{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "collapsed": true,
        "pycharm": {
          "name": "#%% md\n"
        }
      },
      "source": [
        "# La loi de Zipf illustrée avec gutenbergr en R 🇫🇷\n",
        "\n",
        "[Xiaoou WANG](https://scholar.google.fr/citations?user=vKAMMpwAAAAJ&hl=en)\n",
        "\n",
        "Il s'agit de montrer quelques aspects de la loi de Zipf avec 3 textes (Wuthering Heights, Madame Bovary et Faust) récupérés du site Gutenberg.\n",
        "\n",
        "## Packages utilisés\n",
        "\n",
        "Les trois packages/ensemble de packages utilisés sont `gutenbergr`,`tidyverse` et `gridExtra` (pour afficher des multi-plots)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "ExecuteTime": {
          "end_time": "2021-03-21T16:14:34.957457Z",
          "start_time": "2021-03-21T16:14:34.938Z"
        },
        "pycharm": {
          "name": "#%%\n"
        }
      },
      "outputs": [],
      "source": [
        "options(tidyverse.quiet = TRUE) # omettre les warnings pour ne pas encombrer le document\n",
        "library(gutenbergr)\n",
        "library(tidyverse)\n",
        "library(gridExtra)\n",
        "options(gridExtra.quiet = TRUE)\n",
        "# center plot title\n",
        "theme_update(plot.title = element_text(hjust = 0.5))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Workflow général\n",
        "\n",
        "1. On prédéfinit les titres des 3 œuvres qui seront proposées à l'utiliseur et produit par la suite un dataframe contenant les métadatas\n",
        "\n",
        "```r\n",
        "eng.title <- \"Wuthering Heights\"\n",
        "fr.title <- \"Madame Bovary\"\n",
        "de.title <- \"Faust: Der Tragödie erster Teil\"\n",
        "\n",
        "# Get meta infos\n",
        "metas <- get_books_meta(eng.title, fr.title, de.title)\n",
        "```\n",
        "\n",
        "2. On définit une fonction main qui comprend toutes les étapes restantes allant de la saisie de l'utilisateur jusqu'à la génération et de la sauvegarde des graphes.\n",
        "\n",
        "```r\n",
        "main(metas)\n",
        "```"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2021-03-21T12:48:42.368717Z",
          "start_time": "2021-03-21T12:48:42.351Z"
        }
      },
      "source": [
        "## Explication de la fonction get_books_meta\n",
        "\n",
        "La fonction `get_books_meta` prend les titres des 3 oeuvres comme entrées et produit un dataframe des informations des 3 œuvres en question en filtrant les métadatas du package `guternbergr` sur les colonnes `titre` et `language`."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "ExecuteTime": {
          "end_time": "2021-03-21T12:50:00.372958Z",
          "start_time": "2021-03-21T12:50:00.334Z"
        }
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<table>\n",
              "<caption>A tibble: 3 × 8</caption>\n",
              "<thead>\n",
              "\t<tr><th scope=col>gutenberg_id</th><th scope=col>title</th><th scope=col>author</th><th scope=col>gutenberg_author_id</th><th scope=col>language</th><th scope=col>gutenberg_bookshelf</th><th scope=col>rights</th><th scope=col>has_text</th></tr>\n",
              "\t<tr><th scope=col>&lt;int&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;lgl&gt;</th></tr>\n",
              "</thead>\n",
              "<tbody>\n",
              "\t<tr><td>  768</td><td>Wuthering Heights              </td><td>Brontë, Emily              </td><td>405</td><td>en</td><td>Gothic Fiction/Movie Books/Best Books Ever Listings                         </td><td>Public domain in the USA.</td><td>TRUE</td></tr>\n",
              "\t<tr><td>14155</td><td>Madame Bovary                  </td><td>Flaubert, Gustave          </td><td>574</td><td>fr</td><td>Best Books Ever Listings/FR Littérature/Banned Books from Anne Haight's list</td><td>Public domain in the USA.</td><td>TRUE</td></tr>\n",
