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IndicatorQuantiles.R
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# Script to calculate specified quantile and standard deviation threshold values for each parameter
# in the SEACAR DDI data exports for use in flagging unusual values for QA/QC.
#
# Author: Stephen R. Durham, PhD
# Florida Department of Environmental Protection
#
# Date: 01/08/2024
# Updated: 01/23/2024
# Setup------------------------------------------
#Load libraries
library(tidyverse)
library(data.table)
library(doFuture)
library(lubridate)
library(stringr)
library(openxlsx)
library(git2r)
options(scipen = 999) #prevent scientific notation in outputs
#Process new data export downloads if needed
# downloaddate <- as_date("2024-01-23")
# zips <- file.info(list.files("C:/Users/steph/Downloads/", full.names = TRUE, pattern="*.zip"))
# zips <- subset(zips, date(zips$mtime) == downloaddate)
#
# for(z in row.names(zips)){
# unzip(z, exdir = here::here("SEACARdata"), junkpaths = TRUE)
#
# while(TRUE %in% str_detect(list.files(here::here("SEACARdata")), ".zip$")){
# for(zz in list.files(here::here("SEACARdata"), full.names = TRUE, pattern = ".zip$")){
# unzip(zz, exdir = here::here("SEACARdata"), junkpaths = TRUE)
# file.remove(zz)
# }
# }
# # file.remove(z)
# }
#Set quantile and number of standard deviation values that should be used
quant_low <- 0.001
quant_high <- 0.999
num_sds <- 3
#Set the strings that need to be interpreted as NA values when loading data files
nas <- c("NULL", "NA", "")
#Identify and check date for reference thresholds file
reffilename <- "Database_Thresholds.xlsx"
reffilepath <- list.files(here::here("output/ScriptResults/"), pattern = reffilename, full.names = TRUE)
ref_info <- file.info(reffilepath)
warning(paste0("The supplied ref. file (", reffilename, ") was created ", ref_info$ctime, " and last modified ", ref_info$mtime, ". Proceed if you are sure this is the most up-to-date version."))
#Specify GitHub user info
github_user = "srdurham"
github_email = "[email protected]"
#Get current script git commit and path, and create a version label
gitcommit_script <- system("git rev-list HEAD -1 IndicatorQuantiles.R", intern=TRUE) #NOTE: this command only looks within the current branch (assumes the user is already using 'main').
scriptpath <- rstudioapi::getSourceEditorContext()$path
scriptname <- str_sub(scriptpath, max(str_locate_all(scriptpath, "/")[[1]]) + 1, -1)
scriptversion <- paste0(scriptname, ", Git Commit ID: ", gitcommit_script)
#This script can be run using parallel processing to speed it up, but note that progress messages will not be printed to the console.
#To run in parallel, uncomment the following line and any other lines marked with "uncomment for parallel use" and comment lines marked
#"comment for parallel use".
# options(future.globals.maxSize = 6291456000) #only necessary if using the parallel processing version of the script
#List data files
seacardat <- list.files(here::here("SEACARdata"), full.names = TRUE, pattern = ".txt")
#Set which parameters to skip (if needed)
parstoskip <- c()
#Remove all but one file for each habitat x parameter
seacardat_forit <- c(subset(seacardat, str_detect(seacardat, "Combined_WQ_WC_NUT_cont_|Species Richness - ", negate = TRUE)), subset(seacardat, str_detect(seacardat, "Combined_WQ_WC_NUT_cont_.+NE"))) #, subset(seacardat, str_detect(seacardat, "Species Richness - "))[1])
speciesdat <- sort(subset(seacardat_forit, str_detect(seacardat_forit, "CORAL|CW|NEKTON|SAV")))
names(speciesdat) <- c("coral", "cw", "nekton", "sav")
# Load reference thresholds file-------------------------------
refdat <- setDT(read.xlsx(reffilepath, sheet = 1, startRow = 3))
refdat[, `:=` (ActionNeededDate = as_date(ActionNeededDate, origin = "1899-12-30"),
ScriptLatestRunVersion = as.character(ScriptLatestRunVersion),
ScriptLatestRunDate = as_date(ScriptLatestRunDate, origin = "1899-12-30"))][, `:=` (ScriptLatestRunVersion = scriptversion,
ScriptLatestRunDate = Sys.Date())]
# Load and combine the data exports that include species information------------------------------------
spec_dat <- lapply(speciesdat, function(x){
assign("dt", fread(x, sep = "|", na.strings = nas))
dt[, export := str_sub(x, max(str_locate_all(x, "/")[[1]][,2]) + 1, -1)]
})
spec_dat <- rbindlist(spec_dat, fill = TRUE, idcol = TRUE)
spec_dat[, `:=` (rowcode = paste0(.id, RowID), Habitat = fcase(.id == "sav", "Submerged Aquatic Vegetation",
.id == "coral", "Coral/Coral Reef",
.id == "nekton", "Water Column",
.id == "cw", "Coastal Wetlands",
default = NA))]
isspspec <- spec_dat[CommonIdentifier %in% c("Total seagrass", "Total SAV") & ParameterName %in% c("Percent Cover", "Percent Occurrence"), ]
isspspec_habs <- unique(isspspec$Habitat)
spec_dat2 <- merge(spec_dat[!(rowcode %in% isspspec$rowcode), ], refdat[Calculated == 0 & isSpeciesSpecific == 0 & is.na(QuadSize_m2) & IndicatorName != "Grazers and Reef Dependent Species", -c("QuadSize_m2")], by = c("Habitat", "ParameterName"), all.x = TRUE)
spec_dat2[ParameterName %in% c("Count", "Presence/Absence", "Standard Length") & SpeciesGroup1 %in% c("Grazers and reef dependent species", "Reef Fish"), `:=` (IndicatorID = 11,
IndicatorName = "Grazers and Reef Dependent Species",
ParameterID = fcase(ParameterName == "Count", 46,
ParameterName == "Presence/Absence", 47,
ParameterName == "Standard Length", 81),
ThresholdID = fcase(ParameterName == "Count", 49,
ParameterName == "Presence/Absence", 50,
ParameterName == "Standard Length", 87))]
#This merging step could be simplified if there was a row in the refdat table for coral percent cover with isSpeciesSpecific == 1 to cover the records in the coral export that have CommonIdentifier == "Total SAV" or "Total seagrass".
