Exploring_Britain_and_UK_Handbooks.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exploring Britain and UK Handbooks\n",
    "\n",
    "Created in July-September 2020 for the National Library of Scotland's Data Foundry by Lucy Havens, Digital Library Research Intern"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### About the *Britain and UK Handbooks* Dataset\n",
    "The data consists of digitised text from select Britain and UK Handbooks produced between 1954 and 2005.  A central statistics bureau (the Central Statistical Office until 1 April 1966, when it merged with the Office of Population Censuses and Surveys and became the Office for National Statistics) produced the Handbooks each year to communicate information about the UK that would impress international diplomats.  The Handbooks provide a factual skeleton of the UK, focusing on reporting quantitative information and a civil service perspective.\n",
    "\n",
    "* Data format: digitised text\n",
    "* Data creation process: Optical Character Recognition (OCR)\n",
    "* Data source: https://data.nls.uk/digitised-collections/britain-uk-handbooks/"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class='alert alert-block alert-warning'>\n",
    "    <b>Before you begin:</b> If you are interacting with this Notebook in Binder, please note that there is a memory limit (see top right corner) that may prevent the entire Notebook from running due to the large size of the dataset.  Installing Jupyter Lab or Jupyter Notebook locally will allow you to run the entire Notebeook on your own computer without running into memory limitations.\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***\n",
    "### Table of Contents\n",
    "0. [Preparation](#0.-Preparation)\n",
    "1. [Data Cleaning and Standardisation](#1.-Data-Cleaning-and-Standardisation)\n",
    "2. [Summary Statistics](#2.-Summary-Statistics)\n",
    "3. [Exploratory Analysis](#3.-Exploratory-Analysis)\n",
    "***\n",
    "### Citations\n",
    "* Alex, Beatrice and Llewellyn, Clare. (2020) *Library Carpentry: Text & Data Mining*. Centre for Data, Culture & Society, University of Edinburgh. http://librarycarpentry.org/lc-tdm/.\n",
    "* Bird, Steven and Klein, Ewan and Loper, Edward. (2019) *Natural Language Processing with Python – Analyzing Text with the Natural Language Toolkit*.  O'Reilly Media. 978-0-596-51649-9. https://www.nltk.org/book/."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 0. Preparation\n",
    "Import libraries to use for cleaning, summarizing and exploring the data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "# To prevent SSL certificate failure\n",
    "import os, ssl\n",
    "if (not os.environ.get('PYTHONHTTPSVERIFY', '') and\n",
    "    getattr(ssl, '_create_unverified_context', None)):\n",
    "    ssl._create_default_https_context = ssl._create_unverified_context\n",
    "\n",
    "# Libraries for data loading\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import string\n",
    "import re\n",
    "\n",
    "# Libraries for visualization\n",
    "import altair as alt\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Libraries for text analysis\n",
    "import nltk\n",
    "from nltk.tokenize import word_tokenize, sent_tokenize\n",
    "nltk.download('punkt')\n",
    "from nltk.corpus import PlaintextCorpusReader\n",
    "nltk.download('wordnet')\n",
    "from nltk.corpus import wordnet\n",
    "nltk.download('stopwords')\n",
    "from nltk.corpus import stopwords\n",
    "from nltk.text import Text\n",
    "from nltk.stem.porter import PorterStemmer\n",
    "from nltk.probability import FreqDist\n",
    "nltk.download('averaged_perceptron_tagger')\n",
    "from nltk.tag import pos_tag\n",
    "nltk.download('tagsets')  # part of speech tags\n",
    "from nltk.draw.dispersion import dispersion_plot as displt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To explore the text in the *Britain and UK Handbooks* collection, we'll mainly use the **Natural Language Toolkit (NLTK)**, a library written for the programming language Python.\n",
    "\n",
    "The nls-text-handbooks folder (downloadable as *Just the text* data from the website at the top of this notebook) contains TXT files of digitised text, with numerical names, as well as a CSV inventory file and a TXT ReadMe file.  Load only the TXT files of digitised text and **tokenise** the text (which splits a string into separate words, numbers, and punctuation):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['BRITAIN', '1979', '3W', '+', 'L', 'Capita', '!', 'Edinburgh', 'Population', '5']\n"
     ]
    }
   ],
   "source": [
    "corpus_folder = 'data/nls-text-handbooks/'\n",
    "wordlists = PlaintextCorpusReader(corpus_folder, '\\d.*', encoding='latin1')\n",
    "corpus_tokens = wordlists.words()\n",
    "print(corpus_tokens[:10])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It's hard to get a sense of how accurately the text has been digitised from this list of 10 tokens, so let's look at one of these words in context.  To see phrases in which \"Edinburgh\" is used, we can use the concordance() method:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Displaying 20 of 2579 matches:\n",
      "BRITAIN 1979 3W + L Capita ! Edinburgh Population 5 , 196 / GOO ENGLAND A\n",
      "ondon WC1V 6HB 13a Castle Street , Edinburgh EH2 3AR 41 The Hayes , Cardiff CF1\n",
      "ield Liverpool Manchester Bradford Edinburgh Bristol Belfast Coventry Cardiff s\n",
      "Counsellors of State ( the Duke of Edinburgh , the four adult persons next in s\n",
      "ments , accompanied by the Duke of Edinburgh , and undertakes lengthy tours in \n",
      "y government bookshops in London , Edinburgh , Cardiff , Belfast , Manchester ,\n",
      "five Scottish departments based in Edinburgh and known as the Scottish Office .\n",
      " is centred in the Crown Office in Edinburgh . The Parliamentary Draftsmen for \n",
      ". The main seat of the court is in Edinburgh where all appeals are heard . All \n",
      " The Court of Session sits only in Edinburgh , and has jurisdiction to deal wit\n",
      "ersities are : Aberdeen , Dundee , Edinburgh , Glasgow , Heriot - Watt ( Edinbu\n",
      "nburgh , Glasgow , Heriot - Watt ( Edinburgh ), St . Andrews , Stirling , and S\n",
      ". Andrews , Glasgow , Aberdeen and Edinburgh from the fifteenth and sixteenth c\n",
      "the Pentland Hills to the south of Edinburgh . Over 98 per cent of the land in \n",
      " , a major commercial centre , and Edinburgh , Scotland s capital , an administ\n",
      " , bife and Dundee , as well as in Edinburgh , where this and other modern indu\n",
      "c Services Station , East Craigs , Edinburgh , provide scientific and technical\n",
      "similar service between London and Edinburgh in May 1978 and the construction o\n",
      "t England and on the route linking Edinburgh , Newcastle upon Tyne , Birmingham\n",
      "e been introduced from Heathrow to Edinburgh and Belfast . Joint shuttle servic\n"
     ]
    }
   ],
   "source": [
    "t = Text(corpus_tokens)\n",
    "t.concordance('Edinburgh', lines=20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I'm guessing `bife` should be `Fife` as it's closely followed by `Dundee`, but overall not so bad!\n",
    "\n",
    "We can also load individual files from the nls-text-handbooks folder:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['GH', '.', 'fl-', '[', 'IASG0', '>', 'J^RSEI', 'nice', ']', 'ROME']\n"
     ]
    }
   ],
   "source": [
    "file = open('data/nls-text-handbooks/205336772.txt', 'r')\n",
    "sample_text = file.read()\n",
    "sample_tokens = word_tokenize(sample_text)\n",
    "print(sample_tokens[:10])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "However, in this Notebook, we're interested in the entire dataset, so we'll use all its files.  Let's find out just how many files, and just how much text, we're working with.\n",
    "\n",
    "#### 0.1 Dataset Size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total...\n",
      "  Tokens in Handbooks Data: 16606800\n",
      "  Sentences in Handbooks Data: 584618\n",
      "  Files in Handbooks Data: 50\n"
     ]
    }
   ],
   "source": [
    "def corpusStatistics(plaintext_corpus_read_lists):\n",
    "    total_tokens = 0\n",
    "    total_sents = 0\n",
    "    total_files = 0\n",
    "    for fileid in plaintext_corpus_read_lists.fileids():\n",
    "        total_tokens += len(plaintext_corpus_read_lists.words(fileid))\n",
    "        total_sents += len(plaintext_corpus_read_lists.sents(fileid))\n",
    "        total_files += 1\n",
    "    print(\"Total...\")\n",
    "    print(\"  Tokens in Handbooks Data:\", total_tokens)\n",
    "    print(\"  Sentences in Handbooks Data:\", total_sents)\n",
    "    print(\"  Files in Handbooks Data:\", total_files)\n",
    "\n",
    "corpusStatistics(wordlists)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that I've print ``Tokens`` rather than words, though the NLTK method used to count those was ``.words()``.  This is because words in NLTK include punctuation marks and digits, in addition to alphabetic words.\n",
    "\n",
    "Across the 50 files that make up the Handbooks dataset, there are over 90 million characters (which could be words, numbers, punctuation, abbreviations, etc.), over 16 million words, and nearly 600,000 sentences.  Of course, OCR isn't perfect, so these numbers are estimates, not precise totals."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Variables that store the words and sentences in our dataset will be useful for future analysis.  Let's create those now:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def getWordsSents(plaintext_corpus_read_lists):\n",
    "    all_words = []\n",
    "    all_sents = []\n",
    "    for fileid in plaintext_corpus_read_lists.fileids():\n",
    "        \n",
    "        file_words = plaintext_corpus_read_lists.words(fileid)\n",
    "        all_words += [str(word) for word in file_words  if word.isalpha()]\n",
    "        \n",
    "        file_sents = sent_tokenize(plaintext_corpus_read_lists.raw(fileid))  #plaintext_corpus_read_lists.sents(fileid)\n",
    "        all_sents += [str(sent) for sent in file_sents]\n",
    "        \n",
