NLPF_01_R.ipynb

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  "metadata": {
    "colab": {
      "name": "NLPF-01-R",
      "provenance": [],
      "authorship_tag": "ABX9TyPCW4KO46TLWWqALl+OgkLz",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "ir",
      "display_name": "R"
    },
    "language_info": {
      "name": "R"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/satuelisa/NLPF/blob/main/NLPF_01_R.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "pM2QwdGj-drW",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b198fda6-ec99-4ddf-a614-38f49b049436"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Installing package into ‘/usr/local/lib/R/site-library’\n",
            "(as ‘lib’ is unspecified)\n",
            "\n",
            "also installing the dependencies ‘Rcpp’, ‘triebeard’, ‘lazyeval’, ‘urltools’\n",
            "\n",
            "\n"
          ]
        }
      ],
      "source": [
        "install.packages('gutenbergr') # check https://cran.r-project.org/web/packages/gutenbergr/vignettes/intro.html for documentation, this is like using pip in python\n",
        "library(gutenbergr)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "mirror <- \"http://mirrors.xmission.com/gutenberg/\"\n",
        "book <- gutenberg_download(768, mirror = mirror) # this one is Wuthering Heights"
      ],
      "metadata": {
        "id": "OLDRH8cvkKsT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "What did we get, actually? We can use *head* and *tail* to look at how it starts and ends."
      ],
      "metadata": {
        "id": "mbEfOQF6lRXm"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "head(book)\n",
        "tail(book)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 556
        },
        "id": "_vG_BWPElTAw",
        "outputId": "1dffcd78-980e-4618-f53a-ba9d6a22ab11"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<table class=\"dataframe\">\n",
              "<caption>A tibble: 6 × 2</caption>\n",
              "<thead>\n",
              "\t<tr><th scope=col>gutenberg_id</th><th scope=col>text</th></tr>\n",
              "\t<tr><th scope=col>&lt;int&gt;</th><th scope=col>&lt;chr&gt;</th></tr>\n",
              "</thead>\n",
              "<tbody>\n",
              "\t<tr><td>768</td><td>Wuthering Heights</td></tr>\n",
              "\t<tr><td>768</td><td>                 </td></tr>\n",
              "\t<tr><td>768</td><td>by Emily Brontë  </td></tr>\n",
              "\t<tr><td>768</td><td>                 </td></tr>\n",
              "\t<tr><td>768</td><td>                 </td></tr>\n",
              "\t<tr><td>768</td><td>                 </td></tr>\n",
              "</tbody>\n",
              "</table>\n"
            ],
            "text/markdown": "\nA tibble: 6 × 2\n\n| gutenberg_id &lt;int&gt; | text &lt;chr&gt; |\n|---|---|\n| 768 | Wuthering Heights |\n| 768 | <!----> |\n| 768 | by Emily Brontë   |\n| 768 | <!----> |\n| 768 | <!----> |\n| 768 | <!----> |\n\n",
            "text/latex": "A tibble: 6 × 2\n\\begin{tabular}{ll}\n gutenberg\\_id & text\\\\\n <int> & <chr>\\\\\n\\hline\n\t 768 & Wuthering Heights\\\\\n\t 768 &                  \\\\\n\t 768 & by Emily Brontë  \\\\\n\t 768 &                  \\\\\n\t 768 &                  \\\\\n\t 768 &                  \\\\\n\\end{tabular}\n",
            "text/plain": [
              "  gutenberg_id text             \n",
              "1 768          Wuthering Heights\n",
              "2 768                           \n",
              "3 768          by Emily Brontë  \n",
              "4 768                           \n",
              "5 768                           \n",
              "6 768                           "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<table class=\"dataframe\">\n",
              "<caption>A tibble: 6 × 2</caption>\n",
              "<thead>\n",
              "\t<tr><th scope=col>gutenberg_id</th><th scope=col>text</th></tr>\n",
              "\t<tr><th scope=col>&lt;int&gt;</th><th scope=col>&lt;chr&gt;</th></tr>\n",
              "</thead>\n",
              "<tbody>\n",
              "\t<tr><td>768</td><td>still bare.                                                        </td></tr>\n",
              "\t<tr><td>768</td><td>                                                                   </td></tr>\n",
              "\t<tr><td>768</td><td>I lingered round them, under that benign sky: watched the moths    </td></tr>\n",
              "\t<tr><td>768</td><td>fluttering among the heath and harebells, listened to the soft wind</td></tr>\n",
