Rodgers_FinalProject.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "Final Project\n",
        "\n",
        "Dependency Parsing\n",
        "\n",
        "Mary E. Rodgers\n",
        "\n",
        "\n",
        "# Introduction\n",
        "\n",
        "At three years old, children should have around 1000  words [1]. However, children diagnosed with autism spectrum disorder (ASD) acquire language at a lower rate than their peers. When assessing the language development of a child, both on and off the spectrum, several measures can be used. Previous research has focused on less reliable outcomes such as the mean length of utterance [2], but a new focus has been on the number of nominal subjects in active declarative sentences. Current research indicates that the number of diverse nouns in the subject position (i.e., nominal subjects) a caregiver models for their child may have a positive effect on number of words that a child acquires during early language acquisition [2, 3].\n",
        "\n",
        "In this paper, dependency parsing will be used to calculate the number of nominal subjects used by caregivers during play-based interactions with their toddlers with autism. The dependencies will be parsed using SpaCY, an open-sourced Python library for natural language processing (NLP) [4].\n",
        "\n",
        "The primary research question of this paper are\n",
        "1. What is the correlation between the number of nominal subjects modeled by caregivers and the child's number of different words at baseline?\n",
        "\n",
        "2. What is the correlation between the number of nominal subjects modeled by caregivers at post-testing and the child's number of different words at follow-up, and by group?\n",
        "\n",
        "3. Is there a significant difference in the number of nominal subjects for the TX and BAU caregiver groups at post-test?"
      ],
      "metadata": {
        "id": "HFwawRrqc_RC"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Method\n",
        "\n",
        "## Corpus\n",
        "\n",
        "The body of texts, or corpus, for this research paper are the transcripts between caregivers playing with their toddlers (a Child-Caregvier Interaction (CCX) with autism from a previously completed research study [2] that took place at Vanderbilt and Northwestern University. In this longitudional randomized clinical trial (RCT), caregivers of toddlers with ASD were randomized equally into the treatment (TX) or business as usual (BAU) condition. All caregivers consented into the study received developmental information about their child and payment for their participation. Caregivers in the TX group also had a therapist train them in using Enhanced Milieu Teaching (EMT), a naturalistic language intervention strategy, with their child. During the CCX, caregivers were instructed to play with their child as they typically would for 10 minutes. The CCX was filmed and later transcribed by research team members naive to the randomization of the participating dyad. A CCX was collected at pre-, post-, and follow-up testing. All available CCX transcripts were used in the analysis.\n",
        "\n",
        "The CCX transcripts were imported in as .txt files from their native software, Systematic Analysis of Language Transcripts (SALT) [6]. This resulted in 301 transcripts, with 113 from pre-test (T00), 101 from post-test (T01), and 87 from follow-up (T02).\n",
        "\n",
        "The SALT transcripts were cleaned using regular expression [7] and pandas [8] in a previous round of analysis. The currently body of work included additional data cleaning using pandas to convert the SALT transcription conventions into text that could be analyzed by SpaCy.\n",
        "\n",
        "## Dependency Parsing in the Roberts et al. (2022) Corpus\n",
        "\n",
        "In Google Colab [9], python code [10] was used with SpaCY [5] to identify the number of nominal subjects caregivers used at all assessment time points. The nominal subjects were the predictor variable for a previously hand-coded child outcome in the original study, number of different words (NDW).\n",
        "\n",
        "## Statistical Analysis\n",
        "\n",
        "In Google Colab [9] a correlation were run between the number of nouns caregivers used at post-testing and the number of different words children had at follow-up testing for all group (all, BAU, TX). Additionally, a two-sample t- test was used assess the mean difference in the number of nominal subjects used by each group at post-test."
      ],
      "metadata": {
        "id": "W8wShO6BgLJZ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# NLP Analysis\n",
        "\n",
        "The steps taken in this NLP analysis included:\n",
        "(1) Accessing and cleaning the CCX transcripts in Google Colab [9] using Regular Expression [7] and pandas [8];\n",
        "(2) Using SpaCy [5] to parse the nominal subjects by group (TX or BAU); (3) appending the SpaCy results to pandas [8] dataframe and exporting the dataframe; and (4) statistically analzing the nominal subject dependencies for significance in regards to treatment condition."
      ],
      "metadata": {
        "id": "TULIJyhHkwaA"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Github\n",
        "\n",
        "All publicaly available code will be stored on [Github](https://github.com/Mary-E-Rodgers/NLP-Analysis-using-SpaCy---Dependency-Parsing) [11].\n",
        "\n",
        "The transcripts used in this analysis will not be made availabe.\n",
        "\n",
        "Please contact the author with any questions:\n",
        "\n",
        ">mary.e.rodgers@vanderbilt.edu\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "dOMAvpMtIx6L"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Step 1: Accessing and cleaning the transcripts\n",
        "Allow Google colab to access the data you have on your Google Drive, and map the directory location of your data."
      ],
      "metadata": {
        "id": "RHjIRF1wkYWL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "-ryEUUDj1h7X",
        "outputId": "e13ab0e4-6034-467d-8d7e-b4fb0f0d464d"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive # this imports Google Drive (Drive) into Google Colab (Colab)\n",
        "\n",
        "drive.mount('/content/drive') # this mounts Drive into Colab\n",
        "\n",
        "!ls \"/content/drive/My Drive\" # this shows the content on your Drive\n",
        "\n",
        "import os # this package deciphers which operating system you are using\n",
        "\n",
        "os.chdir(\"/content/drive/MyDrive/Professional/4_PhD/Classes/9_Fall_2023/SPED7500_ComputationalLanguageAnalysis/Assignments/FinalProject\") # this is where you change the directory to where your data is located on Drive\n",
        "\n",
        "os.listdir() #this lists what you now have available"
      ],
      "metadata": {
        "id": "oZ6AuoiWkvbG",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "1861844e-7d4c-4eb8-8110-7104a2cf38f4"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n",
            "shell-init: error retrieving current directory: getcwd: cannot access parent directories: Transport endpoint is not connected\n",
            "Personal  Professional\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['Rodgers_FinalProject.ipynb',\n",
              " 'Final Project rubric.docx',\n",
              " 'DOSAGE_DATA_TRANSCRIPT.csv',\n",
              " 'Rodgers_FinalProject.docx',\n",
              " 'Data_Assignment_Rubric.docx',\n",
              " 'Example_Papers',\n",
              " 'DOSAGE_DATA_TRANSCRIPT_CLEAN.csv',\n",
              " 'DOSAGE_DATA_DEPENDENCY.csv',\n",
              " 'DOSAGE_DATA_DEPENDENCY_BAU.csv',\n",
              " 'DOSAGE_DATA_DEPENDENCY_TX.csv',\n",
              " 'Rodgers_FinalProject.ipynb - Colaboratory.pdf']"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Import pandas"
      ],
      "metadata": {
        "id": "xtx_S9UTNmw5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd # you will need this data manipulation"
      ],
      "metadata": {
        "id": "sJbvhJ9KNoeS"
      },
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Open the .csv file containing your data"
      ],
      "metadata": {
        "id": "_0iGWdgU2UMQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "DOSAGE_DATA = pd.read_csv('DOSAGE_DATA_TRANSCRIPT.csv', encoding = \"ISO-8859-1\") # read in the original dosage data set, which contains group randomization\n",
        "DOSAGE_DATA.rename(columns = {'ï»¿ID':'ID'}, inplace = True)  # fix column name\n",
        "DOSAGE_DATA = DOSAGE_DATA[DOSAGE_DATA['ID'].notna()]\n",
        "\n",
        "DOSAGE_DATA"
      ],
      "metadata": {
        "id": "pKIUOFKWPjzC",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 877
        },
        "outputId": "8eb62e50-4d30-44ab-eb37-0806f6a9f2cd"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
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              "..          ...     ...             ...               ...   \n",
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              "                                        T00_TRANSCRIPT  \\\n",
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              "..                                                 ...   \n",
              "115  \\nwhat's in theret?\\n.\\nwhat else we got.\\noh ...   \n",
              "116  ï»¿\\nlook Scott.\\nlet's sit.\\nok  go.\\nok.\\nsi...   \n",
              "117  ï»¿why you only takeing the girls out of the b...   \n",
              "118  ï»¿let'ssee whiokay.\\nxx over here.\\nit's not ...   \n",
              "119  ï»¿ Scott xxx.\\ndo you want to play with this?...   \n",
              "\n",
              "     T00_CCX_ChildTotalUtterances_unprompted  T00_CCX_ChildNDW_unprompted  \\\n",
              "0                                        3.0                          2.0   \n",
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              "\n",
              "     Unnamed: 74  Unnamed: 75  Unnamed: 76  Unnamed: 77  Unnamed: 78  \\\n",
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              "      <td>1.0</td>\n",
              "      <td>ï»¿\\nlook Scott.\\nlet's sit.\\nok  go.\\nok.\\nsi...</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>117</th>\n",
              "      <td>117</td>\n",
              "      <td>2055.0</td>\n",
              "      <td>Intervention</td>\n",
              "      <td>1.0</td>\n",
              "      <td>ï»¿why you only takeing the girls out of the b...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>118</th>\n",
              "      <td>118</td>\n",
              "      <td>2056.0</td>\n",
              "      <td>Intervention</td>\n",
              "      <td>1.0</td>\n",
              "      <td>ï»¿let'ssee whiokay.\\nxx over here.\\nit's not ...</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>119</th>\n",
              "      <td>119</td>\n",
              "      <td>2059.0</td>\n",
              "      <td>Intervention</td>\n",
              "      <td>1.0</td>\n",
              "      <td>ï»¿ Scott xxx.\\ndo you want to play with this?...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>120 rows × 84 columns</p>\n",
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              "\n",
              "  @keyframes spin {\n",
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              "    async function quickchart(key) {\n",
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              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
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              "        const charts = await google.colab.kernel.invokeFunction(\n",
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              "\n",
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              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('DOSAGE_DATA')\"\n",
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              "\n",
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              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Use pandas for data cleaning\n",
        "\n",
        "Because the transcripts were transcribed in SALT (Miller et al., 2020), they were written using a notation form that allowed the software to do some summarization. This will need to be corrected for SpaCy to work."
