Nothing
R/load_data.R
In tidycensus: Load US Census Boundary and Attribute Data as 'tidyverse' and 'sf'-Ready Data Frames
Defines functions
load_data_flows
load_data_pums_vacant
load_data_pums
load_data_estimates
load_data_decennial
load_data_acs
format_variables_acs
format_variables_acs <- function(variables) {
# find code in 'data-raw/no_moe_vars.R' to pull these vars from api
no_moes <- c("B00001_001", "B00002_001", "B98001_001", "B98001_002", "B98002_001",
"B98002_002", "B98002_003", "B98003_001", "B98011_001", "B98012_001",
"B98012_002", "B98012_003", "B98013_001", "B98013_002", "B98013_003",
"B98013_004", "B98013_005", "B98013_006", "B98013_007", "B98014_001",
"B98021_001", "B98021_002", "B98021_003", "B98021_004", "B98021_005",
"B98021_006", "B98021_007", "B98021_008", "B98021_009", "B98021_010",
"B98022_001", "B98022_002", "B98022_003", "B98022_004", "B98022_005",
"B98022_006", "B98022_007", "B98022_008", "B98022_009", "B98022_010",
"B98031_001", "B98032_001", "B99011_001", "B99011_002", "B99011_003",
"B99012_001", "B99012_002", "B99012_003", "B99021_001", "B99021_002",
"B99021_003", "B99031_001", "B99031_002", "B99031_003", "B99051_001",
"B99051_002", "B99051_003", "B99051_004", "B99051_005", "B99051_006",
"B99051_007", "B99052_001", "B99052_002", "B99052_003", "B99052_004",
"B99052_005", "B99052_006", "B99052_007", "B99052PR_001", "B99052PR_002",
"B99052PR_003", "B99052PR_004", "B99052PR_005", "B99052PR_006",
"B99052PR_007", "B99053_001", "B99053_002", "B99053_003", "B99061_001",
"B99061_002", "B99061_003", "B99071_001", "B99071_002", "B99071_003",
"B99072_001", "B99072_002", "B99072_003", "B99072_004", "B99072_005",
"B99072_006", "B99072_007", "B99080_001", "B99080_002", "B99080_003",
"B99081_001", "B99081_002", "B99081_003", "B99081_004", "B99081_005",
"B99082_001", "B99082_002", "B99082_003", "B99082_004", "B99082_005",
"B99083_001", "B99083_002", "B99083_003", "B99083_004", "B99083_005",
"B99084_001", "B99084_002", "B99084_003", "B99084_004", "B99084_005",
"B99085_001", "B99085_002", "B99085_003", "B99086_001", "B99086_002",
"B99086_003", "B99087_001", "B99087_002", "B99087_003", "B99087_004",
"B99087_005", "B99088_001", "B99088_002", "B99088_003", "B99088_004",
"B99088_005", "B99089_001", "B99089_002", "B99089_003", "B99092_001",
"B99092_002", "B99092_003", "B99102_001", "B99102_002", "B99102_003",
"B99103_001", "B99103_002", "B99103_003", "B99104_001", "B99104_002",
"B99104_003", "B99104_004", "B99104_005", "B99104_006", "B99104_007",
"B99121_001", "B99121_002", "B99121_003", "B99122_001", "B99122_002",
"B99122_003", "B99123_001", "B99123_002", "B99123_003", "B99124_001",
"B99124_002", "B99124_003", "B99125_001", "B99125_002", "B99125_003",
"B99126_001", "B99126_002", "B99126_003", "B99131_001", "B99131_002",
"B99131_003", "B99132_001", "B99132_002", "B99132_003", "B99141_001",
"B99141_002", "B99141_003", "B99142_001", "B99142_002", "B99142_003",
"B99151_001", "B99151_002", "B99151_003", "B99152_001", "B99152_002",
"B99152_003", "B99161_001", "B99161_002", "B99161_003", "B99162_001",
"B99162_002", "B99162_003", "B99162_004", "B99162_005", "B99162_006",
"B99162_007", "B99163_001", "B99163_002", "B99163_003", "B99163_004",
"B99163_005", "B99171_001", "B99171_002", "B99171_003", "B99171_004",
"B99171_005", "B99171_006", "B99171_007", "B99171_008", "B99171_009",
"B99171_010", "B99171_011", "B99171_012", "B99171_013", "B99171_014",
"B99171_015", "B99172_001", "B99172_002", "B99172_003", "B99172_004",
"B99172_005", "B99172_006", "B99172_007", "B99172_008", "B99172_009",
"B99172_010", "B99172_011", "B99172_012", "B99172_013", "B99172_014",
"B99172_015", "B99181_001", "B99181_002", "B99181_003", "B99182_001",
"B99182_002", "B99182_003", "B99183_001", "B99183_002", "B99183_003",
"B99184_001", "B99184_002", "B99184_003", "B99185_001", "B99185_002",
"B99185_003", "B99186_001", "B99186_002", "B99186_003", "B99187_001",
"B99187_002", "B99187_003", "B99187_004", "B99187_005", "B99187_006",
"B99187_007", "B99191_001", "B99191_002", "B99191_003", "B99191_004",
"B99191_005", "B99191_006", "B99191_007", "B99191_008", "B99192_001",
"B99192_002", "B99192_003", "B99192_004", "B99192_005", "B99192_006",
"B99192_007", "B99192_008", "B99193_001", "B99193_002", "B99193_003",
"B99193_004", "B99193_005", "B99193_006", "B99193_007", "B99193_008",
"B99194_001", "B99194_002", "B99194_003", "B99194_004", "B99194_005",
"B99194_006", "B99194_007", "B99194_008", "B99201_001", "B99201_002",
"B99201_003", "B99201_004", "B99201_005", "B99201_006", "B99201_007",
"B99201_008", "B99211_001", "B99211_002", "B99211_003", "B99212_001",
"B99212_002", "B99212_003", "B99221_001", "B99221_002", "B99221_003",
"B99231_001", "B99231_002", "B99231_003", "B99232_001", "B99232_002",
"B99232_003", "B99233_001", "B99233_002", "B99233_003", "B99233_004",
"B99233_005", "B99234_001", "B99234_002", "B99234_003", "B99234_004",
"B99234_005", "B99241_001", "B99241_002", "B99241_003", "B99242_001",
"B99242_002", "B99242_003", "B99243_001", "B99243_002", "B99243_003",
"B99244_001", "B99244_002", "B99244_003", "B99245_001", "B99245_002",
"B99245_003", "B99246_001", "B99246_002", "B99246_003", "B992510_001",
"B992510_002", "B992510_003", "B992511_001", "B992511_002", "B992511_003",
"B992512_001", "B992512_002", "B992512_003", "B992513_001", "B992513_002",
"B992513_003", "B992514_001", "B992514_002", "B992514_003", "B992515_001",
"B992515_002", "B992515_003", "B992516_001", "B992516_002", "B992516_003",
"B992518_001", "B992518_002", "B992518_003", "B992519_001", "B992519_002",
"B992519_003", "B99252_001", "B99252_002", "B99252_003", "B992520_001",
"B992520_002", "B992520_003", "B992521_001", "B992521_002", "B992521_003",
"B992522_001", "B992522_002", "B992522_003", "B992522_004", "B992522_005",
"B992522_006", "B992522_007", "B992523_001", "B992523_002", "B992523_003",
"B99253_001", "B99253_002", "B99253_003", "B99254_001", "B99254_002",
"B99254_003", "B99255_001", "B99255_002", "B99255_003", "B99256_001",
"B99256_002", "B99256_003", "B99257_001", "B99257_002", "B99257_003",
"B99258_001", "B99258_002", "B99258_003", "B99259_001", "B99259_002",
"B99259_003", "B992701_001", "B992701_002", "B992701_003", "B992702_001",
"B992702_002", "B992702_003", "B992703_001", "B992703_002", "B992703_003",
"B992704_001", "B992704_002", "B992704_003", "B992705_001", "B992705_002",
"B992705_003", "B992706_001", "B992706_002", "B992706_003", "B992707_001",
"B992707_002", "B992707_003", "B992708_001", "B992708_002", "B992708_003",
"B992709_001", "B992709_002", "B992709_003", "B99281_001", "B99281_002",
"B99281_003", "B99282_001", "B99282_002", "B99282_003", "B99282_004",
"B99282_005", "B99282_006", "B99282_007", "B99282_008", "B99282_009",
"B99283_001", "B99283_002", "B99283_003", "B99283_004", "B99283_005")
# First, remove E or M if user has put it in
variables1 <- map_chr(variables, function(x) {
if (str_sub(x, -1) %in% c("E", "M")) {
x <- str_sub(x, 1, -2)
} else {
x <- x
}
})
# Now, make unique
variables2 <- unique(variables1)
# The comparison profile doesn't have MOEs, so account for that
if (!any(grepl("^CP[0-9].", variables2))) {
# Next, separate into vars with and without MOEs
variables2a <- variables2[!variables2 %in% no_moes]
variables2_nomoe <- variables2[variables2 %in% no_moes]
# Now, expand with both E and M if MOE is applicable
variables3 <- map_chr(variables2a, function(y) paste0(y, c("E", "M"), collapse = ","))
if (length(variables2_nomoe)) {
variables3_nomoe <- paste0(variables2_nomoe, "E")
variables3 <- c(variables3, variables3_nomoe)
}
} else {
variables3 <- paste0(variables2, "E")
}
# Now, put together all these strings if need be
var <- paste0(variables3, collapse = ",")
return(var)
}
load_data_acs <- function(geography, formatted_variables, key, year, state = NULL,
county = NULL, zcta = NULL, survey, show_call = FALSE) {
base <- paste("https://api.census.gov/data",
as.character(year), "acs",
survey, sep = "/")
if (grepl("^DP", formatted_variables)) {
message("Using the ACS Data Profile")
base <- paste0(base, "/profile")
}
if (grepl("^S[0-9].", formatted_variables)) {
message("Using the ACS Subject Tables")
base <- paste0(base, "/subject")
}
if (grepl("^CP[0-9].", formatted_variables)) {
message("Using the ACS Comparison Profile")
base <- paste0(base, "/cprofile")
}
for_area <- paste0(geography, ":*")
if (!is.null(zcta)) {
# if (is.null(state)) {
# stop("The `zcta` argument requires specifying a state.", call. = FALSE)
# }
if (!is.null(county)) {
stop("ZCTAs do not nest within counties, so `county` should not be specified.",
call. = FALSE)
}
if (!is.null(state)) {
state <- map_chr(state, function(x) {
validate_state(x)
})
in_area <- paste0("state:", state)
} else {
in_area <- NULL
}
for_area <- paste0(zcta, collapse = ",")
vars_to_get <- paste0(formatted_variables, ",NAME")
call <- GET(base, query = list(get = vars_to_get,
"for" = paste0(geography, ":", for_area),
"in" = in_area,
key = key))
} else if (!is.null(state) && is.null(zcta)) {
state <- map_chr(state, function(x) {
validate_state(x)
})
if (length(state) > 1) {
state <- paste0(state, collapse = ",")
}
if (geography == "state") {
for_area <- paste0("state:", state)
}
if (!is.null(county)) {
county <- map_chr(county, function(x) {
validate_county(state, x)
})
if (length(county) > 1) {
county <- paste0(county, collapse = ",")
}
if (geography == "county") {
for_area <- paste0("county:", county)
in_area <- paste0("state:", state)
} else {
in_area <- paste0("state:", state,
"+county:", county)
}
} else {
if (geography == "block group" && is.null(county)) {
in_area <- paste0("state:", state,
"&in=county:*")
} else {
in_area <- paste0("state:", state)
}
}
vars_to_get <- paste0(formatted_variables, ",NAME")
if (geography == "state" && !is.null(state)) {
call <- GET(base, query = list(get = vars_to_get,
"for" = for_area,
key = key))
} else {
call <- GET(base, query = list(get = vars_to_get,
"for" = for_area,
"in" = in_area,
key = key))
}
} else {
vars_to_get <- paste0(formatted_variables, ",NAME")
call <- GET(base, query = list(get = vars_to_get,
"for" = paste0(geography, ":*"),
key = key))
}
if (show_call) {
call_url <- gsub("&key.*", "", call$url)
message(paste("Census API call:", call_url))