for(h in isspspec_habs){
if(h == "Submerged Aquatic Vegetation"){
spec_dat2b <- merge(spec_dat[rowcode %in% isspspec$rowcode & Habitat == h, ], refdat[Calculated == 0 & isSpeciesSpecific == 1 & is.na(QuadSize_m2) & Habitat == h, -c("QuadSize_m2", "Habitat")], by = c("ParameterName"), all.x = TRUE)
} else{
spec_dat2b <- merge(spec_dat[rowcode %in% isspspec$rowcode & Habitat == h, ], refdat[Calculated == 0 & is.na(QuadSize_m2) & Habitat == h, -c("QuadSize_m2", "Habitat")], by = c("ParameterName"), all.x = TRUE)
}
spec_dat2 <- rbind(spec_dat2, spec_dat2b)
}
spec_dat <- copy(spec_dat2)
rm(isspspec, isspspec_habs, spec_dat2, spec_dat2b)
gc()
# plan(multisession, workers = 10) #uncomment for parallel use
#comment for parallel use
qs <- data.table(primaryHab = character(),
primaryCombTab = character(),
primaryIndID = integer(),
primaryInd = character(),
primaryParID = integer(),
primaryParNm = character(),
primaryThrID = integer(),
habitat = character(),
combinedTable = character(),
indicatorID = integer(),
indicatorName = character(),
parameterID = integer(),
parameterName = character(),
thresholdID = integer(),
calculated = logical(),
isSpeciesSpec = logical(),
QuadSize_m2 = numeric(),
median = numeric(),
iqr = numeric(),
qval_low = numeric(),
qval_high = numeric(),
q_low = numeric(),
q_high = numeric(),
mean = numeric(),
sd = numeric(),
num_sds = integer(),
sdn_low = numeric(),
sdn_high = numeric(),
n_tot = integer(),
n_q_low = integer(),
n_q_high = integer(),
n_sdn_low = integer(),
n_sdn_high = integer(),
pid = integer(),
export = character())
pars <- data.table(file = character(),
param = character())
# Build new quantiles summary data----------------------------------------------------------------
# qs <- foreach(file = seacardat_forit, .combine = rbind) %dofuture% { #uncomment for parallel use
for(file in seacardat_forit){ #comment for parallel use
file_short <- str_sub(file, max(str_locate_all(file, "/")[[1]][,2]) + 1, -1)
qs_dat <- data.table(primaryHab = character(),
primaryCombTab = character(),
primaryIndID = integer(),
primaryInd = character(),
primaryParID = integer(),
primaryParNm = character(),
primaryThrID = integer(),
habitat = character(),
combinedTable = character(),
indicatorID = integer(),
indicatorName = character(),
parameterID = integer(),
parameterName = character(),
thresholdID = integer(),
calculated = logical(),
isSpeciesSpec = logical(),
QuadSize_m2 = numeric(),
median = numeric(),
iqr = numeric(),
qval_low = numeric(),
qval_high = numeric(),
q_low = numeric(),
q_high = numeric(),
mean = numeric(),
sd = numeric(),
num_sds = integer(),
sdn_low = numeric(),
sdn_high = numeric(),
n_tot = integer(),
n_q_low = integer(),
n_q_high = integer(),
n_sdn_low = integer(),
n_sdn_high = integer(),
pid = integer(),
export = character())
cat("Starting ", file_short, "\n", sep = "")
if(str_detect(file, "Combined_WQ_WC_NUT_cont_")){
## Continuous WQ-----------------------------------------------------------
#Load and combine all regional continuous WQ files
#Exception for loading dissolved oxygen data because the parameter name "Dissolved Oxygen" is an exact subset of "Dissolved Oxygen Saturation"
if(str_detect(file, "Dissolved_Oxygen_Saturation")){
cont_dat <- lapply(subset(seacardat, str_detect(seacardat, str_sub(file, str_locate(file, "Combined_WQ_WC_NUT_cont_Dissolved_Oxygen_Saturation.+NE")[1], str_locate(file, "Combined_WQ_WC_NUT_cont_Dissolved_Oxygen_Saturation.+NE")[2] - 2))), function(x){
assign(paste0("cont_", which(str_detect(subset(seacardat, str_detect(seacardat, "Combined_WQ_WC_NUT_cont_Dissolved_Oxygen_Saturation")), x))),
fread(x, sep = "|", na.strings = nas))
#record the export name in the data.table
eval(as.name(paste0("cont_", which(str_detect(subset(seacardat, str_detect(seacardat, "Combined_WQ_WC_NUT_cont_Dissolved_Oxygen_Saturation")), x)))))[, export := str_sub(x, max(str_locate_all(file, "/")[[1]][,2]) + 1, -1)]
})
#Keep track of the parameter and the export file it came from
lapply(cont_dat, function(x){
pars_f <- data.table(file = unique(x$export),
param = unique(x$ParameterName))
pars <<- rbind(pars, pars_f)
})
} else{
#remove "Dissolved Oxygen Saturation" file before selecting and loading data files
seacardat_sub <- subset(seacardat, str_detect(seacardat, "Dissolved_Oxygen_Saturation", negate = TRUE))
cont_dat <- lapply(subset(seacardat_sub, str_detect(seacardat_sub, str_sub(file, str_locate(file, "Combined_WQ_WC_NUT_cont_.+NE")[1], str_locate(file, "Combined_WQ_WC_NUT_cont_.+NE")[2] - 2))), function(x){
assign(paste0("cont_", which(str_detect(subset(seacardat_sub, str_detect(seacardat_sub, "Combined_WQ_WC_NUT_cont_")), x))),
fread(x, sep = "|", na.strings = nas))
eval(as.name(paste0("cont_", which(str_detect(subset(seacardat_sub, str_detect(seacardat_sub, "Combined_WQ_WC_NUT_cont_")), x)))))[, export := str_sub(x, max(str_locate_all(file, "/")[[1]][,2]) + 1, -1)]
})
#Keep track of the parameter and the export file it came from
lapply(cont_dat, function(x){
pars_f <- data.table(file = unique(x$export),
param = unique(x$ParameterName))
pars <<- rbind(pars, pars_f)
})
}
#Combine the loaded data tables and add the Habitat and 'refdat' columns
cont_dat <- rbindlist(cont_dat)
cont_dat[, Habitat := "Water Column"]
cont_dat <- merge(cont_dat, refdat[CombinedTable == "Continuous WQ" & ParameterName %in% unique(cont_dat$ParameterName), ], by = c("Habitat", "ParameterName"), all.x = TRUE)
#Calculate quantile and standard deviation results for each parameter
for(par in unique(cont_dat$ParameterName)){
#if the parameter is in the skip parameters list, move on to the next parameter
if(par %in% parstoskip) next
parid <- cont_dat[ParameterName == par, unique(ParameterID)]
cont_dat_par <- cont_dat[ParameterName == par & !is.na(ResultValue) & MADup == 1 & Include == 1, .(habitat = list(unique(Habitat)),
combinedTable = list(unique(CombinedTable)),
indicatorID = list(unique(IndicatorID)),
indicatorName = list(unique(IndicatorName)),
parameterID = list(unique(ParameterID)),
parameterName = unique(ParameterName),
thresholdID = list(unique(ThresholdID)),
calculated = unique(Calculated),
isSpeciesSpec = unique(isSpeciesSpecific),
median = median(ResultValue),
iqr = IQR(ResultValue),
qval_low = quant_low,
qval_high = quant_high,
q_low = quantile(ResultValue, probs = quant_low),
q_high = quantile(ResultValue, probs = quant_high),
mean = mean(ResultValue),
sd = sd(ResultValue),
num_sds = num_sds,
sdn_low = mean(ResultValue) - (num_sds * sd(ResultValue)),
sdn_high = mean(ResultValue) + (num_sds * sd(ResultValue)),
n_tot = length(ResultValue),
n_q_low = nrow(cont_dat[ParameterName == par & !is.na(ResultValue) & MADup == 1 & Include == 1 & ResultValue < cont_dat[ParameterName == par & !is.na(ResultValue) & MADup == 1 & Include == 1, quantile(ResultValue, probs = quant_low)], ]),
