INEP_PNAD-main

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INEP_PNAD-main/Comparação INEP.Rmd
---
title: "R Notebook"
output: html_notebook
---
```{r}
library(PNADcIBGE)
library(PNADc.table)
library(survey)
library(dplyr)
library(ggplot2)
library(reshape2)
library(patchwork)
library(gridExtra)
library(readxl)
library(forcats)
```
```{r}
design_2019.4 <- get_pnadc(2019, interview = 1)
design_2015.4 <- get_pnadc(2015, interview = 1)
gc()
```
```{r}
UF_interesse <- c("Rondônia","Acre","Amazonas","Roraima","Pará","Amapá","Tocantins","Maranhão","Piauí","Ceará","Rio Grande do Norte","Paraíba","Pernambuco","Alagoas","Sergipe","Bahia","Minas Gerais","Espírito Santo","Rio de Janeiro","São Paulo","Paraná","Santa Catarina","Rio Grande do Sul","Mato Grosso do Sul","Mato Grosso","Goiás","Distrito Federal")
ensino_ <- c("Pré-escola","Regular do ensino fundamental","Regular do ensino médio")
```
```{r}
#2019
V2010_4 <- as.character(design_2019.4$variables$V2010)
V2010_4 <- as.data.frame(V2010_4)
raca <- as.character(design_2019.4$variables$V2010)
V2010_4[raca == "Preta"|raca == "Parda", 1] <- "Negra"
V2010_4[raca == "Indígena"|raca == "Amarela" | raca == "Ignorado", 1] <- "Outros"
design_2019.4$variables <- bind_cols(design_2019.4$variables, V2010_4)
design_2019.4$variables$V2010_4 <- as.factor(design_2019.4$variables$V2010_4)
#2015
V2010_4 <- as.character(design_2015.4$variables$V2010)
V2010_4 <- as.data.frame(V2010_4)
raca <- as.character(design_2015.4$variables$V2010)
V2010_4[raca == "Preta"|raca == "Parda", 1] <- "Negra"
V2010_4[raca == "Indígena"|raca == "Amarela" | raca == "Ignorado", 1] <- "Outros"
design_2015.4$variables <- bind_cols(design_2015.4$variables, V2010_4)
design_2015.4$variables$V2010_4 <- as.factor(design_2015.4$variables$V2010_4)
```
```{r}
i <- 1
j <- 1
data_final <- data_aux
for (i in 1:length(ensino_)) {
 for (j in 1:length(UF_interesse)) {
tabela <- svyby(
 ~V2010_4,
 ~V3003A + V3002A,
 design = subset(design_2015.4, UF %in% UF_interesse[j] & V3003A %in% ensino_[i]),
 FUN = svymean,
 na.rm = TRUE,
 multicore = TRUE,
 na.rm.by = TRUE,
 na.rm.all = TRUE,
 keep.names = FALSE,
 )
data_aux <- data.frame(
 UF = rep(UF_interesse[j], 6),
 ETAPA = c(rep(ensino_[i],6)),
 REDE = c(rep(tabela[1,2],3),rep(tabela[2,2],3)),
 COR = c("Branca", "Negra", "Outros","Branca", "Negra", "Outros"),
 taxa_pd = c(tabela[1,3],tabela[1,4],tabela[1,5],tabela[2,3],tabela[2,4],tabela[2,5]),
 se.pd = c(tabela[1,6],tabela[1,7],tabela[1,8],tabela[2,6],tabela[2,7],tabela[2,8])
)
data_final <- bind_rows(data_final, data_aux)
 }
}
data_final <- data_final[-(1:6),]
#Nivel Brasil
data_final.2 <- data_aux
for (i in 1:length(ensino_)) {
tabela <- svyby(
 ~V2010_4,
 ~V3003A + V3002A,
 design = subset(design_2015.4, V3003A %in% ensino_[i]),
 FUN = svymean,
 na.rm = TRUE,
 multicore = TRUE,
 na.rm.by = TRUE,
 na.rm.all = TRUE,
 keep.names = FALSE,
 )
data_aux <- data.frame(
 UF = rep("Brasil", 6),
 ETAPA = c(rep(ensino_[i],6)),
 REDE = c(rep(tabela[1,2],3),rep(tabela[2,2],3)),
 COR = c("Branca", "Negra", "Outros","Branca", "Negra", "Outros"),
 taxa_pd = c(tabela[1,3],tabela[1,4],tabela[1,5],tabela[2,3],tabela[2,4],tabela[2,5]),
 se.pd = c(tabela[1,6],tabela[1,7],tabela[1,8],tabela[2,6],tabela[2,7],tabela[2,8])
)
data_final.2 <- bind_rows(data_final.2, data_aux)
}
data_final.2 <- data_final.2[-(1:6),]
data_final.2 <- bind_rows(data_final.2, data_final)
write.csv2(data_final.2, file = "Pnad_2015_rede.csv")
```
#Total 2015
```{r}
i <- 1
j <- 1
data_final <- data_aux
for (i in 1:length(ensino_)) {
 for (j in 1:length(UF_interesse)) {
tabela <- svyby(
 ~V2010_4,
 ~V3003A + V3002A,
 design = subset(design_2015.4, UF %in% UF_interesse[j] & V3003A %in% ensino_[i]),
 FUN = svytotal,
 na.rm = TRUE,
 multicore = TRUE,
 na.rm.by = TRUE,
 na.rm.all = TRUE,
 keep.names = FALSE,
 )
data_aux <- data.frame(
 UF = rep(UF_interesse[j], 6),
 ETAPA = c(rep(ensino_[i],6)),
 REDE = c(rep(tabela[1,2],3),rep(tabela[2,2],3)),
 COR = c("Branca", "Negra", "Outros","Branca", "Negra", "Outros"),
 total_pd = c(tabela[1,3],tabela[1,4],tabela[1,5],tabela[2,3],tabela[2,4],tabela[2,5]),
 se.pd = c(tabela[1,6],tabela[1,7],tabela[1,8],tabela[2,6],tabela[2,7],tabela[2,8])
)
data_final <- bind_rows(data_final, data_aux)
 }
}
data_final <- data_final[-(1:6),]
#Nivel Brasil
data_final.2 <- data_aux
for (i in 1:length(ensino_)) {
tabela <- svyby(
 ~V2010_4,
 ~V3003A + V3002A,
 design = subset(design_2015.4, V3003A %in% ensino_[i]),
 FUN = svytotal,
 na.rm = TRUE,
 multicore = TRUE,
 na.rm.by = TRUE,
 na.rm.all = TRUE,
 keep.names = FALSE,
 )
data_aux <- data.frame(
 UF = rep("Brasil", 6),
 ETAPA = c(rep(ensino_[i],6)),
 REDE = c(rep(tabela[1,2],3),rep(tabela[2,2],3)),
 COR = c("Branca", "Negra", "Outros","Branca", "Negra", "Outros"),
 total_pd = c(tabela[1,3],tabela[1,4],tabela[1,5],tabela[2,3],tabela[2,4],tabela[2,5]),
 se.pd = c(tabela[1,6],tabela[1,7],tabela[1,8],tabela[2,6],tabela[2,7],tabela[2,8])
)
data_final.2 <- bind_rows(data_final.2, data_aux)
}
data_final.2 <- data_final.2[-(1:6),]
data_final.2 <- bind_rows(data_final.2, data_final)
write.csv2(data_final.2, file = "Pnad_2015_total_rede.csv")
```
#2019
```{r}
i <- 1
j <- 1
data_final <- data_aux
for (i in 1:length(ensino_)) {
 for (j in 1:length(UF_interesse)) {
tabela <- svyby(
 ~V2010_4,
 ~V3003A + V3002A,
 design = subset(design_2019.4, UF %in% UF_interesse[j] & V3003A %in% ensino_[i]),
 FUN = svymean,
 na.rm = TRUE,
 multicore = TRUE,
 na.rm.by = TRUE,
 na.rm.all = TRUE,
 keep.names = FALSE,
 )
data_aux <- data.frame(
 UF = rep(UF_interesse[j], 6),
 ETAPA = c(rep(ensino_[i],6)),
 REDE = c(rep(tabela[1,2],3),rep(tabela[2,2],3)),
 COR = c("Branca", "Negra", "Outros","Branca", "Negra", "Outros"),
 taxa_pd = c(tabela[1,3],tabela[1,4],tabela[1,5],tabela[2,3],tabela[2,4],tabela[2,5]),
 se.pd = c(tabela[1,6],tabela[1,7],tabela[1,8],tabela[2,6],tabela[2,7],tabela[2,8])
)
data_final <- bind_rows(data_final, data_aux)
 }
}
data_final <- data_final[-(1:6),]
#Nivel Brasil
data_final.2 <- data_aux
for (i in 1:length(ensino_)) {
tabela <- svyby(
 ~V2010_4,
 ~V3003A + V3002A,
 design = subset(design_2019.4, V3003A %in% ensino_[i]),
 FUN = svymean,
 na.rm = TRUE,
 multicore = TRUE,
 na.rm.by = TRUE,
 na.rm.all = TRUE,
 keep.names = FALSE,
 )
data_aux <- data.frame(
 UF = rep("Brasil", 6),
 ETAPA = c(rep(ensino_[i],6)),
 REDE = c(rep(tabela[1,2],3),rep(tabela[2,2],3)),
 COR = c("Branca", "Negra", "Outros","Branca", "Negra", "Outros"),
 taxa_pd = c(tabela[1,3],tabela[1,4],tabela[1,5],tabela[2,3],tabela[2,4],tabela[2,5]),
 se.pd = c(tabela[1,6],tabela[1,7],tabela[1,8],tabela[2,6],tabela[2,7],tabela[2,8])
)
data_final.2 <- bind_rows(data_final.2, data_aux)
}
data_final.2 <- data_final.2[-(1:6),]
data_final.2 <- bind_rows(data_final.2, data_final)
write.csv2(data_final.2, file = "Pnad_2019_rede.csv")
```
#TOTAL 2019
```{r}
i <- 1
j <- 1
data_final <- data_aux
for (i in 1:length(ensino_)) {
 for (j in 1:length(UF_interesse)) {
tabela <- svyby(
 ~V2010_4,
 ~V3003A + V3002A,
 design = subset(design_2019.4, UF %in% UF_interesse[j] & V3003A %in% ensino_[i]),
 FUN = svytotal,
 na.rm = TRUE,
 multicore = TRUE,
 na.rm.by = TRUE,
 na.rm.all = TRUE,
 keep.names = FALSE,
 )
data_aux <- data.frame(
 UF = rep(UF_interesse[j], 6),
 ETAPA = c(rep(ensino_[i],6)),
 REDE = c(rep(tabela[1,2],3),rep(tabela[2,2],3)),
 COR = c("Branca", "Negra", "Outros","Branca", "Negra", "Outros"),
 total_pd = c(tabela[1,3],tabela[1,4],tabela[1,5],tabela[2,3],tabela[2,4],tabela[2,5]),
 se.pd = c(tabela[1,6],tabela[1,7],tabela[1,8],tabela[2,6],tabela[2,7],tabela[2,8])
)
data_final <- bind_rows(data_final, data_aux)
 }
}
data_final <-
data_final[-(1:6),]
#Nivel Brasil
data_final.2 <- data_aux
for (i in 1:length(ensino_)) {
tabela <- svyby(
 ~V2010_4,
 ~V3003A + V3002A,
 design = subset(design_2019.4, V3003A %in% ensino_[i]),
 FUN = svytotal,
 na.rm = TRUE,
 multicore = TRUE,
 na.rm.by = TRUE,
 na.rm.all = TRUE,
 keep.names = FALSE,
 )
data_aux <- data.frame(
 UF = rep("Brasil", 6),
 ETAPA = c(rep(ensino_[i],6)),
 REDE = c(rep(tabela[1,2],3),rep(tabela[2,2],3)),
 COR = c("Branca", "Negra", "Outros","Branca", "Negra", "Outros"),
 total_pd = c(tabela[1,3],tabela[1,4],tabela[1,5],tabela[2,3],tabela[2,4],tabela[2,5]),
 se.pd = c(tabela[1,6],tabela[1,7],tabela[1,8],tabela[2,6],tabela[2,7],tabela[2,8])
)
data_final.2 <- bind_rows(data_final.2, data_aux)
}
data_final.2 <- data_final.2[-(1:6),]
data_final.2 <- bind_rows(data_final.2, data_final)
write.csv2(data_final.2, file = "Pnad_2019_total_rede.csv")
```
#Sem divisão por rede
#2015
```{r}
i <- 1
j <- 1
data_final <- data_aux
for (i in 1:length(ensino_)) {
 for (j in 1:length(UF_interesse)) {
tabela <- svyby(
 ~V2010_4,
 ~V3003A,
 design = subset(design_2015.4, UF %in% UF_interesse[j] & V3003A %in% ensino_[i]),
 FUN = svymean,
 na.rm = TRUE,
 multicore = TRUE,
 na.rm.by = TRUE,
 na.rm.all = TRUE,
 keep.names = FALSE,
 )
data_aux <- data.frame(
 UF = rep(UF_interesse[j], 3),
 ETAPA = rep(ensino_[i],3),
 COR = c("Branca", "Negra", "Outros"),
 taxa_pd = c(tabela[1,2],tabela[1,3],tabela[1,4]),
 se.pd = c(tabela[1,5],tabela[1,6],tabela[1,7])
)
data_final <- bind_rows(data_final, data_aux)
 }
}
data_final <- data_final[-(1:3),]
#Nivel Brasil
data_final.2 <- data_aux
for (i in 1:length(ensino_)) {
tabela <- svyby(
 ~V2010_4,
 ~V3003A,
 design = subset(design_2015.4, V3003A %in% ensino_[i]),
 FUN = svymean,
 na.rm = TRUE,
 multicore = TRUE,
 na.rm.by = TRUE,
 na.rm.all = TRUE,
 keep.names = FALSE,
 )
data_aux <- data.frame(
 UF = rep("Brasil", 3),
 ETAPA = rep(ensino_[i],3),
 COR = c("Branca", "Negra", "Outros"),
 taxa_pd = c(tabela[1,2],tabela[1,3],tabela[1,4]),
 se.pd = c(tabela[1,5],tabela[1,6],tabela[1,7])
)
data_final.2 <- bind_rows(data_final.2, data_aux)
}
data_final.2 <- data_final.2[-(1:3),]
data_final.2 <- bind_rows(data_final.2, data_final)
write.csv2(data_final.2, file = "Pnad_2015.csv")
```
#TOTAL 2015
```{r}
i <- 1
j <- 1
data_final <- data_aux
for (i in 1:length(ensino_)) {
 for (j in 1:length(UF_interesse)) {
tabela <- svyby(
 ~V2010_4,
 ~V3003A,
 design = subset(design_2015.4, UF %in% UF_interesse[j] & V3003A %in% ensino_[i]),
 FUN = svytotal,
 na.rm = TRUE,
 multicore = TRUE,
 na.rm.by = TRUE,
 na.rm.all = TRUE,
 keep.names = FALSE,
 )
data_aux <- data.frame(
 UF = rep(UF_interesse[j], 3),
 ETAPA = rep(ensino_[i],3),
 COR = c("Branca", "Negra", "Outros"),
 total_pd = c(tabela[1,2],tabela[1,3],tabela[1,4]),
 se.pd = c(tabela[1,5],tabela[1,6],tabela[1,7])
)
data_final <- bind_rows(data_final, data_aux)
 }
}
data_final <- data_final[-(1:3),]
#Nivel Brasil
data_final.2 <- data_aux
for (i in 1:length(ensino_)) {
tabela <- svyby(
 ~V2010_4,
 ~V3003A,
 design = subset(design_2015.4, V3003A %in% ensino_[i]),
 FUN = svytotal,
 na.rm = TRUE,
 multicore = TRUE,
 na.rm.by = TRUE,
 na.rm.all = TRUE,
 keep.names = FALSE,
 )
data_aux <- data.frame(
 UF = rep("Brasil", 3),
 ETAPA = rep(ensino_[i],3),
 COR = c("Branca", "Negra", "Outros"),