              "\t<tr><td>768</td><td>breathing through the grass, and wondered how any one could ever   </td></tr>\n",
              "\t<tr><td>768</td><td>imagine unquiet slumbers for the sleepers in that quiet earth.     </td></tr>\n",
              "</tbody>\n",
              "</table>\n"
            ],
            "text/markdown": "\nA tibble: 6 × 2\n\n| gutenberg_id &lt;int&gt; | text &lt;chr&gt; |\n|---|---|\n| 768 | still bare.                                                         |\n| 768 | <!----> |\n| 768 | I lingered round them, under that benign sky: watched the moths     |\n| 768 | fluttering among the heath and harebells, listened to the soft wind |\n| 768 | breathing through the grass, and wondered how any one could ever    |\n| 768 | imagine unquiet slumbers for the sleepers in that quiet earth.      |\n\n",
            "text/latex": "A tibble: 6 × 2\n\\begin{tabular}{ll}\n gutenberg\\_id & text\\\\\n <int> & <chr>\\\\\n\\hline\n\t 768 & still bare.                                                        \\\\\n\t 768 &                                                                    \\\\\n\t 768 & I lingered round them, under that benign sky: watched the moths    \\\\\n\t 768 & fluttering among the heath and harebells, listened to the soft wind\\\\\n\t 768 & breathing through the grass, and wondered how any one could ever   \\\\\n\t 768 & imagine unquiet slumbers for the sleepers in that quiet earth.     \\\\\n\\end{tabular}\n",
            "text/plain": [
              "  gutenberg_id\n",
              "1 768         \n",
              "2 768         \n",
              "3 768         \n",
              "4 768         \n",
              "5 768         \n",
              "6 768         \n",
              "  text                                                               \n",
              "1 still bare.                                                        \n",
              "2                                                                    \n",
              "3 I lingered round them, under that benign sky: watched the moths    \n",
              "4 fluttering among the heath and harebells, listened to the soft wind\n",
              "5 breathing through the grass, and wondered how any one could ever   \n",
              "6 imagine unquiet slumbers for the sleepers in that quiet earth.     "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "So, we have a... *tibble*? It seems to contain one line of text per row. The textbook points us towards the *tidytext* library to chop this into chapters like we did in Python for *Pride and Prejudice*."
      ],
      "metadata": {
        "id": "0M_YC7ldlieR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "install.packages(\"tidytext\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HGuiyjsclxJy",
        "outputId": "2d5081b2-a19f-4288-f216-b9b7daf3b12f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Installing package into ‘/usr/local/lib/R/site-library’\n",
            "(as ‘lib’ is unspecified)\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "library(tidytext)\n",
        "library(dplyr) # should be pre-installed\n",
        "library(stringr) # as should this"
      ],
      "metadata": {
        "id": "opLGTz-fnCzp"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "clean <- book %>% mutate(linenumber = row_number(), chapter = cumsum(str_detect(text, regex(\"^chapter [\\\\divxlc]\", ignore_case = TRUE))))\n",
        "# that %>% is just the R way to write \"forward pipe\", much like | in linux\n",
        "head(clean)\n",
        "tail(clean)\n",
        "max(clean$chapter)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 573
        },
        "id": "ROONKhHtnC-4",
        "outputId": "470fd020-8d17-42c0-e30a-72e36b738cf8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<table class=\"dataframe\">\n",
              "<caption>A tibble: 6 × 4</caption>\n",
              "<thead>\n",
              "\t<tr><th scope=col>gutenberg_id</th><th scope=col>text</th><th scope=col>linenumber</th><th scope=col>chapter</th></tr>\n",
              "\t<tr><th scope=col>&lt;int&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th></tr>\n",
              "</thead>\n",
              "<tbody>\n",
              "\t<tr><td>768</td><td>Wuthering Heights</td><td>1</td><td>0</td></tr>\n",
              "\t<tr><td>768</td><td>                 </td><td>2</td><td>0</td></tr>\n",
              "\t<tr><td>768</td><td>by Emily Brontë  </td><td>3</td><td>0</td></tr>\n",
              "\t<tr><td>768</td><td>                 </td><td>4</td><td>0</td></tr>\n",
