ui.R
In ejanalysis/batch.summarizer: Summarize Multiple Buffer Reports from the (nonpublic) Batch Tool for EJSCREEN
if (testing) { cat('Starting UI\n') }
library(shiny) # http://shiny.rstudio.com
shinyUI(
fluidPage(
# http://datatables.net/release-datatables/extensions/FixedColumns/js/dataTables.fixedColumns.js
# # BUT NOW SEE http://rstudio.github.io/DT/
tagList(
singleton(tags$head(tags$script(src='//cdn.datatables.net/fixedcolumns/3.0.0/js/dataTables.fixedColumns.js',type='text/javascript'))),
singleton(tags$head(tags$link(href='//cdn.datatables.net/fixedcolumns/3.0.0/css/dataTables.fixedColumns.css',rel='stylesheet',type='text/css')))
),
# tagList(
# singleton(tags$head(tags$script(src='//cdn.datatables.net/fixedheader/2.1.2/js/dataTables.fixedHeader.min.js',type='text/javascript'))),
# singleton(tags$head(tags$link(href='//cdn.datatables.net/fixedheader/2.1.2/css/dataTables.fixedHeader.css',rel='stylesheet',type='text/css')))
# ),
titlePanel(
h3(textOutput('titletext')),
windowTitle = 'Batch Summarizer'
),
tabsetPanel(
id='tabset1',
selected = default.tab,
####################################################################################
tabPanel(
"Details",
h4('*Still developing this feature - Will be able to specify which fields to compare to user-defined thresholds, in up to 3 groups of fields'),
h4('e.g., See how many sites or people have an EJ Index for traffic at or above the 95th percentile nationwide'),
# *** use dynamic ui to allow user to flexibly specify multiple thresholds in multiple groups,
# via selectize to specify groups of columns (threshnames list), groups of thresholds (threshold list), etc.
column(
4,
#h5('Selected plus default variables to compare to thresholds:'),
#textOutput('mythreshnames.toprint'),
textInput('threshgroup1', label='Name for 1st set of comparisons', value=threshgroup.default[[1]]),
numericInput('threshold1', label='Threshold value(s) for 1st set of comparisons (e.g. %ile 1-100):', value=threshold.default[[1]][1]),
wellPanel(
uiOutput('thresholdPICKS1', inline=FALSE)
)
),
column(
4,
textInput('threshgroup2', label='Name for 2d set of comparisons', value=threshgroup.default[[2]]),
numericInput('threshold2', label='Threshold value(s) for 2d set of comparisons (e.g. %ile 1-100):', value=threshold.default[[2]][1]),
wellPanel(
uiOutput('thresholdPICKS2', inline=FALSE)
)
),
column(
4,
textInput('threshgroup3', label='Name for 3d set of comparisons', value=threshgroup.default[[3]]),
numericInput('threshold3', label='Threshold value(s) for 3d set of comparisons (e.g. %ile 1-100):', value=threshold.default[[3]][1]),
wellPanel(
uiOutput('thresholdPICKS3', inline=FALSE)
)
)
),
####################################################################################
tabPanel(
"Upload",
br(),
#h4(textOutput("name1", container = span)),
fluidRow(
column(
4,
h4(textInput('batchname', "Name this analysis", "Example Dataset"))
),
# Get standard report on each site
# to have correct population counts and stats for any single site,
# BUT people who live near >1 site are accounted for >1 times here,
# so cannot use this to examine total population or distributions across all unique individuals who are near 1+ sites
column(
4,
h4('Upload batch buffer output that has standard report stats for each site'),
wellPanel(
radioButtons('notuploadingtoserver', 'Running locally on file too big to upload?',
c('Y', 'N'), selected = 'N'),
# uiOutput('filepicker', inline = FALSE),
conditionalPanel(
condition = "input.notuploadingtoserver == 'N'",
fileInput('file1', 'Browse to upload file of batch results to summarize',
accept = c('text/csv', 'text/txt', '.txt', 'text/comma-separated-values, text/plain', '.csv'))
#actionButton('browsedfilebutton', 'Upload browsed filename'),
#h4('OR'),
),
conditionalPanel(
condition = "input.notuploadingtoserver == 'Y'",
textInput('localbigfilename', 'Enter filename to specify local file of batch results to read and summarize (if too large to upload)', ''),
#submitButton(text = 'Read entered filename')
actionButton('localfilebutton', 'Read entered filename')
),
textOutput('sitecount.text2'),
#textOutput(input$localbigfilename),
tags$hr(),
h5(fileInput('file2', 'Select file of fieldname mapping to rename fields to other than defaults',
accept = c('text/csv', 'text/txt', '.txt', 'text/comma-separated-values, text/plain', '.csv')))
)),
####### Get standard report on each site BUT where population counts have been reduced
# to avoid double counting people near >1 site, so
# this table can be used to compile aggregate stats across all unique individuals.
column(
4,
h4('Upload batch buffer output with stats adjusted to stop double counting people'),
wellPanel(
radioButtons('notuploadingtoserverpop', 'Running locally on file too big to upload?',
c('Y', 'N'), selected = 'N'),
# uiOutput('filepicker', inline = FALSE),
conditionalPanel(
condition = "input.notuploadingtoserverpop == 'N'",
fileInput('file1.pop', 'Browse to upload file of batch results to summarize',
accept = c('text/csv', 'text/txt', '.txt', 'text/comma-separated-values, text/plain', '.csv'))
#actionButton('browsedfilebutton', 'Upload browsed filename'),
#h4('OR'),
),
conditionalPanel(
condition = "input.notuploadingtoserverpop == 'Y'",
textInput('localbigfilename.pop', 'Enter filename to specify local file of batch results to read and summarize (if too large to upload)', ''),
#submitButton(text = 'Read entered filename')
actionButton('localfilebutton.pop', 'Read entered filename')
),
textOutput('sitecount.text2.pop'),
#textOutput(input$localbigfilename),
tags$hr(),
h5(fileInput('file2.pop', 'Select file of fieldname mapping to rename fields to other than defaults',
accept = c('text/csv', 'text/txt', '.txt', 'text/comma-separated-values, text/plain', '.csv')))
)),
#######
column(
4,
h4('Analysis settings'),
checkboxGroupInput('probs', 'Percentiles of sites & residents to calculate:', choices = probs.default.choices,
selected = probs.default)
