R/age_adjusted/other_scripts/age_adjusted_comorb_dst.R

In gabrielburcea/cvindia: Analysis of symptoms

#' age_adjusted_comorb_dst
#'
#' @param data 
#' @param country_select 
#' @param comorb 
#' @param table 
#'
#' @return
#' @export
#'
#' @examples
age_adjusted_comorb_dst <- function(data, country_select, comorb, table_gen =  TRUE) {
  
  data_select <- data %>%
    dplyr::select(id, age, Country, asthma, diabetes_type_one, diabetes_type_two, obesity, hypertension, heart_disease, lung_condition, 
                  liver_disease, kidney_disease) %>%
    dplyr::mutate(age_recoded = replace(age, age > 100, NA_real_)) %>%
    dplyr::rename(country = Country)
  
  #get the age bands
  data_age_band <- data_select %>%
    dplyr::mutate(age_recoded_band = dplyr::case_when(
      age_recoded == 0 | age_recoded <= 19 ~ '0-19',
      age_recoded == 20 | age_recoded <= 39 ~ '20-39',
      age_recoded == 40 | age_recoded <= 59 ~ '40-59',
      age_recoded >= 60 ~ "60+"))
  
  
  # get the country age_recoded_bands counts
  
  data_country_age_band <- data_age_band %>%
    dplyr::select(id, country, age_recoded_band) %>%
    dplyr::filter(country == country_select) %>%
    dplyr::group_by(age_recoded_band) %>%
    dplyr::summarise(count_age_study_pop = dplyr::n())
  
  
  # get the country age_recoded_bands and specific pre-existing condition 
  # 1.use pivot_loger to get comorbidities as rows 
  age_band_comorbidities_pv <- data_age_band %>%
    tidyr::pivot_longer(cols = 4:12, 
                        names_to = "comorbidities", 
                        values_to = "Bolean") %>%
    dplyr::filter(Bolean != "No")
  
  # get the counts of a pre-existing cond for a certain country across age_bands
  cnt_country_age_band_comorb <- age_band_comorbidities_pv %>%
    dplyr::filter(country == country_select & comorbidities == comorb) %>%
    dplyr::group_by(age_recoded_band, country, comorbidities) %>%
    dplyr::summarise(count_study_pop_comorb = dplyr::n()) %>%
    tidyr::drop_na()
  
  # join the data country age band and age band comorbidities
  dt_study_pop_age_comorb <- dplyr::left_join(data_country_age_band, cnt_country_age_band_comorb) %>% 
    tidyr::drop_na()
  
  dt_study_pop <- dt_study_pop_age_comorb %>%
    dplyr::mutate(study_comorbidity_rate = count_study_pop_comorb/count_age_study_pop)
  
  # get the counts of respondents by age band in the standard population
  dt_cnt_age_band_standard_pop <- data_age_band %>%
    dplyr::group_by(age_recoded_band) %>%
    dplyr::summarise(count_age_group_standard_pop = dplyr::n()) %>% 
    dplyr::ungroup() %>%
    tidyr::drop_na()
  
  # join the dt_cnt_age_band_standard_pop to dt_study_pop_age_comorb
  
  study_pop_standard_pop <- dplyr::left_join(dt_study_pop, dt_cnt_age_band_standard_pop)
  
  # get the expected study population comorbidity
  
  dt_expected_study_pop_comorb <- study_pop_standard_pop %>%
    dplyr::mutate(expected_comorb_study_pop = count_age_group_standard_pop * study_comorbidity_rate) %>%
    tidyr::drop_na()
  
  
  
  final_adj_comorb_age_adj_rate <- dplyr::left_join(dt_expected_study_pop_comorb, study_pop_standard_pop)
  
  # get the total expected study pop comorb and total comorb age group in standard pop and the get total expected comorb in the study pop
  dt_total_expected_standard_pop <- final_adj_comorb_age_adj_rate %>%
    dplyr::ungroup() %>%
    dplyr::mutate(total_expected_study_pop_comorb = sum(expected_comorb_study_pop)) %>%
    dplyr::mutate(total_counts_stnd_pop = sum(count_age_group_standard_pop)) %>%
    dplyr::mutate(standardised_existing_cond_rate = total_expected_study_pop_comorb/total_counts_stnd_pop *100)
  
  dt_select_final <- round(dt_total_expected_standard_pop[1, 9], digits = 2)
  
  title <- "Standardised"
  add_to_title <- comorb
  add_last_word <- " rate in"
  country = country_select
  
  whole_title <- paste(title, add_to_title, add_last_word, country)
  
  whole_title

  #########################################
  #########################################
  
