{ "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 2019 Tabagismo" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Bibliotecas Utilizadas" ] }, { "cell_type": "code", "execution_count": 53, "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": 54, "metadata": { "collapsed": true, "jupyter": { "outputs_hidden": true } }, "outputs": [ { "data": { "text/html": [ "\n", "
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\n" ], "text/latex": [ "\\begin{enumerate*}\n", "\\item 'V0001'\n", "\\item 'V0024'\n", "\\item 'UPA\\_PNS'\n", "\\item 'V0006\\_PNS'\n", "\\item 'V0015'\n", "\\item 'V0020'\n", "\\item 'V0022'\n", "\\item 'V0026'\n", "\\item 'V0031'\n", "\\item 'V0025A'\n", "\\item 'V0025B'\n", "\\item 'A001'\n", "\\item 'A002010'\n", "\\item 'A003010'\n", "\\item 'A004010'\n", "\\item 'A01001'\n", "\\item 'A011'\n", "\\item 'A005010'\n", "\\item 'A005012'\n", "\\item 'A00601'\n", "\\item 'A009010'\n", "\\item 'A01401'\n", "\\item 'A01402'\n", "\\item 'A01403'\n", "\\item 'A01501'\n", "\\item 'A016010'\n", "\\item 'A018011'\n", "\\item 'A018012'\n", "\\item 'A018013'\n", "\\item 'A018014'\n", "\\item 'A018015'\n", "\\item 'A018016'\n", "\\item 'A018017'\n", "\\item 'A018018'\n", "\\item 'A018019'\n", "\\item 'A018020'\n", "\\item 'A018021'\n", "\\item 'A018022'\n", "\\item 'A018023'\n", "\\item 'A018024'\n", "\\item 'A018025'\n", "\\item 'A018026'\n", "\\item 'A018027'\n", "\\item 'A018028'\n", "\\item 'A01901'\n", "\\item 'A02101'\n", "\\item 'A02102'\n", "\\item 'A02201'\n", "\\item 'A02305'\n", "\\item 'A02306'\n", "\\item 'A02307'\n", "\\item 'A02308'\n", "\\item 'A02401'\n", "\\item 'A02402'\n", "\\item 'B001'\n", "\\item 'B002'\n", "\\item 'B003'\n", "\\item 'B004'\n", "\\item 'C001'\n", "\\item 'C00301'\n", "\\item 'C004'\n", "\\item 'C006'\n", "\\item 'C00701'\n", "\\item 'C00702'\n", "\\item 'C00703'\n", "\\item 'C008'\n", "\\item 'C009'\n", "\\item 'C01001'\n", "\\item 'C010010'\n", "\\item 'C013'\n", "\\item 'C014'\n", "\\item 'C015'\n", "\\item 'C016'\n", "\\item 'C017'\n", "\\item 'C018'\n", "\\item 'C01801'\n", "\\item 'C011'\n", "\\item 'C012'\n", "\\item 'D001'\n", "\\item 'D00201'\n", "\\item 'D00202'\n", "\\item 'D00301'\n", "\\item 'D00501'\n", "\\item 'D006'\n", "\\item 'D00601'\n", "\\item 'D007'\n", "\\item 'D008'\n", "\\item 'D00901'\n", "\\item 'D010'\n", "\\item 'D01101'\n", "\\item 'D01201'\n", "\\item 'D01301'\n", "\\item 'D01302'\n", "\\item 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\n", "[397] \"P07007\" \"P07101\" \"P07201\" \"Q00101\" \"Q00201\" \"Q00202\" \n", "[403] \"Q003\" \"Q00401\" \"Q00502\" \"Q00503\" \"Q00601\" \"Q00602\" \n", "[409] \"Q00801\" \"Q009\" \"Q010\" \"Q01101\" \"Q01202\" \"Q014\" \n", "[415] \"Q015\" \"Q016\" \"Q017\" \"Q018010\" \"Q018011\" \"Q018012\" \n", "[421] \"Q018013\" \"Q018014\" \"Q018015\" \"Q018016\" \"Q018017\" \"Q01910\" \n", "[427] \"Q019101\" \"Q01911\" \"Q019111\" \"Q01912\" \"Q019121\" \"Q01913\" \n", "[433] \"Q019131\" \"Q022\" \"Q02301\" \"Q026\" \"Q02701\" \"Q028\" \n", "[439] \"Q02901\" \"Q03001\" \"Q03002\" \"Q031\" \"Q03201\" \"Q03302\" \n", "[445] \"Q03303\" \"Q03403\" \"Q03404\" \"Q03601\" \"Q03701\" \"Q03801\" \n", "[451] \"Q03802\" \"Q03803\" \"Q03804\" \"Q03805\" \"Q03806\" \"Q03807\" \n", "[457] \"Q03901\" \"