download ACS 5year data from Census API, at blockgroup resolution (slowly if for entire US)

Usage

acs_bybg(
  variables = c(pop = "B01001_001"),
  table = NULL,
  year = NULL,
  cache_table = FALSE,
  output = "wide",
  state = stateinfo$ST,
  county = NULL,
  zcta = NULL,
  geometry = FALSE,
  keep_geo_vars = FALSE,
  summary_var = NULL,
  key = NULL,
  moe_level = 90,
  survey = "acs5",
  show_call = FALSE,
  geography = "block group",
  dropname = TRUE,
  ...
)

Arguments

variables

Vector of variables - see tidycensus::get_acs()

table

see tidycensus::get_acs()

EJSCREEN-relevant key tables are listed in the details section here.

year

optional, e.g., 2024 means ACS5 data covering 2020-2024. Tries to use the most recent available if not specified.

cache_table

see tidycensus::get_acs()

output

see get_acs() from the tidycensus package

state

Default is 2-character abbreviations, vector of all US States, DC, and PR.

county

see get_acs() from the tidycensus package

zcta

see get_acs() from the tidycensus package

geometry

see get_acs() from the tidycensus package

keep_geo_vars

see get_acs() from the tidycensus package

summary_var

see get_acs() from the tidycensus package

key

see get_acs() from the tidycensus package

moe_level

see get_acs() from the tidycensus package

survey

see get_acs() from the tidycensus package

show_call

see get_acs() from the tidycensus package

geography

"block group" (but it also will recognize you meant "block group" or "tract" if you omit the space or capitalize by accident)

dropname

whether to drop the column called NAME

...

see get_acs() from the tidycensus package

Value

A data.table (not tibble, and not just a data.frame)

Details

See newer ACSdownload::get_acs_new() as used in calc_blockgroupstats_acs() etc., which will download ACS nationwide data by table instead of using acs_bybg(), which queried the API state-by-state.

acs_bybg() requires a Census Bureau API key: tidycensus (>= 1.8) now errors (no longer just warns) without one. Set it once with tidycensus::census_api_key("YOUR KEY", install = TRUE) (stores envt var CENSUS_API_KEY), then restart R. See tidycensus package help and ?tidycensus::census_api_key.

NOTES ON KEY TABLES IN ACS THAT ARE RELEVANT TO EJSCREEN:

x <- tidycensus::load_variables(acs_endyear(guess_census_has_published = TRUE), "acs5")
  ## tables_ejscreen_acs
tables = tables_ejscreen_acs
acstabs2 <- paste0(tables, "_")
mytables <- data.table::rbindlist(lapply(acstabs2, function(z) {
  x[substr(x$name,1,7) %in% z, ][1, ]
  }))
print(mytables)

  # see details of ALL the variables in these tables
# for (i in 1:NROW(mytables)) {
#    x[substr(x$name,1,7) %in% substr(mytables[i,]$name,1,7), ] |> print(n=50)
# }

 # disability is by tract only:

 cbind(unique(grep("disab", x$concept, value = TRUE, ignore.case = TRUE) ))
 # x[substr(x$name,1,6) %in% "B18101" & x$geography %in% "block group", ] |> print(n=50) # none
 x[substr(x$name,1,7) %in% "B18101_"  , ] |> print(n=50)

Examples

if (FALSE) { # \dontrun{
## All states, full table
# newvars <- acs_bybg(table = "B01001")

## One state, some variables
newvars <- acs_bybg(c(pop = "B01001_001", y = "B01001_002"), state = "DC")

## Format new data to match rows of blockgroupstats

data.table::setnames(newvars, "GEOID", "bgfips")
dim(newvars)
newvars <- newvars[blockgroupstats[,.(bgfips, ST)], ,  on = "bgfips"]
dim(blockgroupstats)
dim(newvars)
newvars
newvars[ST == "DC", ]

## Calculate a new indicator for each blockgroup, using ACS data

mystates = c("DC", 'RI')
newvars <- acs_bybg(variables = c("B01001_001", paste0("B01001_0", 31:39)),
  state = mystates)
data.table::setnames(newvars, "GEOID", "bgfips")
newvars[, ST := fips2stateabbrev(bgfips)]
names(newvars) <- gsub("E$", "", names(newvars))

# provide formulas for calculating new indicators from ACS raw data:
formula1 <- c(
 " pop = B01001_001",
 " age1849female = (B01001_031 + B01001_032 + B01001_033 + B01001_034 +
      B01001_035 + B01001_036 + B01001_037 + B01001_038 + B01001_039)",
 " pct1849female = ifelse(pop == 0, 0, age1849female / pop)"
 )
newvars <- calc_ejam(newvars, formulas = formula1,
  keep.old = c("bgid", "ST", "pop", 'bgfips'))

newvars[, pct1849female := round(100 * pct1849female, 1)]
mapfast(newvars[1:10,], column_names = colnames(newvars),
     labels = gsub('pct1849female', 'Women 18-49 as % of residents',
              gsub('age1849female', 'Count of women ages 18-49',
             fixcolnames(colnames(newvars), 'r', 'long'))))


## ACS tables and variables most relevant to EJSCREEN

acsinfo <- tidycensus::load_variables(acs_endyear(guess_census_has_published = TRUE), "acs5")
# or x = EJAM:::acs_table_info()
ejscreentables <-  as.vector(tables_ejscreen_acs)

acstabs2 <- paste0(ejscreentables, "_")
acsinfo$table = gsub("_.*", "", acsinfo$name)
myacsinfo <- acsinfo[acsinfo$table %in% ejscreentables, ]
mytables <- data.table::rbindlist(lapply(ejscreentables, function(z) {
  acsinfo[acsinfo$table %in% z, ][1,]
}))
ejscreen_tables <-  mytables$table # same as ejscreentables

myvars <- myacsinfo$name #

if ("want to run example that takes >15 minutes" == "yes") {
  # VERY SLOWLY download data for all these tables
  # in ALL STATES and DC and PR but not Island Areas
  mystates <- stateinfo2[stateinfo2$is.usa.plus.pr, ]$ST
  ## PR must be handled separately. see e.g., B05001PR
  mystates = mystates[mystates != "PR"]
  ### takes time to download each table for each state:
  system.time({
    newvars <- acs_bybg(variables = myvars, state = mystates)
  })
  data.table::setnames(newvars, "GEOID", "bgfips")
  newvars[, ST := fips2stateabbrev(bgfips)]
  names(newvars) <- gsub("E$", "", names(newvars))
  dim(newvars) # 239781 rows, 370 columns
  # columns are variable estimates and margin of error values
  t(head(newvars))
  ejscreen_acs = newvars
  save(ejscreen_acs, file="ejscreen_acs.rda")
}
} # }