 total_pd = c(tabela[1,2],tabela[1,3],tabela[1,4]),
 se.pd = c(tabela[1,5],tabela[1,6],tabela[1,7])
)
data_final.2 <- bind_rows(data_final.2, data_aux)
}
data_final.2 <- data_final.2[-(1:3),]
data_final.2 <- bind_rows(data_final.2, data_final)
write.csv2(data_final.2, file = "Pnad_2015_Total.csv")
```
#2019
```{r}
i <- 1
j <- 1
data_final <- data_aux
for (i in 1:length(ensino_)) {
 for (j in 1:length(UF_interesse)) {
tabela <- svyby(
 ~V2010_4,
 ~V3003A,
 design = subset(design_2019.4, UF %in% UF_interesse[j] & V3003A %in% ensino_[i]),
 FUN = svymean,
 na.rm = TRUE,
 multicore = TRUE,
 na.rm.by = TRUE,
 na.rm.all = TRUE,
 keep.names = FALSE,
 )
data_aux <- data.frame(
 UF = rep(UF_interesse[j], 3),
 ETAPA = rep(ensino_[i],3),
 COR = c("Branca", "Negra", "Outros"),
 taxa_pd = c(tabela[1,2],tabela[1,3],tabela[1,4]),
 se.pd = c(tabela[1,5],tabela[1,6],tabela[1,7])
)
data_final <- bind_rows(data_final, data_aux)
 }
}
data_final <- data_final[-(1:3),]
#Nivel Brasil
data_final.2 <- data_aux
for (i in 1:length(ensino_)) {
tabela <- svyby(
 ~V2010_4,
 ~V3003A,
 design = subset(design_2019.4, V3003A %in% ensino_[i]),
 FUN = svymean,
 na.rm = TRUE,
 multicore = TRUE,
 na.rm.by = TRUE,
 na.rm.all = TRUE,
 keep.names = FALSE,
 )
data_aux <- data.frame(
 UF = rep("Brasil", 3),
 ETAPA = rep(ensino_[i],3),
 COR = c("Branca", "Negra", "Outros"),
 taxa_pd = c(tabela[1,2],tabela[1,3],tabela[1,4]),
 se.pd = c(tabela[1,5],tabela[1,6],tabela[1,7])
)
data_final.2 <- bind_rows(data_final.2, data_aux)
}
data_final.2 <- data_final.2[-(1:3),]
data_final.2 <- bind_rows(data_final.2, data_final)
write.csv2(data_final.2, file = "Pnad_2019.csv")
```
#TOTAL 2019
```{r}
i <- 1
j <- 1
data_final <- data_aux
for (i in 1:length(ensino_)) {
 for (j in 1:length(UF_interesse)) {
tabela <- svyby(
 ~V2010_4,
 ~V3003A,
 design = subset(design_2019.4, UF %in% UF_interesse[j] & V3003A %in% ensino_[i]),
 FUN = svytotal,
 na.rm = TRUE,
 multicore = TRUE,
 na.rm.by = TRUE,
 na.rm.all = TRUE,
 keep.names = FALSE,
 )
data_aux <- data.frame(
 UF = rep(UF_interesse[j], 3),
 ETAPA = rep(ensino_[i],3),
 COR = c("Branca", "Negra", "Outros"),
 total_pd = c(tabela[1,2],tabela[1,3],tabela[1,4]),
 se.pd = c(tabela[1,5],tabela[1,6],tabela[1,7])
)
data_final <- bind_rows(data_final, data_aux)
 }
}
data_final <- data_final[-(1:3),]
#Nivel Brasil
data_final.2 <- data_aux
for (i in 1:length(ensino_)) {
tabela <- svyby(
 ~V2010_4,
 ~V3003A,
 design = subset(design_2019.4, V3003A %in% ensino_[i]),
 FUN = svytotal,
 na.rm = TRUE,
 multicore = TRUE,
 na.rm.by = TRUE,
 na.rm.all = TRUE,
 keep.names = FALSE,
 )
data_aux <- data.frame(
 UF = rep("Brasil", 3),
 ETAPA = rep(ensino_[i],3),
 COR = c("Branca", "Negra", "Outros"),
 total_pd = c(tabela[1,2],tabela[1,3],tabela[1,4]),
 se.pd = c(tabela[1,5],tabela[1,6],tabela[1,7])
)
data_final.2 <- bind_rows(data_final.2, data_aux)
}
data_final.2 <- data_final.2[-(1:3),]
data_final.2 <- bind_rows(data_final.2, data_final)
write.csv2(data_final.2, file = "Pnad_2019_Total.csv")
```
#eja
```{r}
load(fs::path_home("Design_PNADc_2019_1"))
design_2019.1 <- design_PNADc
load(fs::path_home("Design_PNADc_2015_1"))
design_2015.1 <- design_PNADc
gc()
```
```{r}
#2019
ensino_ <- c("Educação de jovens e adultos (EJA) do ensino fundamental", "Educação de jovens e adultos (EJA) do ensino médio")
V3003A_2 = as.character(design_2019.1$variables$V3003A)
V3003A_2 <- as.data.frame(V3003A_2)
eja = as.character(design_2019.1$variables$V3003A)
eja[is.na(eja)] <- "NA"
V3003A_2[eja == ensino_[1] | eja == ensino_[2], 1] <- "EJA"
design_2019.1$variables <- bind_cols(design_2019.1$variables, V3003A_2)
design_2019.1$variables$V3003A_2 <- as.factor(design_2019.1$variables$V3003A_2)
#2015
ensino_ <- c("Educação de jovens e adultos (EJA) ou supletivo do ensino fundamental", "Educação de jovens e adultos (EJA) ou supletivo do ensino médio")
V3003A_2 = as.character(design_2015.1$variables$V3003)
V3003A_2 <- as.data.frame(V3003A_2)
eja = as.character(design_2015.1$variables$V3003)
eja[is.na(eja)] <- "NA"
V3003A_2[eja == ensino_[1] | eja == ensino_[2], 1] <- "EJA"
design_2015.1$variables <- bind_cols(design_2015.1$variables, V3003A_2)
design_2015.1$variables$V3003A_2
<- as.factor(design_2015.1$variables$V3003A_2)
```
```{r}
#2019
V2010_4 <- as.character(design_2019.1$variables$V2010)
V2010_4 <- as.data.frame(V2010_4)
raca <- as.character(design_2019.1$variables$V2010)
V2010_4[raca == "Preta"|raca == "Parda", 1] <- "Negra"
V2010_4[raca == "Indígena"|raca == "Amarela" | raca == "Ignorado", 1] <- "Outros"
design_2019.1$variables <- bind_cols(design_2019.1$variables, V2010_4)
design_2019.1$variables$V2010_4 <- as.factor(design_2019.1$variables$V2010_4)
#2015
V2010_4 <- as.character(design_2015.1$variables$V2010)
V2010_4 <- as.data.frame(V2010_4)
raca <- as.character(design_2015.1$variables$V2010)
V2010_4[raca == "Preta"|raca == "Parda", 1] <- "Negra"
V2010_4[raca == "Indígena"|raca == "Amarela" | raca == "Ignorado", 1] <- "Outros"
design_2015.1$variables <- bind_cols(design_2015.1$variables, V2010_4)
design_2015.1$variables$V2010_4 <- as.factor(design_2015.1$variables$V2010_4)
```
```{r}
UF_interesse <- c("Rondônia","Acre","Amazonas","Roraima","Pará","Amapá","Tocantins","Maranhão","Piauí","Ceará","Rio Grande do Norte","Paraíba","Pernambuco","Alagoas","Sergipe","Bahia","Minas Gerais","Espírito Santo","Rio de Janeiro","São Paulo","Paraná","Santa Catarina","Rio Grande do Sul","Mato Grosso do Sul","Mato Grosso","Goiás","Distrito Federal")
```
```{r}
i <- 1
j <- 1
data_final <- data_aux
for (j in 1:length(UF_interesse)) {
tabela <- svyby(
 ~V2010_4,
 ~V3003A_2,
 design = subset(design_2015.1, UF %in% UF_interesse[j] & V3003A_2 %in% "EJA"),
 FUN = svymean,
 na.rm = TRUE,
 multicore = TRUE,
 na.rm.by = TRUE,
 na.rm.all = TRUE,
 keep.names = FALSE,
 )
data_aux <- data.frame(
 UF = rep(UF_interesse[j], 3),
 ETAPA = rep("EJA",3),
 COR = c("Branca", "Negra", "Outros"),
 taxa_pd = c(tabela[1,2],tabela[1,3],tabela[1,4]),
 se.pd = c(tabela[1,5],tabela[1,6],tabela[1,7])
)
data_final <- bind_rows(data_final, data_aux)
}
data_final <- data_final[-(1:3),]
#Brasil
data_final.2 <- data_aux
tabela <- svyby(
 ~V2010_4,
 ~V3003A_2,
 design = subset(design_2015.1, V3003A_2 %in% "EJA"),
 FUN = svymean,
 na.rm = TRUE,
 multicore = TRUE,
 na.rm.by = TRUE,
 na.rm.all = TRUE,
 keep.names = FALSE,
 )
data_aux <- data.frame(
 UF = rep("Brasil", 3),
 ETAPA = rep("EJA",3),
 COR = c("Branca", "Negra", "Outros"),
 taxa_pd = c(tabela[1,2],tabela[1,3],tabela[1,4]),
 se.pd = c(tabela[1,5],tabela[1,6],tabela[1,7])
)
data_final.2 <- bind_rows(data_aux, data_final)
write.csv2(data_final.2, file = "Pnad_EJA_2015.1.csv")
```
#2019
```{r}
i <- 1
j <- 1
data_final <- data_aux
for (j in 1:length(UF_interesse)) {
tabela <- svyby(
 ~V2010_4,
 ~V3003A_2,
 design = subset(design_2019.1, UF %in% UF_interesse[j] & V3003A_2 %in% "EJA"),
 FUN = svymean,
 na.rm = TRUE,
 multicore = TRUE,
 na.rm.by = TRUE,
 na.rm.all = TRUE,
 keep.names = FALSE,
 )
data_aux <- data.frame(
 UF = rep(UF_interesse[j], 3),
 ETAPA = rep("EJA",3),
 COR = c("Branca", "Negra", "Outros"),
 taxa_pd = c(tabela[1,2],tabela[1,3],tabela[1,4]),
 se.pd = c(tabela[1,5],tabela[1,6],tabela[1,7])
)
data_final <- bind_rows(data_final, data_aux)
}
data_final <- data_final[-(1:3),]
#Brasil
data_final.2 <- data_aux
tabela <- svyby(
 ~V2010_4,
 ~V3003A_2,
 design = subset(design_2019.1, V3003A_2 %in% "EJA"),
 FUN = svymean,
 na.rm = TRUE,
 multicore = TRUE,
 na.rm.by = TRUE,
 na.rm.all = TRUE,
 keep.names = FALSE,
 )
data_aux <- data.frame(
 UF = rep("Brasil", 3),
 ETAPA = rep("EJA",3),
 COR = c("Branca", "Negra", "Outros"),
 taxa_pd = c(tabela[1,2],tabela[1,3],tabela[1,4]),
 se.pd = c(tabela[1,5],tabela[1,6],tabela[1,7])
)
data_final.2 <- bind_rows(data_aux, data_final)
write.csv2(data_final.2, file = "Pnad_EJA_2019.1.csv")
```
INEP_PNAD-main/Comparação INEP.nb.html
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library(PNADcIBGE)
library(PNADc.table)
library(survey)
library(dplyr)
library(ggplot2)
library(reshape2)
library(patchwork)
library(gridExtra)
library(readxl)
library(forcats)
design_2019.4 <- get_pnadc(2019, interview = 1)
design_2015.4 <- get_pnadc(2015, interview = 1)
gc()
UF_interesse <- c("Rondônia","Acre","Amazonas","Roraima","Pará","Amapá","Tocantins","Maranhão","Piauí","Ceará","Rio Grande do Norte","Paraíba","Pernambuco","Alagoas","Sergipe","Bahia","Minas Gerais","Espírito Santo","Rio de Janeiro","São Paulo","Paraná","Santa Catarina","Rio Grande do Sul","Mato Grosso do Sul","Mato Grosso","Goiás","Distrito Federal")
ensino_ <- c("Pré-escola","Regular do ensino fundamental","Regular do ensino médio")
#2019
V2010_4 <- as.character(design_2019.4$variables$V2010)
V2010_4 <- as.data.frame(V2010_4)
raca <- as.character(design_2019.4$variables$V2010)
V2010_4[raca == "Preta"|raca == "Parda", 1] <- "Negra"
V2010_4[raca == "Indígena"|raca == "Amarela" | raca == "Ignorado", 1] <- "Outros"
design_2019.4$variables <- bind_cols(design_2019.4$variables, V2010_4)
design_2019.4$variables$V2010_4 <- as.factor(design_2019.4$variables$V2010_4)
#2015
V2010_4 <- as.character(design_2015.4$variables$V2010)
V2010_4 <- as.data.frame(V2010_4)
raca <- as.character(design_2015.4$variables$V2010)
V2010_4[raca == "Preta"|raca == "Parda", 1] <- "Negra"
V2010_4[raca == "Indígena"|raca == "Amarela" | raca == "Ignorado", 1] <- "Outros"
design_2015.4$variables <- bind_cols(design_2015.4$variables, V2010_4)
design_2015.4$variables$V2010_4 <- as.factor(design_2015.4$variables$V2010_4)
for (i in 1:length(ensino_)) {
 for (j in 1:length(UF_interesse)) {
tabela <- svyby(
 ~V2010_4,
 ~V3003A + V3002A,
 design = subset(design_2015.4, UF %in% UF_interesse[j] & V3003A %in% ensino_[i]),
 FUN = svymean,
 na.rm = TRUE,
 multicore = TRUE,
 na.rm.by = TRUE,
 na.rm.all = TRUE,
 keep.names = FALSE,
 )
data_aux <- data.frame(
 UF = rep(UF_interesse[j], 6),
 ETAPA = c(rep(ensino_[i],6)),
 REDE = c(rep(tabela[1,2],3),rep(tabela[2,2],3)),
 COR = c("Branca", "Negra", "Outros","Branca", "Negra", "Outros"),
 taxa_pd = c(tabela[1,3],tabela[1,4],tabela[1,5],tabela[2,3],tabela[2,4],tabela[2,5]),
 se.pd = c(tabela[1,6],tabela[1,7],tabela[1,8],tabela[2,6],tabela[2,7],tabela[2,8])
)
data_final <- bind_rows(data_final, data_aux)
 }
}
Warning: 13 replicates gave NA results and were discarded.Warning: 29 replicates gave NA results and were discarded.Warning: 2 replicates gave NA results and were discarded.Warning: 2 replicates gave NA results and were discarded.Warning: 3 replicates gave NA results and were discarded.Warning: 1 replicates gave NA results and were discarded.Warning: 12 replicates gave NA results and were discarded.Warning: 8 replicates gave NA results and were discarded.Warning: 40 replicates gave NA results and were discarded.Warning: 34 replicates gave NA results and were discarded.Warning: 7 replicates gave NA results and were discarded.Warning: 5 replicates gave NA results and were discarded.