}
# Make sure call status returns 200, else, print the error message for the user.
if (call$status_code != 200) {
msg <- content(call, as = "text")
if (grepl("The requested resource is not available", msg)) {
stop("One or more of your requested variables is likely not available at the requested geography. Please refine your selection.", call. = FALSE)
} else {
stop(sprintf("Your API call has errors. The API message returned is %s.", msg), call. = FALSE)
}
}
# callStatus <- http_status(call)
# if (callStatus$reason != "OK") {
# message(callStatus$category, " ", callStatus$reason, " ", callStatus$message)
# } else {
# content <- content(call, as = "text")
# }
#
# validate_call(content = content, geography = geography, year = year,
# dataset = survey)
content <- content(call, as = "text")
if (grepl("You included a key with this request", content)) {
stop("You have supplied an invalid or inactive API key. To obtain a valid API key, visit https://api.census.gov/data/key_signup.html. To activate your key, be sure to click the link provided to you in the email from the Census Bureau that contained your key.", call. = FALSE)
}
dat <- fromJSON(content)
colnames(dat) <- dat[1,]
dat <- as_tibble(dat)
dat <- dat[-1,]
var_vector <- unlist(strsplit(formatted_variables, split = ","))
dat[var_vector] <- lapply(dat[var_vector], as.numeric)
v2 <- c(var_vector, "NAME")
# Get the geography ID variables
id_vars <- names(dat)[! names(dat) %in% v2]
# Paste into a GEOID column
dat$GEOID <- do.call(paste0, dat[id_vars])
# Now, remove them
dat <- dat[, !(names(dat) %in% id_vars)]
return(dat)
}
load_data_decennial <- function(geography, variables, key, year, sumfile, pop_group,
state = NULL, county = NULL, show_call = FALSE) {
var <- paste0(variables, collapse = ",")
if (year == 1990) {
vars_to_get <- paste0(var, ",ANPSADPI")
} else {
vars_to_get <- paste0(var, ",NAME")
}
if (!is.null(pop_group)) {
if (pop_group == "all") {
vars_to_get <- paste0(vars_to_get, ",POPGROUP")
pop_group <- NULL
}
}
base <- paste0("https://api.census.gov/data/",
year,
"/dec/",
sumfile)
# if (year == 2010) {
# base <- paste0("https://api.census.gov/data/",
# year,
# "/dec/",
# sumfile)
# } else {
# base <- paste0("https://api.census.gov/data/",
# year,
# "/",
# sumfile)
# }
for_area <- paste0(geography, ":*")
if (!is.null(state)) {
state <- map_chr(state, function(x) {
validate_state(x)
})
if (length(state) > 1) {
state <- paste0(state, collapse = ",")
}
if (geography == "state") {
for_area <- paste0("state:", state)
}
if (!is.null(county)) {
county <- map_chr(county, function(x) {
validate_county(state, x)
})
if (length(county) > 1) {
county <- paste0(county, collapse = ",")
}
if (geography == "county") {
for_area <- paste0("county:", county)
in_area <- paste0("state:", state)
} else {
in_area <- paste0("state:", state,
"+county:", county)
}
} else {
if ((geography == "block group" || geography == "block") && is.null(county)) {
in_area <- paste0("state:", state,
"&in=county:*")
} else {
in_area <- paste0("state:", state)
}
}
if (geography == "state" && !is.null(state)) {
call <- GET(base, query = list(get = vars_to_get,
"for" = for_area,
key = key,
"POPGROUP" = pop_group))
} else {
call <- GET(base, query = list(get = vars_to_get,
"for" = for_area,
"in" = in_area,
key = key,
"POPGROUP" = pop_group))
}
}
else {
call <- GET(base, query = list(get = vars_to_get,
"for" = paste0(geography, ":*"),
key = key,
"POPGROUP" = pop_group))
}
if (show_call) {
call_url <- gsub("&key.*", "", call$url)
message(paste("Census API call:", call_url))
}
# Make sure call status returns 200, else, print the error message for the user.
# Try to handle 204's here
if (call$status_code == 204) {
if (sumfile == "ddhca") {
rlang::abort(c("Your DDHC-A request returned No Content from the API.",
"i" = "The DDHC-A file uses an 'adaptive design' where data availability varies by geography and by population group.",
"i" = "Read Section 3-1 at https://www2.census.gov/programs-surveys/decennial/2020/technical-documentation/complete-tech-docs/detailed-demographic-and-housing-characteristics-file-a/2020census-detailed-dhc-a-techdoc.pdf for more information.",
"i" = "In tidycensus, use the function `check_ddhca_groups()` to see if your data is available."))
} else {
rlang::abort("No content was returned from the API. Please refine your selection.")
}
}
if (call$status_code != 200) {
msg <- content(call, as = "text")
if (grepl("The requested resource is not available", msg)) {
stop("One or more of your requested variables is likely not available at the requested geography. Please refine your selection.", call. = FALSE)
} else if (grepl("unknown variable", msg) && length(variables) > 1 && year < 2010) {
stop(sprintf("The Census API has returned the error message %s.\nThis may be due to mixing SF1 and SF3 variables. If so, separate your requests to SF1 and SF3 when using `get_decennial()` by using the `sumfile` argument in separate calls.", msg))
} else {
stop(sprintf("Your API call has errors. The API message returned is %s.", msg), call. = FALSE)
}
}
# callStatus <- http_status(call)
# if (callStatus$reason != "OK") {
# if (sumfile == "sf1") {
# message("Checking SF3 API for data...")
# } else {
# message(callStatus$category, " ", callStatus$reason, " ", callStatus$message)
# }
# } else {
# content <- content(call, as = "text")
# }
content <- content(call, as = "text")
if (grepl("You included a key with this request", content)) {
stop("You have supplied an invalid or inactive API key. To obtain a valid API key, visit https://api.census.gov/data/key_signup.html. To activate your key, be sure to click the link provided to you in the email from the Census Bureau that contained your key.", call. = FALSE)
}
# Fix issue in SF3 2000 API - https://github.com/walkerke/tidycensus/issues/22
if (year == 2000 && sumfile == "sf3") {
content <- str_replace(content, 'O"Brien', "O'Brien")
content <- str_replace(content, 'Prince George"s', "Prince George's")
content <- str_replace(content, 'Queen Anne"s', "Queen Anne's")
content <- str_replace(content, 'St. Mary"s', "St. Mary's")
}
dat <- fromJSON(content)
colnames(dat) <- dat[1,]
dat <- as_tibble(dat)
dat <- dat[-1,]
if (year == 1990) {
dat <- rename(dat, NAME = ANPSADPI)
}
vnum <- variables[variables != "POPGROUP"]
dat[vnum] <- lapply(dat[vnum], as.numeric)
v2 <- c(variables, "NAME", "POPGROUP")
# Get the geography ID variables
id_vars <- names(dat)[! names(dat) %in% v2]
# Paste into a GEOID column
# Apply tract band-aid (GH issue #317)
if ("tract" %in% id_vars && year == 2000) {
dat$tract <- stringr::str_pad(dat$tract, 6, "right", "0")
}
dat$GEOID <- do.call(paste0, dat[id_vars])
# Fix issues with ZCTAs if necessary
if (geography == "zip code tabulation area") {
l <- unique(nchar(dat$GEOID))
if (l == 7) {
dat$GEOID <- str_sub(dat$GEOID, 3, 7)
}
}
# Now, remove them
dat <- dat[, !(names(dat) %in% id_vars)]
return(dat)
}
load_data_estimates <- function(geography, product = NULL, variables = NULL, key, year,
time_series, state = NULL, county = NULL, show_call = FALSE) {
if (!is.null(product)) {
if (!product %in% c("population", "components",
"charagegroups", "housing")) {
stop("You have selected an invalid product. Valid requests are 'population', 'components', 'characteristics', and 'housing'.", call. = FALSE)
}
}
for_area <- paste0(geography, ":*")
if (year >= 2019) {
geo_name <- "NAME"
} else {
geo_name <- "GEONAME"
}
if (is.null(variables)) {
if (is.null(product)) {
stop("Either a product or a vector of variables (from a single product) is required.", call. = FALSE)
} else {
if (product == "population") {
vars_to_get <- paste0(geo_name, ",POP,DENSITY")
} else if (product == "components") {
vars_to_get <- paste0(geo_name, ",BIRTHS,DEATHS,DOMESTICMIG,INTERNATIONALMIG,NATURALINC,NETMIG,RBIRTH,RDEATH,RDOMESTICMIG,RINTERNATIONALMIG,RNATURALINC,RNETMIG")
} else if (product == "housing") {
vars_to_get <- paste0(geo_name, ",HUEST")
} else {
stop("Other products are not yet supported.", call. = FALSE)
}
}
} else {
if (!is.null(product)) {
if (product != "charagegroups") {
stop("Please specify either a product or a vector of variables, but not both.", call. = FALSE)
}
} else {
if (all(variables %in% c("POP", "DENSITY"))) {
product <- "population"
} else if (all(variables %in% c("BIRTHS", "DEATHS", "DOMESTICMIG", "INTERNATIONALMIG", "NATURALINC", "NETMIG", "RBIRTH", "RDEATH", "RDOMESTICMIG", "RINTERNATIONALMIG", "RNATURALINC", "RNETMIG"))) {
product <- "components"
} else if (all(variables %in% c("HUEST"))) {
product <- "housing"
} else {
stop(
paste('Variables must be a subset of only one of the following sets of valid variables:',
'for population estimates, c("POP", "DENSITY");',
'for housing unit estimates, c("HUEST");',
'and for components of change estimates, c("BIRTHS", "DEATHS", "DOMESTICMIG", "INTERNATIONALMIG", "NATURALINC", "NETMIG", "RBIRTH", "RDEATH", "RDOMESTICMIG", "RINTERNATIONALMIG", "RNATURALINC", "RNETMIG").'),
call. = FALSE)
}
}
vars_to_get <- paste0(geo_name, ",", paste0(variables, collapse = ","))
}
if (time_series) {
if (product == "components") {
if (year >= 2018) {
vars_to_get <- paste0(vars_to_get, ",PERIOD_CODE")
} else {
vars_to_get <- paste0(vars_to_get, ",PERIOD")
}
} else {
if (year >= 2018) {
vars_to_get <- paste0(vars_to_get, ",DATE_CODE")
} else {
vars_to_get <- paste0(vars_to_get, ",DATE_")
}
}
}
base <- sprintf("https://api.census.gov/data/%s/pep/%s", year, product)
if (!is.null(state)) {
state <- map_chr(state, function(x) {
validate_state(x)
})
if (length(state) > 1) {
state <- paste0(state, collapse = ",")
}
if (geography == "state") {
for_area <- paste0("state:", state)
}
if (!is.null(county)) {
county <- map_chr(county, function(x) {
validate_county(state, x)
})
if (length(county) > 1) {
county <- paste0(county, collapse = ",")
}
if (geography == "county") {
for_area <- paste0("county:", county)
in_area <- paste0("state:", state)
} else {
in_area <- paste0("state:", state,
"+county:", county)
}
} else {
in_area <- paste0("state:", state)
}
if (geography == "state" && !is.null(state)) {
call <- GET(base, query = list(get = vars_to_get,
"for" = for_area,
key = key))
} else {
call <- GET(base, query = list(get = vars_to_get,
"for" = for_area,
"in" = in_area,
key = key))
}
}
else {
call <- GET(base, query = list(get = vars_to_get,
"for" = paste0(geography, ":*"),
key = key))
}
if (show_call) {
call_url <- gsub("&key.*", "", call$url)
message(paste("Census API call:", call_url))
