fix bullet point order to match tables/figs, fix nbsp's in template text

Author: MargaretSiple-NOAA

markdown/ComplexResultTemplate.Rmd

@@ -4,12 +4,12 @@
 <!-- These are the summary bullet points. -->
 - The {{spp_name_informal_sen}} includes {{f2}} and {{f4}}.
 
-- {{spp_name_informal_sen}} was the `r scales::ordinal({{xth}})` most abundant group caught in the {{year}} {{SRVY}} survey. Their total biomass was estimated to be {{species_biomass}}, which is a {{spp_percent_change}}% {{spp_incr_decr}} from {{compareyr}} (Table `r officer::run_reference(paste0("table1-","{{species_code}}"))`, Fig. `r officer::run_reference(paste0("threepanel-","{{species_code}}"))`).
-
--   The largest estimated biomass for the {{spp_name_informal_low}} was in the {{nmfs_highest_biomass}} statistical area and the depth range with the largest estimated biomass was {{depth_highest_biomass}} (Table `r officer::run_reference(paste0("table2-","{{species_code}}"))`).
+- {{spp_name_informal_sen}} was the `r scales::ordinal({{xth}})` most abundant group caught in the {{year}} {{SRVY}} survey. Their total biomass was estimated to be {{species_biomass}}, which is a {{spp_percent_change}}% {{spp_incr_decr}} from {{compareyr}} (Fig. `r officer::run_reference(paste0("threepanel-","{{species_code}}"))`).
 
 -   The highest CPUEs for the {{spp_name_informal}} were recorded in the {{nmfs_highest_cpue[1]}} and {{nmfs_highest_cpue[2]}} statistical areas (Table `r officer::run_reference(paste0("table1-","{{species_code}}"))` and Fig. `r officer::run_reference(paste0("cpue-","{{species_code}}"))`).
 
+-   The largest estimated biomass for the {{spp_name_informal_low}} was in the {{nmfs_highest_biomass}} statistical area and the depth range with the largest estimated biomass was {{depth_highest_biomass}} (Table `r officer::run_reference(paste0("table2-","{{species_code}}"))`).
+
 -   Length compositions and size by depth are not shown for complexes; these data are available upon request.
 
 `r if({{show.text}}){paste0("- ","{{sex_diff_sentence}}") }`
@@ -21,7 +21,7 @@ table3s_list[["{{ species_code }}"]] |>
   #table3s_list[[as.character("NRSSRS")]] |>
    flextable() |>
   flextable::theme_vanilla() |>
-  flextable::fontsize(size = 9, part = "all") |>
+  flextable::fontsize(size = 10, part = "all") |>
   flextable::fit_to_width(max_width = 8.25, unit = "in") |>
   flextable::align(align = "center", part = "all") |>
   flextable::align(j = 1, align = "left", part = "body") |>
@@ -30,6 +30,7 @@ table3s_list[["{{ species_code }}"]] |>
   flextable::set_header_labels(
     `Total haul count` = "Total \n haul count",
     `Hauls with positive catch` = "Hauls w/\npositive catch",
+    `Percent positive tows` = "% \npositive \ntows",
     #`CPUE (kg/km2)` = "CPUE\n(kg/km2)",
     `Biomass (t)` = "Biomass\n(t)",
     `% biomass in area` = "% \n biomass \n in area"

markdown/DATA_REPORT.Rmd

@@ -305,7 +305,8 @@ for (i in 1:nrow(report_species)) { # nrow(report_species)
     dplyr::filter(biomass_numeric == max(biomass_numeric, na.rm = TRUE)) |>
     dplyr::select(`Depth (m)`) |>
     as.character()
-  depth_highest_biomass <- paste0(depth_highest_biomass0, "&nbsp;", "m")
+  #depth_highest_biomass <- paste0(depth_highest_biomass0, "&nbsp;", "m")
+  depth_highest_biomass <- gsub(pattern = " ", replacement = "&nbsp;", x = depth_highest_biomass0)
   # 
   # Octopus
   if (species_code == 78403) { # true/false, if it's for octopus don't make length comp or length scatter figs and use a different template maybe