    "    return all_words, all_sents\n",
    "        \n",
    "handbooks_words, handbooks_sents = getWordsSents(wordlists)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['BRITAIN', 'L', 'Capita', 'Edinburgh', 'Population', 'GOO', 'ENGLAND', 'Area', 'km', 'miles']\n",
      "['AIRWAYS', 'ADEN', 'ALWAYS', 'BAHAMAS', 'AIRWAYS', 'ASSOCIATES', 'August', 'r', 'MORESBY', 'T']\n",
      "\n",
      "Capita! 1979\n",
      "Capita!q.miles.kmGOO\n",
      "^xt:. i - <1.. i 'i&rr\n",
      "u.\n",
      "(between pp 390 and 391).t structure390);olomgssalaries\n",
      "LABI!BOâr\n",
      "CHICAGO!EAIATED BY BRITISH OVERSEAS AIRWAYS-BRITISH EUROPEAN AIRWA YS-TRANS-CANADA AIR LINES â¢ Q ANT AS EMPIRE AIRWAYS\n",
      "GIBRALTAR!\n",
      "GRAND CAYMAN.\n",
      "MORESBY%TN A1RWA VS â¢ TASMAN EMPIRE AIRWA YS- BRITISH WEST INDIAN AIRWAYS-ADEN ALWAYS BAHAMAS AIRWAYS'* ASSOCIATES\n"
     ]
    }
   ],
   "source": [
    "print(handbooks_words[:10])\n",
    "print(handbooks_words[-10:])\n",
    "print()\n",
    "sample_sentences = handbooks_sents[:5] + handbooks_sents[-5:]\n",
    "for s in sample_sentences:\n",
    "    # remove new lines and tabs at the start and end of sentences\n",
    "    s = s.strip('\\n')\n",
    "    s = s.strip('\\t')\n",
    "    # remove new lines and tabs in the middle of sentences\n",
    "    s = s.replace('\\n','')\n",
    "    s = s.replace('\\t','')\n",
    "    print(s)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`bife` isn't the only word the OCR incorrectly digitised.  To get a sense of how much of the digitised text we can perform meaningful analysis on, let's figure out how many of NLTK's \"words\" are actually recognisable English words.  We'll use [WordNet](https://wordnet.princeton.edu/),* a database of English words, to evaluate which of NLTK's \"words\" are not valid English words.  Section 1. Data Cleaning and Standardisation walks through how to estimate the amount digitisation mistakes.\n",
    "***\n",
    "  **Princeton University \"About WordNet.\" WordNet. Princeton University. 2010.*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 0.2 Identifying Subsets of the Data\n",
    "Before we move onto cleaning and standardisation, we'll create a lists and dictionary that will help us easily access subsets of the Handbooks data.  First need to load the inventory (CSV file) that lists which file name corresponds with which text in the Handbooks dataset.  When you open the inventory in Microsoft Excel or a text editor, you can see that there are no column names.  The Python library [Pandas](https://pandas.pydata.org/docs/), which reads CSV files, calls these column names the ``header``.  When we use Pandas to read the inventory, we'll create our own header by specifying that the CSV file as ``None`` and providing a list of column ``names``.\n",
    "\n",
    "When Pandas (abbreviated ``pd`` when we loaded libraries in the first cell of this notebook) reads a CSV file, it creates a table called a **DataFrame** from that data.  Let's see what the Handbooks inventory DataFrame looks like:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fileid</th>\n",
       "      <th>title</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>189742208.txt</td>\n",
       "      <td>Britain: An official handbook - 1979 - GII.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>189742209.txt</td>\n",
       "      <td>Britain: An official handbook - 1980 - GII.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>189742210.txt</td>\n",
       "      <td>Britain: An official handbook - 1981 - GII.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>189742211.txt</td>\n",
       "      <td>Britain: An official handbook - 1982 - GII.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>189742212.txt</td>\n",
       "      <td>Britain: An official handbook - 1983 - GII.11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          fileid                                          title\n",
       "0  189742208.txt  Britain: An official handbook - 1979 - GII.11\n",
       "1  189742209.txt  Britain: An official handbook - 1980 - GII.11\n",
       "2  189742210.txt  Britain: An official handbook - 1981 - GII.11\n",
       "3  189742211.txt  Britain: An official handbook - 1982 - GII.11\n",
       "4  189742212.txt  Britain: An official handbook - 1983 - GII.11"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('data/nls-text-handbooks/handbooks-inventory.csv', header=None, names=['fileid', 'title'])\n",
    "df.head()  # df.head() returns the first 5 rows of a table, df.tail() returns the last 5 rows of the table, and df returns the entire table"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It looks like the titles of the handbooks in this collection changes over time!  The returned DataFrame (the inventory table) isn't showing the entire titles, so let's increase the maximum width of the columns:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fileid</th>\n",
       "      <th>title</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>204486117.txt</td>\n",
       "      <td>Britain: The official yearbook of the United Kingdom - 2001 - GII.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>204882221.txt</td>\n",
       "      <td>UK: The official yearbook of the United Kingdom of Great Britain and Northern Ireland - 2002 - GII.11 SER</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>204882222.txt</td>\n",
       "      <td>UK: The official yearbook of the United Kingdom of Great Britain and Northern Ireland - 2003 - GII.11 SER</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>204882223.txt</td>\n",
       "      <td>UK: The official yearbook of the United Kingdom of Great Britain and Northern Ireland - 2005 - GII.11 SER</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>205336772.txt</td>\n",
       "      <td>Britain: An official handbook - 1955 - GII.11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           fileid  \\\n",
       "45  204486117.txt   \n",
       "46  204882221.txt   \n",
       "47  204882222.txt   \n",
       "48  204882223.txt   \n",
       "49  205336772.txt   \n",
       "\n",
       "                                                                                                        title  \n",
       "45                                       Britain: The official yearbook of the United Kingdom - 2001 - GII.11  \n",
       "46  UK: The official yearbook of the United Kingdom of Great Britain and Northern Ireland - 2002 - GII.11 SER  \n",
       "47  UK: The official yearbook of the United Kingdom of Great Britain and Northern Ireland - 2003 - GII.11 SER  \n",
       "48  UK: The official yearbook of the United Kingdom of Great Britain and Northern Ireland - 2005 - GII.11 SER  \n",
       "49                                                              Britain: An official handbook - 1955 - GII.11  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.set_option('display.max_colwidth', 150)\n",
    "df.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Perfect!  Now we can see the full titles for each Handbook file.  It looks like they're not in chronological order, though, so let's sort them from earliest to latest publication date.\n",
    "\n",
    "**Step 1:** Let's extract the date from each title of the Handbooks dataset using [Regular Expressions](https://www.w3schools.com/python/python_regex.asp), which enable us to specify patterns to look for that may be a combination of letters, digits, punctuation, or white space:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1979, 1980, 1981, 1982, 1983, 1984, 1985, 1986, 1987, 1988, 1989, 1990, 1954, 1956, 1958, 1959, 1960, 1961, 1962, 1963, 1964, 1965, 1966, 1967, 1968, 1969, 1970, 1971, 1972, 1973, 1974, 1975, 1976, 1977, 1978, 1991, 1992, 1993, 1994, 1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003, 2005, 1955]\n"
     ]
    }
   ],
   "source": [
    "titles = list(df['title'])\n",
    "dates = []\n",
    "for title in titles:\n",
    "    # Write a Regular Expression to extract the date from the title\n",
    "    # (I find it helpful to text out Regular Expressions with Pythex: https://pythex.org/)\n",
    "    # and turn the date into an Integer, so we can analyse it like a number\n",
    "    yr = int((re.search('\\d{4}', title))[0])\n",
    "    dates += [yr]\n",
    "print(dates)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Step 2:** Next, we'll associate the extracted years with their titles and fileids, adding them to the DataFrame in a column named `year`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['year'] = dates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.sort_values(by=['year'], ascending=True, inplace=True)  # 'inplace=False' would create a different DataFrame that's sorted, instead of sorting the 'df' DataFrame\n",
    "# df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-info\">\n",
    "<b>Try It!</b> Uncomment the last line of code in the cell above by removing the '#' before 'df' to see the sorted DataFrame.  Are the titles ordered chronologically?\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With a DataFrame, we can access individual cells, for example: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "204486084.txt Britain: An official handbook - 1954 - GII.11\n",
      "UK: The official yearbook of the United Kingdom of Great Britain and Northern Ireland - 2005 - GII.11 SER\n",
      "205336772.txt\n"
     ]
    }
   ],
   "source": [
    "# To view the first (index = 0) row's fileid and title:\n",
    "print(df.iloc[0][0], df.iloc[0][1])\n",
    "# To view a title value given a fileid value:\n",
    "print(df[df.fileid == '204882223.txt']['title'].values[0])\n",
    "# To view a fileid value given a title value\n",
    "print(df.loc[df['title'] == 'Britain: An official handbook - 1955 - GII.11'].values[0][0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can create a two dictionaries of fileids and their associated journal titles, so we can easily identify which wordlists correspond with which text in the Handbooks dataset (you can uncomment any lines of code by removing the `#` to see what they print):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The title for the file ID at index 10:\n",
