R/healthplus.R

In spatialanalysis/geodaData: Spatial Analysis Datasets for Teaching

#' 2000 Health, Income and Diversity
#'
#' Income, race, and public health statistics for US counties.
#'
#' Sf object, unprojected. EPSG 4326: WGS84.
#'
#' @format An sf data frame with 3984 rows, 64 variables, and a geometry column:
#' \describe{
#'	\item{	cartodb_id	}{	Carto ID	}
#'	\item{	countyfp	}{	County FIPS ID	}
#'	\item{	statefp	}{	State FIPS code	}
#'	\item{	statename	}{	State name	}
#'	\item{	countyname	}{	County name and State	}
#'	\item{	countyfips	}{	County FIPS code	}
#'	\item{	ratio	}{	The county’s median income divided by the state’s median income	}
#'	\item{	cty_pop200	}{	County’s population in 2000	}
#'	\item{	le_agg_q1	}{	Female life expectancy, 1st Income Quartile, not adjusted for race.	}
#'	\item{	le_raceadj	}{	Female life expectancy, 1st Income Quartile, adjusted for race.	}
#'	\item{	le_agg_q2	}{	Female life expectancy, 2nd Income Quartile, not adjusted for race	}
#'	\item{	le_racea_1	}{	Female life expectancy, 2nd Income Quartile, adjusted for race	}
#'	\item{	le_agg_q3	}{	Female life expectancy, 3rd Income Quartile, not adjusted for race	}
#'	\item{	le_racea_2	}{	Female life expectancy, 3rd Income Quartile, adjusted for race	}
#'	\item{	le_agg_q4	}{	Female life expectancy, 4th Income Quartile, not adjusted for race	}
#'	\item{	le_racea_3	}{	Female life expectancy, 4th Income Quartile, adjusted for race	}
#'	\item{	le_agg_q11	}{	Male life expectancy, 1st Income Quartile, not adjusted for race	}
#'	\item{	le_racea_4	}{	Male life expectancy, 1st Income Quartile, adjusted for race	}
#'	\item{	le_agg_q21	}{	Male life expectancy, 2nd Income Quartile, not adjusted for race	}
#'	\item{	le_racea_5	}{	Male life expectancy, 2nd Income Quartile, adjusted for race	}
#'	\item{	le_agg_q31	}{	Male life expectancy, 3rd Income Quartile, not adjusted for race	}
#'	\item{	le_racea_6	}{	Male life expectancy, 3rd Income Quartile, adjusted for race	}
#'	\item{	le_agg_q41	}{	Male life expectancy, 4th Income Quartile, not adjusted for race	}
#'	\item{	le_racea_7	}{	Male life expectancy, 4th Income Quartile, adjusted for race	}
#'	\item{	sd_le_agg	}{	Standard error for female life expectancy, 1st income Quartile, not adjusted for race.	}
#'	\item{	sd_le_race	}{	Standard error for female life expectancy, 1st income Quartile, adjusted for race.	}
#'	\item{	sd_le_agg1	}{	Standard error for female life expectancy, 2nd income Quartile, not adjusted for race.	}
#'	\item{	sd_le_ra_1	}{	Standard error for female life expectancy, 2nd income Quartile, adjusted for race.	}
#'	\item{	sd_le_ag_1	}{	Standard error for female life expectancy, 3rd income Quartile, not adjusted for race.	}
#'	\item{	sd_le_ra_2	}{	Standard error for female life expectancy, 3rd income Quartile, adjusted for race.	}
#'	\item{	sd_le_ag_2	}{	Standard error for female life expectancy, 4th income Quartile, not adjusted for race.	}
#'	\item{	sd_le_ra_3	}{	Standard error for female life expectancy, 4th income Quartile, adjusted for race.	}
#'	\item{	sd_le_ag_3	}{	Standard error for male life expectancy, 1st income Quartile, not adjusted for race.	}
#'	\item{	sd_le_ra_4	}{	Standard error for male life expectancy, 1st income Quartile, adjusted for race.	}
#'	\item{	sd_le_ag_4	}{	Standard error for male life expectancy, 2nd income Quartile, not adjusted for race.	}
#'	\item{	sd_le_ra_5	}{	Standard error for male life expectancy, 2nd income Quartile, adjusted for race.	}
#'	\item{	sd_le_ag_5	}{	Standard error for male life expectancy, 3rd income Quartile, not adjusted for race.	}
#'	\item{	sd_le_ra_6	}{	Standard error for male life expectancy, 3rd income Quartile, adjusted for race.	}
#'	\item{	sd_le_ag_6	}{	Standard error for male life expectancy, 4th income Quartile, not adjusted for race.	}
#'	\item{	sd_le_ra_7	}{	Standard error for male life expectancy, 4th income Quartile, adjusted for race.	}
#'	\item{	count_q1_F	}{	Female count, 1st income quartile.	}
#'	\item{	count_q2_F	}{	Female count, 2nd income quartile.	}
#'	\item{	count_q3_F	}{	Female count, 3rd income quartile.	}
#'	\item{	count_q4_F	}{	Female count, 4th income quartile.	}
#'	\item{	count_q1_M	}{	Male count, 1st income quartile.	}
#'	\item{	count_q2_M	}{	Male count, 2nd income quartile.	}
#'	\item{	count_q3_M	}{	Male count, 3rd income quartile.	}
#'	\item{	count_q4_M	}{	Male count, 4th income quartile.	}
#'	\item{	Diversity	}{	Diversity Index	}
#'	\item{	Asianalon	}{	Asian alone, percent	}
#'	\item{	NativeHaw	}{	Native Hawaiian, percent	}
#'	\item{	TwoorMor	}{	Two or more races, percent	}
#'	\item{	Hispanico	}{	Hispanic, percent	}
#'	\item{	Whitealon	}{	White alone, percent	}
#'	}
#' @source Multiple sources: (1) ratio from \url{https://philpierdo2.carto.com/me}, (2) race and diversity index source: \url{https://www.kaggle.com/mikejohnsonjr/us-counties-diversity-index} and (3) income and life expectancy variables obtained from: Chetty, Stepner, Abraham, Lin, Scuderi, Turner, Bergeron, and Cutler (2016). The Association Between Income and Life Expectancy in the United States, 2001-2014. Health Statistics by County.
#'
#' @examples
#' if (requireNamespace("sf", quietly = TRUE)) {
#'   library(sf)
#'   data(healthplus)
#'
#'   plot(healthplus["le_agg_q1"])
#' }
"healthplus"