              "\t<tr><td> 2229</td><td>Faust: Der Tragödie erster Teil</td><td>Goethe, Johann Wolfgang von</td><td>586</td><td>de</td><td>Harvard Classics/Best Books Ever Listings/DE Drama                          </td><td>Public domain in the USA.</td><td>TRUE</td></tr>\n",
              "</tbody>\n",
              "</table>\n"
            ],
            "text/latex": [
              "A tibble: 3 × 8\n",
              "\\begin{tabular}{llllllll}\n",
              " gutenberg\\_id & title & author & gutenberg\\_author\\_id & language & gutenberg\\_bookshelf & rights & has\\_text\\\\\n",
              " <int> & <chr> & <chr> & <int> & <chr> & <chr> & <chr> & <lgl>\\\\\n",
              "\\hline\n",
              "\t   768 & Wuthering Heights               & Brontë, Emily               & 405 & en & Gothic Fiction/Movie Books/Best Books Ever Listings                          & Public domain in the USA. & TRUE\\\\\n",
              "\t 14155 & Madame Bovary                   & Flaubert, Gustave           & 574 & fr & Best Books Ever Listings/FR Littérature/Banned Books from Anne Haight's list & Public domain in the USA. & TRUE\\\\\n",
              "\t  2229 & Faust: Der Tragödie erster Teil & Goethe, Johann Wolfgang von & 586 & de & Harvard Classics/Best Books Ever Listings/DE Drama                           & Public domain in the USA. & TRUE\\\\\n",
              "\\end{tabular}\n"
            ],
            "text/markdown": [
              "\n",
              "A tibble: 3 × 8\n",
              "\n",
              "| gutenberg_id &lt;int&gt; | title &lt;chr&gt; | author &lt;chr&gt; | gutenberg_author_id &lt;int&gt; | language &lt;chr&gt; | gutenberg_bookshelf &lt;chr&gt; | rights &lt;chr&gt; | has_text &lt;lgl&gt; |\n",
              "|---|---|---|---|---|---|---|---|\n",
              "|   768 | Wuthering Heights               | Brontë, Emily               | 405 | en | Gothic Fiction/Movie Books/Best Books Ever Listings                          | Public domain in the USA. | TRUE |\n",
              "| 14155 | Madame Bovary                   | Flaubert, Gustave           | 574 | fr | Best Books Ever Listings/FR Littérature/Banned Books from Anne Haight's list | Public domain in the USA. | TRUE |\n",
              "|  2229 | Faust: Der Tragödie erster Teil | Goethe, Johann Wolfgang von | 586 | de | Harvard Classics/Best Books Ever Listings/DE Drama                           | Public domain in the USA. | TRUE |\n",
              "\n"
            ],
            "text/plain": [
              "  gutenberg_id title                           author                     \n",
              "1   768        Wuthering Heights               Brontë, Emily              \n",
              "2 14155        Madame Bovary                   Flaubert, Gustave          \n",
              "3  2229        Faust: Der Tragödie erster Teil Goethe, Johann Wolfgang von\n",
              "  gutenberg_author_id language\n",
              "1 405                 en      \n",
              "2 574                 fr      \n",
              "3 586                 de      \n",
              "  gutenberg_bookshelf                                                         \n",
              "1 Gothic Fiction/Movie Books/Best Books Ever Listings                         \n",
              "2 Best Books Ever Listings/FR Littérature/Banned Books from Anne Haight's list\n",
              "3 Harvard Classics/Best Books Ever Listings/DE Drama                          \n",
              "  rights                    has_text\n",
              "1 Public domain in the USA. TRUE    \n",
              "2 Public domain in the USA. TRUE    \n",
              "3 Public domain in the USA. TRUE    "
