diff --git a/R/convert_output.R b/R/convert_output.R
index ba5f5326..c9082d5c 100644
--- a/R/convert_output.R
+++ b/R/convert_output.R
@@ -2190,9 +2190,9 @@ convert_output <- function(
   # temporarily add call to local csv so I can test
   # con_file <- glue::glue("~/GitHub/stockplotr/inst/resources/{model}_var_names.csv")
   var_names_sheet <- utils::read.csv(con_file, na.strings = "")
-  
+
   if (tolower(model) == "bam") {
-    var_names_sheet <- var_names_sheet |> 
+    var_names_sheet <- var_names_sheet |>
       dplyr::mutate(label = tolower(label))
   }
 
diff --git a/R/plot_biomass.R b/R/plot_biomass.R
index 45dbf3f4..199d793c 100644
--- a/R/plot_biomass.R
+++ b/R/plot_biomass.R
@@ -84,10 +84,10 @@ plot_biomass <- function(
     cli::cli_alert_warning("Scale amount is not applicable when relative = TRUE. Resetting scale_amount to 1.")
     scale_amount <- 1
   }
-  
+
   orig_group <- group
   orig_facet <- facet
-  
+
   # Filter data for biomass
   # TODO: determine method to ID that first point in the timeseries is actually Bunfished ref pt
   prepared_data <- filter_data(
@@ -177,10 +177,14 @@ plot_biomass <- function(
   ### Make RDA ----
   if (make_rda) {
     if (relative) {
-      rel.B.min <- calc_kqs(returned_kq = "rel.B.min",
-                            final = final)
-      rel.B.max <- calc_kqs(returned_kq = "rel.B.max",
-                            final = final)
+      rel.B.min <- calc_kqs(
+        returned_kq = "rel.B.min",
+        final = final
+      )
+      rel.B.max <- calc_kqs(
+        returned_kq = "rel.B.max",
+        final = final
+      )
 
       # calculate & export key quantities
       export_kqs(rel.B.min, rel.B.max)
@@ -188,10 +192,14 @@ plot_biomass <- function(
       # Add key quantities to captions/alt text
       insert_kqs(rel.B.min, rel.B.max)
     } else {
-      B.min <- calc_kqs(returned_kq = "B.min",
-                        prepared_data = prepared_data)
-      B.max <- calc_kqs(returned_kq = "B.max",
-                        prepared_data = prepared_data)
+      B.min <- calc_kqs(
+        returned_kq = "B.min",
+        prepared_data = prepared_data
+      )
+      B.max <- calc_kqs(
+        returned_kq = "B.max",
+        prepared_data = prepared_data
+      )
 
       export_kqs(B.min, B.max)
       insert_kqs(B.min, B.max)
@@ -199,41 +207,61 @@ plot_biomass <- function(
 
     B.ref.pt <- as.character(ref_line)
     B.units <- as.character(unit_label)
-    B.start.year <- calc_kqs(returned_kq = "B.start.year",
-                             prepared_data = prepared_data)
-    B.end.year <- calc_kqs(returned_kq = "B.end.year", 
-                           prepared_data = prepared_data)
-    B.terminal.year <- calc_kqs(returned_kq = "B.terminal.year",
-                                dat = dat,
-                                relative = relative)
+    B.start.year <- calc_kqs(
+      returned_kq = "B.start.year",
+      prepared_data = prepared_data
+    )
+    B.end.year <- calc_kqs(
+      returned_kq = "B.end.year",
+      prepared_data = prepared_data
+    )
+    B.terminal.year <- calc_kqs(
+      returned_kq = "B.terminal.year",
+      dat = dat,
+      relative = relative
+    )
 
