R/metric.values.R
In leppott/MMIcalc: Calculate multi-metric index (MMI) values and scores.
Defines functions
metric.values
metric.values.bugs
metric.values.fish
metric.values.algae
Documented in
metric.values
#' Calculate metric values
#'
#' This function calculates metric values for bugs, fish, and algae.
#' Inputs are a data frame of taxa, SampleID, and community.
#' NonUnique = taxa to be excluded from taxa metrics (i.e., ambiguous taxa in the sample)
#' Benthic Macroinvertebrates
#' Required fields: SampleID (provided by user), Count, NonUnique, FinalID, Order, Family, Tribe, Genus, FFG, TolVal, Habit, Voltinism, BCG_Atr
#' FFG = CF, CG, SC, SH, PR
#' Habit = BU, CB, CN, SP, SW
#' Voltinism = multivoltine, semivoltine, univoltine
#' Fish
#' Required fields: SampleID (provided by user), Count, NonUnique, FinalID, Genus, Trophic, Lithophil, StWidAvg, StLength, MassTotal
#' Trophic =
#' Lithophil =
#' Stratum =
#' Tolerance =
#' Metric names have a prefix and suffix. The prefix follows the naming convention listed below. The suffix is a short name of the group being measured. Only phylogenetic names are first letter caps.
#' ni = number of individuals
#' nt = number of taxa
#' pi = percent of individuals
#' pt = percent of taxa
#' x = index
#'
#
#' @param fun.DF Data frame of taxa (list required fields)
#' @param fun.SampID Sample ID column name
#' @param fun.Community Community name for which to calculate metric values (bugs, fish, or algae)
#' @param fun.MetricNames Optional vector of metric names to be returned. If none are supplied then all will be returned.
#' @param boo.Adjust Optional boolean value on whether to perform adjustments of values prior to scoring. Default = FALSE.
#' @return data frame of SampleID and metric values
#' @examples
#' # Index, Benthic Macroinvertebrates, genus
#' myIndex <- "MBSS.2005.Bugs"
#' # Thresholds
#' thresh <- metrics_scoring
#' # get metric names for myIndex
#' (myMetrics.Bugs <- as.character(droplevels(unique(thresh[thresh[,"Index.Name"]==myIndex,"Metric"]))))
#' # Taxa Data
#' myDF.Bugs <- taxa_bugs_genus
#' myMetric.Values.Bugs <- metric.values(myDF.Bugs, "SampleID", "bugs", myMetrics.Bugs)
#' View(myMetric.Values.Bugs)
#'
#' # Index, Benthic Macroinvertebrates, family
#' myIndex <- "MSW.1999.Bugs"
#' # Thresholds
#' thresh <- metrics_scoring
#' # get metric names for myIndex
#' (myMetrics.Bugs <- as.character(droplevels(unique(thresh[thresh[,"Index.Name"]==myIndex,"Metric"]))))
#' # Taxa Data
#' myDF.Bugs <- taxa_bugs_family
#' myMetric.Values.Bugs <- metric.values(myDF.Bugs, "SampleID", "bugs", myMetrics.Bugs)
#' View(myMetric.Values.Bugs)
#'
#' # Index, Fish
#' myIndex <- "MBSS.2005.Fish"
#' # Thresholds
#' thresh <- metrics_scoring
#' # get metric names for myIndex
#' (myMetrics.Fish <- as.character(droplevels(unique(thresh[thresh[,"Index.Name"]==myIndex,"Metric"]))))
#' # Taxa Data
#' myDF.Fish <- taxa_fish
#' myMetric.Values.Fish <- metric.values(myDF.Fish, "SampleID", "fish", myMetrics.Fish, TRUE)
#' View(myMetric.Values.Fish)
#
#' @export
metric.values <- function(fun.DF, fun.SampID, fun.Community, fun.MetricNames=NULL, boo.Adjust=FALSE){##FUNCTION.metric.values.START
# convert community to lowercase
fun.Community <- tolower(fun.Community)
# run the proper sub function
if (fun.Community=="bugs") {##IF.START
metric.values.bugs(fun.DF, fun.SampID, fun.MetricNames, boo.Adjust)
} else if(fun.Community=="fish"){
metric.values.fish(fun.DF, fun.SampID, fun.MetricNames, boo.Adjust)
} else if(fun.Community=="algae"){
metric.values.algae(fun.DF, fun.SampID, fun.MetricNames, boo.Adjust)
}##IF.END
}##FUNCTION.metric.values.START
#
#
#' @export
metric.values.bugs <- function(myDF, SampleID, MetricNames=NULL, boo.Adjust){##FUNCTION.metric.values.bugs.START
# Remove Non-Target Taxa
myDF <- myDF[myDF[,"NonTarget"]==0,]
# Add extra columns for FFG and Habit (need unique values for functions in summarise)
# each will be TRUE or FALSE
myDF["Habit_BU"] <- grepl("BU",toupper(myDF[,"Habit"]))
myDF["Habit_CB"] <- grepl("CB",toupper(myDF[,"Habit"]))
myDF["Habit_CN"] <- grepl("CN",toupper(myDF[,"Habit"]))
myDF["Habit_SP"] <- grepl("SP",toupper(myDF[,"Habit"]))
myDF["Habit_SW"] <- grepl("SW",toupper(myDF[,"Habit"]))
myDF["FFG_col"] <- grepl("col",tolower(myDF[,"FFG"]))
myDF["FFG_fil"] <- grepl("fil",tolower(myDF[,"FFG"]))
myDF["FFG_pre"] <- grepl("pre",tolower(myDF[,"FFG"]))
myDF["FFG_scr"] <- grepl("scr",tolower(myDF[,"FFG"]))
myDF["FFG_shr"] <- grepl("shr",tolower(myDF[,"FFG"]))
# Calculate Metrics (could have used pipe, %>%)
met.val <- dplyr::summarise(dplyr::group_by(myDF, SampleID, Index.Name, Index.Region)