Q04002\" \"Q042\" \"Q043\" \"Q044\" \"Q045\" \n", "[463] \"Q046011\" \"Q046012\" \"Q046013\" \"Q046014\" \"Q046015\" \"Q046016\" \n", "[469] \"Q046017\" \"Q046018\" \"Q046019\" \"Q046020\" \"Q046021\" \"Q04707\" \n", "[475] \"Q047071\" \"Q04708\" \"Q047081\" \"Q04709\" \"Q047091\" \"Q04710\" \n", "[481] \"Q047101\" \"Q04711\" \"Q047111\" \"Q050\" \"Q05101\" \"Q05301\" \n", "[487] \"Q05401\" \"Q055011\" \"Q055012\" \"Q055013\" \"Q055014\" \"Q055015\" \n", "[493] \"Q055016\" \"Q056\" \"Q05701\" \"Q058\" \"Q05901\" \"Q060\" \n", "[499] \"Q061\" \"Q06207\" \"Q06208\" \"Q06209\" \"Q06210\" \"Q06211\" \n", "[505] \"Q06212\" \"Q06306\" \"Q06307\" \"Q06308\" \"Q06309\" \"Q06310\" \n", "[511] \"Q06311\" \"Q064\" \"Q06506\" \"Q06507\" \"Q06508\" \"Q06509\" \n", "[517] \"Q06601\" \"Q067\" \"Q068\" \"Q070\" \"Q07208\" \"Q07209\" \n", "[523] \"Q07210\" \"Q07211\" \"Q07212\" \"Q07213\" \"Q073\" \"Q074\" \n", "[529] \"Q075\" \"Q076\" \"Q07601\" \"Q07704\" \"Q07705\" \"Q07706\" \n", "[535] \"Q07707\" \"Q07708\" \"Q07709\" \"Q07710\" \"Q07711\" \"Q078\" \n", "[541] \"Q079\" \"Q080\" \"Q08107\" \"Q08108\" \"Q08109\" \"Q08110\" \n", "[547] \"Q08111\" \"Q082\" \"Q083\" \"Q084\" \"Q085\" \"Q08607\" \n", "[553] \"Q08608\" \"Q08609\" \"Q08610\" \"Q08611\" \"Q087\" \"Q088\" \n", "[559] \"Q08901\" \"Q09007\" \"Q09008\" \"Q09009\" \"Q09010\" \"Q09011\" \n", "[565] \"Q091\" \"Q092\" \"Q09201\" \"Q09202\" \"Q09301\" \"Q094\" \n", "[571] \"Q09502\" \"Q09605\" \"Q09606\" \"Q09607\" \"Q098\" \"Q100\" \n", "[577] \"Q10101\" \"Q10202\" \"Q104\" \"Q105\" \"Q106\" \"Q10701\" \n", "[583] \"Q109\" \"Q11006\" \"Q11007\" \"Q11008\" \"Q11009\" \"Q11010\" \n", "[589] \"Q111\" \"Q11201\" \"Q11405\" \"Q11406\" \"Q11407\" \"Q11408\" \n", "[595] \"Q115\" \"Q11604\" \"Q11605\" \"Q11606\" \"Q11607\" \"Q11701\" \n", "[601] \"Q11806\" \"Q11807\" \"Q11808\" \"Q11809\" \"Q119\" \"Q120\" \n", "[607] \"Q12102\" \"Q12103\" \"Q12104\" \"Q12105\" \"Q12106\" \"Q12107\" \n", "[613] \"Q12108\" \"Q12109\" \"Q121010\" \"Q121011\" \"Q121012\" \"Q121013\" \n", "[619] \"Q121014\" \"Q121015\" \"Q121016\" \"Q12201\" \"Q12301\" \"Q124\" \n", "[625] \"Q125\" \"Q12501\" \"Q12607\" \"Q12608\" \"Q12609\" \"Q12610\" \n", "[631] \"Q127\" \"Q128\" \"Q132\" \"Q133\" \"Q134\" \"U00204\" \n", "[637] \"U00205\" \"U00206\" \"U00207\" \"U00208\" \"U00101\" \"U00401\" \n", "[643] \"U005\" \"U006\" \"U00902\" \"U01002\" \"U014\" \"U02001\" \n", "[649] \"U02101\" \"U02302\" \"U02303\" \"U02402\" \"U02403\" \"U02501\" \n", "[655] \"W001\" \"W00101\" \"W00102\" \"W00103\" \"W00201\" \"W00202\" \n", "[661] \"W00203\" \"V0028\" \"V0029\" \"V0030\" \"V00281\" \"V00291\" \n", "[667] \"V00301\" \"V00282\" \"V00292\" \"V00302\" \"V00283\" \"V00293\" \n", "[673] \"V00303\" \"VDC001\" \"VDC003\" \"VDD004A\" \"VDE001\" \"VDE002\" \n", "[679] \"VDF002\" \"VDF003\" \"VDF004\" \"VDDATA\" " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#Carregando banco de dados para R versão 3.5.0 ou superior\n", "load(\"\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Definição de peso e filtragem de respondentes do questionário" ] }, { "cell_type": "code", "execution_count": 55, "metadata": {}, "outputs": [ { "data": { "text/plain": [ " Min. 1st Qu. Median Mean 3rd Qu. Max. \n", " 0.00582 0.27579 0.56358 1.03597 1.16822 63.29775 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#Selecionando registros válidos para o módulo P e calculando peso amostral - summary de verificação\n", "pns2019.1<-pns2019v3 %>% filter(V0025A==1)\n", "pns2019.1<-pns2019.1 %>% mutate(peso_morador_selec=((V00291*(94114/168426190))))\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": 56, "metadata": { "collapsed": true, "jupyter": { "outputs_hidden": true } }, "outputs": [ { "data": { "text/html": [ "