      " Britain: An official handbook - 1965 - GII.11\n"
     ]
    }
   ],
   "source": [
    "# 1. Obtain a list of all file IDs\n",
    "fileids = list(df[\"fileid\"])\n",
    "# print(\"Sample file IDs from list of file IDs:\\n\", fileids[-5:])\n",
    "# print()\n",
    "\n",
    "# 2. Obtain a list of all titles\n",
    "titles = list(df[\"title\"])\n",
    "# print(\"Sample titles from list of titles:\\n\", titles[-5:])\n",
    "# print()\n",
    "\n",
    "# 3. Create a dictionary where the keys are file IDs and the values are titles\n",
    "inventory = dict(zip(fileids, titles))\n",
    "# print(inventory)\n",
    "# print()\n",
    "\n",
    "# 4. Pick a file ID by its index number...\n",
    "i = 10\n",
    "a_file_id = fileids[i]\n",
    "# ... and get the title corresponding with the file ID in the inventory dictionary\n",
    "print(\"The title for the file ID at index \" + str(i) + \":\\n\", inventory[a_file_id])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Python's Natural Language Toolkit (NLTK) library, which we use for text analysis later on, stores the lists of tokens (``worldists`` in the `corpus_tokens` variable we created) by the file IDs, so it's useful to be able to match the file IDs with their handbook text! \n",
    "\n",
    "*However*, the previous `corpus_tokens` is based on the unsorted collection of `wordlists` for the Handbooks dataset.  We'll fix that in the next section."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. Data Cleaning and Standardisation\n",
    "\n",
    "There are several ways to standardise, or \"normalise,\" text, with each way providing suitable text for different types of analysis.  For example, to study the vocabulary of a text-based dataset, it's useful to remove punctuation and digits, lowercase the remaining alphabetic words, and then reduce those words to their root form (with stemming or lemmatisation, for example).  Alternatively, to identify people and places using named entity recognition, it's important to keep capitalisation in words and keep words in the context of their sentences.\n",
    "\n",
    "Additionally, when working with a range of files published at different times, sorting the files and their wordlists chronologically is useful so that you can study changes in vocabulary or topics over time."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Step 1:** In the previous section, [Preparation](#0.-Preparation), we tokenized the Handbooks dataset, creating a list of the words and a list of the sentences in each file.  However, the files were not sorted when we created those lists, so we'll create new lists of word tokens and sentence tokens using on the sorted list of Handbooks files, `fileid`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def getSortedWordsSents(plaintext_corpus_read_lists):\n",
    "    all_words = []\n",
    "    all_words_lower = []\n",
    "    all_sents = []\n",
    "    \n",
    "    # Iterate through the list of SORTED fileids so that \n",
    "    # the words and sentences are tokenized in chronological order\n",
    "    for fileid in fileids:\n",
    "        file_words = plaintext_corpus_read_lists.words(fileid)\n",
    "        all_words_lower += [str(word).lower() for word in file_words if word.isalpha()]\n",
    "        all_words += [str(word) for word in file_words  if word.isalpha()]\n",
    "        file_sents = sent_tokenize(plaintext_corpus_read_lists.raw(fileid))  \n",
    "        all_sents += [str(sent) for sent in file_sents]\n",
    "        \n",
    "    return all_words, all_words_lower, all_sents\n",
    "        \n",
    "handbooks_words, handbooks_words_lower, handbooks_sents = getSortedWordsSents(wordlists)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Step 2:** To get an estimate of how accurately OCR digitised the Handbooks, though, we'll use words in the sense that they are recognizable words in the English language.  Let's write a regular expression that can tell us whether a string is a word or abbreviation:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "isWord = re.compile('[a-zA-z.]+')  # include single letters and abbreviations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Step 3:** Lastly, let's use that regular expression to write a function to distinguish words recognisable English words from unrecognisable strings:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "def removeNonEnglishWords(list_of_strings):\n",
    "    english_only = []\n",
    "    nonenglish = []\n",
    "    for s in list_of_strings:\n",
    "        test = isWord.match(s)            # fails if has characters other than letters or a period\n",
    "        if (test != None):\n",
    "            passed = test.group()   # get the matching string\n",
    "            if wordnet.synsets(passed):  # see if WordNet recognizes the matching string\n",
    "                english_only.append(passed)\n",
    "            else:\n",
    "                nonenglish.append(passed)\n",
    "        else:\n",
    "            nonenglish.append(passed)\n",
    "    return english_only, nonenglish\n",
    "                \n",
    "recognised, unrecognised = removeNonEnglishWords(handbooks_words)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total alphabetic words recognised in WordNet: 9652429\n",
      "Total alphabetic words NOT reccognised in WordNet: 3422753\n",
      "Percentage of alphabetic words that are unrecognised in WordNet: 35.460017369721136 %\n"
     ]
    }
   ],
   "source": [
    "print(\"Total alphabetic words recognised in WordNet:\", len(recognised))\n",
    "print(\"Total alphabetic words NOT reccognised in WordNet:\", len(unrecognised))\n",
    "print(\"Percentage of alphabetic words that are unrecognised in WordNet:\", (len(unrecognised)/len(recognised))*100, \"%\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that these totals and percentage should be used as rough estimates, not precise calculations.  WordNet may not recognise some British English terms or Scottish terms since it was developed at Princeton, an American university.  There are other data sources that provide lists of valid words to which you could compare words from the Handbooks dataset.  Using a combination of several sources of valid English words could provide more accurate estimates."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1.2 Reducing to Root Forms\n",
    "In addition to tokenisation, **lemmatisation** is a method of standardising, or \"normalising,\" text.  NLTK's WordNet Lemmatizer reduces a token to its root *only* if the reduction of the token results in a word that's recognized as an English word in WordNet.  Here's what that looks like:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['be', 'placed', 'on', 'general', 'sale', 'and', 'it', 'wa', 'finally', 'decided', 'to', 'do', 'this', 'after', 'a', 'recommendation', 'by', 'the', 'inter', 'departmental', 'committee', 'on', 'social', 'and', 'economic', 'research', 'the', 'handbook', 'contains', 'factual', 'and', 'statistical', 'information', 'compiled', 'from', 'authoritative', 'and', 'official', 'source', 'about', 'the', 'united', 'kingdom', 'it', 'people', 'and', 'it', 'institution', 'it', 'doe', 'not', 'claim', 'to', 'be', 'comprehensive', 'it', 'principal', 'purpose', 'is', 'to', 'provide', 'basic', 'data', 'on', 'the', 'main', 'aspect', 'of', 'national', 'administration', 'and', 'national', 'economy', 'and', 'to', 'give', 'an', 'account', 'of', 'the', 'part', 'played', 'by', 'the', 'government', 'in', 'the', 'life', 'of', 'the', 'community', 'in', 'considering', 'it', 'content', 'reader', 'in', 'the', 'united', 'kingdom']\n"
     ]
    }
   ],
   "source": [
    "# Lemmatize the text (reduce words to their root ONLY if the root is considered a word in WordNet)\n",
    "wnl = nltk.WordNetLemmatizer()\n",
    "lemmatised = [wnl.lemmatize(t) for t in handbooks_words_lower if t.isalpha()]  # only include alphabetic tokens\n",
    "print(lemmatised[500:600])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Summary Statistics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2.1 Frequencies and Sizes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we've created some different cuts of the Handbooks dataset, let's start investigating the frequency of terms as they appear across the dataset.  One way to do so is with a **frequency distribution**, which is a line chart that shows how many times a token appears in the dataset."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's plot the frequency distribution using tokens that were recognised by WordNet as English words, excluding stop words (for example: a, an, the), digits, and punctuation:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total tokens after filtering: 7219577\n"
     ]
    }
   ],
   "source": [
    "rec_min_two_letters = [t.lower() for t in recognised if len(t) > 2]\n",
    "to_exclude = set(stopwords.words('english') + list(string.punctuation) + list(string.digits) + ['also', 'per', '000', 'one', 'many', 'may', 'two', 'see'])\n",
    "filtered_rec_tokens = [t for t in rec_min_two_letters if not t in to_exclude]\n",
    "\n",
    "fdist_ft_rec = FreqDist(filtered_rec_tokens)\n",
    "print(\"Total tokens after filtering:\", fdist_ft_rec.N())  # count the total tokens after filtering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1add77668>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plt.figure(figsize = (20, 8))\n",
    "plt.rc('font', size=12)\n",
    "\n",
    "number_of_tokens = 30 # Try increasing or decreasing this number to view more or fewer tokens in the visualization\n",
    "fdist_ft_rec.plot(number_of_tokens, title='Frequency Distribution for ' + str(number_of_tokens) + ' Most Common Tokens among Recognized English Words in the Handbooks Dataset (excluding stop words)')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can create another data visualisation, one that illustrates when specific words are used within the Handbooks dataset.  This is called a **Lexical Dispersion Plot**.  Since capitalisation is important for identifying place names, we'll use the `handbooks_words` list rather than the `handbooks_words_lower` list for the plot.  We'll pick some place names (the list of `targets`) to see when they appear:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1296x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "corpus_text = Text(handbooks_words)\n",