}
# Make sure call status returns 200, else, print the error message for the user.
if (call$status_code != 200) {
msg <- content(call, as = "text")
if (grepl("The requested resource is not available", msg)) {
stop("One or more of your requested variables is likely not available at the requested geography. Please refine your selection.", call. = FALSE)
} else {
stop(sprintf("Your API call has errors. The API message returned is %s.", msg), call. = FALSE)
}
}
content <- content(call, as = "text")
if (grepl("You included a key with this request", content)) {
stop("You have supplied an invalid or inactive API key. To obtain a valid API key, visit https://api.census.gov/data/key_signup.html. To activate your key, be sure to click the link provided to you in the email from the Census Bureau that contained your key.", call. = FALSE)
}
dat <- fromJSON(content)
colnames(dat) <- dat[1,]
dat <- as_tibble(dat)
dat <- dat[-1,]
var_vector <- unlist(strsplit(vars_to_get, split = ","))
var_vector <- var_vector[var_vector != geo_name]
dat[var_vector] <- lapply(dat[var_vector], as.numeric)
v2 <- c(var_vector, geo_name)
if ("county" %in% names(dat)) {
dat$county <- stringr::str_pad(dat$county, 3, "left", "0")
}
# Get the geography ID variables
id_vars <- names(dat)[! names(dat) %in% v2]
# Paste into a GEOID column
dat$GEOID <- do.call(paste0, dat[id_vars])
# Now, remove them
dat <- dat[, !(names(dat) %in% id_vars)]
return(dat)
}
load_data_pums <- function(variables, state, puma, key, year, survey,
variables_filter, recode, show_call) {
# for which years is data dictionary available in pums_variables?
# we'll use this a couple times later on
recode_years <- 2017:2023
base <- sprintf("https://api.census.gov/data/%s/acs/%s/pums",
year, survey)
if (!is.null(puma)) {
if (length(state) > 1) {
stop('When requesting PUMAs for more than one state, you must set state to "multiple" and set puma to a named vector of state/PUMA pairs.', call. = FALSE)
}
# pumas in multiple states can be requested with a named vector of
# state / puma pairs in the puma argument of get_pums()
if (state == "multiple") {
# print FIPS code of states used just once
purrr::walk(unique(names(puma)), validate_state)
geo <- purrr::map2_chr(names(puma), unname(puma), function(x, y) {
paste0("7950000US", suppressMessages(validate_state(x)), y)
})
geo <- paste0(geo, collapse = ",")
} else {
# if PUMAs requested are in one state only
state <- validate_state(state)
geo <- purrr::map_chr(puma, function(x) {
paste0("7950000US", state, x)
})
if (length(puma) > 1) {
geo <- paste0(geo, collapse = ",")
}
}
} else {
# if no PUMAs specified, get all PUMAs in each state requested
if (!is.null(state)) {
geo <- purrr::map_chr(state, function(x) {
paste0("0400000US", validate_state(x))
})
} else {
geo <- NULL
}
if (length(state) > 1) {
geo <- paste0(geo, collapse = ",")
}
}
# Handle variables & variable filter
# If a variables filter is supplied, those variables should be removed from
# variables (if applicable)
if (!is.null(variables_filter)) {
filter_names <- names(variables_filter)
variables <- variables[!variables %in% filter_names]
if (length(variables) > 0) {
var <- paste0(variables, collapse = ",")
# Handle housing-only query
if ("SPORDER" %in% filter_names) {
vars_to_get <- paste0("SERIALNO,WGTP,PWGTP,", var)
} else {
vars_to_get <- paste0("SERIALNO,SPORDER,WGTP,PWGTP,", var)
}
} else {
if ("SPORDER" %in% filter_names) {
vars_to_get <- "SERIALNO,WGTP,PWGTP"
} else {
vars_to_get <- "SERIALNO,SPORDER,WGTP,PWGTP"
}
}
# If geo is NULL, state should be added back in here
if (is.null(geo)) {
vars_to_get <- paste0(vars_to_get, ",ST")
}
# Combine the default query with the variables filter query
query_default <- list(get = vars_to_get,
ucgid = geo,
key = key)
# Collapse vectors if supplied
variables_filter_collapsed <- purrr::map(variables_filter,
~{paste0(.x, collapse = ",")})
query <- c(
list(get = vars_to_get),
variables_filter_collapsed,
list(ucgid = geo, key = key)
)
} else {
var <- paste0(variables, collapse = ",")
vars_to_get <- paste0("SERIALNO,SPORDER,WGTP,PWGTP,", var)
# If geo is NULL, state should be added back in here
if (is.null(geo)) {
if (year > 2022) {
vars_to_get <- paste0(vars_to_get, ",STATE")
} else {
vars_to_get <- paste0(vars_to_get, ",ST")
}
}
query <- list(get = vars_to_get,
ucgid = geo,
key = key)
}
call <- httr::GET(base,
query = query,
httr::progress())
if (show_call) {
call_url <- gsub("&key.*", "", call$url)
message(paste("Census API call:", call_url))
}
# Make sure call status returns 200, else, print the error message for the user.
if (call$status_code != 200) {
msg <- content(call, as = "text")
if (grepl("The requested resource is not available", msg)) {
stop("One or more of your requested variables is likely not available at the requested geography. Please refine your selection.", call. = FALSE)
} else {
stop(sprintf("Your API call has errors. The API message returned is %s.", msg), call. = FALSE)
}
}
content <- httr::content(call, as = "text")
if (grepl("You included a key with this request", content)) {
stop("You have supplied an invalid or inactive API key. To obtain a valid API key, visit https://api.census.gov/data/key_signup.html. To activate your key, be sure to click the link provided to you in the email from the Census Bureau that contained your key.", call. = FALSE)
}
dat <- jsonlite::fromJSON(content)
colnames(dat) <- dat[1,]
dat <- dplyr::as_tibble(dat, .name_repair = "minimal")
dat <- dat[-1,]
# Convert the weights columns to numeric
dat <- dat %>%
dplyr::mutate(
dplyr::across(
.cols = dplyr::contains("WGTP"),
as.numeric
)
)
# dat$WGTP <- as.numeric(dat$WGTP)
# dat$PWGTP <- as.numeric(dat$PWGTP)
# Filter the pums lookup table for the selected year and survey
pums_variables_filter <- tidycensus::pums_variables %>%
filter(year == !!year, survey == !!survey)
# Do some clean up of the API response: pad returned values with 0s when
# necessary to match data dictionary codes and
# convert variables to numeric according to data dictionary
# Only works for years included in pums_variables data dictionary
if (year %in% recode_years) {
var_val_length <- pums_variables_filter %>%
filter(!is.na(.data$val_length)) %>%
distinct(.data$var_code, .data$val_length, .data$val_na)
num_vars <- pums_variables_filter %>%
filter(.data$data_type == "num") %>%
distinct(.data$var_code) %>%
pull()
# For all variables in which we know what the length should be, pad with 0s
dat_padded <- suppressWarnings(
dat %>%
select(.data$SERIALNO, .data$SPORDER, any_of(var_val_length$var_code)) %>%
pivot_longer(
cols = -c(.data$SERIALNO, .data$SPORDER),
names_to = "var_code",
values_to = "val"
) %>%
left_join(var_val_length, by = "var_code") %>%
mutate(
val = ifelse(!is.na(.data$val_na) & .data$val_na == .data$val, strrep("b", .data$val_length), .data$val),
val = ifelse(.data$var_code != "NAICSP", str_pad(.data$val, .data$val_length, pad = "0"), .data$val),
val = ifelse(.data$var_code == "NAICSP" & .data$val == "*", "bbbbbbbb", .data$val), # special NULL value returned by API for this var
) %>%
select(-.data$val_length, -.data$val_na) %>%
pivot_wider(
names_from = .data$var_code,
values_from = .data$val
)
)
# Rejoin padded variables to API return
dat <- dat %>%
select(.data$SERIALNO, .data$SPORDER, !any_of(var_val_length$var_code)) %>%
left_join(dat_padded, by = c("SERIALNO", "SPORDER")) %>%
mutate_at(vars(any_of(num_vars)), as.double)
}
# Do you want to return value labels also?
if (recode) {
# Only works for years included in pums_variables data dictionary
if (year %in% recode_years) {
var_lookup <- pums_variables_filter %>%
select(.data$var_code, val = .data$val_min, .data$val_label)
# Vector of variables that are possible to recode
vars_to_recode <- pums_variables_filter %>%
filter(.data$recode) %>%
distinct(.data$var_code) %>%
pull()
# Pivot to long format and join variable codes to lookup table with labels
recoded_long <- suppressWarnings(
dat %>%
select(.data$SERIALNO, .data$SPORDER, any_of(vars_to_recode)) %>%
pivot_longer(
cols = -c(.data$SERIALNO, .data$SPORDER),
names_to = "var_code",
values_to = "val"
) %>%
left_join(var_lookup, by = c("var_code", "val")) %>%
select(-.data$val)
)
# Create a "pivot spec" with nicer names for the labeled columns
# https://tidyr.tidyverse.org/articles/pivot.html#wider-1
spec <- recoded_long %>%
build_wider_spec(
names_from = .data$var_code,
values_from = .data$val_label
) %>%
mutate(.name = paste0(.data$var_code, "_label", ""))
recoded_wide <- recoded_long %>%
pivot_wider_spec(spec)
recode_v <- recoded_long %>% pull(var_code) %>% unique()
for (var in recode_v){
order_v <- var_lookup %>%
filter(var_code==var) %>%
pull(val_label)
var_label <- str_c(var, "_label")
recoded_wide[[var_label]] <-
readr::parse_factor(recoded_wide[[var_label]], ordered=TRUE, levels=order_v)
}
# Join recoded columns to API return
dat <- dat %>%
left_join(recoded_wide, by = c("SERIALNO", "SPORDER"))
} else {
message(paste("Recoding is currently supported for",
min(recode_years), "-", max(recode_years),
"data. Returning original data only."))
