code/exploratory_analysis.ipynb

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    "# Exploratory Analysis\n",
    "\n",
    "Man Ho Wong, University of Pittsburgh | April 23, 2022\n",
    "\n",
    "*Jupyter notebooks are not always rendered properly on GitHub. Please [view this notebook here on nbviewer](https://nbviewer.org/github/Data-Science-for-Linguists-2022/Child-Vocab-Development/blob/main/code/exploratory_analysis.ipynb) if that happens.*\n",
    "\n",
    "##### Script header"
   ]
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   "id": "2c724a13-8313-43bd-a208-44b4a4b1de8f",
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   "source": [
    "# Python packages and modules required\n",
    "from collections import Counter\n",
    "from heapq import nlargest\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import pickle\n",
    "import pylangacq                       # For reading CHAT files\n",
    "\n",
    "# For showing progress bar (optional)\n",
    "#   This is useful for estimating the efficiency of the code.\n",
    "from tqdm import tqdm\n",
    "\n",
    "# Unpickle processed data_idx\n",
    "data_idx = pickle.load(open('../data/childes/data_idx_processed.pkl', 'rb'))"
   ]
  },
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    "---\n",
    "\n",
    "# 1 Overview\n",
    "\n",
    "> Data processing pipeline:  \n",
    ">[`data_curation.ipynb`](https://github.com/Data-Science-for-Linguists-2022/Child-Vocab-Development/blob/main/code/data_curation.ipynb)  \n",
    ">[`data_preprocessing.ipynb`](https://github.com/Data-Science-for-Linguists-2022/Child-Vocab-Development/blob/main/code/data_preprocessing.ipynb)    \n",
    ">`exploratory_analysis.ipynb`  <-- You are here  \n",
    ">[`vocabulary_analysis.ipynb`](https://github.com/Data-Science-for-Linguists-2022/Child-Vocab-Development/blob/main/code/vocabulary_analysis.ipynb)\n",
    "\n",
    "This notebook is the third step of the data processing and analysis pipeline of the project. The goal here is to evaluate the quality of the dataset processed by [`data_preprocessing.ipynb`](https://github.com/Data-Science-for-Linguists-2022/Child-Vocab-Development/blob/main/code/data_preprocessing.ipynb). Besides, I will also look at what limitations the dataset has and whether further data cleaning is needed. Specifically, I will use Mean length of utterance (MLU), a linguistic measurement commonly used in the assessment of child language development, as a tool assess the processed dataset quantitatively.\n",
    "\n",
    "## 1.1 Objectives\n",
    "\n",
    "Here are the objectives of this notebook:\n",
    "1. Identify the tools available for measuring MLU:\n",
    "    - Assess the tools available in the `PyLangAcq` package and identify their limitations\n",
    "    - Develop custom functions specifically designed for this project\n",
    "2. Evaluate the quality and limitations of the processed dataset with MLU:\n",
    "    - Is the sample size sufficient?\n",
    "    - Is the sample distribution balanced so that fair comparison between different groups can be made?\n",
    "    - Can we really combine different corpora?\n",
    "\n",
    "### Table of contents\n",
    "\n",
    "1. [Overview](#1-overview)\n",
    "    - 1.1 [Objectives](#11-objectives)\n",
    "    - 1.2 [Quick recap](#12-quick-recap)\n",
    "    - 1.3 [Efficiency and analysis steps](#13-efficiency-and-analysis-steps)\n",
    "2. [Evaluating dataset with MLU](#2-evaluating-dataset-with-mlu)\n",
    "    - 2.1 [Using `PyLangAcq` to get MLU](#21-using-pylangacq-to-get-mlu)\n",
    "    - 2.2 [Custom functions to get MLU](#22-custom-functions-to-get-mlu)\n",
    "    - 2.3 [Comparing MLU-m between childen of different SES](#23-comparing-mlu-m-between-childen-of-different-ses)\n",
    "    - 2.4 [Sample distribution by age](#24-sample-distribution-by-age)\n",
    "3. [Checking effects of recording length on MLU](#3-checking-effects-of-recording-length-on-mlu)\n",
    "4. [Summary](#4-summary)\n"
   ]
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    "## 1.2 Quick recap\n",
    "\n",
    "The DataFrame `data_idx` contains basic information about each CHAT file and it serves as an index for reading the CHAT files. Before analysis, the target CHAT file(s) should be first read into a `PyLangAcq` `reader` object. Depending on the type of analysis, one CHAT file or multiple CHAT files (e.g. all files from a group) can be read into a `reader` object. Users can then access the data stored in the `reader` by calling relevant methods and attributes."
