data-raw/fns/transform.R

In ready4-dev/youthvars: Describe and Validate Youth Mental Health Economic Datasets

transform_csnl_example_ds <- function(ds_df){ # New to youthvars
  ds_tb <- ds_df %>%
    tibble::as_tibble() %>%
    dplyr::mutate(c_p_diag_grouped = dplyr::case_when(as.character(DiagnosisPrimary) %in% c("Acute stress disorder",
                                                                                            "Adjustment disorder",
                                                                                            "Agoraphobia",
                                                                                            "Anxiety symptoms",
                                                                                            "Bipolar disorder",
                                                                                            "Cyclothymic disorder",
                                                                                            "Depressive disorder NOS",
                                                                                            "Depressive symptoms",
                                                                                            "Dysthymia",
                                                                                            "Generalised anxiety disorder",
                                                                                            "Major depressive disorder",
                                                                                            "Mixed anxiety and depressive symptoms",
                                                                                            "Obsessive-compulsive disorder",
                                                                                            "Other affective disorder",
                                                                                            "Other anxiety disorder",
                                                                                            "Panic disorder",
                                                                                            "Post-traumatic stress disorder",
                                                                                            "Separation anxiety disorder",
                                                                                            "Social phobia",
                                                                                            "Stress related") ~ "Anxiety and Depression",
                                                      as.character(DiagnosisPrimary) %in% c("Attention deficit hyperactivity disorder (ADHD)",
                                                                                            "Conduct disorder",
                                                                                            "Feeding and Eating Disorders",
                                                                                            "Gender Dysphoria",
                                                                                            "Neurocognitive Disorders",
                                                                                            "Neurodevelopmental Disorders",
                                                                                            "Oppositional defiant disorder",
                                                                                            "Other",
                                                                                            "Personality Disorders",
                                                                                            "Pervasive developmental disorder",
                                                                                            "Schizoaffective disorder",
                                                                                            "Schizophrenia",
                                                                                            "Sleep-Wake Disorders",
                                                                                            "Somatic Symptom and Related Disorders" ) ~ "Other Mental Disorder",
                                                      as.character(DiagnosisPrimary) %in% c("Alcohol dependence",
                                                                                            "Other drug dependence") ~ "Substance Use",
                                                      as.character(DiagnosisPrimary) %in% c("Not applicable (e.g. for non-Mental Health related services, or service provider not qualified to give diagnosis)",