              "\t<tr><td>768</td><td>                 </td><td>5</td><td>0</td></tr>\n",
              "\t<tr><td>768</td><td>                 </td><td>6</td><td>0</td></tr>\n",
              "</tbody>\n",
              "</table>\n"
            ],
            "text/markdown": "\nA tibble: 6 × 4\n\n| gutenberg_id &lt;int&gt; | text &lt;chr&gt; | linenumber &lt;int&gt; | chapter &lt;int&gt; |\n|---|---|---|---|\n| 768 | Wuthering Heights | 1 | 0 |\n| 768 | <!----> | 2 | 0 |\n| 768 | by Emily Brontë   | 3 | 0 |\n| 768 | <!----> | 4 | 0 |\n| 768 | <!----> | 5 | 0 |\n| 768 | <!----> | 6 | 0 |\n\n",
            "text/latex": "A tibble: 6 × 4\n\\begin{tabular}{llll}\n gutenberg\\_id & text & linenumber & chapter\\\\\n <int> & <chr> & <int> & <int>\\\\\n\\hline\n\t 768 & Wuthering Heights & 1 & 0\\\\\n\t 768 &                   & 2 & 0\\\\\n\t 768 & by Emily Brontë   & 3 & 0\\\\\n\t 768 &                   & 4 & 0\\\\\n\t 768 &                   & 5 & 0\\\\\n\t 768 &                   & 6 & 0\\\\\n\\end{tabular}\n",
            "text/plain": [
              "  gutenberg_id text              linenumber chapter\n",
              "1 768          Wuthering Heights 1          0      \n",
              "2 768                            2          0      \n",
              "3 768          by Emily Brontë   3          0      \n",
              "4 768                            4          0      \n",
              "5 768                            5          0      \n",
              "6 768                            6          0      "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<table class=\"dataframe\">\n",
              "<caption>A tibble: 6 × 4</caption>\n",
              "<thead>\n",
              "\t<tr><th scope=col>gutenberg_id</th><th scope=col>text</th><th scope=col>linenumber</th><th scope=col>chapter</th></tr>\n",
              "\t<tr><th scope=col>&lt;int&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th></tr>\n",
              "</thead>\n",
              "<tbody>\n",
              "\t<tr><td>768</td><td>still bare.                                                        </td><td>12337</td><td>34</td></tr>\n",
              "\t<tr><td>768</td><td>                                                                   </td><td>12338</td><td>34</td></tr>\n",
              "\t<tr><td>768</td><td>I lingered round them, under that benign sky: watched the moths    </td><td>12339</td><td>34</td></tr>\n",
              "\t<tr><td>768</td><td>fluttering among the heath and harebells, listened to the soft wind</td><td>12340</td><td>34</td></tr>\n",
              "\t<tr><td>768</td><td>breathing through the grass, and wondered how any one could ever   </td><td>12341</td><td>34</td></tr>\n",
              "\t<tr><td>768</td><td>imagine unquiet slumbers for the sleepers in that quiet earth.     </td><td>12342</td><td>34</td></tr>\n",
              "</tbody>\n",
              "</table>\n"
            ],
            "text/markdown": "\nA tibble: 6 × 4\n\n| gutenberg_id &lt;int&gt; | text &lt;chr&gt; | linenumber &lt;int&gt; | chapter &lt;int&gt; |\n|---|---|---|---|\n| 768 | still bare.                                                         | 12337 | 34 |\n| 768 | <!----> | 12338 | 34 |\n| 768 | I lingered round them, under that benign sky: watched the moths     | 12339 | 34 |\n| 768 | fluttering among the heath and harebells, listened to the soft wind | 12340 | 34 |\n| 768 | breathing through the grass, and wondered how any one could ever    | 12341 | 34 |\n| 768 | imagine unquiet slumbers for the sleepers in that quiet earth.      | 12342 | 34 |\n\n",
            "text/latex": "A tibble: 6 × 4\n\\begin{tabular}{llll}\n gutenberg\\_id & text & linenumber & chapter\\\\\n <int> & <chr> & <int> & <int>\\\\\n\\hline\n\t 768 & still bare.                                                         & 12337 & 34\\\\\n\t 768 &                                                                     & 12338 & 34\\\\\n\t 768 & I lingered round them, under that benign sky: watched the moths     & 12339 & 34\\\\\n\t 768 & fluttering among the heath and harebells, listened to the soft wind & 12340 & 34\\\\\n\t 768 & breathing through the grass, and wondered how any one could ever    & 12341 & 34\\\\\n\t 768 & imagine unquiet slumbers for the sleepers in that quiet earth.      & 12342 & 34\\\\\n\\end{tabular}\n",
            "text/plain": [
              "  gutenberg_id\n",
              "1 768         \n",
              "2 768         \n",
              "3 768         \n",
              "4 768         \n",
              "5 768         \n",
