R_tasks/prepare_DASS21.R

In gorkang/jsPsychHelpeR: Standardize and automatize data preparation and analysis of jsPsych experiments created with jsPsychMaker

##' Prepare DASS21
##'
##' Template for the functions to prepare specific tasks. Most of this file should not be changed
##' Things to change: 
##'   - Name of function: prepare_DASS21 -> prepare_[value of short_name_scale_str] 
##'   - dimensions parameter in standardized_names()
##'   - 2 [ADAPT] chunks
##'
##' @title prepare_DASS21
##'
##' @param short_name_scale_str 
##' @param DF_clean
##'
##' @return
##' @author gorkang
##' @export
prepare_DASS21 <- function(DF_clean, short_name_scale_str, output_formats) {
  
  # DEBUG
  # targets::tar_load_globals()
  # jsPsychHelpeR::debug_function(prepare_DASS21)
  
  # [ADAPT]: Items to ignore and reverse ---------------------------------------
  # ****************************************************************************
  
  items_to_ignore = c("000") # Ignore these items: If nothing to ignore, keep items_to_ignore = c("00")
  items_to_reverse = c("000") # Reverse these items: If nothing to reverse, keep  items_to_reverse = c("00")
  
  items_dimensions = list(
    depresion = c("003", "005", "010", "013", "016", "017", "021"),
    ansiedad = c("002", "004", "007", "009", "015", "019", "020"),
    estres = c("001", "006", "008", "011", "012", "014", "018")
    )
  
  # [END ADAPT]: ***************************************************************
  # ****************************************************************************
  
  
  # Standardized names ------------------------------------------------------
  names_list = standardized_names(short_name_scale = short_name_scale_str, 
                                  dimensions = names(items_dimensions),
                                  help_names = FALSE) # [KEEP as FALSE]
  
  # Create long -------------------------------------------------------------
  DF_long_RAW = create_raw_long(DF_clean, 
                                short_name_scale = short_name_scale_str, 
                                numeric_responses = FALSE, # [TRUE or FALSE]
                                is_experiment = FALSE, 
                                keep_time = FALSE, # Keep time stamp for each response
                                help_prepare = FALSE) # Show n of items, responses,... [CHANGE to TRUE to debug] 
  
  
  
  # Create long DIR ------------------------------------------------------------
  
  DF_long_DIR = 
    DF_long_RAW |> 
   dplyr::select(id, trialid, RAW) |>
    
    
    # [ADAPT]: RAW to DIR for individual items -----------------------------------
  # ****************************************************************************
  
  # Transformations
  dplyr::mutate(
    DIR =
     dplyr::case_when(
        RAW %in% c("0 No me aplicó", "0 (No me aplicó)", "No me ha ocurrido") ~ 0,
        RAW %in% c("1 Me aplicó un poco, o durante parte del tiempo", "Me ha ocurrido un poco, o durante parte del tiempo") ~ 1,
        RAW %in% c("2 Me aplicó bastante, o durante una buena parte del tiempo", "Me ha ocurrido bastante, o durante una buena parte del tiempo") ~ 2,
        RAW %in% c("3 Me aplicó mucho, o la mayor parte del tiempo", "Me ha ocurrido mucho, o la mayor parte del tiempo") ~ 3,
        is.na(RAW) ~ NA_real_,
        trialid %in% paste0(short_name_scale_str, "_", items_to_ignore) ~ NA_real_, # OR NA_character_
        TRUE ~ 9999
      )
  ) |> 
    
    # Invert items
    dplyr::mutate(
      DIR = 
       dplyr::case_when(
          DIR == 9999 ~ DIR, # To keep the missing values unchanged
          trialid %in% paste0(short_name_scale_str, "_", items_to_reverse) ~ (6 - DIR),
          TRUE ~ DIR
        )
    )
  
  # [END ADAPT]: ***************************************************************
  # ****************************************************************************
  
  
  # Create DF_wide_RAW_DIR -----------------------------------------------------
  DF_wide_RAW =
    DF_long_DIR |> 
    tidyr::pivot_wider(
      names_from = trialid, 
      values_from = c(RAW, DIR),
      names_glue = "{trialid}_{.value}") |> 
    
    # NAs for RAW and DIR items
    dplyr::mutate(!!names_list$name_RAW_NA := rowSums(is.na(across((-matches(paste0(short_name_scale_str, "_", items_to_ignore, "_RAW")) & matches("_RAW$"))))),
                  !!names_list$name_DIR_NA := rowSums(is.na(across((-matches(paste0(short_name_scale_str, "_", items_to_ignore, "_DIR")) & matches("_DIR$"))))))
  
  
  
  # [ADAPT]: Scales and dimensions calculations --------------------------------
  # ****************************************************************************
  # [USE STANDARD NAMES FOR Scales and dimensions: name_DIRt, name_DIRd1, etc.] Check with: standardized_names(help_names = TRUE)
  
  DF_wide_RAW_DIR =
    DF_wide_RAW |> 
    dplyr::mutate(
      
      # Multiplied by 2 to make the scores equivalent to the complete DASS scale. See DASS21_en-AU.pdf Note 3 in Table 3.
      !!names_list$name_DIRd[1] := rowSums(across(all_of(paste0(short_name_scale_str, "_", items_dimensions[[1]], "_DIR"))), na.rm = TRUE) * 2,
      !!names_list$name_DIRd[2] := rowSums(across(all_of(paste0(short_name_scale_str, "_", items_dimensions[[2]], "_DIR"))), na.rm = TRUE) * 2,
      !!names_list$name_DIRd[3] := rowSums(across(all_of(paste0(short_name_scale_str, "_", items_dimensions[[3]], "_DIR"))), na.rm = TRUE) * 2

    )
  
  # [END ADAPT]: ***************************************************************
  # ****************************************************************************
  
  
  # CHECK NAs -------------------------------------------------------------------
  check_NAs(DF_wide_RAW_DIR)
  
  # Save files --------------------------------------------------------------
  save_files(DF_wide_RAW_DIR, short_name_scale = short_name_scale_str, is_scale = TRUE, output_formats = output_formats)
  
  # Output of function ---------------------------------------------------------
  return(DF_wide_RAW_DIR) 
  
}