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server.R
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server.R
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# server.R file
options(shiny.maxRequestSize=60*1024^2)
server <- function(input, output, session) {
# for Desktop bat file
session$onSessionEnded(function() {
stopApp()
})
############# Data Tab ##########################
dataFile <- callModule(csvFile, "dataFile",
stringsAsFactors = FALSE, skip = 10)
WQP <- reactive({
if(is.null(dataFile())){
return(NULL)
} else {
dat <- dataFile()
dat <- data.table(dat)
dat[, Characteristic := str_to_title(as.character(Characteristic))] # Capitalizing all first letters of words since there was duplication caused by inconsistency in whether words were capitalized or not
dat[, ActivityStartDate := as.Date(ActivityStartDate)] # '%Y-%m-%d')
dat[, MonthDay := format(ActivityStartDate, format = "%m-%d")]
dat$Result <- as.numeric(dat$Result)
dat <- dat[!is.na(Result)]
setnames(dat, c("ActivityMediaName", "ResultSampleFractionText"), c("Media", "Sample_Fraction"))
datcols <- c("LatitudeMeasure", "LongitudeMeasure")
dat[, (datcols) := lapply(.SD, as.numeric), .SDcols = datcols]
return(dat)
}})
output$meta <- renderUI({
data <- WQP()
records <- nrow(data)
char <- length(unique(data$Characteristic))
station <- length(unique(data$Station))
org <- length(unique(data$OrganizationFormalName))
minDate <- min(data$ActivityStartDate)
maxDate <- max(data$ActivityStartDate)
fluidRow(p(h4(paste("The file contains data from", station, "stations in",
org, "organization(s) for the period", minDate, "to", maxDate,
". The data include", char, "characteristics (parameters) in", records, "rows."))))
})
output$discoveryDataTable <- DT::renderDataTable(
data.frame(WQP()), escape = -1, rownames = FALSE,
extensions = 'Buttons', options = list(dom = 'lfrBtip', buttons = I('colvis'),
pageLength = 100,
lengthMenu = c(100, 200, 500),
columnDefs = list(list(visible = FALSE, targets = list(1,3,4,6,8,9,10,11,12,
13, 14, 15, 16, 17, 18,
19, 20, 21, 22, 23, 24,
25, 26, 27, 28, 29, 30,
32, 35, 36, 37, 38, 39, 40,
41, 42, 43, 44, 45, 46, 47,
48, 49, 50, 51, 52, 53, 54,
55, 56, 57, 58, 59, 60, 61,
62, 63, 64, 65, 66, 67, 68)))
), server = TRUE
)
############# Criteria Tab ##########################
# Generate the empty criteria template
criteria_gen <- reactive({
Criteria <- data.table(WQP())
Criteria <- Criteria[,.(Count = .N), by = c("Characteristic", "Unit", "Media", "Sample_Fraction") ]
setnames(Criteria, c("Characteristic", "Unit"), c("PARM", "Units") )
Criteria <- Criteria[, .(PARM, Units, Media, Sample_Fraction)]
Criteria$USE_OR_CLASS <- ""
Criteria$WBODY <- ""
Criteria$ECOREGION <- ""
Criteria$Criterion <- ""
Criteria$Limit <- ""
Criteria$Comparison <- ""
Criteria$AverageTime <- ""
Criteria$MinSamples <- ""
Criteria$SeasonStartDate <- ""
Criteria$SeasonEndDate <- ""
return(Criteria)
})
output$Criteria_outfile <- downloadHandler(
filename = function() {
paste('Criteria-', Sys.Date(), '.csv', sep='')
},
content = function(con) {
write.table(criteria_gen(), con, row.names = FALSE, quote = TRUE, sep = ",")
})
criteriaFile <- callModule(csvFile, "criteriaFile",
stringsAsFactors = FALSE)
output$criteria_table <- DT::renderDataTable(
criteriaFile(), escape = -1, rownames = FALSE
)
############# Metals analysis Tab ##########################
output$Criteria_metals <- downloadHandler(
filename = function() {
paste('Metals-', Sys.Date(), '.csv', sep='')
},
content = function(con) {
write.table(metalsCriteria, con, row.names = FALSE, quote = TRUE, sep = ",")
})
metalsFile <- callModule(csvFile, "metalscriteriaFile",
stringsAsFactors = FALSE)
output$metals_table <- DT::renderDataTable(
metalsFile(), escape = -1, rownames = FALSE
)
# Prepping the metals file and combining it with the criteria file
metalsPrep <- reactive({