    "targets = ['UK', 'Britain', 'British', 'England', 'English', 'Scotland', 'Scottish', 'Ireland', 'Irish', 'Wales', 'Welsh']\n",
    "plt.figure(figsize=(18,10))\n",
    "plt.rc('font', size=12)\n",
    "displt(corpus_text, targets, ignore_case=True, title='Lexical Dispersion Plot of UK and Ireland Place-related Words in the Handbooks Dataset')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since we sorted the Handbooks tokens by date, the lexical dispersion plot is showing changes in the use of our list of target words (tokens) over time, with the earliest publications to the left and the most recent publications to the right!  We can see that the word `UK` is used with increasing frequency over time.  We can also see that `English`, `Welsh`, and `Irish` occur less than `British` and `Scottish`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class='alert alert-block alert-info'>\n",
    "    <b>Try It!</b> Instead of tokens, visualise the frequency distribution of lemmas or stems, and pick your own list of targets!  You could try looking at sports, industries (such as mining), or other economy-related topics, for example.\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2.2 Uniqueness and Variety\n",
    "\n",
    "Another way to summarise the Handbooks dataset is to look at the uniqueness and variety of word usage.  We can obtain the **vocabulary** of the text by creating a set of unique tokens that occur in the dataset, as well as creating a set of unique *lemmatised* tokens that occur in the dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Unique tokens: 72489\n",
      "Unique lowercase tokens: 57422\n",
      "Unique lemmatised (lowercase) tokens: 52713\n",
      "\n",
      "Unique recognised words: 36742\n",
      "Unique unrecognised words: 33606\n"
     ]
    }
   ],
   "source": [
    "# Remove duplicate tokens from the text (obtain the vocabulary of the text)\n",
    "t_vocab = set(handbooks_words)\n",
    "t_vocab_lower = set(handbooks_words_lower)\n",
    "lemma_vocab = set(lemmatised)\n",
    "print(\"Unique tokens:\", len(t_vocab))\n",
    "print(\"Unique lowercase tokens:\", len(t_vocab_lower))\n",
    "print(\"Unique lemmatised (lowercase) tokens:\", len(lemma_vocab))\n",
    "print()\n",
    "rec_vocab = set(recognised)\n",
    "unrec_vocab = set(unrecognised)\n",
    "print(\"Unique recognised words:\", len(rec_vocab))\n",
    "print(\"Unique unrecognised words:\", len(unrec_vocab))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The vocabulary of the entire Handbooks dataset contains 70,922 unique words, 36,780 of which are recognised English words in WordNet. The lemmatised vocabulary of the dataset contains 66,172 words."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['digitalâ', 'rusedskiâ', 'jermanent', 'leggett', 'gave', 'bso', 'portpatrick', 'certifies', 'gzji', 'minimised', 'wu', 'cordial', 'conductivity', 'hedge', 'vlalochnagar', 'ltuaâ', 'lities', 'respectâ', 'gnvqs', 'bc', 'branching', 'weartieayp', 'thalmic', 'toria', 'theddl', 'unfurnished', 'pont', 'oocno', 'rac', 'informatics', 'ductive', 'ccea', 'maintenâ', 'lte', 'coid', 'minehead', 'kabul', 'chad', 'btec', 'waketield', 'admmisters', 'welj', 'câ', 'granddaughter', 'federation', 'hide', 'vegetarian', 'acrobat', 'perchaâ', 'underneath', 'ratesâ', 'emulsifier', 'ogc', 'cathay', 'iqsl', 'centerpiece', 'yearbook', 'vohi', 'minoritiesâ', 'baofe', 'uci', 'enact', 'monofilament', 'mics', 'betws', 'supplying', 'printerâ', 'rih', 'confecâ', 'loiga', 'jobless', 'repatriated', 'fomy', 'apprenticeshipsâ', 'dam', 'foundry', 'halsey', 'sponsqred', 'sssp', 'talbot', 'filler', 'supplement', 'intelsatâ', 'fcambridge', 'arrested', 'wdges', 'romantic', 'carrying', 'vjz', 'hn', 'ransfersâ', 'asks', 'territorv', 'abandoned', 'corres', 'rehousing', 'traffickersâ', 'reaching', 'upheaval', 'utc']\n"
     ]
    }
   ],
   "source": [
    "print(list(lemma_vocab)[:100])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since the Handbooks dataset contains multiple publications (one file per year of publication), we could try picking a subset of publications, or even a single publication, and then compare the vocabulary across different publications.  What patterns would you expect to see?  How might the lexical diversity of the Handbooks dataset compare to the lexical diversity of novels in the Lewis Grassic Gibbon First Editions collection?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. Exploratory Analysis\n",
    "\n",
    "Let's group the Handbooks TXT files into 10-year periods so that we can investigate patterns in the Handbooks' text over time, comparing one decade to the next.\n",
    "\n",
    "We'll group the Handbooks using [Regular Expressions](https://www.w3schools.com/python/python_regex.asp) on their titles to identify the decade in which they were published:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['204486084.txt', '205336772.txt', '204486085.txt', '204486086.txt', '204486087.txt']\n"
     ]
    }
   ],
   "source": [
    "# Make a dictionary with years as keys and fileids as values\n",
    "list_of_years = list(df['year'])\n",
    "fileid_to_year = dict(zip(fileids, list_of_years))\n",
    "\n",
    "# Create a list for each decade during which the Handbooks were published:\n",
    "fifties = [f for f in fileids if re.match('195\\d{1}', str(fileid_to_year[f]))]\n",
    "print(fifties)\n",
    "sixties = [f for f in fileids if re.match('196\\d{1}', str(fileid_to_year[f]))]\n",
    "seventies = [f for f in fileids if re.match('197\\d{1}', str(fileid_to_year[f]))]\n",
    "eighties = [f for f in fileids if re.match('198\\d{1}', str(fileid_to_year[f]))]\n",
    "nineties = [f for f in fileids if re.match('199\\d{1}', str(fileid_to_year[f]))]\n",
    "twotho = [f for f in fileids if re.match('200\\d{1}', str(fileid_to_year[f]))]\n",
    "\n",
    "# Check that the decade lists' lengths sum to the length of the list of all fileids (an error is thrown if they don't)\n",
    "assert len(fifties) + len(sixties) + len(seventies) + len(eighties) + len(nineties) + len(twotho) == len(fileids)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# INPUT: a wordlist (from the PlaintextCorpusReader - see section 0. Preparation),\n",
    "#        a year and a list of fileids associated with that year\n",
    "# OUTPUT: a list of word tokens for fileids from the inputted year\n",
    "def getTokens(plaintext_corpus_read_lists, decade_files):\n",
    "    all_words = []\n",
    "    for fileid in decade_files:\n",
    "        file_words = plaintext_corpus_read_lists.words(fileid)\n",
    "        all_words += [str(word) for word in file_words  if word.isalpha()]  # isalpha() removes non-letter tokens\n",
    "    return all_words"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['m', 'Gti', 'i', 'BRITAIN', 'An', 'Official', 'Handbook', 'BRITAIN', 'An', 'Official', 'Handbook', 'PREPARED', 'BY', 'THE', 'CENTRAL', 'OFFICE', 'OF', 'INFORMATION', 'AND', 'PUBLISHED', 'BY', 'HER', 'MAJESTYâ', 'S', 'STATIONERY', 'OFFICE', 'LONDON', 'Crown', 'Copyright', 'Reserved', 'HER', 'MAJESTYâ', 'S', 'STATIONERY', 'OFFICE', 'Copies', 'of', 'this', 'book', 'may', 'be', 'had', 'from', 'H', 'M', 'Stationery', 'Office', 'York', 'House', 'Kingsway', 'London', 'W', 'C', 'Oxford', 'St', 'London', 'W', 'i', 'orders', 'by', 'post', 'to', 'be', 'sent', 'to', 'P', 'O', 'Box', 'London', 'S', 'E', 'i', 'Castle', 'St', 'Edinburgh', 'King', 'St', 'Manchester', 'Edmund', 'St', 'Birmingham', 'St', 'Andrewâ', 's', 'Crescent', 'Cardiff', 'Tower', 'Lane', 'Bristol', 'Chichester', 'St', 'Belfast', 'or', 'through', 'any', 'bookseller', 'Obtainable', 'in', 'the', 'United']\n"
     ]
    }
   ],
   "source": [
    "fifties_tokens = getTokens(wordlists, fifties)\n",
    "print(fifties_tokens[:100])\n",
    "sixties_tokens = getTokens(wordlists, sixties)\n",
    "seventies_tokens = getTokens(wordlists, seventies)\n",
    "eighties_tokens = getTokens(wordlists, eighties)\n",
    "nineties_tokens = getTokens(wordlists, nineties)\n",
    "twotho_tokens = getTokens(wordlists, twotho)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Great!  Now we can analyse the Handbooks dataset by 10-year periods!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3.1 How are the United Kingdom and Great Britain portrayed?  How does this change over time?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The Handbooks were written for an international audience to impress people with the success and strength of Britain and the UK.  Let's investigate how Britain and the UK are portrayed:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Displaying 10 of 49467 matches:\n",
      " BRITAIN 1979 3W + L Capita ! Edinburgh Popu\n",
      "ges and salaries 1973 - 78 318 Maps Britain inside back cover Economic planning\n",
      "icity 255 Some minerals produced in Britain â ¢ 261 Main railway passenger rou\n",
      "ween pp 390 and 391 ). Introduction Britain 1979 is the thirtieth official hand\n",
      "onery Office throughout the world . Britain 1979 is primarily concerned to desc\n",
      "ember 1 978 THE PHYSICAL BACKGROUND Britain , formally known as the United King\n",
      "nown as the United Kingdom of Great Britain and Northern Ireland , constitutes \n",
      "ope . The largest islands are Great Britain ( comprising the mainlands of Engla\n",
      "d the Channel Islands between Great Britain and France have a large measure of \n",
      "ed Kingdom 244 , 103 94 , 249 Great Britain 229 , 983 88 , 797 England 130 , 44\n"
     ]
    }
   ],
   "source": [
    "t = Text(corpus_tokens)\n",
    "t.concordance('Britain', lines=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Frequency (percentage) of Britain and the UK in Handbooks dataset:\n",
      " - Britain: 0.2972731087031905 %\n",
      " - GB: 0.0018202423492078353 %\n",
      " - UK: 0.07828571717013194 %\n"
     ]
    }
   ],
   "source": [
    "fdist = FreqDist(handbooks_words)\n",
    "print(\"Frequency (percentage) of Britain and the UK in Handbooks dataset:\")\n",
    "print(\" - Britain:\", (fdist.freq('Britain'))*100, \"%\")\n",
    "print(\" - GB:\", (fdist.freq('GB'))*100, \"%\")\n",