              "6 768         \n",
              "  text                                                               \n",
              "1 still bare.                                                        \n",
              "2                                                                    \n",
              "3 I lingered round them, under that benign sky: watched the moths    \n",
              "4 fluttering among the heath and harebells, listened to the soft wind\n",
              "5 breathing through the grass, and wondered how any one could ever   \n",
              "6 imagine unquiet slumbers for the sleepers in that quiet earth.     \n",
              "  linenumber chapter\n",
              "1 12337      34     \n",
              "2 12338      34     \n",
              "3 12339      34     \n",
              "4 12340      34     \n",
              "5 12341      34     \n",
              "6 12342      34     "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "34"
            ],
            "text/markdown": "34",
            "text/latex": "34",
            "text/plain": [
              "[1] 34"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tidy <- clean %>% unnest_tokens(word, text)\n",
        "head(tidy)\n",
        "tail(tidy)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 556
        },
        "id": "2Ip7Ugh-nmKE",
        "outputId": "80d36316-0b25-447d-e90f-8ae3252fc38d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<table class=\"dataframe\">\n",
              "<caption>A tibble: 6 × 4</caption>\n",
              "<thead>\n",
              "\t<tr><th scope=col>gutenberg_id</th><th scope=col>linenumber</th><th scope=col>chapter</th><th scope=col>word</th></tr>\n",
              "\t<tr><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;chr&gt;</th></tr>\n",
              "</thead>\n",
              "<tbody>\n",
              "\t<tr><td>768</td><td>1</td><td>0</td><td>wuthering</td></tr>\n",
              "\t<tr><td>768</td><td>1</td><td>0</td><td>heights  </td></tr>\n",
              "\t<tr><td>768</td><td>3</td><td>0</td><td>by       </td></tr>\n",
              "\t<tr><td>768</td><td>3</td><td>0</td><td>emily    </td></tr>\n",
              "\t<tr><td>768</td><td>3</td><td>0</td><td>brontë   </td></tr>\n",
              "\t<tr><td>768</td><td>8</td><td>1</td><td>chapter  </td></tr>\n",
              "</tbody>\n",
              "</table>\n"
            ],
            "text/markdown": "\nA tibble: 6 × 4\n\n| gutenberg_id &lt;int&gt; | linenumber &lt;int&gt; | chapter &lt;int&gt; | word &lt;chr&gt; |\n|---|---|---|---|\n| 768 | 1 | 0 | wuthering |\n| 768 | 1 | 0 | heights   |\n| 768 | 3 | 0 | by        |\n| 768 | 3 | 0 | emily     |\n| 768 | 3 | 0 | brontë    |\n| 768 | 8 | 1 | chapter   |\n\n",
            "text/latex": "A tibble: 6 × 4\n\\begin{tabular}{llll}\n gutenberg\\_id & linenumber & chapter & word\\\\\n <int> & <int> & <int> & <chr>\\\\\n\\hline\n\t 768 & 1 & 0 & wuthering\\\\\n\t 768 & 1 & 0 & heights  \\\\\n\t 768 & 3 & 0 & by       \\\\\n\t 768 & 3 & 0 & emily    \\\\\n\t 768 & 3 & 0 & brontë   \\\\\n\t 768 & 8 & 1 & chapter  \\\\\n\\end{tabular}\n",
            "text/plain": [
              "  gutenberg_id linenumber chapter word     \n",
              "1 768          1          0       wuthering\n",
              "2 768          1          0       heights  \n",
              "3 768          3          0       by       \n",
              "4 768          3          0       emily    \n",
              "5 768          3          0       brontë   \n",
              "6 768          8          1       chapter  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<table class=\"dataframe\">\n",
              "<caption>A tibble: 6 × 4</caption>\n",
              "<thead>\n",
              "\t<tr><th scope=col>gutenberg_id</th><th scope=col>linenumber</th><th scope=col>chapter</th><th scope=col>word</th></tr>\n",
              "\t<tr><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;chr&gt;</th></tr>\n",
              "</thead>\n",
              "<tbody>\n",
              "\t<tr><td>768</td><td>12342</td><td>34</td><td>the     </td></tr>\n",
              "\t<tr><td>768</td><td>12342</td><td>34</td><td>sleepers</td></tr>\n",
              "\t<tr><td>768</td><td>12342</td><td>34</td><td>in      </td></tr>\n",
              "\t<tr><td>768</td><td>12342</td><td>34</td><td>that    </td></tr>\n",
              "\t<tr><td>768</td><td>12342</td><td>34</td><td>quiet   </td></tr>\n",
              "\t<tr><td>768</td><td>12342</td><td>34</td><td>earth   </td></tr>\n",
              "</tbody>\n",
