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04_analysis.R
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############################################
# # ACS # #
############################################
order <- c("Orleans", "Jefferson", "New Orleans Metro Area", "United States")
orderHisp <- c("Orleans", "Jefferson", "New Orleans Metro Area", "United States")
load("inputs/totalpop_metro.RData")
load("inputs/hispanRaw.RData")
load("inputs/RacepopestRaw.RData")
hispanRaw[hispanRaw == -555555555] <- 0
#Hispanic Origin
hispan <- hispanRaw %>%
filter(placename %in% c("Orleans", "Jefferson","New Orleans Metro Area", "United States")) %>%
#slice(match(orderHisp, place)) %>%
mutate(Cubanpct = TotCuba / TotalHisporLat,
Dominicanpct = TotDomin / TotalHisporLat,
Mexicanpct = TotMex / TotalHisporLat,
PuertoRicanpct = TotPR / TotalHisporLat,
Honduranpct = TotHond / TotalHisporLat,
Guatemalanpct = TotGuat / TotalHisporLat,
Nicaraguanpct = TotNicarag / TotalHisporLat,
Salvadoranpct = TotSalva / TotalHisporLat,
OtherCApct = TotOtherCA / TotalHisporLat,
SouthAmericanpct =TotSA / TotalHisporLat,
Otherpct = TotOtherHisporLat / TotalHisporLat,
# for US/other geo sig testing
CubanUS = rep(Cubanpct[4], 4),
DominicanUS = rep(Dominicanpct[4],4),
MexicanUS = rep(Mexicanpct[4],4),
PuertoRicanUS = rep(PuertoRicanpct[4],4),
HonduranUS = rep(Honduranpct[4],4),
GuatemalanUS = rep(Guatemalanpct[4],4),
NicaraguanUS = rep(Nicaraguanpct[4],4),
SalvadoranUS = rep(Salvadoranpct[4],4),
OtherCAUS = rep(OtherCApct[4],4),
SouthAmericanUS = rep(SouthAmericanpct[4],4),
OtherUS = rep(Otherpct[4],4),
CubanMoeProp = moeprop(y = TotalHisporLat, moex = TotCubaMOE, moey = TotalHisporLatMOE, p = Cubanpct),
DominicanMoeProp = moeprop(y = TotalHisporLat, moex = TotDominMOE, moey = TotalHisporLatMOE, p = Dominicanpct),
MexicanMoeProp = moeprop(y = TotalHisporLat, moex = TotMexMOE, moey = TotalHisporLatMOE, p = Mexicanpct),
PuertoRicanMoeProp =moeprop(y = TotalHisporLat, moex = TotPRMOE, moey = TotalHisporLatMOE, p = PuertoRicanpct),
HonduranMoeProp = moeprop(y = TotalHisporLat, moex = TotHondMOE, moey = TotalHisporLatMOE, p = Honduranpct),
GuatemalanMoeProp = moeprop(y = TotalHisporLat, moex = TotGuatMOE, moey = TotalHisporLatMOE, p = Guatemalanpct),
NicaraguanMoeProp = moeprop(y = TotalHisporLat, moex = TotNicaragMOE, moey = TotalHisporLatMOE, p = Nicaraguanpct),
SalvadoranMoeProp = moeprop(y = TotalHisporLat, moex = TotSalvaMOE, moey = TotalHisporLatMOE, p = Salvadoranpct),
OtherCAMoeProp = moeprop(y = TotalHisporLat, moex = TotOtherCAMOE, moey = TotalHisporLatMOE, p = OtherCApct),
SouthAmericanMoeProp = moeprop(y = TotalHisporLat, moex = TotSAMOE, moey = TotalHisporLatMOE, p = SouthAmericanpct),
OtherMoeProp= moeprop(y = TotalHisporLat, moex = TotOtherHisporLatMOE, moey = TotalHisporLatMOE, p = Otherpct),
###adding US MOEs to the stat test###
CubanUSMOE = rep(CubanMoeProp[4], 4),
DominicanUSMOE = rep(DominicanMoeProp[4],4),
MexicanUSMOE = rep(MexicanMoeProp[4],4),
PuertoRicanUSMOE = rep(PuertoRicanMoeProp[4],4),
HonduranUSMOE = rep(HonduranMoeProp[4],4),
GuatemalanUSMOE = rep(GuatemalanMoeProp[4],4),
NicaraguanUSMOE = rep(NicaraguanMoeProp[4],4),
SalvadoranUSMOE = rep(SalvadoranMoeProp[4],4),
OtherCAUSMOE = rep(OtherCAMoeProp[4],4),
SouthAmericanUSMOE = rep(SouthAmericanMoeProp[4],4),
OtherUSMOE = rep(OtherMoeProp[4],4),
######
CubanSIG = stattest(x=CubanUS, moex = CubanUSMOE, y=Cubanpct, moey = CubanMoeProp),
DominicanSIG = stattest(x=DominicanUS, moex = DominicanUSMOE, y=Dominicanpct, moey = DominicanMoeProp),
MexicanSIG =stattest(x=MexicanUS, moex = MexicanUSMOE, y=Mexicanpct, moey = MexicanMoeProp),
PuertoRicanSIG = stattest(x=PuertoRicanUS, moex = PuertoRicanUSMOE, y=PuertoRicanpct, moey=PuertoRicanMoeProp),
HonduranSIG = stattest(x=HonduranUS, moex = HonduranUSMOE, y=Honduranpct, moey = PuertoRicanMoeProp),
GuatemalanSIG = stattest( x=GuatemalanUS, moex = GuatemalanUSMOE, y=Guatemalanpct, moey = GuatemalanMoeProp),
NicaraguanSIG = stattest(x=NicaraguanUS, moex = NicaraguanUSMOE, y = Nicaraguanpct, moey = NicaraguanMoeProp),
SalvadoranSIG = stattest(x=SalvadoranUS, moex = SalvadoranUSMOE, y = Salvadoranpct, moey = SalvadoranMoeProp),
OtherCASIG = stattest(x=OtherCAUS, moex = OtherCAUSMOE, y = OtherCApct, moey = OtherCAMoeProp),
SouthAmericanSIG= stattest(x=SouthAmericanUS, moex = SouthAmericanUSMOE, y=SouthAmericanpct, moey = SouthAmericanMoeProp),
OtherSIG = stattest(x=OtherUS, moex = OtherUSMOE, y=Otherpct, moey = OtherMoeProp))
hispanCSV <- hispan %>%
select(place, (contains("pct"))) %>%
pivot_longer(-c("place"), names_to = "Hispanic origin", values_to = "Value") %>%
pivot_wider(id_cols = c("Hispanic origin"), names_from = "place", values_from = "Value")
write.csv(hispanCSV, "outputs/spreadsheets/hispan.csv")
#storage_write_csv(hispanCSV, cont_proj, "who_lives/2024/outputs/hispan.csv")
#Households with own children under 18
load("inputs/hwcRaw.RData")
load("inputs/hwc2000Raw.RData")
hwc <- hwcRaw %>%
left_join(hwc2000Raw %>% select(-place), by = "placename") %>% #do something about the mismatch place codes
mutate(tothwc2000 = Married15to64_2000 + MaleHH15to64_2000 + FemaleHH15to64_2000 + Married65plus_2000 + MaleHH65plus_2000 + FemaleHH65plus_2000,
tothwc2000MOE = moeagg(cbind(Married15to64_2000MOE, MaleHH15to64_2000MOE, FemaleHH15to64_2000MOE, Married65plus_2000MOE, MaleHH65plus_2000MOE, FemaleHH65plus_2000MOE)),
pcthwc2000 = tothwc2000 / TotalHH_2000,
tothwc = Married + MaleHH + FemaleHH,
pcthwc = tothwc/TotalHH,
moeagg = moeagg(cbind(MarriedMOE, MaleHHMOE, FemaleHHMOE)),
moeprop = moeprop(y = TotalHH, moex = moeagg, moey = TotalHHMOE, p = pcthwc),
moeprop2000 = moeprop(y = TotalHH_2000, moex = tothwc2000MOE, moey = TotalHH_2000MOE, p = pcthwc2000),
significant = stattest(x=pcthwc2000,moex = moeprop2000, y=pcthwc,moey = moeprop))
hwcCSV <- hwc %>%
select(place, (contains("pct"))) %>%
pivot_longer(-c("place"), names_to = "hwc", values_to = "Value") %>%
mutate(name = paste( place, hwc, sep = "-"),
year = year) %>%
select(-place) %>%
pivot_wider(id_cols = c("year"), names_from = "name", values_from = "Value")
write.csv(hwcCSV, "outputs/spreadsheets/hwc.csv")
