Demographics.RmdThe following code chucks are utilized for pulling demographic information from the ACS database built into the tidycensus package. The following code is there to test the reliability of the data to provide neat data sources for further calculations and visualizations.
acs5 <- load_variables(2020, "acs5")
view(acs5)
Total <- c(Total = "B18101_001")
DisabledMale <- c("Under 5 years" = "B18101_004", "5 to 17 years" = "B18101_007", "18 to 34 years" = "B18101_010", "35 to 64 years" = "B18101_013", "65 to 74 years" = "B18101_016", "75 years and over" = "B18101_019")
DisabledFemale <- c("Under 5 years" = "B18101_023", "5 to 17 years" = "B18101_026", "18 to 34 years" = "B18101_029", "35 to 64 years" = "B18101_032", "65 to 74 years" = "B18101_035", "75 years and over" = "B18101_035")
DisabledPop <- get_acs(geography = "county",
variables = c(Total, DisabledMale, DisabledFemale),
year = 2020,
state = "IA")
DisabledPop <- DisabledPop %>%
group_by(NAME, variable) %>%
summarise(estimate = sum(estimate),
moe = sum(moe)) %>%
pivot_wider(names_from = variable, values_from = c(estimate, moe))
DisabledPop$NAME <- gsub(" County, Iowa", "", DisabledPop$NAME)
names(DisabledPop)[names(DisabledPop) == "NAME"] <- "County"
CV_Under5 <- ((DisabledPop$`moe_Under 5 years` / 1.645) / DisabledPop$`estimate_Under 5 years`) * 100
CV_5to17 <- ((DisabledPop$`moe_5 to 17 years` / 1.645) / DisabledPop$`estimate_5 to 17 years`) * 100
CV_18to34 <- ((DisabledPop$`moe_18 to 34 years` / 1.645) / DisabledPop$`estimate_18 to 34 years`) * 100
CV_35to64 <- ((DisabledPop$`moe_35 to 64 years` / 1.645) / DisabledPop$`estimate_35 to 64 years`) * 100
CV_65to74 <- ((DisabledPop$`moe_65 to 74 years` / 1.645) / DisabledPop$`estimate_65 to 74 years`) * 100
CV_75andOver <- ((DisabledPop$`moe_75 years and over` / 1.645) / DisabledPop$`estimate_75 years and over`) * 100
DisabledPop$CV_Under5 <- ifelse(CV_Under5 > 30, "Low reliability",
ifelse(CV_Under5 <= 15, "High reliability",
"Median reliability"))
DisabledPop$CV_5to17 <- ifelse(CV_5to17 > 30, "Low reliability",
ifelse(CV_5to17 <= 15, "High reliability",
"Median reliability"))
DisabledPop$CV_18to34 <- ifelse(CV_18to34 > 30, "Low reliability",
ifelse(CV_18to34 <= 15, "High reliability",
"Median reliability"))
DisabledPop$CV_35to64 <- ifelse(CV_35to64 > 30, "Low reliability",
ifelse(CV_35to64 <= 15, "High reliability",
"Median reliability"))
DisabledPop$CV_65to74 <- ifelse(CV_65to74 > 30, "Low reliability",
ifelse(CV_65to74 <= 15, "High reliability",
"Median reliability"))
DisabledPop$CV_75andOver <- ifelse(CV_75andOver > 30, "Low reliability",
ifelse(CV_75andOver <= 15, "High reliability",
"Median reliability"))
DisabledHearing <- c("Under 5 years" = c("B18102_004", "B18102_023"), "5 to 17 years" = c("B18102_007", "B18102_026"), "18 to 34 years" = c("B18102_010", "B18102_029"), "35 to 64 years" = c("B18102_013", "B18102_032"), "65 to 74 years" = c("B18102_016", "B18102_035"), "75 and over" = c("B18102_019", "B18102_038"))
DisabledVision <- c("Under 5 years" = c("B18103_004", "B18103_023"), "5 to 17 years" = c("B18103_007", "B18103_026"), "18 to 34 years" = c("B18103_010", "B18103_029"), "35 to 64 years" = c("B18103_013", "B18103_032"), "65 to 74 years" = c("B18103_016", "B18103_035"), "75 and over" = c("B18103_019", "B18103_038"))
DisabledCognitive <- c("5 to 17 years" = c("B18104_004", "B18104_020"), "18 to 34 years" = c("B18104_007", "B18104_023"), "35 to 64 years" = c("B18104_010", "B18104_026"), "65 to 74 years" = c("B18104_013", "B18104_029"), "75 and over" = c("B18104_016", "B18104_032"))
