Table of Contents

1. Loading libraries
2. Collect the data with web scraping
3. Visualization of the proposed salaries
4. Mapping job locations
5. Analyzing Job descriptions
   1. Downloading and cleaning each job description
   2. Annotation procedure with udpipe Package
   3. Most common nouns
   4. Extracting key words for resume writing
6. Conclusion
7. Custom functions

Loading libraries

The first step is to import several packages:

# General
library(tidyverse)
# Webscraping 
library(rvest)
library(RSelenium)
# Geo data
library(tidygeocoder)
library(leaflet)
library(rnaturalearth)
library(sf)
# NLP
library(udpipe)
library(textrank)
library(wordcloud)
# Cleaning
library(stringr)
# Additional functions presented at the end of the post 
source('scraping_functions.R') 

Collect the data with web scraping

In the beginning of this project, I was using read_html() from rvest to access and download the webpage from Indeed. However, Indeed pages are protected by an anti-scrapping software that blocked any of my requests even though scraping is not forbidden on the pages I am interested in (I checked the robots.txt page).

This is why I decided to access the pages with Rselenium which allows to run an headless browser. We first navigate to the page corresponding to the search results of data scientist jobs in France:

url = "https://fr.indeed.com/jobs?q=data%20scientist&l=France&from=searchOnHP"

# Headless Firefox browser
exCap <- list("moz:firefoxOptions" = list(args = list('--headless')))
rD <- rsDriver(browser = "firefox", extraCapabilities = exCap, port=1111L,
                verbose = F)
remDr <- rD$client

# Navigate to the url
remDr$navigate(url)

# Store page source 
web_page <- remDr$getPageSource(header = TRUE)[[1]] %>% read_html()

To scrape a specific information on a webpage you need to follow these steps:

1. Find on the web page the element/text/data you want to scrape
2. Find the associated xpath or css selector with the developer tool of chrome or firefox ( tutorial here ! )
3. Extract the element with hmtl_element() by indicating the xpath or css selector
4. Transform the data to text with html_text2()
5. Clean the data if necessary

Here is the example with the number of listed data science jobs in France:

web_page %>%
  html_element(css = "div.jobsearch-JobCountAndSortPane-jobCount") %>% # selecting with css 
  html_text2() %>% # Transform to text
  str_remove_all("[^0-9.-]") %>% # Clean the data to only get numbers
  substr(start = 2, stop = 8) %>% 
  as.numeric()
## [1] 1761

For now, we can only scrape the data from the first page. However, I am interested in all the job posts and I need to access the other pages ! After navigating through the first 3 pages of listed jobs, I remarked a pattern in the URL address (valid at the time of writing), this means that with a line of code, I can produce a list containing the URLs for the first 40 pages.

Once I have the list, the only thing left is to loop over all the URLs with some delay (good practice for web-scraping), collect the data and clean it with custom functions (at the end of the post):

# Creating URL link corresponding to the first 40 pages
base_url = "https://fr.indeed.com/jobs?q=data%20scientist&l=France&start="
url_list <- c(url, paste0(base_url, as.character(seq(from=10, to=400, by=10))))

# Looping through the URL list
res <- list()
for(i in 1:length(url_list)){
  # Navigate to the URL
  remDr$navigate(url_list[i])
  
  # Store page source 
  web_page <- remDr$getPageSource(header = TRUE)[[1]] %>% read_html()

  # Job title 
  job_title <- web_page %>%
    html_elements(css = ".mosaic-provider-jobcards .result") %>%
    html_elements(css = ".resultContent") %>%
    html_element("h2") %>%
    html_text2() %>%
    str_replace(".css.*;\\}", "")

  # URL for job post 
  job_url <- web_page %>%
    html_elements(css = ".mosaic-provider-jobcards .result")%>%
    html_elements(css = ".resultContent") %>%
    html_element("h2") %>%
    html_element("a") %>%
    html_attr('href') %>%
    lapply(function(x){paste0("https://fr.indeed.com", x)}) %>%
    unlist()
  
  # Data about company
  company_info <- web_page %>%
    html_elements(css = ".mosaic-provider-jobcards .result")%>%
    html_elements(css = ".resultContent")%>%
    html_element(css = ".company_location")%>%
    html_text2() %>%
    lapply(FUN = tidy_comploc) %>% # Function to clean the textual data
    do.call(rbind, .)

