Changed projected biomass csv entry from figure to table; updated key…

… quantities coding in write_captions
nmfs-ost · Jan 16, 2025 · 442e89c · 442e89c
1 parent a1f3989
commit 442e89c
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diff --git a/R/write_captions.R b/R/write_captions.R
@@ -24,6 +24,11 @@ write_captions <- function(dat, # converted model output object
     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.
@@ -63,7 +68,6 @@ write_captions <- function(dat, # converted model output object
                   module_name == "TIME_SERIES" | module_name == "t.series", # SS3 and BAM target module names
                   is.na(fleet),
                   is.na(age)) |>
-    dplyr::mutate(estimate = as.numeric(estimate)) |>
     dplyr::slice(which.min(year)) |>
     dplyr::select(year) |>
     as.numeric()
@@ -80,7 +84,6 @@ write_captions <- function(dat, # converted model output object
                   module_name == "TIME_SERIES" | module_name == "t.series", # SS3 and BAM target module names
                   is.na(fleet),
                   is.na(age)) |>
-    dplyr::mutate(estimate = as.numeric(estimate)) |>
     dplyr::slice(which.min(estimate)) |>
     dplyr::select(estimate) |>
     as.numeric() |>
@@ -92,7 +95,6 @@ write_captions <- function(dat, # converted model output object
                   module_name == "TIME_SERIES" | module_name == "t.series", # SS3 and BAM target module names
                   is.na(fleet),
                   is.na(age)) |>
-    dplyr::mutate(estimate = as.numeric(estimate)) |>
     dplyr::slice(which.max(estimate)) |>
     dplyr::select(estimate) |>
     as.numeric() |>
@@ -173,7 +175,7 @@ write_captions <- function(dat, # converted model output object
     } else {
     F.min <- F.min |>
       dplyr::group_by(age)|>
-      dplyr::summarize(val = min(estimate)) |>
+      dplyr::summarize(val = max(estimate)) |>
       dplyr::slice(which.min(val)) |>
       dplyr::select(val) |>
       as.numeric() |>
@@ -191,7 +193,7 @@ write_captions <- function(dat, # converted model output object
         dplyr::filter(module_name %in% c("TIME_SERIES","t.series")) |>
         dplyr::slice(which.max(estimate)) |>
         as.numeric() |>
-        signif(2)
+        round(digits = 2)
     }
   } else {
     F.max <- F.max |>
@@ -200,7 +202,7 @@ write_captions <- function(dat, # converted model output object
       dplyr::slice(which.max(val)) |>
       dplyr::select(val) |>
       as.numeric() |>
-      signif(2)
+      round(digits = 2)
   }
 
 
@@ -227,9 +229,6 @@ write_captions <- function(dat, # converted model output object
       # t.series is associated with a conversion from BAM output and CATCH with SS3 converted output
       !is.na(fleet)
     ) |>
-    dplyr::mutate(
-      year = as.numeric(year)
-    ) |>
     dplyr::slice(which.min(year)) |>
     dplyr::select(year) |>
     as.numeric()
@@ -241,9 +240,6 @@ write_captions <- function(dat, # converted model output object
       # t.series is associated with a conversion from BAM output and CATCH with SS3 converted output
       !is.na(fleet)
     ) |>
-    dplyr::mutate(
-      year = as.numeric(year)
-    ) |>
     dplyr::slice(which.max(year)) |>
     dplyr::select(year) |>
     as.numeric()
@@ -258,10 +254,6 @@ write_captions <- function(dat, # converted model output object
       # t.series is associated with a conversion from BAM output and CATCH with SS3 converted output
       !is.na(fleet)
     ) |>
-    dplyr::mutate(
-      estimate = as.numeric(estimate),
-      year = as.numeric(year)
-    ) |>
     dplyr::slice(which.min(estimate)) |>
     dplyr::select(estimate) |>
     as.numeric() |>
@@ -274,17 +266,13 @@ write_captions <- function(dat, # converted model output object
       # t.series is associated with a conversion from BAM output and CATCH with SS3 converted output
       !is.na(fleet)
     ) |>
-    dplyr::mutate(
-      estimate = as.numeric(estimate),
-      year = as.numeric(year)
-    ) |>
     dplyr::slice(which.max(estimate)) |>
     dplyr::select(estimate) |>
     as.numeric() |>
     round(digits = 2)
 