DisabledAmbulatory <- c("5 to 17 years" = c("B18105_004", "B18105_020"), "18 to 34 years" = c("B18105_007", "B18105_023"), "35 to 64 years" = c("B18105_010", "B18105_026"), "65 to 74 years" = c("B18105_013", "B18105_029"), "75 and over" = c("B18105_016", "B18105_032"))
DisabledSelfCare <- c("5 to 17 years" = c("B18106_004", "B18106_020"), "18 to 34 years" = c("B18106_007", "B18106_023"), "35 to 64 years" = c("B18106_010", "B18106_026"), "65 to 74 years" = c("B18106_013", "B18106_029"), "75 and over" = c("B18106_016", "B18106_032"))
DisabledIndependentLiving <- c("18 to 34 years" = c("B18107_007", "B18107_023"), "35 to 64 years" = c("B18107_010", "B18107_026"), "65 to 74 years" = c("B18107_010", "B18107_023"), "75 and over" = c("B18107_013", "B18107_026"))
DisabledType <- get_acs(geography = "county",
variables = c(Hearing = DisabledHearing, Vision = DisabledVision, Cognitive = DisabledCognitive, Ambulatory = DisabledAmbulatory, "Self-care" = DisabledSelfCare, "Independent living" = DisabledIndependentLiving),
year = 2020,
state = "IA")
DisabledType$variable <- gsub("(years|over).*", "\\1", DisabledType$variable)
DisabledType <- DisabledType %>%
group_by(NAME, variable) %>%
summarise(estimate = sum(estimate),
moe = sum(moe))
DisabledType_estimate <- aggregate(estimate ~ NAME, data = DisabledType, sum) %>%
slice(rep(1:n(), each = 6))
DisabledType_moe <- aggregate(moe ~ NAME, data = DisabledType, sum) %>%
slice(rep(1:n(), each = 6))
DisabledType_pivot <- DisabledType %>% separate(variable, into = c("Type", "Age"), sep = "\\.") %>%
pivot_wider(names_from = Age, values_from = c(estimate, moe)) %>%
ungroup() %>%
mutate(estimate_total = DisabledType_estimate$estimate,
moe_total = DisabledType_moe$moe) %>%
mutate(Percentage = rowSums(DisabledType_pivot[3:8], na.rm = TRUE) / DisabledType_estimate$estimate * 100)
names(DisabledType_pivot) <- gsub("estimate_", "", names(DisabledType_pivot))
DisabledType_pivot$NAME <- gsub(" County, Iowa", "", DisabledType_pivot$NAME)
names(DisabledType_pivot)[names(DisabledType_pivot) == "NAME"] <- "County"
Aged18to34 <- c(Total = c("B18101_009", "B18101_028"), Disabled = c("B18101_010", "B18101_029"))
Aged35to64 <- c(Total = c("B18101_012", "B18101_031"), Disabled = c("B18101_013", "B18101_032"))
population <- get_acs(geography = "county",
variables = c("18 to 34 years" = Aged18to34, "35 to 64 years" = Aged35to64),
year = 2020,
state = "IA")
population$variable <- str_sub(population$variable, end = -2)
population <- population %>%
group_by(NAME, variable) %>%
summarise(estimate = sum(estimate),
moe = sum(moe)) %>%
pivot_wider(names_from = variable,
values_from = c(estimate, moe))
names(population) <- gsub("estimate_", "", names(population))
population$NAME <- gsub(" County, Iowa", "", population$NAME)
names(population)[names(population) == "NAME"] <- "County"
CV_18_to_34_Disabled <- ((population$`moe_18 to 34 years.Disabled` / 1.645) / population$`18 to 34 years.Disabled`) * 100
CV_35_to_64_Disabled <- ((population$`moe_35 to 64 years.Disabled` / 1.645) / population$`35 to 64 years.Disabled`) * 100
population$CV_18_to_34_Disabled <- ifelse(CV_18_to_34_Disabled > 30, "Low reliability",
ifelse(CV_18_to_34_Disabled <= 15, "High reliability",
"Median reliability"))
population$CV_35_to_64_Disabled <- ifelse(CV_35_to_64_Disabled > 30, "Low reliability",
ifelse(CV_35_to_64_Disabled <= 15, "High reliability",
"Median reliability"))
DisabledHearing <- c("18 to 34 years" = c("B18102_010", "B18102_029"), "35 to 64 years" = c("B18102_013", "B18102_032"))
DisabledVision <- c("18 to 34 years" = c("B18103_010", "B18103_029"), "35 to 64 years" = c("B18103_007", "B18103_032"))
DisabledCognitive <- c("18 to 34 years" = c("B18104_007", "B18104_023"), "35 to 64 years" = c("B18104_010", "B18104_026"))