#
# individuals, total
,ni_total=sum(Count)
#
# number of individuals
,ni_Ephem=sum(Count[Order=="Ephemeroptera"])
,ni_Trich=sum(Count[Order=="Trichoptera"])
,ni_Pleco=sum(Count[Order=="Plecoptera"])
,ni_EPT=sum(Count[Order=="Ephemeroptera" | Order=="Trichoptera" | Order=="Plecoptera"])
#
# percent individuals
,pi_Amph=sum(Count[Order=="Amphipoda"]) / ni_total
,pi_Bival=sum(Count[Class=="Bivalvia"]) / ni_total
,pi_Caen=sum(Count[Family=="Caenidae"]) / ni_total
,pi_Coleo=sum(Count[Order=="Coleoptera"]) / ni_total
# Cole2Odon,
# Colesensitive
,pi_Corb=sum(Count[Genus=="Corbicula"]) / ni_total
#CruMol
#Crus
,pi_Deca=sum(Count[Order=="Decapoda"]) / ni_total
,pi_Dipt=sum(Count[Order=="Diptera"]) / ni_total
,pi_Ephem=sum(Count[Order=="Ephemeroptera"]) / ni_total
#EphemNoCaen
#EPTsenstive
,pi_EPT=sum(Count[Order=="Ephemeroptera" | Order=="Trichoptera" | Order=="Plecoptera"]) / ni_total
,pi_Gast=sum(Count[Class=="Gastropoda"]) / ni_total
,pi_Iso=sum(Count[Order=="Isopoda"]) / ni_total
#Moll
,pi_NonIns=sum(Count[Order!="Insecta" | is.na(Class)]) / ni_total
,pi_Odon=sum(Count[Order=="Odonata"]) / ni_total
#oligo
,pi_Pleco=sum(Count[Order=="Plecoptera"]) / ni_total
,pi_Trich=sum(Count[Order=="Trichoptera"]) / ni_total
,pi_Tubif=sum(Count[Family=="Tubificidae"]) / ni_total
#
# number of taxa
,nt_total=dplyr::n_distinct(FinalID[NonUnique!=1])
,nt_Coleo=dplyr::n_distinct(FinalID[NonUnique!=1 & Order=="Coleoptera"])
# ,nt_CruMol=dplyr::n_distinct(FinalID[NonUnique!=1 & (Phylum=="Mollusca" | SubPhylum="Crustacea")])
,nt_Dipt=dplyr::n_distinct(FinalID[NonUnique!=1 & Order=="Diptera"])
,nt_Ephem=dplyr::n_distinct(FinalID[NonUnique!=1 & Order=="Ephemeroptera"])
,nt_EPT=dplyr::n_distinct(FinalID[NonUnique!=1 & (Order=="Ephemeroptera"| Order=="Trichoptera" | Order=="Plecoptera")])
,nt_Oligo=dplyr::n_distinct(FinalID[NonUnique!=1 & Class=="Oligochaeta"])
,nt_Pleco=dplyr::n_distinct(FinalID[NonUnique!=1 & Order=="Plecoptera"])
,nt_Ptero=dplyr::n_distinct(FinalID[NonUnique!=1 & Genus=="Pteronarcys"])
,nt_Trich=dplyr::n_distinct(FinalID[NonUnique!=1 & Order=="Trichoptera"])
# Amph, Bival, Gast, Deca, Insect, Isopod, intolMol, Oligo, POET, Tubif
# intol
#
# Midges
,nt_Chiro=n_distinct(FinalID[NonUnique!=1 & Family=="Chironomidae"])
,pi_Chiro=sum(Count[Family=="Chironomidae"]) / ni_total
#,pi_CrCh2Chi
#,pi_Orth2Chi
#,nt_Ortho
#MB_pi_OrthocladiinaeCricotopusChironomus2Chironomidae
,pi_Tanyt=sum(Count[Tribe=="Tanytarsini"]) / ni_total
#,pi-Tnyt2Chi,
# COC2Chi
# tanyp
# tanyp2Chir
#
# percent of taxa
# Amph, POET, Bival, Chiro, Deca, Dip, Gast, Iso, NonIns, Toler
# / nt_total
#
# tolerance
,nt_tv_intol=n_distinct(FinalID[NonUnique!=1 & TolVal>=7])
,nt_tv_toler=n_distinct(FinalID[NonUnique!=1 & TolVal<=3])
,pi_tv_intolurb=sum(Count[TolVal>=7])/sum(Count[!is.na(TolVal)])
# pi_Baet2Eph, pi_Hyd2EPT, pi_Hyd2Tri, pi_intol, pi_toler, , nt_intMol,
# pt toler
#
# ffg
,nt_ffg_col=dplyr::n_distinct(FinalID[NonUnique!=1 & FFG_col==TRUE])
,nt_ffg_filt=dplyr::n_distinct(FinalID[NonUnique!=1 & FFG_fil==TRUE])
,nt_ffg_pred=dplyr::n_distinct(FinalID[NonUnique!=1 & FFG_pre==TRUE])
,nt_ffg_scrap=dplyr::n_distinct(FinalID[NonUnique!=1 & FFG_scr==TRUE])
,nt_ffg_shred=dplyr::n_distinct(FinalID[NonUnique!=1 & FFG_shr==TRUE])
,pi_ffg_col=dplyr::n_distinct(Count[NonUnique!=1 & FFG_col==TRUE]) / ni_total
,pi_ffg_filt=dplyr::n_distinct(Count[NonUnique!=1 & FFG_fil==TRUE]) / ni_total
,pi_ffg_pred=dplyr::n_distinct(Count[NonUnique!=1 & FFG_pre==TRUE]) / ni_total
,pi_ffg_scrap=dplyr::n_distinct(Count[NonUnique!=1 & FFG_scr==TRUE]) / ni_total
,pi_ffg_shred=dplyr::n_distinct(Count[NonUnique!=1 & FFG_shr==TRUE]) / ni_total
# pt for cllct, filtr, pred, scrap, shred
#
# habit (need to be wild card)
,pi_habit_burrow=sum(Count[Habit_BU==TRUE]) / ni_total
,pi_habit_clmbrs=sum(Count[Habit_CB==TRUE]) / ni_total
,pi_habit_clngrs=sum(Count[Habit_CN==TRUE]) / ni_total
,pi_habit_sprawl=sum(Count[Habit_SP==TRUE]) / ni_total
,pi_habit_swmmrs=sum(Count[Habit_SW==TRUE]) / ni_total
,nt_habit_burrow=dplyr::n_distinct(FinalID[NonUnique!=1 & Habit_BU==TRUE])
,nt_habit_clmbrs=dplyr::n_distinct(FinalID[NonUnique!=1 & Habit_CB==TRUE])
,nt_habit_clngrs=dplyr::n_distinct(FinalID[NonUnique!=1 & Habit_CN==TRUE])
,nt_habit_sprawl=dplyr::n_distinct(FinalID[NonUnique!=1 & Habit_SP==TRUE])
,nt_habit_swmmrs=dplyr::n_distinct(FinalID[NonUnique!=1 & Habit_SW==TRUE])
# pt for each
#
# voltinism
# pi and nt for mltvol, semvol, univol
,pi_volt_multi=sum(Count[Voltinism=="multivoltine"]) / ni_total