      ],
      "metadata": {
        "id": "3Fj4UPhOjAny"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# REMOVE\n",
        "# remove overlap symbols before punctuation: <\n",
        "DOSAGE_DATA['T00_TRANSCRIPT'] = DOSAGE_DATA['T00_TRANSCRIPT'].str.replace('<', '')\n",
        "DOSAGE_DATA['T01_TRANSCRIPT'] = DOSAGE_DATA['T01_TRANSCRIPT'].str.replace('<', '')\n",
        "DOSAGE_DATA['T02_TRANSCRIPT'] = DOSAGE_DATA['T02_TRANSCRIPT'].str.replace('<', '')\n",
        "\n",
        "# remove soft brackets: {\n",
        "DOSAGE_DATA['T00_TRANSCRIPT'] = DOSAGE_DATA['T00_TRANSCRIPT'].str.replace('{', '')\n",
        "DOSAGE_DATA['T01_TRANSCRIPT'] = DOSAGE_DATA['T01_TRANSCRIPT'].str.replace('{', '')\n",
        "DOSAGE_DATA['T02_TRANSCRIPT'] = DOSAGE_DATA['T02_TRANSCRIPT'].str.replace('{', '')\n",
        "\n",
        "# remove soft brackets: }\n",
        "DOSAGE_DATA['T00_TRANSCRIPT'] = DOSAGE_DATA['T00_TRANSCRIPT'].str.replace('}', '')\n",
        "DOSAGE_DATA['T01_TRANSCRIPT'] = DOSAGE_DATA['T01_TRANSCRIPT'].str.replace('}', '')\n",
        "DOSAGE_DATA['T02_TRANSCRIPT'] = DOSAGE_DATA['T02_TRANSCRIPT'].str.replace('}', '')\n",
        "\n",
        "# remove parenthese: (\n",
        "DOSAGE_DATA['T00_TRANSCRIPT'] = DOSAGE_DATA['T00_TRANSCRIPT'].str.replace('(', '')\n",
        "DOSAGE_DATA['T01_TRANSCRIPT'] = DOSAGE_DATA['T01_TRANSCRIPT'].str.replace('(', '')\n",
        "DOSAGE_DATA['T02_TRANSCRIPT'] = DOSAGE_DATA['T02_TRANSCRIPT'].str.replace('(', '')\n",
        "\n",
        "# remove parenthese: )\n",
        "DOSAGE_DATA['T00_TRANSCRIPT'] = DOSAGE_DATA['T00_TRANSCRIPT'].str.replace(')', '')\n",
        "DOSAGE_DATA['T01_TRANSCRIPT'] = DOSAGE_DATA['T01_TRANSCRIPT'].str.replace(')', '')\n",
        "DOSAGE_DATA['T02_TRANSCRIPT'] = DOSAGE_DATA['T02_TRANSCRIPT'].str.replace(')', '')\n",
        "\n",
        "# remove symbols left by previous regular expression cleaning: ï\n",
        "DOSAGE_DATA['T00_TRANSCRIPT'] = DOSAGE_DATA['T00_TRANSCRIPT'].str.replace('ï', '')\n",
        "DOSAGE_DATA['T01_TRANSCRIPT'] = DOSAGE_DATA['T01_TRANSCRIPT'].str.replace('ï', '')\n",
        "DOSAGE_DATA['T02_TRANSCRIPT'] = DOSAGE_DATA['T02_TRANSCRIPT'].str.replace('ï', '')\n",
        "\n",
        "# remove symbols left by previous regular expression cleaning: »\n",
        "DOSAGE_DATA['T00_TRANSCRIPT'] = DOSAGE_DATA['T00_TRANSCRIPT'].str.replace('»', '')\n",
        "DOSAGE_DATA['T01_TRANSCRIPT'] = DOSAGE_DATA['T01_TRANSCRIPT'].str.replace('»', '')\n",
        "DOSAGE_DATA['T02_TRANSCRIPT'] = DOSAGE_DATA['T02_TRANSCRIPT'].str.replace('»', '')\n",
        "\n",
        "# remove symbols left by previous regular expression cleaning: ¿\n",
        "DOSAGE_DATA['T00_TRANSCRIPT'] = DOSAGE_DATA['T00_TRANSCRIPT'].str.replace('¿', '')\n",
        "DOSAGE_DATA['T01_TRANSCRIPT'] = DOSAGE_DATA['T01_TRANSCRIPT'].str.replace('¿', '')\n",
        "DOSAGE_DATA['T02_TRANSCRIPT'] = DOSAGE_DATA['T02_TRANSCRIPT'].str.replace('¿', '')\n",
        "###\n",
        "\n",
        "# REPLACE\n",
        "# replace words left by prevoius pandas cleaning: letsee\n",
        "DOSAGE_DATA['T00_TRANSCRIPT'] = DOSAGE_DATA['T00_TRANSCRIPT'].str.replace(\"let'ssee\", \"let's see\") #switched to double '' here so hyphens in the transcript could be captured\n",
        "DOSAGE_DATA['T01_TRANSCRIPT'] = DOSAGE_DATA['T01_TRANSCRIPT'].str.replace(\"let'ssee\", \"let's see\")\n",
        "DOSAGE_DATA['T02_TRANSCRIPT'] = DOSAGE_DATA['T02_TRANSCRIPT'].str.replace(\"let'ssee\", \"let's see\")\n",
        "\n",
        "# replace punctuation left by prevoius pandas cleaning: > -> .\n",
        "DOSAGE_DATA['T00_TRANSCRIPT'] = DOSAGE_DATA['T00_TRANSCRIPT'].str.replace(\">\", \".\")\n",
        "DOSAGE_DATA['T01_TRANSCRIPT'] = DOSAGE_DATA['T01_TRANSCRIPT'].str.replace(\">\", \".\")\n",
        "DOSAGE_DATA['T02_TRANSCRIPT'] = DOSAGE_DATA['T02_TRANSCRIPT'].str.replace(\">\", \".\")\n",
        "\n",
        "# replace punctuation left by prevoius pandas cleaning: ~ -> .\n",
        "DOSAGE_DATA['T00_TRANSCRIPT'] = DOSAGE_DATA['T00_TRANSCRIPT'].str.replace(\"~\", \".\")\n",
        "DOSAGE_DATA['T01_TRANSCRIPT'] = DOSAGE_DATA['T01_TRANSCRIPT'].str.replace(\"~\", \".\")\n",
        "DOSAGE_DATA['T02_TRANSCRIPT'] = DOSAGE_DATA['T02_TRANSCRIPT'].str.replace(\"~\", \".\")\n",
        "\n",
        "# replace punctuation left by prevoius pandas cleaning: ^ -> .\n",
        "DOSAGE_DATA['T00_TRANSCRIPT'] = DOSAGE_DATA['T00_TRANSCRIPT'].str.replace(\"^\", \".\")\n",
        "DOSAGE_DATA['T01_TRANSCRIPT'] = DOSAGE_DATA['T01_TRANSCRIPT'].str.replace(\"^\", \".\")\n",
        "DOSAGE_DATA['T02_TRANSCRIPT'] = DOSAGE_DATA['T02_TRANSCRIPT'].str.replace(\"^\", \".\")\n",
        "\n",
        "# replace words left by prevoius pandas cleaning: runing -> running\n",
        "DOSAGE_DATA['T00_TRANSCRIPT'] = DOSAGE_DATA['T00_TRANSCRIPT'].str.replace(\"runing\", \"running\")\n",
        "DOSAGE_DATA['T01_TRANSCRIPT'] = DOSAGE_DATA['T01_TRANSCRIPT'].str.replace(\"runing\", \"running\")\n",
        "DOSAGE_DATA['T02_TRANSCRIPT'] = DOSAGE_DATA['T02_TRANSCRIPT'].str.replace(\"runing\", \"running\")\n",
        "\n",
        "# replace words left by prevoius pandas cleaning: biteing -> biting\n",
        "DOSAGE_DATA['T00_TRANSCRIPT'] = DOSAGE_DATA['T00_TRANSCRIPT'].str.replace(\"biteing\", \"biting\")\n",
        "DOSAGE_DATA['T01_TRANSCRIPT'] = DOSAGE_DATA['T01_TRANSCRIPT'].str.replace(\"biteing\", \"biting\")\n",
        "DOSAGE_DATA['T02_TRANSCRIPT'] = DOSAGE_DATA['T02_TRANSCRIPT'].str.replace(\"biteing\", \"biting\")\n",
        "\n",
        "# replace words left by prevoius pandas cleaning: gos -> go's\n",
        "DOSAGE_DATA['T00_TRANSCRIPT'] = DOSAGE_DATA['T00_TRANSCRIPT'].str.replace(\"gos\", \"go's\")\n",
        "DOSAGE_DATA['T01_TRANSCRIPT'] = DOSAGE_DATA['T01_TRANSCRIPT'].str.replace(\"gos\", \"go's\")\n",
        "DOSAGE_DATA['T02_TRANSCRIPT'] = DOSAGE_DATA['T02_TRANSCRIPT'].str.replace(\"gos\", \"go's\")\n",
        "\n",
        "# replace words left by prevoius pandas cleaning: wanna -> want to\n",
        "DOSAGE_DATA['T00_TRANSCRIPT'] = DOSAGE_DATA['T00_TRANSCRIPT'].str.replace(\"wanna\", \"want to\")\n",
        "DOSAGE_DATA['T01_TRANSCRIPT'] = DOSAGE_DATA['T01_TRANSCRIPT'].str.replace(\"wanna\", \"want to\")\n",
        "DOSAGE_DATA['T02_TRANSCRIPT'] = DOSAGE_DATA['T02_TRANSCRIPT'].str.replace(\"wanna\", \"want to\")\n",
        "\n",
        "# replace words left by prevoius pandas cleaning: lookit -> look at it\n",
        "DOSAGE_DATA['T00_TRANSCRIPT'] = DOSAGE_DATA['T00_TRANSCRIPT'].str.replace(\"lookit\", \"look at it\")\n",
        "DOSAGE_DATA['T01_TRANSCRIPT'] = DOSAGE_DATA['T01_TRANSCRIPT'].str.replace(\"lookit\", \"look at it\")\n",
        "DOSAGE_DATA['T02_TRANSCRIPT'] = DOSAGE_DATA['T02_TRANSCRIPT'].str.replace(\"lookit\", \"look at it\")\n",
        "\n",
