Companies_often_give_the _wrong_name_to_job_openings.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The aim of this project is to take the data in https://www.kaggle.com/sl6149/data-scientist-job-market-in-the-us?select=fulltimeCHI.csv about IT related job hiring in 2018 in the US and get some insight on the job market. \n",
    "\n",
    "For the moment, the most important result I found out is that companies give the wrong name to machine learning engineering openings and software engineering openings around 40% of the time, whereas data scientist and data analyst openings have the wrong name 20% of the time. These numbers imply that companies may fail to attract good candidates because an applicant has up to a 30% probability to apply for the wrong job.\n",
    "\n",
    "To give a better idea on how I obtained such values, I identified the ideal employee type for each job opening in the dataset by finding the most important tasks from the job description with non-negative matrix factorization techniques. I then compared the ideal employee type with the job title for each opening, and found out that the two do not often match\n",
    "\n",
    "The complete analysis with all the passages is performed below. Let's start by loading the necessary R packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loading tidyverse: ggplot2\n",
      "Loading tidyverse: tibble\n",
      "Loading tidyverse: tidyr\n",
      "Loading tidyverse: readr\n",
      "Loading tidyverse: purrr\n",
      "Loading tidyverse: dplyr\n",
      "Conflicts with tidy packages ---------------------------------------------------\n",
      "filter(): dplyr, stats\n",
      "lag():    dplyr, stats\n",
      "\n",
      "Attaching package: ‘data.table’\n",
      "\n",
      "The following objects are masked from ‘package:dplyr’:\n",
      "\n",
      "    between, first, last\n",
      "\n",
      "The following object is masked from ‘package:purrr’:\n",
      "\n",
      "    transpose\n",
      "\n",
      "Loading required package: NLP\n",
      "\n",
      "Attaching package: ‘NLP’\n",
      "\n",
      "The following object is masked from ‘package:ggplot2’:\n",
      "\n",
      "    annotate\n",
      "\n",
      "Loading required package: pkgmaker\n",
      "Loading required package: registry\n",
      "\n",
      "Attaching package: ‘pkgmaker’\n",
      "\n",
      "The following object is masked from ‘package:base’:\n",
      "\n",
      "    isNamespaceLoaded\n",
      "\n",
      "Loading required package: rngtools\n",
      "Loading required package: cluster\n",
      "NMF - BioConductor layer [NO: missing Biobase] | Shared memory capabilities [NO: bigmemory] | Cores 3/4\n",
      "  To enable the Bioconductor layer, try: install.extras('\n",
      "NMF\n",
      "') [with Bioconductor repository enabled]\n",
      "  To enable shared memory capabilities, try: install.extras('\n",
      "NMF\n",
      "')\n",
      "\n",
      "Attaching package: ‘gplots’\n",
      "\n",
      "The following object is masked from ‘package:stats’:\n",
      "\n",
      "    lowess\n",
      "\n"
     ]
    }
   ],
   "source": [
    "library(tidyverse)\n",
    "library(data.table)\n",
    "library(tm)\n",
    "library(slam)\n",
    "library(stringr)\n",
    "library(NMF)\n",
    "library(gplots)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As second step, let's import the data using fread, and then let's check a bit the structure of the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classes ‘data.table’ and 'data.frame':\t6964 obs. of  5 variables:\n",
      " $ position   : chr  \"Development Director\" \"An Ostentatiously-Excitable Principal Research Assistant to Chief Scientist\" \"Data Scientist\" \"Data Analyst\" ...\n",
      " $ company    : chr  \"ALS TDI\" \"The Hexagon Lavish\" \"Xpert Staffing\" \"Operation HOPE\" ...\n",
      " $ description: chr  \"Development Director\\nALS Therapy Development Institute has an immediate opening for Development Directors. Rep\"| __truncated__ \"Job Description\\n\\n\\\"\\\"The road that leads to accomplishment is narrow.\\\"\\\" -Desmond\\nOstentatious is a strong \"| __truncated__ \"Growing company located in the Atlanta, GA area is currently looking to add a Data Scientist to their team. The\"| __truncated__ \"DEPARTMENT: Program OperationsPOSITION LOCATIONS: Atlanta, GA.ACCOUNTABILITY:  President of Program OperationsF\"| __truncated__ ...\n",
      " $ reviews    : int  NA NA NA 44 550 385 385 899 3343 4494 ...\n",
      " $ location   : chr  \"Atlanta, GA 30301 \" \"Atlanta, GA\" \"Atlanta, GA\" \"Atlanta, GA 30303 \" ...\n",
      " - attr(*, \".internal.selfref\")=<externalptr> \n"
     ]
    }
   ],
   "source": [
    "# import data and inspect dataset\n",
    "job_descriptions <- fread('alldata.csv')\n",
    "str(job_descriptions)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This crude inspection reveals that the data are quite messy. Let's start to fix two easy details <br>\n",
    "1. the same city appears with different names because postal codes are sometimes included <br>\n",
    "2. States appear within the location and not as a separate column. <br>\n",
    "\n",
    "To solve this issue, I first split the string a few times to isolate City and State information, and throw away postal codes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classes ‘data.table’ and 'data.frame':\t6964 obs. of  6 variables:\n",
      " $ position   : chr  \"Development Director\" \"An Ostentatiously-Excitable Principal Research Assistant to Chief Scientist\" \"Data Scientist\" \"Data Analyst\" ...\n",