metals <- data.table(metalsFile())
metals[is.na(metals)] <- ""
cols <- c("m", "b", "Hardness_default", "CF")
metals[, (cols) := lapply(.SD, as.numeric), .SDcols = cols]
# Calculate conversion factors (CF) that are hardness-dependent
metals[is.na(CF) & !is.na(Hardness_default) & PARM == "Lead", CF := leadCF(Hardness_default)]
metals[is.na(CF) & !is.na(Hardness_default) & PARM == "Cadmium"
& grepl("acute", Criterion, ignore.case = TRUE), CF := cadCF_acute(Hardness_default)]
metals[is.na(CF) & !is.na(Hardness_default) & PARM == "Cadmium"
& grepl("chronic", Criterion, ignore.case = TRUE), CF := cadCF_chronic(Hardness_default)]
# Calculating the limits (criteria) against which the result will be compared
metals[grepl("acute", Criterion, ignore.case = TRUE),
Limit := round(limit_CMC(Hardness_default, m, b, CF), 2)]
metals[grepl("chronic", Criterion, ignore.case = TRUE),
Limit := round(limit_CCC(Hardness_default, m, b, CF), 2)]
# Removing columns no longer required
metals[, c("m", "b", "Hardness_default", "CF") := NULL]
# Adding a "Comparison" column to calculate exceedences later on
metals[, Comparison := "GT"]
metals[, ECOREGION := ""]
# Renaming criteria names
metals[Criterion == "Freshwater Acute", Criterion := "FW_Acute"]
metals[Criterion == "Freshwater Chronic", Criterion := "FW_Chronic"]
metals[Criterion == "Saltwater Acute", Criterion := "SW_Acute"]
metals[Criterion == "Saltwater Chronic", Criterion := "sW_Chronic"]
return(data.frame(metals))
})
criteria <- reactive({
metals <- data.table(metalsPrep())
criterion <- data.table(criteriaFile())
# combining the metals and criteria files
criterion <- rbind(criterion, metals, use.names = TRUE, fill = TRUE)
return(criterion)
})
############################# Stations tab ###############################
# Generate the empty stations template
station_gen<-reactive({
if(is.null(WQP())){
return(NULL)
} else {
dat <- data.table(WQP())
stations <- dat[,.(Name = last(Name), Organization=last(Organization), OrganizationFormalName=last(OrganizationFormalName), LatitudeMeasure = last(LatitudeMeasure),
LongitudeMeasure = last(LongitudeMeasure)), by = Station ]
stations$AssessmentUnit <- ""
return(stations)
}})
output$Station_outfile <- downloadHandler(
filename = function() {
paste('Stations-', Sys.Date(), '.csv', sep='')
},
content = function(con) {
write.table(station_gen(), con, row.names = FALSE, sep = ",")
})
stationsFile <- callModule(csvFile, "stationsFile",
stringsAsFactors = FALSE)
output$stations_table <- DT::renderDataTable(
stationsFile(), escape = -1, rownames = FALSE
)
############################# Assessment Units tab ###############################
# Generate the empty assessment units template
assessunit_gen<-reactive({
if(is.null(stationsFile())){
return(NULL)
} else {
dat <- data.table(stationsFile())
AssesU <- dat[ , list(AssessmentUnit)]
AssesU$USE_OR_CLASS <- ""
AssesU$WBODY <- ""
AssesU$ECOREGION <- ""
return(AssesU)
}})
output$AssessmentUnit_outfile <- downloadHandler(
filename = function() {
paste('Assessment_Units-', Sys.Date(), '.csv', sep='')
},
content = function(con) {
write.table(assessunit_gen(), con, row.names = FALSE, sep = ",")
})
assessmentUnitFile <- callModule(csvFile, "assessmentUnitFile",
stringsAsFactors = FALSE)
output$assessmentUnit_table <- DT::renderDataTable(
assessmentUnitFile(), escape = -1, rownames = FALSE
)
######################## Analysis tab ###################################
data_prep <- reactive({
# Organize the inputs
dat <- WQP()
stations <- stationsFile()
assesU <- assessmentUnitFile()
criterion <- criteria()
stations[is.na(stations)] <- ""
assesU[is.na(assesU)] <- ""
criterion[is.na(criterion)] <- ""
stations <- data.table(stations)
assesU <- data.table(assesU)
criterion <- data.table(criterion)
criterion[, nc := nchar(SeasonStartDate)]
criterion[, SeasonStartDate := as.character(SeasonStartDate)]