#Total 2015
#2019
i <- 1
j <- 1
data_final <- data_aux
for (i in 1:length(ensino_)) {
 for (j in 1:length(UF_interesse)) {
tabela <- svyby(
 ~V2010_4,
 ~V3003A + V3002A,
 design = subset(design_2019.4, UF %in% UF_interesse[j] & V3003A %in% ensino_[i]),
 FUN = svymean,
 na.rm = TRUE,
 multicore = TRUE,
 na.rm.by = TRUE,
 na.rm.all = TRUE,
 keep.names = FALSE,
 )
data_aux <- data.frame(
 UF = rep(UF_interesse[j], 6),
 ETAPA = c(rep(ensino_[i],6)),
 REDE = c(rep(tabela[1,2],3),rep(tabela[2,2],3)),
 COR = c("Branca", "Negra", "Outros","Branca", "Negra", "Outros"),
 taxa_pd = c(tabela[1,3],tabela[1,4],tabela[1,5],tabela[2,3],tabela[2,4],tabela[2,5]),
 se.pd = c(tabela[1,6],tabela[1,7],tabela[1,8],tabela[2,6],tabela[2,7],tabela[2,8])
)
data_final <- bind_rows(data_final, data_aux)
 }
}
Warning: 8 replicates gave NA results and were discarded.
data_final <- data_final[-(1:6),]
#Nivel
Brasil
data_final.2 <- data_aux
for (i in 1:length(ensino_)) {
tabela <- svyby(
 ~V2010_4,
 ~V3003A + V3002A,
 design = subset(design_2019.4, V3003A %in% ensino_[i]),
 FUN = svymean,
 na.rm = TRUE,
 multicore = TRUE,
 na.rm.by = TRUE,
 na.rm.all = TRUE,
 keep.names = FALSE,
 )
data_aux <- data.frame(
 UF = rep("Brasil", 6),
 ETAPA = c(rep(ensino_[i],6)),
 REDE = c(rep(tabela[1,2],3),rep(tabela[2,2],3)),
 COR = c("Branca", "Negra", "Outros","Branca", "Negra", "Outros"),
 taxa_pd = c(tabela[1,3],tabela[1,4],tabela[1,5],tabela[2,3],tabela[2,4],tabela[2,5]),
 se.pd = c(tabela[1,6],tabela[1,7],tabela[1,8],tabela[2,6],tabela[2,7],tabela[2,8])
)
data_final.2 <- bind_rows(data_final.2, data_aux)
}
data_final.2 <- data_final.2[-(1:6),]
data_final.2 <- bind_rows(data_final.2, data_final)
write.csv2(data_final.2, file = "Pnad_2019_rede.csv")
#TOTAL 2019
i <- 1
j <- 1
data_final <- data_aux
for (i in 1:length(ensino_)) {
 for (j in 1:length(UF_interesse)) {
tabela <- svyby(
 ~V2010_4,
 ~V3003A + V3002A,
 design = subset(design_2019.4, UF %in% UF_interesse[j] & V3003A %in% ensino_[i]),
 FUN = svytotal,
 na.rm = TRUE,
 multicore = TRUE,
 na.rm.by = TRUE,
 na.rm.all = TRUE,
 keep.names = FALSE,
 )
data_aux <- data.frame(
 UF = rep(UF_interesse[j], 6),
 ETAPA = c(rep(ensino_[i],6)),
 REDE = c(rep(tabela[1,2],3),rep(tabela[2,2],3)),
 COR = c("Branca", "Negra", "Outros","Branca", "Negra", "Outros"),
 total_pd = c(tabela[1,3],tabela[1,4],tabela[1,5],tabela[2,3],tabela[2,4],tabela[2,5]),
 se.pd = c(tabela[1,6],tabela[1,7],tabela[1,8],tabela[2,6],tabela[2,7],tabela[2,8])
)
data_final <- bind_rows(data_final, data_aux)
 }
}
data_final <- data_final[-(1:6),]
#Nivel Brasil
data_final.2 <- data_aux
for (i in 1:length(ensino_)) {
tabela <- svyby(
 ~V2010_4,
 ~V3003A + V3002A,
 design = subset(design_2019.4, V3003A %in% ensino_[i]),
 FUN = svytotal,
 na.rm = TRUE,
 multicore = TRUE,
 na.rm.by = TRUE,
 na.rm.all = TRUE,
 keep.names = FALSE,
 )
data_aux <- data.frame(
 UF = rep("Brasil", 6),
 ETAPA = c(rep(ensino_[i],6)),
 REDE = c(rep(tabela[1,2],3),rep(tabela[2,2],3)),
 COR = c("Branca", "Negra", "Outros","Branca", "Negra", "Outros"),
 total_pd = c(tabela[1,3],tabela[1,4],tabela[1,5],tabela[2,3],tabela[2,4],tabela[2,5]),
 se.pd = c(tabela[1,6],tabela[1,7],tabela[1,8],tabela[2,6],tabela[2,7],tabela[2,8])
)
data_final.2 <- bind_rows(data_final.2, data_aux)
}
data_final.2 <- data_final.2[-(1:6),]
data_final.2 <- bind_rows(data_final.2, data_final)
write.csv2(data_final.2, file = "Pnad_2019_total_rede.csv")
#Sem divisão por rede #2015
i <- 1
j <- 1
data_final <- data_aux
for (i in 1:length(ensino_)) {
 for (j in 1:length(UF_interesse)) {
tabela <- svyby(
 ~V2010_4,
 ~V3003A,
 design = subset(design_2015.4, UF %in% UF_interesse[j] & V3003A %in% ensino_[i]),
 FUN = svymean,
 na.rm = TRUE,
 multicore = TRUE,
 na.rm.by = TRUE,
 na.rm.all = TRUE,
 keep.names = FALSE,
 )
data_aux <- data.frame(
 UF = rep(UF_interesse[j], 3),
 ETAPA = rep(ensino_[i],3),
 COR = c("Branca", "Negra", "Outros"),
 taxa_pd = c(tabela[1,2],tabela[1,3],tabela[1,4]),
 se.pd = c(tabela[1,5],tabela[1,6],tabela[1,7])
)
data_final <- bind_rows(data_final, data_aux)
 }
}
data_final <- data_final[-(1:3),]
#Nivel Brasil
data_final.2 <- data_aux
for (i in 1:length(ensino_)) {
tabela <- svyby(
 ~V2010_4,
 ~V3003A,
 design = subset(design_2015.4, V3003A %in% ensino_[i]),
 FUN = svymean,
 na.rm = TRUE,
 multicore = TRUE,
 na.rm.by = TRUE,
 na.rm.all = TRUE,
 keep.names = FALSE,
 )
data_aux <- data.frame(
 UF = rep("Brasil", 3),
 ETAPA = rep(ensino_[i],3),
 COR = c("Branca", "Negra", "Outros"),
 taxa_pd = c(tabela[1,2],tabela[1,3],tabela[1,4]),
 se.pd = c(tabela[1,5],tabela[1,6],tabela[1,7])
)
data_final.2 <- bind_rows(data_final.2, data_aux)
}
data_final.2 <- data_final.2[-(1:3),]
data_final.2 <- bind_rows(data_final.2, data_final)
write.csv2(data_final.2, file = "Pnad_2015.csv")
#TOTAL 2015
i <- 1
j <- 1
data_final <- data_aux
for (i in 1:length(ensino_)) {
 for (j in 1:length(UF_interesse)) {
tabela <- svyby(
 ~V2010_4,
 ~V3003A,
 design = subset(design_2015.4, UF %in% UF_interesse[j] & V3003A %in% ensino_[i]),
 FUN = svytotal,
 na.rm = TRUE,
 multicore = TRUE,
 na.rm.by = TRUE,
 na.rm.all = TRUE,
 keep.names = FALSE,
 )
data_aux <- data.frame(
 UF = rep(UF_interesse[j], 3),
 ETAPA = rep(ensino_[i],3),
 COR = c("Branca", "Negra", "Outros"),
 total_pd = c(tabela[1,2],tabela[1,3],tabela[1,4]),
 se.pd = c(tabela[1,5],tabela[1,6],tabela[1,7])
)
data_final <- bind_rows(data_final, data_aux)
 }
}
data_final <- data_final[-(1:3),]
#Nivel Brasil
data_final.2 <- data_aux
for (i in 1:length(ensino_)) {
tabela <- svyby(
 ~V2010_4,
 ~V3003A,
 design = subset(design_2015.4, V3003A %in% ensino_[i]),
 FUN = svytotal,
 na.rm = TRUE,
 multicore = TRUE,
 na.rm.by = TRUE,
 na.rm.all = TRUE,
 keep.names = FALSE,
 )
data_aux <- data.frame(
 UF = rep("Brasil", 3),
 ETAPA = rep(ensino_[i],3),
 COR = c("Branca", "Negra", "Outros"),
 total_pd = c(tabela[1,2],tabela[1,3],tabela[1,4]),
 se.pd = c(tabela[1,5],tabela[1,6],tabela[1,7])
)
data_final.2 <- bind_rows(data_final.2, data_aux)
}
data_final.2 <- data_final.2[-(1:3),]
data_final.2 <- bind_rows(data_final.2, data_final)
write.csv2(data_final.2, file = "Pnad_2015_Total.csv")
#2019
i <- 1
j <- 1
data_final <- data_aux
for (i in 1:length(ensino_)) {
 for (j in 1:length(UF_interesse)) {
tabela <- svyby(
 ~V2010_4,
 ~V3003A,
 design = subset(design_2019.4, UF %in% UF_interesse[j] & V3003A %in% ensino_[i]),
 FUN = svymean,
 na.rm = TRUE,
 multicore = TRUE,
 na.rm.by = TRUE,
 na.rm.all = TRUE,
 keep.names = FALSE,
 )
data_aux <- data.frame(
 UF = rep(UF_interesse[j], 3),
 ETAPA = rep(ensino_[i],3),
 COR = c("Branca", "Negra", "Outros"),
 taxa_pd = c(tabela[1,2],tabela[1,3],tabela[1,4]),
 se.pd = c(tabela[1,5],tabela[1,6],tabela[1,7])
)
data_final <- bind_rows(data_final, data_aux)
 }
}
data_final <- data_final[-(1:3),]
#Nivel Brasil
data_final.2 <- data_aux
for (i in 1:length(ensino_)) {
tabela <- svyby(
 ~V2010_4,
 ~V3003A,
 design = subset(design_2019.4, V3003A %in% ensino_[i]),
 FUN = svymean,
 na.rm = TRUE,
 multicore = TRUE,
 na.rm.by = TRUE,
 na.rm.all = TRUE,
 keep.names = FALSE,
 )
data_aux <- data.frame(
 UF = rep("Brasil", 3),
 ETAPA = rep(ensino_[i],3),
 COR = c("Branca", "Negra", "Outros"),
 taxa_pd = c(tabela[1,2],tabela[1,3],tabela[1,4]),
 se.pd = c(tabela[1,5],tabela[1,6],tabela[1,7])
)
data_final.2 <- bind_rows(data_final.2, data_aux)
}
data_final.2 <- data_final.2[-(1:3),]
data_final.2 <- bind_rows(data_final.2, data_final)
write.csv2(data_final.2, file = "Pnad_2019.csv")
#TOTAL 2019
i <- 1
j <- 1
data_final <- data_aux
for (i in 1:length(ensino_)) {
 for (j in 1:length(UF_interesse)) {
tabela <- svyby(
 ~V2010_4,
 ~V3003A,
 design = subset(design_2019.4, UF %in% UF_interesse[j] & V3003A %in% ensino_[i]),
 FUN = svytotal,
 na.rm = TRUE,
 multicore = TRUE,
 na.rm.by = TRUE,
 na.rm.all = TRUE,
 keep.names = FALSE,
 )
data_aux <- data.frame(
 UF = rep(UF_interesse[j], 3),
 ETAPA = rep(ensino_[i],3),
 COR = c("Branca", "Negra", "Outros"),
 total_pd = c(tabela[1,2],tabela[1,3],tabela[1,4]),
 se.pd = c(tabela[1,5],tabela[1,6],tabela[1,7])
)
data_final <- bind_rows(data_final, data_aux)
 }
}
data_final <- data_final[-(1:3),]
#Nivel Brasil
data_final.2 <- data_aux
for (i in 1:length(ensino_)) {
tabela <- svyby(
 ~V2010_4,
 ~V3003A,
 design = subset(design_2019.4, V3003A %in% ensino_[i]),
 FUN = svytotal,
 na.rm = TRUE,
 multicore = TRUE,
 na.rm.by = TRUE,
 na.rm.all = TRUE,
 keep.names = FALSE,
 )
data_aux <- data.frame(
 UF = rep("Brasil", 3),
 ETAPA = rep(ensino_[i],3),
 COR = c("Branca", "Negra", "Outros"),
 total_pd = c(tabela[1,2],tabela[1,3],tabela[1,4]),
 se.pd = c(tabela[1,5],tabela[1,6],tabela[1,7])
)
data_final.2 <- bind_rows(data_final.2,
data_aux)
}
data_final.2 <- data_final.2[-(1:3),]
data_final.2 <- bind_rows(data_final.2, data_final)
write.csv2(data_final.2, file = "Pnad_2019_Total.csv")
#eja
load(fs::path_home("Design_PNADc_2019_1"))
design_2019.1 <- design_PNADc
load(fs::path_home("Design_PNADc_2015_1"))
design_2015.1 <- design_PNADc
gc()
 used (Mb) gc trigger (Mb) max used (Mb)
Ncells 3804547 203.2 6290145 336.0 6290145 336.0
Vcells 626636501 4780.9 1024386238 7815.5 1066985292 8140.5
#2019
ensino_ <- c("Educação de jovens e adultos (EJA) do ensino fundamental", "Educação de jovens e adultos (EJA) do ensino médio")
V3003A_2 = as.character(design_2019.1$variables$V3003A)
V3003A_2 <- as.data.frame(V3003A_2)
eja = as.character(design_2019.1$variables$V3003A)
eja[is.na(eja)] <- "NA"
V3003A_2[eja == ensino_[1] | eja == ensino_[2], 1] <- "EJA"
design_2019.1$variables <- bind_cols(design_2019.1$variables, V3003A_2)
design_2019.1$variables$V3003A_2 <- as.factor(design_2019.1$variables$V3003A_2)
#2015
ensino_ <- c("Educação de jovens e adultos (EJA) ou supletivo do ensino fundamental", "Educação de jovens e adultos (EJA) ou supletivo do ensino médio")
V3003A_2 = as.character(design_2015.1$variables$V3003)
V3003A_2 <- as.data.frame(V3003A_2)
eja = as.character(design_2015.1$variables$V3003)
eja[is.na(eja)] <- "NA"
V3003A_2[eja == ensino_[1] | eja == ensino_[2], 1] <- "EJA"
design_2015.1$variables <- bind_cols(design_2015.1$variables, V3003A_2)
design_2015.1$variables$V3003A_2 <- as.factor(design_2015.1$variables$V3003A_2)
#2019
V2010_4 <- as.character(design_2019.1$variables$V2010)
V2010_4 <- as.data.frame(V2010_4)
raca <- as.character(design_2019.1$variables$V2010)
V2010_4[raca == "Preta"|raca == "Parda", 1] <- "Negra"
V2010_4[raca == "Indígena"|raca == "Amarela" | raca == "Ignorado", 1] <- "Outros"
design_2019.1$variables <- bind_cols(design_2019.1$variables, V2010_4)
design_2019.1$variables$V2010_4 <- as.factor(design_2019.1$variables$V2010_4)
#2015
V2010_4 <- as.character(design_2015.1$variables$V2010)
V2010_4 <- as.data.frame(V2010_4)
raca <- as.character(design_2015.1$variables$V2010)
V2010_4[raca == "Preta"|raca == "Parda", 1] <- "Negra"
V2010_4[raca == "Indígena"|raca == "Amarela" | raca == "Ignorado", 1] <- "Outros"
design_2015.1$variables <- bind_cols(design_2015.1$variables, V2010_4)
design_2015.1$variables$V2010_4 <- as.factor(design_2015.1$variables$V2010_4)
UF_interesse <- c("Rondônia","Acre","Amazonas","Roraima","Pará","Amapá","Tocantins","Maranhão","Piauí","Ceará","Rio Grande do Norte","Paraíba","Pernambuco","Alagoas","Sergipe","Bahia","Minas Gerais","Espírito Santo","Rio de Janeiro","São Paulo","Paraná","Santa Catarina","Rio Grande do Sul","Mato Grosso do Sul","Mato Grosso","Goiás","Distrito Federal")
for (j in 1:length(UF_interesse)) {
tabela <- svyby(
 ~V2010_4,
 ~V3003A_2,
 design = subset(design_2015.1, V3003A_2 %in% "EJA"),
 FUN = svymean,
 na.rm = TRUE,
 multicore = TRUE,
 na.rm.by = TRUE,
 na.rm.all = TRUE,
 keep.names = FALSE,
 )
data_aux <- data.frame(
 UF = rep(UF_interesse[j], 3),
 ETAPA = rep("EJA",3),
 COR = c("Branca", "Negra", "Outros"),
 taxa_pd = c(tabela[1,2],tabela[1,3],tabela[1,4]),
 se.pd = c(tabela[1,5],tabela[1,6],tabela[1,7])
)
data_final.2 <- bind_rows(data_final.2, data_aux)
}
Error in list2(...) : object 'data_final.2' not found
#2019
i <- 1
j <- 1
data_final <- data_aux
for (j in 1:length(UF_interesse)) {