      " $ company    : chr  \"ALS TDI\" \"The Hexagon Lavish\" \"Xpert Staffing\" \"Operation HOPE\" ...\n",
      " $ description: chr  \"Development Director\\nALS Therapy Development Institute has an immediate opening for Development Directors. Rep\"| __truncated__ \"Job Description\\n\\n\\\"\\\"The road that leads to accomplishment is narrow.\\\"\\\" -Desmond\\nOstentatious is a strong \"| __truncated__ \"Growing company located in the Atlanta, GA area is currently looking to add a Data Scientist to their team. The\"| __truncated__ \"DEPARTMENT: Program OperationsPOSITION LOCATIONS: Atlanta, GA.ACCOUNTABILITY:  President of Program OperationsF\"| __truncated__ ...\n",
      " $ reviews    : int  NA NA NA 44 550 385 385 899 3343 4494 ...\n",
      " $ city       : chr  \"Atlanta\" \"Atlanta\" \"Atlanta\" \"Atlanta\" ...\n",
      " $ state      : chr  \"GA\" \"GA\" \"GA\" \"GA\" ...\n"
     ]
    }
   ],
   "source": [
    "# split location into city and state and throw away postal codes\n",
    "job_descriptions <- job_descriptions %>%\n",
    "    separate(location, c('city', 'state'), ', ', fill = \"left\") %>%\n",
    "    separate(state, c('state'), ' ', extra = 'drop') \n",
    "\n",
    "# inspect dataset\n",
    "str(job_descriptions)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A second feature that is immediately clear from the structure of the dataframe with explicit locations is that the job titles are pretty heterogeneous. To get a better sense of what it's in there, I will attempt use the tm package to count the frequency of ngrams made by 2 words. Such choice is justified because common positions for data-related jobs are combination of two words (e.g. \"data science\", \"data engineer\", \"data analyst\", \"machine learning\") "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<<TermDocumentMatrix (terms: 44, documents: 6964)>>\n",
       "Non-/sparse entries: 5470/300946\n",
       "Sparsity           : 98%\n",
       "Maximal term length: 24\n",
       "Weighting          : term frequency (tf)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {},
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA0gAAANICAYAAAD958/bAAAEGWlDQ1BrQ0dDb2xvclNwYWNl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\nEAEREAEREAEREIGcJ/AfWVSzmDIGD9IAAAAASUVORK5CYII=",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# preprocess job position column to remove numbers, punctuation, capital letters and stopwords\n",
    "job_descriptions <- job_descriptions %>%\n",
    "    mutate(position = tolower(position)) %>%\n",
    "    mutate(position = removeNumbers(position)) %>%\n",
    "    mutate(position = removePunctuation(position)) %>% \n",
    "    mutate(position = removeWords(position, stopwords(\"english\")))\n",
    "\n",
    "# create corpus from the position descriptions \n",
    "corpus_positions <- VCorpus(VectorSource(job_descriptions$position))\n",
    "\n",
    "# create function that computes ngrams of 2 words\n",
    "NLP_tokenizer <- function(x) {\n",
    "  unlist(lapply(ngrams(words(x), 2), paste, collapse = \"_\"), use.names = FALSE)\n",
    "}\n",
    "\n",
    "# create term-document-matrix for the job titles and create dataframe with frequencies of bigrams\n",
    "TDM_positions <- TermDocumentMatrix(corpus_positions, control = list(tokenize = NLP_tokenizer))\n",
    "TDM_positions <- removeSparseTerms(TDM_positions, 0.995)\n",
    "TDM_positions                          \n",
    "                                    \n",
    "# extract frequency of bigrams in the whole corpus (with plot)\n",
    "frequency_of_position_names <- data.frame(frequency = row_sums(as.matrix(TDM_positions)))\n",
    "frequency_of_position_names <- rownames_to_column(frequency_of_position_names, \"position_name\") %>%\n",
    "    arrange(desc(frequency))\n",
    "\n",
    "frequency_of_position_names %>%\n",
    "    ggplot(aes(x = reorder(position_name, frequency), y = frequency)) +\n",
    "    geom_col() + \n",
    "    coord_flip()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This crude analysis of bigram frequencies allows me to identify the bigrams associated to the  positions that interests me most (and possible common name variants):\n",
    "1. \"data science\", \" data scientis\"\n",
    "2. \"software engineer\", \"software development\", \"development engineer\"\n",
    "3. \"data engineer\"\n",
    "4. \"data analyst\", \"quantitative analyst\", \"data analytics\", \"data analysis\".\n",
    "5. \"machine learning engineer\", \"machine learning scientist\", \"engineer machine learning\" (These trigrams have been inferred from bigrams that relate to them)\n",
    "\n",
    "\n",
    "This allows me to give an approximate label to the job titles according to these braod categories by matchinging these frequent strings (identifiers) with job labels."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# define indentifiers\n",
    "data_science_identifiers <- \"\\\\bdata science\\\\b|\\\\bdata scientist\\\\b\"\n",
    "software_engineer_identifiers <- \"\\\\bsoftware engineer\\\\b|\\\\bsoftware development\\\\b|\\\\bdevelopment engineer\\\\b\"\n",
    "data_engineer_identifiers <- \"\\\\bdata engineer\\\\b\"\n",
    "data_analyst_identifiers <- \"\\\\bdata analyst\\\\b|\\\\bquantitative analyst\\\\b|\\\\bdata analytics\\\\b|\\\\bdata analysis\\\\b\"\n",
    "machine_learning_engineer_identifiers <- \"\\\\bmachine learning engineer\\\\b|\\\\bmachine learning scientist\\\\b|\\\\bengineer machine learning\\\\b\"\n",
    "\n",
    "# build list of job labels associated with the corresponding string identifiers\n",
