R/example.not.shiny.R
In ejanalysis/batch.summarizer: Summarize Multiple Buffer Reports from the (nonpublic) Batch Tool for EJSCREEN
if (1 == 0) {
# Example of using batch.summarize directly
# *** These (newer versions) are also found in the packages called analyze.stuff and ejanalysis, (and ejscreen pkg provides popupunits)
# available at http://ejanalysis.github.io
#
source('pct.above.R') # returns percent of rows (or wtd people) that have value above specified cutoff (or mean as default), for each column of data.frame
source('count.above.R') # returns count of how many rows (or wtd people) have value above specified cutoff (or mean as default), for each column of data.frame
source('cols.above.count.R') # returns count of how many cols (e.g. how many variables or indicators) have value above specified cutoff (or mean as default), for each row of data.frame
source('flagged.R') # creates flag that is TRUE if at least one of 12 indicators is > cutoff (EJ index >50th or 80th or 95th%ile, for example), for each of many rows of a data.frame
source('rowMaxs.R') # returns Max of each row
source('rowMins.R') # returns Min of each row
source('colMaxs.R') # returns Max of each col
source('colMins.R') # returns Min of each col
source('wtd.colMeans.R') # returns wtd.mean of each col
source('lead.zeroes.R') # add leading zeroes as needed to fix FIPS that had been stored as numeric
source('change.fieldnames.R') # updated function that helps change or resort fieldnames using a map file mapping old to new names
# source('wilcoxon.pvalues.r') # for statistical significance testing - from air
source('batch.read.R')
source('batch.clean.R')
source('batch.summarize.R')
require(Hmisc) # various useful functions for data analysis
# library(plotrix) # for better weighted.hist than ggplot2 can provide.
# require(ggplot2) # for geom_histogram() that allows weights to be used. plotrix package also does wtd hist, but better.
options(stringsAsFactors = FALSE)
######################### #
# define the functions
######################### #
readclean <- function(file, namesfile) {
lookup <- read.csv(namesfile)
batch.clean(read.csv(file), oldcolnames = lookup$oldnames, newcolnames = lookup$newnames)
}
# get summary stats cols and rows
batch.all <- function(sitefile, popfile, namesfile, ...) {
return(batch.summarize(
readclean(sitefile, namesfile),
readclean(popfile, namesfile)
))
## or
# test.sitestats <- read.csv(sitefile) # or batch.read() is same thing
# test.popstats <- read.csv(popfile)
# test.lookup <- read.csv(namesfile)
#
# test.sitestats <- batch.clean(test.sitestats, oldcolnames = test.lookup$oldnames, newcolnames = test.lookup$newnames )
# test.popstats <- batch.clean(test.popstats, oldcolnames = test.lookup$oldnames, newcolnames = test.lookup$newnames )
#
# test.outlist <- batch.summarize(test.sitestats, test.popstats, ...)
# return(test.outlist)
}
######################### #
# use the functions
######################### #
sitedata <- readclean("R/sample_ejscreen_batch_output.csv", 'map_batch_to_friendly_fieldnames_2016.csv') # or "map_batch_to_friendly_fieldnames_2018.csv"
sitedata1 <- readclean("Export_Output_Example2.csv", 'map batch to friendly fieldnames v1.csv') # or "map_batch_to_friendly_fieldnames_2018.csv"
popdata <- readclean("R/sample_ejscreen_batch_output.csv", 'map_batch_to_friendly_fieldnames_2016.csv') # or "map_batch_to_friendly_fieldnames_2018.csv"
#names(sitedata)
# test.outlist <- batch.summarize(sitedata, popdata)
# or,
# get summary stats cols and rows:
test.outlist <- batch.all(
sitefile = "Export_Output_Example2.csv",
popfile = "Export_Output_Example2.pop.csv",
namesfile = 'map batch to friendly fieldnames v1.csv'
)
# test.outlist <- batch.all(
# sitefile = "R/sample_ejscreen_batch_output.csv",
# popfile = "R/sample_ejscreen_batch_output.csv",
# namesfile = 'map_batch_to_friendly_fieldnames_2016.csv' # or "map_batch_to_friendly_fieldnames_2018.csv"
# )
# FAILS AS OF 8/27/16
######################### #
# View results
######################### #
test.outlist$cols # one row per site, one col per summary stat
# cbind(sitedata, test.outlist$cols)[ 1:7, c(1:7, 178:181)]
t(cbind(sitedata, test.outlist$cols)[ 1:2, c(1:22, 170:173)])
#test.outlist$rows[1:4, 1:20 ] # one row per summary stat before t(), one col per indicator before t()
round( t(test.outlist$rows[1:4, 1:20 ]), 1)
