notebooks/data-analysis-pilot.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Importing required packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from collections import Counter\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Filtering out some test participants (used to test the survey system), added dataset column and deleted \"instruction\" and \"configHash Column\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>participantId</th>\n",
       "      <th>trialId</th>\n",
       "      <th>trialOrder</th>\n",
       "      <th>responseId</th>\n",
       "      <th>status</th>\n",
       "      <th>percentComplete</th>\n",
       "      <th>description</th>\n",
       "      <th>instruction</th>\n",
       "      <th>responsePrompt</th>\n",
       "      <th>answer</th>\n",
       "      <th>correctAnswer</th>\n",
       "      <th>responseMin</th>\n",
       "      <th>responseMax</th>\n",
       "      <th>startTime</th>\n",
       "      <th>endTime</th>\n",
       "      <th>duration</th>\n",
       "      <th>configHash</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2033c860-b467-42e3-ab2e-19ca44e65b0a</td>\n",
       "      <td>Consent</td>\n",
       "      <td>1</td>\n",
       "      <td>id</td>\n",
       "      <td>completed</td>\n",
       "      <td>100.0</td>\n",
       "      <td>undefined</td>\n",
       "      <td>undefined</td>\n",
       "      <td>Your Prolific ID</td>\n",
       "      <td>Rahat Zaman</td>\n",
       "      <td>undefined</td>\n",
       "      <td>undefined</td>\n",
       "      <td>undefined</td>\n",
       "      <td>2024-07-31T03:26:03.202Z</td>\n",
       "      <td>2024-07-31T03:27:28.983Z</td>\n",
       "      <td>85781</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2033c860-b467-42e3-ab2e-19ca44e65b0a</td>\n",
       "      <td>Consent</td>\n",
       "      <td>1</td>\n",
       "      <td>consent</td>\n",
       "      <td>completed</td>\n",
       "      <td>100.0</td>\n",
       "      <td>undefined</td>\n",
       "      <td>undefined</td>\n",
       "      <td>Do you consent to the study and wish to continue?</td>\n",
       "      <td>yes</td>\n",
       "      <td>undefined</td>\n",
       "      <td>undefined</td>\n",
       "      <td>undefined</td>\n",
       "      <td>2024-07-31T03:26:03.202Z</td>\n",
       "      <td>2024-07-31T03:27:28.983Z</td>\n",
       "      <td>85781</td>\n",
       "      <td>ecdced17dc1fd86b8b83f4914646d257990133f9423074...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2033c860-b467-42e3-ab2e-19ca44e65b0a</td>\n",
       "      <td>Training 1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>completed</td>\n",
       "      <td>100.0</td>\n",
       "      <td>undefined</td>\n",
       "      <td>Please answer the following questions:</td>\n",
       "      <td>What is the largest set?</td>\n",
       "      <td>Romance</td>\n",
       "      <td>Action</td>\n",
       "      <td>undefined</td>\n",
       "      <td>undefined</td>\n",
       "      <td>2024-07-31T03:48:25.846Z</td>\n",
       "      <td>2024-07-31T03:52:51.094Z</td>\n",
       "      <td>265248</td>\n",
       "      <td>ecdced17dc1fd86b8b83f4914646d257990133f9423074...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2033c860-b467-42e3-ab2e-19ca44e65b0a</td>\n",
       "      <td>Training 1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>completed</td>\n",
       "      <td>100.0</td>\n",
       "      <td>undefined</td>\n",
       "      <td>Please answer the following questions:</td>\n",
       "      <td>What is the largest intersection?</td>\n",
       "      <td>Empty Intersection (no sets)</td>\n",
       "      <td>Empty Intersection (no sets)</td>\n",
       "      <td>undefined</td>\n",
       "      <td>undefined</td>\n",
       "      <td>2024-07-31T03:48:25.846Z</td>\n",
       "      <td>2024-07-31T03:52:51.094Z</td>\n",
       "      <td>265248</td>\n",
       "      <td>ecdced17dc1fd86b8b83f4914646d257990133f9423074...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2033c860-b467-42e3-ab2e-19ca44e65b0a</td>\n",
       "      <td>Training 1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>completed</td>\n",
       "      <td>100.0</td>\n",
       "      <td>undefined</td>\n",
       "      <td>Please answer the following questions:</td>\n",
       "      <td>How many sets make up the largest intersection?</td>\n",
       "      <td>It is the empty intersection (no sets)</td>\n",
       "      <td>It is the empty intersection (no sets)</td>\n",
       "      <td>undefined</td>\n",
       "      <td>undefined</td>\n",
       "      <td>2024-07-31T03:48:25.846Z</td>\n",
       "      <td>2024-07-31T03:52:51.094Z</td>\n",
       "      <td>265248</td>\n",
       "      <td>ecdced17dc1fd86b8b83f4914646d257990133f9423074...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2033c860-b467-42e3-ab2e-19ca44e65b0a</td>\n",
       "      <td>Training 2</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>completed</td>\n",
       "      <td>100.0</td>\n",
       "      <td>undefined</td>\n",
       "      <td>Please answer the following questions:</td>\n",
       "      <td>What is the largest set?</td>\n",
       "      <td>Adventure</td>\n",
       "      <td>Adventure</td>\n",
       "      <td>undefined</td>\n",
       "      <td>undefined</td>\n",
       "      <td>2024-07-31T03:52:51.112Z</td>\n",
       "      <td>2024-07-31T04:01:08.980Z</td>\n",
       "      <td>497868</td>\n",
       "      <td>ecdced17dc1fd86b8b83f4914646d257990133f9423074...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2033c860-b467-42e3-ab2e-19ca44e65b0a</td>\n",
       "      <td>Training 2</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>completed</td>\n",
       "      <td>100.0</td>\n",
       "      <td>undefined</td>\n",
       "      <td>Please answer the following questions:</td>\n",
       "      <td>What is the largest intersection?</td>\n",