    "labels = c(\"data_science\", \"software_engineer\", \"data_engineer\", \"data_analyst\", \"machine_learning_engineer\")\n",
    "identifiers <- c(data_science_identifiers, software_engineer_identifiers, data_engineer_identifiers, data_analyst_identifiers, machine_learning_engineer_identifiers)\n",
    "labeling_via_identifiers <- list(labels, identifiers)\n",
    "\n",
    "# define function that combines together identifiers from different job labels\n",
    "exclude_identifier <- function(labeling_conditions = labeling_via_identifiers, index_to_exclude = NULL){\n",
    "    if(is.null(index_to_exclude))\n",
    "        to_exclude <- paste(labeling_conditions[[2]][], collapse = \"|\")\n",
    "    else\n",
    "        to_exclude <- paste(labeling_conditions[[2]][-index_to_exclude], collapse = \"|\")\n",
    "    return(to_exclude)\n",
    "}\n",
    "\n",
    "# define function that identify strings with at least one label from the ideintifiers\n",
    "# of one job type and no identifiers from the other job types\n",
    "find_position_with_unique_identifier_class <- function(position, index_of_interest, labeling_conditions = labeling_via_identifiers){\n",
    "    possesses_desired_identifiers <- str_detect(job_descriptions$position, labeling_conditions[[2]][index_of_interest])\n",
    "    possesses_undesired_identifiers <- str_detect(job_descriptions$position, exclude_identifier(labeling_conditions = labeling_via_identifiers, index_of_interest))\n",
    "    return(possesses_desired_identifiers & (!possesses_undesired_identifiers))\n",
    "}\n",
    "\n",
    "\n",
    "# assign label to job_descriptions depending on the identifier found in the position column\n",
    "job_descriptions <- job_descriptions %>%\n",
    "    mutate(position_label_from_bigrams = case_when(\n",
    "        find_position_with_unique_identifier_class(position, 1) == TRUE ~ \"data_scientist\",\n",
    "        find_position_with_unique_identifier_class(position, 2) == TRUE ~ \"software_engineer\",\n",
    "        find_position_with_unique_identifier_class(position, 3) == TRUE ~ \"data_engineer\",\n",
    "        find_position_with_unique_identifier_class(position, 4) == TRUE ~ \"data_analyst\",\n",
    "        find_position_with_unique_identifier_class(position, 5) == TRUE ~ \"machine_learning_engineer\",\n",
    "        str_detect(position, exclude_identifier()) == FALSE ~ \"other\",\n",
    "        TRUE ~ \"mixed\"\n",
    "    ))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now the final is to prepare the description field so that I can identify the building patterns of the job descriptions with non-negative matrix factorization. The idea is to identify if there is a match between the job title (defined as the label given to with 2-gram analysis of the job title) and the most important job task in the job description (identified as the most important pattern from the nonegative matrix factorization of the job description). To this end, let's process the job descriptions by removing stopwords, punctuation, numbers and by stemming the words in the job description."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# prepare job descriptions for NLP analysis\n",
    "job_descriptions <- job_descriptions %>%\n",
    "    mutate(description = tolower(description)) %>%\n",
    "    mutate(description = removePunctuation(description)) %>% \n",
    "    mutate(description = removeNumbers(description)) %>%\n",
    "    mutate(description = removeWords(description, stopwords(\"english\"))) %>%\n",
    "    mutate(description = stemDocument(description, language = \"english\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This formally concludes data preparation. It's time to make some exploratory analysis.\n",
    "\n",
    "The first thing I want to inspect is job demands in various states which does not correspond to tha classification \"other\". This should give an indicative information where one type of job is most desired.\n",
    "\n",
    "The information I want to gather is the following\n",
    "1. Where are the majority of jobs located? ANSWER: California with New York lagging behind.\n",
    "2. Which job position is most desired in every state? ANSWER: Nationwide, Data science leads as the most requested job. Second place goes to software engineer.\n",
    "3. Is the predominance of data science openings also a local phenomenon? ANSWER: Almost. The only exceptions are Washington and Colorado where data science is only the second most common job position name. This allows to state that having an abundance of data science positions in every state is a common trend, but with some notable exceptions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA0gAAANICAYAAAD958/bAAAEGWlDQ1BrQ0dDb2xvclNwYWNl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\ngAABAgQIECBAgEC5Bf4ftRP6IDkTAvUAAAAASUVORK5CYII=",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA0gAAANICAYAAAD958/bAAAEGWlDQ1BrQ0dDb2xvclNwYWNl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\npWyTqT8AAAAASUVORK5CYII=",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Filter out jobs with label \"others\"\n",
    "job_descriptions_without_others <- job_descriptions %>%\n",
    "    filter(position_label_from_bigrams != \"other\")\n",
    "\n",
    "# aggregate data to count which jobs are in high demand in each state, both in absolute and relative terms\n",
    "job_descriptions_summary <- job_descriptions_without_others %>%\n",
    "    group_by(state, position_label_from_bigrams) %>%\n",
    "    summarize(jobs_by_position_in_state = n()) %>%\n",
    "    mutate(total_jobs_in_state = sum(jobs_by_position_in_state)) %>%\n",