n_q_high = nrow(cont_dat[ParameterName == par & !is.na(ResultValue) & MADup == 1 & Include == 1 & ResultValue > cont_dat[ParameterName == par & !is.na(ResultValue) & MADup == 1 & Include == 1, quantile(ResultValue, probs = quant_high)], ]),
n_sdn_low = nrow(cont_dat[ParameterName == par & !is.na(ResultValue) & MADup == 1 & Include == 1 & ResultValue < cont_dat[ParameterName == par & !is.na(ResultValue) & MADup == 1 & Include == 1, mean(ResultValue) - (num_sds * sd(ResultValue))], ]),
n_sdn_high = nrow(cont_dat[ParameterName == par & !is.na(ResultValue) & MADup == 1 & Include == 1 & ResultValue > cont_dat[ParameterName == par & !is.na(ResultValue) & MADup == 1 & Include == 1, mean(ResultValue) + (num_sds * sd(ResultValue))], ]))]
#Some parameters exist across export files, so record which parameter these calculations are for specifically
cont_dat_par[, `:=` (primaryHab = "Water Column",
primaryCombTab = "Continuous WQ",
primaryIndID = cont_dat[ParameterID == parid, unique(IndicatorID)],
primaryInd = cont_dat[ParameterID == parid, unique(IndicatorName)],
primaryParID = parid,
primaryParNm = cont_dat[ParameterID == parid, unique(ParameterName)],
primaryThrID = cont_dat[ParameterID == parid, unique(ThresholdID)],
QuadSize_m2 = NA,
pid = Sys.getpid(), #processor ID (when not running the script in parallel, these will all be the same)
export = paste0("example: ", file_short))]
#Record the parameter results in the 'qs_dat' data.table
qs_dat <- rbind(qs_dat, cont_dat_par)
#print a progress message
cat("\t", parid, ": ", par, "\n", sep = "")
}
#Record the parameter x export file results in the 'qs' data.table
qs <- rbind(qs, qs_dat) #comment for parallel use
#Remove unnecessary data objects to save memory
rm(cont_dat, cont_dat_par, pars_f)
#print a progress message
cat("\t Done! \n\n", sep = "")
# qs_dat #uncomment for parallel use
} else if(str_detect(file, "Combined_WQ_WC_NUT_")){
## Discrete WQ-----------------------------------------------------------
#Load the data file and specify the habitat
dat <- fread(file, sep = "|", na.strings = nas)
dat[, Habitat := "Water Column"]
#Merge refdat variables with the data file separately by calculated versus uncalculated, then recombine
dat1 <- merge(dat[str_detect(SEACAR_QAQCFlagCode, "1Q", negate = TRUE), ], refdat[CombinedTable == "Discrete WQ" & Calculated == 0 & ParameterName %in% unique(dat$ParameterName), ], by = c("Habitat", "ParameterName"))
dat2 <- merge(dat, refdat[CombinedTable == "Discrete WQ" & Calculated == 1 & ParameterName %in% unique(dat$ParameterName), ], by = c("Habitat", "ParameterName"))
dat <- rbind(dat1, dat2)
#Record the parameters in the data file to 'pars' object
pars_f <- data.table(file = file_short,
param = dat[!is.na(ResultValue), unique(ParameterName)])
pars <- rbind(pars, pars_f)
#Calculate quantile and standard deviation results for each parameter
for(par in unique(dat$ParameterName)){
#if the parameter is in the skip parameters list, move on to the next parameter
if(par %in% parstoskip) next
parid <- dat[ParameterName == par, unique(ParameterID)]
#If parameter is "Total Nitrogen", calculate quantiles/SDs separately for "uncalculated" records
if(par == "Total Nitrogen"){
dat_par_all <- dat[ParameterName == par & !is.na(ResultValue) & MADup == 1 & Include == 1 & Calculated == 1, .(habitat = list(unique(Habitat)),
combinedTable = list(unique(CombinedTable)),
indicatorID = list(unique(IndicatorID)),
indicatorName = list(unique(IndicatorName)),
parameterID = list(unique(ParameterID)),
parameterName = unique(ParameterName),
thresholdID = list(unique(ThresholdID)),
calculated = unique(Calculated),
isSpeciesSpec = unique(isSpeciesSpecific),
median = median(ResultValue),
iqr = IQR(ResultValue),
qval_low = quant_low,
qval_high = quant_high,
q_low = quantile(ResultValue, probs = quant_low),
q_high = quantile(ResultValue, probs = quant_high),
mean = mean(ResultValue),
sd = sd(ResultValue),
num_sds = num_sds,
sdn_low = mean(ResultValue) - (num_sds * sd(ResultValue)),
sdn_high = mean(ResultValue) + (num_sds * sd(ResultValue)),
n_tot = length(ResultValue),
n_q_low = nrow(dat[ParameterName == par & !is.na(ResultValue) & MADup == 1 & Include == 1 & ResultValue < dat[ParameterName == par & !is.na(ResultValue) & MADup == 1 & Include == 1, quantile(ResultValue, probs = quant_low)], ]),
n_q_high = nrow(dat[ParameterName == par & !is.na(ResultValue) & MADup == 1 & Include == 1 & ResultValue > dat[ParameterName == par & !is.na(ResultValue) & MADup == 1 & Include == 1, quantile(ResultValue, probs = quant_high)], ]),
n_sdn_low = nrow(dat[ParameterName == par & !is.na(ResultValue) & MADup == 1 & Include == 1 & ResultValue < dat[ParameterName == par & !is.na(ResultValue) & MADup == 1 & Include == 1, mean(ResultValue) - (num_sds * sd(ResultValue))], ]),
n_sdn_high = nrow(dat[ParameterName == par & !is.na(ResultValue) & MADup == 1 & Include == 1 & ResultValue > dat[ParameterName == par & !is.na(ResultValue) & MADup == 1 & Include == 1, mean(ResultValue) + (num_sds * sd(ResultValue))], ]))]
dat_par_nocalc <- dat[ParameterName == par & !is.na(ResultValue) & MADup == 1 & Include == 1 & str_detect(SEACAR_QAQCFlagCode, "1Q", negate = TRUE) & Calculated == 0,
.(habitat = list(unique(Habitat)),
combinedTable = list(unique(CombinedTable)),
indicatorID = list(unique(IndicatorID)),
indicatorName = list(unique(IndicatorName)),
parameterID = list(unique(ParameterID)),
parameterName = unique(ParameterName),
thresholdID = list(unique(ThresholdID)),
calculated = unique(Calculated),
isSpeciesSpec = unique(isSpeciesSpecific),
median = median(ResultValue),
iqr = IQR(ResultValue),
qval_low = quant_low,
qval_high = quant_high,
q_low = quantile(ResultValue, probs = quant_low),
q_high = quantile(ResultValue, probs = quant_high),
mean = mean(ResultValue),
sd = sd(ResultValue),
num_sds = num_sds,
sdn_low = mean(ResultValue) - (num_sds * sd(ResultValue)),
sdn_high = mean(ResultValue) + (num_sds * sd(ResultValue)),
n_tot = length(ResultValue),
n_q_low = nrow(dat[ParameterName == par & !is.na(ResultValue) & MADup == 1 & Include == 1 & ResultValue < dat[ParameterName == par & !is.na(ResultValue) & MADup == 1 & Include == 1, quantile(ResultValue, probs = quant_low)], ]),
n_q_high = nrow(dat[ParameterName == par & !is.na(ResultValue) & MADup == 1 & Include == 1 & ResultValue > dat[ParameterName == par & !is.na(ResultValue) & MADup == 1 & Include == 1, quantile(ResultValue, probs = quant_high)], ]),
n_sdn_low = nrow(dat[ParameterName == par & !is.na(ResultValue) & MADup == 1 & Include == 1 & ResultValue < dat[ParameterName == par & !is.na(ResultValue) & MADup == 1 & Include == 1, mean(ResultValue) - (num_sds * sd(ResultValue))], ]),
n_sdn_high = nrow(dat[ParameterName == par & !is.na(ResultValue) & MADup == 1 & Include == 1 & ResultValue > dat[ParameterName == par & !is.na(ResultValue) & MADup == 1 & Include == 1, mean(ResultValue) + (num_sds * sd(ResultValue))], ]))]
dat_par <- rbind(dat_par_all, dat_par_nocalc)
