R/processData.R
In ksauby/Panhandleproc: Data processing functions for Panhandle data.
Defines functions
processData
Documented in
processData
#' Process Panhandle dataset
#'
#' @param dataset Dataset
#' @description Process data:
#' \itemize{
#' \item rename locations, variables, and species
#' \item format variables
#' \item process date and check for duplicate entries
#' \item create "Visit Number" field
#' \item create "Fruit Presence" ariable
#' \item Classify sites as coastal or inland
#' \item Create variables for insect presence during the entire study and during the current study
#' \item Create season variable
#' \item Classify surveys as complete
#' \item calculate plant volume
#' \item create PlantID2
#' }
#'
#' @export
#' @importFrom zoo as.yearmon
#' @import data.table
processData <- function(dataset) {
timeseries %<>% mutate(
Location = replace(
Location,
which(Location=="MexicoBeach"),
"MB"
),
Location = replace(
Location,
which(Location=="Sweetwater"),
"TSP"
),
Location = replace(
Location,
which(Location=="Nokuse"),
"NP"
)
)
# create unique PlantID variable
timeseries %<>% mutate(PlantID = paste(Location, ID, sep=""))
# remame column namesnames
timeseries %<>%
setnames("Height", "Height_t") %>%
setnames("Segments", "Size_t") %>%
setnames("MEPR", "ME_t") %>%
setnames("CACA", "CA_t") %>%
setnames("DACT", "DA_t") %>%
setnames("CHVI", "CH_t") %>%
setnames("Width", "Width_t") %>%
setnames("Fruit", "Fruit_t")
# Rename Species factor labels
timeseries %<>% mutate(
Species = replace(
Species,
which(Species=="humifusa"),
"Opuntia humifusa"
),
Species = replace(
Species,
which(Species=="stricta"),
"Opuntia stricta"
)
)
# format as factor
timeseries$Location %<>% as.factor
timeseries$Species %<>% as.factor
# Change Zeros to NAs
# timeseries[timeseries$PlantSize_t==0, ]
# timeseries[timeseries$PlantHeight_t==0, ]
# timeseries[timeseries$Width_t==0, ]
# There should never be observations of 0 segments, 0 height, or 0 width
# replace 0 with NA
cnames <- c("Size_t", "Height_t", "Width_t")
timeseries %<>% as.data.table
for (cname in cnames) {
timeseries[, (cname) := Zero_is_NA_Function(timeseries[[cname]])]
}
############################################################################
# Process TIME
############################################################################
timeseries$Date %<>% as.Date("%m/%d/%y")
timeseries %<>%
mutate(
Month = month(Date),
MonthYear = as.character(as.yearmon(Date)),
DaysSinceStart = as.numeric(Date - min(Date)),
VisitNum = 0
) %>%
as.data.frame
########################## ERROR MESSAGE
# check first duplicate data entries
dups <- timeseries %>%
group_by(Location, ID, Date) %>%
dplyr::summarise(n.obs = length(Species)) %>%
filter(n.obs > 1)
if (dim(dups)[1] > 0) {
stop("Duplicates observations for a PlantID, Date combination are
present in the dataset.")
}
#--------------------------------------------------------------------------#
# create "Visit Number" field
#--------------------------------------------------------------------------#
timeseries %<>% as.data.table
timeseries[Date >= "2009-01-21" & Date <= "2009-01-26", VisitNum := 1]
timeseries[Date >= "2009-04-25" & Date <= "2009-04-30", VisitNum := 2]
timeseries[Date >= "2009-07-23" & Date <= "2009-07-28", VisitNum := 3]
timeseries[Date >= "2009-10-23" & Date <= "2009-10-28", VisitNum := 4]
timeseries[Date >= "2010-05-14" & Date <= "2010-05-19", VisitNum := 5]
timeseries[Date >= "2010-11-15" & Date <= "2010-11-20", VisitNum := 6]
timeseries[Date >= "2011-05-12" & Date <= "2011-05-17", VisitNum := 7]
timeseries[Date >= "2011-12-15" & Date <= "2012-01-17", VisitNum := 8]
timeseries[Date >= "2012-05-13" & Date <= "2012-05-16", VisitNum := 9]
timeseries[Date >= "2012-12-14" & Date <= "2012-12-19", VisitNum := 10]
timeseries[Date >= "2013-05-18" & Date <= "2013-05-23", VisitNum := 11]
timeseries[Date >= "2014-01-12" & Date <= "2014-01-17", VisitNum := 12]
########################## ERROR MESSAGE
# check for erroneous dates
date_errors <- filter(timeseries, VisitNum=="0")
# timeseries <- timeseries[which(timeseries$VisitNum!="0"), ]
if (dim(date_errors)[1] > 0) {
stop("At least one observation was given a visit number of 0.")