#storage_write_csv(hwcCSV, cont_proj, "who_lives/2024/outputs/hwc.csv")
#One-person households
load("inputs/singRaw.RData")
load("inputs/singRaw2000.RData") #weird formatting, fixing
singRaw2000_1 <- singRaw2000 %>% select(placename, POP, TotalHH_2000, TotalHH_2000MOE) %>% na.omit()
singRaw2000_2 <- singRaw2000 %>% select(placename, POP, SingleHH_2000, SingleHH_2000MOE) %>% na.omit()
singRaw2000 <- singRaw2000_1 %>% left_join(singRaw2000_2, by = c("placename", "POP"))
sing <- singRaw %>%
left_join(singRaw2000, by = "placename") %>%
mutate(census2000 = SingleHH_2000 / TotalHH_2000,
census2000MOE = moeprop(TotalHH_2000, SingleHH_2000MOE, TotalHH_2000MOE, census2000),
pctsing = SingleHH/TotalHH,
moeprop = moeprop(y = TotalHH, moex = SingleHHMOE, moey = TotalMOE, p = pctsing),
significant = stattest(x=census2000, moex = census2000MOE, y=pctsing,moey = moeprop))
singCSV <- sing %>%
select(place, census2000, (contains("pct"))) %>%
pivot_longer(-c("place"), names_to = "sing", values_to = "Value") %>%
mutate(name = paste( place, sing, sep = "-"),
year = 2023) %>%
select(-place) %>%
pivot_wider(id_cols = c("year"), names_from = "name", values_from = "Value")
write.csv(singCSV, "outputs/spreadsheets/sing.csv")
#storage_write_csv(singCSV, cont_proj, "who_lives/2024/outputs/sing.csv")
#Less than a high school degree, adults 25 and older
load("inputs/hsRaw.RData")
load("inputs/hsRaw2000.RData")
hs <- hsRaw %>%
left_join(hsRaw2000 %>% select(-place), by = "placename") %>%
mutate(totless2000= MaleNS_2000 + Male4_2000 + Male5to6_2000 + Male7to8_2000 + Male9_2000 + Male10_2000 + Male11_2000 + Male12ND_2000 + FemaleNS_2000 + Female4_2000 + Female5to6_2000 + Female7to8_2000 + Female9_2000 + Female10_2000 + Female11_2000 + Female12ND_2000,
totless2000MOE = moeagg(cbind(MaleNS_2000MOE, Male4_2000MOE, Male5to6_2000MOE, Male7to8_2000MOE, Male9_2000MOE, Male10_2000MOE, Male11_2000MOE, Male12ND_2000MOE, FemaleNS_2000MOE, Female4_2000MOE, Female5to6_2000MOE, Female7to8_2000MOE, Female9_2000MOE, Female10_2000MOE, Female11_2000MOE, Female12ND_2000MOE)),
census2000 = totless2000 / Total_2000,
census2000MOE = moeprop(Total_2000, totless2000MOE, Total_2000MOE, census2000),
totless = Male9 + Male9to12 + Female9 + Female9to12,
pctless = totless/Total,
moeagg = moeagg(cbind(Male9MOE, Male9to12MOE, Female9MOE, Female9to12MOE)),
moeprop = moeprop(y = Total, moex = moeagg, moey = TotalMOE, p = pctless),
significant = stattest(x=census2000, moex = census2000MOE, y=pctless,moey=moeprop))
hsCSV <- hs %>%
select(place, census2000, (contains("pct"))) %>%
pivot_longer(-c("place"), names_to = "hs", values_to = "Value") %>%
mutate(name = paste( place, hs, sep = "-"),
year = 2023) %>%
select(-place) %>%
pivot_wider(id_cols = c("year"), names_from = "name", values_from = "Value")
write.csv(hsCSV, "outputs/spreadsheets/hs.csv")
#storage_write_csv(hsCSV, cont_proj, "who_lives/2024/outputs/hs.csv")
#Bachelor's degree or higher, adults 25 and older
load("inputs/bachRaw.RData")
load("inputs/bachRaw2000.RData")
bach <- bachRaw %>%
left_join(bachRaw2000 %>% select(-place), by = "placename") %>%
mutate(totbach2000 = MaleBach_2000 + MaleMasters_2000 + MaleProf_2000 + MaleDoc_2000 + FemaleBach_2000 + FemaleMasters_2000 + FemaleProf_2000 + FemaleDoc_2000,
totbach2000MOE = moeagg(cbind(MaleBach_2000MOE, MaleMasters_2000MOE, MaleProf_2000MOE, MaleDoc_2000MOE, FemaleBach_2000MOE, FemaleMasters_2000MOE, FemaleProf_2000MOE, FemaleDoc_2000MOE)),
census2000 = totbach2000 / Total_2000,
census2000MOE = moeprop(Total_2000, totbach2000MOE, Total_2000MOE, census2000),
totbach = MaleBach + MaleGradProf + FemaleBach + FemaleGradProf,
pctbach = totbach / Total,
moeagg = moeagg(cbind(MaleBachMOE, MaleGradProfMOE, FemaleBachMOE, FemaleGradProfMOE)),
moeprop = moeprop(y = Total, moex = moeagg, moey = TotalMOE, p = pctbach),
significant = stattest(x=census2000, moex = census2000MOE, y=pctbach,moey = moeprop))
bachCSV <- bach %>%
select(place, census2000, (contains("pct"))) %>%
pivot_longer(-c("place"), names_to = "bach", values_to = "Value") %>%
mutate(name = paste( place, bach, sep = "-"),
year = 2023) %>%
select(-place) %>%
pivot_wider(id_cols = c("year"), names_from = "name", values_from = "Value")
write.csv(bachCSV, "outputs/spreadsheets/bach.csv")
#storage_write_csv(bachCSV, cont_proj, "who_lives/2024/outputs/bach.csv")
#Median household income, 201* inflation-adjusted dollars
load("inputs/medhhRaw.RData")
load("inputs/medhhRaw2000.RData")
medhh <- medhhRaw %>%
left_join(medhhRaw2000 %>% select(-place), by = "placename") %>%
mutate(census2000 = ifelse(placename == "New Orleans Metro Area", 0, MedianHHIncome_2000 *cpi99),
census2000MOE = MedianHHIncome_2000MOE * cpi99,
significant = stattest(x=census2000,moex = census2000MOE, y=MedianHHIncome,moey=MedianHHIncomeMOE))
medhhCSV <- medhh %>%
select(place, census2000, MedianHHIncome) %>%
pivot_longer(-c("place"), names_to = "medhh", values_to = "Value") %>%
mutate(name = paste( place, medhh, sep = "-"),
year = 2023) %>%
select(-place) %>%
pivot_wider(id_cols = c("year"), names_from = "name", values_from = "Value")
write.csv(medhhCSV, "outputs/spreadsheets/medhh.csv")
#storage_write_csv(medhhCSV, cont_proj, "who_lives/2024/outputs/medhh.csv")
#Internet access
load("inputs/intaRaw.RData")
inta <- intaRaw %>%
mutate(cellonlypct = CellOnly / Total,
cellmoeprop = moeprop(y = Total, moex = CellOnlyMOE, moey = TotalMOE, p = cellonlypct),
nosubpct = NoSubscript / Total,
nosubmoeprop = moeprop(y = Total, moex = NoSubscriptMOE, moey = TotalMOE, p = nosubpct),
noaccpct = NoAccess / Total,
noaccmoeprop = moeprop(y = Total, moex = NoAccessMOE, moey = TotalMOE, p = noaccpct),
broadband = Total-(CellOnly +NoSubscript+NoAccess),
broadbandMOE = moeagg(cbind(TotalMOE, CellOnlyMOE, NoSubscriptMOE, NoAccessMOE)),
broadbandpct = broadband / Total,
broadbandmoeprop = moeprop(y= Total, moex = broadbandMOE, moey = TotalMOE, p = broadbandpct),
cellonlyUS = rep(cellonlypct[4],4),
nosubUS = rep(nosubpct[4],4),
noaccUS = rep(noaccpct[4],4),
broadbandUS = rep(broadbandpct[4],4),
cellonlyUSMOE = rep(cellmoeprop[4],4),
nosubUSMOE = rep(nosubmoeprop[4],4),
noaccUSMOE = rep(noaccmoeprop[4],4),