,pi_volt_semi=sum(Count[Voltinism=="semivoltine"]) / ni_total
,pi_volt_uni=sum(Count[Voltinism=="univoltine"]) / ni_total
,nt_volt_multi=dplyr::n_distinct(FinalID[NonUnique!=1 & Voltinism=="multivoltine"])
,nt_volt_semi=dplyr::n_distinct(FinalID[NonUnique!=1 & Voltinism=="semivoltine"])
,nt_volt_uni=dplyr::n_distinct(FinalID[NonUnique!=1 & Voltinism=="univoltine"])
#
# indices
,pi_dom01=max(Count)/ni_total
#,x_Becks.Class1=n_distinct(Count[NonUnique!=1 & TolVal>=0 & TolVal<=2.5])
#,x_Becks.Class2=n_distinct(Count[NonUnique!=1 & TolVal>=2.5 & TolVal<=4])
,x_Becks=(2*dplyr::n_distinct(FinalID[NonUnique!=1 & TolVal>=0 & TolVal<=2.5]))+(1*dplyr::n_distinct(FinalID[NonUnique!=1 & TolVal>=2.5 & TolVal<=4]))
#,x_HBI_num=sum(Count*TolVal)
#,x_HBI_denom=sum(Count[!is.na(TolVal) & TolVal>0])
,x_HBI=sum(Count*TolVal)/sum(Count[!is.na(TolVal) & TolVal>0])
# ,x_Shan_Num=log(3.14)
# ,x_Shan_e=x_Shan_Num/log(exp(1))
# ,x_Shan_2=x_Shan_Num/log(2)
# ,x_Shan_10=x_Shan_Num/log(10)
#, x_D Simpson
#, x_Hbe
#, x_D_Mg Margalef
#, x_H
# Pielou
# H / Hmax Hmax is log(nt_total)
#
# BCG
,nt_BCG_att123=dplyr::n_distinct(FinalID[NonUnique!=1 & (BCG_Atr=="1" | BCG_Atr=="2" | BCG_Atr=="3")])
# nt_att 12, 123, 2, 23, 234, 4, 5, 5, 56
# nt_EPT_att123
# pi_att 12, 123, 23, 45, 5, 56
# pi_dom01_att 4, 5, 56
# pi_dom05_att 123, not 456
# pi_EPT_att123
# pt_att 12, 123, 23, 234, 5, 56
# pt_EPT_att 123
#
)## met.val.END
# replace NA with 0
met.val[is.na(met.val)] <- 0
# # subset to only metrics specified by user
if (!is.null(MetricNames)){
met.val <- met.val[,c(SampleID,"Index.Name","Index.Region", "ni_total", MetricNames)]
}
# df to report back
return(met.val)
}##FUNCTION.metric.values.bugs.END
#
#
#' @export
metric.values.fish <- function(myDF, SampleID, MetricNames=NULL, boo.Adjust){##FUNCTION.metric.values.fish.START
# Remove Non-Target Taxa
myDF <- myDF[myDF[,"NonTarget"]==0,]
# set case on fields
myFlds <- c("FinalID", "Genus", "Trophic", "Stratum", "Guild", "Tolerance")
for (i in myFlds) {
myDF[,i] <- tolower(myDF[,i])
}
# Calculate Metrics (could have used pipe, %>%)
met.val <- dplyr::summarise(dplyr::group_by(myDF, SampleID, Index.Name, Index.Region, Catchment)
#
# MBSS 2005, 11 metrics
#
# individuals, total
,ni_total=sum(Count)
#
# number of individuals
# Num Benthic Species
#,ni_Ephem=sum(Count[Order=="Ephemeroptera"])
#,ni_Trich=sum(Count[Order=="Trichoptera"])
#,ni_Pleco=sum(Count[Order=="Plecoptera"])
#,ni_EPT=sum(Count[Order=="Ephemeroptera" | Order=="Trichoptera" | Order=="Plecoptera"])
#
# percent individuals
# % RBS
,pi_rbs=sum(Count[Genus=="hypentelium"|Genus=="moxostoma"|Genus=="minytrema"|Genus=="erimyzon"])/ni_total
# Pct Brook Trout
,pi_brooktrout=sum(Count[FinalID=="brook trout"])/ni_total
# Pct Sculpins
,pi_sculpin=sum(Count[Genus=="cottus"|Genus=="myoxocephalus"])/ni_total
# ,pi_Amph=sum(Count[Order=="Amphipoda"]) / ni_total
# ,pi_Bival=sum(Count[Class=="Bivalvia"]) / ni_total
# ,pi_Caen=sum(Count[Family=="Caenidae"]) / ni_total
# ,pi_Coleo=sum(Count[Order=="Coleoptera"]) / ni_total
#
#
# ,pi_Corb=sum(Count[Genus=="Corbicula"]) / ni_total
#
#
# ,pi_Deca=sum(Count[Order=="Decapoda"]) / ni_total
# ,pi_Dipt=sum(Count[Order=="Diptera"]) / ni_total
# ,pi_Ephem=sum(Count[Order=="Ephemeroptera"]) / ni_total
#
#
# ,pi_EPT=sum(Count[Order=="Ephemeroptera" | Order=="Trichoptera" | Order=="Plecoptera"]) / ni_total
# ,pi_Gast=sum(Count[Class=="Gastropoda"]) / ni_total
# ,pi_Iso=sum(Count[Order=="Isopoda"]) / ni_total
#
# ,pi_NonIns=sum(Count[Order!="Insecta" | is.na(Class)]) / ni_total
# ,pi_Odon=sum(Count[Order=="Odonata"]) / ni_total
#
# ,pi_Pleco=sum(Count[Order=="Plecoptera"]) / ni_total
# ,pi_Trich=sum(Count[Order=="Trichoptera"]) / ni_total
# ,pi_Tubif=sum(Count[Family=="Tubificidae"]) / ni_total
#
#
# number of taxa
,nt_total=dplyr::n_distinct(FinalID[NonUnique!=1])
,nt_benthic=dplyr::n_distinct(FinalID[NonUnique!=1 & Stratum=="b"])
# ,nt_Coleo=n_distinct(Count[NonUnique!=1 & Order=="Coleoptera"])
# #,nt_CruMol=n_distinct(Count[NonUnique!=1 & (Phylum=="Mollusca" | Subphylum="Crustacea")])
# ,nt_Ephem=n_distinct(Count[NonUnique!=1 & Order=="Ephemeroptera"])
# ,nt_EPT=n_distinct(Count[NonUnique!=1 & (Order=="Ephemeroptera"| Order=="Trichoptera" | Order=="Plecoptera")])
# ,nt_Oligo=n_distinct(Count[NonUnique!=1 & Class=="Oligochaeta"])
# ,nt_Pleco=n_distinct(Count[NonUnique!=1 & Order=="Plecoptera"])
# ,nt_Ptero=n_distinct(Count[NonUnique!=1 & Genus=="Pteronarcys"])
# ,nt_Trich=n_distinct(Count[NonUnique!=1 & Order=="Trichoptera"])
# # Amph, Bival, Gast, Deca, Insect, Isopod, intolMol, Oligo, POET, Tubif
# intol
# #
# # Midges
# ,nt_Chiro=n_distinct(Count[NonUnique!=1 & Family=="Chironomidae"])
# ,pi_Chiro=sum(Count[Family=="Chironomidae"]) / ni_total
# #MB_pi_OrthocladiinaeCricotopusChironomus2Chironomidae
# ,pi_Tanyt=sum(Count[Tribe=="Tanytarsini"]) / ni_total
#
# percent of taxa
# Amph, POET, Bival, Chiro, Deca, Dip, Gast, Iso, NonIns, Toler
# / nt_total
#
# tolerance
# % Tolerant
,pi_tv_toler=sum(Count[Tolerance=="tol"])/ni_total