}
############################################################################
# Fruit Presence
############################################################################
# make FruitPres variable
timeseries %<>%
mutate(FruitPres_t = ifelse(Fruit_t>0, 1, 0)) %>%
as.data.table %>%
# add NAs
.[is.na(Fruit_t), FruitPres_t := NA]
############################################################################
# Classify sites as coastal or inland
############################################################################
timeseries %<>%
mutate(Coastal = as.integer(NA)) %>%
as.data.table %>%
# Nokuse and TSP are given 0 because they are not coastal
.[Location=="N" | Location=="TSP", Coastal := 0] %>%
.[Location!="N" & Location!="TSP", Coastal := 1]
timeseries$Coastal %<>% as.factor
############################################################################
# Insect presence during entire study
############################################################################
#--------------------------------------------------------------------------#
# INSECT ever present?
#--------------------------------------------------------------------------#
timeseries %<>%
group_by(PlantID) %>%
mutate(
CACAPresent = ifelse(sum(CA_t, na.rm=T) > 0, 1, 0),
MEPRPresent = ifelse(sum(ME_t, na.rm=T) > 0, 1, 0),
DACTPresent = ifelse(sum(DA_t, na.rm=T) > 0, 1, 0),
CHVIPresent = ifelse(sum(CH_t, na.rm=T) > 0, 1, 0)
)
############################################################################
# Insect presence during current survey
############################################################################
timeseries %<>% as.data.frame
timeseries$NumInsectSpecies_t <- timeseries %>%
dplyr::select(ME_t, CA_t, CH_t, DA_t) %>%
apply(., 1, mysum
)
############################################################################
# SEASON
############################################################################
timeseries$Date %<>%
strptime("%Y-%m-%d") %>%
as.POSIXct(format="%Y-%m-%d")
timeseries %<>% as.data.frame %>% assignSeason
timeseries$Date %<>% as.Date
############################################################################
# Classify Surveys as Complete
############################################################################
timeseries$complete_insect_surveys <- ifelse(
rowSums(
is.na(
timeseries[, c("ME_t", "CA_t", "CH_t", "DA_t")]
)
)==0, 1, 0
)
timeseries$complete_surveys <- ifelse(
rowSums(
is.na(
timeseries[,
c(
# insects
"ME_t",
"CA_t",
"CH_t",
"DA_t",
# number of segments
"Size_t",
# flowers and fruit
"Fruit_t"
)
]
)
)==0, 1, 0
)
############################################################################