######################### #
# > test.fulltable$cols
# Max of variables Number of variables at/above threshold of 80
# 1 7946 39
# 2 1487 7
# 3 14003 53
# 4 106245 68
# round( t(head(test.fulltable$rows[ , 1:20 ] , 2)), 2)
#
# Average site Average person
# OBJECTID 21.50 14.44
# FACID 1020.50 1013.44
# name NA NA
# lat 37.25 40.42
# lon -88.67 -85.25
# pop 29973.21 138436.48
# radius.miles 3.00 3.00
# ST NA NA
# statename NA NA
# REGION 5.10 4.46
# VSI.eo 38.00 54.19
# us.avg.VSI.eo 35.00 35.00
# region.avg.VSI.eo 34.40 31.53
# state.avg.VSI.eo 33.24 33.13
# pctile.VSI.eo 56.85 73.79
# region.pctile.VSI.eo 57.80 75.68
# state.pctile.VSI.eo 60.44 75.64
# pctlowinc 41.44 49.39
# us.avg.pctlowinc 34.00 34.00
# region.avg.pctlowinc 34.64 32.51
# >
# > names(test.sitestats)
# [1] "OBJECTID"
# [2] "FACID"
# [3] "name"
# [4] "lat"
# [5] "lon"
# [6] "pop"
# [7] "radius.miles"
# [8] "ST"
# [9] "statename"
# [10] "REGION"
# [11] "VSI.eo"
# [12] "us.avg.VSI.eo"
# etc.
# cbind(sapply(test.sitestats, class))
# name "character"
# lat "numeric"
# lon "numeric"
# pop "numeric"
# radius.miles "numeric"
# ST "character"
# statename "character"
#
# List of 2
# $ rows: num [1:24, 1:179] 21.5 14.4 21.5 13 1 ...
# ..- attr(*, "dimnames")=List of 2
# .. ..$ : chr [1:24] "Average site" "Average person" "Median site" "Median person" ...
# .. ..$ : chr [1:179] "OBJECTID" "FACID" "name" "lat" ...
# $ cols: num [1:42, 1:2] 7946 1487 14003 106245 344991 ...
# ..- attr(*, "dimnames")=List of 2
# .. ..$ : chr [1:42] "1" "2" "3" "4" ...
# .. ..$ : chr [1:2] "Max of variables" "Number of variables at/above threshold of 80"
############## #
# As script instead of function:
#
# test.mydemofile <- "Export_Output_Example2.csv"
# test.mydemofile.pop <- "Export_Output_Example2.pop.csv"
# test.mynamesfile.default <- "map batch to friendly fieldnames v1.csv"
# options(stringsAsFactors = FALSE)
# test.sitestats <- read.csv(test.mydemofile)
# test.popstats <- read.csv(test.mydemofile.pop)
# test.lookup <- read.csv(test.mynamesfile.default)
# test.sitestats <- batch.clean(test.sitestats, oldcolnames = test.lookup$oldnames, newcolnames = test.lookup$newnames )
# test.popstats <- batch.clean(test.popstats, oldcolnames = test.lookup$oldnames, newcolnames = test.lookup$newnames )
# test.fulltable <- batch.summarize(test.sitestats, test.popstats)
# rm(test.lookup,test.mydemofile,test.mydemofile.pop,test.mynamesfile.default,test.sitestats,test.popstats)
# rm(test.fulltable)
####################### #
############### as used within server.R
# batch.summarize <- function(sitestats, popstats, cols = 'all',
# wtscolname = 'pop', wts=popstats[ , wtscolname],
# probs=c(0,0.25,0.50,0.75,0.80,0.90,0.95,0.99,1),
# threshold=list(80), threshnames=list(colnames(sitestats)), threshgroup=list('variables'),
# na.rm=TRUE,
# rowfun.picked='all', colfun.picked='all') {
#
# outlist <- reactive({
# x <- batch.summarize(
# sitestats = fulltabler(), popstats = fulltabler.pop(),
# wtscolname = mywtsname,
# #wts = fulltabler()[ , mywtsname],
# cols = mycolnames(),
# threshnames = mythreshnames(), threshold = mythresholds(), threshgroup = mythreshgroups(),
# colfun.picked = colfun.picked, rowfun.picked = rowfun.picked,
# probs = as.numeric( input$probs ), na.rm = na.rm
# )
# ... other steps to further clean up results here
###############
