></dl>\n"
      ],
      "text/latex": [
       "\\begin{description*}\n",
       "\\item[Rondônia] 2176\n",
       "\\item[Acre] 2380\n",
       "\\item[Amazonas] 3479\n",
       "\\item[Roraima] 2238\n",
       "\\item[Pará] 3853\n",
       "\\item[Amapá] 1554\n",
       "\\item[Tocantins] 1922\n",
       "\\item[Maranhão] 5080\n",
       "\\item[Piauí] 2740\n",
       "\\item[Ceará] 4265\n",
       "\\item[Rio Grande do Norte] 2962\n",
       "\\item[Paraíba] 3158\n",
       "\\item[Pernambuco] 4083\n",
       "\\item[Alagoas] 2987\n",
       "\\item[Sergipe] 2610\n",
       "\\item[Bahia] 3659\n",
       "\\item[Minas Gerais] 5209\n",
       "\\item[Espírito Santo] 3541\n",
       "\\item[Rio de Janeiro] 4966\n",
       "\\item[São Paulo] 6114\n",
       "\\item[Paraná] 3967\n",
       "\\item[Santa Catarina] 3738\n",
       "\\item[Rio Grande do Sul] 3767\n",
       "\\item[Mato Grosso do Sul] 2863\n",
       "\\item[Mato Grosso] 2468\n",
       "\\item[Goiás] 2702\n",
       "\\item[Distrito Federal] 2365\n",
       "\\end{description*}\n"
      ],
      "text/markdown": [
       "Rondônia\n",
       ":   2176Acre\n",
       ":   2380Amazonas\n",
       ":   3479Roraima\n",
       ":   2238Pará\n",
       ":   3853Amapá\n",
       ":   1554Tocantins\n",
       ":   1922Maranhão\n",
       ":   5080Piauí\n",
       ":   2740Ceará\n",
       ":   4265Rio Grande do Norte\n",
       ":   2962Paraíba\n",
       ":   3158Pernambuco\n",
       ":   4083Alagoas\n",
       ":   2987Sergipe\n",
       ":   2610Bahia\n",
       ":   3659Minas Gerais\n",
       ":   5209Espírito Santo\n",
       ":   3541Rio de Janeiro\n",
       ":   4966São Paulo\n",
       ":   6114Paraná\n",
       ":   3967Santa Catarina\n",
       ":   3738Rio Grande do Sul\n",
       ":   3767Mato Grosso do Sul\n",
       ":   2863Mato Grosso\n",
       ":   2468Goiás\n",
       ":   2702Distrito Federal\n",
       ":   2365\n",
       "\n"
      ],
      "text/plain": [
       "           Rondônia                Acre            Amazonas             Roraima \n",
       "               2176                2380                3479                2238 \n",
       "               Pará               Amapá           Tocantins            Maranhão \n",
       "               3853                1554                1922                5080 \n",
       "              Piauí               Ceará Rio Grande do Norte             Paraíba \n",
       "               2740                4265                2962                3158 \n",
       "         Pernambuco             Alagoas             Sergipe               Bahia \n",
       "               4083                2987                2610                3659 \n",
       "       Minas Gerais      Espírito Santo      Rio de Janeiro           São Paulo \n",
       "               5209                3541                4966                6114 \n",
       "             Paraná      Santa Catarina   Rio Grande do Sul  Mato Grosso do Sul \n",
       "               3967                3738                3767                2863 \n",
       "        Mato Grosso               Goiás    Distrito Federal \n",
       "               2468                2702                2365 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Estados - UFs\n",
    "pns2019.1 <- pns2019.1 %>% rename(Unidades_da_Federacao=V0001)\n",
    "pns2019.1$Unidades_da_Federacao<-factor(pns2019.1$Unidades_da_Federacao, levels=c(11,12,13,14,15,16,17,21,22,23,24,25,26,27,28,29,31,32,33,35,41,42,43,50,51,52,53),\n",
    "                                       label=c(\"Rondônia\",\"Acre\",\"Amazonas\",\"Roraima\",\"Pará\",\"Amapá\",\"Tocantins\",\"Maranhão\",\"Piauí\",\"Ceará\",\n",
    "                                        \"Rio Grande do Norte\",\"Paraíba\",\"Pernambuco\",\"Alagoas\",\"Sergipe\",\"Bahia\",\n",
    "                                        \"Minas Gerais\",\"Espírito Santo\",\"Rio de Janeiro\",\"São Paulo\",\n",
    "                                        \"Paraná\",\"Santa Catarina\",\"Rio Grande do Sul\", \n",
    "                                        \"Mato Grosso do Sul\",\"Mato Grosso\",\"Goiás\",\"Distrito Federal\"))\n",
    "summary(pns2019.1$Unidades_da_Federacao)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Grandes Regiões"
   ]
  },
  {
   "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>Norte</dt><dd>17602</dd><dt>Nordeste</dt><dd>31544</dd><dt>Sudeste</dt><dd>19830</dd><dt>Sul</dt><dd>11472</dd><dt>Centro-Oeste</dt><dd>10398</dd></dl>\n"
      ],
      "text/latex": [
       "\\begin{description*}\n",
       "\\item[Norte] 17602\n",
       "\\item[Nordeste] 31544\n",
       "\\item[Sudeste] 19830\n",
       "\\item[Sul] 11472\n",
       "\\item[Centro-Oeste] 10398\n",
       "\\end{description*}\n"
      ],
      "text/markdown": [
       "Norte\n",
       ":   17602Nordeste\n",
       ":   31544Sudeste\n",
       ":   19830Sul\n",
       ":   11472Centro-Oeste\n",
       ":   10398\n",
       "\n"
      ],
      "text/plain": [
       "       Norte     Nordeste      Sudeste          Sul Centro-Oeste \n",
       "       17602        31544        19830        11472        10398 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Grandes Regiões\n",
    "pns2019.1 <- pns2019.1 %>% \n",
    "  mutate(GrandesRegioes = fct_collapse(Unidades_da_Federacao, \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": 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>Porto Velho</dt><dd>2176</dd><dt>Rio Branco</dt><dd>2380</dd><dt>Manaus</dt><dd>3479</dd><dt>Boa Vista</dt><dd>2238</dd><dt>Belém</dt><dd>3853</dd><dt>Macapá</dt><dd>1554</dd><dt>Palmas</dt><dd>1922</dd><dt>São Luís</dt><dd>5080</dd><dt>Teresina</dt><dd>2740</dd><dt>Fortaleza</dt><dd>4265</dd><dt>Natal</dt><dd>2962</dd><dt>João Pessoa</dt><dd>3158</dd><dt>Recife</dt><dd>4083</dd><dt>Maceió</dt><dd>2987</dd><dt>Aracaju</dt><dd>2610</dd><dt>Salvador</dt><dd>3659</dd><dt>Belo Horizonte</dt><dd>5209</dd><dt>Vitória</dt><dd>3541</dd><dt>Rio de Janeiro</dt><dd>4966</dd><dt>São Paulo</dt><dd>6114</dd><dt>Curitiba</dt><dd>3967</dd><dt>Florianópolis</dt><dd>3738</dd><dt>Porto Alegre</dt><dd>3767</dd><dt>Campo Grande</dt><dd>2863</dd><dt>Cuiabá</dt><dd>2468</dd><dt>Goiânia</dt><dd>2702</dd><dt>Brasília</dt><dd>2365</dd></dl>\n"
      ],
      "text/latex": [
       "\\begin{description*}\n",
       "\\item[Porto Velho] 2176\n",
       "\\item[Rio Branco] 2380\n",
       "\\item[Manaus] 3479\n",
       "\\item[Boa Vista] 2238\n",
       "\\item[Belém] 3853\n",
       "\\item[Macapá] 1554\n",
       "\\item[Palmas] 1922\n",
       "\\item[São Luís] 5080\n",
       "\\item[Teresina] 2740\n",
       "\\item[Fortaleza] 4265\n",
       "\\item[Natal] 2962\n",
       "\\item[João Pessoa] 3158\n",
       "\\item[Recife] 4083\n",
       "\\item[Maceió] 2987\n",
       "\\item[Aracaju] 2610\n",
       "\\item[Salvador] 3659\n",
       "\\item[Belo Horizonte] 5209\n",
       "\\item[Vitória] 3541\n",
       "\\item[Rio de Janeiro] 4966\n",
       "\\item[São Paulo] 6114\n",
       "\\item[Curitiba] 3967\n",
       "\\item[Florianópolis] 3738\n",
       "\\item[Porto Alegre] 3767\n",
       "\\item[Campo Grande] 2863\n",
       "\\item[Cuiabá] 2468\n",
       "\\item[Goiânia] 2702\n",
       "\\item[Brasília] 2365\n",
       "\\end{description*}\n"
      ],
      "text/markdown": [
       "Porto Velho\n",
       ":   2176Rio Branco\n",
       ":   2380Manaus\n",
       ":   3479Boa Vista\n",
       ":   2238Belém\n",
       ":   3853Macapá\n",
       ":   1554Palmas\n",
       ":   1922São Luís\n",
       ":   5080Teresina\n",
       ":   2740Fortaleza\n",
       ":   4265Natal\n",
       ":   2962João Pessoa\n",
       ":   3158Recife\n",
       ":   4083Maceió\n",
       ":   2987Aracaju\n",
       ":   2610Salvador\n",
       ":   3659Belo Horizonte\n",
       ":   5209Vitória\n",
       ":   3541Rio de Janeiro\n",
       ":   4966São Paulo\n",
       ":   6114Curitiba\n",
       ":   3967Florianópolis\n",
       ":   3738Porto Alegre\n",
       ":   3767Campo Grande\n",
       ":   2863Cuiabá\n",
       ":   2468Goiânia\n",
       ":   2702Brasília\n",
       ":   2365\n",
       "\n"
      ],
      "text/plain": [
       "   Porto Velho     Rio Branco         Manaus      Boa Vista          Belém \n",
       "          2176           2380           3479           2238           3853 \n",
       "        Macapá         Palmas       São Luís       Teresina      Fortaleza \n",
       "          1554           1922           5080           2740           4265 \n",
       "         Natal    João Pessoa         Recife         Maceió        Aracaju \n",
       "          2962           3158           4083           2987           2610 \n",
       "      Salvador Belo Horizonte        Vitória Rio de Janeiro      São Paulo \n",
       "          3659           5209           3541           4966           6114 \n",
       "      Curitiba  Florianópolis   Porto Alegre   Campo Grande         Cuiabá \n",
       "          3967           3738           3767           2863           2468 \n",
       "       Goiânia       Brasília \n",
       "          2702           2365 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Capital\n",
    "pns2019.1<- pns2019.1 %>% mutate(Capital= fct_collapse(Unidades_da_Federacao,\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": 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>18 a 24 anos</dt><dd>8145</dd><dt>25 a 29 anos</dt><dd>7249</dd><dt>30 a 39 anos</dt><dd>18150</dd><dt>40 anos ou mais</dt><dd>54987</dd><dt>NA's</dt><dd>2315</dd></dl>\n"
      ],
      "text/latex": [
       "\\begin{description*}\n",
       "\\item[18 a 24 anos] 8145\n",
       "\\item[25 a 29 anos] 7249\n",
       "\\item[30 a 39 anos] 18150\n",
       "\\item[40 anos ou mais] 54987\n",
       "\\item[NA's] 2315\n",
       "\\end{description*}\n"
      ],
      "text/markdown": [
       "18 a 24 anos\n",
       ":   814525 a 29 anos\n",
       ":   724930 a 39 anos\n",