}
}
return(dat)
}
load_data_pums_vacant <- function(variables, state, puma, key, year, survey,
variables_filter, recode, show_call) {
# for which years is data dictionary available in pums_variables?
# we'll use this a couple times later on
recode_years <- 2017:2023
base <- sprintf("https://api.census.gov/data/%s/acs/%s/pums",
year, survey)
# Parse the variables for housing vs. person variables
housing_catalog <- pums_variables %>%
dplyr::filter(level != "person" | is.na(level)) %>%
dplyr::pull(var_code) %>%
unique()
housing_variables <- variables[variables %in% housing_catalog]
if (!is.null(puma)) {
if (length(state) > 1) {
stop('When requesting PUMAs for more than one state, you must set state to "multiple" and set puma to a named vector of state/PUMA pairs.', call. = FALSE)
}
# pumas in multiple states can be requested with a named vector of
# state / puma pairs in the puma argument of get_pums()
if (state == "multiple") {
# print FIPS code of states used just once
purrr::walk(unique(names(puma)), validate_state)
geo <- purrr::map2_chr(names(puma), unname(puma), function(x, y) {
paste0("7950000US", suppressMessages(validate_state(x)), y)
})
geo <- paste0(geo, collapse = ",")
} else {
# if PUMAs requested are in one state only
state <- validate_state(state)
geo <- purrr::map_chr(puma, function(x) {
paste0("7950000US", state, x)
})
if (length(puma) > 1) {
geo <- paste0(geo, collapse = ",")
}
}
} else {
# if no PUMAs specified, get all PUMAs in each state requested
if (!is.null(state)) {
geo <- purrr::map_chr(state, function(x) {
paste0("0400000US", validate_state(x))
})
} else {
geo <- NULL
}
if (length(state) > 1) {
geo <- paste0(geo, collapse = ",")
}
}
# Handle variables & variable filter
# In this scenario, the "filter" is for vacant households
# This will be specified on the `get_pums()` side
if (!is.null(variables_filter)) {
filter_names <- names(variables_filter)
housing_variables <- housing_variables[!housing_variables %in% filter_names]
var <- paste0(housing_variables, collapse = ",")
vars_to_get <- paste0("SERIALNO,WGTP,", var)
# If geo is NULL, state should be added back in here
if (is.null(geo)) {
if (year > 2022) {
vars_to_get <- paste0(vars_to_get, ",STATE")
} else {
vars_to_get <- paste0(vars_to_get, ",ST")
}
}
# Combine the default query with the variables filter query
query_default <- list(get = vars_to_get,
ucgid = geo,
key = key)
# Collapse vectors if supplied
variables_filter_collapsed <- purrr::map(variables_filter,
~{paste0(.x, collapse = ",")})
query <- c(
list(get = vars_to_get),
variables_filter_collapsed,
list(ucgid = geo, key = key)
)
} else {
# Not implemented here
# var <- paste0(variables, collapse = ",")
#
# vars_to_get <- paste0("SERIALNO,SPORDER,WGTP,PWGTP,", var)
#
# # If geo is NULL, state should be added back in here
# if (is.null(geo)) {
# vars_to_get <- paste0(vars_to_get, ",ST")
# }
#
# query <- list(get = vars_to_get,
# ucgid = geo,
# key = key)
}
call <- httr::GET(base,
query = query,
httr::progress())
if (show_call) {
call_url <- gsub("&key.*", "", call$url)
message(paste("Census API call:", call_url))
}
# Make sure call status returns 200, else, print the error message for the user.
if (call$status_code != 200) {
msg <- content(call, as = "text")
if (grepl("The requested resource is not available", msg)) {
stop("One or more of your requested variables is likely not available at the requested geography. Please refine your selection.", call. = FALSE)
} else {
stop(sprintf("Your API call has errors. The API message returned is %s.", msg), call. = FALSE)
}
}
content <- httr::content(call, as = "text")
if (grepl("You included a key with this request", content)) {
stop("You have supplied an invalid or inactive API key. To obtain a valid API key, visit https://api.census.gov/data/key_signup.html. To activate your key, be sure to click the link provided to you in the email from the Census Bureau that contained your key.", call. = FALSE)
}
dat <- jsonlite::fromJSON(content)
colnames(dat) <- dat[1,]
dat <- dplyr::as_tibble(dat, .name_repair = "minimal")
dat <- dat[-1,]
# Convert the weights columns to numeric
dat <- dat %>%
dplyr::mutate(
dplyr::across(
.cols = dplyr::contains("WGTP"),
as.numeric
)
)
# dat$WGTP <- as.numeric(dat$WGTP)
# dat$PWGTP <- as.numeric(dat$PWGTP)
# Filter the pums lookup table for the selected year and survey
pums_variables_filter <- tidycensus::pums_variables %>%
filter(year == !!year, survey == !!survey)
# Do some clean up of the API response: pad returned values with 0s when
# necessary to match data dictionary codes and
# convert variables to numeric according to data dictionary
# Only works for years included in pums_variables data dictionary
if (year %in% recode_years) {
var_val_length <- pums_variables_filter %>%
dplyr::filter(!is.na(.data$val_length)) %>%
dplyr::distinct(.data$var_code, .data$val_length, .data$val_na)
num_vars <- pums_variables_filter %>%
dplyr::filter(.data$data_type == "num") %>%
dplyr::distinct(.data$var_code) %>%
dplyr::pull()
# For all variables in which we know what the length should be, pad with 0s
dat_padded <- suppressWarnings(
dat %>%
dplyr::select(.data$SERIALNO, dplyr::any_of(var_val_length$var_code)) %>%
tidyr::pivot_longer(
cols = -c(.data$SERIALNO),
names_to = "var_code",
values_to = "val"
) %>%
dplyr::left_join(var_val_length, by = "var_code") %>%
dplyr::mutate(
val = ifelse(!is.na(.data$val_na) & .data$val_na == .data$val, strrep("b", .data$val_length), .data$val),
val = ifelse(.data$var_code != "NAICSP", str_pad(.data$val, .data$val_length, pad = "0"), .data$val),
val = ifelse(.data$var_code == "NAICSP" & .data$val == "*", "bbbbbbbb", .data$val), # special NULL value returned by API for this var
) %>%
dplyr::select(-.data$val_length, -.data$val_na) %>%
tidyr::pivot_wider(
names_from = .data$var_code,
values_from = .data$val
)
)
# Rejoin padded variables to API return
dat <- dat %>%
dplyr::select(.data$SERIALNO, !dplyr::any_of(var_val_length$var_code)) %>%
dplyr::left_join(dat_padded, by = c("SERIALNO")) %>%
dplyr::mutate_at(vars(dplyr::any_of(num_vars)), as.double)
}
# Do you want to return value labels also?
if (recode) {
# Only works for years included in pums_variables data dictionary
if (year %in% recode_years) {
var_lookup <- pums_variables_filter %>%
dplyr::select(.data$var_code, val = .data$val_min, .data$val_label)
# Vector of variables that are possible to recode
vars_to_recode <- pums_variables_filter %>%
dplyr::filter(.data$recode) %>%
dplyr::distinct(.data$var_code) %>%
dplyr::pull()
# Pivot to long format and join variable codes to lookup table with labels
recoded_long <- suppressWarnings(
dat %>%
dplyr::select(.data$SERIALNO, dplyr::any_of(vars_to_recode)) %>%
tidyr::pivot_longer(
cols = -c(.data$SERIALNO),
names_to = "var_code",
values_to = "val"
) %>%
dplyr::left_join(var_lookup, by = c("var_code", "val")) %>%
dplyr::select(-.data$val)
)
# Create a "pivot spec" with nicer names for the labeled columns
# https://tidyr.tidyverse.org/articles/pivot.html#wider-1
spec <- recoded_long %>%
tidyr::build_wider_spec(
names_from = .data$var_code,
values_from = .data$val_label
) %>%
dplyr::mutate(.name = paste0(.data$var_code, "_label", ""))
recoded_wide <- recoded_long %>%
tidyr::pivot_wider_spec(spec)
recode_v <- recoded_long %>% dplyr::pull(var_code) %>% unique()
for (var in recode_v){
order_v <- var_lookup %>%
dplyr::filter(var_code==var) %>%
dplyr::pull(val_label)
var_label <- stringr::str_c(var, "_label")
recoded_wide[[var_label]] <-
readr::parse_factor(recoded_wide[[var_label]], ordered=TRUE, levels=order_v)
}
# Join recoded columns to API return
dat <- dat %>%
dplyr::left_join(recoded_wide, by = c("SERIALNO"))
} else {
message(paste("Recoding is currently supported for",
min(recode_years), "-", max(recode_years),
"data. Returning original data only."))
}
}
return(dat)
}
load_data_flows <- function(geography, variables, key, year, state = NULL,
county = NULL, msa = NULL, show_call = FALSE) {
base <- paste("https://api.census.gov/data", year, "acs", "flows", sep = "/")
# collapse variables in a single string for api call
vars_to_get <- paste0(variables, collapse = ",")
# get all in a given geography unless specified
for_area <- paste0(geography, ":*")
in_area <- NULL
# turn text states into fips if specified, else get all states
# collapse into string for api call
if (!is.null(state)) {
state <- paste(map_chr(state, ~ validate_state(.x)), collapse = ",")
} else {
state <- "*"
}
# turn text counties into fips if specified, validate with specified state
# collapse into string for api call
# can only filter counties for one state
if (!is.null(county)) {
county <- paste(map_chr(county, ~ validate_county(state, .x)), collapse = ",")
}
# if msa codes specified, collapse into string for api call
if (!is.null(msa)) {
msa <- paste0(msa, collapse = ",")
}
# define counties (for) within states (in) for filtering in api call
if (geography == "county") {
if (!is.null(county)) {
for_area <- paste0("county:", county)
}
if (!is.null(state)) {
in_area <- paste0("state:", state)
}
}
# if county is defined, construct for call using state and county
# otherwise filter only at state level
if (geography == "county subdivision") {
if (!is.null(county)) {
in_area <- paste0("state:", state, " ", "county:", county)
} else {
in_area <- paste0("state:", state)
}
}
# if msas are specified construct for call to filter by msa
if (geography == "metropolitan statistical area/micropolitan statistical area") {
# before 2016 msa geography name in api is different
if (year <= 2015) {
geography <- "metropolitan statistical areas"
}
if (!is.null(msa)) {
for_area <- paste0(geography, ":", msa)
} else {
for_area <- paste0(geography, ":*")
}
}
# build query for api call
# for calls where in_area is not defined, "in" will be NULL and not included in GET()
query <- list(get = vars_to_get,
"for" = for_area,
"in" = in_area,
key = key)
# execute api call
call <- GET(base, query = query)
# print nicely formatted api call if requested
if (show_call) {
call_url <- gsub("&key.*", "", call$url)
message(paste("Census API call:", utils::URLdecode(call_url)))