   ]
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>file_path</th>\n",
       "      <th>corpus</th>\n",
       "      <th>year</th>\n",
       "      <th>participants</th>\n",
       "      <th>name</th>\n",
       "      <th>child_id</th>\n",
       "      <th>age_d</th>\n",
       "      <th>age_m</th>\n",
       "      <th>sex</th>\n",
       "      <th>group</th>\n",
       "      <th>ses</th>\n",
       "      <th>mot_edu</th>\n",
       "      <th>situation</th>\n",
       "      <th>activities</th>\n",
       "      <th>study_type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>../data/childes/Bates/Free20/amy.cha</td>\n",
       "      <td>Bates</td>\n",
       "      <td>NaN</td>\n",
       "      <td>{CHI, MOT}</td>\n",
       "      <td>Target_Child</td>\n",
       "      <td>Bates_Target_Child</td>\n",
       "      <td>600</td>\n",
       "      <td>20.0</td>\n",
       "      <td>female</td>\n",
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       "      <td>unspecified</td>\n",
       "      <td>unspecified</td>\n",
       "      <td>unspecified</td>\n",
       "      <td>cross, toyplay, TD</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>../data/childes/Bates/Free20/betty.cha</td>\n",
       "      <td>Bates</td>\n",
       "      <td>NaN</td>\n",
       "      <td>{CHI, MOT}</td>\n",
       "      <td>Betty</td>\n",
       "      <td>Bates_Betty</td>\n",
       "      <td>600</td>\n",
       "      <td>20.0</td>\n",
       "      <td>female</td>\n",
       "      <td>TD</td>\n",
       "      <td>MC</td>\n",
       "      <td>unspecified</td>\n",
       "      <td>unspecified</td>\n",
       "      <td>unspecified</td>\n",
       "      <td>cross, toyplay, TD</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>../data/childes/Bates/Free20/chuck.cha</td>\n",
       "      <td>Bates</td>\n",
       "      <td>NaN</td>\n",
       "      <td>{CHI, MOT}</td>\n",
       "      <td>Chuck</td>\n",
       "      <td>Bates_Chuck</td>\n",
       "      <td>600</td>\n",
       "      <td>20.0</td>\n",
       "      <td>male</td>\n",
       "      <td>TD</td>\n",
       "      <td>MC</td>\n",
       "      <td>unspecified</td>\n",
       "      <td>unspecified</td>\n",
       "      <td>unspecified</td>\n",
       "      <td>cross, toyplay, TD</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>../data/childes/Bates/Free20/doug.cha</td>\n",
       "      <td>Bates</td>\n",
       "      <td>NaN</td>\n",
       "      <td>{CHI, MOT}</td>\n",
       "      <td>Doug</td>\n",
       "      <td>Bates_Doug</td>\n",
       "      <td>600</td>\n",
       "      <td>20.0</td>\n",
       "      <td>male</td>\n",
       "      <td>TD</td>\n",
       "      <td>MC</td>\n",
       "      <td>unspecified</td>\n",
       "      <td>unspecified</td>\n",
       "      <td>unspecified</td>\n",
       "      <td>cross, toyplay, TD</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>../data/childes/Bates/Free20/ed.cha</td>\n",
       "      <td>Bates</td>\n",
       "      <td>NaN</td>\n",
       "      <td>{CHI, MOT}</td>\n",
       "      <td>Ed</td>\n",
       "      <td>Bates_Ed</td>\n",
       "      <td>600</td>\n",
       "      <td>20.0</td>\n",
       "      <td>male</td>\n",
       "      <td>TD</td>\n",
       "      <td>MC</td>\n",
       "      <td>unspecified</td>\n",
       "      <td>unspecified</td>\n",
       "      <td>unspecified</td>\n",
       "      <td>cross, toyplay, TD</td>\n",
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       "                                file_path corpus  year participants  \\\n",
       "0    ../data/childes/Bates/Free20/amy.cha  Bates   NaN   {CHI, MOT}   \n",
       "1  ../data/childes/Bates/Free20/betty.cha  Bates   NaN   {CHI, MOT}   \n",
       "2  ../data/childes/Bates/Free20/chuck.cha  Bates   NaN   {CHI, MOT}   \n",