  #Get the tables for analysing the robstness 
  
  
  # data_select %>%
  #   dplyr::select(id, age, asthma, diabetes_type_one, diabetes_type_two, obesity, hypertension, heart_disease, lung_condition, 
  #                 liver_disease, kidney_disease) %>%
  #   dplyr::mutate(age_recoded = replace(age, age > 100, NA_real_))
  # 
  #get the age bands
  data_age_band <- data_select %>%
    dplyr::mutate(age_recoded_band = dplyr::case_when(
      age_recoded == 0 | age_recoded <= 19 ~ '0-19',
      age_recoded == 20 | age_recoded <= 39 ~ '20-39',
      age_recoded == 40 | age_recoded <= 59 ~ '40-59',
      age_recoded >= 60 ~ "60+")) %>%
    dplyr::mutate(age_recoded_band_5plus = dplyr::case_when(
      age_recoded == 5 | age_recoded <= 24 ~ '5-24',
      age_recoded == 25 | age_recoded <= 44 ~ '25-44',
      age_recoded == 45 | age_recoded <= 64 ~ '45-64',
      age_recoded >= 65 ~ "65+")) %>%
    dplyr::mutate(age_recoded_band_10plus = dplyr::case_when(
      age_recoded == 10 | age_recoded <= 29 ~ '10-29',
      age_recoded == 30 | age_recoded <= 49 ~ '30-49',
      age_recoded == 50 | age_recoded <= 69 ~ '50-69',
      age_recoded >= 70 ~ "70+"))
  
  
  
  # get the country age_recoded_bands counts
  data_country_age_band <- data_age_band %>%
    dplyr::select(id, country, age_recoded_band) %>%
    dplyr::filter(country == country_select) %>%
    dplyr::group_by(age_recoded_band) %>%
    dplyr::summarise(count_age_study_pop = dplyr::n()) %>%
    dplyr::ungroup() %>%
    tidyr::drop_na()
  
  data_country_age_band_5plus <- data_age_band %>%
    dplyr::select(id, country, age_recoded_band_5plus) %>%
    dplyr::filter(country == country_select) %>%
    dplyr::group_by(age_recoded_band_5plus) %>%
    dplyr::summarise(count_age_study_pop_5plus = dplyr::n()) %>%
    dplyr::ungroup() %>%
    tidyr::drop_na()
  
  data_country_age_band_10plus <- data_age_band %>%
    dplyr::select(id, country, age_recoded_band_10plus) %>%
    dplyr::filter(country == country_select) %>%
    dplyr::group_by(age_recoded_band_10plus) %>%
    dplyr::summarise(count_age_study_pop_10plus = dplyr::n()) %>%
    dplyr::ungroup() %>%
    tidyr::drop_na()
  
  
  data_country_age_band_j <- bind_cols(data_country_age_band, data_country_age_band_5plus, data_country_age_band_10plus)
  
  # get the country age_recoded_bands and specific pre-existing condition 
  # 1.use pivot_loger to get comorbidities as rows 
  age_band_comorbidities_pv <- data_age_band %>%
    tidyr::pivot_longer(cols = 4:12, 
                        names_to = "comorbidities", 
                        values_to = "Bolean") %>%
    dplyr::filter(Bolean != "No")
  
  
  
  # get the counts of a pre-existing cond for a certain country across age_bands
  cnt_country_age_band_comorb <- age_band_comorbidities_pv %>%
    dplyr::filter(country == country_select & comorbidities == comorb) %>%
    dplyr::group_by(age_recoded_band, country, comorbidities) %>%
    dplyr::summarise(count_study_pop_comorb = dplyr::n())
  
  cnt_country_age_band_comorb_5plus <- age_band_comorbidities_pv %>%
    dplyr::filter(country == country_select & comorbidities == comorb) %>%
    dplyr::group_by(age_recoded_band_5plus) %>%
    dplyr::summarise(count_study_pop_comorb_5plus = dplyr::n()) 
  
  cnt_country_age_band_comorb_10plus <- age_band_comorbidities_pv %>%
    dplyr::filter(country == country_select & comorbidities == comorb) %>%
    dplyr::group_by(age_recoded_band_10plus) %>%
    dplyr::summarise(count_study_pop_comorb_10plus = dplyr::n()) 
  
  
  cnt_country_age_band_comorb_j <- bind_cols(cnt_country_age_band_comorb, cnt_country_age_band_comorb_5plus, cnt_country_age_band_comorb_10plus)
  