#Some parameters exist across export files, so record which parameter these calculations are for specifically
dat_par[, `:=` (primaryHab = rep("Water Column", 2),
primaryCombTab = rep("Discrete WQ", 2),
primaryIndID = rep(dat[ParameterID == parid, unique(IndicatorID)], 2),
primaryInd = rep(dat[ParameterID == parid, unique(IndicatorName)], 2),
primaryParID = rep(parid, 2),
primaryParNm = rep(dat[ParameterID == parid, unique(ParameterName)], 2),
primaryThrID = dat[ParameterID == parid, sort(unique(ThresholdID), decreasing = TRUE)],
QuadSize_m2 = NA,
pid = Sys.getpid(), #processor ID (when not running the script in parallel, these will all be the same)
export = file_short)]
} else{
dat_par <- dat[ParameterName == par & !is.na(ResultValue) & MADup == 1 & Include == 1, .(habitat = list(unique(Habitat)),
combinedTable = list(unique(CombinedTable)),
indicatorID = list(unique(IndicatorID)),
indicatorName = list(unique(IndicatorName)),
parameterID = list(unique(ParameterID)),
parameterName = unique(ParameterName),
thresholdID = list(unique(ThresholdID)),
calculated = unique(Calculated),
isSpeciesSpec = unique(isSpeciesSpecific),
median = median(ResultValue),
iqr = IQR(ResultValue),
qval_low = quant_low,
qval_high = quant_high,
q_low = quantile(ResultValue, probs = quant_low),
q_high = quantile(ResultValue, probs = quant_high),
mean = mean(ResultValue),
sd = sd(ResultValue),
num_sds = num_sds,
sdn_low = mean(ResultValue) - (num_sds * sd(ResultValue)),
sdn_high = mean(ResultValue) + (num_sds * sd(ResultValue)),
n_tot = length(ResultValue),
n_q_low = nrow(dat[ParameterName == par & !is.na(ResultValue) & MADup == 1 & Include == 1 & ResultValue < dat[ParameterName == par & !is.na(ResultValue) & MADup == 1 & Include == 1, quantile(ResultValue, probs = quant_low)], ]),
n_q_high = nrow(dat[ParameterName == par & !is.na(ResultValue) & MADup == 1 & Include == 1 & ResultValue > dat[ParameterName == par & !is.na(ResultValue) & MADup == 1 & Include == 1, quantile(ResultValue, probs = quant_high)], ]),
n_sdn_low = nrow(dat[ParameterName == par & !is.na(ResultValue) & MADup == 1 & Include == 1 & ResultValue < dat[ParameterName == par & !is.na(ResultValue) & MADup == 1 & Include == 1, mean(ResultValue) - (num_sds * sd(ResultValue))], ]),
n_sdn_high = nrow(dat[ParameterName == par & !is.na(ResultValue) & MADup == 1 & Include == 1 & ResultValue > dat[ParameterName == par & !is.na(ResultValue) & MADup == 1 & Include == 1, mean(ResultValue) + (num_sds * sd(ResultValue))], ]))]
#Some parameters exist across export files, so record which parameter these calculations are for specifically
dat_par[, `:=` (primaryHab = "Water Column",
primaryCombTab = "Discrete WQ",
primaryIndID = dat[ParameterID == parid, unique(IndicatorID)],
primaryInd = dat[ParameterID == parid, unique(IndicatorName)],
primaryParID = parid,
primaryParNm = dat[ParameterID == parid, unique(ParameterName)],
primaryThrID = dat[ParameterID == parid, unique(ThresholdID)],
QuadSize_m2 = NA,
pid = Sys.getpid(), #processor ID (when not running the script in parallel, these will all be the same)
export = file_short)]
}
#Record the parameter results in the 'qs_dat' data.table
qs_dat <- rbind(qs_dat, dat_par)
#Print a progress message
cat("\t", parid, ": ", par, "\n", sep = "")
}
#Record the parameter x export file results in the 'qs' data.table
qs <- rbind(qs, qs_dat) #comment for parallel use
#Remove unnecessary data objects to save memory
if(par == "Total Nitrogen"){
rm(dat, dat_par, dat_par_all, dat_par_nocalc, pars_f)
} else{
rm(dat, dat_par, pars_f)
}
#print a progress message
cat("\t Done! \n\n", sep = "")
# qs_dat #uncomment for parallel use
} else if(str_detect(file, "All_CW_Parameters")){
## Coastal Wetlands----------------------------------
#use combined species data object
dat <- spec_dat
#Only needed for old-style wide-format exports
# dat <- melt(dat,
# measure.vars = c("[PercentCover-SpeciesComposition_%]", "[StemDensity_#/m2]", "[Total/CanopyPercentCover-SpeciesComposition_%]", "[BasalArea_m2/ha]"),
# variable.name = "ParameterName",
# value.name = "ResultValue")
# dat[, ResultValue := as.numeric(ResultValue)]
#Record parameters in the 'pars' object
pars_f <- data.table(file = file_short,
param = dat[.id == "cw" & !is.na(ResultValue), unique(ParameterName)])
pars <- rbind(pars, pars_f)
#Calculate quantile and standard deviation results for each parameter
for(par in dat[.id == "cw", unique(ParameterName)]){
#if the parameter is in the skip parameters list, move on to the next parameter
if(par %in% parstoskip) next
parid <- dat[.id == "cw" & ParameterName == par, unique(ParameterID)]
dat_par <- dat[ParameterName == par & !is.na(ResultValue) & MADup == 1 & str_detect(SpeciesGroup1, "Mangroves|Marsh|Invasive"), .(habitat = list(unique(Habitat)),
combinedTable = list(unique(CombinedTable)),
indicatorID = list(unique(IndicatorID)),
indicatorName = list(unique(IndicatorName)),
parameterID = list(unique(ParameterID)),
parameterName = unique(ParameterName),
thresholdID = list(unique(ThresholdID)),
calculated = unique(Calculated),
isSpeciesSpec = unique(isSpeciesSpecific),
export = list(unique(export)),
median = median(ResultValue),
iqr = IQR(ResultValue),
qval_low = quant_low,
qval_high = quant_high,
q_low = quantile(ResultValue, probs = quant_low),
q_high = quantile(ResultValue, probs = quant_high),
mean = mean(ResultValue),
sd = sd(ResultValue),
num_sds = num_sds,
sdn_low = mean(ResultValue) - (num_sds * sd(ResultValue)),
sdn_high = mean(ResultValue) + (num_sds * sd(ResultValue)),
n_tot = length(ResultValue),
n_q_low = nrow(dat[ParameterName == par & !is.na(ResultValue) & MADup == 1 & ResultValue < dat[ParameterName == par & !is.na(ResultValue) & MADup == 1, quantile(ResultValue, probs = quant_low)], ]),
n_q_high = nrow(dat[ParameterName == par & !is.na(ResultValue) & MADup == 1 & ResultValue > dat[ParameterName == par & !is.na(ResultValue) & MADup == 1, quantile(ResultValue, probs = quant_high)], ]),
n_sdn_low = nrow(dat[ParameterName == par & !is.na(ResultValue) & MADup == 1 & ResultValue < dat[ParameterName == par & !is.na(ResultValue) & MADup == 1, mean(ResultValue) - (num_sds * sd(ResultValue))], ]),
n_sdn_high = nrow(dat[ParameterName == par & !is.na(ResultValue) & MADup == 1 & ResultValue > dat[ParameterName == par & !is.na(ResultValue) & MADup == 1, mean(ResultValue) + (num_sds * sd(ResultValue))], ]))]
#Some parameters exist across export files, so record which parameter these calculations are for specifically
dat_par[, `:=` (primaryHab = "Coastal Wetlands",
primaryCombTab = "CW",
primaryIndID = ifelse(purrr::is_empty(parid), refdat[CombinedTable == "CW" & ParameterName == par, unique(IndicatorID)], dat[ParameterID == parid, unique(IndicatorID)]),
primaryInd = ifelse(purrr::is_empty(parid), refdat[CombinedTable == "CW" & ParameterName == par, unique(IndicatorName)], dat[ParameterID == parid, unique(IndicatorName)]),
