Importing my CV

To do things properly and to make it reproducible for you, I will start from my CV in PDF version.

# Downloading the CV with {pdftools}
curriculum <- pdftools::pdf_text("MVaugoyeauCV.pdf") %>% 
  str_split("\n", simplify = TRUE) %>% 
  t() %>% 
  set_colnames(c("page1", "page2")) %>% 
  as_tibble() %>% 
  gather("page", "texte")

Harvesting a list of words in French

Not all words are interesting, so you should remove unnecessary words like and,le,`to….
For the English language, this list is directly available in the {tidytext} package but there are none (to my knowledge) in French.

So I got it from the count words site. This site has the advantage of offering a lot of languages!

# Creation of the sotp word list in French / use of {rvest} and {xml2}
stop_words_fr <-
  xml2::read_html("https://countwordsfree.com/stopwords/french") %>%
  rvest::html_table() %>%
  purrr::pluck(1) %>%
  dplyr::select(X2) %>%
  as_tibble() %>%
  rename(mot = X2) %>%
  add_row(mot = c("vaugoyeau", "jeunes", "al", "impact", "factor", "été", "issus", "marie", "annee", "création", "influence", "paris"))

Word frequency measurement

The first step is to create a list of simple words.

Be careful with the accents, here, I removed them.

# creation of the word list
texte <- curriculum %>% 
  mutate(texte = texte %>% 
           str_remove_all("\\d") %>%
           str_replace_all("[:punct:]", " ") %>% 
           chartr(old = "àâäçéèêëîïôöùûüÿ", 
                  new = "aaaceeeeiioouuuy")
         ) %>% 
  unnest_tokens(mot, texte) %>%
  anti_join(
    stop_words_fr %>% 
      mutate(
        mot = mot %>% 
          chartr(old = "àâäçéèêëîïôöùûüÿ", new = "aaaceeeeiioouuuy")
        )
    ) %>%
  anti_join(
    stop_words %>% 
      mutate(
        mot = word %>% 
          chartr(old = "àâäçéèêëîïôöùûüÿ", new = "aaaceeeeiioouuuy")
        )
    ) %>% 
  mutate(
    mot = mot %>% 
      str_remove_all("s$") %>% 
      str_replace_all(
        c(
          "analys\\w+" = "analyse",
          "biostatistique" = "statistiques",
          "automatiser" = "automatisation",
          "experi\\w+" = "experimentation",
          "evolutive" = "evolution",
          "urba\\w+" = "urbanisation",
          "universit\\w+" = "universite",
          "permi" = "permis",
          "paru" = "parus",
          "ox[iy]d\\w+" = "oxydatif"))
    ) %>% 
  count(mot, sort = TRUE) 

#  and associated graphic
texte %>%
  filter(n > 2) %>% 
  ggplot() +
  aes(reorder(mot, n), n) +
  geom_bar(stat = "identity", fill = "orange") +
  geom_text(
    aes(label= as.character(n)), 
    check_overlap = TRUE, 
    size = 4) + 
  coord_flip() + 
  xlab(" ") + 
  ylab("nombre de répétition") + 
  labs(title = "Mots les plus récurrents") +
  theme_classic()

If you look carefully at the code you will see that I have made lemmatisation, that is, I have sought to exploit the meaning of the words and not the word as such.

For example, analyst, analyze or analysis have the same meaning for me of analysis so I have grouped them under the same word.

There may be an official list for lemmatisation, but I don’t know it, sorry!

Bigram measurement

A single word does not always mean something, hence the need to use n-grammms as well (here I would stop at the bigram, but it is of course possible to search for the number of words you want!).

# Creation of the bigram file for expressions
bigram <- curriculum %>% 
  mutate(texte = texte %>% 
           str_remove_all("\\d") %>%
           str_replace_all("[:punct:]", " ") %>% 
           chartr(old = "àâäçéèêëîïôöùûüÿ", 
                  new = "aaaceeeeiioouuuy")
         ) %>% 
  unnest_tokens(bigramme, texte, token = "ngrams", n = 2) %>%
  separate(bigramme, c("mot1", "mot2"), sep = " ") %>% 
  filter(!mot1 %in% (stop_words_fr$mot %>% chartr(old = "àâäçéèêëîïôöùûüÿ", new = "aaaceeeeiioouuuy"))) %>% 
  filter(!mot2 %in% (stop_words_fr$mot %>% chartr(old = "àâäçéèêëîïôöùûüÿ", new = "aaaceeeeiioouuuy"))) %>% 
  filter(!mot1 %in% (stop_words$word %>% chartr(old = "àâäçéèêëîïôöùûüÿ", new = "aaaceeeeiioouuuy"))) %>% 
  filter(!mot2 %in% (stop_words$word %>% chartr(old = "àâäçéèêëîïôöùûüÿ", new = "aaaceeeeiioouuuy"))) %>% 
  select(- page) %>% 
  mutate_all(
    ~ str_remove_all(., "s$") %>% 
      str_replace_all(
        c(
          "analys\\w+" = "analyse",
          "biostatistique" = "statistiques",
          "automatiser" = "automatisation",
          "experi\\w+" = "experimentation",
          "evolutive" = "evolution",
          "urba\\w+" = "urbanisation",
          "universit\\w+" = "universite",
          "permi" = "permis",
          "paru" = "parus",
          "ox[iy]d\\w+" = "oxydatif"))
    ) %>% 
  unite(bigramme, mot1, mot2, sep = " ") %>% 
  count(bigramme, sort = TRUE) 

#  and graphic representation
bigram %>%
  filter(n > 1) %>% 
  ggplot() +
  aes(reorder(bigramme, n), n) +
  geom_bar(stat = "identity", fill = "cyan") +
  geom_text(
    aes(label= as.character(n)), 
    check_overlap = TRUE, 
    size = 4) + 
  coord_flip() + 
  xlab(" ") + 
  ylab("nombre de répétition") + 
  labs(title = "Expressions les plus utilisées") +
  theme_classic()