R/tabs.reformat.R
In ejanalysis/batch.summarizer: Summarize Multiple Buffer Reports from the (nonpublic) Batch Tool for EJSCREEN
Defines functions
tabs.reformat
Documented in
tabs.reformat
#' @title reformats tabular data saved from a single buffer report - probably obsolete
#' @description based on code from make.finaltable.R
#' @param fulltable input is fulltable, a set of tabular data from a buffer standard report in EJSCREEN
#' @param folder default is working directory
#' @param colcount colcount is 17 for circular buffer report, but differs for block group and maybe noncircular buffer reports
#' @author ejanalyst info@ejanalysis.com
#' @return the output is the output
tabs.reformat <- function(fulltable, folder=getwd(), colcount=17 ) {
warning(' # DOES NOT WORK YET -- IT IS CREATING PAIRS OF ROWS INSTEAD OF 2 COLS, ONE FOR EACH BUFFER REPORT I USED:')
# > t(batchout)
# [,1] [,2]
# ID "ejscreenbuffersavedfromtabular1" "ejscreenbuffersavedfromtabular1"
# lon "-74.04961" "-74.04961"
# lat "40.86902" "40.86902"
# miles "1" "1"
# State "NEW JERSEY" "NEW JERSEY"
# REGION "2" "2"
# pop "19214" "19214"
# EJ Index for Lead Paint Indicator fieldname "EJ Index for Lead Paint Indicator" "NATA Neurological Hazard Index*"
# EJ Index for Lead Paint Indicator fieldname "EJ Index for Lead Paint Indicator" "NATA Respiratory Hazard Index*"
# EJ Index for NATA Air Toxics Cancer Risk* fieldname "EJ Index for NATA Air Toxics Cancer Risk*" "NATA Respiratory Hazard Index*"
# EJ Index for NATA Air Toxics Cancer Risk* fieldname "EJ Index for NATA Air Toxics Cancer Risk*" "NPL Proximity (site count/km distance)"
# EJ Index for NATA Diesel PM* fieldname "EJ Index for NATA Diesel PM*" "NPL Proximity (site count/km distance)"
# EJ Index for NATA Diesel PM* fieldname "EJ Index for NATA Diesel PM*" "Ozone (ppb)"
# EJ Index for NATA Neurological Hazard Index* fieldname "EJ Index for NATA Neurological Hazard Index*" "Ozone (ppb)"
# EJ Index for NATA Neurological Hazard Index* fieldname "EJ Index for NATA Neurological Hazard Index*" "Particulate Matter (PM 2.5 in ug/m3)"
# EJ Index for NATA Respiratory Hazard Index* fieldname "EJ Index for NATA Respiratory Hazard Index*" "Particulate Matter (PM 2.5 in ug/m3)"
# EJ Index for NATA Respiratory Hazard Index* fieldname "EJ Index for NATA Respiratory Hazard Index*" "RMP Proximity (facility count/km distance)"
# EJ Index for NPL Proximity fieldname "EJ Index for NPL Proximity" "RMP Proximity (facility count/km distance)"
#
##################### #
# INPUT FORMAT
##################### #
# str(fulltable) # format of circular buffer tabular view saved as csv and parsed/compiled by tabs.compile():
# 'data.frame': 62 obs. of 17 variables:
# $ ID : chr "ejscreenbuffersavedfromtabular1" "ejscreenbuffersavedfromtabular1" "ejscreenbuffersavedfromtabular1" "ejscreenbuffersavedfromtabular1" ...
# $ lon : num -74 -74 -74 -74 -74 ...
# $ lat : num 40.9 40.9 40.9 40.9 40.9 ...
# $ miles : chr "1" "1" "1" "1" ...
# $ State : chr "NEW JERSEY" "NEW JERSEY" "NEW JERSEY" "NEW JERSEY" ...
# $ REGION : chr "2" "2" "2" "2" ...
# $ pop : num 19214 19214 19214 19214 19214 ...
# $ fieldgroup : chr "EJ" "EJ" "EJ" "EJ" ...
# $ fieldname : chr "EJ Index for Lead Paint Indicator" "EJ Index for NATA Air Toxics Cancer Risk*" "EJ Index for NATA Diesel PM*" "EJ Index for NATA Neurological Hazard Index*" ...