Sim
11706
Não
79140
\n" ], "text/latex": [ "\\begin{description*}\n", "\\item[Sim] 11706\n", "\\item[Não] 79140\n", "\\end{description*}\n" ], "text/markdown": [ "Sim\n", ": 11706Não\n", ": 79140\n", "\n" ], "text/plain": [ " Sim Não \n", "11706 79140 " ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
Sim
11386
Não
79460
\n" ], "text/latex": [ "\\begin{description*}\n", "\\item[Sim] 11386\n", "\\item[Não] 79460\n", "\\end{description*}\n" ], "text/markdown": [ "Sim\n", ": 11386Não\n", ": 79460\n", "\n" ], "text/plain": [ " Sim Não \n", "11386 79460 " ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
Sim
24224
Não
66622
\n" ], "text/latex": [ "\\begin{description*}\n", "\\item[Sim] 24224\n", "\\item[Não] 66622\n", "\\end{description*}\n" ], "text/markdown": [ "Sim\n", ": 24224Não\n", ": 66622\n", "\n" ], "text/plain": [ " Sim Não \n", "24224 66622 " ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
Sim
5700
Não
85146
\n" ], "text/latex": [ "\\begin{description*}\n", "\\item[Sim] 5700\n", "\\item[Não] 85146\n", "\\end{description*}\n" ], "text/markdown": [ "Sim\n", ": 5700Não\n", ": 85146\n", "\n" ], "text/plain": [ " Sim Não \n", " 5700 85146 " ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
Sim
6295
Não
84551
\n" ], "text/latex": [ "\\begin{description*}\n", "\\item[Sim] 6295\n", "\\item[Não] 84551\n", "\\end{description*}\n" ], "text/markdown": [ "Sim\n", ": 6295Não\n", ": 84551\n", "\n" ], "text/plain": [ " Sim Não \n", " 6295 84551 " ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
Sim
11155
Não
79691
\n" ], "text/latex": [ "\\begin{description*}\n", "\\item[Sim] 11155\n", "\\item[Não] 79691\n", "\\end{description*}\n" ], "text/markdown": [ "Sim\n", ": 11155Não\n", ": 79691\n", "\n" ], "text/plain": [ " Sim Não \n", "11155 79691 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#Desfechos - Indicadores\n", "\n", "#P019P - Uso atual de produtos derivados do tabaco \n", "pns2019.1 <- pns2019.1 %>% mutate(P019P = ifelse(P050 %in% 1:2, 1,\n", " ifelse(P067 %in% 1:2, 1, 2)))\n", "pns2019.1$P019P<-factor(pns2019.1$P019P, levels=c(1,2), labels=c(\"Sim\",\"Não\"))\n", "summary(pns2019.1$P019P)\n", "\n", "#P020P - Fumo atual do tabaco \n", "pns2019.1 <- pns2019.1 %>% mutate(P020P = ifelse(P050 %in% 1:2, 1, 2))\n", "pns2019.1$P020P<-factor(pns2019.1$P020P, levels=c(1,2), labels=c(\"Sim\",\"Não\"))\n", "summary(pns2019.1$P020P)\n", "\n", "#P021P - Ex-fumantes de tabaco\n", "pns2019.1 <- pns2019.1 %>% mutate(P021P = ifelse(P050 == 3 & P052 %in% 1:2,1,2))\n", "pns2019.1$P021P<-factor(pns2019.1$P021P, levels=c(1,2), labels=c(\"Sim\",\"Não\"))\n", "summary(pns2019.1$P021P)\n", "\n", "#P022P - Tentativa de parar de fumar nos últimos 12 meses entre os fumantes atuais de tabaco\n", "pns2019.1 <- pns2019.1 %>% mutate(P022P = if_else(P060==1 | P05901==0,1,2,missing=2)) \n", "pns2019.1$P022P<-factor(pns2019.1$P022P, levels=c(1,2), labels=c(\"Sim\",\"Não\"))\n", "summary(pns2019.1$P022P)\n", "\n", "\n", "#P023P - Fumo passivo no domícilio entre os não fumantes\n", "pns2019.1 <- pns2019.1 %>% mutate(P023P = if_else(P050==3 & P068<=3,1,2,missing=2)) \n", "pns2019.1$P023P<-factor(pns2019.1$P023P, levels=c(1,2), labels=c(\"Sim\",\"Não\"))\n", "summary(pns2019.1$P023P)\n", "\n", "\n", "#P024P - Fumo atual de cigarro\n", "pns2019.1 <- pns2019.1 %>% mutate(P024P = ifelse((P050%in%1:2) & (P05401<=4 | P05404<=4 | P05407<=4),1,2)) \n", "pns2019.1$P024P<-factor(pns2019.1$P024P, levels=c(1,2), labels=c(\"Sim\",\"Não\"))\n", "summary(pns2019.1$P024P)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Definições de abrangências" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Situação urbana ou rural" ] }, { "cell_type": "code", "execution_count": 57, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
urbano
69873
rural
20973
\n" ], "text/latex": [ "\\begin{description*}\n", "\\item[urbano] 69873\n", "\\item[rural] 20973\n", "\\end{description*}\n" ], "text/markdown": [ "urbano\n", ": 69873rural\n", ": 20973\n", "\n" ], "text/plain": [ "urbano rural \n", " 69873 20973 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#Situação Urbano ou Rural\n", "pns2019.1 <- pns2019.1 %>% rename(Sit_Urbano_Rural=V0026)\n", "pns2019.1$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": [ "### Sexo" ] }, { "cell_type": "code", "execution_count": 58, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
Masculino
42799
Feminino
48047
\n" ], "text/latex": [ "\\begin{description*}\n", "\\item[Masculino] 42799\n", "\\item[Feminino] 48047\n", "\\end{description*}\n" ], "text/markdown": [ "Masculino\n", ": 42799Feminino\n", ": 48047\n", "\n" ], "text/plain": [ "Masculino Feminino \n", " 42799 48047 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#Sexo\n", "pns2019.1 <- pns2019.1 %>% rename(Sexo=C006)\n", "pns2019.1$Sexo<-factor(pns2019.1$Sexo, levels=c(1,2), labels=c(\"Masculino\", \"Feminino\"))\n", "summary(pns2019.1$Sexo)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### UF" ] }, { "cell_type": "code", "execution_count": 59, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
Rondônia
2176
Acre
2380
Amazonas
3479
Roraima
2238
Pará
3853
Amapá
1554
Tocantins
1922
Maranhão
5080
Piauí
2740
Ceará
4265
Rio Grande do Norte
2962
Paraíba
3158
Pernambuco
4083
Alagoas
2987
Sergipe
2610
Bahia
3659
Minas Gerais
5209
Espírito Santo
3541
Rio de Janeiro
4966
São Paulo
6114
Paraná
3967
Santa Catarina
3738
Rio Grande do Sul
3767
Mato Grosso do Sul
2863
Mato Grosso
2468
Goiás
2702
Distrito Federal
2365
\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": 60, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
Norte
17602
Nordeste
31544
Sudeste
19830
Sul
11472
Centro-Oeste
10398
\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$GrandesRegioes)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Faixa Etária" ] }, { "cell_type": "code", "execution_count": 61, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
18 a 29 anos
15394
30 a 44 anos
26754
45 a 59 anos
23655
60 a 74 anos
16767
75 anos ou mais
5961
NA's
2315
