data-raw/Baystudy.R
In sbashevkin/LTMRdata: Data for the IEP Long Term Monitoring Survey Review
## code to prepare `Baystudy` dataset goes here
require(readr)
require(wql)
require(dplyr)
require(tidyr)
require(lubridate)
require(readxl)
require(LTMRdata)
require(stringr)
library(rvest)
# Station locations -------------------------------------------------------
stations_baystudy <- read_excel(file.path("data-raw", "Baystudy", "Bay Study_Station Coordinates for Distribution_04May2020.xlsx"))%>%
separate(Latitude, into=c("Lat_Deg", "Lat_Min"), sep = "°", convert=T)%>% # Convert to decimal degrees
separate(Longitude, into=c("Lon_Deg", "Lon_Min"), sep = "°", convert=T)%>%
mutate(Latitude=Lat_Deg+Lat_Min/60,
Longitude=Lon_Deg-Lon_Min/60)%>%
dplyr::select(Station, Latitude, Longitude)%>%
dplyr::filter(Station!="211E")%>% # Kathy said W location of station 211 is more often used, so using those coordinates
mutate(Station=recode(Station, `211W`="211"))
# Import lookup tables ----------------------------------------------------
options(timeout = 999999)
# Path<-file.path(tempdir(), "BayStudy_AccessDatabase_1980-2021.zip")
# Path_origin<-file.path(tempdir())
#Downloading MWT_data.zip----
# Grab file name of Access DB, which changes per year
session <- session("https://filelib.wildlife.ca.gov/Public/BayStudy/Access_Database/")
links <- html_elements(session, "a") %>%
html_attr("href")
dbLink <- links[which(grepl("\\.zip", links))]
fileName <- regmatches(dbLink, regexpr("(BayStudy_).*", dbLink))
download.file(paste0("https://filelib.wildlife.ca.gov", dbLink),
file.path(tempdir(), fileName), mode = "wb", method = "libcurl")
unzip(file.path(tempdir(), fileName), exdir = tempdir())
accessFile <- unzip(file.path(tempdir(), fileName), list = T) %>%
pull(Name) %>%
.[which(grepl("\\.accdb", .))]
# File path to Access database (Salvage)
source(file.path("data-raw", "bridgeAccess.R"))
db_path <- file.path(tempdir(), accessFile)
keepTables <- c("TideCodes_LookUp","WaveCodes_LookUp","CloudCover_LookUp",
"SalinTemp","BoatStation","BoatTow",
"Fish Catch Data","Fish Length Data")
BayStudyTables <- bridgeAccess(db_path,
tables = keepTables,
script = file.path("data-raw", "connectAccess.R"))
# # If you've chosen to read csv --------------------------------------------
# BayStudyTables <- list()
#
# BayStudyTables$TideCodes_LookUp <- read_csv(file.path("data-raw", "Baystudy", "TideCodes_LookUp.csv"),
# col_types=cols_only(Tide="i", Description="c"))
#
# BayStudyTables$WaveCodes_LookUp <- read_csv(file.path("data-raw", "Baystudy", "WaveCodes_LookUp.csv"),
# col_types=cols_only(Waves="i", Description="c"))
#
# BayStudyTables$CloudCover_LookUp <- read_csv(file.path("data-raw", "Baystudy", "CloudCover_LookUp.csv"),
# col_types=cols_only(CloudCover="i", Description="c"))
#
# BayStudyTables$SalinTemp <- read_csv(file.path("data-raw", "Baystudy", "SalinTemp.csv"),
# col_types = cols_only(Year="i", Survey="i", Station="c",
# ECSurf="d", ECAvg="d", ECBott="d",
# TempSurf="d", TempAvg="d", TempBott="d"))
#
# BayStudyTables$BoatStation <- read_csv(file.path("data-raw", "Baystudy", "BoatStation.csv"),
# col_types = cols_only(Year="i", Survey="i", Station="c",
# Date="c", Depth="d", Secchi="d",
# SubstrateCode="c", Waves="i", CloudCover="i",
# Tide="i", StationComment="c"))
# # # Will need this line depending on the export type you exploy
# # mutate(Date = as.POSIXct(Date, format = "%m/%d/%Y %H:%M:%S", tz = "America/Los_Angeles"))
#
# BayStudyTables$BoatTow <- read_csv(file.path("data-raw", "Baystudy", "BoatTow.csv"),
# col_types = cols_only(Year="i", Survey="i", Station="c", Net="i",
# Tow="i", Time="c", Bearing="d", Tide="i",
# Direction="i", CatchCode="i", Duration="d",
# StartMeter="d", EndMeter="d", TotalMeter="d",
# StartLong="d", EndLong="d", StartLat="d",
# EndLat="d", Distance="d", TowComment="c"))
# # # Will need this line depending on the export type you exploy
# # mutate(Time = as.POSIXct(Time, format = "%m/%d/%Y %H:%M:%S", tz = "America/Los_Angeles"))
#
# BayStudyTables$`Fish Catch Data` <- read_csv(file.path("data-raw", "Baystudy", "Fish Catch Data.csv"),
# col_types=cols_only(Year="i", Survey="i", Station="c",
# Net="i", AlphaCode="c",
# SizeGroup="i", QtsCaught="d", QtsSubsampled="d",
# PlusCount="d"))
#
# BayStudyTables$`Fish Length Data` <- read_csv(file.path("data-raw", "Baystudy", "Fish Length Data.csv"),
# col_types=cols_only(Year="i", Survey="i", Station="c",
# Net="i", AlphaCode="c",
# SizeGroup="i", Length="d", Frequency="d"))
# Environ data ----
tidecodes_baystudy <- BayStudyTables$TideCodes_LookUp %>%
transmute(Tide = as.integer(Tide),
Description = as.character(Description))
wavecodes_baystudy <- BayStudyTables$WaveCodes_LookUp %>%
transmute(Waves = as.integer(Waves),
Description = as.character(Description))
cloudcovercodes_baystudy <- BayStudyTables$CloudCover_LookUp %>%
transmute(CloudCover = as.integer(CloudCover),
Description = as.character(Description))
# Sample-level data -------------------------------------------------------
salintemp_baystudy <- BayStudyTables$SalinTemp %>%
transmute(Year = as.integer(Year), Survey = as.integer(Survey),
Station = as.character(Station),
across(c(ECSurf, ECAvg, ECBott, TempSurf, TempAvg, TempBott),
as.double))
# Station-visit-level data -----
boatstation_baystudy <- BayStudyTables$BoatStation %>%
transmute(Year = as.integer(Year), Survey = as.integer(Survey),
Station = as.character(Station), Date = as.character(Date),
Depth = as.double(Depth), Secchi = as.double(Secchi),
SubstrateCode = as.character(SubstrateCode),
across(c(Waves, CloudCover, Tide), as.integer),
StationComment = as.character(StationComment))
boatstation_baystudy <- boatstation_baystudy %>%
mutate(Date=parse_date_time(Date, "%Y-%m-%d", tz="America/Los_Angeles"))%>% # What about when you read this from a csv file?
left_join(tidecodes_baystudy, by="Tide")%>% # Convert tide codes to values
dplyr::select(-Tide, -SubstrateCode)%>%
rename(Tidestation=Description)%>%
left_join(wavecodes_baystudy, by="Waves")%>% # Convert wave codes to values
dplyr::select(-Waves)%>%
rename(Waves=Description)%>%
left_join(cloudcovercodes_baystudy, by="CloudCover")%>% # Convert cloud cover codes to values
dplyr::select(-CloudCover)%>%
rename(CloudCover=Description)
# Tow-level data -----
boattow_baystudy <- BayStudyTables$BoatTow %>%
transmute(Year = as.integer(Year), Survey = as.integer(Survey),
Station = as.character(Station), Net = as.integer(Net),
Tow = as.integer(Tow), Time = as.character(Time),
Bearing = as.double(Bearing),
across(c(Tide, Direction, CatchCode), as.integer),
across(c(Duration, StartMeter, EndMeter, TotalMeter,
StartLong, EndLong, StartLat, EndLat, Distance), as.double),
TowComment = as.character(TowComment))
boattow_baystudy <- boattow_baystudy %>%
dplyr::select(-StartLong, -EndLong, -StartLat, -EndLat, -StartMeter, -EndMeter)%>% # Removing survey lats/longs and start/end meters for now
mutate(Tow_direction=recode(Direction, `1`="With current", `2`="Against current", `3`="Slack or cross-current"))%>% # Convert tow direction codes to values
left_join(tidecodes_baystudy, by="Tide")%>% # Convert tide codes to values
dplyr::select(-Tide, -Direction)%>%
rename(Method=Net, Tidetow=Description)%>%
mutate(Method=recode(Method, `1`="Midwater trawl", `2`="Otter trawl", `3`="EL"))%>% # Convert method codes to values
mutate(Tow_volume=if_else(Method=="Midwater trawl", TotalMeter * 0.02687 * 10.7, NA_real_),# Calculate tow volume using formulas in database metadata
Tow_area=if_else(Method=="Otter trawl", Distance*1852*3.42, NA_real_))%>% # Calculate tow area using formulas in database metadata, after first converting nautical miles to meters)
dplyr::filter(Method%in%c("Midwater trawl", "Otter trawl"))%>% # Only include midwater and otter trawls. This currently does not do anything since those are the only two methods in the data.