# $ raw : chr "" "" "" "" ...
# $ stateavg : chr "" "" "" "" ...
# $ statepctile : chr "80" "N/A" "N/A" "N/A" ...
# $ regionavg : chr "" "" "" "" ...
# $ regionpctile: chr "73" "N/A" "N/A" "N/A" ...
# $ usavg : chr "" "" "" "" ...
# $ uspctile : chr "85" "N/A" "N/A" "N/A" ...
##################### #
# OUTPUT FORMAT:
##################### #
# "Export_Output_Example2.txt" is an example of batch tool output:
#
# OBJECTID,FACID,NAME,LAT,LON,totpop,buff,stabbr,statename,region,S_E_TSDF_PER,R_P_TRAFFIC,S_E_PM25_PER,R_P_CANCER,S_P_DIESEL,N_D_INDEX,RAW_E_RMP,R_E_PM25,R_D_LESSHS,R_E_DIESEL,RAW_D_OVER64,N_E_TSDF,R_E_LEAD_PER,R_E_RMP_PER,S_E_DIESEL_PER,RAW_E_RESP,R_D_INDEX_PER,RAW_D_LESSHS,N_E_TRAFFIC,S_E_NEURO_PER,N_P_NPL,S_D_INDEX,S_D_MINOR,S_D_LESSHS,S_P_RESP,N_E_PM25_PER,RAW_D_INDEX,N_E_NEURO_PER,RAW_D_UNDER5,RAW_E_LEAD,R_E_NPL_PER,S_E_RESP_PER,S_E_O3_PER,N_P_PM25,S_D_LESSHS_PER,N_E_DIESEL_PER,S_D_INCOME_PER,RAW_E_NPL,R_D_MINOR_PER,S_E_TRAFFIC,R_P_TSDF,RAW_E_TSDF,N_P_CANCER,RAW_E_NEURO,S_E_DIESEL,RAW_D_INCOME,N_P_RMP,N_E_O3_PER,S_E_O3,R_E_RESP,S_E_RESP,N_E_DIESEL,N_D_INDEX_PER,N_E_RMP_PER,RAW_D_MINOR,N_E_CANCER_PER,R_E_O3_PER,S_D_INDEX_PER,N_E_RMP,R_P_LEAD,R_E_NEURO,N_E_LEAD,S_E_RMP_PER,R_E_RMP,RAW_E_DIESEL,R_D_LING_PER,R_E_TRAFFIC,R_E_LEAD,R_D_OVER64_PER,N_P_NEURO,R_E_CANCER_PER,R_E_NPDES_PER,N_E_CANCER,N_D_MINOR_PER,S_E_TSDF,S_E_NPL,R_D_OVER64,S_D_MINOR_PER,S_P_TSDF,S_P_RMP,N_E_PM25,R_E_TSDF,S_E_RMP,RAW_D_LING,S_E_TRAFFIC_PER,S_P_PM25,S_E_LEAD,R_P_NEURO,S_D_LING,N_E_NPL,R_E_DIESEL_PER,R_D_LESSHS_PER,R_P_O3,N_E_TRAFFIC_PER,RAW_E_NPDES,N_E_NPDES,N_E_NEURO,R_P_DIESEL,N_E_RESP_PER,R_E_TSDF_PER,RAW_E_TRAFFIC,R_D_INDEX,R_P_PM25,N_D_UNDER5_PER,N_D_LESSHS_PER,R_E_NPDES,N_D_LING,S_E_PM25,N_E_NPL_PER,R_E_NEURO_PER,R_D_MINOR,N_P_TSDF,S_D_LING_PER,R_P_NPL,S_P_NPDES,S_E_NPDES_PER,N_D_UNDER5,S_E_NPL_PER,S_E_CANCER_PER,N_E_RESP,N_D_LESSHS,S_D_UNDER5,N_P_LEAD,RAW_E_CANCER,S_P_TRAFFIC,N_E_NPDES_PER,R_E_TRAFFIC_PER,N_P_NPDES,RAW_E_O3,N_P_O3,R_E_O3,N_E_O3,N_E_TSDF_PER,R_E_RESP_PER,S_D_OVER64,N_D_INCOME,R_E_NPL,R_D_UNDER5_PER,R_P_RESP,R_P_NPDES,S_P_O3,N_P_DIESEL,N_D_OVER64_PER,R_P_RMP,N_P_TRAFFIC,N_E_LEAD_PER,S_E_NPDES,S_D_OVER64_PER,S_P_NPL,N_D_MINOR,RAW_E_PM25,N_D_LING_PER,S_D_INCOME,S_P_NEURO,N_P_RESP,N_D_OVER64,S_D_UNDER5_PER,R_D_LING,R_E_CANCER,S_E_CANCER,S_P_CANCER,N_D_INCOME_PER,R_D_INCOME_PER,S_E_NEURO,R_D_INCOME,R_E_PM25_PER,R_D_UNDER5,S_E_LEAD_PER,S_P_LEAD
# 1,1000,BRASW Facility,32.4,-94.7,"7,946",3 miles,TX,Texas,6,97,74,44,75,61,35%,1.2,9.44,19%,0.733,13%,0.054,74,92,37,1.5,72,23%,110,99,95,47%,55%,20%,66,27,61%,92,7%,0.27,97,54,51,77,64,49,70,0.29,69,91,96,0.33,80,0.12,0.913,52%,95,34,42.9,1.4,1.5,0.824,83,95,69%,72,47,68,0.31,84,0.043,0.3,91,0.42,0.478,66,81,0.18,63,90,87,73,49,79,0.073,0.067,11%,62,94,86,10.7,0.062,0.47,6%,60,61,0.17,92,9%,0.096,50,67,68,60,0.32,0.25,0.063,70,43,97,55,44%,68,62,78,0.35,5%,9.63,94,99,49%,97,56,95,74,71,7%,97,84,2.3,15%,8%,84,56,69,81,64,89,43.8,79,43.6,46.3,98,63,10%,34%,0.063,52,73,79,62,78,55,89,80,57,0.38,69,93,36%,9.44,74,39%,89,76,13%,49,7%,42,44,68,79,71,0.044,39%,47,7%,76,82
##################### #
#header <- c('OBJECTID', 'FACID', 'NAME', 'LAT', 'LON', 'totpop', 'buff', 'stabbr', 'statename', 'region', 'S_E_TSDF_PER', 'R_P_TRAFFIC', 'S_E_PM25_PER', 'R_P_CANCER', 'S_P_DIESEL', 'N_D_INDEX', 'RAW_E_RMP', 'R_E_PM25', 'R_D_LESSHS', 'R_E_DIESEL', 'RAW_D_OVER64', 'N_E_TSDF', 'R_E_LEAD_PER', 'R_E_RMP_PER', 'S_E_DIESEL_PER', 'RAW_E_RESP', 'R_D_INDEX_PER', 'RAW_D_LESSHS', 'N_E_TRAFFIC', 'S_E_NEURO_PER', 'N_P_NPL', 'S_D_INDEX', 'S_D_MINOR', 'S_D_LESSHS', 'S_P_RESP', 'N_E_PM25_PER', 'RAW_D_INDEX', 'N_E_NEURO_PER', 'RAW_D_UNDER5', 'RAW_E_LEAD', 'R_E_NPL_PER', 'S_E_RESP_PER', 'S_E_O3_PER', 'N_P_PM25', 'S_D_LESSHS_PER', 'N_E_DIESEL_PER', 'S_D_INCOME_PER', 'RAW_E_NPL', 'R_D_MINOR_PER', 'S_E_TRAFFIC', 'R_P_TSDF', 'RAW_E_TSDF', 'N_P_CANCER', 'RAW_E_NEURO', 'S_E_DIESEL', 'RAW_D_INCOME', 'N_P_RMP', 'N_E_O3_PER', 'S_E_O3', 'R_E_RESP', 'S_E_RESP', 'N_E_DIESEL', 'N_D_INDEX_PER', 'N_E_RMP_PER', 'RAW_D_MINOR', 'N_E_CANCER_PER', 'R_E_O3_PER', 'S_D_INDEX_PER', 'N_E_RMP', 'R_P_LEAD', 'R_E_NEURO', 'N_E_LEAD', 'S_E_RMP_PER', 'R_E_RMP', 'RAW_E_DIESEL', 'R_D_LING_PER', 'R_E_TRAFFIC', 'R_E_LEAD', 'R_D_OVER64_PER', 'N_P_NEURO', 'R_E_CANCER_PER', 'R_E_NPDES_PER', 'N_E_CANCER', 'N_D_MINOR_PER', 'S_E_TSDF', 'S_E_NPL', 'R_D_OVER64', 'S_D_MINOR_PER', 