criterion[nc < 4, SeasonStartDate := paste0("0", SeasonStartDate)]
criterion[, SeasonStartDate := paste0("2000", SeasonStartDate)]
criterion[, SeasonStartDate := as.Date(SeasonStartDate, format = "%Y%m%d")]
criterion[, SeasonStartDate := format(SeasonStartDate, format = "%m-%d")]
criterion[, nc := nchar(SeasonEndDate)]
criterion[, SeasonEndDate := as.character(SeasonEndDate)]
criterion[nc < 4, SeasonEndDate := paste0("0", SeasonEndDate)]
criterion[, SeasonEndDate := paste0("2000", SeasonEndDate)]
criterion[, SeasonEndDate := as.Date(SeasonEndDate, format = "%Y%m%d")]
criterion[, SeasonEndDate := format(SeasonEndDate, format = "%m-%d")]
criterion[, nc := NULL]
cols <- c("WBODY", "ECOREGION", "Sample_Fraction", "Media", "Criterion", "Comparison", "Units")
criterion[, (cols) := lapply(.SD, as.character), .SDcols = cols]
# To be used with "Filtered data"
cols2 <- c("Station", "Characteristic", "Unit", "Media","Sample_Fraction", "Name", "OrganizationFormalName")
dat[, (cols2) := lapply(.SD, as.character), .SDcols = cols2]
assesU[, AssessmentUnit := as.character(AssessmentUnit)]
stations[, AssessmentUnit := as.character(AssessmentUnit)]
step1 <- merge(stations, assesU, by = "AssessmentUnit", allow.cartesian = TRUE)
step2A <- merge(step1, criterion, by=c("USE_OR_CLASS", "WBODY", "ECOREGION"), allow.cartesian = TRUE)
setnames(step2A, "PARM", "Characteristic")
setnames(dat, "Unit", "Units")
step3A <- merge(step2A, dat, by = c("Station", "Characteristic", "Units", "Media","Sample_Fraction",
"Name", "Organization", "OrganizationFormalName",
"LatitudeMeasure", "LongitudeMeasure"), all = TRUE,
allow.cartesian = TRUE)
step3A <- step3A[!is.na(Result)]
cols3 <- c("Station", "Characteristic", "Units", "Sample_Fraction",
"OrganizationFormalName", "WBODY", "Media", "ECOREGION",
"Criterion", "USE_OR_CLASS")
step3A[, (cols3) := lapply(.SD, as.character), .SDcols = cols3]
return(step3A)
})
rolling <- reactive({
data <- data_prep()
data <- data[grepl("rolling", Criterion, ignore.case = TRUE)]
data[, maxDate := max(ActivityStartDate, na.rm = TRUE), by = c("Station", "Characteristic",
"AssessmentUnit", "Units",
"USE_OR_CLASS", "WBODY", "Media","Sample_Fraction",
"Sample_Fraction", "ECOREGION" )]
data[, MinSamples := as.numeric(MinSamples)]
data[is.na(MinSamples), MinSamples := 0]
data[, id := paste(Station, Characteristic,
AssessmentUnit, Units,
USE_OR_CLASS, WBODY, Media,
Sample_Fraction, ECOREGION, sep = "_")]
data[, count := .N, by ="id"]
data <- data[count >= MinSamples]
ids <- unique(data$id)
all_ids <- lapply(ids, function(y){
sub_date <- data[id == y]
dates <- as.Date(unique(sub_date$ActivityStartDate))
if(!is.na(dates)){
sapply(dates, function(x) {
endDate <- x + as.numeric(unique(sub_date$AverageTime))
if(is.null(endDate)){
paste("EndDate doesn't exist")
} else {
if(endDate > unique(sub_date$maxDate)) {
# return()
res = "End date is after the latest date for this parameter/station combination"
} else {
subsub <- sub_date[ActivityStartDate >= as.Date(x) & ActivityStartDate <= endDate]
res <- mean(subsub$Result, na.rm = TRUE)
}
sub_date[ActivityStartDate == as.Date(x), RollingAvg := res]
}
})
return(sub_date)
} else {
return()
}
})
rollingAverages <- rbindlist(all_ids, use.names = TRUE)
rollingAverages[, c("id", "count") := NULL]
rollingAverages <- rollingAverages[, list(Station, Characteristic, ActivityStartDate,
AssessmentUnit, Units, USE_OR_CLASS, WBODY,
Media, Sample_Fraction, ECOREGION, RollingAvg)]
setkey(rollingAverages)
rollingAverages <- rollingAverages[!duplicated(rollingAverages)]
return(rollingAverages)
})
ANALYSIS <- eventReactive(input$RunAnalysis, {
test <- data_prep()
rollingAverages <- rolling()
test <- merge(test, rollingAverages, by = c("Station", "Characteristic", "ActivityStartDate",
"AssessmentUnit", "Units" , "USE_OR_CLASS", "Media",
"WBODY","Sample_Fraction","ECOREGION" ), all = TRUE)