# can add settings here, e.g.
# checkboxInput('header', 'Header', TRUE),
# radioButtons('sep', 'Separator',
# c(Comma=',', Semicolon=';', Tab='\t'), ','),
# radioButtons('quote', 'Quote',
# c(None='', 'Double Quote'='"', 'Single Quote'="'"), '"'),
)
),
tags$hr(),
h5('Required format for input files in 2018 version:'),
tags$br(),
h5(' OBJECTID,facid,name,lat,lon,_ACSTOTPOP,_ACSIPOVBAS,_ACSTOTHH,_ACSEDUCBAS,_PRE1960,_buff,_stabbr,_statename,_region,'),
h5(' _S_P_PM25,_R_P_PM25,_N_P_PM25,_S_P_O3,_R_P_O3,_N_P_O3,_S_P_DIESEL,_R_P_DIESEL,_N_P_DIESEL,_S_P_CANCER,_R_P_CANCER,_N_P_CANCER,'),
h5(' _S_P_RESP,_R_P_RESP,_N_P_RESP,_S_P_TRAFFIC,_R_P_TRAFFIC,_N_P_TRAFFIC,_S_P_LEAD,_R_P_LEAD,_N_P_LEAD,'),
h5(' _S_P_NPL,_R_P_NPL,_N_P_NPL,_S_P_RMP,_R_P_RMP,_N_P_RMP,_S_P_TSDF,_R_P_TSDF,_N_P_TSDF,_S_P_NPDES,_R_P_NPDES,_N_P_NPDES,'),
h5(' _RAW_E_PM25,_S_E_PM25_PER,_R_E_PM25_PER,_N_E_PM25_PER,_S_E_PM25,_R_E_PM25,_N_E_PM25,'),
h5(' _RAW_E_O3,_S_E_O3_PER,_R_E_O3_PER,_N_E_O3_PER,_S_E_O3,_R_E_O3,_N_E_O3,'),
h5(' _RAW_E_DIESEL,_S_E_DIESEL_PER,_R_E_DIESEL_PER,_N_E_DIESEL_PER,_S_E_DIESEL,_R_E_DIESEL,_N_E_DIESEL,'),
h5(' _RAW_E_CANCER,_S_E_CANCER_PER,_R_E_CANCER_PER,_N_E_CANCER_PER,_S_E_CANCER,_R_E_CANCER,_N_E_CANCER,'),
h5(' _RAW_E_RESP,_S_E_RESP_PER,_R_E_RESP_PER,_N_E_RESP_PER,_S_E_RESP,_R_E_RESP,_N_E_RESP,'),
h5(' _RAW_E_TRAFFIC,_S_E_TRAFFIC_PER,_R_E_TRAFFIC_PER,_N_E_TRAFFIC_PER,_S_E_TRAFFIC,_R_E_TRAFFIC,_N_E_TRAFFIC,'),
h5(' _RAW_E_LEAD,_S_E_LEAD_PER,_R_E_LEAD_PER,_N_E_LEAD_PER,_S_E_LEAD,_R_E_LEAD,_N_E_LEAD,'),
h5(' _RAW_E_NPL,_S_E_NPL_PER,_R_E_NPL_PER,_N_E_NPL_PER,_S_E_NPL,_R_E_NPL,_N_E_NPL,'),
h5(' _RAW_E_RMP,_S_E_RMP_PER,_R_E_RMP_PER,_N_E_RMP_PER,_S_E_RMP,_R_E_RMP,_N_E_RMP,'),
h5(' _RAW_E_TSDF,_S_E_TSDF_PER,_R_E_TSDF_PER,_N_E_TSDF_PER,_S_E_TSDF,_R_E_TSDF,_N_E_TSDF,'),
h5(' _RAW_E_NPDES,_S_E_NPDES_PER,_R_E_NPDES_PER,_N_E_NPDES_PER,_S_E_NPDES,_R_E_NPDES,_N_E_NPDES,'),
h5(' _RAW_D_INDEX,_S_D_INDEX_PER,_R_D_INDEX_PER,_N_D_INDEX_PER,_S_D_INDEX,_R_D_INDEX,_N_D_INDEX,'),
h5(' _RAW_D_MINOR,_S_D_MINOR_PER,_R_D_MINOR_PER,_N_D_MINOR_PER,_S_D_MINOR,_R_D_MINOR,_N_D_MINOR,'),
h5(' _RAW_D_INCOME,_S_D_INCOME_PER,_R_D_INCOME_PER,_N_D_INCOME_PER,_S_D_INCOME,_R_D_INCOME,_N_D_INCOME,'),
h5(' _RAW_D_LING,_S_D_LING_PER,_R_D_LING_PER,_N_D_LING_PER,_S_D_LING,_R_D_LING,_N_D_LING,'),
h5(' _RAW_D_LESSHS,_S_D_LESSHS_PER,_R_D_LESSHS_PER,_N_D_LESSHS_PER,_S_D_LESSHS,_R_D_LESSHS,_N_D_LESSHS,'),
h5(' _RAW_D_UNDER5,_S_D_UNDER5_PER,_R_D_UNDER5_PER,_N_D_UNDER5_PER,_S_D_UNDER5,_R_D_UNDER5,_N_D_UNDER5,'),
h5(' _RAW_D_OVER64,_S_D_OVER64_PER,_R_D_OVER64_PER,_N_D_OVER64_PER,_S_D_OVER64,_R_D_OVER64,_N_D_OVER64'),
h5(' 1,id234234234,JOHN DOE FACILITY,44.122999999999998,-69.123000000000005,845,836,412,665,284,1 kilometers,ME,Maine,'),
h5(' 1,36,43,31,33,45,28,30,53,36,29,47,32,39,57,40,10,21,16,6,17,4,32,49,26,63,69,50,26,45,23,10,11,11,6.78,37,19,7,6.91,7.37,9.53,34.9,69,7,9,34,39.6,42.5,0.312,70,'),
h5(' <50th,<50th,0.379,0.713,0.938,25,73,<50th,<50th,23,33,40,0.7,47,<50th,<50th,0.88,1.5,1.8,150,84,63,58,85,320,600,0.58,83,65,80,0.37,0.45,0.29,0.043,62,30,45,0.062,'),
h5(' 0.14,0.12,0.026,16,5,3,0.35,0.56,0.72,0.36,67,33,46,0.58,2.5,4.3,0.0055,85,81,77,0.11,0.11,30,19%,51,53,27,19%,24%,36%,1%,16,5,3,6%,23%,38%,36%,60,75,59,33%,25%,34%,0%,'),
h5(' 68,45,44,1%,4%,4%,1%,7,11,8,8%,10%,13%,1%,7,8,6,5%,5%,6%,27%,88,92,92,18%,16%,14%'),
tags$br(),
h5('Required format for input files (2016 version):'),
tags$br(),
h5(' "ID","LAT","LONG", '),
h5(' "POP100","mins","pctmin","lowinc","pctlowinc","lths","pctlths","lingiso","pctlingiso","under5","pctunder5","over64","pctover64", '),
h5(' "traffic.score","pctpre1960","pm","o3","cancer","dpm","resp","proximity.tsdf","proximity.rmp","proximity.npl","proximity.npdes", '),
#neuro
h5(' "VSI.eo","VSI.svi6", '),
h5(' "inedx_EJ_Traffic","inedx_EJ_Lead","inedx_EJ_PM","inedx_EJ_Ozone","inedx_EJ_Cancer","inedx_EJ_DPM","inedx_EJ_Resp","inedx_EJ_proximity.tsdf","inedx_EJ_proximity.rmp","inedx_EJ_proximity.npl","inedx_EJ_proximity.npdes", '),