'S_P_TSDF', 'S_P_RMP', 'N_E_PM25', 'R_E_TSDF', 'S_E_RMP', 'RAW_D_LING', 'S_E_TRAFFIC_PER', 'S_P_PM25', 'S_E_LEAD', 'R_P_NEURO', 'S_D_LING', 'N_E_NPL', 'R_E_DIESEL_PER', 'R_D_LESSHS_PER', 'R_P_O3', 'N_E_TRAFFIC_PER', 'RAW_E_NPDES', 'N_E_NPDES', 'N_E_NEURO', 'R_P_DIESEL', 'N_E_RESP_PER', 'R_E_TSDF_PER', 'RAW_E_TRAFFIC', 'R_D_INDEX', 'R_P_PM25', 'N_D_UNDER5_PER', 'N_D_LESSHS_PER', 'R_E_NPDES', 'N_D_LING', 'S_E_PM25', 'N_E_NPL_PER', 'R_E_NEURO_PER', 'R_D_MINOR', 'N_P_TSDF', 'S_D_LING_PER', 'R_P_NPL', 'S_P_NPDES', 'S_E_NPDES_PER', 'N_D_UNDER5', 'S_E_NPL_PER', 'S_E_CANCER_PER', 'N_E_RESP', 'N_D_LESSHS', 'S_D_UNDER5', 'N_P_LEAD', 'RAW_E_CANCER', 'S_P_TRAFFIC', 'N_E_NPDES_PER', 'R_E_TRAFFIC_PER', 'N_P_NPDES', 'RAW_E_O3', 'N_P_O3', 'R_E_O3', 'N_E_O3', 'N_E_TSDF_PER', 'R_E_RESP_PER', 'S_D_OVER64', 'N_D_INCOME', 'R_E_NPL', 'R_D_UNDER5_PER', 'R_P_RESP', 'R_P_NPDES', 'S_P_O3', 'N_P_DIESEL', 'N_D_OVER64_PER', 'R_P_RMP', 'N_P_TRAFFIC', 'N_E_LEAD_PER', 'S_E_NPDES', 'S_D_OVER64_PER', 'S_P_NPL', 'N_D_MINOR', 'RAW_E_PM25', 'N_D_LING_PER', 'S_D_INCOME', 'S_P_NEURO', 'N_P_RESP', 'N_D_OVER64', 'S_D_UNDER5_PER', 'R_D_LING', 'R_E_CANCER', 'S_E_CANCER', 'S_P_CANCER', 'N_D_INCOME_PER', 'R_D_INCOME_PER', 'S_E_NEURO', 'R_D_INCOME', 'R_E_PM25_PER', 'R_D_UNDER5', 'S_E_LEAD_PER', 'S_P_LEAD')
if (missing(fulltable)) {stop('fulltable must be provided as output from tabs.compile()')}
################################################### #
# SHORTEN/WIDEN THE COMPILATION TO HAVE 1 ROW PER SITE, ALL OTHER DATA IN NUMEROUS COLUMNS
################################################### #
# We have already checked to make sure every tabular txt file output has
# 31 rows, or varcount.typical, or rowcount, in case that may be set differently (31 means 12 EJ, 12 envt, 7 demog)
# FIGURE OUT HOW MANY DATA ELEMENTS WERE IN THE REPORT SPLIT INTO ID.etc.rowcount and the rest of colcount
#
# $ ID : chr "ejscreenbuffersavedfromtabular1" "ejscreenbuffersavedfromtabular1" "ejscreenbuffersavedfromtabular1" "ejscreenbuffersavedfromtabular1" ...
# $ lon : num -74 -74 -74 -74 -74 ...
# $ lat : num 40.9 40.9 40.9 40.9 40.9 ...
# $ miles : chr "1" "1" "1" "1" ...
# $ State : chr "NEW JERSEY" "NEW JERSEY" "NEW JERSEY" "NEW JERSEY" ...
# $ REGION : chr "2" "2" "2" "2" ...
# $ pop : num 19214 19214 19214 19214 19214 ...
# $ fieldgroup : chr "EJ" "EJ" "EJ" "EJ" ...
# $ fieldname : chr "EJ Index for Lead Paint Indicator" "EJ Index for NATA Air Toxics Cancer Risk*" "EJ Index for NATA Diesel PM*" "EJ Index for NATA Neurological Hazard Index*" ...
# $ raw : chr "" "" "" "" ...
# $ stateavg : chr "" "" "" "" ...
# $ statepctile : chr "80" "N/A" "N/A" "N/A" ...
# $ regionavg : chr "" "" "" "" ...
# $ regionpctile: chr "73" "N/A" "N/A" "N/A" ...
# $ usavg : chr "" "" "" "" ...
# $ uspctile : chr "85" "N/A" "N/A" "N/A" ...
#
# The # of rows like ID, State, REGION, pop, etc. is different for buffer vs block group reports. (FIPS for a block group vs lat/lon/miles for buffers)
# There are currently 9 data rows including fieldgroup:
# fieldgroup, fieldname, raw, stateavg, statepctile, regionavg, regionpctile, usavg, uspctile
# (row called fieldgroups says EJ, envt, or demog, etc.)
# So the count of Basic rows like ID, State, etc. is found this way: (should be 5 if block groups and was 7 if buffers).
varcount.typical <- 31 #??? how many indicators (e.g., env, demog, or EJ Indexes)
#colcount <- 17 # how many stats on each indicator (e.g., US avg.) for circular buffers at least... not necessarily for block group reports or non circular buffers
ID.etc.rowcount <- colcount - 9 # used to be 7 , if circular buffers
# This pointcount should match the length of filelist, and is the number of points/buffers/zones/places analyzed.
pointcount <- length(fulltable[, 1]) / varcount.typical
if (floor(pointcount) != pointcount) {
cat("\n ERROR - Table length is not an integer multiple of number of places/buffers.\n")
#logwrite("ERROR - Table length is not an integer multiple of number of places/buffers.")
}
# make finaltable which will have only 1 row per ID instead of 31 (varcount.typical),
# but 31*7 or 31*5 columns to the right instead of just 7 if buffers (or 5 if these are block group not buffer reports).
# Take every 31 or so rows and make those into one row of the shorter/wider table, by creating 7 (or 5) new columns for each of those rows.
