diff --git a/R/write_captions.R b/R/write_captions.R
index 563f4fe..fa3d447 100644
--- a/R/write_captions.R
+++ b/R/write_captions.R
@@ -17,497 +17,487 @@ write_captions <- function(dat, # converted model output object
                            dir = NULL,
                            year = NULL){
 
-  # import pre-written captions and alt text that include placeholders
-  # for key quantities (e.g., 'start_year' is the placeholder for the
-  # assessment's start year)
-  caps_alttext <- utils::read.csv(
-    system.file("resources", "captions_alt_text_template.csv", package = "satf")
-  )
-
-  dat <- dat |>
-    dplyr::mutate(estimate = as.numeric(estimate),
-                  year = as.numeric(year),
-                  age = as.numeric(age))
-
-  # extract key quantities
-  # REMINDERS:
-  # -the variable names must exactly match those in the captions/alt text csv.
-
-  # suppress warnings
-  options(warn=-1)
-
-  # FIGURES-----
-
-  ## kobe plot- don't code quantities yet
-  # kobe.end.year <-
-  # value of B/B(MSY) at the end year
-  # B.BMSY.end.yr <- B/BMSY
-  # --B = time series of biomass, last year
-  # --BMSY = dplyr::filter(grepl('b_target', label) | grepl('b_msy', label) | c(grepl('fishing_mortality_msy', label) & is.na(year)))
-  #   CHECK: if length > 1, then select b_target
-
-  # value of F/F(MSY) at the end year
-  # F.FMSY.end.yr <-
-
-  # object that should be "is" or "is not" and answers the question,
-  # "the stock overfishing status ... overfished"
-  # overfished.status.is.isnot <-
-
-  # object that should be "is" or "is not" and answers the question,
-  # "the stock ... experiencing overfishing"
-  # overfishing.status.is.isnot <-
-
-
-  ## Biomass plot
-  # biomass reference point
-  # B.ref.pt : added with add_more_key_quants
-
-  # start year of biomass plot
-  B.start.year <- dat |>
-    dplyr::filter(label == "biomass",
-                  module_name == "TIME_SERIES" | module_name == "t.series", # SS3 and BAM target module names
-                  is.na(fleet),
-                  is.na(age)) |>
-    dplyr::slice(which.min(year)) |>
-    dplyr::select(year) |>
-    as.numeric()
-
-  # end year of biomass plot
-  # B.end.year : added with add_more_key_quants
-
-  # units of B (plural)
-  # B.units : added with add_more_key_quants
-
-  # minimum B
-  B.min <- dat |>
-    dplyr::filter(label == "biomass",
-                  module_name == "TIME_SERIES" | module_name == "t.series", # SS3 and BAM target module names
-                  is.na(fleet),
-                  is.na(age)) |>
-    dplyr::slice(which.min(estimate)) |>
-    dplyr::select(estimate) |>
-    as.numeric() |>
-    round(digits = 2)
-
-  # maximum B
-  B.max <- dat |>
-    dplyr::filter(label == "biomass",
-                  module_name == "TIME_SERIES" | module_name == "t.series", # SS3 and BAM target module names
-                  is.na(fleet),
-                  is.na(age)) |>
-    dplyr::slice(which.max(estimate)) |>
-    dplyr::select(estimate) |>
-    as.numeric() |>
-    round(digits = 2)
-
-  # TODO: uncomment and recode once we get clarity about how to extract this value properly
-  # R0
-  # R0 <- dat |>
-  #   dplyr::filter(
-  #     # pull from BAM
-  #     grepl('^recruitment$', label) & module_name == "parms" |
-  #        grepl('^R0', label) |
-  #     # pull from SS3
-  #       grepl('recruitment_virgin', label)
-  #                  ) |>
-  #   dplyr::pull(estimate) |>
-  #   unique() |>
-  #   as.numeric() |>
-  #   round(digits = 2)
-
-  # Bend <-
-
-  # TODO: uncomment and recode once we get clarity about how to extract this value properly
-  # Target biomass
-  # Btarg <- dat |>
-  #   dplyr::filter(c(grepl('biomass', label) & grepl('target', label) & estimate >1) | label == 'biomass_msy') |>
-  #   dplyr::pull(estimate) |>
-  #   as.numeric() |>
-  #   round(digits = 2)
-
-  # Bmsy <-
-
-
-  # TODO: uncomment and recode once we get clarity about how to extract Btarg properly
-  ## relative B
-  # relative B min
-  # rel.B.min <- (B.min / Btarg) |>
-  #   round(digits = 2)
-  #
-  # # relative B max
-  # rel.B.max <- (B.max / Btarg) |>
-  #   round(digits = 2)
-
-  # TODO: uncomment and recode once we get clarity about how to extract this value properly
-  ## mortality (F) plot
-  # F reference point
-  # F.ref.pt <- dat |>
-  #   dplyr::filter(
-  #     label == stringr::str_to_lower("F_targ") |
-  #       label == stringr::str_to_lower("F_proxy") |
-  #       label == stringr::str_to_lower("F_msy") |
-  #       label == "F_target"
-  #       # label == "F40"
-  #       # label == "F30"
-  #       # label == "F50"
-  #       # label == "F_initial"
-  #       # label == "Fmsy"
-  #   ) |>
-  #   dplyr::filter(module_name == "DERIVED_QUANTITIES" | module_name == "parms") |>
-  #   dplyr::pull(estimate) |>
-  #   as.numeric() |>
-  #   round(digits = 2)
-
-  # start year of F plot
-  F.start.year <- dat |>
-    dplyr::filter(label == 'fishing_mortality') |>
-    dplyr::slice(which.min(year)) |>
-    dplyr::select(year) |>
-    as.numeric()
-
-  # terminal fishing mortality
-  # F.end.year : added with add_more_key_quants
-
-  # minimum F
-  F.min <- dat |>
-    dplyr::filter(label == 'fishing_mortality')
-
-  if(length(unique(F.min$age)) == 1){
-    if(is.na(unique(F.min$age))){
-    F.min <- F.min |>
-      dplyr::filter(module_name %in% c("TIME_SERIES","t.series")) |>
+  # only extract key quantities/export new csv if not present
+  if (file.exists(fs::path(dir,
+                           "captions_alt_text.csv"))){
+    message("Captions and alternative text file (captions_alt_text.csv) already exists; write_captions() will not run. To extract new key quantities and make a new captions_alt_text.csv file, delete existing captions_alt_text.csv and rerun write_captions().")
+  } else {
+
+    # import pre-written captions and alt text that include placeholders
+    # for key quantities (e.g., 'start_year' is the placeholder for the
+    # assessment's start year)
+    caps_alttext <- utils::read.csv(
+      system.file("resources", "captions_alt_text_template.csv", package = "satf")
+    )
+
+    dat <- dat |>
+      dplyr::mutate(estimate = as.numeric(estimate),
+                    year = as.numeric(year),
+                    age = as.numeric(age))
+
+    # extract key quantities
+    # REMINDERS:
+    # -the variable names must exactly match those in the captions/alt text csv.
+
+    # suppress warnings
+    options(warn=-1)
+
+    # FIGURES-----
+
+    ## kobe plot- don't code quantities yet
+    # kobe.end.year <-
+    # value of B/B(MSY) at the end year
+    # B.BMSY.end.yr <- B/BMSY
+    # --B = time series of biomass, last year
+    # --BMSY = dplyr::filter(grepl('b_target', label) | grepl('b_msy', label) | c(grepl('fishing_mortality_msy', label) & is.na(year)))
+    #   CHECK: if length > 1, then select b_target
+
+    # value of F/F(MSY) at the end year
+    # F.FMSY.end.yr <-
+
+    # object that should be "is" or "is not" and answers the question,
+    # "the stock overfishing status ... overfished"
+    # overfished.status.is.isnot <-
+
+    # object that should be "is" or "is not" and answers the question,
+    # "the stock ... experiencing overfishing"
+    # overfishing.status.is.isnot <-
+
+
+    ## Biomass plot
+    # biomass reference point
+    # B.ref.pt : added with add_more_key_quants
+
+    # start year of biomass plot
+    B.start.year <- dat |>
+      dplyr::filter(label == "biomass",
+                    module_name == "TIME_SERIES" | module_name == "t.series", # SS3 and BAM target module names
+                    is.na(fleet),
+                    is.na(age)) |>
+      dplyr::slice(which.min(year)) |>
+      dplyr::select(year) |>
+      as.numeric()
+
+    # end year of biomass plot
+    # B.end.year : added with add_more_key_quants
+
+    # units of B (plural)
+    # B.units : added with add_more_key_quants
+
+    # minimum B
+    B.min <- dat |>
+      dplyr::filter(label == "biomass",
+                    module_name == "TIME_SERIES" | module_name == "t.series", # SS3 and BAM target module names
+                    is.na(fleet),
+                    is.na(age)) |>
       dplyr::slice(which.min(estimate)) |>
+      dplyr::select(estimate) |>
       as.numeric() |>
       round(digits = 2)
-    }
-    } else {
-    F.min <- F.min |>
-      dplyr::group_by(age)|>
-      dplyr::summarize(val = max(estimate)) |>
-      dplyr::slice(which.min(val)) |>
-      dplyr::select(val) |>
+
+    # maximum B
+    B.max <- dat |>
+      dplyr::filter(label == "biomass",
+                    module_name == "TIME_SERIES" | module_name == "t.series", # SS3 and BAM target module names
+                    is.na(fleet),
+                    is.na(age)) |>
+      dplyr::slice(which.max(estimate)) |>
+      dplyr::select(estimate) |>
       as.numeric() |>
       round(digits = 2)
-  }
 
