R_tasks/prepare_BART.R

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

##' Prepare BART
##'
##' Template for the functions to prepare specific tasks. Most of this file should not be changed
##' Things to change: 
##'   - Name of function: prepare_PRFBM -> prepare_[value of short_name_scale_str] 
##'   - dimensions parameter in standardized_names()
##'   - 2 [ADAPT] chunks
##'
##' @title prepare_PRFBM
##'
##' @param short_name_scale_str 
##' @param DF_clean
##'
##' @return
##' @author gorkang
##' @export
prepare_BART <- function(DF_clean, short_name_scale_str, output_formats) {
  
  # DEBUG
  # targets::tar_load_globals()
  # jsPsychHelpeR::debug_function(prepare_BART)
  
  # Numero de infladas de cada globo que no revento
  # en que inflada reventaron 
  # recaudacion total
  # cantidad de globos que revientan
  # 
   
  # dimensions = c("InflatesSafe", "InflatesExplode", "TotalIncome", "NumberExplode")
  # BART_01_round_money
  # BART_01_rounds
  # BART_01_status
  # BART_01_total_money

  # Standardized names ------------------------------------------------------
  names_list = standardized_names(short_name_scale = short_name_scale_str, 
                     # dimensions = c("InflatesSafe", "InflatesExplode", "TotalIncome", "NumberExplode"), # Use names of dimensions, "" or comment out line
                     dimensions = c("meanRoundsSafe", "meanRoundsExplode", "numberSafe", "numberExplode", "totalMoney"),
                     help_names = FALSE) # help_names = FALSE once the script is ready
  
  # Create long -------------------------------------------------------------
  DF_long_RAW = create_raw_long(DF_clean, short_name_scale = short_name_scale_str, numeric_responses = FALSE, help_prepare = FALSE)
  
  
  # Create long DIR ------------------------------------------------------------
  
  # [ADAPT]: Items to ignore and reverse ---------------------------------------
  # ****************************************************************************
  
  items_to_ignore = c("00|00") # Ignore the following items: If nothing to ignore, keep "00|00"
  items_to_reverse = c("00|00") # Reverse the following items: If nothing to ignore, keep "00|00"
  
  # [END ADAPT]: ***************************************************************
  # ****************************************************************************
  
  
  
  
  DF_long_DIR = 
    DF_long_RAW |> 
   dplyr::select(id, trialid, RAW) |>
    
    
    # [ADAPT]: RAW to DIR for individual items -----------------------------------
  # ****************************************************************************
  
  # Transformations
  dplyr::mutate(
    DIR = RAW
  ) 
  
  # [END ADAPT]: ***************************************************************
  # ****************************************************************************
  
  
  # Create DF_wide_RAW_DIR -----------------------------------------------------
  
  
  # dimensions = c("MeanRounds_safe", "MeanRounds_explode", "N_safe", "N_explode", "TotalMoney")
  # BART_01_round_money
  # BART_01_rounds
  # BART_01_status
  # BART_01_total_money
  
  
  
  
  # METHOD 1 -----------------------
  
  DF_temp =
    DF_long_RAW |>
   dplyr::select(id, trialid, RAW) |>

    # Should be fixed
    # dplyr::mutate(trialid =
    #         dplyr::case_when(
    #            grepl("round_money", trialid) ~ gsub("(.*)round_money", "\\1RoundMoney", trialid),
    #            grepl("total_money", trialid) ~ gsub("(.*)total_money", "\\1TotalMoney", trialid),
    #            grepl("explode_rounds", trialid) ~ gsub("(.*)explode_rounds", "\\1ExplodeRounds", trialid),
    #            TRUE ~ trialid)) |>
    tidyr::separate(trialid, into = c("BART", "trialnum", "variable"), sep = "_") |>
    tidyr::pivot_wider(names_from = variable, values_from = RAW) |> 
    dplyr::mutate(status = stringr::str_to_sentence(status))


  DF_wide_Dimensions =
    DF_temp |>
    dplyr::group_by(id, status) |>
    dplyr::summarise(BART_meanRounds = mean(as.numeric(rounds), na.rm = TRUE),
                     BART_number = dplyr::n(), 
                     .groups = "drop") |>
    tidyr::pivot_wider(names_from = status, values_from = c(BART_meanRounds, BART_number), names_sep = "") |>
    dplyr::left_join(DF_temp |>
                       dplyr::group_by(id) |>
                       dplyr::summarise(BART_totalMoney = max(as.numeric(totalMoney)), .groups = "drop"), 
                     by = "id"
                     ) |> 
    dplyr::rename_with(~paste0(., "_DIRd"), BART_meanRoundsExplode:BART_totalMoney) |> 
    dplyr::mutate(dplyr::across(dplyr::ends_with("_DIRd"), ~tidyr::replace_na(.x, 0)))

  
  
  # DF_wide_Dimensions
    
  
  # USE name_DIRd1, name_DIRd2, name_DIRd3, name_DIRd4, name_DIRd5

  
  
  DF_wide_RAW_DIR =
    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$name_RAW_NA := rowSums(is.na(dplyr::select(., -matches(items_to_ignore) & matches("_RAW")))),
           !!names_list$name_DIR_NA := rowSums(is.na(dplyr::select(., -matches(items_to_ignore) & 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)

    
    dplyr::left_join(DF_wide_Dimensions, by = "id")  
  # dplyr::mutate(
  #   
  #   # Score Dimensions (see standardized_names(help_names = TRUE) for instructions)
  #   !!names_list$name_DIRd[1] := rowMeans(select(., matches("01") & matches("_DIR$")), na.rm = TRUE), 
  #   !!names_list$name_DIRd[2] := rowMeans(select(., matches("02|03") & matches("_DIR$")), na.rm = TRUE), 
  #   !!names_list$name_DIRd[3] := rowMeans(select(., matches("04_beneficio|06_beneficio") & matches("_DIR$")), na.rm = TRUE), 
  #   !!names_list$name_DIRd[4] := rowMeans(select(., matches("05_beneficio|07_beneficio") & matches("_DIR$")), na.rm = TRUE)
  #   
  #   
  #   # Score Scale
  #   # !!names_list$name_DIRt := rowSums(across(all_of(matches("_DIR$"))), na.rm = TRUE)
  #   
  # )  
      
  
  # [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) 
  
}