R - GT Package systematically calculate summary_row percentages

Question

I have a table that I made using the GT package, but I cannot get summary_row percentage to calculate correctly (green row).

Is there a way to systematically take the sum of the column and divide by the Total N. So for example, Female percent should be 8/11 (73%).

    summary_rows(
    groups = TRUE,
    columns = vars(total, NRA, FN, HN, BN, AIN, AN, NHN, WN, TN, MN, UN), #N columns
    fns = list(TOTAL = "sum"),
    decimals = 0,
    use_seps = TRUE
  ) %>% 
  summary_rows(
    groups = TRUE,
    columns = vars(FP,NRAP,HP,BP, AIP, AP, NHP, WP, TP, MP, UP), #Percentages columns
    fns = list(TOTAL = ~ sum(.)/sum(total)),
    decimals = 0,
    formatter = fmt_percent,
    use_seps = TRUE
  ) %>% 

In the code above, the first set of summary_rows is for computing N, which is easy because its a simple sum. This is my attempt at getting summary row percentages. fns = list(TOTAL = ~ sum(.)/sum(total)),

I have tried looking at similar questions/answers and couldnt find quite what I was looking for.

Thanks

Edit - asked for full code

t2= ds %>% group_by(Division, Department) %>% 
  summarise(Category = "University Total",
            Division = "University",
            Total = round(n(),digits = 2), 
            FN = n_distinct(Pernr[Gender == "Female"]),
            FP = n_distinct(Pernr[Gender == "Female"])/Total,
            NRA = n_distinct(Pernr[`i_OLD_FCR_Race Eth` == "NRA"]),
            NRAP = n_distinct(Pernr[`i_OLD_FCR_Race Eth` == "NRA"])/Total,
            HN = n_distinct(Pernr[`i_OLD_FCR_Race Eth` == "Hispanic"]),
            HP = n_distinct(Pernr[`i_OLD_FCR_Race Eth` == "Hispanic"])/Total,
            BN = n_distinct(Pernr[`i_OLD_FCR_Race Eth` == "Black or African American"]),
            BP = n_distinct(Pernr[`i_OLD_FCR_Race Eth` == "Black or African American"])/Total,
            AIN = n_distinct(Pernr[`i_OLD_FCR_Race Eth` == "American Indian or Alaska Native"]),
            AIP = n_distinct(Pernr[`i_OLD_FCR_Race Eth` == "American Indian or Alaska Native"])/Total,
            AN = n_distinct(Pernr[`i_OLD_FCR_Race Eth` == "Asian"]),
            AP = n_distinct(Pernr[`i_OLD_FCR_Race Eth` == "Asian"])/Total,
            NHN = n_distinct(Pernr[`i_OLD_FCR_Race Eth` == "Native Hawaiian or Other Pacific Islander"]),
            NHP = n_distinct(Pernr[`i_OLD_FCR_Race Eth` == "Native Hawaiian or Other Pacific Islander"])/Total,
            WN = n_distinct(Pernr[`i_OLD_FCR_Race Eth` == "White"]),
            WP = n_distinct(Pernr[`i_OLD_FCR_Race Eth` == "White"])/Total,
            TN = n_distinct(Pernr[`i_OLD_FCR_Race Eth` == "Two or more races"]),
            TP = n_distinct(Pernr[`i_OLD_FCR_Race Eth` == "Two or more races"])/Total,
            MN = n_distinct(Pernr[Minority == "Minority"]),
            MP = n_distinct(Pernr[Minority == "Minority"])/Total,
            UN = n_distinct(Pernr[URM == "URM"]),
            UP = n_distinct(Pernr[URM == "URM"])/Total
  )

    gtfcr = t2 %>% 
  gt(rowname_col="Department") %>%  
  tab_header(
    title = "Table 2: FCR Fall 2021"
  ) %>% 
  tab_spanner(
    label = md("*Female*"),
    columns = vars(FN, FP)
  ) %>% 
  tab_spanner(
    label = md("*NRA*"),
    columns = vars(NRA, NRAP)
  ) %>% 
  tab_spanner(
    label = md("*Hispanic*"),
    columns = vars(HN, HP)
  ) %>% 
  tab_spanner(
    label = md("*Black*"),
    columns = vars(BN, BP)
  ) %>% 
  tab_spanner(
    label = md("*Amer Ind*"),
    columns = vars(AIN, AIP)
  ) %>% 
  tab_spanner(
    label = md("*Asian*"),
    columns = vars(AN, AP)
  ) %>% 
  tab_spanner(
    label = md("*Nat Haw*"),
    columns = vars(NHN, NHP)
  ) %>% 
  tab_spanner(
    label = md("*White*"),
    columns = vars(WN, WP)
  ) %>% 
  tab_spanner(
    label = md("*Two or More*"),
    columns = vars(TN, TP)
  ) %>% 
  tab_spanner(
    label = md("*Minority*"),
    columns = vars(MN,MP)
  ) %>% 
  tab_spanner(
    label = md("*URM*"),
    columns = vars(UN,UP)
  ) %>% 
  fmt_percent(
    columns = vars(NRAP,FP,HP,BP, AIP, AP, NHP, WP, TP, MP, UP),
    decimals = 0
  ) %>% 
  cols_align(align = "center", columns = TRUE) %>%
  data_color(
    columns = vars(total),
    colors = scales::col_numeric(
      palette = "grey86"#paletteer::paletteer_d(palette = "nord::") 
      %>% as.character(),
      domain = NULL
    ),
    alpha = 0.8
  ) %>% 
  summary_rows(
    groups = TRUE,
    columns = vars(total, NRA, FN, HN, BN, AIN, AN, NHN, WN, TN, MN, UN), #N columns
    fns = list(TOTAL = "sum"),
    decimals = 0,
    use_seps = TRUE
  ) %>% 
  summary_rows(
    groups = TRUE,
    columns = vars(FP,NRAP,HP,BP, AIP, AP, NHP, WP, TP, MP, UP), #Percentages columns
    fns = list(TOTAL = ~ sum(.)/sum(total)),
    decimals = 0,
    formatter = fmt_percent,
    use_seps = TRUE
  ) %>% 
  tab_options(
    summary_row.background.color = "#ACEACE80",
    grand_summary_row.background.color = "#990000",
    row_group.background.color = "#FFEFDB80",
    heading.background.color = "#EFFBFC",
    column_labels.background.color = "#EFFBFC",
    stub.background.color = "#EFFBFC",
    table.font.color = "#323232",
    table_body.hlines.color = "#989898",
    table_body.border.top.color = "#989898",
    heading.border.bottom.color = "#989898",
    row_group.border.top.color = "#989898",
    row_group.border.bottom.style = "none",
    stub.border.style = "dashed",
    stub.border.color = "#989898",
    stub.border.width = "1px",
    summary_row.border.color = "#989898",
    table.width = "60%"
  ) %>%
  cols_label(
    NRA = "N",
    NRAP = "%",
    FN = "N",
    FP = "%",
    HN = "N",
    HP = "%",
    BN = "N",
    BP = "%",
    AIN = "N",
    AIP = "%",
    AN = "N",
    AP = "%",
    NHN = "N",
    NHP = "%",
    WN = "N",
    WP = "%",
    TN = "N",
    TP = "%",
    MN = "N",
    MP = "%",
    UN = "N",
    UP = "%"
  ) %>% 
  opt_all_caps()