+    # TODO: uncomment and recode once we get clarity about how to extract this value properly
+    # R0
+    # R0 <- dat |>
+    #   dplyr::filter(
+    #     # pull from BAM
+    #     grepl('^recruitment$', label) & module_name == "parms" |
+    #        grepl('^R0', label) |
+    #     # pull from SS3
+    #       grepl('recruitment_virgin', label)
+    #                  ) |>
+    #   dplyr::pull(estimate) |>
+    #   unique() |>
+    #   as.numeric() |>
+    #   round(digits = 2)
+
+    # Bend <-
+
+    # TODO: uncomment and recode once we get clarity about how to extract this value properly
+    # Target biomass
+    # Btarg <- dat |>
+    #   dplyr::filter(c(grepl('biomass', label) & grepl('target', label) & estimate >1) | label == 'biomass_msy') |>
+    #   dplyr::pull(estimate) |>
+    #   as.numeric() |>
+    #   round(digits = 2)
+
+    # Bmsy <-
+
+
+    # TODO: uncomment and recode once we get clarity about how to extract Btarg properly
+    ## relative B
+    # relative B min
+    # rel.B.min <- (B.min / Btarg) |>
+    #   round(digits = 2)
+    #
+    # # relative B max
+    # rel.B.max <- (B.max / Btarg) |>
+    #   round(digits = 2)
+
+    # TODO: uncomment and recode once we get clarity about how to extract this value properly
+    ## mortality (F) plot
+    # F reference point
+    # F.ref.pt <- dat |>
+    #   dplyr::filter(
+    #     label == stringr::str_to_lower("F_targ") |
+    #       label == stringr::str_to_lower("F_proxy") |
+    #       label == stringr::str_to_lower("F_msy") |
+    #       label == "F_target"
+    #       # label == "F40"
+    #       # label == "F30"
+    #       # label == "F50"
+    #       # label == "F_initial"
+    #       # label == "Fmsy"
+    #   ) |>
+    #   dplyr::filter(module_name == "DERIVED_QUANTITIES" | module_name == "parms") |>
+    #   dplyr::pull(estimate) |>
+    #   as.numeric() |>
+    #   round(digits = 2)
+
+    # start year of F plot
+    F.start.year <- dat |>
+      dplyr::filter(label == 'fishing_mortality') |>
+      dplyr::slice(which.min(year)) |>
+      dplyr::select(year) |>
+      as.numeric()
 
-  # maximum F
-  F.max <- dat |>
-    dplyr::filter(label == 'fishing_mortality')
+    # terminal fishing mortality
+    # F.end.year : added with add_more_key_quants
 
-  if(length(unique(F.max$age)) == 1){
-    if(is.na(unique(F.max$age))){
+    # minimum F
+    F.min <- dat |>
+      dplyr::filter(label == 'fishing_mortality')
+
+    if(length(unique(F.min$age)) == 1){
+      if(is.na(unique(F.min$age))){
       F.min <- F.min |>
         dplyr::filter(module_name %in% c("TIME_SERIES","t.series")) |>
-        dplyr::slice(which.max(estimate)) |>
+        dplyr::slice(which.min(estimate)) |>
+        as.numeric() |>
+        round(digits = 2)
+      }
+      } else {
+      F.min <- F.min |>
+        dplyr::group_by(age)|>
+        dplyr::summarize(val = max(estimate)) |>
+        dplyr::slice(which.min(val)) |>
+        dplyr::select(val) |>
         as.numeric() |>
         round(digits = 2)
     }
-  } else {
-    F.max <- F.max |>
-      dplyr::group_by(age)|>
-      dplyr::summarize(val = max(estimate)) |>
-      dplyr::slice(which.max(val)) |>
-      dplyr::select(val) |>
-      as.numeric() |>
-      round(digits = 2)
-  }
 
-  # TODO: uncomment and recode once we get clarity about how to extract this value properly
-  # fishing mortality at msy
-  # Ftarg <- dat |>
-  #   dplyr::filter(grepl('f_target', label) |
-  #                   grepl('f_msy', label) |
-  #                   c(grepl('fishing_mortality_msy', label) & is.na(year))) |>
-  #   dplyr::pull(estimate) |>
-  #   as.numeric() |>
-  #   round(digits = 2)
 
-  # Terminal year F respective to F target
-  # F.Ftarg : added with add_more_key_quants
+    # maximum F
+    F.max <- dat |>
+      dplyr::filter(label == 'fishing_mortality')
 
+    if(length(unique(F.max$age)) == 1){
+      if(is.na(unique(F.max$age))){
+        F.min <- F.min |>
+          dplyr::filter(module_name %in% c("TIME_SERIES","t.series")) |>
+          dplyr::slice(which.max(estimate)) |>
+          as.numeric() |>
+          round(digits = 2)
+      }
+    } else {
+      F.max <- F.max |>
+        dplyr::group_by(age)|>
+        dplyr::summarize(val = max(estimate)) |>
+        dplyr::slice(which.max(val)) |>
+        dplyr::select(val) |>
+        as.numeric() |>
+        round(digits = 2)
+    }
 
-  ## landings plot
-
-  # start year of landings plot
-  landings.start.year <- dat |>
-    dplyr::filter(
-      c(module_name == "t.series" & grepl("landings_observed", label)) | c(module_name == "CATCH" & grepl("ret_bio", label)),
-      # t.series is associated with a conversion from BAM output and CATCH with SS3 converted output
-      !is.na(fleet)
-    ) |>
-    dplyr::slice(which.min(year)) |>
-    dplyr::select(year) |>
-    as.numeric()
+    # TODO: uncomment and recode once we get clarity about how to extract this value properly
+    # fishing mortality at msy
+    # Ftarg <- dat |>
+    #   dplyr::filter(grepl('f_target', label) |
+    #                   grepl('f_msy', label) |
+    #                   c(grepl('fishing_mortality_msy', label) & is.na(year))) |>
+    #   dplyr::pull(estimate) |>
+    #   as.numeric() |>
+    #   round(digits = 2)
 
-  # end year of landings plot
-  landings.end.year <- dat |>
-    dplyr::filter(
-      c(module_name == "t.series" & grepl("landings_observed", label)) | c(module_name == "CATCH" & grepl("ret_bio", label)),
-      # t.series is associated with a conversion from BAM output and CATCH with SS3 converted output
-      !is.na(fleet)
-    ) |>
-    dplyr::slice(which.max(year)) |>
-    dplyr::select(year) |>
-    as.numeric()
+    # Terminal year F respective to F target
+    # F.Ftarg : added with add_more_key_quants
 
-  # units of landings (plural)
-  # landings.units : added with add_more_key_quants
 
-  # minimum landings
-  landings.min <- dat |>
-    dplyr::filter(
-      c(module_name == "t.series" & grepl("landings_observed", label)) | c(module_name == "CATCH" & grepl("ret_bio", label)),
-      # t.series is associated with a conversion from BAM output and CATCH with SS3 converted output
-      !is.na(fleet)
-    ) |>
-    dplyr::slice(which.min(estimate)) |>
-    dplyr::select(estimate) |>
-    as.numeric() |>
-    round(digits = 2)
+    ## landings plot
 
-  # maximum landings
-  landings.max <- dat |>
-    dplyr::filter(
-      c(module_name == "t.series" & grepl("landings_observed", label)) | c(module_name == "CATCH" & grepl("ret_bio", label)),
-      # t.series is associated with a conversion from BAM output and CATCH with SS3 converted output
-      !is.na(fleet)
-    ) |>
-    dplyr::group_by(fleet, year) |>
-    dplyr::summarise(max_est = max(estimate)) |>
-    dplyr::filter(!is.na(max_est)) |>
-    dplyr::group_by(year) |>
-    dplyr::summarise(max_est_yr = sum(max_est)) |>
-    dplyr::slice(which.max(max_est_yr)) |>
-    dplyr::select(max_est_yr) |>
-    as.numeric() |>
-    round(digits = 2)
-
-  ## natural mortality (M)- bam examples have label as natural_mortality
-  ## but other formats don't (in input)
-  # minimum age of M
-  if ("natural_mortality" %in% dat$label){
-    M.age.min <- dat |>
-      dplyr::filter(label == "natural_mortality") |>
-      dplyr::select(age) |>
-      dplyr::filter(!is.na(age)) |>
-      dplyr::slice(which.min(age)) |>
-      as.numeric()
-  } else {
-    M.age.min <- dat |>
-    #  dplyr::filter(label == "natural_mortality") |>
-      dplyr::select(age) |>
-      dplyr::filter(!is.na(age)) |>
-      dplyr::slice(which.min(age)) |>
+    # start year of landings plot
+    landings.start.year <- dat |>
+      dplyr::filter(
+        c(module_name == "t.series" & grepl("landings_observed", label)) | c(module_name == "CATCH" & grepl("ret_bio", label)),
+        # t.series is associated with a conversion from BAM output and CATCH with SS3 converted output
+        !is.na(fleet)
+      ) |>
+      dplyr::slice(which.min(year)) |>
+      dplyr::select(year) |>
       as.numeric()
-  }
 