#,pi_tv_intolurb=sum(Count[TolVal>=7])/sum(Count[!is.na(TolVal)])
# pi_Baet2Eph, pi_Hyd2EPT, pi_Hyd2Tri, pi_intol, pi_toler, nt_intol, nt_intMol, nt_toler
# pt toler
#
# ffg
# Feeding
# % Lithophilic spawners
,pi_lithophil=sum(Count[Lithophil=="Yes"])/ni_total
# % gen, omn, invert
,pi_genomninvrt=sum(Count[Trophic=="generalist" | Trophic=="omnivore" | Trophic=="invertivore"])/ ni_total
# % insectivore
,pi_insectivore=sum(Count[Trophic=="insectivore"])/ ni_total
#
# ,nt_ffg_scrap=n_distinct(Count[NonUnique!=1 & FFG=="SC"])
# ,pi_ffg_scrap=n_distinct(Count[NonUnique!=1 & FFG=="SC"]) / ni_total
# # pi and nt for cllct, filtr, pred,scrap, shred
#
# # habit (need to be wild card)
# ,pi_habit_burrow=sum(Count[Habit=="BU"]) / ni_total
# ,pi_habit_clngrs=sum(Count[Habit=="CN"]) / ni_total
# ,pi_habit_clmbrs=sum(Count[Habit=="CB"]) / ni_total
# ,pi_habit_sprawl=sum(Count[Habit=="SP"]) / ni_total
# ,pi_habit_swmmrs=sum(Count[Habit=="SW"]) / ni_total
# ,nt_habit_burrow=n_distinct(Count[NonUnique!=1 & Habit=="BU"])
# ,nt_habit_clngrs=n_distinct(Count[NonUnique!=1 & Habit=="CN"])
# ,nt_habit_clmbrs=n_distinct(Count[NonUnique!=1 & Habit=="CB"])
# ,nt_habit_sprawl=n_distinct(Count[NonUnique!=1 & Habit=="SP"])
# ,nt_habit_swmmrs=n_distinct(Count[NonUnique!=1 & Habit=="SW"])
# # pt Swmmr
# #
# # voltinism
# # pi and nt for mltvol, semvol, univol
# ,pi_volt_multi=sum(Count[Voltinism=="multivoltine"]) / ni_total
# ,pi_volt_semi=sum(Count[Voltinism=="semivoltine"]) / ni_total
# ,pi_volt_uni=sum(Count[Voltinism=="univoltine"]) / ni_total
# ,nt_volt_multi=n_distinct(Count[NonUnique!=1 & Voltinism=="multivoltine"])
# ,nt_volt_semi=n_distinct(Count[NonUnique!=1 & Voltinism=="semivoltine"])
# ,nt_volt_uni=n_distinct(Count[NonUnique!=1 & Voltinism=="univoltine"])
#
# indices
#,pi_dom01/2/3/5 #last? or nth
,pi_dom01=max(Count)/ni_total
# Other
,area=max(StWidAvg)*max(StLength)
# Abund / sq meter
,ni_m2=ni_total/area #/(StWidAvg*StLength)
# biomass per square meter
,x_biomass_m2=max(MassTotal)/area #/(StWidAvg*StLength)
# #
# # BCG
# ,nt_BCG_att123=n_distinct(Count[NonUnique!=1 & (BCG_Atr=="1" | BCG_Atr=="2" | BCG_Atr=="3")])
#
)## met.val.END
# replace NA with 0
met.val[is.na(met.val)] <- 0
# subset to only metrics specified by user
if (!is.null(MetricNames)){
met.val <- met.val[,c(SampleID, "Index.Name", "Index.Region", "Catchment", "ni_total", MetricNames)]
}
# Adjust metrics
if (boo.Adjust==TRUE) {##IF.boo.Ajust.START
# MBSS.2005.Fish
# nt_benthic
met.val[,"nt_benthic_Obs"] <- met.val[,"nt_benthic"]
# Expected constants
## m
met.val[,"nt_benthic_m"] <- NA
met.val[,"nt_benthic_m"][met.val[,"Index.Region"]=="COASTAL"] <- 1.69
met.val[,"nt_benthic_m"][met.val[,"Index.Region"]=="EPIEDMONT"] <- 1.25
met.val[,"nt_benthic_m"][met.val[,"Index.Region"]=="HIGHLAND"] <- 1.23
## b
met.val[,"nt_benthic_b"] <- NA
met.val[,"nt_benthic_b"][met.val[,"Index.Region"]=="COASTAL"] <- -3.33
met.val[,"nt_benthic_b"][met.val[,"Index.Region"]=="EPIEDMONT"] <- -2.36
met.val[,"nt_benthic_b"][met.val[,"Index.Region"]=="HIGHLAND"] <- -2.35
# Calc Expected
met.val[,"nt_benthic_Exp"] <- (met.val[,"nt_benthic_m"] * log10(met.val[,"Catchment"])) + met.val[,"nt_benthic_b"]
# Calc Adjusted
met.val[,"nt_benthic_Adj"] <- met.val[,"nt_benthic_Obs"] / met.val[,"nt_benthic_Exp"]
# Rename base metric with adjusted value
met.val[,"nt_benthic"] <- met.val[,"nt_benthic_Adj"]
}##IF.boo.Ajust.END
#
# df to report back
return(met.val)
}##FUNCTION.metric.values.fish.END
#
#
#' @export
metric.values.algae <- function(myDF, SampleID, MetricNames=NULL, boo.Adjust){##FUNCTION.metric.values.algae.START
# Calculate Metrics (could have used pipe, %>%)
met.val <- dplyr::summarise(dplyr::group_by(myDF, SampleID, "Index.Name", "Index.Region")
#
# individuals, total
,ni_total=sum(Count)
#
)##met.val.END
# replace NA with 0
met.val[is.na(met.val)] <- 0
# subset to only metrics specified by user
if (!is.null(MetricNames)){
met.val <- met.val[,c(SampleID, "Index.Name", "Index.Region", "ni_total", MetricNames)]
}
# df to report back
return(met.val)
}##FUNCTION.metric.values.algae.END
leppott/MMIcalc documentation built on May 21, 2019, 5:10 a.m.
