{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "![](https://github.com/bigdata-icict/ETL-Dataiku-DSS/raw/master/tutoriais/pcdas_1.5.png)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Notebook para criação de tabela de indicadores da PNS - módulo P 2013 Atividade Física " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Bibliotecas Utilizadas" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "#Lendo pacotes necessários\n", "library(survey)\n", "library(ggplot2)\n", "library(dplyr)\n", "library(foreign)\n", "library(forcats)\n", "library(tidyverse)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Carregando microdados da PNS" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "#Carregando banco de dados para R versão 3.5.0 ou superior\n", "load(\"\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Definição do peso e filtragem de respondentes do questionario" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ " Min. 1st Qu. Median Mean 3rd Qu. Max. \n", " 0.004156 0.243959 0.521557 1.000000 1.147413 31.179597 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#Selecionando registros válidos para o módulo P e calculando peso amostral - summary de verificação\n", "pns2013.1<-pns2013 %>% filter(M001==1)\n", "pns2013.1<-pns2013.1 %>% mutate(peso_morador_selec=((V00291*(60202/145572211))))\n", "pns2013.1<-pns2013.1 %>% filter(!is.na(peso_morador_selec))\n", "summary(pns2013.1$peso_morador_selec)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Criação de variáveis dos indicadores" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
Sim
17769
Nao
42433
\n" ], "text/latex": [ "\\begin{description*}\n", "\\item[Sim] 17769\n", "\\item[Nao] 42433\n", "\\end{description*}\n" ], "text/markdown": [ "Sim\n", ": 17769Nao\n", ": 42433\n", "\n" ], "text/plain": [ " Sim Nao \n", "17769 42433 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#Desfechos - Indicadores\n", "\n", "#P017P - Tempo de televisão de 3h ou mais por dia\n", "pns2013.1$P045[which(is.na(pns2013.1$P045))] <- 0\n", "pns2013.1 <- pns2013.1 %>% filter(P045!=9) %>% mutate(P017P = ifelse(P045%in% 4:7, 1,2))\n", "pns2013.1$P017P<-factor(pns2013.1$P017P, levels=c(1,2), labels=c(\"Sim\",\"Nao\"))\n", "summary(pns2013.1$P017P)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Definições de abrangências" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Situação urbana ou rural" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
Urbano
49245
Rural
10957
\n" ], "text/latex": [ "\\begin{description*}\n", "\\item[Urbano] 49245\n", "\\item[Rural] 10957\n", "\\end{description*}\n" ], "text/markdown": [ "Urbano\n", ": 49245Rural\n", ": 10957\n", "\n" ], "text/plain": [ "Urbano Rural \n", " 49245 10957 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#Situação Urbano ou Rural\n", "pns2013.1 <- pns2013.1 %>% rename(Sit_Urbano_Rural=V0026)\n", "pns2013.1$Sit_Urbano_Rural<-factor(pns2013.1$Sit_Urbano_Rural, levels=c(1,2), labels=c(\"Urbano\", \"Rural\"))\n", "summary(pns2013.1$Sit_Urbano_Rural)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Sexo" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
Masculino
25920
Feminino
34282