    "print(\" - UK:\", (fdist.freq('UK'))*100, \"%\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's create Frequency Distribution visualisations for the Handbooks published in the fifties and the 2000s.  \n",
    "\n",
    "**Step 1:** First, we'll lowercase the words and remove stopwords from the lists:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "to_exclude = stopwords.words('english') + ['â', 'per', 'cent']\n",
    "fifties_filtered = [w.lower() for w in fifties_tokens if not w.lower() in to_exclude]\n",
    "twotho_filtered = [w.lower() for w in twotho_tokens if not w.lower() in to_exclude]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Step 2:** Then, we'll stem the words in both lists:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fifties stems: ['purpos', 'great', 'britain', 'compris', 'england', 'wale', 'scotland', 'posit', 'complic', 'fact']\n",
      "Twotho stems: ['postal', 'area', 'england', 'counti', 'unitari', 'author', 'sinc', 'april', 'region', 'england']\n"
     ]
    }
   ],
   "source": [
    "porter = nltk.PorterStemmer()\n",
    "porter_stemmed = [porter.stem(w) for w in fifties_filtered]\n",
    "print(\"Fifties stems:\", porter_stemmed[590:600])  \n",
    "porter_stemmed = [porter.stem(w) for w in twotho_filtered]\n",
    "print(\"Twotho stems:\", porter_stemmed[590:600])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Step 3:** Lastly, we'll calculate the frequency distributions of stems for the 1950s and 2000s, and visualise those distributions:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x161a31c50>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fdist_fifties = FreqDist(fifties_filtered)\n",
    "fdist_twotho = FreqDist(twotho_filtered)\n",
    "\n",
    "number_of_tokens = 10 # Try increasing or decreasing this number to view more or fewer tokens in the visualization\n",
    "plt.figure(figsize = (10, 6))\n",
    "plt.rc('font', size=12)\n",
    "fdist_fifties.plot(number_of_tokens, title='Frequency Distribution of Top ' + str(number_of_tokens) + ' Stems in Handbooks from 1954-59')\n",
    "\n",
    "plt.figure(figsize = (10, 6))\n",
    "plt.rc('font', size=12)\n",
    "fdist_twotho.plot(number_of_tokens, title='Frequency Distribution of Top ' + str(number_of_tokens) + ' Stems in Handbooks from 2000-05')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Be careful when comparing these graphs to pay attention to the different scales on their y axes!\n",
    "\n",
    "#### 3.1 Visualising Words Over Time\n",
    "**Step 1:** Let's pick some words that relate to various industries that may appear in the Handbooks and visualise their occurrences over time:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "targets = ['mining', 'technology', 'shipbuilding', 'football', 'medicine', 'research', 'digital']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class='alert alert-box alert-info'>\n",
    "    <b>Try It!</b> How could you use the Contents pages in the Handbooks to look at the topics that are added, removed, or maintained over the years each Handbook has been published?\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Step 2:** Let's group the Handbooks by decade to study these words' occurrences in the Handbooks based on their decade of publication:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "# INPUT: a capitalised word (in String format)\n",
    "# OUTPUT: a list of the ratios of the inputted word's occurrence\n",
    "#         (lowercased and capitalised) to all words in each \n",
    "#         decade group of Handbooks\n",
    "def wordOccurrenceByDecade(word):\n",
    "    word_occurs = []\n",
    "    tokens_lists = [fifties_tokens, sixties_tokens, seventies_tokens, eighties_tokens, nineties_tokens, twotho_tokens]\n",
    "    for decade in tokens_lists:\n",
    "        word_count_capital = decade.count(word)\n",
    "        word_lower = word.lower()\n",
    "        word_count_lower = decade.count(word_lower)\n",
    "        total_words = len(decade)\n",
    "        occurrence = (word_count_capital + word_count_lower)/total_words\n",
    "        word_occurs += [occurrence]\n",
    "    return word_occurs\n",
    "\n",
    "digital = wordOccurrenceByDecade('Digital')\n",
    "mining = wordOccurrenceByDecade('Mining')\n",
    "shipbuilding = wordOccurrenceByDecade('Shipbuilding')\n",
    "technology = wordOccurrenceByDecade('Technology')\n",
    "football = wordOccurrenceByDecade('Football')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Step 3:** Now we'll create a DataFrame of the occurrence data to view the occurrence of the words by decade of Handbooks publications, and we can export the DataFrame as a CSV file so it can be opened in Microsoft Excel or loaded into another Jupyter Notebook as a DataFrame:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Digital</th>\n",
       "      <th>Football</th>\n",
       "      <th>Mining</th>\n",
       "      <th>Shipbuilding</th>\n",
       "      <th>Technology</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1950s</th>\n",
       "      <td>0.000004</td>\n",
       "      <td>0.000038</td>\n",
       "      <td>0.000095</td>\n",
       "      <td>0.000117</td>\n",
       "      <td>0.000072</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960s</th>\n",
       "      <td>0.000006</td>\n",
       "      <td>0.000140</td>\n",
       "      <td>0.000078</td>\n",
       "      <td>0.000092</td>\n",
       "      <td>0.000275</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1970s</th>\n",
       "      <td>0.000008</td>\n",
       "      <td>0.000115</td>\n",
       "      <td>0.000098</td>\n",
       "      <td>0.000084</td>\n",
       "      <td>0.000299</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1980s</th>\n",
       "      <td>0.000049</td>\n",
       "      <td>0.000141</td>\n",
       "      <td>0.000089</td>\n",
       "      <td>0.000084</td>\n",
       "      <td>0.000516</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990s</th>\n",
       "      <td>0.000092</td>\n",
       "      <td>0.000202</td>\n",
       "      <td>0.000063</td>\n",
       "      <td>0.000030</td>\n",
       "      <td>0.000800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000s</th>\n",
       "      <td>0.000182</td>\n",
       "      <td>0.000186</td>\n",
       "      <td>0.000054</td>\n",
       "      <td>0.000027</td>\n",
       "      <td>0.000606</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Digital  Football    Mining  Shipbuilding  Technology\n",
       "1950s  0.000004  0.000038  0.000095      0.000117    0.000072\n",
       "1960s  0.000006  0.000140  0.000078      0.000092    0.000275\n",
       "1970s  0.000008  0.000115  0.000098      0.000084    0.000299\n",
       "1980s  0.000049  0.000141  0.000089      0.000084    0.000516\n",
       "1990s  0.000092  0.000202  0.000063      0.000030    0.000800\n",
       "2000s  0.000182  0.000186  0.000054      0.000027    0.000606"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "col_names = ['1950s', '1960s', '1970s', '1980s', '1990s', '2000s' ]\n",
    "row_names = ['Digital', 'Football', 'Mining', 'Shipbuilding', 'Technology']\n",
    "industry_df = pd.DataFrame(data=[digital, football, mining, shipbuilding, technology], columns=col_names, index=row_names)\n",
    "industry_df.to_csv('handbooks_industry_occurrences.csv')\n",
    "industry_df\n",
    "\n",
    "# Transpose the data to rotate the columns and rows of a DataFrame with '.T' or '.transpose()'\n",
    "industry_df = industry_df.T\n",
    "industry_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Step 4:** Using [Altair](https://altair-viz.github.io/index.html), we can visualise the occurrence of a single word over the decades of Handbooks publications:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<div id=\"altair-viz-377d21762f6c455ab239115a65e12d9d\"></div>\n",
       "<script type=\"text/javascript\">\n",
       "  (function(spec, embedOpt){\n",
       "    let outputDiv = document.currentScript.previousElementSibling;\n",
       "    if (outputDiv.id !== \"altair-viz-377d21762f6c455ab239115a65e12d9d\") {\n",
       "      outputDiv = document.getElementById(\"altair-viz-377d21762f6c455ab239115a65e12d9d\");\n",
       "    }\n",
       "    const paths = {\n",
       "      \"vega\": \"https://cdn.jsdelivr.net/npm//vega@5?noext\",\n",
       "      \"vega-lib\": \"https://cdn.jsdelivr.net/npm//vega-lib?noext\",\n",
       "      \"vega-lite\": \"https://cdn.jsdelivr.net/npm//vega-lite@4.8.1?noext\",\n",
       "      \"vega-embed\": \"https://cdn.jsdelivr.net/npm//vega-embed@6?noext\",\n",
       "    };\n",
       "\n",
       "    function loadScript(lib) {\n",
       "      return new Promise(function(resolve, reject) {\n",
       "        var s = document.createElement('script');\n",
       "        s.src = paths[lib];\n",
       "        s.async = true;\n",
       "        s.onload = () => resolve(paths[lib]);\n",
       "        s.onerror = () => reject(`Error loading script: ${paths[lib]}`);\n",
       "        document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
       "      });\n",
       "    }\n",
       "\n",
       "    function showError(err) {\n",
       "      outputDiv.innerHTML = `<div class=\"error\" style=\"color:red;\">${err}</div>`;\n",
       "      throw err;\n",
       "    }\n",
       "\n",
       "    function displayChart(vegaEmbed) {\n",
       "      vegaEmbed(outputDiv, spec, embedOpt)\n",
       "        .catch(err => showError(`Javascript Error: ${err.message}<br>This usually means there's a typo in your chart specification. See the javascript console for the full traceback.`));\n",
       "    }\n",
       "\n",
       "    if(typeof define === \"function\" && define.amd) {\n",
       "      requirejs.config({paths});\n",
       "      require([\"vega-embed\"], displayChart, err => showError(`Error loading script: ${err.message}`));\n",
       "    } else if (typeof vegaEmbed === \"function\") {\n",
       "      displayChart(vegaEmbed);\n",
       "    } else {\n",
       "      loadScript(\"vega\")\n",
       "        .then(() => loadScript(\"vega-lite\"))\n",
       "        .then(() => loadScript(\"vega-embed\"))\n",
       "        .catch(showError)\n",