# Identifying exceedences
test[, Exceed := FALSE]
test[Comparison == "GT" & Result > Limit, Exceed := TRUE]
test[Comparison == "LT" & Result < Limit, Exceed := TRUE]
test[, Geomean_AU := geomean(Result), by = c("AssessmentUnit", "Characteristic",
"Units", "USE_OR_CLASS", "WBODY", "Media",
"Sample_Fraction", "ECOREGION")]
test[, Geomean_Station := geomean(Result), by = c("Station", "Characteristic",
"Units", "USE_OR_CLASS", "WBODY", "Media",
"Sample_Fraction", "ECOREGION")]
test[Criterion == "Geomean_Station" & Geomean_Station > Limit,
Exceed := TRUE]
test[Criterion == "Geomean_AU" & Geomean_AU > Limit,
Exceed := TRUE]
test[, `:=` (SeasonStartDate = as.character(SeasonStartDate),
SeasonEndDate = as.character(SeasonEndDate))]
test[grepl("season", Criterion, ignore.case = TRUE) & !is.na(SeasonEndDate) & !is.na(SeasonStartDate) & Result > Limit &
MonthDay > SeasonStartDate & MonthDay < SeasonEndDate,
Exceed := TRUE]
test[grepl("Roll", Criterion, ignore.case = TRUE), Exceed := FALSE]
test[grepl("Roll", Criterion, ignore.case = TRUE) &
!is.na(RollingAvg) &
RollingAvg > Limit, Exceed := TRUE]
return(test)
})
table_dat<-reactive({
table_dat<-ANALYSIS()
table_dat[is.na(table_dat)]<-0
cols <- c("Station", "Characteristic", "USE_OR_CLASS", "WBODY", "Sample_Fraction",
"ECOREGION", "Criterion", "Name", "Organization")
table_dat[, (cols) := lapply(.SD, as.character), .SDcols = cols]
table_dat[, `:=` (num_exceed_total = sum(Exceed),
count_total = .N), by = c("Station",
"Characteristic",
"USE_OR_CLASS",
"WBODY",
"ECOREGION",
"Media",
"Sample_Fraction",
"Criterion",
"Comparison")]
table_dat<-table_dat[, .(AssessmentUnit = last(AssessmentUnit),
Name = last(Name),
num_exceed = sum(Exceed),
count = .N,
num_exceed_total = last(num_exceed_total),
count_total = last(count_total),
Lat = last(LatitudeMeasure),
Long = last(LongitudeMeasure)), by = c("Station",
"Characteristic",
"USE_OR_CLASS",
"WBODY",
"ECOREGION",
"Media",
"Sample_Fraction",
"Criterion",
"Comparison")]
table_dat[, Perc_Exceed := round((num_exceed_total/count_total) * 100, digits = 2)]
table_dat[, Perc_Exceed_Map := ifelse(Perc_Exceed == 0, 1, Perc_Exceed)]
table_dat[Criterion == 0, Criterion := "Not Analyzed"]
table_dat[USE_OR_CLASS == 0, USE_OR_CLASS := "Not Analyzed"]
table_dat[AssessmentUnit == 0, AssessmentUnit := "Not Specified"]
table_dat[Sample_Fraction == 0, Sample_Fraction := "Not Specified"]
# Moving the assessment unit column to the first position
setcolorder(table_dat, c("AssessmentUnit", setdiff(names(table_dat), "AssessmentUnit")))
return(table_dat)
})
output$Save_Analysis <- downloadHandler(
filename = function() {
paste('Analysis-', Sys.Date(), '.csv', sep='')
},
content = function(con) {
write.table(ANALYSIS(), con, row.names = F, col.names = TRUE, sep = ",")
})
output$analysis_table =
DT::renderDataTable(table_dat(),
rownames = FALSE,
filter = 'top',
extensions = 'Buttons', escape = -1, options = list(dom = 'lfrBtip', buttons = I('colvis'),
columnDefs = list(list(visible = FALSE, targets = list(4, 5, 6, 7, 9, 10, 13, 14, 15, 17))
))
)
################## Attains-compatible output ############################################
attains <- reactive({
stations <- data.table(stationsFile())
assesU <- data.table(assessmentUnitFile())
stations[is.na(stations)] <- ""
assesU[is.na(assesU)] <- ""
assesU[, AssessmentUnit := as.character(AssessmentUnit)]
stations[, AssessmentUnit := as.character(AssessmentUnit)]
table_dat <- table_dat()
stationNames <- stations[, list(Station, AssessmentUnit)]
stations2 <- merge(stationNames, assesU, by = "AssessmentUnit", allow.cartesian = TRUE)
au <- merge(stations2, table_dat, by = c("Station", "USE_OR_CLASS", "WBODY","ECOREGION", "AssessmentUnit") )
au[, station_count := length(unique(Station)), by = "AssessmentUnit"]
au2 <- au[, .(num_exceed_total = sum(num_exceed_total, na.rm = TRUE),
count_total = sum(count_total, na.rm = TRUE)