       "      <td>Empty Intersection (no sets)</td>\n",
       "      <td>Adventure</td>\n",
       "      <td>undefined</td>\n",
       "      <td>undefined</td>\n",
       "      <td>2024-07-31T03:52:51.112Z</td>\n",
       "      <td>2024-07-31T04:01:08.980Z</td>\n",
       "      <td>497868</td>\n",
       "      <td>ecdced17dc1fd86b8b83f4914646d257990133f9423074...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2033c860-b467-42e3-ab2e-19ca44e65b0a</td>\n",
       "      <td>Training 2</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>completed</td>\n",
       "      <td>100.0</td>\n",
       "      <td>undefined</td>\n",
       "      <td>Please answer the following questions:</td>\n",
       "      <td>How many sets make up the largest intersection?</td>\n",
       "      <td>It is the empty intersection (no sets)</td>\n",
       "      <td>It is only of a single set</td>\n",
       "      <td>undefined</td>\n",
       "      <td>undefined</td>\n",
       "      <td>2024-07-31T03:52:51.112Z</td>\n",
       "      <td>2024-07-31T04:01:08.980Z</td>\n",
       "      <td>497868</td>\n",
       "      <td>ecdced17dc1fd86b8b83f4914646d257990133f9423074...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2033c860-b467-42e3-ab2e-19ca44e65b0a</td>\n",
       "      <td>Training 3</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>completed</td>\n",
       "      <td>100.0</td>\n",
       "      <td>undefined</td>\n",
       "      <td>Please answer the following questions:</td>\n",
       "      <td>What is the largest set?</td>\n",
       "      <td>Drama</td>\n",
       "      <td>Drama</td>\n",
       "      <td>undefined</td>\n",
       "      <td>undefined</td>\n",
       "      <td>2024-07-31T04:01:08.993Z</td>\n",
       "      <td>2024-07-31T04:01:40.418Z</td>\n",
       "      <td>31425</td>\n",
       "      <td>ecdced17dc1fd86b8b83f4914646d257990133f9423074...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2033c860-b467-42e3-ab2e-19ca44e65b0a</td>\n",
       "      <td>Training 3</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>completed</td>\n",
       "      <td>100.0</td>\n",
       "      <td>undefined</td>\n",
       "      <td>Please answer the following questions:</td>\n",
       "      <td>What is the largest intersection?</td>\n",
       "      <td>Drama</td>\n",
       "      <td>Drama</td>\n",
       "      <td>undefined</td>\n",
       "      <td>undefined</td>\n",
       "      <td>2024-07-31T04:01:08.993Z</td>\n",
       "      <td>2024-07-31T04:01:40.418Z</td>\n",
       "      <td>31425</td>\n",
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       "    </tr>\n",
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      ],
      "text/plain": [
       "                          participantId     trialId  trialOrder responseId  \\\n",
       "0  2033c860-b467-42e3-ab2e-19ca44e65b0a     Consent           1         id   \n",
       "1  2033c860-b467-42e3-ab2e-19ca44e65b0a     Consent           1    consent   \n",
       "2  2033c860-b467-42e3-ab2e-19ca44e65b0a  Training 1           3          1   \n",
       "3  2033c860-b467-42e3-ab2e-19ca44e65b0a  Training 1           3          2   \n",
       "4  2033c860-b467-42e3-ab2e-19ca44e65b0a  Training 1           3          3   \n",
       "5  2033c860-b467-42e3-ab2e-19ca44e65b0a  Training 2           4          1   \n",
       "6  2033c860-b467-42e3-ab2e-19ca44e65b0a  Training 2           4          2   \n",
       "7  2033c860-b467-42e3-ab2e-19ca44e65b0a  Training 2           4          3   \n",
       "8  2033c860-b467-42e3-ab2e-19ca44e65b0a  Training 3           5          1   \n",
       "9  2033c860-b467-42e3-ab2e-19ca44e65b0a  Training 3           5          2   \n",
       "\n",
       "      status  percentComplete description  \\\n",
       "0  completed            100.0   undefined   \n",
       "1  completed            100.0   undefined   \n",
       "2  completed            100.0   undefined   \n",
       "3  completed            100.0   undefined   \n",
       "4  completed            100.0   undefined   \n",
       "5  completed            100.0   undefined   \n",
       "6  completed            100.0   undefined   \n",
       "7  completed            100.0   undefined   \n",
       "8  completed            100.0   undefined   \n",
       "9  completed            100.0   undefined   \n",
       "\n",
       "                              instruction  \\\n",
       "0                               undefined   \n",
       "1                               undefined   \n",
       "2  Please answer the following questions:   \n",
       "3  Please answer the following questions:   \n",
       "4  Please answer the following questions:   \n",
       "5  Please answer the following questions:   \n",
       "6  Please answer the following questions:   \n",
       "7  Please answer the following questions:   \n",
       "8  Please answer the following questions:   \n",
       "9  Please answer the following questions:   \n",
       "\n",
       "                                      responsePrompt  \\\n",
       "0                                   Your Prolific ID   \n",
       "1  Do you consent to the study and wish to continue?   \n",
       "2                           What is the largest set?   \n",
       "3                  What is the largest intersection?   \n",
       "4    How many sets make up the largest intersection?   \n",
       "5                           What is the largest set?   \n",
       "6                  What is the largest intersection?   \n",
       "7    How many sets make up the largest intersection?   \n",
       "8                           What is the largest set?   \n",
       "9                  What is the largest intersection?   \n",
       "\n",
       "                                   answer  \\\n",
       "0                             Rahat Zaman   \n",
       "1                                     yes   \n",
       "2                                 Romance   \n",