DisabledAmbulatory <- c("18 to 34 years" = c("B18105_007", "B18105_023"), "35 to 64 years" = c("B18105_010", "B18105_026"))
DisabledSelfCare <- c("18 to 34 years" = c("B18106_007", "B18106_023"), "35 to 64 years" = c("B18106_010", "B18106_026"))
DisabledIndependentLiving <- c("18 to 34 years" = c("B18107_007", "B18107_023"), "35 to 64 years" = c("B18107_010", "B18107_026"))
DisabledType <- get_acs(geography = "county",
variables = c(Hearing = DisabledHearing, Vision = DisabledVision, Cognitive = DisabledCognitive, Ambulatory = DisabledAmbulatory, "Self-care" = DisabledSelfCare, "Independent living" = DisabledIndependentLiving),
year = 2020,
state = "IA")
DisabledType$variable <- gsub("(years).*", "\\1", DisabledType$variable)
DisabledType <- DisabledType %>%
group_by(NAME, variable) %>%
summarise(estimate = sum(estimate),
moe = sum(moe))
DisabledType_pivot <- DisabledType %>% separate(variable, into = c("Type", "Age"), sep = "\\.") %>%
pivot_wider(names_from = Type, values_from = c(estimate, moe))
DisabledType_sum <- aggregate(estimate ~ NAME, data = DisabledType, sum) %>%
slice(rep(1:n(), each = 2))
DisabledType_pivot <- DisabledType_pivot %>%
ungroup() %>%
mutate(Percentage = rowSums(DisabledType_pivot[3:8] / DisabledType_sum$estimate) * 100)
names(DisabledType_pivot) <- gsub("estimate_", "", names(DisabledType_pivot))
DisabledType_pivot$NAME <- gsub(" County, Iowa", "", DisabledType_pivot$NAME)
names(DisabledType_pivot)[names(DisabledType_pivot) == "NAME"] <- "County"
CV_Cognitive <- ((DisabledType_pivot$moe_Cognitive / 1.645) / DisabledType_pivot$Cognitive) * 100
CV_Hearing <- ((DisabledType_pivot$moe_Hearing / 1.645) / DisabledType_pivot$Hearing) * 100
CV_IndependentLiving <- ((DisabledType_pivot$`moe_Independent living` / 1.645) / DisabledType_pivot$`Independent living`) * 100
CV_Selfcare <- ((DisabledType_pivot$`moe_Self-care` / 1.645) / DisabledType_pivot$`Self-care`) * 100
CV_Vision <- ((DisabledType_pivot$moe_Vision / 1.645) / DisabledType_pivot$Vision) * 100
CV_Ambulatory <- ((DisabledType_pivot$moe_Ambulatory / 1.645) / DisabledType_pivot$Ambulatory) * 100
DisabledType_pivot$CV_Cognitive <- ifelse(CV_Cognitive > 30, "Low reliability",
ifelse(CV_Cognitive <= 15, "High reliability",
"Median reliability"))
DisabledType_pivot$CV_Hearing <- ifelse(CV_Hearing > 30, "Low reliability",
ifelse(CV_Hearing <= 15, "High reliability",
"Median reliability"))
DisabledType_pivot$CV_IndependentLiving <- ifelse(CV_IndependentLiving > 30, "Low reliability",
ifelse(CV_IndependentLiving <= 15, "High reliability",
"Median reliability"))
DisabledType_pivot$CV_Selfcare <- ifelse(CV_Selfcare > 30, "Low reliability",
ifelse(CV_Selfcare <= 15, "High reliability",
"Median reliability"))
DisabledType_pivot$CV_Vision <- ifelse(CV_Vision > 30, "Low reliability",
ifelse(CV_Vision <= 15, "High reliability",
"Median reliability"))
DisabledType_pivot$CV_Ambulatory <- ifelse(CV_Ambulatory > 30, "Low reliability",
ifelse(CV_Ambulatory <= 15, "High reliability",
"Median reliability"))
White <- c("Total White"= "B18101A_005", "Disabled White" = "B18101A_006")
Black <- c("Total Black" ="B18101B_005", "Disabled Black" = "B18101B_006")
IndianAlaska <- c("Total American Indian and Alaska Native" = "B18101C_005", "Disabled American Indian and Alaska Native" = "B18101C_006")
Asian <- c("Total Asian" = "B18101D_005", "Disabled Asian" = "B18101D_006")
HawaiianPacific <- c("Total Native Hawaiian and Other Pacific Islander" = "B18101E_005", "Disabled Native Hawaiian and Other Pacific Islander" = "B18101E_006")
OtherAlone <- c("Total Some Other Race Alone" = "B18101F_005", "Disabled Some Other Race Alone" = "B18101F_006")