\n" ], "text/latex": [ "\\begin{description*}\n", "\\item[18 a 29 anos] 15394\n", "\\item[30 a 44 anos] 26754\n", "\\item[45 a 59 anos] 23655\n", "\\item[60 a 74 anos] 16767\n", "\\item[75 anos ou mais] 5961\n", "\\item[NA's] 2315\n", "\\end{description*}\n" ], "text/markdown": [ "18 a 29 anos\n", ": 1539430 a 44 anos\n", ": 2675445 a 59 anos\n", ": 2365560 a 74 anos\n", ": 1676775 anos ou mais\n", ": 5961NA's\n", ": 2315\n", "\n" ], "text/plain": [ " 18 a 29 anos 30 a 44 anos 45 a 59 anos 60 a 74 anos 75 anos ou mais \n", " 15394 26754 23655 16767 5961 \n", " NA's \n", " 2315 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#Faixas Etárias\n", "\n", "pns2019.1 <- pns2019.1 %>% mutate(faixa_idade=cut(C008,\n", " breaks = c(18,30, 45, 60, 75,Inf),\n", " labels = c(\"18 a 29 anos\",\"30 a 44 anos\",\"45 a 59 anos\",\"60 a 74 anos\",\"75 anos ou mais\"), \n", " ordered_result = TRUE, right = FALSE))\n", "summary(pns2019.1$faixa_idade) " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Raça" ] }, { "cell_type": "code", "execution_count": 62, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
Branca
33133
Preta
10345
Parda
45994
NA's
1374
\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$Raca)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Renda per capita" ] }, { "cell_type": "code", "execution_count": 63, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
Até 1/2 SM
23697
1/2 até 1 SM
26406
1 até 2 SM
22466
2 até 3 SM
7612
Mais de 3 SM
10665
\n" ], "text/latex": [ "\\begin{description*}\n", "\\item[Até 1/2 SM] 23697\n", "\\item[1/2 até 1 SM] 26406\n", "\\item[1 até 2 SM] 22466\n", "\\item[2 até 3 SM] 7612\n", "\\item[Mais de 3 SM] 10665\n", "\\end{description*}\n" ], "text/markdown": [ "Até 1/2 SM\n", ": 236971/2 até 1 SM\n", ": 264061 até 2 SM\n", ": 224662 até 3 SM\n", ": 7612Mais de 3 SM\n", ": 10665\n", "\n" ], "text/plain": [ " Até 1/2 SM 1/2 até 1 SM 1 até 2 SM 2 até 3 SM Mais de 3 SM \n", " 23697 26406 22466 7612 10665 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#Rendimento domiciliar per capita\n", "pns2019.1 <- pns2019.1 %>% mutate(rend_per_capita = ifelse(VDF004 %in% 1:2, 1, \n", " ifelse(VDF004%in% 3, 2, \n", " ifelse(VDF004%in% 4, 3,\n", " ifelse(VDF004%in% 5, 4,5)))))\n", "\n", "pns2019.1$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": 64, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
Sem instrução e fundamental incompleto
36276
Fundamental completo e médio incompleto
13520
Médio completo e superior incompleto
27433
Superior completo
13617
\n" ], "text/latex": [ "\\begin{description*}\n", "\\item[Sem instrução e fundamental incompleto] 36276\n", "\\item[Fundamental completo e médio incompleto] 13520\n", "\\item[Médio completo e superior incompleto] 27433\n", "\\item[Superior completo] 13617\n", "\\end{description*}\n" ], "text/markdown": [ "Sem instrução e fundamental incompleto\n", ": 36276Fundamental completo e médio incompleto\n", ": 13520Médio completo e superior incompleto\n", ": 27433Superior completo\n", ": 13617\n", "\n" ], "text/plain": [ " Sem instrução e fundamental incompleto Fundamental completo e médio incompleto \n", " 36276 13520 \n", " Médio completo e superior incompleto Superior completo \n", " 27433 13617 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Escolaridade\n", "\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(\"Sem instrução e fundamental incompleto\",\"Fundamental completo e médio incompleto\",\n", " \"Médio completo e superior incompleto\",\"Superior completo\"))\n", "summary(pns2019.1$gescol)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Capital" ] }, { "cell_type": "code", "execution_count": 65, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