       "3            Empty Intersection (no sets)   \n",
       "4  It is the empty intersection (no sets)   \n",
       "5                               Adventure   \n",
       "6            Empty Intersection (no sets)   \n",
       "7  It is the empty intersection (no sets)   \n",
       "8                                   Drama   \n",
       "9                                   Drama   \n",
       "\n",
       "                            correctAnswer responseMin responseMax  \\\n",
       "0                               undefined   undefined   undefined   \n",
       "1                               undefined   undefined   undefined   \n",
       "2                                  Action   undefined   undefined   \n",
       "3            Empty Intersection (no sets)   undefined   undefined   \n",
       "4  It is the empty intersection (no sets)   undefined   undefined   \n",
       "5                               Adventure   undefined   undefined   \n",
       "6                               Adventure   undefined   undefined   \n",
       "7              It is only of a single set   undefined   undefined   \n",
       "8                                   Drama   undefined   undefined   \n",
       "9                                   Drama   undefined   undefined   \n",
       "\n",
       "                  startTime                   endTime  duration  \\\n",
       "0  2024-07-31T03:26:03.202Z  2024-07-31T03:27:28.983Z     85781   \n",
       "1  2024-07-31T03:26:03.202Z  2024-07-31T03:27:28.983Z     85781   \n",
       "2  2024-07-31T03:48:25.846Z  2024-07-31T03:52:51.094Z    265248   \n",
       "3  2024-07-31T03:48:25.846Z  2024-07-31T03:52:51.094Z    265248   \n",
       "4  2024-07-31T03:48:25.846Z  2024-07-31T03:52:51.094Z    265248   \n",
       "5  2024-07-31T03:52:51.112Z  2024-07-31T04:01:08.980Z    497868   \n",
       "6  2024-07-31T03:52:51.112Z  2024-07-31T04:01:08.980Z    497868   \n",
       "7  2024-07-31T03:52:51.112Z  2024-07-31T04:01:08.980Z    497868   \n",
       "8  2024-07-31T04:01:08.993Z  2024-07-31T04:01:40.418Z     31425   \n",
       "9  2024-07-31T04:01:08.993Z  2024-07-31T04:01:40.418Z     31425   \n",
       "\n",
       "                                          configHash  \n",
       "0  ecdced17dc1fd86b8b83f4914646d257990133f9423074...  \n",
       "1  ecdced17dc1fd86b8b83f4914646d257990133f9423074...  \n",
       "2  ecdced17dc1fd86b8b83f4914646d257990133f9423074...  \n",
       "3  ecdced17dc1fd86b8b83f4914646d257990133f9423074...  \n",
       "4  ecdced17dc1fd86b8b83f4914646d257990133f9423074...  \n",
       "5  ecdced17dc1fd86b8b83f4914646d257990133f9423074...  \n",
       "6  ecdced17dc1fd86b8b83f4914646d257990133f9423074...  \n",
       "7  ecdced17dc1fd86b8b83f4914646d257990133f9423074...  \n",
       "8  ecdced17dc1fd86b8b83f4914646d257990133f9423074...  \n",
       "9  ecdced17dc1fd86b8b83f4914646d257990133f9423074...  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load the CSV file\n",
    "file_path = './data/prepilot-pilot-raw.csv'\n",
    "df = pd.read_csv(file_path)\n",
    "\n",
    "df.head(10)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Adding the correct answers (was not added in first two pilots)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>participantId</th>\n",
       "      <th>trialId</th>\n",
       "      <th>trialOrder</th>\n",
       "      <th>responseId</th>\n",
       "      <th>status</th>\n",
       "      <th>percentComplete</th>\n",
       "      <th>description</th>\n",
       "      <th>instruction</th>\n",
       "      <th>responsePrompt</th>\n",
       "      <th>answer</th>\n",
       "      <th>correctAnswer</th>\n",
       "      <th>responseMin</th>\n",
       "      <th>responseMax</th>\n",
       "      <th>startTime</th>\n",
       "      <th>endTime</th>\n",
       "      <th>duration</th>\n",
       "      <th>configHash</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2033c860-b467-42e3-ab2e-19ca44e65b0a</td>\n",
       "      <td>Consent</td>\n",
       "      <td>1</td>\n",
       "      <td>id</td>\n",
       "      <td>completed</td>\n",
       "      <td>100.0</td>\n",
       "      <td>undefined</td>\n",
       "      <td>undefined</td>\n",
       "      <td>Your Prolific ID</td>\n",
       "      <td>Rahat Zaman</td>\n",
       "      <td>undefined</td>\n",
       "      <td>undefined</td>\n",
       "      <td>undefined</td>\n",
       "      <td>2024-07-31T03:26:03.202Z</td>\n",
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       "      <th>1</th>\n",
       "      <td>2033c860-b467-42e3-ab2e-19ca44e65b0a</td>\n",
       "      <td>Consent</td>\n",
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       "      <td>consent</td>\n",
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       "      <td>100.0</td>\n",
       "      <td>undefined</td>\n",
       "      <td>undefined</td>\n",
       "      <td>Do you consent to the study and wish to continue?</td>\n",
       "      <td>yes</td>\n",
       "      <td>undefined</td>\n",
       "      <td>undefined</td>\n",
       "      <td>undefined</td>\n",
       "      <td>2024-07-31T03:26:03.202Z</td>\n",
       "      <td>2024-07-31T03:27:28.983Z</td>\n",
       "      <td>85781</td>\n",
       "      <td>ecdced17dc1fd86b8b83f4914646d257990133f9423074...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2033c860-b467-42e3-ab2e-19ca44e65b0a</td>\n",
       "      <td>Training 1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>completed</td>\n",
       "      <td>100.0</td>\n",
       "      <td>undefined</td>\n",
       "      <td>Please answer the following questions:</td>\n",
       "      <td>What is the largest set?</td>\n",
       "      <td>Romance</td>\n",
       "      <td>Action</td>\n",
       "      <td>undefined</td>\n",
       "      <td>undefined</td>\n",
       "      <td>2024-07-31T03:48:25.846Z</td>\n",
       "      <td>2024-07-31T03:52:51.094Z</td>\n",
