R/util_fbs_child_v4dat.R

In alainapearce/kellertools: Keller Lab Data Tools

#' util_fbs_child_v4dat: Process raw qualtrics visit 4 data for the child
#'
#' This function loads the .sav raw data file for the child visit 4 data that was collected via Qualtrics and cleans the data. Cleaning the data involves:
#' 1) extracting all variable descriptions,
#' 2) selecting relevant data columns,
#' 3) removing all practice events (e.g., 999)
#' 4) re-ordering and re-name data columns
#' 5) reformatting dates to be appropriate and computer readable: YYYY-MM-DD
#' 6) re-calculate manual variables
#' 7) fix 99's / create prefer not to answer database
#' 8) random fixes to factor level names and variable descriptions
#'
#' The databases MUST follow the naming convention: Child_V4_YYYY-MM-DD.sav
#'
#' @inheritParams util_fbs_parent_v1dat
#' @inheritParams util_fbs_parent_v1dat
#'
#'
#' @return A list containing: 1) data: data.frame with raw, cleaned data from child visit 4 Qualtrics; 2) dict: all variable descriptions; 3) pna_data: data.frame marking participants who 'prefered not to answer' (pna) specific questions; and 4) pna_dict: all variable descriptions for pna_data
#'
#' @examples
#' #if in same working directory as data:
#' child_v4_dat <- util_fbs_child_v4dat('Child_V4')
#'
#' \dontrun{
#' #file_pattern must be a string. The following will not run:
#' child_v4_dat <- util_fbs_child_v4dat(Child_V4)
#'
#' #file_pattern must have the respondent ('Child') and visit number ('V4'). If just enter 'Child', the script will not run because it will return multiple files for different parent visits. The following will not run:
#' child_v4_dat <- util_fbs_child_v4dat('Child')
#' }
#'
#'
#' @export
#'
util_fbs_child_v4dat <- function(file_pattern, data_path) {

    #### 1. Set up/initial checks #####

    # check that file_pattern exist and is a string

    filepat_arg <- methods::hasArg(file_pattern)

    if (isTRUE(filepat_arg) & !is.character(file_pattern)) {
        stop("file_pattern must be entered as a string: e.g., 'Child_V4'")
    } else if (isFALSE(filepat_arg)) {
        stop("file_pattern must set to the a string matching the name of the raw data file for child visit: e.g., 'Child_V4'")
    }

    # check datapath
    datapath_arg <- methods::hasArg(data_path)

    if (isTRUE(datapath_arg)) {
        if (!is.character(data_path)) {
            stop("data_path must be entered as a string: e.g., '.../Participant_Data/untouchedRaw/'")
        }
    }

    #### 2. Load Data #####
    # Verified visit dates
    if (isTRUE(datapath_arg)) {

        #check pattern of directories specified in Data manual
        visit_dates_path <- list.files(path = data_path, pattern = 'verified_visit_dates', full.names = TRUE)

    } else {
        visit_dates_path <- list.files(pattern = 'verified_visit_dates', full.names = TRUE)
    }

    # check number of files found
    if (length(visit_dates_path) > 1) {
        stop("More than one file matched 'verified_visit_dates'. If have more than 1 file matching the pattern in the directory, may need to move one.")
    } else if (length(visit_dates_path) == 0) {
        stop("No files found for file_pattern 'verified_visit_dates'. Be sure the data_path is correct and that the file exists.")
    }

    # check if file exists
    visit_dates_exists <- file.exists(visit_dates_path)

    # load data if it exists
    if (isTRUE(visit_dates_exists)) {
        visit_dates <- read.csv(visit_dates_path, header = TRUE)

    } else {

        if (isTRUE(datapath_arg)) {
            stop("File does not exist. Check data_path entered")
        } else {
            stop("File does not exist. Check that the data exists in current working directory")
        }
    }