#"inedx_EJ_Neuro",
h5(' "BLOCKID","Distance.x", '),
h5(' "BLOCKGROUPFIPS","STUSAB","STATE","COUNTY","TRACT","BLKGRP","BLOCK","REGION", '),
h5(' "N_D_INDEX_PER", '),
h5(' "N_E_NPDES_PER","N_E_TSDF_PER","N_E_RMP_PER","N_E_NPL_PER","N_E_LEAD_PER","N_E_TRAFFIC_PER","N_E_RESP_PER","N_E_CANCER_PER","N_E_DIESEL_PER","N_E_O3_PER","N_E_PM25_PER", '),
#"N_E_NEURO_PER",
h5(' "N_D_MINOR_PER","N_D_INCOME_PER","N_D_LESSHS_PER","N_D_LING_PER","N_D_UNDER5_PER","N_D_OVER64_PER", '),
h5(' "N_D_INDEX", '),
h5(' "N_E_NPDES","N_E_TSDF","N_E_RMP","N_E_NPL","N_E_LEAD","N_E_TRAFFIC","N_E_RESP","N_E_CANCER","N_E_DIESEL","N_E_O3","N_E_PM25", '),
#"N_E_NEURO",
h5(' "N_D_MINOR","N_D_INCOME","N_D_LESSHS","N_D_LING","N_D_UNDER5","N_D_OVER64", '),
h5(' "R_D_INDEX_PER", '),
h5(' "R_E_NPDES_PER","R_E_TSDF_PER","R_E_RMP_PER","R_E_NPL_PER","R_E_LEAD_PER","R_E_TRAFFIC_PER","R_E_RESP_PER","R_E_CANCER_PER","R_E_DIESEL_PER","R_E_O3_PER","R_E_PM25_PER", '),
#"R_E_NEURO_PER",
h5(' "R_D_MINOR_PER","R_D_INCOME_PER","R_D_LESSHS_PER","R_D_LING_PER","R_D_UNDER5_PER","R_D_OVER64_PER", '),
h5(' "R_D_INDEX", '),
h5(' "R_E_NPDES","R_E_TSDF","R_E_RMP","R_E_NPL","R_E_LEAD","R_E_TRAFFIC","R_E_RESP","R_E_CANCER","R_E_DIESEL","R_E_O3","R_E_PM25", '),
#"R_E_NEURO",
h5(' "R_D_MINOR","R_D_INCOME","R_D_LESSHS","R_D_LING","R_D_UNDER5","R_D_OVER64", '),
h5(' "S_D_INDEX_PER", '),
h5(' "S_E_NPDES_PER","S_E_TSDF_PER","S_E_RMP_PER","S_E_NPL_PER","S_E_LEAD_PER","S_E_TRAFFIC_PER","S_E_RESP_PER","S_E_CANCER_PER","S_E_DIESEL_PER","S_E_O3_PER","S_E_PM25_PER", '),
#"S_E_NEURO_PER",
h5(' "S_D_MINOR_PER","S_D_INCOME_PER","S_D_LESSHS_PER","S_D_LING_PER","S_D_UNDER5_PER","S_D_OVER64_PER", '),
h5(' "S_D_INDEX", '),
h5(' "S_E_NPDES","S_E_TSDF","S_E_RMP","S_E_NPL","S_E_LEAD","S_E_TRAFFIC","S_E_RESP","S_E_CANCER","S_E_DIESEL","S_E_O3","S_E_PM25", '),
h5(' "S_D_MINOR","S_D_INCOME","S_D_LESSHS","S_D_LING","S_D_UNDER5","S_D_OVER64" '),
#"S_E_NEURO",
tags$hr(),
h5('Required format for input file (2015 version):'),
tags$br(),
h5('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'),
h5('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')
),
####################################################################################
tabPanel(
"Barplots",
plotOutput('barplots'),
downloadButton('download.barplot', 'Download'),
fluidRow(
h4('Barplot settings'),
column(4, radioButtons('bartype', h5('Indicator type'), list('Demographic'='Demographic', 'Environmental'='Environmental','EJ'='EJ'))),
column(3, radioButtons('barvartype', 'Data Type', list('Percentile of population'='pctile', 'Raw data'='raw'))),
column(3, radioButtons('barvarmean', 'Statistic', list('Median'='med', 'Average'='avg')))
)
),
####################################################################################
tabPanel(
"Exec sum",
# HOW TO SAVE AS PDF MOST EASILY? ***
br(),
h4(paste('Executive Summary - ','', sep = '')),
#h4(textOutput('titletext2')), # already in header above the tab
tags$hr(),
h4(textOutput('popsitecounts.out2')),
fluidRow(
column(
4,
wellPanel(
h4('Demographic:'),
br(),
h5(textOutput('execsum1')),
# br(),
h5(textOutput('execsum2')),
br(),
h5(textOutput('execsum3')),
numericInput('execsum.threshold.d', 'Percentile threshold:', 95, min=0,max=100,step=5),
br(),
h5(textOutput('execsum4'))
)
),
column(
4,
wellPanel(
h4('Environmental:'),
br(),
h5(textOutput('execsum5')),
br(),
h5(textOutput('execsum5b')),
selectInput('execsum.e.selected', h5('Environmental factor:'),
names.e.friendly,
selected=1)
)
),
column(
4,
wellPanel(
h4('EJ:'),
br(),
h5(textOutput('execsum6')),
numericInput('execsum.threshold', 'Percentile threshold:', 95, min=0,max=100,step=5)
),
h6('** Note: More precisely, they reside in buffer zones near these sites where the average person in the buffer zone has a block group indicator value that is at/above the block group values of the specified % of the US population.')