\n" ], "text/latex": [ "\\begin{description*}\n", "\\item[Masculino] 25920\n", "\\item[Feminino] 34282\n", "\\end{description*}\n" ], "text/markdown": [ "Masculino\n", ": 25920Feminino\n", ": 34282\n", "\n" ], "text/plain": [ "Masculino Feminino \n", " 25920 34282 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#Sexo\n", "pns2013.1 <- pns2013.1 %>% rename(Sexo=C006)\n", "pns2013.1$Sexo<-factor(pns2013.1$Sexo, levels=c(1,2), labels=c(\"Masculino\", \"Feminino\"))\n", "summary(pns2013.1$Sexo)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### UF" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
Rondonia
1694
Acre
1814
Amazonas
2586
Roraima
1591
Para
2004
Amapa
1332
Tocantins
1515
Maranhao
1774
Piaui
1804
Ceara
2560
Rio Grande do Norte
1691
Paraiba
1943
Pernambuco
2591
Alagoas
1748
Sergipe
1553
Bahia
2641
Minas Gerais
3779
Espirito Santo
1724
Rio de Janeiro
3486
Sao Paulo
5305
Parana
3012
Santa Catarina
1623
Rio Grande do Sul
2913
Mato Grosso do Sul
1809
Mato Grosso
1476
Goias
2423
Distrito Federal
1811
\n" ], "text/latex": [ "\\begin{description*}\n", "\\item[Rondonia] 1694\n", "\\item[Acre] 1814\n", "\\item[Amazonas] 2586\n", "\\item[Roraima] 1591\n", "\\item[Para] 2004\n", "\\item[Amapa] 1332\n", "\\item[Tocantins] 1515\n", "\\item[Maranhao] 1774\n", "\\item[Piaui] 1804\n", "\\item[Ceara] 2560\n", "\\item[Rio Grande do Norte] 1691\n", "\\item[Paraiba] 1943\n", "\\item[Pernambuco] 2591\n", "\\item[Alagoas] 1748\n", "\\item[Sergipe] 1553\n", "\\item[Bahia] 2641\n", "\\item[Minas Gerais] 3779\n", "\\item[Espirito Santo] 1724\n", "\\item[Rio de Janeiro] 3486\n", "\\item[Sao Paulo] 5305\n", "\\item[Parana] 3012\n", "\\item[Santa Catarina] 1623\n", "\\item[Rio Grande do Sul] 2913\n", "\\item[Mato Grosso do Sul] 1809\n", "\\item[Mato Grosso] 1476\n", "\\item[Goias] 2423\n", "\\item[Distrito Federal] 1811\n", "\\end{description*}\n" ], "text/markdown": [ "Rondonia\n", ": 1694Acre\n", ": 1814Amazonas\n", ": 2586Roraima\n", ": 1591Para\n", ": 2004Amapa\n", ": 1332Tocantins\n", ": 1515Maranhao\n", ": 1774Piaui\n", ": 1804Ceara\n", ": 2560Rio Grande do Norte\n", ": 1691Paraiba\n", ": 1943Pernambuco\n", ": 2591Alagoas\n", ": 1748Sergipe\n", ": 1553Bahia\n", ": 2641Minas Gerais\n", ": 3779Espirito Santo\n", ": 1724Rio de Janeiro\n", ": 3486Sao Paulo\n", ": 5305Parana\n", ": 3012Santa Catarina\n", ": 1623Rio Grande do Sul\n", ": 2913Mato Grosso do Sul\n", ": 1809Mato Grosso\n", ": 1476Goias\n", ": 2423Distrito Federal\n", ": 1811\n", "\n" ], "text/plain": [ " Rondonia Acre Amazonas Roraima \n", " 1694 1814 2586 1591 \n", " Para Amapa Tocantins Maranhao \n", " 2004 1332 1515 1774 \n", " Piaui Ceara Rio Grande do Norte Paraiba \n", " 1804 2560 1691 1943 \n", " Pernambuco Alagoas Sergipe Bahia \n", " 2591 1748 1553 2641 \n", " Minas Gerais Espirito Santo Rio de Janeiro Sao Paulo \n", " 3779 1724 3486 5305 \n", " Parana Santa Catarina Rio Grande do Sul Mato Grosso do Sul \n", " 3012 1623 2913 1809 \n", " Mato Grosso Goias Distrito Federal \n", " 1476 2423 1811 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#Estados - UFs\n", "pns2013.1 <- pns2013.1 %>% rename(Unidades_da_Federacao=V0001)\n", "pns2013.1$Unidades_da_Federacao<-factor(pns2013.1$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(\"Rondonia\",\"Acre\",\"Amazonas\",\"Roraima\",\"Para\",\"Amapa\",\"Tocantins\",\"Maranhao\",\"Piaui\",\"Ceara\",\n", " \"Rio Grande do Norte\",\"Paraiba\",\"Pernambuco\",\"Alagoas\",\"Sergipe\",\"Bahia\",\n", " \"Minas Gerais\",\"Espirito Santo\",\"Rio de Janeiro\",\"Sao Paulo\",\n", " \"Parana\",\"Santa Catarina\",\"Rio Grande do Sul\", \n", " \"Mato Grosso do Sul\",\"Mato Grosso\",\"Goias\",\"Distrito Federal\"))\n", "summary(pns2013.1$Unidades_da_Federacao)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Grandes Regiões" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