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "get_books_meta <- function(eng, fr, de) {\n",
        "  # This function takes titles of 3 books in english\n",
        "  # french and german and returns a dataframe of meta information\n",
        "  english.meta <- gutenberg_metadata %>%\n",
        "    filter(title == eng) %>%\n",
        "    filter(language == \"en\")\n",
        "  french.meta <- gutenberg_metadata %>%\n",
        "    filter(title == fr) %>%\n",
        "    filter(language == \"fr\")\n",
        "  german.meta <- gutenberg_metadata %>%\n",
        "    filter(title == de) %>%\n",
        "    filter(language == \"de\")\n",
        "  return(rbind(english.meta, french.meta, german.meta))\n",
        "}\n",
        "\n",
        "eng.title <- \"Wuthering Heights\"\n",
        "fr.title <- \"Madame Bovary\"\n",
        "de.title <- \"Faust: Der Tragödie erster Teil\"\n",
        "\n",
        "# Get meta infos\n",
        "metas <- get_books_meta(eng.title, fr.title, de.title)\n",
        "metas"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Explication de la fonction main\n",
        "\n",
        "- Dans `main`, on exécute la fonction `welcome_message` qui affiche les informations des 3 oeuvres."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "ExecuteTime": {
          "end_time": "2021-03-21T16:06:33.215120Z",
          "start_time": "2021-03-21T16:06:33.202Z"
        }
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Here are the books in our database.\n",
            "\n",
            "[1] \"book id   book title                      language\"\n",
            "[1] \"768       Wuthering Heights                     en\" \n",
            "[2] \"14155     Madame Bovary                         fr\" \n",
            "[3] \"2229      Faust: Der Tragödie erster Teil        de\"\n",
            "\n",
            "\n",
            "\n",
            "Tell us the book id on which you want to test the zipf's law.\n",
            "\n",
            "\n"
          ]
        }
      ],
      "source": [
        "welcome_message <- function(metas) {\n",
        "  # This functions takes a df of metainfo and prints the information in a human-friendly fashion #\n",
        "  cat(\"Here are the books in our database.\\n\\n\")\n",
        "  print(sprintf(\"%-10s%-30s%10s\", \"book id\", \"book title\", \"language\"))\n",
        "  print(sprintf(\"%-10d%-30s%10s\", metas$gutenberg_id, metas$title, metas$language))\n",
        "  cat(\"\\n\\n\\nTell us the book id on which you want to test the zipf's law.\\n\\n\\n\")\n",
        "}\n",
        "welcome_message(metas)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "- Ensuite on demande à l'utilisateur de saisir le book id. Notez que si le book id ne figure pas dans la base de données on affiche un avertissement et relance la fonction `main`.\n",
        "\n",
        "```r\n",
        "book.id <- readline()\n",
        "# convert the book id into integer\n",
        "book.id <- as.integer(book.id)\n",
        "# check id validity\n",
        "if (!(book.id %in% metas$gutenberg_id)) {\n",
        "cat(\"\\n\\n\\U0001f609!Book id not found!\\U0001f609 Please type an id existing in our database.\\n\\n\")\n",
        "main(metas)\n",
        "}\n",
        "```"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "- Si le book id figure dans la base de donnée on stocke le titre du livre et normalise le texte.\n",
        "\n",
        "```r\n",
        "book.chosen <- metas %>% filter(gutenberg_id == book.id)\n",
        "book.title <- book.chosen$title\n",
        "# normalize the long text and convert to words\n",
        "text <- normalize_text(book.id)\n",
        "```"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "- La fonction `normalize_text` fait notamment les choses suivantes :\n",
        "\n",
