kellertools: Keller Lab Data Tools

Documented in fbs_databases

#' fbs_databases: Generate all desired databases for the Food and Brain Study
#'
#' This function will generate up-to-date databases for the Food and Brain Study. The following databases are will be created unless only specific databases are specified:
#' 1) Demographics database: includes child (e.g., age, sex, pubertal status), parent characteristics (e.g., age, sex, education, alcohol use/abuse), and household characteristics (e.g., food insecurity, income)
#' 2) Anthropometrics: includes child and parent height/weight and child adiposity (DXA), actigraphy, physical activity, and sleep
#' 3) Intake: includes child liking ratings, Freddy Fullness, and intake for all eating paradigms
#' 4) Food Related Behaviors and Traits: includes all questionnaires assessing child or parent food related behaviors and/or traits (e.g., CEBQ, CFQ, TFEQ, etc.) and body image
#' 5) (Neuro)Psychological Assessments: includes the WASI and all questionnaires assessing the child's cognitive and/or psychosocial functioning (e.g., BRIEF, BIS/BAS, anxiety, etc.)
#' 6) Delay Discounting: includes the modeling results for delay discounting
#' 7) Interoception: includes all data related to the heart beat interoception task
#' 8) Notes: this is a reference databases that includes any research and/or parent notes or updates from each visit
#' 9) Prefer Not to Answer: this is a reference databases that includes any questions the 'prefer not to answer' option was marked. Only saved in case there is a desire to determine the number of responses on an item that were missing versus 'prefer not to answer'.
#' 10) Microstrucure: includes databases related to microstructure coding of meals
#'
#' To process the raw data, the raw databases from Qualtrics MUST follow the naming convention: Child_V1_YYYY-MM-DD.sav, Child_V1_Home_YYY-MM-DD.sav, Child_V1_Lab_YYY-MM-DD.sav, and Parent_V1_YYY-MM-DD.sav. The databases must all be in the SAME directory to be processed if the data_path is not entered and the directory organization does not follow the structure laid out in the DataManual.
#'
#' @param databases (optional) list of strings to indicate which databases to process. If not entered, all databases will be generated. Options include: 1) 'demo' for Demographic, 2) 'anthro' for Anthropometrics, 3) 'intake' for Intake, 4) 'food_qs' for food-related questionnaires, 5) 'psych_qs' for cognitive and psych related data, 6) 'dd' for Delay Discounting, 7) 'intero' for Interoception data, 8) 'notes' for Notes, 9) 'pna' for Prefer Not to Answer, and 10) 'micro' for microstructure data (note - microstructure will always be in separate databases)
#' @param model_DD Indicate if delay discounting data should be modeled. This will take an addition 3-5 minutes of processing time. Default = FALSE. The Delay Discounting database will only be generate if set to TRUE.
#' @param write_dat indicate whether to write databases. Default is TRUE.
#' @param write_path (optional) a string with the path indicating where to save the generated databases if write_dat is TRUE (default option). If no path is given, databases will be written to working directory.
#' @param return_data indicate whether to return databases to the environment. Default is FALSE.
#' @inheritParams util_fbs_parent_v1dat
#' @param child_file_pattern (optional) This is only needed if file naming deviates from the standard naming: e.g., Child_V#_*sav'. Only enter the string indicating a respondent (e.g., 'Child').
#' @param parent_file_pattern (optional) This is only needed if file naming deviates from the standard naming: e.g., Parent_V#_*sav'. Only enter the string indicating a respondent (e.g., 'Child').
#' @param visit_file_pattern (optional) This is only needed if file naming deviates from the standard naming for visits: e.g., V# . Only enter the string indicating a visit (e.g., 'V' for 'V1' or 'Visit_' for 'Visit_1').
#'
#' @return A list containing all databases that were generated
#'
#' @examples
#' #if in same working directory as data, want all databases, and the databases to be written to working directory:
#' fbs_data_proc <- fbs_databases(model_DD = TRUE)
#'
#' #if only want the Demographics and Intake databases written to working directory:
#' fbs_data_proc <- fbs_databases(databases = c('demo', 'intake'))
#'
#' #if want to work with the Demographics database but not write it to a file:
#' fbs_data_proc <- fbs_databases(databases = 'demo', write_dat = FALSE)
#'
#' \dontrun{
#' #databases must be strings. The following will not run:
#' fbs_data_proc <- fbs_databases(databases = c(demo, intake))
#'
#' }
#'
#' @seealso Raw data from Qualtrics is processed using the following scripts: \code{\link{util_fbs_merge_v1dat}}, \code{\link{util_fbs_merge_v2dat}}, \code{\link{util_fbs_merge_v3dat}}, \code{\link{util_fbs_merge_v4dat}}, \code{\link{util_fbs_merge_v5dat}}, \code{\link{util_fbs_merge_v6dat}}, \code{\link{util_fbs_merge_v7dat}}
#'
#'
#' @export

fbs_databases <- function(databases, model_DD = FALSE, write_dat = TRUE, write_path, data_path, return_data = FALSE, child_file_pattern, parent_file_pattern, visit_file_pattern) {

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

    # check the file patterns

    c_filepat_arg <- methods::hasArg(child_file_pattern)

    if (isTRUE(c_filepat_arg)) {
        if (!is.character(child_file_pattern)) {
            stop("child_file_pattern must be entered as a string: e.g., 'Child'")
        } else {
            child_fp <- child_file_pattern
        }
    } else if (isFALSE(c_filepat_arg)) {
        child_fp <- 'Child'
    }

    p_filepat_arg <- methods::hasArg(parent_file_pattern)

    if (isTRUE(p_filepat_arg)) {
        if (!is.character(parent_file_pattern)) {
            stop("parent_file_pattern must be entered as a string: e.g., 'Parent'")
        } else {
            parent_fp <- parent_file_pattern
        }
    } else if (isFALSE(p_filepat_arg)) {
        parent_fp <- 'Parent'
    }

    v_filepat_arg <- methods::hasArg(visit_file_pattern)

    if (isTRUE(p_filepat_arg)) {
        if (!is.character(visit_file_pattern)) {
            stop("parent_file_pattern must be entered as a string: e.g., 'V' or 'Visit_'")
        } else {
            visit_fp <- visit_file_pattern
        }
    } else if (isFALSE(p_filepat_arg)) {
        visit_fp <- 'V'
    }

    # 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/")
        }

      #make universal to 'Untouched_Raw'
      if (grepl('Qualtrics_Raw', data_path, fixed = TRUE)){
        data_path <- gsub('Qualtrics_Raw', '', data_path)
      } else if (grepl('Microstructure_Raw', data_path, fixed = TRUE)){
        data_path <- gsub('Microstructure_Raw', '', data_path)
      }
    }

    # check databases argument
    databases_arg <- methods::hasArg(databases)

    database_options <- c('demo', 'anthro', 'intake', 'food_qs', 'psych_qs', 'dd', 'intero', 'notes', 'pna', 'micro')

    if (isTRUE(databases_arg)) {

        databases_string <- sapply(databases, FUN = is.character)
        ndatabases <- length(databases)

        if (sum(databases_string) != ndatabases) {
            stop("Not all items listed in databases are strings. All databases must be entered as strings and matach the following options: 'demo', 'anthro', 'intake', 'food_qs', 'psych_qs', 'dd', 'intero', 'notes', 'pna'.")
        }

        #check if entered databases match database_options
        if (sum(databases %in% database_options) != ndatabases){
            stop("Not all items listed in databases match available options. Options include: 'demo', 'anthro', 'intake', 'food_qs', 'psych_qs', 'dd', 'intero', 'notes', 'pna', 'micro'.")
        }
    } else {
        databases <- NA
    }

    # check write_path
    writepath_arg <- methods::hasArg(write_path)

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

    #### 2. Get Visit Databases ####

    ## Visit 1 data - need for all databases

    if (isTRUE(datapath_arg)){
        v1_data <- util_fbs_merge_v1(child_file_pattern = paste0(child_fp, '_', visit_fp, '1'), parent_file_pattern = paste0(parent_fp, '_', visit_fp, '1'), data_path = paste0(data_path, '/Qualtrics_Raw/'))
    } else {
        v1_data <- util_fbs_merge_v1(child_file_pattern = paste0(child_fp, '_', visit_fp, '1'), parent_file_pattern = paste0(parent_fp, '_', visit_fp, '1'))
    }