       "3   ../data/childes/Bates/Free20/doug.cha  Bates   NaN   {CHI, MOT}   \n",
       "4     ../data/childes/Bates/Free20/ed.cha  Bates   NaN   {CHI, MOT}   \n",
       "\n",
       "           name            child_id  age_d  age_m     sex group ses  \\\n",
       "0  Target_Child  Bates_Target_Child    600   20.0  female    TD  MC   \n",
       "1         Betty         Bates_Betty    600   20.0  female    TD  MC   \n",
       "2         Chuck         Bates_Chuck    600   20.0    male    TD  MC   \n",
       "3          Doug          Bates_Doug    600   20.0    male    TD  MC   \n",
       "4            Ed            Bates_Ed    600   20.0    male    TD  MC   \n",
       "\n",
       "       mot_edu    situation   activities          study_type  \n",
       "0  unspecified  unspecified  unspecified  cross, toyplay, TD  \n",
       "1  unspecified  unspecified  unspecified  cross, toyplay, TD  \n",
       "2  unspecified  unspecified  unspecified  cross, toyplay, TD  \n",
       "3  unspecified  unspecified  unspecified  cross, toyplay, TD  \n",
       "4  unspecified  unspecified  unspecified  cross, toyplay, TD  "
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   "source": [
    "data_idx.head()"
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    "## 1.3 Efficiency and analysis steps\n",
    "\n",
    "For this project, most of the linguistic analyses do not take too much time and resources. Actually, the process that has relatively high computational cost is the reading of CHAT files into a `reader` object. Since each CHAT file could contain a large amount of data (e.g., participant age, utterances, tokens, annotation, etc.), handling all CHAT files in the dataset as one `reader` object not only results in high memory usage, but it's also very inconvenient to manipulate `reader` containing lots of files(e.g., indexing and slicing with loops). Besides, not all CHAT files will be used in every single analysis. Therefore, instead of reading all CHAT files at once, I will only read the files I need for each linguistic analysis. Although some files may need to be read again for the next analysis, I will only need to handle smaller `reader` object(s) each time and this is more efficient practically.\n",
    "\n",
    "Below are the general steps for analysis:\n",
    "1. Select the CHAT files needed for the analysis from `data_idx`\n",
    "2. Read the needed CHAT files into `reader`\n",
    "3. Perform the analysis"
   ]
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   "source": [
    "---\n",
    "\n",
    "# 2 Evaluating dataset with MLU\n",
    "\n",
    "A common way to measure a child's linguistic productivity is to look at the child's mean length of utterance (MLU). In General, MLU increases with age during early development. It can be measured by morphemes (MLU-m) or by words (MLU-w):  \n",
    "`MLU-m = number of morphemes / number of utterances`\n",
    "\n",
    "The accuracy of MLU depends on the overall quality of the data, which relies on: \n",
    "- correctly parsed speech\n",
    "- Sample size (total number of utterances)\n",
    "\n",
    "Therefore, by measuring MLU, we can assess whether the quality of the processed dataset is high enough for the study of vocabulary development: we should see an increase in MLU during development. Besides, if there is any effect of socio-economic status (SES)  on vocabulary development, it should also be reflected by differences in MLU among different SES groups.\n",
    "\n",
    "Let's take a look at an example, 'Adam' from the Brown corpus:"
   ]
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    "# Read 'Adam' in Brown corpus into a reader object\n",
    "path = '../data/data_samples/Brown.zip'\n",
    "adam = pylangacq.Reader.from_zip(path, 'Adam')"