    # Qualtrics data
    if (isTRUE(datapath_arg)) {
        qv4_child_pathlist <- list.files(path = data_path, pattern = file_pattern, full.names = TRUE)
    } else {
        qv4_child_pathlist <- paste0(pattern = file_pattern, full.names = TRUE)
    }

    # check for WASI files
    wasi_file <- grepl('WASI', qv4_child_pathlist, fixed = TRUE)


    # check number of files found
    if (length(qv4_child_pathlist) - sum(wasi_file) > 1) {
        stop("More than one file matched the file_pattern. Be sure thefile_pattern specifies both the respondent (Parent/Child) and visit number (V#). If have more than 1 file matching the pattern in the directory, may need to move to enter a more specific file_pattern than is standard.")
    } else if (length(qv4_child_pathlist) == 0) {
        stop('No files found. Be sure the data_path and file_pattern are correct and that the file exists')
    } else {

        #get child qualtrics path
        if (sum(wasi_file) > 0) {
          qv4_child_path <- qv4_child_pathlist[wasi_file == FALSE]
        } else {
          qv4_child_path <- qv4_child_pathlist
        }
    }

    # check that file is of type '.sav'
    if (!grepl('.sav', qv4_child_path, fixed = TRUE)){
        stop("The file found is not an SPSS database (.sav)")
    }

    # check if file exists
    qv4_child_exists <- file.exists(qv4_child_path)

    # load data if it exists
    if (isTRUE(qv4_child_exists)) {
        qv4_child_dat <- as.data.frame(haven::read_spss(qv4_child_path))

    } else {
        if (isTRUE(datapath_arg)) {
            stop("File does not exist. Check file_pattern and data_path entered")
        } else {
            stop("File does not exist. Check file_pattern and that the data exists in current working directory")
        }
    }

    # check for and load WASI data
    if (sum(wasi_file) == 1){
      qv4_child_WASIpath <- qv4_child_pathlist[wasi_file]

      # check that file is of type '.sav'
      if (!grepl('.sav', qv4_child_WASIpath, fixed = TRUE)){
        stop("The WASI file found is not an SPSS database (.sav)")
      }

      # check if WASI exists
      qv4_child_WASI_exists <- file.exists(qv4_child_WASIpath)

      # load data if it exists
      if (isTRUE(qv4_child_WASI_exists)) {
        qv4_child_WASIdat <- as.data.frame(haven::read_spss(qv4_child_WASIpath))

      } else {
        if (isTRUE(datapath_arg)) {
          stop("WASI file does not exist. Check file_pattern and data_path entered")
        } else {
          stop("WASI file does not exist. Check file_pattern and that the data exists in current working directory")
        }
      }
    } else if (sum(wasi_file) > 1){
      stop("More than one file matched the WASI file_pattern. If have more than 1 file matching the pattern in the directory, may need to move to enter a more specific file_pattern than is standard.")
    }

    #### 3. Clean Data #####

    # 1) extract variable labels/descriptions ####
    qv4_child_labels <- lapply(qv4_child_dat, function(x) attributes(x)$label)

    # 2) selecting relevant data columns ####
    qv4_child_clean <- qv4_child_dat[c(1, 18:20, 25:33, 38:40, 42:46, 48:61, 66, 69:96, 208:208)]

    ## update labels
    qv4_child_clean_labels <- qv4_child_labels[c(1, 18:20, 25:33, 38:40, 42:46, 48:61, 66, 69:96, 208:208)]


    # 3) removing all practice events (e.g., 999) ####
    qv4_child_clean <- qv4_child_clean[!is.na(qv4_child_clean[["ID"]]) & qv4_child_clean[["ID"]] < 999, ]

    # 4) re-ordering and re-name data columns ####
    # general order: 1) child information (ID. date), 2) freddies, 3) food VAS 4) intakes (meal, meal duration), 5) CWC, CBIS, Parent Responsiveness, alien snack 6) notes

    qv4_child_clean <- qv4_child_clean[c(2, 1, 3:4, 37:38, 5:16, 39:63, 17:36, 64:65)]

    qv4_child_clean_labels <- qv4_child_clean_labels[c(2, 1, 3:4, 37:36, 5:16, 39:63, 17:36, 64:65)]