    ## Visit 2 - need for Anthroprometrics, food/eating behavior, and cog/psych databases
    # requires the V4 parent database so check both v2_datestr_arg and v4_datestr_arg
    if (isFALSE(databases_arg) | 'anthro' %in% databases | 'food_qs' %in% databases | 'psych_qs' %in% databases | 'intake' %in% databases | 'notes' %in% databases | 'pna' %in% databases){

        if (isTRUE(datapath_arg)){
            v2_data <- util_fbs_merge_v2(child_file_pattern = paste0(child_fp, '_', visit_fp, '2'), parent_file_pattern = paste0(parent_fp, '_', visit_fp, '2'), parentV4_file_pattern = paste0(parent_fp, '_', visit_fp, '4'), data_path = paste0(data_path, '/Qualtrics_Raw/'))
        } else {
            v2_data <- util_fbs_merge_v2(child_file_pattern = paste0(child_fp, '_', visit_fp, '2'), parent_file_pattern = paste0(parent_fp, '_', visit_fp, '2'), parentV4_file_pattern = paste0(parent_fp, '_', visit_fp, '4'))
        }
    }

    ## Visit 3 data - need for the food/eating behavior, cog/psych, and delay discounting databases
    if (isFALSE(databases_arg) | 'food_qs' %in% databases | 'psych_qs' %in% databases | 'dd' %in% databases | 'intake' %in% databases | 'notes' %in% databases | 'pna' %in% databases){

        if (isTRUE(datapath_arg)){
            v3_data <- util_fbs_merge_v3(child_file_pattern = paste0(child_fp, '_', visit_fp, '3'), parent_file_pattern = paste0(parent_fp, '_', visit_fp, '3'), data_path = paste0(data_path, '/Qualtrics_Raw/'), model_DD = model_DD)
        } else {
            v3_data <- util_fbs_merge_v3(child_file_pattern = paste0(child_fp, '_', visit_fp, '3'), parent_file_pattern = paste0(parent_fp, '_', visit_fp, '3'), model_DD = model_DD)
        }
    }

    ## Visit 4 data - need for the demographics, food/eating behavior, and cog/psych databases
    if (isFALSE(databases_arg) | 'demo' %in% databases | 'food_qs' %in% databases | 'psych_qs' %in% databases | 'intake' %in% databases | 'notes' %in% databases | 'pna' %in% databases){
        if (isTRUE(datapath_arg)){
            v4_data <- util_fbs_merge_v4(child_file_pattern = paste0(child_fp, '_', visit_fp, '4'), parent_file_pattern = paste0(parent_fp, '_', visit_fp, '4'), data_path = paste0(data_path, '/Qualtrics_Raw/'))
        } else {
            v4_data <- util_fbs_merge_v4(child_file_pattern = paste0(child_fp, '_', visit_fp, '4'), parent_file_pattern = paste0(parent_fp, '_', visit_fp, '4'))
        }
    }

    ## Visit 5 data - need for the demographics database
    if (isFALSE(databases_arg) | 'demo' %in% databases | 'intake' %in% databases | 'food_qs' %in% databases | 'notes' %in% databases | 'pna' %in% databases){
        if (isTRUE(datapath_arg)){
            v5_data <- util_fbs_merge_v5(child_file_pattern = paste0(child_fp, '_', visit_fp, '5'), parent_file_pattern = paste0(parent_fp, '_', visit_fp, '5'), data_path = paste0(data_path, '/Qualtrics_Raw/'))
        } else {
            v5_data <- util_fbs_merge_v5(child_file_pattern = paste0(child_fp, '_', visit_fp, '5'), parent_file_pattern = paste0(parent_fp, '_', visit_fp, '5'))
        }
    }

    ## Visit 6 data - need for the fMRI database
    if (isFALSE(databases_arg) | 'food_qs' %in% databases | 'notes' %in% databases | 'pna' %in% databases){
        if (isTRUE(datapath_arg)){
            v6_data <- util_fbs_merge_v6(child_file_pattern = paste0(child_fp, '_', visit_fp, '6'), parent_file_pattern = paste0(parent_fp, '_', visit_fp, '6'), data_path = paste0(data_path, '/Qualtrics_Raw/'))
        } else {
            v6_data <- util_fbs_merge_v6(child_file_pattern = paste0(child_fp, '_', visit_fp, '6'), parent_file_pattern = paste0(parent_fp, '_', visit_fp, '6'))
        }
    }

    ## Visit 7 data - need for the demographics, anthroprometrics, food/eating behavior, and cog/psych databases
    if (isFALSE(databases_arg) | 'demo' %in% databases | 'anthro' %in% databases | 'food_qs' %in% databases | 'psych_qs' %in% databases | 'intake' %in% databases | 'notes' %in% databases | 'pna' %in% databases){
        if (isTRUE(datapath_arg)){
            v7_data <- util_fbs_merge_v7(child_file_pattern = paste0(child_fp, '_', visit_fp, '7'), parent_file_pattern = paste0(parent_fp, '_', visit_fp, '7'), data_path = paste0(data_path, '/Qualtrics_Raw/'))
        } else {
            v7_data <- util_fbs_merge_v7(child_file_pattern = paste0(child_fp, '_', visit_fp, '7'), parent_file_pattern = paste0(parent_fp, '_', visit_fp, '7'))
        }
    }

    ## Microstructure data
    if (isFALSE(databases_arg) | 'micro' %in% databases){
      if (isTRUE(datapath_arg)){
        micro_data <- util_fbs_merge_micro(data_path = paste0(data_path, '/Microstructure_Raw/'))
      } else {
        micro_data <- util_fbs_merge_micro()
      }
    }

    #### 3. Make Databases #####

    #empty list
    database_return <- list()

    #get cross-database variables
    common_demo_data <- v1_data[['data']][c(1:13, 20:21, 60:64, 94:97, 232:233)]
    common_demo_labels <- v1_data[['dict']][c(1:13, 20:21, 60:64, 94:97, 232:233)]

    names(common_demo_data)[2] <- 'v1_date'
    names(common_demo_labels)[2] <- 'v1_date'

    ## 3b) Demographics ####
    if (isFALSE(databases_arg) | 'demo' %in% databases){

        #visit 1
        v1_demo_data <- v1_data[['data']][c(1:13, 20:21, 60:61, 63, 94:97, 232:233, 14:19, 22:59, 65:87)]
        v1_demo_labels <- v1_data[['dict']][c(1:13, 20:21, 60:61, 63, 94:97, 232:233, 14:19, 22:59, 65:87)]

        names(v1_demo_data)[2] <- 'v1_date'
        names(v1_demo_labels)[2] <- 'v1_date'

        #visit 4
        v4_demo_data <- v4_data[['data']][c(1:2, 6:99)]
        v4_demo_labels <- v4_data[['dict']][c(1:2, 6:99)]

        names(v4_demo_data)[2] <- 'v4_date'
        names(v4_demo_labels)[2] <- 'v4_date'

        #visit 5
        v5_demo_data <- v5_data[['data']][c(1:34)]
        v5_demo_labels <- v5_data[['dict']][c(1:34)]

        names(v5_demo_data)[2] <- 'v5_date'
        names(v5_demo_labels)[2] <- 'v5_date'

        #visit 7
        v7_demo_data <- v7_data[['data']][c(1:2, 5:120)]
        v7_demo_labels <- v7_data[['dict']][c(1:2, 5:120)]

        names(v7_demo_data)[2] <- 'v7_date'
        names(v7_demo_labels)[2] <- 'v7_date'

        #re-name variables with 'v7_' and add 'Visit 7 - ' to labels
        for (v in 3:ncol(v7_demo_data)){
            #names
            var_name <- names(v7_demo_data)[v]
            v7_name <- paste0('v7_', var_name)
            names(v7_demo_data)[v] <- v7_name

            #labels
            v7_demo_labels[[var_name]] <- paste0('Visit 7 - ', v7_demo_labels[[var_name]])
        }

        #make names match
        names(v7_demo_labels) <- names(v7_demo_data)

        ## merge databases

        #weird issue - couldn't rbind due to some `labels` to logical issue - brute force fix
        set_attr_hfssm_hh2 <- attributes(v4_demo_data[['hfssm_hh2']])
        set_attr_hfssm_hh3 <- attributes(v4_demo_data[['hfssm_hh3']])
        set_attr_hfssm_hh4 <- attributes(v4_demo_data[['hfssm_hh4']])
        set_attr_hfssm_ch1 <- attributes(v4_demo_data[['hfssm_ch1']])
        set_attr_hfssm_ch2 <- attributes(v4_demo_data[['hfssm_ch2']])
        set_attr_hfssm_ch3 <- attributes(v4_demo_data[['hfssm_ch3']])
        set_attr_hfssm_ch5a <- attributes(v4_demo_data[['hfssm_ch5a']])
        set_attr_hfias_category <- attributes(v4_demo_data[['hfias_category']])

        v4_demo_data[c(5:7, 15:17, 20, 50)] <- sapply(v4_demo_data[c(5:7, 15:17, 20, 50)], as.numeric)


        demo_v1v4_data <- merge(v1_demo_data, v4_demo_data, by = 'id', all = TRUE)
        demo_v1v4v5_data <- merge(demo_v1v4_data, v5_demo_data, by = 'id', all = TRUE)
        demo_v1v4v5v7_data <- merge(demo_v1v4v5_data, v7_demo_data, by = 'id', all = TRUE)