}
# Make sure call status returns 200, else, print the error message for the user.
if (call$status_code != 200) {
msg <- content(call, as = "text")
if (grepl("The requested resource is not available", msg)) {
stop("One or more of your requested variables is likely not available at the requested geography. Please refine your selection.", call. = FALSE)
} else {
stop(sprintf("Your API call has errors. The API message returned is %s.", msg), call. = FALSE)
}
}
# convert json response to text
content <- content(call, as = "text")
if (grepl("You included a key with this request", content)) {
stop("You have supplied an invalid or inactive API key. To obtain a valid API key, visit https://api.census.gov/data/key_signup.html. To activate your key, be sure to click the link provided to you in the email from the Census Bureau that contained your key.", call. = FALSE)
}
# convert response to json
dat <- fromJSON(content)
# first row of response are colnames
colnames(dat) <- dat[1, ]
dat <- dat[-1, ]
# convert to tibble
dat <- as_tibble(dat)
# remove columns that contains geographies automatically returned by api - we already have this as GEOID1
dat <- dat[, !(names(dat) %in% c("state", "county", "county subdivision",
"metropolitan statistical area/micropolitan statistical area",
"metropolitan statistical areas"))]
# convert vars to numeric, skip geoids and names
str_vars <- variables[grepl("_NAME|GEOID|^STATE|^COUNTY|^MCD|^METRO", variables)]
num_vars <- variables[!variables %in% str_vars]
dat[num_vars] <- lapply(dat[num_vars], as.numeric)
return(dat)
}
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Defines functions load_data_flows load_data_pums_vacant load_data_pums load_data_estimates load_data_decennial load_data_acs format_variables_acs
format_variables_acs <- function(variables) {
# find code in 'data-raw/no_moe_vars.R' to pull these vars from api
no_moes <- c("B00001_001", "B00002_001", "B98001_001", "B98001_002", "B98002_001",
"B98002_002", "B98002_003", "B98003_001", "B98011_001", "B98012_001",
"B98012_002", "B98012_003", "B98013_001", "B98013_002", "B98013_003",
"B98013_004", "B98013_005", "B98013_006", "B98013_007", "B98014_001",
"B98021_001", "B98021_002", "B98021_003", "B98021_004", "B98021_005",
"B98021_006", "B98021_007", "B98021_008", "B98021_009", "B98021_010",
"B98022_001", "B98022_002", "B98022_003", "B98022_004", "B98022_005",
"B98022_006", "B98022_007", "B98022_008", "B98022_009", "B98022_010",
"B98031_001", "B98032_001", "B99011_001", "B99011_002", "B99011_003",
"B99012_001", "B99012_002", "B99012_003", "B99021_001", "B99021_002",
"B99021_003", "B99031_001", "B99031_002", "B99031_003", "B99051_001",
"B99051_002", "B99051_003", "B99051_004", "B99051_005", "B99051_006",
"B99051_007", "B99052_001", "B99052_002", "B99052_003", "B99052_004",
"B99052_005", "B99052_006", "B99052_007", "B99052PR_001", "B99052PR_002",
"B99052PR_003", "B99052PR_004", "B99052PR_005", "B99052PR_006",
"B99052PR_007", "B99053_001", "B99053_002", "B99053_003", "B99061_001",
"B99061_002", "B99061_003", "B99071_001", "B99071_002", "B99071_003",
"B99072_001", "B99072_002", "B99072_003", "B99072_004", "B99072_005",
"B99072_006", "B99072_007", "B99080_001", "B99080_002", "B99080_003",
"B99081_001", "B99081_002", "B99081_003", "B99081_004", "B99081_005",
"B99082_001", "B99082_002", "B99082_003", "B99082_004", "B99082_005",
"B99083_001", "B99083_002", "B99083_003", "B99083_004", "B99083_005",
"B99084_001", "B99084_002", "B99084_003", "B99084_004", "B99084_005",
"B99085_001", "B99085_002", "B99085_003", "B99086_001", "B99086_002",
"B99086_003", "B99087_001", "B99087_002", "B99087_003", "B99087_004",
"B99087_005", "B99088_001", "B99088_002", "B99088_003", "B99088_004",
"B99088_005", "B99089_001", "B99089_002", "B99089_003", "B99092_001",
"B99092_002", "B99092_003", "B99102_001", "B99102_002", "B99102_003",
"B99103_001", "B99103_002", "B99103_003", "B99104_001", "B99104_002",
"B99104_003", "B99104_004", "B99104_005", "B99104_006", "B99104_007",
"B99121_001", "B99121_002", "B99121_003", "B99122_001", "B99122_002",
"B99122_003", "B99123_001", "B99123_002", "B99123_003", "B99124_001",
"B99124_002", "B99124_003", "B99125_001", "B99125_002", "B99125_003",
"B99126_001", "B99126_002", "B99126_003", "B99131_001", "B99131_002",
"B99131_003", "B99132_001", "B99132_002", "B99132_003", "B99141_001",
"B99141_002", "B99141_003", "B99142_001", "B99142_002", "B99142_003",
"B99151_001", "B99151_002", "B99151_003", "B99152_001", "B99152_002",
"B99152_003", "B99161_001", "B99161_002", "B99161_003", "B99162_001",
"B99162_002", "B99162_003", "B99162_004", "B99162_005", "B99162_006",
"B99162_007", "B99163_001", "B99163_002", "B99163_003", "B99163_004",
"B99163_005", "B99171_001", "B99171_002", "B99171_003", "B99171_004",
"B99171_005", "B99171_006", "B99171_007", "B99171_008", "B99171_009",
"B99171_010", "B99171_011", "B99171_012", "B99171_013", "B99171_014",
"B99171_015", "B99172_001", "B99172_002", "B99172_003", "B99172_004",
"B99172_005", "B99172_006", "B99172_007", "B99172_008", "B99172_009",
"B99172_010", "B99172_011", "B99172_012", "B99172_013", "B99172_014",
"B99172_015", "B99181_001", "B99181_002", "B99181_003", "B99182_001",
"B99182_002", "B99182_003", "B99183_001", "B99183_002", "B99183_003",
"B99184_001", "B99184_002", "B99184_003", "B99185_001", "B99185_002",
"B99185_003", "B99186_001", "B99186_002", "B99186_003", "B99187_001",
"B99187_002", "B99187_003", "B99187_004", "B99187_005", "B99187_006",
"B99187_007", "B99191_001", "B99191_002", "B99191_003", "B99191_004",
"B99191_005", "B99191_006", "B99191_007", "B99191_008", "B99192_001",
"B99192_002", "B99192_003", "B99192_004", "B99192_005", "B99192_006",
"B99192_007", "B99192_008", "B99193_001", "B99193_002", "B99193_003",
"B99193_004", "B99193_005", "B99193_006", "B99193_007", "B99193_008",
"B99194_001", "B99194_002", "B99194_003", "B99194_004", "B99194_005",
"B99194_006", "B99194_007", "B99194_008", "B99201_001", "B99201_002",
"B99201_003", "B99201_004", "B99201_005", "B99201_006", "B99201_007",
"B99201_008", "B99211_001", "B99211_002", "B99211_003", "B99212_001",
"B99212_002", "B99212_003", "B99221_001", "B99221_002", "B99221_003",
"B99231_001", "B99231_002", "B99231_003", "B99232_001", "B99232_002",
"B99232_003", "B99233_001", "B99233_002", "B99233_003", "B99233_004",
"B99233_005", "B99234_001", "B99234_002", "B99234_003", "B99234_004",
"B99234_005", "B99241_001", "B99241_002", "B99241_003", "B99242_001",
"B99242_002", "B99242_003", "B99243_001", "B99243_002", "B99243_003",
"B99244_001", "B99244_002", "B99244_003", "B99245_001", "B99245_002",
"B99245_003", "B99246_001", "B99246_002", "B99246_003", "B992510_001",
"B992510_002", "B992510_003", "B992511_001", "B992511_002", "B992511_003",
"B992512_001", "B992512_002", "B992512_003", "B992513_001", "B992513_002",
"B992513_003", "B992514_001", "B992514_002", "B992514_003", "B992515_001",
"B992515_002", "B992515_003", "B992516_001", "B992516_002", "B992516_003",
"B992518_001", "B992518_002", "B992518_003", "B992519_001", "B992519_002",
"B992519_003", "B99252_001", "B99252_002", "B99252_003", "B992520_001",
"B992520_002", "B992520_003", "B992521_001", "B992521_002", "B992521_003",
"B992522_001", "B992522_002", "B992522_003", "B992522_004", "B992522_005",
"B992522_006", "B992522_007", "B992523_001", "B992523_002", "B992523_003",
"B99253_001", "B99253_002", "B99253_003", "B99254_001", "B99254_002",
"B99254_003", "B99255_001", "B99255_002", "B99255_003", "B99256_001",
"B99256_002", "B99256_003", "B99257_001", "B99257_002", "B99257_003",
"B99258_001", "B99258_002", "B99258_003", "B99259_001", "B99259_002",
"B99259_003", "B992701_001", "B992701_002", "B992701_003", "B992702_001",
"B992702_002", "B992702_003", "B992703_001", "B992703_002", "B992703_003",
"B992704_001", "B992704_002", "B992704_003", "B992705_001", "B992705_002",
"B992705_003", "B992706_001", "B992706_002", "B992706_003", "B992707_001",
"B992707_002", "B992707_003", "B992708_001", "B992708_002", "B992708_003",
"B992709_001", "B992709_002", "B992709_003", "B99281_001", "B99281_002",
"B99281_003", "B99282_001", "B99282_002", "B99282_003", "B99282_004",
"B99282_005", "B99282_006", "B99282_007", "B99282_008", "B99282_009",
"B99283_001", "B99283_002", "B99283_003", "B99283_004", "B99283_005")
# First, remove E or M if user has put it in
variables1 <- map_chr(variables, function(x) {
if (str_sub(x, -1) %in% c("E", "M")) {
x <- str_sub(x, 1, -2)
} else {
x <- x
}
})
# Now, make unique
variables2 <- unique(variables1)
# The comparison profile doesn't have MOEs, so account for that
if (!any(grepl("^CP[0-9].", variables2))) {
# Next, separate into vars with and without MOEs
variables2a <- variables2[!variables2 %in% no_moes]
variables2_nomoe <- variables2[variables2 %in% no_moes]
# Now, expand with both E and M if MOE is applicable
variables3 <- map_chr(variables2a, function(y) paste0(y, c("E", "M"), collapse = ","))
if (length(variables2_nomoe)) {
variables3_nomoe <- paste0(variables2_nomoe, "E")
variables3 <- c(variables3, variables3_nomoe)
}
} else {
variables3 <- paste0(variables2, "E")
}
# Now, put together all these strings if need be
var <- paste0(variables3, collapse = ",")
return(var)
}
load_data_acs <- function(geography, formatted_variables, key, year, state = NULL,
county = NULL, zcta = NULL, survey, show_call = FALSE) {
base <- paste("https://api.census.gov/data",
as.character(year), "acs",
survey, sep = "/")
if (grepl("^DP", formatted_variables)) {
message("Using the ACS Data Profile")
base <- paste0(base, "/profile")
}
if (grepl("^S[0-9].", formatted_variables)) {
message("Using the ACS Subject Tables")
base <- paste0(base, "/subject")
}
if (grepl("^CP[0-9].", formatted_variables)) {
message("Using the ACS Comparison Profile")
base <- paste0(base, "/cprofile")
}
for_area <- paste0(geography, ":*")
if (!is.null(zcta)) {
# if (is.null(state)) {
# stop("The `zcta` argument requires specifying a state.", call. = FALSE)
# }
if (!is.null(county)) {
stop("ZCTAs do not nest within counties, so `county` should not be specified.",
call. = FALSE)
}
if (!is.null(state)) {
state <- map_chr(state, function(x) {
validate_state(x)
})
in_area <- paste0("state:", state)
} else {
in_area <- NULL
}
for_area <- paste0(zcta, collapse = ",")
vars_to_get <- paste0(formatted_variables, ",NAME")
call <- GET(base, query = list(get = vars_to_get,
"for" = paste0(geography, ":", for_area),
"in" = in_area,
key = key))
} else if (!is.null(state) && is.null(zcta)) {
state <- map_chr(state, function(x) {
validate_state(x)
})
if (length(state) > 1) {
state <- paste0(state, collapse = ",")
}
if (geography == "state") {
for_area <- paste0("state:", state)
}
if (!is.null(county)) {
county <- map_chr(county, function(x) {
validate_county(state, x)
})
if (length(county) > 1) {
county <- paste0(county, collapse = ",")
}
if (geography == "county") {
for_area <- paste0("county:", county)
in_area <- paste0("state:", state)
} else {
in_area <- paste0("state:", state,
"+county:", county)
}
} else {
if (geography == "block group" && is.null(county)) {
in_area <- paste0("state:", state,
"&in=county:*")
} else {
in_area <- paste0("state:", state)
}
}
vars_to_get <- paste0(formatted_variables, ",NAME")
if (geography == "state" && !is.null(state)) {
call <- GET(base, query = list(get = vars_to_get,
"for" = for_area,
key = key))
} else {
call <- GET(base, query = list(get = vars_to_get,
"for" = for_area,
"in" = in_area,
key = key))
}
} else {
vars_to_get <- paste0(formatted_variables, ",NAME")
call <- GET(base, query = list(get = vars_to_get,
"for" = paste0(geography, ":*"),
key = key))
}
if (show_call) {
call_url <- gsub("&key.*", "", call$url)
message(paste("Census API call:", call_url))
