Actividad Clase 6

datos <- medical_costs %>%
      select(Sex:Region, Costo='Medical Cost') %>%
      filter(Sex == 'male') %>%
      group_by(Region) %>%
      summarise(Avg_Costo = mean(Costo))

region <- datos$Region
RegionCosto <- round(datos$Avg_Costo,2)



bar <- barplot(RegionCosto, names.arg = region,
        main = "Promedio de Costos Medicos Por Region",
        ylab = "Costo Promedio",
        xlab = "Regiones",
        ylim = c(0, max(RegionCosto) * 1.2),
        col = "blue"
        )
text(bar, RegionCosto + 1, labels = RegionCosto, pos=3)

datos <- medical_costs %>%
  select(Age, Costo='Medical Cost') %>%
  group_by(Age) %>%
  summarise(Avg_Costo = mean(Costo))

plot(datos,
        main = "Promedio de Costos Medicos Por Edad",
        ylab = "Costo Promedio",
        xlab = "Edad"
     )

datos2 <- medical_costs %>%
  select(Age:Smoker , Costo='Medical Cost') %>%
  filter(Smoker == 'yes') %>%
  group_by(Age) %>%
  summarise(Avg_Costo = mean(Costo))

plot(datos2,
        main = "Promedio de Costos Medicos Por Edad En Personas Que Fuman",
        ylab = "Costo Promedio",
        xlab = "Edad"
     )

datos3 <- medical_costs %>%
  select(Age:Smoker , Costo='Medical Cost') %>%
  filter(Smoker == 'no') %>%
  group_by(Age) %>%
  summarise(Avg_Costo = mean(Costo))

plot(datos3,
        main = "Promedio de Costos Medicos Por Edad En Personas Que No Fuman",
        ylab = "Costo Promedio",
        xlab = "Edad"
     )

datos4 <- medical_costs %>%
  select(Smoker, Costo='Medical Cost') %>%
  group_by(Smoker) %>%
  summarise(Avg_Costo = mean(Costo))


habitos <- datos4$Smoker
costo <- round(datos4$Avg_Costo,2)
  
  

plot <- barplot(costo, names.arg = habitos,
        main = "Ingerencia de consumo de tabaco en costos medicos",
        ylab = "Costo Promedio",
        xlab = "habitos",
        ylim = c(0, max(costo) * 1.2),
        col = "blue"
        )
text(plot, costo + 1, labels = costo, pos=3)

generos <- medical_costs %>%
  select(Sex,  Costo='Medical Cost') %>%
  group_by(Sex) %>%
  summarise(Avg_Costo = mean(Costo))

sex <- generos$Sex
costosex <- round(generos$Avg_Costo,2)

plotgen <- barplot(costosex, names.arg = sex,
        main = "Promedio de Costos Medicos por Sexo en personas que fuman",
        ylab = "Costo Promedio",
        xlab = "habitos",
        ylim = c(0, max(costosex) * 1.2),
        col = "blue"
        )
text(plotgen, costosex + 1, labels = costosex, pos=3)

generosnf <- medical_costs %>%
  select(Sex, Smoker, Costo='Medical Cost') %>%
  filter(Smoker == 'no') %>%
  group_by(Sex, Smoker) %>%
  summarise(Avg_Costo = mean(Costo))

## `summarise()` has grouped output by 'Sex'. You can override using the `.groups`
## argument.

sexnf <- generosnf$Sex
costosexnf <- round(generosnf$Avg_Costo,2)

plotnf <- barplot(costosexnf, names.arg = sexnf,
        main = "Promedio De Costos Medicos Por Sexo En Personas Que No Fuman",
        ylab = "Costo Promedio",
        xlab = "habitos",
        ylim = c(0, max(costosexnf) * 1.2),
        col = "blue"
        )
text(plotnf, costosexnf + 1, labels = costosexnf, pos=3)

generosf <- medical_costs %>%
  select(Sex, Smoker, Costo='Medical Cost') %>%
  filter(Smoker == 'yes') %>%
  group_by(Sex) %>%
  summarise(Avg_Costo = mean(Costo))

sexf <- generosf$Sex
costosexf <- round(generosf$Avg_Costo,2)

barsexf <- barplot(costosexf, names.arg = sexf,
        main = "Promedio de Costos Medicos por Sexo",
        ylab = "Costo Promedio",
        xlab = "habitos",
        ylim = c(0, max(costosexf) * 1.2),
        col = "blue"
        )
text(barsexf, costosexf + 1, labels = costosexf, pos=3)