# Calculate Volume
############################################################################
timeseries %<>% mutate(
Cone_t = pi * (((Width_t)/4)^2) * Height_t / 3,
Cylinder_Tall_t = pi * ((Width_t/2)^2) * Height_t
)
############################################################################
# Create PlantID2
############################################################################
timeseries$PlantID2 <- factor(timeseries$PlantID,
levels=c(
# BLSP humifusa
"BLSPOH2",
"BLSPOH4",
"BLSPOH5",
"BLSPOH6",
"BLSPOH7",
"BLSPOH8",
"BLSPOH9",
"BLSPOH10",
"BLSPOH11",
"BLSPOH11B",
"BLSPOH11C",
"BLSPOH12",
"BLSPOH13",
"BLSPOH14",
"BLSPOH15",
"BLSPOH16",
"BLSPOH17",
"BLSPOH18",
"BLSPOH19",
"BLSPOH20",
# HBSP humifusa
"HBSPOH1",
"HBSPOH2",
"HBSPOH3",
"HBSPOH4",
"HBSPOH5",
"HBSPOH6",
"HBSPOH7",
"HBSPOH8",
"HBSPOH9",
"HBSPOH10",
"HBSPOH11",
"HBSPOH12",
"HBSPOH13",
"HBSPOH14",
"HBSPOH15",
"HBSPOH16",
"HBSPOH17",
"HBSPOH18",
# HBSP stricta
"HBSPOS1",
"HBSPOS2",
"HBSPOS3",
"HBSPOS4",
"HBSPOS5",
"HBSPOS6",
"HBSPOS6B",
"HBSPOS7",
"HBSPOS8",
"HBSPOS9",
"HBSPOS10",
"HBSPOS11" ,
"HBSPOS12",
"HBSPOS13",
"HBSPOS14",
"HBSPOS15",
"HBSPOS16",
"HBSPOS17",
"HBSPOS18" ,
# Mexico Beach
"MBOS1",
"MBOS4",
"MBOS5",
"MBOS6",
"MBOS7",
"MBOS10",
"MBOS11",
"MBOS12",
"MBOS13" ,
"MBOS13B",
"MBOS14",
"MBOS15",
"MBOS16",
"MBOS17",
"MBOS18",
"MBOS18B",
"MBOS19",
"MBOS20",
# N
"NPOH1",
"NPOH1B",
"NPOH2",
"NPOH3" ,
"NPOH4",
"NPOH5",
"NPOH6",
"NPOH7",
"NPOH7B",
"NPOH8",
"NPOH8B",
"NPOH9",
"NPOH9B",
"NPOH10",
"NPOH11",
"NPOH11B",
"NPOH12",
"NPOH13",
"NPOH14",
"NPOH14B",
"NPOH15",
"NPOH16",
"NPOH16B",
"NPOH17",
"NPOH17B",
"NPOH18",
"NPOH19" ,
"NPOH19B",
"NPOH20",
"NPOH20B",
# SASP humifusa
"SASPOH1",
"SASPOH2",
"SASPOH3",
"SASPOH4",
"SASPOH5" ,
"SASPOH6",
"SASPOH6B",
"SASPOH7",
"SASPOH8",
"SASPOH9",
"SASPOH10",
"SASPOH11",
"SASPOH11B",
"SASPOH12" ,
"SASPOH13",
"SASPOH14",
"SASPOH15",
# SASP stricta
"SASPOS1",
"SASPOS2",
"SASPOS3" ,
"SASPOS4",
"SASPOS5",
"SASPOS6",
"SASPOS7",
"SASPOS7B",
"SASPOS8",
"SASPOS9" ,
"SASPOS9B",
"SASPOS10" ,
"SASPOS11",
"SASPOS12",
"SASPOS13",
"SASPOS14",
"SASPOS15",
# TSP
"TSPOH1",
"TSPOH1B",
"TSPOH2" ,
"TSPOH3",
"TSPOH4",
"TSPOH5",
"TSPOH6",
"TSPOH7",
"TSPOH8" ,
"TSPOH9",
"TSPOH10",
"TSPOH11",
"TSPOH12",
"TSPOH13",
"TSPOH14" ,
"TSPOH15",
"TSPOH16",
"TSPOH17",
"TSPOH18",
"TSPOH19",
"TSPOH20"
)
)
levels(timeseries$PlantID2) <- c(
paste("BLSP", 1:20),
paste("HBSP", 1:18),
paste("HBSP", 1:19),
paste("MB", 1:18),
paste("NP", 1:30),
paste("SASP", 1:17),
paste("SASP", 1:17),
paste("TSP", 1:21)
)
############################################################################
return(timeseries)
}
ksauby/Panhandleproc documentation built on May 20, 2019, 7:02 p.m.