}
# Make sure call status returns 200, else, print the error message for the user.
if (call$status_code != 200) {
msg <- content(call, as = "text")
if (grepl("The requested resource is not available", msg)) {
stop("One or more of your requested variables is likely not available at the requested geography. Please refine your selection.", call. = FALSE)
} else {
stop(sprintf("Your API call has errors. The API message returned is %s.", msg), call. = FALSE)
}
}
# callStatus <- http_status(call)
# if (callStatus$reason != "OK") {
# message(callStatus$category, " ", callStatus$reason, " ", callStatus$message)
# } else {
# content <- content(call, as = "text")
# }
#
# validate_call(content = content, geography = geography, year = year,
# dataset = survey)
content <- content(call, as = "text")
if (grepl("You included a key with this request", content)) {
stop("You have supplied an invalid or inactive API key. To obtain a valid API key, visit https://api.census.gov/data/key_signup.html. To activate your key, be sure to click the link provided to you in the email from the Census Bureau that contained your key.", call. = FALSE)
}
dat <- fromJSON(content)
colnames(dat) <- dat[1,]
dat <- as_tibble(dat)
dat <- dat[-1,]
var_vector <- unlist(strsplit(formatted_variables, split = ","))
dat[var_vector] <- lapply(dat[var_vector], as.numeric)
v2 <- c(var_vector, "NAME")
# Get the geography ID variables
id_vars <- names(dat)[! names(dat) %in% v2]
# Paste into a GEOID column
dat$GEOID <- do.call(paste0, dat[id_vars])
# Now, remove them
dat <- dat[, !(names(dat) %in% id_vars)]
return(dat)
}
load_data_decennial <- function(geography, variables, key, year, sumfile, pop_group,
state = NULL, county = NULL, show_call = FALSE) {
var <- paste0(variables, collapse = ",")
if (year == 1990) {
vars_to_get <- paste0(var, ",ANPSADPI")
} else {
vars_to_get <- paste0(var, ",NAME")
}
if (!is.null(pop_group)) {
if (pop_group == "all") {
vars_to_get <- paste0(vars_to_get, ",POPGROUP")
pop_group <- NULL
}
}
base <- paste0("https://api.census.gov/data/",
year,
"/dec/",
sumfile)
# if (year == 2010) {
# base <- paste0("https://api.census.gov/data/",
# year,
# "/dec/",
# sumfile)
# } else {
# base <- paste0("https://api.census.gov/data/",
# year,
# "/",
# sumfile)
# }
for_area <- paste0(geography, ":*")
if (!is.null(state)) {
state <- map_chr(state, function(x) {
validate_state(x)
})
if (length(state) > 1) {
state <- paste0(state, collapse = ",")
}
if (geography == "state") {
for_area <- paste0("state:", state)
}
if (!is.null(county)) {
county <- map_chr(county, function(x) {
validate_county(state, x)
})
if (length(county) > 1) {
county <- paste0(county, collapse = ",")
}
if (geography == "county") {
for_area <- paste0("county:", county)
in_area <- paste0("state:", state)
} else {
in_area <- paste0("state:", state,
"+county:", county)
}
} else {
if ((geography == "block group" || geography == "block") && is.null(county)) {
in_area <- paste0("state:", state,
"&in=county:*")
} else {
in_area <- paste0("state:", state)
}
}
if (geography == "state" && !is.null(state)) {
call <- GET(base, query = list(get = vars_to_get,
"for" = for_area,
key = key,
"POPGROUP" = pop_group))
} else {
call <- GET(base, query = list(get = vars_to_get,
"for" = for_area,
"in" = in_area,
key = key,
"POPGROUP" = pop_group))
}
}
else {
call <- GET(base, query = list(get = vars_to_get,
"for" = paste0(geography, ":*"),
key = key,
"POPGROUP" = pop_group))
}
if (show_call) {
call_url <- gsub("&key.*", "", call$url)
message(paste("Census API call:", call_url))
}
# Make sure call status returns 200, else, print the error message for the user.
# Try to handle 204's here
if (call$status_code == 204) {
if (sumfile == "ddhca") {
rlang::abort(c("Your DDHC-A request returned No Content from the API.",
"i" = "The DDHC-A file uses an 'adaptive design' where data availability varies by geography and by population group.",
"i" = "Read Section 3-1 at https://www2.census.gov/programs-surveys/decennial/2020/technical-documentation/complete-tech-docs/detailed-demographic-and-housing-characteristics-file-a/2020census-detailed-dhc-a-techdoc.pdf for more information.",
"i" = "In tidycensus, use the function `check_ddhca_groups()` to see if your data is available."))
} else {
rlang::abort("No content was returned from the API. Please refine your selection.")
}
}
if (call$status_code != 200) {
msg <- content(call, as = "text")
if (grepl("The requested resource is not available", msg)) {
stop("One or more of your requested variables is likely not available at the requested geography. Please refine your selection.", call. = FALSE)
} else if (grepl("unknown variable", msg) && length(variables) > 1 && year < 2010) {
stop(sprintf("The Census API has returned the error message %s.\nThis may be due to mixing SF1 and SF3 variables. If so, separate your requests to SF1 and SF3 when using `get_decennial()` by using the `sumfile` argument in separate calls.", msg))
} else {
stop(sprintf("Your API call has errors. The API message returned is %s.", msg), call. = FALSE)
}
}
# callStatus <- http_status(call)
# if (callStatus$reason != "OK") {
# if (sumfile == "sf1") {
# message("Checking SF3 API for data...")
# } else {
# message(callStatus$category, " ", callStatus$reason, " ", callStatus$message)
# }
# } else {
# content <- content(call, as = "text")
# }
content <- content(call, as = "text")
if (grepl("You included a key with this request", content)) {
stop("You have supplied an invalid or inactive API key. To obtain a valid API key, visit https://api.census.gov/data/key_signup.html. To activate your key, be sure to click the link provided to you in the email from the Census Bureau that contained your key.", call. = FALSE)
}
# Fix issue in SF3 2000 API - https://github.com/walkerke/tidycensus/issues/22
if (year == 2000 && sumfile == "sf3") {
content <- str_replace(content, 'O"Brien', "O'Brien")
content <- str_replace(content, 'Prince George"s', "Prince George's")
content <- str_replace(content, 'Queen Anne"s', "Queen Anne's")
content <- str_replace(content, 'St. Mary"s', "St. Mary's")
}
dat <- fromJSON(content)
colnames(dat) <- dat[1,]
dat <- as_tibble(dat)
dat <- dat[-1,]
if (year == 1990) {
dat <- rename(dat, NAME = ANPSADPI)
}
vnum <- variables[variables != "POPGROUP"]
dat[vnum] <- lapply(dat[vnum], as.numeric)
v2 <- c(variables, "NAME", "POPGROUP")
# Get the geography ID variables
id_vars <- names(dat)[! names(dat) %in% v2]
# Paste into a GEOID column
# Apply tract band-aid (GH issue #317)
if ("tract" %in% id_vars && year == 2000) {
dat$tract <- stringr::str_pad(dat$tract, 6, "right", "0")
}
dat$GEOID <- do.call(paste0, dat[id_vars])
# Fix issues with ZCTAs if necessary
if (geography == "zip code tabulation area") {
l <- unique(nchar(dat$GEOID))
if (l == 7) {
dat$GEOID <- str_sub(dat$GEOID, 3, 7)
}
}
# Now, remove them
dat <- dat[, !(names(dat) %in% id_vars)]
return(dat)
}
load_data_estimates <- function(geography, product = NULL, variables = NULL, key, year,
time_series, state = NULL, county = NULL, show_call = FALSE) {
if (!is.null(product)) {
if (!product %in% c("population", "components",
"charagegroups", "housing")) {
stop("You have selected an invalid product. Valid requests are 'population', 'components', 'characteristics', and 'housing'.", call. = FALSE)
}
}
for_area <- paste0(geography, ":*")
if (year >= 2019) {
geo_name <- "NAME"
} else {
geo_name <- "GEONAME"
}
if (is.null(variables)) {
if (is.null(product)) {
stop("Either a product or a vector of variables (from a single product) is required.", call. = FALSE)
} else {
if (product == "population") {
vars_to_get <- paste0(geo_name, ",POP,DENSITY")
} else if (product == "components") {
vars_to_get <- paste0(geo_name, ",BIRTHS,DEATHS,DOMESTICMIG,INTERNATIONALMIG,NATURALINC,NETMIG,RBIRTH,RDEATH,RDOMESTICMIG,RINTERNATIONALMIG,RNATURALINC,RNETMIG")
} else if (product == "housing") {
vars_to_get <- paste0(geo_name, ",HUEST")
} else {
stop("Other products are not yet supported.", call. = FALSE)
}
}
} else {
if (!is.null(product)) {
if (product != "charagegroups") {
stop("Please specify either a product or a vector of variables, but not both.", call. = FALSE)
}
} else {
if (all(variables %in% c("POP", "DENSITY"))) {
product <- "population"
} else if (all(variables %in% c("BIRTHS", "DEATHS", "DOMESTICMIG", "INTERNATIONALMIG", "NATURALINC", "NETMIG", "RBIRTH", "RDEATH", "RDOMESTICMIG", "RINTERNATIONALMIG", "RNATURALINC", "RNETMIG"))) {
product <- "components"
} else if (all(variables %in% c("HUEST"))) {
product <- "housing"
} else {
stop(
paste('Variables must be a subset of only one of the following sets of valid variables:',
'for population estimates, c("POP", "DENSITY");',
'for housing unit estimates, c("HUEST");',
'and for components of change estimates, c("BIRTHS", "DEATHS", "DOMESTICMIG", "INTERNATIONALMIG", "NATURALINC", "NETMIG", "RBIRTH", "RDEATH", "RDOMESTICMIG", "RINTERNATIONALMIG", "RNATURALINC", "RNETMIG").'),
call. = FALSE)
}
}
vars_to_get <- paste0(geo_name, ",", paste0(variables, collapse = ","))
}
if (time_series) {
if (product == "components") {
if (year >= 2018) {
vars_to_get <- paste0(vars_to_get, ",PERIOD_CODE")
} else {
vars_to_get <- paste0(vars_to_get, ",PERIOD")
}
} else {
if (year >= 2018) {
vars_to_get <- paste0(vars_to_get, ",DATE_CODE")
} else {
vars_to_get <- paste0(vars_to_get, ",DATE_")
}
}
}
base <- sprintf("https://api.census.gov/data/%s/pep/%s", year, product)
if (!is.null(state)) {
state <- map_chr(state, function(x) {
validate_state(x)
})
if (length(state) > 1) {
state <- paste0(state, collapse = ",")
}
if (geography == "state") {
for_area <- paste0("state:", state)
}
if (!is.null(county)) {
county <- map_chr(county, function(x) {
validate_county(state, x)
})
if (length(county) > 1) {
county <- paste0(county, collapse = ",")
}
if (geography == "county") {
for_area <- paste0("county:", county)
in_area <- paste0("state:", state)
} else {
in_area <- paste0("state:", state,
"+county:", county)
}
} else {
in_area <- paste0("state:", state)
}
if (geography == "state" && !is.null(state)) {
call <- GET(base, query = list(get = vars_to_get,
"for" = for_area,
key = key))
} else {
call <- GET(base, query = list(get = vars_to_get,
"for" = for_area,
"in" = in_area,
key = key))
}
}
else {
call <- GET(base, query = list(get = vars_to_get,
"for" = paste0(geography, ":*"),
key = key))
}
if (show_call) {
call_url <- gsub("&key.*", "", call$url)
message(paste("Census API call:", call_url))