-  ## natural mortality (M)
-  ## Should this be filtered by label = natural_mortality too?
+  ## natural mortality (M)- bam examples have label as natural_mortality
+  ## but other formats don't (in input) (use if statement)
   # minimum age of M
   # M.age.min <- dat |>
   #   dplyr::select(age) |>
@@ -300,13 +288,13 @@ write_captions <- function(dat, # converted model output object
   #   as.numeric()
 
   # minimum M rate
-  # The "petrale_sole_std_output.csv" doesn't have a natural_mortality label...
-  # doesn't work
   # M.rate.min <- dat |>
   #   dplyr::filter(
   #     grepl("natural_mortality", label)) |>
   # -label = natural_mortality (min); est in est col
-  # M.rate.max <- # maximum M rate
+
+  # maximum M rate
+  # M.rate.max <-
   # -label = natural_mortality (min); est in est col
 
 
@@ -481,8 +469,6 @@ 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::mutate(estimate = as.numeric(estimate),
-                  year = as.numeric(year)) |>
     dplyr::slice(which.min(estimate)) |>
     dplyr::select(estimate) |>
     as.numeric() |>
@@ -499,8 +485,6 @@ 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::mutate(estimate = as.numeric(estimate),
-                  year = as.numeric(year)) |>
     dplyr::slice(which.max(estimate)) |>
     dplyr::select(estimate) |>
     as.numeric() |>
@@ -520,8 +504,6 @@ 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::mutate(estimate = as.numeric(estimate),
-                  year = as.numeric(year)) |>
     dplyr::slice(which.min(estimate)) |>
     dplyr::select(estimate) |>
     as.numeric() |>
@@ -538,8 +520,6 @@ 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::mutate(estimate = as.numeric(estimate),
-                  year = as.numeric(year)) |>
     dplyr::slice(which.max(estimate)) |>
     dplyr::select(estimate) |>
     as.numeric() |>
@@ -561,8 +541,6 @@ 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::mutate(estimate = as.numeric(estimate),
-                  year = as.numeric(year)) |>
     dplyr::slice(which.min(year)) |>
     dplyr::select(year) |>
     as.numeric()
@@ -593,8 +571,6 @@ 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::mutate(estimate = as.numeric(estimate),
-                  year = as.numeric(year)) |>
     dplyr::slice(which.min(year)) |>
     dplyr::select(year) |>
     as.numeric()
@@ -613,8 +589,6 @@ 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::mutate(estimate = as.numeric(estimate),
-                  year = as.numeric(year)) |>
     dplyr::slice(which.min(estimate)) |>
     dplyr::select(estimate) |>
     as.numeric() |>
@@ -645,9 +619,6 @@ write_captions <- function(dat, # converted model output object
     label == "spawning_biomass",
     module_name %in% c("DERIVED_QUANTITIES", "t.series")
   ) |>
-    dplyr::mutate(
-      estimate = as.numeric(estimate),
-      year = as.numeric(year)) |>
     dplyr::slice(which.min(year)) |>
     dplyr::select(year) |>
     as.numeric() |>
@@ -665,9 +636,6 @@ write_captions <- function(dat, # converted model output object
       label == "spawning_biomass",
       module_name %in% c("DERIVED_QUANTITIES", "t.series")
     ) |>
-    dplyr::mutate(
-      estimate = as.numeric(estimate),
-      year = as.numeric(year)) |>
     dplyr::slice(which.min(estimate)) |>
     dplyr::select(estimate) |>
     as.numeric() |>
@@ -679,9 +647,6 @@ write_captions <- function(dat, # converted model output object
       label == "spawning_biomass",
       module_name %in% c("DERIVED_QUANTITIES", "t.series")
     ) |>
-    dplyr::mutate(
-      estimate = as.numeric(estimate),
-      year = as.numeric(year)) |>
     dplyr::slice(which.max(estimate)) |>
     dplyr::select(estimate) |>
     as.numeric() |>
@@ -732,35 +697,67 @@ write_captions <- function(dat, # converted model output object
     as.numeric() |>
     round(digits = 2)
 
