library(acs)

api.key.install(key="7876242c28cec9d378629ab9ba095801e0651d07")

#acs.tables.install()

zip_geo = geo.make(zip.code = "*")

####RACE####

race.data = acs.fetch(geography=zip_geo, table.number="B03002", col.names = "pretty", endyear = 2015)

zip_demographics = data.frame(region = as.character(geography(race.data)$zipcodetabulationarea),
                              total_population = as.numeric(estimate(race.data[,1])))

zip_demographics$region = as.character(zip_demographics$region)

race_df = data.frame(white_alone_not_hispanic = as.numeric(estimate(race.data[,3])),
                     black_alone_not_hispanic = as.numeric(estimate(race.data[,4])),
                     asian_alone_not_hispanic = as.numeric(estimate(race.data[,6])),
                     hispanic_all_races = as.numeric(estimate(race.data[,12])))

zip_demographics$percent_white = (race_df$white_alone_not_hispanic / zip_demographics$total_population * 100)
zip_demographics$percent_black = (race_df$black_alone_not_hispanic / zip_demographics$total_population * 100)
zip_demographics$percent_asian = (race_df$asian_alone_not_hispanic / zip_demographics$total_population * 100)
zip_demographics$percent_hispanic = (race_df$hispanic_all_races / zip_demographics$total_population * 100)

#race data table B19013 (columns 1,3,4,6)
#pop by povert status table B06012 columns 1,2,3,4

######POVERTY######

poverty.data = acs.fetch(geography=zip_geo, table.number="B06012", col.names = "pretty", endyear = 2015)
poverty.data<-na.omit(poverty.data)

zip_poverty = data.frame(region = as.character(geography(poverty.data)$zipcodetabulationarea),
                              total_population = as.numeric(estimate(poverty.data[,1])))
zip_poverty<-na.omit(zip_poverty)

zip_poverty$region = as.character(zip_poverty$region)

mytable <- results(acs.lookup(endyear=2015, table.number="B06012"))$variable.name

poverty_df = data.frame(Below_100_percent_of_the_poverty_level = as.numeric(estimate(poverty.data[,2])),
                        Hundred_to_149_percent_of_the_poverty_level = as.numeric(estimate(poverty.data[,3])),
                        At_or_above_150_percent_of_the_poverty_level = as.numeric(estimate(poverty.data[,4])))
poverty_df<-na.omit(poverty_df)

zip_poverty$percent_below100percent= (poverty_df$Below_100_percent_of_the_poverty_level / zip_poverty$total_population * 100)
zip_poverty$percent_100to149percent = (poverty_df$Hundred_to_149_percent_of_the_poverty_level / zip_poverty$total_population * 100)
zip_poverty$percent_above149percent= (poverty_df$At_or_above_150_percent_of_the_poverty_level / zip_poverty$total_population * 100)