),
by = c("AssessmentUnit", "USE_OR_CLASS", "Characteristic", "station_count")]
au2[, Perc_Exceed := round((num_exceed_total/count_total) * 100, 1)]
au2[, USE_ATTAINMENT_CODE := ""]
setnames(au2, c("AssessmentUnit", "USE_OR_CLASS", "Characteristic"),
c("ASSESSMENT_UNIT_ID", "USE_NAME", "PARAM_NAME"))
setcolorder(au2, c("ASSESSMENT_UNIT_ID", "USE_NAME", "USE_ATTAINMENT_CODE",
"PARAM_NAME","num_exceed_total", "count_total",
"Perc_Exceed", "station_count"))
return(au2)
})
output$attains_table =
DT::renderDataTable(attains(),
rownames = FALSE,
filter = 'top')
# Downloading the ATTAINS analysis table
output$Save_ATTAINS_table <- downloadHandler(
filename = function() {
paste('ATTAINS-', Sys.Date(), '.csv', sep='')
},
content = function(con) {
write.table(attains(), con, row.names = F, col.names = TRUE, sep = ",")
})
########### Trends analysis ############################################
output$trend_selects <- renderUI({
fluidRow(column(6,
selectizeInput("time_choice", h4("Select time unit of analysis"),
choices = c("Month", "Year")),
selectizeInput("tie", h4("If multiple observations exist for same date, use:"),
choices = c("Mean" = "a",
"Median" = "m")),
checkboxInput("correct", h4("Correct for correlation between blocks (should only be chosen if there are more than nine years of data)"),
value = FALSE)
),
column(6,
selectizeInput("block_choice", h4("Select unit of analysis"),
choices = c("Stations", "Assessment Units")),
numericInput("p_choice", h4("Select the p-value"),
value = 0.05)
)
)
})
trends_prep <- reactive({
data <- copy(WQP()) #[Characteristic == input$parm_trend]
if(is.element("Unit", names(data))){
setnames(data, "Unit", "Units")
}
data <- merge(data, stationsFile(), by = "Station")
data_trend <- merge(data, assessmentUnitFile(),
"AssessmentUnit", allow.cartesian = TRUE)
data <- data_trend[, list(Station, Characteristic, Units, Result, ActivityStartDate, AssessmentUnit)]
data <- data[!duplicated(data)]
data <- gen_date(data)
data[, date_char := as.character(ActivityStartDate)]
data[, year := as.numeric(tstrsplit(date_char, "-")[[1]])]
data[, month := as.numeric(tstrsplit(date_char, "-")[[2]])]
data[, month_dec := month * (1/12) ]
data[, date := year + month_dec]
data[, stationID := as.numeric(factor(Station))]
data[, assessUID := as.numeric(factor(AssessmentUnit))]
return(data)
})
trends_result <- eventReactive(input$RunTrendAnalysis, {
dataT <- trends_prep()
dataT[, charunit := paste(Characteristic, Units, sep = "_")]
if(input$block_choice == "Stations"){
if(input$time_choice == "Year"){
dataT[, count := length(unique(year)), by = c("Station", "Characteristic", "Units")]
} else {
dataT[, count := length(unique(date)), by = c("Station", "Characteristic", "Units")]
}
data_less <- dataT[count < 4]
data_less <- data_less[, list(Station, Characteristic, Units)]
data_less <- data_less[!duplicated(data_less)]
if(input$time_choice == "Year"){
data_less[, Kendall_score := "Less than four years in the sample"]
} else {
data_less[, Kendall_score := "Less than four months in the sample"]
}
dataT <- dataT[count >= 4]
if(nrow(dataT) > 0) {
stations <- unique(dataT$Station)
all_results <- lapply(stations, function(x){
data_s <- dataT[Station == x]
chars <- unique(data_s$charunit)
station_result <- lapply(chars, function(y){
data <- data_s[charunit == y]
if(input$time_choice == "Year"){
timeUnit <- data$year
} else {
timeUnit <- data$date
}
trend <- rkt(timeUnit, data$Result, rep = "a")
character <- unique(data[charunit == y, Characteristic])
unit <- unique(data[charunit == y, Units])
result <- data.table(Station = x,
Characteristic = character,
Units = unit,
Kendall_score = round(trend[[2]], 4),
Theil_Sen_slope = round(trend[[3]], 4),
Kendall_tau = round(trend[[12]], 4),
p_value = round(trend[[1]], 4),
variance = round(trend[[4]], 4)
)
return(result)
})
sub_result <- rbindlist(station_result, use.names = TRUE)