# keep rows unique to one ID (some of these are stored as factors but that is OK)
# There are 7 (8 now) such rows currently if they are buffer reports: ID, lon, lat, miles, State, REGION, pop *****************
# BUT there are 5 such rows if they are block group reports: ID, FIPS, State, REGION, pop
# so but we will use variable varcount.typical or rowcount
# print('fulltable str:')
# print( str(fulltable) )
finaltable <- data.frame(unique(fulltable[ , 1:ID.etc.rowcount]))
# can assume that retains the original order of the rows, or use merge to join on ID
row.names(finaltable) <- NULL
# column 8 if buffer, col 6 if block group report, is fieldname- it gets the specific type of field appended to it.
# note that last column is fieldgroup so leave that last one out
# The result is for example, "Ozone (Env) regionavg"
for (mycolname in names(fulltable[ , (ID.etc.rowcount + 2):(colcount - 1)]) ) {
c.this <- matrix(fulltable[ , mycolname], ncol = varcount.typical, nrow = pointcount, byrow = TRUE)
colnames(c.this) <- paste(fulltable$fieldname[1:varcount.typical], mycolname)
finaltable <- cbind(finaltable, c.this, stringsAsFactors = FALSE)
}
# To view results so far:
# dim(finaltable)
# [1] 62 225 **** should only be 2 rows now? not 31+31 !
# t(head(finaltable,2))
#header <- c('OBJECTID', 'FACID', 'NAME', 'LAT', 'LON', 'totpop', 'buff', 'stabbr', 'statename', 'region', 'S_E_TSDF_PER', 'R_P_TRAFFIC', 'S_E_PM25_PER', 'R_P_CANCER', 'S_P_DIESEL', 'N_D_INDEX', 'RAW_E_RMP', 'R_E_PM25', 'R_D_LESSHS', 'R_E_DIESEL', 'RAW_D_OVER64', 'N_E_TSDF', 'R_E_LEAD_PER', 'R_E_RMP_PER', 'S_E_DIESEL_PER', 'RAW_E_RESP', 'R_D_INDEX_PER', 'RAW_D_LESSHS', 'N_E_TRAFFIC', 'S_E_NEURO_PER', 'N_P_NPL', 'S_D_INDEX', 'S_D_MINOR', 'S_D_LESSHS', 'S_P_RESP', 'N_E_PM25_PER', 'RAW_D_INDEX', 'N_E_NEURO_PER', 'RAW_D_UNDER5', 'RAW_E_LEAD', 'R_E_NPL_PER', 'S_E_RESP_PER', 'S_E_O3_PER', 'N_P_PM25', 'S_D_LESSHS_PER', 'N_E_DIESEL_PER', 'S_D_INCOME_PER', 'RAW_E_NPL', 'R_D_MINOR_PER', 'S_E_TRAFFIC', 'R_P_TSDF', 'RAW_E_TSDF', 'N_P_CANCER', 'RAW_E_NEURO', 'S_E_DIESEL', 'RAW_D_INCOME', 'N_P_RMP', 'N_E_O3_PER', 'S_E_O3', 'R_E_RESP', 'S_E_RESP', 'N_E_DIESEL', 'N_D_INDEX_PER', 'N_E_RMP_PER', 'RAW_D_MINOR', 'N_E_CANCER_PER', 'R_E_O3_PER', 'S_D_INDEX_PER', 'N_E_RMP', 'R_P_LEAD', 'R_E_NEURO', 'N_E_LEAD', 'S_E_RMP_PER', 'R_E_RMP', 'RAW_E_DIESEL', 'R_D_LING_PER', 'R_E_TRAFFIC', 'R_E_LEAD', 'R_D_OVER64_PER', 'N_P_NEURO', 'R_E_CANCER_PER', 'R_E_NPDES_PER', 'N_E_CANCER', 'N_D_MINOR_PER', 'S_E_TSDF', 'S_E_NPL', 'R_D_OVER64', 'S_D_MINOR_PER', 'S_P_TSDF', 'S_P_RMP', 'N_E_PM25', 'R_E_TSDF', 'S_E_RMP', 'RAW_D_LING', 'S_E_TRAFFIC_PER', 'S_P_PM25', 'S_E_LEAD', 'R_P_NEURO', 'S_D_LING', 'N_E_NPL', 'R_E_DIESEL_PER', 'R_D_LESSHS_PER', 'R_P_O3', 'N_E_TRAFFIC_PER', 'RAW_E_NPDES', 'N_E_NPDES', 'N_E_NEURO', 'R_P_DIESEL', 'N_E_RESP_PER', 'R_E_TSDF_PER', 'RAW_E_TRAFFIC', 'R_D_INDEX', 'R_P_PM25', 'N_D_UNDER5_PER', 'N_D_LESSHS_PER', 'R_E_NPDES', 'N_D_LING', 'S_E_PM25', 'N_E_NPL_PER', 'R_E_NEURO_PER', 'R_D_MINOR', 'N_P_TSDF', 'S_D_LING_PER', 'R_P_NPL', 'S_P_NPDES', 'S_E_NPDES_PER', 'N_D_UNDER5', 'S_E_NPL_PER', 'S_E_CANCER_PER', 'N_E_RESP', 'N_D_LESSHS', 'S_D_UNDER5', 'N_P_LEAD', 'RAW_E_CANCER', 'S_P_TRAFFIC', 'N_E_NPDES_PER', 'R_E_TRAFFIC_PER', 'N_P_NPDES', 'RAW_E_O3', 'N_P_O3', 'R_E_O3', 'N_E_O3', 'N_E_TSDF_PER', 'R_E_RESP_PER', 'S_D_OVER64', 'N_D_INCOME', 'R_E_NPL', 'R_D_UNDER5_PER', 'R_P_RESP', 'R_P_NPDES', 'S_P_O3', 'N_P_DIESEL', 'N_D_OVER64_PER', 'R_P_RMP', 'N_P_TRAFFIC', 'N_E_LEAD_PER', 'S_E_NPDES', 'S_D_OVER64_PER', 'S_P_NPL', 'N_D_MINOR', 'RAW_E_PM25', 'N_D_LING_PER', 'S_D_INCOME', 'S_P_NEURO', 'N_P_RESP', 'N_D_OVER64', 'S_D_UNDER5_PER', 'R_D_LING', 'R_E_CANCER', 'S_E_CANCER', 'S_P_CANCER', 'N_D_INCOME_PER', 'R_D_INCOME_PER', 'S_E_NEURO', 'R_D_INCOME', 'R_E_PM25_PER', 'R_D_UNDER5', 'S_E_LEAD_PER', 'S_P_LEAD')
return(finaltable)
}
ejanalysis/batch.summarizer documentation built on Sept. 6, 2021, 3:41 a.m.