    ## re-name variables\
    names(qv4_child_clean) <- c("id", "start_date", "dob", "age", "freddy_pre_meal", "freddy_post_meal", "vas_mac_cheese", "vas_chkn_nug", "vas_broccoli", "vas_grape", "vas_water", "rank_mac_cheese", "rank_chkn_nug", "rank_broccoli", "rank_grape", "meal_start", "meal_end", "meal_dur", "noplate_chkn_nug_g", "plate_chkn_nug_g", "post_chkn_nug_g", "consumed_chkn_nug_g", "noplate_mac_cheese_g", "plate_mac_cheese_g", "post_mac_cheese_g", "consumed_mac_cheese_g", "noplate_grapes_g", "plate_grapes_g", "post_grapes_g", "consumed_grapes_g", "noplate_margerine_g", "noplate_broccoli_g", "plate_broccoli_g", "post_broccoli_g", "consumed_broccoli_g", "noplate_ketchup_g", "plate_ketchup_g", "post_ketchup_g", "consumed_ketchup_g", "noplate_water_g", "plate_water_g", "post_water_g", "consumed_water_g", "cwc1", "cwc2", "cwc3", "cwc4", "cwc5", "cbis_perc_male", "cbis_ideal_male", "cbis_perc_female", "cbis_ideal_female", "psi_resp_mom1", "psi_resp_mom2", "psi_resp_mom3", "psi_resp_mom4", "psi_resp_mom5", "psi_resp_dad1", "psi_resp_dad2", "psi_resp_dad3", "psi_resp_dad4", "psi_resp_dad5", "spacegame_reward", "food_initials", "child_notes")

    ## update data labels
    names(qv4_child_clean_labels) <- names(qv4_child_clean)

    # 5) reformatting dates to be appropriate and computer readable YYYY-MM-DD ####
    #format start date
    qv4_child_clean[["start_date"]] <- lubridate::ymd(as.Date(qv4_child_clean[["start_date"]]))

    # dates are fomrated as dd-mstr-yy
    visit_dates[['RO1_V4_Date']] <- lubridate::ymd(as.Date(visit_dates[['RO1_V4_Date']], format = "%d-%b-%y"))

    # add validated dates
    names(visit_dates)[1] <- 'id'
    qv4_child_clean <- merge(qv4_child_clean, visit_dates[c('id', 'RO1_V4_Date')], by = 'id', all.x = TRUE, all.y = FALSE)

    #update start_date
    qv4_child_clean[["start_date"]] <- ifelse(!is.na(qv4_child_clean[['RO1_V4_Date']]), as.character(qv4_child_clean[['RO1_V4_Date']]), as.character(qv4_child_clean[["start_date"]]))

    #remove RO1_V date column
    qv4_child_clean <- qv4_child_clean[, names(qv4_child_clean) != "RO1_V4_Date"]

    # add label
    qv4_child_clean_labels[["start_date"]] <- "date from participant contacts databases ('verified_visit_dates*.csv) converted to format yyyy-mm-dd in R. If no date in database, uses start_date metadata from qualtrics"


    ## freddy fullness as numeric
    qv4_child_clean[c(5:6, 12:15, 18)] <- sapply(qv4_child_clean[c(5:6, 12:15, 18)], FUN = as.numeric)


    # 6) re-calculate manual variables ####

    ## re-calculate all intake values

    # get all intake variables
    intake_vars <- names(qv4_child_clean)[c(19:43)]

    # make all intake variables numeric NOTE - there is a whole row I am not manually fixing as every value has ','
    # instead of '.'
    for (var in 1:length(intake_vars)) {
        var_name <- intake_vars[[var]]

        qv4_child_clean[[var_name]] <- ifelse(qv4_child_clean[[var_name]] == "-" | qv4_child_clean[[var_name]] == "NA",
            NA, qv4_child_clean[[var_name]])

        if (is.character(qv4_child_clean[[var_name]])) {
            qv4_child_clean[[var_name]] <- as.numeric(qv4_child_clean[[var_name]])
        }
    }