)
)
),
####################################################################################
tabPanel(
'Map',
h4('Map of uploaded sites and circular buffers'),
tags$hr(),
leafletOutput('map.sites'),
tags$hr(),
fluidRow(
column(
4,
wellPanel(
textOutput('sitecount.text')
)
),
column(
4,
h5('Hover over large marker for site name.'),
h5('Click on point for site info.'),
h5('Zoom in to see buffers around sites.')
),
column(
4,
radioButtons('markertype', label='Marker style', choices= list('Large (easier to click but sites overlap)' = 'big', 'Small' = 'small'),
selected = 'small' # default for example dataset which is small set so use big markers? small is more often useful though.
)
)
)
),
####################################################################################
tabPanel(
"States",
br(),
h4(textOutput('popsitecounts.out')),
tags$hr(),
fluidRow(
column(
5,
dataTableOutput('counts.by.state.out'),
downloadButton('download.states', 'Download States Table')
),
column(
5,
dataTableOutput('counts.by.region.out'),
downloadButton('download.regions', 'Download Regions Table')
)
)
),
####################################################################################
tabPanel(
"Demog tables",
#h4(textOutput("name3", container = span)),
br(),
h4('Table 1. Does the average person at these sites (nearby population as a whole) have demographics above the US average?'),
tableOutput("table1"),
downloadButton('download.table1', 'Download Table 1'),
tags$hr(),
h4('Table 2. Does the average site have demographics above the US average?'),
tableOutput("table2"),
downloadButton('download.table2', 'Download Table 2'),
tags$hr(),
h4('Table 3. Do most of these sites have demographics above the US average (not median)?'),
tableOutput("table3"),
downloadButton('download.table3', 'Download Table 3')
),
####################################################################################
tabPanel(
"Envt tables",
#h4(textOutput("name3", container = span)),
br(),
h4('Table 1e. Does the average person at these sites (nearby population as a whole) have environmental indicators above the US average?'),
tableOutput("table1e"),
downloadButton('download.table1e', 'Download Table 1e'),
tags$hr(),
h4('Table 2e. Does the average site have environmental indicators above the US average?'),
tableOutput("table2e"),
downloadButton('download.table2e', 'Download Table 2e'),
tags$hr(),
h4('Table 3e. Do most of these sites have environmental indicators above the US average (not median)?'),
tableOutput("table3e"),
downloadButton('download.table3e', 'Download Table 3e')
),
####################################################################################
tabPanel(
"Batch Details",
downloadButton('download.rowsout.only', 'Download stats only'),
downloadButton('download.rowsout', 'Download stats plus every site'),
dataTableOutput("rowsout"),
h5('Tip: Enter text (e.g., Demog, EJ, Env for Category column, and pctile, state, statepctile, etc. for the Type column) in the filter boxes at the bottoms of columns to limit view to certain rows.'),
h5('Tip: Click a heading (e.g., Type) twice to sort descending, then Shift-click another column (e.g., Average person) twice for descending secondary sort (to sort on 2d col within each group in 1st col)')
#h5('NOTE: SORTING DOES NOT WORK YET - NUMBERS ARE SORTED AS IF THEY WERE TEXT... TO BE FIXED SOON')
),
####################################################################################
tabPanel(
"Site Details",
downloadButton('download.colsout.only', 'Download stats only'),
downloadButton('download.colsout', 'Download stats plus every site'),
dataTableOutput("colsout"),
h5('Tip: Click a heading (e.g., State) to sort, then Shift-click another column for secondary sort (to sort on 2d within each group in 1st column)'),
h5('Tip: Enter text in the filter box at the bottom of a column to focus on one State or search for one site by name.')
),
####################################################################################
tabPanel(
"Histograms",
fluidRow(
column(
3,
selectInput('myvar.friendly.base', h5('Indicator'),
c(names.d.friendly, names.e.friendly, names.ej.friendly),
selected = 1)
),
column(9, plotOutput('histograms') )
),
downloadButton('download.histogram', 'Download'),
# this presumes new variable names are as in default file
# myvar.base <- 'VSI.eo' # *** BUT IF IT IS A SUMMARY STAT LIKE ??? this won't work in hist(fulltable[ , myvar]) since it is in outlist()$rows not in fulltable
fluidRow(
h4("Histogram settings"),
column(
2,
radioButtons('sites.or.people', label = h5('Distribution across sites or people (pop.wtd.)'), list('Sites' = 'Sites','People' = 'People'), selected = 'People' )
),
column(
2,
radioButtons('refzone', label = h5('Percentile Zone'), list('US' = 'us', 'Region' = 'region', 'State' = 'state'), selected = 'us')
),
column(
2,
radioButtons('refstat', label = h5('Data type'), list('Percentile of population' = 'pctile', 'Raw data' = 'raw'))
),
column(
2,
sliderInput('bincount', label = h5('Bins'), 5, 100, step = 5, value = 10)
)
)
),
####################################################################################
tabPanel(
"Map counties",
h3('US County Map, Census 2010'),
selectInput("mapvar",
label = "Choose a variable to display on map",
choices = c("Percent Non-White", "Percent White", "Percent Black", "Percent Hispanic", "Percent Asian"), # names.d.friendly,
selected = 1),
sliderInput("range",
label = "Range of interest:",
min = 0, max = 100, value = c(0, 100)),
plotOutput("map")
) # ,
####################################################################################
# tabPanel(
# textInput( "plotly.search", "Search what to plot", '' ),
# graphOutput("plotly.chart")
# ),
####################################################################################
####################################################################################
# tabPanel(
# 'debug',
# verbatimTextOutput('debugginginfo')
# )
####################################################################################
)
)
)
ejanalysis/batch.summarizer documentation built on Sept. 6, 2021, 3:41 a.m.
