Information about the libraries, environment, sources used and their execution is reported. Aditional information is provided within section tabs. Navigating through the report is also possible through the table of contents. Tables reported, can be dynamically filtered, searched ordered and exported into various formats.

Environment

R version

## [1] "R version 4.0.5 (2021-03-31)"

Libraries initialisation

## Loading required package: magrittr
## Loading required package: DT
## Loading required package: text2vec
## Loading required package: stringdist
## 
## Attaching package: 'stringdist'
## The following object is masked from 'package:magrittr':
## 
##     extract
## Loading required package: parallel
## Loading required package: stopwords

../000.core/00.01.libraries.R completed in 1.69 seconds

sourceTimeNeeded <- c( sourceTimeNeeded, timeNeeded)


source.starting.time <- proc.time()[3]


## Base functions

# EPAS-DS
# @authors ds@eworx.gr
repository <- "/data/generic/"

getSourcePath <- function(filename, baseFolder = repository){
    return(paste(baseFolder, filename, sep = ""))
}

readData <- function(filename, colClasses = c(), baseFolder = repository, header = TRUE, sep = "\t", encoding = "UTF-8", stringsAsFactors = TRUE, na.strings = c("", "NULL"), verbose = FALSE){
    if(length(colClasses) == 0)
        return (data.table::fread(input = getSourcePath(filename, baseFolder), header = header, sep = sep, encoding = encoding, stringsAsFactors = stringsAsFactors, verbose = verbose, showProgress = TRUE, na.strings = na.strings ) )
    return (data.table::fread(input = getSourcePath(filename, baseFolder), colClasses = colClasses, header = header, sep = sep, encoding = encoding, verbose = verbose, showProgress = TRUE,   na.strings = na.strings )  )
}

#rds for small disk space & fst for fast load
saveBinary <- function(data, filename = filename, baseFolder = repository, format = "rds"){
    fileName <- getSourcePath(filename, baseFolder)
    dir.create(dirname(fileName), recursive = TRUE, showWarnings = FALSE)
    if(format == "rds") saveRDS(data, fileName)
    if(format == "fst") fst::write_fst(data, fileName)
}

#alternative for rough read write operations
saveRDS_ <- function(object, file){
  dir.create(dirname(file), recursive = TRUE, showWarnings = FALSE)
  saveRDS(object, file)
}

#rds for small disk space & fst for fast load
loadBinary <- function(filename, baseFolder = repository, format = "rds", as.data.table = TRUE){
  if(format == "rds"){return(readRDS(getSourcePath(filename, baseFolder)))}
  if(format == "fst"){return(fst::read_fst(getSourcePath(filename, baseFolder), as.data.table = as.data.table))}
}

rowColumns <- function(data){
    return(paste( format(nrow(data),  big.mark=","), "Rows X ", ncol(data), "Columns"))
}

publishIncludeCss <- function(){
    sourceFile <- "/data/jobs/wp41.analysis/000.core/include.css"
    destinatinoFile <- "/data/tmpfs/results/include.css"
    if (!file.exists(destinatinoFile)) {
        return (file.copy(sourceFile, destinatinoFile))
    }else{
        return(TRUE);
    }

}
#as the mountstorage is on memory make sure the asset include.css is there.

summariseTable <- function(data){
    return(data.frame(unclass(summary(data)), check.names = FALSE, stringsAsFactors = FALSE))
    #return(do.call(cbind, lapply(data, summary)))
}

factoriseCharacterColumns <- function(data){
    for(name in names(data)){
        if( class(data[[name]]) =="character"){
            data[[name]] <- as.factor(data[[name]])
        }
    }
    return(data) 
}

############################
# https://rstudio.github.io/DT/010-style.html
#https://rpubs.com/marschmi/RMarkdown

capitalise <- function(x) paste0(toupper(substring(x, 1, 1)), substring(x, 2, nchar(x)))


styliseDTNumericalColumn <- function(data, result, columnName, color, columnsName_original ){

    if(columnName%in% columnsName_original){
        result <- result %>%   formatStyle(
            columnName,
            background = styleColorBar(data[[columnName]], color),
            backgroundSize = '100% 90%',
            backgroundRepeat = 'no-repeat',
            backgroundPosition = 'center'
        )
    }
    return(result)