     # SS3, FIMS
     if ("spawning_biomass_msy" %in% dat$label) {
-      sb_msy <- calc_kqs(returned_kq = "sb_msy",
-                         dat = dat)
-      b_sb_msy <- calc_kqs(returned_kq = "b_sb_msy",
-                           dat = dat)
-      B.msy <- calc_kqs(returned_kq = "B.msy",
-                        dat = dat)
+      sb_msy <- calc_kqs(
+        returned_kq = "sb_msy",
+        dat = dat
+      )
+      b_sb_msy <- calc_kqs(
+        returned_kq = "b_sb_msy",
+        dat = dat
+      )
+      B.msy <- calc_kqs(
+        returned_kq = "B.msy",
+        dat = dat
+      )
       B.msy.min <- NA
       B.msy.max <- NA
-    # BAM
+      # BAM
     } else if ("bmsy" %in% dat$label) {
-      B.msy <- calc_kqs(returned_kq = "B.msy", 
-                        dat = dat)
-      B.msy.min_uncert <- calc_kqs(returned_kq = "B.msy.min_uncert",
-                                   dat = dat)
-      B.msy.min <- calc_kqs(returned_kq = "B.msy.min", 
-                            dat = dat)
-      B.msy.max <- calc_kqs(returned_kq = "B.msy.max",
-                            dat = dat)
-    # Rceattle
+      B.msy <- calc_kqs(
+        returned_kq = "B.msy",
+        dat = dat
+      )
+      B.msy.min_uncert <- calc_kqs(
+        returned_kq = "B.msy.min_uncert",
+        dat = dat
+      )
+      B.msy.min <- calc_kqs(
+        returned_kq = "B.msy.min",
+        dat = dat
+      )
+      B.msy.max <- calc_kqs(
+        returned_kq = "B.msy.max",
+        dat = dat
+      )
+      # Rceattle
     } else {
       B.msy <- NA
       B.msy.min <- NA
       B.msy.max <- NA
     }
-    
+
     export_kqs(
       B.ref.pt,
       B.units,
diff --git a/R/plot_fishing_mortality.R b/R/plot_fishing_mortality.R
index 56b01401..551b2379 100644
--- a/R/plot_fishing_mortality.R
+++ b/R/plot_fishing_mortality.R
@@ -87,10 +87,14 @@ plot_fishing_mortality <- function(
 
   ### Make RDA ----
   if (make_rda) {
-    F.min <- calc_kqs(returned_kq = "F.min",
-                      prepared_data = prepared_data)
-    F.max <- calc_kqs(returned_kq = "F.max",
-                      prepared_data = prepared_data)
+    F.min <- calc_kqs(
+      returned_kq = "F.min",
+      prepared_data = prepared_data
+    )
+    F.max <- calc_kqs(
+      returned_kq = "F.max",
+      prepared_data = prepared_data
+    )
 
     export_kqs(F.min, F.max)
     insert_kqs(F.min, F.max)
@@ -98,15 +102,20 @@ plot_fishing_mortality <- function(
     F.ref.pt <- as.character(ref_line)
     F.start.year <- min(prepared_data$year)
     F.end.year <- max(prepared_data$year)
-    F.terminal.year <- calc_kqs(returned_kq = "F.terminal.year",
+    F.terminal.year <- calc_kqs(
+      returned_kq = "F.terminal.year",
+      dat = dat
+    )
+
+    F.target <- calc_kqs(
+      returned_kq = "F.target",
+      dat = dat
+    )
+    f.limit <- calc_kqs(
+      returned_kq = "f.limit",
       dat = dat
     )
-    
-    F.target <- calc_kqs(returned_kq = "F.target",
-                         dat = dat)
-    f.limit <- calc_kqs(returned_kq = "f.limit",
-                        dat = dat)
-    
+
     export_kqs(
       F.ref.pt,
       F.start.year,
diff --git a/R/plot_spawning_biomass.R b/R/plot_spawning_biomass.R
index 33a67fca..c3cad064 100644
--- a/R/plot_spawning_biomass.R
+++ b/R/plot_spawning_biomass.R
@@ -242,13 +242,17 @@ plot_spawning_biomass <- function(
   ### Make RDA ----
   if (make_rda) {
     if (relative) {
-      rel.ssb.min <- calc_kqs(returned_kq = "rel.ssb.min",
-                              final = final)
-      
-     
-      rel.ssb.max <- calc_kqs(returned_kq = "rel.ssb.max",
-                              final = final)
-        
+      rel.ssb.min <- calc_kqs(
+        returned_kq = "rel.ssb.min",
+        final = final
+      )
+
+
+      rel.ssb.max <- calc_kqs(
+        returned_kq = "rel.ssb.max",
+        final = final
+      )
+
       # calculate & export key quantities
       export_kqs(rel.ssb.min, rel.ssb.max)
 