       ":   1815040 anos ou mais\n",
       ":   54987NA's\n",
       ":   2315\n",
       "\n"
      ],
      "text/plain": [
       "   18 a 24 anos    25 a 29 anos    30 a 39 anos 40 anos ou mais            NA's \n",
       "           8145            7249           18150           54987            2315 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Faixas Etárias\n",
    "pns2019.1 <- pns2019.1 %>% mutate(fx_idade_S=cut(C008,\n",
    "  breaks = c(18,25,30,40,120),\n",
    "  labels = c(\"18 a 24 anos\", \"25 a 29 anos\", \"30 a 39 anos\", \"40 anos ou mais\"), \n",
    "  ordered_result = TRUE, right = FALSE))\n",
    "summary(pns2019.1$fx_idade_S)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Raça"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>\n",
       ".dl-inline {width: auto; margin:0; padding: 0}\n",
       ".dl-inline>dt, .dl-inline>dd {float: none; width: auto; display: inline-block}\n",
       ".dl-inline>dt::after {content: \":\\0020\"; padding-right: .5ex}\n",
       ".dl-inline>dt:not(:first-of-type) {padding-left: .5ex}\n",
       "</style><dl class=dl-inline><dt>Branca</dt><dd>33133</dd><dt>Preta</dt><dd>10345</dd><dt>Parda</dt><dd>45994</dd><dt>NA's</dt><dd>1374</dd></dl>\n"
      ],
      "text/latex": [
       "\\begin{description*}\n",
       "\\item[Branca] 33133\n",
       "\\item[Preta] 10345\n",
       "\\item[Parda] 45994\n",
       "\\item[NA's] 1374\n",
       "\\end{description*}\n"
      ],
      "text/markdown": [
       "Branca\n",
       ":   33133Preta\n",
       ":   10345Parda\n",
       ":   45994NA's\n",
       ":   1374\n",
       "\n"
      ],
      "text/plain": [
       "Branca  Preta  Parda   NA's \n",
       " 33133  10345  45994   1374 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Raça\n",
    "pns2019.1 <- pns2019.1 %>% mutate(Raca= ifelse(C009==1, 1, \n",
    "                        ifelse(C009==2, 2, \n",
    "                            ifelse(C009==4, 3, 9))))\n",
    "\n",
    "pns2019.1$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": 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>Até 1/2 SM</dt><dd>23697</dd><dt>1/2 até 1 SM</dt><dd>26406</dd><dt>1 até 2 SM</dt><dd>22466</dd><dt>2 até 3 SM</dt><dd>7612</dd><dt>Mais de 3 SM</dt><dd>10643</dd><dt>NA's</dt><dd>22</dd></dl>\n"
      ],
      "text/latex": [
       "\\begin{description*}\n",
       "\\item[Até 1/2 SM] 23697\n",
       "\\item[1/2 até 1 SM] 26406\n",
       "\\item[1 até 2 SM] 22466\n",
       "\\item[2 até 3 SM] 7612\n",
       "\\item[Mais de 3 SM] 10643\n",
       "\\item[NA's] 22\n",
       "\\end{description*}\n"
      ],
      "text/markdown": [
       "Até 1/2 SM\n",
       ":   236971/2 até 1 SM\n",
       ":   264061 até 2 SM\n",
       ":   224662 até 3 SM\n",
       ":   7612Mais de 3 SM\n",
       ":   10643NA's\n",
       ":   22\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",
       "       23697        26406        22466         7612        10643           22 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Rendimento domiciliar per capita\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(is.na(VDF004)==TRUE, NA_real_, 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": [
    "### Escolaridade"
   ]
  },
  {
   "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>Fundamental incompleto ou equivalente</dt><dd>36276</dd><dt>Médio incompleto ou equivalente</dt><dd>13520</dd><dt>Superior incompleto ou equivalente</dt><dd>27433</dd><dt>Superior completo</dt><dd>13617</dd></dl>\n"
      ],
      "text/latex": [
       "\\begin{description*}\n",
       "\\item[Fundamental incompleto ou equivalente] 36276\n",
       "\\item[Médio incompleto ou equivalente] 13520\n",
       "\\item[Superior incompleto ou equivalente] 27433\n",
       "\\item[Superior completo] 13617\n",
       "\\end{description*}\n"
      ],
      "text/markdown": [
       "Fundamental incompleto ou equivalente\n",
       ":   36276Médio incompleto ou equivalente\n",
       ":   13520Superior incompleto ou equivalente\n",
       ":   27433Superior completo\n",
       ":   13617\n",
       "\n"
      ],
      "text/plain": [
       "Fundamental incompleto ou equivalente       Médio incompleto ou equivalente \n",
       "                                36276                                 13520 \n",
       "   Superior incompleto ou equivalente                     Superior completo \n",
       "                                27433                                 13617 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Escolaridade\n",
    "pns2019.1 <- pns2019.1 %>% mutate(gescol = ifelse(VDD004A %in% 1:2, 1, \n",
    "                        ifelse(VDD004A%in% 3:4, 2, \n",
    "                        ifelse(VDD004A%in% 5:6, 3,4\n",
    "                        ))))\n",
    "\n",
    "pns2019.1$gescol<-factor(pns2019.1$gescol, levels=c(1,2,3,4), \n",
    "                                  labels=c(\"Fundamental incompleto ou equivalente\",\"Médio incompleto ou equivalente\",\n",
    "                                           \"Superior incompleto ou equivalente\",\"Superior completo\"))\n",
    "summary(pns2019.1$gescol)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Criando indicadores"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Filtrando base de indicadores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "     V0024            UPA_PNS      peso_morador_selec      C008       \n",
       " 1210010: 1167   140001681:   18   Min.   : 0.00562   Min.   : 15.00  \n",
       " 1410011:  792   140003815:   18   1st Qu.: 0.26621   1st Qu.: 32.00  \n",
       " 2710111:  779   140005777:   18   Median : 0.54401   Median : 45.00  \n",
       " 2410011:  745   140006746:   18   Mean   : 1.00000   Mean   : 46.39  \n",
       " 5010011:  738   140007081:   18   3rd Qu.: 1.12765   3rd Qu.: 60.00  \n",
       " 3210011:  711   140007715:   18   Max.   :61.09981   Max.   :107.00  \n",
       " (Other):85914   (Other)  :90738                                      \n",
       "      C006            C009           V0031       Sit_Urbano_Rural\n",
       " Min.   :1.000   Min.   :1.000   Min.   :1.000   urbano:69873    \n",
       " 1st Qu.:1.000   1st Qu.:1.000   1st Qu.:1.000   rural :20973    \n",
       " Median :2.000   Median :4.000   Median :2.000                   \n",
       " Mean   :1.529   Mean   :2.679   Mean   :2.605                   \n",
       " 3rd Qu.:2.000   3rd Qu.:4.000   3rd Qu.:4.000                   \n",
       " Max.   :2.000   Max.   :9.000   Max.   :4.000                   \n",
       "                                                                 \n",
       "    Unidades_da_Federacao      GrandesRegioes            Capital     \n",
       " São Paulo     : 6114     Norte       :17602   São Paulo     : 6114  \n",
       " Minas Gerais  : 5209     Nordeste    :31544   Belo Horizonte: 5209  \n",
       " Maranhão      : 5080     Sudeste     :19830   São Luís      : 5080  \n",
       " Rio de Janeiro: 4966     Sul         :11472   Rio de Janeiro: 4966  \n",
       " Ceará         : 4265     Centro-Oeste:10398   Fortaleza     : 4265  \n",
       " Pernambuco    : 4083                          Recife        : 4083  \n",
       " (Other)       :61129                          (Other)       :61129  \n",
       "           fx_idade_S        Raca           rend_per_capita \n",
       " 18 a 24 anos   : 8145   Branca:33133   Até 1/2 SM  :23697  \n",
       " 25 a 29 anos   : 7249   Preta :10345   1/2 até 1 SM:26406  \n",
       " 30 a 39 anos   :18150   Parda :45994   1 até 2 SM  :22466  \n",
       " 40 anos ou mais:54987   NA's  : 1374   2 até 3 SM  : 7612  \n",
       " NA's           : 2315                  Mais de 3 SM:10643  \n",
       "                                        NA's        :   22  \n",
       "                                                            \n",
       "                                   gescol       S011P        S012P      \n",
       " Fundamental incompleto ou equivalente:36276   Sim : 2131   Sim : 2071  \n",
       " Médio incompleto ou equivalente      :13520   Não :  603   Não :  663  \n",
       " Superior incompleto ou equivalente   :27433   NA's:88112   NA's:88112  \n",
       " Superior completo                    :13617                            \n",
       "                                                                        \n",
       "                                                                        \n",
       "                                                                        \n",
       "  S013P        S014P        S015P            S068      \n",
       " Sim : 2246   Sim : 2172   Sim : 2423   Min.   :1.00   \n",
       " Não :  488   Não :  562   Não :  311   1st Qu.:1.00   \n",
       " NA's:88112   NA's:88112   NA's:88112   Median :1.00   \n",
       "                                        Mean   :1.03   \n",
       "                                        3rd Qu.:1.00   \n",
       "                                        Max.   :2.00   \n",
       "                                        NA's   :87936  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Selecionando variáveis para cálculo de indicadores no survey\n",
    "pns2019Ssurvey<- pns2019.1 %>% select(\"V0024\",\"UPA_PNS\",\"peso_morador_selec\", \"C008\", \"C006\", \"C009\", \"V0031\", \n",
    "                                      \"Sit_Urbano_Rural\", \"Unidades_da_Federacao\", \"GrandesRegioes\",  \"Capital\", \"fx_idade_S\", \"Raca\", \"rend_per_capita\", \"gescol\",\n",
    "                                      \"S011P\", \"S012P\", \"S013P\", \"S014P\", \"S015P\", \"S068\") \n",
    "summary(pns2019Ssurvey)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exporta tabela filtrada com os dados específicos - Módulo S - Parte3 2019"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Salvando csv para cálculo de indicadores no survey\n",
    "diretorio_saida <- \"<coloque aqui o diretório>\"\n",