broadbandUSMOE = rep(broadbandmoeprop[4],4),
cellonlySIG = stattest(x=cellonlyUS, moex = cellonlyUSMOE, y = cellonlypct, moey = cellmoeprop),
nosubSIG = stattest(x = nosubUS, moex = nosubUSMOE,y = nosubpct, moey = nosubmoeprop),
noaccSIG = stattest(x = noaccUS, moex = noaccUSMOE, y = noaccpct, moey = noaccmoeprop),
broadbandSIG = stattest(x = broadbandUS, moex = broadbandUSMOE, y = broadbandpct, moey = broadbandmoeprop))
intaCSV <- inta %>%
select(place, (contains("pct"))) %>%
pivot_longer(-c("place"), names_to = "inta", values_to = "Value") %>%
pivot_wider(id_cols = c("inta"), names_from = "place", values_from = "Value")
write.csv(intaCSV, "outputs/spreadsheets/inta.csv")
#storage_write_csv(intaCSV, cont_proj, "who_lives/2024/outputs/inta.csv")
#Poverty rate, population for whom poverty has been determined
load("inputs/povRaw.RData")
load("inputs/povRaw2000.RData")
pov <- povRaw %>%
left_join(povRaw2000 %>% select(-place), by = "placename") %>%
mutate(sf1999= BelowPov_2000 / Total_2000,
sf1999MOE = moeprop(Total_2000, BelowPov_2000MOE, Total_2000MOE, p = sf1999),
pctpov = BelowPov / Total,
moeprop = moeprop(y = Total, moex = BelowPovMOE, moey = TotalMOE, p = pctpov),
significant = stattest(x=sf1999, moex = sf1999MOE, y=pctpov,moey=moeprop))
povCSV <- pov %>%
select(place, sf1999, (contains("pct"))) %>%
pivot_longer(-c("place"), names_to = "pov", values_to = "Value") %>%
mutate(name = paste( place, pov, sep = "-"),
year = 2023) %>%
select(-place) %>%
pivot_wider(id_cols = c("year"), names_from = "name", values_from = "Value")
write.csv(povCSV, "outputs/spreadsheets/pov.csv")
#storage_write_csv(povCSV, cont_proj, "who_lives/2024/outputs/pov.csv")
#Children in poverty, population for whom poverty has been determined
load("inputs/childpovRaw.RData")
load("inputs/childpovRaw2000.RData")
childpov <- childpovRaw %>%
left_join(childpovRaw2000 %>% select(-place), by = "placename") %>%
mutate(BelowPov_2000 = BelowPovUnder5Years_2000 + BelowPov5Years_2000 + BelowPov6to11Years_2000 + BelowPov12to17Years_2000,
BelowPov_2000MOE = moeagg(cbind(BelowPovUnder5Years_2000MOE,BelowPov5Years_2000MOE,BelowPov6to11Years_2000MOE,BelowPov12to17Years_2000MOE)),
Total_2000 = BelowPovUnder5Years_2000 + BelowPov5Years_2000 + BelowPov6to11Years_2000 + BelowPov12to17Years_2000 + AbovePovUnder5Years_2000 + AbovePov5Years_2000 + AbovePov6to11Years_2000 + AbovePov12to17_2000,
Total_2000MOE = moeagg(cbind(BelowPovUnder5Years_2000MOE, BelowPov5Years_2000MOE,BelowPov6to11Years_2000MOE,BelowPov12to17Years_2000MOE , AbovePovUnder5Years_2000MOE , AbovePov5Years_2000MOE,AbovePov6to11Years_2000MOE ,AbovePov12to17_2000MOE)),
sf1999= BelowPov_2000 / Total_2000,
sf1999MOE = moeprop(Total_2000, BelowPov_2000MOE, Total_2000MOE, sf1999),
TotChildPov = BelowPovFemaleChild + BelowPovMaleChild + AbovePovFemaleChild + AbovePovMaleChild,
moeaggtot = moeagg(cbind(BelowPovFemaleChildMOE, BelowPovMaleChildMOE, AbovePovFemaleChildMOE, AbovePovMaleChildMOE)),
TotBelowChildPov = BelowPovFemaleChild + BelowPovMaleChild,
moeaggbelow = moeagg(cbind(BelowPovFemaleChildMOE, BelowPovMaleChildMOE)),
pctBelowChildPov = TotBelowChildPov / TotChildPov,
moeprop = moeprop(y=TotChildPov, moex = moeaggbelow, moey = moeaggtot, p=pctBelowChildPov),
significant = stattest(x=sf1999, moex = sf1999MOE, y=pctBelowChildPov,moey=moeprop))
childpovCSV <- childpov %>%
select(place, sf1999, (contains("pct"))) %>%
pivot_longer(-c("place"), names_to = "childpov", values_to = "Value") %>%
mutate(name = paste( place, childpov, sep = "-"),
year = 2023) %>%
select(-place) %>%
pivot_wider(id_cols = c("year"), names_from = "name", values_from = "Value")
write.csv(childpovCSV, "outputs/spreadsheets/childpov.csv")
#storage_write_csv(childpovCSV, cont_proj, "who_lives/2024/outputs/childpov.csv")
#Households without access to a vehicle
load("inputs/vehRaw.RData")
load("inputs/vehRaw2000.RData")
veh <- vehRaw %>%
left_join(vehRaw2000 %>% select(-place), by = "placename") %>%
mutate(noveh2000 = OwnNoVehAvail_2000 + RentNoVehAvail_2000,
noveh2000MOE = moeagg(cbind(OwnNoVehAvail_2000MOE, RentNoVehAvail_2000MOE)),
census2000 = noveh2000 / Total_2000,
census2000MOE = moeprop(Total_2000, noveh2000MOE, Total_2000MOE, census2000),
vehpct = NoVehAvail / Total,
moeprop = moeprop(y = Total, moex = NoVehAvailMOE, moey = TotalMOE, p = vehpct),
significant = stattest(x=census2000, moex= census2000MOE,y=vehpct,moey = moeprop))
vehCSV <- veh %>%
select(place, census2000, (contains("pct"))) %>%
pivot_longer(-c("place"), names_to = "veh", values_to = "Value") %>%
mutate(name = paste( place, veh, sep = "-"),
year = 2023) %>%
select(-place) %>%
pivot_wider(id_cols = c("year"), names_from = "name", values_from = "Value")
write.csv(vehCSV, "outputs/spreadsheets/veh.csv")
#storage_write_csv(vehCSV, cont_proj, "who_lives/2024/outputs/veh.csv")
#Population not U.S. citizens at birth
load("inputs/forborRaw.RData")
load("inputs/forborRaw2000.RData")
#Population not U.S. citizens at birth
load("inputs/forborRaw.RData")
load("inputs/forborRaw2000.RData")
forborRaw2000_1 <- forborRaw2000 %>% select(placename, Total_2000, Total_2000MOE) %>% mutate(Total_2000MOE = 0) %>% na.omit()
forborRaw2000_2 <- forborRaw2000 %>% select(-c(place, POP, Total_2000, Total_2000MOE))%>% na.omit()
forborRaw2000 <- forborRaw2000_1 %>% left_join(forborRaw2000_2, by = "placename")
forbor <- forborRaw %>%
left_join(forborRaw2000, by = "placename") %>%
mutate(TotalPopMOE = ifelse(TotalPopMOE < 0, 0, TotalPopMOE)) %>%
mutate(forbor2000 = TotForeign95to00_2000 + TotForegin90to94_2000 + TotForegin85to89_2000 + TotForeign80to84_2000 + TotForegin75to79_2000 + TotForeign70to74_2000 + TotForegin65to69_2000 + TotForeginPre65_2000,
forbor2000MOE = moeagg(cbind(TotForeign95to00_2000MOE, TotForegin90to94_2000MOE, TotForegin85to89_2000MOE, TotForeign80to84_2000MOE, TotForegin75to79_2000MOE, TotForeign70to74_2000MOE, TotForegin65to69_2000MOE, TotForeginPre65_2000MOE)),
census2000= forbor2000 / Total_2000,
census2000MOE = moeprop(Total_2000, forbor2000MOE, Total_2000MOE, census2000),
forborpct = (TotForeign00to09 + TotForeign90to99 + TotForeignPre90 + TotForeign10on) / TotalPop,