1 Norte
12536
2 Nordeste
18305
3 Sudeste
14294
4 Sul
7548
5 Centro-Oeste
7519
\n" ], "text/latex": [ "\\begin{description*}\n", "\\item[1 Norte] 12536\n", "\\item[2 Nordeste] 18305\n", "\\item[3 Sudeste] 14294\n", "\\item[4 Sul] 7548\n", "\\item[5 Centro-Oeste] 7519\n", "\\end{description*}\n" ], "text/markdown": [ "1 Norte\n", ": 125362 Nordeste\n", ": 183053 Sudeste\n", ": 142944 Sul\n", ": 75485 Centro-Oeste\n", ": 7519\n", "\n" ], "text/plain": [ " 1 Norte 2 Nordeste 3 Sudeste 4 Sul 5 Centro-Oeste \n", " 12536 18305 14294 7548 7519 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#Grandes Regiões\n", "pns2013.1 <- pns2013.1 %>% \n", " mutate(GrandesRegioes = fct_collapse(Unidades_da_Federacao, \n", " `1 Norte` = c(\"Rondonia\",\"Acre\",\"Amazonas\",\"Roraima\",\"Para\", \"Amapa\",\"Tocantins\"),\n", " `2 Nordeste` = c(\"Maranhao\", \"Piaui\", \"Ceara\", \"Rio Grande do Norte\", \"Paraiba\",\"Pernambuco\", \"Alagoas\",\"Sergipe\",\"Bahia\"),\n", " `3 Sudeste` = c(\"Minas Gerais\", \"Espirito Santo\",\"Rio de Janeiro\", \"Sao Paulo\"), \n", " `4 Sul` = c(\"Parana\", \"Santa Catarina\", \"Rio Grande do Sul\"),\n", " `5 Centro-Oeste`= c(\"Mato Grosso do Sul\",\"Mato Grosso\", \"Goias\", \"Distrito Federal\")))\n", "summary(pns2013.1$GrandesRegioes)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Capital" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
Porto Velho
1694
Rio Branco
1814
Manaus
2586
Boa Vista
1591
Belem
2004
Macapa
1332
Palmas
1515
Sao Luis
1774
Teresina
1804
Fortaleza
2560
Natal
1691
Joao Pessoa
1943
Recife
2591
Maceio
1748
Aracaju
1553
Salvador
2641
Belo Horizonte
3779
Vitoria
1724
Rio de Janeiro
3486
Sao Paulo
5305
Curitiba
3012
Florianopolis
1623
Porto Alegre
2913
Campo Grande
1809
Cuiaba
1476
Goiania
2423
Brasilia
1811
\n" ], "text/latex": [ "\\begin{description*}\n", "\\item[Porto Velho] 1694\n", "\\item[Rio Branco ] 1814\n", "\\item[Manaus] 2586\n", "\\item[Boa Vista] 1591\n", "\\item[Belem] 2004\n", "\\item[Macapa] 1332\n", "\\item[Palmas] 1515\n", "\\item[Sao Luis] 1774\n", "\\item[Teresina] 1804\n", "\\item[Fortaleza] 2560\n", "\\item[Natal] 1691\n", "\\item[Joao Pessoa] 1943\n", "\\item[Recife] 2591\n", "\\item[Maceio] 1748\n", "\\item[Aracaju] 1553\n", "\\item[Salvador] 2641\n", "\\item[Belo Horizonte] 3779\n", "\\item[Vitoria] 1724\n", "\\item[Rio de Janeiro] 3486\n", "\\item[Sao Paulo] 5305\n", "\\item[Curitiba] 3012\n", "\\item[Florianopolis] 1623\n", "\\item[Porto Alegre] 2913\n", "\\item[Campo Grande] 1809\n", "\\item[Cuiaba] 1476\n", "\\item[Goiania] 2423\n", "\\item[Brasilia] 1811\n", "\\end{description*}\n" ], "text/markdown": [ "Porto Velho\n", ": 1694Rio Branco \n", ": 1814Manaus\n", ": 2586Boa Vista\n", ": 1591Belem\n", ": 2004Macapa\n", ": 1332Palmas\n", ": 1515Sao Luis\n", ": 1774Teresina\n", ": 1804Fortaleza\n", ": 2560Natal\n", ": 1691Joao Pessoa\n", ": 1943Recife\n", ": 2591Maceio\n", ": 1748Aracaju\n", ": 1553Salvador\n", ": 2641Belo Horizonte\n", ": 3779Vitoria\n", ": 1724Rio de Janeiro\n", ": 