    "write.csv(pns2019Ssurvey, file.path(diretorio_saida, \"pns2019Ssurvey.csv\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cria plano amostral complexo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "desPNS=svydesign(id=~UPA_PNS, strat=~V0024, weight=~peso_morador_selec, nest=TRUE, \n",
    "                 data=pns2019Ssurvey)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "#survey design S006P a S010P\n",
    "desPNSS=subset(desPNS, C006==2 & C008>=18 & S068==1)\n",
    "desPNSS_C=subset(desPNS, C006==2 & C008>=18 & S068==1 & V0031==1)\n",
    "desPNSS_R=subset(desPNS, C006==2 & C008>=18 & S068==1 & !is.na(Raca))\n",
    "desPNSS_Rend=subset(desPNS, C006==2 & C008>=18 & S068==1 & !is.na(rend_per_capita))"
   ]
  },
  {
   "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": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "matrizIndicadores = data.frame()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Definição de variáveis para iteração dos indicadores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "ListaIndicadores = c(~S011P, ~S012P, ~S013P, ~S014P, ~S015P)\n",
    "ListaIndicadoresTexto = c(\"S011P\", \"S012P\", \"S013P\", \"S014P\", \"S015P\" )\n",
    "ListaTotais = c('Brasil','Capital')\n",
    "Ano <- \"2019\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "ListaDominios = c(~Raca,~rend_per_capita,~fx_idade_S,~Sit_Urbano_Rural,\n",
    "                      ~Unidades_da_Federacao,~GrandesRegioes,~Capital,~gescol)\n",
    "ListaDominiosTexto= c(\"raça\",\"rend_per_capita\",\"fx_idade_S\",\"urb_rur\",\n",
    "                          \"uf\",\"região\",\"capital\",\"gescol\") "
   ]
  },
  {
   "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; j <- 1\n",
    "    #Para cada dominio\n",
    "    for (dominio in ListaDominios){\n",
    "           #design especifico para capital que é subconjunto do dataframe total\n",
    "           #design geral para capital   \n",
    "           if (ListaDominiosTexto[j]==\"capital\"){\n",
    "             dataframe_indicador<-svyby( indicador , dominio , desPNSS_C , svymean,vartype= c(\"ci\",\"cv\"))\n",
    "           #design geral para raça\n",
    "           } else if  (ListaDominiosTexto[j]==\"raça\"){\n",
    "             dataframe_indicador<-svyby( indicador , dominio , desPNSS_R , svymean,vartype= c(\"ci\",\"cv\"))   \n",
    "           #design geral para renda per capita\n",
    "           } else if  (ListaDominiosTexto[j]==\"rend_per_capita\"){\n",
    "             dataframe_indicador<-svyby( indicador , dominio , desPNSS_Rend , svymean,vartype= c(\"ci\",\"cv\"))   \n",
    "           #design geral para o subconjunto maior que 18 anos \n",
    "           } else { \n",
    "             dataframe_indicador<-svyby( indicador , dominio , desPNSS , svymean,vartype= c(\"ci\",\"cv\"))   \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\",\"Nao\",\"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",
    "}"
   ]
  },
  {
   "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",
    "# para cada indicador\n",
    "for(indicador in ListaIndicadores){\n",
    "    i <- i + 1\n",
    "    # para os totais Brasil e total das capitais\n",
    "    for(total in ListaTotais){\n",
    "        dataframe_indicador <- data.frame()\n",
    "        dataframe_indicador_S <- data.frame()\n",
    "        # Uso do design que é subconjunto do dataset para cada Capital\n",
    "        if (total == \"Capital\"){\n",
    "            #Indicadores que são subconjunto do dataset total\n",
    "            dataframe_indicador <- svymean(indicador,desPNSS_C)\n",
    "        } else {\n",
    "            dataframe_indicador <- svymean(indicador,desPNSS)\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",
    "        \n",
    "        dataframe_indicador_S$abr_tipo <- \"total\"\n",
    "        dataframe_indicador_S$abr_nome <- total\n",
    "        dataframe_indicador_S$Ano <- Ano\n",
    "        #Uso design do subconjunto para raça/cor que inclui preta,branca e parda  \n",
    "        #  as outras raças não possuiam dados suficientes para os dominios dos indicadores\n",
    "                \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",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.frame: 10 × 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;chr&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>S011PSim</th><td>total</td><td>Brasil </td><td>2019</td><td>S011P</td><td>0.7970506</td><td>0.7729093</td><td>0.8211920</td><td>0.015453529</td></tr>\n",
       "\t<tr><th scope=row>S011PSim1</th><td>total</td><td>Capital</td><td>2019</td><td>S011P</td><td>0.7856012</td><td>0.7367190</td><td>0.8344834</td><td>0.031746843</td></tr>\n",
       "\t<tr><th scope=row>S012PSim</th><td>total</td><td>Brasil </td><td>2019</td><td>S012P</td><td>0.7709437</td><td>0.7450416</td><td>0.7968458</td><td>0.017142104</td></tr>\n",
       "\t<tr><th scope=row>S012PSim1</th><td>total</td><td>Capital</td><td>2019</td><td>S012P</td><td>0.7605274</td><td>0.7105432</td><td>0.8105116</td><td>0.033532798</td></tr>\n",
       "\t<tr><th scope=row>S013PSim</th><td>total</td><td>Brasil </td><td>2019</td><td>S013P</td><td>0.8423751</td><td>0.8206783</td><td>0.8640719</td><td>0.013141415</td></tr>\n",
       "\t<tr><th scope=row>S013PSim1</th><td>total</td><td>Capital</td><td>2019</td><td>S013P</td><td>0.8497500</td><td>0.8098841</td><td>0.8896158</td><td>0.023936576</td></tr>\n",
       "\t<tr><th scope=row>S014PSim</th><td>total</td><td>Brasil </td><td>2019</td><td>S014P</td><td>0.8117146</td><td>0.7881055</td><td>0.8353238</td><td>0.014839855</td></tr>\n",
       "\t<tr><th scope=row>S014PSim1</th><td>total</td><td>Capital</td><td>2019</td><td>S014P</td><td>0.8189464</td><td>0.7762082</td><td>0.8616846</td><td>0.026626396</td></tr>\n",
       "\t<tr><th scope=row>S015PSim</th><td>total</td><td>Brasil </td><td>2019</td><td>S015P</td><td>0.8997072</td><td>0.8831155</td><td>0.9162988</td><td>0.009408937</td></tr>\n",
       "\t<tr><th scope=row>S015PSim1</th><td>total</td><td>Capital</td><td>2019</td><td>S015P</td><td>0.8832092</td><td>0.8432095</td><td>0.9232089</td><td>0.023107083</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 10 × 8\n",
       "\\begin{tabular}{r|llllllll}\n",
       "  & abr\\_tipo & abr\\_nome & Ano & Indicador & Sim & LowerS & UpperS & cvS\\\\\n",
       "  & <chr> & <chr> & <chr> & <chr> & <dbl> & <dbl> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\tS011PSim & total & Brasil  & 2019 & S011P & 0.7970506 & 0.7729093 & 0.8211920 & 0.015453529\\\\\n",
       "\tS011PSim1 & total & Capital & 2019 & S011P & 0.7856012 & 0.7367190 & 0.8344834 & 0.031746843\\\\\n",
       "\tS012PSim & total & Brasil  & 2019 & S012P & 0.7709437 & 0.7450416 & 0.7968458 & 0.017142104\\\\\n",
       "\tS012PSim1 & total & Capital & 2019 & S012P & 0.7605274 & 0.7105432 & 0.8105116 & 0.033532798\\\\\n",
       "\tS013PSim & total & Brasil  & 2019 & S013P & 0.8423751 & 0.8206783 & 0.8640719 & 0.013141415\\\\\n",
       "\tS013PSim1 & total & Capital & 2019 & S013P & 0.8497500 & 0.8098841 & 0.8896158 & 0.023936576\\\\\n",
       "\tS014PSim & total & Brasil  & 2019 & S014P & 0.8117146 & 0.7881055 & 0.8353238 & 0.014839855\\\\\n",
       "\tS014PSim1 & total & Capital & 2019 & S014P & 0.8189464 & 0.7762082 & 0.8616846 & 0.026626396\\\\\n",
       "\tS015PSim & total & Brasil  & 2019 & S015P & 0.8997072 & 0.8831155 & 0.9162988 & 0.009408937\\\\\n",
       "\tS015PSim1 & total & Capital & 2019 & S015P & 0.8832092 & 0.8432095 & 0.9232089 & 0.023107083\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 10 × 8\n",
       "\n",
       "| <!--/--> | abr_tipo &lt;chr&gt; | abr_nome &lt;chr&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",
       "| S011PSim | total | Brasil  | 2019 | S011P | 0.7970506 | 0.7729093 | 0.8211920 | 0.015453529 |\n",
       "| S011PSim1 | total | Capital | 2019 | S011P | 0.7856012 | 0.7367190 | 0.8344834 | 0.031746843 |\n",
       "| S012PSim | total | Brasil  | 2019 | S012P | 0.7709437 | 0.7450416 | 0.7968458 | 0.017142104 |\n",
       "| S012PSim1 | total | Capital | 2019 | S012P | 0.7605274 | 0.7105432 | 0.8105116 | 0.033532798 |\n",
       "| S013PSim | total | Brasil  | 2019 | S013P | 0.8423751 | 0.8206783 | 0.8640719 | 0.013141415 |\n",
       "| S013PSim1 | total | Capital | 2019 | S013P | 0.8497500 | 0.8098841 | 0.8896158 | 0.023936576 |\n",
       "| S014PSim | total | Brasil  | 2019 | S014P | 0.8117146 | 0.7881055 | 0.8353238 | 0.014839855 |\n",
       "| S014PSim1 | total | Capital | 2019 | S014P | 0.8189464 | 0.7762082 | 0.8616846 | 0.026626396 |\n",
       "| S015PSim | total | Brasil  | 2019 | S015P | 0.8997072 | 0.8831155 | 0.9162988 | 0.009408937 |\n",
       "| S015PSim1 | total | Capital | 2019 | S015P | 0.8832092 | 0.8432095 | 0.9232089 | 0.023107083 |\n",
       "\n"
      ],
      "text/plain": [
       "          abr_tipo abr_nome Ano  Indicador Sim       LowerS    UpperS   \n",
       "S011PSim  total    Brasil   2019 S011P     0.7970506 0.7729093 0.8211920\n",
       "S011PSim1 total    Capital  2019 S011P     0.7856012 0.7367190 0.8344834\n",
       "S012PSim  total    Brasil   2019 S012P     0.7709437 0.7450416 0.7968458\n",
       "S012PSim1 total    Capital  2019 S012P     0.7605274 0.7105432 0.8105116\n",
       "S013PSim  total    Brasil   2019 S013P     0.8423751 0.8206783 0.8640719\n",
       "S013PSim1 total    Capital  2019 S013P     0.8497500 0.8098841 0.8896158\n",
       "S014PSim  total    Brasil   2019 S014P     0.8117146 0.7881055 0.8353238\n",
       "S014PSim1 total    Capital  2019 S014P     0.8189464 0.7762082 0.8616846\n",
       "S015PSim  total    Brasil   2019 S015P     0.8997072 0.8831155 0.9162988\n",
       "S015PSim1 total    Capital  2019 S015P     0.8832092 0.8432095 0.9232089\n",
       "          cvS        \n",
       "S011PSim  0.015453529\n",
       "S011PSim1 0.031746843\n",
       "S012PSim  0.017142104\n",
       "S012PSim1 0.033532798\n",