        #reset labels due to brute force fix
        attributes(demo_v1v4v5v7_data[['hfssm_hh2']]) <- set_attr_hfssm_hh2
        attributes(demo_v1v4v5v7_data[['hfssm_hh3']]) <- set_attr_hfssm_hh3
        attributes(demo_v1v4v5v7_data[['hfssm_hh4']]) <- set_attr_hfssm_hh4
        attributes(demo_v1v4v5v7_data[['hfssm_ch1']]) <- set_attr_hfssm_ch1
        attributes(demo_v1v4v5v7_data[['hfssm_ch2']]) <- set_attr_hfssm_ch2
        attributes(demo_v1v4v5v7_data[['hfssm_ch3']]) <- set_attr_hfssm_ch3
        attributes(demo_v1v4v5v7_data[['hfssm_ch5a']]) <- set_attr_hfssm_ch5a
        attributes(demo_v1v4v5v7_data[['hfias_category']]) <- set_attr_hfias_category

        class(demo_v1v4v5v7_data[["hfssm_hh2"]]) <- c("haven_labelled", "vctrs_vctr", "double")
        class(demo_v1v4v5v7_data[["hfssm_hh3"]]) <- c("haven_labelled", "vctrs_vctr", "double")
        class(demo_v1v4v5v7_data[["hfssm_hh4"]]) <- c("haven_labelled", "vctrs_vctr", "double")
        class(demo_v1v4v5v7_data[["hfssm_ch1"]]) <- c("haven_labelled", "vctrs_vctr", "double")
        class(demo_v1v4v5v7_data[["hfssm_ch2"]]) <- c("haven_labelled", "vctrs_vctr", "double")
        class(demo_v1v4v5v7_data[["hfssm_ch3"]]) <- c("haven_labelled", "vctrs_vctr", "double")
        class(demo_v1v4v5v7_data[["hfssm_ch5a"]]) <- c("haven_labelled", "vctrs_vctr", "double")
        class(demo_v1v4v5v7_data[["hfias_category"]]) <- c("haven_labelled", "vctrs_vctr", "double")

        #get labels
        demographic_labels <- c(v1_demo_labels, v4_demo_labels[2:length(v4_demo_labels)], v5_demo_labels[2:length(v5_demo_labels)], v7_demo_labels[2:length(v7_demo_labels)])

        # ensure labels are up to date
        demographic_data = sjlabelled::set_label(demo_v1v4v5v7_data, label = matrix(unlist(demographic_labels, use.names = FALSE)))

        # add to list
        database_return <- c(database_return, list(demo_data = demographic_data, demo_dict = demographic_labels))

        # write out
        if (isTRUE(write_dat)){

            #data dictionary
            demo_dict <- labelled::generate_dictionary(demographic_data, details = TRUE)
            demo_dict$label <- matrix(unlist(demographic_labels, use.names = FALSE))
            names(demo_dict)[1] <- 'column'

            #interprets the value_labels as list so need to make everything a character
            demo_dict_write <- sapply(demo_dict[c(1:3, 6:8, 12:13)], FUN = as.character)

            if (isTRUE(writepath_arg)){
                haven::write_sav(demographic_data, path = paste0(write_path, 'demographics_data.sav'))
                write.csv(demo_dict_write, file = paste0(write_path, 'dict-demographics_data.csv'), row.names = FALSE)
            } else {
                haven::write_sav(demographic_data, path = 'demographics_data.sav')
                write.csv(demo_dict_write, file = 'dict-demographics_data.csv', row.names = FALSE)
            }
        }
    }

    ## 3b) Anthropometrics ####
    if (isFALSE(databases_arg) | 'anthro' %in% databases){

        #visit 1
        v1_anthro_data <- v1_data[['data']][c(1, 88:336)]
        v1_anthro_labels <- v1_data[['dict']][c(1, 88:336)]

        #visit 2
        v2_anthro_data <- v2_data[['data']][c(1:2, 4:29)]
        v2_anthro_labels <- v2_data[['dict']][c(1:2, 4:29)]

        names(v2_anthro_data)[2] <- 'v2_date'
        names(v2_anthro_labels)[2] <- 'v2_date'

        #visit 7
        v7_anthro_data <- v7_data[['data']][c(1:2, 121:369, 376:398)]
        v7_anthro_labels <- v7_data[['dict']][c(1:2, 121:369, 376:398)]

        names(v7_anthro_data)[2] <- 'v7_date'
        names(v7_anthro_labels)[2] <- 'v7_date'

        #re-name variables with 'v7_' and add 'Visit 7 - ' to labels
        for (v in 3:ncol(v7_anthro_data)){
            #names
            var_name <- names(v7_anthro_data)[v]
            v7_name <- paste0('v7_', var_name)
            names(v7_anthro_data)[v] <- v7_name

            #labels
            v7_anthro_labels[[var_name]] <- paste0('Visit 7 - ', v7_anthro_labels[[var_name]])
        }

        #make names match
        names(v7_anthro_labels) <- names(v7_anthro_data)

        ## merge databases from v1
        anthro_demov1_data <- merge(common_demo_data[1:20], v1_anthro_data, by = 'id', all = TRUE)

        ## merge databases - set all.x = FALSE so only get the participants with data at later visits/have not been screened out
        anthro_demov1v2_data <- merge(anthro_demov1_data, v2_anthro_data, by = 'id', all.x = FALSE, all.y = TRUE)

        ## other merges - set all = TRUE so get all participants in visits 2-7
        anthro_demov1v2v7_data <- merge(anthro_demov1v2_data, v7_anthro_data, by = 'id', all = TRUE)

        #get labels
        anthroprometric_labels <- c(common_demo_labels[1:20], v1_anthro_labels[2:length(v1_anthro_labels)], v2_anthro_labels[2:length(v2_anthro_labels)], v7_anthro_labels[2:length(v7_anthro_labels)])

        # ensure labels are up to date
        anthroprometric_data = sjlabelled::set_label(anthro_demov1v2v7_data, label = matrix(unlist(anthroprometric_labels, use.names = FALSE)))

        # add to list
        database_return <- c(database_return, list(anthro_data = anthroprometric_data, anthro_dict = anthroprometric_labels))

        # write out
        if (isTRUE(write_dat)){

            #data dictionary
            antho_dict <- labelled::generate_dictionary(anthroprometric_data, details = TRUE)
            antho_dict$label <- matrix(unlist(anthroprometric_labels, use.names = FALSE))
            names(antho_dict)[1] <- 'column'

            #interprets the value_labels as list so need to make everything a character
            antho_dict_write <- sapply(antho_dict[c(1:3, 6:8, 12:13)], FUN = as.character)

            if (isTRUE(writepath_arg)){
                haven::write_sav(anthroprometric_data, path = paste0(write_path, 'anthro_data.sav'))
                write.csv(antho_dict_write, file = paste0(write_path, 'dict-anthro_data.csv'), row.names = FALSE)
            } else {
                haven::write_sav(anthroprometric_data, path = 'anthro_data.sav')
                write.csv(antho_dict_write, file = 'dict_anthro_data.csv', row.names = FALSE)
            }
        }

    }

    ## 3c) Intake ####
    if (isFALSE(databases_arg) | 'intake' %in% databases){

        #visit 1
        v1_intake_data <- v1_data[['data']][c(1, 337:505)]
        v1_intake_labels <- v1_data[['dict']][c(1, 337:505)]

        #re-name variables with 'v1_' and add 'Visit 1 - ' to labels
        for (v in 2:ncol(v1_intake_data)){
            #names
            var_name <- names(v1_intake_data)[v]
            v1_name <- paste0('v1_', var_name)
            names(v1_intake_data)[v] <- v1_name

            #labels
            v1_intake_labels[[var_name]] <- paste0('Visit 1 - ', v1_intake_labels[[var_name]])
        }

        names(v1_intake_labels) <- names(v1_intake_data)

        #visit 2
        v2_intake_data <- v2_data[['data']][c(1:2, 30:76)]
        v2_intake_labels <- v2_data[['dict']][c(1:2, 30:76)]

        names(v2_intake_data)[2] <- 'v2_date'
        names(v2_intake_labels)[2] <- 'v2_date'

        #re-name variables with 'v2_' and add 'Visit 2 - ' to labels
        for (v in 3:ncol(v2_intake_data)){
            #names
            var_name <- names(v2_intake_data)[v]
            v2_name <- paste0('v2_', var_name)
            names(v2_intake_data)[v] <- v2_name