}


reportTabularData <- function(data, anonymize=TRUE){

    if(anonymize)return()

    columnsName <- names(data)
    columnsName <- lapply(columnsName, capitalise)
    columnsName_original <- names(data)

    result <-
        DT::datatable(
            data,
            class = 'cell-border stripe',
            filter = 'top',
            rownames = FALSE,
            colnames = columnsName,
            extensions = 'Buttons',
            options = list(
                pageLength = 20,
                columnDefs = list(list(className = 'dt-left', targets = "_all")),
                dom = 'Bfrtip',
                buttons = c('copy', 'csv', 'excel', 'pdf'),
                searchHighlight = TRUE,
                initComplete = JS(
                    "function(settings, json) {",
                        "$(this.api().table().header()).css({'border': '1px solid'});",
                    "}"
                )
            )

        )


    result <- styliseDTNumericalColumn(data,result, "Count", 'steelblue', columnsName_original)
    result <- styliseDTNumericalColumn(data,result, "sourceTimeNeeded", '#808080', columnsName_original)
    result <- styliseDTNumericalColumn(data,result, "timeNeeded", '#808080', columnsName_original)
    #result <- styliseDTNumericalColumn(data,result, "percentMatch", '#5fba7d', columnsName_original)
    result <- styliseDTNumericalColumn(data,result, "percentMatch", '#4682b4', columnsName_original)

 
 

    return(result)
}

fonts <- list(
 sans = "DejaVu Serif",
  mono = "DejaVu Serif",
  `Times New Roman` = "DejaVu Serif"
)

#read_xml_to_list <- function(filepath, is.gz = FALSE){
#   if(is.gz){  
#       temp_data <- paste0(repository, "data/delete.me")
#       result <- xmlToList(xmlParse(gunzip(filepath, destname = temp_data, remove =FALSE)))
#       Sys.chmod(file.path(temp_data), "777", use_umask = FALSE)
#       unlink(temp_data)
#       result
#   }else{
#       xmlToList(xmlParse(filepath))
#   }
#}

#transpose_list_to_dt <- function(data_list){
#  dt <- t(as.data.table(data_list))
#  dt <- as.data.table(dt)
#  dt[, (names(dt)) := lapply(.SD, unlist), .SDcols = 1:ncol(dt)]
#  dt[, (names(dt)) := lapply(.SD, unlist), .SDcols = 1:ncol(dt)]
#  names(dt) <- names(data_list[[1]])
#  dt
#}

cleansingCorpus <- function(
    htmlString, rem.html =TRUE, rem.http = TRUE, rem.newline = TRUE,
    rem.nonalphanum = TRUE, rem.longwords = TRUE, rem.space = TRUE, 
    tolower = TRUE, add.space.to.numbers = TRUE, rem.country.begin = FALSE,
    rem.nonalphanum.begin = FALSE, rem.space.begin = FALSE
){
  if(rem.html){text <- gsub("<.*?>", " ", htmlString)} # removing html commands
  if(rem.http){text <- gsub(" ?(f|ht)tp(s?)://(.*)[.][a-z]+", " ", text)} #removing http destinations
  if(rem.newline){text <- gsub("[\r\n\t]", " ", text)} 
  if(rem.nonalphanum){text <- gsub("[^[:alpha:]]", " ", text)} #removing non-alphanumeric
  if(rem.longwords){text <- gsub("\\w{35,}", " ", text)} ##Removing words with more than 30 letters
  if(rem.space){text <- gsub("\\s+", " ", text)}  #removing excess space 
  if(tolower){text <- tolower(text)}
  if(add.space.to.numbers){    #add space between number and letters
    text <- gsub("([0-9])([[:alpha:]])", "\\1 \\2", text)
    text <- gsub("([[:alpha:]]|[.])([0-9])", "\\1 \\2", text)
  }
  if(rem.space.begin){text <- gsub("^[[:space:]]*", "", text)} 
  if(rem.country.begin){text <- gsub("^EU", "", text)} #remove country codes from the beginning of the text
  if(rem.nonalphanum.begin){text <- gsub("^[?–-]*", "", text)} #remove special characters identified in the beginning of text
  if(rem.space.begin){text <- gsub("^[[:space:]]*", "", text)}
  trimws(text)
}
#This function removes dates that are "relics" from the xml parsing
removeDates <- function(text){
  days <-  "(Sunday,|Monday,|Tuesday,|Wednesday,|Thursday,|Friday,|Saturday,)"
  months <- "(January|February|March|April|May|June|July|August|September|October|November|December|Months)"
  date_form1 <- paste(days, months, "([0-9]|[0-9][0-9]), [0-9][0-9][0-9][0-9]")
  date_form2 <- "\\?[0-9][0-9][0-9][0-9]"
  text <- gsub(date_form1, " ", text)
  gsub(date_form2, " ", text)
}