}
# test.mydemofile <- "Export_Output_Example2.csv"
# e.g.
# This shows it for the 2015 version:
# > t(test.sitestats[1:2,])
# 1 2
# OBJECTID "1" "2"
# FACID "1000" "1001"
# NAME "BRASW Facility" "EHZST Facility"
# LAT "32.4" "33.7"
# LON "-94.7" "-94.4"
# totpop "7,946" "1,487"
# buff "3 miles" "3 miles"
# stabbr "TX" "AR"
# statename "Texas" "Arkansas"
# region "6" "6"
# S_E_TSDF_PER "97" "98"
# R_P_TRAFFIC "74" "30"
# S_E_PM25_PER "44" "29"
# R_P_CANCER "75" "35"
# S_P_DIESEL "61" "49"
# N_D_INDEX "35%" "35%"
# RAW_E_RMP "1.2" "0.038"
# R_E_PM25 "9.44" "9.44"
# R_D_LESSHS "19%" "19%"
# R_E_DIESEL "0.733" "0.733"
# RAW_D_OVER64 "13%" "18%"
# N_E_TSDF "0.054" "0.054"
# R_E_LEAD_PER "74" "69"
# R_E_RMP_PER "92" "5"
# S_E_DIESEL_PER "37" "36"
# RAW_E_RESP "1.5" "0.76"
# R_D_INDEX_PER "72" "33"
# RAW_D_LESSHS "23%" "14%"
# N_E_TRAFFIC "110" "110"
# S_E_NEURO_PER "99" "13"
# N_P_NPL "95" "51"
# S_D_INDEX "47%" "34%"
# S_D_MINOR "55%" "26%"
# S_D_LESSHS "20%" "17%"
# S_P_RESP "66" "54"
# N_E_PM25_PER "27" "27"
# RAW_D_INDEX "61%" "29%"
# N_E_NEURO_PER "92" "4"
# RAW_D_UNDER5 "7%" "3%"
# RAW_E_LEAD "0.27" "0.22"
# R_E_NPL_PER "97" "41"
# S_E_RESP_PER "54" "26"
# S_E_O3_PER "51" "60"
# N_P_PM25 "77" "53"
# S_D_LESSHS_PER "64" "43"
# N_E_DIESEL_PER "49" "12"
# S_D_INCOME_PER "70" "48"
# RAW_E_NPL "0.29" "0.024"
# R_D_MINOR_PER "69" "18"
# S_E_TRAFFIC "91" "64"
# R_P_TSDF "96" "7"
# RAW_E_TSDF "0.33" "0.33"
# N_P_CANCER "80" "52"
# RAW_E_NEURO "0.12" "0.023"
# S_E_DIESEL "0.913" "0.245"
# RAW_D_INCOME "52%" "41%"
# N_P_RMP "95" "56"
# N_E_O3_PER "34" "37"
# S_E_O3 "42.9" "44.5"
# R_E_RESP "1.4" "1.4"
# S_E_RESP "1.5" "1.1"
# N_E_DIESEL "0.824" "0.824"
# N_D_INDEX_PER "83" "50"
# N_E_RMP_PER "95" "8"
# RAW_D_MINOR "69%" "17%"
# N_E_CANCER_PER "72" "33"
# R_E_O3_PER "47" "53"
# S_D_INDEX_PER "68" "50"
# N_E_RMP "0.31" "0.31"
# R_P_LEAD "84" "18"
# R_E_NEURO "0.043" "0.043"
# N_E_LEAD "0.3" "0.3"
# S_E_RMP_PER "91" "17"
# R_E_RMP "0.42" "0.42"
# RAW_E_DIESEL "0.478" "0.0875"
# R_D_LING_PER "66" "36"
# R_E_TRAFFIC "81" "81"
# R_E_LEAD "0.18" "0.18"
# R_D_OVER64_PER "63" "83"
# N_P_NEURO "90" "55"
# R_E_CANCER_PER "87" "39"
# R_E_NPDES_PER "73" "5"
# N_E_CANCER "49" "49"
# N_D_MINOR_PER "79" "38"
# S_E_TSDF "0.073" "0.046"
# S_E_NPL "0.067" "0.033"
# R_D_OVER64 "11%" "11%"
# S_D_MINOR_PER "62" "51"
# S_P_TSDF "94" "4"
# S_P_RMP "86" "56"
# N_E_PM25 "10.7" "10.7"
# R_E_TSDF "0.062" "0.062"
# S_E_RMP "0.47" "0.33"
# RAW_D_LING "6%" "0%"
# S_E_TRAFFIC_PER "60" "44"
# S_P_PM25 "61" "53"
# S_E_LEAD "0.17" "0.17"
# R_P_NEURO "92" "38"
# S_D_LING "9%" "2%"
# N_E_NPL "0.096" "0.096"
