#' @title Connect to ePIRLS Data
#' @description Opens a connection to an ePIRLS data file and
#'              returns an \code{edsurvey.data.frame} with 
#'              information about the file and data.
#' @param path a character value to the full directory to the ePIRLS extracted SPSS (.sav) set of data
#' @param countries a character vector of the country/countries to include using
#'                  the three-digit ISO country code.  
#'                  A list of country codes can be found on Wikipedia at
#'                  \url{https://en.wikipedia.org/wiki/ISO_3166-1#Current_codes},
#'                  or other online sources. Consult the \emph{ePIRLS User Guide} to help determine what countries
#'                  are included within a specific testing year of ePIRLS.
#'                  To select all countries, use a wildcard value of \strong{\code{*}}.
#' @param forceReread a logical value to force rereading of all processed data. 
#'                    The default value of \code{FALSE} will speed up the \code{read_ePIRLS} function by 
#'                    using existing read-in data already processed.
#' @param verbose a logical value to either print or suppress status message output.
#'                The default value is \code{TRUE}.
#' @details Reads in the unzipped files downloaded from the ePIRLS international database(s) using the \href{http://rms.iea-dpc.org/}{IEA Study Data Repository}. 
#'          Data files require the SPSS data file (.sav) format using the default filenames.
#' @details An ePIRLS \code{edsurvey.data.frame} includes three distinct data levels: 
#'          \itemize{
#'               \item student
#'               \item school
#'               \item teacher
#'          }
#'          When the \code{getData} function is called using a ePIRLS \code{edsurvey.data.frame},
#'          the requested data variables are inspected, and it handles any necessary data merges automatically. 
#'          Note that the \code{school} data will always be returned merged to the \code{student}
#'          data, even if only \code{school} variables are requested.
#'          Only if \code{teacher} variables are requested by the \code{getData} call, will cause \code{teacher} data to be merged.
#'          A \code{student} can be linked to many \code{teachers}, which varies widely between countries.
#' @details Please note that calling the \code{dim} function for an ePIRLS \code{edsurvey.data.frame} will result in 
#'          the row count as if the \code{teacher} dataset was merged.
#'          This row count will be considered the \code{full data N} of the \code{edsurvey.data.frame}, even if no \code{teacher} data were included in an analysis.  
#'          The column count returned by \code{dim} will be the count of unique column variables across all three data levels.
#' @return
#'  an \code{edsurvey.data.frame} for a single specified country or an \code{edsurvey.data.frame.list} if multiple countries specified
#' @seealso \code{\link{readNAEP}}, \code{\link{readTIMSS}}, \code{\link{getData}}, and \code{\link{download_ePIRLS}}
#' @author Tom Fink
#' @example man/examples/read_ePIRLS.R
#' @importFrom haven read_sav
#' @import tibble
#' @export
read_ePIRLS <- function(path,
                      verbose=TRUE) {
  path <- suppressWarnings(normalizePath(unique(path), winslash = "/"))
    stop(paste0("The argument ", sQuote("path"), " cannot be located: ", pasteItems(dQuote(path[!dir.exists(path)])), "."))
    stop(paste0("The argument ", sQuote("forceReread"), " must be a logical value."))
    stop(paste0("The argument ", sQuote("verbose"), " must be a logical value."))
  countries <- tolower(unique(countries))
  gradeLvl <- 4 #PIRLS is only 4th grade data
  gradeL <- "a" 
  if(unlist(countries)[1]=="*"){ #user has requested data for all countries::grab all country codes
    countries <- unique(tolower(substring(list.files(path, 
                                                     pattern=paste0("^", gradeL, ".....(",
                                                                    paste(get_ePIRLSYearCodes(), collapse = "|"), ")\\.sav$"), full.names=FALSE, ignore.case = TRUE),4,6)))
  #gather datafile listing::be sure we only pickup PIRLS years based on the yearcodes
  filenames <- list.files(path,
                          pattern=paste0("^", gradeL, "..", "(",paste(countries, collapse="|"), ")(",
                                         paste(get_ePIRLSYearCodes(), collapse = "|"), ")","\\.sav$"), full.names=TRUE, ignore.case = TRUE)
  if(length(filenames) == 0) {
    stop(paste0("Could not find any ePIRLS datafiles for countries ", paste(sQuote(countries), collapse=", "),
                " in the following folder(s): ", pasteItems(dQuote(path)), "."))