       "      <td>265248</td>\n",
       "      <td>ecdced17dc1fd86b8b83f4914646d257990133f9423074...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2033c860-b467-42e3-ab2e-19ca44e65b0a</td>\n",
       "      <td>Training 1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>completed</td>\n",
       "      <td>100.0</td>\n",
       "      <td>undefined</td>\n",
       "      <td>Please answer the following questions:</td>\n",
       "      <td>What is the largest intersection?</td>\n",
       "      <td>Empty Intersection (no sets)</td>\n",
       "      <td>Empty Intersection (no sets)</td>\n",
       "      <td>undefined</td>\n",
       "      <td>undefined</td>\n",
       "      <td>2024-07-31T03:48:25.846Z</td>\n",
       "      <td>2024-07-31T03:52:51.094Z</td>\n",
       "      <td>265248</td>\n",
       "      <td>ecdced17dc1fd86b8b83f4914646d257990133f9423074...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2033c860-b467-42e3-ab2e-19ca44e65b0a</td>\n",
       "      <td>Training 1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>completed</td>\n",
       "      <td>100.0</td>\n",
       "      <td>undefined</td>\n",
       "      <td>Please answer the following questions:</td>\n",
       "      <td>How many sets make up the largest intersection?</td>\n",
       "      <td>It is the empty intersection (no sets)</td>\n",
       "      <td>It is the empty intersection (no sets)</td>\n",
       "      <td>undefined</td>\n",
       "      <td>undefined</td>\n",
       "      <td>2024-07-31T03:48:25.846Z</td>\n",
       "      <td>2024-07-31T03:52:51.094Z</td>\n",
       "      <td>265248</td>\n",
       "      <td>ecdced17dc1fd86b8b83f4914646d257990133f9423074...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2033c860-b467-42e3-ab2e-19ca44e65b0a</td>\n",
       "      <td>Training 2</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>completed</td>\n",
       "      <td>100.0</td>\n",
       "      <td>undefined</td>\n",
       "      <td>Please answer the following questions:</td>\n",
       "      <td>What is the largest set?</td>\n",
       "      <td>Adventure</td>\n",
       "      <td>Adventure</td>\n",
       "      <td>undefined</td>\n",
       "      <td>undefined</td>\n",
       "      <td>2024-07-31T03:52:51.112Z</td>\n",
       "      <td>2024-07-31T04:01:08.980Z</td>\n",
       "      <td>497868</td>\n",
       "      <td>ecdced17dc1fd86b8b83f4914646d257990133f9423074...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2033c860-b467-42e3-ab2e-19ca44e65b0a</td>\n",
       "      <td>Training 2</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>completed</td>\n",
       "      <td>100.0</td>\n",
       "      <td>undefined</td>\n",
       "      <td>Please answer the following questions:</td>\n",
       "      <td>What is the largest intersection?</td>\n",
       "      <td>Empty Intersection (no sets)</td>\n",
       "      <td>Adventure</td>\n",
       "      <td>undefined</td>\n",
       "      <td>undefined</td>\n",
       "      <td>2024-07-31T03:52:51.112Z</td>\n",
       "      <td>2024-07-31T04:01:08.980Z</td>\n",
       "      <td>497868</td>\n",
       "      <td>ecdced17dc1fd86b8b83f4914646d257990133f9423074...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2033c860-b467-42e3-ab2e-19ca44e65b0a</td>\n",
       "      <td>Training 2</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>completed</td>\n",
       "      <td>100.0</td>\n",
       "      <td>undefined</td>\n",
       "      <td>Please answer the following questions:</td>\n",
       "      <td>How many sets make up the largest intersection?</td>\n",
       "      <td>It is the empty intersection (no sets)</td>\n",
       "      <td>It is only of a single set</td>\n",
       "      <td>undefined</td>\n",
       "      <td>undefined</td>\n",
       "      <td>2024-07-31T03:52:51.112Z</td>\n",
       "      <td>2024-07-31T04:01:08.980Z</td>\n",
       "      <td>497868</td>\n",
       "      <td>ecdced17dc1fd86b8b83f4914646d257990133f9423074...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2033c860-b467-42e3-ab2e-19ca44e65b0a</td>\n",
       "      <td>Training 3</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>completed</td>\n",
       "      <td>100.0</td>\n",
       "      <td>undefined</td>\n",
       "      <td>Please answer the following questions:</td>\n",
       "      <td>What is the largest set?</td>\n",
       "      <td>Drama</td>\n",
       "      <td>Drama</td>\n",
       "      <td>undefined</td>\n",
       "      <td>undefined</td>\n",
       "      <td>2024-07-31T04:01:08.993Z</td>\n",
       "      <td>2024-07-31T04:01:40.418Z</td>\n",
       "      <td>31425</td>\n",
       "      <td>ecdced17dc1fd86b8b83f4914646d257990133f9423074...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2033c860-b467-42e3-ab2e-19ca44e65b0a</td>\n",
       "      <td>Training 3</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>completed</td>\n",
       "      <td>100.0</td>\n",
       "      <td>undefined</td>\n",
       "      <td>Please answer the following questions:</td>\n",
       "      <td>What is the largest intersection?</td>\n",
       "      <td>Drama</td>\n",
       "      <td>Drama</td>\n",
       "      <td>undefined</td>\n",
       "      <td>undefined</td>\n",
       "      <td>2024-07-31T04:01:08.993Z</td>\n",
       "      <td>2024-07-31T04:01:40.418Z</td>\n",
       "      <td>31425</td>\n",
       "      <td>ecdced17dc1fd86b8b83f4914646d257990133f9423074...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          participantId     trialId  trialOrder responseId  \\\n",
       "0  2033c860-b467-42e3-ab2e-19ca44e65b0a     Consent           1         id   \n",
       "1  2033c860-b467-42e3-ab2e-19ca44e65b0a     Consent           1    consent   \n",
       "2  2033c860-b467-42e3-ab2e-19ca44e65b0a  Training 1           3          1   \n",
       "3  2033c860-b467-42e3-ab2e-19ca44e65b0a  Training 1           3          2   \n",