    # get all foods served - extract prefix and thne postfix in name
    food_strs_g <- unique(sapply(intake_vars, function(x) gsub(".*plate_|.*post_|.*consumed_", "\\1", x), USE.NAMES = FALSE))
    food_strs <- unique(sapply(food_strs_g, function(x) gsub("_g.*", "\\1", x), USE.NAMES = FALSE))

    # loop through foods
    for (f in 1:length(food_strs)) {

        # no post weights for margerine
        if (food_strs[f] != "margerine") {
            # get variable names for plate* and post* weights
            plate_var <- paste0("plate_", food_strs[f], "_g")
            post_var <- paste0("post_", food_strs[f], "_g")
            consumed_var <- paste0("consumed_", food_strs[f], "_g")

            # calculate amount consumed
            qv4_child_clean[[consumed_var]] <- qv4_child_clean[[plate_var]] - qv4_child_clean[[post_var]]
            qv4_child_clean[[consumed_var]] <- ifelse(qv4_child_clean[[consumed_var]] < 0, 0, qv4_child_clean[[consumed_var]])

            # update labels
            qv4_child_clean_labels[[consumed_var]] <- paste0(qv4_child_clean_labels[[consumed_var]], " - recalcuated difference in R with values < 0 set to 0")
        }
    }

    # 7) fix 99's ####

    ## check for labels/99 option: 1) if 99's exist, make a 'prefer not to answer' (pna) variable to go in pna database, 2) replace 99's with NA and make variable numeric

    ## make pna database
    qv4_child_pna <- data.frame(id = qv4_child_clean[["id"]])
    qv4_child_pna_labels <- lapply(qv4_child_pna, function(x) attributes(x)$label)
    qv4_child_pna_labels[["id"]] <- qv4_child_clean_labels[["id"]]

    pna_label <- "Note: prefer not to answer (pna) marked NA - see pna database for which were pna rather than missing NA"

    ## Fix 99/Don't want to answer in CWC, CBIS, PSI - Parent Responsiveness (levels are OK starting with 1; all are categorical variables)
    level99_issue_catvars <- names(qv4_child_clean)[c(44:62)]

    for (v in 1:length(level99_issue_catvars)) {
        # get variable name
        pvar <- level99_issue_catvars[v]

        # if has '99' value, create new pna variable marking pna == 1
        if (is.element(99, qv4_child_clean[[pvar]])) {
            pna_dat <- ifelse(is.na(qv4_child_clean[[pvar]]), 0, ifelse(qv4_child_clean[[pvar]] == 99, 1, 0))

            new_pna <- length(names(qv4_child_pna)) + 1
            qv4_child_pna[[new_pna]] <- pna_dat

            names(qv4_child_pna)[new_pna] <- paste0(pvar, "_pna")

            # add label to pna database
            qv4_child_pna_labels[[paste0(pvar, "_pna")]] <- paste0("prefer not to answer marked for variable ", pvar, ": ", qv4_child_clean_labels[[pvar]])

            # update true data label (only want to pna label if needed)
            qv4_child_clean_labels[[pvar]] <- paste0(qv4_child_clean_labels[[pvar]], " -- ", pna_label)

        }

        # drop 99 level label labels only update if had 99 - done in if statement above
        qv4_child_clean[[pvar]] <- sjlabelled::remove_labels(qv4_child_clean[[pvar]], labels = "Don't want to answer")

        # extract variable attributes
        pvar_attr <- attributes(qv4_child_clean[[pvar]])

        # replace 99 values
        qv4_child_clean[[pvar]] <- ifelse(is.na(qv4_child_clean[[pvar]]) | qv4_child_clean[[pvar]] == 99, NA, qv4_child_clean[[pvar]])