  fSubPart <- tolower(substring(basename(filenames), 1, 8)) #includes a (4th grade), country code, and year code
  fileYrs <- sort(unique(tolower(substring(basename(filenames),7,8))))
  procCountryData <- list()
  iProcCountry <- 0 #index counter for adding countries to the list
  for(yrCode in fileYrs){ #loop through all the year codes
    for(cntry in countries){
      ePIRLSfiles <- list()#empty list
      ePIRLSfiles <- c("acg", #school background
                      "asa", #student achievement
                      "asg", #student background
                      "ash", #student home background (Special file::might not always be present)
                      "asr", #within-country scoring reliability
                      "ast", #student-teacher linkage
                      "atg") #teacher background
      fnames <- NULL # be sure to clear this out so leftovers are not kept from a previous loop
      fnames <- sapply(ePIRLSfiles, function(f) {
        filenames[(fSubPart %in% paste0(f,cntry, yrCode))] #added check here to only grab files for our specific grade level, country, and year
      }, simplify=FALSE)
      hasMissing <- sapply(fnames, function(g) {
      }, simplify=TRUE)
      hasExcess <- sapply(fnames, function(h) {
      }, simplify=TRUE)
      hasData <- sum(nchar(unlist(fnames)))>0
      #test for any missing files::also check for any duplicate or multiple files
      if (sum(hasMissing)>0 && hasData==TRUE) {
        stop(paste0("Missing ePIRLS datafile(s) for country ", dQuote(cntry), " ", pasteItems(ePIRLSfiles[hasMissing]), "."))
      if (sum(hasExcess)>0 && hasData==TRUE){
        stop(paste0("Excess/duplicate ePIRLS datafile(s) for country ", dQuote(cntry), " ", pasteItems(ePIRLSfiles[hasExcess]), "."))
      #test if there are any files for this country/year combination, if not, we can skip this loop iteration as it does not exist
      if (hasData==FALSE) {
      iProcCountry <- iProcCountry + 1 #update the processed country index value after we confirm that there is data to process
      processedData <- list()
        processArgs <- list(dataFolderPath = unique(dirname(unlist(fnames))), #specify only the directory in which the files exist
                            countryCode = cntry, 
                            fnames = fnames, 
                            fileYrs = yrCode, 
                            forceReread = forceReread, 
                            verbose = verbose)
        retryProc <- tryCatch({processedData <- do.call("process_ePIRLS", processArgs, quote = TRUE)
                              }, error = function(e){
                                TRUE #flag to retry
                              }, warning = function(w){
                                TRUE #flag to retry

        if (retryProc){
          processArgs[["forceReread"]] <- TRUE #try it again reprocessing the data
          processedData <- tryCatch(do.call("process_ePIRLS", processArgs, quote = TRUE),
                                    error = function(e){
                                      stop(paste0("Unable to process ePIRLS data for country code ", dQuote(cntry),
                                                  " having year code ", dQuote(yrCode) ," at folder path(s) ", pasteItems(dQuote(path)),
                                                  ". Possible file corruption with source data.",
                                                  " Error message: ", e))
      }#end if(hasData==TRUE)

      processedData$userConditions <- list()
      processedData$defaultConditions <- NULL
      testJKprefix <- c("JK", "JK.TCHWGT") #have any jk prefix values here that are applicable for this dataset
      weights <- NULL #default value
      for(i in 1:length(testJKprefix)){
        ujkz <- unique(tolower(grep(paste0("^","(", testJKprefix[i] ,")","[1-9]"), c(names(processedData$dataList$student), names(processedData$dataList$teacher)), value = TRUE, ignore.case = TRUE)))
        ujkz <- gsub(tolower(testJKprefix[i]), "", ujkz, fixed = TRUE) #remove jk to leave the numeric values
            tmpWgt <- list()
            tmpWgt[[1]] <- list(jkbase="jk", jksuffixes=as.character(ujkz))
            names(tmpWgt)[[1]] <- "totwgt"