            #labels
            v2_intake_labels[[var_name]] <- paste0('Visit 2 - ', v2_intake_labels[[var_name]])
        }

        names(v2_intake_labels) <- names(v2_intake_data)

        #visit 3
        v3_intake_data <- v3_data[['data']][1:49]
        v3_intake_labels <- v3_data[['dict']][1:49]

        names(v3_intake_data)[2] <- 'v3_date'
        names(v3_intake_labels)[2] <- 'v3_date'

        #re-name variables with 'v3_' and add 'Visit 3 - ' to labels
        for (v in 3:ncol(v3_intake_data)){
            #names
            var_name <- names(v3_intake_data)[v]
            v3_name <- paste0('v3_', var_name)
            names(v3_intake_data)[v] <- v3_name

            #labels
            v3_intake_labels[[var_name]] <- paste0('Visit 3 - ', v3_intake_labels[[var_name]])
        }

        names(v3_intake_labels) <- names(v3_intake_data)

        #visit 4
        v4_intake_data <- v4_data[['data']][c(1:2, 100:146)]
        v4_intake_labels <- v4_data[['dict']][c(1:2, 100:146)]

        names(v4_intake_data)[2] <- 'v4_date'
        names(v4_intake_labels)[2] <- 'v4_date'

        #re-name variables with 'v4_' and add 'Visit 4 - ' to labels
        for (v in 3:ncol(v4_intake_data)){
            #names
            var_name <- names(v4_intake_data)[v]
            v4_name <- paste0('v4_', var_name)
            names(v4_intake_data)[v] <- v4_name

            #labels
            v4_intake_labels[[var_name]] <- paste0('Visit 4 - ', v4_intake_labels[[var_name]])
        }

        names(v4_intake_labels) <- names(v4_intake_data)

        #visit 5
        v5_intake_data <- v5_data[['data']][c(1:2, 35:81)]
        v5_intake_labels <- v5_data[['dict']][c(1:2, 35:81)]

        names(v5_intake_data)[2] <- 'v5_date'
        names(v5_intake_labels)[2] <- 'v5_date'

        #re-name variables with 'v5_' and add 'Visit 5 - ' to labels
        for (v in 3:ncol(v5_intake_data)){
            #names
            var_name <- names(v5_intake_data)[v]
            v5_name <- paste0('v5_', var_name)
            names(v5_intake_data)[v] <- v5_name

            #labels
            v5_intake_labels[[var_name]] <- paste0('Visit 5 - ', v5_intake_labels[[var_name]])
        }

        names(v5_intake_labels) <- names(v5_intake_data)

        #visit 7
        v7_intake_data <- v7_data[['data']][c(1:2, 399:571)]
        v7_intake_labels <- v7_data[['dict']][c(1:2, 399:571)]

        names(v7_intake_data)[2] <- 'v7_date'
        names(v7_intake_labels)[2] <- 'v7_date'

        #re-name variables with 'v7_' and add 'Visit 7 - ' to labels
        for (v in 3:ncol(v7_intake_data)){
            #names
            var_name <- names(v7_intake_data)[v]
            v7_name <- paste0('v7_', var_name)
            names(v7_intake_data)[v] <- v7_name

            #labels
            v7_intake_labels[[var_name]] <- paste0('Visit 7 - ', v7_intake_labels[[var_name]])
        }

        #make names match
        names(v7_intake_labels) <- names(v7_intake_data)

        ## merge databases from v1
        intake_demov1_data <- merge(common_demo_data, v1_intake_data, by = 'id', all = TRUE)

        ## merge databases - set all.x = FALSE so only get the participants with data at later visits/have not been screened out
        intake_demov1v2_data <- merge(intake_demov1_data, v2_intake_data, by = 'id', all.x = FALSE, all.y = TRUE)

        ## other merges - set all = TRUE so get all participants in visits 2-7
        intake_demov1v2v3_data <- merge(intake_demov1v2_data, v3_intake_data, by = 'id', all = TRUE)
        intake_demov1v2v3v4_data <- merge(intake_demov1v2v3_data, v4_intake_data, by = 'id', all = TRUE)
        intake_demov1v2v3v4v5_data <- merge(intake_demov1v2v3v4_data, v5_intake_data, by = 'id', all = TRUE)
        intake_demov1v23v4v5v7_data <- merge(intake_demov1v2v3v4v5_data, v7_intake_data, by = 'id', all = TRUE)

        #get labels
        intake_labels <- c(common_demo_labels, v1_intake_labels[2:length(v1_intake_labels)], v2_intake_labels[2:length(v2_intake_labels)], v3_intake_labels[2:length(v3_intake_labels)], v4_intake_labels[2:length(v4_intake_labels)], v5_intake_labels[2:length(v5_intake_labels)], v7_intake_labels[2:length(v7_intake_labels)])

        # ensure labels are up to date
        intake_data = sjlabelled::set_label(intake_demov1v23v4v5v7_data, label = matrix(unlist(intake_labels, use.names = FALSE)))

        ## add sort by portion size to database

        ps_vars <- c('id', 'date', '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', 'consumed_chkn_nug_kcal', 'noplate_mac_cheese_g', 'plate_mac_cheese_g', 'post_mac_cheese_g', 'consumed_mac_cheese_g', 'consumed_mac_cheese_kcal', 'noplate_grapes_g', 'plate_grapes_g', 'post_grapes_g', 'consumed_grapes_g', 'consumed_grapes_kcal', 'noplate_broccoli_g', 'plate_broccoli_g', 'post_broccoli_g', 'consumed_broccoli_g', 'consumed_broccoli_kcal', 'noplate_ketchup_g', 'plate_ketchup_g', 'post_ketchup_g', 'consumed_ketchup_g', 'consumed_ketchup_kcal', 'noplate_water_g', 'plate_water_g', 'post_water_g', 'consumed_water_g', 'total_g', 'total_kcal')

        ps_convert_fn <- function(intake_data, ps, ps_var) {

            if (ps_var == 'id'){
                ps_var_dat <- intake_data[['id']]
            } else {

                #visit vars
                v2_var_name <- paste0('v2_', ps_var)
                v3_var_name <- paste0('v3_', ps_var)
                v4_var_name <- paste0('v4_', ps_var)
                v5_var_name <- paste0('v5_', ps_var)

                #get new variable added to dataset
                ps_value <- ps -1

                if (ps_var == 'date'){
                    ps_var_dat <- ifelse(intake_data[['v2_meal_ps']] == ps_value, as.character(intake_data[[v2_var_name]]), ifelse(intake_data[['v3_meal_ps']] == ps_value, as.character(intake_data[[v3_var_name]]), ifelse(intake_data[['v4_meal_ps']] == ps_value, as.character(intake_data[[v4_var_name]]), ifelse(intake_data[['v5_meal_ps']] == ps_value, as.character(intake_data[[v5_var_name]]), NA))))
                } else {
                    ps_var_dat <- ifelse(intake_data[['v2_meal_ps']] == ps_value, intake_data[[v2_var_name]], ifelse(intake_data[['v3_meal_ps']] == ps_value, intake_data[[v3_var_name]], ifelse(intake_data[['v4_meal_ps']] == ps_value, intake_data[[v4_var_name]], ifelse(intake_data[['v5_meal_ps']] == ps_value, intake_data[[v5_var_name]], NA))))
                }
            }

            return(ps_var_dat)
        }

        ## portion size data
        ps1_intake_data <- as.data.frame(sapply(ps_vars, FUN = ps_convert_fn, intake_data = intake_data, ps = 1, simplify = TRUE, USE.NAMES = TRUE))
        names(ps1_intake_data)[2:ncol(ps1_intake_data)] <- paste0('ps1_', ps_vars[2:length(ps_vars)])
        ps1_intake_data[c(1, 3:13, 16:47)] <- sapply(ps1_intake_data[c(1, 3:13, 16:47)], as.numeric)
        intake_data <- merge(intake_data, ps1_intake_data, by = 'id',  all = TRUE)

        ps2_intake_data <- as.data.frame(sapply(ps_vars, FUN = ps_convert_fn, intake_data = intake_data, ps = 2, simplify = TRUE, USE.NAMES = TRUE))
        names(ps2_intake_data)[2:ncol(ps2_intake_data)] <- paste0('ps2_', ps_vars[2:length(ps_vars)])
        ps2_intake_data[c(1, 3:13, 16:47)] <- sapply(ps2_intake_data[c(1, 3:13, 16:47)], as.numeric)
        intake_data <- merge(intake_data, ps2_intake_data, by = 'id',  all = TRUE)

        ps3_intake_data <- as.data.frame(sapply(ps_vars, FUN = ps_convert_fn, intake_data = intake_data, ps = 3, simplify = TRUE, USE.NAMES = TRUE))
        names(ps3_intake_data)[2:ncol(ps3_intake_data)] <- paste0('ps3_', ps_vars[2:length(ps_vars)])
        ps3_intake_data[c(1, 3:13, 16:47)] <- sapply(ps3_intake_data[c(1, 3:13, 16:47)], as.numeric)
        intake_data <- merge(intake_data, ps3_intake_data, by = 'id',  all = TRUE)

        ps4_intake_data <- as.data.frame(sapply(ps_vars, FUN = ps_convert_fn, intake_data = intake_data, ps = 4, simplify = TRUE, USE.NAMES = TRUE))
        names(ps4_intake_data)[2:ncol(ps4_intake_data)] <- paste0('ps4_', ps_vars[2:length(ps_vars)])
        ps4_intake_data[c(1, 3:13, 16:47)] <- sapply(ps4_intake_data[c(1, 3:13, 16:47)], as.numeric)
        intake_data <- merge(intake_data, ps4_intake_data, by = 'id',  all = TRUE)


