getBioMatches: Biological and Chemistry data matches.
In leppott/CASTfxn: Functions for Causal Assessment Screening Tool (CAST)
Description
Usage
Arguments
Details
Value
Examples
View source: R/getBioMatches.R
Description
Get Biological (Algae or BMI) samples and chemistry sample matches.
Usage
1
2
getBioMatches(stressors, list.data, list.SiteSummary, data.SampSummary,
data.chem.raw, data.bio.metrics, biocomm = "BMI")
Arguments
stressors
stressors
list.data
data list
list.SiteSummary
Site summary data; output of getSiteInfo function.
data.SampSummary
x
data.chem.raw
x
data.bio.metrics
Biological metric data (algae or BMI).
biocomm
Biological community; algae or BMI. Default = "BMI"
Details
Matched biological (algae/BMI) and chem samples.
Required objects:
* data.SampSumamry; StationID_Master, CollDate, ChemSampleID, PhabSampID, BMI.Metrics.SampID, Algae.Metrics.SampID
* data.chem.raw; StationID_Master, ChemSampleID
Value
A summary list; all.b.str, cl.b.str, site.b.str, all.b.rsp, cl.b.rsp
, and site.b.rsp.
Examples
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# Example 1, BMI
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.cluster <- data_Cluster_Hi # need for getSiteInfo and getChemDataSubsets
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
site.COMID <- list.SiteSummary$COMID
site.Clusters <- list.SiteSummary$ClustIDs
# data, example included with package
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)
# Data 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$site.chem
# set cutoff for possible stressor identification
probsLow <- 0.10
probsHigh <- 0.90
#
dir_sub <- "SiteInfo"
# Run getStressorList
list.stressors <- getStressorList(TargetSiteID, site.Clusters, chem.info, cluster.chem
, cluster.samps, ref.sites, site.chem
, probsHigh, probsLow, dir_sub)
# Data getBioMatches
## remove "none"
stressors <- list.stressors$stressors[list.stressors$stressors != "none"]
# Run getBioMatches
biocomm <- "BMI"
data.bio.metrics <- data_BMIMetrics
list.MatchBioData <- getBioMatches(stressors, list.data, list.SiteSummary, data.SampSummary
, data.chem.raw, data.bio.metrics, biocomm)
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# Example 2, Algae
TargetSiteID <- "LCBEN002.57"
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, example included with package
data.chem.raw <- data_Chem
data.chem.info <- data_ChemInfo
site.COMID <- list.SiteSummary$COMID
site.Clusters <- list.SiteSummary$ClustIDs
# Run getChemDataSubsets
list.data <- getChemDataSubsets(TargetSiteID, comid=site.COMID, cluster=site.Clusters
, data.cluster=data.cluster, data.Stations.Info=data.Stations.Info
, data.chem.raw=data.chem.raw, data.chem.info=data.chem.info)
# Data getStressorList
chem.info <- list.data$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)
# Data getBioMatches
## remove "none"
stressors <- list.stressors$stressors[list.stressors$stressors != "none"]
# Run getBioMatches
biocomm <- "algae"
data.bio.metrics <- data_AlgMetrics
list.MatchBioData <- getBioMatches(stressors, list.data, list.SiteSummary, data.SampSummary
, data.chem.raw, data.bio.metrics, biocomm)
leppott/CASTfxn documentation built on Sept. 6, 2019, 11:04 p.m.
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Description Usage Arguments Details Value Examples
View source: R/getBioMatches.R
Description
Get Biological (Algae or BMI) samples and chemistry sample matches.
Usage
1 2 | getBioMatches(stressors, list.data, list.SiteSummary, data.SampSummary,
data.chem.raw, data.bio.metrics, biocomm = "BMI")
|
Arguments
stressors |
stressors |
list.data |
data list |
list.SiteSummary |
Site summary data; output of getSiteInfo function. |
data.SampSummary |
x |
data.chem.raw |
x |
data.bio.metrics |
Biological metric data (algae or BMI). |
biocomm |
Biological community; algae or BMI. Default = "BMI" |
Details
Matched biological (algae/BMI) and chem samples.
Required objects:
* data.SampSumamry; StationID_Master, CollDate, ChemSampleID, PhabSampID, BMI.Metrics.SampID, Algae.Metrics.SampID
* data.chem.raw; StationID_Master, ChemSampleID
Value
A summary list; all.b.str, cl.b.str, site.b.str, all.b.rsp, cl.b.rsp , and site.b.rsp.
Examples
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 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 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 | # Example 1, BMI
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.cluster <- data_Cluster_Hi # need for getSiteInfo and getChemDataSubsets
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
site.COMID <- list.SiteSummary$COMID
site.Clusters <- list.SiteSummary$ClustIDs
# data, example included with package
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)
# Data 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$site.chem
# set cutoff for possible stressor identification
probsLow <- 0.10
probsHigh <- 0.90
#
dir_sub <- "SiteInfo"
# Run getStressorList
list.stressors <- getStressorList(TargetSiteID, site.Clusters, chem.info, cluster.chem
, cluster.samps, ref.sites, site.chem
, probsHigh, probsLow, dir_sub)
# Data getBioMatches
## remove "none"
stressors <- list.stressors$stressors[list.stressors$stressors != "none"]
# Run getBioMatches
biocomm <- "BMI"
data.bio.metrics <- data_BMIMetrics
list.MatchBioData <- getBioMatches(stressors, list.data, list.SiteSummary, data.SampSummary
, data.chem.raw, data.bio.metrics, biocomm)
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# Example 2, Algae
TargetSiteID <- "LCBEN002.57"
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, example included with package
data.chem.raw <- data_Chem
data.chem.info <- data_ChemInfo
site.COMID <- list.SiteSummary$COMID
site.Clusters <- list.SiteSummary$ClustIDs
# Run getChemDataSubsets
list.data <- getChemDataSubsets(TargetSiteID, comid=site.COMID, cluster=site.Clusters
, data.cluster=data.cluster, data.Stations.Info=data.Stations.Info
, data.chem.raw=data.chem.raw, data.chem.info=data.chem.info)
# Data getStressorList
chem.info <- list.data$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)
# Data getBioMatches
## remove "none"
stressors <- list.stressors$stressors[list.stressors$stressors != "none"]
# Run getBioMatches
biocomm <- "algae"
data.bio.metrics <- data_AlgMetrics
list.MatchBioData <- getBioMatches(stressors, list.data, list.SiteSummary, data.SampSummary
, data.chem.raw, data.bio.metrics, biocomm)
|
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