R Package Documentation
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Defines functions processData
Documented in processData
#' Process Panhandle dataset
#'
#' @param dataset Dataset
#' @description Process data:
#' \itemize{
#' \item rename locations, variables, and species
#' \item format variables
#' \item process date and check for duplicate entries
#' \item create "Visit Number" field
#' \item create "Fruit Presence" ariable
#' \item Classify sites as coastal or inland
#' \item Create variables for insect presence during the entire study and during the current study
#' \item Create season variable
#' \item Classify surveys as complete
#' \item calculate plant volume
#' \item create PlantID2
#' }
#'
#' @export
#' @importFrom zoo as.yearmon
#' @import data.table
processData <- function(dataset) {
timeseries %<>% mutate(
Location = replace(
Location,
which(Location=="MexicoBeach"),
"MB"
),
Location = replace(
Location,
which(Location=="Sweetwater"),
"TSP"
),
Location = replace(
Location,
which(Location=="Nokuse"),
"NP"
)
)
# create unique PlantID variable
timeseries %<>% mutate(PlantID = paste(Location, ID, sep=""))
# remame column namesnames
timeseries %<>%
setnames("Height", "Height_t") %>%
setnames("Segments", "Size_t") %>%
setnames("MEPR", "ME_t") %>%
setnames("CACA", "CA_t") %>%
setnames("DACT", "DA_t") %>%
setnames("CHVI", "CH_t") %>%
setnames("Width", "Width_t") %>%
setnames("Fruit", "Fruit_t")
# Rename Species factor labels
timeseries %<>% mutate(
Species = replace(
Species,
which(Species=="humifusa"),
"Opuntia humifusa"
),
Species = replace(
Species,
which(Species=="stricta"),
"Opuntia stricta"
)
)
# format as factor
timeseries$Location %<>% as.factor
timeseries$Species %<>% as.factor
# Change Zeros to NAs
# timeseries[timeseries$PlantSize_t==0, ]
# timeseries[timeseries$PlantHeight_t==0, ]
# timeseries[timeseries$Width_t==0, ]
# There should never be observations of 0 segments, 0 height, or 0 width
# replace 0 with NA
cnames <- c("Size_t", "Height_t", "Width_t")
timeseries %<>% as.data.table
for (cname in cnames) {
timeseries[, (cname) := Zero_is_NA_Function(timeseries[[cname]])]
}
############################################################################
# Process TIME
############################################################################
timeseries$Date %<>% as.Date("%m/%d/%y")
timeseries %<>%
mutate(
Month = month(Date),
MonthYear = as.character(as.yearmon(Date)),
DaysSinceStart = as.numeric(Date - min(Date)),
VisitNum = 0
) %>%
as.data.frame
########################## ERROR MESSAGE
# check first duplicate data entries
dups <- timeseries %>%
group_by(Location, ID, Date) %>%
dplyr::summarise(n.obs = length(Species)) %>%
filter(n.obs > 1)
if (dim(dups)[1] > 0) {
stop("Duplicates observations for a PlantID, Date combination are
present in the dataset.")
}
#--------------------------------------------------------------------------#
# create "Visit Number" field
#--------------------------------------------------------------------------#
timeseries %<>% as.data.table
timeseries[Date >= "2009-01-21" & Date <= "2009-01-26", VisitNum := 1]
timeseries[Date >= "2009-04-25" & Date <= "2009-04-30", VisitNum := 2]
timeseries[Date >= "2009-07-23" & Date <= "2009-07-28", VisitNum := 3]
timeseries[Date >= "2009-10-23" & Date <= "2009-10-28", VisitNum := 4]
timeseries[Date >= "2010-05-14" & Date <= "2010-05-19", VisitNum := 5]
timeseries[Date >= "2010-11-15" & Date <= "2010-11-20", VisitNum := 6]
timeseries[Date >= "2011-05-12" & Date <= "2011-05-17", VisitNum := 7]
timeseries[Date >= "2011-12-15" & Date <= "2012-01-17", VisitNum := 8]
timeseries[Date >= "2012-05-13" & Date <= "2012-05-16", VisitNum := 9]
timeseries[Date >= "2012-12-14" & Date <= "2012-12-19", VisitNum := 10]
timeseries[Date >= "2013-05-18" & Date <= "2013-05-23", VisitNum := 11]
timeseries[Date >= "2014-01-12" & Date <= "2014-01-17", VisitNum := 12]
########################## ERROR MESSAGE
# check for erroneous dates
date_errors <- filter(timeseries, VisitNum=="0")
# timeseries <- timeseries[which(timeseries$VisitNum!="0"), ]
if (dim(date_errors)[1] > 0) {
stop("At least one observation was given a visit number of 0.")