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Defines functions tabs.reformat
Documented in tabs.reformat
#' @title reformats tabular data saved from a single buffer report - probably obsolete
#' @description based on code from make.finaltable.R
#' @param fulltable input is fulltable, a set of tabular data from a buffer standard report in EJSCREEN
#' @param folder default is working directory
#' @param colcount colcount is 17 for circular buffer report, but differs for block group and maybe noncircular buffer reports
#' @author ejanalyst info@ejanalysis.com
#' @return the output is the output
tabs.reformat <- function(fulltable, folder=getwd(), colcount=17 ) {
warning(' # DOES NOT WORK YET -- IT IS CREATING PAIRS OF ROWS INSTEAD OF 2 COLS, ONE FOR EACH BUFFER REPORT I USED:')
# > t(batchout)
# [,1] [,2]
# ID "ejscreenbuffersavedfromtabular1" "ejscreenbuffersavedfromtabular1"
# lon "-74.04961" "-74.04961"
# lat "40.86902" "40.86902"
# miles "1" "1"
# State "NEW JERSEY" "NEW JERSEY"
# REGION "2" "2"
# pop "19214" "19214"
# EJ Index for Lead Paint Indicator fieldname "EJ Index for Lead Paint Indicator" "NATA Neurological Hazard Index*"
# EJ Index for Lead Paint Indicator fieldname "EJ Index for Lead Paint Indicator" "NATA Respiratory Hazard Index*"
# EJ Index for NATA Air Toxics Cancer Risk* fieldname "EJ Index for NATA Air Toxics Cancer Risk*" "NATA Respiratory Hazard Index*"
# EJ Index for NATA Air Toxics Cancer Risk* fieldname "EJ Index for NATA Air Toxics Cancer Risk*" "NPL Proximity (site count/km distance)"
# EJ Index for NATA Diesel PM* fieldname "EJ Index for NATA Diesel PM*" "NPL Proximity (site count/km distance)"
# EJ Index for NATA Diesel PM* fieldname "EJ Index for NATA Diesel PM*" "Ozone (ppb)"
# EJ Index for NATA Neurological Hazard Index* fieldname "EJ Index for NATA Neurological Hazard Index*" "Ozone (ppb)"
# EJ Index for NATA Neurological Hazard Index* fieldname "EJ Index for NATA Neurological Hazard Index*" "Particulate Matter (PM 2.5 in ug/m3)"
# EJ Index for NATA Respiratory Hazard Index* fieldname "EJ Index for NATA Respiratory Hazard Index*" "Particulate Matter (PM 2.5 in ug/m3)"
# EJ Index for NATA Respiratory Hazard Index* fieldname "EJ Index for NATA Respiratory Hazard Index*" "RMP Proximity (facility count/km distance)"
# EJ Index for NPL Proximity fieldname "EJ Index for NPL Proximity" "RMP Proximity (facility count/km distance)"
#
##################### #
# INPUT FORMAT
##################### #
# str(fulltable) # format of circular buffer tabular view saved as csv and parsed/compiled by tabs.compile():
# 'data.frame': 62 obs. of 17 variables:
# $ ID : chr "ejscreenbuffersavedfromtabular1" "ejscreenbuffersavedfromtabular1" "ejscreenbuffersavedfromtabular1" "ejscreenbuffersavedfromtabular1" ...
# $ lon : num -74 -74 -74 -74 -74 ...
# $ lat : num 40.9 40.9 40.9 40.9 40.9 ...
# $ miles : chr "1" "1" "1" "1" ...
# $ State : chr "NEW JERSEY" "NEW JERSEY" "NEW JERSEY" "NEW JERSEY" ...
# $ REGION : chr "2" "2" "2" "2" ...
# $ pop : num 19214 19214 19214 19214 19214 ...
# $ fieldgroup : chr "EJ" "EJ" "EJ" "EJ" ...
# $ fieldname : chr "EJ Index for Lead Paint Indicator" "EJ Index for NATA Air Toxics Cancer Risk*" "EJ Index for NATA Diesel PM*" "EJ Index for NATA Neurological Hazard Index*" ...