       "        .then(() => displayChart(vegaEmbed));\n",
       "    }\n",
       "  })({\"config\": {\"view\": {\"continuousWidth\": 400, \"continuousHeight\": 300, \"strokeWidth\": 0}, \"axis\": {\"grid\": false, \"labelAngle\": 0}}, \"data\": {\"name\": \"data-cccfaf53ae78668a39ead2550cfd7eff\"}, \"mark\": {\"type\": \"bar\", \"size\": 60}, \"encoding\": {\"x\": {\"type\": \"nominal\", \"field\": \"decade\"}, \"y\": {\"type\": \"quantitative\", \"axis\": {\"format\": \"%\", \"title\": \"Occurrence\"}, \"field\": \"occurrence\"}}, \"title\": \"Occurrence of 'Digital' and 'digital' in Handbooks by Decade\", \"width\": 440, \"$schema\": \"https://vega.github.io/schema/vega-lite/v4.8.1.json\", \"datasets\": {\"data-cccfaf53ae78668a39ead2550cfd7eff\": [{\"decade\": \"1950s\", \"occurrence\": 4.420643698770265e-06}, {\"decade\": \"1960s\", \"occurrence\": 5.502931858914457e-06}, {\"decade\": \"1970s\", \"occurrence\": 8.482218763361904e-06}, {\"decade\": \"1980s\", \"occurrence\": 4.875173595708442e-05}, {\"decade\": \"1990s\", \"occurrence\": 9.213005804193656e-05}, {\"decade\": \"2000s\", \"occurrence\": 0.0001817709685795897}]}}, {\"mode\": \"vega-lite\"});\n",
       "</script>"
      ],
      "text/plain": [
       "alt.Chart(...)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "source = pd.DataFrame({\n",
    "    'decade': col_names,\n",
    "    'occurrence': list(industry_df['Digital'])\n",
    "})\n",
    "\n",
    "alt.Chart(source, title=\"Occurrence of 'Digital' and 'digital' in Handbooks by Decade\").mark_bar(size=60).encode(\n",
    "    x='decade',\n",
    "    y=alt.Y('occurrence', axis=alt.Axis(format='%', title='Occurrence'))\n",
    ").configure_axis(\n",
    "    grid=False,\n",
    "    labelAngle=0\n",
    ").configure_view(\n",
    "    strokeWidth=0\n",
    ").properties(\n",
    "    width=440\n",
    ")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ta da!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class='alert alert-box alert-info'>\n",
    "    <b>Try It!</b> Can you edit the bar chart to show the occurrence of another word by decade, such as mining?  Then, try editing the code to visualise the occurrence of a word of your choosing, not one of the words already in our target list!\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also use Altair for other types of visualisations, such as line charts that display the occurrence of all the words per decade in a single plot.  To plot the occurrence of multiple words at once, we need to create a DataFrame with a slightly different structure...\n",
    "\n",
    "First, though, we'll calculate the occurrence of words in every Handbook:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "def wordOccurrenceByFile(word, wordlists, fileids):\n",
    "    word_occurs = []\n",
    "    for file in fileids:\n",
    "        file_words = wordlists.words(file)\n",
    "        \n",
    "        word_count_capital = file_words.count(word)\n",
    "        word_lower = word.lower()\n",
    "        word_count_lower = file_words.count(word_lower)\n",
    "        \n",
    "        total_words = len(list(file_words))\n",
    "        occurrence = (word_count_capital + word_count_lower)/total_words\n",
    "        word_occurs += [occurrence]\n",
    "        \n",
    "    return word_occurs\n",
    "\n",
    "digital = wordOccurrenceByFile('Digital', wordlists, fileids)\n",
    "mining = wordOccurrenceByFile('Mining', wordlists, fileids)\n",
    "shipbuilding = wordOccurrenceByFile('Shipbuilding', wordlists, fileids)\n",
    "technology = wordOccurrenceByFile('Technology', wordlists, fileids)\n",
    "football = wordOccurrenceByFile('Football', wordlists, fileids)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "assert (len(digital) == len(fileids))\n",
    "assert (len(digital) == len(mining))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we'll create a new DataFrame:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>word</th>\n",
       "      <th>occurrence</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>245</th>\n",
       "      <td>football</td>\n",
       "      <td>0.000151</td>\n",
       "      <td>2000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>246</th>\n",
       "      <td>football</td>\n",
       "      <td>0.000164</td>\n",
       "      <td>2001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>247</th>\n",
       "      <td>football</td>\n",
       "      <td>0.000152</td>\n",
       "      <td>2002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>248</th>\n",
       "      <td>football</td>\n",
       "      <td>0.000162</td>\n",
       "      <td>2003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>249</th>\n",
       "      <td>football</td>\n",
       "      <td>0.000096</td>\n",
       "      <td>2005</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         word  occurrence  year\n",
       "245  football    0.000151  2000\n",
       "246  football    0.000164  2001\n",
       "247  football    0.000152  2002\n",
       "248  football    0.000162  2003\n",
       "249  football    0.000096  2005"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "word = (['digital'] * (len(digital))) + (['mining'] * (len(mining))) + (['shipbuilding'] * (len(shipbuilding))) + (['technology'] * (len(technology))) + (['football'] * (len(football)))\n",
    "\n",
    "occurrence = digital + mining + shipbuilding + technology + football\n",
    "\n",
    "yrs = list(df['year'])\n",
    "year = yrs * 5\n",
    "\n",
    "word_df = pd.DataFrame({'word': word, 'occurrence': occurrence, 'year': year})\n",
    "word_df.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using that DataFrame, we'll visualise the occurrence of all the words in every Handbook in our dataset:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<div id=\"altair-viz-a71174ed770d4c2e952cb1c2635dd880\"></div>\n",
       "<script type=\"text/javascript\">\n",
       "  (function(spec, embedOpt){\n",
       "    let outputDiv = document.currentScript.previousElementSibling;\n",
       "    if (outputDiv.id !== \"altair-viz-a71174ed770d4c2e952cb1c2635dd880\") {\n",
       "      outputDiv = document.getElementById(\"altair-viz-a71174ed770d4c2e952cb1c2635dd880\");\n",
       "    }\n",
       "    const paths = {\n",
       "      \"vega\": \"https://cdn.jsdelivr.net/npm//vega@5?noext\",\n",
       "      \"vega-lib\": \"https://cdn.jsdelivr.net/npm//vega-lib?noext\",\n",
       "      \"vega-lite\": \"https://cdn.jsdelivr.net/npm//vega-lite@4.8.1?noext\",\n",
       "      \"vega-embed\": \"https://cdn.jsdelivr.net/npm//vega-embed@6?noext\",\n",
       "    };\n",
       "\n",
       "    function loadScript(lib) {\n",
       "      return new Promise(function(resolve, reject) {\n",
       "        var s = document.createElement('script');\n",
       "        s.src = paths[lib];\n",
       "        s.async = true;\n",
       "        s.onload = () => resolve(paths[lib]);\n",
       "        s.onerror = () => reject(`Error loading script: ${paths[lib]}`);\n",
       "        document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
       "      });\n",
       "    }\n",
       "\n",
       "    function showError(err) {\n",
       "      outputDiv.innerHTML = `<div class=\"error\" style=\"color:red;\">${err}</div>`;\n",
       "      throw err;\n",
       "    }\n",
       "\n",
       "    function displayChart(vegaEmbed) {\n",
       "      vegaEmbed(outputDiv, spec, embedOpt)\n",
       "        .catch(err => showError(`Javascript Error: ${err.message}<br>This usually means there's a typo in your chart specification. See the javascript console for the full traceback.`));\n",
       "    }\n",
       "\n",
       "    if(typeof define === \"function\" && define.amd) {\n",
       "      requirejs.config({paths});\n",
       "      require([\"vega-embed\"], displayChart, err => showError(`Error loading script: ${err.message}`));\n",
       "    } else if (typeof vegaEmbed === \"function\") {\n",
       "      displayChart(vegaEmbed);\n",
       "    } else {\n",
       "      loadScript(\"vega\")\n",
       "        .then(() => loadScript(\"vega-lite\"))\n",
       "        .then(() => loadScript(\"vega-embed\"))\n",
       "        .catch(showError)\n",
       "        .then(() => displayChart(vegaEmbed));\n",
       "    }\n",
       "  })({\"config\": {\"view\": {\"continuousWidth\": 400, \"continuousHeight\": 300}}, \"data\": {\"name\": \"data-11ee16e0dac5e0ccb5f74a2be34117dc\"}, \"mark\": \"line\", \"encoding\": {\"color\": {\"type\": \"nominal\", \"field\": \"word\"}, \"tooltip\": {\"type\": \"nominal\", \"field\": \"word\"}, \"x\": {\"type\": \"ordinal\", \"field\": \"year\"}, \"y\": {\"type\": \"quantitative\", \"axis\": {\"format\": \"%\"}, \"field\": \"occurrence\"}}, \"title\": \"Occurrence of Select Words in the Britain and UK Handbooks\", \"$schema\": \"https://vega.github.io/schema/vega-lite/v4.8.1.json\", \"datasets\": {\"data-11ee16e0dac5e0ccb5f74a2be34117dc\": [{\"word\": \"digital\", \"occurrence\": 4.606363230166152e-06, \"year\": 1954}, {\"word\": \"digital\", \"occurrence\": 3.8050302499904872e-06, \"year\": 1955}, {\"word\": \"digital\", \"occurrence\": 3.3612541511488768e-06, \"year\": 1956}, {\"word\": \"digital\", \"occurrence\": 2.990359082318605e-06, \"year\": 1958}, {\"word\": \"digital\", \"occurrence\": 2.9441292590509895e-06, \"year\": 1959}, {\"word\": \"digital\", \"occurrence\": 2.666865792645851e-06, \"year\": 1960}, {\"word\": \"digital\", \"occurrence\": 2.6434186805111314e-06, \"year\": 1961}, {\"word\": \"digital\", \"occurrence\": 2.643439643664336e-06, \"year\": 1962}, {\"word\": \"digital\", \"occurrence\": 2.6253678796741395e-06, \"year\": 1963}, {\"word\": \"digital\", \"occurrence\": 5.172864189037149e-06, \"year\": 1964}, {\"word\": \"digital\", \"occurrence\": 5.162102937494677e-06, \"year\": 1965}, {\"word\": \"digital\", \"occurrence\": 5.2195472042800285e-06, \"year\": 1966}, {\"word\": \"digital\", \"occurrence\": 5.536102307170637e-06, \"year\": 1967}, {\"word\": \"digital\", \"occurrence\": 8.282099346542362e-06, \"year\": 1968}, {\"word\": \"digital\", \"occurrence\": 2.8662082644249095e-06, \"year\": 1969}, {\"word\": \"digital\", \"occurrence\": 2.8554132925199594e-06, \"year\": 