R Package Documentation
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Note that we can't provide technical support on individual packages. You should contact the package authors for that.
if (testing) { cat('Starting UI\n') }
library(shiny) # http://shiny.rstudio.com
shinyUI(
fluidPage(
# http://datatables.net/release-datatables/extensions/FixedColumns/js/dataTables.fixedColumns.js
# # BUT NOW SEE http://rstudio.github.io/DT/
tagList(
singleton(tags$head(tags$script(src='//cdn.datatables.net/fixedcolumns/3.0.0/js/dataTables.fixedColumns.js',type='text/javascript'))),
singleton(tags$head(tags$link(href='//cdn.datatables.net/fixedcolumns/3.0.0/css/dataTables.fixedColumns.css',rel='stylesheet',type='text/css')))
),
# tagList(
# singleton(tags$head(tags$script(src='//cdn.datatables.net/fixedheader/2.1.2/js/dataTables.fixedHeader.min.js',type='text/javascript'))),
# singleton(tags$head(tags$link(href='//cdn.datatables.net/fixedheader/2.1.2/css/dataTables.fixedHeader.css',rel='stylesheet',type='text/css')))
# ),
titlePanel(
h3(textOutput('titletext')),
windowTitle = 'Batch Summarizer'
),
tabsetPanel(
id='tabset1',
selected = default.tab,
####################################################################################
tabPanel(
"Details",
h4('*Still developing this feature - Will be able to specify which fields to compare to user-defined thresholds, in up to 3 groups of fields'),
h4('e.g., See how many sites or people have an EJ Index for traffic at or above the 95th percentile nationwide'),
# *** use dynamic ui to allow user to flexibly specify multiple thresholds in multiple groups,
# via selectize to specify groups of columns (threshnames list), groups of thresholds (threshold list), etc.
column(
4,
#h5('Selected plus default variables to compare to thresholds:'),
#textOutput('mythreshnames.toprint'),
textInput('threshgroup1', label='Name for 1st set of comparisons', value=threshgroup.default[[1]]),
numericInput('threshold1', label='Threshold value(s) for 1st set of comparisons (e.g. %ile 1-100):', value=threshold.default[[1]][1]),
wellPanel(
uiOutput('thresholdPICKS1', inline=FALSE)
)
),
column(
4,
textInput('threshgroup2', label='Name for 2d set of comparisons', value=threshgroup.default[[2]]),
numericInput('threshold2', label='Threshold value(s) for 2d set of comparisons (e.g. %ile 1-100):', value=threshold.default[[2]][1]),
wellPanel(
uiOutput('thresholdPICKS2', inline=FALSE)
)
),
column(
4,
textInput('threshgroup3', label='Name for 3d set of comparisons', value=threshgroup.default[[3]]),
numericInput('threshold3', label='Threshold value(s) for 3d set of comparisons (e.g. %ile 1-100):', value=threshold.default[[3]][1]),
wellPanel(
uiOutput('thresholdPICKS3', inline=FALSE)
)
)
),
####################################################################################
tabPanel(
"Upload",
br(),
#h4(textOutput("name1", container = span)),
fluidRow(
column(
4,
h4(textInput('batchname', "Name this analysis", "Example Dataset"))
),
# Get standard report on each site
# to have correct population counts and stats for any single site,
# BUT people who live near >1 site are accounted for >1 times here,
# so cannot use this to examine total population or distributions across all unique individuals who are near 1+ sites
column(
4,
h4('Upload batch buffer output that has standard report stats for each site'),
wellPanel(
radioButtons('notuploadingtoserver', 'Running locally on file too big to upload?',
c('Y', 'N'), selected = 'N'),
# uiOutput('filepicker', inline = FALSE),
conditionalPanel(
condition = "input.notuploadingtoserver == 'N'",
fileInput('file1', 'Browse to upload file of batch results to summarize',
accept = c('text/csv', 'text/txt', '.txt', 'text/comma-separated-values, text/plain', '.csv'))
#actionButton('browsedfilebutton', 'Upload browsed filename'),
#h4('OR'),
),
conditionalPanel(
condition = "input.notuploadingtoserver == 'Y'",
textInput('localbigfilename', 'Enter filename to specify local file of batch results to read and summarize (if too large to upload)', ''),
#submitButton(text = 'Read entered filename')
actionButton('localfilebutton', 'Read entered filename')
),
textOutput('sitecount.text2'),
#textOutput(input$localbigfilename),
tags$hr(),
h5(fileInput('file2', 'Select file of fieldname mapping to rename fields to other than defaults',
accept = c('text/csv', 'text/txt', '.txt', 'text/comma-separated-values, text/plain', '.csv')))
)),
####### Get standard report on each site BUT where population counts have been reduced
# to avoid double counting people near >1 site, so
# this table can be used to compile aggregate stats across all unique individuals.
column(
4,
h4('Upload batch buffer output with stats adjusted to stop double counting people'),
wellPanel(
radioButtons('notuploadingtoserverpop', 'Running locally on file too big to upload?',
c('Y', 'N'), selected = 'N'),
# uiOutput('filepicker', inline = FALSE),
conditionalPanel(
condition = "input.notuploadingtoserverpop == 'N'",
fileInput('file1.pop', 'Browse to upload file of batch results to summarize',
accept = c('text/csv', 'text/txt', '.txt', 'text/comma-separated-values, text/plain', '.csv'))
#actionButton('browsedfilebutton', 'Upload browsed filename'),
#h4('OR'),
),
conditionalPanel(
condition = "input.notuploadingtoserverpop == 'Y'",
textInput('localbigfilename.pop', 'Enter filename to specify local file of batch results to read and summarize (if too large to upload)', ''),
#submitButton(text = 'Read entered filename')
actionButton('localfilebutton.pop', 'Read entered filename')
),
textOutput('sitecount.text2.pop'),
#textOutput(input$localbigfilename),
tags$hr(),
h5(fileInput('file2.pop', 'Select file of fieldname mapping to rename fields to other than defaults',
accept = c('text/csv', 'text/txt', '.txt', 'text/comma-separated-values, text/plain', '.csv')))
)),
#######
column(
4,
h4('Analysis settings'),
checkboxGroupInput('probs', 'Percentiles of sites & residents to calculate:', choices = probs.default.choices,
selected = probs.default)
