fix: adapt to new r4ss column names

Error was caused by change r4ss/r4ss#512 which
changed FltSvy to fleet and Yr to year. This change affected petrale,
opakapaka, and sardine.

content/NWFSC-petrale.qmd

@@ -77,8 +77,8 @@ source("R/get_ss3_data.R")
 # by assigning to fleet -1, these get filtered by get_ss3_data()
 
 # only include age comps with fleet = -4 or 1:
-ss3dat$agecomp <- ss3dat$agecomp |> dplyr::filter(FltSvy %in% c(-4, 1))
-ss3dat$agecomp$FltSvy <- abs(ss3dat$agecomp$FltSvy)
+ss3dat$agecomp <- ss3dat$agecomp |> dplyr::filter(fleet %in% c(-4, 1))
+ss3dat$agecomp$fleet <- abs(ss3dat$agecomp$fleet)
 
 # convert SS3 data into FIMS format using function defined in the R directory
 mydat <- get_ss3_data(ss3dat, fleets = c(1,4), ages = ages)

content/PIFS-opakapaka.qmd

@@ -53,8 +53,13 @@ load(file.path(getwd(), "data_files", "opaka_model.RDS"))
 ## Should now have ss3dat, ss3ctl, and ss3rep in environment
 ## RDS file can also be found at
 #githubURL <- "https://github.com/MOshima-PIFSC/Opaka-FIMS-Case-Study/blob/main/Model/FIMS-em/1/em/opaka_model.RDS"
+# replace ss3dat and ss3ctl loaded from RDS file with new versions 
+# using current r4ss to read ss_new files from github
+opaka_input <- r4ss::SS_read("https://raw.githubusercontent.com/MOshima-PIFSC/Opaka-FIMS-Case-Study/refs/heads/main/Model/FIMS-em/1/em/", ss_new = TRUE)
+ss3dat <- opaka_input$dat
+ss3ctl <- opaka_input$ctl
 
-## Function written by Ian Taylor to get SS3 data into FIMSFrame format
+## Function written by Ian Taylor and Meg Oshima to get SS3 data into FIMSFrame format
 source("./R/get_ss3_data.R")
 
 # Define the dimensions ----

content/R/get_ss3_data.R

@@ -102,39 +102,39 @@ get_ss3_data <- function(dat, fleets, ages) {
   # further processing
   age_info <-
     dat$agecomp |>
-    dplyr::filter(FltSvy %in% fleets) |> # filter by requested fleets
-    dplyr::mutate(FltSvy = abs(FltSvy)) |> # convert any negative fleet to positive
-    dplyr::select(!dplyr::starts_with("m", ignore.case = FALSE)) |> # exclude male comps
+    dplyr::filter(fleet %in% fleets) |> # filter by requested fleets
+    dplyr::mutate(fleet = abs(fleet)) |> # convert any negative fleet to positive
+    dplyr::select(!dplyr::matches("^m[0-9]")) |> # exclude male comps
     tidyr::pivot_longer( # convert columns f1...f17 to values in a new "age" colum of a longer table
-      cols = dplyr::starts_with(c("f", "a"), ignore.case = FALSE),
+      cols = dplyr::matches("^f[0-9]") | dplyr::matches("^a[0-9]"), # 2-sex model uses f1, f2, ...; 1-sex model uses a1, a2, ...
       names_to = "age",
       values_to = "value"
     ) |>
     dplyr::mutate(age = as.numeric(substring(age, first = 2))) |> # convert "f17" to 17
-    dplyr::select(Yr, FltSvy, Nsamp, age, value)
+    dplyr::select(year, fleet, Nsamp, age, value)
 
   # add -999 for missing years
   # create empty data frame for all combinations of year, fleet, and age
   age_info_empty <- tidyr::expand_grid(
-    Yr = years,
-    FltSvy = fleets,
+    year = years,
+    fleet = fleets,
     age = ages
   ) |> dplyr::mutate(Nsamp = 1, value = -999 - 0.001)
   # combine the two data frames and remove redundant rows
   # NOTE: this removes some age comp data because there
   # were years with multiple observations from the same fleet
   # due to multiple ageing error matrices
   age_info <- rbind(age_info, age_info_empty) |>
-    dplyr::distinct(Yr, FltSvy, age, .keep_all = TRUE) |>
-    dplyr::arrange(FltSvy, Yr, age)
+    dplyr::distinct(year, fleet, age, .keep_all = TRUE) |>
+    dplyr::arrange(fleet, year, age)
 
   # finish converting age comps to FIMSFrame format
   agecomps <- data.frame(
     type = "age",
-    name = paste0("fleet", abs(age_info$FltSvy)), # abs to include fleet == -4
+    name = paste0("fleet", abs(age_info$fleet)), # abs to include fleet == -4
     age = age_info$age,
-    datestart = paste0(age_info$Yr, "-01-01"),
-    dateend = paste0(age_info$Yr, "-12-31"),
+    datestart = paste0(age_info$year, "-01-01"),
+    dateend = paste0(age_info$year, "-12-31"),
     value = age_info$value + 0.001, # add constant to avoid 0 values
     unit = "",
     # Q: should uncertainty here be the total sample size across bins, or the samples within the bin?

content/SWFSC-sardine.qmd

@@ -330,15 +330,15 @@ ewaa_growth <- methods::new(EWAAgrowth)
 ewaa_growth$ages <- ages
 # NOTE: getting weight-at-age vector from
 # petrale_output$wtatage |>
-#   dplyr::filter(Sex == 1 & Fleet == -1 & Yr == 1876) |>
+#   dplyr::filter(sex == 1 & fleet == -1 & year == 1876) |>
 #   dplyr::select(paste(0:40)) |>
 #   round(4)
 # ewaa_growth$weights <- c(0.019490,0.077760,0.108865,
 #                          0.133855,0.154360,0.174905,0.184200,
 #                          0.196460,0.214155)
 
 
-ewaa_growth$weights <- wtatage %>% filter(Fleet == 1, Yr == 2010) %>% select(as.character(0:10))  %>% t %>%
+ewaa_growth$weights <- wtatage %>% filter(fleet == 1, year == 2010) %>% select(as.character(0:10))  %>% t %>%
   as.vector
 
 # maturity