1970}, {\"word\": \"digital\", \"occurrence\": 2.8112551410828393e-06, \"year\": 1971}, {\"word\": \"digital\", \"occurrence\": 5.644344225976965e-06, \"year\": 1972}, {\"word\": \"digital\", \"occurrence\": 8.557247989046722e-06, \"year\": 1973}, {\"word\": \"digital\", \"occurrence\": 1.1455180175665187e-05, \"year\": 1974}, {\"word\": \"digital\", \"occurrence\": 8.960653769299008e-06, \"year\": 1975}, {\"word\": \"digital\", \"occurrence\": 5.966747317201238e-06, \"year\": 1976}, {\"word\": \"digital\", \"occurrence\": 3.063490847821092e-06, \"year\": 1977}, {\"word\": \"digital\", \"occurrence\": 6.338722303745234e-06, \"year\": 1978}, {\"word\": \"digital\", \"occurrence\": 9.621891728059681e-06, \"year\": 1979}, {\"word\": \"digital\", \"occurrence\": 1.601804272332355e-05, \"year\": 1980}, {\"word\": \"digital\", \"occurrence\": 1.580053405805116e-05, \"year\": 1981}, {\"word\": \"digital\", \"occurrence\": 1.542751574570826e-05, \"year\": 1982}, {\"word\": \"digital\", \"occurrence\": 2.6434546175487624e-05, \"year\": 1983}, {\"word\": \"digital\", \"occurrence\": 5.100479445067836e-05, \"year\": 1984}, {\"word\": \"digital\", \"occurrence\": 5.01007796452094e-05, \"year\": 1985}, {\"word\": \"digital\", \"occurrence\": 5.064408422834694e-05, \"year\": 1986}, {\"word\": \"digital\", \"occurrence\": 5.585597536751486e-05, \"year\": 1987}, {\"word\": \"digital\", \"occurrence\": 5.851697336445059e-05, \"year\": 1988}, {\"word\": \"digital\", \"occurrence\": 5.678914191606565e-05, \"year\": 1989}, {\"word\": \"digital\", \"occurrence\": 5.6567662781306274e-05, \"year\": 1990}, {\"word\": \"digital\", \"occurrence\": 5.9924243141039594e-05, \"year\": 1991}, {\"word\": \"digital\", \"occurrence\": 5.895776543132807e-05, \"year\": 1992}, {\"word\": \"digital\", \"occurrence\": 5.7418099460907844e-05, \"year\": 1993}, {\"word\": \"digital\", \"occurrence\": 5.656197464138137e-05, \"year\": 1994}, {\"word\": \"digital\", \"occurrence\": 5.433338967907078e-05, \"year\": 1995}, {\"word\": \"digital\", \"occurrence\": 7.932730445819452e-05, \"year\": 1996}, {\"word\": \"digital\", \"occurrence\": 0.00010331011182621564, \"year\": 1997}, {\"word\": \"digital\", \"occurrence\": 0.00010278576471256501, \"year\": 1998}, {\"word\": \"digital\", \"occurrence\": 0.00010319207484865162, \"year\": 1999}, {\"word\": \"digital\", \"occurrence\": 0.00013209795327431197, \"year\": 2000}, {\"word\": \"digital\", \"occurrence\": 0.00012414747663650147, \"year\": 2001}, {\"word\": \"digital\", \"occurrence\": 0.00014973205842177157, \"year\": 2002}, {\"word\": \"digital\", \"occurrence\": 0.0001371595357424034, \"year\": 2003}, {\"word\": \"digital\", \"occurrence\": 0.00016550457540068125, \"year\": 2005}, {\"word\": \"mining\", \"occurrence\": 0.00010133999106365534, \"year\": 1954}, {\"word\": \"mining\", \"occurrence\": 7.610060499980975e-05, \"year\": 1955}, {\"word\": \"mining\", \"occurrence\": 7.730884547642417e-05, \"year\": 1956}, {\"word\": \"mining\", \"occurrence\": 6.578789981100931e-05, \"year\": 1958}, {\"word\": \"mining\", \"occurrence\": 5.888258518101979e-05, \"year\": 1959}, {\"word\": \"mining\", \"occurrence\": 5.333731585291702e-05, \"year\": 1960}, {\"word\": \"mining\", \"occurrence\": 4.758153624920036e-05, \"year\": 1961}, {\"word\": \"mining\", \"occurrence\": 5.551223251695106e-05, \"year\": 1962}, {\"word\": \"mining\", \"occurrence\": 7.35103006308759e-05, \"year\": 1963}, {\"word\": \"mining\", \"occurrence\": 6.466080236296437e-05, \"year\": 1964}, {\"word\": \"mining\", \"occurrence\": 5.678313231244144e-05, \"year\": 1965}, {\"word\": \"mining\", \"occurrence\": 6.263456645136034e-05, \"year\": 1966}, {\"word\": \"mining\", \"occurrence\": 6.920127883963296e-05, \"year\": 1967}, {\"word\": \"mining\", \"occurrence\": 6.34960949901581e-05, \"year\": 1968}, {\"word\": \"mining\", \"occurrence\": 6.305658181734802e-05, \"year\": 1969}, {\"word\": \"mining\", \"occurrence\": 6.28190924354391e-05, \"year\": 1970}, {\"word\": \"mining\", \"occurrence\": 5.9036357962739625e-05, \"year\": 1971}, {\"word\": \"mining\", \"occurrence\": 6.208778648574662e-05, \"year\": 1972}, {\"word\": \"mining\", \"occurrence\": 5.990073592332706e-05, \"year\": 1973}, {\"word\": \"mining\", \"occurrence\": 6.300349096615854e-05, \"year\": 1974}, {\"word\": \"mining\", \"occurrence\": 8.960653769299008e-05, \"year\": 1975}, {\"word\": \"mining\", \"occurrence\": 8.950120975801857e-05, \"year\": 1976}, {\"word\": \"mining\", \"occurrence\": 9.803170713027495e-05, \"year\": 1977}, {\"word\": \"mining\", \"occurrence\": 8.557275110056065e-05, \"year\": 1978}, {\"word\": \"mining\", \"occurrence\": 8.980432279522369e-05, \"year\": 1979}, {\"word\": \"mining\", \"occurrence\": 9.610825633994131e-05, \"year\": 1980}, {\"word\": \"mining\", \"occurrence\": 8.532288391347627e-05, \"year\": 1981}, {\"word\": \"mining\", \"occurrence\": 9.256509447424954e-05, \"year\": 1982}, {\"word\": \"mining\", \"occurrence\": 9.063272974452899e-05, \"year\": 1983}, {\"word\": \"mining\", \"occurrence\": 7.846891453950517e-05, \"year\": 1984}, {\"word\": \"mining\", \"occurrence\": 6.937031027798224e-05, \"year\": 1985}, {\"word\": \"mining\", \"occurrence\": 5.426151881608601e-05, \"year\": 1986}, {\"word\": \"mining\", \"occurrence\": 6.283797228845422e-05, \"year\": 1987}, {\"word\": \"mining\", \"occurrence\": 5.163262355686817e-05, \"year\": 1988}, {\"word\": \"mining\", \"occurrence\": 4.008645311722281e-05, \"year\": 1989}, {\"word\": \"mining\", \"occurrence\": 3.771177518753752e-05, \"year\": 1990}, {\"word\": \"mining\", \"occurrence\": 3.784689040486711e-05, \"year\": 1991}, {\"word\": \"mining\", \"occurrence\": 3.814914233791817e-05, \"year\": 1992}, {\"word\": \"mining\", \"occurrence\": 3.1898944144948805e-05, \"year\": 1993}, {\"word\": \"mining\", \"occurrence\": 5.341964271686018e-05, \"year\": 1994}, {\"word\": \"mining\", \"occurrence\": 6.640747627441984e-05, \"year\": 1995}, {\"word\": \"mining\", \"occurrence\": 7.345120783166159e-05, \"year\": 1996}, {\"word\": \"mining\", \"occurrence\": 6.142763405883092e-05, \"year\": 1997}, {\"word\": \"mining\", \"occurrence\": 5.680265944641751e-05, \"year\": 1998}, {\"word\": \"mining\", \"occurrence\": 4.4981160831463526e-05, \"year\": 1999}, {\"word\": \"mining\", \"occurrence\": 5.548114037521103e-05, \"year\": 2000}, {\"word\": \"mining\", \"occurrence\": 3.698009942363874e-05, \"year\": 2001}, {\"word\": \"mining\", \"occurrence\": 3.15225386151098e-05, \"year\": 2002}, {\"word\": \"mining\", \"occurrence\": 5.2120623582113296e-05, \"year\": 2003}, {\"word\": \"mining\", \"occurrence\": 3.470257226143316e-05, \"year\": 2005}, {\"word\": \"shipbuilding\", \"occurrence\": 0.00010594635429382149, \"year\": 1954}, {\"word\": \"shipbuilding\", \"occurrence\": 0.00010273581674974317, \"year\": 1955}, {\"word\": \"shipbuilding\", \"occurrence\": 8.067009962757304e-05, \"year\": 1956}, {\"word\": \"shipbuilding\", \"occurrence\": 9.270113155187675e-05, \"year\": 1958}, {\"word\": \"shipbuilding\", \"occurrence\": 7.949148999437671e-05, \"year\": 1959}, {\"word\": \"shipbuilding\", \"occurrence\": 8.000597377937553e-05, \"year\": 1960}, {\"word\": \"shipbuilding\", \"occurrence\": 7.137230437380054e-05, \"year\": 1961}, {\"word\": \"shipbuilding\", \"occurrence\": 7.665974966626575e-05, \"year\": 1962}, {\"word\": \"shipbuilding\", \"occurrence\": 6.825956487152762e-05, \"year\": 1963}, {\"word\": \"shipbuilding\", \"occurrence\": 8.535225911911296e-05, \"year\": 1964}, {\"word\": \"shipbuilding\", \"occurrence\": 7.48504925936728e-05, \"year\": 1965}, {\"word\": \"shipbuilding\", \"occurrence\": 7.568343446206042e-05, \"year\": 1966}, {\"word\": \"shipbuilding\", \"occurrence\": 5.812907422529168e-05, \"year\": 1967}, {\"word\": \"shipbuilding\", \"occurrence\": 6.073539520797732e-05, \"year\": 1968}, {\"word\": \"shipbuilding\", \"occurrence\": 6.01903735529231e-05, \"year\": 1969}, {\"word\": \"shipbuilding\", \"occurrence\": 6.567450572795907e-05, \"year\": 1970}, {\"word\": \"shipbuilding\", \"occurrence\": 5.9036357962739625e-05, \"year\": 1971}, {\"word\": \"shipbuilding\", \"occurrence\": 6.208778648574662e-05, \"year\": 1972}, {\"word\": \"shipbuilding\", \"occurrence\": 6.845798391237377e-05, \"year\": 1973}, {\"word\": \"shipbuilding\", \"occurrence\": 6.013969592224224e-05, \"year\": 1974}, {\"word\": \"shipbuilding\", \"occurrence\": 6.272457638509306e-05, \"year\": 1975}, {\"word\": \"shipbuilding\", \"occurrence\": 5.6684099513411754e-05, \"year\": 1976}, {\"word\": \"shipbuilding\", \"occurrence\": 6.739679865206403e-05, \"year\": 1977}, {\"word\": \"shipbuilding\", \"occurrence\": 7.28953064930702e-05, \"year\": 1978}, {\"word\": \"shipbuilding\", \"occurrence\": 7.056053933910433e-05, \"year\": 1979}, {\"word\": \"shipbuilding\", \"occurrence\": 8.009021361661776e-05, \"year\": 1980}, {\"word\": \"shipbuilding\", \"occurrence\": 7.268245666703534e-05, \"year\": 1981}, {\"word\": \"shipbuilding\", \"occurrence\": 8.870821553782249e-05, \"year\": 1982}, {\"word\": \"shipbuilding\", \"occurrence\": 7.175091104775211e-05, \"year\": 1983}, {\"word\": \"shipbuilding\", \"occurrence\": 7.846891453950517e-05, \"year\": 1984}, {\"word\": \"shipbuilding\", \"occurrence\": 8.093202865764596e-05, \"year\": 1985}, {\"word\": \"shipbuilding\", \"occurrence\": 6.873125716704228e-05, \"year\": 1986}, {\"word\": \"shipbuilding\", \"occurrence\": 5.585597536751486e-05, \"year\": 1987}, {\"word\": \"shipbuilding\", \"occurrence\": 4.819044865307696e-05, \"year\": 1988}, {\"word\": \"shipbuilding\", \"occurrence\": 3.6745915357454244e-05, \"year\": 1989}, {\"word\": \"shipbuilding\", \"occurrence\": 3.142647932294793e-05, \"year\": 1990}, {\"word\": \"shipbuilding\", \"occurrence\": 2.8385167803650332e-05, \"year\": 1991}, {\"word\": \"shipbuilding\", \"occurrence\": 2.4276726942311562e-05, \"year\": 1992}, {\"word\": \"shipbuilding\", \"occurrence\": 2.5519155315959042e-05, \"year\": 1993}, {\"word\": \"shipbuilding\", \"occurrence\": 2.5138655396169497e-05, \"year\": 1994}, {\"word\": \"shipbuilding\", \"occurrence\": 2.716669483953539e-05, \"year\": 1995}, {\"word\": \"shipbuilding\", \"occurrence\": 2.0566338192865242e-05, \"year\": 1996}, {\"word\": \"shipbuilding\", \"occurrence\": 2.2337321475938517e-05, \"year\": 