            weights <- c(weights,tmpWgt)
            tmpWgt <- list()
            tmpWgt[[1]] <- list(jkbase="jk.tchwgt", jksuffixes=as.character(ujkz))
            names(tmpWgt)[[1]] <- "tchwgt"
            weights <- c(weights,tmpWgt)
      attr(weights, "default") <- "totwgt"
      processedData$weights <-  weights
      processedData$pvvars <- buildPVVARS_ePIRLS(processedData$dataListFF$student, defaultPV = "rrea")
      processedData$subject <- c("Reading")
      processedData$year <- convert_ePIRLSYearCode(yrCode)
      processedData$assessmentCode <- "International"
      processedData$dataType <- "Student Data"
      processedData$gradeLevel <- "Grade 4"
      #achievment level cutpoints as defined by PIRLS documentation
      processedData$achievementLevels <- c("625", "550", "475", "400")
      names(processedData$achievementLevels) <- c("Advanced International Benchmark", "High International Benchmark", "Intermediate International Benchmark", "Low International Benchmark")
      processedData$omittedLevels <- c('Multiple', 
                                       'OMITTED OR INVALID', 
                                       'NOT REACHED', 
                                       'INVALID RESPONSE', 
                                       'NOT APPLICABLE', 
                                       'LOGICALLY NOT APPLICABLE', 

      processedData$survey <- "ePIRLS"
      processedData$country <- get_ePIRLSCountryName(cntry)
      procCountryData[[iProcCountry]] <- edsurvey.data.frame(userConditions = processedData$userConditions,
                                                             defaultConditions = processedData$defaultConditions,
                                                             dataList = build_ePIRLS_dataList(processedData$dataList$student,
                                                             weights = processedData$weights,
                                                             pvvars = processedData$pvvars,
                                                             subject = processedData$subject,
                                                             year = processedData$year,
                                                             assessmentCode = processedData$assessmentCode,
                                                             dataType = processedData$dataType,
                                                             gradeLevel = processedData$gradeLevel,
                                                             achievementLevels = processedData$achievementLevels,
                                                             omittedLevels = processedData$omittedLevels,
                                                             survey = processedData$survey,
                                                             country = processedData$country,
                                                             psuVar = "jkrep",
                                                             stratumVar = "jkzone",
                                                             jkSumMultiplier = 0.5) #defined by the method of JK weight replication used (JK2)
    }#end country loop
  }#end for(fileYr in fileYrs)
  if (iProcCountry > 1) {
    return(edsurvey.data.frame.list(procCountryData)) #return full list.  let edsurvey.data.frame.list constructor build covs
  } else {
    # just one country

#@param yrCode a character value used in the PIRLS filenaming structure to identify the specific year (e.g. m1, m2, m6)
#@return a numeric 4 digit year value
convert_ePIRLSYearCode <- function(yrCode){
  yrTest <- tolower(sort(unique(yrCode)))
  yrTest[yrTest %in% "e1"] <- 2016

get_ePIRLSYearCodes <- function(){
  #retrieve the TIMMS years based on their filenaming structure
  yrVals = c("e1")
  names(yrVals) = c(2016)

#builds the list of pvvars from the passed fileformat data.frame
buildPVVARS_ePIRLS <- function(fileFormat, defaultPV = "rrea"){
  pvFields <- subset(fileFormat, nchar(fileFormat$Type)>0) #type is identified in writeTibbleToFWFReturnFileFormat function
  constructs <- unique(pvFields$Type)
  pvvars <- vector("list", length(constructs))
  names(pvvars) <- constructs
  for(i in names(pvvars)){