# R_E_DIESEL_PER "50" "17"
# R_D_LESSHS_PER "67" "46"
# R_P_O3 "68" "36"
# N_E_TRAFFIC_PER "60" "35"
# RAW_E_NPDES "0.32" "0.031"
# N_E_NPDES "0.25" "0.25"
# N_E_NEURO "0.063" "0.063"
# R_P_DIESEL "70" "37"
# N_E_RESP_PER "43" "15"
# R_E_TSDF_PER "97" "97"
# RAW_E_TRAFFIC "55" "19"
# R_D_INDEX "44%" "44%"
# R_P_PM25 "68" "35"
# N_D_UNDER5_PER "62" "20"
# N_D_LESSHS_PER "78" "58"
# R_E_NPDES "0.35" "0.35"
# N_D_LING "5%" "5%"
# S_E_PM25 "9.63" "9.70"
# N_E_NPL_PER "94" "29"
# R_E_NEURO_PER "99" "6"
# R_D_MINOR "49%" "49%"
# N_P_TSDF "97" "7"
# S_D_LING_PER "56" "67"
# R_P_NPL "95" "34"
# S_P_NPDES "74" "58"
# S_E_NPDES_PER "71" "8"
# N_D_UNDER5 "7%" "7%"
# S_E_NPL_PER "97" "68"
# S_E_CANCER_PER "84" "49"
# N_E_RESP "2.3" "2.3"
# N_D_LESSHS "15%" "15%"
# S_D_UNDER5 "8%" "7%"
# N_P_LEAD "84" "39"
# RAW_E_CANCER "56" "38"
# S_P_TRAFFIC "69" "40"
# N_E_NPDES_PER "81" "4"
# R_E_TRAFFIC_PER "64" "35"
# N_P_NPDES "89" "56"
# RAW_E_O3 "43.8" "44.4"
# N_P_O3 "79" "52"
# R_E_O3 "43.6" "43.6"
# N_E_O3 "46.3" "46.3"
# N_E_TSDF_PER "98" "98"
# R_E_RESP_PER "63" "22"
# S_D_OVER64 "10%" "14%"
# N_D_INCOME "34%" "34%"
# R_E_NPL "0.063" "0.063"
# R_D_UNDER5_PER "52" "15"
# R_P_RESP "73" "37"
# R_P_NPDES "79" "40"
# S_P_O3 "62" "53"
# N_P_DIESEL "78" "54"
# N_D_OVER64_PER "55" "78"
# R_P_RMP "89" "40"
# N_P_TRAFFIC "80" "45"
# N_E_LEAD_PER "57" "51"
# S_E_NPDES "0.38" "0.25"
# S_D_OVER64_PER "69" "74"
# S_P_NPL "93" "46"
# N_D_MINOR "36%" "36%"
# RAW_E_PM25 "9.44" "9.45"
# N_D_LING_PER "74" "45"
# S_D_INCOME "39%" "42%"
# S_P_NEURO "89" "55"
# N_P_RESP "76" "54"
# N_D_OVER64 "13%" "13%"
# S_D_UNDER5_PER "49" "19"
# R_D_LING "7%" "7%"
# R_E_CANCER "42" "42"
# S_E_CANCER "44" "40"
# S_P_CANCER "68" "53"
# N_D_INCOME_PER "79" "65"
# R_D_INCOME_PER "71" "55"
# S_E_NEURO "0.044" "0.038"
# R_D_INCOME "39%" "39%"
# R_E_PM25_PER "47" "47"
# R_D_UNDER5 "7%" "7%"
# S_E_LEAD_PER "76" "71"
# S_P_LEAD "82" "33"
# Assume same lookup.fieldnames will be used for both sitestats and popstats,
# since they should have identical formats,
# and this shows it for the 2015 version:
# test.mynamesfile.default <- "map batch to friendly fieldnames v1.csv"
# e.g.
# oldnames newnames
# 1 OBJECTID OBJECTID
# 2 FACID FACID
# 3 Registry_I registryID
# 4 NAME name
# 5 LAT lat
# 6 LON lon
# 7 totpop pop
# 8 buff radius.miles
# 9 stabbr ST
# 10 statename statename
# 11 region REGION
# 12 RAW_D_INDEX VSI.eo
# 13 N_D_INDEX us.avg.VSI.eo
# 14 R_D_INDEX region.avg.VSI.eo
# 15 S_D_INDEX state.avg.VSI.eo
# 16 N_D_INDEX_PER pctile.VSI.eo
# 17 R_D_INDEX_PER region.pctile.VSI.eo
# 18 S_D_INDEX_PER state.pctile.VSI.eo
# 19 RAW_D_INCOME pctlowinc
# 20 N_D_INCOME us.avg.pctlowinc
# 21 R_D_INCOME region.avg.pctlowinc
# 22 S_D_INCOME state.avg.pctlowinc
# 23 N_D_INCOME_PER pctile.pctlowinc
# 24 R_D_INCOME_PER region.pctile.pctlowinc
# 25 S_D_INCOME_PER state.pctile.pctlowinc
# 26 RAW_D_MINOR pctmin
# 27
ejanalysis/batch.summarizer documentation built on Sept. 6, 2021, 3:41 a.m.