-  # maximum age of M
-  if ("natural_mortality" %in% dat$label){
-    M.age.max <- dat |>
-      dplyr::filter(label == "natural_mortality") |>
-      dplyr::select(age) |>
-      dplyr::filter(!is.na(age)) |>
-      dplyr::slice(which.max(age)) |>
+    # end year of landings plot
+    landings.end.year <- dat |>
+      dplyr::filter(
+        c(module_name == "t.series" & grepl("landings_observed", label)) | c(module_name == "CATCH" & grepl("ret_bio", label)),
+        # t.series is associated with a conversion from BAM output and CATCH with SS3 converted output
+        !is.na(fleet)
+      ) |>
+      dplyr::slice(which.max(year)) |>
+      dplyr::select(year) |>
       as.numeric()
-  } else {
-    M.age.max <- dat |>
+
+    # units of landings (plural)
+    # landings.units : added with add_more_key_quants
+
+    # minimum landings
+    landings.min <- dat |>
+      dplyr::filter(
+        c(module_name == "t.series" & grepl("landings_observed", label)) | c(module_name == "CATCH" & grepl("ret_bio", label)),
+        # t.series is associated with a conversion from BAM output and CATCH with SS3 converted output
+        !is.na(fleet)
+      ) |>
+      dplyr::slice(which.min(estimate)) |>
+      dplyr::select(estimate) |>
+      as.numeric() |>
+      round(digits = 2)
+
+    # maximum landings
+    landings.max <- dat |>
+      dplyr::filter(
+        c(module_name == "t.series" & grepl("landings_observed", label)) | c(module_name == "CATCH" & grepl("ret_bio", label)),
+        # t.series is associated with a conversion from BAM output and CATCH with SS3 converted output
+        !is.na(fleet)
+      ) |>
+      dplyr::group_by(fleet, year) |>
+      dplyr::summarise(max_est = max(estimate)) |>
+      dplyr::filter(!is.na(max_est)) |>
+      dplyr::group_by(year) |>
+      dplyr::summarise(max_est_yr = sum(max_est)) |>
+      dplyr::slice(which.max(max_est_yr)) |>
+      dplyr::select(max_est_yr) |>
+      as.numeric() |>
+      round(digits = 2)
+
+    ## natural mortality (M)- bam examples have label as natural_mortality
+    ## but other formats don't (in input)
+    # minimum age of M
+    if ("natural_mortality" %in% dat$label){
+      M.age.min <- dat |>
+        dplyr::filter(label == "natural_mortality") |>
+        dplyr::select(age) |>
+        dplyr::filter(!is.na(age)) |>
+        dplyr::slice(which.min(age)) |>
+        as.numeric()
+    } else {
+      M.age.min <- dat |>
       #  dplyr::filter(label == "natural_mortality") |>
-      dplyr::select(age) |>
-      dplyr::filter(!is.na(age)) |>
-      dplyr::slice(which.max(age)) |>
-      as.numeric()
-  }
+        dplyr::select(age) |>
+        dplyr::filter(!is.na(age)) |>
+        dplyr::slice(which.min(age)) |>
+        as.numeric()
+    }
 
-  # minimum M rate- don't code quantities yet (see how it's coded in future fig)
-  # M.rate.min <- dat |>
-  #   dplyr::filter(
-  #     grepl("natural_mortality", label)) |>
-  # -label = natural_mortality (min); est in est col
+    # maximum age of M
+    if ("natural_mortality" %in% dat$label){
+      M.age.max <- dat |>
+        dplyr::filter(label == "natural_mortality") |>
+        dplyr::select(age) |>
+        dplyr::filter(!is.na(age)) |>
+        dplyr::slice(which.max(age)) |>
+        as.numeric()
+    } else {
+      M.age.max <- dat |>
+        #  dplyr::filter(label == "natural_mortality") |>
+        dplyr::select(age) |>
+        dplyr::filter(!is.na(age)) |>
+        dplyr::slice(which.max(age)) |>
+        as.numeric()
+    }
 
-  # maximum M rate- don't code quantities yet (see how it's coded in future fig)
-  # M.rate.max <-
-  # -label = natural_mortality (min); est in est col
+    # minimum M rate- don't code quantities yet (see how it's coded in future fig)
+    # M.rate.min <- dat |>
+    #   dplyr::filter(
+    #     grepl("natural_mortality", label)) |>
+    # -label = natural_mortality (min); est in est col
 
+    # maximum M rate- don't code quantities yet (see how it's coded in future fig)
+    # M.rate.max <-
+    # -label = natural_mortality (min); est in est col
 
-  ## vonB LAA (von Bertalanffy growth function + length at age)- don't code quantities yet
-  # vonb.age.min <- # minimum vonB age
-  # vonb.age.max <- # maximum vonB age
 
-  # vonB length units (plural)
-  # vonb.length.units : added with add_more_key_quants
+    ## vonB LAA (von Bertalanffy growth function + length at age)- don't code quantities yet
+    # vonb.age.min <- # minimum vonB age
+    # vonb.age.max <- # maximum vonB age
 
-  # vonb.length.min <- # minimum vonB length
-  # vonb.length.max <- # minimum vonB length
+    # vonB length units (plural)
+    # vonb.length.units : added with add_more_key_quants
 
+    # vonb.length.min <- # minimum vonB length
+    # vonb.length.max <- # minimum vonB length
 
-  ## length-type conversion plot- don't code quantities yet
-  # total length units (plural)
-  # total.length.units : added with add_more_key_quants
 
-  # total.length.min <- # minimum total length
-  # total.length.max <- # maximum total length
-  # fork length units (plural)
-  # fork.length.units : added with add_more_key_quants
+    ## length-type conversion plot- don't code quantities yet
+    # total length units (plural)
+    # total.length.units : added with add_more_key_quants
 
-  # fork.length.min <- # minimum fork length
-  # fork.length.max <- # maximum fork length
+    # total.length.min <- # minimum total length
+    # total.length.max <- # maximum total length
+    # fork length units (plural)
+    # fork.length.units : added with add_more_key_quants
 
+    # fork.length.min <- # minimum fork length
+    # fork.length.max <- # maximum fork length
 
-  ## weight-length conversion plot- don't code quantities yet
-  # length units (plural)
-  # wl.length.units : added with add_more_key_quants
 
-  # wl.length.min <- # minimum length
-  # wl.length.max <- # maximum length
+    ## weight-length conversion plot- don't code quantities yet
+    # length units (plural)
+    # wl.length.units : added with add_more_key_quants
 
-  # weight units (plural)
-  # wl.weight.units : added with add_more_key_quants
+    # wl.length.min <- # minimum length
+    # wl.length.max <- # maximum length
 
-  # wl.weight.min <- # minimum weight
-  # wl.weight.max <- # maximum weight
+    # weight units (plural)
+    # wl.weight.units : added with add_more_key_quants
 
+    # wl.weight.min <- # minimum weight
+    # wl.weight.max <- # maximum weight
 
-  ## maturity schedule (proportion mature)- don't code quantities yet
-  # length units (plural)
-  # prop.mat.length.units : added with add_more_key_quants
 
-  # prop.mat.length.min <- # minimum length
-  # prop.mat.length.max <- # maximum length
+    ## maturity schedule (proportion mature)- don't code quantities yet
+    # length units (plural)
+    # prop.mat.length.units : added with add_more_key_quants
 
+    # prop.mat.length.min <- # minimum length
+    # prop.mat.length.max <- # maximum length
 
-  ## fecundity at length- don't code quantities yet
-  # length units (plural)
-  # fecundity.length.units : added with add_more_key_quants
 
-  # fecundity.length.min <- # minimum length
-  # fecundity.length.max <- # maximum length
+    ## fecundity at length- don't code quantities yet
+    # length units (plural)
+    # fecundity.length.units : added with add_more_key_quants
 
-  # fecundity units (plural)
-  # fecundity.units : added with add_more_key_quants
+    # fecundity.length.min <- # minimum length
+    # fecundity.length.max <- # maximum length
 
-  # fecundity.min <- # minimum fecundity
-  # fecundity.max <- # maximum fecundity
+    # fecundity units (plural)
+    # fecundity.units : added with add_more_key_quants
 
+    # fecundity.min <- # minimum fecundity
+    # fecundity.max <- # maximum fecundity
 
-  ## CAA (catch at age)- don't code quantities yet
-  # fleet.or.survey.name <- # fleet or survey name (SHARED with CAL, below)
-  # caa.age.min <- # minimum age group
-  # caa.age.max <- # maximum age group
 
-  ## CAL (catch at length)- don't code quantities yet
-  # cal.length.min <- # minimum length group
-  # cal.length.max <- # maximum length group
+    ## CAA (catch at age)- don't code quantities yet
+    # fleet.or.survey.name <- # fleet or survey name (SHARED with CAL, below)
+    # caa.age.min <- # minimum age group
+    # caa.age.max <- # maximum age group
 
+    ## CAL (catch at length)- don't code quantities yet
+    # cal.length.min <- # minimum length group
+    # cal.length.max <- # maximum length group
 
-  ## CPUE indices plot- don't code quantities yet
-  # cpue.start.year <- # start year of CPUE indices plot
-  # cpue.end.year <- # end year of CPUE indices plot
 
-  # CPUE units (plural) (SHARED with mod.fit.abun, below)
-  # cpue.units : added with add_more_key_quants
+    ## CPUE indices plot- don't code quantities yet
+    # cpue.start.year <- # start year of CPUE indices plot
+    # cpue.end.year <- # end year of CPUE indices plot
 
-  # cpue.min <- # minimum CPUE (SHARED with mod_fit_abun, below)
-  # cpue.max <- # maximum CPUE (SHARED with mod_fit_abun, below)
+    # CPUE units (plural) (SHARED with mod.fit.abun, below)
+    # cpue.units : added with add_more_key_quants
 
+    # cpue.min <- # minimum CPUE (SHARED with mod_fit_abun, below)
+    # cpue.max <- # maximum CPUE (SHARED with mod_fit_abun, below)
 
-  ## NAA (numbers at age)
-  # start year of NAA plot
-  pop.naa.start.year.min <- dat |>
-    dplyr::filter(label == "abundance" & !is.na(year)) |>
-    dplyr::slice(which.min(year)) |>
-    dplyr::select(year) |>
-    as.numeric()
-
-  # end year of NAA plot
-  pop.naa.end.year.max <- dat |>
-  dplyr::filter(label == "abundance" & !is.na(year)) |>
-    dplyr::slice(which.max(year)) |>
-    dplyr::select(year) |>
-    as.numeric()
-
-  # minimum age
-  pop.naa.age.min <- dat |>
-    dplyr::filter(label == "abundance" & !is.na(year)) |>
-    dplyr::slice(which.min(age)) |>
-    dplyr::select(age) |>
-    as.numeric()
 