xmlToDataTable <- function(xmlData, itemNames){
  itemList <- lapply(itemNames,
    function(x){
      xml_text(xml_find_all(xmlData, paste0(".//item/", x)))
    }
  )
  names(itemList) <- xmlItems
  as.data.table(itemList)
}

cleanCorpusHtml <- function(text){
  unlist(lapply(text, function(x){
    if(nchar(x) > 0){
        # because nodes were starting with tag keywords in li, we relocate at the end so the information remains and the description 
        # starts with the main content
        html <- gsub(">","> ", x) # add spaces after html tags so these aren't concatenated 
        xml <- read_xml(html, as_html = TRUE)
        lis <- xml_find_all(xml, ".//li")
        xml_remove(lis)
        text <- paste( paste(xml_text(xml), collapse ="") , paste(xml_text(lis) , collapse =""), collapse ="")
        text <- gsub("\\s+"," ",  text)

    }else {""}
  }))
}

#Split equally a vector into chunks of number n_chunks
equal_split <- function(vct, n_chunks) {
  lim <- length(vct)
  fstep <- lim%/%n_chunks
  idx_list <- list()
  for(i in seq(n_chunks - 1)){
    idx_list[[i]] <- vct[((i-1)*fstep + 1):(i*(fstep))]
  }
  idx_list[[n_chunks]] <- vct[((n_chunks - 1)*fstep + 1):(lim)]
  return(idx_list)
}

#Function that takes a vector, and returns thresholded first 10 sorted indexes
getThresholdOrderRwmd <- function(vct, idVec, threshold = 1e-6, numHead = 10){
    vct <- ifelse(vct > threshold, vct, Inf)
    indexVec <- head(order(vct), numHead)
    idVec[indexVec]
}


#Function to read xml nodes in description
maintainElements <- function(nodes, elementType = "a", attribute = "href"){
    xml_attr(xml_find_all(nodes, paste0(".//", elementType)), attribute)
}

#Function to add results to datatable
elementsToDataTable <- function(result, elementType){
    if(length(result) > 0)
        data.table(elementType = elementType, attributeValue = result)
    else
        data.table()
}

#Function to retrieve urls from text
keepHtmlElements <- function(feedItem){
    nodes <- read_xml(paste0("<div>",  feedItem, "</div>"), as_html = TRUE)
    rbind(
        elementsToDataTable(maintainElements(nodes, "a", "href"), "link"),
        elementsToDataTable(maintainElements(nodes, "img", "src"), "image"),
        elementsToDataTable(maintainElements(nodes, "img-src", "src"), "image")
        #All "img-src" are NA
    )
}

#retrieve list of parameters in a http request query
getQueryParams <- function(url){
  query <- httr::parse_url(url)$query
  queryValues <- unlist(query)
  queryNames <- names(query)
  dat <- data.table(varName = queryNames, value = queryValues)
  dat[queryValues != ""]
}

keepCountryName <- function(string){
  string <- gsub(".*_", "", string)
  gsub("\\..*", "", string)
}

keepNTokens <- function(string, num){
    tokenList <- strsplit(string, split = " ")
    sapply(tokenList, function(tokens){
        tokens <- sort(tokens)
        tokensShift <- shift(tokens, -num, fill = FALSE)
        paste(tokens[tokens != tokensShift], collapse = " ")    
    })
}

findTFIDF <- function(corpus, stopwords, normalize = "double", min_char = 1) {

  tokensList <- strsplit(corpus[, text], " ")
  names(tokensList) <- corpus[, code]

  tokensDT <- lapply(tokensList, as.data.table) %>% 
    rbindlist(idcol = TRUE) %>%
        setnames(c("class", "term"))

  tokensDT <- tokensDT[!term %in% stopwords][nchar(term) > min_char]
    #inverse document frequency smooth  
    idfDT <- tokensDT[!duplicated(tokensDT)][, .(docFreq = .N), by = "term"]
    idfDT[, idf :=  log(length(unique(tokensDT$class)) / (docFreq + 1)) + 1]    

  tfDT <- tokensDT[, .(term_count = .N), by = c("class", "term")]

  if(normalize == "double")tfDT[, tf := 0.5 + 0.5 * term_count / max(term_count), by = "class"]
  if(normalize == "log")tfDT[, tf := log(1 +term_count)]
  
  merge(tfDT, idfDT, on = "term")[, tfIdf := tf*idf ][, .(term, class, tfIdf)]
    