})
all <- rbindlist(all_results, use.names = TRUE)
all[, Trend := trend_sig(Theil_Sen_slope, p_value, input$p_choice), by = c("Station", "Characteristic", "Units")]
} else {
if(input$time_choice == "Year"){
all <- data.table(Station = "No station/characteristic pair has at least four sampling years")
} else {
all <- data.table(Station = "No station/characteristic pair has at least four sampling months")
}
}
all <- rbind(all, data_less, use.names = TRUE, fill = TRUE)
} else if(input$block_choice == "Assessment Units") {
if(input$time_choice == "Year"){
dataT[, count := length(unique(year)), by = c("AssessmentUnit", "Characteristic", "Units")]
} else {
dataT[, count := length(unique(date)), by = c("AssessmentUnit", "Characteristic", "Units")]
}
data_less <- dataT[count < 4]
data_less <- data_less[, list(AssessmentUnit, Characteristic, Units)]
data_less <- data_less[!duplicated(data_less)]
if(input$time_choice == "Year"){
data_less[, Kendall_score := "Less than four years in the sample"]
} else {
data_less[, Kendall_score := "Less than four months in the sample"]
}
dataT <- dataT[count >= 4]
if(nrow(dataT) > 0) {
AU <- unique(dataT$AssessmentUnit)
all_results <- lapply(AU, function(x){
data_s <- dataT[AssessmentUnit == x]
chars <- unique(data_s$charunit)
au_result <- lapply(chars, function(y){
data <- data_s[charunit == y]
if(input$time_choice == "Year"){
timeUnit <- data$year
} else {
timeUnit <- data$date
}
trend <- rkt(timeUnit, data$Result, data$stationID, rep = input$tie)
character <- unique(data[charunit == y, Characteristic])
unit <- unique(data[charunit == y, Units])
result <- data.table(AssessmentUnit = x,
Characteristic = y,
Units = unit,
Kendall_score = round(trend[[2]], 4),
Theil_Sen_slope = round(trend[[3]], 4),
Kendall_tau = round(trend[[12]], 4),
p_value = round(trend[[1]], 4),
variance = round(trend[[4]], 4)
)
return(result)
})
sub_result <- rbindlist(au_result, use.names = TRUE)
})
all <- rbindlist(all_results, use.names = TRUE)
all[, Trend := trend_sig(Theil_Sen_slope, p_value, input$p_choice), by = c("AssessmentUnit", "Characteristic")]
} else {
if(input$time_choice == "Year"){
all <- data.table(AssessmentUnit = "No assessment unit/characteristic pair has at least four sampling years")
} else {
all <- data.table(AssessmentUnit = "No assessment unit/characteristic pair has at least four sampling months")
}
}
all <- rbind(all, data_less, use.names = TRUE, fill = TRUE)
}
return(all)
})
output$trends_table <- DT::renderDataTable(
trends_result(), escape = -1, rownames = FALSE,
filter = 'top'
)
# Downloading the criteria analysis table
output$Save_Analysis_trends <- downloadHandler(
filename = function() {
paste('Trends_Analysis-', Sys.Date(), '.csv', sep='')
},
content = function(con) {
write.table(trends_result(), con, row.names = F, col.names = TRUE, sep = ",")
})
output$tbl_text <- renderText({
str(trends_prep())
})
################################ Map tab ##########################################
output$ParamMAP <- renderUI({
data <- ANALYSIS()
data[, CharUnit := paste(Characteristic, " (", Units, ")", sep = "")]
selectizeInput("param_map", label = p("Select a parameter"),
choices = unique(data[, CharUnit]), multiple = TRUE,
selected = if(input$param_sel==1){
unique(data[, CharUnit])
} else {NULL})
})
output$Class_Use_MAP <- renderUI({
data <- ANALYSIS()
selectizeInput("useclass_map", label = p("Select a use or class"),
choices = unique(data[!is.na(USE_OR_CLASS), USE_OR_CLASS]), multiple = TRUE,
selected = if(input$useclass_sel==1){
unique(data[!is.na(USE_OR_CLASS), USE_OR_CLASS])
} else {NULL})
})
output$Eco_MAP <- renderUI({
data <- ANALYSIS()
selectizeInput("eco_map", label = p("Select an ecoregion"),
choices = unique(data[, ECOREGION]), multiple = TRUE,
selected = if(input$eco_sel==1){
unique(data[, ECOREGION])
} else {NULL})
})
output$MediaMAP <- renderUI({