diff --git a/R/plot_stock_recruitment.R b/R/plot_stock_recruitment.R
index 87db9481..e22a0f4d 100644
--- a/R/plot_stock_recruitment.R
+++ b/R/plot_stock_recruitment.R
@@ -178,8 +178,10 @@ plot_stock_recruitment <- function(
   # Make RDA
   if (make_rda) {
     # Obtain relevant key quantities for captions/alt text
-    sr.age.min <- calc_kqs(returned_kq = "sr.age.min",
-                           dat = dat)
+    sr.age.min <- calc_kqs(
+      returned_kq = "sr.age.min",
+      dat = dat
+    )
     sr.ssb.units <- spawning_biomass_label
     sr.ssb.min <- min(sr$spawning_biomass, na.rm = TRUE) |> round(digits = 3)
     sr.ssb.max <- max(sr$spawning_biomass, na.rm = TRUE) |> round(digits = 3)
diff --git a/R/process_data.R b/R/process_data.R
index b503e37e..a31454f0 100644
--- a/R/process_data.R
+++ b/R/process_data.R
@@ -533,7 +533,7 @@ process_table <- function(
       dplyr::rename(
         !!mod_uncert_lab := uncertainty
       ) |>
-    # set values to strings to include trailing zeros from rounding and     # format large estimate values with commas
+      # set values to strings to include trailing zeros from rounding and     # format large estimate values with commas
       dplyr::mutate(estimate = formatC(estimate, format = "f", digits = digits, big.mark = ",")) |>
       tidyr::pivot_wider(
         id_cols = dplyr::all_of(c(stringr::str_to_title(mod_cols))),
diff --git a/R/table_index.R b/R/table_index.R
index 46c3d05e..23d397a1 100644
--- a/R/table_index.R
+++ b/R/table_index.R
@@ -48,18 +48,17 @@
 #' )
 #' }
 table_index <- function(
-    dat,
-    era = NULL,
-    interactive = TRUE,
-    group = NULL,
-    method = "sum",
-    module = NULL,
-    label = NULL,
-    digits = 2,
-    make_rda = FALSE,
-    tables_dir = getwd()
-    ) {
-  
+  dat,
+  era = NULL,
+  interactive = TRUE,
+  group = NULL,
+  method = "sum",
+  module = NULL,
+  label = NULL,
+  digits = 2,
+  make_rda = FALSE,
+  tables_dir = getwd()
+) {
   # TODO: do group and facet need to be uncommented and updated?
   # Filter data for landings
   prepared_data <- filter_data(
@@ -71,14 +70,16 @@ table_index <- function(
     scale_amount = 1,
     interactive = interactive
   ) |>
-    dplyr::mutate(estimate = round(as.numeric(estimate), digits = digits),
-                  uncertainty = round(as.numeric(uncertainty), digits = digits))
-  
+    dplyr::mutate(
+      estimate = round(as.numeric(estimate), digits = digits),
+      uncertainty = round(as.numeric(uncertainty), digits = digits)
+    )
+
   # Add check if there is any data
   if (nrow(prepared_data) == 0) {
     cli::cli_abort("No index data found.")
   }
-  
+
   # get uncertainty label by model
   uncert_lab <- prepared_data |>
     dplyr::filter(!is.na(uncertainty_label)) |>
@@ -86,22 +87,22 @@ table_index <- function(
     dplyr::reframe(unique_uncert = unique(uncertainty_label)) # changed to reframe -- may cause errors
   uncert_lab <- stats::setNames(uncert_lab$unique_uncert, uncert_lab$model)
   # if (length(unique(uncert_lab)) == 1) uncert_lab <- unique(uncert_lab) # might need this line
-  