tabela <- svyby(
 ~V2010_4,
 ~V3003A_2,
 design = subset(design_2019.1, UF %in% UF_interesse[j] & V3003A_2 %in% "EJA"),
 FUN = svymean,
 na.rm = TRUE,
 multicore = TRUE,
 na.rm.by = TRUE,
 na.rm.all = TRUE,
 keep.names = FALSE,
 )
data_aux <- data.frame(
 UF = rep(UF_interesse[j], 3),
 ETAPA = rep("EJA",3),
 COR = c("Branca", "Negra", "Outros"),
 taxa_pd = c(tabela[1,2],tabela[1,3],tabela[1,4]),
 se.pd = c(tabela[1,5],tabela[1,6],tabela[1,7])
)
data_final <- bind_rows(data_final, data_aux)
}
data_final <- data_final[-(1:3),]
#Brasil
data_final.2 <- data_aux
tabela <- svyby(
 ~V2010_4,
 ~V3003A_2,
 design = subset(design_2019.1, V3003A_2 %in% "EJA"),
 FUN = svymean,
 na.rm = TRUE,
 multicore = TRUE,
 na.rm.by = TRUE,
 na.rm.all = TRUE,
 keep.names = FALSE,
 )
data_aux <- data.frame(
 UF = rep("Brasil", 3),
 ETAPA = rep("EJA",3),
 COR = c("Branca", "Negra", "Outros"),
 taxa_pd = c(tabela[1,2],tabela[1,3],tabela[1,4]),
 se.pd = c(tabela[1,5],tabela[1,6],tabela[1,7])
)
data_final.2 <- bind_rows(data_aux, data_final)
write.csv2(data_final.2, file = "Pnad_EJA_2019.1.csv")
---
title: "R Notebook"
output: html_notebook
---

```{r}
library(PNADcIBGE)
library(PNADc.table)
library(survey)
library(dplyr)
library(ggplot2)
library(reshape2)
library(patchwork)
library(gridExtra)
library(readxl)
library(forcats)
```

```{r}
design_2019.4 <- get_pnadc(2019, interview = 1)

design_2015.4 <- get_pnadc(2015, interview = 1)

gc()
```

```{r}
UF_interesse <- c("Rondônia","Acre","Amazonas","Roraima","Pará","Amapá","Tocantins","Maranhão","Piauí","Ceará","Rio Grande do Norte","Paraíba","Pernambuco","Alagoas","Sergipe","Bahia","Minas Gerais","Espírito Santo","Rio de Janeiro","São Paulo","Paraná","Santa Catarina","Rio Grande do Sul","Mato Grosso do Sul","Mato Grosso","Goiás","Distrito Federal")

ensino_ <- c("Pré-escola","Regular do ensino fundamental","Regular do ensino médio")
```

```{r}
#2019
V2010_4 <- as.character(design_2019.4$variables$V2010)
V2010_4 <- as.data.frame(V2010_4)

raca <- as.character(design_2019.4$variables$V2010)

V2010_4[raca == "Preta"|raca == "Parda", 1] <- "Negra"
V2010_4[raca == "Indígena"|raca == "Amarela" | raca == "Ignorado", 1] <- "Outros"

design_2019.4$variables <- bind_cols(design_2019.4$variables, V2010_4)

design_2019.4$variables$V2010_4 <- as.factor(design_2019.4$variables$V2010_4)

#2015
V2010_4 <- as.character(design_2015.4$variables$V2010)
V2010_4 <- as.data.frame(V2010_4)

raca <- as.character(design_2015.4$variables$V2010)

V2010_4[raca == "Preta"|raca == "Parda", 1] <- "Negra"
V2010_4[raca == "Indígena"|raca == "Amarela" | raca == "Ignorado", 1] <- "Outros"

design_2015.4$variables <- bind_cols(design_2015.4$variables, V2010_4)

design_2015.4$variables$V2010_4 <- as.factor(design_2015.4$variables$V2010_4)

```

```{r}
i <- 1
j <- 1

data_final <- data_aux

for (i in 1:length(ensino_)) {
  for (j in 1:length(UF_interesse)) {
tabela <- svyby(
  ~V2010_4,
  ~V3003A + V3002A,
  design = subset(design_2015.4, UF %in% UF_interesse[j] & V3003A %in% ensino_[i]),
  FUN = svymean,
  na.rm = TRUE,
  multicore = TRUE,
  na.rm.by = TRUE,
  na.rm.all = TRUE,
  keep.names = FALSE,
  )

data_aux <- data.frame(
  UF = rep(UF_interesse[j], 6),
  ETAPA = c(rep(ensino_[i],6)),
  REDE = c(rep(tabela[1,2],3),rep(tabela[2,2],3)),
  COR = c("Branca", "Negra", "Outros","Branca", "Negra", "Outros"),
  taxa_pd = c(tabela[1,3],tabela[1,4],tabela[1,5],tabela[2,3],tabela[2,4],tabela[2,5]),
  se.pd = c(tabela[1,6],tabela[1,7],tabela[1,8],tabela[2,6],tabela[2,7],tabela[2,8])
)

data_final <- bind_rows(data_final, data_aux)
  }
}

data_final <- data_final[-(1:6),]

#Nivel Brasil
data_final.2 <- data_aux

for (i in 1:length(ensino_)) {
tabela <- svyby(
  ~V2010_4,
  ~V3003A + V3002A,
  design = subset(design_2015.4, V3003A %in% ensino_[i]),
  FUN = svymean,
  na.rm = TRUE,
  multicore = TRUE,
  na.rm.by = TRUE,
  na.rm.all = TRUE,
  keep.names = FALSE,
  )

data_aux <- data.frame(
  UF = rep("Brasil", 6),
  ETAPA = c(rep(ensino_[i],6)),
  REDE = c(rep(tabela[1,2],3),rep(tabela[2,2],3)),
  COR = c("Branca", "Negra", "Outros","Branca", "Negra", "Outros"),
  taxa_pd = c(tabela[1,3],tabela[1,4],tabela[1,5],tabela[2,3],tabela[2,4],tabela[2,5]),
  se.pd = c(tabela[1,6],tabela[1,7],tabela[1,8],tabela[2,6],tabela[2,7],tabela[2,8])
)

data_final.2 <- bind_rows(data_final.2, data_aux)
}

data_final.2 <- data_final.2[-(1:6),]


data_final.2 <- bind_rows(data_final.2, data_final)

write.csv2(data_final.2, file = "Pnad_2015_rede.csv")
```

#Total 2015
```{r}
i <- 1
j <- 1

data_final <- data_aux

for (i in 1:length(ensino_)) {
  for (j in 1:length(UF_interesse)) {
tabela <- svyby(
  ~V2010_4,
  ~V3003A + V3002A,
  design = subset(design_2015.4, UF %in% UF_interesse[j] & V3003A %in% ensino_[i]),
  FUN = svytotal,
  na.rm = TRUE,
  multicore = TRUE,
  na.rm.by = TRUE,
  na.rm.all = TRUE,
  keep.names = FALSE,
  )

data_aux <- data.frame(
  UF = rep(UF_interesse[j], 6),
  ETAPA = c(rep(ensino_[i],6)),
  REDE = c(rep(tabela[1,2],3),rep(tabela[2,2],3)),
  COR = c("Branca", "Negra", "Outros","Branca", "Negra", "Outros"),
  total_pd = c(tabela[1,3],tabela[1,4],tabela[1,5],tabela[2,3],tabela[2,4],tabela[2,5]),
  se.pd = c(tabela[1,6],tabela[1,7],tabela[1,8],tabela[2,6],tabela[2,7],tabela[2,8])
)

data_final <- bind_rows(data_final, data_aux)
  }
}

data_final <- data_final[-(1:6),]

#Nivel Brasil
data_final.2 <- data_aux

for (i in 1:length(ensino_)) {
tabela <- svyby(
  ~V2010_4,
  ~V3003A + V3002A,
  design = subset(design_2015.4, V3003A %in% ensino_[i]),
  FUN = svytotal,
  na.rm = TRUE,
  multicore = TRUE,
  na.rm.by = TRUE,
  na.rm.all = TRUE,
  keep.names = FALSE,
  )

data_aux <- data.frame(
  UF = rep("Brasil", 6),
  ETAPA = c(rep(ensino_[i],6)),
  REDE = c(rep(tabela[1,2],3),rep(tabela[2,2],3)),
  COR = c("Branca", "Negra", "Outros","Branca", "Negra", "Outros"),
  total_pd = c(tabela[1,3],tabela[1,4],tabela[1,5],tabela[2,3],tabela[2,4],tabela[2,5]),
  se.pd = c(tabela[1,6],tabela[1,7],tabela[1,8],tabela[2,6],tabela[2,7],tabela[2,8])
)

data_final.2 <- bind_rows(data_final.2, data_aux)
}

data_final.2 <- data_final.2[-(1:6),]


data_final.2 <- bind_rows(data_final.2, data_final)

write.csv2(data_final.2, file = "Pnad_2015_total_rede.csv")
```


#2019
```{r}
i <- 1
j <- 1

data_final <- data_aux

for (i in 1:length(ensino_)) {
  for (j in 1:length(UF_interesse)) {
tabela <- svyby(
  ~V2010_4,
  ~V3003A + V3002A,
  design = subset(design_2019.4, UF %in% UF_interesse[j] & V3003A %in% ensino_[i]),
  FUN = svymean,
  na.rm = TRUE,
  multicore = TRUE,
  na.rm.by = TRUE,
  na.rm.all = TRUE,
  keep.names = FALSE,
  )

data_aux <- data.frame(
  UF = rep(UF_interesse[j], 6),
  ETAPA = c(rep(ensino_[i],6)),
  REDE = c(rep(tabela[1,2],3),rep(tabela[2,2],3)),
  COR = c("Branca", "Negra", "Outros","Branca", "Negra", "Outros"),
  taxa_pd = c(tabela[1,3],tabela[1,4],tabela[1,5],tabela[2,3],tabela[2,4],tabela[2,5]),
  se.pd = c(tabela[1,6],tabela[1,7],tabela[1,8],tabela[2,6],tabela[2,7],tabela[2,8])
)

data_final <- bind_rows(data_final, data_aux)
  }
}

data_final <- data_final[-(1:6),]

#Nivel Brasil
data_final.2 <- data_aux

for (i in 1:length(ensino_)) {
tabela <- svyby(
  ~V2010_4,
  ~V3003A + V3002A,
  design = subset(design_2019.4, V3003A %in% ensino_[i]),
  FUN = svymean,
  na.rm = TRUE,
  multicore = TRUE,
  na.rm.by = TRUE,
  na.rm.all = TRUE,
  keep.names = FALSE,
  )

data_aux <- data.frame(
  UF = rep("Brasil", 6),
  ETAPA = c(rep(ensino_[i],6)),
  REDE = c(rep(tabela[1,2],3),rep(tabela[2,2],3)),
  COR = c("Branca", "Negra", "Outros","Branca", "Negra", "Outros"),
  taxa_pd = c(tabela[1,3],tabela[1,4],tabela[1,5],tabela[2,3],tabela[2,4],tabela[2,5]),
  se.pd = c(tabela[1,6],tabela[1,7],tabela[1,8],tabela[2,6],tabela[2,7],tabela[2,8])
)

data_final.2 <- bind_rows(data_final.2, data_aux)
}

data_final.2 <- data_final.2[-(1:6),]


data_final.2 <- bind_rows(data_final.2, data_final)

write.csv2(data_final.2, file = "Pnad_2019_rede.csv")
```

#TOTAL 2019
```{r}
i <- 1
j <- 1

data_final <- data_aux

for (i in 1:length(ensino_)) {
  for (j in 1:length(UF_interesse)) {
tabela <- svyby(
  ~V2010_4,
  ~V3003A + V3002A,
  design = subset(design_2019.4, UF %in% UF_interesse[j] & V3003A %in% ensino_[i]),
  FUN = svytotal,
  na.rm = TRUE,
  multicore = TRUE,
  na.rm.by = TRUE,
  na.rm.all = TRUE,
  keep.names = FALSE,
  )

data_aux <- data.frame(
  UF = rep(UF_interesse[j], 6),
  ETAPA = c(rep(ensino_[i],6)),
  REDE = c(rep(tabela[1,2],3),rep(tabela[2,2],3)),
  COR = c("Branca", "Negra", "Outros","Branca", "Negra", "Outros"),
  total_pd = c(tabela[1,3],tabela[1,4],tabela[1,5],tabela[2,3],tabela[2,4],tabela[2,5]),
  se.pd = c(tabela[1,6],tabela[1,7],tabela[1,8],tabela[2,6],tabela[2,7],tabela[2,8])
)

data_final <- bind_rows(data_final, data_aux)
  }
}

data_final <- data_final[-(1:6),]

#Nivel Brasil
data_final.2 <- data_aux

for (i in 1:length(ensino_)) {
tabela <- svyby(
  ~V2010_4,
  ~V3003A + V3002A,
  design = subset(design_2019.4, V3003A %in% ensino_[i]),
  FUN = svytotal,
  na.rm = TRUE,
  multicore = TRUE,
  na.rm.by = TRUE,
  na.rm.all = TRUE,
  keep.names = FALSE,
  )

data_aux <- data.frame(
  UF = rep("Brasil", 6),
  ETAPA = c(rep(ensino_[i],6)),
  REDE = c(rep(tabela[1,2],3),rep(tabela[2,2],3)),
  COR = c("Branca", "Negra", "Outros","Branca", "Negra", "Outros"),
  total_pd = c(tabela[1,3],tabela[1,4],tabela[1,5],tabela[2,3],tabela[2,4],tabela[2,5]),
  se.pd = c(tabela[1,6],tabela[1,7],tabela[1,8],tabela[2,6],tabela[2,7],tabela[2,8])
)

data_final.2 <- bind_rows(data_final.2, data_aux)
}

data_final.2 <- data_final.2[-(1:6),]


data_final.2 <- bind_rows(data_final.2, data_final)

write.csv2(data_final.2, file = "Pnad_2019_total_rede.csv")
```


#Sem divisão por rede
#2015
```{r}
i <- 1
j <- 1

data_final <- data_aux

for (i in 1:length(ensino_)) {
  for (j in 1:length(UF_interesse)) {
tabela <- svyby(
  ~V2010_4,
  ~V3003A,
  design = subset(design_2015.4, UF %in% UF_interesse[j] & V3003A %in% ensino_[i]),
  FUN = svymean,
  na.rm = TRUE,
  multicore = TRUE,
  na.rm.by = TRUE,
  na.rm.all = TRUE,
  keep.names = FALSE,
  )

data_aux <- data.frame(
  UF = rep(UF_interesse[j], 3),
  ETAPA = rep(ensino_[i],3),
  COR = c("Branca", "Negra", "Outros"),
  taxa_pd = c(tabela[1,2],tabela[1,3],tabela[1,4]),
  se.pd = c(tabela[1,5],tabela[1,6],tabela[1,7])
)

data_final <- bind_rows(data_final, data_aux)
  }
}

data_final <- data_final[-(1:3),]

#Nivel Brasil
data_final.2 <- data_aux

for (i in 1:length(ensino_)) {
tabela <- svyby(
  ~V2010_4,
  ~V3003A,
  design = subset(design_2015.4, V3003A %in% ensino_[i]),
  FUN = svymean,
  na.rm = TRUE,
  multicore = TRUE,
  na.rm.by = TRUE,
  na.rm.all = TRUE,
  keep.names = FALSE,
  )

data_aux <- data.frame(
  UF = rep("Brasil", 3),
  ETAPA = rep(ensino_[i],3),
  COR = c("Branca", "Negra", "Outros"),
  taxa_pd = c(tabela[1,2],tabela[1,3],tabela[1,4]),
  se.pd = c(tabela[1,5],tabela[1,6],tabela[1,7])
)

data_final.2 <- bind_rows(data_final.2, data_aux)
}

data_final.2 <- data_final.2[-(1:3),]


data_final.2 <- bind_rows(data_final.2, data_final)

write.csv2(data_final.2, file = "Pnad_2015.csv")
```

#TOTAL 2015
```{r}
i <- 1
j <- 1

data_final <- data_aux

for (i in 1:length(ensino_)) {
  for (j in 1:length(UF_interesse)) {
tabela <- svyby(
  ~V2010_4,
  ~V3003A,
  design = subset(design_2015.4, UF %in% UF_interesse[j] & V3003A %in% ensino_[i]),
  FUN = svytotal,
  na.rm = TRUE,
  multicore = TRUE,
  na.rm.by = TRUE,
  na.rm.all = TRUE,
  keep.names = FALSE,
  )

data_aux <- data.frame(
  UF = rep(UF_interesse[j], 3),
  ETAPA = rep(ensino_[i],3),
  COR = c("Branca", "Negra", "Outros"),
  total_pd = c(tabela[1,2],tabela[1,3],tabela[1,4]),
  se.pd = c(tabela[1,5],tabela[1,6],tabela[1,7])
)

data_final <- bind_rows(data_final, data_aux)
  }
}

data_final <- data_final[-(1:3),]

#Nivel Brasil
data_final.2 <- data_aux

for (i in 1:length(ensino_)) {
tabela <- svyby(
  ~V2010_4,
  ~V3003A,
  design = subset(design_2015.4, V3003A %in% ensino_[i]),
  FUN = svytotal,
  na.rm = TRUE,
