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#' @importFrom acs acs.fetch geo.make
get_tracts_by_state = function(state_fips)
{
counties = acs.fetch(geography = geo.make(state = state_fips, county = "*"),
table.number = "B01003",
endyear = 2015)
geo.make(state = state_fips,
county = as.numeric(geography(counties)[[3]]),
tract = "*")
}
#' @importFrom acs acs.fetch geo.make
get_tracts_by_state_and_county = function(state_fips, county_fips)
{
stopifnot(!is.null(county_fips))
# choroplethr requires users to use a numeric form of county fips codes.
# but acs package here requires the county argument here to be just the
# 3 digit county part of the fips code.
# Manhattan is 61, not 36061
county_fips = as.character(county_fips)
len = nchar(county_fips)
county_fips = substr(county_fips, len-2, len)
county_fips = as.numeric(county_fips)
counties = acs.fetch(geography = geo.make(state = state_fips, county = county_fips),
table.number = "B01003",
endyear = 2015)
geo.make(state = state_fips,
county = as.numeric(geography(counties)[[3]]),
tract = "*")
}
#' Get a handful of demographic variables on Census Tracts in a State from the US Census Bureau as a data.frame.
#'
#' The data comes from the American Community Survey (ACS). The variables are: total population, percent White
#' not Hispanic, Percent Black or African American not Hispanic, percent Asian not Hispanic,
#' percent Hispanic all races, per-capita income, median rent and median age.
#' @param state_name The name of the state. See ?state.regions for proper spelling and capitalization.
#' @param county_fips An optional vector of county fips codes within the state. Useful to set because getting data on all tracts can be slow.
#' @param endyear The end year for the survey
#' @param span The span of the survey
#' @references The choroplethr guide to Census data: http://www.arilamstein.com/open-source/choroplethr/mapping-us-census-data/
#' @references A list of all ACS Surveys: http://factfinder.census.gov/faces/affhelp/jsf/pages/metadata.xhtml?lang=en&type=survey&id=survey.en.ACS_ACS
#' @importFrom acs geo.make acs.fetch geography estimate
#' @importFrom utils data
#' @importFrom acs acs.fetch
#' @export
get_tract_demographics = function(state_name, county_fips = NULL, endyear=2013, span=5)
{
state_fips = get_state_fips_from_name(state_name)
if (is.null(county_fips)) {
tracts = get_tracts_by_state(state_fips)
} else {
tracts = get_tracts_by_state_and_county(state_fips, county_fips)
}
race.data = acs::acs.fetch(geography = tracts,
table.number = "B03002",
col.names = "pretty",
endyear = endyear,
span = span)
# dummy to get proper regions
dummy.df = convert_acs_obj_to_df("tract", race.data, 1, FALSE)
# convert to a data.frame
df_race = data.frame(region = dummy.df$region,
total_population = as.numeric(acs::estimate(race.data[,1])),
white_alone_not_hispanic = as.numeric(acs::estimate(race.data[,3])),
black_alone_not_hispanic = as.numeric(acs::estimate(race.data[,4])),
asian_alone_not_hispanic = as.numeric(acs::estimate(race.data[,6])),
hispanic_all_races = as.numeric(acs::estimate(race.data[,12])))
df_race$region = as.character(df_race$region) # no idea why, but it's a factor before this line
df_race$percent_white = round(df_race$white_alone_not_hispanic / df_race$total_population * 100)
df_race$percent_black = round(df_race$black_alone_not_hispanic / df_race$total_population * 100)
df_race$percent_asian = round(df_race$asian_alone_not_hispanic / df_race$total_population * 100)
df_race$percent_hispanic = round(df_race$hispanic_all_races / df_race$total_population * 100)
df_race = df_race[, c("region", "total_population", "percent_white", "percent_black", "percent_asian", "percent_hispanic")]
# per capita income
df_income = get_tract_acs_data(tracts, "B19301", endyear=endyear, span=span)[[1]]
colnames(df_income)[[2]] = "per_capita_income"
# median rent
df_rent = get_tract_acs_data(tracts, "B25058", endyear=endyear, span=span)[[1]]
colnames(df_rent)[[2]] = "median_rent"
# median age
df_age = get_tract_acs_data(tracts, "B01002", endyear=endyear, span=span, column_idx=1)[[1]]
colnames(df_age)[[2]] = "median_age"
df_demographics = merge(df_race , df_income, all.x=TRUE)
df_demographics = merge(df_demographics, df_rent , all.x=TRUE)
df_demographics = merge(df_demographics, df_age , all.x=TRUE)
# making the region numeric is the easiest way to handle leading 0's
df_demographics$region = as.numeric(df_demographics$region)
df_demographics
}
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