                                                                                            "Diagnosis not yet assessed or requires further assessment",
                                                                                            "No diagnosis (and no sub-syndromal mental health problems)") ~ "Not applicable",
                                                      is.na(DiagnosisPrimary) ~ NA_character_,
                                                      T ~ "Uncategorised") %>%
                    as.factor()) %>%
    dplyr::rename(c_days_cut_back = K12_DaysCutDown,
                  c_days_unable = K11_DaysTotallyUnable,
                  CHU9D = chu9_total_w,
                  c_p_diag_s = DiagnosisPrimary,
                  d_age = Age,
                  d_ATSI = ATSI,
                  d_CALD = CALD, # Uncomment when dictionary is updated
                  d_employed = Working,
                  d_employment_type = EmploymentType,
                  d_gender = Gender,
                  d_studying = Studying,
                  K10 = K10_total,
                  MLT = MLT_mean,
                  s_IRSD = IRSD,
                  s_remoteness = Remoteness,
                  validation_aqol_c = aqol6d_total_c,
                  validation_aqol_w = aqol6d_total_w)
  ds_tb <- ds_tb %>%
    dplyr::mutate(dplyr::across(c(dplyr::starts_with("aqol6d_q"),
                                  dplyr::starts_with("chu9_q"),
                                  dplyr::starts_with("c_days_"),
                                  d_age,
                                  K10,
                                  s_IRSD,
                                  SOFAS), ~as.integer(.x))) %>%
    dplyr::mutate(dplyr::across(c(c_p_diag_s,
                                  d_ATSI,
                                  d_gender), ~as.factor(.x)))
  ds_tb <- ds_tb %>%
    dplyr::mutate(c_days_oor = c_days_cut_back + c_days_unable) %>%
    dplyr::mutate(d_studying_working = dplyr::case_when(purrr::map2_lgl(as.character(d_employed), as.character(d_studying), ~ is.na(.x) | is.na(.y)) ~ NA_character_,
                                                        purrr::map2_lgl(as.character(d_employed), as.character(d_studying), ~ .x == "No" && .y == "No") ~ "Not studying or working",
                                                        purrr::map2_lgl(as.character(d_employed), as.character(d_studying), ~ .x == "No" && .y == "Yes") ~ "Studying only",
                                                        purrr::map2_lgl(as.character(d_employed), as.character(d_studying), ~ .x == "Yes" && .y == "No") ~ "Working only",
                                                        purrr::map2_lgl(as.character(d_employed), as.character(d_studying), ~ .x == "Yes" && .y == "Yes") ~ "Studying and working",
                                                        T ~ "Uncategorised"
    ) %>% as.factor()) %>%
    dplyr::mutate(difference_mauis = validation_aqol_w - CHU9D) %>%
    dplyr::mutate(difference_aqol_calcs = NA_real_)
  ds_tb <- add_uids_to_tbs_ls(list(ds_tb),"Participant_") %>% purrr::pluck(1)  %>% ready4::remove_lbls_from_df()
  return(ds_tb)
}
transform_ds_for_item_plt <- function(data_tb,
                                      var_nm_1L_chr,
                                      round_var_nm_1L_chr = "round"){
  tfd_data_tb <- data_tb %>%
    dplyr::filter(!is.na(!!as.name(var_nm_1L_chr) ))
  if(!identical(round_var_nm_1L_chr, character(0))){
    tfd_data_tb <- tfd_data_tb %>%
      dplyr::group_by(!!rlang::sym(round_var_nm_1L_chr), !!as.name(var_nm_1L_chr) )
  }else{
    tfd_data_tb <- tfd_data_tb %>%
      dplyr::group_by(!!as.name(var_nm_1L_chr) )
  }
  tfd_data_tb <- tfd_data_tb %>%
    dplyr::summarise(n = dplyr::n())
  if(!identical(round_var_nm_1L_chr, character(0)))
    tfd_data_tb <- tfd_data_tb %>%
    dplyr::group_by(!!rlang::sym(round_var_nm_1L_chr))
  tfd_data_tb <- tfd_data_tb %>%
    dplyr::mutate(y = n/sum(n))
  return(tfd_data_tb)
}
transform_ds_for_tstng <- function (data_tb, depnt_var_nm_1L_chr = "aqol6d_total_w", depnt_var_max_val_1L_dbl = 0.999,
                                    candidate_predrs_chr = NA_character_, covar_var_nms_chr = NA_character_,
                                    round_var_nm_1L_chr = "round", round_val_1L_chr = "Baseline",
                                    remove_all_msng_1L_lgl = F)
{
  vars_to_keep_chr <- c(depnt_var_nm_1L_chr, candidate_predrs_chr,
                        covar_var_nms_chr) %>% purrr::discard(is.na)
  tfd_data_tb <- data_tb