}
# Make sure call status returns 200, else, print the error message for the user.
if (call$status_code != 200) {
msg <- content(call, as = "text")
if (grepl("The requested resource is not available", msg)) {
stop("One or more of your requested variables is likely not available at the requested geography. Please refine your selection.", call. = FALSE)
} else {
stop(sprintf("Your API call has errors. The API message returned is %s.", msg), call. = FALSE)
}
}
content <- content(call, as = "text")
if (grepl("You included a key with this request", content)) {
stop("You have supplied an invalid or inactive API key. To obtain a valid API key, visit https://api.census.gov/data/key_signup.html. To activate your key, be sure to click the link provided to you in the email from the Census Bureau that contained your key.", call. = FALSE)
}
dat <- fromJSON(content)
colnames(dat) <- dat[1,]
dat <- as_tibble(dat)
dat <- dat[-1,]
var_vector <- unlist(strsplit(vars_to_get, split = ","))
var_vector <- var_vector[var_vector != geo_name]
dat[var_vector] <- lapply(dat[var_vector], as.numeric)
v2 <- c(var_vector, geo_name)
if ("county" %in% names(dat)) {
dat$county <- stringr::str_pad(dat$county, 3, "left", "0")
}
# Get the geography ID variables
id_vars <- names(dat)[! names(dat) %in% v2]
# Paste into a GEOID column
dat$GEOID <- do.call(paste0, dat[id_vars])
# Now, remove them
dat <- dat[, !(names(dat) %in% id_vars)]
return(dat)
}
load_data_pums <- function(variables, state, puma, key, year, survey,
variables_filter, recode, show_call) {
# for which years is data dictionary available in pums_variables?
# we'll use this a couple times later on
recode_years <- 2017:2023
base <- sprintf("https://api.census.gov/data/%s/acs/%s/pums",
year, survey)
if (!is.null(puma)) {
if (length(state) > 1) {
stop('When requesting PUMAs for more than one state, you must set state to "multiple" and set puma to a named vector of state/PUMA pairs.', call. = FALSE)
}
# pumas in multiple states can be requested with a named vector of
# state / puma pairs in the puma argument of get_pums()
if (state == "multiple") {
# print FIPS code of states used just once
purrr::walk(unique(names(puma)), validate_state)
geo <- purrr::map2_chr(names(puma), unname(puma), function(x, y) {
paste0("7950000US", suppressMessages(validate_state(x)), y)
})
geo <- paste0(geo, collapse = ",")
} else {
# if PUMAs requested are in one state only
state <- validate_state(state)
geo <- purrr::map_chr(puma, function(x) {
paste0("7950000US", state, x)
})
if (length(puma) > 1) {
geo <- paste0(geo, collapse = ",")
}
}
} else {
# if no PUMAs specified, get all PUMAs in each state requested
if (!is.null(state)) {
geo <- purrr::map_chr(state, function(x) {
paste0("0400000US", validate_state(x))
})
} else {
geo <- NULL
}
if (length(state) > 1) {
geo <- paste0(geo, collapse = ",")
}
}
# Handle variables & variable filter
# If a variables filter is supplied, those variables should be removed from
# variables (if applicable)
if (!is.null(variables_filter)) {
filter_names <- names(variables_filter)
variables <- variables[!variables %in% filter_names]
if (length(variables) > 0) {
var <- paste0(variables, collapse = ",")
# Handle housing-only query
if ("SPORDER" %in% filter_names) {
vars_to_get <- paste0("SERIALNO,WGTP,PWGTP,", var)
} else {
vars_to_get <- paste0("SERIALNO,SPORDER,WGTP,PWGTP,", var)
}
} else {
if ("SPORDER" %in% filter_names) {
vars_to_get <- "SERIALNO,WGTP,PWGTP"
} else {
vars_to_get <- "SERIALNO,SPORDER,WGTP,PWGTP"
}
}
# If geo is NULL, state should be added back in here
if (is.null(geo)) {
vars_to_get <- paste0(vars_to_get, ",ST")
}
# Combine the default query with the variables filter query
query_default <- list(get = vars_to_get,
ucgid = geo,
key = key)
# Collapse vectors if supplied
variables_filter_collapsed <- purrr::map(variables_filter,
~{paste0(.x, collapse = ",")})
query <- c(
list(get = vars_to_get),
variables_filter_collapsed,
list(ucgid = geo, key = key)
)
} else {
var <- paste0(variables, collapse = ",")
vars_to_get <- paste0("SERIALNO,SPORDER,WGTP,PWGTP,", var)
# If geo is NULL, state should be added back in here
if (is.null(geo)) {
if (year > 2022) {
vars_to_get <- paste0(vars_to_get, ",STATE")
} else {
vars_to_get <- paste0(vars_to_get, ",ST")
}
}
query <- list(get = vars_to_get,
ucgid = geo,
key = key)
}
call <- httr::GET(base,
query = query,
httr::progress())
if (show_call) {
call_url <- gsub("&key.*", "", call$url)
message(paste("Census API call:", call_url))
}
# Make sure call status returns 200, else, print the error message for the user.
if (call$status_code != 200) {
msg <- content(call, as = "text")
if (grepl("The requested resource is not available", msg)) {
stop("One or more of your requested variables is likely not available at the requested geography. Please refine your selection.", call. = FALSE)
} else {
stop(sprintf("Your API call has errors. The API message returned is %s.", msg), call. = FALSE)
}
}
content <- httr::content(call, as = "text")
if (grepl("You included a key with this request", content)) {
stop("You have supplied an invalid or inactive API key. To obtain a valid API key, visit https://api.census.gov/data/key_signup.html. To activate your key, be sure to click the link provided to you in the email from the Census Bureau that contained your key.", call. = FALSE)
}
dat <- jsonlite::fromJSON(content)
colnames(dat) <- dat[1,]
dat <- dplyr::as_tibble(dat, .name_repair = "minimal")
dat <- dat[-1,]
# Convert the weights columns to numeric
dat <- dat %>%
dplyr::mutate(
dplyr::across(
.cols = dplyr::contains("WGTP"),
as.numeric
)
)
# dat$WGTP <- as.numeric(dat$WGTP)
# dat$PWGTP <- as.numeric(dat$PWGTP)
# Filter the pums lookup table for the selected year and survey
pums_variables_filter <- tidycensus::pums_variables %>%
filter(year == !!year, survey == !!survey)
# Do some clean up of the API response: pad returned values with 0s when
# necessary to match data dictionary codes and
# convert variables to numeric according to data dictionary
# Only works for years included in pums_variables data dictionary
if (year %in% recode_years) {
var_val_length <- pums_variables_filter %>%
filter(!is.na(.data$val_length)) %>%
distinct(.data$var_code, .data$val_length, .data$val_na)
num_vars <- pums_variables_filter %>%
filter(.data$data_type == "num") %>%
distinct(.data$var_code) %>%
pull()
# For all variables in which we know what the length should be, pad with 0s
dat_padded <- suppressWarnings(
dat %>%
select(.data$SERIALNO, .data$SPORDER, any_of(var_val_length$var_code)) %>%
pivot_longer(
cols = -c(.data$SERIALNO, .data$SPORDER),
names_to = "var_code",
values_to = "val"
) %>%
left_join(var_val_length, by = "var_code") %>%
mutate(
val = ifelse(!is.na(.data$val_na) & .data$val_na == .data$val, strrep("b", .data$val_length), .data$val),
val = ifelse(.data$var_code != "NAICSP", str_pad(.data$val, .data$val_length, pad = "0"), .data$val),
val = ifelse(.data$var_code == "NAICSP" & .data$val == "*", "bbbbbbbb", .data$val), # special NULL value returned by API for this var
) %>%
select(-.data$val_length, -.data$val_na) %>%
pivot_wider(
names_from = .data$var_code,
values_from = .data$val
)
)
# Rejoin padded variables to API return
dat <- dat %>%
select(.data$SERIALNO, .data$SPORDER, !any_of(var_val_length$var_code)) %>%
left_join(dat_padded, by = c("SERIALNO", "SPORDER")) %>%
mutate_at(vars(any_of(num_vars)), as.double)
}
# Do you want to return value labels also?
if (recode) {
# Only works for years included in pums_variables data dictionary
if (year %in% recode_years) {
var_lookup <- pums_variables_filter %>%
select(.data$var_code, val = .data$val_min, .data$val_label)
# Vector of variables that are possible to recode
vars_to_recode <- pums_variables_filter %>%
filter(.data$recode) %>%
distinct(.data$var_code) %>%
pull()
# Pivot to long format and join variable codes to lookup table with labels
recoded_long <- suppressWarnings(
dat %>%
select(.data$SERIALNO, .data$SPORDER, any_of(vars_to_recode)) %>%
pivot_longer(
cols = -c(.data$SERIALNO, .data$SPORDER),
names_to = "var_code",
values_to = "val"
) %>%
left_join(var_lookup, by = c("var_code", "val")) %>%
select(-.data$val)
)
# Create a "pivot spec" with nicer names for the labeled columns
# https://tidyr.tidyverse.org/articles/pivot.html#wider-1
spec <- recoded_long %>%
build_wider_spec(
names_from = .data$var_code,
values_from = .data$val_label
) %>%
mutate(.name = paste0(.data$var_code, "_label", ""))
recoded_wide <- recoded_long %>%
pivot_wider_spec(spec)
recode_v <- recoded_long %>% pull(var_code) %>% unique()
for (var in recode_v){
order_v <- var_lookup %>%
filter(var_code==var) %>%
pull(val_label)
var_label <- str_c(var, "_label")
recoded_wide[[var_label]] <-
readr::parse_factor(recoded_wide[[var_label]], ordered=TRUE, levels=order_v)
}
# Join recoded columns to API return
dat <- dat %>%
left_join(recoded_wide, by = c("SERIALNO", "SPORDER"))
} else {
message(paste("Recoding is currently supported for",
min(recode_years), "-", max(recode_years),
"data. Returning original data only."))