   ]
  },
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   "execution_count": 4,
   "id": "33b699b1-e48d-438d-bb06-2689b01c52fc",
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     "data": {
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       "<table>  <tr>\n",
       "    <td>*CHI:</td>\n",
       "    <td style=\"text-align: left\">don't</td>\n",
       "    <td style=\"text-align: left\">CLITIC</td>\n",
       "    <td style=\"text-align: left\">dog</td>\n",
       "    <td style=\"text-align: left\">.</td>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <td>%mor:</td>\n",
       "    <td style=\"text-align: left\">mod|do</td>\n",
       "    <td style=\"text-align: left\">neg|not</td>\n",
       "    <td style=\"text-align: left\">v|dog</td>\n",
       "    <td style=\"text-align: left\">.</td>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <td>%gra:</td>\n",
       "    <td style=\"text-align: left\">1|3|AUX</td>\n",
       "    <td style=\"text-align: left\">2|1|NEG</td>\n",
       "    <td style=\"text-align: left\">3|0|ROOT</td>\n",
       "    <td style=\"text-align: left\">4|3|PUNCT</td>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <td>%com:</td>\n",
       "    <td colspan=\"4\" style=\"text-align: left\">Adam repeated these utterances several times</td>\n",
       "  </tr>\n",
       "</table>"
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       "Utterance(participant='CHI', tokens=[Token(word=\"don't\", pos='mod', mor='do', gra=Gra(dep=1, head=3, rel='AUX')), Token(word='CLITIC', pos='neg', mor='not', gra=Gra(dep=2, head=1, rel='NEG')), Token(word='dog', pos='v', mor='dog', gra=Gra(dep=3, head=0, rel='ROOT')), Token(word='.', pos='.', mor='', gra=Gra(dep=4, head=3, rel='PUNCT'))], time_marks=None, tiers={'CHI': \"don't dog .\", '%mor': 'mod|do~neg|not v|dog .', '%gra': '1|3|AUX 2|1|NEG 3|0|ROOT 4|3|PUNCT', '%com': 'Adam repeated these utterances several times'})"
      ]
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   ],
   "source": [
    "# get 2nd utterance of 4th file in 'adam'\n",
    "adam[5].utterances()[1]"
   ]
  },
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    "## 2.1 Using `PyLangAcq` to get MLU\n",
    "\n",
    "In the above utterance, \"Don't dog.\", there are two words (\"don't\" and \"dog\") but three morphemes (\"do\", \"not\", \"dog\"), so the length of utterance by words and by morphemes would be 2 and 3 respectively. To get Adam's MLU-w or MLU-m, you can measure the length of each utterance in  `adam` and then calculate the average length for all utterances. `PyLangAcq` has two functions which you can use to get the MLU easily:"
   ]
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MLU-w: [2.2562874251497007]\n",
      "MLU-m: [3.1988023952095808]\n"
     ]
    }
   ],
   "source": [
    "# print MLU of 4th file in 'adam'\n",
    "print('MLU-w:', adam[5].mluw())\n",
    "print('MLU-m:', adam[5].mlum())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "28389644-3c23-4cb2-99b3-a605af991076",
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   "source": [
    "These functions are very helpful for getting a sense of how your data looks like, but there are a few limitations that make them unsuited for some studies. For example, these functions count the punctuation period (.) as a word or empty string (e.g., the annotation for the punctuation period; some corpora also annotate gestures or actions) as a morpheme. Consequently, utterances with no words but just punctuation would be included in the calculation of the mean. Besides, the `PyLangAcq` functions only return average values: you cannot get the distribution of utterance length, nor the median length (MdLU) or other MLU variants such as MLU5-w (mean length of five longest utterances by words)."