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Defines functions metric.values metric.values.bugs metric.values.fish metric.values.algae
Documented in metric.values
#' Calculate metric values
#'
#' This function calculates metric values for bugs, fish, and algae.
#' Inputs are a data frame of taxa, SampleID, and community.
#' NonUnique = taxa to be excluded from taxa metrics (i.e., ambiguous taxa in the sample)
#' Benthic Macroinvertebrates
#' Required fields: SampleID (provided by user), Count, NonUnique, FinalID, Order, Family, Tribe, Genus, FFG, TolVal, Habit, Voltinism, BCG_Atr
#' FFG = CF, CG, SC, SH, PR
#' Habit = BU, CB, CN, SP, SW
#' Voltinism = multivoltine, semivoltine, univoltine
#' Fish
#' Required fields: SampleID (provided by user), Count, NonUnique, FinalID, Genus, Trophic, Lithophil, StWidAvg, StLength, MassTotal
#' Trophic =
#' Lithophil =
#' Stratum =
#' Tolerance =
#' Metric names have a prefix and suffix. The prefix follows the naming convention listed below. The suffix is a short name of the group being measured. Only phylogenetic names are first letter caps.
#' ni = number of individuals
#' nt = number of taxa
#' pi = percent of individuals
#' pt = percent of taxa
#' x = index
#'
#
#' @param fun.DF Data frame of taxa (list required fields)
#' @param fun.SampID Sample ID column name
#' @param fun.Community Community name for which to calculate metric values (bugs, fish, or algae)
#' @param fun.MetricNames Optional vector of metric names to be returned. If none are supplied then all will be returned.
#' @param boo.Adjust Optional boolean value on whether to perform adjustments of values prior to scoring. Default = FALSE.
#' @return data frame of SampleID and metric values
#' @examples
#' # Index, Benthic Macroinvertebrates, genus
#' myIndex <- "MBSS.2005.Bugs"
#' # Thresholds
#' thresh <- metrics_scoring
#' # get metric names for myIndex
#' (myMetrics.Bugs <- as.character(droplevels(unique(thresh[thresh[,"Index.Name"]==myIndex,"Metric"]))))
#' # Taxa Data
#' myDF.Bugs <- taxa_bugs_genus
#' myMetric.Values.Bugs <- metric.values(myDF.Bugs, "SampleID", "bugs", myMetrics.Bugs)
#' View(myMetric.Values.Bugs)
#'
#' # Index, Benthic Macroinvertebrates, family
#' myIndex <- "MSW.1999.Bugs"
#' # Thresholds
#' thresh <- metrics_scoring
#' # get metric names for myIndex
#' (myMetrics.Bugs <- as.character(droplevels(unique(thresh[thresh[,"Index.Name"]==myIndex,"Metric"]))))
#' # Taxa Data
#' myDF.Bugs <- taxa_bugs_family
#' myMetric.Values.Bugs <- metric.values(myDF.Bugs, "SampleID", "bugs", myMetrics.Bugs)
#' View(myMetric.Values.Bugs)
#'
#' # Index, Fish
#' myIndex <- "MBSS.2005.Fish"
#' # Thresholds
#' thresh <- metrics_scoring
#' # get metric names for myIndex
#' (myMetrics.Fish <- as.character(droplevels(unique(thresh[thresh[,"Index.Name"]==myIndex,"Metric"]))))
#' # Taxa Data
#' myDF.Fish <- taxa_fish
#' myMetric.Values.Fish <- metric.values(myDF.Fish, "SampleID", "fish", myMetrics.Fish, TRUE)
#' View(myMetric.Values.Fish)
#
#' @export
metric.values <- function(fun.DF, fun.SampID, fun.Community, fun.MetricNames=NULL, boo.Adjust=FALSE){##FUNCTION.metric.values.START
# convert community to lowercase
fun.Community <- tolower(fun.Community)
# run the proper sub function
if (fun.Community=="bugs") {##IF.START
metric.values.bugs(fun.DF, fun.SampID, fun.MetricNames, boo.Adjust)
} else if(fun.Community=="fish"){
metric.values.fish(fun.DF, fun.SampID, fun.MetricNames, boo.Adjust)
} else if(fun.Community=="algae"){
metric.values.algae(fun.DF, fun.SampID, fun.MetricNames, boo.Adjust)
}##IF.END
}##FUNCTION.metric.values.START
#
#
#' @export
metric.values.bugs <- function(myDF, SampleID, MetricNames=NULL, boo.Adjust){##FUNCTION.metric.values.bugs.START
# Remove Non-Target Taxa
myDF <- myDF[myDF[,"NonTarget"]==0,]
# Add extra columns for FFG and Habit (need unique values for functions in summarise)
# each will be TRUE or FALSE
myDF["Habit_BU"] <- grepl("BU",toupper(myDF[,"Habit"]))
myDF["Habit_CB"] <- grepl("CB",toupper(myDF[,"Habit"]))
myDF["Habit_CN"] <- grepl("CN",toupper(myDF[,"Habit"]))
myDF["Habit_SP"] <- grepl("SP",toupper(myDF[,"Habit"]))
myDF["Habit_SW"] <- grepl("SW",toupper(myDF[,"Habit"]))
myDF["FFG_col"] <- grepl("col",tolower(myDF[,"FFG"]))
myDF["FFG_fil"] <- grepl("fil",tolower(myDF[,"FFG"]))
myDF["FFG_pre"] <- grepl("pre",tolower(myDF[,"FFG"]))
myDF["FFG_scr"] <- grepl("scr",tolower(myDF[,"FFG"]))
myDF["FFG_shr"] <- grepl("shr",tolower(myDF[,"FFG"]))
# Calculate Metrics (could have used pipe, %>%)
met.val <- dplyr::summarise(dplyr::group_by(myDF, SampleID, Index.Name, Index.Region)