primaryParID = ifelse(purrr::is_empty(parid), refdat[CombinedTable == "CW" & ParameterName == par, unique(ParameterID)], parid),
primaryParNm = ifelse(purrr::is_empty(parid), refdat[CombinedTable == "CW" & ParameterName == par, unique(ParameterName)], dat[ParameterID == parid, unique(ParameterName)]),
primaryThrID = ifelse(purrr::is_empty(parid), refdat[CombinedTable == "CW" & ParameterName == par, unique(ThresholdID)], dat[ParameterID == parid, unique(ThresholdID)]),
QuadSize_m2 = NA,
pid = Sys.getpid())] #processor ID (when not running the script in parallel, these will all be the same)
#Record the parameter results in the 'qs_dat' data.table
qs_dat <- rbind(qs_dat, dat_par)
#print a progress message
cat("\t", ifelse(purrr::is_empty(parid), refdat[CombinedTable == "CW" & ParameterName == par, unique(ParameterID)], parid), ": ", par, "\n", sep = "")
}
#Record the parameter x export file results in the 'qs' data.table
qs <- rbind(qs, qs_dat) #comment for parallel use
#Remove unnecessary data objects to save memory
rm(dat, dat_par, pars_f)
#print a progress message
cat("\t Done! \n\n", sep = "")
# qs_dat #uncomment for parallel use
} else if(str_detect(file, "All_CORAL_Parameters")){
## Coral-------------------------------------------------
# #Only needed when coral exports were separated by region
# coral_dat <- lapply(subset(seacardat, str_detect(seacardat, "Species Richness - ")), function(x){
# assign(paste0("coral_", which(str_detect(subset(seacardat, str_detect(seacardat, "Species Richness - ")), x))),
# fread(x))
# })
#
# coral_dat <- rbindlist(coral_dat)
#use combined species data object
dat <- spec_dat
# #Only needed for old-style wide formatted exports
# dat <- melt(coral_dat,
# measure.vars = c("[PercentCover-SpeciesComposition_%]", "[%LiveTissue_%]", "Height_cm", "Width_cm", "Diameter_cm"),
# variable.name = "ParameterName",
# value.name = "ResultValue")
# dat[, ResultValue := as.numeric(ResultValue)]
#Record parameters in the 'pars' object
pars_f <- data.table(file = file_short,
param = dat[.id == "coral" & !is.na(ResultValue), unique(ParameterName)])
pars <- rbind(pars, pars_f)
#Calculate quantile and standard deviation results for each parameter
for(par in dat[.id == "coral", unique(ParameterName)]){
#if the parameter is in the skip parameters list, move on to the next parameter
if(par %in% parstoskip) next
#Get the Coral parameter ID (exclude "Grazers and Reef Dependent Species" indicator)
parid <- dat[.id == "coral" & ParameterName == par & IndicatorName != "Grazers and Reef Dependent Species", unique(ParameterID)]
#Calculate quantiles/SDs for data subset from Coral species groups
dat_par <- dat[ParameterName == par & !is.na(ResultValue) & MADup == 1 & str_detect(SpeciesGroup1, "Coral|Cyanobacteria|Milleporans|Octocoral|Porifera|Scleractinian|Zoanthid"),
.(habitat = list(unique(Habitat)),
combinedTable = list(unique(CombinedTable)),
indicatorID = list(unique(IndicatorID)),
indicatorName = list(unique(IndicatorName)),
parameterID = list(unique(ParameterID)),
parameterName = unique(ParameterName),
thresholdID = list(unique(ThresholdID)),
calculated = unique(Calculated),
isSpeciesSpec = unique(isSpeciesSpecific),
export = list(unique(export)),
median = median(ResultValue),
iqr = IQR(ResultValue),
qval_low = quant_low,
qval_high = quant_high,
q_low = quantile(ResultValue, probs = quant_low),
q_high = quantile(ResultValue, probs = quant_high),
mean = mean(ResultValue),
sd = sd(ResultValue),
num_sds = num_sds,
sdn_low = mean(ResultValue) - (num_sds * sd(ResultValue)),
sdn_high = mean(ResultValue) + (num_sds * sd(ResultValue)),
n_tot = length(ResultValue),
n_q_low = nrow(dat[ParameterName == par & !is.na(ResultValue) & MADup == 1 & ResultValue < dat[ParameterName == par & !is.na(ResultValue) & MADup == 1, quantile(ResultValue, probs = quant_low)], ]),
n_q_high = nrow(dat[ParameterName == par & !is.na(ResultValue) & MADup == 1 & ResultValue > dat[ParameterName == par & !is.na(ResultValue) & MADup == 1, quantile(ResultValue, probs = quant_high)], ]),
n_sdn_low = nrow(dat[ParameterName == par & !is.na(ResultValue) & MADup == 1 & ResultValue < dat[ParameterName == par & !is.na(ResultValue) & MADup == 1, mean(ResultValue) - (num_sds * sd(ResultValue))], ]),
n_sdn_high = nrow(dat[ParameterName == par & !is.na(ResultValue) & MADup == 1 & ResultValue > dat[ParameterName == par & !is.na(ResultValue) & MADup == 1, mean(ResultValue) + (num_sds * sd(ResultValue))], ]))]
#Some parameters exist across export files, so record which parameter these calculations are for specifically
dat_par[, `:=` (primaryHab = "Coral/Coral Reef",
primaryCombTab = "Coral",
primaryIndID = ifelse(purrr::is_empty(parid), refdat[CombinedTable == "Coral" & ParameterName == par, unique(IndicatorID)], dat[ParameterID == parid, unique(IndicatorID)]),
primaryInd = ifelse(purrr::is_empty(parid), refdat[CombinedTable == "Coral" & ParameterName == par, unique(IndicatorName)], dat[ParameterID == parid, unique(IndicatorName)]),
primaryParID = ifelse(purrr::is_empty(parid), refdat[CombinedTable == "Coral" & ParameterName == par, unique(ParameterID)], parid),
primaryParNm = ifelse(purrr::is_empty(parid), refdat[CombinedTable == "Coral" & ParameterName == par, unique(ParameterName)], dat[ParameterID == parid, unique(ParameterName)]),
primaryThrID = ifelse(purrr::is_empty(parid), refdat[CombinedTable == "Coral" & ParameterName == par, unique(ThresholdID)], dat[ParameterID == parid, unique(ThresholdID)]),
QuadSize_m2 = NA,
pid = Sys.getpid())] #processor ID (when not running the script in parallel, these will all be the same)
#Record the parameter results in the 'qs_dat' data.table
qs_dat <- rbind(qs_dat, dat_par)
#print a progress message
cat("\t", ifelse(purrr::is_empty(parid), refdat[CombinedTable == "Coral" & ParameterName == par, unique(ParameterID)], parid), ": ", par, "\n", sep = "")
}
#Record the parameter x export file results in the 'qs' data.table
qs <- rbind(qs, qs_dat) #comment for parallel use
#Remove unnecessary data objects to save memory
rm(dat, dat_par, pars_f)
#print a progress message
cat("\t Done! \n\n", sep = "")
# qs_dat #uncomment for parallel use
} else if(str_detect(file, "All_NEKTON_Parameters")){
## Nekton-------------------------------------------------
#use combined species data object
dat <- spec_dat
dat[.id == "nekton" & SpeciesGroup1 == "", SpeciesGroup1 := NA]
dat[.id == "nekton" & CommonIdentifier == "Ophiothrix angulata", SpeciesGroup1 := "Grazers and reef dependent species"]
#Record parameters in the 'pars' object
pars_f <- data.table(file = file_short,
param = dat[.id == "nekton" & !is.na(ResultValue), unique(ParameterName)])
pars <- rbind(pars, pars_f)
#Calculate quantile and standard deviation results for each parameter
for(par in dat[.id == "nekton", unique(ParameterName)]){
#if the parameter is in the skip parameters list, move on to the next parameter
