sector.R

#Subset the census data by full-time, sector, and sex.
ratio_class_of_worker = census[!(census$class.of.worker == "Not in universe" | 
                                   census$class.of.worker == "Without pay"),]
ratio_class_of_worker = ratio_class_of_worker[(ratio_class_of_worker$full.or.part.time.employment.stat == "Full-time schedules"),]

ratio_class_of_worker = group_by(ratio_class_of_worker, class.of.worker, sex) %>% 
  summarise(., 
            count_geq50 = sum(instance.weight[X == "50000+." & age >= 18]), 
            count_l50 = sum(instance.weight[X == "-50000" & age >= 18]),
            pct_geq50 = (count_geq50 / (count_l50 + count_geq50)) * 100)

#Plot graph.
classPlot_sex = ggplot(ratio_class_of_worker, aes(x = class.of.worker, y = pct_geq50)) + 
  geom_bar(stat = "identity", aes(fill = class.of.worker)) + 
  scale_fill_brewer(name = "Class of Worker", palette = "Set1") + 
  #theme_bw() + 
  ggtitle("Income by Sector 94' - 95'") + 
  xlab("") + 
  ylab("Percent of Sector with Full-time Income >50k") + 
  theme(axis.ticks = element_blank(), axis.text.x = element_blank()) + 
  facet_wrap( ~ sex)
classPlot_sex

######

#Subset data by similarly, but for population percentage measurements.
ratio_class_of_worker_population = census[!(census$class.of.worker == "Not in universe" | 
                                              census$class.of.worker == "Without pay"),]

ratio_class_of_worker_population = ratio_class_of_worker_population[(ratio_class_of_worker_population$full.or.part.time.employment.stat == "Full-time schedules"),]

#Tally the total of males and females.
sector_totals = summarise(ratio_class_of_worker_population, 
                             Both = sum(instance.weight),
                             Males = sum(instance.weight[sex == "Male"]),
                             Females = sum(instance.weight[sex == "Female"]))

#Get percentages of population per sector.
sector_numbers_males = group_by(ratio_class_of_worker_population, class.of.worker, sex) %>%
  summarise(.,
            pct = (sum(instance.weight[sex == "Male"]) / sector_totals$Males) * 100)

sector_numbers_females = group_by(ratio_class_of_worker_population, class.of.worker, sex) %>%
  summarise(.,
            pct = (sum(instance.weight[sex == "Female"]) / sector_totals$Females) * 100)

sector_numbers_males$pct = sector_numbers_males$pct + sector_numbers_females$pct

#Male/female side by side plot.
sectorPlot_mf = ggplot(sector_numbers_males, aes(x = class.of.worker, y = pct)) + 
  geom_bar(stat = "identity", aes(fill = class.of.worker)) + 
  scale_fill_brewer(name = "Class of Worker", palette = "Set1") + 
  #theme_bw() + 
  ggtitle("Sector of Population 94' - 95'") + 
  xlab("") + 
  ylab("Percent of Population in Sector") + 
  theme(axis.ticks = element_blank(), axis.text.x = element_blank()) + 
  facet_wrap( ~ sex)
sectorPlot_mf