       "4  2033c860-b467-42e3-ab2e-19ca44e65b0a  Training 1           3          3   \n",
       "5  2033c860-b467-42e3-ab2e-19ca44e65b0a  Training 2           4          1   \n",
       "6  2033c860-b467-42e3-ab2e-19ca44e65b0a  Training 2           4          2   \n",
       "7  2033c860-b467-42e3-ab2e-19ca44e65b0a  Training 2           4          3   \n",
       "8  2033c860-b467-42e3-ab2e-19ca44e65b0a  Training 3           5          1   \n",
       "9  2033c860-b467-42e3-ab2e-19ca44e65b0a  Training 3           5          2   \n",
       "\n",
       "      status  percentComplete description  \\\n",
       "0  completed            100.0   undefined   \n",
       "1  completed            100.0   undefined   \n",
       "2  completed            100.0   undefined   \n",
       "3  completed            100.0   undefined   \n",
       "4  completed            100.0   undefined   \n",
       "5  completed            100.0   undefined   \n",
       "6  completed            100.0   undefined   \n",
       "7  completed            100.0   undefined   \n",
       "8  completed            100.0   undefined   \n",
       "9  completed            100.0   undefined   \n",
       "\n",
       "                              instruction  \\\n",
       "0                               undefined   \n",
       "1                               undefined   \n",
       "2  Please answer the following questions:   \n",
       "3  Please answer the following questions:   \n",
       "4  Please answer the following questions:   \n",
       "5  Please answer the following questions:   \n",
       "6  Please answer the following questions:   \n",
       "7  Please answer the following questions:   \n",
       "8  Please answer the following questions:   \n",
       "9  Please answer the following questions:   \n",
       "\n",
       "                                      responsePrompt  \\\n",
       "0                                   Your Prolific ID   \n",
       "1  Do you consent to the study and wish to continue?   \n",
       "2                           What is the largest set?   \n",
       "3                  What is the largest intersection?   \n",
       "4    How many sets make up the largest intersection?   \n",
       "5                           What is the largest set?   \n",
       "6                  What is the largest intersection?   \n",
       "7    How many sets make up the largest intersection?   \n",
       "8                           What is the largest set?   \n",
       "9                  What is the largest intersection?   \n",
       "\n",
       "                                   answer  \\\n",
       "0                             Rahat Zaman   \n",
       "1                                     yes   \n",
       "2                                 Romance   \n",
       "3            Empty Intersection (no sets)   \n",
       "4  It is the empty intersection (no sets)   \n",
       "5                               Adventure   \n",
       "6            Empty Intersection (no sets)   \n",
       "7  It is the empty intersection (no sets)   \n",
       "8                                   Drama   \n",
       "9                                   Drama   \n",
       "\n",
       "                            correctAnswer responseMin responseMax  \\\n",
       "0                               undefined   undefined   undefined   \n",
       "1                               undefined   undefined   undefined   \n",
       "2                                  Action   undefined   undefined   \n",
       "3            Empty Intersection (no sets)   undefined   undefined   \n",
       "4  It is the empty intersection (no sets)   undefined   undefined   \n",
       "5                               Adventure   undefined   undefined   \n",
       "6                               Adventure   undefined   undefined   \n",
       "7              It is only of a single set   undefined   undefined   \n",
       "8                                   Drama   undefined   undefined   \n",
       "9                                   Drama   undefined   undefined   \n",
       "\n",
       "                  startTime                   endTime  duration  \\\n",
       "0  2024-07-31T03:26:03.202Z  2024-07-31T03:27:28.983Z     85781   \n",
       "1  2024-07-31T03:26:03.202Z  2024-07-31T03:27:28.983Z     85781   \n",
       "2  2024-07-31T03:48:25.846Z  2024-07-31T03:52:51.094Z    265248   \n",
       "3  2024-07-31T03:48:25.846Z  2024-07-31T03:52:51.094Z    265248   \n",
       "4  2024-07-31T03:48:25.846Z  2024-07-31T03:52:51.094Z    265248   \n",
       "5  2024-07-31T03:52:51.112Z  2024-07-31T04:01:08.980Z    497868   \n",
       "6  2024-07-31T03:52:51.112Z  2024-07-31T04:01:08.980Z    497868   \n",
       "7  2024-07-31T03:52:51.112Z  2024-07-31T04:01:08.980Z    497868   \n",
       "8  2024-07-31T04:01:08.993Z  2024-07-31T04:01:40.418Z     31425   \n",
       "9  2024-07-31T04:01:08.993Z  2024-07-31T04:01:40.418Z     31425   \n",
       "\n",
       "                                          configHash  \n",
       "0  ecdced17dc1fd86b8b83f4914646d257990133f9423074...  \n",
       "1  ecdced17dc1fd86b8b83f4914646d257990133f9423074...  \n",
       "2  ecdced17dc1fd86b8b83f4914646d257990133f9423074...  \n",
       "3  ecdced17dc1fd86b8b83f4914646d257990133f9423074...  \n",
       "4  ecdced17dc1fd86b8b83f4914646d257990133f9423074...  \n",
       "5  ecdced17dc1fd86b8b83f4914646d257990133f9423074...  \n",
       "6  ecdced17dc1fd86b8b83f4914646d257990133f9423074...  \n",
       "7  ecdced17dc1fd86b8b83f4914646d257990133f9423074...  \n",
       "8  ecdced17dc1fd86b8b83f4914646d257990133f9423074...  \n",
       "9  ecdced17dc1fd86b8b83f4914646d257990133f9423074...  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "\n",
    "# Load the CSV file\n",
    "file_path = './data/prepilot-pilot-raw.csv'\n",
    "df = pd.read_csv(file_path)\n",
    "\n",