    "    mutate(job_fraction_by_position_in_state = jobs_by_position_in_state / total_jobs_in_state)\n",
    "\n",
    "# plot total number of non-other jobs in each state\n",
    "where_jobs <- job_descriptions_summary %>%\n",
    "    ggplot(aes(x = reorder(state, total_jobs_in_state), y = total_jobs_in_state)) +\n",
    "    geom_col() +\n",
    "    coord_flip()                                          \n",
    "     \n",
    "# plot fraction of job type demands in each states\n",
    "which_jobs_by_state <- job_descriptions_summary %>%\n",
    "    ggplot(aes(x = state, fill = position_label_from_bigrams, y = job_fraction_by_position_in_state)) + \n",
    "    geom_col(position = \"dodge\")\n",
    "                                                     \n",
    "print(where_jobs)\n",
    "print(which_jobs_by_state)\n",
    "                                      \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I now want to use non-negative matrix factorization to see if the job titles matches the kind of employee that the company is truly looking forward. \n",
    "\n",
    "To perform this analysis I need to teke into account that data scientist openings are much mor common than other posistions. This means that non-negative matrix factorization can be biased toward data science positions. To avoid this effect, I will perform a stratified sampling of the data where each stratum corresponds to the non-other job labels I hae given with the ngram analysis. This approach has also the advantage of reducing the dataset size because the stratum with the smallest number of job descriptions will determine the sample dimension."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# A tibble: 6 x 2\n",
      "  position_label_from_bigrams total_jobs\n",
      "                        <chr>      <int>\n",
      "1                data_analyst        225\n",
      "2               data_engineer        159\n",
      "3              data_scientist       1356\n",
      "4   machine_learning_engineer        168\n",
      "5                       mixed         65\n",
      "6           software_engineer        397\n"
     ]
    }
   ],
   "source": [
    "# Check that data scientist positions seems to exceed other positions\n",
    "jobs_per_label <- job_descriptions_summary %>%\n",
    "    ungroup() %>%\n",
    "    group_by(position_label_from_bigrams) %>%\n",
    "    summarize(total_jobs = sum(jobs_by_position_in_state))\n",
    "print(jobs_per_label)\n",
    "\n",
    "# seeding for sampling reproducibility\n",
    "set.seed(1)\n",
    "\n",
    "# use stratified sample to undersample the original dataset of non-other jobs\n",
    "sample_number_per_strata = min(jobs_per_label$total_jobs)\n",
    "undersampled_job_descriptions_without_others <- job_descriptions_without_others %>%\n",
    "    group_by(position_label_from_bigrams) %>%\n",
    "    sample_n(sample_number_per_strata, replace = FALSE) %>%\n",
    "    ungroup()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It's now time to perform the non negative matrix factorization of this undersampled dataset. To this end, I first produce a Term Document Matrix from the job description. For simplicity, I do so by considering 1-grams and 2-grams only. This should be enough to identify programming languages (usually 1-grams) and most common 2-word combinations like \"team player\"."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create corpus from the position descriptions \n",
    "corpus_job_descriptions <- VCorpus(VectorSource(undersampled_job_descriptions_without_others$description))\n",
    "\n",
    "# overweite NLP_tokenizer to consider n-grams with n = 1,2 \n",
    "NLP_tokenizer <- function(x) {\n",
    "  unlist(lapply(ngrams(words(x), 1:2), paste, collapse = \"_\"), use.names = FALSE)\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<<TermDocumentMatrix (terms: 1157, documents: 390)>>\n",
       "Non-/sparse entries: 71858/379372\n",
       "Sparsity           : 84%\n",
       "Maximal term length: 24\n",
       "Weighting          : term frequency (tf)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# create term-document-matrix for job descriptions\n",
    "TDM_job_descriptions <- TermDocumentMatrix(corpus_job_descriptions, control = list(tokenize = NLP_tokenizer))\n",
    "TDM_job_descriptions <- removeSparseTerms(TDM_job_descriptions, 0.95)         \n",
    "\n",
    "# remove some words that do not bring anything to the analysis \n",
    "# this step comes because I have already performed nonegative factorization on this dataset, and I found out such uninformative words\n",
    "list_of_confusing_words <- c(\"will\", \"use\", \"need\")\n",
    "#as.matrix(TDM_job_descriptions)[\"will\", ]\n",
    "TDM_job_descriptions <- TDM_job_descriptions[!(rownames(TDM_job_descriptions) %in% list_of_confusing_words), ]\n",
    "TDM_job_descriptions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the corpus I can now perform the nonnegative matrix factorization of the job descriptions, and then inspect the fundamental linguistical patterns found by the algorithm. I will use 10 fundamental topics as it should allow to describe enough different topics to discriminate different job requirements."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# perform nonnegative matrix factorization\n",
    "decompostion_rank <- 10\n",
    "nmf_decomposition_job_descriptions <- nmf(as.matrix(TDM_job_descriptions), decompostion_rank, seed = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# extract the fundamental linguistic patterns identified by the non-negative matrix factoriaztion\n",