  # Data about job description
  job_desc <- web_page %>%
    html_elements(css = ".mosaic-provider-jobcards .result")%>%
    html_element(css =".slider_container .jobCardShelfContainer")%>%
    html_text2() %>%
    tidy_job_desc() # Function to clean the textual data related to job desc.

  # Data about salary (when indicated)
  salary_hour <- web_page %>%
    html_elements(css = ".mosaic-provider-jobcards .result .resultContent")%>%
    html_element(css = ".salaryOnly") %>%
    html_text2() %>%
    lapply(FUN = tidy_salary) %>% # Function to clean the data related to salary
    do.call(rbind, .)
  
  # Job posts in the same format
  final_df <- cbind(job_title, company_info, salary_hour, job_desc, job_url)
  colnames(final_df) <- c("Job_title", "Company", "Location", "Rating", "Low_salary", "High_salary", "Contract_info", "Job_desc", "url")
  res[[i]] <- final_df
  
  # Sleep 5 seconds, good practice for web scraping
  Sys.sleep(5)
}

# Gather all the job post in a tibble
final_df <- as_tibble(do.call("rbind", res))

# Final data cleaning
final_df <- final_df %>%
  mutate_at(c("Rating", "Low_salary", "High_salary"), as.numeric)

# Clean job title
final_df$Job_title_c <- clean_job_title(final_df$Job_title)  
final_df$Job_title_c <- as.factor(final_df$Job_title_c)

We have now a tidy data set! Here is a truncated example of the 5 first rows:

# Function to make euro X scale 
euro <- scales::label_dollar(
  prefix = "",
  suffix = "\u20ac",
  big.mark = ".",
  decimal.mark = ","
)

final_df %>%
  filter(Low_salary > 1600) %>% # To remove internships and freelance works
  select(Company, Low_salary, High_salary) %>%
  group_by(Company) %>%
  summarize_if(is.numeric, mean) %>%
  mutate(Mean_salary = rowMeans(cbind(Low_salary, High_salary), na.rm = T),
           Company = fct_reorder(Company, desc(-Mean_salary))) %>%
  ggplot(aes(x = Company)) +
  geom_point(aes(y = Mean_salary), colour = "#267266") +
  geom_linerange(aes(ymin = Low_salary, ymax = High_salary)) +
  geom_hline(aes(yintercept = median(Mean_salary)), lty=2, col='red', alpha = 0.7) +
  scale_y_continuous(labels = euro) +
  ylab("Monthly income") +
  xlab("") +
  coord_flip() +
  theme_bw(base_size = 8)

final_df %>%
  filter(Low_salary > 1600) %>%  # To remove internships and freelance works
  select(Job_title_c, Low_salary, High_salary, Job_type) %>%
  group_by(Job_title_c) %>%
  summarize_if(is.numeric, ~ mean(.x, na.rm = TRUE)) %>%
  mutate(Mean_salary = rowMeans(cbind(Low_salary, High_salary), na.rm = T),
         Job_title_c = fct_reorder(Job_title_c, desc(-Mean_salary))) %>%
  ggplot(aes(x = Job_title_c, y = Mean_salary)) +
  geom_point(aes(y = Mean_salary), colour = "#267266") +
  geom_linerange(aes(ymin = Low_salary, ymax = High_salary)) +
  #geom_label(aes(label = n, Job_title_c, y = 1500), data = count_df) + 
  scale_y_continuous(labels = euro) +
  theme_bw(base_size = 12) +
  xlab("") +
  ylab("Monthly Income") +
  coord_flip()

# Tidy the types and locations of listed jobs
final_df <- tidy_location(final_df)
count_df <- count(final_df %>% filter(Low_salary > 1600), Job_type)
final_df %>%
  filter(Low_salary > 1600) %>% 
  drop_na(Location) %>%
  mutate(Mean_salary = rowMeans(cbind(Low_salary, High_salary), na.rm = T),
         Job_type = as.factor(Job_type)) %>%
    ggplot(aes(x = Job_type, y = Mean_salary)) +
  geom_boxplot(na.rm = TRUE) +
  geom_label(aes(label = n, Job_type, y = 5500), data = count_df) + 
  scale_y_continuous(labels = euro) + 
  theme_bw(base_size = 12) +
  xlab("Job Type") +
  ylab("Income")

# Extract coordinates from town name
final_df <- final_df %>%
  mutate(Loc_tidy_fr = paste(Loc_tidy, 'France')) %>%
  geocode(Loc_tidy_fr, method = 'arcgis', lat = latitude , long = longitude) %>%
  select(- Loc_tidy_fr)

