RR: Relative Risk (RR) by demographic group by indicator based on...

In ejanalysis/ejanalysis: Tools for Environmental Justice (EJ) Analysis

Relative Risk (RR) by demographic group by indicator based on Census data

Description

Finds the ratio of mean indicator value in one demographic subgroup to mean in everyone else, based on data for each spatial unit such as for block groups or tracts.

Usage

RR(e, d, pop, dref, na.rm = TRUE)

Arguments

e	Vector or data.frame or matrix with 1 or more environmental indicator(s) or health risk level (e.g., PM2.5 concentration to which this person or place is exposed), one row per Census unit and one column per indicator.
d	Vector or data.frame or matrix with 1 or more demog groups percentage (as fraction of 1, not 0-100!) of place that is selected demog group (e.g. percent Hispanic) (or d=1 or 0 per row if this is a vector of individuals)
pop	Vector of one row per location providing population count of place (or pop=1 if this is a vector of individuals), to convert d into a count since d is a fraction
dref	Optional vector specifying a reference group for RR calculation by providing what percentage (as fraction of 1, not 0-100!) of place that is individuals in the reference group (or dref= vector of ones and zeroes if this is a vector of individuals)
na.rm	Optional, logical, TRUE by default. Specify if NA values should be removed first.

Details

This function requires, for each Census unit, demographic data on total population and percent in each demographic group, and some indicator(s) for each Census unit, such as health status, exposure estimates, or environmental health risk. For example, given population count, percent Hispanic, and ppm of ozone for each tract, this calculates the ratio of the population mean tract-level ozone concentration among Hispanics divided by the same value among all non-Hispanics. The result is a ratio of means for two demographic groups, or for each of several groups and indicators. Each e (for environmental indicator) or d (for demographic percentage) is specified as a vector over small places like Census blocks or block groups or even individuals (ideally) but then d would be a dummy=1 for selected group and 0 for people not in selected group

note: this currently does not use rrf() & rrfv() but perhaps it would be faster if it did? but rrfv not tested for multiple demog groups

VERIFY/TEST THIS: REMOVES PLACES WITH NA in any one or more of the values used (e, d, pop, dref) in numerators and denominators.

Note also that THIS REMOVES NA VALUES FOR one e factor and not for another, so results can use different places & people for different e factors

Value

numeric results as vector or data.frame

See Also

• RR() to calculate overall disparity metric as relative risk (RR), ratio of mean environmental indicator values across demographic groups

• RR.table() to create 3-D table of RR values, by demographic group by environmental indicator by zone

• RR.table.sort() to sort existing RR table

• RR.table.add() to add zone(s) to existing RR table

• write.RR.tables() to write a file with a table or RR by indicator by group

• pop.ecdf() to compare plots of cumulative frequency distribution of indicator values by group

• RR.cut.if.gone() to find local contribution to RR

• RR.if.address.top.x() to find how much RR would change if top-ranked places had different conditions

• ej.added() to find EJ Index as local contribution to sum of EJ Indexes

• ej.indexes() for local contribution to a variety of overall disparity metrics such as excess risk

Examples

 # See examples for [RR.table()] and [RR.means()] and [RR()]

 ########################################  #

 ##    if just using ejanalysis pkg test data:
 bg <- ejanalysis::bgtest
  enames <- c("pm", "o3", "cancer", "resp", "dpm", "pctpre1960", "traffic.score",
   "proximity.npl", "proximity.rmp", "proximity.tsdf", "proximity.npdes", "ust")
 dnames = c("pctlingiso", "pctlowinc")
 dnames.subgroups.count =  c("hisp", "nhwa", "nhba", "nhaiana",
   "nhaa", "nhnhpia", "nhotheralone", "nhmulti")
 dnames.subgroups.pct = c("pcthisp", "pctnhwa", "pctnhba", "pctnhaiana",
   "pctnhaa", "pctnhnhpia", "pctnhotheralone", "pctnhmulti")

 ##    if EJAM pkg available:
 # bg <- as.data.frame(EJAM::blockgroupstats)
 # enames = EJAM::names_e
 # dnames = EJAM::names_d
 # dnames.subgroups.count = EJAM::names_d_subgroups_count
 # dnames.subgroups.pct  =  EJAM::names_d_subgroups

 ##    if EJAM pkg not available and using ejscreen pkg data:
 # bg <- ejscreen::bg22
 # enames = ejscreen::names.e
 # dnames = ejscreen::names.d
 # dnames.subgroups.count = ejscreen::names.d.subgroups
 # dnames.subgroups.pct  =  ejscreen::names.d.subgroups.pct

 ########################################  #

 x <- ejanalysis::RR(e = bg[, enames], d = bg[, dnames], pop = bg$pop)
 round(x, 2)
 t(round(x, 2))

 sapply(bg[ , dnames], function(z) round(RR(bg[ , enames], z, bg$pop), 2))

ejanalysis::RR(bg$pcthisp, bg$pcthisp, bg$pop)
 #  Avg Hispanic persons local percent Hispanic (their blockgroup)
 #  is 4x as everyone elses on avg,
 #  but avg low income persons local percent low income
 #  is only 1.8x as high as everyone elses.

 cbind(RR = RR(
     e = data.frame(
       local_pct_hispanic = bg$pcthisp,
       local_pct_lowincome = bg$pctlowinc),
     d = cbind(
       Ratio_of_avg_among_hispanics_to_avg_among_nonhispanics = bg$pcthisp,
       avg_among_lowinc_vs_rest_of_pop = bg$pctlowinc),
     bg$pop))

ejanalysis/ejanalysis documentation built on April 2, 2024, 10:12 a.m.