TwoMore <- c("Total Two Or More Races" = "B18101G_005", "Disabled Two Or More Races" = "B18101G_006")
WhiteAlone <- c("Total White Alone" = "B18101H_005", "Disabled White Alone" = "B18101H_006")
HispanicLatino <- c("Total Hispanic Or Latino" = "B18101I_005", "Disabled Hispanic Or Latino" = "B18101I_006")
RaceEthnic_State <- get_acs(geography = "state",
variables = c(White, Black, IndianAlaska, Asian, HawaiianPacific, OtherAlone, TwoMore, WhiteAlone, HispanicLatino),
year = 2020,
state = "IA")
RaceEthnic_County <- get_acs(geography = "county",
variables = c(White, Black, IndianAlaska, Asian, HawaiianPacific, OtherAlone, TwoMore, WhiteAlone, HispanicLatino),
year = 2020,
state = "IA")
RaceEthnic <- rbind(RaceEthnic_State, RaceEthnic_County)
RaceEthnic <- RaceEthnic %>%
pivot_wider(names_from = variable,
values_from = c(estimate, moe))
names(RaceEthnic) <- gsub("estimate_", "", names(RaceEthnic))
RaceEthnic$NAME <- gsub(" County, Iowa", "", RaceEthnic$NAME)
names(RaceEthnic)[names(RaceEthnic) == "NAME"] <- "County"
CV_White_Disabled <- ((RaceEthnic$`moe_Disabled White` / 1.645) / RaceEthnic$`Disabled White`) * 100
CV_Black_Disabled <- ((RaceEthnic$`moe_Disabled Black` / 1.645) / RaceEthnic$`Disabled Black`) * 100
CV_IndianAlaska_Disabled <- ((RaceEthnic$`moe_Disabled American Indian and Alaska Native` / 1.645) / RaceEthnic$`Disabled American Indian and Alaska Native`) * 100
CV_Asian_Disabled <- ((RaceEthnic$`moe_Disabled Asian` / 1.645) / RaceEthnic$`Disabled Asian`) * 100
CV_HawaiianPacific_Disabled <- ((RaceEthnic$`moe_Disabled Native Hawaiian and Other Pacific Islander` / 1.645) / RaceEthnic$`Disabled Native Hawaiian and Other Pacific Islander`) * 100
CV_OtherAlone_Disabled <- ((RaceEthnic$`moe_Disabled Some Other Race Alone` / 1.645) / RaceEthnic$`Disabled Some Other Race Alone`) * 100
CV_TwoMore_Disabled <- ((RaceEthnic$`moe_Disabled Two Or More Races` / 1.645) / RaceEthnic$`Disabled Two Or More Races`) * 100
CV_WhiteAlone_Disabled <- ((RaceEthnic$`moe_Disabled White Alone` / 1.645) / RaceEthnic$`Disabled White Alone`) * 100
CV_HispanicLatino_Disabled <- ((RaceEthnic$`moe_Disabled Hispanic Or Latino` / 1.645) / RaceEthnic$`Disabled Hispanic Or Latino`) * 100
RaceEthnic$CV_White_Disabled <- ifelse(CV_White_Disabled > 30, "Low reliability",
ifelse(CV_White_Disabled <= 15, "High reliability",
"Median reliability"))
RaceEthnic$CV_Black_Disabled <- ifelse(CV_Black_Disabled > 30, "Low reliability",
ifelse(CV_Black_Disabled <= 15, "High reliability",
"Median reliability"))
RaceEthnic$CV_IndianAlaska_Disabled <- ifelse(CV_IndianAlaska_Disabled > 30, "Low reliability",
ifelse(CV_IndianAlaska_Disabled <= 15, "High reliability",
"Median reliability"))
RaceEthnic$CV_Asian_Disabled <- ifelse(CV_Asian_Disabled > 30, "Low reliability",
ifelse(CV_Asian_Disabled <= 15, "High reliability",
"Median reliability"))
RaceEthnic$CV_HawaiianPacific_Disabled <- ifelse(CV_HawaiianPacific_Disabled > 30, "Low reliability",
ifelse(CV_HawaiianPacific_Disabled <= 15, "High reliability",
"Median reliability"))
RaceEthnic$CV_OtherAlone_Disabled <- ifelse(CV_OtherAlone_Disabled > 30, "Low reliability",
ifelse(CV_OtherAlone_Disabled <= 15, "High reliability",
"Median reliability"))
RaceEthnic$CV_TwoMore_Disabled <- ifelse(CV_TwoMore_Disabled > 30, "Low reliability",
ifelse(CV_TwoMore_Disabled <= 15, "High reliability",
"Median reliability"))
RaceEthnic$CV_WhiteAlone_Disabled <- ifelse(CV_WhiteAlone_Disabled > 30, "Low reliability",
ifelse(CV_WhiteAlone_Disabled <= 15, "High reliability",
"Median reliability"))
RaceEthnic$CV_HispanicLatino_Disabled <- ifelse(CV_HispanicLatino_Disabled > 30, "Low reliability",
ifelse(CV_HispanicLatino_Disabled <= 15, "High reliability",
"Median reliability"))