        "# replace words left by prevoius pandas cleaning: gonna -> going to\n",
        "DOSAGE_DATA['T00_TRANSCRIPT'] = DOSAGE_DATA['T00_TRANSCRIPT'].str.replace(\"gonna\", \"going to\")\n",
        "DOSAGE_DATA['T01_TRANSCRIPT'] = DOSAGE_DATA['T01_TRANSCRIPT'].str.replace(\"gonna\", \"going to\")\n",
        "DOSAGE_DATA['T02_TRANSCRIPT'] = DOSAGE_DATA['T02_TRANSCRIPT'].str.replace(\"gonna\", \"going to\")\n",
        "\n",
        "# replace words left by prevoius pandas cleaning: gotta -> got to\n",
        "DOSAGE_DATA['T00_TRANSCRIPT'] = DOSAGE_DATA['T00_TRANSCRIPT'].str.replace(\"gotta\", \"got to\")\n",
        "DOSAGE_DATA['T01_TRANSCRIPT'] = DOSAGE_DATA['T01_TRANSCRIPT'].str.replace(\"gotta\", \"got to\")\n",
        "DOSAGE_DATA['T02_TRANSCRIPT'] = DOSAGE_DATA['T02_TRANSCRIPT'].str.replace(\"gotta\", \"got to\")\n",
        "\n",
        "# replace words left by prevoius pandas cleaning: thereyougo -> there you go\n",
        "DOSAGE_DATA['T00_TRANSCRIPT'] = DOSAGE_DATA['T00_TRANSCRIPT'].str.replace(\"thereyougo\", \"there you go\")\n",
        "DOSAGE_DATA['T01_TRANSCRIPT'] = DOSAGE_DATA['T01_TRANSCRIPT'].str.replace(\"thereyougo\", \"there you go\")\n",
        "DOSAGE_DATA['T02_TRANSCRIPT'] = DOSAGE_DATA['T02_TRANSCRIPT'].str.replace(\"thereyougo\", \"there you go\")\n",
        "\n",
        "# replace words left by prevoius pandas cleaning: readyset -> ready set\n",
        "DOSAGE_DATA['T00_TRANSCRIPT'] = DOSAGE_DATA['T00_TRANSCRIPT'].str.replace(\"readyset\", \"read set\")\n",
        "DOSAGE_DATA['T01_TRANSCRIPT'] = DOSAGE_DATA['T01_TRANSCRIPT'].str.replace(\"readyset\", \"read set\")\n",
        "DOSAGE_DATA['T02_TRANSCRIPT'] = DOSAGE_DATA['T02_TRANSCRIPT'].str.replace(\"readyset\", \"read set\")\n",
        "\n",
        "# replace words left by prevoius pandas cleaning: upsidedown -> upside-down\n",
        "DOSAGE_DATA['T00_TRANSCRIPT'] = DOSAGE_DATA['T00_TRANSCRIPT'].str.replace(\"upsidedown\", \"upside-down\")\n",
        "DOSAGE_DATA['T01_TRANSCRIPT'] = DOSAGE_DATA['T01_TRANSCRIPT'].str.replace(\"upsidedown\", \"upside-down\")\n",
        "DOSAGE_DATA['T02_TRANSCRIPT'] = DOSAGE_DATA['T02_TRANSCRIPT'].str.replace(\"upsidedown\", \"upside-down\")\n",
        "\n",
        "# replace words left by prevoius pandas cleaning: hereyougo -> here you go\n",
        "DOSAGE_DATA['T00_TRANSCRIPT'] = DOSAGE_DATA['T00_TRANSCRIPT'].str.replace(\"hereyougo\", \"here you go\")\n",
        "DOSAGE_DATA['T01_TRANSCRIPT'] = DOSAGE_DATA['T01_TRANSCRIPT'].str.replace(\"hereyougo\", \"here you go\")\n",
        "DOSAGE_DATA['T02_TRANSCRIPT'] = DOSAGE_DATA['T02_TRANSCRIPT'].str.replace(\"hereyougo\", \"here you go\")\n",
        "\n",
        "# replace words left by prevoius pandas cleaning: driveing -> driving\n",
        "DOSAGE_DATA['T00_TRANSCRIPT'] = DOSAGE_DATA['T00_TRANSCRIPT'].str.replace(\"driveing\", \"driving\")\n",
        "DOSAGE_DATA['T01_TRANSCRIPT'] = DOSAGE_DATA['T01_TRANSCRIPT'].str.replace(\"driveing\", \"driving\")\n",
        "DOSAGE_DATA['T02_TRANSCRIPT'] = DOSAGE_DATA['T02_TRANSCRIPT'].str.replace(\"driveing\", \"driving\")\n",
        "\n",
        "# replace words left by prevoius pandas cleaning: syeah -> yeah\n",
        "DOSAGE_DATA['T00_TRANSCRIPT'] = DOSAGE_DATA['T00_TRANSCRIPT'].str.replace(\"syeah\", \"yeah\")\n",
        "DOSAGE_DATA['T01_TRANSCRIPT'] = DOSAGE_DATA['T01_TRANSCRIPT'].str.replace(\"syeah\", \"yeah\")\n",
        "DOSAGE_DATA['T02_TRANSCRIPT'] = DOSAGE_DATA['T02_TRANSCRIPT'].str.replace(\"syeah\", \"yeah\")\n",
        "\n",
        "# replace words left by prevoius pandas cleaning: svroom -> vroom\n",
        "DOSAGE_DATA['T00_TRANSCRIPT'] = DOSAGE_DATA['T00_TRANSCRIPT'].str.replace(\"svroom\", \"vroom\")\n",
        "DOSAGE_DATA['T01_TRANSCRIPT'] = DOSAGE_DATA['T01_TRANSCRIPT'].str.replace(\"svroom\", \"vroom\")\n",
        "DOSAGE_DATA['T02_TRANSCRIPT'] = DOSAGE_DATA['T02_TRANSCRIPT'].str.replace(\"svroom\", \"vroom\")\n",
        "\n",
        "# replace words left by prevoius pandas cleaning: truScott -> Scott\n",
        "DOSAGE_DATA['T00_TRANSCRIPT'] = DOSAGE_DATA['T00_TRANSCRIPT'].str.replace(\"truScott\", \"Scott\") # note, this is not the name of a participant; participant names were denoted in the SALT transcripts by their ID number (i.e., BA1001) and were previously replaced with the course instructors name for this project, \"Scott\"\n",
        "DOSAGE_DATA['T01_TRANSCRIPT'] = DOSAGE_DATA['T01_TRANSCRIPT'].str.replace(\"truScott\", \"Scott\")\n",
        "DOSAGE_DATA['T02_TRANSCRIPT'] = DOSAGE_DATA['T02_TRANSCRIPT'].str.replace(\"truScott\", \"Scott\")\n",
        "\n",
        "# replace words left by prevoius pandas cleaning: trudrive -> drive\n",
        "DOSAGE_DATA['T00_TRANSCRIPT'] = DOSAGE_DATA['T00_TRANSCRIPT'].str.replace(\"trudrive\", \"drive\")\n",
        "DOSAGE_DATA['T01_TRANSCRIPT'] = DOSAGE_DATA['T01_TRANSCRIPT'].str.replace(\"trudrive\", \"drive\")\n",
        "DOSAGE_DATA['T02_TRANSCRIPT'] = DOSAGE_DATA['T02_TRANSCRIPT'].str.replace(\"trudrive\", \"drive\")\n",
        "\n",
        "# replace words left by prevoius pandas cleaning: swiming -> swimming\n",
        "DOSAGE_DATA['T00_TRANSCRIPT'] = DOSAGE_DATA['T00_TRANSCRIPT'].str.replace(\"swiming\", \"swimming\")\n",
        "DOSAGE_DATA['T01_TRANSCRIPT'] = DOSAGE_DATA['T01_TRANSCRIPT'].str.replace(\"swiming\", \"swimming\")\n",
        "DOSAGE_DATA['T02_TRANSCRIPT'] = DOSAGE_DATA['T02_TRANSCRIPT'].str.replace(\"swiming\", \"swimming\")\n",
        "\n",
        "# replace words left by prevoius pandas cleaning: leaveing -> leaving\n",
        "DOSAGE_DATA['T00_TRANSCRIPT'] = DOSAGE_DATA['T00_TRANSCRIPT'].str.replace(\"leaveing\", \"leaving\")\n",
        "DOSAGE_DATA['T01_TRANSCRIPT'] = DOSAGE_DATA['T01_TRANSCRIPT'].str.replace(\"leaveing\", \"leaving\")\n",
        "DOSAGE_DATA['T02_TRANSCRIPT'] = DOSAGE_DATA['T02_TRANSCRIPT'].str.replace(\"leaveing\", \"leaving\")\n",
        "\n",
        "# replace words left by prevoius pandas cleaning: takeing -> taking\n",
        "DOSAGE_DATA['T00_TRANSCRIPT'] = DOSAGE_DATA['T00_TRANSCRIPT'].str.replace(\"takeing\", \"taking\")\n",
        "DOSAGE_DATA['T01_TRANSCRIPT'] = DOSAGE_DATA['T01_TRANSCRIPT'].str.replace(\"takeing\", \"taking\")\n",
        "DOSAGE_DATA['T02_TRANSCRIPT'] = DOSAGE_DATA['T02_TRANSCRIPT'].str.replace(\"takeing\", \"taking\")\n",
        "\n",
        "# replace words left by prevoius pandas cleaning: geting -> getting\n",
        "DOSAGE_DATA['T00_TRANSCRIPT'] = DOSAGE_DATA['T00_TRANSCRIPT'].str.replace(\"geting\", \"getting\")\n",
        "DOSAGE_DATA['T01_TRANSCRIPT'] = DOSAGE_DATA['T01_TRANSCRIPT'].str.replace(\"geting\", \"getting\")\n",
        "DOSAGE_DATA['T02_TRANSCRIPT'] = DOSAGE_DATA['T02_TRANSCRIPT'].str.replace(\"geting\", \"getting\")\n",
        "\n",
        "# replace words left by prevoius pandas cleaning: byebye -> bye bye\n",