}
}
return(dat)
}
load_data_pums_vacant <- function(variables, state, puma, key, year, survey,
variables_filter, recode, show_call) {
# for which years is data dictionary available in pums_variables?
# we'll use this a couple times later on
recode_years <- 2017:2023
base <- sprintf("https://api.census.gov/data/%s/acs/%s/pums",
year, survey)
# Parse the variables for housing vs. person variables
housing_catalog <- pums_variables %>%
dplyr::filter(level != "person" | is.na(level)) %>%
dplyr::pull(var_code) %>%
unique()
housing_variables <- variables[variables %in% housing_catalog]
if (!is.null(puma)) {
if (length(state) > 1) {
stop('When requesting PUMAs for more than one state, you must set state to "multiple" and set puma to a named vector of state/PUMA pairs.', call. = FALSE)
}
# pumas in multiple states can be requested with a named vector of
# state / puma pairs in the puma argument of get_pums()
if (state == "multiple") {
# print FIPS code of states used just once
purrr::walk(unique(names(puma)), validate_state)
geo <- purrr::map2_chr(names(puma), unname(puma), function(x, y) {
paste0("7950000US", suppressMessages(validate_state(x)), y)
})
geo <- paste0(geo, collapse = ",")
} else {
# if PUMAs requested are in one state only
state <- validate_state(state)
geo <- purrr::map_chr(puma, function(x) {
paste0("7950000US", state, x)
})
if (length(puma) > 1) {
geo <- paste0(geo, collapse = ",")
}
}
} else {
# if no PUMAs specified, get all PUMAs in each state requested
if (!is.null(state)) {
geo <- purrr::map_chr(state, function(x) {
paste0("0400000US", validate_state(x))
})
} else {
geo <- NULL
}
if (length(state) > 1) {
geo <- paste0(geo, collapse = ",")
}
}
# Handle variables & variable filter
# In this scenario, the "filter" is for vacant households
# This will be specified on the `get_pums()` side
if (!is.null(variables_filter)) {
filter_names <- names(variables_filter)
housing_variables <- housing_variables[!housing_variables %in% filter_names]
var <- paste0(housing_variables, collapse = ",")
vars_to_get <- paste0("SERIALNO,WGTP,", var)
# If geo is NULL, state should be added back in here
if (is.null(geo)) {
if (year > 2022) {
vars_to_get <- paste0(vars_to_get, ",STATE")
} else {
vars_to_get <- paste0(vars_to_get, ",ST")
}
}
# Combine the default query with the variables filter query
query_default <- list(get = vars_to_get,
ucgid = geo,
key = key)
# Collapse vectors if supplied
variables_filter_collapsed <- purrr::map(variables_filter,
~{paste0(.x, collapse = ",")})
query <- c(
list(get = vars_to_get),
variables_filter_collapsed,
list(ucgid = geo, key = key)
)
} else {
# Not implemented here
# var <- paste0(variables, collapse = ",")
#
# vars_to_get <- paste0("SERIALNO,SPORDER,WGTP,PWGTP,", var)
#
# # If geo is NULL, state should be added back in here
# if (is.null(geo)) {
# vars_to_get <- paste0(vars_to_get, ",ST")
# }
#
# query <- list(get = vars_to_get,
# ucgid = geo,
# key = key)
}
call <- httr::GET(base,
query = query,
httr::progress())
if (show_call) {
call_url <- gsub("&key.*", "", call$url)
message(paste("Census API call:", call_url))
}
# Make sure call status returns 200, else, print the error message for the user.
if (call$status_code != 200) {
msg <- content(call, as = "text")
if (grepl("The requested resource is not available", msg)) {
stop("One or more of your requested variables is likely not available at the requested geography. Please refine your selection.", call. = FALSE)
} else {
stop(sprintf("Your API call has errors. The API message returned is %s.", msg), call. = FALSE)
}
}
content <- httr::content(call, as = "text")
if (grepl("You included a key with this request", content)) {
stop("You have supplied an invalid or inactive API key. To obtain a valid API key, visit https://api.census.gov/data/key_signup.html. To activate your key, be sure to click the link provided to you in the email from the Census Bureau that contained your key.", call. = FALSE)
}
dat <- jsonlite::fromJSON(content)
colnames(dat) <- dat[1,]
dat <- dplyr::as_tibble(dat, .name_repair = "minimal")
dat <- dat[-1,]
# Convert the weights columns to numeric
dat <- dat %>%
dplyr::mutate(
dplyr::across(
.cols = dplyr::contains("WGTP"),
as.numeric
)
)
# dat$WGTP <- as.numeric(dat$WGTP)
# dat$PWGTP <- as.numeric(dat$PWGTP)
# Filter the pums lookup table for the selected year and survey
pums_variables_filter <- tidycensus::pums_variables %>%
filter(year == !!year, survey == !!survey)
# Do some clean up of the API response: pad returned values with 0s when
# necessary to match data dictionary codes and
# convert variables to numeric according to data dictionary
# Only works for years included in pums_variables data dictionary
if (year %in% recode_years) {
var_val_length <- pums_variables_filter %>%
dplyr::filter(!is.na(.data$val_length)) %>%
dplyr::distinct(.data$var_code, .data$val_length, .data$val_na)
num_vars <- pums_variables_filter %>%
dplyr::filter(.data$data_type == "num") %>%
dplyr::distinct(.data$var_code) %>%
dplyr::pull()
# For all variables in which we know what the length should be, pad with 0s
dat_padded <- suppressWarnings(
dat %>%
dplyr::select(.data$SERIALNO, dplyr::any_of(var_val_length$var_code)) %>%
tidyr::pivot_longer(
cols = -c(.data$SERIALNO),
names_to = "var_code",
values_to = "val"
) %>%
dplyr::left_join(var_val_length, by = "var_code") %>%
dplyr::mutate(
val = ifelse(!is.na(.data$val_na) & .data$val_na == .data$val, strrep("b", .data$val_length), .data$val),
val = ifelse(.data$var_code != "NAICSP", str_pad(.data$val, .data$val_length, pad = "0"), .data$val),
val = ifelse(.data$var_code == "NAICSP" & .data$val == "*", "bbbbbbbb", .data$val), # special NULL value returned by API for this var
) %>%
dplyr::select(-.data$val_length, -.data$val_na) %>%
tidyr::pivot_wider(
names_from = .data$var_code,
values_from = .data$val
)
)
# Rejoin padded variables to API return
dat <- dat %>%
dplyr::select(.data$SERIALNO, !dplyr::any_of(var_val_length$var_code)) %>%
dplyr::left_join(dat_padded, by = c("SERIALNO")) %>%
dplyr::mutate_at(vars(dplyr::any_of(num_vars)), as.double)
}
# Do you want to return value labels also?
if (recode) {
# Only works for years included in pums_variables data dictionary
if (year %in% recode_years) {
var_lookup <- pums_variables_filter %>%
dplyr::select(.data$var_code, val = .data$val_min, .data$val_label)
# Vector of variables that are possible to recode
vars_to_recode <- pums_variables_filter %>%
dplyr::filter(.data$recode) %>%
dplyr::distinct(.data$var_code) %>%
dplyr::pull()
# Pivot to long format and join variable codes to lookup table with labels
recoded_long <- suppressWarnings(
dat %>%
dplyr::select(.data$SERIALNO, dplyr::any_of(vars_to_recode)) %>%
tidyr::pivot_longer(
cols = -c(.data$SERIALNO),
names_to = "var_code",
values_to = "val"
) %>%
dplyr::left_join(var_lookup, by = c("var_code", "val")) %>%
dplyr::select(-.data$val)
)
# Create a "pivot spec" with nicer names for the labeled columns
# https://tidyr.tidyverse.org/articles/pivot.html#wider-1
spec <- recoded_long %>%
tidyr::build_wider_spec(
names_from = .data$var_code,
values_from = .data$val_label
) %>%
dplyr::mutate(.name = paste0(.data$var_code, "_label", ""))
recoded_wide <- recoded_long %>%
tidyr::pivot_wider_spec(spec)
recode_v <- recoded_long %>% dplyr::pull(var_code) %>% unique()
for (var in recode_v){
order_v <- var_lookup %>%
dplyr::filter(var_code==var) %>%
dplyr::pull(val_label)
var_label <- stringr::str_c(var, "_label")
recoded_wide[[var_label]] <-
readr::parse_factor(recoded_wide[[var_label]], ordered=TRUE, levels=order_v)
}
# Join recoded columns to API return
dat <- dat %>%
dplyr::left_join(recoded_wide, by = c("SERIALNO"))
} else {
message(paste("Recoding is currently supported for",
min(recode_years), "-", max(recode_years),
"data. Returning original data only."))
}
}
return(dat)
}
load_data_flows <- function(geography, variables, key, year, state = NULL,
county = NULL, msa = NULL, show_call = FALSE) {
base <- paste("https://api.census.gov/data", year, "acs", "flows", sep = "/")
# collapse variables in a single string for api call
vars_to_get <- paste0(variables, collapse = ",")
# get all in a given geography unless specified
for_area <- paste0(geography, ":*")
in_area <- NULL
# turn text states into fips if specified, else get all states
# collapse into string for api call
if (!is.null(state)) {
state <- paste(map_chr(state, ~ validate_state(.x)), collapse = ",")
} else {
state <- "*"
}
# turn text counties into fips if specified, validate with specified state
# collapse into string for api call
# can only filter counties for one state
if (!is.null(county)) {
county <- paste(map_chr(county, ~ validate_county(state, .x)), collapse = ",")
}
# if msa codes specified, collapse into string for api call
if (!is.null(msa)) {
msa <- paste0(msa, collapse = ",")
}
# define counties (for) within states (in) for filtering in api call
if (geography == "county") {
if (!is.null(county)) {
for_area <- paste0("county:", county)
}
if (!is.null(state)) {
in_area <- paste0("state:", state)
}
}
# if county is defined, construct for call using state and county
# otherwise filter only at state level
if (geography == "county subdivision") {
if (!is.null(county)) {
in_area <- paste0("state:", state, " ", "county:", county)
} else {
in_area <- paste0("state:", state)
}
}
# if msas are specified construct for call to filter by msa
if (geography == "metropolitan statistical area/micropolitan statistical area") {
# before 2016 msa geography name in api is different
if (year <= 2015) {
geography <- "metropolitan statistical areas"
}
if (!is.null(msa)) {
for_area <- paste0(geography, ":", msa)
} else {
for_area <- paste0(geography, ":*")
}
}
# build query for api call
# for calls where in_area is not defined, "in" will be NULL and not included in GET()
query <- list(get = vars_to_get,
"for" = for_area,
"in" = in_area,
key = key)
# execute api call
call <- GET(base, query = query)
# print nicely formatted api call if requested
if (show_call) {
call_url <- gsub("&key.*", "", call$url)
message(paste("Census API call:", utils::URLdecode(call_url)))
}
# Make sure call status returns 200, else, print the error message for the user.
if (call$status_code != 200) {
msg <- content(call, as = "text")
if (grepl("The requested resource is not available", msg)) {
stop("One or more of your requested variables is likely not available at the requested geography. Please refine your selection.", call. = FALSE)
} else {
stop(sprintf("Your API call has errors. The API message returned is %s.", msg), call. = FALSE)
}
}
# convert json response to text
content <- content(call, as = "text")
if (grepl("You included a key with this request", content)) {
stop("You have supplied an invalid or inactive API key. To obtain a valid API key, visit https://api.census.gov/data/key_signup.html. To activate your key, be sure to click the link provided to you in the email from the Census Bureau that contained your key.", call. = FALSE)
}
# convert response to json
dat <- fromJSON(content)
# first row of response are colnames
colnames(dat) <- dat[1, ]
dat <- dat[-1, ]
# convert to tibble
dat <- as_tibble(dat)
# remove columns that contains geographies automatically returned by api - we already have this as GEOID1
dat <- dat[, !(names(dat) %in% c("state", "county", "county subdivision",
"metropolitan statistical area/micropolitan statistical area",
"metropolitan statistical areas"))]
# convert vars to numeric, skip geoids and names
str_vars <- variables[grepl("_NAME|GEOID|^STATE|^COUNTY|^MCD|^METRO", variables)]
num_vars <- variables[!variables %in% str_vars]
dat[num_vars] <- lapply(dat[num_vars], as.numeric)
return(dat)
}
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