# can add settings here, e.g.
# checkboxInput('header', 'Header', TRUE),
# radioButtons('sep', 'Separator',
# c(Comma=',', Semicolon=';', Tab='\t'), ','),
# radioButtons('quote', 'Quote',
# c(None='', 'Double Quote'='"', 'Single Quote'="'"), '"'),
)
),
tags$hr(),
h5('Required format for input files in 2018 version:'),
tags$br(),
h5(' OBJECTID,facid,name,lat,lon,_ACSTOTPOP,_ACSIPOVBAS,_ACSTOTHH,_ACSEDUCBAS,_PRE1960,_buff,_stabbr,_statename,_region,'),
h5(' _S_P_PM25,_R_P_PM25,_N_P_PM25,_S_P_O3,_R_P_O3,_N_P_O3,_S_P_DIESEL,_R_P_DIESEL,_N_P_DIESEL,_S_P_CANCER,_R_P_CANCER,_N_P_CANCER,'),
h5(' _S_P_RESP,_R_P_RESP,_N_P_RESP,_S_P_TRAFFIC,_R_P_TRAFFIC,_N_P_TRAFFIC,_S_P_LEAD,_R_P_LEAD,_N_P_LEAD,'),
h5(' _S_P_NPL,_R_P_NPL,_N_P_NPL,_S_P_RMP,_R_P_RMP,_N_P_RMP,_S_P_TSDF,_R_P_TSDF,_N_P_TSDF,_S_P_NPDES,_R_P_NPDES,_N_P_NPDES,'),
h5(' _RAW_E_PM25,_S_E_PM25_PER,_R_E_PM25_PER,_N_E_PM25_PER,_S_E_PM25,_R_E_PM25,_N_E_PM25,'),
h5(' _RAW_E_O3,_S_E_O3_PER,_R_E_O3_PER,_N_E_O3_PER,_S_E_O3,_R_E_O3,_N_E_O3,'),
h5(' _RAW_E_DIESEL,_S_E_DIESEL_PER,_R_E_DIESEL_PER,_N_E_DIESEL_PER,_S_E_DIESEL,_R_E_DIESEL,_N_E_DIESEL,'),
h5(' _RAW_E_CANCER,_S_E_CANCER_PER,_R_E_CANCER_PER,_N_E_CANCER_PER,_S_E_CANCER,_R_E_CANCER,_N_E_CANCER,'),
h5(' _RAW_E_RESP,_S_E_RESP_PER,_R_E_RESP_PER,_N_E_RESP_PER,_S_E_RESP,_R_E_RESP,_N_E_RESP,'),
h5(' _RAW_E_TRAFFIC,_S_E_TRAFFIC_PER,_R_E_TRAFFIC_PER,_N_E_TRAFFIC_PER,_S_E_TRAFFIC,_R_E_TRAFFIC,_N_E_TRAFFIC,'),
h5(' _RAW_E_LEAD,_S_E_LEAD_PER,_R_E_LEAD_PER,_N_E_LEAD_PER,_S_E_LEAD,_R_E_LEAD,_N_E_LEAD,'),
h5(' _RAW_E_NPL,_S_E_NPL_PER,_R_E_NPL_PER,_N_E_NPL_PER,_S_E_NPL,_R_E_NPL,_N_E_NPL,'),
h5(' _RAW_E_RMP,_S_E_RMP_PER,_R_E_RMP_PER,_N_E_RMP_PER,_S_E_RMP,_R_E_RMP,_N_E_RMP,'),
h5(' _RAW_E_TSDF,_S_E_TSDF_PER,_R_E_TSDF_PER,_N_E_TSDF_PER,_S_E_TSDF,_R_E_TSDF,_N_E_TSDF,'),
h5(' _RAW_E_NPDES,_S_E_NPDES_PER,_R_E_NPDES_PER,_N_E_NPDES_PER,_S_E_NPDES,_R_E_NPDES,_N_E_NPDES,'),
h5(' _RAW_D_INDEX,_S_D_INDEX_PER,_R_D_INDEX_PER,_N_D_INDEX_PER,_S_D_INDEX,_R_D_INDEX,_N_D_INDEX,'),
h5(' _RAW_D_MINOR,_S_D_MINOR_PER,_R_D_MINOR_PER,_N_D_MINOR_PER,_S_D_MINOR,_R_D_MINOR,_N_D_MINOR,'),
h5(' _RAW_D_INCOME,_S_D_INCOME_PER,_R_D_INCOME_PER,_N_D_INCOME_PER,_S_D_INCOME,_R_D_INCOME,_N_D_INCOME,'),
h5(' _RAW_D_LING,_S_D_LING_PER,_R_D_LING_PER,_N_D_LING_PER,_S_D_LING,_R_D_LING,_N_D_LING,'),
h5(' _RAW_D_LESSHS,_S_D_LESSHS_PER,_R_D_LESSHS_PER,_N_D_LESSHS_PER,_S_D_LESSHS,_R_D_LESSHS,_N_D_LESSHS,'),
h5(' _RAW_D_UNDER5,_S_D_UNDER5_PER,_R_D_UNDER5_PER,_N_D_UNDER5_PER,_S_D_UNDER5,_R_D_UNDER5,_N_D_UNDER5,'),
h5(' _RAW_D_OVER64,_S_D_OVER64_PER,_R_D_OVER64_PER,_N_D_OVER64_PER,_S_D_OVER64,_R_D_OVER64,_N_D_OVER64'),
h5(' 1,id234234234,JOHN DOE FACILITY,44.122999999999998,-69.123000000000005,845,836,412,665,284,1 kilometers,ME,Maine,'),
h5(' 1,36,43,31,33,45,28,30,53,36,29,47,32,39,57,40,10,21,16,6,17,4,32,49,26,63,69,50,26,45,23,10,11,11,6.78,37,19,7,6.91,7.37,9.53,34.9,69,7,9,34,39.6,42.5,0.312,70,'),
h5(' <50th,<50th,0.379,0.713,0.938,25,73,<50th,<50th,23,33,40,0.7,47,<50th,<50th,0.88,1.5,1.8,150,84,63,58,85,320,600,0.58,83,65,80,0.37,0.45,0.29,0.043,62,30,45,0.062,'),
h5(' 0.14,0.12,0.026,16,5,3,0.35,0.56,0.72,0.36,67,33,46,0.58,2.5,4.3,0.0055,85,81,77,0.11,0.11,30,19%,51,53,27,19%,24%,36%,1%,16,5,3,6%,23%,38%,36%,60,75,59,33%,25%,34%,0%,'),
h5(' 68,45,44,1%,4%,4%,1%,7,11,8,8%,10%,13%,1%,7,8,6,5%,5%,6%,27%,88,92,92,18%,16%,14%'),
tags$br(),
h5('Required format for input files (2016 version):'),
tags$br(),
h5(' "ID","LAT","LONG", '),
h5(' "POP100","mins","pctmin","lowinc","pctlowinc","lths","pctlths","lingiso","pctlingiso","under5","pctunder5","over64","pctover64", '),
h5(' "traffic.score","pctpre1960","pm","o3","cancer","dpm","resp","proximity.tsdf","proximity.rmp","proximity.npl","proximity.npdes", '),
#neuro
h5(' "VSI.eo","VSI.svi6", '),
h5(' "inedx_EJ_Traffic","inedx_EJ_Lead","inedx_EJ_PM","inedx_EJ_Ozone","inedx_EJ_Cancer","inedx_EJ_DPM","inedx_EJ_Resp","inedx_EJ_proximity.tsdf","inedx_EJ_proximity.rmp","inedx_EJ_proximity.npl","inedx_EJ_proximity.npdes", '),