mutate(TowStatus=recode(Tow, `0`="Invalid", `1`="Valid", `2`="Valid", `51`="Valid", `52`="Invalid",
`53`="Invalid", `54`="Valid", `55`="Valid", `56`="Valid", `57`="Valid", `58`="Invalid"))%>% # Classify TowStatus into Valid or Invalid tows
dplyr::select(-Tow, -TotalMeter, -Distance) %>% # Remove unneeded variables
# fixing time zone to tbe pst
mutate(Time = lubridate::force_tz(as.POSIXct(Time, format = "%Y-%m-%d %H:%M:%S"), tz = "America/Los_Angeles"))
# All sample-level data -----
env_baystudy <- left_join(boattow_baystudy, boatstation_baystudy, by=c("Year", "Survey", "Station"))%>% # Join together station-visit and tow - level data
mutate(Tide=if_else(is.na(Tidetow), Tidestation, Tidetow), # Tide was sometimes recorded at each station visit and sometimes at each tow
Datetime=parse_date_time(if_else(is.na(Time), NA_character_, paste0(Date, " ", hour(Time), ":", minute(Time))), "%Y-%m-%d %%H:%M", tz="America/Los_Angeles"),
SampleID=1:nrow(.))%>% # Create identifier for each sample (tow)
left_join(stations_baystudy, by="Station")%>% # Add station locations
left_join(salintemp_baystudy, by=c("Year", "Survey", "Station"))%>% #Add salinity and temperature data
dplyr::select(-Tidestation, -Tidetow, -Time) # Remove unneeded variables
rm(tidecodes_baystudy, wavecodes_baystudy, cloudcovercodes_baystudy, boattow_baystudy, boatstation_baystudy, salintemp_baystudy, stations_baystudy) # Clean up
# Catch data --------------------------------------------------------------
catch_baystudy <- BayStudyTables$`Fish Catch Data` %>%
transmute(Year = as.integer(Year), Survey = as.integer(Survey),
Station = as.character(Station), Net = as.integer(Net),
AlphaCode = as.character(AlphaCode), SizeGroup = as.integer(SizeGroup),
across(c(QtsCaught, QtsSubsampled, PlusCount), as.double))
catch_baystudy <- catch_baystudy %>%
rename(Method=Net)%>%
mutate(Method=recode(Method, `1`="Midwater trawl", `2`="Otter trawl", `3`="EL"))%>% # Convert method codes to values
dplyr::filter(Method%in%c("Midwater trawl", "Otter trawl"))%>% #Only keep midwater and otter trawls
left_join(Species%>% # Add species names
dplyr::select(AlphaCode=Baystudy_Code, Taxa)%>%
dplyr::filter(!is.na(AlphaCode)),
by="AlphaCode")%>%
dplyr::select(-AlphaCode) # Remove unneeded variable
# Length data -------------------------------------------------------------
length_baystudy <- BayStudyTables$`Fish Length Data` %>%
transmute(Year = as.integer(Year), Survey = as.integer(Survey),
Station = as.character(Station), Net = as.integer(Net),
AlphaCode = as.character(AlphaCode), SizeGroup = as.integer(SizeGroup),
Length = as.double(Length), Frequency = as.double(Frequency))
length_baystudy <- length_baystudy %>%
rename(Method=Net)%>%
mutate(Method=recode(Method, `1`="Midwater trawl", `2`="Otter trawl", `3`="EL"), # Convert method codes to values
AlphaCode = toupper(AlphaCode))%>% # Make alphacodes consistent
dplyr::filter(Method%in%c("Midwater trawl", "Otter trawl"))%>% #Only keep midwater and otter trawls
left_join(Species%>% # Add species names
dplyr::select(AlphaCode=Baystudy_Code, Taxa)%>%
dplyr::filter(!is.na(AlphaCode)),
by="AlphaCode")%>%
dplyr::select(-AlphaCode) # Remove unneeded variable
# Join length and catch data ----
lengthcatch_baystudy<-catch_baystudy%>%
full_join(length_baystudy%>% # This first join will just include total number measured
group_by(Year, Survey, Station, Method, SizeGroup, Taxa)%>%