        ## update/add portion size labels
        for (ps in 1:4) {
            for (v in 2:length(ps_vars)){
                #new portion size name
                ps_var_name <- paste0('ps', ps, '_', ps_vars[v])

                #visit vars
                v2_var_name <- paste0('v2_', ps_vars[v])

                #get label information with visit information removed
                novisit_label <- gsub('Visit 2 - ', '', intake_labels[[v2_var_name]], fixed = TRUE)

                #add new label with portion size information
                intake_labels[[ps_var_name]] <- paste0('Portion Size ', ps, ': ', novisit_label)
            }
        }

        #update and match labels
        names(intake_labels) <- names(intake_data)
        intake_data = sjlabelled::set_label(intake_data, label = matrix(unlist(intake_labels, use.names = FALSE)))

        # add to list
        database_return <- c(database_return, list(intake_data = intake_data, intake_dict = intake_labels))

        # write out
        if (isTRUE(write_dat)){

            #data dictionary
            intake_dict <- labelled::generate_dictionary(intake_data, details = TRUE)
            intake_dict$label <- matrix(unlist(intake_labels, use.names = FALSE))
            names(intake_dict)[1] <- 'column'

            #interprets the value_labels as list so need to make everything a character
            intake_dict_write <- sapply(intake_dict[c(1:3, 6:8, 12:13)], FUN = as.character)

            if (isTRUE(writepath_arg)){
                haven::write_sav(intake_data, path = paste0(write_path, 'intake_data.sav'))
                write.csv(intake_dict_write, file = paste0(write_path, 'dict-intake_data.csv'), row.names = FALSE)
            } else {
                haven::write_sav(intake_data, path = 'intake_data.sav')
                write.csv(intake_dict_write, file = 'dict-intake_data.csv', row.names = FALSE)
            }
        }
    }

    ## 3d) Eating Behavior/Food Intake Questionnaires ####
    if (isFALSE(databases_arg) | 'food_qs' %in% databases){

        #visit 1
        v1_foodqs_data <- v1_data[['data']][c(1, 506:821)]
        v1_foodqs_labels <- v1_data[['dict']][c(1, 506:821)]

        #visit 2
        v2_foodqs_data <- v2_data[['data']][c(1:2, 77:299)]
        v2_foodqs_labels <- v2_data[['dict']][c(1:2, 77:299)]

        names(v2_foodqs_data)[2] <- 'v2_date'
        names(v2_foodqs_labels)[2] <- 'v2_date'

        #visit 3
        v3_foodqs_data <- v3_data[['data']][c(1:2, 50:160)]
        v3_foodqs_labels <- v3_data[['dict']][c(1:2, 50:160)]

        names(v3_foodqs_data)[2] <- 'v3_date'
        names(v3_foodqs_labels)[2] <- 'v3_date'

        #visit 4
        v4_foodqs_data <- v4_data[['data']][c(1:2, 147:158)]
        v4_foodqs_labels <- v4_data[['dict']][c(1:2, 147:158)]

        names(v4_foodqs_data)[2] <- 'v4_date'
        names(v4_foodqs_labels)[2] <- 'v4_date'

        #visit 5
        v5_foodqs_data <- v5_data[['data']][c(1:2, 82:104)]
        v5_foodqs_labels <- v5_data[['dict']][c(1:2, 82:104)]

        names(v5_foodqs_data)[2] <- 'v5_date'
        names(v5_foodqs_labels)[2] <- 'v5_date'

        #visit 6
        v6_foodqs_data <- v6_data[['data']][c(1:2, 330:336)]
        v6_foodqs_labels <- v6_data[['dict']][c(1:2, 330:336)]

        names(v6_foodqs_data)[2] <- 'v6_date'
        names(v6_foodqs_labels)[2] <- 'v6_date'

        #visit 7
        v7_foodqs_data <- v7_data[['data']][c(1:2, 370:375, 572:887)]
        v7_foodqs_labels <- v7_data[['dict']][c(1:2, 370:375, 572:887)]

        names(v7_foodqs_data)[2] <- 'v7_date'
        names(v7_foodqs_labels)[2] <- 'v7_date'

        #re-name variables with 'v7_' and add 'Visit 7 - ' to labels
        for (v in 3:ncol(v7_foodqs_data)){
            #names
            var_name <- names(v7_foodqs_data)[v]
            v7_name <- paste0('v7_', var_name)
            names(v7_foodqs_data)[v] <- v7_name

            #labels
            v7_foodqs_labels[[var_name]] <- paste0('Visit 7 - ', v7_foodqs_labels[[var_name]])
        }

        #make names match
        names(v7_foodqs_labels) <- names(v7_foodqs_data)

        ## merge databases from v1
        foodqs_demov1_data <- merge(common_demo_data, v1_foodqs_data, by = 'id', all = TRUE)

        ## merge databases - set all.x = FALSE so only get the participants with data at later visits/have not been screened out
        foodqs_demov1v2_data <- merge(foodqs_demov1_data, v2_foodqs_data, by = 'id', all.x = FALSE, all.y = TRUE)

        ## other merges - set all = TRUE so get all participants in visits 2-7
        foodqs_demov1v2v3_data <- merge(foodqs_demov1v2_data, v3_foodqs_data, by = 'id', all = TRUE)
        foodqs_demov1v2v3v4_data <- merge(foodqs_demov1v2v3_data, v4_foodqs_data, by = 'id', all = TRUE)
        foodqs_demov1v2v3v4v5_data <- merge(foodqs_demov1v2v3v4_data, v5_foodqs_data, by = 'id', all = TRUE)
        foodqs_demov1v2v3v4v5v6_data <- merge(foodqs_demov1v2v3v4v5_data, v6_foodqs_data, by = 'id', all = TRUE)
        foodqs_demov1v2v3v4v5v6v7_data <- merge(foodqs_demov1v2v3v4v5v6_data, v7_foodqs_data, by = 'id', all = TRUE)

        #get labels
        foodqs_labels <- c(common_demo_labels, v1_foodqs_labels[2:length(v1_foodqs_labels)], v2_foodqs_labels[2:length(v2_foodqs_labels)], v3_foodqs_labels[2:length(v3_foodqs_labels)], v4_foodqs_labels[2:length(v4_foodqs_labels)], v5_foodqs_labels[2:length(v5_foodqs_labels)], v6_foodqs_labels[2:length(v6_foodqs_labels)], v7_foodqs_labels[2:length(v7_foodqs_labels)])

        # ensure labels are up to date
        foodqs_data = sjlabelled::set_label(foodqs_demov1v2v3v4v5v6v7_data, label = matrix(unlist(foodqs_labels, use.names = FALSE)))

        # add to list
        database_return <- c(database_return, list(foodqs_data = foodqs_data, foodqs_dict = foodqs_labels))

        #write out
        if (isTRUE(write_dat)){

            #data dictionary
            foodqs_dict <- labelled::generate_dictionary(foodqs_data, details = TRUE)
            foodqs_dict$label <- matrix(unlist(foodqs_labels, use.names = FALSE))
            names(foodqs_dict)[1] <- 'column'

            #interprets the value_labels as list so need to make everything a character
            foodqs_dict_write <- sapply(foodqs_dict[c(1:3, 6:8, 12:13)], FUN = as.character)

            if (isTRUE(writepath_arg)){
                haven::write_sav(foodqs_data, path = paste0(write_path, 'qs_eatbeh_bodyimage.sav'))
                write.csv(foodqs_dict_write, file = paste0(write_path, 'dict-qs_eatbeh_bodyimage.csv'), row.names = FALSE)
            } else {
                haven::write_sav(foodqs_data, path = 'qs_eatbeh_bodyimage.sav')
                write.csv(foodqs_dict_write, file = 'dict-qs_eatbeh_bodyimage.csv', row.names = FALSE)
            }
        }
    }