-  #TODO: LEFT OFF HERE (identifying how to extract quantities with Sam, Steve)
+
   ## pop_naa_baa (population numbers at age and population biomass at age)
-  # pop.naa.baa.start.year <- # start year of spr plot
-  # pop.naa.baa.end.year <- # end year of spr plot
-  # pop.naa.baa.fish.min <- # minimum number of fish
-  # pop.naa.baa.fish.max <- # maximum number of fish
+  # start year of pop_naa_baa plot
+  pop.naa.baa.start.year <- dat |>
+    dplyr::filter(label == "abundance") |>
+    dplyr::slice(which.min(year)) |>
+    dplyr::select(year) |>
+    as.numeric()
+
+  # end year of pop_naa_baa plot
+  pop.naa.baa.end.year <- landings.end.year
+
+  # minimum number of fish
+  pop.naa.baa.fish.min <- dat |>
+    dplyr::filter(label == "abundance") |>
+    dplyr::group_by(year) |>
+    dplyr::summarize(val = max(estimate)) |>
+    dplyr::slice(which.min(val)) |>
+    dplyr::select(val) |>
+    as.numeric() |>
+    round(digits = 2)
+
+  # maximum number of fish
+  pop.naa.baa.fish.max <- dat |>
+    dplyr::filter(label == "abundance") |>
+    dplyr::group_by(year) |>
+    dplyr::summarize(val = max(estimate)) |>
+    dplyr::slice(which.max(val)) |>
+    dplyr::select(val) |>
+    as.numeric() |>
+    round(digits = 2)
+
 
   ## proj_catch (projected catch)
-  # proj.catch.units <- # projected catch units (plural)
-  # proj.catch.start.year <- # start year of projected catch plot
-  # proj.catch.end.year <- # end year of projected catch plot
-  # proj.catch.min <- # minimum projected catch
-  # proj.catch.max <- # maximum projected catch
-
-  ## proj_biomass (projected biomass)
-  # proj.biomass.units <- # projected biomass units (plural)
-  # proj.biomass.start.year <- # start year of projected biomass plot
-  # proj.biomass.end.year <- # end year of projected biomass plot
-  # proj.biomass.min <- # minimum projected biomass
-  # proj.biomass.max <- # maximum projected biomass
-  # proj.biomass.ref.pt <- # projected biomass reference point
-  # proj.biomass.ref.pt.units <- # projected biomass reference point units
-
-  ## Other
-  # tot.catch <-
-  # M <-
-  # steep <-
-  # SBmsy <-
-  # fSB <-
+  # 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
+
+  # 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()
+
+  # minimum projected catch
+  proj.catch.min <- dat |>
+    dplyr::filter(label == "catch" & module_name == "DERIVED_QUANTITIES") |>
+    dplyr::slice(which.min(estimate)) |>
+    dplyr::select(estimate) |>
+    as.numeric()
+
+  # 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()
 
   # TABLES-----
 
@@ -949,38 +946,21 @@ write_captions <- function(dat, # converted model output object
    ## spr (spawning potential ratio)
    'spr.min' = as.character(spr.min),
    'spr.max' = as.character(spr.max),
-   'spr.ref.pt' = as.character(spr.ref.pt)#,
+   'spr.ref.pt' = as.character(spr.ref.pt),
 