#
# individuals, total
,ni_total=sum(Count)
#
# number of individuals
,ni_Ephem=sum(Count[Order=="Ephemeroptera"])
,ni_Trich=sum(Count[Order=="Trichoptera"])
,ni_Pleco=sum(Count[Order=="Plecoptera"])
,ni_EPT=sum(Count[Order=="Ephemeroptera" | Order=="Trichoptera" | Order=="Plecoptera"])
#
# percent individuals
,pi_Amph=sum(Count[Order=="Amphipoda"]) / ni_total
,pi_Bival=sum(Count[Class=="Bivalvia"]) / ni_total
,pi_Caen=sum(Count[Family=="Caenidae"]) / ni_total
,pi_Coleo=sum(Count[Order=="Coleoptera"]) / ni_total
# Cole2Odon,
# Colesensitive
,pi_Corb=sum(Count[Genus=="Corbicula"]) / ni_total
#CruMol
#Crus
,pi_Deca=sum(Count[Order=="Decapoda"]) / ni_total
,pi_Dipt=sum(Count[Order=="Diptera"]) / ni_total
,pi_Ephem=sum(Count[Order=="Ephemeroptera"]) / ni_total
#EphemNoCaen
#EPTsenstive
,pi_EPT=sum(Count[Order=="Ephemeroptera" | Order=="Trichoptera" | Order=="Plecoptera"]) / ni_total
,pi_Gast=sum(Count[Class=="Gastropoda"]) / ni_total
,pi_Iso=sum(Count[Order=="Isopoda"]) / ni_total
#Moll
,pi_NonIns=sum(Count[Order!="Insecta" | is.na(Class)]) / ni_total
,pi_Odon=sum(Count[Order=="Odonata"]) / ni_total
#oligo
,pi_Pleco=sum(Count[Order=="Plecoptera"]) / ni_total
,pi_Trich=sum(Count[Order=="Trichoptera"]) / ni_total
,pi_Tubif=sum(Count[Family=="Tubificidae"]) / ni_total
#
# number of taxa
,nt_total=dplyr::n_distinct(FinalID[NonUnique!=1])
,nt_Coleo=dplyr::n_distinct(FinalID[NonUnique!=1 & Order=="Coleoptera"])
# ,nt_CruMol=dplyr::n_distinct(FinalID[NonUnique!=1 & (Phylum=="Mollusca" | SubPhylum="Crustacea")])
,nt_Dipt=dplyr::n_distinct(FinalID[NonUnique!=1 & Order=="Diptera"])
,nt_Ephem=dplyr::n_distinct(FinalID[NonUnique!=1 & Order=="Ephemeroptera"])
,nt_EPT=dplyr::n_distinct(FinalID[NonUnique!=1 & (Order=="Ephemeroptera"| Order=="Trichoptera" | Order=="Plecoptera")])
,nt_Oligo=dplyr::n_distinct(FinalID[NonUnique!=1 & Class=="Oligochaeta"])
,nt_Pleco=dplyr::n_distinct(FinalID[NonUnique!=1 & Order=="Plecoptera"])
,nt_Ptero=dplyr::n_distinct(FinalID[NonUnique!=1 & Genus=="Pteronarcys"])
,nt_Trich=dplyr::n_distinct(FinalID[NonUnique!=1 & Order=="Trichoptera"])
# Amph, Bival, Gast, Deca, Insect, Isopod, intolMol, Oligo, POET, Tubif
# intol
#
# Midges
,nt_Chiro=n_distinct(FinalID[NonUnique!=1 & Family=="Chironomidae"])
,pi_Chiro=sum(Count[Family=="Chironomidae"]) / ni_total
#,pi_CrCh2Chi
#,pi_Orth2Chi
#,nt_Ortho
#MB_pi_OrthocladiinaeCricotopusChironomus2Chironomidae
,pi_Tanyt=sum(Count[Tribe=="Tanytarsini"]) / ni_total
#,pi-Tnyt2Chi,
# COC2Chi
# tanyp
# tanyp2Chir
#
# percent of taxa
# Amph, POET, Bival, Chiro, Deca, Dip, Gast, Iso, NonIns, Toler
# / nt_total
#
# tolerance
,nt_tv_intol=n_distinct(FinalID[NonUnique!=1 & TolVal>=7])
,nt_tv_toler=n_distinct(FinalID[NonUnique!=1 & TolVal<=3])
,pi_tv_intolurb=sum(Count[TolVal>=7])/sum(Count[!is.na(TolVal)])
# pi_Baet2Eph, pi_Hyd2EPT, pi_Hyd2Tri, pi_intol, pi_toler, , nt_intMol,
# pt toler
#
# ffg
,nt_ffg_col=dplyr::n_distinct(FinalID[NonUnique!=1 & FFG_col==TRUE])
,nt_ffg_filt=dplyr::n_distinct(FinalID[NonUnique!=1 & FFG_fil==TRUE])
,nt_ffg_pred=dplyr::n_distinct(FinalID[NonUnique!=1 & FFG_pre==TRUE])
,nt_ffg_scrap=dplyr::n_distinct(FinalID[NonUnique!=1 & FFG_scr==TRUE])
,nt_ffg_shred=dplyr::n_distinct(FinalID[NonUnique!=1 & FFG_shr==TRUE])
,pi_ffg_col=dplyr::n_distinct(Count[NonUnique!=1 & FFG_col==TRUE]) / ni_total
,pi_ffg_filt=dplyr::n_distinct(Count[NonUnique!=1 & FFG_fil==TRUE]) / ni_total
,pi_ffg_pred=dplyr::n_distinct(Count[NonUnique!=1 & FFG_pre==TRUE]) / ni_total
,pi_ffg_scrap=dplyr::n_distinct(Count[NonUnique!=1 & FFG_scr==TRUE]) / ni_total
,pi_ffg_shred=dplyr::n_distinct(Count[NonUnique!=1 & FFG_shr==TRUE]) / ni_total
# pt for cllct, filtr, pred, scrap, shred
#
# habit (need to be wild card)
,pi_habit_burrow=sum(Count[Habit_BU==TRUE]) / ni_total
,pi_habit_clmbrs=sum(Count[Habit_CB==TRUE]) / ni_total
,pi_habit_clngrs=sum(Count[Habit_CN==TRUE]) / ni_total
,pi_habit_sprawl=sum(Count[Habit_SP==TRUE]) / ni_total
,pi_habit_swmmrs=sum(Count[Habit_SW==TRUE]) / ni_total
,nt_habit_burrow=dplyr::n_distinct(FinalID[NonUnique!=1 & Habit_BU==TRUE])
,nt_habit_clmbrs=dplyr::n_distinct(FinalID[NonUnique!=1 & Habit_CB==TRUE])
,nt_habit_clngrs=dplyr::n_distinct(FinalID[NonUnique!=1 & Habit_CN==TRUE])
,nt_habit_sprawl=dplyr::n_distinct(FinalID[NonUnique!=1 & Habit_SP==TRUE])
,nt_habit_swmmrs=dplyr::n_distinct(FinalID[NonUnique!=1 & Habit_SW==TRUE])
# pt for each
#
# voltinism
# pi and nt for mltvol, semvol, univol
,pi_volt_multi=sum(Count[Voltinism=="multivoltine"]) / ni_total
,pi_volt_semi=sum(Count[Voltinism=="semivoltine"]) / ni_total