forbormoeagg = moeagg(cbind(TotForeign00to09MOE, TotForeign90to99MOE, TotForeignPre90MOE, TotForeign10onMOE)),
forbormoeprop = moeprop(y=TotalPop, moex = forbormoeagg, moey = TotalPopMOE, p=forborpct),
significant = stattest(x=census2000,moex = census2000MOE, y=forborpct,moey=forbormoeprop))
forborCSV <- forbor %>%
select(place, census2000, (contains("pct"))) %>%
pivot_longer(-c("place"), names_to = "forbor", values_to = "Value") %>%
mutate(name = paste( place, forbor, sep = "-"),
year = 2023) %>%
select(-place) %>%
pivot_wider(id_cols = c("year"), names_from = "name", values_from = "Value")
write.csv(forborCSV, "outputs/spreadsheets/forbor.csv")
#storage_write_csv(forborCSV, cont_proj, "who_lives/2024/outputs/forbor.csv")
#Population who moved in the past year
load("inputs/mobRaw.RData")
load("inputs/mob04Raw.RData")
mob <- mobRaw %>%
left_join(mob04Raw, by = "placename") %>%
mutate(mobabroadpct = TotMovedfromAbroad / Total,
mobStatespct = TotMovedbtwnStates / Total,
difparishpct = TotMovedinState / Total,
withinparishpct = TotMovedinCty / Total,
samehousepct = TotSameHouse / Total,
mobabroadmoeprop = moeprop(y=Total,moex = TotMovedfromAbroadMOE,moey = TotalMOE,p=mobabroadpct),
mobStatesmoeprop = moeprop(y=Total,moex = TotMovedbtwnStatesMOE,moey = TotalMOE,p=mobStatespct),
difparishmoeprop = moeprop(y=Total,moex = TotMovedinStateMOE,moey = TotalMOE,p=difparishpct),
withinparishmoeprop = moeprop(y=Total,moex = TotMovedinCtyMOE,moey = TotalMOE,p=withinparishpct),
samehousemoeprop = moeprop(y=Total, moex = TotSameHouseMOE,moey = TotalMOE,p=samehousepct))
mob <- mob %>% mutate_all(funs(replace_na(.,0))) %>%
mutate(
abroadSIG = stattest(x=sf2004mobabroadpct, moex = ifelse(is.na(sf2004mobabroadpctMOE), 0, sf2004mobabroadpctMOE), y=mobabroadpct, moey = mobabroadmoeprop),
statesSIG = stattest(x=sf2004mobStatespct, moex = ifelse(is.na(sf2004mobStatespctMOE), 0, sf2004mobStatespctMOE), y=mobStatespct,moey = mobStatesmoeprop),
difparishSIG = stattest(x=sf2004difparishpct, moex = ifelse(is.na(sf2004difparishpctMOE), 0, sf2004difparishpctMOE), y =difparishpct, moey = difparishmoeprop),
withinparishSIG = stattest(x=sf2004withinparishpct, moex = ifelse(is.na(sf2004withinparishpctMOE), 0 , sf2004withinparishpctMOE), y=withinparishpct, moey = withinparishmoeprop),
samhouseSIG = stattest (x=sf2004samehousepct, moex = ifelse(is.na(sf2004samehousepctMOE), 0, sf2004samehousepctMOE), y=samehousepct, moey = samehousemoeprop))
mobCSV <- mob %>%
select(place, (contains("sf2004") & !contains("MOE")), (contains("pct"))) %>%
pivot_longer(-c("place"), names_to = "mob", values_to = "Value") %>%
mutate(year = ifelse(grepl("2004", mob), "2004", "2023"),
header = paste(place, year, sep = "-"),
mobfinal = ifelse(grepl("abroad", mob), "abroad", mob),
mobfinal = ifelse(grepl("tates", mob), "tates", mobfinal),
mobfinal = ifelse(grepl("difparish", mob), "difparish", mobfinal),
mobfinal = ifelse(grepl("withinparish", mob), "withinparish", mobfinal),
mobfinal = ifelse(grepl("samehouse", mob), "samehouse", mobfinal),
mobfinal = factor(mobfinal, levels = c("samehouse", "withinparish", "difparish", "tates", "abroad"))) %>%
arrange(mobfinal) %>%
pivot_wider(id_cols = c("mobfinal"), names_from = "header", values_from = "Value")
#write.csv(mobCSV, "outputs/spreadsheets/mob.csv")
#storage_write_csv(mobCSV, cont_proj, "who_lives/2024/outputs/mob.csv")
## mobility sig testing for written analysis
OPmoeagg <- moeagg(cbind(mob$TotMovedinStateMOE[1],mob$TotMovedbtwnStatesMOE[1],mob$TotMovedfromAbroadMOE[1]))
OPpct <- (mob$TotMovedinState[1]+mob$TotMovedbtwnStates[1]+mob$TotMovedfromAbroad[1])/mob$Total[1]
OPmoeprop <- moeprop(y=mob$Total[1], moex = OPmoeagg, moey = mob$TotalMOE[1], p= OPpct)
OPsig <- stattest(x = (mob$sf2004mobabroadpct[1]+mob$sf2004mobStatespct[1]+mob$sf2004difparishpct[1]), y=OPpct, moey=OPmoeprop, moex = moeagg(cbind(mob$sf2004mobabroadpctMOE[1], mob$sf2004mobStatespctMOE[1],mob$sf2004difparishpctMOE[1])))
jeffmoeagg <- moeagg(cbind(mob$TotMovedinStateMOE[2],mob$TotMovedbtwnStatesMOE[2],mob$TotMovedfromAbroadMOE[2]))
jeffpct <- (mob$TotMovedinState[2]+mob$TotMovedbtwnStates[2]+mob$TotMovedfromAbroad[2])/mob$Total[2]
jeffmoeprop <- moeprop(y=mob$Total[2], moex = jeffmoeagg, moey = mob$TotalMOE[2], p= jeffpct)
jeffsig <- stattest(x = (mob$sf2004mobabroadpct[2]+mob$sf2004mobStatespct[2]+mob$sf2004difparishpct[2]), y=jeffpct, moey=jeffmoeprop, moex = moeagg(cbind(mob$sf2004mobabroadpctMOE[2], mob$sf2004mobStatespctMOE[2],mob$sf2004difparishpctMOE[2])))
#Homeownership rates
load("inputs/hoRaw.RData")
load("inputs/hoRaw2000.RData")
ho <- hoRaw %>% left_join(hoRaw2000 %>% select(-place), by = "placename") %>%
mutate(Ownerpct = Owner / Total,
Ownerpct2000 = Owner_2000 / Total_2000,
Ownermoeprop = moeprop(y=Total,moex = OwnerMOE,moey = TotalMOE,p=Ownerpct),
Ownerpct2000moe = moeprop(y = Total_2000, moex = Owner_2000MOE, moey = Total_2000MOE, p = Ownerpct2000),
significant = stattest(x=Ownerpct2000 ,y=Ownerpct,moey = Ownermoeprop, moex = Ownerpct2000moe))
hoCSV <- ho %>%
select(place, (contains("pct"))) %>% select(place, Ownerpct2000, Ownerpct) %>%
pivot_longer(-c("place"), names_to = "ho", values_to = "Value") %>%
mutate(name = paste( place, ho, sep = "-"),
year = 2023) %>%
select(-place) %>%
pivot_wider(id_cols = c("year"), names_from = "name", values_from = "Value")
write.csv(hoCSV, "outputs/spreadsheets/ho.csv")
#storage_write_csv(hoCSV, cont_proj, "who_lives/2024/outputs/ho.csv")
#Homeowners without a mortgage
load("inputs/honomoRaw.RData")
load("inputs/honomoRaw2000.RData")
honomo <- honomoRaw %>%
left_join(honomoRaw2000 %>% select(-place), by = "placename") %>%
mutate(census2000 = NoMortgage_2000 / Total_2000,
census2000MOE = moeprop(Total_2000, NoMortgage_2000MOE, Total_2000MOE, census2000),
honomopct = NoMortgage / Total,
moeprop = moeprop(y=Total,moex =NoMortgageMOE,moey = TotalMOE,p=honomopct),
significant = stattest(x=census2000, moex = census2000MOE, y=honomopct,moey=moeprop))
honomoCSV <- honomo %>%
select(place, census2000, (contains("pct"))) %>%
pivot_longer(-c("place"), names_to = "honomo", values_to = "Value") %>%
mutate(name = paste( place, honomo, sep = "-"),
year = 2023) %>%
select(-place) %>%