  if(!identical(round_var_nm_1L_chr, character(0)) && (!is.na(round_var_nm_1L_chr) & !is.na(round_val_1L_chr)))
    tfd_data_tb <- tfd_data_tb %>%
    dplyr::filter(!!rlang::sym(round_var_nm_1L_chr) == round_val_1L_chr)
  tfd_data_tb <- tfd_data_tb %>%
    dplyr::select(!!!rlang::syms(vars_to_keep_chr)) %>%
    dplyr::mutate(`:=`(!!rlang::sym(depnt_var_nm_1L_chr), ifelse(!!rlang::sym(depnt_var_nm_1L_chr) >
                                                                   depnt_var_max_val_1L_dbl, depnt_var_max_val_1L_dbl, !!rlang::sym(depnt_var_nm_1L_chr))))
  if (remove_all_msng_1L_lgl)
    tfd_data_tb <- tfd_data_tb %>% stats::na.omit()
  return(tfd_data_tb)
}
transform_ds_with_rename_lup <- function(ds_tb,
                                         rename_lup,
                                         target_var_nms_chr = NULL){
  if(is.null(target_var_nms_chr))
    target_var_nms_chr <- intersect(names(ds_tb),rename_lup$old_nms_chr)
  tfmd_ds_tb <- dplyr::rename_with(ds_tb,
                                   .cols = target_var_nms_chr,
                                   ~ ready4::get_from_lup_obj(rename_lup,
                                                                 match_value_xx = .x,
                                                                 match_var_nm_1L_chr = "old_nms_chr",
                                                                 target_var_nm_1L_chr = "new_nms_chr",
                                                                 evaluate_1L_lgl = F))
  return(tfmd_ds_tb)

}
transform_raw_ds_for_analysis <- function (raw_ds_tb) # Previously transform_raw_aqol_tb_to_aqol6d_tb
{
  transformed_ds_tb <- raw_ds_tb %>% dplyr::mutate(d_agegroup = cut(d_age,
                                                              breaks = c(11, 17, 30), labels = c("Age 12-17", "Age 18-26"))) %>%
    dplyr::mutate(round = factor(round, labels = c("Baseline",
                                                   "Follow-up"))) %>%
    dplyr::mutate(Gender = factor(ifelse(d_gender == "Genderqueer/gender nonconforming/agender" |
                                           d_gender == "Transgender", "Other", as.character(d_gender)))) %>%
    dplyr::mutate(Region = as.factor(ifelse(s_centre == "Canberra" |
                                              s_centre == "Southport" | s_centre == "Knox", "Metro",
                                            "Regional"))) %>%
    dplyr::mutate(CALD = factor(ifelse(d_country_bir_s == "Other" | d_english_home == "No" | d_english_native == "No",
                                       "Yes",
                                       "No"
                                       ))) %>%
    dplyr::mutate(d_sexual_ori_s = dplyr::case_when(d_sexual_ori_s == "Straight" ~ "Heterosexual",
                                                    T ~ as.character(d_sexual_ori_s)) %>% factor()) %>%
    dplyr::select(fkClientID, round, d_interview_date,
                  d_age, d_agegroup, Gender, d_gender,
                  d_sex_birth_s, d_sexual_ori_s,
                  d_ATSI,
                  CALD, d_country_bir_s, d_english_home, d_english_native,
                  Region,
                  d_studying_working,
                  d_relation_s,
                  s_centre,
                  c_p_diag_s, c_clinical_staging_s,
                  k6_total, phq9_total, bads_total, gad7_total, oasis_total, scared_total,
                  c_sofas,
                  dplyr::contains("aqol6d")) %>%
    dplyr::rename(PHQ9 = phq9_total,
                  BADS = bads_total,
                  GAD7 = gad7_total,
                  OASIS = oasis_total,
                  SCARED = scared_total,
                  K6 = k6_total,
                  SOFAS = c_sofas)
  transformed_ds_tb <- transformed_ds_tb %>%
    dplyr::select(names(transformed_ds_tb)[!(names(transformed_ds_tb) %>% purrr::map_lgl(~ startsWith(.x,"aqol6d_sub") | startsWith(.x,"aqol6d_tot") | startsWith(.x,"aqol6d_fla")))])
  # transformed_ds_tb <- transformed_ds_tb %>%
  #   dplyr::select(c(c("fkClientID","round"),names(transformed_ds_tb)[!startsWith(names(transformed_ds_tb),"aqol6d_q") & !(names(transformed_ds_tb) %in% c("fkClientID","round"))],names(transformed_ds_tb)[startsWith(names(transformed_ds_tb),"aqol6d_q")]))
  return(transformed_ds_tb)
}
transform_tb_for_merged_col_1 <- function(df,
                                          output_type_1L_chr = "PDF"){
  df[[1]] <- as.character(df[[1]])
  rle.lengths <- rle(df[[1]])$lengths
  first <- !duplicated(df[[1]])
  df[[1]][!first] <- ""
  if(output_type_1L_chr == "PDF")
    df[[1]][first] <- paste0("\\midrule\\multirow{", rle.lengths, "}{*}{\\textbf{", df[[1]][first], "}}")
  return(df)
}