       "S013PSim  0.013141415\n",
       "S013PSim1 0.023936576\n",
       "S014PSim  0.014839855\n",
       "S014PSim1 0.026626396\n",
       "S015PSim  0.009408937\n",
       "S015PSim1 0.023107083"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "matriz_totais"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Unindo tabela de indicadores e de totais"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "matrizIndicadores<-rbind(matrizIndicadores,matriz_totais)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Visualizando tabela de indicadores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.frame: 395 × 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>Branca</th><td>raça           </td><td>Branca             </td><td>2019</td><td>S011P</td><td>0.7873750</td><td>0.7422421</td><td>0.8325079</td><td>0.02924581</td></tr>\n",
       "\t<tr><th scope=row>Preta</th><td>raça           </td><td>Preta              </td><td>2019</td><td>S011P</td><td>0.7794471</td><td>0.7046830</td><td>0.8542112</td><td>0.04893935</td></tr>\n",
       "\t<tr><th scope=row>Parda</th><td>raça           </td><td>Parda              </td><td>2019</td><td>S011P</td><td>0.8112202</td><td>0.7821922</td><td>0.8402482</td><td>0.01825704</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>S011P</td><td>0.7674890</td><td>0.7344969</td><td>0.8004810</td><td>0.02193252</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>S011P</td><td>0.8227927</td><td>0.7772524</td><td>0.8683330</td><td>0.02823954</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>S011P</td><td>0.8572637</td><td>0.7962834</td><td>0.9182440</td><td>0.03629337</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>S011P</td><td>0.7506448</td><td>0.6173035</td><td>0.8839861</td><td>0.09063210</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>S011P</td><td>0.7643860</td><td>0.6571495</td><td>0.8716225</td><td>0.07157838</td></tr>\n",
       "\t<tr><th scope=row>18 a 24 anos</th><td>fx_idade_S     </td><td>18 a 24 anos       </td><td>2019</td><td>S011P</td><td>0.7642557</td><td>0.7172469</td><td>0.8112645</td><td>0.03138288</td></tr>\n",
       "\t<tr><th scope=row>25 a 29 anos</th><td>fx_idade_S     </td><td>25 a 29 anos       </td><td>2019</td><td>S011P</td><td>0.7897381</td><td>0.7367238</td><td>0.8427525</td><td>0.03425013</td></tr>\n",
       "\t<tr><th scope=row>30 a 39 anos</th><td>fx_idade_S     </td><td>30 a 39 anos       </td><td>2019</td><td>S011P</td><td>0.8265024</td><td>0.7909434</td><td>0.8620615</td><td>0.02195119</td></tr>\n",
       "\t<tr><th scope=row>40 anos ou mais</th><td>fx_idade_S     </td><td>40 anos ou mais    </td><td>2019</td><td>S011P</td><td>0.7832286</td><td>0.6845172</td><td>0.8819400</td><td>0.06430293</td></tr>\n",
       "\t<tr><th scope=row>urbano</th><td>urb_rur        </td><td>urbano             </td><td>2019</td><td>S011P</td><td>0.8123256</td><td>0.7851979</td><td>0.8394532</td><td>0.01703859</td></tr>\n",
       "\t<tr><th scope=row>rural</th><td>urb_rur        </td><td>rural              </td><td>2019</td><td>S011P</td><td>0.7123457</td><td>0.6653805</td><td>0.7593109</td><td>0.03363856</td></tr>\n",
       "\t<tr><th scope=row>Rondônia</th><td>uf             </td><td>Rondônia           </td><td>2019</td><td>S011P</td><td>0.8113854</td><td>0.7112533</td><td>0.9115175</td><td>0.06296482</td></tr>\n",
       "\t<tr><th scope=row>Acre</th><td>uf             </td><td>Acre               </td><td>2019</td><td>S011P</td><td>0.8715134</td><td>0.7777214</td><td>0.9653054</td><td>0.05490903</td></tr>\n",
       "\t<tr><th scope=row>Amazonas</th><td>uf             </td><td>Amazonas           </td><td>2019</td><td>S011P</td><td>0.7675780</td><td>0.6671789</td><td>0.8679770</td><td>0.06673581</td></tr>\n",
       "\t<tr><th scope=row>Roraima</th><td>uf             </td><td>Roraima            </td><td>2019</td><td>S011P</td><td>0.8092680</td><td>0.6458560</td><td>0.9726801</td><td>0.10302523</td></tr>\n",
       "\t<tr><th scope=row>Pará</th><td>uf             </td><td>Pará               </td><td>2019</td><td>S011P</td><td>0.7977664</td><td>0.7155590</td><td>0.8799738</td><td>0.05257592</td></tr>\n",
       "\t<tr><th scope=row>Amapá</th><td>uf             </td><td>Amapá              </td><td>2019</td><td>S011P</td><td>0.8772571</td><td>0.7620805</td><td>0.9924338</td><td>0.06698682</td></tr>\n",
       "\t<tr><th scope=row>Tocantins</th><td>uf             </td><td>Tocantins          </td><td>2019</td><td>S011P</td><td>0.8597343</td><td>0.7646931</td><td>0.9547755</td><td>0.05640264</td></tr>\n",
       "\t<tr><th scope=row>Maranhão</th><td>uf             </td><td>Maranhão           </td><td>2019</td><td>S011P</td><td>0.6763398</td><td>0.5883963</td><td>0.7642833</td><td>0.06634235</td></tr>\n",
       "\t<tr><th scope=row>Piauí</th><td>uf             </td><td>Piauí              </td><td>2019</td><td>S011P</td><td>0.7918508</td><td>0.6875284</td><td>0.8961731</td><td>0.06721805</td></tr>\n",
       "\t<tr><th scope=row>Ceará</th><td>uf             </td><td>Ceará              </td><td>2019</td><td>S011P</td><td>0.7318008</td><td>0.6417493</td><td>0.8218522</td><td>0.06278410</td></tr>\n",
       "\t<tr><th scope=row>Rio Grande do Norte</th><td>uf             </td><td>Rio Grande do Norte</td><td>2019</td><td>S011P</td><td>0.8062034</td><td>0.7020560</td><td>0.9103508</td><td>0.06591067</td></tr>\n",
       "\t<tr><th scope=row>Paraíba</th><td>uf             </td><td>Paraíba            </td><td>2019</td><td>S011P</td><td>0.6354086</td><td>0.5038523</td><td>0.7669648</td><td>0.10563561</td></tr>\n",
       "\t<tr><th scope=row>Pernambuco</th><td>uf             </td><td>Pernambuco         </td><td>2019</td><td>S011P</td><td>0.7903354</td><td>0.7019489</td><td>0.8787220</td><td>0.05705934</td></tr>\n",
       "\t<tr><th scope=row>Alagoas</th><td>uf             </td><td>Alagoas            </td><td>2019</td><td>S011P</td><td>0.7448166</td><td>0.6421750</td><td>0.8474582</td><td>0.07031143</td></tr>\n",
       "\t<tr><th scope=row>Sergipe</th><td>uf             </td><td>Sergipe            </td><td>2019</td><td>S011P</td><td>0.7055517</td><td>0.5524472</td><td>0.8586563</td><td>0.11071620</td></tr>\n",
       "\t<tr><th scope=row>Bahia</th><td>uf             </td><td>Bahia              </td><td>2019</td><td>S011P</td><td>0.8109277</td><td>0.6995516</td><td>0.9223039</td><td>0.07007479</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>João Pessoa4</th><td>capital</td><td>João Pessoa                          </td><td>2019</td><td>S015P</td><td>0.9058899</td><td>0.8197244</td><td>0.9920555</td><td>0.048529968</td></tr>\n",
       "\t<tr><th scope=row>Recife4</th><td>capital</td><td>Recife                               </td><td>2019</td><td>S015P</td><td>0.9442654</td><td>0.8641629</td><td>1.0243678</td><td>0.043281640</td></tr>\n",
       "\t<tr><th scope=row>Maceió4</th><td>capital</td><td>Maceió                               </td><td>2019</td><td>S015P</td><td>0.9317416</td><td>0.8584334</td><td>1.0050497</td><td>0.040142881</td></tr>\n",
       "\t<tr><th scope=row>Aracaju4</th><td>capital</td><td>Aracaju                              </td><td>2019</td><td>S015P</td><td>0.9253580</td><td>0.8200606</td><td>1.0306555</td><td>0.058057736</td></tr>\n",
       "\t<tr><th scope=row>Salvador4</th><td>capital</td><td>Salvador                             </td><td>2019</td><td>S015P</td><td>0.8851483</td><td>0.7279619</td><td>1.0423347</td><td>0.090604712</td></tr>\n",
       "\t<tr><th scope=row>Belo Horizonte4</th><td>capital</td><td>Belo Horizonte                       </td><td>2019</td><td>S015P</td><td>0.7831622</td><td>0.6265665</td><td>0.9397578</td><td>0.102018733</td></tr>\n",
       "\t<tr><th scope=row>Vitória4</th><td>capital</td><td>Vitória                              </td><td>2019</td><td>S015P</td><td>0.8295784</td><td>0.6389091</td><td>1.0202477</td><td>0.117266844</td></tr>\n",
       "\t<tr><th scope=row>Rio de Janeiro9</th><td>capital</td><td>Rio de Janeiro                       </td><td>2019</td><td>S015P</td><td>0.9830347</td><td>0.9513834</td><td>1.0146860</td><td>0.016427602</td></tr>\n",
       "\t<tr><th scope=row>São Paulo8</th><td>capital</td><td>São Paulo                            </td><td>2019</td><td>S015P</td><td>0.7818142</td><td>0.6130362</td><td>0.9505922</td><td>0.110144846</td></tr>\n",
       "\t<tr><th scope=row>Curitiba4</th><td>capital</td><td>Curitiba                             </td><td>2019</td><td>S015P</td><td>0.8708550</td><td>0.7478725</td><td>0.9938376</td><td>0.072052572</td></tr>\n",
       "\t<tr><th scope=row>Florianópolis4</th><td>capital</td><td>Florianópolis                        </td><td>2019</td><td>S015P</td><td>0.7691331</td><td>0.5210958</td><td>1.0171704</td><td>0.164538473</td></tr>\n",
       "\t<tr><th scope=row>Porto Alegre4</th><td>capital</td><td>Porto Alegre                         </td><td>2019</td><td>S015P</td><td>1.0000000</td><td>1.0000000</td><td>1.0000000</td><td>0.000000000</td></tr>\n",
       "\t<tr><th scope=row>Campo Grande4</th><td>capital</td><td>Campo Grande                         </td><td>2019</td><td>S015P</td><td>0.9588850</td><td>0.9041423</td><td>1.0136277</td><td>0.029128061</td></tr>\n",
       "\t<tr><th scope=row>Cuiabá4</th><td>capital</td><td>Cuiabá                               </td><td>2019</td><td>S015P</td><td>0.8613471</td><td>0.6884873</td><td>1.0342069</td><td>0.102392414</td></tr>\n",
       "\t<tr><th scope=row>Goiânia4</th><td>capital</td><td>Goiânia                              </td><td>2019</td><td>S015P</td><td>0.9112432</td><td>0.7458644</td><td>1.0766219</td><td>0.092597080</td></tr>\n",
       "\t<tr><th scope=row>Brasília4</th><td>capital</td><td>Brasília                             </td><td>2019</td><td>S015P</td><td>0.8353633</td><td>0.7275334</td><td>0.9431932</td><td>0.065859086</td></tr>\n",
       "\t<tr><th scope=row>Fundamental incompleto ou equivalente4</th><td>gescol </td><td>Fundamental incompleto ou equivalente</td><td>2019</td><td>S015P</td><td>0.8420161</td><td>0.7906370</td><td>0.8933952</td><td>0.031132788</td></tr>\n",