    "fundamental_patterns <- data.frame(basis(nmf_decomposition_job_descriptions))\n",
    "fundamental_patterns <- rownames_to_column(fundamental_patterns, \"word\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, let's have a look at the fundamental linguistic patterns to see if they identify some group of job tasks or interesting features of the job descriptions. I will make the identification by inspecting the 30 most important words in each one of the linguistic pattern identified by the nonenegative matrix factorization algorithm."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"investigate pattern X1\"\n",
      "     1      2        3        4      5         6         7        8     \n",
      "word \"data\" \"analyt\" \"experi\" \"busi\" \"statist\" \"analysi\" \"scienc\" \"work\"\n",
      "     9       10          11        12               13            14     \n",
      "word \"model\" \"scientist\" \"insight\" \"data_scientist\" \"data_scienc\" \"skill\"\n",
      "     15       16     17    18         19    20       21          \n",
      "word \"custom\" \"role\" \"sql\" \"communic\" \"set\" \"advanc\" \"understand\"\n",
      "     22             23        24     25       26     27     28         29      \n",
      "word \"data_analysi\" \"product\" \"tool\" \"analyz\" \"year\" \"larg\" \"data_set\" \"report\"\n",
      "     30       \n",
      "word \"present\"\n",
      "[1] \"investigate pattern X2\"\n",
      "     1       2        3              4        5       6      7          \n",
      "word \"learn\" \"machin\" \"machin_learn\" \"experi\" \"model\" \"data\" \"algorithm\"\n",
      "     8        9        10        11     12         13      14        15       \n",
      "word \"scienc\" \"comput\" \"problem\" \"deep\" \"research\" \"appli\" \"develop\" \"languag\"\n",
      "     16       17     18           19        20      21     22      23       \n",
      "word \"system\" \"work\" \"deep_learn\" \"predict\" \"relat\" \"year\" \"engin\" \"process\"\n",
      "     24         25       26        27              28        29      30       \n",
      "word \"techniqu\" \"python\" \"compani\" \"comput_scienc\" \"statist\" \"field\" \"product\"\n",
      "[1] \"investigate pattern X3\"\n",
      "     1      2        3       4      5     6            7           8         \n",
      "word \"data\" \"experi\" \"engin\" \"work\" \"big\" \"data_engin\" \"technolog\" \"big_data\"\n",
      "     9         10     11      12       13        14      15       16    \n",
      "word \"develop\" \"team\" \"build\" \"design\" \"pipelin\" \"solut\" \"analyt\" \"busi\"\n",
      "     17        18        19      20     21       22        23          24      \n",
      "word \"process\" \"support\" \"spark\" \"tool\" \"scienc\" \"databas\" \"implement\" \"system\"\n",
      "     25      26      27       28              29    30      \n",
      "word \"manag\" \"sourc\" \"python\" \"infrastructur\" \"sql\" \"hadoop\"\n",
      "[1] \"investigate pattern X4\"\n",
      "     1         2         3        4        5      6       7        8     \n",
      "word \"softwar\" \"develop\" \"design\" \"experi\" \"work\" \"engin\" \"system\" \"team\"\n",
      "     9        10        11     12          13     14        15      16      \n",
      "word \"requir\" \"product\" \"abil\" \"technolog\" \"test\" \"technic\" \"skill\" \"applic\"\n",
      "     17              18                19       20       21         22    \n",
      "word \"softwar_engin\" \"softwar_develop\" \"custom\" \"includ\" \"communic\" \"code\"\n",
      "     23         24       25       26        27           28        29     \n",
      "word \"knowledg\" \"strong\" \"servic\" \"perform\" \"understand\" \"program\" \"manag\"\n",
      "     30       \n",
      "word \"process\"\n",
      "[1] \"investigate pattern X5\"\n",
      "     1        2        3       4         5         6         7      8      \n",
      "word \"experi\" \"amazon\" \"engin\" \"languag\" \"softwar\" \"develop\" \"team\" \"learn\"\n",
      "     9        10             11       12        13       14       15     \n",
      "word \"machin\" \"machin_learn\" \"custom\" \"product\" \"qualif\" \"system\" \"build\"\n",
      "     16     17       18     19     20     21       22      23          24      \n",
      "word \"data\" \"comput\" \"work\" \"high\" \"year\" \"design\" \"solut\" \"scientist\" \"servic\"\n",
      "     25             26      27         28       29    30         \n",
      "word \"basic_qualif\" \"basic\" \"descript\" \"scienc\" \"job\" \"technolog\"\n",
      "[1] \"investigate pattern X6\"\n",
      "     1      2         3      4       5       6      7        8         9     \n",
      "word \"team\" \"product\" \"work\" \"build\" \"engin\" \"data\" \"experi\" \"compani\" \"help\"\n",
      "     10    11      12      13         14     15      16     17              \n",
      "word \"new\" \"learn\" \"peopl\" \"platform\" \"make\" \"world\" \"look\" \"data_scientist\"\n",
      "     18          19          20         21     22      23    24     25     \n",
      "word \"scientist\" \"technolog\" \"opportun\" \"grow\" \"everi\" \"way\" \"best\" \"scale\"\n",
      "     26       27     28     29     30       \n",
      "word \"market\" \"high\" \"code\" \"user\" \"environ\"\n",
      "[1] \"investigate pattern X7\"\n",
      "     1        2        3      4        5      6        7        8     \n",
      "word \"status\" \"employ\" \"data\" \"applic\" \"work\" \"gender\" \"disabl\" \"race\"\n",
      "     9          10       11        12       13      14        15        \n",