              "</table>\n"
            ],
            "text/markdown": "\nA tibble: 6 × 4\n\n| gutenberg_id &lt;int&gt; | linenumber &lt;int&gt; | chapter &lt;int&gt; | word &lt;chr&gt; |\n|---|---|---|---|\n| 768 | 12342 | 34 | the      |\n| 768 | 12342 | 34 | sleepers |\n| 768 | 12342 | 34 | in       |\n| 768 | 12342 | 34 | that     |\n| 768 | 12342 | 34 | quiet    |\n| 768 | 12342 | 34 | earth    |\n\n",
            "text/latex": "A tibble: 6 × 4\n\\begin{tabular}{llll}\n gutenberg\\_id & linenumber & chapter & word\\\\\n <int> & <int> & <int> & <chr>\\\\\n\\hline\n\t 768 & 12342 & 34 & the     \\\\\n\t 768 & 12342 & 34 & sleepers\\\\\n\t 768 & 12342 & 34 & in      \\\\\n\t 768 & 12342 & 34 & that    \\\\\n\t 768 & 12342 & 34 & quiet   \\\\\n\t 768 & 12342 & 34 & earth   \\\\\n\\end{tabular}\n",
            "text/plain": [
              "  gutenberg_id linenumber chapter word    \n",
              "1 768          12342      34      the     \n",
              "2 768          12342      34      sleepers\n",
              "3 768          12342      34      in      \n",
              "4 768          12342      34      that    \n",
              "5 768          12342      34      quiet   \n",
              "6 768          12342      34      earth   "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now we can eliminate stop words like we did in Python using NLTK."
      ],
      "metadata": {
        "id": "hk6kkUJjnv_c"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data(stop_words)\n",
        "relevant <- tidy %>% anti_join(stop_words)\n",
        "head(relevant)\n",
        "tail(relevant)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 573
        },
        "id": "Vz7v-kpqn0UG",
        "outputId": "1a6747eb-636f-4ff9-db6b-ed93ab6e8b99"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\u001b[1m\u001b[22mJoining, by = \"word\"\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<table class=\"dataframe\">\n",
              "<caption>A tibble: 6 × 4</caption>\n",
              "<thead>\n",
              "\t<tr><th scope=col>gutenberg_id</th><th scope=col>linenumber</th><th scope=col>chapter</th><th scope=col>word</th></tr>\n",
              "\t<tr><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;chr&gt;</th></tr>\n",
              "</thead>\n",
              "<tbody>\n",
              "\t<tr><td>768</td><td> 1</td><td>0</td><td>wuthering</td></tr>\n",
              "\t<tr><td>768</td><td> 1</td><td>0</td><td>heights  </td></tr>\n",
              "\t<tr><td>768</td><td> 3</td><td>0</td><td>emily    </td></tr>\n",
              "\t<tr><td>768</td><td> 3</td><td>0</td><td>brontë   </td></tr>\n",
              "\t<tr><td>768</td><td> 8</td><td>1</td><td>chapter  </td></tr>\n",
              "\t<tr><td>768</td><td>11</td><td>1</td><td>1801     </td></tr>\n",
              "</tbody>\n",
              "</table>\n"
            ],
            "text/markdown": "\nA tibble: 6 × 4\n\n| gutenberg_id &lt;int&gt; | linenumber &lt;int&gt; | chapter &lt;int&gt; | word &lt;chr&gt; |\n|---|---|---|---|\n| 768 |  1 | 0 | wuthering |\n| 768 |  1 | 0 | heights   |\n| 768 |  3 | 0 | emily     |\n| 768 |  3 | 0 | brontë    |\n| 768 |  8 | 1 | chapter   |\n| 768 | 11 | 1 | 1801      |\n\n",
            "text/latex": "A tibble: 6 × 4\n\\begin{tabular}{llll}\n gutenberg\\_id & linenumber & chapter & word\\\\\n <int> & <int> & <int> & <chr>\\\\\n\\hline\n\t 768 &  1 & 0 & wuthering\\\\\n\t 768 &  1 & 0 & heights  \\\\\n\t 768 &  3 & 0 & emily    \\\\\n\t 768 &  3 & 0 & brontë   \\\\\n\t 768 &  8 & 1 & chapter  \\\\\n\t 768 & 11 & 1 & 1801     \\\\\n\\end{tabular}\n",
            "text/plain": [
              "  gutenberg_id linenumber chapter word     \n",
              "1 768           1         0       wuthering\n",
              "2 768           1         0       heights  \n",
              "3 768           3         0       emily    \n",
              "4 768           3         0       brontë   \n",
              "5 768           8         1       chapter  \n",
              "6 768          11         1       1801     "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<table class=\"dataframe\">\n",
              "<caption>A tibble: 6 × 4</caption>\n",
              "<thead>\n",
              "\t<tr><th scope=col>gutenberg_id</th><th scope=col>linenumber</th><th scope=col>chapter</th><th scope=col>word</th></tr>\n",
              "\t<tr><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;chr&gt;</th></tr>\n",
              "</thead>\n",
              "<tbody>\n",