       "\t<tr><th scope=row>Médio incompleto ou equivalente4</th><td>gescol </td><td>Médio incompleto ou equivalente      </td><td>2019</td><td>S015P</td><td>0.8999967</td><td>0.8673529</td><td>0.9326406</td><td>0.018505991</td></tr>\n",
       "\t<tr><th scope=row>Superior incompleto ou equivalente4</th><td>gescol </td><td>Superior incompleto ou equivalente   </td><td>2019</td><td>S015P</td><td>0.9163796</td><td>0.8947965</td><td>0.9379628</td><td>0.012016843</td></tr>\n",
       "\t<tr><th scope=row>Superior completo4</th><td>gescol </td><td>Superior completo                    </td><td>2019</td><td>S015P</td><td>0.9154731</td><td>0.8764884</td><td>0.9544578</td><td>0.021727051</td></tr>\n",
       "\t<tr><th scope=row>S011PSim</th><td>total  </td><td>Brasil                               </td><td>2019</td><td>S011P</td><td>0.7970506</td><td>0.7729093</td><td>0.8211920</td><td>0.015453529</td></tr>\n",
       "\t<tr><th scope=row>S011PSim1</th><td>total  </td><td>Capital                              </td><td>2019</td><td>S011P</td><td>0.7856012</td><td>0.7367190</td><td>0.8344834</td><td>0.031746843</td></tr>\n",
       "\t<tr><th scope=row>S012PSim</th><td>total  </td><td>Brasil                               </td><td>2019</td><td>S012P</td><td>0.7709437</td><td>0.7450416</td><td>0.7968458</td><td>0.017142104</td></tr>\n",
       "\t<tr><th scope=row>S012PSim1</th><td>total  </td><td>Capital                              </td><td>2019</td><td>S012P</td><td>0.7605274</td><td>0.7105432</td><td>0.8105116</td><td>0.033532798</td></tr>\n",
       "\t<tr><th scope=row>S013PSim</th><td>total  </td><td>Brasil                               </td><td>2019</td><td>S013P</td><td>0.8423751</td><td>0.8206783</td><td>0.8640719</td><td>0.013141415</td></tr>\n",
       "\t<tr><th scope=row>S013PSim1</th><td>total  </td><td>Capital                              </td><td>2019</td><td>S013P</td><td>0.8497500</td><td>0.8098841</td><td>0.8896158</td><td>0.023936576</td></tr>\n",
       "\t<tr><th scope=row>S014PSim</th><td>total  </td><td>Brasil                               </td><td>2019</td><td>S014P</td><td>0.8117146</td><td>0.7881055</td><td>0.8353238</td><td>0.014839855</td></tr>\n",
       "\t<tr><th scope=row>S014PSim1</th><td>total  </td><td>Capital                              </td><td>2019</td><td>S014P</td><td>0.8189464</td><td>0.7762082</td><td>0.8616846</td><td>0.026626396</td></tr>\n",
       "\t<tr><th scope=row>S015PSim</th><td>total  </td><td>Brasil                               </td><td>2019</td><td>S015P</td><td>0.8997072</td><td>0.8831155</td><td>0.9162988</td><td>0.009408937</td></tr>\n",
       "\t<tr><th scope=row>S015PSim1</th><td>total  </td><td>Capital                              </td><td>2019</td><td>S015P</td><td>0.8832092</td><td>0.8432095</td><td>0.9232089</td><td>0.023107083</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 395 × 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",
       "\tBranca & raça            & Branca              & 2019 & S011P & 0.7873750 & 0.7422421 & 0.8325079 & 0.02924581\\\\\n",
       "\tPreta & raça            & Preta               & 2019 & S011P & 0.7794471 & 0.7046830 & 0.8542112 & 0.04893935\\\\\n",
       "\tParda & raça            & Parda               & 2019 & S011P & 0.8112202 & 0.7821922 & 0.8402482 & 0.01825704\\\\\n",
       "\tAté 1/2 SM & rend\\_per\\_capita & Até 1/2 SM          & 2019 & S011P & 0.7674890 & 0.7344969 & 0.8004810 & 0.02193252\\\\\n",
       "\t1/2 até 1 SM & rend\\_per\\_capita & 1/2 até 1 SM        & 2019 & S011P & 0.8227927 & 0.7772524 & 0.8683330 & 0.02823954\\\\\n",
       "\t1 até 2 SM & rend\\_per\\_capita & 1 até 2 SM          & 2019 & S011P & 0.8572637 & 0.7962834 & 0.9182440 & 0.03629337\\\\\n",
       "\t2 até 3 SM & rend\\_per\\_capita & 2 até 3 SM          & 2019 & S011P & 0.7506448 & 0.6173035 & 0.8839861 & 0.09063210\\\\\n",
       "\tMais de 3 SM & rend\\_per\\_capita & Mais de 3 SM        & 2019 & S011P & 0.7643860 & 0.6571495 & 0.8716225 & 0.07157838\\\\\n",
       "\t18 a 24 anos & fx\\_idade\\_S      & 18 a 24 anos        & 2019 & S011P & 0.7642557 & 0.7172469 & 0.8112645 & 0.03138288\\\\\n",
       "\t25 a 29 anos & fx\\_idade\\_S      & 25 a 29 anos        & 2019 & S011P & 0.7897381 & 0.7367238 & 0.8427525 & 0.03425013\\\\\n",
       "\t30 a 39 anos & fx\\_idade\\_S      & 30 a 39 anos        & 2019 & S011P & 0.8265024 & 0.7909434 & 0.8620615 & 0.02195119\\\\\n",
       "\t40 anos ou mais & fx\\_idade\\_S      & 40 anos ou mais     & 2019 & S011P & 0.7832286 & 0.6845172 & 0.8819400 & 0.06430293\\\\\n",
       "\turbano & urb\\_rur         & urbano              & 2019 & S011P & 0.8123256 & 0.7851979 & 0.8394532 & 0.01703859\\\\\n",
       "\trural & urb\\_rur         & rural               & 2019 & S011P & 0.7123457 & 0.6653805 & 0.7593109 & 0.03363856\\\\\n",
       "\tRondônia & uf              & Rondônia            & 2019 & S011P & 0.8113854 & 0.7112533 & 0.9115175 & 0.06296482\\\\\n",
       "\tAcre & uf              & Acre                & 2019 & S011P & 0.8715134 & 0.7777214 & 0.9653054 & 0.05490903\\\\\n",
       "\tAmazonas & uf              & Amazonas            & 2019 & S011P & 0.7675780 & 0.6671789 & 0.8679770 & 0.06673581\\\\\n",
       "\tRoraima & uf              & Roraima             & 2019 & S011P & 0.8092680 & 0.6458560 & 0.9726801 & 0.10302523\\\\\n",
       "\tPará & uf              & Pará                & 2019 & S011P & 0.7977664 & 0.7155590 & 0.8799738 & 0.05257592\\\\\n",
       "\tAmapá & uf              & Amapá               & 2019 & S011P & 0.8772571 & 0.7620805 & 0.9924338 & 0.06698682\\\\\n",
       "\tTocantins & uf              & Tocantins           & 2019 & S011P & 0.8597343 & 0.7646931 & 0.9547755 & 0.05640264\\\\\n",
       "\tMaranhão & uf              & Maranhão            & 2019 & S011P & 0.6763398 & 0.5883963 & 0.7642833 & 0.06634235\\\\\n",
       "\tPiauí & uf              & Piauí               & 2019 & S011P & 0.7918508 & 0.6875284 & 0.8961731 & 0.06721805\\\\\n",
       "\tCeará & uf              & Ceará               & 2019 & S011P & 0.7318008 & 0.6417493 & 0.8218522 & 0.06278410\\\\\n",
       "\tRio Grande do Norte & uf              & Rio Grande do Norte & 2019 & S011P & 0.8062034 & 0.7020560 & 0.9103508 & 0.06591067\\\\\n",
       "\tParaíba & uf              & Paraíba             & 2019 & S011P & 0.6354086 & 0.5038523 & 0.7669648 & 0.10563561\\\\\n",
       "\tPernambuco & uf              & Pernambuco          & 2019 & S011P & 0.7903354 & 0.7019489 & 0.8787220 & 0.05705934\\\\\n",
       "\tAlagoas & uf              & Alagoas             & 2019 & S011P & 0.7448166 & 0.6421750 & 0.8474582 & 0.07031143\\\\\n",
       "\tSergipe & uf              & Sergipe             & 2019 & S011P & 0.7055517 & 0.5524472 & 0.8586563 & 0.11071620\\\\\n",
       "\tBahia & uf              & Bahia               & 2019 & S011P & 0.8109277 & 0.6995516 & 0.9223039 & 0.07007479\\\\\n",
       "\t⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮\\\\\n",
       "\tJoão Pessoa4 & capital & João Pessoa                           & 2019 & S015P & 0.9058899 & 0.8197244 & 0.9920555 & 0.048529968\\\\\n",
       "\tRecife4 & capital & Recife                                & 2019 & S015P & 0.9442654 & 0.8641629 & 1.0243678 & 0.043281640\\\\\n",
       "\tMaceió4 & capital & Maceió                                & 2019 & S015P & 0.9317416 & 0.8584334 & 1.0050497 & 0.040142881\\\\\n",
       "\tAracaju4 & capital & Aracaju                               & 2019 & S015P & 0.9253580 & 0.8200606 & 1.0306555 & 0.058057736\\\\\n",
       "\tSalvador4 & capital & Salvador                              & 2019 & S015P & 0.8851483 & 0.7279619 & 1.0423347 & 0.090604712\\\\\n",
       "\tBelo Horizonte4 & capital & Belo Horizonte                        & 2019 & S015P & 0.7831622 & 0.6265665 & 0.9397578 & 0.102018733\\\\\n",
       "\tVitória4 & capital & Vitória                               & 2019 & S015P & 0.8295784 & 0.6389091 & 1.0202477 & 0.117266844\\\\\n",
       "\tRio de Janeiro9 & capital & Rio de Janeiro                        & 2019 & S015P & 0.9830347 & 0.9513834 & 1.0146860 & 0.016427602\\\\\n",
       "\tSão Paulo8 & capital & São Paulo                             & 2019 & S015P & 0.7818142 & 0.6130362 & 0.9505922 & 0.110144846\\\\\n",
       "\tCuritiba4 & capital & Curitiba                              & 2019 & S015P & 0.8708550 & 0.7478725 & 0.9938376 & 0.072052572\\\\\n",
       "\tFlorianópolis4 & capital & Florianópolis                         & 2019 & S015P & 0.7691331 & 0.5210958 & 1.0171704 & 0.164538473\\\\\n",
       "\tPorto Alegre4 & capital & Porto Alegre                          & 2019 & S015P & 1.0000000 & 1.0000000 & 1.0000000 & 0.000000000\\\\\n",
       "\tCampo Grande4 & capital & Campo Grande                          & 2019 & S015P & 0.9588850 & 0.9041423 & 1.0136277 & 0.029128061\\\\\n",
       "\tCuiabá4 & capital & Cuiabá                                & 2019 & S015P & 0.8613471 & 0.6884873 & 1.0342069 & 0.102392414\\\\\n",
       "\tGoiânia4 & capital & Goiânia                               & 2019 & S015P & 0.9112432 & 0.7458644 & 1.0766219 & 0.092597080\\\\\n",
       "\tBrasília4 & capital & Brasília                              & 2019 & S015P & 0.8353633 & 0.7275334 & 0.9431932 & 0.065859086\\\\\n",
       "\tFundamental incompleto ou equivalente4 & gescol  & Fundamental incompleto ou equivalente & 2019 & S015P & 0.8420161 & 0.7906370 & 0.8933952 & 0.031132788\\\\\n",
       "\tMédio incompleto ou equivalente4 & gescol  & Médio incompleto ou equivalente       & 2019 & S015P & 0.8999967 & 0.8673529 & 0.9326406 & 0.018505991\\\\\n",
       "\tSuperior incompleto ou equivalente4 & gescol  & Superior incompleto ou equivalente    & 2019 & S015P & 0.9163796 & 0.8947965 & 0.9379628 & 0.012016843\\\\\n",
       "\tSuperior completo4 & gescol  & Superior completo                     & 2019 & S015P & 0.9154731 & 0.8764884 & 0.9544578 & 0.021727051\\\\\n",