      "word \"opportun\" \"nation\" \"protect\" \"origin\" \"color\" \"veteran\" \"religion\"\n",
      "     16       17              18       19      20       21              22   \n",
      "word \"sexual\" \"sexual_orient\" \"orient\" \"equal\" \"inform\" \"nation_origin\" \"age\"\n",
      "     23      24      25       26               27             28      \n",
      "word \"ident\" \"innov\" \"analyt\" \"equal_opportun\" \"gender_ident\" \"regard\"\n",
      "     29        30               \n",
      "word \"qualifi\" \"opportun_employ\"\n",
      "[1] \"investigate pattern X8\"\n",
      "     1        2      3       4       5      6        7      8      9        \n",
      "word \"client\" \"busi\" \"manag\" \"model\" \"team\" \"analyt\" \"work\" \"abil\" \"project\"\n",
      "     10       11        12        13       14     15       16       17    \n",
      "word \"invest\" \"financi\" \"develop\" \"includ\" \"risk\" \"experi\" \"requir\" \"bank\"\n",
      "     18        19       20       21    22         23      24         25       \n",
      "word \"quantit\" \"servic\" \"market\" \"new\" \"research\" \"skill\" \"knowledg\" \"process\"\n",
      "     26       27     28       29         30      \n",
      "word \"corpor\" \"data\" \"strong\" \"communic\" \"global\"\n",
      "[1] \"investigate pattern X9\"\n",
      "     1      2          3        4      5       6        7        8       \n",
      "word \"data\" \"research\" \"requir\" \"work\" \"skill\" \"system\" \"experi\" \"health\"\n",
      "     9         10      11        12        13       14          15      \n",
      "word \"support\" \"posit\" \"program\" \"develop\" \"includ\" \"healthcar\" \"report\"\n",
      "     16       17      18        19       20     21      22    23       \n",
      "word \"inform\" \"relat\" \"analysi\" \"provid\" \"year\" \"manag\" \"new\" \"respons\"\n",
      "     24       25     26      27      28        29        30      \n",
      "word \"candid\" \"oper\" \"degre\" \"medic\" \"collect\" \"project\" \"divers\"\n",
      "[1] \"investigate pattern X10\"\n",
      "     1       2        3       4         5      6           7       8      \n",
      "word \"googl\" \"experi\" \"engin\" \"develop\" \"work\" \"technolog\" \"learn\" \"resum\"\n",
      "     9        10        11       12        13       14         15       16    \n",
      "word \"comput\" \"technic\" \"applic\" \"product\" \"custom\" \"opportun\" \"qualif\" \"team\"\n",
      "     17    18       19        20             21      22           23     \n",
      "word \"one\" \"machin\" \"employe\" \"machin_learn\" \"pleas\" \"regardless\" \"relat\"\n",
      "     24       25        26       27      28      29       30      \n",
      "word \"requir\" \"softwar\" \"disabl\" \"agenc\" \"equal\" \"employ\" \"status\"\n"
     ]
    }
   ],
   "source": [
    "for(i in 1:10){\n",
    "    # create a temporary vector containing the first 30 words of one linguistic pattern\n",
    "    pattern_to_inspect = paste(\"X\", as.character(i), sep = \"\")\n",
    "    temp <- fundamental_patterns %>%\n",
    "        arrange(desc(.[[pattern_to_inspect]])) %>%\n",
    "        head(30) %>%\n",
    "        select(word)    \n",
    "    \n",
    "    # print the first 30 words of one linguistic pattern\n",
    "    print(paste(\"investigate pattern\", pattern_to_inspect))\n",
    "    print(t(temp))\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, a direct inspection shows that\n",
    "1. pattern X1 describes a data analyst or data science position\n",
    "2. pattern X2 describes a machine learning engineer position or a data science position \n",
    "3. pattern X3 describes a data engineer position\n",
    "4. pattern X4 describes a software engineer position\n",
    "5. pattern X5 is a mixture of terms that does not highlight a specific theme.\n",
    "6. pattern X6 has a data science flavor, but it's mostly worlds describing teamwork and the company desired impact \n",
    "7. pattern X7 describes gender/race equal opportunities\n",
    "8. pattern X8 describes fintech-related jobs. It does have some data analyst flavor, but it's not quite clear\n",
    "9. pattern X9 describes both interpersonal skills and healthcare benefits\n",
    "10. pattern X10 contains a mixture of words that mixes various themes already mentioned. I believe it's the pattern describing the common structure of IT job descriptions.\n",
    "\n",
    "Using these identifications, I can relabel the patterns with descriptive names."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# define names for the linguistic pattern ordered according to their X1:X10 names\n",
    "renaming_convention <- c(\"word\", \n",
    "                         \"data_science\",\n",
    "                         \"machine_learning_engineer\",\n",
    "                         \"data_engineer\",\n",
    "                         \"software_engineer\",\n",
    "                         \"mixed_nonspecific_words_1\",\n",
    "                         \"teamwork\",\n",
    "                         \"equal_opportunities\",\n",
    "                         \"fintech\",\n",
    "                         \"soft_skills\",\n",
    "                         \"mixed_nonspecific_words_2\")\n",
    "\n",
    "# rename fundamental patterns with the new descriptive names\n",
    "colnames(fundamental_patterns) = renaming_convention;\n",
    "\n",
    "# create matrix containing the contributions of each linguistic pattern to each job description\n",
    "coefficients_for_fundamental_patterns <- data.frame(t(coef(nmf_decomposition_job_descriptions)))\n",