pivot_wider(id_cols = c("year"), names_from = "name", values_from = "Value")
write.csv(honomoCSV, "outputs/spreadsheets/honomo.csv")
#storage_write_csv(honomoCSV, cont_proj, "who_lives/2024/outputs/honomo.csv")
#Renters with severe housing cost burdens
load("inputs/rentburRaw.RData")
load("inputs/rentburRaw2004.RData")
rentbur <- rentburRaw %>%
left_join(rentburRaw2004, by = "placename") %>%
mutate(#sf2004=c(0.2432,0.2167,0,0.2161,0.2384),#0 for St. tammany missing value ### ****HT commenting out old values, adding ones I pulled from 2004 ACS
#order is "Orleans", "Jefferson", "New Orleans Metro Area", "United States"
sf2004 = ifelse(is.na(rentburpct2004),0,rentburpct2004),
sf2004MOE = ifelse(is.na(rentburpct2004MOE),0,rentburpct2004MOE),
rentburpct = `50orMore`/ (Total - NotComputed),
moeagg = moeagg(cbind(TotalMOE, NotComputedMOE)),
moeprop = moeprop(y=(Total - NotComputed),moex=`50orMoreMOE`,moey = moeagg, p = rentburpct),
significant = stattest(x=sf2004, moex = sf2004MOE, y=rentburpct, moey=moeprop))
rentburCSV <- rentbur %>%
select(place, (contains("2004")) & (!contains("MOE")), (contains("pct"))) %>%
pivot_longer(-c("place"), names_to = "rentbur", values_to = "Value") %>%
mutate(name = paste( place, rentbur, sep = "-"),
year = 2023) %>%
select(-place) %>%
pivot_wider(id_cols = c("year"), names_from = "name", values_from = "Value")
write.csv(rentburCSV, "outputs/spreadsheets/rentbur.csv")
#storage_write_csv(rentburCSV, cont_proj, "who_lives/2024/outputs/rentbur.csv")
#Homeowners with severe housing cost burdens
load("inputs/hoburRaw.RData")
load("inputs/rentburRaw2004.RData") #this one has both variables
hobur <- hoburRaw %>%
left_join(rentburRaw2004, by = "placename") %>%
mutate(sf2004 = ifelse(is.na(hoburpct2004), 0,hoburpct2004),
sf2004MOE = ifelse(is.na(hoburpct2004MOE),0,hoburpct2004MOE), #getting these numbers from line 154 of data-pull.R
hoburpct = (`50orMoreMortgage`+`50orMoreNoMortgage`)/(Total - NotComputedMortgage - NotComputedNoMortgage),
moexagg = moeagg(cbind(`50orMoreMortgageMOE`,`50orMoreNoMortgageMOE`)),
moeyagg = moeagg(cbind(TotalMOE, NotComputedMortgageMOE, NotComputedNoMortgageMOE)),
moeprop = moeprop(y=Total,moex=moexagg,moey=moeyagg,p=hoburpct),
significant = stattest(x=sf2004,moex = sf2004MOE, y=hoburpct, moey=moeprop))
hoburCSV <- hobur %>%
select(place, (contains("2004")) & (!contains("MOE")), (contains("pct"))) %>%
pivot_longer(-c("place"), names_to = "hobur", values_to = "Value") %>%
mutate(name = paste( place, hobur, sep = "-"),
year = 2023) %>%
select(-place) %>%
pivot_wider(id_cols = c("year"), names_from = "name", values_from = "Value")
write.csv(hoburCSV, "outputs/spreadsheets/hobur.csv")
#storage_write_csv(hoburCSV, cont_proj, "who_lives/2024/outputs/hobur.csv")
#Median gross rent, 201* inflation-adjusted dollars
load("inputs/medrentRaw.RData")
#sf2004 <- data.frame(sf2004 = cpi04*c(566,654,0,616,694))### ****HT commenting out old values, adding ones I pulled from 2004 ACS
#order is "Orleans", "Jefferson", "New Orleans Metro Area", "United States"
medrent <- medrentRaw %>%
left_join(rentburRaw2004, by = "placename") %>%
mutate(sf2004 = ifelse(is.na(medgrossrent2004),0,medgrossrent2004) * cpi04,
sf2004MOE = ifelse(is.na(medgrossrent2004),0,medgrossrent2004) * cpi04,
significant = stattest(x=sf2004, moex = sf2004MOE, y=Rent,moey=RentMOE))
medrentCSV <- medrent %>%
select(place, sf2004, Rent) %>%
pivot_longer(-c("place"), names_to = "medrent", values_to = "Value") %>%
mutate(name = paste( place, medrent, sep = "-"),
year = 2023) %>%
select(-place) %>%
pivot_wider(id_cols = c("year"), names_from = "name", values_from = "Value")
write.csv(medrentCSV, "outputs/spreadsheets/medrent.csv")
#storage_write_csv(medrentCSV, cont_proj, "who_lives/2024/outputs/medrent.csv")
#Year structure built, 201* housing units
load("inputs/yrbuiltRaw.RData")
yrbuilt <- yrbuiltRaw %>%
mutate(orLater1990pct = (`1990to1999` + `2000to2009` + `2010to2013` + `2014`)/Total,
mid1950to1989pct = (`1950to1959`+`1960to1969`+`1970to1979`+`1980to1989`)/Total,
orbefore1949pct = (`1940to1949`+`1939`)/Total,
orlater1990moeagg = moeagg(cbind(`1990to1999MOE`,`2000to2009MOE`, `2010to2013MOE`,`2014MOE`)),
mid1950to1989moeagg =moeagg(cbind(`1950to1959MOE`,`1960to1969MOE`,`1970to1979MOE`,`1980to1989MOE`)),
orbefore1949moeagg = moeagg(cbind(`1940to1949MOE`,`1939MOE`)),
orlater1990moeprop = moeprop(y=Total,moex = orlater1990moeagg, moey = TotalMOE,p=orLater1990pct),
mid1950to1989moeprop = moeprop(y=Total,moex = mid1950to1989moeagg, moey = TotalMOE,p=mid1950to1989pct),
orbefore1949moeprop = moeprop(y=Total,moex = orbefore1949moeagg, moey = TotalMOE,p=orbefore1949pct),
orLater1990US = rep(orLater1990pct[4],4),
mid1950to1989US = rep(mid1950to1989pct[4],4),
orbefore1949US = rep(orbefore1949pct[4],4),
orLater1990SIG = stattest(x=orLater1990US, y= orLater1990pct, moey = orlater1990moeprop),
mid1950to1989SIG = stattest(x= mid1950to1989US, y= mid1950to1989pct , moey = mid1950to1989moeprop),
orbefore1949SIG = stattest(x=orbefore1949US, y= orbefore1949pct, moey = orbefore1949moeprop))
yrbuiltCSV <- yrbuilt %>%
select(place, (contains("pct"))) %>%
pivot_longer(-c("place"), names_to = "yrbuilt", values_to = "Value") %>%
pivot_wider(id_cols = c("yrbuilt"), names_from = "place", values_from = "Value")
write.csv(yrbuiltCSV, "outputs/spreadsheets/yrbuilt.csv")
#storage_write_csv(yrbuiltCSV, cont_proj, "who_lives/2024/outputs/yrbuilt.csv")
#Means of transportation to work,
load("inputs/commuteRaw.RData")
load("inputs/commuteRaw2000.RData")
commute <- commuteRaw %>% left_join(commuteRaw2000, by = "placename") %>%
filter(placename %in% c("Orleans", "Jefferson", "New Orleans Metro Area", "United States")) %>%
mutate(census2000drive= DroveAlone_2000,
census2000carpool= Carpool_2000,
census2000publictransit= PublicTransit_2000,
census2000bike= Bike_2000,
census2000walk= Walk_2000,
census2000workhome= Workhome_2000,
census2000other= Other_2000,
Drivepct = DroveAlone / Total,
Carpoolpct= Carpool / Total,
PublicTransitpct = PublicTransit / Total,
bikepct = Bike / Total,
Walkpct = Walk / Total,
Workhomepct = Workhome / Total,
Otherpct = Other / Total,
Drivepct2000 = DroveAlone_2000 / Total_2000,