3486Sao Paulo\n", ": 5305Curitiba\n", ": 3012Florianopolis\n", ": 1623Porto Alegre\n", ": 2913Campo Grande\n", ": 1809Cuiaba\n", ": 1476Goiania\n", ": 2423Brasilia\n", ": 1811\n", "\n" ], "text/plain": [ " Porto Velho Rio Branco Manaus Boa Vista Belem \n", " 1694 1814 2586 1591 2004 \n", " Macapa Palmas Sao Luis Teresina Fortaleza \n", " 1332 1515 1774 1804 2560 \n", " Natal Joao Pessoa Recife Maceio Aracaju \n", " 1691 1943 2591 1748 1553 \n", " Salvador Belo Horizonte Vitoria Rio de Janeiro Sao Paulo \n", " 2641 3779 1724 3486 5305 \n", " Curitiba Florianopolis Porto Alegre Campo Grande Cuiaba \n", " 3012 1623 2913 1809 1476 \n", " Goiania Brasilia \n", " 2423 1811 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#Capital\n", "pns2013.1<- pns2013.1 %>% mutate(Capital= fct_collapse(Unidades_da_Federacao,\n", " `Porto Velho`= \"Rondonia\", \n", " `Boa Vista`= \"Roraima\", \n", " `Rio Branco `= \"Acre\", \n", " `Manaus` = \"Amazonas\",\n", " `Boa Vista`= \"Roraima\",\n", " `Belem` = \"Para\" ,\n", " `Macapa`= \"Amapa\",\n", " `Palmas` = \"Tocantins\",\n", " `Sao Luis` = \"Maranhao\",\n", " `Teresina`= \"Piaui\" ,\n", " `Fortaleza`= \"Ceara\",\n", " `Natal`= \"Rio Grande do Norte\",\n", " `Joao Pessoa`= \"Paraiba\",\n", " `Recife`= \"Pernambuco\",\n", " `Maceio`= \"Alagoas\",\n", " `Aracaju`= \"Sergipe\",\n", " `Salvador`= \"Bahia\",\n", " `Belo Horizonte`= \"Minas Gerais\",\n", " `Vitoria`= \"Espirito Santo\",\n", " `Rio de Janeiro`= \"Rio de Janeiro\",\n", " `Sao Paulo`= \"Sao Paulo\",\n", " `Curitiba`= \"Parana\",\n", " `Florianopolis`= \"Santa Catarina\",\n", " `Porto Alegre`= \"Rio Grande do Sul\",\n", " `Campo Grande`= \"Mato Grosso do Sul\",\n", " `Cuiaba`= \"Mato Grosso\",\n", " `Goiania` = \"Goias\",\n", " `Brasilia`= \"Distrito Federal\"))\n", "summary(pns2013.1$Capital)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Faixa Etária" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
18 a 29 anos
14321
30 a 44 anos
20242
45 a 59 anos
14462
60 a 74 anos
8290
75 anos ou mais
2887
\n" ], "text/latex": [ "\\begin{description*}\n", "\\item[18 a 29 anos] 14321\n", "\\item[30 a 44 anos] 20242\n", "\\item[45 a 59 anos] 14462\n", "\\item[60 a 74 anos] 8290\n", "\\item[75 anos ou mais] 2887\n", "\\end{description*}\n" ], "text/markdown": [ "18 a 29 anos\n", ": 1432130 a 44 anos\n", ": 2024245 a 59 anos\n", ": 1446260 a 74 anos\n", ": 829075 anos ou mais\n", ": 2887\n", "\n" ], "text/plain": [ " 18 a 29 anos 30 a 44 anos 45 a 59 anos 60 a 74 anos 75 anos ou mais \n", " 14321 20242 14462 8290 2887 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#Faixas Etárias\n", "pns2013.1 <- pns2013.1 %>% mutate(faixa_idade=cut(C008,\n", " breaks = c(18,30, 45, 60, 75,Inf),\n", " labels = c(\"18 a 29 anos\",\"30 a 44 anos\",\"45 a 59 anos\",\"60 a 74 anos\",\"75 anos ou mais\"), \n", " ordered_result = TRUE, right = FALSE))\n", "summary(pns2013.1$faixa_idade) " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Raça" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
Branca
24106
Preta
5631
Parda
30465
