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validacao.R
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validacao.R
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## Script de Validacao
list.of.packages <- c("dplyr", "tidyr", "ggplot2", "RCurl","plotly")
new.packages <- list.of.packages[!(list.of.packages %in% installed.packages()[,"Package"])]
if(length(new.packages)) install.packages(new.packages)
library(RCurl)
library(dplyr)
library(tidyr)
library(plotly)
library(ggplot2)
dl_dropbox <- function(x, key) {
bin <- getBinaryURL(paste0("https://dl.dropboxusercontent.com/s/", key, "/", x),
ssl.verifypeer = FALSE)
con <- file(x, open = "wb")
writeBin(bin, con)
close(con)
message(noquote(paste("O arquivo",x , "esta na pasta", getwd())))
}
dl_dropbox("ProbabilityBayes.csv","ghn6jpznwi0zgyc")
dl_dropbox("CBO2002%20-%20PerfilOcupacional.csv","s3vch0ws47dbweq")
dl_dropbox("Previsao%20Empregos.csv", "qygjhx74bipxrlo")
dl_dropbox("RAIS_CBO_1986-2017.csv", "34lp9dbgsf4kage")
results <- read.csv('ProbabilityBayes.csv')
serie <- read.csv('RAIS_CBO_1986-2017.csv')
names(serie)[2] <- 'cbo2002'
# Calculate the total
total.cbo <- serie %>%
mutate(cbo2002 = factor(cbo2002)) %>%
group_by(cbo2002, ano) %>%
summarise(Total=sum(empregados))
# Calculate the median
median.cbo <- results %>%
group_by(COD_OCUPACAO) %>%
summarise(Probability=median(Probability))
colnames(median.cbo)<-c("cbo2002","Probability")
median.cbo <- median.cbo %>%
mutate(cbo2002 = as.character(cbo2002))
# Merge data
final <- inner_join(total.cbo, median.cbo, by = "cbo2002" )
brks <- quantile(final$Probability,probs=c(0.25,0.5,0.75))
soma2 <- final %>%
mutate(Classe2 = cut(Probability,
c(0,brks,1),
labels=c("MB","B","A","MA")) ) %>%
group_by(ano, Classe2) %>%
filter(ano==2017) %>%
summarise(emp=sum(Total))
paste("Número de empregados Alto e Muito Alto em 2017:",
soma2[4,3]+soma2[3,3], sep = "\n") %>% cat()
paste("Percentual de Empregados Alto e Muito Alto 2017:",
100*((soma2[4,3]+soma2[3,3])/sum(soma2[,3])) , sep = "\n") %>% cat()
### Graficos ###
# Figura 3
dl_dropbox("World%20Probability_pt.csv", "huw8obhhabkx59y")
prob <- read.csv2('World%20Probability_pt.csv')
# Remove Different approach
prob <- prob %>%
filter(as.character(Autor) != "Arntz, Gregory, e Zierahn (2016)")
prob$Label <-paste0(as.character(prob$Pais)," - ",as.character(prob$Autor))
mycolors <- RColorBrewer::brewer.pal(5, "Pastel1")[5:1]
world_graph <- prob %>%
ggplot(aes(reorder(Label, Probabilidade), Probabilidade)) +
geom_col(aes(fill = Probabilidade))+
scale_fill_gradientn(colors = mycolors)+
coord_flip() +
labs(x = "País",
y = "Probabilidade") +
theme(axis.text.y=element_text(face = c(rep("plain",3), "bold",
rep("plain",13)) ) )
# Plot estatico
world_graph
# Plot interativo
ggplotly(world_graph)
##############
# forecast <- read.csv('Previsao%20Empregos.csv')
#
# #Calculate the total
# total.for <- forecast %>%
# mutate(cbo2002 = factor(cbo2002)) %>%
# group_by(cbo2002, ano) %>%
# summarise(Total=sum(empregados))
#
# #Calculate the median
# median.cbo <- results %>%
# group_by(COD_OCUPACAO) %>%
# summarise(Probability=median(Probability))
#
# colnames(median.cbo)<-c("cbo2002","Probability")
#
# median.cbo <- median.cbo %>%
# mutate(cbo2002 = as.character(cbo2002))
#
# final3 <- inner_join(total.for, median.cbo, by = "cbo2002" )
#
# quantile(final3$Probability)
# final3$Class<-cut(final3$Probability,breaks = c(0, 0.25, 0.5, 0.75, 1),
# labels = c("Muito Baixo","Baixo","Alto", "Muito Alto"))
#
# final3 <- final3 %>%
# group_by(Class, ano) %>%
# summarise(Total=sum(Total))
#
# table(final3$Class)
#
# #Colors
# mycolors <- RColorBrewer::brewer.pal(4, "Pastel1")
#
# p2<-ggplot(final3 %>% na.omit(), aes(x=ano, y=Total, colour=as.factor(Class), group=as.factor(Class))) +
# geom_line(aes(),lwd=1) + theme_bw() + geom_point() +
# #scale_x_continuous(breaks = sort(unique(final$ano))[seq(1, length(unique(final$ano)), by = 10)]) +
# scale_x_continuous(breaks = c(1990,2000,2010,2020,2030,2040),
# labels = c("1990","2000","2010","2020","2030","2040")) +
# xlab("")+ ylab("Número de Empregados")+
# scale_colour_manual(name="", values = mycolors)+
# #theme(legend.position="bottom", legend.direction = "horizontal", legend.box = "vertical")+
# scale_y_continuous(breaks = c(5000000,10000000,15000000,20000000,25000000,30000000),
# labels = c("5M","10M","15M","20M","25M","30M"))+
# #scale_x_discrete(breaks = c(1990,2000,2010,2020,2030,2040),
# # labels = c("1990","2000","2010","2020","2030","2040"))+
# guides(fill=guide_legend(nrow=2,byrow=TRUE)) +
# theme_minimal()+
# theme(legend.position="bottom", legend.direction = "horizontal", legend.box = "vertical")
#
# ggplotly(p2)