# $ raw : chr "" "" "" "" ...
# $ stateavg : chr "" "" "" "" ...
# $ statepctile : chr "80" "N/A" "N/A" "N/A" ...
# $ regionavg : chr "" "" "" "" ...
# $ regionpctile: chr "73" "N/A" "N/A" "N/A" ...
# $ usavg : chr "" "" "" "" ...
# $ uspctile : chr "85" "N/A" "N/A" "N/A" ...
##################### #
# OUTPUT FORMAT:
##################### #
# "Export_Output_Example2.txt" is an example of batch tool output:
#
# OBJECTID,FACID,NAME,LAT,LON,totpop,buff,stabbr,statename,region,S_E_TSDF_PER,R_P_TRAFFIC,S_E_PM25_PER,R_P_CANCER,S_P_DIESEL,N_D_INDEX,RAW_E_RMP,R_E_PM25,R_D_LESSHS,R_E_DIESEL,RAW_D_OVER64,N_E_TSDF,R_E_LEAD_PER,R_E_RMP_PER,S_E_DIESEL_PER,RAW_E_RESP,R_D_INDEX_PER,RAW_D_LESSHS,N_E_TRAFFIC,S_E_NEURO_PER,N_P_NPL,S_D_INDEX,S_D_MINOR,S_D_LESSHS,S_P_RESP,N_E_PM25_PER,RAW_D_INDEX,N_E_NEURO_PER,RAW_D_UNDER5,RAW_E_LEAD,R_E_NPL_PER,S_E_RESP_PER,S_E_O3_PER,N_P_PM25,S_D_LESSHS_PER,N_E_DIESEL_PER,S_D_INCOME_PER,RAW_E_NPL,R_D_MINOR_PER,S_E_TRAFFIC,R_P_TSDF,RAW_E_TSDF,N_P_CANCER,RAW_E_NEURO,S_E_DIESEL,RAW_D_INCOME,N_P_RMP,N_E_O3_PER,S_E_O3,R_E_RESP,S_E_RESP,N_E_DIESEL,N_D_INDEX_PER,N_E_RMP_PER,RAW_D_MINOR,N_E_CANCER_PER,R_E_O3_PER,S_D_INDEX_PER,N_E_RMP,R_P_LEAD,R_E_NEURO,N_E_LEAD,S_E_RMP_PER,R_E_RMP,RAW_E_DIESEL,R_D_LING_PER,R_E_TRAFFIC,R_E_LEAD,R_D_OVER64_PER,N_P_NEURO,R_E_CANCER_PER,R_E_NPDES_PER,N_E_CANCER,N_D_MINOR_PER,S_E_TSDF,S_E_NPL,R_D_OVER64,S_D_MINOR_PER,S_P_TSDF,S_P_RMP,N_E_PM25,R_E_TSDF,S_E_RMP,RAW_D_LING,S_E_TRAFFIC_PER,S_P_PM25,S_E_LEAD,R_P_NEURO,S_D_LING,N_E_NPL,R_E_DIESEL_PER,R_D_LESSHS_PER,R_P_O3,N_E_TRAFFIC_PER,RAW_E_NPDES,N_E_NPDES,N_E_NEURO,R_P_DIESEL,N_E_RESP_PER,R_E_TSDF_PER,RAW_E_TRAFFIC,R_D_INDEX,R_P_PM25,N_D_UNDER5_PER,N_D_LESSHS_PER,R_E_NPDES,N_D_LING,S_E_PM25,N_E_NPL_PER,R_E_NEURO_PER,R_D_MINOR,N_P_TSDF,S_D_LING_PER,R_P_NPL,S_P_NPDES,S_E_NPDES_PER,N_D_UNDER5,S_E_NPL_PER,S_E_CANCER_PER,N_E_RESP,N_D_LESSHS,S_D_UNDER5,N_P_LEAD,RAW_E_CANCER,S_P_TRAFFIC,N_E_NPDES_PER,R_E_TRAFFIC_PER,N_P_NPDES,RAW_E_O3,N_P_O3,R_E_O3,N_E_O3,N_E_TSDF_PER,R_E_RESP_PER,S_D_OVER64,N_D_INCOME,R_E_NPL,R_D_UNDER5_PER,R_P_RESP,R_P_NPDES,S_P_O3,N_P_DIESEL,N_D_OVER64_PER,R_P_RMP,N_P_TRAFFIC,N_E_LEAD_PER,S_E_NPDES,S_D_OVER64_PER,S_P_NPL,N_D_MINOR,RAW_E_PM25,N_D_LING_PER,S_D_INCOME,S_P_NEURO,N_P_RESP,N_D_OVER64,S_D_UNDER5_PER,R_D_LING,R_E_CANCER,S_E_CANCER,S_P_CANCER,N_D_INCOME_PER,R_D_INCOME_PER,S_E_NEURO,R_D_INCOME,R_E_PM25_PER,R_D_UNDER5,S_E_LEAD_PER,S_P_LEAD
# 1,1000,BRASW Facility,32.4,-94.7,"7,946",3 miles,TX,Texas,6,97,74,44,75,61,35%,1.2,9.44,19%,0.733,13%,0.054,74,92,37,1.5,72,23%,110,99,95,47%,55%,20%,66,27,61%,92,7%,0.27,97,54,51,77,64,49,70,0.29,69,91,96,0.33,80,0.12,0.913,52%,95,34,42.9,1.4,1.5,0.824,83,95,69%,72,47,68,0.31,84,0.043,0.3,91,0.42,0.478,66,81,0.18,63,90,87,73,49,79,0.073,0.067,11%,62,94,86,10.7,0.062,0.47,6%,60,61,0.17,92,9%,0.096,50,67,68,60,0.32,0.25,0.063,70,43,97,55,44%,68,62,78,0.35,5%,9.63,94,99,49%,97,56,95,74,71,7%,97,84,2.3,15%,8%,84,56,69,81,64,89,43.8,79,43.6,46.3,98,63,10%,34%,0.063,52,73,79,62,78,55,89,80,57,0.38,69,93,36%,9.44,74,39%,89,76,13%,49,7%,42,44,68,79,71,0.044,39%,47,7%,76,82
##################### #
#header <- c('OBJECTID', 'FACID', 'NAME', 'LAT', 'LON', 'totpop', 'buff', 'stabbr', 'statename', 'region', 'S_E_TSDF_PER', 'R_P_TRAFFIC', 'S_E_PM25_PER', 'R_P_CANCER', 'S_P_DIESEL', 'N_D_INDEX', 'RAW_E_RMP', 'R_E_PM25', 'R_D_LESSHS', 'R_E_DIESEL', 'RAW_D_OVER64', 'N_E_TSDF', 'R_E_LEAD_PER', 'R_E_RMP_PER', 'S_E_DIESEL_PER', 'RAW_E_RESP', 'R_D_INDEX_PER', 'RAW_D_LESSHS', 'N_E_TRAFFIC', 'S_E_NEURO_PER', 'N_P_NPL', 'S_D_INDEX', 'S_D_MINOR', 'S_D_LESSHS', 'S_P_RESP', 'N_E_PM25_PER', 'RAW_D_INDEX', 'N_E_NEURO_PER', 'RAW_D_UNDER5', 'RAW_E_LEAD', 'R_E_NPL_PER', 'S_E_RESP_PER', 'S_E_O3_PER', 'N_P_PM25', 'S_D_LESSHS_PER', 'N_E_DIESEL_PER', 'S_D_INCOME_PER', 'RAW_E_NPL', 'R_D_MINOR_PER', 'S_E_TRAFFIC', 'R_P_TSDF', 'RAW_E_TSDF', 'N_P_CANCER', 'RAW_E_NEURO', 'S_E_DIESEL', 'RAW_D_INCOME', 'N_P_RMP', 'N_E_O3_PER', 'S_E_O3', 'R_E_RESP', 'S_E_RESP', 'N_E_DIESEL', 'N_D_INDEX_PER', 'N_E_RMP_PER', 'RAW_D_MINOR', 'N_E_CANCER_PER', 'R_E_O3_PER', 'S_D_INDEX_PER', 'N_E_RMP', 'R_P_LEAD', 'R_E_NEURO', 'N_E_LEAD', 'S_E_RMP_PER', 'R_E_RMP', 'RAW_E_DIESEL', 'R_D_LING_PER', 'R_E_TRAFFIC', 'R_E_LEAD', 'R_D_OVER64_PER', 'N_P_NEURO', 'R_E_CANCER_PER', 'R_E_NPDES_PER', 'N_E_CANCER', 'N_D_MINOR_PER', 'S_E_TSDF', 'S_E_NPL', 'R_D_OVER64', 'S_D_MINOR_PER', 