Carpoolpct2000= Carpool_2000 / Total_2000,
PublicTransitpct2000 = PublicTransit_2000 / Total_2000,
bikepct2000 = Bike_2000 / Total_2000,
Walkpct2000 = Walk_2000 / Total_2000,
Workhomepct2000 = Workhome_2000 / Total_2000,
Otherpct2000 = Other_2000 / Total_2000,
Drivemoeprop = moeprop(y=Total, moex=DroveAloneMOE, moey=TotalMOE, p=Drivepct),
carpoolmoeprop = moeprop(y=Total, moex=CarpoolMOE, moey=TotalMOE, p=Carpoolpct),
PublicTransitmoeprop = moeprop(y=Total, moex=PublicTransitMOE, moey=TotalMOE, p=PublicTransitpct),
bikemoeprop = moeprop(y=Total, moex=BikeMOE, moey=TotalMOE, p=bikepct),
Walkmoeprop = moeprop(y=Total, moex=WalkMOE, moey=TotalMOE, p=Walkpct),
workhomemoeprop = moeprop(y=Total, moex=WorkhomeMOE, moey=TotalMOE, p=Workhomepct),
othermoeprop = moeprop(y=Total, moex=OtherMOE, moey=TotalMOE, p=Otherpct),
Drivemoeprop2000 = moeprop(y=Total_2000, moex=DroveAlone_2000MOE, moey=Total_2000MOE, p=Drivepct2000),
carpoolmoeprop2000 = moeprop(y=Total_2000, moex= Carpool_2000MOE, moey=Total_2000MOE, p=Carpoolpct2000),
PublicTransitmoeprop2000 = moeprop(y=Total_2000, moex= PublicTransit_2000MOE, moey=Total_2000MOE, p=PublicTransitpct2000),
bikemoeprop2000 = moeprop(y=Total_2000, moex= Bike_2000MOE, moey=Total_2000MOE, p=bikepct2000),
Walkmoeprop2000 = moeprop(y=Total_2000, moex= Walk_2000MOE, moey=Total_2000MOE, p=Walkpct2000),
workhomemoeprop2000 = moeprop(y=Total_2000, moex=Workhome_2000MOE, moey=Total_2000MOE, p=Workhomepct2000),
othermoeprop2000 = moeprop(y=Total_2000, moex=Other_2000MOE, moey=Total_2000MOE, p=Otherpct2000),
DriveSIG = stattest(x=census2000drive, moex = Drivemoeprop2000,y=Drivepct,moey = Drivemoeprop),
carpoolSIG = stattest (x=census2000carpool, moex = carpoolmoeprop2000, y=Carpoolpct, moey = carpoolmoeprop),
PublicTransitSIG = stattest (x=census2000publictransit, moex = , y=PublicTransitpct, moey = PublicTransitmoeprop),
bikeSIG = stattest(x=census2000bike, moex = PublicTransitmoeprop2000, y = bikepct, moey = bikemoeprop),
walkSIG = stattest (x=census2000walk, moex = Walkmoeprop2000, y = Walkpct, moey = Walkmoeprop),
workhomeSIG = stattest ( x=census2000workhome, moex = workhomemoeprop2000, y=Workhomepct, moey = workhomemoeprop),
otherSIG = stattest (x=census2000other, moex = othermoeprop2000, y= Otherpct, moey = othermoeprop))
commuteCSV <- commute %>%
select(placename, (contains("pct"))) %>%
pivot_longer(-c("placename"), names_to = "commute", values_to = "Value") %>%
mutate(year = ifelse(grepl("2000", commute), 2000, 2023),
header = paste(placename, year, sep = "-"),
commutefinal = ifelse(grepl("ive", commute), "DroveAlone", commute),
commutefinal = ifelse(grepl("arpool", commute), "Carpool", commutefinal),
commutefinal = ifelse(grepl("ublic", commute), "PublicTransit", commutefinal),
commutefinal = ifelse(grepl("Motorcycle", commute), "Motorcycle", commutefinal),
commutefinal = ifelse(grepl("ike", commute), "Bike", commutefinal),
commutefinal = ifelse(grepl("alk", commute), "Walk", commutefinal),
commutefinal = ifelse(grepl("home", commute), "Workhome", commutefinal),
commutefinal = ifelse(grepl("ther", commute), "Other", commutefinal)) %>%
pivot_wider(id_cols = c("commutefinal"), names_from = "header", values_from = "Value") %>%
select(commutefinal, `Orleans-2000`, `Orleans-2023`, `Jefferson-2000`, `Jefferson-2023`, `New Orleans Metro Area-2000`, `New Orleans Metro Area-2023`, `United States-2000`, `United States-2023` )
write.csv(commuteCSV, "outputs/spreadsheets/commute.csv")
#storage_write_csv(commuteCSV, cont_proj, "who_lives/2024/outputs/commute.csv")
############################################
# # PEP # #
############################################
##############################################
# To analyze, filter to:
# whatever combination of demographics you need: race, age, sex, hisp
# whatever geography you need: place
# whatever year of estimate that you need: date
orderDemo <- c("Orleans Parish", "Jefferson Parish", "Plaquemines Parish", "St. Bernard Parish", "St. Charles Parish",
"St. James Parish", "St. John the Baptist Parish", "Metro", "United States")
#allparishesRaw <- load("inputs/allparishesRaw.RData")
#Table 1
load("inputs/totpoprace2000.Rdata")
totpopraceRaw_tot <- totpopraceRaw %>% select(placename, Total_2000) %>% na.omit()
totpopraceRaw_blkwt <- totpopraceRaw %>% select(placename, TotBlackAlone_2000, TotWhiteAlone_2000, TotAsianAlone_2000) %>% na.omit()
totpopraceRaw_hisp <- totpopraceRaw %>% select(placename, TotHispanicAny_2000) %>% na.omit()
totpoprace_2000 <- totpopraceRaw_tot %>%
left_join(totpopraceRaw_blkwt) %>%
left_join(totpopraceRaw_hisp) %>%
select(placename, Total_2000, TotBlackAlone_2000, TotWhiteAlone_2000, TotHispanicAny_2000, TotAsianAlone_2000) %>%
pivot_longer(cols = -placename, names_to = c("var2000", "year"), names_sep = "_", values_to = "val2000") %>%
mutate(raceSimple = case_when(grepl("Total", var2000) ~ "Total",
grepl("Black", var2000) ~ "Black",
grepl("White", var2000) ~ "White",
grepl("Hisp", var2000) ~ "Hispanic",
grepl("Asian", var2000) ~ "Asian")) %>%
select(-c(year, var2000))
AAWhiteHispan2000 <- totpoprace_2000 %>% filter(placename == "Orleans" & raceSimple != "Total") %>% rename(est2000 = val2000)
load("inputs/allparishesRaw2023.RData")
AAWhiteHispan <- allparishesRaw2023 %>%
filter(place == "Orleans Parish") %>%
filter(date == "7/1/2023 population estimate") %>%
filter(age == "Total" & sex == "Total" & (raceSimple == "Black"|raceSimple == "White" |hisp == "Hispanic" | raceSimple == "Asian" )) %>%
mutate(race.fac = factor(.$raceSimple,levels = c("Black", "White","Hispanic", "Asian")))%>% arrange(race.fac) %>%
left_join(AAWhiteHispan2000, by = "raceSimple") %>%
select(raceSimple, race.fac, population, est2000)
write.csv(AAWhiteHispan, "outputs/spreadsheets/AAWhiteHispan.csv")
#storage_write_csv(AAWhiteHispan, cont_proj, "who_lives/2024/outputs/AAWhiteHispan.csv")
#Tables 2
#Remove Louisiana and Us to be able to combine 8 parish estimates for each race/ethnicity to create Metro
#HT : I just switched these around so it would make Metro last... The dataframe is not ordered by the levels of placename factor
#that's the order that the 2000 numbers are going in.