+
   # This needs to be adjusted when comparing different models and diff error
   if (length(uncert_lab) > 1 & length(unique(uncert_lab)) == 1 | length(names(uncert_lab)) == 1) { # prepared_data$model
     # cli::cli_alert_warning("More than one value for uncertainty exists: {uncert_lab}")
     uncert_lab <- uncert_lab[[1]]
     # cli::cli_alert_warning("The first value ({uncert_lab}) will be chosen.")
   }
-  
+
   if (is.na(uncert_lab)) uncert_lab <- "uncertainty"
-  
+
   # get fleet names
   # TODO: change from fleets to id_group AFTER the process data step and adjust throughout the table based on indexing
   fleets <- unique(prepared_data$fleet) |>
     # sort numerically even if fleets are 100% characters
     stringr::str_sort(numeric = TRUE)
-  
+
   # TODO: fix this so that fleet names aren't removed if, e.g., group = "fleet"
   table_data_info <- process_table(
     dat = prepared_data,
@@ -113,12 +114,12 @@ table_index <- function(
   table_data <- table_data_info[[1]]
   indexed_vars <- table_data_info[[2]]
   id_col_vals <- table_data_info[[3]]
-  
+
   # id_group_vals <- sapply(id_cols, function(x) unique(prepared_data[[x]]), simplify = FALSE)
   # TODO: add check if there is a index column for every error column -- if not remove the error (can keep index)
-  
+
   # if (uncert_lab != "") uncert_lab <- glue::glue("({uncert_lab})")
-  
+
   # merge error and index columns and rename
   df_list <- merge_error(
     table_data,
@@ -126,31 +127,30 @@ table_index <- function(
     unit_label = "", # should this be CPUE?
     uncert_lab
   )
-  
+
   # transform dfs into tables
   final <- lapply(df_list, function(df) {
     df |>
       gt::gt() |>
       add_theme()
   })
-  
+
   # export figure to rda if argument = T
   if (make_rda == TRUE) {
-    
     # Caption contains no key quantities for index table
     # So, export captions/alt text csv if absent
     if (!file.exists(fs::path(getwd(), "captions_alt_text.csv"))) {
       caps_alttext <- utils::read.csv(
         system.file("resources", "captions_alt_text_template.csv", package = "stockplotr")
       )
-    # export df with captions and alt text to csv
-    utils::write.csv(
-      x = caps_alttext,
-      file = fs::path(getwd(), "captions_alt_text.csv"),
-      row.names = FALSE
-      )    
+      # export df with captions and alt text to csv
+      utils::write.csv(
+        x = caps_alttext,
+        file = fs::path(getwd(), "captions_alt_text.csv"),
+        row.names = FALSE
+      )
     }
-  
+
     if (length(df_list) == 1) {
       create_rda(
         object = final$label,
@@ -168,7 +168,7 @@ table_index <- function(
     cli::cli_alert_warning("Multiple tables cannot be exported at this time.")
     cli::cli_alert_info("We are currently developing this feature.")
   }
-  
+
   # Send table(s) to viewer
   if (!is.data.frame(table_data)) {
     for (t in final) {
diff --git a/R/utils_rda.R b/R/utils_rda.R
index a5b84d2c..7caf02ca 100644
--- a/R/utils_rda.R
+++ b/R/utils_rda.R
@@ -3,30 +3,28 @@
 # @@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@
 