}
############################################################################
# Fruit Presence
############################################################################
# make FruitPres variable
timeseries %<>%
mutate(FruitPres_t = ifelse(Fruit_t>0, 1, 0)) %>%
as.data.table %>%
# add NAs
.[is.na(Fruit_t), FruitPres_t := NA]
############################################################################
# Classify sites as coastal or inland
############################################################################
timeseries %<>%
mutate(Coastal = as.integer(NA)) %>%
as.data.table %>%
# Nokuse and TSP are given 0 because they are not coastal
.[Location=="N" | Location=="TSP", Coastal := 0] %>%
.[Location!="N" & Location!="TSP", Coastal := 1]
timeseries$Coastal %<>% as.factor
############################################################################
# Insect presence during entire study
############################################################################
#--------------------------------------------------------------------------#
# INSECT ever present?
#--------------------------------------------------------------------------#
timeseries %<>%
group_by(PlantID) %>%
mutate(
CACAPresent = ifelse(sum(CA_t, na.rm=T) > 0, 1, 0),
MEPRPresent = ifelse(sum(ME_t, na.rm=T) > 0, 1, 0),
DACTPresent = ifelse(sum(DA_t, na.rm=T) > 0, 1, 0),
CHVIPresent = ifelse(sum(CH_t, na.rm=T) > 0, 1, 0)
)
############################################################################
# Insect presence during current survey
############################################################################
timeseries %<>% as.data.frame
timeseries$NumInsectSpecies_t <- timeseries %>%
dplyr::select(ME_t, CA_t, CH_t, DA_t) %>%
apply(., 1, mysum
)
############################################################################
# SEASON
############################################################################
timeseries$Date %<>%
strptime("%Y-%m-%d") %>%
as.POSIXct(format="%Y-%m-%d")
timeseries %<>% as.data.frame %>% assignSeason
timeseries$Date %<>% as.Date
############################################################################
# Classify Surveys as Complete
############################################################################
timeseries$complete_insect_surveys <- ifelse(
rowSums(
is.na(
timeseries[, c("ME_t", "CA_t", "CH_t", "DA_t")]
)
)==0, 1, 0
)
timeseries$complete_surveys <- ifelse(
rowSums(
is.na(
timeseries[,
c(
# insects
"ME_t",
"CA_t",
"CH_t",
"DA_t",
# number of segments
"Size_t",
# flowers and fruit
"Fruit_t"
)
]
)
)==0, 1, 0
)
############################################################################