-  # maximum age
-  pop.naa.age.max <- dat |>
+    ## NAA (numbers at age)
+    # start year of NAA plot
+    pop.naa.start.year.min <- dat |>
+      dplyr::filter(label == "abundance" & !is.na(year)) |>
+      dplyr::slice(which.min(year)) |>
+      dplyr::select(year) |>
+      as.numeric()
+
+    # end year of NAA plot
+    pop.naa.end.year.max <- dat |>
     dplyr::filter(label == "abundance" & !is.na(year)) |>
-    dplyr::slice(which.max(age)) |>
-    dplyr::select(age) |>
-    as.numeric()
-
-
-  ## mod_fit_catch (model fit to catch ts)- don't code quantities yet
-  # mod.fit.catch.start.year <- # start year of model fit to catch ts plot
-  # mod.fit.catch.end.year <- # end year of model fit to catch ts plot
-
-  # catch units (plural)
-  # mod.fit.catch.units : added with add_more_key_quants
-
-  # mod.fit.catch.min <- # minimum catch
-  # mod.fit.catch.max <- # maximum catch
-
-
-  ## mod_fit_abun (model fit to abundance indices plot)- don't code quantities yet
-  # start year of model fit to abundance indices plot
-  # mod.fit.abun.start.year <-
-
-  # end year of model fit to abundance indices plot
-  # mod.fit.abun.end.year <-
-
-  ## mod_fit_discards- will be by fleet
-  ## for ss3, obs discards not in output file
-  ## -filter labels as discard_observed | discard_predicted | discard
-  ## -then group_by(year, fleet, label), then summarize(estimate_y = sum(estimate))
-  ## ---then, get the following:
-  ## -change alt text so that we add the line's min/max, but analyst has to describe further
-  # mod.fit.discards.start.year <- # start year of model fit to discards plot
-  # mod.fit.discards.end.year <- # end year of F
-  # mod.fit.discards.units <- # discards units (plural)
-  # mod.fit.discards.min <- # minimum discards
-  # mod.fit.discards.max <- # maximum discards
-
-  ## selectivity- don't code quantities yet
-  # selectivity.start.year <- # start year of selectivity plot
-  # selectivity.end.year <- # end year of selectivity plot
-  # selectivity.length.units <- # length units (plural)
-  # selectivity.length.min <- # minimum length
-  # selectivity.length.max <- # maximum length
-
-
-  ## estimated stock recruitment
-  # youngest-age recruited fish (instead of age-0)
-  sr.age.min <- dat |>
-    dplyr::filter(!is.na(year) & !is.na(age)) |>
-    dplyr::slice(which.min(age)) |>
-    dplyr::select(age) |>
-    as.numeric()
-
-  # ssb units (plural)
-  # sr.ssb.units : added with add_more_key_quants
-
-  # minimum ssb
-  sr.ssb.min <- dat |>
-  dplyr::filter(label == "spawning_biomass",
-                module_name == "TIME_SERIES" | module_name == "t.series",
-                !is.na(year),
-                is.na(fleet) | length(unique(fleet)) <= 1,
-                is.na(sex) | length(unique(sex)) <= 1,
-                is.na(area) | length(unique(area)) <= 1,
-                is.na(growth_pattern) | length(unique(growth_pattern)) <= 1,
-                !year %in% year_exclusions
-  ) |> # SS3 and BAM target module names
-    dplyr::slice(which.min(estimate)) |>
-    dplyr::select(estimate) |>
-    as.numeric() |>
-    round(digits = 2)
-
-  # maximum ssb
-  sr.ssb.max <- dat |>
+      dplyr::slice(which.max(year)) |>
+      dplyr::select(year) |>
+      as.numeric()
+
+    # minimum age
+    pop.naa.age.min <- dat |>
+      dplyr::filter(label == "abundance" & !is.na(year)) |>
+      dplyr::slice(which.min(age)) |>
+      dplyr::select(age) |>
+      as.numeric()
+
+    # maximum age
+    pop.naa.age.max <- dat |>
+      dplyr::filter(label == "abundance" & !is.na(year)) |>
+      dplyr::slice(which.max(age)) |>
+      dplyr::select(age) |>
+      as.numeric()
+
+
+    ## mod_fit_catch (model fit to catch ts)- don't code quantities yet
+    # mod.fit.catch.start.year <- # start year of model fit to catch ts plot
+    # mod.fit.catch.end.year <- # end year of model fit to catch ts plot
+
+    # catch units (plural)
+    # mod.fit.catch.units : added with add_more_key_quants
+
+    # mod.fit.catch.min <- # minimum catch
+    # mod.fit.catch.max <- # maximum catch
+
+
+    ## mod_fit_abun (model fit to abundance indices plot)- don't code quantities yet
+    # start year of model fit to abundance indices plot
+    # mod.fit.abun.start.year <-
+
+    # end year of model fit to abundance indices plot
+    # mod.fit.abun.end.year <-
+
+    ## mod_fit_discards- will be by fleet
+    ## for ss3, obs discards not in output file
+    ## -filter labels as discard_observed | discard_predicted | discard
+    ## -then group_by(year, fleet, label), then summarize(estimate_y = sum(estimate))
+    ## ---then, get the following:
+    ## -change alt text so that we add the line's min/max, but analyst has to describe further
+    # mod.fit.discards.start.year <- # start year of model fit to discards plot
+    # mod.fit.discards.end.year <- # end year of F
+    # mod.fit.discards.units <- # discards units (plural)
+    # mod.fit.discards.min <- # minimum discards
+    # mod.fit.discards.max <- # maximum discards
+
+    ## selectivity- don't code quantities yet
+    # selectivity.start.year <- # start year of selectivity plot
+    # selectivity.end.year <- # end year of selectivity plot
+    # selectivity.length.units <- # length units (plural)
+    # selectivity.length.min <- # minimum length
+    # selectivity.length.max <- # maximum length
+
+
+    ## estimated stock recruitment
+    # youngest-age recruited fish (instead of age-0)
+    sr.age.min <- dat |>
+      dplyr::filter(!is.na(year) & !is.na(age)) |>
+      dplyr::slice(which.min(age)) |>
+      dplyr::select(age) |>
+      as.numeric()
+
+    # ssb units (plural)
+    # sr.ssb.units : added with add_more_key_quants
+
+    # minimum ssb
+    sr.ssb.min <- dat |>
     dplyr::filter(label == "spawning_biomass",
                   module_name == "TIME_SERIES" | module_name == "t.series",
                   !is.na(year),
@@ -517,53 +507,32 @@ write_captions <- function(dat, # converted model output object
                   is.na(growth_pattern) | length(unique(growth_pattern)) <= 1,
                   !year %in% year_exclusions
     ) |> # SS3 and BAM target module names
-    dplyr::slice(which.max(estimate)) |>
-    dplyr::select(estimate) |>
-    as.numeric() |>
-    round(digits = 2)
-
-  # recruitment units (plural)
-  # sr.units : added with add_more_key_quants
-
-  # minimum recruitment
-  sr.min <- dat |>
-  dplyr::filter(label == "recruitment",
-                module_name == "TIME_SERIES" | module_name == "t.series",
-                !is.na(year),
-                is.na(fleet) | length(unique(fleet)) <= 1,
-                is.na(sex) | length(unique(sex)) <= 1,
-                is.na(area) | length(unique(area)) <= 1,
-                is.na(growth_pattern) | length(unique(growth_pattern)) <= 1,
-                !year %in% year_exclusions
-  ) |> # SS3 and BAM target module names
-    dplyr::slice(which.min(estimate)) |>
-    dplyr::select(estimate) |>
-    as.numeric() |>
-    round(digits = 2)
-
-  # maximum recruitment
-  sr.max <- dat |>
-    dplyr::filter(label == "recruitment",
-                  module_name == "TIME_SERIES" | module_name == "t.series",
-                  !is.na(year),
-                  is.na(fleet) | length(unique(fleet)) <= 1,
-                  is.na(sex) | length(unique(sex)) <= 1,
-                  is.na(area) | length(unique(area)) <= 1,
-                  is.na(growth_pattern) | length(unique(growth_pattern)) <= 1,
-                  !year %in% year_exclusions
-    ) |> # SS3 and BAM target module names
-    dplyr::slice(which.max(estimate)) |>
-    dplyr::select(estimate) |>
-    as.numeric() |>
-    round(digits = 2)
+      dplyr::slice(which.min(estimate)) |>
+      dplyr::select(estimate) |>
+      as.numeric() |>
+      round(digits = 2)
 
+    # maximum ssb
+    sr.ssb.max <- dat |>
+      dplyr::filter(label == "spawning_biomass",
+                    module_name == "TIME_SERIES" | module_name == "t.series",
+                    !is.na(year),
+                    is.na(fleet) | length(unique(fleet)) <= 1,
+                    is.na(sex) | length(unique(sex)) <= 1,
+                    is.na(area) | length(unique(area)) <= 1,
+                    is.na(growth_pattern) | length(unique(growth_pattern)) <= 1,
+                    !year %in% year_exclusions
+      ) |> # SS3 and BAM target module names
+      dplyr::slice(which.max(estimate)) |>
+      dplyr::select(estimate) |>
+      as.numeric() |>
+      round(digits = 2)
 
-  ## recruitment ts
-  # recruitment units (plural) - numbers of fish, in thousands
-  # recruitment.units : added with add_more_key_quants
+    # recruitment units (plural)
+    # sr.units : added with add_more_key_quants
 
-  # start year of recruitment ts plot
-  recruitment.start.year <- dat |>
+    # minimum recruitment
+    sr.min <- dat |>
     dplyr::filter(label == "recruitment",
                   module_name == "TIME_SERIES" | module_name == "t.series",
                   !is.na(year),
@@ -573,475 +542,510 @@ write_captions <- function(dat, # converted model output object
                   is.na(growth_pattern) | length(unique(growth_pattern)) <= 1,
                   !year %in% year_exclusions
     ) |> # SS3 and BAM target module names
-    dplyr::slice(which.min(year)) |>
-    dplyr::select(year) |>
-    as.numeric()
-
-  # end year of recruitment ts plot
-  # recruitment.end.year : added with add_more_key_quants
-
-  # TODO: uncomment and recode once we get clarity about how to extract R0 properly
-  ## relative recruitment
-  # # minimum relative recruitment
-  # rel.recruitment.min <- (sr.min / R0) |>
-  #   round(digits = 2)
-  #
-  # # maximum relative recruitment
-  # rel.recruitment.max <- (sr.max / R0) |>
-  #   round(digits = 2)
-
-
-  ## recruitment deviations
-  # start year of recruitment deviations plot
-  recruit.dev.start.year <-  dat |>
-    dplyr::filter(label == "recruitment_deviations" | label == "log_recruitment_deviations",
-                  module_name == "SPAWN_RECRUIT" | module_name == "t.series",
-                  !is.na(year),
-                  is.na(fleet) | length(unique(fleet)) <= 1,
-                  is.na(sex) | length(unique(sex)) <= 1,
-                  is.na(area) | length(unique(area)) <= 1,
-                  is.na(growth_pattern) | length(unique(growth_pattern)) <= 1,
-                  !year %in% year_exclusions
-    ) |> # SS3 and BAM target module names
-    dplyr::slice(which.min(year)) |>
-    dplyr::select(year) |>
-    as.numeric()
+      dplyr::slice(which.min(estimate)) |>
+      dplyr::select(estimate) |>
+      as.numeric() |>
+      round(digits = 2)
 