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if (1 == 0) {
# Example of using batch.summarize directly
# *** These (newer versions) are also found in the packages called analyze.stuff and ejanalysis, (and ejscreen pkg provides popupunits)
# available at http://ejanalysis.github.io
#
source('pct.above.R') # returns percent of rows (or wtd people) that have value above specified cutoff (or mean as default), for each column of data.frame
source('count.above.R') # returns count of how many rows (or wtd people) have value above specified cutoff (or mean as default), for each column of data.frame
source('cols.above.count.R') # returns count of how many cols (e.g. how many variables or indicators) have value above specified cutoff (or mean as default), for each row of data.frame
source('flagged.R') # creates flag that is TRUE if at least one of 12 indicators is > cutoff (EJ index >50th or 80th or 95th%ile, for example), for each of many rows of a data.frame
source('rowMaxs.R') # returns Max of each row
source('rowMins.R') # returns Min of each row
source('colMaxs.R') # returns Max of each col
source('colMins.R') # returns Min of each col
source('wtd.colMeans.R') # returns wtd.mean of each col
source('lead.zeroes.R') # add leading zeroes as needed to fix FIPS that had been stored as numeric
source('change.fieldnames.R') # updated function that helps change or resort fieldnames using a map file mapping old to new names
# source('wilcoxon.pvalues.r') # for statistical significance testing - from air
source('batch.read.R')
source('batch.clean.R')
source('batch.summarize.R')
require(Hmisc) # various useful functions for data analysis
# library(plotrix) # for better weighted.hist than ggplot2 can provide.
# require(ggplot2) # for geom_histogram() that allows weights to be used. plotrix package also does wtd hist, but better.
options(stringsAsFactors = FALSE)
######################### #
# define the functions
######################### #
readclean <- function(file, namesfile) {
lookup <- read.csv(namesfile)
batch.clean(read.csv(file), oldcolnames = lookup$oldnames, newcolnames = lookup$newnames)
}
# get summary stats cols and rows
batch.all <- function(sitefile, popfile, namesfile, ...) {
return(batch.summarize(
readclean(sitefile, namesfile),
readclean(popfile, namesfile)
))
## or
# test.sitestats <- read.csv(sitefile) # or batch.read() is same thing
# test.popstats <- read.csv(popfile)
# test.lookup <- read.csv(namesfile)
#
# test.sitestats <- batch.clean(test.sitestats, oldcolnames = test.lookup$oldnames, newcolnames = test.lookup$newnames )
# test.popstats <- batch.clean(test.popstats, oldcolnames = test.lookup$oldnames, newcolnames = test.lookup$newnames )
#
# test.outlist <- batch.summarize(test.sitestats, test.popstats, ...)
# return(test.outlist)
}
######################### #
# use the functions
######################### #
sitedata <- readclean("R/sample_ejscreen_batch_output.csv", 'map_batch_to_friendly_fieldnames_2016.csv') # or "map_batch_to_friendly_fieldnames_2018.csv"
sitedata1 <- readclean("Export_Output_Example2.csv", 'map batch to friendly fieldnames v1.csv') # or "map_batch_to_friendly_fieldnames_2018.csv"
popdata <- readclean("R/sample_ejscreen_batch_output.csv", 'map_batch_to_friendly_fieldnames_2016.csv') # or "map_batch_to_friendly_fieldnames_2018.csv"
#names(sitedata)
# test.outlist <- batch.summarize(sitedata, popdata)
# or,
# get summary stats cols and rows:
test.outlist <- batch.all(
sitefile = "Export_Output_Example2.csv",
popfile = "Export_Output_Example2.pop.csv",
namesfile = 'map batch to friendly fieldnames v1.csv'
)
# test.outlist <- batch.all(
# sitefile = "R/sample_ejscreen_batch_output.csv",
# popfile = "R/sample_ejscreen_batch_output.csv",
# namesfile = 'map_batch_to_friendly_fieldnames_2016.csv' # or "map_batch_to_friendly_fieldnames_2018.csv"
# )
# FAILS AS OF 8/27/16
######################### #
# View results
######################### #
test.outlist$cols # one row per site, one col per summary stat
# cbind(sitedata, test.outlist$cols)[ 1:7, c(1:7, 178:181)]
t(cbind(sitedata, test.outlist$cols)[ 1:2, c(1:22, 170:173)])
#test.outlist$rows[1:4, 1:20 ] # one row per summary stat before t(), one col per indicator before t()
round( t(test.outlist$rows[1:4, 1:20 ]), 1)