if(par %in% parstoskip) next
#get non-"Grazers and Reef Dependent Species" parameter ID
parid <- dat[.id == "nekton" & ParameterName == par & IndicatorName != "Grazers and Reef Dependent Species", unique(ParameterID)]
#Calculate quantiles/SDs for data subset from all Nekton species groups
dat_par <- dat[ParameterName == par & !is.na(ResultValue) & MADup == 1 & str_detect(SpeciesGroup2, "Nekton"), .(habitat = list(unique(Habitat)),
combinedTable = list(unique(CombinedTable)),
indicatorID = list(unique(IndicatorID)),
indicatorName = list(unique(IndicatorName)),
parameterID = list(unique(ParameterID)),
parameterName = unique(ParameterName),
thresholdID = list(unique(ThresholdID)),
calculated = unique(Calculated),
isSpeciesSpec = unique(isSpeciesSpecific),
export = list(unique(export)),
median = median(ResultValue),
iqr = IQR(ResultValue),
qval_low = quant_low,
qval_high = quant_high,
q_low = quantile(ResultValue, probs = quant_low),
q_high = quantile(ResultValue, probs = quant_high),
mean = mean(ResultValue),
sd = sd(ResultValue),
num_sds = num_sds,
sdn_low = mean(ResultValue) - (num_sds * sd(ResultValue)),
sdn_high = mean(ResultValue) + (num_sds * sd(ResultValue)),
n_tot = length(ResultValue),
n_q_low = nrow(dat[ParameterName == par & !is.na(ResultValue) & MADup == 1 & ResultValue < dat[ParameterName == par & !is.na(ResultValue) & MADup == 1, quantile(ResultValue, probs = quant_low)], ]),
n_q_high = nrow(dat[ParameterName == par & !is.na(ResultValue) & MADup == 1 & ResultValue > dat[ParameterName == par & !is.na(ResultValue) & MADup == 1, quantile(ResultValue, probs = quant_high)], ]),
n_sdn_low = nrow(dat[ParameterName == par & !is.na(ResultValue) & MADup == 1 & ResultValue < dat[ParameterName == par & !is.na(ResultValue) & MADup == 1, mean(ResultValue) - (num_sds * sd(ResultValue))], ]),
n_sdn_high = nrow(dat[ParameterName == par & !is.na(ResultValue) & MADup == 1 & ResultValue > dat[ParameterName == par & !is.na(ResultValue) & MADup == 1, mean(ResultValue) + (num_sds * sd(ResultValue))], ]))]
#Some parameters exist across export files, so record which parameter these calculations are for specifically
dat_par[, `:=` (primaryHab = "Water Column",
primaryCombTab = "Nekton",
primaryIndID = ifelse(purrr::is_empty(parid), refdat[CombinedTable == "Nekton" & ParameterName == par, unique(IndicatorID)], dat[ParameterID == parid, unique(IndicatorID)]),
primaryInd = ifelse(purrr::is_empty(parid), refdat[CombinedTable == "Nekton" & ParameterName == par, unique(IndicatorName)], dat[ParameterID == parid, unique(IndicatorName)]),
primaryParID = ifelse(purrr::is_empty(parid), refdat[CombinedTable == "Nekton" & ParameterName == par, unique(ParameterID)], parid),
primaryParNm = ifelse(purrr::is_empty(parid), refdat[CombinedTable == "Nekton" & ParameterName == par, unique(ParameterName)], dat[ParameterID == parid, unique(ParameterName)]),
primaryThrID = ifelse(purrr::is_empty(parid), refdat[CombinedTable == "Nekton" & ParameterName == par, unique(ThresholdID)], dat[ParameterID == parid, unique(ThresholdID)]),
QuadSize_m2 = NA,
pid = Sys.getpid())] #processor ID (when not running the script in parallel, these will all be the same)
#Record the parameter results in the 'qs_dat' data.table
qs_dat <- rbind(qs_dat, dat_par)
#print a progress message
cat("\t", ifelse(purrr::is_empty(parid), refdat[CombinedTable == "Nekton" & ParameterName == par, unique(ParameterID)], parid), ": ", par, "\n", sep = "")
#get "Grazers and Reef Dependent Species" parameter ID
parid <- dat[.id == "coral" & ParameterName == par & IndicatorName == "Grazers and Reef Dependent Species", unique(ParameterID)]
#Calculate quantiles/SDs for data subset from only the "Grazers and reef dependent species" and "Reef Fish" species groups
dat_par2 <- dat[ParameterName == par & !is.na(ResultValue) & MADup == 1 & SpeciesGroup1 %in% c("Grazers and reef dependent species", "Reef Fish"), .(habitat = list(unique(Habitat)),
combinedTable = list(unique(CombinedTable)),
indicatorID = list(unique(IndicatorID)),
indicatorName = list(unique(IndicatorName)),
parameterID = list(unique(ParameterID)),
parameterName = unique(ParameterName),
thresholdID = list(unique(ThresholdID)),
calculated = unique(Calculated),
isSpeciesSpec = unique(isSpeciesSpecific),
export = list(unique(export)),
median = median(ResultValue),
iqr = IQR(ResultValue),
qval_low = quant_low,
qval_high = quant_high,
q_low = quantile(ResultValue, probs = quant_low),
q_high = quantile(ResultValue, probs = quant_high),
mean = mean(ResultValue),
sd = sd(ResultValue),
num_sds = num_sds,
sdn_low = mean(ResultValue) - (num_sds * sd(ResultValue)),
sdn_high = mean(ResultValue) + (num_sds * sd(ResultValue)),
n_tot = length(ResultValue),
n_q_low = nrow(dat[ParameterName == par & !is.na(ResultValue) & MADup == 1 & ResultValue < dat[ParameterName == par & !is.na(ResultValue) & MADup == 1, quantile(ResultValue, probs = quant_low)], ]),
n_q_high = nrow(dat[ParameterName == par & !is.na(ResultValue) & MADup == 1 & ResultValue > dat[ParameterName == par & !is.na(ResultValue) & MADup == 1, quantile(ResultValue, probs = quant_high)], ]),
n_sdn_low = nrow(dat[ParameterName == par & !is.na(ResultValue) & MADup == 1 & ResultValue < dat[ParameterName == par & !is.na(ResultValue) & MADup == 1, mean(ResultValue) - (num_sds * sd(ResultValue))], ]),
n_sdn_high = nrow(dat[ParameterName == par & !is.na(ResultValue) & MADup == 1 & ResultValue > dat[ParameterName == par & !is.na(ResultValue) & MADup == 1, mean(ResultValue) + (num_sds * sd(ResultValue))], ]))]
#Some parameters exist across export files, so record which parameter these calculations are for specifically
dat_par2[, `:=` (primaryHab = "Coral/Coral Reef",
primaryCombTab = "Coral",
primaryIndID = ifelse(purrr::is_empty(parid), refdat[CombinedTable == "Coral" & IndicatorName == "Grazers and Reef Dependent Species" & ParameterName == par, unique(IndicatorID)], dat[ParameterID == parid, unique(IndicatorID)]),
primaryInd = ifelse(purrr::is_empty(parid), refdat[CombinedTable == "Coral" & IndicatorName == "Grazers and Reef Dependent Species" & ParameterName == par, unique(IndicatorName)], dat[ParameterID == parid, unique(IndicatorName)]),
primaryParID = ifelse(purrr::is_empty(parid), refdat[CombinedTable == "Coral" & IndicatorName == "Grazers and Reef Dependent Species" & ParameterName == par, unique(ParameterID)], parid),
primaryParNm = ifelse(purrr::is_empty(parid), refdat[CombinedTable == "Coral" & IndicatorName == "Grazers and Reef Dependent Species" & ParameterName == par, unique(ParameterName)], dat[ParameterID == parid, unique(ParameterName)]),
primaryThrID = ifelse(purrr::is_empty(parid), refdat[CombinedTable == "Coral" & IndicatorName == "Grazers and Reef Dependent Species" & ParameterName == par, unique(ThresholdID)], dat[ParameterID == parid & IndicatorName == "Grazers and Reef Dependent Species", unique(ThresholdID)]),