  multicore = TRUE,
  na.rm.by = TRUE,
  na.rm.all = TRUE,
  keep.names = FALSE,
  )

data_aux <- data.frame(
  UF = rep("Brasil", 3),
  ETAPA = rep(ensino_[i],3),
  COR = c("Branca", "Negra", "Outros"),
  total_pd = c(tabela[1,2],tabela[1,3],tabela[1,4]),
  se.pd = c(tabela[1,5],tabela[1,6],tabela[1,7])
)

data_final.2 <- bind_rows(data_final.2, data_aux)
}

data_final.2 <- data_final.2[-(1:3),]


data_final.2 <- bind_rows(data_final.2, data_final)

write.csv2(data_final.2, file = "Pnad_2015_Total.csv")
```


#2019
```{r}
i <- 1
j <- 1

data_final <- data_aux

for (i in 1:length(ensino_)) {
  for (j in 1:length(UF_interesse)) {
tabela <- svyby(
  ~V2010_4,
  ~V3003A,
  design = subset(design_2019.4, UF %in% UF_interesse[j] & V3003A %in% ensino_[i]),
  FUN = svymean,
  na.rm = TRUE,
  multicore = TRUE,
  na.rm.by = TRUE,
  na.rm.all = TRUE,
  keep.names = FALSE,
  )

data_aux <- data.frame(
  UF = rep(UF_interesse[j], 3),
  ETAPA = rep(ensino_[i],3),
  COR = c("Branca", "Negra", "Outros"),
  taxa_pd = c(tabela[1,2],tabela[1,3],tabela[1,4]),
  se.pd = c(tabela[1,5],tabela[1,6],tabela[1,7])
)

data_final <- bind_rows(data_final, data_aux)
  }
}

data_final <- data_final[-(1:3),]

#Nivel Brasil
data_final.2 <- data_aux

for (i in 1:length(ensino_)) {
tabela <- svyby(
  ~V2010_4,
  ~V3003A,
  design = subset(design_2019.4, V3003A %in% ensino_[i]),
  FUN = svymean,
  na.rm = TRUE,
  multicore = TRUE,
  na.rm.by = TRUE,
  na.rm.all = TRUE,
  keep.names = FALSE,
  )

data_aux <- data.frame(
  UF = rep("Brasil", 3),
  ETAPA = rep(ensino_[i],3),
  COR = c("Branca", "Negra", "Outros"),
  taxa_pd = c(tabela[1,2],tabela[1,3],tabela[1,4]),
  se.pd = c(tabela[1,5],tabela[1,6],tabela[1,7])
)

data_final.2 <- bind_rows(data_final.2, data_aux)
}

data_final.2 <- data_final.2[-(1:3),]


data_final.2 <- bind_rows(data_final.2, data_final)

write.csv2(data_final.2, file = "Pnad_2019.csv")
```

#TOTAL 2019
```{r}
i <- 1
j <- 1

data_final <- data_aux

for (i in 1:length(ensino_)) {
  for (j in 1:length(UF_interesse)) {
tabela <- svyby(
  ~V2010_4,
  ~V3003A,
  design = subset(design_2019.4, UF %in% UF_interesse[j] & V3003A %in% ensino_[i]),
  FUN = svytotal,
  na.rm = TRUE,
  multicore = TRUE,
  na.rm.by = TRUE,
  na.rm.all = TRUE,
  keep.names = FALSE,
  )

data_aux <- data.frame(
  UF = rep(UF_interesse[j], 3),
  ETAPA = rep(ensino_[i],3),
  COR = c("Branca", "Negra", "Outros"),
  total_pd = c(tabela[1,2],tabela[1,3],tabela[1,4]),
  se.pd = c(tabela[1,5],tabela[1,6],tabela[1,7])
)

data_final <- bind_rows(data_final, data_aux)
  }
}

data_final <- data_final[-(1:3),]

#Nivel Brasil
data_final.2 <- data_aux

for (i in 1:length(ensino_)) {
tabela <- svyby(
  ~V2010_4,
  ~V3003A,
  design = subset(design_2019.4, V3003A %in% ensino_[i]),
  FUN = svytotal,
  na.rm = TRUE,
  multicore = TRUE,
  na.rm.by = TRUE,
  na.rm.all = TRUE,
  keep.names = FALSE,
  )

data_aux <- data.frame(
  UF = rep("Brasil", 3),
  ETAPA = rep(ensino_[i],3),
  COR = c("Branca", "Negra", "Outros"),
  total_pd = c(tabela[1,2],tabela[1,3],tabela[1,4]),
  se.pd = c(tabela[1,5],tabela[1,6],tabela[1,7])
)

data_final.2 <- bind_rows(data_final.2, data_aux)
}

data_final.2 <- data_final.2[-(1:3),]


data_final.2 <- bind_rows(data_final.2, data_final)

write.csv2(data_final.2, file = "Pnad_2019_Total.csv")
```


#eja
```{r}
load(fs::path_home("Design_PNADc_2019_1"))
design_2019.1 <- design_PNADc

load(fs::path_home("Design_PNADc_2015_1"))
design_2015.1 <- design_PNADc

gc()
```

```{r}
#2019
ensino_ <- c("Educação de jovens e adultos (EJA) do ensino fundamental", "Educação de jovens e adultos (EJA) do ensino médio")

V3003A_2 = as.character(design_2019.1$variables$V3003A)
V3003A_2 <- as.data.frame(V3003A_2)

eja = as.character(design_2019.1$variables$V3003A)
eja[is.na(eja)] <- "NA"

V3003A_2[eja == ensino_[1] | eja == ensino_[2], 1] <- "EJA"

design_2019.1$variables <- bind_cols(design_2019.1$variables, V3003A_2)

design_2019.1$variables$V3003A_2 <- as.factor(design_2019.1$variables$V3003A_2)

#2015
ensino_ <- c("Educação de jovens e adultos (EJA) ou supletivo do ensino fundamental", "Educação de jovens e adultos (EJA) ou supletivo do ensino médio")

V3003A_2 = as.character(design_2015.1$variables$V3003)
V3003A_2 <- as.data.frame(V3003A_2)

eja = as.character(design_2015.1$variables$V3003)
eja[is.na(eja)] <- "NA"

V3003A_2[eja == ensino_[1] | eja == ensino_[2], 1] <- "EJA"

design_2015.1$variables <- bind_cols(design_2015.1$variables, V3003A_2)

design_2015.1$variables$V3003A_2 <- as.factor(design_2015.1$variables$V3003A_2)
```

```{r}
#2019
V2010_4 <- as.character(design_2019.1$variables$V2010)
V2010_4 <- as.data.frame(V2010_4)

raca <- as.character(design_2019.1$variables$V2010)

V2010_4[raca == "Preta"|raca == "Parda", 1] <- "Negra"
V2010_4[raca == "Indígena"|raca == "Amarela" | raca == "Ignorado", 1] <- "Outros"

design_2019.1$variables <- bind_cols(design_2019.1$variables, V2010_4)

design_2019.1$variables$V2010_4 <- as.factor(design_2019.1$variables$V2010_4)

#2015
V2010_4 <- as.character(design_2015.1$variables$V2010)
V2010_4 <- as.data.frame(V2010_4)

raca <- as.character(design_2015.1$variables$V2010)

V2010_4[raca == "Preta"|raca == "Parda", 1] <- "Negra"
V2010_4[raca == "Indígena"|raca == "Amarela" | raca == "Ignorado", 1] <- "Outros"

design_2015.1$variables <- bind_cols(design_2015.1$variables, V2010_4)

design_2015.1$variables$V2010_4 <- as.factor(design_2015.1$variables$V2010_4)
```


```{r}
UF_interesse <- c("Rondônia","Acre","Amazonas","Roraima","Pará","Amapá","Tocantins","Maranhão","Piauí","Ceará","Rio Grande do Norte","Paraíba","Pernambuco","Alagoas","Sergipe","Bahia","Minas Gerais","Espírito Santo","Rio de Janeiro","São Paulo","Paraná","Santa Catarina","Rio Grande do Sul","Mato Grosso do Sul","Mato Grosso","Goiás","Distrito Federal")
```


```{r}
i <- 1
j <- 1

data_final <- data_aux

for (j in 1:length(UF_interesse)) {
tabela <- svyby(
  ~V2010_4,
  ~V3003A_2,
  design = subset(design_2015.1, UF %in% UF_interesse[j] & V3003A_2 %in% "EJA"),
  FUN = svymean,
  na.rm = TRUE,
  multicore = TRUE,
  na.rm.by = TRUE,
  na.rm.all = TRUE,
  keep.names = FALSE,
  )

data_aux <- data.frame(
  UF = rep(UF_interesse[j], 3),
  ETAPA = rep("EJA",3),
  COR = c("Branca", "Negra", "Outros"),
  taxa_pd = c(tabela[1,2],tabela[1,3],tabela[1,4]),
  se.pd = c(tabela[1,5],tabela[1,6],tabela[1,7])
)

data_final <- bind_rows(data_final, data_aux)
}

data_final <- data_final[-(1:3),]

#Brasil
data_final.2 <- data_aux

tabela <- svyby(
  ~V2010_4,
  ~V3003A_2,
  design = subset(design_2015.1, V3003A_2 %in% "EJA"),
  FUN = svymean,
  na.rm = TRUE,
  multicore = TRUE,
  na.rm.by = TRUE,
  na.rm.all = TRUE,
  keep.names = FALSE,
  )

data_aux <- data.frame(
  UF = rep("Brasil", 3),
  ETAPA = rep("EJA",3),
  COR = c("Branca", "Negra", "Outros"),
  taxa_pd = c(tabela[1,2],tabela[1,3],tabela[1,4]),
  se.pd = c(tabela[1,5],tabela[1,6],tabela[1,7])
)

data_final.2 <- bind_rows(data_aux, data_final)


write.csv2(data_final.2, file = "Pnad_EJA_2015.1.csv")
```

#2019
```{r}
i <- 1
j <- 1

data_final <- data_aux

for (j in 1:length(UF_interesse)) {
tabela <- svyby(
  ~V2010_4,
  ~V3003A_2,
  design = subset(design_2019.1, UF %in% UF_interesse[j] & V3003A_2 %in% "EJA"),
  FUN = svymean,
  na.rm = TRUE,
  multicore = TRUE,
  na.rm.by = TRUE,
  na.rm.all = TRUE,
  keep.names = FALSE,
  )

data_aux <- data.frame(
  UF = rep(UF_interesse[j], 3),
  ETAPA = rep("EJA",3),
  COR = c("Branca", "Negra", "Outros"),
  taxa_pd = c(tabela[1,2],tabela[1,3],tabela[1,4]),
  se.pd = c(tabela[1,5],tabela[1,6],tabela[1,7])
)

data_final <- bind_rows(data_final, data_aux)
}

data_final <- data_final[-(1:3),]

#Brasil
data_final.2 <- data_aux

tabela <- svyby(
  ~V2010_4,
  ~V3003A_2,
  design = subset(design_2019.1, V3003A_2 %in% "EJA"),
  FUN = svymean,
  na.rm = TRUE,
  multicore = TRUE,
  na.rm.by = TRUE,
  na.rm.all = TRUE,
  keep.names = FALSE,
  )

data_aux <- data.frame(
  UF = rep("Brasil", 3),
  ETAPA = rep("EJA",3),
  COR = c("Branca", "Negra", "Outros"),
  taxa_pd = c(tabela[1,2],tabela[1,3],tabela[1,4]),
  se.pd = c(tabela[1,5],tabela[1,6],tabela[1,7])
)

data_final.2 <- bind_rows(data_aux, data_final)


write.csv2(data_final.2, file = "Pnad_EJA_2019.1.csv")
```
INEP_PNAD-main/DADOS CORRETOS!!!/PNAD_EJA_1519.zip
Pnad_EJA_2015.1.csv
"";"UF";"ETAPA";"COR";"taxa_pd";"se.pd"
"1";"Brasil";"EJA";"Branca";0,29442933778564;0,00992711374807946
"2";"Brasil";"EJA";"Negra";0,699887603342163;0,0101108247901567
"3";"Brasil";"EJA";"Outros";0,00568305887219605;0,00147131607144401
"4";"Rondônia";"EJA";"Banca";0,222728618157961;0,0535391483743995
"5";"Rondônia";"EJA";"Negra";0,770458044648858;0,0525589204898308
"6";"Rondônia";"EJA";"Outros";0,00681333719318063;0,00670223190665608
"7";"Acre";"EJA";"Banca";0,0736473986084425;0,0176829151107278
"8";"Acre";"EJA";"Negra";0,922242174400458;0,018198588695335
"9";"Acre";"EJA";"Outros";0,00411042699109912;0,00441959371313803
"10";"Amazonas";"EJA";"Banca";0,116248073557775;0,0236201337334433
"11";"Amazonas";"EJA";"Negra";0,85403592963263;0,0254897866766097
"12";"Amazonas";"EJA";"Outros";0,0297159968095942;0,0131997788636727
"13";"Roraima";"EJA";"Banca";0,12905586138977;0,0291049765543424
"14";"Roraima";"EJA";"Negra";0,827296028683043;0,0395255484842391
"15";"Roraima";"EJA";"Outros";0,0436481099271872;0,0184591100514798
"16";"Pará";"EJA";"Banca";0,123284588767734;0,0248787238854203
"17";"Pará";"EJA";"Negra";0,873244832206036;0,0251890740909677
"18";"Pará";"EJA";"Outros";0,00347057902623059;0,00348261846497359
"19";"Amapá";"EJA";"Banca";0,156748960396988;0,0751233443064101
"20";"Amapá";"EJA";"Negra";0,843251039603012;0,0751233443064101
"21";"Amapá";"EJA";"Outros";0;0
"22";"Tocantins";"EJA";"Banca";0,231555057106905;0,0704845466195368
"23";"Tocantins";"EJA";"Negra";0,768444942893095;0,0704845466195368
"24";"Tocantins";"EJA";"Outros";0;0
"25";"Maranhão";"EJA";"Banca";0,172243725626851;0,029977056901245
"26";"Maranhão";"EJA";"Negra";0,819251165194912;0,0313483247031916
"27";"Maranhão";"EJA";"Outros";0,00850510917823689;0,00989877161644683
"28";"Piauí";"EJA";"Banca";0,145196649056283;0,0350063717367229
"29";"Piauí";"EJA";"Negra";0,854803350943717;0,0350063717367229
"30";"Piauí";"EJA";"Outros";0;0
"31";"Ceará";"EJA";"Banca";0,221997717248825;0,0341988094351041
"32";"Ceará";"EJA";"Negra";0,768490768316934;0,0354693060700944
"33";"Ceará";"EJA";"Outros";0,00951151443424091;0,0100074359548412
"34";"Rio Grande do Norte";"EJA";"Banca";0,25490058256164;0,0348606995798256
"35";"Rio Grande do Norte";"EJA";"Negra";0,74509941743836;0,0348606995798256
"36";"Rio Grande do Norte";"EJA";"Outros";0;0
"37";"Paraíba";"EJA";"Banca";0,212392409666202;0,0416995785595304
"38";"Paraíba";"EJA";"Negra";0,774186306810218;0,0417446170756632
"39";"Paraíba";"EJA";"Outros";0,0134212835235789;0,0128045760750655
"40";"Pernambuco";"EJA";"Banca";0,190829014546859;0,0351078412058303
"41";"Pernambuco";"EJA";"Negra";0,799706562186138;0,0349984536845556
"42";"Pernambuco";"EJA";"Outros";0,00946442326700231;0,00773531988884463
"43";"Alagoas";"EJA";"Banca";0,120739169452396;0,0296026021958083
"44";"Alagoas";"EJA";"Negra";0,872569045905444;0,0313075745334072
"45";"Alagoas";"EJA";"Outros";0,00669178464215978;0,00630520180147498
"46";"Sergipe";"EJA";"Banca";0,170204490872136;0,0405011076017165
"47";"Sergipe";"EJA";"Negra";0,829795509127864;0,0405011076017165
"48";"Sergipe";"EJA";"Outros";0;0
"49";"Bahia";"EJA";"Banca";0,0642718227150911;0,0174976146793492
"50";"Bahia";"EJA";"Negra";0,931323293180887;0,0185035497460446
"51";"Bahia";"EJA";"Outros";0,00440488410402175;0,00405453404058914
"52";"Minas Gerais";"EJA";"Banca";0,261441078692246;0,0342406137034903
"53";"Minas Gerais";"EJA";"Negra";0,737802059596358;0,0342036110114902
"54";"Minas Gerais";"EJA";"Outros";0,000756861711396053;0,00074909913276059
"55";"Espírito Santo";"EJA";"Banca";0,279274674161392;0,0418890965431776
"56";"Espírito Santo";"EJA";"Negra";0,720725325838609;0,0418890965431775
"57";"Espírito Santo";"EJA";"Outros";0;0
