#' Get a handful of demographic variables on California Census Tracts 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 endyear The end year for the survey
#' @param span The span of the survey
#' @references The choroplethr guide to Census data: http://cran.r-project.org/web/packages/choroplethr/vignettes/e-mapping-us-census-data.html
#' @importFrom acs geo.make acs.fetch geography estimate
#' @export
#' @examples
#' \dontrun{
#' df = get_ca_tract_demographics(endyear=2010, span=5)
#' colnames(df)
#'
#' # analyze the percent of people who are white not hispanic
#' # a boxplot shows the distribution
#' boxplot(df$percent_white)
#'
#' # a choropleth map shows the location of the values in california
#' # set the 'value' column to be the column we want to render
#' df$value = df$percent_white
#' ca_tract_choropleth(df,
#' title="2010 Census Tracts\nPercent White not Hispanic",
#' legend="Percent")
#'
#' # zoom into san francisco county
#' ca_tract_choropleth(df,
#' title="2010 Census Tracts\nPercent White not Hispanic",
#' legend="Percent",
#' county_zoom=6075)
#' }
get_ca_tract_demographics = function(endyear=2013, span=5)
{
all.ca.tracts = get_all_ca_tracts()
race.data = acs::acs.fetch(geography = all.ca.tracts,
table.number = "B03002",
col.names = "pretty",
endyear = endyear,
span = span)
# dummy to get proper regions
dummy.df = convert_acs_obj_to_df(race.data, 1)
# convert to a data.frame
df_race = data.frame(region = dummy.df$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_ca_tract_acs_data("B19301", endyear=endyear, span=span)[[1]]
colnames(df_income)[[2]] = "per_capita_income"
# median rent
df_rent = get_ca_tract_acs_data("B25058", endyear=endyear, span=span)[[1]]
colnames(df_rent)[[2]] = "median_rent"
# median age
df_age = get_ca_tract_acs_data("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)
df_demographics
}
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