1997}, {\"word\": \"shipbuilding\", \"occurrence\": 2.1639108360540004e-05, \"year\": 1998}, {\"word\": \"shipbuilding\", \"occurrence\": 1.8521654460014395e-05, \"year\": 1999}, {\"word\": \"shipbuilding\", \"occurrence\": 2.1135672523889913e-05, \"year\": 2000}, {\"word\": \"shipbuilding\", \"occurrence\": 2.3772921058053475e-05, \"year\": 2001}, {\"word\": \"shipbuilding\", \"occurrence\": 2.1015025743406536e-05, \"year\": 2002}, {\"word\": \"shipbuilding\", \"occurrence\": 1.645914428908841e-05, \"year\": 2003}, {\"word\": \"shipbuilding\", \"occurrence\": 2.402485771945373e-05, \"year\": 2005}, {\"word\": \"technology\", \"occurrence\": 2.763817938099691e-05, \"year\": 1954}, {\"word\": \"technology\", \"occurrence\": 2.2830181499942923e-05, \"year\": 1955}, {\"word\": \"technology\", \"occurrence\": 2.6890033209191014e-05, \"year\": 1956}, {\"word\": \"technology\", \"occurrence\": 6.279754072869071e-05, \"year\": 1958}, {\"word\": \"technology\", \"occurrence\": 0.00012070929962109056, \"year\": 1959}, {\"word\": \"technology\", \"occurrence\": 0.00014401075280287594, \"year\": 1960}, {\"word\": \"technology\", \"occurrence\": 0.00014803144610862337, \"year\": 1961}, {\"word\": \"technology\", \"occurrence\": 0.00015067605968886716, \"year\": 1962}, {\"word\": \"technology\", \"occurrence\": 0.0001732742800584932, \"year\": 1963}, {\"word\": \"technology\", \"occurrence\": 0.00017329095033274448, \"year\": 1964}, {\"word\": \"technology\", \"occurrence\": 0.00017293044840607167, \"year\": 1965}, {\"word\": \"technology\", \"occurrence\": 0.0003314412474717818, \"year\": 1966}, {\"word\": \"technology\", \"occurrence\": 0.00028234121766570244, \"year\": 1967}, {\"word\": \"technology\", \"occurrence\": 0.0002953948766933442, \"year\": 1968}, {\"word\": \"technology\", \"occurrence\": 0.0002751559933847913, \"year\": 1969}, {\"word\": \"technology\", \"occurrence\": 0.00028554132925199594, \"year\": 1970}, {\"word\": \"technology\", \"occurrence\": 0.0003429731272121064, \"year\": 1971}, {\"word\": \"technology\", \"occurrence\": 0.00018344118734425137, \"year\": 1972}, {\"word\": \"technology\", \"occurrence\": 0.00017399737577728335, \"year\": 1973}, {\"word\": \"technology\", \"occurrence\": 0.0001775552927228104, \"year\": 1974}, {\"word\": \"technology\", \"occurrence\": 0.00019116061374504551, \"year\": 1975}, {\"word\": \"technology\", \"occurrence\": 0.00022971977171224765, \"year\": 1976}, {\"word\": \"technology\", \"occurrence\": 0.00020219039595619208, \"year\": 1977}, {\"word\": \"technology\", \"occurrence\": 0.00023136336408670106, \"year\": 1978}, {\"word\": \"technology\", \"occurrence\": 0.00027262026562835764, \"year\": 1979}, {\"word\": \"technology\", \"occurrence\": 0.0002562886835731768, \"year\": 1980}, {\"word\": \"technology\", \"occurrence\": 0.0002654489721752595, \"year\": 1981}, {\"word\": \"technology\", \"occurrence\": 0.00030083655704131104, \"year\": 1982}, {\"word\": \"technology\", \"occurrence\": 0.00030966182662714074, \"year\": 1983}, {\"word\": \"technology\", \"occurrence\": 0.0003805742355166001, \"year\": 1984}, {\"word\": \"technology\", \"occurrence\": 0.0004008062371616752, \"year\": 1985}, {\"word\": \"technology\", \"occurrence\": 0.0004630316272306006, \"year\": 1986}, {\"word\": \"technology\", \"occurrence\": 0.0005027037783076338, \"year\": 1987}, {\"word\": \"technology\", \"occurrence\": 0.0006230336575862093, \"year\": 1988}, {\"word\": \"technology\", \"occurrence\": 0.0006547454009146392, \"year\": 1989}, {\"word\": \"technology\", \"occurrence\": 0.0007228090244278024, \"year\": 1990}, {\"word\": \"technology\", \"occurrence\": 0.0007537839005636032, \"year\": 1991}, {\"word\": \"technology\", \"occurrence\": 0.0006381311081979039, \"year\": 1992}, {\"word\": \"technology\", \"occurrence\": 0.0006890171935308941, \"year\": 1993}, {\"word\": \"technology\", \"occurrence\": 0.0006504627083758857, \"year\": 1994}, {\"word\": \"technology\", \"occurrence\": 0.0006610562410953612, \"year\": 1995}, {\"word\": \"technology\", \"occurrence\": 0.0006316803873522896, \"year\": 1996}, {\"word\": \"technology\", \"occurrence\": 0.0006058998450348323, \"year\": 1997}, {\"word\": \"technology\", \"occurrence\": 0.0006031901455500526, \"year\": 1998}, {\"word\": \"technology\", \"occurrence\": 0.0005635874857118666, \"year\": 1999}, {\"word\": \"technology\", \"occurrence\": 0.0004570589183291194, \"year\": 2000}, {\"word\": \"technology\", \"occurrence\": 0.00048074129250730356, \"year\": 2001}, {\"word\": \"technology\", \"occurrence\": 0.0004912262267521278, \"year\": 2002}, {\"word\": \"technology\", \"occurrence\": 0.0004910311379578042, \"year\": 2003}, {\"word\": \"technology\", \"occurrence\": 0.00044312515349214656, \"year\": 2005}, {\"word\": \"football\", \"occurrence\": 3.224454261116306e-05, \"year\": 1954}, {\"word\": \"football\", \"occurrence\": 3.424527224991439e-05, \"year\": 1955}, {\"word\": \"football\", \"occurrence\": 3.0251287360339892e-05, \"year\": 1956}, {\"word\": \"football\", \"occurrence\": 2.392287265854884e-05, \"year\": 1958}, {\"word\": \"football\", \"occurrence\": 2.9441292590509894e-05, \"year\": 1959}, {\"word\": \"football\", \"occurrence\": 0.00012800955804700084, \"year\": 1960}, {\"word\": \"football\", \"occurrence\": 0.00011102358458146753, \"year\": 1961}, {\"word\": \"football\", \"occurrence\": 0.00011366790467756644, \"year\": 1962}, {\"word\": \"football\", \"occurrence\": 0.000112890818825988, \"year\": 1963}, {\"word\": \"football\", \"occurrence\": 0.00011638944425333585, \"year\": 1964}, {\"word\": \"football\", \"occurrence\": 9.807995581239885e-05, \"year\": 1965}, {\"word\": \"football\", \"occurrence\": 0.00010178117048346055, \"year\": 1966}, {\"word\": \"football\", \"occurrence\": 0.00010241789268265678, \"year\": 1967}, {\"word\": \"football\", \"occurrence\": 9.938519215850834e-05, \"year\": 1968}, {\"word\": \"football\", \"occurrence\": 0.00010604970578372166, \"year\": 1969}, {\"word\": \"football\", \"occurrence\": 0.00010850570511575846, \"year\": 1970}, {\"word\": \"football\", \"occurrence\": 0.00010963895050223073, \"year\": 1971}, {\"word\": \"football\", \"occurrence\": 9.313167972861993e-05, \"year\": 1972}, {\"word\": \"football\", \"occurrence\": 9.127731188316505e-05, \"year\": 1973}, {\"word\": \"football\", \"occurrence\": 9.45052364492378e-05, \"year\": 1974}, {\"word\": \"football\", \"occurrence\": 7.46721147441584e-05, \"year\": 1975}, {\"word\": \"football\", \"occurrence\": 7.458434146501547e-05, \"year\": 1976}, {\"word\": \"football\", \"occurrence\": 7.96507620433484e-05, \"year\": 1977}, {\"word\": \"football\", \"occurrence\": 7.28953064930702e-05, \"year\": 1978}, {\"word\": \"football\", \"occurrence\": 8.0182431067164e-05, \"year\": 1979}, {\"word\": \"football\", \"occurrence\": 7.368299652728833e-05, \"year\": 1980}, {\"word\": \"football\", \"occurrence\": 7.584256347864558e-05, \"year\": 1981}, {\"word\": \"football\", \"occurrence\": 0.00010799261021995781, \"year\": 1982}, {\"word\": \"football\", \"occurrence\": 0.00010951454844130586, \"year\": 1983}, {\"word\": \"football\", \"occurrence\": 0.00010200958890135672, \"year\": 1984}, {\"word\": \"football\", \"occurrence\": 0.00010405546541697337, \"year\": 1985}, {\"word\": \"football\", \"occurrence\": 0.00013022764515860643, \"year\": 1986}, {\"word\": \"football\", \"occurrence\": 0.00014313093687925684, \"year\": 1987}, {\"word\": \"football\", \"occurrence\": 0.00014457134595923088, \"year\": 1988}, {\"word\": \"football\", \"occurrence\": 0.0001536647369493541, \"year\": 1989}, {\"word\": \"football\", \"occurrence\": 0.00015084710075015007, \"year\": 1990}, {\"word\": \"football\", \"occurrence\": 0.0001576953766869463, \"year\": 1991}, {\"word\": \"football\", \"occurrence\": 0.00016300088089837763, \"year\": 1992}, {\"word\": \"football\", \"occurrence\": 0.00016906440396822865, \"year\": 1993}, {\"word\": \"football\", \"occurrence\": 0.00016654359199962291, \"year\": 1994}, {\"word\": \"football\", \"occurrence\": 0.00016903721233488687, \"year\": 1995}, {\"word\": \"football\", \"occurrence\": 0.00017334485048272133, \"year\": 1996}, {\"word\": \"football\", \"occurrence\": 0.00017032207625403118, \"year\": 1997}, {\"word\": \"football\", \"occurrence\": 0.00016499820124911752, \"year\": 1998}, {\"word\": \"football\", \"occurrence\": 0.00015611108759154988, \"year\": 1999}, {\"word\": \"football\", \"occurrence\": 0.00015059166673271564, \"year\": 2000}, {\"word\": \"football\", \"occurrence\": 0.00016376901173325727, \"year\": 2001}, {\"word\": \"football\", \"occurrence\": 0.0001523589366396974, \"year\": 2002}, {\"word\": \"football\", \"occurrence\": 0.00016184825217603605, \"year\": 2003}, {\"word\": \"football\", \"occurrence\": 9.609943087781492e-05, \"year\": 2005}]}}, {\"mode\": \"vega-lite\"});\n",
       "</script>"
      ],
      "text/plain": [
       "alt.Chart(...)"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "alt.Chart(word_df, title=\"Occurrence of Select Words in the Britain and UK Handbooks\").mark_line().encode(\n",
    "    x='year:O',\n",
    "    y=alt.Y('occurrence', axis=alt.Axis(format='%')),\n",
    "    color='word',\n",
    "    tooltip='word'\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class='alert alert-box alert-info'>\n",
    "    <b>Try It!</b> How is Scotland portrayed?  How does the portrayal of Britain and the UK compare or contrast with the portrayal of Scotland?\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "# HINT 1: try using Regular Expressions to search for words related to Scotland and Scottish-ness...\n",
    "\n",
    "# scot_strings = [s for s in handbooks_words_lower if (re.search('scot$', s) or re.search('scot[tcls]+', s))]\n",
    "# print(\"Total tokens related to Scotland:\", len(scot_strings))\n",
    "\n",
    "####################\n",
    "\n",
    "# HINT 2: Sets in Python are similar to Lists except that they can't have repeating items, \n",
    "#         so changing a list to a set is a quick way to remove duplicates from a list!\n",
    "# unique_scot = set(scot_strings)\n",
    "# print(\"Unique tokens related to Scotland:\", len(unique_scot))\n",
    "# print(unique_scot)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}