"58";"Rio de Janeiro";"EJA";"Banca";0,353932264205666;0,0399764038762945
"59";"Rio de Janeiro";"EJA";"Negra";0,646067735794334;0,0399764038762945
"60";"Rio de Janeiro";"EJA";"Outros";0;0
"61";"São Paulo";"EJA";"Banca";0,427961747672664;0,0508947404170718
"62";"São Paulo";"EJA";"Negra";0,56230889369428;0,0507303573569865
"63";"São Paulo";"EJA";"Outros";0,00972935863305559;0,00936491156708691
"64";"Paraná";"EJA";"Banca";0,573513962710304;0,0492343955412591
"65";"Paraná";"EJA";"Negra";0,426486037289696;0,0492343955412591
"66";"Paraná";"EJA";"Outros";0;0
"67";"Santa Catarina";"EJA";"Banca";0,789816444285672;0,0362786298037194
"68";"Santa Catarina";"EJA";"Negra";0,210183555714328;0,0362786298037194
"69";"Santa Catarina";"EJA";"Outros";0;0
"70";"Rio Grande do Sul";"EJA";"Banca";0,724687987337751;0,0416211336979536
"71";"Rio Grande do Sul";"EJA";"Negra";0,275312012662249;0,0416211336979536
"72";"Rio Grande do Sul";"EJA";"Outros";0;0
"73";"Mato Grosso do Sul";"EJA";"Banca";0,32777024476558;0,0541653489317168
"74";"Mato Grosso do Sul";"EJA";"Negra";0,67222975523442;0,0541653489317168
"75";"Mato Grosso do Sul";"EJA";"Outros";0;0
"76";"Mato Grosso";"EJA";"Banca";0,24486231103094;0,0462421599050308
"77";"Mato Grosso";"EJA";"Negra";0,723132329739588;0,0473999182633536
"78";"Mato Grosso";"EJA";"Outros";0,0320053592294715;0,0235697139931643
"79";"Goiás";"EJA";"Banca";0,370580650658546;0,0832878715477262
"80";"Goiás";"EJA";"Negra";0,629419349341454;0,0832878715477262
"81";"Goiás";"EJA";"Outros";0;0
"82";"Distrito Federal";"EJA";"Banca";0,289852608098737;0,0521584033772545
"83";"Distrito Federal";"EJA";"Negra";0,710147391901264;0,0521584033772545
"84";"Distrito Federal";"EJA";"Outros";0;0
Pnad_EJA_2019.1.csv
"";"UF";"ETAPA";"COR";"taxa_pd";"se.pd"
"1";"Brasil";"EJA";"Branca";0,28721953562895;0,0105815538815593
"2";"Brasil";"EJA";"Negra";0,703710511235262;0,0107593020116454
"3";"Brasil";"EJA";"Outros";0,00906995313578855;0,00165861725813644
"4";"Rondônia";"EJA";"Branca";0,136980135261185;0,0596233163011705
"5";"Rondônia";"EJA";"Negra";0,863019864738815;0,0596233163011705
"6";"Rondônia";"EJA";"Outros";0;0
"7";"Acre";"EJA";"Branca";0,109457339634598;0,0302128303189901
"8";"Acre";"EJA";"Negra";0,869329242406929;0,0318744710756394
"9";"Acre";"EJA";"Outros";0,0212134179584736;0,0131410673365094
"10";"Amazonas";"EJA";"Branca";0,0845007427780093;0,0232013923738281
"11";"Amazonas";"EJA";"Negra";0,858269169680587;0,0332183192344936
"12";"Amazonas";"EJA";"Outros";0,0572300875414034;0,0294175963785621
"13";"Roraima";"EJA";"Branca";0,174869735415718;0,0475578530680003
"14";"Roraima";"EJA";"Negra";0,774856950155667;0,0550637370313985
"15";"Roraima";"EJA";"Outros";0,0502733144286147;0,0215358169146161
"16";"Pará";"EJA";"Branca";0,104032305538596;0,0206912806461632
"17";"Pará";"EJA";"Negra";0,891600644533679;0,0212736375848899
"18";"Pará";"EJA";"Outros";0,00436704992772487;0,00444417203380958
"19";"Amapá";"EJA";"Branca";0,0627561983331756;0,042435993932137
"20";"Amapá";"EJA";"Negra";0,937243801666824;0,042435993932137
"21";"Amapá";"EJA";"Outros";0;0
"22";"Tocantins";"EJA";"Branca";0,225233828769511;0,102324087903727
"23";"Tocantins";"EJA";"Negra";0,774766171230489;0,102324087903727
"24";"Tocantins";"EJA";"Outros";0;0
"25";"Maranhão";"EJA";"Branca";0,151520884416433;0,0305228435248675
"26";"Maranhão";"EJA";"Negra";0,816223415943221;0,0358782038924868
"27";"Maranhão";"EJA";"Outros";0,0322556996403453;0,0217541607455608
"28";"Piauí";"EJA";"Branca";0,103200459104026;0,0349061446322936
"29";"Piauí";"EJA";"Negra";0,896799540895974;0,0349061446322936
"30";"Piauí";"EJA";"Outros";0;0
"31";"Ceará";"EJA";"Branca";0,260960899795684;0,0416053292551094
"32";"Ceará";"EJA";"Negra";0,730201826408426;0,0414845564938395
"33";"Ceará";"EJA";"Outros";0,00883727379589002;0,00640504150883497
"34";"Rio Grande do Norte";"EJA";"Branca";0,277359391738856;0,0434076345127194
"35";"Rio Grande do Norte";"EJA";"Negra";0,722640608261144;0,0434076345127194
"36";"Rio Grande do Norte";"EJA";"Outros";0;0
"37";"Paraíba";"EJA";"Branca";0,259336100655143;0,0459530953537649
"38";"Paraíba";"EJA";"Negra";0,740663899344857;0,0459530953537649
"39";"Paraíba";"EJA";"Outros";0;0
"40";"Pernambuco";"EJA";"Branca";0,311239910020231;0,0383010405941698
"41";"Pernambuco";"EJA";"Negra";0,681925830911314;0,0380871159139759
"42";"Pernambuco";"EJA";"Outros";0,00683425906845533;0,00511059268336691
"43";"Alagoas";"EJA";"Branca";0,154691071223622;0,0281072089974557
"44";"Alagoas";"EJA";"Negra";0,834867575131132;0,0292155297915143
"45";"Alagoas";"EJA";"Outros";0,0104413536452462;0,00741733652563913
"46";"Sergipe";"EJA";"Branca";0,147459384270795;0,0309003550385654
"47";"Sergipe";"EJA";"Negra";0,852540615729205;0,0309003550385654
"48";"Sergipe";"EJA";"Outros";0;0
"49";"Bahia";"EJA";"Branca";0,0744090358325185;0,0175959489879373
"50";"Bahia";"EJA";"Negra";0,91517412467654;0,0181265810782123
"51";"Bahia";"EJA";"Outros";0,0104168394909418;0,0070917794631356
"52";"Minas Gerais";"EJA";"Branca";0,235388244625068;0,033369705797605
"53";"Minas Gerais";"EJA";"Negra";0,764611755374932;0,033369705797605
"54";"Minas Gerais";"EJA";"Outros";0;0
"55";"Espírito Santo";"EJA";"Branca";0,157278628332901;0,0333614693591499
"56";"Espírito Santo";"EJA";"Negra";0,83262394814181;0,0334755895868751
"57";"Espírito Santo";"EJA";"Outros";0,0100974235252887;0,00765820357156384
"58";"Rio de Janeiro";"EJA";"Branca";0,355134811827264;0,0525261427868657
"59";"Rio de Janeiro";"EJA";"Negra";0,644865188172736;0,0525261427868657
"60";"Rio de Janeiro";"EJA";"Outros";0;0
"61";"São Paulo";"EJA";"Branca";0,439256949310536;0,043798268562909
"62";"São Paulo";"EJA";"Negra";0,553222754977428;0,0438355321435344
"63";"São Paulo";"EJA";"Outros";0,00752029571203637;0,00636542928641336
"64";"Paraná";"EJA";"Branca";0,479405841198756;0,0438381386133605
"65";"Paraná";"EJA";"Negra";0,506473834341421;0,0446108074484695
"66";"Paraná";"EJA";"Outros";0,0141203244598232;0,00819837096081278
"67";"Santa Catarina";"EJA";"Branca";0,648474847271031;0,0380914104968941
"68";"Santa Catarina";"EJA";"Negra";0,341258319675366;0,038783952589845
"69";"Santa Catarina";"EJA";"Outros";0,0102668330536026;0,00794942832243161
"70";"Rio Grande do Sul";"EJA";"Branca";0,666765807479059;0,0550733591625476
"71";"Rio Grande do Sul";"EJA";"Negra";0,321852903677713;0,0544513765853933
"72";"Rio Grande do Sul";"EJA";"Outros";0,0113812888432276;0,00893415954999311
"73";"Mato Grosso do Sul";"EJA";"Branca";0,329142750973648;0,0540060211538851
"74";"Mato Grosso do Sul";"EJA";"Negra";0,645943309488367;0,0526641113971122
"75";"Mato Grosso do Sul";"EJA";"Outros";0,0249139395379843;0,0165212326882124
"76";"Mato Grosso";"EJA";"Branca";0,187194109083096;0,0434282744603171
"77";"Mato Grosso";"EJA";"Negra";0,802857145535431;0,0448437798854621
"78";"Mato Grosso";"EJA";"Outros";0,00994874538147317;0,00988081157546634
"79";"Goiás";"EJA";"Branca";0,206819604122189;0,0451914749477197
"80";"Goiás";"EJA";"Negra";0,793180395877811;0,0451914749477196
"81";"Goiás";"EJA";"Outros";0;0
"82";"Distrito Federal";"EJA";"Branca";0,172959648991406;0,0432279039055005
"83";"Distrito Federal";"EJA";"Negra";0,813424768885264;0,0460816488968567
"84";"Distrito Federal";"EJA";"Outros";0,0136155821233296;0,0130405163998259
INEP_PNAD-main/DADOS CORRETOS!!!/PNADc_1519.zip
Pnad_2015.csv
"";"UF";"ETAPA";"COR";"taxa_pd";"se.pd"
"1";"Brasil";"Pré-escola";"Branca";0,412778351976768;0,0163640635817766
"2";"Brasil";"Pré-escola";"Negra";0,583520942777325;0,0163632918324064
"3";"Brasil";"Pré-escola";"Outros";0,00370070524590784;0,00229094341143446
"4";"Brasil";"Regular do ensino fundamental";"Branca";0,378403753659015;0,00763503934167071
"5";"Brasil";"Regular do ensino fundamental";"Negra";0,615655541535566;0,00763898203025421
"6";"Brasil";"Regular do ensino fundamental";"Outros";0,00594070480541934;0,00147236672671979
"7";"Brasil";"Regular do ensino médio";"Branca";0,429761295749508;0,011858908985477
"8";"Brasil";"Regular do ensino médio";"Negra";0,56556808862415;0,011741766324333
"9";"Brasil";"Regular do ensino médio";"Outros";0,00467061562634158;0,00135056908972902
"10";"Rondônia";"Pré-escola";"Branca";0,232888096519863;0,0767964532289491
"11";"Rondônia";"Pré-escola";"Negra";0,767111903480137;0,0767964532289491
"12";"Rondônia";"Pré-escola";"Outros";0;0
"13";"Acre";"Pré-escola";"Branca";0,203756597451911;0,0724094068854196
"14";"Acre";"Pré-escola";"Negra";0,766615611832202;0,0731687494281498
"15";"Acre";"Pré-escola";"Outros";0,0296277907158867;0,0267440747706981
"16";"Amazonas";"Pré-escola";"Branca";0,282854112272734;0,0619484302019513
"17";"Amazonas";"Pré-escola";"Negra";0,717145887727266;0,0619484302019513
"18";"Amazonas";"Pré-escola";"Outros";0;0
"19";"Roraima";"Pré-escola";"Branca";0,182825061056314;0,0623680952174103
"20";"Roraima";"Pré-escola";"Negra";0,796560929269309;0,0639344802889233
"21";"Roraima";"Pré-escola";"Outros";0,0206140096743773;0,0231176934098176
"22";"Pará";"Pré-escola";"Branca";0,17415060135238;0,0420701714695505
"23";"Pará";"Pré-escola";"Negra";0,818932077731279;0,0425922964299142
"24";"Pará";"Pré-escola";"Outros";0,00691732091634076;0,00717695807258495
"25";"Amapá";"Pré-escola";"Branca";0,206473241468624;0,125130098792221
"26";"Amapá";"Pré-escola";"Negra";0,793526758531376;0,125130098792221
"27";"Amapá";"Pré-escola";"Outros";0;0
"28";"Tocantins";"Pré-escola";"Branca";0,209627907736299;0,100283311723011
"29";"Tocantins";"Pré-escola";"Negra";0,790372092263701;0,100283311723011
"30";"Tocantins";"Pré-escola";"Outros";0;0
"31";"Maranhão";"Pré-escola";"Branca";0,18152193883214;0,031543234913221
"32";"Maranhão";"Pré-escola";"Negra";0,818478061167859;0,031543234913221
"33";"Maranhão";"Pré-escola";"Outros";0;0
"34";"Piauí";"Pré-escola";"Branca";0,163390188848423;0,0555485323643995
"35";"Piauí";"Pré-escola";"Negra";0,836609811151577;0,0555485323643995
"36";"Piauí";"Pré-escola";"Outros";0;0
"37";"Ceará";"Pré-escola";"Branca";0,260865427740953;0,0475556016901984
"38";"Ceará";"Pré-escola";"Negra";0,728119501740299;0,0473610072161858
"39";"Ceará";"Pré-escola";"Outros";0,0110150705187472;0,0104180309720567
"40";"Rio Grande do Norte";"Pré-escola";"Branca";0,406307234610009;0,0943245840848818
"41";"Rio Grande do Norte";"Pré-escola";"Negra";0,593692765389991;0,0943245840848818
"42";"Rio Grande do Norte";"Pré-escola";"Outros";0;0
"43";"Paraíba";"Pré-escola";"Branca";0,452211544445891;0,0710447041143405
"44";"Paraíba";"Pré-escola";"Negra";0,547788455554109;0,0710447041143405
"45";"Paraíba";"Pré-escola";"Outros";0;0
"46";"Pernambuco";"Pré-escola";"Branca";0,278389643552014;0,0580509293012344
"47";"Pernambuco";"Pré-escola";"Negra";0,721610356447986;0,0580509293012344
"48";"Pernambuco";"Pré-escola";"Outros";0;0
"49";"Alagoas";"Pré-escola";"Branca";0,296525676660017;0,0537753784725063
"50";"Alagoas";"Pré-escola";"Negra";0,703474323339983;0,0537753784725063
"51";"Alagoas";"Pré-escola";"Outros";0;0
"52";"Sergipe";"Pré-escola";"Branca";0,0803434790017076;0,0495805980483807
"53";"Sergipe";"Pré-escola";"Negra";0,919656520998292;0,0495805980483807
"54";"Sergipe";"Pré-escola";"Outros";0;0
"55";"Bahia";"Pré-escola";"Branca";0,205151348013836;0,0518617416137324
"56";"Bahia";"Pré-escola";"Negra";0,794848651986164;0,0518617416137324
"57";"Bahia";"Pré-escola";"Outros";0;0
"58";"Minas Gerais";"Pré-escola";"Branca";0,408584346296899;0,0463776602127686
"59";"Minas Gerais";"Pré-escola";"Negra";0,589409679499973;0,0465436871784717
"60";"Minas Gerais";"Pré-escola";"Outros";0,00200597420312855;0,00207614204036544
"61";"Espírito Santo";"Pré-escola";"Branca";0,413839671976413;0,0608674603175104
"62";"Espírito Santo";"Pré-escola";"Negra";0,586160328023588;0,0608674603175104
"63";"Espírito Santo";"Pré-escola";"Outros";0;0
"64";"Rio de Janeiro";"Pré-escola";"Branca";0,418562627528117;0,0563210035246204
"65";"Rio de Janeiro";"Pré-escola";"Negra";0,581437372471883;0,0563210035246203
"66";"Rio de Janeiro";"Pré-escola";"Outros";0;0
"67";"São Paulo";"Pré-escola";"Branca";0,560541610626968;0,0523798585017135