-  # end year of recruitment deviations plot
-  # recruit.dev.end.year : added with add_more_key_quants
+    # maximum recruitment
+    sr.max <- dat |>
+      dplyr::filter(label == "recruitment",
+                    module_name == "TIME_SERIES" | module_name == "t.series",
+                    !is.na(year),
+                    is.na(fleet) | length(unique(fleet)) <= 1,
+                    is.na(sex) | length(unique(sex)) <= 1,
+                    is.na(area) | length(unique(area)) <= 1,
+                    is.na(growth_pattern) | length(unique(growth_pattern)) <= 1,
+                    !year %in% year_exclusions
+      ) |> # SS3 and BAM target module names
+      dplyr::slice(which.max(estimate)) |>
+      dplyr::select(estimate) |>
+      as.numeric() |>
+      round(digits = 2)
 
-  # minimum recruitment deviation
-  recruit.dev.min <- dat |>
-    dplyr::filter(label == "recruitment_deviations" | label == "log_recruitment_deviations",
-                  module_name == "SPAWN_RECRUIT" | module_name == "t.series",
-                  !is.na(year),
-                  is.na(fleet) | length(unique(fleet)) <= 1,
-                  is.na(sex) | length(unique(sex)) <= 1,
-                  is.na(area) | length(unique(area)) <= 1,
-                  is.na(growth_pattern) | length(unique(growth_pattern)) <= 1,
-                  !year %in% year_exclusions
-    ) |> # SS3 and BAM target module names
-    dplyr::slice(which.min(estimate)) |>
-    dplyr::select(estimate) |>
-    as.numeric() |>
-    round(digits = 2)
-
-  # maximum recruitment deviation
-  recruit.dev.max <- dat |>
-    dplyr::filter(label == "recruitment_deviations" | label == "log_recruitment_deviations",
-                  module_name == "SPAWN_RECRUIT" | module_name == "t.series",
-                  !is.na(year),
-                  is.na(fleet) | length(unique(fleet)) <= 1,
-                  is.na(sex) | length(unique(sex)) <= 1,
-                  is.na(area) | length(unique(area)) <= 1,
-                  is.na(growth_pattern) | length(unique(growth_pattern)) <= 1,
-                  !year %in% year_exclusions
-    ) |> # SS3 and BAM target module names
-    dplyr::slice(which.max(estimate)) |>
-    dplyr::select(estimate) |>
-    as.numeric() |>
-    round(digits = 2)
-
-  ## tot_b (total biomass): same as B plot above
-
-  ## spawning_biomass (ssb)
-  # start year of ssb plot
-  ssb.start.year <- dat |>
-  dplyr::filter(
-    label == "spawning_biomass",
-    module_name %in% c("DERIVED_QUANTITIES", "t.series")
-  ) |>
-    dplyr::slice(which.min(year)) |>
-    dplyr::select(year) |>
-    as.numeric() |>
-    round(digits = 2)
-
-  # end year of ssb plot
-  # ssb.end.year : added with add_more_key_quants
-
-  # ssb units (plural)
-  # ssb.units : added with add_more_key_quants
-
-  # minimum ssb
-  ssb.min <- dat |>
-    dplyr::filter(
-      label == "spawning_biomass",
-      module_name %in% c("DERIVED_QUANTITIES", "t.series")
-    ) |>
-    dplyr::slice(which.min(estimate)) |>
-    dplyr::select(estimate) |>
-    as.numeric() |>
-    round(digits = 2)
 
-  # maximum ssb
-  ssb.max <- dat |>
-    dplyr::filter(
-      label == "spawning_biomass",
-      module_name %in% c("DERIVED_QUANTITIES", "t.series")
-    ) |>
-    dplyr::slice(which.max(estimate)) |>
-    dplyr::select(estimate) |>
-    as.numeric() |>
-    round(digits = 2)
-
-  # ssb reference point
-  # ssb.ref.pt : added with add_more_key_quants
-
-  # TODO: uncomment and recode once we get clarity about how to extract this value properly
-  # ssbtarg <- dat |>
-  #   dplyr::filter(c(grepl('spawning_biomass', label) & grepl('msy$', label) & estimate >1) | label == 'spawning_biomass_msy$') |>
-  #   dplyr::pull(estimate) |>
-  #   as.numeric() |>
-  #   round(digits = 2)
-
-  # TODO: uncomment and recode once we get clarity about how to extract ssbtarg properly
-  ## relative ssb
-  # relative ssb min
-  # rel.ssb.min <- (ssb.min / ssbtarg) |>
-  #   round(digits = 2)
-  #
-  # # relative ssb max
-  # rel.ssb.max <- (ssb.max / ssbtarg) |>
-  #   round(digits = 2)
-
-
-  ## spr (spawning potential ratio)
-  # minimum spr
-  spr.min <- dat |>
-    dplyr::filter(c(grepl('spr', label) |
-                      label == "spr") &
-                    !is.na(year) &
-                    !is.na(estimate)) |>
-    dplyr::slice(which.min(estimate)) |>
-    dplyr::select(estimate) |>
-    as.numeric() |>
-    round(digits = 2)
-
-  # maximum spr
-  spr.max <- dat |>
-    dplyr::filter(c(grepl('spr', label) |
-                      label == "spr") & !is.na(year) & !is.na(estimate)) |>
-    dplyr::slice(which.max(estimate)) |>
-    dplyr::select(estimate) |>
-    as.numeric() |>
-    round(digits = 2)
-
-  # TODO: uncomment and recode once we get clarity about how to extract this value properly
-  # spr reference point
-  # spr.ref.pt <- dat |>
-  # dplyr::filter(label == "spr_msy") |>
-  #   dplyr::select(estimate) |>
-  #   as.numeric()# |>
-  #   round(digits = 2)
-
-
-  ## pop.baa (population biomass at age)
-  # start year of pop.baa plot
-  pop.baa.start.year <- dat |>
-    dplyr::filter(label == "abundance") |>
-    dplyr::slice(which.min(year)) |>
-    dplyr::select(year) |>
-    as.numeric()
-
-  # end year of pop.baa plot
-  pop.baa.end.year <- landings.end.year
-
-  # minimum number of fish
-  pop.baa.fish.min <- dat |>
-    dplyr::filter(label == "abundance" & !is.na(year)) |>
-    dplyr::slice(which.min(estimate)) |>
-    dplyr::select(estimate) |>
-    as.numeric() |>
-    round(digits = 2)
+    ## recruitment ts
+    # recruitment units (plural) - numbers of fish, in thousands
+    # recruitment.units : added with add_more_key_quants
+
+    # start year of recruitment ts plot
+    recruitment.start.year <- dat |>
+      dplyr::filter(label == "recruitment",
+                    module_name == "TIME_SERIES" | module_name == "t.series",
+                    !is.na(year),
+                    is.na(fleet) | length(unique(fleet)) <= 1,
+                    is.na(sex) | length(unique(sex)) <= 1,
+                    is.na(area) | length(unique(area)) <= 1,
+                    is.na(growth_pattern) | length(unique(growth_pattern)) <= 1,
+                    !year %in% year_exclusions
+      ) |> # SS3 and BAM target module names
+      dplyr::slice(which.min(year)) |>
+      dplyr::select(year) |>
+      as.numeric()
 
+    # end year of recruitment ts plot
+    # recruitment.end.year : added with add_more_key_quants
+
+    # TODO: uncomment and recode once we get clarity about how to extract R0 properly
+    ## relative recruitment
+    # # minimum relative recruitment
+    # rel.recruitment.min <- (sr.min / R0) |>
+    #   round(digits = 2)
+    #
+    # # maximum relative recruitment
+    # rel.recruitment.max <- (sr.max / R0) |>
+    #   round(digits = 2)
+
+
+    ## recruitment deviations
+    # start year of recruitment deviations plot
+    recruit.dev.start.year <-  dat |>
+      dplyr::filter(label == "recruitment_deviations" | label == "log_recruitment_deviations",
+                    module_name == "SPAWN_RECRUIT" | module_name == "t.series",
+                    !is.na(year),
+                    is.na(fleet) | length(unique(fleet)) <= 1,
+                    is.na(sex) | length(unique(sex)) <= 1,
+                    is.na(area) | length(unique(area)) <= 1,
+                    is.na(growth_pattern) | length(unique(growth_pattern)) <= 1,
+                    !year %in% year_exclusions
+      ) |> # SS3 and BAM target module names
+      dplyr::slice(which.min(year)) |>
+      dplyr::select(year) |>
+      as.numeric()
 