#"inedx_EJ_Neuro",
h5(' "BLOCKID","Distance.x", '),
h5(' "BLOCKGROUPFIPS","STUSAB","STATE","COUNTY","TRACT","BLKGRP","BLOCK","REGION", '),
h5(' "N_D_INDEX_PER", '),
h5(' "N_E_NPDES_PER","N_E_TSDF_PER","N_E_RMP_PER","N_E_NPL_PER","N_E_LEAD_PER","N_E_TRAFFIC_PER","N_E_RESP_PER","N_E_CANCER_PER","N_E_DIESEL_PER","N_E_O3_PER","N_E_PM25_PER", '),
#"N_E_NEURO_PER",
h5(' "N_D_MINOR_PER","N_D_INCOME_PER","N_D_LESSHS_PER","N_D_LING_PER","N_D_UNDER5_PER","N_D_OVER64_PER", '),
h5(' "N_D_INDEX", '),
h5(' "N_E_NPDES","N_E_TSDF","N_E_RMP","N_E_NPL","N_E_LEAD","N_E_TRAFFIC","N_E_RESP","N_E_CANCER","N_E_DIESEL","N_E_O3","N_E_PM25", '),
#"N_E_NEURO",
h5(' "N_D_MINOR","N_D_INCOME","N_D_LESSHS","N_D_LING","N_D_UNDER5","N_D_OVER64", '),
h5(' "R_D_INDEX_PER", '),
h5(' "R_E_NPDES_PER","R_E_TSDF_PER","R_E_RMP_PER","R_E_NPL_PER","R_E_LEAD_PER","R_E_TRAFFIC_PER","R_E_RESP_PER","R_E_CANCER_PER","R_E_DIESEL_PER","R_E_O3_PER","R_E_PM25_PER", '),
#"R_E_NEURO_PER",
h5(' "R_D_MINOR_PER","R_D_INCOME_PER","R_D_LESSHS_PER","R_D_LING_PER","R_D_UNDER5_PER","R_D_OVER64_PER", '),
h5(' "R_D_INDEX", '),
h5(' "R_E_NPDES","R_E_TSDF","R_E_RMP","R_E_NPL","R_E_LEAD","R_E_TRAFFIC","R_E_RESP","R_E_CANCER","R_E_DIESEL","R_E_O3","R_E_PM25", '),
#"R_E_NEURO",
h5(' "R_D_MINOR","R_D_INCOME","R_D_LESSHS","R_D_LING","R_D_UNDER5","R_D_OVER64", '),
h5(' "S_D_INDEX_PER", '),
h5(' "S_E_NPDES_PER","S_E_TSDF_PER","S_E_RMP_PER","S_E_NPL_PER","S_E_LEAD_PER","S_E_TRAFFIC_PER","S_E_RESP_PER","S_E_CANCER_PER","S_E_DIESEL_PER","S_E_O3_PER","S_E_PM25_PER", '),
#"S_E_NEURO_PER",
h5(' "S_D_MINOR_PER","S_D_INCOME_PER","S_D_LESSHS_PER","S_D_LING_PER","S_D_UNDER5_PER","S_D_OVER64_PER", '),
h5(' "S_D_INDEX", '),
h5(' "S_E_NPDES","S_E_TSDF","S_E_RMP","S_E_NPL","S_E_LEAD","S_E_TRAFFIC","S_E_RESP","S_E_CANCER","S_E_DIESEL","S_E_O3","S_E_PM25", '),
h5(' "S_D_MINOR","S_D_INCOME","S_D_LESSHS","S_D_LING","S_D_UNDER5","S_D_OVER64" '),
#"S_E_NEURO",
tags$hr(),
h5('Required format for input file (2015 version):'),
tags$br(),
h5('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'),
h5('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')
),
####################################################################################
tabPanel(
"Barplots",
plotOutput('barplots'),
downloadButton('download.barplot', 'Download'),
fluidRow(
h4('Barplot settings'),
column(4, radioButtons('bartype', h5('Indicator type'), list('Demographic'='Demographic', 'Environmental'='Environmental','EJ'='EJ'))),
column(3, radioButtons('barvartype', 'Data Type', list('Percentile of population'='pctile', 'Raw data'='raw'))),
column(3, radioButtons('barvarmean', 'Statistic', list('Median'='med', 'Average'='avg')))
)
),
####################################################################################
tabPanel(
"Exec sum",
# HOW TO SAVE AS PDF MOST EASILY? ***
br(),
h4(paste('Executive Summary - ','', sep = '')),
#h4(textOutput('titletext2')), # already in header above the tab
tags$hr(),
h4(textOutput('popsitecounts.out2')),
fluidRow(
column(
4,
wellPanel(
h4('Demographic:'),
br(),
h5(textOutput('execsum1')),
# br(),
h5(textOutput('execsum2')),
br(),
h5(textOutput('execsum3')),
numericInput('execsum.threshold.d', 'Percentile threshold:', 95, min=0,max=100,step=5),
br(),
h5(textOutput('execsum4'))
)
),
column(
4,
wellPanel(
h4('Environmental:'),
br(),
h5(textOutput('execsum5')),
br(),
h5(textOutput('execsum5b')),
selectInput('execsum.e.selected', h5('Environmental factor:'),
names.e.friendly,
selected=1)
)
),
column(
4,
wellPanel(
h4('EJ:'),
br(),
h5(textOutput('execsum6')),
numericInput('execsum.threshold', 'Percentile threshold:', 95, min=0,max=100,step=5)
),
h6('** Note: More precisely, they reside in buffer zones near these sites where the average person in the buffer zone has a block group indicator value that is at/above the block group values of the specified % of the US population.')