"68";"São Paulo";"Pré-escola";"Negra";0,428614258298102;0,0523492654751865
"69";"São Paulo";"Pré-escola";"Outros";0,0108441310749298;0,0113211401748262
"70";"Paraná";"Pré-escola";"Branca";0,680873584099763;0,0556490601982355
"71";"Paraná";"Pré-escola";"Negra";0,319126415900237;0,0556490601982355
"72";"Paraná";"Pré-escola";"Outros";0;0
"73";"Santa Catarina";"Pré-escola";"Branca";0,804558796791192;0,0407749120495422
"74";"Santa Catarina";"Pré-escola";"Negra";0,195441203208808;0,0407749120495422
"75";"Santa Catarina";"Pré-escola";"Outros";0;0
"76";"Rio Grande do Sul";"Pré-escola";"Branca";0,772313368860082;0,0521517179734022
"77";"Rio Grande do Sul";"Pré-escola";"Negra";0,227686631139918;0,0521517179734022
"78";"Rio Grande do Sul";"Pré-escola";"Outros";0;0
"79";"Mato Grosso do Sul";"Pré-escola";"Branca";0,492385595955493;0,105982343375552
"80";"Mato Grosso do Sul";"Pré-escola";"Negra";0,507614404044507;0,105982343375552
"81";"Mato Grosso do Sul";"Pré-escola";"Outros";0;0
"82";"Mato Grosso";"Pré-escola";"Branca";0,42548596222901;0,0697053452628135
"83";"Mato Grosso";"Pré-escola";"Negra";0,57451403777099;0,0697053452628135
"84";"Mato Grosso";"Pré-escola";"Outros";0;0
"85";"Goiás";"Pré-escola";"Branca";0,442408204378677;0,0693714463314862
"86";"Goiás";"Pré-escola";"Negra";0,549528648130884;0,0686669817877335
"87";"Goiás";"Pré-escola";"Outros";0,00806314749043874;0,00860078929603778
"88";"Distrito Federal";"Pré-escola";"Branca";0,293232466316714;0,0895064550929871
"89";"Distrito Federal";"Pré-escola";"Negra";0,706767533683286;0,0895064550929871
"90";"Distrito Federal";"Pré-escola";"Outros";0;0
"91";"Rondônia";"Regular do ensino fundamental";"Branca";0,178029397365104;0,0313053291302499
"92";"Rondônia";"Regular do ensino fundamental";"Negra";0,818151395081892;0,0316348747785816
"93";"Rondônia";"Regular do ensino fundamental";"Outros";0,00381920755300372;0,00404085669574903
"94";"Acre";"Regular do ensino fundamental";"Branca";0,168916416461627;0,0304975893309344
"95";"Acre";"Regular do ensino fundamental";"Negra";0,824471628247513;0,0302470644854511
"96";"Acre";"Regular do ensino fundamental";"Outros";0,00661195529086024;0,00542787119933256
"97";"Amazonas";"Regular do ensino fundamental";"Branca";0,132138684998032;0,0218269803609948
"98";"Amazonas";"Regular do ensino fundamental";"Negra";0,861411074517617;0,0215859967356111
"99";"Amazonas";"Regular do ensino fundamental";"Outros";0,00645024048435043;0,00336304547258563
"100";"Roraima";"Regular do ensino fundamental";"Branca";0,167731921239948;0,032270398826515
"101";"Roraima";"Regular do ensino fundamental";"Negra";0,812493907577545;0,0374837642295959
"102";"Roraima";"Regular do ensino fundamental";"Outros";0,0197741711825078;0,0125926199907963
"103";"Pará";"Regular do ensino fundamental";"Branca";0,183063715107851;0,0194917365465596
"104";"Pará";"Regular do ensino fundamental";"Negra";0,80833172445654;0,0210225156706109
"105";"Pará";"Regular do ensino fundamental";"Outros";0,00860456043560889;0,00734341807462325
"106";"Amapá";"Regular do ensino fundamental";"Branca";0,23924444368873;0,0375885870369331
"107";"Amapá";"Regular do ensino fundamental";"Negra";0,754015681794871;0,0365664346661301
"108";"Amapá";"Regular do ensino fundamental";"Outros";0,00673987451639884;0,00937082432156287
"109";"Tocantins";"Regular do ensino fundamental";"Branca";0,21554316268846;0,0319200475355473
"110";"Tocantins";"Regular do ensino fundamental";"Negra";0,78445683731154;0,0319200475355473
"111";"Tocantins";"Regular do ensino fundamental";"Outros";0;0
"112";"Maranhão";"Regular do ensino fundamental";"Branca";0,155171748344504;0,0130359791147396
"113";"Maranhão";"Regular do ensino fundamental";"Negra";0,842959483331992;0,0131631473865949
"114";"Maranhão";"Regular do ensino fundamental";"Outros";0,001868768323504;0,00133102519121355
"115";"Piauí";"Regular do ensino fundamental";"Branca";0,183557439937069;0,0253966278644547
"116";"Piauí";"Regular do ensino fundamental";"Negra";0,816442560062931;0,0253966278644547
"117";"Piauí";"Regular do ensino fundamental";"Outros";0;0
"118";"Ceará";"Regular do ensino fundamental";"Branca";0,223637526088619;0,0216490126579503
"119";"Ceará";"Regular do ensino fundamental";"Negra";0,77097851672176;0,0216041162730903
"120";"Ceará";"Regular do ensino fundamental";"Outros";0,00538395718962065;0,00234807799470573
"121";"Rio Grande do Norte";"Regular do ensino fundamental";"Branca";0,385573361426587;0,0315167286634365
"122";"Rio Grande do Norte";"Regular do ensino fundamental";"Negra";0,614426638573413;0,0315167286634365
"123";"Rio Grande do Norte";"Regular do ensino fundamental";"Outros";0;0
"124";"Paraíba";"Regular do ensino fundamental";"Branca";0,333932387525179;0,0240451087702643
"125";"Paraíba";"Regular do ensino fundamental";"Negra";0,666067612474821;0,0240451087702643
"126";"Paraíba";"Regular do ensino fundamental";"Outros";0;0
"127";"Pernambuco";"Regular do ensino fundamental";"Branca";0,283222975219219;0,0195699686858349
"128";"Pernambuco";"Regular do ensino fundamental";"Negra";0,715552143792473;0,0192996557757731
"129";"Pernambuco";"Regular do ensino fundamental";"Outros";0,00122488098830735;0,00121136236954235
"130";"Alagoas";"Regular do ensino fundamental";"Branca";0,215942190840417;0,0193698896054751
"131";"Alagoas";"Regular do ensino fundamental";"Negra";0,776308430259992;0,0196772781309106
"132";"Alagoas";"Regular do ensino fundamental";"Outros";0,00774937889959136;0,00317939551432194
"133";"Sergipe";"Regular do ensino fundamental";"Branca";0,174314379335561;0,0291215902923302
"134";"Sergipe";"Regular do ensino fundamental";"Negra";0,818522881025459;0,0293636509471333
"135";"Sergipe";"Regular do ensino fundamental";"Outros";0,00716273963897925;0,00421104270417263
"136";"Bahia";"Regular do ensino fundamental";"Branca";0,193941136541721;0,0204690154856277
"137";"Bahia";"Regular do ensino fundamental";"Negra";0,79904744598885;0,0206805518417966
"138";"Bahia";"Regular do ensino fundamental";"Outros";0,00701141746942847;0,00313158207287121
"139";"Minas Gerais";"Regular do ensino fundamental";"Branca";0,372802876484216;0,0282540814493133
"140";"Minas Gerais";"Regular do ensino fundamental";"Negra";0,624684884858187;0,0285730413988035
"141";"Minas Gerais";"Regular do ensino fundamental";"Outros";0,0025122386575967;0,00185070451505941
"142";"Espírito Santo";"Regular do ensino fundamental";"Branca";0,333560900604485;0,0347905173948996
"143";"Espírito Santo";"Regular do ensino fundamental";"Negra";0,661458763570939;0,0345678657099985
"144";"Espírito Santo";"Regular do ensino fundamental";"Outros";0,00498033582457554;0,00340789769124288
"145";"Rio de Janeiro";"Regular do ensino fundamental";"Branca";0,343468088200225;0,0182084583609381
"146";"Rio de Janeiro";"Regular do ensino fundamental";"Negra";0,656531911799775;0,0182084583609381
"147";"Rio de Janeiro";"Regular do ensino fundamental";"Outros";0;0
"148";"São Paulo";"Regular do ensino fundamental";"Branca";0,565061079560921;0,0255519322449901
"149";"São Paulo";"Regular do ensino fundamental";"Negra";0,420568200359259;0,0256271859935365
"150";"São Paulo";"Regular do ensino fundamental";"Outros";0,0143707200798202;0,00734015832607516
"151";"Paraná";"Regular do ensino fundamental";"Branca";0,6468699457992;0,0209021589538509
"152";"Paraná";"Regular do ensino fundamental";"Negra";0,34232159465449;0,0207014602545556
"153";"Paraná";"Regular do ensino fundamental";"Outros";0,0108084595463104;0,00443155754702909
"154";"Santa Catarina";"Regular do ensino fundamental";"Branca";0,792950228321468;0,0220889818849064
"155";"Santa Catarina";"Regular do ensino fundamental";"Negra";0,205185493548555;0,021344343245198
"156";"Santa Catarina";"Regular do ensino fundamental";"Outros";0,00186427812997752;0,00187759235423187
"157";"Rio Grande do Sul";"Regular do ensino fundamental";"Branca";0,753446629358872;0,0232821201105632
"158";"Rio Grande do Sul";"Regular do ensino fundamental";"Negra";0,244685544794052;0,0233078470801259
"159";"Rio Grande do Sul";"Regular do ensino fundamental";"Outros";0,00186782584707546;0,00182537767892362
"160";"Mato Grosso do Sul";"Regular do ensino
fundamental";"Branca";0,373909459121649;0,0375323744641716
"161";"Mato Grosso do Sul";"Regular do ensino fundamental";"Negra";0,618765365048818;0,0382315487692638
"162";"Mato Grosso do Sul";"Regular do ensino fundamental";"Outros";0,00732517582953221;0,00632973309144334
"163";"Mato Grosso";"Regular do ensino fundamental";"Branca";0,307302214291154;0,0396890769368409
"164";"Mato Grosso";"Regular do ensino fundamental";"Negra";0,690118282124656;0,0391036466168293
"165";"Mato Grosso";"Regular do ensino fundamental";"Outros";0,00257950358419055;0,00258906679875333
"166";"Goiás";"Regular do ensino fundamental";"Branca";0,348105017545759;0,0271591543877836
"167";"Goiás";"Regular do ensino fundamental";"Negra";0,651466937773159;0,0270851367052642
"168";"Goiás";"Regular do ensino fundamental";"Outros";0,000428044681082218;0,00044740712143778
"169";"Distrito Federal";"Regular do ensino fundamental";"Branca";0,410570253881641;0,0534268744569832
"170";"Distrito Federal";"Regular do ensino fundamental";"Negra";0,579071458265764;0,0536155065122806
"171";"Distrito Federal";"Regular do ensino fundamental";"Outros";0,0103582878525949;0,00590641700530343
"172";"Rondônia";"Regular do ensino médio";"Branca";0,0985984870109147;0,035809963278302
"173";"Rondônia";"Regular do ensino médio";"Negra";0,891035983379127;0,0400545379864203
"174";"Rondônia";"Regular do ensino médio";"Outros";0,0103655296099586;0,0108866554358784
"175";"Acre";"Regular do ensino médio";"Branca";0,157031708144561;0,0482851733081259
"176";"Acre";"Regular do ensino médio";"Negra";0,842968291855439;0,0482851733081259
"177";"Acre";"Regular do ensino médio";"Outros";0;0
"178";"Amazonas";"Regular do ensino médio";"Branca";0,171697138972415;0,0330836011620846
"179";"Amazonas";"Regular do ensino médio";"Negra";0,828302861027585;0,0330836011620846
"180";"Amazonas";"Regular do ensino médio";"Outros";0;0
"181";"Roraima";"Regular do ensino médio";"Branca";0,169298948122966;0,0573857694936296
"182";"Roraima";"Regular do ensino médio";"Negra";0,772939227168689;0,0646507963427781
"183";"Roraima";"Regular do ensino médio";"Outros";0,0577618247083451;0,0356341024395649
"184";"Pará";"Regular do ensino médio";"Branca";0,197083999461903;0,0354682698440738
"185";"Pará";"Regular do ensino médio";"Negra";0,775444287448434;0,0386269523244018
"186";"Pará";"Regular do ensino médio";"Outros";0,0274717130896627;0,0159409923745748
"187";"Amapá";"Regular do ensino médio";"Branca";0,217417887741239;0,0580422910497963
"188";"Amapá";"Regular do ensino médio";"Negra";0,78258211225876;0,0580422910497963
"189";"Amapá";"Regular do ensino médio";"Outros";0;0
"190";"Tocantins";"Regular do ensino médio";"Branca";0,176758577347096;0,0416311640338052
"191";"Tocantins";"Regular do ensino médio";"Negra";0,823241422652904;0,0416311640338052
"192";"Tocantins";"Regular do ensino médio";"Outros";0;0
"193";"Maranhão";"Regular do ensino médio";"Branca";0,207702975908557;0,0245631113445318
"194";"Maranhão";"Regular do ensino médio";"Negra";0,791470321646539;0,0246378128595338
"195";"Maranhão";"Regular do ensino médio";"Outros";0,000826702444903262;0,00089396126724433
"196";"Piauí";"Regular do ensino médio";"Branca";0,147732646147137;0,0368622593940302
"197";"Piauí";"Regular do ensino médio";"Negra";0,852267353852863;0,0368622593940302
"198";"Piauí";"Regular do ensino médio";"Outros";0;0
"199";"Ceará";"Regular do ensino médio";"Branca";0,300967683355307;0,0345084399394716
"200";"Ceará";"Regular do ensino médio";"Negra";0,689096082126679;0,034641595517169
"201";"Ceará";"Regular do ensino médio";"Outros";0,00993623451801381;0,00587992674077754
"202";"Rio Grande do Norte";"Regular do ensino médio";"Branca";0,376098900994183;0,0427742560016683
"203";"Rio Grande do Norte";"Regular do ensino médio";"Negra";0,623901099005817;0,0427742560016684
"204";"Rio Grande do Norte";"Regular do ensino médio";"Outros";0;0