              "\t<tr><td>768</td><td>12342</td><td>34</td><td>imagine </td></tr>\n",
              "\t<tr><td>768</td><td>12342</td><td>34</td><td>unquiet </td></tr>\n",
              "\t<tr><td>768</td><td>12342</td><td>34</td><td>slumbers</td></tr>\n",
              "\t<tr><td>768</td><td>12342</td><td>34</td><td>sleepers</td></tr>\n",
              "\t<tr><td>768</td><td>12342</td><td>34</td><td>quiet   </td></tr>\n",
              "\t<tr><td>768</td><td>12342</td><td>34</td><td>earth   </td></tr>\n",
              "</tbody>\n",
              "</table>\n"
            ],
            "text/markdown": "\nA tibble: 6 × 4\n\n| gutenberg_id &lt;int&gt; | linenumber &lt;int&gt; | chapter &lt;int&gt; | word &lt;chr&gt; |\n|---|---|---|---|\n| 768 | 12342 | 34 | imagine  |\n| 768 | 12342 | 34 | unquiet  |\n| 768 | 12342 | 34 | slumbers |\n| 768 | 12342 | 34 | sleepers |\n| 768 | 12342 | 34 | quiet    |\n| 768 | 12342 | 34 | earth    |\n\n",
            "text/latex": "A tibble: 6 × 4\n\\begin{tabular}{llll}\n gutenberg\\_id & linenumber & chapter & word\\\\\n <int> & <int> & <int> & <chr>\\\\\n\\hline\n\t 768 & 12342 & 34 & imagine \\\\\n\t 768 & 12342 & 34 & unquiet \\\\\n\t 768 & 12342 & 34 & slumbers\\\\\n\t 768 & 12342 & 34 & sleepers\\\\\n\t 768 & 12342 & 34 & quiet   \\\\\n\t 768 & 12342 & 34 & earth   \\\\\n\\end{tabular}\n",
            "text/plain": [
              "  gutenberg_id linenumber chapter word    \n",
              "1 768          12342      34      imagine \n",
              "2 768          12342      34      unquiet \n",
              "3 768          12342      34      slumbers\n",
              "4 768          12342      34      sleepers\n",
              "5 768          12342      34      quiet   \n",
              "6 768          12342      34      earth   "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We can access the word frequencies with ease."
      ],
      "metadata": {
        "id": "e78vf2ZnoDX-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "library(ggplot2)\n",
        "threshold <- 150\n",
        "freq <- relevant %>% count(word, sort = TRUE)\n",
        "head(freq)\n",
        "top <- freq %>% filter (n > threshold) %>% mutate(word = reorder(word, n))\n",
        "ggplot(data = top, aes(word, n)) + geom_col() + coord_flip() # horizontal bar chart"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 706
        },
        "id": "YNvSOL0ZoGK9",
        "outputId": "61a80d59-7596-4835-d547-6c9f1676fdd8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<table class=\"dataframe\">\n",
              "<caption>A tibble: 6 × 2</caption>\n",
              "<thead>\n",
              "\t<tr><th scope=col>word</th><th scope=col>n</th></tr>\n",
              "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;int&gt;</th></tr>\n",
              "</thead>\n",
              "<tbody>\n",
              "\t<tr><td>heathcliff</td><td>421</td></tr>\n",
              "\t<tr><td>linton    </td><td>346</td></tr>\n",
              "\t<tr><td>catherine </td><td>336</td></tr>\n",
              "\t<tr><td>i’m       </td><td>192</td></tr>\n",
              "\t<tr><td>i’ll      </td><td>189</td></tr>\n",
              "\t<tr><td>master    </td><td>185</td></tr>\n",
              "</tbody>\n",
              "</table>\n"
            ],
            "text/markdown": "\nA tibble: 6 × 2\n\n| word &lt;chr&gt; | n &lt;int&gt; |\n|---|---|\n| heathcliff | 421 |\n| linton     | 346 |\n| catherine  | 336 |\n| i’m        | 192 |\n| i’ll       | 189 |\n| master     | 185 |\n\n",
            "text/latex": "A tibble: 6 × 2\n\\begin{tabular}{ll}\n word & n\\\\\n <chr> & <int>\\\\\n\\hline\n\t heathcliff & 421\\\\\n\t linton     & 346\\\\\n\t catherine  & 336\\\\\n\t i’m        & 192\\\\\n\t i’ll       & 189\\\\\n\t master     & 185\\\\\n\\end{tabular}\n",
            "text/plain": [
              "  word       n  \n",
              "1 heathcliff 421\n",
              "2 linton     346\n",
              "3 catherine  336\n",
              "4 i’m        192\n",
              "5 i’ll       189\n",
              "6 master     185"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "plot without title"
            ],
            "image/png": 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JDckj46QhIjJLekj46QxAjJLemjIyQxQnJL+ugISYyQ3JI+OkISIyS3pI+OkMQI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\nrkJggg=="
          },
          "metadata": {
            "image/png": {
              "width": 420,
              "height": 420
            }
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "How about the document-term frequencies?"