QuadSize_m2 = NA,
pid = Sys.getpid())] #processor ID (when not running the script in parallel, these will all be the same)
#Record the parameter results in the 'qs_dat' data.table
qs_dat <- distinct(rbind(qs_dat, dat_par2))
#print a progress message
cat("\t", ifelse(purrr::is_empty(parid), refdat[CombinedTable == "Coral" & IndicatorName == "Grazers and Reef Dependent Species" & ParameterName == par, unique(ParameterID)], parid), ": ", par, "\n", sep = "")
}
#Record the parameter x export file results in the 'qs' data.table
qs <- rbind(qs, qs_dat) #comment for parallel use
#Remove unnecessary data objects to save memory
rm(dat, dat_par, dat_par2, pars_f)
#print a progress message
cat("\t Done! \n\n", sep = "")
# qs_dat #uncomment for parallel use
} else if(str_detect(file, "All_Oyster_Parameters")){
## Oyster-----------------------------------------
#load oyster data file and make some temporary fixes that aren't already in the DDI export
dat <- fread(file, sep = "|", na.strings = nas)
dat[QuadSize_m2 == 0.06, QuadSize_m2 := 0.0625]
dat[ProgramID == 4042 & is.na(QuadSize_m2), QuadSize_m2 := fcase(SampleDate == as_date("2014-06-11"), 1,
SampleDate >= as_date("2014-11-11") & SampleDate <= as_date("2015-01-22"), 0.33,
SampleDate >= as_date("2015-03-04"), 0.0625)]
dat[ProgramID == 5035, QuadSize_m2 := NA]
dat[, Habitat := "Oyster/Oyster Reef"]
#Merge refdat columns into the oyster data.table, but only include "QuadSize_m2" as a grouping variable for the 'number of oysters counted...' and 'shell height' variables
dat1 <- merge(dat, refdat[CombinedTable == "Oyster" & ParameterID %in% c(26, 51, 27), -c("QuadSize_m2")], by = c("Habitat", "ParameterName"))
dat2 <- merge(dat, refdat[CombinedTable == "Oyster" & !(ParameterID %in% c(26, 51, 27)), ], by = c("Habitat", "ParameterName", "QuadSize_m2"))
dat <- rbind(dat1, dat2)
#Record parameters in the 'pars' object
pars_f <- data.table(file = file_short,
param = dat[!is.na(ResultValue), unique(ParameterName)])
pars <- rbind(pars, pars_f)
#Calculate quantile and standard deviation results for each parameter
for(par in unique(dat$ParameterName)){
#if the parameter is in the skip parameters list, move on to the next parameter
if(par %in% parstoskip) next
parid <- dat[ParameterName == par, unique(ParameterID)]
#Calculate quantiles and SDs separately depending on whether parameter values need to be grouped by 'QuadSize_m2'
if(par %in% c("Density", "Reef Height", "Percent Live")){
dat_par <- dat[ParameterName == par & !is.na(ResultValue) & MADup == 1, .(habitat = list(unique(Habitat)),
combinedTable = list(unique(CombinedTable)),
indicatorID = list(unique(IndicatorID)),
indicatorName = list(unique(IndicatorName)),
parameterID = list(unique(ParameterID)),
parameterName = unique(ParameterName),
thresholdID = list(unique(ThresholdID)),
calculated = unique(Calculated),
isSpeciesSpec = unique(isSpeciesSpecific),
median = median(ResultValue),
iqr = IQR(ResultValue),
qval_low = quant_low,
qval_high = quant_high,
q_low = quantile(ResultValue, probs = quant_low),
q_high = quantile(ResultValue, probs = quant_high),
mean = mean(ResultValue),
sd = sd(ResultValue),
num_sds = num_sds,
sdn_low = mean(ResultValue) - (num_sds * sd(ResultValue)),
sdn_high = mean(ResultValue) + (num_sds * sd(ResultValue)),
n_tot = length(ResultValue),
n_q_low = nrow(dat[ParameterName == par & !is.na(ResultValue) & MADup == 1 & ResultValue < dat[ParameterName == par & !is.na(ResultValue) & MADup == 1, quantile(ResultValue, probs = quant_low)], ]),
n_q_high = nrow(dat[ParameterName == par & !is.na(ResultValue) & MADup == 1 & ResultValue > dat[ParameterName == par & !is.na(ResultValue) & MADup == 1, quantile(ResultValue, probs = quant_high)], ]),
n_sdn_low = nrow(dat[ParameterName == par & !is.na(ResultValue) & MADup == 1 & ResultValue < dat[ParameterName == par & !is.na(ResultValue) & MADup == 1, mean(ResultValue) - (num_sds * sd(ResultValue))], ]),
n_sdn_high = nrow(dat[ParameterName == par & !is.na(ResultValue) & MADup == 1 & ResultValue > dat[ParameterName == par & !is.na(ResultValue) & MADup == 1, mean(ResultValue) + (num_sds * sd(ResultValue))], ]))]
#Some parameters exist across export files, so record which parameter these calculations are for specifically
dat_par[, `:=` (primaryHab = "Oyster/Oyster Reef",
primaryCombTab = "Oyster",
primaryIndID = dat[ParameterID == parid, unique(IndicatorID)],
primaryInd = dat[ParameterID == parid, unique(IndicatorName)],
primaryParID = parid,
primaryParNm = dat[ParameterID == parid, unique(ParameterName)],
primaryThrID = dat[ParameterID == parid, unique(ThresholdID)],
QuadSize_m2 = NA,
pid = Sys.getpid(), #processor ID (when not running the script in parallel, these will all be the same)
export = file_short)]
} else{
dat_par <- dat[ParameterName == par & !is.na(ResultValue) & MADup == 1, .(habitat = list(unique(Habitat)),
combinedTable = list(unique(CombinedTable)),
indicatorID = list(unique(IndicatorID)),
indicatorName = list(unique(IndicatorName)),
parameterID = list(unique(ParameterID)),
parameterName = unique(ParameterName),
thresholdID = list(unique(ThresholdID)),
calculated = unique(Calculated),
isSpeciesSpec = unique(isSpeciesSpecific),
median = median(ResultValue),
iqr = IQR(ResultValue),
qval_low = quant_low,
qval_high = quant_high,
q_low = quantile(ResultValue, probs = quant_low),
q_high = quantile(ResultValue, probs = quant_high),
mean = mean(ResultValue),
sd = sd(ResultValue),
num_sds = num_sds,
sdn_low = mean(ResultValue) - (num_sds * sd(ResultValue)),
sdn_high = mean(ResultValue) + (num_sds * sd(ResultValue)),
n_tot = length(ResultValue),
n_q_low = nrow(dat[ParameterName == par & !is.na(ResultValue) & MADup == 1 & ResultValue < dat[ParameterName == par & !is.na(ResultValue) & MADup == 1, quantile(ResultValue, probs = quant_low)], ]),
n_q_high = nrow(dat[ParameterName == par & !is.na(ResultValue) & MADup == 1 & ResultValue > dat[ParameterName == par & !is.na(ResultValue) & MADup == 1, quantile(ResultValue, probs = quant_high)], ]),
n_sdn_low = nrow(dat[ParameterName == par & !is.na(ResultValue) & MADup == 1 & ResultValue < dat[ParameterName == par & !is.na(ResultValue) & MADup == 1, mean(ResultValue) - (num_sds * sd(ResultValue))], ]),
n_sdn_high = nrow(dat[ParameterName == par & !is.na(ResultValue) & MADup == 1 & ResultValue > dat[ParameterName == par & !is.na(ResultValue) & MADup == 1, mean(ResultValue) + (num_sds * sd(ResultValue))], ])), by = QuadSize_m2]
#Some parameters exist across export files, so record which parameter these calculations are for specifically
dat_par[, `:=` (primaryHab = "Oyster/Oyster Reef",
primaryCombTab = "Oyster",
primaryIndID = dat[ParameterID == parid, unique(IndicatorID)],
primaryInd = dat[ParameterID == parid, unique(IndicatorName)],
primaryParID = parid,
primaryParNm = dat[ParameterID == parid, unique(ParameterName)],