ParishDemo2<- allparishesRaw2023 %>%
filter(PlaceName %in% c("Orleans", "Jefferson", "Plaquemines", "St. Bernard", "St. Charles",
"St. James", "St. John the Baptist")) %>%
filter(date == "7/1/2023 population estimate") %>%
filter(age == "Total" & sex == "Total") %>%
group_by(raceSimple)%>% unique() %>%
summarise(population=sum(population)) %>% mutate(PlaceName = "Metro") %>% select(PlaceName, population, raceSimple)
ParishDemo3 <- allparishesRaw2023 %>% filter(PlaceName == "United States") %>%
filter(date == "7/1/2023 population estimate") %>%
filter(age == "Total" & sex == "Total") %>%
select(PlaceName, population, raceSimple)
ParishDemo1<- allparishesRaw2023 %>%
filter(PlaceName %in% c("Orleans", "Jefferson", "Plaquemines", "St. Bernard", "St. Charles",
"St. James", "St. John the Baptist")) %>% arrange((PlaceName)) %>%
filter(date == "7/1/2023 population estimate") %>%
filter(age == "Total" & sex == "Total") %>%
select(PlaceName, population, raceSimple) %>%
bind_rows(.,ParishDemo2, ParishDemo3)
#reshape data from long to wide for easy analysis
load("inputs/totpoprace2000.Rdata")
totpopraceRaw_tot <- totpopraceRaw %>% select(placename, Total_2000) %>% na.omit()
totpopraceRaw_blkwt <- totpopraceRaw %>% select(placename, TotBlackAlone_2000, TotWhiteAlone_2000, TotAsianAlone_2000) %>% na.omit()
totpopraceRaw_hisp <- totpopraceRaw %>% select(placename, TotHispanicAny_2000) %>% na.omit()
totpoprace_2000 <- totpopraceRaw_tot %>%
left_join(totpopraceRaw_blkwt) %>%
left_join(totpopraceRaw_hisp) %>%
select(placename, Total_2000, TotBlackAlone_2000, TotWhiteAlone_2000, TotHispanicAny_2000, TotAsianAlone_2000) %>%
pivot_longer(cols = -placename, names_to = c("var2000", "year"), names_sep = "_", values_to = "val2000") %>%
mutate(raceSimple = case_when(grepl("Total", var2000) ~ "Total",
grepl("Black", var2000) ~ "Black",
grepl("White", var2000) ~ "White",
grepl("Hisp", var2000) ~ "Hispanic",
grepl("Asian", var2000) ~ "Asian")) %>%
select(-c(year, var2000))
ParishDemo <- ParishDemo1 %>%
mutate(placename2 = case_when(PlaceName == "Metro" ~ "New Orleans Metro Area",
T ~ PlaceName)) %>%
left_join(totpoprace_2000, by = c("placename2" = "placename", "raceSimple")) %>%
pivot_wider(names_from = raceSimple, values_from = c(population, val2000)) %>%
mutate(PlaceName = factor(PlaceName, levels = c("Orleans", "Jefferson", "Plaquemines",
"St. Bernard","St. Charles", "St. James",
"St. John the Baptist", "New Orleans Metro Area", "United States"))) %>%
mutate(pctwhite = as.numeric(population_White)/ as.numeric(population_Total),
pctblack = as.numeric(population_Black) / as.numeric(population_Total),
pctasian = as.numeric(population_Asian) / as.numeric(population_Total),
pcthisp = as.numeric(population_Hispanic) / as.numeric(population_Total),
white2000 = val2000_White / val2000_Total,
black2000 = val2000_Black / val2000_Total,
asian2000 = val2000_Asian / val2000_Total,
hispanic2000 = val2000_Hispanic / val2000_Total
# white2000=c(.266,.645,.688,.844,.705,.497,.51 ,.853,.547,.691),
# black2000=c(.667,.227,.233,.076,.251,.492,.446,.098,.373,.121),
# asian2000=c(.023,.031,.026,.013,.006,0 ,.005,.008,.021,.037),
# hispanic2000=c(.031,.071,.016,.051,.028,.006,.029,.025,.044,.125)
) #%>%
#.[-2,]
orleansdemo_csv <- ParishDemo %>% filter(PlaceName == "Orleans") %>% select(PlaceName, population_Total:population_Hispanic) %>%
pivot_longer(cols = -PlaceName, names_to = "race", values_to = "est2023")
write.csv(orleansdemo_csv, "outputs/spreadsheets/orleansdemo.csv")
#storage_write_csv(orleansdemo_csv, cont_proj, "who_lives/2024/outputs/orleansdemo.csv")
parishdemo_csv <- ParishDemo %>% select(PlaceName, pctwhite:hispanic2000) %>%
pivot_longer(cols = -PlaceName, names_to = c("race", "year"), names_sep = "2", values_to = "val") %>%
mutate(year = case_when(year == "000" ~ "2000",
is.na(year) ~ "2023",
T ~ year),
race = factor(case_when(grepl("white", race) ~ "White, non-Hispanic",
grepl("black", race) ~ "Black",
grepl("hisp", race) ~ "Hispanic, any race",
grepl("asian", race) ~ "Asian"), levels = c("White, non-Hispanic", "Black", "Hispanic, any race", "Asian"))) %>%
arrange(PlaceName, year) %>%
pivot_wider(names_from = c(PlaceName, year), values_from = val) %>%
arrange(race)
write.csv(parishdemo_csv, "outputs/spreadsheets/ParishDemo.csv")
#storage_write_csv(parishdemo_csv, cont_proj, "who_lives/2024/outputs/ParishDemo.csv")
#Table 3 Hispanic population change by population
HispanicPop <- allparishesRaw2023 %>%
filter(PlaceName %in% c("Orleans", "Jefferson", "Plaquemines", "St. Bernard", "St. Charles",
"St. James", "St. John the Baptist")) %>%
filter(age == "Total" & sex == "Total") %>%
filter(raceSimple=="Hispanic")%>%
select(PlaceName, population) %>%
left_join(totpopraceRaw_hisp, by = c("PlaceName" = "placename")) %>%
rename(est2000 = TotHispanicAny_2000)
#Table 5 Hispanic population for parishes in metro by year
HispanicPopyears <- allparishesRaw2023 %>%
filter(age == "Total" & sex == "Total") %>%
filter(raceSimple=="Hispanic") %>%
filter(PlaceName %in% c("Orleans", "Jefferson", "Plaquemines", "St. Bernard", "St. Charles",
"St. James", "St. John the Baptist"))
HispanicPopyears <- HispanicPopyears %>%
select(PlaceName, date, population) %>%
pivot_wider(id_cols = date, names_from = PlaceName, values_from = population )
write.csv(HispanicPopyears, "outputs/spreadsheets/HispanicPopyears.csv")
#storage_write_csv(HispanicPopyears, cont_proj, "who_lives/2024/outputs/HispanicPopyears.csv")
load("inputs/hisppopestRaw.RData")
HISPpopM <- hisppopestRaw %>%
mutate(year = as.numeric(year)) %>%
filter(place %in% c("Orleans", "Jefferson", "Plaquemines", "St. Bernard", "St. Charles",
"St. James", "St. John the Baptist")) %>%
add_row(year = 2000, place= "Orleans", POP= totpopraceRaw_hisp$TotHispanicAny_2000[totpopraceRaw_hisp$placename == "Orleans"]) %>%
add_row(year = 2000, place= "Jefferson", POP= totpopraceRaw_hisp$TotHispanicAny_2000[totpopraceRaw_hisp$placename == "Jefferson"]) %>%
add_row(year = 2000, place= "Plaquemines", POP= totpopraceRaw_hisp$TotHispanicAny_2000[totpopraceRaw_hisp$placename == "Plaquemines"]) %>%
add_row(year = 2000, place= "St. Bernard", POP= totpopraceRaw_hisp$TotHispanicAny_2000[totpopraceRaw_hisp$placename == "St. Bernard"]) %>%
add_row(year = 2000, place= "St. Charles", POP= totpopraceRaw_hisp$TotHispanicAny_2000[totpopraceRaw_hisp$placename == "St. Charles"]) %>%
add_row(year = 2000, place= "St. James", POP= totpopraceRaw_hisp$TotHispanicAny_2000[totpopraceRaw_hisp$placename == "St. James"]) %>%
add_row(year = 2000, place= "St. John the Baptist", POP= totpopraceRaw_hisp$TotHispanicAny_2000[totpopraceRaw_hisp$placename == "St. John the Baptist"]) %>%
select(place, year, POP) %>% unique()
HISPpopM_CSV <- HISPpopM %>% pivot_wider(names_from = "place", values_from = POP) %>%
select(year, Orleans, Jefferson, Plaquemines, `St. Bernard`, `St. Charles`, `St. James`, `St. John the Baptist`) %>%
arrange(year)
write.csv(HISPpopM_CSV, "outputs/spreadsheets/HISPpopM.csv")
#storage_write_csv(HISPpopM_CSV, cont_proj, "who_lives/2024/outputs/HISPpopM_CSV.csv")
load("inputs/medageRaw.RData")
#Table 5 Population by age group
load("inputs/totpopage2000.RData")
age2000 <- totpopage2000Raw %>%
rename_with(~str_replace(., "_2000", "")) %>%
select(-contains("MOE")) %>%
mutate("Under 5 years" = MaleUnder1yr + Male1yr + Male2yr + Male3yr + Male4yr + FemaleUnder1 + Female1yr + Female2yr + Female3yr + Female4yr,