######################### #
# > test.fulltable$cols
# Max of variables Number of variables at/above threshold of 80
# 1 7946 39
# 2 1487 7
# 3 14003 53
# 4 106245 68
# round( t(head(test.fulltable$rows[ , 1:20 ] , 2)), 2)
#
# Average site Average person
# OBJECTID 21.50 14.44
# FACID 1020.50 1013.44
# name NA NA
# lat 37.25 40.42
# lon -88.67 -85.25
# pop 29973.21 138436.48
# radius.miles 3.00 3.00
# ST NA NA
# statename NA NA
# REGION 5.10 4.46
# VSI.eo 38.00 54.19
# us.avg.VSI.eo 35.00 35.00
# region.avg.VSI.eo 34.40 31.53
# state.avg.VSI.eo 33.24 33.13
# pctile.VSI.eo 56.85 73.79
# region.pctile.VSI.eo 57.80 75.68
# state.pctile.VSI.eo 60.44 75.64
# pctlowinc 41.44 49.39
# us.avg.pctlowinc 34.00 34.00
# region.avg.pctlowinc 34.64 32.51
# >
# > names(test.sitestats)
# [1] "OBJECTID"
# [2] "FACID"
# [3] "name"
# [4] "lat"
# [5] "lon"
# [6] "pop"
# [7] "radius.miles"
# [8] "ST"
# [9] "statename"
# [10] "REGION"
# [11] "VSI.eo"
# [12] "us.avg.VSI.eo"
# etc.
# cbind(sapply(test.sitestats, class))
# name "character"
# lat "numeric"
# lon "numeric"
# pop "numeric"
# radius.miles "numeric"
# ST "character"
# statename "character"
#
# List of 2
# $ rows: num [1:24, 1:179] 21.5 14.4 21.5 13 1 ...
# ..- attr(*, "dimnames")=List of 2
# .. ..$ : chr [1:24] "Average site" "Average person" "Median site" "Median person" ...
# .. ..$ : chr [1:179] "OBJECTID" "FACID" "name" "lat" ...
# $ cols: num [1:42, 1:2] 7946 1487 14003 106245 344991 ...
# ..- attr(*, "dimnames")=List of 2
# .. ..$ : chr [1:42] "1" "2" "3" "4" ...
# .. ..$ : chr [1:2] "Max of variables" "Number of variables at/above threshold of 80"
############## #
# As script instead of function:
#
# test.mydemofile <- "Export_Output_Example2.csv"
# test.mydemofile.pop <- "Export_Output_Example2.pop.csv"
# test.mynamesfile.default <- "map batch to friendly fieldnames v1.csv"
# options(stringsAsFactors = FALSE)
# test.sitestats <- read.csv(test.mydemofile)
# test.popstats <- read.csv(test.mydemofile.pop)
# test.lookup <- read.csv(test.mynamesfile.default)
# test.sitestats <- batch.clean(test.sitestats, oldcolnames = test.lookup$oldnames, newcolnames = test.lookup$newnames )
# test.popstats <- batch.clean(test.popstats, oldcolnames = test.lookup$oldnames, newcolnames = test.lookup$newnames )
# test.fulltable <- batch.summarize(test.sitestats, test.popstats)
# rm(test.lookup,test.mydemofile,test.mydemofile.pop,test.mynamesfile.default,test.sitestats,test.popstats)
# rm(test.fulltable)
####################### #
############### as used within server.R
# batch.summarize <- function(sitestats, popstats, cols = 'all',
# wtscolname = 'pop', wts=popstats[ , wtscolname],
# probs=c(0,0.25,0.50,0.75,0.80,0.90,0.95,0.99,1),
# threshold=list(80), threshnames=list(colnames(sitestats)), threshgroup=list('variables'),
# na.rm=TRUE,
# rowfun.picked='all', colfun.picked='all') {
#
# outlist <- reactive({
# x <- batch.summarize(
# sitestats = fulltabler(), popstats = fulltabler.pop(),
# wtscolname = mywtsname,
# #wts = fulltabler()[ , mywtsname],
# cols = mycolnames(),
# threshnames = mythreshnames(), threshold = mythresholds(), threshgroup = mythreshgroups(),
# colfun.picked = colfun.picked, rowfun.picked = rowfun.picked,
# probs = as.numeric( input$probs ), na.rm = na.rm
# )
# ... other steps to further clean up results here
###############