    varList <- tolower(sort(pvFields$variableName[pvFields$Type == i]))
    pvvars[[i]] <- list(varnames=varList)
  #test if defaultPV in the list and make it default::otherwise set it to the first pvvar in the list
  if (defaultPV %in% names(pvvars)){
    attr(pvvars, "default") <- defaultPV
    attr(pvvars, "default") <- names(pvvars)[1]
  return (pvvars)

#@param dataFolderPath a character value of the initial folder path provided to the 'readPIRLS' call to find the .sav SPSS files
#@param countryCode a character value of the 3-digit country code we want to process
#@param fnames a character vector of the specific filenames that are needed for this country, generally there should be 7 files specified
process_ePIRLS <- function(dataFolderPath, countryCode, fnames, fileYrs, forceReread, verbose) {
  yearCode <- unlist(fileYrs)[1]
  metaCacheFP <- list.files(dataFolderPath,
                            pattern=paste0("^a", "(",paste(countryCode), ")",
                                           yearCode, "\\.meta$"), full.names=TRUE, ignore.case = TRUE)
  #grab the FWF .txt files for this country/year if they are existing
  txtCacheFWF <- list.files(dataFolderPath,
                            pattern=paste0("^a..", "(",paste(countryCode), ")",
                                           yearCode, "\\.txt$"), full.names=TRUE, ignore.case = TRUE)
  #determine if we can use the .meta RDS file for reading, OR process the data and create the .meta RDS
  if(length(metaCacheFP)==0 || length(txtCacheFWF)<3 || forceReread==TRUE){ #ensure we have a full dataset of cache files
    runProcessing <- TRUE
    cacheFile <- readRDS(unlist(metaCacheFP)[1])
    if (cacheMetaReqUpdate(cacheFile$cacheFileVer, "PIRLS")){ #cacheMetaReqUpdates resides in its own R file
      runProcessing <- TRUE
      #rebuild the file connections from the .meta serialized cache file using the stored fileFormats
      studentLAF <- getFWFLaFConnection(txtCacheFWF[tolower(substr(basename(txtCacheFWF),1,3))=="asg"], cacheFile$dataListFF$student)
      schoolLAF <- getFWFLaFConnection(txtCacheFWF[tolower(substr(basename(txtCacheFWF),1,3))=="acg"], cacheFile$dataListFF$school)
      teacherLAF <- getFWFLaFConnection(txtCacheFWF[tolower(substr(basename(txtCacheFWF),1,3))=="atg"], cacheFile$dataListFF$teacher)
      dataList <- list(student = studentLAF, school = schoolLAF, teacher = teacherLAF) #ORDER THE dataList in a heirarchy, ie. student list should be first
      dataListFF <- cacheFile$dataListFF
      dataListMeta <- cacheFile$dataListMeta
      runProcessing <- FALSE
  } #end if(length(metaCacheFP)==0 || length(txtCacheFWF)<3 || forceReread==TRUE)
      cat(paste0("Processing data for country ", dQuote(countryCode),".\n"))
    #SCHOOL LEVEL===================================================
    acg <- unlist(fnames["acg"])[1]
    schoolFP <- gsub(".sav$", "\\.txt", unlist(fnames["acg"])[1], ignore.case = TRUE)
    schoolDF1 <- read_sav(acg, user_na = TRUE)
    schoolDF1 <- UnclassCols(schoolDF1)
    colnames(schoolDF1) <- toupper(colnames(schoolDF1))
    ffsch <- writeTibbleToFWFReturnFileFormat(schoolDF1, schoolFP )  
    #STUDENT LEVEL==================================================
    asa <- unlist(fnames["asa"])[1]
    asg <- unlist(fnames["asg"])[1]
    ash <- unlist(fnames["ash"])[1]
    asr <- unlist(fnames["asr"])[1]
    stuDF1 <- read_sav(asa, user_na = TRUE)
    stuDF1 <- UnclassCols(stuDF1)
    colnames(stuDF1) <- toupper(colnames(stuDF1))
    ids1 <- grep("^ID", names(stuDF1), ignore.case=TRUE, value=TRUE)
    stuDF2 <- read_sav(asg, user_na = TRUE)
    stuDF2 <- UnclassCols(stuDF2)
    colnames(stuDF2) <- toupper(colnames(stuDF2))
    ids2 <- grep("^ID", names(stuDF2), ignore.case=TRUE, value=TRUE)
    ids12 <- ids1[ids1 %in% ids2]
    ids12 <- ids12[!(ids12 %in% c("IDPUNCH", "IDGRADER"))] #IDPUNCH should be omitted for merging
    mm <- mergeTibble(stuDF1,
                      suffixes=c("", ".junk"))
    mm <- mm[,names(mm)[!grepl("\\.junk$",names(mm))]]
    if(nrow(stuDF1) != nrow(mm)) {
      stop(paste0("Failed consistency check for filetype ", sQuote("asa"), " country code ", sQuote(tolower(countryCode)), ". ",
                  "Please email [email protected] for assistance."))
