Creating a Publication-Ready Table in R with flextable

Step 1: Load Required Libraries

# Clear environment and console
rm(list = ls())

# **Load required libraries**
if (!requireNamespace("pacman", quietly = TRUE)) install.packages("pacman")

pacman::p_load(
  lubridate,  # Simplifies the manipulation of dates and times in R (e.g., formatting, extracting components)
  dplyr,      # Provides tools for data manipulation, helpful for filtering and summarizing date-based data
  stringr,    # Simplifies string manipulation
  tidyr,      # Reshaping and tidying data
  zoo,        # For working with time series data, including rolling calculations and handling missing data
  tsibble,    # Provides tools for handling time series data, including date-time indexes and features for forecasting
  readr,      # For reading date-time data from CSV
  haven,      # Data from other statistical software formats (SPSS, SAS, Stata)
  forecast,   # Useful for time series forecasting, especially when working with seasonal or trend-based data
  ggplot2,    # For visualizing date-time trends in data (e.g., time series plots)
  janitor,    # Tabulation, cleaning column names, adding totals and proportions
  plotly,     # Interactive visualizations
  # Documentation/reporting
  prettydoc,      # Pretty document templates
  flexdashboard,  # Interactive dashboards
  quarto,         # For rendering and publishing documents with the Quarto framework
  yaml,           # YAML document processing
  # Tabulation
  flextable,  # For creating styled, publication-ready tables
  gt,         # Grammar of tables
  reactable,  # Interactive tables
  scales,     # For number formatting (e.g., commas)
  officer,    # For exporting tables/reports to Word or PowerPoint
  tidyverse,
  DT,
  knitr,
  kableExtra
)

setwd("D:/OneDrive/ncid/GitHub/table_automation")
load("Vaccination_uptake.RDATA")

vacc_1524 <- vacc_data |>
  mutate(across(
    c(
      age_group,
      gender,
      marital_status,
      vacc_service_year,
      vacc_quarter,
      vacc_weekday
    ),
    as.character
  ))

kable(head(vacc_1524, 200),
      caption = "First 200 Rows of vacc_1524 (Ages 15-24 Vaccination Data)",
      format = "markdown")  |>   # Or "html", "latex"
  kable_styling(bootstrap_options = c("striped", "hover")) |>
  scroll_box(height = "400px")  # Scroll for 200 rows

First 200 Rows of vacc_1524 (Ages 15-24 Vaccination Data)