       "\tS011PSim & total   & Brasil                                & 2019 & S011P & 0.7970506 & 0.7729093 & 0.8211920 & 0.015453529\\\\\n",
       "\tS011PSim1 & total   & Capital                               & 2019 & S011P & 0.7856012 & 0.7367190 & 0.8344834 & 0.031746843\\\\\n",
       "\tS012PSim & total   & Brasil                                & 2019 & S012P & 0.7709437 & 0.7450416 & 0.7968458 & 0.017142104\\\\\n",
       "\tS012PSim1 & total   & Capital                               & 2019 & S012P & 0.7605274 & 0.7105432 & 0.8105116 & 0.033532798\\\\\n",
       "\tS013PSim & total   & Brasil                                & 2019 & S013P & 0.8423751 & 0.8206783 & 0.8640719 & 0.013141415\\\\\n",
       "\tS013PSim1 & total   & Capital                               & 2019 & S013P & 0.8497500 & 0.8098841 & 0.8896158 & 0.023936576\\\\\n",
       "\tS014PSim & total   & Brasil                                & 2019 & S014P & 0.8117146 & 0.7881055 & 0.8353238 & 0.014839855\\\\\n",
       "\tS014PSim1 & total   & Capital                               & 2019 & S014P & 0.8189464 & 0.7762082 & 0.8616846 & 0.026626396\\\\\n",
       "\tS015PSim & total   & Brasil                                & 2019 & S015P & 0.8997072 & 0.8831155 & 0.9162988 & 0.009408937\\\\\n",
       "\tS015PSim1 & total   & Capital                               & 2019 & S015P & 0.8832092 & 0.8432095 & 0.9232089 & 0.023107083\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 395 × 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",
       "| Branca | raça            | Branca              | 2019 | S011P | 0.7873750 | 0.7422421 | 0.8325079 | 0.02924581 |\n",
       "| Preta | raça            | Preta               | 2019 | S011P | 0.7794471 | 0.7046830 | 0.8542112 | 0.04893935 |\n",
       "| Parda | raça            | Parda               | 2019 | S011P | 0.8112202 | 0.7821922 | 0.8402482 | 0.01825704 |\n",
       "| Até 1/2 SM | rend_per_capita | Até 1/2 SM          | 2019 | S011P | 0.7674890 | 0.7344969 | 0.8004810 | 0.02193252 |\n",
       "| 1/2 até 1 SM | rend_per_capita | 1/2 até 1 SM        | 2019 | S011P | 0.8227927 | 0.7772524 | 0.8683330 | 0.02823954 |\n",
       "| 1 até 2 SM | rend_per_capita | 1 até 2 SM          | 2019 | S011P | 0.8572637 | 0.7962834 | 0.9182440 | 0.03629337 |\n",
       "| 2 até 3 SM | rend_per_capita | 2 até 3 SM          | 2019 | S011P | 0.7506448 | 0.6173035 | 0.8839861 | 0.09063210 |\n",
       "| Mais de 3 SM | rend_per_capita | Mais de 3 SM        | 2019 | S011P | 0.7643860 | 0.6571495 | 0.8716225 | 0.07157838 |\n",
       "| 18 a 24 anos | fx_idade_S      | 18 a 24 anos        | 2019 | S011P | 0.7642557 | 0.7172469 | 0.8112645 | 0.03138288 |\n",
       "| 25 a 29 anos | fx_idade_S      | 25 a 29 anos        | 2019 | S011P | 0.7897381 | 0.7367238 | 0.8427525 | 0.03425013 |\n",
       "| 30 a 39 anos | fx_idade_S      | 30 a 39 anos        | 2019 | S011P | 0.8265024 | 0.7909434 | 0.8620615 | 0.02195119 |\n",
       "| 40 anos ou mais | fx_idade_S      | 40 anos ou mais     | 2019 | S011P | 0.7832286 | 0.6845172 | 0.8819400 | 0.06430293 |\n",
       "| urbano | urb_rur         | urbano              | 2019 | S011P | 0.8123256 | 0.7851979 | 0.8394532 | 0.01703859 |\n",
       "| rural | urb_rur         | rural               | 2019 | S011P | 0.7123457 | 0.6653805 | 0.7593109 | 0.03363856 |\n",
       "| Rondônia | uf              | Rondônia            | 2019 | S011P | 0.8113854 | 0.7112533 | 0.9115175 | 0.06296482 |\n",
       "| Acre | uf              | Acre                | 2019 | S011P | 0.8715134 | 0.7777214 | 0.9653054 | 0.05490903 |\n",
       "| Amazonas | uf              | Amazonas            | 2019 | S011P | 0.7675780 | 0.6671789 | 0.8679770 | 0.06673581 |\n",
       "| Roraima | uf              | Roraima             | 2019 | S011P | 0.8092680 | 0.6458560 | 0.9726801 | 0.10302523 |\n",
       "| Pará | uf              | Pará                | 2019 | S011P | 0.7977664 | 0.7155590 | 0.8799738 | 0.05257592 |\n",
       "| Amapá | uf              | Amapá               | 2019 | S011P | 0.8772571 | 0.7620805 | 0.9924338 | 0.06698682 |\n",
       "| Tocantins | uf              | Tocantins           | 2019 | S011P | 0.8597343 | 0.7646931 | 0.9547755 | 0.05640264 |\n",
       "| Maranhão | uf              | Maranhão            | 2019 | S011P | 0.6763398 | 0.5883963 | 0.7642833 | 0.06634235 |\n",
       "| Piauí | uf              | Piauí               | 2019 | S011P | 0.7918508 | 0.6875284 | 0.8961731 | 0.06721805 |\n",
       "| Ceará | uf              | Ceará               | 2019 | S011P | 0.7318008 | 0.6417493 | 0.8218522 | 0.06278410 |\n",
       "| Rio Grande do Norte | uf              | Rio Grande do Norte | 2019 | S011P | 0.8062034 | 0.7020560 | 0.9103508 | 0.06591067 |\n",
       "| Paraíba | uf              | Paraíba             | 2019 | S011P | 0.6354086 | 0.5038523 | 0.7669648 | 0.10563561 |\n",
       "| Pernambuco | uf              | Pernambuco          | 2019 | S011P | 0.7903354 | 0.7019489 | 0.8787220 | 0.05705934 |\n",
       "| Alagoas | uf              | Alagoas             | 2019 | S011P | 0.7448166 | 0.6421750 | 0.8474582 | 0.07031143 |\n",
       "| Sergipe | uf              | Sergipe             | 2019 | S011P | 0.7055517 | 0.5524472 | 0.8586563 | 0.11071620 |\n",
       "| Bahia | uf              | Bahia               | 2019 | S011P | 0.8109277 | 0.6995516 | 0.9223039 | 0.07007479 |\n",
       "| ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ |\n",
       "| João Pessoa4 | capital | João Pessoa                           | 2019 | S015P | 0.9058899 | 0.8197244 | 0.9920555 | 0.048529968 |\n",
       "| Recife4 | capital | Recife                                | 2019 | S015P | 0.9442654 | 0.8641629 | 1.0243678 | 0.043281640 |\n",
       "| Maceió4 | capital | Maceió                                | 2019 | S015P | 0.9317416 | 0.8584334 | 1.0050497 | 0.040142881 |\n",
       "| Aracaju4 | capital | Aracaju                               | 2019 | S015P | 0.9253580 | 0.8200606 | 1.0306555 | 0.058057736 |\n",
       "| Salvador4 | capital | Salvador                              | 2019 | S015P | 0.8851483 | 0.7279619 | 1.0423347 | 0.090604712 |\n",
       "| Belo Horizonte4 | capital | Belo Horizonte                        | 2019 | S015P | 0.7831622 | 0.6265665 | 0.9397578 | 0.102018733 |\n",
       "| Vitória4 | capital | Vitória                               | 2019 | S015P | 0.8295784 | 0.6389091 | 1.0202477 | 0.117266844 |\n",
       "| Rio de Janeiro9 | capital | Rio de Janeiro                        | 2019 | S015P | 0.9830347 | 0.9513834 | 1.0146860 | 0.016427602 |\n",
       "| São Paulo8 | capital | São Paulo                             | 2019 | S015P | 0.7818142 | 0.6130362 | 0.9505922 | 0.110144846 |\n",
       "| Curitiba4 | capital | Curitiba                              | 2019 | S015P | 0.8708550 | 0.7478725 | 0.9938376 | 0.072052572 |\n",
       "| Florianópolis4 | capital | Florianópolis                         | 2019 | S015P | 0.7691331 | 0.5210958 | 1.0171704 | 0.164538473 |\n",
       "| Porto Alegre4 | capital | Porto Alegre                          | 2019 | S015P | 1.0000000 | 1.0000000 | 1.0000000 | 0.000000000 |\n",
       "| Campo Grande4 | capital | Campo Grande                          | 2019 | S015P | 0.9588850 | 0.9041423 | 1.0136277 | 0.029128061 |\n",
       "| Cuiabá4 | capital | Cuiabá                                | 2019 | S015P | 0.8613471 | 0.6884873 | 1.0342069 | 0.102392414 |\n",
       "| Goiânia4 | capital | Goiânia                               | 2019 | S015P | 0.9112432 | 0.7458644 | 1.0766219 | 0.092597080 |\n",
       "| Brasília4 | capital | Brasília                              | 2019 | S015P | 0.8353633 | 0.7275334 | 0.9431932 | 0.065859086 |\n",
       "| Fundamental incompleto ou equivalente4 | gescol  | Fundamental incompleto ou equivalente | 2019 | S015P | 0.8420161 | 0.7906370 | 0.8933952 | 0.031132788 |\n",
       "| Médio incompleto ou equivalente4 | gescol  | Médio incompleto ou equivalente       | 2019 | S015P | 0.8999967 | 0.8673529 | 0.9326406 | 0.018505991 |\n",
       "| Superior incompleto ou equivalente4 | gescol  | Superior incompleto ou equivalente    | 2019 | S015P | 0.9163796 | 0.8947965 | 0.9379628 | 0.012016843 |\n",
       "| Superior completo4 | gescol  | Superior completo                     | 2019 | S015P | 0.9154731 | 0.8764884 | 0.9544578 | 0.021727051 |\n",
       "| S011PSim | total   | Brasil                                | 2019 | S011P | 0.7970506 | 0.7729093 | 0.8211920 | 0.015453529 |\n",
       "| S011PSim1 | total   | Capital                               | 2019 | S011P | 0.7856012 | 0.7367190 | 0.8344834 | 0.031746843 |\n",
       "| S012PSim | total   | Brasil                                | 2019 | S012P | 0.7709437 | 0.7450416 | 0.7968458 | 0.017142104 |\n",
       "| S012PSim1 | total   | Capital                               | 2019 | S012P | 0.7605274 | 0.7105432 | 0.8105116 | 0.033532798 |\n",
       "| S013PSim | total   | Brasil                                | 2019 | S013P | 0.8423751 | 0.8206783 | 0.8640719 | 0.013141415 |\n",
       "| S013PSim1 | total   | Capital                               | 2019 | S013P | 0.8497500 | 0.8098841 | 0.8896158 | 0.023936576 |\n",
       "| S014PSim | total   | Brasil                                | 2019 | S014P | 0.8117146 | 0.7881055 | 0.8353238 | 0.014839855 |\n",
       "| S014PSim1 | total   | Capital                               | 2019 | S014P | 0.8189464 | 0.7762082 | 0.8616846 | 0.026626396 |\n",
       "| S015PSim | total   | Brasil                                | 2019 | S015P | 0.8997072 | 0.8831155 | 0.9162988 | 0.009408937 |\n",
       "| S015PSim1 | total   | Capital                               | 2019 | S015P | 0.8832092 | 0.8432095 | 0.9232089 | 0.023107083 |\n",
       "\n"
      ],
      "text/plain": [
       "                                       abr_tipo       \n",
       "Branca                                 raça           \n",