    "# Define the correct answers for each specific trialId and responseId\n",
    "correct_answers_map = {\n",
    "    'Covid-Vis-Q1': {\n",
    "        'voq1': '6',\n",
    "        'voq2': 'Fatigue',\n",
    "        'voq3': 'Anosmia,Fatigue',\n",
    "        'voq4': '281',\n",
    "        'voq5': 'It is the intersection of 2-3 sets',\n",
    "        'voq6': 'Diverging a lot',\n",
    "        'voq7': 'Yes',\n",
    "        'voq8': 'Yes'\n",
    "    },\n",
    "    'Covid-Text-Q1': {\n",
    "        'voq1': '6',\n",
    "        'voq2': 'Fatigue',\n",
    "        'voq3': 'Anosmia,Fatigue',\n",
    "        'voq4': '281',\n",
    "        'voq5': 'It is the intersection of 2-3 sets',\n",
    "        'voq6': 'Diverging a lot',\n",
    "        'voq7': 'Yes',\n",
    "        'voq8': 'Yes'\n",
    "    },\n",
    "    'Covid-Both-Q1': {\n",
    "        'voq1': '6',\n",
    "        'voq2': 'Fatigue',\n",
    "        'voq3': 'Anosmia,Fatigue',\n",
    "        'voq4': '281',\n",
    "        'voq5': 'It is the intersection of 2-3 sets',\n",
    "        'voq6': 'Diverging a lot',\n",
    "        'voq7': 'Yes',\n",
    "        'voq8': 'Yes'\n",
    "    },\n",
    "    'Tennis-Vis-Q1': {\n",
    "        'voq1': '4',\n",
    "        'voq2': 'Australian',\n",
    "        'voq3': 'French Open',\n",
    "        'voq4': '23',\n",
    "        'voq5': 'It is only of a single set',\n",
    "        'voq6': 'Roughly equal',\n",
    "        'voq7': 'No',\n",
    "        'voq8': 'Yes'\n",
    "    },\n",
    "    'Tennis-Text-Q1': {\n",
    "        'voq1': '4',\n",
    "        'voq2': 'Australian',\n",
    "        'voq3': 'French Open',\n",
    "        'voq4': '23',\n",
    "        'voq5': 'It is only of a single set',\n",
    "        'voq6': 'Roughly equal',\n",
    "        'voq7': 'No',\n",
    "        'voq8': 'Yes'\n",
    "    },\n",
    "    'Tennis-Both-Q1': {\n",
    "        'voq1': '4',\n",
    "        'voq2': 'Australian',\n",
    "        'voq3': 'French Open',\n",
    "        'voq4': '23',\n",
    "        'voq5': 'It is only of a single set',\n",
    "        'voq6': 'Roughly equal',\n",
    "        'voq7': 'No',\n",
    "        'voq8': 'Yes'\n",
    "    },\n",
    "    'Organizations-Vis-Q1': {\n",
    "        'voq1': '8',\n",
    "        'voq2': 'UPU',\n",
    "        'voq3': 'Interpol,UN,UNESCO,UPU,WHO',\n",
    "        'voq4': '117',\n",
    "        'voq5': 'It is the intersection of many sets',\n",
    "        'voq6': 'Diverging a lot',\n",
    "        'voq7': 'Yes',\n",
    "        'voq8': 'No'\n",
    "    },\n",
    "    'Organizations-Text-Q1': {\n",
    "        'voq1': '8',\n",
    "        'voq2': 'UPU',\n",
    "        'voq3': 'Interpol,UN,UNESCO,UPU,WHO',\n",
    "        'voq4': '117',\n",
    "        'voq5': 'It is the intersection of many sets',\n",
    "        'voq6': 'Diverging a lot',\n",
    "        'voq7': 'Yes',\n",
    "        'voq8': 'No'\n",
    "    },\n",
    "    'Organizations-Both-Q1': {\n",
    "        'voq1': '8',\n",
    "        'voq2': 'UPU',\n",
    "        'voq3': 'Interpol,UN,UNESCO,UPU,WHO',\n",
    "        'voq4': '117',\n",
    "        'voq5': 'It is the intersection of many sets',\n",
    "        'voq6': 'Diverging a lot',\n",
    "        'voq7': 'Yes',\n",
    "        'voq8': 'No'\n",
    "    }\n",
    "}\n",
    "\n",
    "# Update the correctAnswer column based on the trialId and responseId\n",
    "for trial_id, responses in correct_answers_map.items():\n",
    "    for response_id, correct_answer in responses.items():\n",
    "        condition = (df['trialId'] == trial_id) & (df['responseId'] == response_id)\n",
    "        df.loc[condition, 'correctAnswer'] = correct_answer\n",
    "\n",
    "# Display the updated dataframe\n",
    "df.head(10)  # Displaying the first 20 rows for verification\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Add a field named isCorrectlyAnswered"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>participantId</th>\n",
       "      <th>trialId</th>\n",
       "      <th>trialOrder</th>\n",
       "      <th>responseId</th>\n",
       "      <th>status</th>\n",
       "      <th>percentComplete</th>\n",
       "      <th>description</th>\n",
       "      <th>instruction</th>\n",
       "      <th>responsePrompt</th>\n",
       "      <th>answer</th>\n",
       "      <th>correctAnswer</th>\n",
       "      <th>isCorrectlyAnswered</th>\n",
       "      <th>responseMin</th>\n",
       "      <th>responseMax</th>\n",
       "      <th>startTime</th>\n",
       "      <th>endTime</th>\n",
       "      <th>duration</th>\n",
       "      <th>configHash</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2033c860-b467-42e3-ab2e-19ca44e65b0a</td>\n",
       "      <td>Consent</td>\n",
       "      <td>1</td>\n",
       "      <td>id</td>\n",
       "      <td>completed</td>\n",
       "      <td>100.0</td>\n",
       "      <td>undefined</td>\n",
       "      <td>undefined</td>\n",
       "      <td>Your Prolific ID</td>\n",
       "      <td>Rahat Zaman</td>\n",
       "      <td>undefined</td>\n",
       "      <td>undefined</td>\n",
       "      <td>undefined</td>\n",
       "      <td>undefined</td>\n",
       "      <td>2024-07-31T03:26:03.202Z</td>\n",
       "      <td>2024-07-31T03:27:28.983Z</td>\n",
       "      <td>85781</td>\n",
       "      <td>ecdced17dc1fd86b8b83f4914646d257990133f9423074...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2033c860-b467-42e3-ab2e-19ca44e65b0a</td>\n",
       "      <td>Consent</td>\n",
       "      <td>1</td>\n",
       "      <td>consent</td>\n",
       "      <td>completed</td>\n",
       "      <td>100.0</td>\n",
       "      <td>undefined</td>\n",
       "      <td>undefined</td>\n",
       "      <td>Do you consent to the study and wish to continue?</td>\n",
       "      <td>yes</td>\n",
       "      <td>undefined</td>\n",
       "      <td>undefined</td>\n",
       "      <td>undefined</td>\n",