      ],
      "metadata": {
        "id": "_EZIgYTGplIL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "cf <- relevant %>% count(word, chapter) # frequencies of words per chapter\n",
        "head(cf)\n",
        "threshold <- 5 # now, mind you, this is a chapter-level threshold, whereas in Python we used a book-level one\n",
        "fcf <- cf %>% filter(n > threshold) # a filtered version to keep the matrix small so we can browse it easily\n",
        "head(fcf)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 556
        },
        "id": "_H0fGYrWpn4m",
        "outputId": "8e7f1e06-c4a1-49a5-f5b5-23d52ee54fce"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<table class=\"dataframe\">\n",
              "<caption>A tibble: 6 × 3</caption>\n",
              "<thead>\n",
              "\t<tr><th scope=col>word</th><th scope=col>chapter</th><th scope=col>n</th></tr>\n",
              "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th></tr>\n",
              "</thead>\n",
              "<tbody>\n",
              "\t<tr><td>_all_   </td><td>13</td><td>1</td></tr>\n",
              "\t<tr><td>_almost_</td><td>29</td><td>1</td></tr>\n",
              "\t<tr><td>_almost_</td><td>34</td><td>1</td></tr>\n",
              "\t<tr><td>_am_    </td><td> 9</td><td>1</td></tr>\n",
              "\t<tr><td>_am_    </td><td>24</td><td>1</td></tr>\n",
              "\t<tr><td>_am_    </td><td>27</td><td>1</td></tr>\n",
              "</tbody>\n",
              "</table>\n"
            ],
            "text/markdown": "\nA tibble: 6 × 3\n\n| word &lt;chr&gt; | chapter &lt;int&gt; | n &lt;int&gt; |\n|---|---|---|\n| _all_    | 13 | 1 |\n| _almost_ | 29 | 1 |\n| _almost_ | 34 | 1 |\n| _am_     |  9 | 1 |\n| _am_     | 24 | 1 |\n| _am_     | 27 | 1 |\n\n",
            "text/latex": "A tibble: 6 × 3\n\\begin{tabular}{lll}\n word & chapter & n\\\\\n <chr> & <int> & <int>\\\\\n\\hline\n\t \\_all\\_    & 13 & 1\\\\\n\t \\_almost\\_ & 29 & 1\\\\\n\t \\_almost\\_ & 34 & 1\\\\\n\t \\_am\\_     &  9 & 1\\\\\n\t \\_am\\_     & 24 & 1\\\\\n\t \\_am\\_     & 27 & 1\\\\\n\\end{tabular}\n",
            "text/plain": [
              "  word     chapter n\n",
              "1 _all_    13      1\n",
              "2 _almost_ 29      1\n",
              "3 _almost_ 34      1\n",
              "4 _am_      9      1\n",
              "5 _am_     24      1\n",
              "6 _am_     27      1"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<table class=\"dataframe\">\n",
              "<caption>A tibble: 6 × 3</caption>\n",
              "<thead>\n",
              "\t<tr><th scope=col>word</th><th scope=col>chapter</th><th scope=col>n</th></tr>\n",
              "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th></tr>\n",
              "</thead>\n",
              "<tbody>\n",
              "\t<tr><td>afraid  </td><td>27</td><td> 8</td></tr>\n",
              "\t<tr><td>answer  </td><td>11</td><td> 7</td></tr>\n",
              "\t<tr><td>answered</td><td> 9</td><td>10</td></tr>\n",
              "\t<tr><td>answered</td><td>10</td><td> 9</td></tr>\n",
              "\t<tr><td>answered</td><td>12</td><td> 8</td></tr>\n",
              "\t<tr><td>answered</td><td>17</td><td> 7</td></tr>\n",
              "</tbody>\n",
              "</table>\n"
            ],
            "text/markdown": "\nA tibble: 6 × 3\n\n| word &lt;chr&gt; | chapter &lt;int&gt; | n &lt;int&gt; |\n|---|---|---|\n| afraid   | 27 |  8 |\n| answer   | 11 |  7 |\n| answered |  9 | 10 |\n| answered | 10 |  9 |\n| answered | 12 |  8 |\n| answered | 17 |  7 |\n\n",
            "text/latex": "A tibble: 6 × 3\n\\begin{tabular}{lll}\n word & chapter & n\\\\\n <chr> & <int> & <int>\\\\\n\\hline\n\t afraid   & 27 &  8\\\\\n\t answer   & 11 &  7\\\\\n\t answered &  9 & 10\\\\\n\t answered & 10 &  9\\\\\n\t answered & 12 &  8\\\\\n\t answered & 17 &  7\\\\\n\\end{tabular}\n",