category	age_group	gender	marital_status	vacc_service_year	vacc_quarter	vacc_weekday
Respiratory vaccines	0-19	Male	Married	2019	1	Sunday
Respiratory vaccines	50-59	Female	NA	2019	4	Wednesday
Respiratory vaccines	0-19	Female	Married	2024	1	Sunday
Respiratory vaccines	0-19	Male	Married	2024	1	Monday
Respiratory vaccines	20-29	Male	Married	2021	3	Saturday
Respiratory vaccines	40-49	Male	Married	2021	4	Sunday
Respiratory vaccines	20-29	Female	Single/widowed	2019	3	Sunday
Respiratory vaccines	20-29	Female	Married	2017	4	Wednesday
Respiratory vaccines	0-19	Male	Married	2019	3	Wednesday
Respiratory vaccines	70+	Male	Married	2016	3	Saturday
Respiratory vaccines	70+	Female	Single/widowed	2016	4	Thursday
Respiratory vaccines	40-49	Male	Married	2017	3	Tuesday
Respiratory vaccines	0-19	Male	Single/widowed	2019	1	Tuesday
Respiratory vaccines	50-59	Male	Single/widowed	2022	4	Tuesday
Respiratory vaccines	60-69	Female	Married	2017	1	Thursday
Respiratory vaccines	40-49	Male	Single/widowed	2021	1	Monday
Respiratory vaccines	20-29	Female	Married	2023	4	Wednesday
Respiratory vaccines	20-29	Male	Single/widowed	2016	3	Friday
Respiratory vaccines	70+	Female	Divorced	2024	4	Thursday
Respiratory vaccines	30-39	Male	Married	2019	1	Wednesday
Respiratory vaccines	0-19	Female	Divorced	2018	1	Monday
Respiratory vaccines	0-19	Male	Single/widowed	2024	3	Sunday
Respiratory vaccines	30-39	Male	Divorced	2017	4	Thursday
Respiratory vaccines	40-49	Female	Divorced	2023	4	Wednesday
Respiratory vaccines	50-59	Male	Single/widowed	2016	2	Wednesday
Respiratory vaccines	70+	Male	Single/widowed	2019	1	Friday
Respiratory vaccines	50-59	Male	Married	2023	4	Tuesday
Respiratory vaccines	50-59	Male	Divorced	2016	3	Monday
Respiratory vaccines	40-49	Female	Single/widowed	2016	4	Saturday
Respiratory vaccines	20-29	Female	Single/widowed	2019	4	Saturday
Respiratory vaccines	40-49	Male	Single/widowed	2020	3	Thursday
Respiratory vaccines	30-39	Female	Married	2024	2	Friday
Respiratory vaccines	20-29	Female	Divorced	2020	2	Tuesday
Respiratory vaccines	0-19	Male	Single/widowed	2017	3	Wednesday
Respiratory vaccines	20-29	Male	Married	2023	2	Sunday
Respiratory vaccines	60-69	Female	Married	2019	2	Tuesday
Respiratory vaccines	30-39	Male	Married	2024	2	Sunday
Respiratory vaccines	70+	Male	Single/widowed	2023	3	Sunday
Respiratory vaccines	60-69	Female	Divorced	2023	4	Thursday
Respiratory vaccines	20-29	Female	Married	2021	4	Monday
Respiratory vaccines	40-49	Female	Married	2024	3	Thursday
Respiratory vaccines	40-49	Female	NA	2020	2	Friday
Respiratory vaccines	60-69	Female	Married	2016	1	Wednesday
Respiratory vaccines	20-29	Female	Single/widowed	2019	2	Thursday
Respiratory vaccines	50-59	Female	Single/widowed	2016	4	Thursday
Respiratory vaccines	40-49	Male	NA	2016	4	Saturday
Respiratory vaccines	50-59	Female	Married	2021	3	Wednesday
Respiratory vaccines	40-49	Female	Divorced	2021	4	Friday
Respiratory vaccines	20-29	Male	Married	2024	1	Monday
Respiratory vaccines	20-29	Male	Married	2019	2	Friday
Respiratory vaccines	30-39	Female	Married	2016	1	Thursday
Respiratory vaccines	70+	Male	Married	2017	1	Tuesday
Respiratory vaccines	0-19	Male	Divorced	2019	3	Thursday
Respiratory vaccines	50-59	Male	Married	2022	3	Sunday
Respiratory vaccines	20-29	Female	Married	2017	1	Monday
Respiratory vaccines	20-29	Male	Married	2019	1	Wednesday
Respiratory vaccines	60-69	Female	Divorced	2015	3	Monday
Respiratory vaccines	60-69	Female	Divorced	2020	1	Tuesday
Respiratory vaccines	20-29	Female	NA	2024	4	Wednesday
Respiratory vaccines	40-49	Male	Married	2019	3	Tuesday
Respiratory vaccines	30-39	Male	Married	2023	1	Friday
Respiratory vaccines	60-69	Female	Married	2016	4	Monday
Respiratory vaccines	50-59	Male	Married	2024	2	Friday
Respiratory vaccines	20-29	Male	Single/widowed	2016	2	Wednesday
Respiratory vaccines	70+	Female	Single/widowed	2021	4	Monday
