getStressorList: Stressor List
In leppott/CASTfxn: Functions for Causal Assessment Screening Tool (CAST)

Description Usage Arguments Details Value Examples

Get stressor list.

getStressorList(TargetSiteID, site.Clusters, chem.info, cluster.chem,
  cluster.samps, ref.sites, site.chem, probsHigh, probsLow,
  biocomm = "bmi", dir_results = file.path(getwd(), "Results"),
  dir_sub = "CandidateCauses")

`TargetSiteID`	Site ID
`site.Clusters`	Clusters
`chem.info`	chem information
`cluster.chem`	chem data cluster.
`cluster.samps`	sample cluster.
`ref.sites`	reference sites
`site.chem`	Chem sites
`probsHigh`	probabilities, high
`probsLow`	probabilities, low
`biocomm`	Biological community; algae or BMI. Default = "BMI".
`dir_results`	Directory to save plots. Default = working directory and Results.
`dir_sub`	Subdirectory for outputs from this function. Default = "SiteInfo"

Box plots of each stressor, grouped by category.

Required objects: all specified as inputs.

chem.info need to include DirIncStress. Valid values are 'inc' or 'dec'.

A jpeg in the "Results" subdirectory of the working directory with box plots. Also returns a list of stressors; stressors and site.stressor.pctrank.

TargetSiteID <- "SRCKN001.61"
dir_results <- file.path(getwd(), "Results")

# Data getSiteInfo
# data, example included with package
data.Stations.Info <- data_Sites        # need for getSiteInfo and getChemDataSubsets
data.SampSummary   <- data_SampSummary
data.303d.ComID    <- data_303d
data.bmi.metrics   <- data_BMIMetrics
data.algae.metrics <- data_AlgMetrics
data.mod           <- data_ReachMod

# Cluster based on elevation category  # need for getSiteInfo and getChemDataSubsets
elev_cat <- toupper(data.Stations.Info[data.Stations.Info[,"StationID_Master"]==TargetSiteID
                    , "ElevCategory"])
if(elev_cat=="HI"){
   data.cluster <- data_Cluster_Hi
} else if(elev_cat=="LO") {
   data.cluster <- data_Cluster_Lo
}

# Map data
# San Diego
#flowline <- rgdal::readOGR(dsn = "data_gis/NHDv2_Flowline_Ecoreg85", layer = "NHDv2_eco85_Project")
#outline <- rgdal::readOGR(dsn = "data_gis/Eco85", layer = "Ecoregion85")
# AZ
map_flowline  <- data_GIS_Flow_HI
map_flowline2 <- data_GIS_Flow_LO
if(elev_cat=="HI"){
   map_flowline <- data_GIS_Flow_HI
} else if(elev_cat=="LO") {
   map_flowline <- data_GIS_Flow_LO
}
map_outline   <- data_GIS_AZ_Outline
# Project site data to USGS Albers Equal Area
usgs.aea <- "+proj=aea +lat_1=29.5 +lat_2=45.5 +lat_0=23
              +lon_0=-96 +x_0=0 +y_0=0 +datum=NAD83
              +units=m +no_defs +ellps=GRS80 +towgs84=0,0,0"
# projection for outline
my.aea <- "+proj=aea +lat_1=20 +lat_2=60 +lat_0=40 +lon_0=-96 +x_0=0 +y_0=0 
           +datum=NAD83 +units=m +no_defs +ellps=GRS80 +towgs84=0,0,0"
map_proj <- my.aea
#
dir_sub <- "SiteInfo"

# Run getSiteInfo
list.SiteSummary <- getSiteInfo(TargetSiteID, dir_results, data.Stations.Info
                                , data.SampSummary, data.303d.ComID
                                , data.bmi.metrics, data.algae.metrics
                                , data.cluster, data.mod
                                , map_proj, map_outline, map_flowline
                                , dir_sub=dir_sub)

# Data getChemDataSubsets
# data import, example 
# data.chem.raw <- read.delim(paste(myDir.Data,"data.chem.raw.tab",sep=""),na.strings = c(""," "))
# data.chem.info <- read.delim(paste(myDir.Data,"data.chem.info.tab",sep=""))
# data, example included with package
site.COMID <- list.SiteSummary$COMID
site.Clusters <- list.SiteSummary$ClustIDs
data.chem.raw <- data_Chem
data.chem.info <- data_ChemInfo

# Run getChemDataSubsets
list.data <- getChemDataSubsets(TargetSiteID, comid=site.COMID, cluster=site.Clusters
                                , data.cluster=data.cluster, data.Stations.Info=data.Stations.Info
                                , data.chem.raw=data.chem.raw, data.chem.info=data.chem.info)

# datasets getStressorList
chem.info <- list.data$chem.info
cluster.chem <- list.data$cluster.chem
cluster.samps <- list.data$cluster.samps
ref.sites <- list.data$ref.sites
site.chem <- list.data$site.chem
dir_sub <- "CandidateCauses"

# set cutoff for possible stressor identification
probsLow <- 0.10
probsHigh <- 0.90 
biocomm <- "bmi"

# Run getStressorList
list.stressors <- getStressorList(TargetSiteID, site.Clusters, chem.info, cluster.chem
                                 , cluster.samps, ref.sites, site.chem
                                 , probsHigh, probsLow, biocomm, dir_results
                                 , dir_sub)