}
# test.mydemofile <- "Export_Output_Example2.csv"
# e.g.
# This shows it for the 2015 version:
# > t(test.sitestats[1:2,])
# 1 2
# OBJECTID "1" "2"
# FACID "1000" "1001"
# NAME "BRASW Facility" "EHZST Facility"
# LAT "32.4" "33.7"
# LON "-94.7" "-94.4"
# totpop "7,946" "1,487"
# buff "3 miles" "3 miles"
# stabbr "TX" "AR"
# statename "Texas" "Arkansas"
# region "6" "6"
# S_E_TSDF_PER "97" "98"
# R_P_TRAFFIC "74" "30"
# S_E_PM25_PER "44" "29"
# R_P_CANCER "75" "35"
# S_P_DIESEL "61" "49"
# N_D_INDEX "35%" "35%"
# RAW_E_RMP "1.2" "0.038"
# R_E_PM25 "9.44" "9.44"
# R_D_LESSHS "19%" "19%"
# R_E_DIESEL "0.733" "0.733"
# RAW_D_OVER64 "13%" "18%"
# N_E_TSDF "0.054" "0.054"
# R_E_LEAD_PER "74" "69"
# R_E_RMP_PER "92" "5"
# S_E_DIESEL_PER "37" "36"
# RAW_E_RESP "1.5" "0.76"
# R_D_INDEX_PER "72" "33"
# RAW_D_LESSHS "23%" "14%"
# N_E_TRAFFIC "110" "110"
# S_E_NEURO_PER "99" "13"
# N_P_NPL "95" "51"
# S_D_INDEX "47%" "34%"
# S_D_MINOR "55%" "26%"
# S_D_LESSHS "20%" "17%"
# S_P_RESP "66" "54"
# N_E_PM25_PER "27" "27"
# RAW_D_INDEX "61%" "29%"
# N_E_NEURO_PER "92" "4"
# RAW_D_UNDER5 "7%" "3%"
# RAW_E_LEAD "0.27" "0.22"
# R_E_NPL_PER "97" "41"
# S_E_RESP_PER "54" "26"
# S_E_O3_PER "51" "60"
# N_P_PM25 "77" "53"
# S_D_LESSHS_PER "64" "43"
# N_E_DIESEL_PER "49" "12"
# S_D_INCOME_PER "70" "48"
# RAW_E_NPL "0.29" "0.024"
# R_D_MINOR_PER "69" "18"
# S_E_TRAFFIC "91" "64"
# R_P_TSDF "96" "7"
# RAW_E_TSDF "0.33" "0.33"
# N_P_CANCER "80" "52"
# RAW_E_NEURO "0.12" "0.023"
# S_E_DIESEL "0.913" "0.245"
# RAW_D_INCOME "52%" "41%"
# N_P_RMP "95" "56"
# N_E_O3_PER "34" "37"
# S_E_O3 "42.9" "44.5"
# R_E_RESP "1.4" "1.4"
# S_E_RESP "1.5" "1.1"
# N_E_DIESEL "0.824" "0.824"
# N_D_INDEX_PER "83" "50"
# N_E_RMP_PER "95" "8"
# RAW_D_MINOR "69%" "17%"
# N_E_CANCER_PER "72" "33"
# R_E_O3_PER "47" "53"
# S_D_INDEX_PER "68" "50"
# N_E_RMP "0.31" "0.31"
# R_P_LEAD "84" "18"
# R_E_NEURO "0.043" "0.043"
# N_E_LEAD "0.3" "0.3"
# S_E_RMP_PER "91" "17"
# R_E_RMP "0.42" "0.42"
# RAW_E_DIESEL "0.478" "0.0875"
# R_D_LING_PER "66" "36"
# R_E_TRAFFIC "81" "81"
# R_E_LEAD "0.18" "0.18"
# R_D_OVER64_PER "63" "83"
# N_P_NEURO "90" "55"
# R_E_CANCER_PER "87" "39"
# R_E_NPDES_PER "73" "5"
# N_E_CANCER "49" "49"
# N_D_MINOR_PER "79" "38"
# S_E_TSDF "0.073" "0.046"
# S_E_NPL "0.067" "0.033"
# R_D_OVER64 "11%" "11%"
# S_D_MINOR_PER "62" "51"
# S_P_TSDF "94" "4"
# S_P_RMP "86" "56"
# N_E_PM25 "10.7" "10.7"
# R_E_TSDF "0.062" "0.062"
# S_E_RMP "0.47" "0.33"
# RAW_D_LING "6%" "0%"
# S_E_TRAFFIC_PER "60" "44"
# S_P_PM25 "61" "53"
# S_E_LEAD "0.17" "0.17"
# R_P_NEURO "92" "38"
# S_D_LING "9%" "2%"
# N_E_NPL "0.096" "0.096"
# R_E_DIESEL_PER "50" "17"
# R_D_LESSHS_PER "67" "46"
# R_P_O3 "68" "36"
# N_E_TRAFFIC_PER "60" "35"
# RAW_E_NPDES "0.32" "0.031"
# N_E_NPDES "0.25" "0.25"
# N_E_NEURO "0.063" "0.063"
# R_P_DIESEL "70" "37"
# N_E_RESP_PER "43" "15"
# R_E_TSDF_PER "97" "97"
# RAW_E_TRAFFIC "55" "19"
# R_D_INDEX "44%" "44%"
# R_P_PM25 "68" "35"
# N_D_UNDER5_PER "62" "20"
# N_D_LESSHS_PER "78" "58"
# R_E_NPDES "0.35" "0.35"
# N_D_LING "5%" "5%"
# S_E_PM25 "9.63" "9.70"
# N_E_NPL_PER "94" "29"
# R_E_NEURO_PER "99" "6"
# R_D_MINOR "49%" "49%"
# N_P_TSDF "97" "7"
# S_D_LING_PER "56" "67"
# R_P_NPL "95" "34"
# S_P_NPDES "74" "58"
# S_E_NPDES_PER "71" "8"
# N_D_UNDER5 "7%" "7%"
# S_E_NPL_PER "97" "68"
# S_E_CANCER_PER "84" "49"
# N_E_RESP "2.3" "2.3"
# N_D_LESSHS "15%" "15%"
# S_D_UNDER5 "8%" "7%"
# N_P_LEAD "84" "39"
# RAW_E_CANCER "56" "38"
# S_P_TRAFFIC "69" "40"
# N_E_NPDES_PER "81" "4"
# R_E_TRAFFIC_PER "64" "35"
# N_P_NPDES "89" "56"
# RAW_E_O3 "43.8" "44.4"
# N_P_O3 "79" "52"
# R_E_O3 "43.6" "43.6"
# N_E_O3 "46.3" "46.3"
# N_E_TSDF_PER "98" "98"
# R_E_RESP_PER "63" "22"
# S_D_OVER64 "10%" "14%"
# N_D_INCOME "34%" "34%"
# R_E_NPL "0.063" "0.063"
# R_D_UNDER5_PER "52" "15"
# R_P_RESP "73" "37"
# R_P_NPDES "79" "40"
# S_P_O3 "62" "53"
# N_P_DIESEL "78" "54"
# N_D_OVER64_PER "55" "78"
# R_P_RMP "89" "40"
# N_P_TRAFFIC "80" "45"
# N_E_LEAD_PER "57" "51"
# S_E_NPDES "0.38" "0.25"
# S_D_OVER64_PER "69" "74"
# S_P_NPL "93" "46"
# N_D_MINOR "36%" "36%"
# RAW_E_PM25 "9.44" "9.45"
# N_D_LING_PER "74" "45"
# S_D_INCOME "39%" "42%"
# S_P_NEURO "89" "55"
# N_P_RESP "76" "54"
# N_D_OVER64 "13%" "13%"
# S_D_UNDER5_PER "49" "19"
# R_D_LING "7%" "7%"
# R_E_CANCER "42" "42"
# S_E_CANCER "44" "40"
# S_P_CANCER "68" "53"
# N_D_INCOME_PER "79" "65"
# R_D_INCOME_PER "71" "55"
# S_E_NEURO "0.044" "0.038"
# R_D_INCOME "39%" "39%"
# R_E_PM25_PER "47" "47"
# R_D_UNDER5 "7%" "7%"
# S_E_LEAD_PER "76" "71"
# S_P_LEAD "82" "33"
# Assume same lookup.fieldnames will be used for both sitestats and popstats,
# since they should have identical formats,
# and this shows it for the 2015 version:
# test.mynamesfile.default <- "map batch to friendly fieldnames v1.csv"
# e.g.
# oldnames newnames
# 1 OBJECTID OBJECTID
# 2 FACID FACID
# 3 Registry_I registryID
# 4 NAME name
# 5 LAT lat
# 6 LON lon
# 7 totpop pop
# 8 buff radius.miles
# 9 stabbr ST
# 10 statename statename
# 11 region REGION
# 12 RAW_D_INDEX VSI.eo
# 13 N_D_INDEX us.avg.VSI.eo
# 14 R_D_INDEX region.avg.VSI.eo
# 15 S_D_INDEX state.avg.VSI.eo
# 16 N_D_INDEX_PER pctile.VSI.eo
# 17 R_D_INDEX_PER region.pctile.VSI.eo
# 18 S_D_INDEX_PER state.pctile.VSI.eo
# 19 RAW_D_INCOME pctlowinc
# 20 N_D_INCOME us.avg.pctlowinc
# 21 R_D_INCOME region.avg.pctlowinc
# 22 S_D_INCOME state.avg.pctlowinc
# 23 N_D_INCOME_PER pctile.pctlowinc
# 24 R_D_INCOME_PER region.pctile.pctlowinc
# 25 S_D_INCOME_PER state.pctile.pctlowinc
# 26 RAW_D_MINOR pctmin
# 27
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