summarize(TotalMeasured=sum(Frequency))%>% # Calculate total number of each species of fish measured in each sample
ungroup(),
by=c("Year", "Survey", "Station", "Method", "SizeGroup", "Taxa"))%>%
dplyr::filter(!is.na(TotalMeasured) & !is.na(PlusCount))%>% # Remove any rows corresponding to 0 fish measured and 0 fish counted
mutate(TotalCatch=(TotalMeasured+PlusCount)*(QtsCaught/QtsSubsampled))%>% # Calculate total number of each species caught in each sample
dplyr::select(-PlusCount, -QtsCaught, -QtsSubsampled)%>% # Remove unneeded variables
right_join(length_baystudy, by=c("Year", "Survey", "Station", "Method", "SizeGroup", "Taxa"),multiple="all")%>% # Now join all measurements
mutate(Count = (Frequency/TotalMeasured)*TotalCatch)%>% # Calculate adjusted size frequency
dplyr::select(-TotalMeasured, -TotalCatch, -Frequency) # Remove unneeded variables
# Create final datasets ---------------------------------------------------
Baystudy <- env_baystudy%>% # Start with sample-level data to retain samples with no catch (nets empty of fish)
left_join(lengthcatch_baystudy, by=c("Year", "Survey", "Station", "Method"),multiple="all")%>% # Add length and catch data
mutate(Sal_surf=ec2pss(ECSurf/1000, t=25), # Calculate salinity from conductivity
Sal_avg=ec2pss(ECAvg/1000, t=25),
Sal_bott=ec2pss(ECBott/1000, t=25),
Source="Bay Study",
SampleID=paste(Source, SampleID), # Add unique identifier for each sample across all studies
Length_NA_flag=if_else(is.na(Length), "No fish caught", NA_character_), # Add reasoning for an NA lengths (all "No Fish Caught" for Baystudy)
Taxa=stringr::str_remove(Taxa, " \\((.*)"))%>% # Remove life stage info from Taxa
dplyr::select(-ECSurf, -ECAvg, -ECBott, -CatchCode, -SizeGroup, -StationComment)%>% # Remove unneeded variables
group_by_at(vars(-Count))%>%
summarise(Count=sum(Count), .groups="drop")%>% # Add up counts when the same species and length were recorded multiple times for a sample
rename(Notes_tow=TowComment, Tow_status=TowStatus,
Weather=CloudCover, Temp_surf=TempSurf, Temp_avg=TempAvg,
Temp_bott=TempBott, Tow_duration=Duration)%>%
dplyr::select(-Bearing, -Waves, -Weather, -Temp_avg, -Temp_bott, -Sal_avg, -Sal_bott)%>% # Remove extra environmental variables
dplyr::filter(Tow_status=="Valid")%>% # Remove invalid tows
mutate(Count=if_else(Length_NA_flag=="No fish caught", 0, Count, missing=Count))%>% # Transform all counts for 'No fish caught' to 0.
dplyr::select(Source, Station, Latitude, Longitude, Date, Datetime, Year, Survey, # Reorder variables for consistency
Depth, SampleID, Method, Tide, Sal_surf, Temp_surf, Secchi,
Tow_duration, Tow_area, Tow_volume, Tow_direction, Taxa, Length, Count, Length_NA_flag, Notes_tow)
# Just measured lengths
Baystudy_measured_lengths<-length_baystudy%>%
inner_join(Baystudy%>% # Inner join to remove invalid tows
dplyr::select(SampleID, Year, Survey, Station, Method)%>% # Add SampleID and Method to length data
distinct(),
by=c("Year", "Survey", "Station", "Method"))%>%
mutate(Taxa=stringr::str_remove(Taxa, " \\((.*)"))%>% #Remove life stage from Taxa
dplyr::select(SampleID, Taxa, Size_group=SizeGroup, Length, Count=Frequency)# Reorder variables for consistency
Baystudy <- Baystudy%>%
dplyr::select(-Year)# Remove unneeded variables
usethis::use_data(Baystudy, Baystudy_measured_lengths, overwrite = TRUE, compress="xz") # Save compressed data to /data folder
sbashevkin/LTMRdata documentation built on Oct. 16, 2024, 7:09 p.m.