 #' Calculate key quantities
-#' 
+#'
 #' @param returned_kq String. Key quantity to be returned.
 #' @param prepared_data Data frame. The prepared_data object.
 #' @param dat Data frame. The original data object.
 #' @param final ggplot2 object. The final figure.
 #' @param relative Logical. TRUE/FALSE; specify whether to set y-axis values relative to the ref_line value.
 #' @param ... Additional arguments to be passed to the function.
-#' 
+#'
 #' @return The value of the specified key quantity.
-#' 
+#'
 #' @examples \dontrun{
 #' calc_kqs(
 #'   returned_kq = "F.min",
 #'   dat = dat
 #' )
 #' }
-
 calc_kqs <- function(returned_kq,
                      prepared_data = NULL,
                      dat = NULL,
                      final = NULL,
                      relative = NULL,
-                     ...){
-  
+                     ...) {
   # plot_stock_recruitment()-----
   if (returned_kq == "sr.age.min") {
     return(
@@ -37,7 +35,7 @@ calc_kqs <- function(returned_kq,
         as.numeric()
     )
   }
-  
+
   # plot_spawning_biomass()-----
   if (returned_kq == "rel.ssb.min") {
     return(
@@ -48,7 +46,7 @@ calc_kqs <- function(returned_kq,
         round(digits = 2)
     )
   }
-  
+
   if (returned_kq == "rel.ssb.max") {
     return(
       ggplot2::ggplot_build(final)[["data"]][[2]] |>
@@ -58,7 +56,7 @@ calc_kqs <- function(returned_kq,
         round(digits = 2)
     )
   }
-  
+
   # plot_biomass()-----
   if (returned_kq == "rel.B.min") {
     return(
@@ -68,7 +66,7 @@ calc_kqs <- function(returned_kq,
         min()
     )
   }
-  
+
   if (returned_kq == "rel.B.max") {
     return(
       ggplot2::ggplot_build(final)[["data"]][[2]] |>
@@ -77,23 +75,23 @@ calc_kqs <- function(returned_kq,
         max()
     )
   }
-  
+
   if (returned_kq == "B.min") {
     return(min(prepared_data$estimate))
   }
-  
+
   if (returned_kq == "B.max") {
     return(max(prepared_data$estimate))
   }
-  
+
   if (returned_kq == "B.start.year") {
     return(min(prepared_data$year) |> round(digits = 3))
   }
-  
+
   if (returned_kq == "B.end.year") {
     return(max(prepared_data$year) |> round(digits = 3))
   }
-  
+
   if (returned_kq == "B.terminal.year") {
     return(
       filter_data(
@@ -112,7 +110,7 @@ calc_kqs <- function(returned_kq,
         unique()
     )
   }
-  
+
   if (returned_kq == "sb_msy") {
     return(
       dat |>
@@ -121,7 +119,7 @@ calc_kqs <- function(returned_kq,
         as.numeric()
     )
   }
-  
+
   if (returned_kq == "b_sb_msy") {
     return(
       dat |>
@@ -130,7 +128,7 @@ calc_kqs <- function(returned_kq,
         as.numeric()
     )
   }
-  
+
   if (returned_kq == "B.msy") {
     if ("spawning_biomass_msy" %in% dat$label) {
       sb_msy <- dat |>
@@ -151,7 +149,7 @@ calc_kqs <- function(returned_kq,
       )
     }
   }
-  
+
   if (returned_kq == "B.msy.min_uncert") {
     return(
       dat |>
@@ -160,19 +158,31 @@ calc_kqs <- function(returned_kq,
         as.numeric()
     )
   }
-  
+
   if (returned_kq == "B.msy.min") {
-    B.msy <- dat |> dplyr::filter(label == "bmsy") |> dplyr::select(estimate) |> as.numeric()
-    B.msy.min_uncert <- dat |> dplyr::filter(label == "bmsy") |> dplyr::select(uncertainty) |> as.numeric()
+    B.msy <- dat |>
+      dplyr::filter(label == "bmsy") |>
+      dplyr::select(estimate) |>
+      as.numeric()
+    B.msy.min_uncert <- dat |>
+      dplyr::filter(label == "bmsy") |>
+      dplyr::select(uncertainty) |>
+      as.numeric()
     return(as.numeric(B.msy) - as.numeric(B.msy.min_uncert))
   }
-  
+
   if (returned_kq == "B.msy.max") {
-    B.msy <- dat |> dplyr::filter(label == "bmsy") |> dplyr::select(estimate) |> as.numeric()
-    B.msy.min_uncert <- dat |> dplyr::filter(label == "bmsy") |> dplyr::select(uncertainty) |> as.numeric()
+    B.msy <- dat |>
+      dplyr::filter(label == "bmsy") |>
+      dplyr::select(estimate) |>
+      as.numeric()
+    B.msy.min_uncert <- dat |>
+      dplyr::filter(label == "bmsy") |>
+      dplyr::select(uncertainty) |>
+      as.numeric()
     return(as.numeric(B.msy) + as.numeric(B.msy.min_uncert))
   }
-  
+
   # plot_fishing_mortality()-----
   if (returned_kq == "F.min") {
     return(
@@ -182,7 +192,7 @@ calc_kqs <- function(returned_kq,
         round(digits = 3)
     )
   }
-  
+
   if (returned_kq == "F.max") {
     return(
       prepared_data$estimate |>
@@ -191,7 +201,7 @@ calc_kqs <- function(returned_kq,
         round(digits = 3)
     )
   }
-  
+
   if (returned_kq == "F.terminal.year") {
     return(
       filter_data(
@@ -210,16 +220,16 @@ calc_kqs <- function(returned_kq,
         unique()
     )
   }
-  
+
   if (returned_kq == "F.target") {
     return(
       dat |>
-        dplyr::filter(grepl('f_target', label) | grepl('f_msy', label) | (grepl('fishing_mortality_msy', label) & is.na(year))) |>
+        dplyr::filter(grepl("f_target", label) | grepl("f_msy", label) | (grepl("fishing_mortality_msy", label) & is.na(year))) |>
         dplyr::pull(estimate) |>
         round(digits = 3)
     )
   }
-  
+
   if (returned_kq == "f.limit") {
     if ("log_flimit" %in% unique(dat$label)) {
       return(
@@ -236,7 +246,6 @@ calc_kqs <- function(returned_kq,
 }
 