}

tidyJsonData <- function(jsonList){
    if(length(jsonList) == 0)return(NULL)
    unlistOccupations <- jsonList %>% unlist
    codesMaleFemale <- names(unlistOccupations)
    epasMapping <- data.table(unlistOccupations)
    epasMapping[ , code := gsub("\\.[[:alpha:]]$", "", codesMaleFemale)]
    uniqueMappingBoolean <- epasMapping[ , unlistOccupations != c(unlistOccupations[-1], F), by = code]$V1
    codesEpasDB <- epasMapping[uniqueMappingBoolean]
    names(codesEpasDB) <- c("title", "code")
    codesEpasDB[ , title := cleansingCorpus(title)]
}


###########################################################################################################

Libraries version

Session info

## R version 4.0.5 (2021-03-31)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 20.04.2 LTS
## 
## Matrix products: default
## BLAS/LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.8.so
## 
## locale:
##  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
##  [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
##  [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=C             
##  [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
##  [9] LC_ADDRESS=C               LC_TELEPHONE=C            
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       
## 
## attached base packages:
## [1] parallel  stats     graphics  grDevices utils     datasets  methods  
## [8] base     
## 
## other attached packages:
## [1] stopwords_2.2      stringdist_0.9.6.3 text2vec_0.6       DT_0.18           
## [5] magrittr_2.0.1     rmarkdown_2.8      data.table_1.14.0 
## 
## loaded via a namespace (and not attached):
##  [1] Rcpp_1.0.6           mlapi_0.1.0          knitr_1.33          
##  [4] RhpcBLASctl_0.20-137 float_0.2-4          lattice_0.20-41     
##  [7] R6_2.5.0             rlang_0.4.11         lgr_0.4.2           
## [10] stringr_1.4.0        highr_0.9            tools_4.0.5         
## [13] grid_4.0.5           xfun_0.23            jquerylib_0.1.4     
## [16] htmltools_0.5.1.1    yaml_2.2.1           digest_0.6.27       
## [19] rsparse_0.4.0        crayon_1.4.1         Matrix_1.3-2        
## [22] vctrs_0.3.8          sass_0.4.0           htmlwidgets_1.5.3   
## [25] evaluate_0.14        stringi_1.6.2        compiler_4.0.5      
## [28] bslib_0.2.5          jsonlite_1.7.2

../000.core/00.02.base.functions.R completed in 0.12 seconds

Loading data.

Loading EPAS word tokens.

  • Type of data: list.

  • Dimensions: ****.

  • Column Names: ar, bg, cs, da, de, el, en-us, en, es, et, fi, fr, ga, hr, hu, is, it, lt, lv, mt, nl, no, pl, pt, ro, sk, sl, sv.

Loading multilingual EPAS vocabulary.

  • Type of data: list.

  • Dimensions: ****.

  • Column Names: ar, bg, cs, da, de, el, en-us, en, es, et, fi, fr, ga, hr, hu, is, it, lt, lv, mt, nl, no, pl, pt, ro, sk, sl, sv.

Loading frequency table.

  • Type of data: data.table, data.frame.

  • Dimensions: 1053923, 4.

  • Column Names: id, locale, label, index.

  • Type of data: data.table, data.frame.

  • Dimensions: 2741, 2.

  • Column Names: code, count.

String Distance Identifier

This process maps words retrieved from free-text to vocabulary terms of the EPAS backend. The Optimal String Alignment distance is used that allows more types of edit operations. These computations are expensive so a low level interface to C with multithreading is used.

Preparing vocabulary

  • Mapping free-text to EPAS vocabulary.
## Warning: 'stopwords(language = "el")' is deprecated.
## Use 'stopwords(language = "el", source = "misc")' instead.
## See help("Deprecated")

Split the matches in multiple chunks to reduce space complexity.

Use the matches to find the top 10 recommended ESCO occupations for each free-text occupation entry.

Join the free-text and the predicted occupations.

Add the english occupation title next to each predicted occupation.

Add the ISCO level 3 label next to each predicted occupation.

Group the predicted occupations by ISCO lvl 3, sum the weights and keep the ISCO lvl 3 labels with the highest weight sum in a temp variable.

Then, add a new column to the predictions that will contain the predicted ISCO level 3.

Finally, keep the occupations that belong to the ISCO lvl 3 with the highest sum and from those occupations, keep the one with the highest weight inside that ISCO lvl.

  • Percentage of matching: 0.8982781.

03.00.process.data.R completed in 224.46 seconds

Computation metrics

Computational report

Completed in 270.38 seconds.

Subpart metrics

## NULL

End of report

Reports index

Eworx S.A. DSENSE report for EPAS.

– end of report –