-  # maximum number of fish
-  pop.baa.fish.max <- dat |>
-    dplyr::filter(label == "abundance" & !is.na(year)) |>
-    dplyr::slice(which.max(estimate)) |>
-    dplyr::select(estimate) |>
-    as.numeric() |>
-    round(digits = 2)
+    # end year of recruitment deviations plot
+    # recruit.dev.end.year : added with add_more_key_quants
+
+    # minimum recruitment deviation
+    recruit.dev.min <- dat |>
+      dplyr::filter(label == "recruitment_deviations" | label == "log_recruitment_deviations",
+                    module_name == "SPAWN_RECRUIT" | module_name == "t.series",
+                    !is.na(year),
+                    is.na(fleet) | length(unique(fleet)) <= 1,
+                    is.na(sex) | length(unique(sex)) <= 1,
+                    is.na(area) | length(unique(area)) <= 1,
+                    is.na(growth_pattern) | length(unique(growth_pattern)) <= 1,
+                    !year %in% year_exclusions
+      ) |> # SS3 and BAM target module names
+      dplyr::slice(which.min(estimate)) |>
+      dplyr::select(estimate) |>
+      as.numeric() |>
+      round(digits = 2)
 
+    # maximum recruitment deviation
+    recruit.dev.max <- dat |>
+      dplyr::filter(label == "recruitment_deviations" | label == "log_recruitment_deviations",
+                    module_name == "SPAWN_RECRUIT" | module_name == "t.series",
+                    !is.na(year),
+                    is.na(fleet) | length(unique(fleet)) <= 1,
+                    is.na(sex) | length(unique(sex)) <= 1,
+                    is.na(area) | length(unique(area)) <= 1,
+                    is.na(growth_pattern) | length(unique(growth_pattern)) <= 1,
+                    !year %in% year_exclusions
+      ) |> # SS3 and BAM target module names
+      dplyr::slice(which.max(estimate)) |>
+      dplyr::select(estimate) |>
+      as.numeric() |>
+      round(digits = 2)
 
-  ## proj_catch (projected catch)
-  # projected catch units (plural)
-  # proj.catch.units <- # probably mt, but wait until figure coded
-  # --then add into add_more_key_quants()
+    ## tot_b (total biomass): same as B plot above
 
-  # start year of projected catch plot
-  proj.catch.start.year <- landings.end.year + 1
+    ## spawning_biomass (ssb)
+    # start year of ssb plot
+    ssb.start.year <- dat |>
+    dplyr::filter(
+      label == "spawning_biomass",
+      module_name %in% c("DERIVED_QUANTITIES", "t.series")
+    ) |>
+      dplyr::slice(which.min(year)) |>
+      dplyr::select(year) |>
+      as.numeric() |>
+      round(digits = 2)
 
-  # end year of projected catch plot
-  proj.catch.end.year <- dat |>
-    dplyr::filter(label == "catch" & module_name == "DERIVED_QUANTITIES") |>
-    dplyr::slice(which.max(year)) |>
-    dplyr::select(year) |>
-    as.numeric()
+    # end year of ssb plot
+    # ssb.end.year : added with add_more_key_quants
 
-  # minimum projected catch
-  proj.catch.min <- dat |>
-    # no BAM file has catch; will be NA
-    dplyr::filter(label == "catch" & module_name == "DERIVED_QUANTITIES") |>
-    dplyr::slice(which.min(estimate)) |>
-    dplyr::select(estimate) |>
-    as.numeric()
+    # ssb units (plural)
+    # ssb.units : added with add_more_key_quants
 
-  # maximum projected catch
-  proj.catch.max <- dat |>
-    dplyr::filter(label == "catch" & module_name == "DERIVED_QUANTITIES") |>
-    dplyr::slice(which.max(estimate)) |>
-    dplyr::select(estimate) |>
-    as.numeric()
+    # minimum ssb
+    ssb.min <- dat |>
+      dplyr::filter(
+        label == "spawning_biomass",
+        module_name %in% c("DERIVED_QUANTITIES", "t.series")
+      ) |>
+      dplyr::slice(which.min(estimate)) |>
+      dplyr::select(estimate) |>
+      as.numeric() |>
+      round(digits = 2)
 
-  # TABLES-----
+    # maximum ssb
+    ssb.max <- dat |>
+      dplyr::filter(
+        label == "spawning_biomass",
+        module_name %in% c("DERIVED_QUANTITIES", "t.series")
+      ) |>
+      dplyr::slice(which.max(estimate)) |>
+      dplyr::select(estimate) |>
+      as.numeric() |>
+      round(digits = 2)
 
-  ## catch
-  # catch.fleet <- # fleet
+    # ssb reference point
+    # ssb.ref.pt : added with add_more_key_quants
+
+    # TODO: uncomment and recode once we get clarity about how to extract this value properly
+    # ssbtarg <- dat |>
+    #   dplyr::filter(c(grepl('spawning_biomass', label) & grepl('msy$', label) & estimate >1) | label == 'spawning_biomass_msy$') |>
+    #   dplyr::pull(estimate) |>
+    #   as.numeric() |>
+    #   round(digits = 2)
+
+    # TODO: uncomment and recode once we get clarity about how to extract ssbtarg properly
+    ## relative ssb
+    # relative ssb min
+    # rel.ssb.min <- (ssb.min / ssbtarg) |>
+    #   round(digits = 2)
+    #
+    # # relative ssb max
+    # rel.ssb.max <- (ssb.max / ssbtarg) |>
+    #   round(digits = 2)
+
+
+    ## spr (spawning potential ratio)
+    # minimum spr
+    spr.min <- dat |>
+      dplyr::filter(c(grepl('spr', label) |
+                        label == "spr") &
+                      !is.na(year) &
+                      !is.na(estimate)) |>
+      dplyr::slice(which.min(estimate)) |>
+      dplyr::select(estimate) |>
+      as.numeric() |>
+      round(digits = 2)
 
-  ## landings
-  # landings.tbl.units <- # landings units; remove if units already in table
+    # maximum spr
+    spr.max <- dat |>
+      dplyr::filter(c(grepl('spr', label) |
+                        label == "spr") & !is.na(year) & !is.na(estimate)) |>
+      dplyr::slice(which.max(estimate)) |>
+      dplyr::select(estimate) |>
+      as.numeric() |>
+      round(digits = 2)
 
-  ## discards
-  # discards.tbl.units <- # discards units
+    # TODO: uncomment and recode once we get clarity about how to extract this value properly
+    # spr reference point
+    # spr.ref.pt <- dat |>
+    # dplyr::filter(label == "spr_msy") |>
+    #   dplyr::select(estimate) |>
+    #   as.numeric()# |>
+    #   round(digits = 2)
+
+
+    ## pop.baa (population biomass at age)
+    # start year of pop.baa plot
+    pop.baa.start.year <- dat |>
+      dplyr::filter(label == "abundance") |>
+      dplyr::slice(which.min(year)) |>
+      dplyr::select(year) |>
+      as.numeric()
 
-  ## catchability
-  # catchability.fleet <- # fleet
+    # end year of pop.baa plot
+    pop.baa.end.year <- landings.end.year
 
+    # minimum number of fish
+    pop.baa.fish.min <- dat |>
+      dplyr::filter(label == "abundance" & !is.na(year)) |>
+      dplyr::slice(which.min(estimate)) |>
+      dplyr::select(estimate) |>
+      as.numeric() |>
+      round(digits = 2)
 
-  # add in more quantities here, and update the quantities above
 
-  # substitute quantity placeholders in the captions/alt text with
-  # the real values, extracted above
+    # maximum number of fish
+    pop.baa.fish.max <- dat |>
+      dplyr::filter(label == "abundance" & !is.na(year)) |>
+      dplyr::slice(which.max(estimate)) |>
+      dplyr::select(estimate) |>
+      as.numeric() |>
+      round(digits = 2)
 
 
+    ## proj_catch (projected catch)
+    # projected catch units (plural)
+    # proj.catch.units <- # probably mt, but wait until figure coded
+    # --then add into add_more_key_quants()
 
+    # start year of projected catch plot
+    proj.catch.start.year <- landings.end.year + 1
 
-  # make list with all placeholders
-  # uncomment placeholders once uncommented, above
-  patterns_replacements <- c(
-    # FIGURES-----
+    # end year of projected catch plot
+    proj.catch.end.year <- dat |>
+      dplyr::filter(label == "catch" & module_name == "DERIVED_QUANTITIES") |>
+      dplyr::slice(which.max(year)) |>
+      dplyr::select(year) |>
+      as.numeric()
 
-   ## kobe plot
-   # 'kobe.end.year' = as.character(kobe.end.year),
-   # 'B.BMSY.end.yr' = as.character(B.BMSY.end.yr),
-   # 'F.FMSY.end.yr' = as.character(F.FMSY.end.yr),
-   # 'overfished.status.is.isnot' = as.character(overfished.status.is.isnot),
-   # 'overfishing.status.is.isnot' = as.character(overfishing.status.is.isnot),
-
-   ## Relative biomass plot
-   # NOTE: moving this above biomass so rel.B.min isn't changed to "rel." + B.min (etc.)
-   # 'rel.B.min' = as.character(rel.B.min),
-   # 'rel.B.max' = as.character(rel.B.max),
-
-   ## Biomass plot
-   'B.start.year' = as.character(B.start.year),
-   'B.min' = as.character(B.min),
-   'B.max' = as.character(B.max),
-  # 'R0' = as.character(R0),
-   # 'Bend' = as.character(Bend),
-  # 'Btarg' = as.character(Btarg),
-   # 'Bmsy' = as.character(Bmsy),
+    # minimum projected catch
+    proj.catch.min <- dat |>
+      # no BAM file has catch; will be NA
+      dplyr::filter(label == "catch" & module_name == "DERIVED_QUANTITIES") |>
+      dplyr::slice(which.min(estimate)) |>
+      dplyr::select(estimate) |>
+      as.numeric()
 
-    ## mortality (F) plot
-  # 'F.ref.pt' = as.character(F.ref.pt),
-   'F.start.year' = as.character(F.start.year),
-   'F.min' = as.character(F.min),
-   'F.max' = as.character(F.max),
-  # 'Ftarg' = as.character(Ftarg),
+    # maximum projected catch
+    proj.catch.max <- dat |>
+      dplyr::filter(label == "catch" & module_name == "DERIVED_QUANTITIES") |>
+      dplyr::slice(which.max(estimate)) |>
+      dplyr::select(estimate) |>
+      as.numeric()
 