'S_P_TSDF', 'S_P_RMP', 'N_E_PM25', 'R_E_TSDF', 'S_E_RMP', 'RAW_D_LING', 'S_E_TRAFFIC_PER', 'S_P_PM25', 'S_E_LEAD', 'R_P_NEURO', 'S_D_LING', 'N_E_NPL', 'R_E_DIESEL_PER', 'R_D_LESSHS_PER', 'R_P_O3', 'N_E_TRAFFIC_PER', 'RAW_E_NPDES', 'N_E_NPDES', 'N_E_NEURO', 'R_P_DIESEL', 'N_E_RESP_PER', 'R_E_TSDF_PER', 'RAW_E_TRAFFIC', 'R_D_INDEX', 'R_P_PM25', 'N_D_UNDER5_PER', 'N_D_LESSHS_PER', 'R_E_NPDES', 'N_D_LING', 'S_E_PM25', 'N_E_NPL_PER', 'R_E_NEURO_PER', 'R_D_MINOR', 'N_P_TSDF', 'S_D_LING_PER', 'R_P_NPL', 'S_P_NPDES', 'S_E_NPDES_PER', 'N_D_UNDER5', 'S_E_NPL_PER', 'S_E_CANCER_PER', 'N_E_RESP', 'N_D_LESSHS', 'S_D_UNDER5', 'N_P_LEAD', 'RAW_E_CANCER', 'S_P_TRAFFIC', 'N_E_NPDES_PER', 'R_E_TRAFFIC_PER', 'N_P_NPDES', 'RAW_E_O3', 'N_P_O3', 'R_E_O3', 'N_E_O3', 'N_E_TSDF_PER', 'R_E_RESP_PER', 'S_D_OVER64', 'N_D_INCOME', 'R_E_NPL', 'R_D_UNDER5_PER', 'R_P_RESP', 'R_P_NPDES', 'S_P_O3', 'N_P_DIESEL', 'N_D_OVER64_PER', 'R_P_RMP', 'N_P_TRAFFIC', 'N_E_LEAD_PER', 'S_E_NPDES', 'S_D_OVER64_PER', 'S_P_NPL', 'N_D_MINOR', 'RAW_E_PM25', 'N_D_LING_PER', 'S_D_INCOME', 'S_P_NEURO', 'N_P_RESP', 'N_D_OVER64', 'S_D_UNDER5_PER', 'R_D_LING', 'R_E_CANCER', 'S_E_CANCER', 'S_P_CANCER', 'N_D_INCOME_PER', 'R_D_INCOME_PER', 'S_E_NEURO', 'R_D_INCOME', 'R_E_PM25_PER', 'R_D_UNDER5', 'S_E_LEAD_PER', 'S_P_LEAD')
if (missing(fulltable)) {stop('fulltable must be provided as output from tabs.compile()')}
################################################### #
# SHORTEN/WIDEN THE COMPILATION TO HAVE 1 ROW PER SITE, ALL OTHER DATA IN NUMEROUS COLUMNS
################################################### #
# We have already checked to make sure every tabular txt file output has
# 31 rows, or varcount.typical, or rowcount, in case that may be set differently (31 means 12 EJ, 12 envt, 7 demog)
# FIGURE OUT HOW MANY DATA ELEMENTS WERE IN THE REPORT SPLIT INTO ID.etc.rowcount and the rest of colcount
#
# $ ID : chr "ejscreenbuffersavedfromtabular1" "ejscreenbuffersavedfromtabular1" "ejscreenbuffersavedfromtabular1" "ejscreenbuffersavedfromtabular1" ...
# $ lon : num -74 -74 -74 -74 -74 ...
# $ lat : num 40.9 40.9 40.9 40.9 40.9 ...
# $ miles : chr "1" "1" "1" "1" ...
# $ State : chr "NEW JERSEY" "NEW JERSEY" "NEW JERSEY" "NEW JERSEY" ...
# $ REGION : chr "2" "2" "2" "2" ...
# $ pop : num 19214 19214 19214 19214 19214 ...
# $ fieldgroup : chr "EJ" "EJ" "EJ" "EJ" ...
# $ fieldname : chr "EJ Index for Lead Paint Indicator" "EJ Index for NATA Air Toxics Cancer Risk*" "EJ Index for NATA Diesel PM*" "EJ Index for NATA Neurological Hazard Index*" ...
# $ raw : chr "" "" "" "" ...
# $ stateavg : chr "" "" "" "" ...
# $ statepctile : chr "80" "N/A" "N/A" "N/A" ...
# $ regionavg : chr "" "" "" "" ...
# $ regionpctile: chr "73" "N/A" "N/A" "N/A" ...
# $ usavg : chr "" "" "" "" ...
# $ uspctile : chr "85" "N/A" "N/A" "N/A" ...
#
# The # of rows like ID, State, REGION, pop, etc. is different for buffer vs block group reports. (FIPS for a block group vs lat/lon/miles for buffers)
# There are currently 9 data rows including fieldgroup:
# fieldgroup, fieldname, raw, stateavg, statepctile, regionavg, regionpctile, usavg, uspctile
# (row called fieldgroups says EJ, envt, or demog, etc.)
# So the count of Basic rows like ID, State, etc. is found this way: (should be 5 if block groups and was 7 if buffers).
varcount.typical <- 31 #??? how many indicators (e.g., env, demog, or EJ Indexes)
#colcount <- 17 # how many stats on each indicator (e.g., US avg.) for circular buffers at least... not necessarily for block group reports or non circular buffers
ID.etc.rowcount <- colcount - 9 # used to be 7 , if circular buffers
# This pointcount should match the length of filelist, and is the number of points/buffers/zones/places analyzed.
pointcount <- length(fulltable[, 1]) / varcount.typical
if (floor(pointcount) != pointcount) {
cat("\n ERROR - Table length is not an integer multiple of number of places/buffers.\n")
#logwrite("ERROR - Table length is not an integer multiple of number of places/buffers.")
}
# make finaltable which will have only 1 row per ID instead of 31 (varcount.typical),
# but 31*7 or 31*5 columns to the right instead of just 7 if buffers (or 5 if these are block group not buffer reports).
# Take every 31 or so rows and make those into one row of the shorter/wider table, by creating 7 (or 5) new columns for each of those rows.