        "DOSAGE_DATA['T00_TRANSCRIPT'] = DOSAGE_DATA['T00_TRANSCRIPT'].str.replace(\"byebye\", \"bye bye\")\n",
        "DOSAGE_DATA['T01_TRANSCRIPT'] = DOSAGE_DATA['T01_TRANSCRIPT'].str.replace(\"byebye\", \"bye bye\")\n",
        "DOSAGE_DATA['T02_TRANSCRIPT'] = DOSAGE_DATA['T02_TRANSCRIPT'].str.replace(\"byebye\", \"bye bye\")\n",
        "\n",
        "# replace words left by prevoius pandas cleaning: rideing -> riding\n",
        "DOSAGE_DATA['T00_TRANSCRIPT'] = DOSAGE_DATA['T00_TRANSCRIPT'].str.replace(\"rideing\", \"riding\")\n",
        "DOSAGE_DATA['T01_TRANSCRIPT'] = DOSAGE_DATA['T01_TRANSCRIPT'].str.replace(\"rideing\", \"riding\")\n",
        "DOSAGE_DATA['T02_TRANSCRIPT'] = DOSAGE_DATA['T02_TRANSCRIPT'].str.replace(\"rideing\", \"riding\")\n",
        "\n",
        "# replace words left by prevoius pandas cleaning: its -> it's\n",
        "DOSAGE_DATA['T00_TRANSCRIPT'] = DOSAGE_DATA['T00_TRANSCRIPT'].str.replace(\"its\", \"it's\")\n",
        "DOSAGE_DATA['T01_TRANSCRIPT'] = DOSAGE_DATA['T01_TRANSCRIPT'].str.replace(\"its\", \"it's\")\n",
        "DOSAGE_DATA['T02_TRANSCRIPT'] = DOSAGE_DATA['T02_TRANSCRIPT'].str.replace(\"its\", \"it's\")\n",
        "###"
      ],
      "metadata": {
        "id": "sITeDsSkicYc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "17f6fe60-de7b-4861-fcea-af6f001e697c"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-24-11aa7a4332c0>:8: FutureWarning: The default value of regex will change from True to False in a future version. In addition, single character regular expressions will *not* be treated as literal strings when regex=True.\n",
            "  DOSAGE_DATA['T00_TRANSCRIPT'] = DOSAGE_DATA['T00_TRANSCRIPT'].str.replace('{', '')\n",
            "<ipython-input-24-11aa7a4332c0>:9: FutureWarning: The default value of regex will change from True to False in a future version. In addition, single character regular expressions will *not* be treated as literal strings when regex=True.\n",
            "  DOSAGE_DATA['T01_TRANSCRIPT'] = DOSAGE_DATA['T01_TRANSCRIPT'].str.replace('{', '')\n",
            "<ipython-input-24-11aa7a4332c0>:10: FutureWarning: The default value of regex will change from True to False in a future version. In addition, single character regular expressions will *not* be treated as literal strings when regex=True.\n",
            "  DOSAGE_DATA['T02_TRANSCRIPT'] = DOSAGE_DATA['T02_TRANSCRIPT'].str.replace('{', '')\n",
            "<ipython-input-24-11aa7a4332c0>:13: FutureWarning: The default value of regex will change from True to False in a future version. In addition, single character regular expressions will *not* be treated as literal strings when regex=True.\n",
            "  DOSAGE_DATA['T00_TRANSCRIPT'] = DOSAGE_DATA['T00_TRANSCRIPT'].str.replace('}', '')\n",
            "<ipython-input-24-11aa7a4332c0>:14: FutureWarning: The default value of regex will change from True to False in a future version. In addition, single character regular expressions will *not* be treated as literal strings when regex=True.\n",
            "  DOSAGE_DATA['T01_TRANSCRIPT'] = DOSAGE_DATA['T01_TRANSCRIPT'].str.replace('}', '')\n",
            "<ipython-input-24-11aa7a4332c0>:15: FutureWarning: The default value of regex will change from True to False in a future version. In addition, single character regular expressions will *not* be treated as literal strings when regex=True.\n",
            "  DOSAGE_DATA['T02_TRANSCRIPT'] = DOSAGE_DATA['T02_TRANSCRIPT'].str.replace('}', '')\n",
            "<ipython-input-24-11aa7a4332c0>:18: FutureWarning: The default value of regex will change from True to False in a future version. In addition, single character regular expressions will *not* be treated as literal strings when regex=True.\n",
            "  DOSAGE_DATA['T00_TRANSCRIPT'] = DOSAGE_DATA['T00_TRANSCRIPT'].str.replace('(', '')\n",
            "<ipython-input-24-11aa7a4332c0>:19: FutureWarning: The default value of regex will change from True to False in a future version. In addition, single character regular expressions will *not* be treated as literal strings when regex=True.\n",
            "  DOSAGE_DATA['T01_TRANSCRIPT'] = DOSAGE_DATA['T01_TRANSCRIPT'].str.replace('(', '')\n",
            "<ipython-input-24-11aa7a4332c0>:20: FutureWarning: The default value of regex will change from True to False in a future version. In addition, single character regular expressions will *not* be treated as literal strings when regex=True.\n",
            "  DOSAGE_DATA['T02_TRANSCRIPT'] = DOSAGE_DATA['T02_TRANSCRIPT'].str.replace('(', '')\n",
            "<ipython-input-24-11aa7a4332c0>:23: FutureWarning: The default value of regex will change from True to False in a future version. In addition, single character regular expressions will *not* be treated as literal strings when regex=True.\n",
            "  DOSAGE_DATA['T00_TRANSCRIPT'] = DOSAGE_DATA['T00_TRANSCRIPT'].str.replace(')', '')\n",
            "<ipython-input-24-11aa7a4332c0>:24: FutureWarning: The default value of regex will change from True to False in a future version. In addition, single character regular expressions will *not* be treated as literal strings when regex=True.\n",
            "  DOSAGE_DATA['T01_TRANSCRIPT'] = DOSAGE_DATA['T01_TRANSCRIPT'].str.replace(')', '')\n",
            "<ipython-input-24-11aa7a4332c0>:25: FutureWarning: The default value of regex will change from True to False in a future version. In addition, single character regular expressions will *not* be treated as literal strings when regex=True.\n",
            "  DOSAGE_DATA['T02_TRANSCRIPT'] = DOSAGE_DATA['T02_TRANSCRIPT'].str.replace(')', '')\n",
            "<ipython-input-24-11aa7a4332c0>:60: FutureWarning: The default value of regex will change from True to False in a future version. In addition, single character regular expressions will *not* be treated as literal strings when regex=True.\n",
            "  DOSAGE_DATA['T00_TRANSCRIPT'] = DOSAGE_DATA['T00_TRANSCRIPT'].str.replace(\"^\", \".\")\n",
            "<ipython-input-24-11aa7a4332c0>:61: FutureWarning: The default value of regex will change from True to False in a future version. In addition, single character regular expressions will *not* be treated as literal strings when regex=True.\n",
            "  DOSAGE_DATA['T01_TRANSCRIPT'] = DOSAGE_DATA['T01_TRANSCRIPT'].str.replace(\"^\", \".\")\n",
            "<ipython-input-24-11aa7a4332c0>:62: FutureWarning: The default value of regex will change from True to False in a future version. In addition, single character regular expressions will *not* be treated as literal strings when regex=True.\n",
            "  DOSAGE_DATA['T02_TRANSCRIPT'] = DOSAGE_DATA['T02_TRANSCRIPT'].str.replace(\"^\", \".\")\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Visually look at a transcript to check for other errors that need to be removed or replaced."