-    ## landings plot
-   'landings.start.year' = as.character(landings.start.year),
-   'landings.end.year' = as.character(landings.end.year),
-   'landings.min' = as.character(landings.min),
-   'landings.max' = as.character(landings.max),
-
-   ## natural mortality (M)
-   'M.age.min' = as.character(M.age.min),
-   'M.age.max' = as.character(M.age.max),
-   # 'M.rate.min' = as.character(M.rate.min),
-   # 'M.rate.max' = as.character(M.rate.max),
-
-   ## vonB LAA (von Bertalanffy growth function + length at age)
-   # 'vonb.age.min' = as.character(vonb.age.min),
-   # 'vonb.age.max' = as.character(vonb.age.max),
-   # 'vonb.length.units' = as.character(vonb.length.units),
-   # 'vonb.length.min' = as.character(vonb.length.min),
-   # 'vonb.length.max' = as.character(vonb.length.max),
-
-    ## length-type conversion plot
-   # 'total.length.units' = as.character(total.length.units),
-   # 'total.length.min' = as.character(total.length.min),
-   # 'total.length.max' = as.character(total.length.max),
-   # 'fork.length.units' = as.character(fork.length.units),
-   # 'fork.length.min' = as.character(fork.length.min),
-   # 'fork.length.max' = as.character(fork.length.max),
-
-   ## weight-length conversion plot
-   # 'wl.length.units' = as.character(wl.length.units),
-   # 'wl.length.min' = as.character(wl.length.min),
-   # 'wl.length.max' = as.character(wl.length.max),
-   # 'wl.weight.units' = as.character(wl.weight.units),
-   # 'wl.weight.min' = as.character(wl.weight.min),
-   # 'wl.weight.max' = as.character(wl.weight.max),
-
-   ## maturity schedule (proportion mature)
-   # 'prop.mat.length.units' = as.character(prop.mat.length.units),
-   # 'prop.mat.length.min' = as.character(prop.mat.length.min),
-   # 'prop.mat.length.max' = as.character(prop.mat.length.max),
-
-   ## fecundity at length
-   # 'fecundity.length.units' = as.character(fecundity.length.units),
-   # 'fecundity.length.min' = as.character(fecundity.length.min),
-   # 'fecundity.length.max' = as.character(fecundity.length.max),
-   # 'fecundity.units' = as.character(fecundity.units),
-   # 'fecundity.min' = as.character(fecundity.min),
-   # 'fecundity.max' = as.character(fecundity.max),
-
-   ## CAA (catch at age)
-   # 'fleet.or.survey.name' = as.character(fleet.or.survey.name),
-   # 'caa.age.min' = as.character(caa.age.min),
-   # 'caa.age.max' = as.character(caa.age.max),
-
-   ## CAL (catch at length)
-   # 'cal.length.min' = as.character(cal.length.min),
-   # 'cal.length.max' = as.character(cal.length.max),
-
-   ## CPUE indices plot
-   # 'cpue.start.year' = as.character(cpue.start.year),
-   # 'cpue.end.year' = as.character(cpue.end.year),
-   # 'cpue.min' = as.character(cpue.min),
-   # 'cpue.max' = as.character(cpue.max),
-
-   ## NAA (numbers at age)
-   'pop.naa.start.year.min' = as.character(pop.naa.start.year.min),
-   'pop.naa.end.year.max' = as.character(pop.naa.end.year.max),
-   'pop.naa.age.min' = as.character(pop.naa.age.min),
-   'pop.naa.age.max' = as.character(pop.naa.age.max),
-
-   ## mod_fit_catch (model fit to catch ts)
-   # 'mod.fit.catch.start.year' = as.character(mod.fit.catch.start.year),
-   # 'mod.fit.catch.end.year' = as.character(mod.fit.catch.end.year),
-   # 'mod.fit.catch.units' = as.character(mod.fit.catch.units),
-   # 'mod.fit.catch.min' = as.character(mod.fit.catch.min),
-   # 'mod.fit.catch.max' = as.character(mod.fit.catch.max),
-
-   ## mod_fit_abun (model fit to abundance indices plot)
-   # 'mod.fit.abun.start.year' = as.character(mod.fit.abun.start.year),
-   # 'mod.fit.abun.end.year' = as.character(mod.fit.abun.end.year),
-
-   ## mod_fit_discards
-   # 'mod.fit.discards.start.year' = as.character(mod.fit.discards.start.year),
-   # 'mod.fit.discards.end.year' = as.character(mod.fit.discards.end.year),
-   # 'mod.fit.discards.units' = as.character(mod.fit.discards.units),
-   # 'mod.fit.discards.min' = as.character(mod.fit.discards.min),
-   # 'mod.fit.discards.max' = as.character(mod.fit.discards.max),
-
-   ## selectivity
-   # 'selectivity.start.year' = as.character(selectivity.start.year),
-   # 'selectivity.end.year' = as.character(selectivity.end.year),
-   # 'selectivity.length.units' = as.character(selectivity.length.units),
-   # 'selectivity.length.min' = as.character(selectivity.length.min),
-   # 'selectivity.length.max' = as.character(selectivity.length.max),
-
-   ## estimated stock recruitment (aka spawning stock biomass)
-   # 'sr.age.min' = as.character(sr.age.min),
-   'sr.ssb.min' = as.character(sr.ssb.min),
-   'sr.ssb.max' = as.character(sr.ssb.max),
-   'sr.min' = as.character(sr.min),
-   'sr.max' = as.character(sr.max),
-
-   # relative recruitment ts
-   # NOTE: moving this above recruitment so rel.recruitment.min isn't changed
-   # to "rel." + recruitment.min (etc.)
-  # 'rel.recruitment.min' = as.character(rel.recruitment.min),
-  # 'rel.recruitment.max' = as.character(rel.recruitment.max),
-
-   ## recruitment ts
-   'recruitment.start.year' = as.character(recruitment.start.year),
-
-   ## recruitment deviations
-   'recruit.dev.start.year' = as.character(recruit.dev.start.year),
-   'recruit.dev.min' = as.character(recruit.dev.min),
-   'recruit.dev.max' = as.character(recruit.dev.max),
-
-   # relative ssb
-   # NOTE: moving this above recruitment so rel.ssb.min isn't changed to
-   # "rel." + ssb.min), etc.
- #  'rel.ssb.min' = as.character(rel.ssb.min),
- #  'rel.ssb.max' = as.character(rel.ssb.max),
-
-   ## spawning.biomass (ssb)
-   'ssb.start.year' = as.character(ssb.start.year),
-   'ssb.min' = as.character(ssb.min),
-   'ssb.max' = as.character(ssb.max),
-   # 'ssbtarg' = as.character(ssbtarg),
-
-
-   ## spr (spawning potential ratio)
-   'spr.min' = as.character(spr.min),
-   'spr.max' = as.character(spr.max),
- #  'spr.ref.pt' = as.character(spr.ref.pt),
-
-   # ## pop.baa (population biomass at age)
-   'pop.baa.start.year' = as.character(pop.baa.start.year),
-   'pop.baa.end.year' = as.character(pop.baa.end.year),
-   'pop.baa.fish.min' = as.character(pop.baa.fish.min),
-   'pop.baa.fish.max' = as.character(pop.baa.fish.max),
-
-   ## proj_catch (projected catch)
-   # 'proj.catch.units' = as.character(proj.catch.units),
-   'proj.catch.start.year' = as.character(proj.catch.start.year),
-   'proj.catch.end.year' = as.character(proj.catch.end.year),
-   'proj.catch.min' = as.character(proj.catch.min),
-   'proj.catch.max' = as.character(proj.catch.max)#,
-
-   # # TABLES-----
-   #
-   # ## catch
-   # 'catch.fleet' = as.character(catch.fleet),
-   #
-   # ## landings
-   # 'landings.tbl.units' = as.character(landings.tbl.units),
-   #
-   # ## discards
-   # 'discards.tbl.units' = as.character(discards.tbl.units),
-   #
-   # ## catchability
-   # 'catchability.fleet' = as.character(catchability.fleet)
-
-  )
-
-  # If a value in patterns_replacements = NA, then make it "NA"
-  # to avoid errors
-  patterns_replacements <- tidyr::replace_na(patterns_replacements, "NA")
-
-  # take the values associated with the quantities and replace the df's
-  # placeholders with them. For example, if ssb_min = 10, this will replace
-  # "the minimum ssb = ssb_min" with "the minimum ssb = 10".
-
-  # replace values in caption column
-  caps_alttext$caption <- stringr::str_replace_all(
-    caps_alttext$caption,
-    patterns_replacements
-  )
-
-  # replace values in alt text column
-  caps_alttext$alt_text <- stringr::str_replace_all(
-    caps_alttext$alt_text,
-    patterns_replacements
-  )
-
-  # export df with updated captions and alt text to csv
- utils::write.csv(x = caps_alttext,
-            file = fs::path(dir,
-                             "captions_alt_text.csv"),
-            row.names=FALSE)
-
-
-
- # enable warnings again
- options(warn=0)
+    # TABLES-----
+
+    ## catch
+    # catch.fleet <- # fleet
+
+    ## landings
+    # landings.tbl.units <- # landings units; remove if units already in table
+
+    ## discards
+    # discards.tbl.units <- # discards units
+
+    ## catchability
+    # catchability.fleet <- # fleet
+
+
+    # add in more quantities here, and update the quantities above
+
+    # substitute quantity placeholders in the captions/alt text with
+    # the real values, extracted above
+
+
+
+
+    # make list with all placeholders
+    # uncomment placeholders once uncommented, above
+    patterns_replacements <- c(
+      # FIGURES-----
+
+     ## kobe plot
+     # 'kobe.end.year' = as.character(kobe.end.year),
+     # 'B.BMSY.end.yr' = as.character(B.BMSY.end.yr),
+     # 'F.FMSY.end.yr' = as.character(F.FMSY.end.yr),
+     # 'overfished.status.is.isnot' = as.character(overfished.status.is.isnot),
+     # 'overfishing.status.is.isnot' = as.character(overfishing.status.is.isnot),
+
+     ## Relative biomass plot