"5 to 9" = Male5yr + Male6yr + Male7yr + Male8yr +Male9yr + Female5yr + Female6yr + Female7yr + Female8yr + Female9yr ,
"10 to 14" = Male10yr + Male11yr + Male12yr + Male13yr + Male14yr + Female10yr + Female11yr + Female12yr + Female13yr + Female14yr ,
"15 to 19" = Male15yr + Male16yr + Male17yr + Male18yr + Male19yr + Female15yr + Female16yr + Female17yr + Female18yr + Female19yr ,
"20 to 24" = Male20yr + Male21yr + Male22to24 + Female20yr + Female21yr + Female22to24 ,
"25 to 29" = Male25to29 + Female25to29,
"30 to 34" = Male30to34 + Female30to34,
"35 to 39" = Male35to39 + Female35to39,
"40 to 44" = Male40to44 + Female40to44,
"45 to 49" = Male45to49 + Female45to49,
"50 to 54" = Male50to54 + Female50to54,
"55 to 59" = Male55to59 + Female55to59,
"60 to 64" = Male60to61 + Male62to64 + Female60to61 + Female62to64,
"65 to 69" = Male65to66 + Male67to69 + Female65to66 + Female67to69,
"70 to 74" = Male70to74 + Female70to74,
"75 to 79" = Male75to79 + Female75to79,
"80 to 84" = Male80to84 + Female80to84,
"85 plus" = MaleOver85 + FemaleOver85,
PlaceName = case_when(place == "051" ~ "Jefferson",
place == "071" ~ "Orleans",
place == "075" ~ "Plaquemines",
place == "087" ~ "St. Bernard",
place == "089" ~ "St. Charles",
place == "093" ~ "St. James",
place == "095" ~ "St. John the Baptist",
place == "MSA_2023" ~ "New Orleans Metro Area",
place == "1" ~ "United States")) %>%
select(PlaceName, `Under 5 years`:`85 plus`) %>% pivot_longer(cols = -PlaceName, names_to = "age", values_to = "est2000")
orderAge <- c(rep("Jefferson",18),rep("Orleans", 18),rep("Plaquemines",18),
rep("St. Bernard", 18),rep("St. Charles", 18),rep("St. James", 18),
rep("St. John The Baptist", 18))
Agepop <- allparishesRaw2023 %>%
filter(age== "Under 5 years" | age== "5 to 9"| age== "10 to 14" | age== "15 to 19"|
age=="20 to 24"| age== "25 to 29"| age== "30 to 34"| age== "35 to 39"| age== "40 to 44"
| age== "45 to 49" | age=="50 to 54"| age== "55 to 59"| age== "60 to 64"| age== "65 to 69"|
age== "70 to 74"| age== "75 to 79"| age== "80 to 84"| age== "85 plus")%>%
filter(raceSimple=="Total")%>%
filter(sex=="Total")%>%
filter(date == "7/1/2023 population estimate") %>%
filter(PlaceName %in% c("Orleans", "Jefferson", "Plaquemines", "St. Bernard", "St. Charles",
"St. James", "St. John the Baptist")) %>%
arrange(factor(PlaceName, levels = c("Jefferson","Orleans","Plaquemines",
"St. Bernard","St. Charles","St. James",
"St. John the Baptist"))) %>%
select(age, PlaceName, population) %>%
left_join(age2000, by = c("age", "PlaceName"))
Agepop_csv <- Agepop %>%
mutate(est22 = population,
est00 = est2000) %>%
pivot_wider(id_cols = age, names_from = PlaceName, values_from = c(est00, est22)) %>% select(est00_Orleans, est22_Orleans,
est00_Jefferson, est22_Jefferson,
est00_Plaquemines, est22_Plaquemines,
`est00_St. Bernard`, `est22_St. Bernard`,
`est00_St. Charles`, `est22_St. Charles`,
`est00_St. James`, `est22_St. James`,
`est00_St. John the Baptist`, `est22_St. John the Baptist`)
write.csv(Agepop_csv, "outputs/spreadsheets/Agepop.csv")
#storage_write_csv(Agepop_csv, cont_proj, "who_lives/2024/outputs/Agepop.csv")
#Table 6 Under 18 population
#Different than estimates from google sheets but aligns with American fact finder
popunder18_2000 <- totpopage2000Raw %>%
rename_with(~str_replace(., "_2000", "")) %>%
select(-contains("MOE")) %>%
mutate(est2000 = MaleUnder1yr + Male1yr + Male2yr + Male3yr + Male4yr + FemaleUnder1 + Female1yr + Female2yr + Female3yr + Female4yr +
Male5yr + Male6yr + Male7yr + Male8yr +Male9yr + Female5yr + Female6yr + Female7yr + Female8yr + Female9yr +
Male10yr + Male11yr + Male12yr + Male13yr + Male14yr + Female10yr + Female11yr + Female12yr + Female13yr + Female14yr +
Male15yr + Male16yr + Male17yr + Female15yr + Female16yr + Female17yr,
PlaceName = case_when(place == "051" ~ "Jefferson",
place == "071" ~ "Orleans",
place == "075" ~ "Plaquemines",
place == "087" ~ "St. Bernard",
place == "089" ~ "St. Charles",
place == "093" ~ "St. James",
place == "095" ~ "St. John the Baptist",
place == "MSA_2023" ~ "Metro",
place == "1" ~ "United States")) %>%
select(PlaceName, est2000)
under18pars<-allparishesRaw2023 %>%
filter(age=="18 years and over" | age=="Total")%>%
filter(raceSimple=="Total")%>%
filter(sex=="Total")%>%
filter(date == "7/1/2023 population estimate") %>%
filter(PlaceName %in% c("Orleans", "Jefferson", "Plaquemines", "St. Bernard", "St. Charles",
"St. James", "St. John the Baptist")) %>%
select(age, PlaceName, population)
#Creating metro
under18metro<- under18pars %>%
group_by(age)%>%
summarise(population=sum(population))
under18metro$PlaceName <- "Metro"
#stack back with other under 18
popunder18 <-bind_rows(under18pars,under18metro) %>%
spread(., age, population) %>%
mutate(under18 = Total-`18 years and over`) %>%
filter(PlaceName == "Orleans" | PlaceName =="Jefferson" | PlaceName == "Metro")%>%
select(PlaceName, under18) %>%
left_join(popunder18_2000, by = c("PlaceName"))
popunder18CSV <- popunder18 %>%
select(PlaceName, est2000, under18) %>%
pivot_longer(-c("PlaceName"), names_to = "under18", values_to = "Value") %>%
mutate(name = paste(PlaceName, under18, sep = "-"),
year = 2023) %>%
select(-PlaceName) %>%
pivot_wider(id_cols = c("year"), names_from = "name", values_from = "Value") %>%
select(`Orleans-est2000`, `Orleans-under18`, `Jefferson-est2000`, `Jefferson-under18`, `Metro-est2000`, `Metro-under18`)
write.csv(popunder18CSV, "outputs/spreadsheets/under18.csv")
#storage_write_csv(popunder18CSV, cont_proj, "who_lives/2024/outputs/under18.csv")
###########################
# Jenna's analysis expanded
###########################
childPovProspInd <- read_csv("inputs/indicator expansion drafts/ProspInd_tables_WhoLives2022/childPov.csv")
homeownershipProspInd <- read_csv("inputs/indicator expansion drafts/ProspInd_tables_WhoLives2022/homeownership.csv")
educationalAttainmentProspInd <- read_csv("inputs/indicator expansion drafts/ProspInd_tables_WhoLives2022/educationalAttainment.csv")
medHHincProspInd <- read_csv("inputs/indicator expansion drafts/ProspInd_tables_WhoLives2022/medHHinc.csv")
### Median household income ###
###
load("inputs/medhhRaw_exp.RData")
medhh_exp <- medhhRaw_exp %>%
mutate(placenames = NA,
placenames = ifelse(place == "051", "Jefferson", placenames),
placenames = ifelse(place == "071", "Orleans", placenames),
placenames = ifelse(place == "35380","Metro",placenames),
placenames = ifelse(place == "1", "U.S.", placenames)) %>%
mutate(place.fac = factor(.$placenames,levels = c("Orleans", "Jefferson","Metro", "U.S."))) %>%
select(-place, -placenames, -placename, -contains("MOE")) %>%
pivot_longer(-place.fac,names_to = "var",values_to = "val")
### Across geos median hh income bar chart ###
medhh.totals <- medhh_exp %>%
filter(var == "MedianHHIncome")%>%
mutate(var.fac = factor(.$var, levels = c("Black","White,\nnon-Hispanic","Asian","Hispanic,\nany race")))
medhh.race <- medhh_exp %>%
filter(var != "MedianHHIncome") %>%
mutate(var = ifelse(grepl("asian",var), "Asian",var),
var = ifelse(grepl("blk",var), "Black", var),
var = ifelse(grepl("hisp",var), "Hispanic,\nany race", var),
var = ifelse(grepl("wht",var), "White,\nnon-Hispanic", var)) %>%
mutate(var.fac = factor(.$var, levels = c("Black","White,\nnon-Hispanic","Asian","Hispanic,\nany race")))