data <- ANALYSIS()
selectizeInput("media_map", label = p("Select media "),
choices = unique(data[, Media]), multiple = TRUE,
selected = if(input$media_sel == 1){
unique(data[, Media])
} else {NULL})
})
output$SampleMAP <- renderUI({
data <- ANALYSIS()
selectizeInput("sample_map", label = p("Select a sample fraction"),
choices = unique(data[, Sample_Fraction]), multiple = TRUE,
selected = if(input$sample_sel==1){
unique(data[, Sample_Fraction])
} else {NULL})
})
output$WB_MAP <- renderUI({
data <- ANALYSIS()
selectizeInput("wb_map", label = p("Select a water body"),
choices = unique(data[, WBODY]), multiple = TRUE,
selected = if(input$wb_sel==1){
unique(data[, WBODY])
} else {NULL})
})
spfilter_dat <- eventReactive (input$submit_filters, {
data <- ANALYSIS()
data[, CharUnit := paste(Characteristic, " (", Units, ")", sep = "")]
if(!is.null(input$param_map) & !unique(data$CharUnit) %in% c("", NA)) {
data <- data[CharUnit %in% input$param_map]
}
if(!is.null(input$useclass_map) & !unique(data$USE_OR_CLASS) %in% c("", NA)){
data <- data[USE_OR_CLASS %in% input$useclass_map]
}
if(!is.null(input$eco_map) & !unique(data$ECOREGION) %in% c("", NA)){
data <- data[ECOREGION %in% input$eco_map]
}
if(!is.null(input$media_map) & !unique(data$Media) %in% c("", NA)){
data <- data[Media %in% input$media_map]
}
if(!is.null(input$sample_map) & !unique(data$Sample_Fraction) %in% c("", NA)){
data <- data[Sample_Fraction %in% input$sample_map]
}
if(!is.null(input$wb_map) & !unique(data$WBODY) %in% c("", NA)){
data <- data[WBODY %in% input$wb_map]
}
return(data)
})
displayed_data <- reactive({
if(input$submit_filters == 0){
data <- ANALYSIS()
} else {
data <- spfilter_dat()
}
return(data)
})
# Adding text if the analysis hasn't been run and the map isn't displayed
output$non_display <- renderUI({
h3("")
if(input$RunAnalysis){
h3("")
} else {
h3("Please run the criteria analysis in the Analysis tab by clicking the 'Run Analysis' button ",
style = "text-align:center ; color: #990000 ;")
}
})
# Adding text under map only if the map will be drawn
output$map_text <- renderUI({
if(input$RunAnalysis){
tags$ul(tags$li("The station markers are scaled based on the # of exceedances/# of measurements."),
tags$li("Stations marked with black circles have no exceedance records in the dataset."))
}
})
min_date <- reactive({
data <- displayed_data()
min_date <- min(unique(data$ActivityStartDate), na.rm = TRUE)
})
max_date <- reactive({
data <- displayed_data()
max_date <- max(unique(data$ActivityStartDate), na.rm = TRUE)
})
map_df <- reactive({
data <- displayed_data()
data[, `:=` (num_exceed_total = sum(Exceed, na.rm = TRUE),
count_total = .N), by = c("Station")]
data <- data[, .(Name = last(Name),
num_exceed = sum(Exceed),
count = .N,
num_exceed_total = last(num_exceed_total),
count_total = last(count_total),
Lat = last(as.numeric(as.character(LatitudeMeasure))),
Long = last(as.numeric(as.character(LongitudeMeasure))),
AssessmentUnit = last(AssessmentUnit)
), by = c("Station")]
data[, Perc_Exceed := (num_exceed_total/count_total) * 100]
data[, Perc_Exceed_Map := ifelse(Perc_Exceed == 0, 1, Perc_Exceed)]
return(data)
})
output$map_data =
DT::renderDataTable(spfilter_dat(),
rownames = FALSE,
filter = 'top')
output$map<-renderLeaflet({
radiusFactor <- 50
leaflet(map_df()) %>%
fitBounds(lng1 = ~min(Long), lat1 = ~min(Lat), lng2 = ~max(Long), lat2 = ~max(Lat)) %>%
addTiles( "//{s}.tiles.mapbox.com/v3/jcheng.map-5ebohr46/{z}/{x}/{y}.png") %>%
clearMarkers() %>%
addCircleMarkers(
lat =~Lat,
lng = ~Long,
radius = ~(log(Perc_Exceed_Map) + 2) * radiusFactor / 5^2,
layerId = row.names(map_df()),
clusterOptions = markerClusterOptions()
)
})
# Render polygons
observe({
pal <- colorFactor("Set1", domain = map_df()$AssessmentUnit)
radiusFactor <- 50
map <- leafletProxy("map", data = map_df())
map %>% clearMarkers()