# Map of France from rnaturalearth package
france <- ne_states(country = "France", returnclass = "sf") %>% 
  filter(!name %in% c("Guyane française", "Martinique", "Guadeloupe", "La Réunion", "Mayotte"))

# Transform location to st point 
test <- st_sf(final_df, geom= lapply(1:nrow(final_df), function(x){st_point(c(final_df$longitude[x],final_df$latitude[x]))}))
st_crs(test) <- 4326

# St_join by departments 
joined <- france %>%
  st_join(test, left = T)

# Custom breaks for visual representation
my_breaks = c(0, 2, 5, 10, 30, 50, 100, 260)

joined %>% 
  mutate(region=as.factor(name)) %>% 
  group_by(region) %>% 
  summarize(Job_number=n()) %>% 
  mutate(Job_number = cut(Job_number, my_breaks)) %>% 
  ggplot() +
  geom_sf(aes(fill=Job_number), col='grey', lwd=0.2) + 
  scale_fill_brewer("Job number",palette = "GnBu") + 
  theme_bw()

# Plot leaflet map
final_df %>%
  mutate(pop_up_text = sprintf("<b>%s</b> <br/> %s",
                                     Job_title, Company)) %>% # Make popup text
  leaflet() %>%
  setView(lng = 2.36, lat = 46.31, zoom = 5.2) %>% # Center of France
  addProviderTiles(providers$CartoDB.Positron) %>%
  addMarkers(
    popup = ~as.character(pop_up_text),
    clusterOptions = markerClusterOptions()
  )

Analyzing job descriptions

Nowadays most of the resumes are scanned and interpreted by an applicant tracking system (ATS). To make things simple, this system looks for key words in your resume and assess the match with the job you are applying for. It is therefore important to describe your experiences with specific key words to improve the chances of getting to the next step of the hiring process.

But what key words should I include in my resume ? Let’s answer this question by analyzing the job descriptions of data scientist jobs.

# Loop through all the URLs
job_descriptions <- list()
pb <- txtProgressBar(min = 1, max = length(final_df$url), style = 3)
for(i in 1:length(final_df$url)){
  remDr$navigate(final_df$url[i])
  web_page <- remDr$getPageSource(header = TRUE)[[1]] %>% read_html()
  job_descriptions[[i]] <- web_page %>%
        html_elements(css = ".jobsearch-JobComponent-description") %>%
      html_text2()
  Sys.sleep(2)
  setTxtProgressBar(pb, i)
}
# Gathering in dataframe
job_descriptions <- as.data.frame(do.call("rbind", job_descriptions))
names(job_descriptions) <- c("Description")

# Binding to same table:
final_df <- cbind(final_df, job_descriptions)

# Homogenize with custom function
final_df$Description_c <- lapply(final_df$Description, function(x){clean_job_desc(x)[[2]]})
final_df$Language <- textcat::textcat(final_df$Description)

# Restricting the analysis to Data scientist post written in french
desc_data_scientist <- final_df %>%
  filter((Job_title_c == "data scientist") & (Language == "french")) %>%
  select(Description_c)

ud_model <- udpipe_download_model(language = "french") # Download the model if necessary
ud_model <- udpipe_load_model(ud_model$file_model) 

# Annotate the descriptions 
x <- udpipe_annotate(ud_model, x = paste(desc_data_scientist, collapse = " "))
x <- as.data.frame(x)

stats <- subset(x, upos %in% "NOUN")
stats <- txt_freq(x = stats$lemma)

stats %>%
  top_n(50, freq) %>%
  mutate(key = as.factor(key),
         key = fct_reorder(key, freq)) %>%
  ggplot(aes(x = key, y = freq)) +
  geom_bar(stat = 'identity') +
  coord_flip() + 
  ylab("Most common nouns") + 
  theme_bw()

stats <- keywords_rake(x = x,
                       term = "token",# Search on token
                       group = c("doc_id", "sentence_id"), # On every post 
                       relevant = x$upos %in% c("NOUN", "ADJ"),  # Only among noun and adj.
                       ngram_max = 2, n_min = 2, sep = " ")

stats <- subset(stats, stats$freq >= 5 & stats$rake > 3)

stats %>% 
  arrange(desc(rake)) %>% 
  head()
##                     keyword ngram freq     rake
## 1 intelligence artificielle     2    9 9.368889
## 2             tableaux bord     2    5 8.504274
## 3      formation supérieure     2    5 8.374725
## 4        modèles prédictifs     2   15 7.581294
## 5         force proposition     2    6 7.190238
## 6        production échelle     2    5 7.034038
wordcloud(words = stats$keyword, freq = stats$freq, min.freq = 3,
          max.words=100, random.order=FALSE, rot.per=0.35,
          colors=brewer.pal(8, "Dark2"), scale = c(2.5, .5))

Conclusion

I hope I convinced you that it is possible to optimize your job search with Data Science!