 
-
 # TODO: update key quantities functions to work with a specified 'dir' instead of default 'getwd()'?
 # TODO: update key quantities functions for plots that need to extract values from KQs csv to calculate their own, dependent KQs (none present in current plotting functions)
 
diff --git a/R/utils_table.R b/R/utils_table.R
index 9f785482..0935073a 100644
--- a/R/utils_table.R
+++ b/R/utils_table.R
@@ -194,11 +194,11 @@ check_label_differences <- function(dat, index_variables, id_group = NULL) {
 #' to reduce redundancy in the table.
 #'
 merge_error <- function(
-    table_data,
-    id_col_vals,
-    unit_label,
-    uncert_lab
-    ) {
+  table_data,
+  id_col_vals,
+  unit_label,
+  uncert_lab
+) {
   # TODO: change fleets to grouping when the data is indexed by factors other than fleet
   lapply(table_data, function(tab_dat) {
     label_cols <- names(tab_dat)[-c(1, grep(glue::glue("^{uncert_lab}"), names(tab_dat)))]
@@ -211,12 +211,13 @@ merge_error <- function(
         tolower(l_col),
         paste(
           stringr::str_escape(unlist(id_col_vals, use.names = FALSE)),
-          collapse = "|")
+          collapse = "|"
         )
-      
+      )
+
       # Identify which uncert col aligns with l_col
       uncert_col <- uncert_cols[grep(l_col, uncert_cols)]
-      
+
       # adjust tab dat to combine the uncert_col value into the l_col = l_col (uncert_col)
       tab_dat <- tab_dat |>
         dplyr::mutate(
@@ -226,7 +227,7 @@ merge_error <- function(
             # maybe not good practice to insert dash?
             # ifelse(
             #   is.na(.data[[l_col]]),
-              "-"
+            "-"
             #   as.character(.data[[l_col]])
             # )
           )
@@ -234,13 +235,13 @@ merge_error <- function(
         # Remove uncertainty colummn id'd in this step of the loop
         dplyr::select(-dplyr::all_of(uncert_col))
     } # close loop combining label and uncertainty
-    
+
     # Adjust all header label names now
     header_labs <- stringr::str_replace_all(colnames(tab_dat), "_", " ") |>
       stringr::str_to_title()
     header_labs2 <- glue::glue("{header_labs[-1]}{ifelse(unit_label!='', paste0(' ', unit_label,' '), ' ')}({uncert_lab})")
     colnames(tab_dat) <- c(header_labs[1], header_labs2)
-    
+
     return(tab_dat)
   }) # close and end lapply
 }
diff --git a/tests/testthat/test-table_index.R b/tests/testthat/test-table_index.R
index cc3df6e7..12cb3855 100644
--- a/tests/testthat/test-table_index.R
+++ b/tests/testthat/test-table_index.R
@@ -6,7 +6,7 @@ test_that("table_index generates plots without errors", {
       interactive = FALSE
     )
   )
-  
+
   # expect error-free plot with many arguments
   expect_no_error(
     table_index(
@@ -15,8 +15,8 @@ test_that("table_index generates plots without errors", {
       tables_dir = getwd()
     )
   )
-  
-  
+
+
   # expect gt object is returned
   # adjust this test to work for multiple output tables
   # expect_s3_class(
@@ -40,11 +40,11 @@ test_that("rda file made when indicated", {
     make_rda = TRUE,
     tables_dir = getwd()
   )
-  
+
   # expect that both tables dir and the index_table.rda file exist
   expect_true(dir.exists(fs::path(getwd(), "tables")))
   expect_true(file.exists(fs::path(getwd(), "tables", "index_table.rda")))
-  
+
   # erase temporary testing files
   file.remove(fs::path(getwd(), "captions_alt_text.csv"))
   unlink(fs::path(getwd(), "tables"), recursive = T)