       "      <td>undefined</td>\n",
       "      <td>2024-07-31T03:26:03.202Z</td>\n",
       "      <td>2024-07-31T03:27:28.983Z</td>\n",
       "      <td>85781</td>\n",
       "      <td>ecdced17dc1fd86b8b83f4914646d257990133f9423074...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2033c860-b467-42e3-ab2e-19ca44e65b0a</td>\n",
       "      <td>Training 1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>completed</td>\n",
       "      <td>100.0</td>\n",
       "      <td>undefined</td>\n",
       "      <td>Please answer the following questions:</td>\n",
       "      <td>What is the largest set?</td>\n",
       "      <td>Romance</td>\n",
       "      <td>Action</td>\n",
       "      <td>False</td>\n",
       "      <td>undefined</td>\n",
       "      <td>undefined</td>\n",
       "      <td>2024-07-31T03:48:25.846Z</td>\n",
       "      <td>2024-07-31T03:52:51.094Z</td>\n",
       "      <td>265248</td>\n",
       "      <td>ecdced17dc1fd86b8b83f4914646d257990133f9423074...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2033c860-b467-42e3-ab2e-19ca44e65b0a</td>\n",
       "      <td>Training 1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>completed</td>\n",
       "      <td>100.0</td>\n",
       "      <td>undefined</td>\n",
       "      <td>Please answer the following questions:</td>\n",
       "      <td>What is the largest intersection?</td>\n",
       "      <td>Empty Intersection (no sets)</td>\n",
       "      <td>Empty Intersection (no sets)</td>\n",
       "      <td>True</td>\n",
       "      <td>undefined</td>\n",
       "      <td>undefined</td>\n",
       "      <td>2024-07-31T03:48:25.846Z</td>\n",
       "      <td>2024-07-31T03:52:51.094Z</td>\n",
       "      <td>265248</td>\n",
       "      <td>ecdced17dc1fd86b8b83f4914646d257990133f9423074...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2033c860-b467-42e3-ab2e-19ca44e65b0a</td>\n",
       "      <td>Training 1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>completed</td>\n",
       "      <td>100.0</td>\n",
       "      <td>undefined</td>\n",
       "      <td>Please answer the following questions:</td>\n",
       "      <td>How many sets make up the largest intersection?</td>\n",
       "      <td>It is the empty intersection (no sets)</td>\n",
       "      <td>It is the empty intersection (no sets)</td>\n",
       "      <td>True</td>\n",
       "      <td>undefined</td>\n",
       "      <td>undefined</td>\n",
       "      <td>2024-07-31T03:48:25.846Z</td>\n",
       "      <td>2024-07-31T03:52:51.094Z</td>\n",
       "      <td>265248</td>\n",
       "      <td>ecdced17dc1fd86b8b83f4914646d257990133f9423074...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2033c860-b467-42e3-ab2e-19ca44e65b0a</td>\n",
       "      <td>Training 2</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>completed</td>\n",
       "      <td>100.0</td>\n",
       "      <td>undefined</td>\n",
       "      <td>Please answer the following questions:</td>\n",
       "      <td>What is the largest set?</td>\n",
       "      <td>Adventure</td>\n",
       "      <td>Adventure</td>\n",
       "      <td>True</td>\n",
       "      <td>undefined</td>\n",
       "      <td>undefined</td>\n",
       "      <td>2024-07-31T03:52:51.112Z</td>\n",
       "      <td>2024-07-31T04:01:08.980Z</td>\n",
       "      <td>497868</td>\n",
       "      <td>ecdced17dc1fd86b8b83f4914646d257990133f9423074...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2033c860-b467-42e3-ab2e-19ca44e65b0a</td>\n",
       "      <td>Training 2</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>completed</td>\n",
       "      <td>100.0</td>\n",
       "      <td>undefined</td>\n",
       "      <td>Please answer the following questions:</td>\n",
       "      <td>What is the largest intersection?</td>\n",
       "      <td>Empty Intersection (no sets)</td>\n",
       "      <td>Adventure</td>\n",
       "      <td>False</td>\n",
       "      <td>undefined</td>\n",
       "      <td>undefined</td>\n",
       "      <td>2024-07-31T03:52:51.112Z</td>\n",
       "      <td>2024-07-31T04:01:08.980Z</td>\n",
       "      <td>497868</td>\n",
       "      <td>ecdced17dc1fd86b8b83f4914646d257990133f9423074...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2033c860-b467-42e3-ab2e-19ca44e65b0a</td>\n",
       "      <td>Training 2</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>completed</td>\n",
       "      <td>100.0</td>\n",
       "      <td>undefined</td>\n",
       "      <td>Please answer the following questions:</td>\n",
       "      <td>How many sets make up the largest intersection?</td>\n",
       "      <td>It is the empty intersection (no sets)</td>\n",
       "      <td>It is only of a single set</td>\n",
       "      <td>False</td>\n",
       "      <td>undefined</td>\n",
       "      <td>undefined</td>\n",
       "      <td>2024-07-31T03:52:51.112Z</td>\n",
       "      <td>2024-07-31T04:01:08.980Z</td>\n",
       "      <td>497868</td>\n",
       "      <td>ecdced17dc1fd86b8b83f4914646d257990133f9423074...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2033c860-b467-42e3-ab2e-19ca44e65b0a</td>\n",
       "      <td>Training 3</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>completed</td>\n",
       "      <td>100.0</td>\n",
       "      <td>undefined</td>\n",
       "      <td>Please answer the following questions:</td>\n",
       "      <td>What is the largest set?</td>\n",
       "      <td>Drama</td>\n",
       "      <td>Drama</td>\n",
       "      <td>True</td>\n",
       "      <td>undefined</td>\n",
       "      <td>undefined</td>\n",
       "      <td>2024-07-31T04:01:08.993Z</td>\n",
       "      <td>2024-07-31T04:01:40.418Z</td>\n",
       "      <td>31425</td>\n",
       "      <td>ecdced17dc1fd86b8b83f4914646d257990133f9423074...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2033c860-b467-42e3-ab2e-19ca44e65b0a</td>\n",