        "  - Normalisation proprement dite : transformer toutes lettres en minuscules, remplacer les non caractères (ponctuation) et les whitespaces par l'espace, supprimer les espaces au début et à la fin des lignes de textes, supprimer les espaces multiples, supprimer les lignes vides etc.\n",
        "  - Coller les éléments du vecteur texte en un vecteur de caractères de longueur 1.\n",
        "  - Découper le vecteur de caractères de longueur 1 en mots et retourner les mots."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {
        "ExecuteTime": {
          "end_time": "2021-03-21T16:18:26.909893Z",
          "start_time": "2021-03-21T16:18:26.899Z"
        }
      },
      "outputs": [],
      "source": [
        "normalize_text <- function(id) {\n",
        "  # This function takes a book id and returns normalized words vector #\n",
        "  text <- gutenberg_download(id, mirror = \"http://mirrors.xmission.com/gutenberg/\")\n",
        "  body <- text$text\n",
        "  # tackle non english texts\n",
        "  body <- iconv(body, \"latin1\", \"UTF-8\")\n",
        "  body.lower <- tolower(body)\n",
        "  # replace whitespace characters/non words by space for easy split\n",
        "  body.lower <- gsub(\"[^a-zA-Z\\\\s]\", \" \", body.lower)\n",
        "  # remove spaces at the beginning and end of texts\n",
        "  body.lower <- gsub(\"(^ | +$)\", \"\", body.lower) \n",
        "  # remove multiple spaces\n",
        "  body.lower <- gsub(\" +\", \" \", body.lower) \n",
        "  # remove empty lines\n",
        "  body.lower <- body.lower[which(body.lower != \"\")]\n",
        "  # make the whole text a vector of length 1\n",
        "  body.v <- paste(body.lower, collapse = \" \") \n",
        "  # split the text to words\n",
        "  body.words <- unlist(strsplit(body.v, \"\\\\W+\")) \n",
        "  return(body.words)\n",
        "}"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "- Ensuite on passe à la fonction `get_freq` qui retourne un dataframe dont les colonnes sont :\n",
        "  - `mot`\n",
        "  - `fréquence absolue`\n",
        "  - `fréquence relative`\n",
        "  - `rang`"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "ExecuteTime": {
          "end_time": "2021-03-21T16:08:24.155810Z",
          "start_time": "2021-03-21T16:08:24.145Z"
        }
      },
      "outputs": [],
      "source": [
        "get_freq <- function(text) {\n",
        "  # This function takes a words vector and returns a dataframe\n",
        "  # with word, count, relative freq, rank as columns\n",
        "  \n",
        "  # get frequency table and convert to tibble\n",
        "  text.freq <- table(text)\n",
        "  sorted_text.freq <- sort(text.freq, decreasing = TRUE)\n",
        "  df <- as_tibble(sorted_text.freq)\n",
        "  # reaarange the tibble to get rank info and relative frequency\n",
        "  df <- df %>%\n",
        "    rename(word = text) %>% # rename the default text column to word\n",
        "    mutate(total = sum(n)) %>% # create a total n column\n",
        "    mutate(\n",
        "      rank = row_number(), # create rank column\n",
        "      `term frequency` = n / total # create the relative frequency column\n",
        "    )\n",
        "  return(df)\n",
        "}"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2021-03-21T16:08:30.030725Z",
          "start_time": "2021-03-21T16:08:30.004Z"
        }
      },
      "source": [
        "- Ensuite la fonction `make_plots` génère un objet liste comprenant 3 graphes :\n",
        "  - Le premier affiche les 20 mots les plus fréquents du texte.\n",
        "  - Le deuxième affiche la distribution des fréquences relatives.\n",
        "  - Le troisième affiche sur l'axe X le logarithme du rang de chaque mot et l'axe Y le logarithme de sa fréquence relative."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 36,