      ],
      "metadata": {
        "id": "W2PiO6Z6Xt-s"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(DOSAGE_DATA.loc[0,\"T00_TRANSCRIPT\"])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "IfCyTG6SXmpb",
        "outputId": "086a32a5-ee49-4673-f3b8-731d51c3d5d9"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "what is that?\n",
            "these are not blo.\n",
            "say open.\n",
            "yay, there you go.\n",
            "xx put an animal in there.\n",
            "uh oh.\n",
            "no.\n",
            "xx here.\n",
            "go like that.\n",
            "look.\n",
            "look it's a table.\n",
            "look, you going to sit down?\n",
            "look.\n",
            "no.\n",
            "uh oh.\n",
            "look it's a table.\n",
            "it's like your little table balook.\n",
            "look Scott look.\n",
            "they going to sit down.\n",
            "aww.\n",
            "look, it's going to eat.\n",
            "look, it's going to eat.\n",
            "going to put it down.\n",
            "sit down.\n",
            "you want to open it?\n",
            "you want to put it away?\n",
            "there you go.\n",
            "there you go.\n",
            "oh.\n",
            "oh.\n",
            "again.\n",
            "you want Mommy to do it?\n",
            "yay, we did it.\n",
            "uh oh.\n",
            "uh, no don't break them.\n",
            "oh you still want to build them.\n",
            "here put this on first.\n",
            ".\n",
            "does it work?\n",
            "what are you looking for?\n",
            "do you need Mommy to help you?\n",
            "i don't think that's going to fit bud.\n",
            "oh  you did it.\n",
            "yeah you did it.\n",
            "yay.\n",
            "uh oh.\n",
            "oh.\n",
            "xx.\n",
            "you want to put it in the bag?\n",
            " put it in here.\n",
            "there you go.\n",
            "yay.\n",
            "get the little yay, you did it.\n",
            "no?\n",
            "you want to put it bai'll hold it open.\n",
            "yay.\n",
            ".\n",
            "you want to jump?\n",
            "look.\n",
            " you want to play with this?\n",
            "here, you want to build a ramp?\n",
            "say .\n",
            "say oh.\n",
            "now you do it.\n",
            " look.\n",
            "one.\n",
            "be oh, you almost hit.\n",
            "now you do it.\n",
            "you do it.\n",
            "say one.\n",
            "uh oh.\n",
            "here I'll hold one.\n",
            "no.\n",
            "here I'll hold one.\n",
            "no.\n",
            "say one.\n",
            "you didn't ok.\n",
            "oh.\n",
            "look.\n",
            "Scott uh oh.\n",
            "say vroom.\n",
            "say  vroom.\n",
            "it's going to go  vroom.\n",
            " vroom.\n",
            " vroom.\n",
            "uh oh.\n",
            "yay, you put it away.\n",
            "yeah?\n",
            "oh no.\n",
            " what is that?\n",
            "what is that?\n",
            "is it a does it feel weird?\n",
            "look.\n",
            "look.\n",
            "oh.\n",
            "your juisay thank you.\n",
            "thank you.\n",
            "hey, say all gone.\n",
            "you done with that too?\n",
            "yay.\n",
            "who is that?\n",
            "say hello.\n",
            "hello.\n",
            "uh oh.\n",
            " hello.\n",
            "hello.\n",
            "say I'm going to get you.\n",
            "-10:41\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Save the dataframe with the clean transcript"
      ],
      "metadata": {
        "id": "LftUwE9OuPdb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "DOSAGE_DATA.to_csv('DOSAGE_DATA_TRANSCRIPT_CLEAN.csv')  # save the cleaned data set"
      ],
      "metadata": {
        "id": "I3XNr4aZuJ2X"
      },
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Step 2: Using SpaCy to parse the nominal subjects\n",
        "Call in SpaCY, and run the dependency parser on the clean transcript data"
      ],
      "metadata": {
        "id": "Tz3BhZrYKE82"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Enable safe divide [12]; this prevents errors when there is a potential to divide by 0"
      ],
      "metadata": {
        "id": "67osmawiYJUZ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def safe_divide(a, b): # writing a function to prevent errors if dividing by zero [12]\n",
        "    if b != 0: #\n",
        "        return a/b\n",
        "    else:\n",
        "        return 0"
      ],
      "metadata": {
        "id": "N7f7P-PT-fTu"
      },
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Import and load SpaCy"
      ],
      "metadata": {
        "id": "G2TasO2-Yi51"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import spacy # import spaCy\n",
        "\n",
        "import spacy.cli # importing command line interface\n",
        "\n",
        "spacy.cli.download(\"en_core_web_lg\") # download the large SpaCy package\n",
        "\n",
        "nlp = spacy.load(\"en_core_web_lg\")  # specifly \"nlp\" for SpaCy # NOTE 50 second run time"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "dT_t7HQfYksz",
        "outputId": "9b0180e2-999c-48f3-ca48-9efe95b0ed68"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[38;5;2m✔ Download and installation successful\u001b[0m\n",
            "You can now load the package via spacy.load('en_core_web_lg')\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "id": "-yuM0x9-i8zP"
      },
      "outputs": [],
      "source": [
        "DOSAGE_DATA['T00_TRANSCRIPT'] = DOSAGE_DATA['T00_TRANSCRIPT'].str.lower() #lower case letters T00\n",
        "\n",
        "DOSAGE_DATA['T01_TRANSCRIPT'] = DOSAGE_DATA['T01_TRANSCRIPT'].str.lower()\n",
        "\n",
        "DOSAGE_DATA['T02_TRANSCRIPT'] = DOSAGE_DATA['T02_TRANSCRIPT'].str.lower()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# # Create a practice dataframe to verify the count nominal subjects\n",
        "# prac_sent = [\"his tree is green. your trees are green. Marys book is purple. The presidents tie is blue. The cow is hungry.\"] # creation of a practice sentence\n",
        "\n",
        "# prac_df = pd.DataFrame(prac_sent, columns=['text']) # creation of a practice dataframe\n",
        "\n",
        "# prac_df # print the practice data frame"
      ],
      "metadata": {
        "id": "HSnsXZbm-rO4"
      },
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# # Universal dependency tags practice df\n",
        "\n",
        "# nw_final = []\n",
        "# nsubj_final = []\n",
        "# nsubj_unique_final = []\n",
        "\n",
        "# for index, row in prac_df.iterrows(): #go through the practice df by row\n",
        "#     nw = 0\n",
        "#     nsubj = 0\n",
        "#     nsubj_unique = 0\n",
        "#     text = str(row[\"text\"]) # text to string for SpaCy processing\n",
        "#     tokenized_doc = nlp(text) # putting the text in spaCy\n",
        "#     for token in tokenized_doc:\n",
        "#         if not token.is_punct and not token.pos_ == \"SPACE\": # counts as a word if it is not puncutation\n",
        "#           print(token.text, token.pos_, token.dep_)\n",
        "#           nw +=1 # adds to new word count for each token not a punctuation\n",
        "#         if token.dep_ == \"nsubj\": # capatalization matters here; use lowercase for \"nsubj\"\n",
        "#           nsubj +=1 # adds to nominal subject count for each token\n",
        "\n",
        "# nw_final.append(nw)\n",
        "# nsubj_final.append(nsubj)\n",
        "\n",
        "# print(nw_final)\n",
        "# print(nsubj_final)"
      ],
      "metadata": {
        "id": "Vav2Vi93BfrS"
      },
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# T00 Universal dependency tags\n",
        "\n",
        "T00_nw_final = []\n",
        "T00_nsubj_final = []\n",
        "\n",
        "for index, row in DOSAGE_DATA.iterrows(): #go through the Pandas df by row\n",
        "  T00_nw = 0\n",
        "  T00_nsubj = 0\n",
        "  text = str(row[\"T00_TRANSCRIPT\"]) # text to sring for SpaCy processing\n",
        "  T00_tokenized_doc = nlp(text) # putting the text in SpaCy\n",
        "  for token in T00_tokenized_doc:\n",
        "    # print(token.text, token.pos_)\n",
        "    if not token.is_punct and not token.pos_ == \"SPACE\": # counts as a word if it is not puncutation\n",
        "      # print(token.text, token.pos_, token.dep_) # testing hypothesis\n",
        "      T00_nw += 1 # adds to new word count for each token not a punctuation\n",
        "    if token.dep_ == \"nsubj\":\n",
        "      # print (token.dep)\n",
        "      T00_nsubj += 1\n",
        "  T00_nw_final.append(T00_nw)\n",
        "  T00_nsubj_final.append(T00_nsubj)\n",
        "\n",
        "print(T00_nsubj_final)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "lXH5OO5Q_Ujk",
        "outputId": "2ed7d362-5fcb-4db9-d7e7-46a39d64eea5"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[61, 0, 45, 39, 71, 89, 33, 42, 47, 46, 40, 38, 48, 50, 21, 28, 15, 69, 40, 72, 91, 0, 59, 65, 98, 61, 37, 110, 72, 64, 27, 17, 131, 59, 97, 69, 90, 41, 104, 93, 23, 61, 46, 0, 57, 57, 62, 32, 79, 45, 53, 78, 61, 60, 67, 45, 77, 124, 44, 38, 29, 50, 86, 52, 96, 88, 47, 53, 78, 44, 81, 0, 83, 63, 81, 70, 0, 92, 106, 79, 0, 12, 0, 51, 63, 43, 48, 65, 25, 32, 79, 80, 54, 32, 72, 26, 49, 53, 43, 70, 70, 29, 85, 24, 54, 42, 97, 86, 71, 59, 37, 83, 59, 43, 50, 45, 21, 85, 56, 76]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# T01 Universal dependency tags\n",
        "## 1. Copy/Paste T00 code into notepad\n",
        "## 2. Ctrl+H\n",
        "## 3. Replace T00 --> T01\n",
        "\n",
        "# T01 Universal dependency tags\n",
        "\n",
        "T01_nw_final = []\n",
        "T01_nsubj_final = []\n",
        "\n",
        "for index, row in DOSAGE_DATA.iterrows():\n",
        "  T01_nw = 0\n",
        "  T01_nsubj = 0\n",
        "  text = str(row[\"T01_TRANSCRIPT\"])\n",
        "  T01_tokenized_doc = nlp(text)\n",
        "  for token in T01_tokenized_doc:\n",
        "    if not token.is_punct and not token.pos_ == \"SPACE\":\n",
        "      T01_nw += 1\n",