   ]
  },
  {
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   "id": "904f869e-ed7f-4263-8b59-bedf01384a8b",
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   "source": [
    "## 2.2 Custom functions to get MLU\n",
    "\n",
    "To overcome the limitations of `PyLangAcq` functions, I will use my custom functions in the analysis. These functions are not declared here in this notebook but in a separate `.py` file, because they will also be called in other notebooks as well. The functions are stored in [`./etc/pittchat.py`](https://github.com/Data-Science-for-Linguists-2022/Child-Vocab-Development/blob/main/code/etc/pittchat.py):"
   ]
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  {
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   "execution_count": 6,
   "id": "85a8f841-a214-41cb-a09a-52ab49c80ab8",
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   "source": [
    "from etc.pittchat import utt_len_w as utt_len_w\n",
    "from etc.pittchat import utt_len_m as utt_len_m"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f683fcd7-c179-452b-8166-4997a08013c9",
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   "source": [
    "Usage examples:"
   ]
  },
  {
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   "id": "8ed0242e-51bd-430f-87d9-5c62bd8216b2",
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Length of first 10 utterances by words:     [6, 2, 5, 1, 2, 2, 3, 1, 2, 2]\n",
      "Length of first 10 utterances by morphemes: [6, 3, 5, 1, 2, 2, 2, 1, 1, 2]\n",
      "MLU-w: 2.258992805755396\n",
      "MLU-m: 2.276155717761557\n",
      "MdLU-w: 2.0\n",
      "MLU5-m: 9.4\n"
     ]
    }
   ],
   "source": [
    "POS_IGNORE = [\"n:prop\"]  # POS of tokens to be ignored (e.g. proper noun)\n",
    "\n",
    "LUw_list = utt_len_w(adam[5])\n",
    "LUm_list = utt_len_m(adam[5], ignore=POS_IGNORE)\n",
    "\n",
    "print('Length of first 10 utterances by words:    ', LUw_list[0:10])\n",
    "print('Length of first 10 utterances by morphemes:', LUm_list[0:10])\n",
    "\n",
    "print('MLU-w:', np.mean(LUw_list))\n",
    "print('MLU-m:', np.mean(LUm_list))\n",
    "print('MdLU-w:', np.median(LUw_list))\n",
    "\n",
    "print('MLU5-m:', np.mean(nlargest(5, LUm_list)))  # MLU-m of first 5 utterances,\n",
    "                                                  #   see section 3"
   ]
  },
  {
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   "id": "fd0bc407-0a26-4378-9771-a69e75d3b501",
   "metadata": {},
   "source": [
    "Documentation can be found in the function's docstring. For example:"
   ]
  },
  {
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     "data": {
      "text/plain": [
       "\u001b[1;31mSignature:\u001b[0m  \u001b[0mutt_len_m\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mf_reader\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mparticipants\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'CHI'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mignore\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m->\u001b[0m \u001b[0mList\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mfloat\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
       "\u001b[1;31mDocstring:\u001b[0m\n",
       "Get a list of utterance length by morphemes (MLU-m) for all the utterances\n",
       "in the f_reader. \n",
       "\n",
       "Parameters\n",
       "f_reader : pylangacq.Reader object\n",
       "           A pylangacq reader object of *one* CHAT file, or a reader of a\n",
       "           collection of CHAT files indexed to *one* CHAT file.\n",
       "participants : str or list[str], optional, default 'CHI' \n",
       "               The participant(s) whose tokens will be extracted from.\n",
       "ignore : str or list[str], optional, default []\n",
       "         The POS to be ignored.\n",
       "\n",
       "Returns\n",
       "List[float] or 0 if no utterance found\n",
       "\n",
       "Remarks\n",
       "- POS_TO_IGNORE (basic list of POS to be ignored) is the same as in\n",
       "  pylangacq.Reader.mlum, with the addition of '.' and 'None'.\n",
       "\u001b[1;31mFile:\u001b[0m      d:\\manho\\documents\\data_science\\child-vocab-development\\code\\etc\\pittchat.py\n",
       "\u001b[1;31mType:\u001b[0m      function\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "? utt_len_m"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8c8a4138-5b2c-40c9-8c02-a55e31c0434c",