      "metadata": {
        "ExecuteTime": {
          "end_time": "2021-03-21T16:21:48.767329Z",
          "start_time": "2021-03-21T16:21:48.756Z"
        }
      },
      "outputs": [],
      "source": [
        "make_plots <- function(df, title) {\n",
        "  # This function takes a df and title \n",
        "  # with 3 plots related to Zipf's law as output \n",
        "  # Plot scatterplot of top20 words (word vs freq)\n",
        "  p.top20 <- ggplot(df[1:20, ], aes(x = reorder(word, -n),\n",
        "  y = n, group = 1, label = word)) +\n",
        "    geom_text(check_overlap = TRUE) + # declare labels\n",
        "    ggtitle(paste(\"Top 20 most common words in\", title)) +\n",
        "    xlab(\"Word\") +\n",
        "    ylab(\"Absolute frequency\")\n",
        "  # plot the non-log relative frequency distribution\n",
        "  p.nonlog <- ggplot(df, aes(`term frequency`)) +\n",
        "    geom_histogram(show.legend = FALSE) +\n",
        "    xlim(NA, 0.0005) +\n",
        "    ggtitle(paste(\"Relative frequency distribution of words in\", title)) +\n",
        "    xlab(\"Relative frequency\") +\n",
        "    ylab(\"Percentage\")\n",
        "  # plot the log zip's distribution\n",
        "  p.log <- ggplot(df, aes(rank, `term frequency`)) +\n",
        "    geom_line(size = 1.1, alpha = 0.8, show.legend = FALSE) +\n",
        "    scale_x_log10() +\n",
        "    scale_y_log10() +\n",
        "    ggtitle(paste(\"Standard plot of Zipf's law using words in\", title)) +\n",
        "    xlab(\"Relative frequency (log)\") +\n",
        "    ylab(\"Rank\")\n",
        "  return(list(p.top20, p.nonlog, p.log))\n",
        "}"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Ensemble de la fonction main"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 37,
      "metadata": {
        "ExecuteTime": {
          "end_time": "2021-03-21T16:21:53.822336Z",
          "start_time": "2021-03-21T16:21:53.811Z"
        }
      },
      "outputs": [],
      "source": [
        "main <- function(metas) {\n",
        "  # This function asks the user to choose a book id\n",
        "  # displays top 20 most common words\n",
        "  # plot and save 3 plots illustrating the zipf's law\n",
        "  \n",
        "  welcome_message(metas)\n",
        "  book.id <- readline()\n",
        "  # convert the book id into integer\n",
        "  book.id <- as.integer(book.id)\n",
        "  # check id validity\n",
        "  if (!(book.id %in% metas$gutenberg_id)) {\n",
        "    cat(\"\\n\\n\\U0001f609!Book id not found!\\U0001f609 \n",
        "    Please type an id existing in our database.\\n\\n\")\n",
        "    main(metas)\n",
        "  } else {\n",
        "    book.chosen <- metas %>% filter(gutenberg_id == book.id)\n",
        "    book.title <- book.chosen$title\n",
        "    # normalize the long text and convert to words\n",
        "    text <- normalize_text(book.id)\n",
        "    # get frequency table as dataframe\n",
        "    df.freq <- get_freq(text)\n",
        "    # show top 20 most common words\n",
        "    cat(paste0(\"\\n\\n\\nThe book you chose was \", book.title, \".\\n\\n\\n\"))\n",
        "    cat(\"Here is the list of the top 20 most frequent words in this text.\\n\\n\\n\")\n",
        "    print(df.freq[1:20, ])\n",
        "    # make plots\n",
        "    plots <- make_plots(df.freq, book.title)\n",
        "    # combine and show all 3 graphes\n",
        "    grid.arrange(plots[[1]], plots[[2]], plots[[3]], nrow = 3)\n",
        "    # save the plot we don't use it for the jupyter notebook illustrated on website\n",
        "#     g <- arrangeGrob(plots[[1]], plots[[2]], plots[[3]], nrow = 3)\n",
        "#     ggsave(file = paste0(\"zipf_\", book.title, \"_xiaoouWang.png\"), g)\n",