Respiratory vaccines	60-69	Female	Single/widowed	2015	4	Tuesday
Respiratory vaccines	20-29	Female	Single/widowed	2024	4	Tuesday
Respiratory vaccines	20-29	Male	Married	2017	1	Wednesday
Respiratory vaccines	50-59	Male	NA	2017	3	Monday
Respiratory vaccines	0-19	Male	Married	2020	2	Friday
Respiratory vaccines	0-19	Male	Married	2020	4	Friday
Respiratory vaccines	70+	Female	Single/widowed	2015	2	Friday
Respiratory vaccines	40-49	Female	Single/widowed	2019	2	Monday
Respiratory vaccines	50-59	Female	Married	2022	2	Thursday
Respiratory vaccines	20-29	Male	Divorced	2018	2	Tuesday
Respiratory vaccines	0-19	Male	Married	2017	1	Saturday
Respiratory vaccines	50-59	Male	NA	2022	4	Saturday
Respiratory vaccines	40-49	Female	NA	2021	3	Friday
Respiratory vaccines	40-49	Male	Single/widowed	2022	1	Wednesday
Respiratory vaccines	0-19	Female	Divorced	2020	2	Tuesday
Respiratory vaccines	30-39	Male	Single/widowed	2023	2	Saturday
Respiratory vaccines	30-39	Female	Married	2021	2	Friday
Respiratory vaccines	50-59	Male	Single/widowed	2024	2	Sunday
Respiratory vaccines	50-59	Female	Married	2016	3	Monday
Respiratory vaccines	40-49	Male	NA	2017	2	Saturday
Respiratory vaccines	60-69	Male	Single/widowed	2015	2	Sunday
Respiratory vaccines	50-59	Male	NA	2020	4	Tuesday
Respiratory vaccines	40-49	Male	Single/widowed	2019	3	Wednesday
Respiratory vaccines	60-69	Male	Married	2017	2	Monday
Respiratory vaccines	20-29	Male	Divorced	2020	1	Sunday
Respiratory vaccines	20-29	Male	Single/widowed	2018	3	Thursday
Respiratory vaccines	0-19	Male	Married	2024	2	Sunday
Respiratory vaccines	20-29	Male	Divorced	2016	3	Friday
Respiratory vaccines	50-59	Female	Married	2023	1	Wednesday
Respiratory vaccines	60-69	Female	Divorced	2022	4	Monday
Respiratory vaccines	70+	Male	Single/widowed	2022	3	Wednesday
Respiratory vaccines	50-59	Female	Married	2021	2	Friday
Respiratory vaccines	40-49	Female	Married	2017	1	Monday
Respiratory vaccines	0-19	Female	Divorced	2016	2	Thursday
Respiratory vaccines	40-49	Male	Divorced	2015	3	Friday
Respiratory vaccines	70+	Female	Married	2015	2	Wednesday
Respiratory vaccines	20-29	Female	Married	2018	3	Saturday
Respiratory vaccines	20-29	Female	Married	2015	3	Thursday
Respiratory vaccines	30-39	Male	Single/widowed	2015	2	Friday
Respiratory vaccines	50-59	Female	Divorced	2024	3	Saturday
Respiratory vaccines	50-59	Male	NA	2022	4	Monday
Respiratory vaccines	60-69	Female	Single/widowed	2015	3	Monday
Respiratory vaccines	30-39	Male	Married	2016	1	Wednesday
Respiratory vaccines	60-69	Female	NA	2020	4	Friday
Respiratory vaccines	40-49	Male	NA	2017	4	Friday
Respiratory vaccines	50-59	Male	Divorced	2015	2	Monday
Respiratory vaccines	0-19	Male	Married	2024	1	Monday
Respiratory vaccines	40-49	Female	NA	2018	4	Thursday
Respiratory vaccines	0-19	Female	Married	2022	3	Saturday
Respiratory vaccines	0-19	Male	Married	2018	3	Wednesday
Respiratory vaccines	50-59	Male	Divorced	2024	4	Saturday
Respiratory vaccines	60-69	Male	Married	2022	2	Saturday
Respiratory vaccines	30-39	Male	NA	2023	1	Friday
Respiratory vaccines	0-19	Male	Married	2021	3	Thursday
Respiratory vaccines	20-29	Male	Married	2015	4	Wednesday
Respiratory vaccines	50-59	Female	Married	2019	2	Friday
Respiratory vaccines	70+	Male	Married	2018	1	Sunday
Respiratory vaccines	60-69	Male	Single/widowed	2015	3	Saturday
Respiratory vaccines	0-19	Female	Married	2019	4	Tuesday
Respiratory vaccines	50-59	Female	Married	2022	4	Monday
Respiratory vaccines	60-69	Female	Divorced	2020	4	Friday
Respiratory vaccines	50-59	Male	Single/widowed	2016	2	Tuesday
Respiratory vaccines	0-19	Male	Divorced	2019	2	Thursday
Respiratory vaccines	70+	Female	Single/widowed	2018	4	Wednesday
Respiratory vaccines	20-29	Male	Married	2021	2	Thursday
Respiratory vaccines	30-39	Male	Single/widowed	2022	2	Friday
Respiratory vaccines	20-29	Female	Married	2020	2	Friday
Respiratory vaccines	20-29	Female	Married	2018	4	Thursday
