RR.table: Table of Relative Risk results by zone by group by envt risk...

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

Table of Relative Risk results by zone by group by envt risk factor

Description

Make table of Relative Risk results by zone by group by envt risk factor. See source code for notes on this work.

Usage

RR.table(
  mydat,
  Enames = ejscreen::names.e[ejscreen::names.e %in% names(mydat)],
  Dnames = ejscreen::names.d[ejscreen::names.d %in% names(mydat)],
  popcolname = "pop",
  Zcolname,
  testing = FALSE,
  digits = 4
)

Arguments

mydat	Data.frame of input data, one row per geographic unit such as US Census block groups, or tracts.
Enames	names of columns with environmental risk factor data, default is names.e from ejscreen package
Dnames	names of columns with percent (fraction) of population that is in each given demographic group, default is names.d from ejscreen package
popcolname	name of column with total population count, default is pop
Zcolname	name of column with name of zone such as US State name
testing	default is FALSE
digits	Default is 4. How many significant digits to use.

Value

Compiles RR values in array of 3 dimensions: RRS[Dnames, Enames, Zcolnames] Returns a matrix with one demographic group per row, one environmental risk indicator per column, and third dimension for which zone (e.g., which US State)

See Also

RR()

Examples

    # (This is very slow right now)

 # 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

 ########################################  #
 Ratios <- ejanalysis::RR.table(bg, Enames = enames,
   Dnames = c(dnames, dnames.subgroups.pct),
   popcolname = 'pop', digits = 2)

 # done like this, it still has NA values:

 MeansByGroup_and_Ratios <- ejanalysis::RR.means(
   subset(bg, select = enames),
   subset(bg, select = c(dnames, dnames.subgroups.pct)),
   bg$pop)

RRS.US  <- RR.table(mydat = bg, Enames = enames,
  Dnames = c(dnames, dnames.subgroups.pct),
  popcolname = 'pop')
RRS.ST  <- RR.table(mydat = bg, Enames = enames,
  Dnames = c(dnames, dnames.subgroups.pct),
  popcolname = 'pop', Zcolname = 'ST')
RRS <- RR.table.add(RRS.ST, RRS.US)
RRS['pctlowinc', , ]
RRS[ , , 'CA']  # RRS[, , 'PR']
RRS[ , 'pm', ]
RRS.REGION  <- RR.table(mydat = bg,
  Enames = enames,
  Dnames = c(dnames, dnames.subgroups.pct),
  popcolname = 'pop', Zcolname = 'REGION')
RRS2 <- RR.table.add(RRS, RRS.REGION)
RRS2[ , , '8']

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