        # replace attributes
        attributes(qv4_child_clean[[pvar]]) <- pvar_attr
    }

    # 8) random fixes to factor level names and variable descriptions ####
    qv4_child_clean_labels[["meal_start"]] <- "Meal start time"
    qv4_child_clean_labels[["meal_end"]] <- "Meal end time"
    qv4_child_clean_labels[["spacegame_reward"]] <- "Type of candy selected for Space Game reward"

    for (var in 1:length(names(qv4_child_clean))) {
        var_name <- as.character(names(qv4_child_clean)[var])

        # remove v4 prefix from labels
        if (grepl("Visit 4", qv4_child_clean_labels[[var_name]], fixed = TRUE)) {
            qv4_child_clean_labels[[var_name]] <- gsub("Visit 4 ", "", qv4_child_clean_labels[[var_name]])
        }

        if (grepl("V4 -", qv4_child_clean_labels[[var_name]], fixed = TRUE)) {
            qv4_child_clean_labels[[var_name]] <- gsub("V4 - ", "", qv4_child_clean_labels[[var_name]])
        }

        if (grepl("V4", qv4_child_clean_labels[[var_name]], fixed = TRUE)) {
            qv4_child_clean_labels[[var_name]] <- gsub("V4 ", "", qv4_child_clean_labels[[var_name]])
        }

    }

    # 9) Add WASI data ####
    if (sum(wasi_file) > 0){
      if (isTRUE(qv4_child_WASI_exists)){

        ## extract variable labels/descriptions
        qv4_child_WASIlabels <- lapply(qv4_child_WASIdat, function(x) attributes(x)$label)

        ## make lowercase
        names(qv4_child_WASIdat) <- tolower(names(qv4_child_WASIdat))

        qv4_child_WASIdat_clean <- qv4_child_WASIdat[c(18:28)]
        qv4_child_WASIlabels_clean <- qv4_child_WASIlabels[18:28]

        names(qv4_child_WASIdat_clean)[2] <- 'wasi_date'

        #update labels
        names(qv4_child_WASIlabels_clean) <- names(qv4_child_WASIdat_clean)

        qv4_child_clean <- merge(qv4_child_clean, qv4_child_WASIdat_clean, by = 'id', all.x = TRUE, all.y = FALSE)
        qv4_child_clean_labels <- c(qv4_child_clean_labels, qv4_child_WASIlabels_clean[2:11])
      }
    }


    # 10) Format for export ####

    ## 10a) add attributes to pna data
    qv4_child_pna[2:ncol(qv4_child_pna)] <- as.data.frame(lapply(qv4_child_pna[2:ncol(qv4_child_pna)], function(x) sjlabelled::add_labels(x, labels = c(`Did not skip due to prefer not to answer` = 0, `Prefer not to answer` = 1))))

    for (v in 2:ncol(qv4_child_pna)){
        class(qv4_child_pna[[v]]) <- c("haven_labelled", "vctrs_vctr", "double")
    }

    # put data in order of participant ID for ease
    qv4_child_clean <- qv4_child_clean[order(qv4_child_clean[["id"]]), ]

    qv4_child_pna <- qv4_child_pna[order(qv4_child_pna[["id"]]), ]

    # make sure the variable labels match in the dataset
    qv4_child_clean = sjlabelled::set_label(qv4_child_clean, label = matrix(unlist(qv4_child_clean_labels, use.names = FALSE)))

    qv4_child_pna = sjlabelled::set_label(qv4_child_pna, label = matrix(unlist(qv4_child_pna_labels, use.names = FALSE)))

    ## make list of data frame and associated labels
    qv4_child <- list(data = qv4_child_clean, dict = qv4_child_clean_labels, pna_data = qv4_child_pna, pna_dict = qv4_child_pna_labels)

    return(qv4_child)
}