        "    if token.dep_ == \"nsubj\":\n",
        "      T01_nsubj += 1\n",
        "  T01_nw_final.append(T01_nw)\n",
        "  T01_nsubj_final.append(T01_nsubj)\n",
        "\n",
        "print(T01_nsubj_final)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "5pkf7XSqy-J-",
        "outputId": "a09a0b7d-b1f0-42d7-c0ba-7fbf91501e6f"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[56, 0, 59, 20, 28, 93, 47, 66, 73, 118, 30, 49, 54, 73, 34, 0, 38, 126, 0, 95, 60, 65, 55, 70, 0, 37, 41, 126, 46, 88, 27, 41, 79, 0, 0, 51, 0, 66, 84, 76, 53, 0, 65, 83, 42, 105, 59, 0, 47, 73, 82, 0, 94, 60, 0, 83, 50, 0, 29, 154, 56, 74, 32, 0, 39, 35, 39, 3, 70, 39, 58, 37, 31, 11, 46, 44, 0, 49, 76, 19, 4, 20, 0, 47, 35, 45, 28, 0, 15, 14, 28, 18, 35, 11, 0, 1, 0, 3, 0, 47, 17, 7, 28, 32, 15, 16, 27, 30, 25, 7, 6, 42, 13, 15, 7, 22, 11, 17, 17, 23]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# T02 Universal dependency tags\n",
        "## 1. Copy/Paste T01 code into notepad\n",
        "## 2. Ctrl+H\n",
        "## 3. Replace T01 --> T02\n",
        "\n",
        "T02_nw_final = []\n",
        "T02_nsubj_final = []\n",
        "\n",
        "for index, row in DOSAGE_DATA.iterrows():\n",
        "  T02_nw = 0\n",
        "  T02_nsubj = 0\n",
        "  text = str(row[\"T02_TRANSCRIPT\"])\n",
        "  T02_tokenized_doc = nlp(text)\n",
        "  for token in T02_tokenized_doc:\n",
        "    if not token.is_punct and not token.pos_ == \"SPACE\":\n",
        "      T02_nw += 1\n",
        "    if token.dep_ == \"nsubj\":\n",
        "      T02_nsubj += 1\n",
        "  T02_nw_final.append(T02_nw)\n",
        "  T02_nsubj_final.append(T02_nsubj)\n",
        "\n",
        "print(T02_nsubj_final)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "iYx5tR8Z0p9Y",
        "outputId": "fa229258-d84a-4f76-f778-d1e78af798f2"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[58, 0, 42, 0, 66, 78, 52, 46, 73, 67, 48, 0, 61, 84, 41, 0, 73, 97, 0, 82, 0, 82, 71, 69, 0, 65, 38, 105, 54, 0, 44, 32, 73, 0, 0, 47, 0, 56, 110, 85, 81, 0, 48, 73, 0, 106, 0, 0, 52, 31, 115, 0, 68, 94, 0, 99, 0, 82, 53, 41, 47, 83, 127, 0, 53, 49, 55, 44, 0, 55, 49, 29, 76, 24, 51, 36, 0, 77, 92, 53, 0, 28, 0, 64, 64, 47, 38, 0, 23, 4, 27, 51, 47, 7, 0, 27, 0, 11, 0, 38, 17, 0, 0, 37, 11, 44, 0, 0, 92, 13, 22, 63, 0, 18, 15, 0, 13, 60, 48, 30]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Step 3: Appending the spaCy results to pandas dataframe and exporting the dataframe"
      ],
      "metadata": {
        "id": "r8yxh7Edm4oF"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "voyZDgBpi8zT"
      },
      "source": [
        "Merge the number of words and nominal subjects"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# T00\n",
        "DOSAGE_DATA['T00_NW'] = T00_nw_final # creates new pandas column\n",
        "DOSAGE_DATA['T00_NSUBJ'] = T00_nsubj_final\n",
        "\n",
        "# T01\n",
        "DOSAGE_DATA['T01_NW'] = T01_nw_final\n",
        "DOSAGE_DATA['T01_NSUBJ'] = T01_nsubj_final\n",
        "\n",
        "# T02\n",
        "DOSAGE_DATA['T02_NW'] = T02_nw_final\n",
        "DOSAGE_DATA['T02_NSUBJ'] = T02_nsubj_final\n",
        "\n",
        "DOSAGE_DATA  #call dataframe to visually check"
      ],
      "metadata": {
        "id": "wFPMOewEVJR_",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 877
        },
        "outputId": "ae744c81-5352-472b-dd8e-6aa71737bd61"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     Unnamed: 0      ID Condition_Group  Condition_Number  \\\n",
              "0             0  1003.0         Control               2.0   \n",
              "1             1  1004.0         Control               2.0   \n",
              "2             2  1006.0         Control               2.0   \n",
              "3             3  1009.0         Control               2.0   \n",
              "4             4  1010.0         Control               2.0   \n",
              "..          ...     ...             ...               ...   \n",
              "115         115  2051.0    Intervention               1.0   \n",
              "116         116  2053.0    Intervention               1.0   \n",
              "117         117  2055.0    Intervention               1.0   \n",
              "118         118  2056.0    Intervention               1.0   \n",
              "119         119  2059.0    Intervention               1.0   \n",
              "\n",
              "                                        T00_TRANSCRIPT  \\\n",
              "0    what is that?\\nthese are not blo.\\nsay open.\\n...   \n",
              "1                                                  NaN   \n",
              "2    no more kiok.\\nnow.\\ni guess you we'll get it ...   \n",
              "3    ok.\\n.\\nhere let's share.\\nyou going to put hi...   \n",
              "4    how did that happen?\\n  you try.\\nlet's try an...   \n",
              "..                                                 ...   \n",
              "115  \\nwhat's in theret?\\n.\\nwhat else we got.\\noh ...   \n",
              "116  \\nlook scott.\\nlet's sit.\\nok  go.\\nok.\\nsit d...   \n",
              "117  why you only taking the girls out of the bus?\\...   \n",
              "118  let's see whiokay.\\nxx over here.\\nit's not wo...   \n",
              "119   scott xxx.\\ndo you want to play with this?\\ny...   \n",
              "\n",
              "     T00_CCX_ChildTotalUtterances_unprompted  T00_CCX_ChildNDW_unprompted  \\\n",
              "0                                        3.0                          2.0   \n",
              "1                                        NaN                          NaN   \n",
              "2                                        1.0                          1.0   \n",
              "3                                        0.0                          0.0   \n",
              "4                                       11.0                          7.0   \n",
              "..                                       ...                          ...   \n",
              "115                                      0.0                          0.0   \n",
              "116                                      2.0                          2.0   \n",
              "117                                      0.0                          0.0   \n",
              "118                                      2.0                          1.0   \n",
              "119                                      0.0                          0.0   \n",
              "\n",
              "     T00_CCX_mt_sw  T00_CCX_et_sw  T00_CCX_mt_et_sw  ...  Unnamed: 79  \\\n",
              "0              1.0            0.0               1.0  ...          NaN   \n",
              "1              NaN            NaN               NaN  ...          NaN   \n",
              "2              9.0            0.0               9.0  ...          NaN   \n",
              "3              0.0            0.0               0.0  ...          NaN   \n",
              "4             10.0            1.0              11.0  ...          NaN   \n",
              "..             ...            ...               ...  ...          ...   \n",
              "115            1.0            0.0               1.0  ...          NaN   \n",
              "116            1.0            0.0               1.0  ...          NaN   \n",
              "117            0.0            0.0               0.0  ...          NaN   \n",
              "118           10.0            0.0              10.0  ...          NaN   \n",
              "119            3.0            0.0               3.0  ...          NaN   \n",
              "\n",
              "     Unnamed: 80  Unnamed: 81  Unnamed: 82  T00_NW  T00_NSUBJ  T01_NW  \\\n",
              "0            NaN          NaN          NaN     343         61     350   \n",
              "1            NaN          NaN          NaN       1          0       1   \n",
              "2            NaN          NaN          NaN     327         45     368   \n",
              "3            NaN          NaN          NaN     242         39     115   \n",
              "4            NaN          NaN          NaN     447         71     190   \n",
              "..           ...          ...          ...     ...        ...     ...   \n",
              "115          NaN          NaN          NaN     273         45     153   \n",
              "116          NaN          NaN          NaN     141         21      98   \n",
              "117          NaN          NaN          NaN     550         85     133   \n",
              "118          NaN          NaN          NaN     447         56     202   \n",
              "119          NaN          NaN          NaN     439         76     155   \n",
              "\n",
              "     T01_NSUBJ  T02_NW  T02_NSUBJ  \n",
              "0           56     358         58  \n",
              "1            0       1          0  \n",
              "2           59     320         42  \n",
              "3           20       1          0  \n",
              "4           28     453         66  \n",
              "..         ...     ...        ...  \n",
              "115         22       1          0  \n",
              "116         11     115         13  \n",
              "117         17     415         60  \n",
              "118         17     287         48  \n",
              "119         23     199         30  \n",
              "\n",
              "[120 rows x 90 columns]"
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              "      <th>T00_TRANSCRIPT</th>\n",
              "      <th>T00_CCX_ChildTotalUtterances_unprompted</th>\n",
              "      <th>T00_CCX_ChildNDW_unprompted</th>\n",
              "      <th>T00_CCX_mt_sw</th>\n",
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              "      <th>T00_NW</th>\n",
              "      <th>T00_NSUBJ</th>\n",
              "      <th>T01_NW</th>\n",
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              "    <tr>\n",
              "      <th>115</th>\n",
              "      <td>115</td>\n",
              "      <td>2051.0</td>\n",
              "      <td>Intervention</td>\n",
              "      <td>1.0</td>\n",
              "      <td>\\nwhat's in theret?\\n.\\nwhat else we got.\\noh ...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>273</td>\n",