    ## 3e) Cognitive and Psychosocial Questionnaires/Measures ####
    if (isFALSE(databases_arg) | 'psych_qs' %in% databases){

        #visit 2
        v2_psychqs_data <- v2_data[['data']][c(1:2, 300:458)]
        v2_psychqs_labels <- v2_data[['dict']][c(1:2, 300:458)]

        names(v2_psychqs_data)[2] <- 'v2_date'
        names(v2_psychqs_labels)[2] <- 'v2_date'

        #visit 3
        v3_psychqs_data <- v3_data[['data']][c(1:2, 161:248)]
        v3_psychqs_labels <- v3_data[['dict']][c(1:2, 161:248)]

        names(v3_psychqs_data)[2] <- 'v3_date'
        names(v3_psychqs_labels)[2] <- 'v3_date'

        #visit 4
        v4_psychqs_data <- v4_data[['data']][c(1:2, 159:280)]
        v4_psychqs_labels <- v4_data[['dict']][c(1:2, 159:280)]

        names(v4_psychqs_data)[2] <- 'v4_date'
        names(v4_psychqs_labels)[2] <- 'v4_date'

        #visit 7
        v7_psychqs_data <- v7_data[['data']][c(1:2, 888:998)]
        v7_psychqs_labels <- v7_data[['dict']][c(1:2, 888:998)]

        names(v7_psychqs_data)[2] <- 'v7_date'
        names(v7_psychqs_labels)[2] <- 'v7_date'

        #re-name variables with 'v7_' and add 'Visit 7 - ' to labels
        for (v in 3:ncol(v7_psychqs_data)){
            #names
            var_name <- names(v7_psychqs_data)[v]
            v7_name <- paste0('v7_', var_name)
            names(v7_psychqs_data)[v] <- v7_name

            #labels
            v7_psychqs_labels[[var_name]] <- paste0('Visit 7 - ', v7_psychqs_labels[[var_name]])
        }

        #make names match
        names(v7_psychqs_labels) <- names(v7_psychqs_data)

        ## merge databases - set all.x = FALSE so only get the participants with data at later visits/have not been screened out
        psychqs_demov2_data <- merge(common_demo_data, v2_psychqs_data, by = 'id', all.x = FALSE, all.y = TRUE)

        ## other merges - set all = TRUE so get all participants in visits 2-7
        psychqs_demov2v3_data <- merge(psychqs_demov2_data, v3_psychqs_data, by = 'id', all = TRUE)
        psychqs_demov2v3v4_data <- merge(psychqs_demov2v3_data, v4_psychqs_data, by = 'id', all = TRUE)
        psychqs_demov2v3v4v7_data <- merge(psychqs_demov2v3v4_data, v7_psychqs_data, by = 'id', all = TRUE)

        #get labels
        psychqs_labels <- c(common_demo_labels, v2_psychqs_labels[2:length(v2_psychqs_labels)], v3_psychqs_labels[2:length(v3_psychqs_labels)], v4_psychqs_labels[2:length(v4_psychqs_labels)], v7_psychqs_labels[2:length(v7_psychqs_labels)])

        # ensure labels are up to date
        psychqs_data = sjlabelled::set_label(psychqs_demov2v3v4v7_data, label = matrix(unlist(psychqs_labels, use.names = FALSE)))

        # add to list
        database_return <- c(database_return, list(psychqs_data = psychqs_data, psychqs_dict = psychqs_labels))

        #write out
        if (isTRUE(write_dat)){

            #data dictionary
            psychqs_dict <- labelled::generate_dictionary(psychqs_data, details = TRUE)
            psychqs_dict$label <- matrix(unlist(psychqs_labels, use.names = FALSE))
            names(psychqs_dict)[1] <- 'column'

            #interprets the value_labels as list so need to make everything a character
            psychqs_dict_write <- sapply(psychqs_dict[c(1:3, 6:8, 12:13)], FUN = as.character)

            if (isTRUE(writepath_arg)){
                haven::write_sav(psychqs_data, path = paste0(write_path, 'qs_cog_psych_soc.sav'))
                write.csv(psychqs_dict_write, file = paste0(write_path, 'dict-qs_cog_psych_soc.csv'), row.names = FALSE)
            } else {
                haven::write_sav(psychqs_data, path = 'qs_cog_psych_soc.sav')
                write.csv(psychqs_dict_write, file = 'dict-qs_cog_psych_soc.csv', row.names = FALSE)
            }
        }
    }

    ## 3f) Delay Discounting ####
    if (isFALSE(databases_arg) | 'dd' %in% databases ){

        #visit 3
        v3_dd_data <- v3_data[['data']][c(1:2, 249:325)]
        v3_dd_labels <- v3_data[['dict']][c(1:2, 249:325)]

        names(v3_dd_data)[2] <- 'v3_date'
        names(v3_dd_labels)[2] <- 'v3_date'

        ## merge databases - set all.x = FALSE so only get the participants with data at later visits/have not been screened out
        dd_demov3_data <- merge(common_demo_data, v3_dd_data, by = 'id', all.x = FALSE, all.y = TRUE)

        #get labels
        dd_labels <- c(common_demo_labels, v3_dd_labels[2:length(v3_dd_labels)])

        # ensure labels are up to date
        dd_data = sjlabelled::set_label(dd_demov3_data, label = matrix(unlist(dd_labels, use.names = FALSE)))

        # add to list
        database_return <- c(database_return, list(dd_data = dd_data, dd_dict = dd_labels))

        #write out
        if (isTRUE(write_dat)){

            #data dictionary
            dd_dict <- labelled::generate_dictionary(dd_data, details = TRUE)
            dd_dict$label <- matrix(unlist(dd_labels, use.names = FALSE))
            names(dd_dict)[1] <- 'column'

            #interprets the value_labels as list so need to make everything a character
            dd_dict_write <- sapply(dd_dict[c(1:3, 6:8, 12:13)], FUN = as.character)

            if (isTRUE(writepath_arg)){
                haven::write_sav(dd_data, path = paste0(write_path, 'delay_discounting.sav'))
                write.csv(dd_dict_write, file = paste0(write_path, 'dict-delay_discounting.csv'), row.names = FALSE)
            } else {
                haven::write_sav(dd_data, path = 'delay_discounting.sav')
                write.csv(dd_dict_write, file = 'dict_delay_discounting.csv', row.names = FALSE)
            }
        }
    }

    ## 3g) Interoception ####
    if (isFALSE(databases_arg) | 'intero' %in% databases){

        #visit 3
        v5_intero_data <- v5_data[['data']][c(1:2, 105:191)]
        v5_intero_labels <- v5_data[['dict']][c(1:2, 105:191)]

        names(v5_intero_data)[2] <- 'v5_date'
        names(v5_intero_labels)[2] <- 'v5_date'

        ## merge databases - set all.x = FALSE so only get the participants with data at later visits/have not been screened out
        intero_demov5_data <- merge(common_demo_data, v5_intero_data, by = 'id', all.x = FALSE, all.y = TRUE)

        #get labels
        intero_labels <- c(common_demo_labels, v5_intero_labels[2:length(v5_intero_labels)])

        # ensure labels are up to date
        intero_data = sjlabelled::set_label(intero_demov5_data, label = matrix(unlist(intero_labels, use.names = FALSE)))

        # add to list
        database_return <- c(database_return, list(intero_data = intero_data, intero_dict = intero_labels))

        #write out
        if (isTRUE(write_dat)){

            #data dictionary
            intero_dict <- labelled::generate_dictionary(intero_data, details = TRUE)
            intero_dict$label <- matrix(unlist(intero_labels, use.names = FALSE))
            names(intero_dict)[1] <- 'column'

            #interprets the value_labels as list so need to make everything a character
            intero_dict_write <- sapply(intero_dict[c(1:3, 6:8, 12:13)], FUN = as.character)

                if (isTRUE(writepath_arg)){
                haven::write_sav(intero_data, path = paste0(write_path, 'intero_data.sav'))
                write.csv(intero_dict_write, file = paste0(write_path, 'dict-intero_data.csv'), row.names = FALSE)
            } else {
                haven::write_sav(intero_data, path = 'intero_data.sav')
                write.csv(intero_dict_write, file = 'dict-intero_data.csv', row.names = FALSE)
            }
        }
    }

    ## 3h) Notes ####
    if (isFALSE(databases_arg) | 'notes' %in% databases){

        #visit 1
        v1_notes_data <- v1_data[['data']][c(1, 822:825)]
        v1_notes_labels <- v1_data[['dict']][c(1, 822:825)]

        #re-name variables with 'v1_' and add 'Visit 1 - ' to labels
        for (v in 2:ncol(v1_notes_data)){
            #names
            var_name <- names(v1_notes_data)[v]
            v1_name <- paste0('v1_', var_name)
            names(v1_notes_data)[v] <- v1_name