Respiratory vaccines	0-19	Female	Divorced	2024	2	Friday
Respiratory vaccines	30-39	Female	Married	2019	2	Friday
Respiratory vaccines	0-19	Female	NA	2017	4	Tuesday
Respiratory vaccines	50-59	Female	Single/widowed	2023	3	Sunday
Respiratory vaccines	0-19	Female	Married	2021	4	Monday
Respiratory vaccines	30-39	Female	Married	2017	2	Sunday
Respiratory vaccines	70+	Female	Married	2018	1	Sunday
Respiratory vaccines	0-19	Male	Single/widowed	2016	2	Friday
Respiratory vaccines	60-69	Male	Divorced	2017	1	Friday
Respiratory vaccines	60-69	Male	Single/widowed	2016	1	Saturday
Respiratory vaccines	70+	Female	NA	2016	1	Tuesday
Respiratory vaccines	60-69	Female	Single/widowed	2017	3	Thursday
Respiratory vaccines	70+	Male	Married	2022	4	Wednesday
Respiratory vaccines	30-39	Male	Married	2023	1	Saturday
Respiratory vaccines	60-69	Female	Single/widowed	2015	4	Wednesday
Respiratory vaccines	50-59	Female	Married	2015	4	Monday
Respiratory vaccines	0-19	Female	NA	2021	2	Friday
Respiratory vaccines	60-69	Female	Married	2015	4	Wednesday
Respiratory vaccines	0-19	Male	Single/widowed	2019	3	Wednesday
Respiratory vaccines	20-29	Male	Single/widowed	2022	3	Sunday
Respiratory vaccines	70+	Male	NA	2024	4	Friday
Respiratory vaccines	20-29	Female	Divorced	2022	3	Thursday
Respiratory vaccines	60-69	Male	Married	2024	2	Friday
Respiratory vaccines	60-69	Male	Married	2015	2	Friday
Respiratory vaccines	60-69	Female	Single/widowed	2024	2	Thursday
Respiratory vaccines	60-69	Female	Married	2021	3	Monday
Respiratory vaccines	70+	Male	Divorced	2016	4	Wednesday
Respiratory vaccines	60-69	Male	Married	2020	2	Thursday
Respiratory vaccines	40-49	Female	Married	2023	3	Saturday
Respiratory vaccines	40-49	Female	Married	2017	1	Saturday
Respiratory vaccines	70+	Male	Married	2019	4	Saturday
Respiratory vaccines	20-29	Female	Married	2015	2	Friday
Respiratory vaccines	20-29	Male	Divorced	2015	4	Tuesday
Respiratory vaccines	70+	Male	Married	2019	1	Wednesday
Respiratory vaccines	40-49	Male	Divorced	2021	4	Friday
Respiratory vaccines	60-69	Male	Married	2017	3	Friday
Respiratory vaccines	40-49	Female	Divorced	2023	2	Saturday
Respiratory vaccines	20-29	Male	Married	2015	2	Thursday
Respiratory vaccines	0-19	Female	NA	2020	2	Wednesday
Respiratory vaccines	0-19	Male	Single/widowed	2016	2	Friday
Respiratory vaccines	20-29	Male	Single/widowed	2019	2	Monday
Respiratory vaccines	20-29	Male	NA	2018	1	Friday
Respiratory vaccines	0-19	Male	Single/widowed	2020	1	Friday
Respiratory vaccines	40-49	Female	Married	2017	1	Wednesday
Respiratory vaccines	30-39	Male	NA	2023	4	Monday
Respiratory vaccines	50-59	Female	Married	2018	1	Saturday
Respiratory vaccines	60-69	Male	Married	2016	1	Thursday
Respiratory vaccines	60-69	Female	Single/widowed	2016	1	Friday
Respiratory vaccines	20-29	Male	Divorced	2022	1	Friday
Respiratory vaccines	40-49	Male	Married	2024	3	Saturday
Respiratory vaccines	20-29	Male	Married	2015	4	Monday
Respiratory vaccines	70+	Male	Married	2019	3	Wednesday
Respiratory vaccines	30-39	Male	Divorced	2016	2	Saturday
Respiratory vaccines	20-29	Female	Single/widowed	2015	2	Sunday
Respiratory vaccines	0-19	Female	Married	2017	1	Thursday
Respiratory vaccines	40-49	Female	Married	2022	1	Thursday
Respiratory vaccines	60-69	Male	Divorced	2020	3	Friday
Respiratory vaccines	50-59	Male	Married	2023	1	Wednesday
Respiratory vaccines	20-29	Female	Married	2019	1	Saturday
Respiratory vaccines	0-19	Male	Married	2018	2	Friday
Respiratory vaccines	0-19	Female	Married	2021	3	Friday
Respiratory vaccines	50-59	Female	Single/widowed	2018	3	Tuesday
Respiratory vaccines	30-39	Male	Single/widowed	2021	4	Wednesday
Respiratory vaccines	60-69	Female	Married	2021	3	Sunday
Respiratory vaccines	60-69	Male	Married	2020	4	Thursday
Respiratory vaccines	40-49	Male	Married	2018	2	Monday
Respiratory vaccines	20-29	Female	Single/widowed	2015	3	Tuesday