              "      <td>45</td>\n",
              "      <td>153</td>\n",
              "      <td>22</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>116</th>\n",
              "      <td>116</td>\n",
              "      <td>2053.0</td>\n",
              "      <td>Intervention</td>\n",
              "      <td>1.0</td>\n",
              "      <td>\\nlook scott.\\nlet's sit.\\nok  go.\\nok.\\nsit d...</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>141</td>\n",
              "      <td>21</td>\n",
              "      <td>98</td>\n",
              "      <td>11</td>\n",
              "      <td>115</td>\n",
              "      <td>13</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>117</th>\n",
              "      <td>117</td>\n",
              "      <td>2055.0</td>\n",
              "      <td>Intervention</td>\n",
              "      <td>1.0</td>\n",
              "      <td>why you only taking the girls out of the bus?\\...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>550</td>\n",
              "      <td>85</td>\n",
              "      <td>133</td>\n",
              "      <td>17</td>\n",
              "      <td>415</td>\n",
              "      <td>60</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>118</th>\n",
              "      <td>118</td>\n",
              "      <td>2056.0</td>\n",
              "      <td>Intervention</td>\n",
              "      <td>1.0</td>\n",
              "      <td>let's see whiokay.\\nxx over here.\\nit's not wo...</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>447</td>\n",
              "      <td>56</td>\n",
              "      <td>202</td>\n",
              "      <td>17</td>\n",
              "      <td>287</td>\n",
              "      <td>48</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>119</th>\n",
              "      <td>119</td>\n",
              "      <td>2059.0</td>\n",
              "      <td>Intervention</td>\n",
              "      <td>1.0</td>\n",
              "      <td>scott xxx.\\ndo you want to play with this?\\ny...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>439</td>\n",
              "      <td>76</td>\n",
              "      <td>155</td>\n",
              "      <td>23</td>\n",
              "      <td>199</td>\n",
              "      <td>30</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>120 rows × 90 columns</p>\n",
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              "\n",
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              "\n",
              "  .colab-df-quickchart:hover {\n",
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              "    fill: var(--button-hover-fill-color);\n",
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              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
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              "    background-color: var(--disabled-bg-color);\n",
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              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
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              "    20% {\n",
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              "    }\n",
              "  }\n",
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              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
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              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "\n",
              "  <div id=\"id_910b51a4-cfef-4aff-9952-819ddfcb5a73\">\n",
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              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "\n",
              "      [theme=dark] .colab-df-generate {\n",
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              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('DOSAGE_DATA')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
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              "    </button>\n",
              "    <script>\n",
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              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('DOSAGE_DATA');\n",
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              "    </script>\n",
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              "\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "DOSAGE_DATA.to_csv('DOSAGE_DATA_DEPENDENCY.csv')  # save the dataframes"
      ],
      "metadata": {
        "id": "a8QqQgmoc1hJ"
      },
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Step 4: Statistically Analyze the Results"
      ],
      "metadata": {
        "id": "83FkyUq6nREr"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "1. What is the correlation between the number of different nominal subjects modeled by caregivers and the child's number of different words at baseline?"
      ],
      "metadata": {
        "id": "BNGTVqSMnhWy"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Read in the data"
      ],
      "metadata": {
        "id": "EfvJeXzBoN8n"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "ALL_DATA = pd.read_csv('DOSAGE_DATA_DEPENDENCY.csv', encoding = \"ISO-8859-1\") #read in all data using ISO8859-1 encoding\n",
        "TX_DATA = pd.read_csv('DOSAGE_DATA_DEPENDENCY_TX.csv', encoding = \"ISO-8859-1\") #read in TX data using ISO8859-1 encoding\n",
        "BAU_DATA = pd.read_csv('DOSAGE_DATA_DEPENDENCY_BAU.csv', encoding = \"ISO-8859-1\") #read in BAU data using ISO8859-1 encodin\n",
        "\n",
        "ALL_DATA = ALL_DATA[ALL_DATA['T02_CCX_ChildNDW_unprompted'].notna()]\n",
        "TX_DATA = TX_DATA[TX_DATA['T02_CCX_ChildNDW_unprompted'].notna()]\n",
        "BAU_DATA = BAU_DATA[BAU_DATA['T02_CCX_ChildNDW_unprompted'].notna()]"
      ],
      "metadata": {
        "id": "QcOGk3jbpcae"
      },
      "execution_count": 37,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Scatterplot and correlation for RQ1\n",
        "\n",
        "> Import matplot [13]\n",
        "> Import seaborn [14]\n",
        "> Import scipy.stats [15]\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "Z_r3Ue7h4QDA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "dataset = ALL_DATA\n",
        "sns.scatterplot(data = ALL_DATA, x='T00_CCX_ChildNDW_unprompted', y='T00_NSUBJ')\n",
        "\n",
        "import scipy.stats\n",
        "ALL_DATA = ALL_DATA[ALL_DATA['T00_CCX_ChildNDW_unprompted'].notna()]\n",
        "x = ALL_DATA[\"T00_CCX_ChildNDW_unprompted\"]\n",
        "y = ALL_DATA[\"T00_NSUBJ\"]\n",
        "scipy.stats.pearsonr(x, y)[0]    # Pearson's r\n",
        "\n",
        "\n",
        "r, p = scipy.stats.pearsonr(x,y)\n",
        "\n",
        "print(r)\n",
        "print(p)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 485
        },
        "id": "aUz3TRKoo3wO",
        "outputId": "e380d918-e35c-4111-f950-1008ca812841"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.07422653375186446\n",
            "0.48937257630073294\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "2. What is the correlation between the number of different nominal subjects modeled by caregivers at post-testing and the child's number of different words at follow-up, and by group?"
      ],
      "metadata": {
        "id": "OuFOE4NPnsVJ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Scatterplot and correlation for RQ2, All Participants"
      ],
      "metadata": {
        "id": "0_G0cTz447pI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dataset = ALL_DATA\n",
        "sns.scatterplot(data = ALL_DATA, x='T02_CCX_ChildNDW_unprompted', y='T01_NSUBJ')\n",
        "\n",
        "import scipy.stats\n",
        "ALL_DATA = ALL_DATA[ALL_DATA['T02_CCX_ChildNDW_unprompted'].notna()]\n",
        "x = ALL_DATA[\"T02_CCX_ChildNDW_unprompted\"]\n",
        "y = ALL_DATA[\"T01_NSUBJ\"]\n",
        "scipy.stats.pearsonr(x, y)[0]    # Pearson's r\n",
        "\n",
        "r, p = scipy.stats.pearsonr(x,y)\n",
        "\n",
        "print(r)\n",
        "print(p)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 486
        },
        "id": "-CzWkNJj5ATo",
        "outputId": "e2cddc11-6164-4e78-c044-f20bd622117d"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.35522508754717047\n",
            "0.000635628076897923\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Scatterplot and correlation for RQ2, Control Participants"
      ],
      "metadata": {
        "id": "mHCIgDVq5CO7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dataset = BAU_DATA\n",
        "sns.scatterplot(data = BAU_DATA, x='T02_CCX_ChildNDW_unprompted', y='T01_NSUBJ')\n",
        "\n",
        "BAU_DATA = BAU_DATA[BAU_DATA['T02_CCX_ChildNDW_unprompted'].notna()]\n",
        "x = BAU_DATA[\"T02_CCX_ChildNDW_unprompted\"]\n",
        "y = BAU_DATA[\"T01_NSUBJ\"]\n",
        "scipy.stats.pearsonr(x, y)[0]    # Pearson's r\n",
        "\n",
        "r, p = scipy.stats.pearsonr(x,y)\n",
        "\n",
        "print(r)\n",
        "print(p)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 486
        },
        "id": "MumBZRZZpP0d",
        "outputId": "86439368-da8b-4350-b2ee-eeb9cda10e24"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.3886890310308269\n",
            "0.007595607224640189\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Scatterplot and correlation for RQ2, Treatment Participants"
      ],
      "metadata": {
        "id": "p1y9STZW5MJM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dataset = TX_DATA\n",
        "sns.scatterplot(data = TX_DATA, x='T02_CCX_ChildNDW_unprompted', y='T01_NSUBJ')\n",
        "\n",
        "TX_DATA = TX_DATA[TX_DATA['T02_CCX_ChildNDW_unprompted'].notna()]\n",
        "x = TX_DATA[\"T02_CCX_ChildNDW_unprompted\"]\n",
        "y = TX_DATA[\"T01_NSUBJ\"]\n",
        "scipy.stats.pearsonr(x, y)[0]    # Pearson's r\n",
        "\n",
        "r, p = scipy.stats.pearsonr(x,y)\n",
        "\n",
        "print(r)\n",
        "print(p)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 485
        },
        "id": "4vke7iSk5NAf",
        "outputId": "8b4649d8-a8ae-475c-d28d-91d3ee69f0a2"
      },
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.4559547103393025\n",
            "0.001645586277477945\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "3. Is there a significant difference in the number of nominal subjects for the TX and BAU caregiver groups at post-test?"