        "  }\n",
        "}"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Interprétations des graphes\n",
        "\n",
        "On utilisel ici le livre `Faust` comme exemple principal."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 38,
      "metadata": {
        "ExecuteTime": {
          "end_time": "2021-03-21T16:22:02.208769Z",
          "start_time": "2021-03-21T16:21:57.520Z"
        }
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "2229\n",
            "Here are the books in our database.\n",
            "\n",
            "[1] \"book id   book title                      language\"\n",
            "[1] \"768       Wuthering Heights                     en\" \n",
            "[2] \"14155     Madame Bovary                         fr\" \n",
            "[3] \"2229      Faust: Der Tragödie erster Teil        de\"\n",
            "\n",
            "\n",
            "\n",
            "Tell us the book id on which you want to test the zipf's law.\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "The book you chose was Faust: Der Tragödie erster Teil.\n",
            "\n",
            "\n",
            "Here is the list of the top 20 most frequent words in this text.\n",
            "\n",
            "\n",
            "\u001b[38;5;246m# A tibble: 20 x 5\u001b[39m\n",
            "   word               n total  rank `term frequency`\n",
            "   \u001b[3m\u001b[38;5;246m<chr>\u001b[39m\u001b[23m          \u001b[3m\u001b[38;5;246m<int>\u001b[39m\u001b[23m \u001b[3m\u001b[38;5;246m<int>\u001b[39m\u001b[23m \u001b[3m\u001b[38;5;246m<int>\u001b[39m\u001b[23m            \u001b[3m\u001b[38;5;246m<dbl>\u001b[39m\u001b[23m\n",
            "\u001b[38;5;250m 1\u001b[39m und              919 \u001b[4m3\u001b[24m\u001b[4m3\u001b[24m225     1          0.027\u001b[4m7\u001b[24m \n",
            "\u001b[38;5;250m 2\u001b[39m ich              701 \u001b[4m3\u001b[24m\u001b[4m3\u001b[24m225     2          0.021\u001b[4m1\u001b[24m \n",
            "\u001b[38;5;250m 3\u001b[39m die              670 \u001b[4m3\u001b[24m\u001b[4m3\u001b[24m225     3          0.020\u001b[4m2\u001b[24m \n",
            "\u001b[38;5;250m 4\u001b[39m der              607 \u001b[4m3\u001b[24m\u001b[4m3\u001b[24m225     4          0.018\u001b[4m3\u001b[24m \n",
            "\u001b[38;5;250m 5\u001b[39m nicht            426 \u001b[4m3\u001b[24m\u001b[4m3\u001b[24m225     5          0.012\u001b[4m8\u001b[24m \n",
            "\u001b[38;5;250m 6\u001b[39m das              402 \u001b[4m3\u001b[24m\u001b[4m3\u001b[24m225     6          0.012\u001b[4m1\u001b[24m \n",
            "\u001b[38;5;250m 7\u001b[39m ein              399 \u001b[4m3\u001b[24m\u001b[4m3\u001b[24m225     7          0.012\u001b[4m0\u001b[24m \n",
            "\u001b[38;5;250m 8\u001b[39m ist              390 \u001b[4m3\u001b[24m\u001b[4m3\u001b[24m225     8          0.011\u001b[4m7\u001b[24m \n",
            "\u001b[38;5;250m 9\u001b[39m zu               380 \u001b[4m3\u001b[24m\u001b[4m3\u001b[24m225     9          0.011\u001b[4m4\u001b[24m \n",
            "\u001b[38;5;250m10\u001b[39m du               319 \u001b[4m3\u001b[24m\u001b[4m3\u001b[24m225    10          0.009\u001b[4m6\u001b[24m\u001b[4m0\u001b[24m\n",
            "\u001b[38;5;250m11\u001b[39m in               309 \u001b[4m3\u001b[24m\u001b[4m3\u001b[24m225    11          0.009\u001b[4m3\u001b[24m\u001b[4m0\u001b[24m\n",
            "\u001b[38;5;250m12\u001b[39m sie              304 \u001b[4m3\u001b[24m\u001b[4m3\u001b[24m225    12          0.009\u001b[4m1\u001b[24m\u001b[4m5\u001b[24m\n",
            "\u001b[38;5;250m13\u001b[39m es               302 \u001b[4m3\u001b[24m\u001b[4m3\u001b[24m225    13          0.009\u001b[4m0\u001b[24m\u001b[4m9\u001b[24m\n",
            "\u001b[38;5;250m14\u001b[39m so               292 \u001b[4m3\u001b[24m\u001b[4m3\u001b[24m225    14          0.008\u001b[4m7\u001b[24m\u001b[4m9\u001b[24m\n",