   "metadata": {},
   "source": [
    "## 2.3 Comparing MLU-m between childen of different SES\n",
    "\n",
    "I will first try to compare the MLU-m between children of different SES (upper class UC, middle class MC and working class WC). If MLU-m is sufficient to reveal the effects of SES, MLU-w will not be measured. Note that since we are in the exploratory stage of data analysis, I will only evaluate the data visually without statistical tests.\n",
    "\n",
    "Let's read the needed CHAT files first:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "8662f0b2-6016-48ee-9af6-d06276bee3f0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reading CHAT files...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|████████████████████████████████████████████████████████████████████████████████████| 3/3 [01:44<00:00, 34.82s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Done!\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "ses_list = ['UC', 'MC', 'WC']\n",
    "paths = {}\n",
    "readers = {}\n",
    "\n",
    "# read CHAT files into separate readers according to SES \n",
    "print('Reading CHAT files...')\n",
    "for s in tqdm(ses_list):\n",
    "    paths[s] = data_idx[data_idx.ses == s].file_path\n",
    "    readers[s] = pylangacq.Reader.from_files(paths[s])\n",
    "\n",
    "print('Done!')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "892dc2aa-8937-43cc-b2cd-45e5aa6320ac",
   "metadata": {},
   "source": [
    "Next, I will get a list of utterance length (by morphemes) of each utterance in each file and the child age of corresponding files:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "902e0ad2-6829-46fa-9272-61fffcb1f091",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create DataFrames first\n",
    "LUm_list = pd.DataFrame(columns=ses_list)  # List of utterance length (morpheme)\n",
    "age_list = pd.DataFrame(columns=ses_list)  # List of child age\n",
    "\n",
    "# Custom function to convert age in ymd format to age in months\n",
    "from etc.pittchat import get_age_m as get_age_m\n",
    "\n",
    "# For each SES group, get utterance length and child age of each file\n",
    "for s in ses_list:\n",
    "    for i,f in enumerate(readers[s]):  # f = one CHAT file\n",
    "        LUm_list.loc[i,s] = utt_len_m(f)\n",
    "        age_list.loc[i,s] = get_age_m(f.ages()[0])\n",
    "\n",
    "# List of MLU-m for all files\n",
    "MLUm_list = LUm_list.applymap(lambda x: np.mean(x) if (x != np.nan) else x)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "92502af2-14af-4e3e-9541-dc38410614db",
   "metadata": {},
   "source": [
    "Visualizing the data for easier assessment:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "92173c8f-71b6-4698-bfe6-fb933a4f3795",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,5), facecolor='w')\n",
    "cmap = {'UC':'darkorange', 'MC':'k', 'WC':'g'}  # color map\n",
    "\n",
    "# Scatter plot\n",
    "for s in ses_list:\n",
    "    plt.scatter(age_list[s], MLUm_list[s], facecolors=cmap[s], edgecolors='.3')\n",
    "\n",
    "# Formatting\n",
    "plt.xlabel('Age (Month)')\n",
    "plt.ylabel('MLU by morphemes (MLU-m)')\n",
    "plt.legend(ses_list, title='SES', edgecolor='k', loc='upper left')\n",
    "plt.title('Mean Length of Utterance (MLU-m)\\nby Socioeconomic Status (SES)', fontsize=16)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "30b4a20e-dd9a-4cba-be09-093cc529ae53",
   "metadata": {},
   "source": [
    "The figure above shows that MLU increases with age regardless of the child's SES. This suggests that the age effect on MLU is preserved in the dataset after preprocessing - the quality of the dataset is sufficient to reveal the age effect. If we compare the overall trends of MLU between different SES groups, we can also find a possible effect of SES on MLU: within the same age range, UC has the highest MLU and WC has the lowest.\n",
    "\n",
    "While the quality of the dataset is high enough to reveal developmental changes, however, the dataset has a very imbalanced sample distribution for different SES groups. Let's check the sample distribution by age.\n",
    "\n",
    "## 2.4 Sample distribution by age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "fe337649-8aea-4a21-b2ef-3dc4fee1ce03",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,5),facecolor='w')\n",
    "cmap = {'UC':'darkorange', 'MC':'k', 'WC':'g'}  # color map\n",
    "n_bin = int(max(age_list.max()))  # number of bins in histogram\n",