  # join the data country age band and age band comorbidities
  dt_study_pop_age_comorb <- dplyr::left_join(data_country_age_band_j, cnt_country_age_band_comorb_j) %>% 
  tidyr::drop_na()
  
  dt_study_pop <- dt_study_pop_age_comorb %>%
    dplyr::mutate(study_comorbidity_rate = count_study_pop_comorb/count_age_study_pop, 
                  study_comorbidity_rate_5plus = count_study_pop_comorb_5plus/count_age_study_pop_5plus, 
                  study_comorbidity_rate_10plus = count_study_pop_comorb_10plus/count_age_study_pop_10plus)
  
  # get the counts of respondents by age band in the standard population
  dt_cnt_age_band_standard_pop <- data_age_band %>%
    dplyr::group_by(age_recoded_band) %>%
    dplyr::summarise(count_age_group_standard_pop = dplyr::n()) %>% 
    dplyr::ungroup() %>%
    tidyr::drop_na()
  
  dt_cnt_age_band_standard_pop_5plus <- data_age_band %>%
    dplyr::group_by(age_recoded_band_5plus) %>%
    dplyr::summarise(count_age_group_standard_pop_5plus = dplyr::n()) %>% 
    dplyr::ungroup() %>%
    tidyr::drop_na()
  
  dt_cnt_age_band_standard_pop_10plus <- data_age_band %>%
    dplyr::group_by(age_recoded_band_10plus) %>%
    dplyr::summarise(count_age_group_standard_pop_10plus = dplyr::n()) %>% 
    dplyr::ungroup() %>%
    tidyr::drop_na()
  
  
  dt_cnt_age_band_standard_pop_j <- bind_cols(dt_cnt_age_band_standard_pop, dt_cnt_age_band_standard_pop_5plus, dt_cnt_age_band_standard_pop_10plus)
  
  # join the dt_cnt_age_band_standard_pop to dt_study_pop_age_comorb
  
  study_pop_standard_pop <- dplyr::left_join(dt_study_pop, dt_cnt_age_band_standard_pop_j)
  
  # get the expected study population comorbidity
  
  dt_expected_study_pop_comorb <- study_pop_standard_pop %>%
    dplyr::mutate(expected_comorb_study_pop = count_age_group_standard_pop * study_comorbidity_rate, 
                  expected_comorb_study_pop_5plus = count_age_group_standard_pop_5plus * study_comorbidity_rate_5plus, 
                  expected_comorb_study_pop_10plus = count_age_group_standard_pop_10plus * study_comorbidity_rate_10plus) %>%
  tidyr::drop_na()
  
  
  
  final_adj_comorb_age_adj_rate <- dplyr::left_join(dt_expected_study_pop_comorb, study_pop_standard_pop)
  
  # get the total expected study pop comorb and total comorb age group in standard pop and the get total expected comorb in the study pop
  numbers_only <- final_adj_comorb_age_adj_rate %>%
    dplyr::ungroup() %>%
    dplyr::mutate(total_expected_study_pop_comorb = sum(expected_comorb_study_pop), 
                  total_expected_study_pop_comorb_5plus = sum(expected_comorb_study_pop_5plus), 
                  total_expected_study_pop_comorb_10plus = sum(expected_comorb_study_pop_10plus)) %>%
    dplyr::mutate(total_counts_stnd_pop = sum(count_age_group_standard_pop), 
                  total_counts_stnd_pop_5plus = sum(count_age_group_standard_pop_5plus), 
                  total_counts_stnd_pop_10plus = sum(count_age_group_standard_pop_10plus)) %>%
    dplyr::mutate(standardised_existing_cond_rate = total_expected_study_pop_comorb/total_counts_stnd_pop *100, 
                  standardised_existing_cond_rate_5plus = total_expected_study_pop_comorb/total_counts_stnd_pop_5plus *100, 
                  standardised_existing_cond_rate_10plus = total_expected_study_pop_comorb/total_counts_stnd_pop_10plus *100)
  
  numbers_only
  
  
  
  
  if(table_gen == TRUE){ 
    
    dt_total_expected_standard_pop
    
  }else{
    
    numbers_only
    
  }
  
}