# keep rows unique to one ID (some of these are stored as factors but that is OK)
# There are 7 (8 now) such rows currently if they are buffer reports: ID, lon, lat, miles, State, REGION, pop *****************
# BUT there are 5 such rows if they are block group reports: ID, FIPS, State, REGION, pop
# so but we will use variable varcount.typical or rowcount
# print('fulltable str:')
# print( str(fulltable) )
finaltable <- data.frame(unique(fulltable[ , 1:ID.etc.rowcount]))
# can assume that retains the original order of the rows, or use merge to join on ID
row.names(finaltable) <- NULL
# column 8 if buffer, col 6 if block group report, is fieldname- it gets the specific type of field appended to it.
# note that last column is fieldgroup so leave that last one out
# The result is for example, "Ozone (Env) regionavg"
for (mycolname in names(fulltable[ , (ID.etc.rowcount + 2):(colcount - 1)]) ) {
c.this <- matrix(fulltable[ , mycolname], ncol = varcount.typical, nrow = pointcount, byrow = TRUE)
colnames(c.this) <- paste(fulltable$fieldname[1:varcount.typical], mycolname)
finaltable <- cbind(finaltable, c.this, stringsAsFactors = FALSE)
}
# To view results so far:
# dim(finaltable)
# [1] 62 225 **** should only be 2 rows now? not 31+31 !
# t(head(finaltable,2))
#header <- c('OBJECTID', 'FACID', 'NAME', 'LAT', 'LON', 'totpop', 'buff', 'stabbr', 'statename', 'region', 'S_E_TSDF_PER', 'R_P_TRAFFIC', 'S_E_PM25_PER', 'R_P_CANCER', 'S_P_DIESEL', 'N_D_INDEX', 'RAW_E_RMP', 'R_E_PM25', 'R_D_LESSHS', 'R_E_DIESEL', 'RAW_D_OVER64', 'N_E_TSDF', 'R_E_LEAD_PER', 'R_E_RMP_PER', 'S_E_DIESEL_PER', 'RAW_E_RESP', 'R_D_INDEX_PER', 'RAW_D_LESSHS', 'N_E_TRAFFIC', 'S_E_NEURO_PER', 'N_P_NPL', 'S_D_INDEX', 'S_D_MINOR', 'S_D_LESSHS', 'S_P_RESP', 'N_E_PM25_PER', 'RAW_D_INDEX', 'N_E_NEURO_PER', 'RAW_D_UNDER5', 'RAW_E_LEAD', 'R_E_NPL_PER', 'S_E_RESP_PER', 'S_E_O3_PER', 'N_P_PM25', 'S_D_LESSHS_PER', 'N_E_DIESEL_PER', 'S_D_INCOME_PER', 'RAW_E_NPL', 'R_D_MINOR_PER', 'S_E_TRAFFIC', 'R_P_TSDF', 'RAW_E_TSDF', 'N_P_CANCER', 'RAW_E_NEURO', 'S_E_DIESEL', 'RAW_D_INCOME', 'N_P_RMP', 'N_E_O3_PER', 'S_E_O3', 'R_E_RESP', 'S_E_RESP', 'N_E_DIESEL', 'N_D_INDEX_PER', 'N_E_RMP_PER', 'RAW_D_MINOR', 'N_E_CANCER_PER', 'R_E_O3_PER', 'S_D_INDEX_PER', 'N_E_RMP', 'R_P_LEAD', 'R_E_NEURO', 'N_E_LEAD', 'S_E_RMP_PER', 'R_E_RMP', 'RAW_E_DIESEL', 'R_D_LING_PER', 'R_E_TRAFFIC', 'R_E_LEAD', 'R_D_OVER64_PER', 'N_P_NEURO', 'R_E_CANCER_PER', 'R_E_NPDES_PER', 'N_E_CANCER', 'N_D_MINOR_PER', 'S_E_TSDF', 'S_E_NPL', 'R_D_OVER64', 'S_D_MINOR_PER', 'S_P_TSDF', 'S_P_RMP', 'N_E_PM25', 'R_E_TSDF', 'S_E_RMP', 'RAW_D_LING', 'S_E_TRAFFIC_PER', 'S_P_PM25', 'S_E_LEAD', 'R_P_NEURO', 'S_D_LING', 'N_E_NPL', 'R_E_DIESEL_PER', 'R_D_LESSHS_PER', 'R_P_O3', 'N_E_TRAFFIC_PER', 'RAW_E_NPDES', 'N_E_NPDES', 'N_E_NEURO', 'R_P_DIESEL', 'N_E_RESP_PER', 'R_E_TSDF_PER', 'RAW_E_TRAFFIC', 'R_D_INDEX', 'R_P_PM25', 'N_D_UNDER5_PER', 'N_D_LESSHS_PER', 'R_E_NPDES', 'N_D_LING', 'S_E_PM25', 'N_E_NPL_PER', 'R_E_NEURO_PER', 'R_D_MINOR', 'N_P_TSDF', 'S_D_LING_PER', 'R_P_NPL', 'S_P_NPDES', 'S_E_NPDES_PER', 'N_D_UNDER5', 'S_E_NPL_PER', 'S_E_CANCER_PER', 'N_E_RESP', 'N_D_LESSHS', 'S_D_UNDER5', 'N_P_LEAD', 'RAW_E_CANCER', 'S_P_TRAFFIC', 'N_E_NPDES_PER', 'R_E_TRAFFIC_PER', 'N_P_NPDES', 'RAW_E_O3', 'N_P_O3', 'R_E_O3', 'N_E_O3', 'N_E_TSDF_PER', 'R_E_RESP_PER', 'S_D_OVER64', 'N_D_INCOME', 'R_E_NPL', 'R_D_UNDER5_PER', 'R_P_RESP', 'R_P_NPDES', 'S_P_O3', 'N_P_DIESEL', 'N_D_OVER64_PER', 'R_P_RMP', 'N_P_TRAFFIC', 'N_E_LEAD_PER', 'S_E_NPDES', 'S_D_OVER64_PER', 'S_P_NPL', 'N_D_MINOR', 'RAW_E_PM25', 'N_D_LING_PER', 'S_D_INCOME', 'S_P_NEURO', 'N_P_RESP', 'N_D_OVER64', 'S_D_UNDER5_PER', 'R_D_LING', 'R_E_CANCER', 'S_E_CANCER', 'S_P_CANCER', 'N_D_INCOME_PER', 'R_D_INCOME_PER', 'S_E_NEURO', 'R_D_INCOME', 'R_E_PM25_PER', 'R_D_UNDER5', 'S_E_LEAD_PER', 'S_P_LEAD')
return(finaltable)
}
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