Added 2023 FMWT and STN data.

NEWS.md

@@ -12,6 +12,7 @@
 -   Jungbluth, M. J., J. Burns, L. Grimaldo, A. Slaughter, A. Katla, and W. Kimmerer. 2021. Feeding habits and novel prey of larval fishes in the northern San Francisco Estuary. Environmental DNA.1-22. <https://doi.org/10.1002/edn3.226>
 -   Fixed issue where macrozoop biomass was being incorrectly converted to micrograms, resulting in 100X higher biomass values. 
 -   Added FRP 2022 data and adjusted crosswalk to reflect changes to their taxonomic table. 
+-   Added 2023 FMWT/STN data and adjusted zoopdownloader to reflect new date format
 
 # zooper 2.5.1
 

R/zoop_downloader.R

@@ -1005,7 +1005,11 @@ Zoopdownloader <- function(
                                                                  Crangonyx_sp="d", Gammarus_daiberi="d", Grandidierella_japonica="d",
                                                                  Hyalella_sp="d", Unidentified_Amphipod="d", Unidentified_Corophium="d",
                                                                  Unidentified_Gammarus="d"))%>%
-      dplyr::mutate(ID=paste(.data$Year, .data$Project, .data$Survey, .data$Station))
+      dplyr::mutate(ID=paste(.data$Year, .data$Project, .data$Survey, .data$Station)) %>%
+      dplyr::mutate(Datetime = lubridate::parse_date_time(dplyr::if_else(is.na(.data$Time), NA_character_,
+                                                                         paste(.data$Date, .data$Time)), "%Y-%m-%d %H:%M", tz="America/Los_Angeles"),
+                    Date=lubridate::parse_date_time(.data$Date, "%Y-%m-%d", tz="America/Los_Angeles"))
+    #The 2025 data upload has a new date format for Macro (but not meso, and not SMSCG very annoying)
 
     zoo_SMSCG_Macro <- readr::read_csv(file.path(Data_folder, "SMSCG_Macro.csv"),
                                        col_types=readr::cols_only(Project="c", Year="d", Survey="d",
@@ -1021,13 +1025,17 @@ Zoopdownloader <- function(
                                                                   Hyalella_sp="d", Unidentified_Amphipod="d", Unidentified_Corophium="d",
                                                                   Unidentified_Gammarus="d"))%>%
       dplyr::mutate(ID=paste(.data$Year, .data$Project, .data$Survey, .data$Station))%>%
-      dplyr::filter(!.data$ID%in%unique(zoo_FMWT_Macro$ID) & .data$Project%in%c("FMWT", "STN"))
+      dplyr::filter(!.data$ID%in%unique(zoo_FMWT_Macro$ID) & .data$Project%in%c("FMWT", "STN")) %>%
+
+      #put date and time in the right format. Date first, tehn datetime to try and fix a problem where the date randomly came out wrong in the 'datetime' version
+      dplyr::mutate(Date=lubridate::parse_date_time(.data$Date, "%m/%d/%y", tz="America/Los_Angeles"),
+        Datetime = lubridate::parse_date_time(dplyr::if_else(is.na(.data$Time), NA_character_,
+                                                                         paste(.data$Date, .data$Time)), "%Y-%m-%d %H:%M:%S", tz="America/Los_Angeles"))
 
     data.list[["FMWT_Macro"]] <- dplyr::bind_rows(zoo_FMWT_Macro, zoo_SMSCG_Macro)%>%
       dplyr::select(-"ID")%>%
-      dplyr::mutate(Datetime = lubridate::parse_date_time(dplyr::if_else(is.na(.data$Time), NA_character_, paste(.data$Date, .data$Time)), "%m/%d/%Y %H:%M", tz="America/Los_Angeles"),
-                    Date=lubridate::parse_date_time(.data$Date, "%m/%d/%Y", tz="America/Los_Angeles"),
-                    Microcystis = as.character(.data$Microcystis))%>% #create a variable for datetime
+      distinct()%>% #remove any samples duplicated between the SMSCG datset and the FMWTdataset
+      dplyr::mutate(Microcystis = as.character(.data$Microcystis))%>%
       tidyr::pivot_longer(cols=c(-"Project", -"Year", -"Survey", -"Date", -"Datetime",
                                  -"Station", -"Time", -"TideCode",
                                  -"DepthBottom", -"CondSurf", -"CondBott",

data-raw/crosswalk-zoopComb-zoopEnvComb-startDates.R

@@ -31,6 +31,7 @@ zoop<-Zoopdownloader(Data_sets=c("EMP_Meso", "FMWT_Meso", "STN_Meso",
                      Data_folder=tempdir(), Save_object=FALSE, Return_object=TRUE,
                      Redownload_data=TRUE, Crosswalk=crosswalk, Biomass=TRUE)
 
+
 zoopComb <- zoop$Zooplankton
 
 zoopEnvComb <- zoop$Environment
@@ -45,3 +46,4 @@ startDates<-zoopComb%>%
   dplyr::summarise(Startdate = min(Date), .groups="drop")
 
 usethis::use_data(zoopComb, zoopEnvComb, startDates, crosswalk, overwrite = TRUE)
+