            #labels
            v1_notes_labels[[var_name]] <- paste0('Visit 1 - ', v1_notes_labels[[var_name]])
        }

        names(v1_notes_labels) <- names(v1_notes_data)

        #visit 2
        v2_notes_data <- v2_data[['data']][c(1:2, 459:474)]
        v2_notes_labels <- v2_data[['dict']][c(1:2, 459:474)]

        names(v2_notes_data)[2] <- 'v2_date'
        names(v2_notes_labels)[2] <- 'v2_date'

        #re-name variables with 'v2_' and add 'Visit 2 - ' to labels
        for (v in 3:ncol(v2_notes_data)){
            #names
            var_name <- names(v2_notes_data)[v]
            v2_name <- paste0('v2_', var_name)
            names(v2_notes_data)[v] <- v2_name

            #labels
            v2_notes_labels[[var_name]] <- paste0('Visit 2 - ', v2_notes_labels[[var_name]])
        }

        names(v2_notes_labels) <- names(v2_notes_data)

        #visit 3
        if (isTRUE(model_DD)){
            v3_notes_data <- v3_data[['data']][c(1:2, 326:341)]
            v3_notes_labels <- v3_data[['dict']][c(1:2, 326:341)]
        } else {
            v3_notes_data <- v3_data[['data']][c(1:2, 318:333)]
            v3_notes_labels <- v3_data[['dict']][c(1:2, 318:333)]
        }


        names(v3_notes_data)[2] <- 'v3_date'
        names(v3_notes_labels)[2] <- 'v3_date'

        #re-name variables with 'v3_' and add 'Visit 3 - ' to labels
        for (v in 3:ncol(v3_notes_data)){
            #names
            var_name <- names(v3_notes_data)[v]
            v3_name <- paste0('v3_', var_name)
            names(v3_notes_data)[v] <- v3_name

            #labels
            v3_notes_labels[[var_name]] <- paste0('Visit 3 - ', v3_notes_labels[[var_name]])
        }

        names(v3_notes_labels) <- names(v3_notes_data)

        #visit 4
        v4_notes_data <- v4_data[['data']][c(1:2, 271:287)]
        v4_notes_labels <- v4_data[['dict']][c(1:2, 271:287)]

        names(v4_notes_data)[2] <- 'v4_date'
        names(v4_notes_labels)[2] <- 'v4_date'

        #re-name variables with 'v4_' and add 'Visit 4 - ' to labels
        for (v in 3:ncol(v4_notes_data)){
            #names
            var_name <- names(v4_notes_data)[v]
            v4_name <- paste0('v4_', var_name)
            names(v4_notes_data)[v] <- v4_name

            #labels
            v4_notes_labels[[var_name]] <- paste0('Visit 4 - ', v4_notes_labels[[var_name]])
        }

        names(v4_notes_labels) <- names(v4_notes_data)

        #visit 5
        v5_notes_data <- v5_data[['data']][c(1:2, 192:209)]
        v5_notes_labels <- v5_data[['dict']][c(1:2, 192:209)]

        names(v5_notes_data)[2] <- 'v5_date'
        names(v5_notes_labels)[2] <- 'v5_date'

        #re-name variables with 'v5_' and add 'Visit 5 - ' to labels
        for (v in 3:ncol(v5_notes_data)){
            #names
            var_name <- names(v5_notes_data)[v]
            v5_name <- paste0('v5_', var_name)
            names(v5_notes_data)[v] <- v5_name

            #labels
            v5_notes_labels[[var_name]] <- paste0('Visit 5 - ', v5_notes_labels[[var_name]])
        }

        names(v5_notes_labels) <- names(v5_notes_data)

        #visit 6
        v6_notes_data <- v6_data[['data']][c(1:2, 337:360)]
        v6_notes_labels <- v6_data[['dict']][c(1:2, 337:360)]

        names(v6_notes_data)[2] <- 'v6_date'
        names(v6_notes_labels)[2] <- 'v6_date'

        #re-name variables with 'v6_' and add 'Visit 7 - ' to labels
        for (v in 3:ncol(v6_notes_data)){
            #names
            var_name <- names(v6_notes_data)[v]
            v6_name <- paste0('v6_', var_name)
            names(v6_notes_data)[v] <- v6_name

            #labels
            v6_notes_labels[[var_name]] <- paste0('Visit 6 - ', v6_notes_labels[[var_name]])
        }

        #make names match
        names(v6_notes_labels) <- names(v6_notes_data)

        #visit 7
        v7_notes_data <- v7_data[['data']][c(1:2, 999:1014)]
        v7_notes_labels <- v7_data[['dict']][c(1:2, 999:1014)]

        names(v7_notes_data)[2] <- 'v7_date'
        names(v7_notes_labels)[2] <- 'v7_date'

        #re-name variables with 'v7_' and add 'Visit 7 - ' to labels
        for (v in 3:ncol(v7_notes_data)){
            #names
            var_name <- names(v7_notes_data)[v]
            v7_name <- paste0('v7_', var_name)
            names(v7_notes_data)[v] <- v7_name

            #labels
            v7_notes_labels[[var_name]] <- paste0('Visit 7 - ', v7_notes_labels[[var_name]])
        }

        #make names match
        names(v7_notes_labels) <- names(v7_notes_data)

        ## merge databases from v1
        notes_demov1_data <- merge(common_demo_data, v1_notes_data, by = 'id', all = TRUE)

        ## merge databases - set all.x = FALSE so only get the participants with data at later visits/have not been screened out
        notes_demov1v2_data <- merge(notes_demov1_data, v2_notes_data, by = 'id', all = TRUE)

        ## other merges - set all = TRUE so get all participants in visits 2-7
        notes_demov1v2v3_data <- merge(notes_demov1v2_data, v3_notes_data, by = 'id', all = TRUE)
        notes_demov1v2v3v4_data <- merge(notes_demov1v2v3_data, v4_notes_data, by = 'id', all = TRUE)
        notes_demov1v2v3v4v5_data <- merge(notes_demov1v2v3v4_data, v5_notes_data, by = 'id', all = TRUE)
        notes_demov1v2v3v4v5v6_data <- merge(notes_demov1v2v3v4v5_data, v6_notes_data, by = 'id', all = TRUE)
        notes_demov1v23v4v5v6v7_data <- merge(notes_demov1v2v3v4v5v6_data, v7_notes_data, by = 'id', all = TRUE)

        #get labels
        notes_labels <- c(common_demo_labels, v1_notes_labels[2:length(v1_notes_labels)], v2_notes_labels[2:length(v2_notes_labels)], v3_notes_labels[2:length(v3_notes_labels)], v4_notes_labels[2:length(v4_notes_labels)], v5_notes_labels[2:length(v5_notes_labels)], v6_notes_labels[2:length(v6_notes_labels)], v7_notes_labels[2:length(v7_notes_labels)])

        # ensure labels are up to date
        notes_data = sjlabelled::set_label(notes_demov1v23v4v5v6v7_data, label = matrix(unlist(notes_labels, use.names = FALSE)))

        # add to list
        database_return <- c(database_return, list(notes_data = notes_data, notes_dict = notes_labels))

        #write out
        if (isTRUE(write_dat)){

            #data dictionary
            notes_dict <- labelled::generate_dictionary(notes_data, details = TRUE)
            notes_dict$label <- matrix(unlist(notes_labels, use.names = FALSE))
            names(notes_dict)[1] <- 'column'

            #interprets the value_labels as list so need to make everything a character
            notes_dict_write <- sapply(notes_dict[c(1:3, 6:8, 12:13)], FUN = as.character)

            if (isTRUE(writepath_arg)){
                haven::write_sav(notes_data, path = paste0(write_path, 'visit_notes.sav'))
                write.csv(notes_dict_write, file = paste0(write_path, 'dict-visit_notes.csv'), row.names = FALSE)
            } else {
                haven::write_sav(notes_data, path = 'visit_notes.sav')
                write.csv(notes_dict_write, file = 'dict-visit_notes.csv', row.names = FALSE)
            }
        }
    }

    ## 3i) PNA data ####
    if (isFALSE(databases_arg) | 'pna' %in% databases){

        #visit 1
        v1_pna_data <- v1_data[['pna_data']]
        v1_pna_labels <- v1_data[['pna_dict']]

        #re-name variables with 'v1_' and add 'Visit 1 - ' to labels
        for (v in 2:ncol(v1_pna_data)){
            #names
            var_name <- names(v1_pna_data)[v]
            v1_name <- paste0('v1_', var_name)
            names(v1_pna_data)[v] <- v1_name

            #labels
            v1_pna_labels[[var_name]] <- paste0('Visit 1 - ', v1_pna_labels[[var_name]])
        }

        names(v1_pna_labels) <- names(v1_pna_data)