    "\n",
    "# rename the linguistic patterns in the contribution matrix according to the descriptive names\n",
    "colnames(coefficients_for_fundamental_patterns) = renaming_convention[2:11];"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that the linguistic patterns have a descriptive name, we can finally identify the desired candidate for a job opening. To this end, I assume that the most important linguistic pattern defines the job tasks that the ideal candidate has to perform. However, I first need to filter out the patterns that do not identify job tasks. These patterns are\n",
    "1. mixed_nonspecific_words_1\n",
    "2. teamwork\n",
    "3. equal_opportunities\n",
    "4. fintech\n",
    "5. soft_skills\n",
    "6. mixed_nonspecific_words_2\n",
    "Note that I removed the fintech pattern because it seems to specify job sector rather than job tasks."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# define non-informative patterns and filter them out from the matrix with the linguistic pattern contributions to each job description\n",
    "non_descriptive_patterns <- c(\"mixed_nonspecific_words_1\", \"teamwork\", \"equal_opportunities\", \"fintech\", \"soft_skills\", \"mixed_nonspecific_words_2\")\n",
    "coefficients_for_fundamental_job_descriptive_patterns <- coefficients_for_fundamental_patterns %>%\n",
    "    select(- one_of(non_descriptive_patterns))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Having thrown away columns that I found not of interest, I can now label the desired candidate based on the dominant job-related linguistic pattern in each job description"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# identify the most important job-related linguistic pattern in each job description\n",
    "dominant_descriptive_pattern_in_job_description <- colnames(coefficients_for_fundamental_job_descriptive_patterns)[max.col(coefficients_for_fundamental_job_descriptive_patterns)]\n",
    "\n",
    "# Assign the ideal candidate based on the most important job-related linguistic pattern in each job description\n",
    "undersampled_job_descriptions_without_others <- undersampled_job_descriptions_without_others %>%\n",
    "    mutate(ideal_candidate = dominant_descriptive_pattern_in_job_description)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "At this point, we have a way to compare if job titles matches with the ideal candidate that each company wants to hire. To investigate that I use a contingency table between the (label I gave to the) job titles and the ideal candidate I identified with non-negative matrix factorization. To better assess how serious the mismatch is I also include \n",
    "1. a table with the fraction of job titles for each ideal candidate type (tells us if companies know what they are looking for).\n",
    "2. a table with the fraction of ideal candidates for each job title type (tells us how likely will an applicant apply for the wrong positions based on job name alone). \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"contingency table\"\n",
      "                           position_label_from_bigrams\n",
      "ideal_candidate             data_analyst data_engineer data_scientist\n",
      "  data_engineer                       10            58              3\n",
      "  data_science                        43             0             45\n",
      "  machine_learning_engineer            7             1             15\n",
      "  software_engineer                    5             6              2\n",
      "                           position_label_from_bigrams\n",
      "ideal_candidate             machine_learning_engineer mixed software_engineer\n",
      "  data_engineer                                     8    14                 9\n",
      "  data_science                                      2    11                 3\n",
      "  machine_learning_engineer                        48    29                13\n",
      "  software_engineer                                 7    11                40\n",
      "[1] \"table with the fraction of job titles for each ideal candidate type\"\n",
      "                           ideal_candidate\n",
      "position_label_from_bigrams data_engineer data_science\n",
      "  data_analyst                0.098039216  0.413461538\n",
      "  data_engineer               0.568627451  0.000000000\n",
      "  data_scientist              0.029411765  0.432692308\n",
      "  machine_learning_engineer   0.078431373  0.019230769\n",
      "  mixed                       0.137254902  0.105769231\n",
      "  software_engineer           0.088235294  0.028846154\n",
      "                           ideal_candidate\n",
      "position_label_from_bigrams machine_learning_engineer software_engineer\n",
      "  data_analyst                            0.061946903       0.070422535\n",
      "  data_engineer                           0.008849558       0.084507042\n",
      "  data_scientist                          0.132743363       0.028169014\n",
      "  machine_learning_engineer               0.424778761       0.098591549\n",
      "  mixed                                   0.256637168       0.154929577\n",
      "  software_engineer                       0.115044248       0.563380282\n",
      "[1] \"build table with the fraction of ideal candidates for each job title type\"\n",
      "                           position_label_from_bigrams\n",
      "ideal_candidate             data_analyst data_engineer data_scientist\n",
      "  data_engineer               0.15384615    0.89230769     0.04615385\n",