       "Preta                                  raça           \n",
       "Parda                                  raça           \n",
       "Até 1/2 SM                             rend_per_capita\n",
       "1/2 até 1 SM                           rend_per_capita\n",
       "1 até 2 SM                             rend_per_capita\n",
       "2 até 3 SM                             rend_per_capita\n",
       "Mais de 3 SM                           rend_per_capita\n",
       "18 a 24 anos                           fx_idade_S     \n",
       "25 a 29 anos                           fx_idade_S     \n",
       "30 a 39 anos                           fx_idade_S     \n",
       "40 anos ou mais                        fx_idade_S     \n",
       "urbano                                 urb_rur        \n",
       "rural                                  urb_rur        \n",
       "Rondônia                               uf             \n",
       "Acre                                   uf             \n",
       "Amazonas                               uf             \n",
       "Roraima                                uf             \n",
       "Pará                                   uf             \n",
       "Amapá                                  uf             \n",
       "Tocantins                              uf             \n",
       "Maranhão                               uf             \n",
       "Piauí                                  uf             \n",
       "Ceará                                  uf             \n",
       "Rio Grande do Norte                    uf             \n",
       "Paraíba                                uf             \n",
       "Pernambuco                             uf             \n",
       "Alagoas                                uf             \n",
       "Sergipe                                uf             \n",
       "Bahia                                  uf             \n",
       "⋮                                      ⋮              \n",
       "João Pessoa4                           capital        \n",
       "Recife4                                capital        \n",
       "Maceió4                                capital        \n",
       "Aracaju4                               capital        \n",
       "Salvador4                              capital        \n",
       "Belo Horizonte4                        capital        \n",
       "Vitória4                               capital        \n",
       "Rio de Janeiro9                        capital        \n",
       "São Paulo8                             capital        \n",
       "Curitiba4                              capital        \n",
       "Florianópolis4                         capital        \n",
       "Porto Alegre4                          capital        \n",
       "Campo Grande4                          capital        \n",
       "Cuiabá4                                capital        \n",
       "Goiânia4                               capital        \n",
       "Brasília4                              capital        \n",
       "Fundamental incompleto ou equivalente4 gescol         \n",
       "Médio incompleto ou equivalente4       gescol         \n",
       "Superior incompleto ou equivalente4    gescol         \n",
       "Superior completo4                     gescol         \n",
       "S011PSim                               total          \n",
       "S011PSim1                              total          \n",
       "S012PSim                               total          \n",
       "S012PSim1                              total          \n",
       "S013PSim                               total          \n",
       "S013PSim1                              total          \n",
       "S014PSim                               total          \n",
       "S014PSim1                              total          \n",
       "S015PSim                               total          \n",
       "S015PSim1                              total          \n",
       "                                       abr_nome                             \n",
       "Branca                                 Branca                               \n",
       "Preta                                  Preta                                \n",
       "Parda                                  Parda                                \n",
       "Até 1/2 SM                             Até 1/2 SM                           \n",
       "1/2 até 1 SM                           1/2 até 1 SM                         \n",
       "1 até 2 SM                             1 até 2 SM                           \n",
       "2 até 3 SM                             2 até 3 SM                           \n",
       "Mais de 3 SM                           Mais de 3 SM                         \n",
       "18 a 24 anos                           18 a 24 anos                         \n",
       "25 a 29 anos                           25 a 29 anos                         \n",
       "30 a 39 anos                           30 a 39 anos                         \n",
       "40 anos ou mais                        40 anos ou mais                      \n",
       "urbano                                 urbano                               \n",
       "rural                                  rural                                \n",
       "Rondônia                               Rondônia                             \n",
       "Acre                                   Acre                                 \n",
       "Amazonas                               Amazonas                             \n",
       "Roraima                                Roraima                              \n",
       "Pará                                   Pará                                 \n",
       "Amapá                                  Amapá                                \n",
       "Tocantins                              Tocantins                            \n",
       "Maranhão                               Maranhão                             \n",
       "Piauí                                  Piauí                                \n",
       "Ceará                                  Ceará                                \n",
       "Rio Grande do Norte                    Rio Grande do Norte                  \n",
       "Paraíba                                Paraíba                              \n",
       "Pernambuco                             Pernambuco                           \n",
       "Alagoas                                Alagoas                              \n",
       "Sergipe                                Sergipe                              \n",
       "Bahia                                  Bahia                                \n",
       "⋮                                      ⋮                                    \n",
       "João Pessoa4                           João Pessoa                          \n",
       "Recife4                                Recife                               \n",
       "Maceió4                                Maceió                               \n",
       "Aracaju4                               Aracaju                              \n",
       "Salvador4                              Salvador                             \n",
       "Belo Horizonte4                        Belo Horizonte                       \n",
       "Vitória4                               Vitória                              \n",
       "Rio de Janeiro9                        Rio de Janeiro                       \n",
       "São Paulo8                             São Paulo                            \n",
       "Curitiba4                              Curitiba                             \n",
       "Florianópolis4                         Florianópolis                        \n",
       "Porto Alegre4                          Porto Alegre                         \n",
       "Campo Grande4                          Campo Grande                         \n",
       "Cuiabá4                                Cuiabá                               \n",
       "Goiânia4                               Goiânia                              \n",
       "Brasília4                              Brasília                             \n",
       "Fundamental incompleto ou equivalente4 Fundamental incompleto ou equivalente\n",
       "Médio incompleto ou equivalente4       Médio incompleto ou equivalente      \n",
       "Superior incompleto ou equivalente4    Superior incompleto ou equivalente   \n",
       "Superior completo4                     Superior completo                    \n",
       "S011PSim                               Brasil                               \n",
       "S011PSim1                              Capital                              \n",
       "S012PSim                               Brasil                               \n",
       "S012PSim1                              Capital                              \n",
       "S013PSim                               Brasil                               \n",
       "S013PSim1                              Capital                              \n",
       "S014PSim                               Brasil                               \n",
       "S014PSim1                              Capital                              \n",
       "S015PSim                               Brasil                               \n",
       "S015PSim1                              Capital                              \n",
       "                                       Ano  Indicador Sim       LowerS   \n",
       "Branca                                 2019 S011P     0.7873750 0.7422421\n",
       "Preta                                  2019 S011P     0.7794471 0.7046830\n",
       "Parda                                  2019 S011P     0.8112202 0.7821922\n",
       "Até 1/2 SM                             2019 S011P     0.7674890 0.7344969\n",