    if(nrow(stuDF2) != nrow(mm)) {
      stop(paste0("Failed consistency check for filetype ", sQuote("asg"), " country code ", sQuote(tolower(countryCode)), ". ",
                  "Please email [email protected] for assistance."))
    if(min(is.na(ash)) == 0) {
      stuDF3 <- read_sav(ash, user_na = TRUE)
      stuDF3 <- UnclassCols(stuDF3)
      colnames(stuDF3) <- toupper(colnames(stuDF3))
      ids3 <- grep("^ID", names(stuDF3), ignore.case=TRUE, value=TRUE)
      idsmm3 <- ids12[ids12 %in% ids3]
      idsmm3 <- idsmm3[!(idsmm3 %in% c("IDPUNCH", "IDGRADER"))] #IDPUNCH should be omitted for merging
      nr <- nrow(mm)
      mm <- mergeTibble(mm,
                        suffixes=c("", ".junk"))
      mm <- mm[,names(mm)[!grepl("\\.junk$",names(mm))]]
      if(nrow(stuDF1) != nrow(mm)) {
        stop(paste0("Failed consistency check for filetype ", sQuote("ash"), " country code ", sQuote(tolower(countryCode)), ". ",
                    "Please email [email protected] for assistance."))
      if(nr != nrow(mm)) {
        stop(paste0("Failed consistency check for filetype ", sQuote("ash"), " country code ", sQuote(tolower(countryCode)), ". ",
                    "Please email [email protected] for assistance."))
    } else {
      idsmm3 <- ids12
    if(min(is.na(asr)) == 0){
      stuDF4 <- read_sav(asr, user_na = TRUE)
      stuDF4 <- UnclassCols(stuDF4)
      colnames(stuDF4) <- toupper(colnames(stuDF4))
      ids4 <- grep("^ID", names(stuDF4), ignore.case=TRUE, value=TRUE)
      idsmm4 <- idsmm3[idsmm3 %in% ids4]
      idsmm4 <- idsmm4[!(idsmm4 %in% c("IDPUNCH", "IDGRADER"))] #IDPUNCH should be omitted for merging
      #test here for duplicate rows::special case for PIRLS 2001 for 'HKG' datafile having multiple data rows
      #anyDuplicated will return the row index of the first duplicate found. if no duplicates found, then it returns '0'
        stuDF4 <- dropTibbleDupes(stuDF4)
      nr <- nrow(mm)
      mm <- mergeTibble(mm,
                        suffixes=c("", ".junk"))
      mm <- mm[,names(mm)[!grepl("\\.junk$",names(mm))]]
      if(nr != nrow(mm)) {
        stop(paste0("Failed consistency check for filetype ", sQuote("asr"), " country code ", sQuote(tolower(countryCode)), ". ",
                    "Please email [email protected] for assistance."))