)
)
),
####################################################################################
tabPanel(
'Map',
h4('Map of uploaded sites and circular buffers'),
tags$hr(),
leafletOutput('map.sites'),
tags$hr(),
fluidRow(
column(
4,
wellPanel(
textOutput('sitecount.text')
)
),
column(
4,
h5('Hover over large marker for site name.'),
h5('Click on point for site info.'),
h5('Zoom in to see buffers around sites.')
),
column(
4,
radioButtons('markertype', label='Marker style', choices= list('Large (easier to click but sites overlap)' = 'big', 'Small' = 'small'),
selected = 'small' # default for example dataset which is small set so use big markers? small is more often useful though.
)
)
)
),
####################################################################################
tabPanel(
"States",
br(),
h4(textOutput('popsitecounts.out')),
tags$hr(),
fluidRow(
column(
5,
dataTableOutput('counts.by.state.out'),
downloadButton('download.states', 'Download States Table')
),
column(
5,
dataTableOutput('counts.by.region.out'),
downloadButton('download.regions', 'Download Regions Table')
)
)
),
####################################################################################
tabPanel(
"Demog tables",
#h4(textOutput("name3", container = span)),
br(),
h4('Table 1. Does the average person at these sites (nearby population as a whole) have demographics above the US average?'),
tableOutput("table1"),
downloadButton('download.table1', 'Download Table 1'),
tags$hr(),
h4('Table 2. Does the average site have demographics above the US average?'),
tableOutput("table2"),
downloadButton('download.table2', 'Download Table 2'),
tags$hr(),
h4('Table 3. Do most of these sites have demographics above the US average (not median)?'),
tableOutput("table3"),
downloadButton('download.table3', 'Download Table 3')
),
####################################################################################
tabPanel(
"Envt tables",
#h4(textOutput("name3", container = span)),
br(),
h4('Table 1e. Does the average person at these sites (nearby population as a whole) have environmental indicators above the US average?'),
tableOutput("table1e"),
downloadButton('download.table1e', 'Download Table 1e'),
tags$hr(),
h4('Table 2e. Does the average site have environmental indicators above the US average?'),
tableOutput("table2e"),
downloadButton('download.table2e', 'Download Table 2e'),
tags$hr(),
h4('Table 3e. Do most of these sites have environmental indicators above the US average (not median)?'),
tableOutput("table3e"),
downloadButton('download.table3e', 'Download Table 3e')
),
####################################################################################
tabPanel(
"Batch Details",
downloadButton('download.rowsout.only', 'Download stats only'),
downloadButton('download.rowsout', 'Download stats plus every site'),
dataTableOutput("rowsout"),
h5('Tip: Enter text (e.g., Demog, EJ, Env for Category column, and pctile, state, statepctile, etc. for the Type column) in the filter boxes at the bottoms of columns to limit view to certain rows.'),
h5('Tip: Click a heading (e.g., Type) twice to sort descending, then Shift-click another column (e.g., Average person) twice for descending secondary sort (to sort on 2d col within each group in 1st col)')
#h5('NOTE: SORTING DOES NOT WORK YET - NUMBERS ARE SORTED AS IF THEY WERE TEXT... TO BE FIXED SOON')
),
####################################################################################
tabPanel(
"Site Details",
downloadButton('download.colsout.only', 'Download stats only'),
downloadButton('download.colsout', 'Download stats plus every site'),
dataTableOutput("colsout"),
h5('Tip: Click a heading (e.g., State) to sort, then Shift-click another column for secondary sort (to sort on 2d within each group in 1st column)'),
h5('Tip: Enter text in the filter box at the bottom of a column to focus on one State or search for one site by name.')
),
####################################################################################
tabPanel(
"Histograms",
fluidRow(
column(
3,
selectInput('myvar.friendly.base', h5('Indicator'),
c(names.d.friendly, names.e.friendly, names.ej.friendly),
selected = 1)
),
column(9, plotOutput('histograms') )
),
downloadButton('download.histogram', 'Download'),
# this presumes new variable names are as in default file
# myvar.base <- 'VSI.eo' # *** BUT IF IT IS A SUMMARY STAT LIKE ??? this won't work in hist(fulltable[ , myvar]) since it is in outlist()$rows not in fulltable
fluidRow(
h4("Histogram settings"),
column(
2,
radioButtons('sites.or.people', label = h5('Distribution across sites or people (pop.wtd.)'), list('Sites' = 'Sites','People' = 'People'), selected = 'People' )
),
column(
2,
radioButtons('refzone', label = h5('Percentile Zone'), list('US' = 'us', 'Region' = 'region', 'State' = 'state'), selected = 'us')
),
column(
2,
radioButtons('refstat', label = h5('Data type'), list('Percentile of population' = 'pctile', 'Raw data' = 'raw'))
),
column(
2,
sliderInput('bincount', label = h5('Bins'), 5, 100, step = 5, value = 10)
)
)
),
####################################################################################
tabPanel(
"Map counties",
h3('US County Map, Census 2010'),
selectInput("mapvar",
label = "Choose a variable to display on map",
choices = c("Percent Non-White", "Percent White", "Percent Black", "Percent Hispanic", "Percent Asian"), # names.d.friendly,
selected = 1),
sliderInput("range",
label = "Range of interest:",
min = 0, max = 100, value = c(0, 100)),
plotOutput("map")
) # ,
####################################################################################
# tabPanel(
# textInput( "plotly.search", "Search what to plot", '' ),
# graphOutput("plotly.chart")
# ),
####################################################################################
####################################################################################
# tabPanel(
# 'debug',
# verbatimTextOutput('debugginginfo')
# )
####################################################################################
)
)
)
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