    "\n",
    "# Plot histogram for each SES group\n",
    "for s in ses_list:\n",
    "    ages = age_list[s].dropna()  # drop NaN to avoid runtime warning\n",
    "    plt.hist(ages, n_bin, histtype='step', edgecolor=cmap[s])\n",
    "\n",
    "# Formatting\n",
    "plt.xlabel('Age (Month)')\n",
    "plt.ylabel('Sample count')\n",
    "plt.title('Sample Count by Socioeconomic Status (SES)', fontsize=16)\n",
    "plt.legend(ses_list, title='SES', edgecolor='k', loc='best')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "702918a2-2313-483c-9f15-a9e8eab7134e",
   "metadata": {},
   "source": [
    "As shown in the histogram, most of the samples (i.e. CHAT files) for WC have age above 40 months, while there are much fewer samples for UC. For fair comparison, the following analysis will focus on the range of **20 to 42 months** where WC and MC have similar sample distribution and where most UC samples are included. This is also the age range when the difference in vocabulary size among children of different SES was observed in other studies.\n",
    "\n",
    "Note that some age groups seem to have an unusually large sample size. These samples could possibly come from corpora where participant age was well-matched, or from repeated recordings of the same individual participants. At this stage, we still can't tell whether this would influence the analysis. I will come back to this issue later when necessary."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7126e21e-2ad8-4a03-b9e2-7fc5d7c03fe6",
   "metadata": {
    "tags": []
   },
   "source": [
    "---\n",
    "\n",
    "# 3 Checking effects of recording length on MLU\n",
    "\n",
    "One thing we need to consider when combining different datasets is that, data samples may have different lengths of recording. This will affect the values of MLU: For example, samples with short duration only provide a \"cross-sectional\" view of a participant's linguistic productivity, and therefore the measured MLU may not reflect the overall linguistic productivity of the participant. To check whether the length of recording has an impact on the measurement of MLU and thus distorting the observed age or SES effect on vocabulary development, I measured three additional variants of MLU:\n",
    "- **MLU100-m**: MLU-m of first 100 utterances\n",
    "- **MLU5-m**: MLU-m of first 5 utterances\n",
    "- **VLU-m**: Variance of utterance length by morpheme\n",
    "\n",
    "Let's read the dataset again with an age filter, 20 to 42 months, as discussed in the previous section."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "f13a2ce8-197d-4f16-b309-08c4fc14a7c3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reading CHAT files...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:33<00:00, 11.05s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Done!\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "ses_list = ['UC', 'MC', 'WC']\n",
    "paths_ses = {}\n",
    "readers_ses = {}\n",
    "\n",
    "# read CHAT files into separate readers according to SES \n",
    "print('Reading CHAT files...')\n",
    "for s in tqdm(ses_list):\n",
    "    paths_ses[s] = data_idx[(data_idx.ses == s) & \n",
    "                        (data_idx.age_m>=20) & \n",
    "                        (data_idx.age_m<=42)].file_path\n",
    "    readers_ses[s] = pylangacq.Reader.from_files(paths_ses[s])\n",
    "\n",
    "print('Done!')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c5a902c7-b6f5-4506-a3ba-a2a778416d3b",
   "metadata": {},
   "source": [
    "Compute four variants of MLU:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "d6a4348d-e1c8-401c-8d10-3933572f8720",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create DataFrames\n",
    "LUm_list = pd.DataFrame(columns=ses_list)  # List of utterance length (morpheme)\n",
    "age_list = pd.DataFrame(columns=ses_list)  # List of child age\n",
    "\n",
    "# For each SES group, get utterance length and child age of each file\n",
    "for s in ses_list:\n",
    "    for i,f in enumerate(readers_ses[s]):  # f = one CHAT file\n",
    "        LUm_list.loc[i,s] = utt_len_m(f)\n",
    "        age_list.loc[i,s] = get_age_m(f.ages()[0])\n",
    "\n",
    "#-------------------------------------------------------------------------------\n",
    "# Utterance length statistics\n",
    "        \n",
    "# List of MLU-m for all files\n",