Porto Velho
2176
Rio Branco
2380
Manaus
3479
Boa Vista
2238
Belém
3853
Macapá
1554
Palmas
1922
São Luís
5080
Teresina
2740
Fortaleza
4265
Natal
2962
João Pessoa
3158
Recife
4083
Maceió
2987
Aracaju
2610
Salvador
3659
Belo Horizonte
5209
Vitória
3541
Rio de Janeiro
4966
São Paulo
6114
Curitiba
3967
Florianópolis
3738
Porto Alegre
3767
Campo Grande
2863
Cuiabá
2468
Goiânia
2702
Brasília
2365
\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": [ "## Criando Indicadores" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Filtrando base de indicadores" ] }, { "cell_type": "code", "execution_count": 66, "metadata": { "collapsed": true, "jupyter": { "outputs_hidden": true } }, "outputs": [ { "data": { "text/plain": [ " V0024 UPA_PNS peso_morador_selec P019P \n", " Min. :1110011 Min. :110000016 Min. : 0.00582 Sim:11706 \n", " 1st Qu.:2210011 1st Qu.:220001391 1st Qu.: 0.27579 Não:79140 \n", " Median :2853020 Median :280039950 Median : 0.56358 \n", " Mean :2998072 Mean :296695855 Mean : 1.03597 \n", " 3rd Qu.:3553013 3rd Qu.:350588998 3rd Qu.: 1.16822 \n", " Max. :5310220 Max. :530051067 Max. :63.29775 \n", " \n", " P020P P021P P022P P023P P024P C008 \n", " Sim:11386 Sim:24224 Sim: 5700 Sim: 6295 Sim:11155 Min. : 15.00 \n", " Não:79460 Não:66622 Não:85146 Não:84551 Não:79691 1st Qu.: 32.00 \n", " Median : 45.00 \n", " Mean : 46.39 \n", " 3rd Qu.: 60.00 \n", " Max. :107.00 \n", " \n", " C009 V0031 P050 P05901 \n", " Min. :1.000 Min. :1.000 Min. :1.000 Min. : 0.00 \n", " 1st Qu.:1.000 1st Qu.:1.000 1st Qu.:3.000 1st Qu.: 6.00 \n", " Median :4.000 Median :2.000 Median :3.000 Median :15.00 \n", " Mean :2.679 Mean :2.605 Mean :2.762 Mean :17.29 \n", " 3rd Qu.:4.000 3rd Qu.:4.000 3rd Qu.:3.000 3rd Qu.:25.00 \n", " Max. :9.000 Max. :4.000 Max. :3.000 Max. :74.00 \n", " NA's :66622 \n", " Sit_Urbano_Rural Sexo Unidades_da_Federacao\n", " urbano:69873 Masculino:42799 São Paulo : 6114 \n", " rural :20973 Feminino :48047 Minas Gerais : 5209 \n", " Maranhão : 5080 \n", " Rio de Janeiro: 4966 \n", " Ceará : 4265 \n", " Pernambuco : 4083 \n", " (Other) :61129 \n", " GrandesRegioes faixa_idade Raca \n", " Norte :17602 18 a 29 anos :15394 Branca:33133 \n", " Nordeste :31544 30 a 44 anos :26754 Preta :10345 \n", " Sudeste :19830 45 a 59 anos :23655 Parda :45994 \n", " Sul :11472 60 a 74 anos :16767 NA's : 1374 \n", " Centro-Oeste:10398 75 anos ou mais: 5961 \n", " NA's : 2315 \n", " \n", " rend_per_capita gescol \n", " Até 1/2 SM :23697 Sem instrução e fundamental incompleto :36276 \n", " 1/2 até 1 SM:26406 Fundamental completo e médio incompleto:13520 \n", " 1 até 2 SM :22466 Médio completo e superior incompleto :27433 \n", " 2 até 3 SM : 7612 Superior completo :13617 \n", " Mais de 3 SM:10665 \n", " \n", " \n", " Capital \n", " São Paulo : 6114 \n", " Belo Horizonte: 5209 \n", " São Luís : 5080 \n", " Rio de Janeiro: 4966 \n", " Fortaleza : 4265 \n", " Recife : 4083 \n", " (Other) :61129 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#Selecionando variáveis para cálculo de indicadores no survey\n", "pns2019Psurvey<- pns2019.1 %>% select(\"V0024\",\"UPA_PNS\",\"peso_morador_selec\", \"P019P\",\"P020P\",\"P021P\",\"P022P\",\"P023P\",\"P024P\",\n", " \"C008\",\"C009\",\"V0031\",\"P050\",\"P05901\",\"Sit_Urbano_Rural\",\"Sexo\",\"Unidades_da_Federacao\", \"GrandesRegioes\",\n", " \"faixa_idade\", \"Raca\",\"rend_per_capita\",\"gescol\",\"Capital\")\n", "summary(pns2019Psurvey)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Exporta tabela filtrada" ] }, { "cell_type": "code", "execution_count": 67, "metadata": {}, "outputs": [], "source": [ "#Salvando csv para cálculo de indicadores no survey\n", "path <- \"\"\n", "write.csv(pns2019Psurvey, file.path(path, \"pns2019Psurvey.csv\"))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Cria plano amostral complexo" ] }, { "cell_type": "code", "execution_count": 68, "metadata": {}, "outputs": [], "source": [ "#survey design\n", "desPNSP=svydesign(id=~UPA_PNS, strat=~V0024, weight=~peso_morador_selec, nest=TRUE, data=pns2019Psurvey)\n", "desPNSP18=subset(desPNSP, C008>=18)\n", "desPNSPC=svydesign(id=~UPA_PNS, strat=~V0024, weight=~peso_morador_selec, nest=TRUE, data=pns2019Psurvey)\n", "desPNSPC18=subset(desPNSPC, C008>=18 & V0031==1)\n", "desPNSPR18=subset(desPNSP, C008>=18 & C009!=9)" ] }, { "cell_type": "code", "execution_count": 69, "metadata": {}, "outputs": [], "source": [ "#survey design - P022P\n", "desPNSP022P_18=subset(desPNSP, C008>=18 & (P050==1 | P050==2 | P05901==0))\n", "desPNSP022P_R18=subset(desPNSP, C008>=18 & (P050==1 | P050==2 | P05901==0) & C009!=9)\n", "desPNSP022P_C18=subset(desPNSP, C008>=18 & V0031==1 & (P050==1 | P050==2 | P05901==0))" ] }, { "cell_type": "code", "execution_count": 70, "metadata": {}, "outputs": [], "source": [ "#survey design - P023P\n", "desPNSP023P_18=subset(desPNSP, C008>=18 & P050==3)\n", "desPNSP023P_R18=subset(desPNSP, C008>=18 & P050==3 & C009!=9)\n", "desPNSP023P_C18=subset(desPNSP, C008>=18 & V0031==1 & P050==3 & !is.na(P023P))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Criação da tabela de indicadores" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Essa tabela é responsável por unir os indicadores no formato do painel de indicadores" ] }, { "cell_type": "code", "execution_count": 71, "metadata": {}, "outputs": [], "source": [ "matrizIndicadores = data.frame()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Definição de variáveis para iteração dos indicadores" ] }, { "cell_type": "code", "execution_count": 73, "metadata": {}, "outputs": [], "source": [ "ListaIndicadores = c(~P019P,~P020P,~P021P,~P022P,~P023P,~P024P)\n", "ListaIndicadoresTexto = c(\"P019P\",\"P020P\",\"P021P\",\"P022P\",\"P023P\",\"P024P\")\n", "ListaDominios = c(~Sexo,~Raca,~rend_per_capita,~faixa_idade,~Sit_Urbano_Rural,~Unidades_da_Federacao,~GrandesRegioes,~gescol,~Capital)\n", "ListaDominiosTexto = c(\"sexo\",\"raça\",\"rend_per_capita\",\"fx_idade_18\",\"urb_rur\",\"uf\",\"região\",\"gescol\",\"capital\")\n", "ListaTotais = c('Brasil','Capital')\n", "Ano <- \"2019\"" ] }, { "cell_type": "code", "execution_count": 74, "metadata": {}, "outputs": [], "source": [ "#Cálculo dos indicadores usando o pacote survey \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", " if(ListaIndicadoresTexto[i] == \"P022P\"){\n", " dataframe_indicador<-svyby( indicador , dominio , desPNSP022P_C18 , svymean,vartype= \"ci\")\n", " }else if(ListaIndicadoresTexto[i] == \"P023P\"){\n", " dataframe_indicador<-svyby( indicador , dominio , desPNSP023P_C18 , svymean,vartype= \"ci\")\n", " }else{\n", " dataframe_indicador<-svyby( indicador , dominio , desPNSPC18 , svymean,vartype= \"ci\")\n", " }\n", " \n", " }else if (ListaDominiosTexto[j]==\"raça\"){\n", " if(ListaIndicadoresTexto[i] == \"P022P\"){\n", " dataframe_indicador<-svyby( indicador , dominio , desPNSP022P_R18 , svymean,vartype= \"ci\")\n", " }else if(ListaIndicadoresTexto[i] == \"P023P\"){\n", " dataframe_indicador<-svyby( indicador , dominio , desPNSP023P_R18 , svymean,vartype= \"ci\")\n", " }else{\n", " dataframe_indicador<-svyby( indicador , dominio , desPNSPR18 , svymean,vartype= \"ci\")\n", " }\n", " \n", " }else {\n", " if(ListaIndicadoresTexto[i] == \"P022P\"){\n", " dataframe_indicador<-svyby( indicador , dominio , desPNSP022P_18 , svymean,vartype= \"ci\")\n", " }else if(ListaIndicadoresTexto[i] == \"P023P\"){\n", " dataframe_indicador<-svyby( indicador , dominio , desPNSP023P_18 , svymean,vartype= \"ci\")\n", " }else{\n", " dataframe_indicador<-svyby( indicador , dominio , desPNSP18 , svymean,vartype= \"ci\")\n", " }\n", " }\n", " \n", " dataframe_indicador<-data.frame(dataframe_indicador)\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": 75, "metadata": {}, "outputs": [], "source": [ "matriz_totais <- data.frame()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Preenchendo a tabela com as abrangências Brasil e total de capitais" ] }, { "cell_type": "code", "execution_count": null, "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", " if(ListaIndicadoresTexto[i] == \"P022P\"){\n", " dataframe_indicador <- svymean(indicador,desPNSP022P_C18)\n", " }else if(ListaIndicadoresTexto[i] == \"P023P\"){\n", " dataframe_indicador <- svymean(indicador,desPNSP023P_C18) \n", " }else{\n", " dataframe_indicador <- svymean(indicador,desPNSPC18)\n", " }\n", " \n", " } else {\n", " if(ListaIndicadoresTexto[i] == \"P022P\"){\n", " dataframe_indicador <- svymean(indicador,desPNSP022P_18)\n", " }else if(ListaIndicadoresTexto[i] == \"P023P\"){\n", " dataframe_indicador <- svymean(indicador,desPNSP023P_18)\n", " }else{\n", " dataframe_indicador <- svymean(indicador,desPNSP18)\n", " }\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", " print(ListaIndicadoresTexto[i])\n", " dataframe_indicador$abr_tipo <- \"total\"\n", " dataframe_indicador$abr_nome <- total\n", " dataframe_indicador$Ano <- Ano \n", " print(colnames(dataframe_indicador))\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", "}" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Unindo tabela de indicadores e totais" ] }, { "cell_type": "code", "execution_count": 78, "metadata": {}, "outputs": [], "source": [ "matrizIndicadores<-rbind(matrizIndicadores,matriz_totais)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Exportando tabela de indicadores" ] }, { "cell_type": "code", "execution_count": 80, "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 }