+     # NOTE: moving this above biomass so rel.B.min isn't changed to "rel." + B.min (etc.)
+     # 'rel.B.min' = as.character(rel.B.min),
+     # 'rel.B.max' = as.character(rel.B.max),
+
+     ## Biomass plot
+     'B.start.year' = as.character(B.start.year),
+     'B.min' = as.character(B.min),
+     'B.max' = as.character(B.max),
+    # 'R0' = as.character(R0),
+     # 'Bend' = as.character(Bend),
+    # 'Btarg' = as.character(Btarg),
+     # 'Bmsy' = as.character(Bmsy),
+
+      ## mortality (F) plot
+    # 'F.ref.pt' = as.character(F.ref.pt),
+     'F.start.year' = as.character(F.start.year),
+     'F.min' = as.character(F.min),
+     'F.max' = as.character(F.max),
+    # 'Ftarg' = as.character(Ftarg),
+
+      ## landings plot
+     'landings.start.year' = as.character(landings.start.year),
+     'landings.end.year' = as.character(landings.end.year),
+     'landings.min' = as.character(landings.min),
+     'landings.max' = as.character(landings.max),
+
+     ## natural mortality (M)
+     'M.age.min' = as.character(M.age.min),
+     'M.age.max' = as.character(M.age.max),
+     # 'M.rate.min' = as.character(M.rate.min),
+     # 'M.rate.max' = as.character(M.rate.max),
+
+     ## vonB LAA (von Bertalanffy growth function + length at age)
+     # 'vonb.age.min' = as.character(vonb.age.min),
+     # 'vonb.age.max' = as.character(vonb.age.max),
+     # 'vonb.length.units' = as.character(vonb.length.units),
+     # 'vonb.length.min' = as.character(vonb.length.min),
+     # 'vonb.length.max' = as.character(vonb.length.max),
+
+      ## length-type conversion plot
+     # 'total.length.units' = as.character(total.length.units),
+     # 'total.length.min' = as.character(total.length.min),
+     # 'total.length.max' = as.character(total.length.max),
+     # 'fork.length.units' = as.character(fork.length.units),
+     # 'fork.length.min' = as.character(fork.length.min),
+     # 'fork.length.max' = as.character(fork.length.max),
+
+     ## weight-length conversion plot
+     # 'wl.length.units' = as.character(wl.length.units),
+     # 'wl.length.min' = as.character(wl.length.min),
+     # 'wl.length.max' = as.character(wl.length.max),
+     # 'wl.weight.units' = as.character(wl.weight.units),
+     # 'wl.weight.min' = as.character(wl.weight.min),
+     # 'wl.weight.max' = as.character(wl.weight.max),
+
+     ## maturity schedule (proportion mature)
+     # 'prop.mat.length.units' = as.character(prop.mat.length.units),
+     # 'prop.mat.length.min' = as.character(prop.mat.length.min),
+     # 'prop.mat.length.max' = as.character(prop.mat.length.max),
+
+     ## fecundity at length
+     # 'fecundity.length.units' = as.character(fecundity.length.units),
+     # 'fecundity.length.min' = as.character(fecundity.length.min),
+     # 'fecundity.length.max' = as.character(fecundity.length.max),
+     # 'fecundity.units' = as.character(fecundity.units),
+     # 'fecundity.min' = as.character(fecundity.min),
+     # 'fecundity.max' = as.character(fecundity.max),
+
+     ## CAA (catch at age)
+     # 'fleet.or.survey.name' = as.character(fleet.or.survey.name),
+     # 'caa.age.min' = as.character(caa.age.min),
+     # 'caa.age.max' = as.character(caa.age.max),
+
+     ## CAL (catch at length)
+     # 'cal.length.min' = as.character(cal.length.min),
+     # 'cal.length.max' = as.character(cal.length.max),
+
+     ## CPUE indices plot
+     # 'cpue.start.year' = as.character(cpue.start.year),
+     # 'cpue.end.year' = as.character(cpue.end.year),
+     # 'cpue.min' = as.character(cpue.min),
+     # 'cpue.max' = as.character(cpue.max),
+
+     ## NAA (numbers at age)
+     'pop.naa.start.year.min' = as.character(pop.naa.start.year.min),
+     'pop.naa.end.year.max' = as.character(pop.naa.end.year.max),
+     'pop.naa.age.min' = as.character(pop.naa.age.min),
+     'pop.naa.age.max' = as.character(pop.naa.age.max),
+
+     ## mod_fit_catch (model fit to catch ts)
+     # 'mod.fit.catch.start.year' = as.character(mod.fit.catch.start.year),
+     # 'mod.fit.catch.end.year' = as.character(mod.fit.catch.end.year),
+     # 'mod.fit.catch.units' = as.character(mod.fit.catch.units),
+     # 'mod.fit.catch.min' = as.character(mod.fit.catch.min),
+     # 'mod.fit.catch.max' = as.character(mod.fit.catch.max),
+
+     ## mod_fit_abun (model fit to abundance indices plot)
+     # 'mod.fit.abun.start.year' = as.character(mod.fit.abun.start.year),
+     # 'mod.fit.abun.end.year' = as.character(mod.fit.abun.end.year),
+
+     ## mod_fit_discards
+     # 'mod.fit.discards.start.year' = as.character(mod.fit.discards.start.year),
+     # 'mod.fit.discards.end.year' = as.character(mod.fit.discards.end.year),
+     # 'mod.fit.discards.units' = as.character(mod.fit.discards.units),
+     # 'mod.fit.discards.min' = as.character(mod.fit.discards.min),
+     # 'mod.fit.discards.max' = as.character(mod.fit.discards.max),
+
+     ## selectivity
+     # 'selectivity.start.year' = as.character(selectivity.start.year),
+     # 'selectivity.end.year' = as.character(selectivity.end.year),
+     # 'selectivity.length.units' = as.character(selectivity.length.units),
+     # 'selectivity.length.min' = as.character(selectivity.length.min),
+     # 'selectivity.length.max' = as.character(selectivity.length.max),
+
+     ## estimated stock recruitment (aka spawning stock biomass)
+     # 'sr.age.min' = as.character(sr.age.min),
+     'sr.ssb.min' = as.character(sr.ssb.min),
+     'sr.ssb.max' = as.character(sr.ssb.max),
+     'sr.min' = as.character(sr.min),
+     'sr.max' = as.character(sr.max),
+
+     # relative recruitment ts
+     # NOTE: moving this above recruitment so rel.recruitment.min isn't changed
+     # to "rel." + recruitment.min (etc.)
+    # 'rel.recruitment.min' = as.character(rel.recruitment.min),
+    # 'rel.recruitment.max' = as.character(rel.recruitment.max),
+
+     ## recruitment ts
+     'recruitment.start.year' = as.character(recruitment.start.year),
+
+     ## recruitment deviations
+     'recruit.dev.start.year' = as.character(recruit.dev.start.year),
+     'recruit.dev.min' = as.character(recruit.dev.min),
+     'recruit.dev.max' = as.character(recruit.dev.max),
+
+     # relative ssb
+     # NOTE: moving this above recruitment so rel.ssb.min isn't changed to
+     # "rel." + ssb.min), etc.
+   #  'rel.ssb.min' = as.character(rel.ssb.min),
+   #  'rel.ssb.max' = as.character(rel.ssb.max),
+
+     ## spawning.biomass (ssb)
+     'ssb.start.year' = as.character(ssb.start.year),
+     'ssb.min' = as.character(ssb.min),
+     'ssb.max' = as.character(ssb.max),
+     # 'ssbtarg' = as.character(ssbtarg),
+
+
+     ## spr (spawning potential ratio)
+     'spr.min' = as.character(spr.min),
+     'spr.max' = as.character(spr.max),
+   #  'spr.ref.pt' = as.character(spr.ref.pt),
+
+     # ## pop.baa (population biomass at age)
+     'pop.baa.start.year' = as.character(pop.baa.start.year),
+     'pop.baa.end.year' = as.character(pop.baa.end.year),
+     'pop.baa.fish.min' = as.character(pop.baa.fish.min),
+     'pop.baa.fish.max' = as.character(pop.baa.fish.max),
+
+     ## proj_catch (projected catch)
+     # 'proj.catch.units' = as.character(proj.catch.units),
+     'proj.catch.start.year' = as.character(proj.catch.start.year),
+     'proj.catch.end.year' = as.character(proj.catch.end.year),
+     'proj.catch.min' = as.character(proj.catch.min),
+     'proj.catch.max' = as.character(proj.catch.max)#,
+
+     # # TABLES-----
+     #
+     # ## catch
+     # 'catch.fleet' = as.character(catch.fleet),
+     #
+     # ## landings
+     # 'landings.tbl.units' = as.character(landings.tbl.units),
+     #
+     # ## discards
+     # 'discards.tbl.units' = as.character(discards.tbl.units),
+     #
+     # ## catchability
+     # 'catchability.fleet' = as.character(catchability.fleet)
+
+    )
+
+    # If a value in patterns_replacements = NA, then make it "NA"
+    # to avoid errors
+    patterns_replacements <- tidyr::replace_na(patterns_replacements, "NA")
+
+    # take the values associated with the quantities and replace the df's
+    # placeholders with them. For example, if ssb_min = 10, this will replace
+    # "the minimum ssb = ssb_min" with "the minimum ssb = 10".
+
+    # replace values in caption column
+    caps_alttext$caption <- stringr::str_replace_all(
+      caps_alttext$caption,
+      patterns_replacements
+    )
+
+    # replace values in alt text column
+    caps_alttext$alt_text <- stringr::str_replace_all(
+      caps_alttext$alt_text,
+      patterns_replacements
+    )
+
+    # export df with updated captions and alt text to csv
+   utils::write.csv(x = caps_alttext,
+              file = fs::path(dir,
+                               "captions_alt_text.csv"),
+              row.names=FALSE)
+
+   # enable warnings again
+   options(warn=0)
 
+  }
 }
-