# Calculate Volume
############################################################################
timeseries %<>% mutate(
Cone_t = pi * (((Width_t)/4)^2) * Height_t / 3,
Cylinder_Tall_t = pi * ((Width_t/2)^2) * Height_t
)
############################################################################
# Create PlantID2
############################################################################
timeseries$PlantID2 <- factor(timeseries$PlantID,
levels=c(
# BLSP humifusa
"BLSPOH2",
"BLSPOH4",
"BLSPOH5",
"BLSPOH6",
"BLSPOH7",
"BLSPOH8",
"BLSPOH9",
"BLSPOH10",
"BLSPOH11",
"BLSPOH11B",
"BLSPOH11C",
"BLSPOH12",
"BLSPOH13",
"BLSPOH14",
"BLSPOH15",
"BLSPOH16",
"BLSPOH17",
"BLSPOH18",
"BLSPOH19",
"BLSPOH20",
# HBSP humifusa
"HBSPOH1",
"HBSPOH2",
"HBSPOH3",
"HBSPOH4",
"HBSPOH5",
"HBSPOH6",
"HBSPOH7",
"HBSPOH8",
"HBSPOH9",
"HBSPOH10",
"HBSPOH11",
"HBSPOH12",
"HBSPOH13",
"HBSPOH14",
"HBSPOH15",
"HBSPOH16",
"HBSPOH17",
"HBSPOH18",
# HBSP stricta
"HBSPOS1",
"HBSPOS2",
"HBSPOS3",
"HBSPOS4",
"HBSPOS5",
"HBSPOS6",
"HBSPOS6B",
"HBSPOS7",
"HBSPOS8",
"HBSPOS9",
"HBSPOS10",
"HBSPOS11" ,
"HBSPOS12",
"HBSPOS13",
"HBSPOS14",
"HBSPOS15",
"HBSPOS16",
"HBSPOS17",
"HBSPOS18" ,
# Mexico Beach
"MBOS1",
"MBOS4",
"MBOS5",
"MBOS6",
"MBOS7",
"MBOS10",
"MBOS11",
"MBOS12",
"MBOS13" ,
"MBOS13B",
"MBOS14",
"MBOS15",
"MBOS16",
"MBOS17",
"MBOS18",
"MBOS18B",
"MBOS19",
"MBOS20",
# N
"NPOH1",
"NPOH1B",
"NPOH2",
"NPOH3" ,
"NPOH4",
"NPOH5",
"NPOH6",
"NPOH7",
"NPOH7B",
"NPOH8",
"NPOH8B",
"NPOH9",
"NPOH9B",
"NPOH10",
"NPOH11",
"NPOH11B",
"NPOH12",
"NPOH13",
"NPOH14",
"NPOH14B",
"NPOH15",
"NPOH16",
"NPOH16B",
"NPOH17",
"NPOH17B",
"NPOH18",
"NPOH19" ,
"NPOH19B",
"NPOH20",
"NPOH20B",
# SASP humifusa
"SASPOH1",
"SASPOH2",
"SASPOH3",
"SASPOH4",
"SASPOH5" ,
"SASPOH6",
"SASPOH6B",
"SASPOH7",
"SASPOH8",
"SASPOH9",
"SASPOH10",
"SASPOH11",
"SASPOH11B",
"SASPOH12" ,
"SASPOH13",
"SASPOH14",
"SASPOH15",
# SASP stricta
"SASPOS1",
"SASPOS2",
"SASPOS3" ,
"SASPOS4",
"SASPOS5",
"SASPOS6",
"SASPOS7",
"SASPOS7B",
"SASPOS8",
"SASPOS9" ,
"SASPOS9B",
"SASPOS10" ,
"SASPOS11",
"SASPOS12",
"SASPOS13",
"SASPOS14",
"SASPOS15",
# TSP
"TSPOH1",
"TSPOH1B",
"TSPOH2" ,
"TSPOH3",
"TSPOH4",
"TSPOH5",
"TSPOH6",
"TSPOH7",
"TSPOH8" ,
"TSPOH9",
"TSPOH10",
"TSPOH11",
"TSPOH12",
"TSPOH13",
"TSPOH14" ,
"TSPOH15",
"TSPOH16",
"TSPOH17",
"TSPOH18",
"TSPOH19",
"TSPOH20"
)
)
levels(timeseries$PlantID2) <- c(
paste("BLSP", 1:20),
paste("HBSP", 1:18),
paste("HBSP", 1:19),
paste("MB", 1:18),
paste("NP", 1:30),
paste("SASP", 1:17),
paste("SASP", 1:17),
paste("TSP", 1:21)
)
############################################################################
return(timeseries)
}
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