create_tab <- function(data, var, label) {
  tab_main <- data |>
    filter(!is.na({{ var }})) |>
    tabyl({{ var }}, category) |>
    mutate(Category = label, .before = 1) |>
    rename(Label = {{ var }}) |>
    mutate(Label = as.character(Label))

  na_count <- sum(is.na(pull(data, {{ var }})))
  if (na_count > 0) {
    # Dynamically build missing row across all category levels
    missing_row <- data |>
      filter(is.na({{ var }})) |>
      count(category) |>
      pivot_wider(names_from = category, values_from = n, values_fill = 0) |>
      mutate(Category = label, Label = "Missing", .before = 1)

    tab_final <- bind_rows(tab_main, missing_row)
  } else {
    tab_final <- tab_main
  }

  return(tab_final)
}

tab_age <- create_tab(vacc_1524, age_group, "Age")
tab_sex <- create_tab(vacc_1524, gender, "Gender")
tab_marital_status <- create_tab(vacc_1524, marital_status, "Marital status")
tab_year <- create_tab(vacc_1524, vacc_service_year, "Year")
tab_quarter <- create_tab(vacc_1524, vacc_quarter, "Quarter")
tab_weekday <- create_tab(vacc_1524, vacc_weekday, "Weekday")

tab_combined <- bind_rows(
  tab_sex,
  tab_age,
  tab_marital_status,
  tab_year,
  tab_quarter,
  tab_weekday
)
tab_combined

       Category          Label Respiratory vaccines Childhood vaccines Other
         Gender         Female                  445                232    65
         Gender           Male                  510                240    61
            Age           0-19                  146                 66    23
            Age          20-29                  160                 81    17
            Age          30-39                  131                 76    18
            Age          40-49                  134                 58    17
            Age          50-59                  126                 65    19
            Age          60-69                  135                 65    14
            Age            70+                  123                 61    18
 Marital status       Divorced                  117                 52    15
 Marital status        Married                  458                218    61
 Marital status Single/widowed                  283                156    41
 Marital status        Missing                   97                 46     9
           Year           2015                  110                 46     8
           Year           2016                   99                 45    14
           Year           2017                   81                 45    10
           Year           2018                   83                 47    13
           Year           2019                  105                 51    12
           Year           2020                   98                 45    12
           Year           2021                   87                 52    12
           Year           2022                   90                 40     9
           Year           2023                   96                 46    17
           Year           2024                  106                 55    19
        Quarter              1                  237                115    40
        Quarter              2                  237                122    36
        Quarter              3                  228                117    22
        Quarter              4                  253                118    28
        Weekday         Friday                  152                 64    16
        Weekday         Monday                  127                 59    20
        Weekday       Saturday                  111                 68    22
        Weekday         Sunday                  129                 73    12
        Weekday       Thursday                  145                 62    19
        Weekday        Tuesday                  138                 71    19
        Weekday      Wednesday                  153                 75    18

tab_combined <- tab_combined |>
  rowwise() |>
  mutate(
    Total = sum(
      `Respiratory vaccines`,
      `Childhood vaccines`,
      Other,
      na.rm = TRUE
    ),
    is_missing = trimws(tolower(Label)) == "missing",