      "  data_science                0.66153846    0.00000000     0.69230769\n",
      "  machine_learning_engineer   0.10769231    0.01538462     0.23076923\n",
      "  software_engineer           0.07692308    0.09230769     0.03076923\n",
      "                           position_label_from_bigrams\n",
      "ideal_candidate             machine_learning_engineer      mixed\n",
      "  data_engineer                            0.12307692 0.21538462\n",
      "  data_science                             0.03076923 0.16923077\n",
      "  machine_learning_engineer                0.73846154 0.44615385\n",
      "  software_engineer                        0.10769231 0.16923077\n",
      "                           position_label_from_bigrams\n",
      "ideal_candidate             software_engineer\n",
      "  data_engineer                    0.13846154\n",
      "  data_science                     0.04615385\n",
      "  machine_learning_engineer        0.20000000\n",
      "  software_engineer                0.61538462\n"
     ]
    }
   ],
   "source": [
    "# build contingency table and print it\n",
    "contingency_table_labels <- undersampled_job_descriptions_without_others %>%\n",
    "    select(ideal_candidate, position_label_from_bigrams,) %>%\n",
    "    table()\n",
    "print(\"contingency table\")\n",
    "print(contingency_table_labels)\n",
    "\n",
    "# build table with the fraction of job titles for each ideal candidate type\n",
    "frequency_of_dominant_patterns_consistent_with_bigrams = sweep(contingency_table_labels, 1, rowSums(contingency_table_labels), \"/\")\n",
    "print(\"table with the fraction of job titles for each ideal candidate type\")\n",
    "print(t(frequency_of_dominant_patterns_consistent_with_bigrams))\n",
    "\n",
    "# build table with the fraction of ideal candidates for each job title type\n",
    "frequency_of_dominant_patterns_consistent_with_bigrams = sweep(contingency_table_labels, 2, colSums(contingency_table_labels), \"/\")\n",
    "print(\"build table with the fraction of ideal candidates for each job title type\")\n",
    "print(frequency_of_dominant_patterns_consistent_with_bigrams)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These tables show interesting results.\n",
    "\n",
    "On the company side, the table with the fraction of job titles for each ideal candidate type suggest that companies often fail to name the job opening according to their true need. More specifically,\n",
    "1. Companies who needs a person with data engineer skills correctly label the job opening only 3 times out of 5. The biggest confounder are data engineer positions which are mislabeled as a mixture of other job titles or as software engineer jobs.\n",
    "2. Companies that needs people with a data science background label those positions as either as data scientist (40% of the times) or data analyst (40% of the time). This may reflect the inertia in dropping the data analyst job title as obsolete.\n",
    "3. Companies that needs people with a machine learning engneer background opens the positions with the correct job title less than half of the times. 25% of the time those companies includes multiple job titles, whereas they erroneously label thos positions as data scientist and software engineer about 10% of the times each. This may indicate that companies do not clearly understand how to get the attention of machine learning specialists when they need them.\n",
    "4. Companies that need software engineer open positions with that title 3 times out of 5 but they can mislabel them as data engineer, machine learning engineer positions or add extra labels to the job descriptions. This is quite odd because software engineers have been around for quite a long time and their job tasks should be well understood.\n",
    "\n",
    "On the applicant side the table with the fraction of ideal candidates for each job title type elucidates that people will often apply for the wrong position. More specifically:\n",
    "1. People that apply to positions with data science analyst title are applying for the wrong positions 30% of the times. In particular, they might be applying for a machine learning engineer positions (10% of the times) or a data engineer position (10% of the times) instead.\n",
    "2. People that apply to positions with data engineer titles are indeed applying to data engineer positions. The only confounder is that they might be applying to a software engineer position, but this happen only 10% of the times.\n",
    "3. People that apply to position with data science title are applyting to the wrong positions 30% of the times. However, in the majority of these cases people are truly applyionf to a machine learning engineer position. This might not be particularly serious given the similarities between the two jobs.\n",
    "4. People that apply to positions with the machine learning engineer title are applying to a different position 30% of the times. In those cases, peopel are instead applying for data engineer of software engineer positions.\n",
    "5. People that apply to positions with software engineer title may be applying for the wrong position 35% of the times. When this happen, they might be applying for data engineer or machine learning engineer positions instead\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "R",
   "language": "R",
   "name": "ir"
  },
  "language_info": {
   "codemirror_mode": "r",
   "file_extension": ".r",
   "mimetype": "text/x-r-source",
   "name": "R",
   "pygments_lexer": "r",
   "version": "3.4.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}