    # ## pop_naa_baa (population numbers at age and population biomass at age)
-   # 'pop.naa.baa.start.year' = as.character(pop.naa.baa.start.year),
-   # 'pop.naa.baa.end.year' = as.character(pop.naa.baa.end.year),
-   # 'pop.naa.baa.fish.min' = as.character(pop.naa.baa.fish.min),
-   # 'pop.naa.baa.fish.max' = as.character(pop.naa.baa.fish.max),
-   #
-   # ## proj_catch (projected catch)
+   'pop.naa.baa.start.year' = as.character(pop.naa.baa.start.year),
+   'pop.naa.baa.end.year' = as.character(pop.naa.baa.end.year),
+   'pop.naa.baa.fish.min' = as.character(pop.naa.baa.fish.min),
+   'pop.naa.baa.fish.max' = as.character(pop.naa.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),
-   #
-   # ## proj_biomass (projected biomass)
-   # 'proj.biomass.units' = as.character(proj.biomass.units),
-   # 'proj.biomass.start.year' = as.character(proj.biomass.start.year),
-   # 'proj.biomass.end.year' = as.character(proj.biomass.end.year),
-   # 'proj.biomass.min' = as.character(proj.biomass.min),
-   # 'proj.biomass.max' = as.character(proj.biomass.max),
-   # 'proj.biomass.ref.pt' = as.character(proj.biomass.ref.pt),
-   # 'proj.biomass.ref.pt.units' = as.character(proj.biomass.ref.pt.units),
-   #
-   # ## Other
-   # 'tot.catch' = as.character(tot.catch),
-   # 'M' = as.character(M),
-   # 'steep' = as.character(steep),
-   # 'SBmsy' = as.character(SBmsy),
-   # 'fSB' = as.character(fSB),
-   # 'sbtarg' = as.character(sbtarg),
-   #
+   '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
@@ -994,6 +974,7 @@ write_captions <- function(dat, # converted model output object
    #
    # ## catchability
    # 'catchability.fleet' = as.character(catchability.fleet)
+
   )
 
   # take the values associated with the quantities and replace the df's

diff --git a/inst/resources/captions_alt_text_template.csv b/inst/resources/captions_alt_text_template.csv
@@ -28,7 +28,7 @@ relative.spawning.biomass,figure,Model-estimated relative spawning stock biomass
 spr,figure,Model-estimated spawning potential ratio (SPR) (SSB~current~/SSB~target~) time series. The horizontal dashed line represents the spawning potential ratio of the limit reference point at spr.ref.pt.,"Line graph showing model-estimated spawning potential ratio over time. The x axis shows the year, which spans from ssb.start.year to ssb.end.year . The y axis shows model-estimated spawning potential ratio in SSB~current~/SSB~target~, which spans from spr.min to spr.max."
 pop.naa.baa,figure,Model-estimated population numbers at age and population biomass at age over time. The relative size of each bubble for a given year and age indicates the relative abundance or biomass in that category compared with others. ,"Bubble plot showing model-estimated population numbers at age and population biomass at age. The x axis shows the year, which spans from pop.naa.baa.start.year to pop.naa.baa.end.year . The y axis shows numbers of fish, which spans from pop.naa.baa.fish.min to pop.naa.baa.fish.max."
 proj.catch,figure,Forecasted catch in proj.catch.units over future years for different fishing mortality scenarios as indicated in the legend.,"Time series line graph showing forecasted catch over future years for different fishing mortality scenarios. The x axis shows the year, which spans from proj.catch.start.year to proj.catch.end.year . The y axis shows catch in proj.catch.units, which spans from proj.catch.min to proj.catch.max."
-proj.biomass,figure,Forecasted biomass in proj.biomass.units over future years for different fishing mortality scenarios as indicated in the legend. The horizontal dashed line represents the biomass limit reference point. ,"Time series line graph showing forecasted biomass over future years for different fishing mortality scenarios. The horizontal dashed line represents the biomass limit reference point. The x axis shows the year, which spans from proj.biomass.start.year to proj.biomass.end.year . The y axis shows biomass in proj.biomass.units, which spans from proj.biomass.min to proj.biomass.max."
+proj.biomass,table,Forecasted biomass over future years for different fishing mortality scenarios.,
 report.version.model.changes,table,Document version history briefly describing when major changes or updates are made to each version of the report.,
 projection.ts,table,Forecasted catch and biomass over future years for different fishing mortality scenarios. ,
 catch,table,Observed (points) and model estimated (line) catch over time for fleet catch.fleet.,