If this post has caught your interest and you are looking for a new Data Scientist, do not hesitate to contact me on my mail as I am currently looking for a job in France (Hybrid, Remote) or in Europe (Remote).

Custom functions to clean data extracted from the webpage

These functions use several methods such as regular expressions, stop words and conditional statements to clean the textual data.

library(rvest)
library(stringr)
library(httr)
library(tidystopwords)
library(textcat)

# Function to tidy the data related to the company
tidy_comploc <- function(text){
  lst <- str_split(text, pattern = "\n", simplify =T)
  ext_str <- substr(lst[1], nchar(lst[1])-2, nchar(lst[1]))
  res <- suppressWarnings(as.numeric(gsub(',', '.', ext_str)))
  lst[1] <- ifelse(is.na(res), lst[1], substr(lst[1], 1, nchar(lst[1])-3))
  lst[3] <- res
  t(as.matrix(lst))
}

# Function to tidy the short job description provided with the job post
tidy_job_desc <- function(text){
  stopwords <- c("Candidature facile", "Employeur réactif")
  text <- str_remove_all(text, paste(stopwords, collapse = "|"))
  stopwords_2 <- "(Posted|Employer).*"
  text <- str_remove_all(text, stopwords_2)
  text
}

# Function to tidy the salary data if provided
tidy_salary <- function(text){
  if(is.na(text)){
    others <- NA
    sal_low <- NA
    sal_high <- NA
  }else{
    text <- str_split(text, "\n", simplify = T)
    others <- paste(text[str_detect(text, "€", negate = T)], collapse = " | ")
    sal <- text[str_detect(text, "€", negate = F)]
    if(rlang::is_empty(sal)){
      sal_low <- NA
      sal_high <- NA
    }else{
      range_sal <- as.numeric(str_split(str_remove_all(str_replace(sal, "à", "-"), "[^0-9.-]"), "-", simplify = TRUE))
      sal_low <- sort(range_sal)[1]
      sal_high <- sort(range_sal)[2]

      if(str_detect(sal, "an")){
        sal_low <- floor(sal_low/12)
        sal_high <- floor(sal_high/12)
      }
    }
  }
  return(c(as.numeric(sal_low), as.numeric(sal_high), others))
}

# Function to tidy the location of the job (Remote/Hybrid/Onsite) + homogenize 
# location and zip code
tidy_location <- function(final_df){
  final_df$Job_type <- ifelse(final_df$Location == "Télétravail", "Full Remote", ifelse(str_detect(final_df$Location, "Télétravail"), "Hybrid", "On site"))
  final_df$Loc_possibility <- ifelse(str_detect(final_df$Location, "lieu"), "Plusieurs lieux", NA)
  stopwords <- c("Télétravail à", "Télétravail", "à", "hybride")
  final_df$Loc_tidy <- str_remove_all(final_df$Location, paste(stopwords, collapse = "|"))
  final_df$Loc_tidy <- str_remove_all(final_df$Loc_tidy, "[+].*")
  final_df$Loc_tidy <- str_trim(final_df$Loc_tidy)
  final_df$Loc_tidy <-  sapply(final_df$Loc_tidy,
                               function(x){
                                 if(!is.na(suppressWarnings(as.numeric(substr(x, 1, 5))))){
                                   return(paste(substr(x, 7, 30), paste0('(', substr(final_df$Loc_tidy[2], 1, 2), ')')))
                                 }else{
                                   return(x)
                                 }})
  return(final_df)
}

# Function to keep only certain words in text
keep_words <- function(text, keep) {
  words <- strsplit(text, " ")[[1]]
  txt <- paste(words[words %in% keep], collapse = " ")
  return(txt)
}