primaryThrID = dat[ParameterID == parid, unique(ThresholdID)],
pid = Sys.getpid(), #processor ID (when not running the script in parallel, these will all be the same)
export = file_short)]
}
#Record the parameter results in the 'qs_dat' data.table
qs_dat <- rbind(qs_dat, dat_par)
#print a progress message
cat("\t", parid, ": ", par, "\n", sep = "")
}
#Record the parameter x export file results in the 'qs' data.table
qs <- rbind(qs, qs_dat) #comment for parallel use
#Remove unnecessary data objects to save memory
rm(dat, dat_par, dat1, dat2, pars_f)
#print a progress message
cat("\t Done! \n\n", sep = "")
# qs_dat #uncomment for parallel use
} else {
## SAV---------------------------------------------------
#use combined species data object
dat <- spec_dat
#Record parameters in the 'pars' object
pars_f <- data.table(file = file_short,
param = dat[.id == "sav" & !is.na(ResultValue), unique(ParameterName)])
pars <- rbind(pars, pars_f)
#Calculate quantile and standard deviation results for each parameter
for(par in dat[.id == "sav", unique(ParameterName)]){
#if the parameter is in the skip parameters list, move on to the next parameter
if(par %in% parstoskip) next
#Two separate options for calculations depending on whether the 'species specific' vs. 'total' distinction (i.e., 'isSpeciesSpecific' variable) is relevant for the parameter
if(str_detect(par, "Presence|Blanquet|Count")){
parid <- dat[.id == "sav" & ParameterName == par & isSpeciesSpecific == 0, unique(ParameterID)]
dat_par <- dat[ParameterName == par & !is.na(ResultValue) & MADup == 1 & SpeciesGroup1 %in% c("Seagrass", "Macroalgae") & isSpeciesSpecific == 0,
.(habitat = list(unique(Habitat)),
combinedTable = list(unique(CombinedTable)),
indicatorID = list(unique(IndicatorID)),
indicatorName = list(unique(IndicatorName)),
parameterID = list(unique(ParameterID)),
parameterName = unique(ParameterName),
thresholdID = list(unique(ThresholdID)),
calculated = unique(Calculated),
isSpeciesSpec = unique(isSpeciesSpecific),
export = list(unique(export)),
median = median(ResultValue),
iqr = IQR(ResultValue),
qval_low = quant_low,
qval_high = quant_high,
q_low = quantile(ResultValue, probs = quant_low),
q_high = quantile(ResultValue, probs = quant_high),
mean = mean(ResultValue),
sd = sd(ResultValue),
num_sds = num_sds,
sdn_low = mean(ResultValue) - (num_sds * sd(ResultValue)),
sdn_high = mean(ResultValue) + (num_sds * sd(ResultValue)),
n_tot = length(ResultValue),
n_q_low = nrow(dat[ParameterName == par & !is.na(ResultValue) & MADup == 1 & ResultValue < dat[ParameterName == par & !is.na(ResultValue) & MADup == 1, quantile(ResultValue, probs = quant_low)], ]),
n_q_high = nrow(dat[ParameterName == par & !is.na(ResultValue) & MADup == 1 & ResultValue > dat[ParameterName == par & !is.na(ResultValue) & MADup == 1, quantile(ResultValue, probs = quant_high)], ]),
n_sdn_low = nrow(dat[ParameterName == par & !is.na(ResultValue) & MADup == 1 & ResultValue < dat[ParameterName == par & !is.na(ResultValue) & MADup == 1, mean(ResultValue) - (num_sds * sd(ResultValue))], ]),
n_sdn_high = nrow(dat[ParameterName == par & !is.na(ResultValue) & MADup == 1 & ResultValue > dat[ParameterName == par & !is.na(ResultValue) & MADup == 1, mean(ResultValue) + (num_sds * sd(ResultValue))], ]))]
#Some parameters exist across export files, so record which parameter these calculations are for specifically
dat_par[, `:=` (primaryHab = "Submerged Aquatic Vegetation",
primaryCombTab = "SAV",
primaryIndID = ifelse(purrr::is_empty(parid), refdat[CombinedTable == "SAV" & ParameterName == par, unique(IndicatorID)], dat[ParameterID == parid, unique(IndicatorID)]),
primaryInd = ifelse(purrr::is_empty(parid), refdat[CombinedTable == "SAV" & ParameterName == par, unique(IndicatorName)], dat[ParameterID == parid, unique(IndicatorName)]),
primaryParID = ifelse(purrr::is_empty(parid), refdat[CombinedTable == "SAV" & ParameterName == par, unique(ParameterID)], parid),
primaryParNm = ifelse(purrr::is_empty(parid), refdat[CombinedTable == "SAV" & ParameterName == par, unique(ParameterName)], dat[ParameterID == parid, unique(ParameterName)]),
primaryThrID = ifelse(purrr::is_empty(parid), refdat[CombinedTable == "SAV" & ParameterName == par, unique(ThresholdID)], dat[ParameterID == parid, unique(ThresholdID)]),
QuadSize_m2 = NA,
pid = Sys.getpid())] #processor ID (when not running the script in parallel, these will all be the same)
} else{
parid <- dat[.id == "sav" & ParameterName == par & isSpeciesSpecific == 1, unique(ParameterID)]
dat_par <- dat[ParameterName == par & !is.na(ResultValue) & MADup == 1 & SpeciesGroup1 %in% c("Seagrass", "Macroalgae"), .(habitat = list(unique(Habitat)),
combinedTable = list(unique(CombinedTable)),
indicatorID = list(unique(IndicatorID)),
indicatorName = list(unique(IndicatorName)),
parameterID = list(unique(ParameterID)),
parameterName = unique(ParameterName),
thresholdID = list(unique(ThresholdID)),
calculated = unique(Calculated),
export = list(unique(export)),
median = median(ResultValue),
iqr = IQR(ResultValue),
qval_low = quant_low,
qval_high = quant_high,
q_low = quantile(ResultValue, probs = quant_low),
q_high = quantile(ResultValue, probs = quant_high),
mean = mean(ResultValue),
sd = sd(ResultValue),
num_sds = num_sds,
sdn_low = mean(ResultValue) - (num_sds * sd(ResultValue)),
sdn_high = mean(ResultValue) + (num_sds * sd(ResultValue)),
n_tot = length(ResultValue),
n_q_low = nrow(dat[ParameterName == par & !is.na(ResultValue) & MADup == 1 & ResultValue < dat[ParameterName == par & !is.na(ResultValue) & MADup == 1, quantile(ResultValue, probs = quant_low)], ]),
n_q_high = nrow(dat[ParameterName == par & !is.na(ResultValue) & MADup == 1 & ResultValue > dat[ParameterName == par & !is.na(ResultValue) & MADup == 1, quantile(ResultValue, probs = quant_high)], ]),
n_sdn_low = nrow(dat[ParameterName == par & !is.na(ResultValue) & MADup == 1 & ResultValue < dat[ParameterName == par & !is.na(ResultValue) & MADup == 1, mean(ResultValue) - (num_sds * sd(ResultValue))], ]),
n_sdn_high = nrow(dat[ParameterName == par & !is.na(ResultValue) & MADup == 1 & ResultValue > dat[ParameterName == par & !is.na(ResultValue) & MADup == 1, mean(ResultValue) + (num_sds * sd(ResultValue))], ])), by = isSpeciesSpecific]
setnames(dat_par, "isSpeciesSpecific", "isSpeciesSpec")
#Some parameters exist across export files, so record which parameter these calculations are for specifically
dat_par[, `:=` (primaryHab = "Submerged Aquatic Vegetation",
primaryCombTab = "SAV",
primaryIndID = ifelse(purrr::is_empty(parid), refdat[CombinedTable == "SAV" & ParameterName == par & isSpeciesSpecific == isSpeciesSpec, unique(IndicatorID)],
dat[ParameterID == parid & isSpeciesSpecific == isSpeciesSpec, unique(IndicatorID)]),
primaryInd = ifelse(purrr::is_empty(parid), refdat[CombinedTable == "SAV" & ParameterName == par & isSpeciesSpecific == isSpeciesSpec, unique(IndicatorName)],