,pi_volt_uni=sum(Count[Voltinism=="univoltine"]) / ni_total
,nt_volt_multi=dplyr::n_distinct(FinalID[NonUnique!=1 & Voltinism=="multivoltine"])
,nt_volt_semi=dplyr::n_distinct(FinalID[NonUnique!=1 & Voltinism=="semivoltine"])
,nt_volt_uni=dplyr::n_distinct(FinalID[NonUnique!=1 & Voltinism=="univoltine"])
#
# indices
,pi_dom01=max(Count)/ni_total
#,x_Becks.Class1=n_distinct(Count[NonUnique!=1 & TolVal>=0 & TolVal<=2.5])
#,x_Becks.Class2=n_distinct(Count[NonUnique!=1 & TolVal>=2.5 & TolVal<=4])
,x_Becks=(2*dplyr::n_distinct(FinalID[NonUnique!=1 & TolVal>=0 & TolVal<=2.5]))+(1*dplyr::n_distinct(FinalID[NonUnique!=1 & TolVal>=2.5 & TolVal<=4]))
#,x_HBI_num=sum(Count*TolVal)
#,x_HBI_denom=sum(Count[!is.na(TolVal) & TolVal>0])
,x_HBI=sum(Count*TolVal)/sum(Count[!is.na(TolVal) & TolVal>0])
# ,x_Shan_Num=log(3.14)
# ,x_Shan_e=x_Shan_Num/log(exp(1))
# ,x_Shan_2=x_Shan_Num/log(2)
# ,x_Shan_10=x_Shan_Num/log(10)
#, x_D Simpson
#, x_Hbe
#, x_D_Mg Margalef
#, x_H
# Pielou
# H / Hmax Hmax is log(nt_total)
#
# BCG
,nt_BCG_att123=dplyr::n_distinct(FinalID[NonUnique!=1 & (BCG_Atr=="1" | BCG_Atr=="2" | BCG_Atr=="3")])
# nt_att 12, 123, 2, 23, 234, 4, 5, 5, 56
# nt_EPT_att123
# pi_att 12, 123, 23, 45, 5, 56
# pi_dom01_att 4, 5, 56
# pi_dom05_att 123, not 456
# pi_EPT_att123
# pt_att 12, 123, 23, 234, 5, 56
# pt_EPT_att 123
#
)## met.val.END
# replace NA with 0
met.val[is.na(met.val)] <- 0
# # subset to only metrics specified by user
if (!is.null(MetricNames)){
met.val <- met.val[,c(SampleID,"Index.Name","Index.Region", "ni_total", MetricNames)]
}
# df to report back
return(met.val)
}##FUNCTION.metric.values.bugs.END
#
#
#' @export
metric.values.fish <- function(myDF, SampleID, MetricNames=NULL, boo.Adjust){##FUNCTION.metric.values.fish.START
# Remove Non-Target Taxa
myDF <- myDF[myDF[,"NonTarget"]==0,]
# set case on fields
myFlds <- c("FinalID", "Genus", "Trophic", "Stratum", "Guild", "Tolerance")
for (i in myFlds) {
myDF[,i] <- tolower(myDF[,i])
}
# Calculate Metrics (could have used pipe, %>%)
met.val <- dplyr::summarise(dplyr::group_by(myDF, SampleID, Index.Name, Index.Region, Catchment)
#
# MBSS 2005, 11 metrics
#
# individuals, total
,ni_total=sum(Count)
#
# number of individuals
# Num Benthic Species
#,ni_Ephem=sum(Count[Order=="Ephemeroptera"])
#,ni_Trich=sum(Count[Order=="Trichoptera"])
#,ni_Pleco=sum(Count[Order=="Plecoptera"])
#,ni_EPT=sum(Count[Order=="Ephemeroptera" | Order=="Trichoptera" | Order=="Plecoptera"])
#
# percent individuals
# % RBS
,pi_rbs=sum(Count[Genus=="hypentelium"|Genus=="moxostoma"|Genus=="minytrema"|Genus=="erimyzon"])/ni_total
# Pct Brook Trout
,pi_brooktrout=sum(Count[FinalID=="brook trout"])/ni_total
# Pct Sculpins
,pi_sculpin=sum(Count[Genus=="cottus"|Genus=="myoxocephalus"])/ni_total
# ,pi_Amph=sum(Count[Order=="Amphipoda"]) / ni_total
# ,pi_Bival=sum(Count[Class=="Bivalvia"]) / ni_total
# ,pi_Caen=sum(Count[Family=="Caenidae"]) / ni_total
# ,pi_Coleo=sum(Count[Order=="Coleoptera"]) / ni_total
#
#
# ,pi_Corb=sum(Count[Genus=="Corbicula"]) / ni_total
#
#
# ,pi_Deca=sum(Count[Order=="Decapoda"]) / ni_total
# ,pi_Dipt=sum(Count[Order=="Diptera"]) / ni_total
# ,pi_Ephem=sum(Count[Order=="Ephemeroptera"]) / ni_total
#
#
# ,pi_EPT=sum(Count[Order=="Ephemeroptera" | Order=="Trichoptera" | Order=="Plecoptera"]) / ni_total
# ,pi_Gast=sum(Count[Class=="Gastropoda"]) / ni_total
# ,pi_Iso=sum(Count[Order=="Isopoda"]) / ni_total
#
# ,pi_NonIns=sum(Count[Order!="Insecta" | is.na(Class)]) / ni_total
# ,pi_Odon=sum(Count[Order=="Odonata"]) / ni_total
#
# ,pi_Pleco=sum(Count[Order=="Plecoptera"]) / ni_total
# ,pi_Trich=sum(Count[Order=="Trichoptera"]) / ni_total
# ,pi_Tubif=sum(Count[Family=="Tubificidae"]) / ni_total
#
#
# number of taxa
,nt_total=dplyr::n_distinct(FinalID[NonUnique!=1])
,nt_benthic=dplyr::n_distinct(FinalID[NonUnique!=1 & Stratum=="b"])
# ,nt_Coleo=n_distinct(Count[NonUnique!=1 & Order=="Coleoptera"])
# #,nt_CruMol=n_distinct(Count[NonUnique!=1 & (Phylum=="Mollusca" | Subphylum="Crustacea")])
# ,nt_Ephem=n_distinct(Count[NonUnique!=1 & Order=="Ephemeroptera"])
# ,nt_EPT=n_distinct(Count[NonUnique!=1 & (Order=="Ephemeroptera"| Order=="Trichoptera" | Order=="Plecoptera")])
# ,nt_Oligo=n_distinct(Count[NonUnique!=1 & Class=="Oligochaeta"])
# ,nt_Pleco=n_distinct(Count[NonUnique!=1 & Order=="Plecoptera"])
# ,nt_Ptero=n_distinct(Count[NonUnique!=1 & Genus=="Pteronarcys"])
# ,nt_Trich=n_distinct(Count[NonUnique!=1 & Order=="Trichoptera"])
# # Amph, Bival, Gast, Deca, Insect, Isopod, intolMol, Oligo, POET, Tubif
# intol
# #
# # Midges
# ,nt_Chiro=n_distinct(Count[NonUnique!=1 & Family=="Chironomidae"])
# ,pi_Chiro=sum(Count[Family=="Chironomidae"]) / ni_total
# #MB_pi_OrthocladiinaeCricotopusChironomus2Chironomidae
# ,pi_Tanyt=sum(Count[Tribe=="Tanytarsini"]) / ni_total
#
# percent of taxa
# Amph, POET, Bival, Chiro, Deca, Dip, Gast, Iso, NonIns, Toler
# / nt_total
#
# tolerance
# % Tolerant
,pi_tv_toler=sum(Count[Tolerance=="tol"])/ni_total