"205";"Paraíba";"Regular do ensino médio";"Branca";0,273762390973023;0,0508532428405623
"206";"Paraíba";"Regular do ensino médio";"Negra";0,726237609026977;0,0508532428405623
"207";"Paraíba";"Regular do ensino médio";"Outros";0;0
"208";"Pernambuco";"Regular do ensino médio";"Branca";0,302377455417775;0,0367357967316076
"209";"Pernambuco";"Regular do ensino médio";"Negra";0,692764574035786;0,0370610143802723
"210";"Pernambuco";"Regular do ensino médio";"Outros";0,00485797054643885;0,00438059330508412
"211";"Alagoas";"Regular do ensino médio";"Branca";0,217200687245877;0,0298578284853344
"212";"Alagoas";"Regular do ensino médio";"Negra";0,782799312754123;0,0298578284853344
"213";"Alagoas";"Regular do ensino médio";"Outros";0;0
"214";"Sergipe";"Regular do ensino médio";"Branca";0,252205996786849;0,0492334066380791
"215";"Sergipe";"Regular do ensino médio";"Negra";0,742138367229495;0,0489433037199329
"216";"Sergipe";"Regular do ensino médio";"Outros";0,00565563598365596;0,0054310186595771
"217";"Bahia";"Regular do ensino médio";"Branca";0,188256250302598;0,0298852065548314
"218";"Bahia";"Regular do ensino médio";"Negra";0,811743749697402;0,0298852065548314
"219";"Bahia";"Regular do ensino médio";"Outros";0;0
"220";"Minas Gerais";"Regular do ensino médio";"Branca";0,377559153351481;0,0318355726366017
"221";"Minas Gerais";"Regular do ensino médio";"Negra";0,617646100757543;0,0309775280958622
"222";"Minas Gerais";"Regular do ensino médio";"Outros";0,00479474589097635;0,00383750103876388
"223";"Espírito Santo";"Regular do ensino médio";"Branca";0,382128948740956;0,0531521279144349
"224";"Espírito Santo";"Regular do ensino médio";"Negra";0,617871051259044;0,0531521279144349
"225";"Espírito Santo";"Regular do ensino médio";"Outros";0;0
"226";"Rio de Janeiro";"Regular do ensino médio";"Branca";0,440129708077953;0,0361886904765596
"227";"Rio de Janeiro";"Regular do ensino médio";"Negra";0,559870291922047;0,0361886904765596
"228";"Rio de Janeiro";"Regular do ensino médio";"Outros";0;0
"229";"São Paulo";"Regular do ensino médio";"Branca";0,627135633879394;0,0304930363451139
"230";"São Paulo";"Regular do ensino médio";"Negra";0,367612441243197;0,0302652808234026
"231";"São Paulo";"Regular do ensino médio";"Outros";0,00525192487740975;0,00394413631970207
"232";"Paraná";"Regular do ensino médio";"Branca";0,715966067606912;0,0375485770622291
"233";"Paraná";"Regular do ensino médio";"Negra";0,273756949491438;0,0371524617868035
"234";"Paraná";"Regular do ensino médio";"Outros";0,0102769829016505;0,0080654308646402
"235";"Santa Catarina";"Regular do ensino médio";"Branca";0,856349856940249;0,0241493966841434
"236";"Santa Catarina";"Regular do ensino médio";"Negra";0,143650143059751;0,0241493966841434
"237";"Santa Catarina";"Regular do ensino médio";"Outros";0;0
"238";"Rio Grande do Sul";"Regular do ensino médio";"Branca";0,755802458051986;0,0397428287985176
"239";"Rio Grande do Sul";"Regular do ensino médio";"Negra";0,244197541948015;0,0397428287985177
"240";"Rio Grande do Sul";"Regular do ensino médio";"Outros";0;0
"241";"Mato Grosso do Sul";"Regular do ensino médio";"Branca";0,310997558818808;0,0642382262241702
"242";"Mato Grosso do Sul";"Regular do ensino médio";"Negra";0,689002441181192;0,0642382262241702
"243";"Mato Grosso do Sul";"Regular do ensino médio";"Outros";0;0
"244";"Mato Grosso";"Regular do ensino médio";"Branca";0,279886717055662;0,043583477385186
"245";"Mato Grosso";"Regular do ensino médio";"Negra";0,704658643130503;0,0471615497292238
"246";"Mato Grosso";"Regular do ensino médio";"Outros";0,0154546398138344;0,0112983933407213
"247";"Goiás";"Regular do ensino médio";"Branca";0,352517597657291;0,058595492370715
"248";"Goiás";"Regular do ensino médio";"Negra";0,647482402342709;0,0585954923707149
"249";"Goiás";"Regular do ensino médio";"Outros";0;0
"250";"Distrito Federal";"Regular do ensino médio";"Branca";0,348267593465004;0,0627630233362198
"251";"Distrito Federal";"Regular do ensino médio";"Negra";0,651732406534996;0,0627630233362198
"252";"Distrito Federal";"Regular do ensino médio";"Outros";0;0
Pnad_2015_rede.csv
"";"UF";"ETAPA";"REDE";"COR";"taxa_pd";"se.pd"
"1";"Brasil";"Pré-escola";"Rede privada";"Branca";0,552445038133801;0,0305980777925071
"2";"Brasil";"Pré-escola";"Rede privada";"Negra";0,446686204303023;0,0305943854384796
"3";"Brasil";"Pré-escola";"Rede privada";"Outros";0,000868757563176544;0,000897262497524292
"4";"Brasil";"Pré-escola";"Rede pública";"Branca";0,374324290444615;0,0179193852550701
"5";"Brasil";"Pré-escola";"Rede pública";"Negra";0,621195291598851;0,0179662669097878
"6";"Brasil";"Pré-escola";"Rede pública";"Outros";0,00448041795653337;0,00293362931855666
"7";"Brasil";"Regular do ensino fundamental";"Rede privada";"Branca";0,535210594234178;0,0180196870206091
"8";"Brasil";"Regular do ensino fundamental";"Rede privada";"Negra";0,448809057654093;0,0183888810364118
"9";"Brasil";"Regular do ensino fundamental";"Rede privada";"Outros";0,0159803481117285;0,00721692252709104
"10";"Brasil";"Regular do ensino fundamental";"Rede pública";"Branca";0,351164286886356;0,00795470517532392
"11";"Brasil";"Regular do ensino fundamental";"Rede pública";"Negra";0,644639029992792;0,00794780701419518
"12";"Brasil";"Regular do ensino fundamental";"Rede pública";"Outros";0,00419668312085213;0,00116196108709707
"13";"Brasil";"Regular do ensino médio";"Rede privada";"Branca";0,645082600116384;0,026438741568348
"14";"Brasil";"Regular do ensino médio";"Rede privada";"Negra";0,349888543781926;0,0266136925838638
"15";"Brasil";"Regular do ensino médio";"Rede privada";"Outros";0,00502885610169084;0,0036788901454803
"16";"Brasil";"Regular do ensino médio";"Rede pública";"Branca";0,397999621318413;0,0129679715983253
"17";"Brasil";"Regular do ensino médio";"Rede pública";"Negra";0,59738260649022;0,0127592710272917
"18";"Brasil";"Regular do ensino médio";"Rede pública";"Outros";0,00461777219136639;0,00148948689438218
"19";"Rondônia";"Pré-escola";"Rede privada";"Branca";1;0
"20";"Rondônia";"Pré-escola";"Rede privada";"Negra";0;0
"21";"Rondônia";"Pré-escola";"Rede privada";"Outros";0;0
"22";"Rondônia";"Pré-escola";"Rede pública";"Branca";0,133634705155522;0,0748297209847872
"23";"Rondônia";"Pré-escola";"Rede pública";"Negra";0,866365294844478;0,0748297209847872
"24";"Rondônia";"Pré-escola";"Rede pública";"Outros";0;0
"25";"Acre";"Pré-escola";"Rede privada";"Branca";0,417837197150717;0,229855403969307
"26";"Acre";"Pré-escola";"Rede privada";"Negra";0,582162802849283;0,229855403969307
"27";"Acre";"Pré-escola";"Rede privada";"Outros";0;0
"28";"Acre";"Pré-escola";"Rede pública";"Branca";0,179250407466797;0,077107624042884
"29";"Acre";"Pré-escola";"Rede pública";"Negra";0,787730255506515;0,0785034812907331
"30";"Acre";"Pré-escola";"Rede pública";"Outros";0,0330193370266885;0,0296557430959005
"31";"Amazonas";"Pré-escola";"Rede privada";"Branca";0,484928716992874;0,159924990989686
"32";"Amazonas";"Pré-escola";"Rede privada";"Negra";0,515071283007126;0,159924990989686
"33";"Amazonas";"Pré-escola";"Rede privada";"Outros";0;0
"34";"Amazonas";"Pré-escola";"Rede pública";"Branca";0,239663864408032;0,0795530415224808
"35";"Amazonas";"Pré-escola";"Rede pública";"Negra";0,760336135591968;0,0795530415224808
"36";"Amazonas";"Pré-escola";"Rede pública";"Outros";0;0
"37";"Roraima";"Pré-escola";"Rede privada";"Branca";0,546024620942642;0,338396239885028
"38";"Roraima";"Pré-escola";"Rede privada";"Negra";0,453975379057358;0,338396239885028
"39";"Roraima";"Pré-escola";"Rede privada";"Outros";0;0
"40";"Roraima";"Pré-escola";"Rede pública";"Branca";0,167159546726818;0,0624729752165981
"41";"Roraima";"Pré-escola";"Rede pública";"Negra";0,811337320649307;0,0646753531510683
"42";"Roraima";"Pré-escola";"Rede pública";"Outros";0,0215031326238748;0,0242653120574847
"43";"Pará";"Pré-escola";"Rede privada";"Branca";0,154728207857957;0,0764178193107461
"44";"Pará";"Pré-escola";"Rede privada";"Negra";0,845271792142043;0,0764178193107461
"45";"Pará";"Pré-escola";"Rede privada";"Outros";0;0
"46";"Pará";"Pré-escola";"Rede pública";"Branca";0,180561443318326;0,05162511875054
"47";"Pará";"Pré-escola";"Rede pública";"Negra";0,810238002690319;0,0524179812193227
"48";"Pará";"Pré-escola";"Rede pública";"Outros";0,0092005539913542;0,0095969161447615
"49";"Amapá";"Pré-escola";"Rede privada";"Branca";0,272901882417941;0,16928748920225
"50";"Amapá";"Pré-escola";"Rede privada";"Negra";0,727098117582059;0,16928748920225
"51";"Amapá";"Pré-escola";"Rede privada";"Outros";0;0
"52";"Amapá";"Pré-escola";"Rede pública";"Branca";0,17131643173581;0,172698415075334
"53";"Amapá";"Pré-escola";"Rede pública";"Negra";0,828683568264191;0,172698415075334
"54";"Amapá";"Pré-escola";"Rede pública";"Outros";0;0
"55";"Tocantins";"Pré-escola";"Rede pública";"Branca";0,209627907736299;0,100283311723011
"56";"Tocantins";"Pré-escola";"Rede pública";"Negra";0,790372092263701;0,100283311723011
"57";"Tocantins";"Pré-escola";"Rede pública";"Outros";0;0
"58";"Tocantins";"Pré-escola";NA;"Branca";NA;NA
"59";"Tocantins";"Pré-escola";NA;"Negra";NA;NA
"60";"Tocantins";"Pré-escola";NA;"Outros";NA;NA
"61";"Maranhão";"Pré-escola";"Rede privada";"Branca";0,161120163151806;0,123399921556869
"62";"Maranhão";"Pré-escola";"Rede privada";"Negra";0,838879836848194;0,123399921556869
"63";"Maranhão";"Pré-escola";"Rede privada";"Outros";0;0
"64";"Maranhão";"Pré-escola";"Rede pública";"Branca";0,183286266860363;0,0321658892551945
"65";"Maranhão";"Pré-escola";"Rede pública";"Negra";0,816713733139637;0,0321658892551945
"66";"Maranhão";"Pré-escola";"Rede pública";"Outros";0;0
"67";"Piauí";"Pré-escola";"Rede privada";"Branca";0,301512274376842;0,281405115801254
"68";"Piauí";"Pré-escola";"Rede privada";"Negra";0,698487725623158;0,281405115801254
"69";"Piauí";"Pré-escola";"Rede privada";"Outros";0;0
"70";"Piauí";"Pré-escola";"Rede pública";"Branca";0,153908269044493;0,0560658931825214
"71";"Piauí";"Pré-escola";"Rede pública";"Negra";0,846091730955507;0,0560658931825214
"72";"Piauí";"Pré-escola";"Rede pública";"Outros";0;0
"73";"Ceará";"Pré-escola";"Rede privada";"Branca";0,306499248257243;0,124201445059061
"74";"Ceará";"Pré-escola";"Rede privada";"Negra";0,693500751742757;0,124201445059061
"75";"Ceará";"Pré-escola";"Rede privada";"Outros";0;0
"76";"Ceará";"Pré-escola";"Rede pública";"Branca";0,247372878120525;0,0503744494339212
"77";"Ceará";"Pré-escola";"Rede pública";"Negra";0,738355225965325;0,0498327474453658
"78";"Ceará";"Pré-escola";"Rede pública";"Outros";0,0142718959141501;0,0135482537474696
"79";"Rio Grande do Norte";"Pré-escola";"Rede privada";"Branca";0,615793512895961;0,162539092350711
"80";"Rio Grande do Norte";"Pré-escola";"Rede privada";"Negra";0,384206487104039;0,162539092350711
"81";"Rio Grande do Norte";"Pré-escola";"Rede privada";"Outros";0;0
"82";"Rio Grande do Norte";"Pré-escola";"Rede pública";"Branca";0,322973910388789;0,098110699378922
"83";"Rio Grande do Norte";"Pré-escola";"Rede pública";"Negra";0,677026089611211;0,098110699378922
"84";"Rio Grande do Norte";"Pré-escola";"Rede pública";"Outros";0;0
"85";"Paraíba";"Pré-escola";"Rede privada";"Branca";0,588391886026956;0,134967413115428
"86";"Paraíba";"Pré-escola";"Rede privada";"Negra";0,411608113973044;0,134967413115428
"87";"Paraíba";"Pré-escola";"Rede privada";"Outros";0;0
"88";"Paraíba";"Pré-escola";"Rede pública";"Branca";0,400506494211574;0,0827514070883389
"89";"Paraíba";"Pré-escola";"Rede pública";"Negra";0,599493505788426;0,0827514070883389
"90";"Paraíba";"Pré-escola";"Rede pública";"Outros";0;0
"91";"Pernambuco";"Pré-escola";"Rede privada";"Branca";0,421997859149936;0,105871374337738
"92";"Pernambuco";"Pré-escola";"Rede privada";"Negra";0,578002140850064;0,105871374337738
"93";"Pernambuco";"Pré-escola";"Rede privada";"Outros";0;0
"94";"Pernambuco";"Pré-escola";"Rede pública";"Branca";0,215513841098311;0,0655315878627467
"95";"Pernambuco";"Pré-escola";"Rede pública";"Negra";0,784486158901689;0,0655315878627467
"96";"Pernambuco";"Pré-escola";"Rede pública";"Outros";0;0
"97";"Alagoas";"Pré-escola";"Rede privada";"Branca";0,346651739987989;0,110364151651788