      ],
      "metadata": {
        "id": "EUsD4B9BnvBs"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Two Tailed T-test"
      ],
      "metadata": {
        "id": "odwaOiyvdPsX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from scipy.stats import ttest_ind\n",
        "\n",
        "df = ALL_DATA[['Condition_Number', 'T01_NSUBJ']]\n",
        "\n",
        "df.head()\n",
        "\n",
        "group1 = df[df['Condition_Number']==1]\n",
        "group2 = df[df['Condition_Number'] ==2]\n",
        "ttest_ind(group1['T01_NSUBJ'], group2['T01_NSUBJ'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "eFI09g9QpLdl",
        "outputId": "b94ff10b-f82c-4210-e048-6d2b4586f03a"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TtestResult(statistic=-7.678555549595043, pvalue=2.216802087078824e-11, df=87.0)"
            ]
          },
          "metadata": {},
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Boxplot of T-Test Results"
      ],
      "metadata": {
        "id": "hDQansrRwJBZ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Note, the boxplots of the t-test results were produced using the generative AI feature of Google Colab [9]."
      ],
      "metadata": {
        "id": "FUerVDOW0nfv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# prompt: make a boxplot of the t test results\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "df.boxplot(by='Condition_Number', column='T01_NSUBJ')\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 478
        },
        "id": "dDq-t1OGxdCO",
        "outputId": "9cda46f4-7457-4cf3-eb67-9957b9dd2174"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# prompt: On the boxplot label Condition_Number 1.0 with \"Control\" and Condition_Number 2.0 with \"Treatment\"\n",
        "\n",
        "df.boxplot(by='Condition_Number', column='T01_NSUBJ')\n",
        "plt.xlabel('Condition (1.0=Control, 2.0=Treatment)')\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 478
        },
        "id": "glvtI2pWy1EH",
        "outputId": "f0078f66-7256-4a07-f7cd-ad1b8db9c85b"
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# prompt: On the boxplot label the Y axis with \"Number of Nominal Subjects\"\n",
        "\n",
        "df.boxplot(by='Condition_Number', column='T01_NSUBJ')\n",
        "plt.xlabel('Condition (1.0=Control, 2.0=Treatment)')\n",
        "plt.ylabel('Number of Nominal Subjects')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 478
        },
        "id": "emzbeH4VzLLk",
        "outputId": "571ff2f5-2c50-405f-8bbe-e76ba6b40217"
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Results and Discussion\n",
        "\n",
        "In regards to question one, the correlation between the number of nominal subjects modeled by caregivers and the child's number of different unprompted words at baseline was small, and of no signifigance (r = 0.07, p = 0.49).\n",
        "\n",
        "In regards to question two, the correlation between the number of nominal subjects modeled by caregivers at post-testing and the child's number of different words at follow-up was medium but significant (r = 0.36, p < 0.05). When looking at the same correlation but only for the BAU group, the results were slightly stronger but also medium and significant (r = 0.38, p < 0.5). The strongest correlation between nominal subjects by caregivers at post-testing and the child's number of different words at follow-up was found in the treatment group, but still with only a medium effect (r = 0.47, p < 0.05).\n",
        "\n",
        "In regards to question three, there was a significant difference in the number of nominal subjects at post-test for the caregivers between treatment and control (t = -7.68, p < 0.05). This indicates that caregivers who were trained used significantly more nominal subjects than caregivers who were not. The histogram of the t-test results was produced using the generative AI feature of Google Colab [9], as was the .html copy of this notebook.\n",
        "\n",
        "The results of this analysis should be interpreted with caution. Analysis of child baseline data was heavily weighted by the number of children that had zero different unprompted words at baseline. The corpus used for analysis required a large amount of cleaning in an attempt to change the SALT [6] transcription guidelines into a format that could be used validly in SpaCy [5]. A more thorough check of data is advised.\n",
        "\n",
        "Future research should seek larger samples of children when assessing expressive language delay in low-rate groups such as toddlers with autism. A larger sample would allow for equal binning of the number of different words as an outcome variable. Future research should also explore other predictors of child language growth such as the number of unique nominal subjects [3,4]. By identifying significant predictors of child language growth in the language modeled by their caregivers, we can further refine the training of caregivers and clinicians alike. In turn, the language structures modeled by caregivers and clincians can result in positive expressive language outcomes for their children [2, 3, 4]."
      ],
      "metadata": {
        "id": "450xofABob9y"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# References\n",
        "\n",
        "[1] ASHA. Typical speech and language development. 2023. https://www.asha.org/public/speech/development/\n",
        "\n",
        "[2] Roberts, M. Y., Stern, Y. S., Grauzer, J., Nietfeld, J. Thompson, S., Jones, M., Kaat, A. J., Kaiser, A. P. Teaching caregivers to support social communication: Results from a randomized clinical trial of austistic toddlers. American Journal of Speech-Language Pathology. 2022; 32(1)115-127.\n",
        "\n",
        "[3] Hadley, P., McKenna, M., & Rispoli, M. Sentence diversity in\n",
        "early language development: Recommendations for target selection and\n",
        "progress monitoring. American Journal of Speech-Language. 2018; 27:\n",
        "553-565\n",
        "\n",
        "[4] Hadley, P. Exploring sentence diversity at the boundary of\n",
        "typical and impaired language abilities. Journal of Speech,\n",
        "Language, and Hearing Research. 2020; 63: 3236-3251.\n",
        "\n",
        "[5] Honnibal, M., & Montani, I. spaCy 2: Natural language understanding with Bloom embeddings, convolutional neural networks and incremental parsing. 2017.\n",
        "\n",
        "[6] Miller, J. F. Systematic Analysis of Language Transcripts Research V8. Language Analysis Lab, University of Wisconson. 2012.\n",
        "\n",
        "[7] Aho, A. V., Algorithms for finding patterns in strings, Handbook of theoretical computer science. volume a: algorithms and complexity. MIT Press, Cambridge, MA. 1991.\n",
        "\n",
        "[8] The pandas development team. Pandas-dev/pandas: Pandas. 2020.\n",
        "\n",
        "[9] Bisong, E. (2019). Google Colaboratory. In: Building Machine Learning and Deep Learning Models on Google Cloud Platform. Apress, Berkeley, CA.\n",
        "\n",
        "[10] Van Rossum, G., Drake, F. L. Python 3 Reference Manual. Scotts Valley, CA: CreateSpace. 2009.\n",
        "\n",
        "[11] Rodgers, M. E. NLP-Analysis-using-SpaCy---Dependency-Parsing. https://github.com/Mary-E-Rodgers/NLP-Analysis-using-SpaCy---Dependency-Parsing/upload/main\n",
        "\n",
        "[12] Crossley, S. Safe Divide. Python function. 2023.\n",
        "\n",
        "[13] Hunter, J. D. Matplotlib: A 2D graphics environment. Computing in Science & Engineering. 2007; 9(3) 90-95.\n",
        "\n",
        "[14] Waskom, M. L. Seaborn: Statistical data visualization. Journal of Open Source Software. 2021; 6(60) 321.\n",
        "\n",
        "[15] Virtanen, P., Gommers, R., Oliphant, T. E., Haberland, M., Reddy, T., Cournapeau, D., Burvoski, E., Peterson, P., Weckesser, W., Bright, J., van der Walt, S. J., Brett, M., Wilson, J., Millman, K. J., Mayorov, N., Nelson, A. R. J., Jones, E., Kern, R., Larson, E. ... van Mulbregt, P., and SciPy 1.0 Contributors. SciPy 1.0: Fundamental Algorithms for Scientific Computing in Python. Nature Methods. 2020; 3, 261-272."
      ],
      "metadata": {
        "id": "w2JyhDnp2Xab"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# prompt: Make a .html copy of Rodgers_FinalProject.ipynb\n",
        "\n",
        "!jupyter nbconvert Rodgers_FinalProject.ipynb --to html\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Pj1BBUkl9wE8",
        "outputId": "0d4ef8f0-0277-4ad1-e673-3dd590a7e65c"
      },
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[NbConvertApp] Converting notebook Rodgers_FinalProject.ipynb to html\n",
            "[NbConvertApp] Writing 946855 bytes to Rodgers_FinalProject.html\n"
          ]
        }
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "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": "3.9.13"
    }
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  "nbformat": 4,
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