    Respiratory = ifelse(
      is_missing,
      comma(`Respiratory vaccines`),
      paste0(
        comma(`Respiratory vaccines`),
        " (",
        round(100 * `Respiratory vaccines` / Total, 1),
        ")"
      )
    ),

    Childhood = ifelse(
      is_missing,
      comma(`Childhood vaccines`),
      paste0(
        comma(`Childhood vaccines`),
        " (",
        round(100 * `Childhood vaccines` / Total, 1),
        ")"
      )
    ),

    Other_grp = ifelse(
      is_missing,
      comma(Other),
      paste0(comma(Other), " (", round(100 * Other / Total, 1), ")")
    )
  ) |>
  ungroup() |>
  select(Category, Label, Respiratory, Childhood, Other_grp)

tab_combined

# A tibble: 34 × 5
   Category       Label    Respiratory Childhood  Other_grp
   <chr>          <chr>    <chr>       <chr>      <chr>    
 1 Gender         Female   445 (60)    232 (31.3) 65 (8.8) 
 2 Gender         Male     510 (62.9)  240 (29.6) 61 (7.5) 
 3 Age            0-19     146 (62.1)  66 (28.1)  23 (9.8) 
 4 Age            20-29    160 (62)    81 (31.4)  17 (6.6) 
 5 Age            30-39    131 (58.2)  76 (33.8)  18 (8)   
 6 Age            40-49    134 (64.1)  58 (27.8)  17 (8.1) 
 7 Age            50-59    126 (60)    65 (31)    19 (9)   
 8 Age            60-69    135 (63.1)  65 (30.4)  14 (6.5) 
 9 Age            70+      123 (60.9)  61 (30.2)  18 (8.9) 
10 Marital status Divorced 117 (63.6)  52 (28.3)  15 (8.2) 
# ℹ 24 more rows

tab_combined <- tab_combined |>
  group_by(Category) |>
  mutate(Category = ifelse(row_number() == 1, Category, "")) |>
  ungroup()

tab_combined

# A tibble: 34 × 5
   Category         Label    Respiratory Childhood  Other_grp
   <chr>            <chr>    <chr>       <chr>      <chr>    
 1 "Gender"         Female   445 (60)    232 (31.3) 65 (8.8) 
 2 ""               Male     510 (62.9)  240 (29.6) 61 (7.5) 
 3 "Age"            0-19     146 (62.1)  66 (28.1)  23 (9.8) 
 4 ""               20-29    160 (62)    81 (31.4)  17 (6.6) 
 5 ""               30-39    131 (58.2)  76 (33.8)  18 (8)   
 6 ""               40-49    134 (64.1)  58 (27.8)  17 (8.1) 
 7 ""               50-59    126 (60)    65 (31)    19 (9)   
 8 ""               60-69    135 (63.1)  65 (30.4)  14 (6.5) 
 9 ""               70+      123 (60.9)  61 (30.2)  18 (8.9) 
10 "Marital status" Divorced 117 (63.6)  52 (28.3)  15 (8.2) 
# ℹ 24 more rows

totals <- vacc_1524 |> count(category) |> deframe()

rm(
  tab_age,
  tab_sex,
  tab_marital_status,
  tab_year,
  tab_quarter,
  tab_weekday,
  tab_combined,
  #ft_vacc_summary,
  totals,
  create_tab
)

# ------------1. Table without title and footer -------------------------------------------+
library(dplyr)
library(janitor)
library(tibble)
library(scales)
library(flextable)
#.......................Ensure grouping variables are characters............................
vacc_1524 <- vacc_1524 |>
  mutate(across(c(age_group, gender, marital_status, vacc_service_year, vacc_quarter, vacc_weekday), as.character))