      mm <- mm[,names(mm)[!grepl("\\.junk$",names(mm))]]
    stuFP <- gsub(".sav$", "\\.txt", unlist(fnames["asg"])[1], ignore.case = TRUE)
    ffstu <- writeTibbleToFWFReturnFileFormat(mm, stuFP)  
    #Student-Teacher Linkage and Teacher Background=================
    ast <- unlist(fnames["ast"])[1]
    atg <- unlist(fnames["atg"])[1]
    stuTeachDF <- read_sav(ast, user_na = TRUE)
    stuTeachDF <- UnclassCols(stuTeachDF)
    colnames(stuTeachDF) <- toupper(colnames(stuTeachDF))
    teachDF <- read_sav(atg, user_na = TRUE)
    teachDF <- UnclassCols(teachDF)
    colnames(teachDF) <- toupper(colnames(teachDF))
    ids1 <- grep("^ID", names(stuTeachDF), ignore.case=TRUE, value=TRUE)
    ids2 <- grep("^ID", names(teachDF), ignore.case=TRUE, value=TRUE)
    ids12 <- ids1[ids1 %in% ids2]
    ids12 <- ids12[!(ids12 %in% c("IDPUNCH", "IDGRADER", "IDCLASS"))] #IDPUNCH should be omitted for merging
    mm <- mergeTibble(stuTeachDF,
                      suffixes=c("", ".junk"))
    mm <- mm[,names(mm)[!grepl("\\.junk$",names(mm))]]
    if(nrow(stuTeachDF) != nrow(mm)) {
      stop(paste0("Failed consistency check for filetype ", sQuote("atg"), " country code ", sQuote(tolower(countryCode)), ". ",
                  "Please email [email protected] for assistance."))
    teachFP <- gsub(".sav$", "\\.txt", unlist(fnames["atg"])[1], ignore.case = TRUE)
    ffTeach <- writeTibbleToFWFReturnFileFormat(mm, teachFP)
    schoolLAF <- getFWFLaFConnection(schoolFP, ffsch)
    studentLAF <- getFWFLaFConnection(stuFP, ffstu)
    teacherLAF <- getFWFLaFConnection(teachFP, ffTeach)
    #perform any data label corrections/fixes
    ffstu <- ePIRLS_ValueLabelCorrection(ffstu, yearCode)
    #build data list and link metadata object=======================
    dataList <- list(student = studentLAF, school = schoolLAF, teacher = teacherLAF) #ORDER THE dataList in a heirarchy, ie. student list should be first
    dataListFF <- list(student = ffstu, school = ffsch, teacher = ffTeach)
    dataListMeta <- list(student = "", school = "", teacher = "")
    dataListMeta$student <- list(school = "idcntry;idschool", teacher = "idcntry;idstud")
    dataListMeta$school <- list()
    dataListMeta$teacher <- list()
    #save the cachefile to be read-in for the next call
    cacheFile <- list(ver=packageVersion("EdSurvey"),
    saveRDS(cacheFile, file.path(dataFolderPath,paste0("a", countryCode, yearCode,".meta")))
  } else { #used the cache files
      cat(paste0("Found cached data for country code ", dQuote(tolower(countryCode)),".\n"))
  } #end if(runProcessing==TRUE)
  return(list(dataList = dataList,
              dataListFF = dataListFF,
              dataListMeta = dataListMeta)) 

export_ePIRLSToCSV <- function(folderPath, exportPath, cntryCodes, ...){
  sdfList <- read_ePIRLS(folderPath, cntryCodes, ...)
  if (class(sdfList) == "edsurvey.data.frame.list"){
    for(i in 1:length(sdfList$datalist)){
      sdf  <- sdfList$datalist[[i]]
      cntry <- sdf$country
      cat(paste(cntry, "working.\n"))
      data <- getData(sdf, colnames(sdf), dropUnusedLevels = FALSE, omittedLevels = FALSE)
      write.csv(data, file=file.path(exportPath, paste0(cntry, ".csv")), na="", row.names = FALSE)
      cat(paste(cntry, "completed.\n"))
  } else if (class(sdfList) == "edsurvey.data.frame"){
    sdf <- sdfList
    cntry <- sdf$country
    cat(paste(cntry, "working.\n"))
    data <- getData(sdf, colnames(sdf), dropUnusedLevels = FALSE, omittedLevels = FALSE)
    write.csv(data, file=file.path(exportPath, paste0(cntry, ".csv")), na="", row.names = FALSE)
    cat(paste(cntry, "completed.\n"))

#get the full country name to aide the user, so they won't have to track them down.