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## code to prepare `Baystudy` dataset goes here
require(readr)
require(wql)
require(dplyr)
require(tidyr)
require(lubridate)
require(readxl)
require(LTMRdata)
require(stringr)
library(rvest)
# Station locations -------------------------------------------------------
stations_baystudy <- read_excel(file.path("data-raw", "Baystudy", "Bay Study_Station Coordinates for Distribution_04May2020.xlsx"))%>%
separate(Latitude, into=c("Lat_Deg", "Lat_Min"), sep = "°", convert=T)%>% # Convert to decimal degrees
separate(Longitude, into=c("Lon_Deg", "Lon_Min"), sep = "°", convert=T)%>%
mutate(Latitude=Lat_Deg+Lat_Min/60,
Longitude=Lon_Deg-Lon_Min/60)%>%
dplyr::select(Station, Latitude, Longitude)%>%
dplyr::filter(Station!="211E")%>% # Kathy said W location of station 211 is more often used, so using those coordinates
mutate(Station=recode(Station, `211W`="211"))
# Import lookup tables ----------------------------------------------------
options(timeout = 999999)
# Path<-file.path(tempdir(), "BayStudy_AccessDatabase_1980-2021.zip")
# Path_origin<-file.path(tempdir())
#Downloading MWT_data.zip----
# Grab file name of Access DB, which changes per year
session <- session("https://filelib.wildlife.ca.gov/Public/BayStudy/Access_Database/")
links <- html_elements(session, "a") %>%
html_attr("href")
dbLink <- links[which(grepl("\\.zip", links))]
fileName <- regmatches(dbLink, regexpr("(BayStudy_).*", dbLink))
download.file(paste0("https://filelib.wildlife.ca.gov", dbLink),
file.path(tempdir(), fileName), mode = "wb", method = "libcurl")
unzip(file.path(tempdir(), fileName), exdir = tempdir())
accessFile <- unzip(file.path(tempdir(), fileName), list = T) %>%
pull(Name) %>%
.[which(grepl("\\.accdb", .))]
# File path to Access database (Salvage)
source(file.path("data-raw", "bridgeAccess.R"))
db_path <- file.path(tempdir(), accessFile)
keepTables <- c("TideCodes_LookUp","WaveCodes_LookUp","CloudCover_LookUp",
"SalinTemp","BoatStation","BoatTow",
"Fish Catch Data","Fish Length Data")
BayStudyTables <- bridgeAccess(db_path,
tables = keepTables,
script = file.path("data-raw", "connectAccess.R"))
# # If you've chosen to read csv --------------------------------------------
# BayStudyTables <- list()
#
# BayStudyTables$TideCodes_LookUp <- read_csv(file.path("data-raw", "Baystudy", "TideCodes_LookUp.csv"),
# col_types=cols_only(Tide="i", Description="c"))
#
# BayStudyTables$WaveCodes_LookUp <- read_csv(file.path("data-raw", "Baystudy", "WaveCodes_LookUp.csv"),
# col_types=cols_only(Waves="i", Description="c"))
#
# BayStudyTables$CloudCover_LookUp <- read_csv(file.path("data-raw", "Baystudy", "CloudCover_LookUp.csv"),
# col_types=cols_only(CloudCover="i", Description="c"))
#
# BayStudyTables$SalinTemp <- read_csv(file.path("data-raw", "Baystudy", "SalinTemp.csv"),
# col_types = cols_only(Year="i", Survey="i", Station="c",
# ECSurf="d", ECAvg="d", ECBott="d",
# TempSurf="d", TempAvg="d", TempBott="d"))
#
# BayStudyTables$BoatStation <- read_csv(file.path("data-raw", "Baystudy", "BoatStation.csv"),
# col_types = cols_only(Year="i", Survey="i", Station="c",
# Date="c", Depth="d", Secchi="d",
# SubstrateCode="c", Waves="i", CloudCover="i",
# Tide="i", StationComment="c"))
# # # Will need this line depending on the export type you exploy
# # mutate(Date = as.POSIXct(Date, format = "%m/%d/%Y %H:%M:%S", tz = "America/Los_Angeles"))
#
# BayStudyTables$BoatTow <- read_csv(file.path("data-raw", "Baystudy", "BoatTow.csv"),
# col_types = cols_only(Year="i", Survey="i", Station="c", Net="i",
# Tow="i", Time="c", Bearing="d", Tide="i",
# Direction="i", CatchCode="i", Duration="d",
# StartMeter="d", EndMeter="d", TotalMeter="d",
# StartLong="d", EndLong="d", StartLat="d",
# EndLat="d", Distance="d", TowComment="c"))
# # # Will need this line depending on the export type you exploy
# # mutate(Time = as.POSIXct(Time, format = "%m/%d/%Y %H:%M:%S", tz = "America/Los_Angeles"))
#
# BayStudyTables$`Fish Catch Data` <- read_csv(file.path("data-raw", "Baystudy", "Fish Catch Data.csv"),
# col_types=cols_only(Year="i", Survey="i", Station="c",
# Net="i", AlphaCode="c",
# SizeGroup="i", QtsCaught="d", QtsSubsampled="d",
# PlusCount="d"))
#
# BayStudyTables$`Fish Length Data` <- read_csv(file.path("data-raw", "Baystudy", "Fish Length Data.csv"),
# col_types=cols_only(Year="i", Survey="i", Station="c",
# Net="i", AlphaCode="c",
# SizeGroup="i", Length="d", Frequency="d"))
# Environ data ----
tidecodes_baystudy <- BayStudyTables$TideCodes_LookUp %>%
transmute(Tide = as.integer(Tide),
Description = as.character(Description))
wavecodes_baystudy <- BayStudyTables$WaveCodes_LookUp %>%
transmute(Waves = as.integer(Waves),
Description = as.character(Description))
cloudcovercodes_baystudy <- BayStudyTables$CloudCover_LookUp %>%
transmute(CloudCover = as.integer(CloudCover),
Description = as.character(Description))
# Sample-level data -------------------------------------------------------
salintemp_baystudy <- BayStudyTables$SalinTemp %>%
transmute(Year = as.integer(Year), Survey = as.integer(Survey),
Station = as.character(Station),
across(c(ECSurf, ECAvg, ECBott, TempSurf, TempAvg, TempBott),
as.double))
# Station-visit-level data -----
boatstation_baystudy <- BayStudyTables$BoatStation %>%
transmute(Year = as.integer(Year), Survey = as.integer(Survey),
Station = as.character(Station), Date = as.character(Date),
Depth = as.double(Depth), Secchi = as.double(Secchi),
SubstrateCode = as.character(SubstrateCode),
across(c(Waves, CloudCover, Tide), as.integer),
StationComment = as.character(StationComment))
boatstation_baystudy <- boatstation_baystudy %>%
mutate(Date=parse_date_time(Date, "%Y-%m-%d", tz="America/Los_Angeles"))%>% # What about when you read this from a csv file?
left_join(tidecodes_baystudy, by="Tide")%>% # Convert tide codes to values
dplyr::select(-Tide, -SubstrateCode)%>%
rename(Tidestation=Description)%>%
left_join(wavecodes_baystudy, by="Waves")%>% # Convert wave codes to values
dplyr::select(-Waves)%>%
rename(Waves=Description)%>%
left_join(cloudcovercodes_baystudy, by="CloudCover")%>% # Convert cloud cover codes to values
dplyr::select(-CloudCover)%>%
rename(CloudCover=Description)
# Tow-level data -----
boattow_baystudy <- BayStudyTables$BoatTow %>%
transmute(Year = as.integer(Year), Survey = as.integer(Survey),
Station = as.character(Station), Net = as.integer(Net),
Tow = as.integer(Tow), Time = as.character(Time),
Bearing = as.double(Bearing),
across(c(Tide, Direction, CatchCode), as.integer),
across(c(Duration, StartMeter, EndMeter, TotalMeter,
StartLong, EndLong, StartLat, EndLat, Distance), as.double),
TowComment = as.character(TowComment))
boattow_baystudy <- boattow_baystudy %>%
dplyr::select(-StartLong, -EndLong, -StartLat, -EndLat, -StartMeter, -EndMeter)%>% # Removing survey lats/longs and start/end meters for now
mutate(Tow_direction=recode(Direction, `1`="With current", `2`="Against current", `3`="Slack or cross-current"))%>% # Convert tow direction codes to values
left_join(tidecodes_baystudy, by="Tide")%>% # Convert tide codes to values
dplyr::select(-Tide, -Direction)%>%
rename(Method=Net, Tidetow=Description)%>%
mutate(Method=recode(Method, `1`="Midwater trawl", `2`="Otter trawl", `3`="EL"))%>% # Convert method codes to values
mutate(Tow_volume=if_else(Method=="Midwater trawl", TotalMeter * 0.02687 * 10.7, NA_real_),# Calculate tow volume using formulas in database metadata
Tow_area=if_else(Method=="Otter trawl", Distance*1852*3.42, NA_real_))%>% # Calculate tow area using formulas in database metadata, after first converting nautical miles to meters)
dplyr::filter(Method%in%c("Midwater trawl", "Otter trawl"))%>% # Only include midwater and otter trawls. This currently does not do anything since those are the only two methods in the data.