# Homogenize the job title and class them in a few categories
clean_job_title <- function(job_titles){
  job_titles <- tolower(job_titles)
  job_titles <- gsub("[[:punct:]]", " ", job_titles, perl=TRUE)

  words_to_keep <- c("data", "scientist", "junior", "senior", "engineer", "nlp",
                     "analyst", "analytics", "analytic", "science", "sciences",
                     "computer", "vision", "ingenieur", "données", "analyste",
                     "analyses", "lead", "leader", "dataminer", "mining", "chief",
                     "miner", "analyse", 'head')
  job_titles_c <- unlist(sapply(job_titles, function(x){keep_words(x, words_to_keep)}, USE.NAMES = F))
  job_titles_c <- unlist(sapply(job_titles_c, function(x){paste(unique(unlist(str_split(x, " "))), collapse = " ")}, USE.NAMES = F))
  table(job_titles_c)

  data_analytics_ind <-  job_titles_c %in% c("analyses data", "analyst data", "analyste data", "analyste data scientist", "data analyse",
                                             "analyste données", "analytic data scientist", "analytics data", "analytics data engineer", "data analyst engineer",
                                             "data analyst données", "data analyst scientist", "data analyst scientist données", "data analyste", "data analyst analytics",
                                             "data analytics", "data analytics engineer", "data engineer analyst", "data scientist analyst", "data scientist analytics")
  job_titles_c[data_analytics_ind] <- "data analyst"

  data_analytics_j_ind <-  job_titles_c %in% c("junior data analyst", "junior data analytics", "junior data scientist analyst")
  job_titles_c[data_analytics_j_ind] <- "data analyst junior"

  data_scientist_ind <- job_titles_c %in% c("data computer science", "data science", "data science scientist", "data sciences",
                                            "data sciences scientist", "data scientist données", "data scientist sciences",
                                            "données data scientist", "scientist data", "science données", "scientist data",
                                            "scientist data science", "computer data science", "data science données", "data scientist science")
  job_titles_c[data_scientist_ind] <- "data scientist"

  data_scientist_j_ind <- job_titles_c %in% c("junior data scientist")
  job_titles_c[data_scientist_j_ind] <- "data scientist junior"

  data_engineer_ind <- job_titles_c %in% c("data engineer scientist", "data science engineer", "data miner", "data scientist engineer",
                                           "dataminer", "engineer data scientist", "senior data scientist engineer", "ingenieur data scientist")
  job_titles_c[data_engineer_ind] <- "data engineer"

  nlp_data_scientist_ind <- job_titles_c %in% c("data scientist nlp", "nlp data science",
                                                "nlp data scientist", "senior data scientist nlp")
  job_titles_c[nlp_data_scientist_ind] <- "data scientist NLP"

  cv_data_scientist_ind <- job_titles_c %in% c("computer vision data scientist", "data science computer vision",
                                               "data scientist computer vision")
  job_titles_c[cv_data_scientist_ind] <- "data scientist CV"

  lead_data_scientist_ind <- job_titles_c %in% c("chief data", "chief data scientist", "data scientist leader", "lead data scientist",
                                                 "data chief scientist", "lead data scientist senior", "head data science")
  job_titles_c[lead_data_scientist_ind] <- "data scientist lead or higher"
  senior_data_scientist_ind <- job_titles_c %in% c("senior data scientist")
  job_titles_c[senior_data_scientist_ind] <- "data scientist senior"

  senior_data_analytics_ind <- job_titles_c %in% c("senior analytics data scientist", "senior data analyst", "senior data scientist analytics")
  job_titles_c[senior_data_analytics_ind] <- "data analyst senior"


  lead_data_analyst_ind <- job_titles_c %in% c("lead data analyst senior", "lead data analyst")
  job_titles_c[lead_data_analyst_ind] <- "data analyst lead"
  return(job_titles_c)
}

# Function to clean the full job description before word annotation
clean_job_desc <- function(text){
  text <- tolower(text)
  text <- str_replace_all(text, "\n", " ")
  text <- str_remove(text, pattern = "dé.*du poste ")
  text <- str_remove(text, pattern = "analyse de recr.*")
  text <- gsub("(?!&)[[:punct:]+’+…+»+«]", " ", text, perl=TRUE)

  language <- textcat(text)

  if(language == "french"){
    text <- str_replace_all(text, "œ", "oe")
    stopwords <- c("détails", "poste", "description", "informations", "complémentaires", "c", generate_stoplist(language = "French"))
  }else{
    stopwords <- c("description", generate_stoplist(language = "English"))
  }

  text <- str_replace_all(text, paste(stopwords, collapse = " | "), " ")
  text <- str_replace_all(text, paste(stopwords, collapse = " | "), " ")
  text <- str_replace_all(text, paste(stopwords, collapse = " | "), " ")

  return(c(language, text))
}