        #visit 2
        v2_pna_data <- v2_data[['pna_data']]
        v2_pna_labels <- v2_data[['pna_dict']]

        #re-name variables with 'v2_' and add 'Visit 2 - ' to labels
        for (v in 2:ncol(v2_pna_data)){
            #names
            var_name <- names(v2_pna_data)[v]
            v2_name <- paste0('v2_', var_name)
            names(v2_pna_data)[v] <- v2_name

            #labels
            v2_pna_labels[[var_name]] <- paste0('Visit 2 - ', v2_pna_labels[[var_name]])
        }

        names(v2_pna_labels) <- names(v2_pna_data)

        #visit 3
        v3_pna_data <- v3_data[['pna_data']]
        v3_pna_labels <- v3_data[['pna_dict']]

        #re-name variables with 'v3_' and add 'Visit 3 - ' to labels
        for (v in 2:ncol(v3_pna_data)){
            #names
            var_name <- names(v3_pna_data)[v]
            v3_name <- paste0('v3_', var_name)
            names(v3_pna_data)[v] <- v3_name

            #labels
            v3_pna_labels[[var_name]] <- paste0('Visit 3 - ', v3_pna_labels[[var_name]])
        }

        names(v3_pna_labels) <- names(v3_pna_data)

        #visit 4
        v4_pna_data <- v4_data[['pna_data']]
        v4_pna_labels <- v4_data[['pna_dict']]

        #re-name variables with 'v4_' and add 'Visit 4 - ' to labels
        for (v in 2:ncol(v4_pna_data)){
            #names
            var_name <- names(v4_pna_data)[v]
            v4_name <- paste0('v4_', var_name)
            names(v4_pna_data)[v] <- v4_name

            #labels
            v4_pna_labels[[var_name]] <- paste0('Visit 4 - ', v4_pna_labels[[var_name]])
        }

        names(v4_pna_labels) <- names(v4_pna_data)

        #visit 5
        v5_pna_data <- v5_data[['pna_data']]
        v5_pna_labels <- v5_data[['pna_dict']]

        #re-name variables with 'v5_' and add 'Visit 5 - ' to labels
        for (v in 2:ncol(v5_pna_data)){
            #names
            var_name <- names(v5_pna_data)[v]
            v5_name <- paste0('v5_', var_name)
            names(v5_pna_data)[v] <- v5_name

            #labels
            v5_pna_labels[[var_name]] <- paste0('Visit 5 - ', v5_pna_labels[[var_name]])
        }

        names(v5_pna_labels) <- names(v5_pna_data)

        #visit 7
        v7_pna_data <- v7_data[['pna_data']]
        v7_pna_labels <- v7_data[['pna_dict']]

        #re-name variables with 'v7_' and add 'Visit 7 - ' to labels
        for (v in 2:ncol(v7_pna_data)){
            #names
            var_name <- names(v7_pna_data)[v]
            v7_name <- paste0('v7_', var_name)
            names(v7_pna_data)[v] <- v7_name

            #labels
            v7_pna_labels[[var_name]] <- paste0('Visit 7 - ', v7_pna_labels[[var_name]])
        }

        #make names match
        names(v7_pna_labels) <- names(v7_pna_data)

        ## merge databases - set all = TRUE so get all participants in visits 1-7
        pna_v1v2_data <- merge(v1_pna_data, v2_pna_data, by = 'id', all = TRUE)
        pna_v1v2v3_data <- merge(pna_v1v2_data, v3_pna_data, by = 'id', all = TRUE)
        pna_v1v2v3v4_data <- merge(pna_v1v2v3_data, v4_pna_data, by = 'id', all = TRUE)
        pna_v1v2v3v4v5_data <- merge(pna_v1v2v3v4_data, v5_pna_data, by = 'id', all = TRUE)
        pna_v1v2v3v4v5v7_data <- merge(pna_v1v2v3v4v5_data, v7_pna_data, by = 'id', all = TRUE)

        #get labels
        pna_labels <- c(v1_pna_labels, v2_pna_labels[2:length(v2_pna_labels)], v3_pna_labels[2:length(v3_pna_labels)], v4_pna_labels[2:length(v4_pna_labels)], v5_pna_labels[2:length(v5_pna_labels)], v7_pna_labels[2:length(v7_pna_labels)])

        # ensure labels are up to date
        pna_data = sjlabelled::set_label(pna_v1v2v3v4v5v7_data, label = matrix(unlist(pna_labels, use.names = FALSE)))

        # add to list
        database_return <- c(database_return, list(pna_data = pna_data, pna_dict = pna_labels))

        #write out
        if (isTRUE(write_dat)){

            #data dictionary
            pna_dict <- labelled::generate_dictionary(pna_data, details = TRUE)
            pna_dict$label <- matrix(unlist(pna_labels, use.names = FALSE))
            names(pna_dict)[1] <- 'column'

            #interprets the value_labels as list so need to make everything a character
            pna_dict_write <- sapply(pna_dict[c(1:3, 6:8, 12:13)], FUN = as.character)

            if (isTRUE(writepath_arg)){
                haven::write_sav(pna_data, path = paste0(write_path, 'pna_notes.sav'))
                write.csv(pna_dict_write, file = paste0(write_path, 'dict-pna_notes.csv'), row.names = FALSE)
            } else {
                haven::write_sav(pna_data, path = 'pna_notes.sav')
                write.csv(pna_dict_write, file = 'dict-pna_notes.csv', row.names = FALSE)
            }
        }
    }

    ## 3i) Microstructure data ####
    if (isFALSE(databases_arg) | 'micro' %in% databases){

      micro_data[['beh_wide']][['data']][['id']] <- as.numeric(micro_data[['beh_wide']][['data']][['id']])
      micro_data[['event']][['data']][['id']] <- as.numeric(micro_data[['event']][['data']][['id']])

      ## merge databases - set all = TRUE so get all participants in visits 1-7
      beh_wide_demo_data <- merge(common_demo_data, micro_data[['beh_wide']][['data']], by = 'id', all = TRUE)

      event_demo_data <- merge(common_demo_data, micro_data[['event']][['data']], by = 'id', all = TRUE)

      #get labels
      beh_wide_labels <- c(common_demo_labels, micro_data[['beh_wide']][['dict']][2:length(micro_data[['beh_wide']][['dict']])])
      event_labels <- c(common_demo_labels, micro_data[['event']][['dict']][2:length(micro_data[['event']][['dict']])])

      # ensure labels are up to date
      beh_wide_data = sjlabelled::set_label(beh_wide_demo_data, label = matrix(unlist(beh_wide_labels, use.names = FALSE)))

      event_data = sjlabelled::set_label(event_demo_data, label = matrix(unlist(event_labels, use.names = FALSE)))


      # add to list
      database_return <- c(database_return, list(micro_wide = beh_wide_data, micro_wide_dict = beh_wide_labels, micro_event = event_data, micro_event_dict = event_labels))

      #write out
      if (isTRUE(write_dat)){

        #data dictionary
        micro_wide_dict <- labelled::generate_dictionary(beh_wide_data, details = TRUE)
        micro_wide_dict$label <- matrix(unlist(beh_wide_labels, use.names = FALSE))
        names(micro_wide_dict)[1] <- 'column'

        micro_event_dict <- labelled::generate_dictionary(event_data, details = TRUE)
        micro_event_dict$label <- matrix(unlist(event_labels, use.names = FALSE))
        names(micro_event_dict)[1] <- 'column'

        #interprets the value_labels as list so need to make everything a character
        micro_wide_dict_write <- sapply(micro_wide_dict[c(1:3, 6:8, 12:13)], FUN = as.character)

        micro_event_dict_write <- sapply(micro_event_dict[c(1:3, 6:8, 12:13)], FUN = as.character)

        if (isTRUE(writepath_arg)){
          haven::write_sav(beh_wide_data, path = paste0(write_path, 'micro_beh_summary.sav'))
          write.csv(micro_wide_dict_write, file = paste0(write_path, 'dict-micro_beh_summary.csv'), row.names = FALSE)

          haven::write_sav(event_data, path = paste0(write_path, 'micro_events.sav'))
          write.csv(micro_event_dict_write, file = paste0(write_path, 'dict-micro_events.csv'), row.names = FALSE)
        } else {
          haven::write_sav(beh_wide_data, 'micro_beh_summary.sav')
          write.csv(micro_wide_dict, 'dict-micro_beh_summary.csv', row.names = FALSE)

          haven::write_sav(event_data, path = 'micro_events.sav')
          write.csv(micro_event_dict, file = 'dict-micro_events.csv', row.names = FALSE)
        }
      }
    }

    #### 10. Return List #####
    if(isTRUE(return_data)){
        return(database_return)
    }

}