mutate(TowStatus=recode(Tow, `0`="Invalid", `1`="Valid", `2`="Valid", `51`="Valid", `52`="Invalid",
`53`="Invalid", `54`="Valid", `55`="Valid", `56`="Valid", `57`="Valid", `58`="Invalid"))%>% # Classify TowStatus into Valid or Invalid tows
dplyr::select(-Tow, -TotalMeter, -Distance) %>% # Remove unneeded variables
# fixing time zone to tbe pst
mutate(Time = lubridate::force_tz(as.POSIXct(Time, format = "%Y-%m-%d %H:%M:%S"), tz = "America/Los_Angeles"))
# All sample-level data -----
env_baystudy <- left_join(boattow_baystudy, boatstation_baystudy, by=c("Year", "Survey", "Station"))%>% # Join together station-visit and tow - level data
mutate(Tide=if_else(is.na(Tidetow), Tidestation, Tidetow), # Tide was sometimes recorded at each station visit and sometimes at each tow
Datetime=parse_date_time(if_else(is.na(Time), NA_character_, paste0(Date, " ", hour(Time), ":", minute(Time))), "%Y-%m-%d %%H:%M", tz="America/Los_Angeles"),
SampleID=1:nrow(.))%>% # Create identifier for each sample (tow)
left_join(stations_baystudy, by="Station")%>% # Add station locations
left_join(salintemp_baystudy, by=c("Year", "Survey", "Station"))%>% #Add salinity and temperature data
dplyr::select(-Tidestation, -Tidetow, -Time) # Remove unneeded variables
rm(tidecodes_baystudy, wavecodes_baystudy, cloudcovercodes_baystudy, boattow_baystudy, boatstation_baystudy, salintemp_baystudy, stations_baystudy) # Clean up
# Catch data --------------------------------------------------------------
catch_baystudy <- BayStudyTables$`Fish Catch Data` %>%
transmute(Year = as.integer(Year), Survey = as.integer(Survey),
Station = as.character(Station), Net = as.integer(Net),
AlphaCode = as.character(AlphaCode), SizeGroup = as.integer(SizeGroup),
across(c(QtsCaught, QtsSubsampled, PlusCount), as.double))
catch_baystudy <- catch_baystudy %>%
rename(Method=Net)%>%
mutate(Method=recode(Method, `1`="Midwater trawl", `2`="Otter trawl", `3`="EL"))%>% # Convert method codes to values
dplyr::filter(Method%in%c("Midwater trawl", "Otter trawl"))%>% #Only keep midwater and otter trawls
left_join(Species%>% # Add species names
dplyr::select(AlphaCode=Baystudy_Code, Taxa)%>%
dplyr::filter(!is.na(AlphaCode)),
by="AlphaCode")%>%
dplyr::select(-AlphaCode) # Remove unneeded variable
# Length data -------------------------------------------------------------
length_baystudy <- BayStudyTables$`Fish Length Data` %>%
transmute(Year = as.integer(Year), Survey = as.integer(Survey),
Station = as.character(Station), Net = as.integer(Net),
AlphaCode = as.character(AlphaCode), SizeGroup = as.integer(SizeGroup),
Length = as.double(Length), Frequency = as.double(Frequency))
length_baystudy <- length_baystudy %>%
rename(Method=Net)%>%
mutate(Method=recode(Method, `1`="Midwater trawl", `2`="Otter trawl", `3`="EL"), # Convert method codes to values
AlphaCode = toupper(AlphaCode))%>% # Make alphacodes consistent
dplyr::filter(Method%in%c("Midwater trawl", "Otter trawl"))%>% #Only keep midwater and otter trawls
left_join(Species%>% # Add species names
dplyr::select(AlphaCode=Baystudy_Code, Taxa)%>%
dplyr::filter(!is.na(AlphaCode)),
by="AlphaCode")%>%
dplyr::select(-AlphaCode) # Remove unneeded variable
# Join length and catch data ----
lengthcatch_baystudy<-catch_baystudy%>%
full_join(length_baystudy%>% # This first join will just include total number measured
group_by(Year, Survey, Station, Method, SizeGroup, Taxa)%>%