\n" ], "text/latex": [ "\\begin{description*}\n", "\\item[Branca] 24106\n", "\\item[Preta] 5631\n", "\\item[Parda] 30465\n", "\\end{description*}\n" ], "text/markdown": [ "Branca\n", ": 24106Preta\n", ": 5631Parda\n", ": 30465\n", "\n" ], "text/plain": [ "Branca Preta Parda \n", " 24106 5631 30465 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#Raça\n", "pns2013.1 <- pns2013.1 %>% filter(C009!=3|C009!=5|C009!=9)%>% mutate(Raca= ifelse(C009==1, 1, \n", " ifelse(C009==2 , 2, 3)))\n", "pns2013.1$Raca<-factor(pns2013.1$Raca, levels=c(1,2,3),labels=c(\"Branca\", \"Preta\", \"Parda\"))\n", "summary(pns2013.1$Raca)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Renda per capita" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
Ate 1/2 SM
14256
1/2 ate 1 SM
17504
1 ate 2 SM
15493
2 ate 3 SM
5335
Mais de 3 SM
7603
\n" ], "text/latex": [ "\\begin{description*}\n", "\\item[Ate 1/2 SM] 14256\n", "\\item[1/2 ate 1 SM] 17504\n", "\\item[1 ate 2 SM] 15493\n", "\\item[2 ate 3 SM] 5335\n", "\\item[Mais de 3 SM] 7603\n", "\\end{description*}\n" ], "text/markdown": [ "Ate 1/2 SM\n", ": 142561/2 ate 1 SM\n", ": 175041 ate 2 SM\n", ": 154932 ate 3 SM\n", ": 5335Mais de 3 SM\n", ": 7603\n", "\n" ], "text/plain": [ " Ate 1/2 SM 1/2 ate 1 SM 1 ate 2 SM 2 ate 3 SM Mais de 3 SM \n", " 14256 17504 15493 5335 7603 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#Rendimento domiciliar per capita\n", "\n", "pns2013.1 <- pns2013.1 %>% drop_na(VDF003) %>% mutate(rend_per_capita=cut(VDF003,\n", " breaks = c(-Inf,339, 678, 1356, 2034,Inf),\n", " labels=c(\"Ate 1/2 SM\",\"1/2 ate 1 SM\",\"1 ate 2 SM\",\"2 ate 3 SM\",\"Mais de 3 SM\"), \n", " ordered_result = TRUE, right = TRUE, na.exclude= TRUE))\n", "\n", "summary(pns2013.1$rend_per_capita)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Escolaridade" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
Sem instrucao e fundamental incompleto
24080
Fundamental completo e medio incompleto
9212
Medio completo e superior incompleto
19145
Superior completo
7754
\n" ], "text/latex": [ "\\begin{description*}\n", "\\item[Sem instrucao e fundamental incompleto] 24080\n", "\\item[Fundamental completo e medio incompleto] 9212\n", "\\item[Medio completo e superior incompleto] 19145\n", "\\item[Superior completo] 7754\n", "\\end{description*}\n" ], "text/markdown": [ "Sem instrucao e fundamental incompleto\n", ": 24080Fundamental completo e medio incompleto\n", ": 9212Medio completo e superior incompleto\n", ": 19145Superior completo\n", ": 7754\n", "\n" ], "text/plain": [ " Sem instrucao e fundamental incompleto Fundamental completo e medio incompleto \n", " 24080 9212 \n", " Medio completo e superior incompleto Superior completo \n", " 19145 7754 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Escolaridade\n", "pns2013.1 <- pns2013.1 %>% 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", "pns2013.1$gescol<-factor(pns2013.1$gescol, levels=c(1,2,3,4), \n", " labels=c(\"Sem instrucao e fundamental incompleto\",\"Fundamental completo e medio incompleto\",\n", " \"Medio completo e superior incompleto\",\"Superior completo\"))\n", "summary(pns2013.1$gescol)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Criando indicadores" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Filtrando base de indicadores" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "text/plain": [ " V0024 UPA_PNS peso_morador_selec P017P \n", " Min. :1110011 Min. :1100001 Min. : 0.004156 Sim:17766 \n", " 1st Qu.:2210013 1st Qu.:2200075 1st Qu.: 0.243935 Nao:42425 \n", " Median :2951023 Median :2900192 Median : 0.521557 \n", " Mean :3035304 Mean :3007768 Mean : 1.000020 \n", " 3rd Qu.:4110111 3rd Qu.:4100002 3rd Qu.: 1.147380 \n", " Max. :5310220 Max. :5300180 Max. :31.179597 \n", " \n", " C008 V0031 Sit_Urbano_Rural Sexo \n", " Min. : 18.00 Min. :1.000 Urbano:49234 Masculino:25915 \n", " 1st Qu.: 30.00 1st Qu.:1.000 Rural :10957 Feminino :34276 \n", " Median : 41.00 Median :2.000 \n", " Mean : 43.32 Mean :2.308 \n", " 3rd Qu.: 55.00 3rd Qu.:4.000 \n", " Max. :101.00 Max. :4.000 \n", " \n", " Unidades_da_Federacao GrandesRegioes Capital \n", " Sao Paulo : 5304 1 Norte :12535 Sao Paulo : 5304 \n", " Minas Gerais : 3779 2 Nordeste :18302 Belo Horizonte: 3779 \n", " Rio de Janeiro : 3485 3 Sudeste :14291 Rio de Janeiro: 3485 \n", " Parana : 3009 4 Sul : 7545 Curitiba : 3009 \n", " Rio Grande do Sul: 2913 5 Centro-Oeste: 7518 Porto Alegre : 2913 \n", " Bahia : 2640 Salvador : 2640 \n", " (Other) :39061 (Other) :39061 \n", " faixa_idade Raca rend_per_capita \n", " 18 a 29 anos :14315 Branca:24101 Ate 1/2 SM :14256 \n", " 30 a 44 anos :20239 Preta : 5631 1/2 ate 1 SM:17504 \n", " 45 a 59 anos :14461 Parda :30459 1 ate 2 SM :15493 \n", " 60 a 74 anos : 8289 2 ate 3 SM : 5335 \n", " 75 anos ou mais: 2887 Mais de 3 SM: 7603 \n", " \n", " \n", " gescol \n", " Sem instrucao e fundamental incompleto :24080 \n", " Fundamental completo e medio incompleto: 9212 \n", " Medio completo e superior incompleto :19145 \n", " Superior completo : 7754 \n", " \n", " \n", " " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#Selecionando variáveis para cálculo de indicadores no survey_ALTERAR\n", "pns2013Psurvey<- pns2013.1 %>% select(\"V0024\",\"UPA_PNS\",\"peso_morador_selec\", \"P017P\",\n", " \"C008\",\"V0031\", \"Sit_Urbano_Rural\",\"Sexo\",\"Unidades_da_Federacao\", \"GrandesRegioes\",\n", " \"Capital\",\"faixa_idade\", \"Raca\",\"rend_per_capita\",\"gescol\")\n", "summary(pns2013Psurvey)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Exporta tabela filtrada" ] }, { "cell_type": "code", "execution_count": 39, "metadata": {}, "outputs": [], "source": [ "#Salvando csv para cálculo de indicadores no survey_ALTERAR\n", "path <- \"\"\n", "write.csv(pns2013Psurvey, file.path(path, \"pns2013Psurvey.csv\"))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Cria plano amostral complexo" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [], "source": [ "#survey design\n", "desPNSP=svydesign(id=~UPA_PNS, strat=~V0024, weight=~peso_morador_selec, nest=TRUE, data=pns2013Psurvey)\n", "desPNSP18=subset(desPNSP, C008>=18)\n", "desPNSPC=svydesign(id=~UPA_PNS, strat=~V0024, weight=~peso_morador_selec, nest=TRUE, data=pns2013Psurvey)\n", "desPNSPC18=subset(desPNSPC, C008>=18 & V0031==1)" ] }, { "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": 19, "metadata": {}, "outputs": [], "source": [ "matrizIndicadores = data.frame()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Definição de variáveis para iteração dos indicadores" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [], "source": [ "ListaIndicadores = c(~P017P)\n", "ListaIndicadoresTexto = c(\"P017P\")\n", "ListaDominios = c(~Sexo,~Raca,~rend_per_capita,~faixa_idade,~Sit_Urbano_Rural,~Unidades_da_Federacao,~GrandesRegioes,~Capital,~gescol)\n", "ListaDominiosTexto = c(\"Sexo\",\"raca\",\"rend_per_capita\",\"fx_idade_18\",\"urb_rur\",\"uf\",\"regiao\",\"capital\",\"gescol\")\n", "ListaTotais = c('Brasil','Capital')\n", "Ano <- \"2013\"" ] }, { "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": 21, "metadata": {}, "outputs": [], "source": [ "#Cálculo dos indicadores usando o pacote survey - alterar\n", "i <- 0\n", "for( indicador in ListaIndicadores){\n", " i <- i + 1\n", " j <- 1\n", " for (dominio in ListaDominios){\n", " if (ListaDominiosTexto[j]==\"capital\"){\n", " dataframe_indicador<-svyby( indicador , dominio , desPNSPC18 , svymean,vartype=\"ci\")\n", " }else{\n", " dataframe_indicador<-svyby( indicador , dominio , desPNSP18 , svymean,vartype=\"ci\")\n", " }\n", " dataframe_indicador<-data.frame(dataframe_indicador)\n", " \n", " colnames(dataframe_indicador) <- c(\"abr_nome\",\"Sim\",\"Não\",\"LowerS\",\"LowerN\",\"UpperS\",\"UpperN\")\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\",\"Não\",\"LowerS\",\"LowerN\",\"UpperS\",\"UpperN\")\n", " matrizIndicadores <-rbind(matrizIndicadores,dataframe_indicador)\n", " j <- j + 1\n", " \n", " }\n", "}\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Criando a tabela pela abrangência total" ] }, { "cell_type": "code", "execution_count": 22, "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": 23, "metadata": {}, "outputs": [], "source": [ "i=0\n", "for(indicador in ListaIndicadores){\n", " i <- i+1\n", " for(total in ListaTotais){\n", " dataframe_indicador <- data.frame()\n", " dataframe_indicador_S <- data.frame()\n", " dataframe_indicador_N <- data.frame()\n", " if(total == 'Capital'){\n", " dataframe_indicador <- svymean(indicador,desPNSPC18)\n", " }else{\n", " dataframe_indicador <- svymean(indicador,desPNSP18)\n", " }\n", " \n", " dataframe_indicador <- cbind(data.frame(dataframe_indicador),data.frame(confint(dataframe_indicador)))\n", " dataframe_indicador <- dataframe_indicador %>% \n", " select('mean','X2.5..','X97.5..') \n", " dataframe_indicador_S <- dataframe_indicador %>% \n", " slice(1)\n", " dataframe_indicador_N <- dataframe_indicador %>% \n", " slice(2)\n", " dataframe_indicador <- cbind(dataframe_indicador_S,dataframe_indicador_N)\n", " colnames(dataframe_indicador) <- c('Sim','LowerS','UpperS','Não','LowerN','UpperN')\n", " dataframe_indicador <- dataframe_indicador %>% \n", " select('Sim','Não','LowerS','LowerN','UpperS','UpperN')\n", " dataframe_indicador$Indicador <- ListaIndicadoresTexto[i]\n", " dataframe_indicador$abr_tipo <- \"total\"\n", " dataframe_indicador$abr_nome <- total\n", " dataframe_indicador$Ano <- \"2013\" \n", " dataframe_indicador <- dataframe_indicador %>% \n", " select(\"abr_tipo\",\"abr_nome\",\"Ano\",\"Indicador\",\"Sim\",\"Não\",\"LowerS\",\"LowerN\",\"UpperS\",\"UpperN\")\n", " \n", " matriz_totais <-rbind(matriz_totais,dataframe_indicador)\n", " \n", " }\n", "}\n" ] }, { "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": [ "#### Exportando tabela de indicadores" ] }, { "cell_type": "code", "execution_count": 44, "metadata": {}, "outputs": [], "source": [ "write.table(matrizIndicadores,file=\"\",sep = \";\",dec = \",\",row.names = FALSE)" ] } ], "metadata": { "kernelspec": { "display_name": "R", "language": "R", "name": "ir" }, "language_info": { "codemirror_mode": "r", "file_extension": ".r", "mimetype": "text/x-r-source", "name": "R", "pygments_lexer": "r", "version": "3.6.3" } }, "nbformat": 4, "nbformat_minor": 4 }