    "MLUm_list = LUm_list.applymap(lambda x: np.mean(x) if (x != np.nan) else x)\n",
    "\n",
    "# List of MLU100-m for all files (mean length of 5 longest utterances)\n",
    "MLU100m_list = LUm_list.applymap(lambda x: np.mean(nlargest(100, x))\n",
    "                               if ((x != np.nan) and (type(x)==list)) else x)\n",
    "\n",
    "# List of MLU5-m for all files (mean length of 5 longest utterances)\n",
    "MLU5m_list = LUm_list.applymap(lambda x: np.mean(nlargest(5, x))\n",
    "                               if ((x != np.nan) and (type(x)==list)) else x)\n",
    "\n",
    "# List of utterance length variance\n",
    "VLUm_list = LUm_list.applymap(lambda x:  np.var(x) if\n",
    "                                ((x != np.nan) and (type(x)==list)) else 0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b0ccffce-d212-4e25-a5f4-ddcc40a6c8b6",
   "metadata": {},
   "source": [
    "Visualize the data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "78735748-f8bb-41c0-8eaa-97a5861448f5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x720 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a figure with 4 axes (i.e. 4 subplots)\n",
    "fig, ax = plt.subplot_mosaic([['MLU-m', 'MLU100-m'],\n",
    "                              ['MLU5-m','VLU-m']],\n",
    "                              figsize=(12,10), facecolor='w',\n",
    "                              constrained_layout=True)\n",
    "cmap = {'UC':'darkorange', 'MC':'k', 'WC':'g'}  # color map\n",
    "\n",
    "# Plot 4 subplots for each SES group\n",
    "for s in ses_list:\n",
    "    ax['MLU-m'].scatter(age_list[s], MLUm_list[s],\n",
    "                        facecolors=cmap[s], edgecolors='.3')\n",
    "    ax['MLU100-m'].scatter(age_list[s], MLU100m_list[s],\n",
    "                           facecolors=cmap[s], edgecolors='.3')\n",
    "    ax['MLU5-m'].scatter(age_list[s], MLU5m_list[s],\n",
    "                         facecolors=cmap[s], edgecolors='.3')\n",
    "    ax['VLU-m'].scatter(age_list[s], VLUm_list[s],\n",
    "                        facecolors=cmap[s], edgecolors='.3')\n",
    "\n",
    "# Set axis labels, subplot titles and legends\n",
    "for k,v in {'A':'MLU-m', 'B':'MLU100-m', 'C':'MLU5-m', 'D':'VLU-m'}.items():\n",
    "    ax[v].set_xlabel('Age (months)')\n",
    "    ax[v].set_ylabel('{} by morphemes ({})'.format(v[:-2],v))\n",
    "    ax[v].set_title('\\n{}. {}\\n'.format(k,v), loc = 'left')\n",
    "    ax[v].spines[['top','right']].set_visible(False)  # remove top/right spines\n",
    "    # ax[v].legend(ses_list, title='SES', edgecolor='k', loc='upper left')\n",
    "\n",
    "fig.legend(ses_list, title='Socio-economic status (SES)', mode='expand', ncol=3,\n",
    "           loc='lower left', bbox_to_anchor=(0.35,-0.07,0.3,0), edgecolor='none')\n",
    "    \n",
    "# Set figure title\n",
    "fig.suptitle('Utterance Length Statistics by Socioeconomic Status (SES)',fontsize=16)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4350d0c7-a18f-4b20-bcca-5c6f3f010e8c",
   "metadata": {},
   "source": [
    "As shown in the figure above, same effects of age and SES can be observed in all the four variants of MLU, although the degree of effects might be different with different measurements. This demonstrates that, with suitable analysis, it is possible to uncover the effects of age and SES on vocabulary development even the dataset contains samples of different recording lengths. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e2205db3-58d4-44d3-8f84-05034e5bc163",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "# 4 Summary\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d5f01577-7908-4f39-ba9e-557dbd835123",
   "metadata": {},
   "source": [
    "To summarize, this notebook demonstrated that the quality of the processed datasset is sufficient for further analysis by showing that MLU increases during development and SES likely has an effect on MLU. Looking at the sample distribution by participant's age, I decided to focus on the age range of 20 to 42 months for further analysis. With different variants of MLU, I also showed the effects of age and SES on vocabulary development can be uncovered even the dataset consists of samples of different recording lengths. Together, the above observations show that it is possible to use a dataset combined from different studies to investigate the effects of SES on early vocabulary development."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}