if(input$mapcolor == "Assessment_Units") {
map %>% clearMarkerClusters() %>%
addCircleMarkers(data = map_df(),
color = ~pal(AssessmentUnit),
lat =~Lat,
lng = ~Long,
radius = ~(log(Perc_Exceed_Map) + 2) * radiusFactor / 5^2,
layerId = row.names(map_df()),
clusterOptions = markerClusterOptions()
)
} else if(input$mapcolor == "Exceedances") {
map %>% clearMarkerClusters() %>%
addCircleMarkers(data = map_df(),
color = ~ifelse(num_exceed_total == 0, 'black','blue'),
lat =~Lat,
lng = ~Long,
radius = ~(log(Perc_Exceed_Map) + 2) * radiusFactor / 5^2,
layerId = row.names(map_df()),
clusterOptions = markerClusterOptions()
)
}
})
observeEvent(input$map_marker_click, {
leafletProxy("map") %>% clearPopups()
content<- as.character(tagList(
tags$html(
tags$div(style = 'color: #24476B',
tags$ul(
tags$li(h4(paste("Station ID: ", map_df()[row.names(map_df()) == input$map_marker_click["id"], ][["Station"]]))),
tags$li(h4(paste("Station name: ", map_df()[row.names(map_df()) == input$map_marker_click["id"], ][["Name"]]))),
tags$li(h4(paste("Start date: ", min_date()))),
tags$li(h4(paste("End date: ", max_date())))
)
)
)
))
leafletProxy("map") %>% addPopups(lat = input$map_marker_click$lat, lng = input$map_marker_click$lng,
paste(content, '<br></br>',
actionButton("Stat_Summary", "Select this Location",
onclick = 'Shiny.onInputChange(\"button_click\", Math.random())'),
sep = ""))
})
################################# Station Summary ##########################################
# *** Specifying station to be summarized
station_info <- eventReactive(input$button_click,{
map_df()[row.names(map_df()) == input$map_marker_click$id]
})
station_data1 <- eventReactive(input$button_click, {
data <- ANALYSIS()[Station == station_info()$Station ]
data[, CharUnit := paste(Characteristic, " (", Units, ")", sep = "")]
return(data)
})
station_data <- reactive({
data <- station_data1()
data <- data[USE_OR_CLASS == input$Select_Use ]
return(data)
})
output$Station_Summary_Panel <- renderUI({
h4(paste("Summary for station ", station_info()$Name))
})
output$Station_Summary_select<-renderUI({
data <- station_data1()
if(is.null(data)){
h3("Please select a station on the map in the Map tab")
} else {
use_list<-as.character(unique(data[!is.na(USE_OR_CLASS), USE_OR_CLASS]))
fluidRow(column(4), column(4, selectizeInput("Select_Use", "Select a Use or Class", choices = use_list, selected = use_list[1])))
}
})
station_summary <- reactive({
data <- station_data()
stat_summary <- data[, list(Characteristic, ActivityStartDate, Criterion, Exceed)]
stat_summary <- stat_summary[!duplicated(stat_summary)]
stat_summary <- stat_summary[, `:=` (Exceedances = sum(Exceed, na.rm = TRUE),
Measurements = .N), by = c("Characteristic")]
stat_summary <- stat_summary[!duplicated(stat_summary[, list(Characteristic, Exceedances, Measurements)])]
stat_summary[, Exceed := NULL]
})
output$Station_Summary_text<-renderUI({
data <- station_data()
use_list <- as.character(unique(data$USE_OR_CLASS))
name <- station_info()$Name
stat_summary <- station_summary()
measurements <- dim(data)[1]
chars <- length(unique(WQP()[Station == station_info()$Station]$Characteristic))
criterion <- length(stat_summary$Characteristic)
date_low <- min(data$ActivityStartDate)
date_high <- max(data$ActivityStartDate)
fluidRow(
column(7,
p(tagList(
tags$html(
tags$div(style = 'text-align:left',
tags$ul(
tags$li(h5(paste("Station name: ", name))),
tags$li(h5(paste("Start date: ", date_low))),
tags$li(h5(paste("End date: ", date_high))),
tags$li(h5(paste("Class/use: ", input$Select_Use))),
tags$li(h5(paste("Unique characteristics: ", chars))),
tags$li(h5(paste("Measurements: ", measurements))),
tags$li(h5(paste("Number of criteria: ", criterion)))
)
)
)
))
)
)
})
# Adding text when no station is selected
output$chart_text <- renderUI({
h4("If no charts are rendered on this page, please choose a station on the map")
})