publication/Data publication code.R

In InteragencyEcologicalProgram/zooper: Download and Integrate Zooplankton Datasets from the Upper San Francisco Estuary

# Install the version of zooper used to create these data
#remotes::install_github("InteragencyEcologicalProgram/zooper", "v2.3.1")

library(zooper)
library(dplyr)
library(lubridate)


# Zooplankton community data, with taxonomic resolution fixed and all variables retained
zooplankton_community<-Zoopsynther(Data_type="Community")%>% # Create dataset with zooper
  select(Source, Station, Latitude, Longitude, Year, Date, Datetime, SampleID, TowType, AmphipodCode, Tide, #Re-order variables
         BottomDepth, Chl, Secchi, Temperature, Turbidity, Microcystis, pH, DO, SalSurf, SalBott,
         SizeClass, Volume, Phylum, Class, Order, Family, Genus, Species, Taxname, Lifestage, Taxlifestage,
         CPUE, Undersampled)

# Raw zooplankton catch data
zooplankton<-zooper::zoopComb%>% #Pull data from zooper
  select(SampleID, SizeClass, Volume, Taxname, Lifestage, CPUE) # Remove variables present in other tables and Re-order variables

environment<-zooper::zoopEnvComb%>% #Pull data from zooper
  select(Source, Station, Latitude, Longitude, Date, Datetime, SampleID, Tide, TowType, AmphipodCode, # Select and re-order variables
         BottomDepth, Chl, Secchi, Temperature, Turbidity, Microcystis, pH, DO, SalSurf, SalBott)

stations<-zooper::stations%>% #Pull data from zooper
  select(Source, Station, Latitude, Longitude) # Re-order variables

taxonomy <- zooper::crosswalk%>% #Pull data from zooper
  select(Phylum, Class, Order, Family, Genus, Species, Taxname, Level)%>% # Re-order variables and remove variables present in other tables
  distinct() # Remove any duplicated rows after selecting only taxonomic variables

taxa_lists<-zooper::crosswalk%>% #Pull data from zooper
  select(-LI_Meso, -LI_Micro, -Level, -Phylum, -Class, -Order, -Family, -Genus, -Species)%>% # Remove variables present in other tables
  mutate(across(c(EMP_Micro, EMP_Meso, EMP_Macro, STN_Meso, STN_Macro, FMWT_Meso,
                  FMWT_Macro, twentymm_Meso, FRP_Meso, FRP_Macro, YBFMP, DOP_Meso, DOP_Macro),
                ~if_else(is.na(.x), FALSE, TRUE)))%>%# Convert organism codes into a binary TRUE/FALSE indicating if taxa are counted in each survey and size class.
  distinct()%>% # Remove duplicated rows now that organism codes are removed
  mutate(across(c(Intro, EMPstart, EMPend, FMWTstart, FMWTend, twentymmstart,
                  twentymmend, twentymmstart2, DOPstart, DOPend), ~year(.x)),
         across(c(Intro, EMPstart, EMPend, FMWTstart, FMWTend, twentymmstart,
                  twentymmend, twentymmstart2, DOPstart, DOPend), ~if_else(.x<1900 | .x>2400, NA, .x)))%>%
  select(Taxname, Lifestage, EMP_Micro, EMP_Meso, EMP_Macro, STN_Meso, STN_Macro, FMWT_Meso,
         FMWT_Macro, twentymm_Meso, FRP_Meso, FRP_Macro, YBFMP, DOP_Meso, DOP_Macro,
         Intro, EMPstart, EMPend, FMWTSTNstart=FMWTstart, FMWTSTNend=FMWTend, twentymmstart,
         twentymmend, twentymmstart2, DOPstart, DOPend) #Re-oroder and rename variables for consistency

undersampled <- zooper::undersampled%>% #Pull data from zooper
  select(SizeClass, Taxname, Lifestage) # Re-order variables

# The study_metadata tables and biomass_mesomicro tables are pulled directly from their spreadsheets. They are also not present in zooper.