       "1/2 até 1 SM                           2019 S011P     0.8227927 0.7772524\n",
       "1 até 2 SM                             2019 S011P     0.8572637 0.7962834\n",
       "2 até 3 SM                             2019 S011P     0.7506448 0.6173035\n",
       "Mais de 3 SM                           2019 S011P     0.7643860 0.6571495\n",
       "18 a 24 anos                           2019 S011P     0.7642557 0.7172469\n",
       "25 a 29 anos                           2019 S011P     0.7897381 0.7367238\n",
       "30 a 39 anos                           2019 S011P     0.8265024 0.7909434\n",
       "40 anos ou mais                        2019 S011P     0.7832286 0.6845172\n",
       "urbano                                 2019 S011P     0.8123256 0.7851979\n",
       "rural                                  2019 S011P     0.7123457 0.6653805\n",
       "Rondônia                               2019 S011P     0.8113854 0.7112533\n",
       "Acre                                   2019 S011P     0.8715134 0.7777214\n",
       "Amazonas                               2019 S011P     0.7675780 0.6671789\n",
       "Roraima                                2019 S011P     0.8092680 0.6458560\n",
       "Pará                                   2019 S011P     0.7977664 0.7155590\n",
       "Amapá                                  2019 S011P     0.8772571 0.7620805\n",
       "Tocantins                              2019 S011P     0.8597343 0.7646931\n",
       "Maranhão                               2019 S011P     0.6763398 0.5883963\n",
       "Piauí                                  2019 S011P     0.7918508 0.6875284\n",
       "Ceará                                  2019 S011P     0.7318008 0.6417493\n",
       "Rio Grande do Norte                    2019 S011P     0.8062034 0.7020560\n",
       "Paraíba                                2019 S011P     0.6354086 0.5038523\n",
       "Pernambuco                             2019 S011P     0.7903354 0.7019489\n",
       "Alagoas                                2019 S011P     0.7448166 0.6421750\n",
       "Sergipe                                2019 S011P     0.7055517 0.5524472\n",
       "Bahia                                  2019 S011P     0.8109277 0.6995516\n",
       "⋮                                      ⋮    ⋮         ⋮         ⋮        \n",
       "João Pessoa4                           2019 S015P     0.9058899 0.8197244\n",
       "Recife4                                2019 S015P     0.9442654 0.8641629\n",
       "Maceió4                                2019 S015P     0.9317416 0.8584334\n",
       "Aracaju4                               2019 S015P     0.9253580 0.8200606\n",
       "Salvador4                              2019 S015P     0.8851483 0.7279619\n",
       "Belo Horizonte4                        2019 S015P     0.7831622 0.6265665\n",
       "Vitória4                               2019 S015P     0.8295784 0.6389091\n",
       "Rio de Janeiro9                        2019 S015P     0.9830347 0.9513834\n",
       "São Paulo8                             2019 S015P     0.7818142 0.6130362\n",
       "Curitiba4                              2019 S015P     0.8708550 0.7478725\n",
       "Florianópolis4                         2019 S015P     0.7691331 0.5210958\n",
       "Porto Alegre4                          2019 S015P     1.0000000 1.0000000\n",
       "Campo Grande4                          2019 S015P     0.9588850 0.9041423\n",
       "Cuiabá4                                2019 S015P     0.8613471 0.6884873\n",
       "Goiânia4                               2019 S015P     0.9112432 0.7458644\n",
       "Brasília4                              2019 S015P     0.8353633 0.7275334\n",
       "Fundamental incompleto ou equivalente4 2019 S015P     0.8420161 0.7906370\n",
       "Médio incompleto ou equivalente4       2019 S015P     0.8999967 0.8673529\n",
       "Superior incompleto ou equivalente4    2019 S015P     0.9163796 0.8947965\n",
       "Superior completo4                     2019 S015P     0.9154731 0.8764884\n",
       "S011PSim                               2019 S011P     0.7970506 0.7729093\n",
       "S011PSim1                              2019 S011P     0.7856012 0.7367190\n",
       "S012PSim                               2019 S012P     0.7709437 0.7450416\n",
       "S012PSim1                              2019 S012P     0.7605274 0.7105432\n",
       "S013PSim                               2019 S013P     0.8423751 0.8206783\n",
       "S013PSim1                              2019 S013P     0.8497500 0.8098841\n",
       "S014PSim                               2019 S014P     0.8117146 0.7881055\n",
       "S014PSim1                              2019 S014P     0.8189464 0.7762082\n",
       "S015PSim                               2019 S015P     0.8997072 0.8831155\n",
       "S015PSim1                              2019 S015P     0.8832092 0.8432095\n",
       "                                       UpperS    cvS        \n",
       "Branca                                 0.8325079 0.02924581 \n",
       "Preta                                  0.8542112 0.04893935 \n",
       "Parda                                  0.8402482 0.01825704 \n",
       "Até 1/2 SM                             0.8004810 0.02193252 \n",
       "1/2 até 1 SM                           0.8683330 0.02823954 \n",
       "1 até 2 SM                             0.9182440 0.03629337 \n",
       "2 até 3 SM                             0.8839861 0.09063210 \n",
       "Mais de 3 SM                           0.8716225 0.07157838 \n",
       "18 a 24 anos                           0.8112645 0.03138288 \n",
       "25 a 29 anos                           0.8427525 0.03425013 \n",
       "30 a 39 anos                           0.8620615 0.02195119 \n",
       "40 anos ou mais                        0.8819400 0.06430293 \n",
       "urbano                                 0.8394532 0.01703859 \n",
       "rural                                  0.7593109 0.03363856 \n",
       "Rondônia                               0.9115175 0.06296482 \n",
       "Acre                                   0.9653054 0.05490903 \n",
       "Amazonas                               0.8679770 0.06673581 \n",
       "Roraima                                0.9726801 0.10302523 \n",
       "Pará                                   0.8799738 0.05257592 \n",
       "Amapá                                  0.9924338 0.06698682 \n",
       "Tocantins                              0.9547755 0.05640264 \n",
       "Maranhão                               0.7642833 0.06634235 \n",
       "Piauí                                  0.8961731 0.06721805 \n",
       "Ceará                                  0.8218522 0.06278410 \n",
       "Rio Grande do Norte                    0.9103508 0.06591067 \n",
       "Paraíba                                0.7669648 0.10563561 \n",
       "Pernambuco                             0.8787220 0.05705934 \n",
       "Alagoas                                0.8474582 0.07031143 \n",
       "Sergipe                                0.8586563 0.11071620 \n",
       "Bahia                                  0.9223039 0.07007479 \n",
       "⋮                                      ⋮         ⋮          \n",
       "João Pessoa4                           0.9920555 0.048529968\n",
       "Recife4                                1.0243678 0.043281640\n",
       "Maceió4                                1.0050497 0.040142881\n",
       "Aracaju4                               1.0306555 0.058057736\n",
       "Salvador4                              1.0423347 0.090604712\n",
       "Belo Horizonte4                        0.9397578 0.102018733\n",
       "Vitória4                               1.0202477 0.117266844\n",
       "Rio de Janeiro9                        1.0146860 0.016427602\n",
       "São Paulo8                             0.9505922 0.110144846\n",
       "Curitiba4                              0.9938376 0.072052572\n",
       "Florianópolis4                         1.0171704 0.164538473\n",
       "Porto Alegre4                          1.0000000 0.000000000\n",
       "Campo Grande4                          1.0136277 0.029128061\n",
       "Cuiabá4                                1.0342069 0.102392414\n",
       "Goiânia4                               1.0766219 0.092597080\n",
       "Brasília4                              0.9431932 0.065859086\n",
       "Fundamental incompleto ou equivalente4 0.8933952 0.031132788\n",
       "Médio incompleto ou equivalente4       0.9326406 0.018505991\n",
       "Superior incompleto ou equivalente4    0.9379628 0.012016843\n",
       "Superior completo4                     0.9544578 0.021727051\n",
       "S011PSim                               0.8211920 0.015453529\n",
       "S011PSim1                              0.8344834 0.031746843\n",
       "S012PSim                               0.7968458 0.017142104\n",
       "S012PSim1                              0.8105116 0.033532798\n",
       "S013PSim                               0.8640719 0.013141415\n",
       "S013PSim1                              0.8896158 0.023936576\n",
       "S014PSim                               0.8353238 0.014839855\n",
       "S014PSim1                              0.8616846 0.026626396\n",
       "S015PSim                               0.9162988 0.009408937\n",
       "S015PSim1                              0.9232089 0.023107083"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "matrizIndicadores"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Exportando tabela de indicadores calculados - Módulo S  2019"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "diretorio_saida <- \"<coloque aqui o diretório>\"\n",
    "write.table(matrizIndicadores,file=paste0(diretorio_saida,\"Indicadores_2019S_R.csv\"),sep = \";\",dec = \",\",row.names = FALSE)"
   ]
  }
 ],
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   "file_extension": ".r",
   "mimetype": "text/x-r-source",
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