            "text/plain": [
              "  word     chapter n \n",
              "1 afraid   27       8\n",
              "2 answer   11       7\n",
              "3 answered  9      10\n",
              "4 answered 10       9\n",
              "5 answered 12       8\n",
              "6 answered 17       7"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "install.packages(\"quanteda\") # need another library for this\n",
        "library(quanteda)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hNnCbuItrOS4",
        "outputId": "64abc3d0-c849-48c7-cba5-68bd2b474199"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Installing package into ‘/usr/local/lib/R/site-library’\n",
            "(as ‘lib’ is unspecified)\n",
            "\n",
            "also installing the dependencies ‘ISOcodes’, ‘fastmatch’, ‘RcppParallel’, ‘stopwords’, ‘RcppArmadillo’\n",
            "\n",
            "\n",
            "Package version: 3.2.1\n",
            "Unicode version: 10.0\n",
            "ICU version: 60.2\n",
            "\n",
            "Parallel computing: 2 of 2 threads used.\n",
            "\n",
            "See https://quanteda.io for tutorials and examples.\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tdm <- fcf %>% cast_dfm(chapter, word, n)\n",
        "tdm"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 191
        },
        "id": "5LCLS7L7rjBa",
        "outputId": "2f7bded3-9d38-4098-a8e8-1bff23ff1aec"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Document-feature matrix of: 33 documents, 107 features (87.65% sparse) and 0 docvars.\n",
              "    features\n",
              "docs afraid answer answered bed bid book branderham called candle catherine\n",
              "  27      8      0        7   0   0    0          0      0      0        33\n",
              "  11      0      7        0   0   0    0          0      0      0        10\n",
              "  9       0      0       10   0   0    0          0      0      0        19\n",
              "  10      0      0        9   0   0    0          0      6      0        19\n",
              "  12      0      0        8   8   0    0          0      0      0        11\n",
              "  17      0      0        7   0   0    0          0      0      0        13\n",
              "[ reached max_ndoc ... 27 more documents, reached max_nfeat ... 97 more features ]"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "And word clouds in R?"
      ],
      "metadata": {
        "id": "XpuUtRgRt2Wo"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "install.packages(\"quanteda.textplots\") # yes, another library\n",
        "library(quanteda.textplots) # check https://quanteda.io/articles/pkgdown/examples/plotting.html for more ideas"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "p9aWdEp9t3za",
        "outputId": "21f1d26a-9aa2-49e5-f46d-544599576906"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Installing package into ‘/usr/local/lib/R/site-library’\n",
            "(as ‘lib’ is unspecified)\n",
            "\n",
            "also installing the dependencies ‘coda’, ‘extrafontdb’, ‘Rttf2pt1’, ‘statnet.common’, ‘extrafont’, ‘ggrepel’, ‘sna’, ‘igraph’, ‘network’\n",
            "\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "textplot_wordcloud(tdm)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 437
        },
        "id": "NRBa9ukauGtI",
        "outputId": "d68c6254-b2bf-4af0-b50c-43ab3079a601"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "plot without title"
            ],
            "image/png": 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RaLFU8vf5JtwudfTXIX6fcY4OfiZZrN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\nxnnERzrYNpiV71boYE4IqaQGe26k3t476eozf9I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}