# ---------------- Table generation function ....................
create_tab <- function(data, var, label) {
  tab_main <- data |>
    filter(!is.na({{ var }})) |>
    tabyl({{ var }}, category) |>
    mutate(Category = label, .before = 1) |>
    rename(Label = {{ var }}) |>
    mutate(Label = as.character(Label))
  na_count <- sum(is.na(pull(data, {{ var }})))
  
  if (na_count > 0) {
    missing_row <- tibble(
      Category = label,
      Label = "Missing",
      `Respiratory vaccines` = sum(data$vacc_group3 == "Respiratory vaccines" & is.na(pull(data, {{ var }})), na.rm = TRUE),
      `Childhood vaccines` = sum(data$vacc_group3 == "Childhood vaccines" & is.na(pull(data, {{ var }})), na.rm = TRUE),
      Other = sum(data$vacc_group3 == "Other" & is.na(pull(data, {{ var }})), na.rm = TRUE)
    )
    tab_final <- bind_rows(tab_main, missing_row)
  } else {
    tab_final <- tab_main
  }
  
  return(tab_final)  
}

# .................... Create summary tables ....................
tab_age <- create_tab(vacc_1524, age_group, "Age")
tab_sex <- create_tab(vacc_1524, gender, "Gender")
tab_marital_status <- create_tab(vacc_1524, marital_status, "Marital status")
tab_year <- create_tab(vacc_1524, vacc_service_year, "Year")
tab_quarter <- create_tab(vacc_1524, vacc_quarter, "Quarter")
tab_weekday <- create_tab(vacc_1524, vacc_weekday, "Weekday")

# ......................Combine all tabs ....................
tab_combined <- bind_rows(
  tab_sex,
  tab_age,
  tab_marital_status,
  tab_year,
  tab_quarter,
  tab_weekday
)

# ....................... Create row % and format as text ....................
tab_combined <- tab_combined |>
  rowwise() |>
  mutate(
    Total = sum(
      `Respiratory vaccines`,
      `Childhood vaccines`,
      Other,
      na.rm = TRUE
    ),
    is_missing = trimws(tolower(Label)) == "missing",
    
    Respiratory = ifelse(is_missing,
                         comma(`Respiratory vaccines`),
                         paste0(comma(`Respiratory vaccines`), " (", round(100 * `Respiratory vaccines` / Total, 1), ")")),
    
    Childhood = ifelse(is_missing,
                       comma(`Childhood vaccines`),
                       paste0(comma(`Childhood vaccines`), " (", round(100 * `Childhood vaccines` / Total, 1), ")")),
    
    Other_grp = ifelse(is_missing,
                       comma(Other),
                       paste0(comma(Other), " (", round(100 * Other / Total, 1), ")"))  
  ) |>                                                                                   
  ungroup() |>
  select(Category, Label, Respiratory, Childhood, Other_grp)

# ............................. Remove repeated variable names ....................
tab_combined <- tab_combined |>
  group_by(Category) |>
  mutate(Category = ifelse(row_number() == 1, Category, "")) |>
  ungroup()

# ........................... Get total counts per vaccine group ....................
totals <- vacc_1524 |>
  count(category) |>
  deframe()

# .................... Create flextable ....................
ft_vacc_summary <- tab_combined |>
  flextable() |>
  set_header_labels(
    Category = "Variable type",
    Label = "Values",
    Respiratory = "Number (%)ᵃ",
    Childhood = "Number (%)ᵃ",
    Other_grp = "Number (%)ᵃ"
  ) |>
  add_header_row(
    # Second row
    values = c(
      "",
      "",
      paste0(
        "Respiratory vaccines (N = ",
        format(totals["Respiratory vaccines"], big.mark = ","),
        ")"
      ),
      paste0(
        "Childhood vaccines (N = ",
        format(totals["Childhood vaccines"], big.mark = ","),
        ")"
      ),
      paste0("Other (N = ", format(totals["Other"], big.mark = ","), ")")
    )
  ) |>
  add_header_row(
    # First row (merged header)
    values = c("Description", "Vaccination uptake"),
    colwidths = c(2, 3)
  ) |>
  theme_box() |>
  set_table_properties(layout = "autofit", width = 1) |>
  width(j = 1, width = 1.5) |>
  width(j = 2, width = 2.5) |>
  width(j = 3:4, width = 1.5) |>
  align(align = "justify", part = "all") |>
  border_remove() |>
  border(
    part = "header",
    i = 1,
    border.top = fp_border(color = "black", width = 1)
  ) |>
  border(
    part = "header",
    i = 3,
    border.bottom = fp_border(color = "black", width = 1)
  ) |>
  border_inner_h(
    part = "header",
    border = fp_border(color = "black", width = 0.8)
  ) |>
#  border_inner_h(
#    part = "body",
#    border = fp_border(color = "black", width = 0.8)
#  ) |>
#  border(
#    part = "body",
#    i = nrow(tab_combined),
#    border.bottom = fp_border(color = "black", width = 1)
#  ) |>
  set_caption(as_paragraph(as_chunk("Table: Vaccination uptake across service year, quarter, and weekday",
                                  props = fp_text(font.size = 14, font.family = "Times New Roman", bold = FALSE)))) |>
  fontsize(part = "header", size = 12) |>
  fontsize(part = "body", size = 11) |>
  fontsize(part = "footer", size = 10) |>
  font(part = "all", fontname = "Times New Roman") |>
  bg(part = "header", bg = "lightblue") |>
  bg(j = 1:5, i = seq(1, nrow(tab_combined), 2), bg = "white") |>
  bg(j = 1:5, i = seq(2, nrow(tab_combined), 2), bg = "lightgray") |>
  add_footer_lines(
    values = "ᵃ Vaccination proportions were calculated row-wise (i.e., by row)."
  )