summarize(TotalMeasured=sum(Frequency))%>% # Calculate total number of each species of fish measured in each sample
ungroup(),
by=c("Year", "Survey", "Station", "Method", "SizeGroup", "Taxa"))%>%
dplyr::filter(!is.na(TotalMeasured) & !is.na(PlusCount))%>% # Remove any rows corresponding to 0 fish measured and 0 fish counted
mutate(TotalCatch=(TotalMeasured+PlusCount)*(QtsCaught/QtsSubsampled))%>% # Calculate total number of each species caught in each sample
dplyr::select(-PlusCount, -QtsCaught, -QtsSubsampled)%>% # Remove unneeded variables
right_join(length_baystudy, by=c("Year", "Survey", "Station", "Method", "SizeGroup", "Taxa"),multiple="all")%>% # Now join all measurements
mutate(Count = (Frequency/TotalMeasured)*TotalCatch)%>% # Calculate adjusted size frequency
dplyr::select(-TotalMeasured, -TotalCatch, -Frequency) # Remove unneeded variables
# Create final datasets ---------------------------------------------------
Baystudy <- env_baystudy%>% # Start with sample-level data to retain samples with no catch (nets empty of fish)
left_join(lengthcatch_baystudy, by=c("Year", "Survey", "Station", "Method"),multiple="all")%>% # Add length and catch data
mutate(Sal_surf=ec2pss(ECSurf/1000, t=25), # Calculate salinity from conductivity
Sal_avg=ec2pss(ECAvg/1000, t=25),
Sal_bott=ec2pss(ECBott/1000, t=25),
Source="Bay Study",
SampleID=paste(Source, SampleID), # Add unique identifier for each sample across all studies
Length_NA_flag=if_else(is.na(Length), "No fish caught", NA_character_), # Add reasoning for an NA lengths (all "No Fish Caught" for Baystudy)
Taxa=stringr::str_remove(Taxa, " \\((.*)"))%>% # Remove life stage info from Taxa
dplyr::select(-ECSurf, -ECAvg, -ECBott, -CatchCode, -SizeGroup, -StationComment)%>% # Remove unneeded variables
group_by_at(vars(-Count))%>%
summarise(Count=sum(Count), .groups="drop")%>% # Add up counts when the same species and length were recorded multiple times for a sample
rename(Notes_tow=TowComment, Tow_status=TowStatus,
Weather=CloudCover, Temp_surf=TempSurf, Temp_avg=TempAvg,
Temp_bott=TempBott, Tow_duration=Duration)%>%
dplyr::select(-Bearing, -Waves, -Weather, -Temp_avg, -Temp_bott, -Sal_avg, -Sal_bott)%>% # Remove extra environmental variables
dplyr::filter(Tow_status=="Valid")%>% # Remove invalid tows
mutate(Count=if_else(Length_NA_flag=="No fish caught", 0, Count, missing=Count))%>% # Transform all counts for 'No fish caught' to 0.
dplyr::select(Source, Station, Latitude, Longitude, Date, Datetime, Year, Survey, # Reorder variables for consistency
Depth, SampleID, Method, Tide, Sal_surf, Temp_surf, Secchi,
Tow_duration, Tow_area, Tow_volume, Tow_direction, Taxa, Length, Count, Length_NA_flag, Notes_tow)
# Just measured lengths
Baystudy_measured_lengths<-length_baystudy%>%
inner_join(Baystudy%>% # Inner join to remove invalid tows
dplyr::select(SampleID, Year, Survey, Station, Method)%>% # Add SampleID and Method to length data
distinct(),
by=c("Year", "Survey", "Station", "Method"))%>%
mutate(Taxa=stringr::str_remove(Taxa, " \\((.*)"))%>% #Remove life stage from Taxa
dplyr::select(SampleID, Taxa, Size_group=SizeGroup, Length, Count=Frequency)# Reorder variables for consistency
Baystudy <- Baystudy%>%
dplyr::select(-Year)# Remove unneeded variables
usethis::use_data(Baystudy, Baystudy_measured_lengths, overwrite = TRUE, compress="xz") # Save compressed data to /data folder
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