markdown/FishResultTemplate.Rmd

@@ -3,13 +3,13 @@
 
 <!-- These are the summary bullet points. -->
 
--   {{spp_name_informal_sen}} was the `r scales::ordinal({{xth}})` most abundant species caught in the {{year}} {{SRVY}} survey. Their total biomass was estimated to be {{species_biomass}}, which is a {{spp_percent_change}}% {{spp_incr_decr}} from {{compareyr}} (Table `r officer::run_reference(paste0("table1-","{{species_code}}"))`, Fig. `r officer::run_reference(paste0("threepanel-","{{species_code}}"))`).
-
--   The largest estimated biomass for {{spp_name_informal}} was in the {{nmfs_highest_biomass}} statistical area and the depth range with the largest estimated biomass was {{depth_highest_biomass}} (Table `r officer::run_reference(paste0("table2-","{{species_code}}"))`).
+-   {{spp_name_informal_sen}} was the `r scales::ordinal({{xth}})` most abundant species caught in the {{year}} {{SRVY}} survey. Their total biomass was estimated to be {{species_biomass}}, which is a {{spp_percent_change}}% {{spp_incr_decr}} from {{compareyr}} (Fig. `r officer::run_reference(paste0("threepanel-","{{species_code}}"))`).
 
 -   The highest {{spp_name_informal}} CPUEs were recorded in the {{nmfs_highest_cpue[1]}} and {{nmfs_highest_cpue[2]}} statistical areas (Table `r officer::run_reference(paste0("table1-","{{species_code}}"))` and Fig. `r officer::run_reference(paste0("cpue-","{{species_code}}"))`).
 
--   On average, the longest individuals were found in the {{region_longest}} statistical area and in a depth range of {{depth_longest}} (Figure `r officer::run_reference(paste0("lengthdepth-","{{species_code}}"))`). Historical length distributions are provided in Figure `r officer::run_reference(paste0("lengthcomp-","{{species_code}}"))`.
+-   The largest estimated biomass for {{spp_name_informal}} was in the {{nmfs_highest_biomass}} statistical area and the depth range with the largest estimated biomass was {{depth_highest_biomass}} (Table `r officer::run_reference(paste0("table2-","{{species_code}}"))`).
+
+-  Historical length distributions are provided in Figure `r officer::run_reference(paste0("lengthcomp-","{{species_code}}"))`. On average, the longest individuals were found in the {{region_longest}} statistical area and in a depth range of {{depth_longest}} (Fig. `r officer::run_reference(paste0("lengthdepth-","{{species_code}}"))`). 
 
 <!-- `r if({{show.text}}){paste0("- ","{{sex_diff_sentence}}") }` -->
 

markdown/InvertebrateResultTemplate.Rmd

@@ -3,11 +3,12 @@
 
 <!-- These are the summary bullet points. For invertebrates, the two bullet points about size are left out, as there are no size comps for things like giant octopus. -->
 
--  {{spp_name_informal_sen}} was the `r scales::ordinal({{xth}})` most abundant species caught in the {{year}} {{SRVY}} survey. The total biomass was estimated to be {{species_biomass}}, which is a {{spp_percent_change}}% {{spp_incr_decr}} from {{compareyr}} (Table `r officer::run_reference(paste0("table1-","{{species_code}}"))`, Fig. `r officer::run_reference(paste0("threepanel-","{{species_code}}"))`).
+-  {{spp_name_informal_sen}} was the `r scales::ordinal({{xth}})` most abundant species caught in the {{year}} {{SRVY}} survey. The total biomass was estimated to be {{species_biomass}}, which is a {{spp_percent_change}}% {{spp_incr_decr}} from {{compareyr}} (Fig. `r officer::run_reference(paste0("threepanel-","{{species_code}}"))`).
+
+-  The highest {{spp_name_informal}} CPUEs were recorded in the {{nmfs_highest_cpue[1]}} and {{nmfs_highest_cpue[2]}} statistical areas (Table `r officer::run_reference(paste0("table1-",{{species_code}}))` and Fig. `r officer::run_reference(paste0("cpue-",{{species_code}}))`). 
 
 -  The largest estimated biomass for {{spp_name_informal}} was in the {{nmfs_highest_biomass}} statistical area, and the depth range with the largest estimated biomass was {{depth_highest_biomass}} (Table `r officer::run_reference(paste0("table2-","{{species_code}}"))`).
 
--  The highest {{spp_name_informal}} CPUEs were recorded in the {{nmfs_highest_cpue[1]}} and {{nmfs_highest_cpue[2]}} statistical areas (Table `r officer::run_reference(paste0("table1-",{{species_code}}))` and Fig. `r officer::run_reference(paste0("cpue-",{{species_code}}))`). 
 
 \newpage
 ```{r echo=FALSE,tab.cap='Summary by survey districts and depth intervals of {{year}} {{region}} trawl effort (number of trawl hauls), number of hauls containing {{spp_name_informal}}, their mean CPUE and biomass estimates, and average individual weight.', tab.id=paste0("table1-",{{species_code}}),  tab.cap.style = "Table caption",tab.align="left"}