            "\u001b[38;5;250m15\u001b[39m mephistopheles   283 \u001b[4m3\u001b[24m\u001b[4m3\u001b[24m225    15          0.008\u001b[4m5\u001b[24m\u001b[4m2\u001b[24m\n",
            "\u001b[38;5;250m16\u001b[39m den              279 \u001b[4m3\u001b[24m\u001b[4m3\u001b[24m225    16          0.008\u001b[4m4\u001b[24m\u001b[4m0\u001b[24m\n",
            "\u001b[38;5;250m17\u001b[39m mit              274 \u001b[4m3\u001b[24m\u001b[4m3\u001b[24m225    17          0.008\u001b[4m2\u001b[24m\u001b[4m5\u001b[24m\n",
            "\u001b[38;5;250m18\u001b[39m sich             267 \u001b[4m3\u001b[24m\u001b[4m3\u001b[24m225    18          0.008\u001b[4m0\u001b[24m\u001b[4m4\u001b[24m\n",
            "\u001b[38;5;250m19\u001b[39m faust            266 \u001b[4m3\u001b[24m\u001b[4m3\u001b[24m225    19          0.008\u001b[4m0\u001b[24m\u001b[4m1\u001b[24m\n",
            "\u001b[38;5;250m20\u001b[39m mir              264 \u001b[4m3\u001b[24m\u001b[4m3\u001b[24m225    20          0.007\u001b[4m9\u001b[24m\u001b[4m5\u001b[24m\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.\n",
            "\n",
            "Warning message:\n",
            "“Removed 256 rows containing non-finite values (stat_bin).”\n",
            "Warning message:\n",
            "“Removed 1 rows containing missing values (geom_bar).”\n"
          ]
        },
        {
          "data": {
            "image/png": 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I8iO6Z8CYz8xOmnn568Tv7e534LOoES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\nX3XGeaF7EPkic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        "### 6.1 Graphe des 20 mots les plus courants\n",
        "\n",
        "On remarquera surtout que les tokens les plus fréquents sont des mots fonctionnels, excepté `faust` et `mephistopheles` qui sont les deux personnages principaux de l'histoire.\n",
        "\n",
        "### 6.2 Gaphe de la distribution des fréquences relatives\n",
        "\n",
        "On remarquera surtout que les tokens fréquents (à gauche) et les tokens rares (à droite) coexistent dans le même texte.\n",
        "\n",
        "La distribution n'est pas du tout une distribution normale. Elle a une longue queue (beaucoup de mots rares). Les mots les plus fréquents occupent déjà une grande partie du texte.\n",
        "\n",
        "Il est à noter que le choix de `binwidth` est important pour rendre le graph plus lisse, comme on peut remarquer sur le même graphe de `Wuthering Heights` (plus bas). Cela montre l'intérêt du graphe 3 qui illustre mieux et en termes plus concrets la loi de Zipf.\n",
        "\n",
        "### 6.3 Graphe des logarithmes des rangs et des fréquences relatives\n",
        "\n",
        "En effet, la loi de Zipf (d'abord formulée par Jean-Baptiste Estoup) stipule que dans un texte donné, la fréquence d'occurrence `f(n)` (nous prenons ici la fréquence relative) d'un mot est liée à son rang `n` dans l'ordre des fréquences par une loi de la forme $$f(n)=\\frac{K}{n}$$ où K est une constante.\n",
        "\n",
        "Si l'on prend les logarithmes de chaque côté, la relation devient linéaire et théoriquement on doit observer une ligne droite, ce qui n'est pas le cas ici. Cela montre que `bien que la formule de Zipf donne l’allure générale des courbes, elle en représente très mal les détails` (Mandelbrot (1954: 12))."
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        "## Références\n",
        "\n",
        "Mandelbrot, Benoit. 1954. Structure formelle des textes et communcation. Word 10:1–27.\n",
        "\n",
        "Cours de Guillaume Desagulier intitulé linguistique outillée et traitements statistiques : https://corpling.modyco.fr/workshops/M2TAL/2.descriptive.stats.html"
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