#,pi_tv_intolurb=sum(Count[TolVal>=7])/sum(Count[!is.na(TolVal)])
# pi_Baet2Eph, pi_Hyd2EPT, pi_Hyd2Tri, pi_intol, pi_toler, nt_intol, nt_intMol, nt_toler
# pt toler
#
# ffg
# Feeding
# % Lithophilic spawners
,pi_lithophil=sum(Count[Lithophil=="Yes"])/ni_total
# % gen, omn, invert
,pi_genomninvrt=sum(Count[Trophic=="generalist" | Trophic=="omnivore" | Trophic=="invertivore"])/ ni_total
# % insectivore
,pi_insectivore=sum(Count[Trophic=="insectivore"])/ ni_total
#
# ,nt_ffg_scrap=n_distinct(Count[NonUnique!=1 & FFG=="SC"])
# ,pi_ffg_scrap=n_distinct(Count[NonUnique!=1 & FFG=="SC"]) / ni_total
# # pi and nt for cllct, filtr, pred,scrap, shred
#
# # habit (need to be wild card)
# ,pi_habit_burrow=sum(Count[Habit=="BU"]) / ni_total
# ,pi_habit_clngrs=sum(Count[Habit=="CN"]) / ni_total
# ,pi_habit_clmbrs=sum(Count[Habit=="CB"]) / ni_total
# ,pi_habit_sprawl=sum(Count[Habit=="SP"]) / ni_total
# ,pi_habit_swmmrs=sum(Count[Habit=="SW"]) / ni_total
# ,nt_habit_burrow=n_distinct(Count[NonUnique!=1 & Habit=="BU"])
# ,nt_habit_clngrs=n_distinct(Count[NonUnique!=1 & Habit=="CN"])
# ,nt_habit_clmbrs=n_distinct(Count[NonUnique!=1 & Habit=="CB"])
# ,nt_habit_sprawl=n_distinct(Count[NonUnique!=1 & Habit=="SP"])
# ,nt_habit_swmmrs=n_distinct(Count[NonUnique!=1 & Habit=="SW"])
# # pt Swmmr
# #
# # voltinism
# # pi and nt for mltvol, semvol, univol
# ,pi_volt_multi=sum(Count[Voltinism=="multivoltine"]) / ni_total
# ,pi_volt_semi=sum(Count[Voltinism=="semivoltine"]) / ni_total
# ,pi_volt_uni=sum(Count[Voltinism=="univoltine"]) / ni_total
# ,nt_volt_multi=n_distinct(Count[NonUnique!=1 & Voltinism=="multivoltine"])
# ,nt_volt_semi=n_distinct(Count[NonUnique!=1 & Voltinism=="semivoltine"])
# ,nt_volt_uni=n_distinct(Count[NonUnique!=1 & Voltinism=="univoltine"])
#
# indices
#,pi_dom01/2/3/5 #last? or nth
,pi_dom01=max(Count)/ni_total
# Other
,area=max(StWidAvg)*max(StLength)
# Abund / sq meter
,ni_m2=ni_total/area #/(StWidAvg*StLength)
# biomass per square meter
,x_biomass_m2=max(MassTotal)/area #/(StWidAvg*StLength)
# #
# # BCG
# ,nt_BCG_att123=n_distinct(Count[NonUnique!=1 & (BCG_Atr=="1" | BCG_Atr=="2" | BCG_Atr=="3")])
#
)## met.val.END
# replace NA with 0
met.val[is.na(met.val)] <- 0
# subset to only metrics specified by user
if (!is.null(MetricNames)){
met.val <- met.val[,c(SampleID, "Index.Name", "Index.Region", "Catchment", "ni_total", MetricNames)]
}
# Adjust metrics
if (boo.Adjust==TRUE) {##IF.boo.Ajust.START
# MBSS.2005.Fish
# nt_benthic
met.val[,"nt_benthic_Obs"] <- met.val[,"nt_benthic"]
# Expected constants
## m
met.val[,"nt_benthic_m"] <- NA
met.val[,"nt_benthic_m"][met.val[,"Index.Region"]=="COASTAL"] <- 1.69
met.val[,"nt_benthic_m"][met.val[,"Index.Region"]=="EPIEDMONT"] <- 1.25
met.val[,"nt_benthic_m"][met.val[,"Index.Region"]=="HIGHLAND"] <- 1.23
## b
met.val[,"nt_benthic_b"] <- NA
met.val[,"nt_benthic_b"][met.val[,"Index.Region"]=="COASTAL"] <- -3.33
met.val[,"nt_benthic_b"][met.val[,"Index.Region"]=="EPIEDMONT"] <- -2.36
met.val[,"nt_benthic_b"][met.val[,"Index.Region"]=="HIGHLAND"] <- -2.35
# Calc Expected
met.val[,"nt_benthic_Exp"] <- (met.val[,"nt_benthic_m"] * log10(met.val[,"Catchment"])) + met.val[,"nt_benthic_b"]
# Calc Adjusted
met.val[,"nt_benthic_Adj"] <- met.val[,"nt_benthic_Obs"] / met.val[,"nt_benthic_Exp"]
# Rename base metric with adjusted value
met.val[,"nt_benthic"] <- met.val[,"nt_benthic_Adj"]
}##IF.boo.Ajust.END
#
# df to report back
return(met.val)
}##FUNCTION.metric.values.fish.END
#
#
#' @export
metric.values.algae <- function(myDF, SampleID, MetricNames=NULL, boo.Adjust){##FUNCTION.metric.values.algae.START
# Calculate Metrics (could have used pipe, %>%)
met.val <- dplyr::summarise(dplyr::group_by(myDF, SampleID, "Index.Name", "Index.Region")
#
# individuals, total
,ni_total=sum(Count)
#
)##met.val.END
# replace NA with 0
met.val[is.na(met.val)] <- 0
# subset to only metrics specified by user
if (!is.null(MetricNames)){
met.val <- met.val[,c(SampleID, "Index.Name", "Index.Region", "ni_total", MetricNames)]
}
# df to report back
return(met.val)
}##FUNCTION.metric.values.algae.END
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