# --- Display the table ---
ft_vacc_summary

Description		Vaccination uptake
		Respiratory vaccines (N = 955)	Childhood vaccines (N = 472)	Other (N = 126)
Variable type	Values	Number (%)ᵃ	Number (%)ᵃ	Number (%)ᵃ
Gender	Female	445 (60)	232 (31.3)	65 (8.8)
	Male	510 (62.9)	240 (29.6)	61 (7.5)
Age	0-19	146 (62.1)	66 (28.1)	23 (9.8)
	20-29	160 (62)	81 (31.4)	17 (6.6)
	30-39	131 (58.2)	76 (33.8)	18 (8)
	40-49	134 (64.1)	58 (27.8)	17 (8.1)
	50-59	126 (60)	65 (31)	19 (9)
	60-69	135 (63.1)	65 (30.4)	14 (6.5)
	70+	123 (60.9)	61 (30.2)	18 (8.9)
Marital status	Divorced	117 (63.6)	52 (28.3)	15 (8.2)
	Married	458 (62.1)	218 (29.6)	61 (8.3)
	Single/widowed	283 (59)	156 (32.5)	41 (8.5)
	Missing	0	0	0
Year	2015	110 (67.1)	46 (28)	8 (4.9)
	2016	99 (62.7)	45 (28.5)	14 (8.9)
	2017	81 (59.6)	45 (33.1)	10 (7.4)
	2018	83 (58)	47 (32.9)	13 (9.1)
	2019	105 (62.5)	51 (30.4)	12 (7.1)
	2020	98 (63.2)	45 (29)	12 (7.7)
	2021	87 (57.6)	52 (34.4)	12 (7.9)
	2022	90 (64.7)	40 (28.8)	9 (6.5)
	2023	96 (60.4)	46 (28.9)	17 (10.7)
	2024	106 (58.9)	55 (30.6)	19 (10.6)
Quarter	1	237 (60.5)	115 (29.3)	40 (10.2)
	2	237 (60)	122 (30.9)	36 (9.1)
	3	228 (62.1)	117 (31.9)	22 (6)
	4	253 (63.4)	118 (29.6)	28 (7)
Weekday	Friday	152 (65.5)	64 (27.6)	16 (6.9)
	Monday	127 (61.7)	59 (28.6)	20 (9.7)
	Saturday	111 (55.2)	68 (33.8)	22 (10.9)
	Sunday	129 (60.3)	73 (34.1)	12 (5.6)
	Thursday	145 (64.2)	62 (27.4)	19 (8.4)
	Tuesday	138 (60.5)	71 (31.1)	19 (8.3)
	Wednesday	153 (62.2)	75 (30.5)	18 (7.3)
ᵃ Vaccination proportions were calculated row-wise (i.e., by row).

datatable(
  tab_combined,
  rownames = FALSE,
  extensions = 'Buttons',
  options = list(
    dom = 'Bfrtip',
    buttons = c('copy', 'csv', 'excel', 'pdf', 'print'),
    pageLength = 25,
    autoWidth = TRUE,
    columnDefs = list(list(width = '200px', targets = c(0, 1)))
  ),
  caption = htmltools::tags$caption(
    style = 'caption-side: top; font-size: 18px; font-family: "Times New Roman"; font-weight: bold;',
    'Table: Vaccination uptake across service year, quarter, and weekday'
  ),
  class = 'stripe hover cell-border'
)