#cntryCode should be the 3 character country code vector defined in the data filename scheme (e.g., usa = United States, swe = Sweden)
#if a match is not found, this funtion will return a character value indicating it is unknown '(unknown) CountryCode: xxx'
get_ePIRLSCountryName <- function(countryCode){
  cntryCodeDF <- data.frame(
    cntryCode = c("aad", "adu", "are",
                  "irl", "isr", "ita",
                  "sgp", "svn", "swe",
    cntryName = c("Abu Dhabi, UAE", "Dubai, UAE", "United Arab Emirates",
                  "Ireland", "Israel", "Italy",
                  "Norway (5th Grade)",
                  "Singapore", "Slovenia", "Sweden",
                  "Chinese Taipei",
                  "United States"),
    stringsAsFactors = FALSE) #be sure to not create any factors::factors not needed at all
  lookupNames <- vector(mode = "character", length = length(countryCode))
  for(i in 1:length(countryCode)){
    testName <- cntryCodeDF[cntryCodeDF$cntryCode==countryCode[i], "cntryName"]
    if(length(testName)==0){ #test if no value found
      testName <- paste("(unknown) CountryCode:", countryCode[i])
    lookupNames[i] <- testName

#perform any label modifications here.  these should not be needed but we found an issue with the read_sav
#not correclty applying the labels to these specific variables
ePIRLS_ValueLabelCorrection <- function(fileFormat, yrCode){
  if(tolower(yrCode)=="e1"){ #for 2016 only
    valLbl <- fileFormat$labelValues[tolower(fileFormat$variableName)=="en11mtiml"] #the value label we are going to model after (correctly applied)
      varPattern <- "EN11(M|R|B|Z|T)TIM(L|S)"
      fileFormat$labelValues[grepl(varPattern, fileFormat$variableName, ignore.case = TRUE)] <- valLbl

#builds the PIRLS dataList object
build_ePIRLS_dataList <- function(stuLaf, stuFF, schLaf, schFF, tchLaf, tchFF){
  dataList <- list()
  #build the list hierarchical based on the order in which the data levels would be merged in getData
  dataList[["Student"]] <- dataListItem(lafObject = stuLaf,
                                        fileFormat = stuFF,
                                        levelLabel = "Student",
                                        forceMerge = TRUE,
                                        parentMergeLevels = NULL,
                                        parentMergeVars = NULL,
                                        mergeVars = NULL,
                                        ignoreVars = NULL,
                                        isDimLevel = FALSE)
  dataList[["School"]] <- dataListItem(lafObject = schLaf,
                                       fileFormat = schFF,
                                       levelLabel = "School",
                                       forceMerge = FALSE,
                                       parentMergeLevels = c("Student", "Student"),
                                       parentMergeVars = c("idcntry", "idschool"),
                                       mergeVars = c("idcntry", "idschool"),
                                       ignoreVars = names(schLaf)[names(schLaf) %in% names(stuLaf)],
                                       isDimLevel = FALSE)
  dataList[["Teacher"]] <- dataListItem(lafObject = tchLaf,
                                        fileFormat = tchFF,
                                        levelLabel = "Teacher",
                                        forceMerge = FALSE,
                                        parentMergeLevels = c("Student", "Student"),
                                        parentMergeVars = c("idcntry", "idstud"),
                                        mergeVars = c("idcntry", "idstud"),
                                        ignoreVars = names(tchLaf)[names(tchLaf) %in% names(stuLaf)],
                                        isDimLevel = TRUE)

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EdSurvey documentation built on May 2, 2019, 7:30 a.m.