       "      <td>Training 3</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>completed</td>\n",
       "      <td>100.0</td>\n",
       "      <td>undefined</td>\n",
       "      <td>Please answer the following questions:</td>\n",
       "      <td>What is the largest intersection?</td>\n",
       "      <td>Drama</td>\n",
       "      <td>Drama</td>\n",
       "      <td>True</td>\n",
       "      <td>undefined</td>\n",
       "      <td>undefined</td>\n",
       "      <td>2024-07-31T04:01:08.993Z</td>\n",
       "      <td>2024-07-31T04:01:40.418Z</td>\n",
       "      <td>31425</td>\n",
       "      <td>ecdced17dc1fd86b8b83f4914646d257990133f9423074...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          participantId     trialId  trialOrder responseId  \\\n",
       "0  2033c860-b467-42e3-ab2e-19ca44e65b0a     Consent           1         id   \n",
       "1  2033c860-b467-42e3-ab2e-19ca44e65b0a     Consent           1    consent   \n",
       "2  2033c860-b467-42e3-ab2e-19ca44e65b0a  Training 1           3          1   \n",
       "3  2033c860-b467-42e3-ab2e-19ca44e65b0a  Training 1           3          2   \n",
       "4  2033c860-b467-42e3-ab2e-19ca44e65b0a  Training 1           3          3   \n",
       "5  2033c860-b467-42e3-ab2e-19ca44e65b0a  Training 2           4          1   \n",
       "6  2033c860-b467-42e3-ab2e-19ca44e65b0a  Training 2           4          2   \n",
       "7  2033c860-b467-42e3-ab2e-19ca44e65b0a  Training 2           4          3   \n",
       "8  2033c860-b467-42e3-ab2e-19ca44e65b0a  Training 3           5          1   \n",
       "9  2033c860-b467-42e3-ab2e-19ca44e65b0a  Training 3           5          2   \n",
       "\n",
       "      status  percentComplete description  \\\n",
       "0  completed            100.0   undefined   \n",
       "1  completed            100.0   undefined   \n",
       "2  completed            100.0   undefined   \n",
       "3  completed            100.0   undefined   \n",
       "4  completed            100.0   undefined   \n",
       "5  completed            100.0   undefined   \n",
       "6  completed            100.0   undefined   \n",
       "7  completed            100.0   undefined   \n",
       "8  completed            100.0   undefined   \n",
       "9  completed            100.0   undefined   \n",
       "\n",
       "                              instruction  \\\n",
       "0                               undefined   \n",
       "1                               undefined   \n",
       "2  Please answer the following questions:   \n",
       "3  Please answer the following questions:   \n",
       "4  Please answer the following questions:   \n",
       "5  Please answer the following questions:   \n",
       "6  Please answer the following questions:   \n",
       "7  Please answer the following questions:   \n",
       "8  Please answer the following questions:   \n",
       "9  Please answer the following questions:   \n",
       "\n",
       "                                      responsePrompt  \\\n",
       "0                                   Your Prolific ID   \n",
       "1  Do you consent to the study and wish to continue?   \n",
       "2                           What is the largest set?   \n",
       "3                  What is the largest intersection?   \n",
       "4    How many sets make up the largest intersection?   \n",
       "5                           What is the largest set?   \n",
       "6                  What is the largest intersection?   \n",
       "7    How many sets make up the largest intersection?   \n",
       "8                           What is the largest set?   \n",
       "9                  What is the largest intersection?   \n",
       "\n",
       "                                   answer  \\\n",
       "0                             Rahat Zaman   \n",
       "1                                     yes   \n",
       "2                                 Romance   \n",
       "3            Empty Intersection (no sets)   \n",
       "4  It is the empty intersection (no sets)   \n",
       "5                               Adventure   \n",
       "6            Empty Intersection (no sets)   \n",
       "7  It is the empty intersection (no sets)   \n",
       "8                                   Drama   \n",
       "9                                   Drama   \n",
       "\n",
       "                            correctAnswer isCorrectlyAnswered responseMin  \\\n",
       "0                               undefined           undefined   undefined   \n",
       "1                               undefined           undefined   undefined   \n",
       "2                                  Action               False   undefined   \n",
       "3            Empty Intersection (no sets)                True   undefined   \n",
       "4  It is the empty intersection (no sets)                True   undefined   \n",
       "5                               Adventure                True   undefined   \n",
       "6                               Adventure               False   undefined   \n",
       "7              It is only of a single set               False   undefined   \n",
       "8                                   Drama                True   undefined   \n",
       "9                                   Drama                True   undefined   \n",
       "\n",
       "  responseMax                 startTime                   endTime  duration  \\\n",
       "0   undefined  2024-07-31T03:26:03.202Z  2024-07-31T03:27:28.983Z     85781   \n",
       "1   undefined  2024-07-31T03:26:03.202Z  2024-07-31T03:27:28.983Z     85781   \n",
       "2   undefined  2024-07-31T03:48:25.846Z  2024-07-31T03:52:51.094Z    265248   \n",
       "3   undefined  2024-07-31T03:48:25.846Z  2024-07-31T03:52:51.094Z    265248   \n",
       "4   undefined  2024-07-31T03:48:25.846Z  2024-07-31T03:52:51.094Z    265248   \n",
       "5   undefined  2024-07-31T03:52:51.112Z  2024-07-31T04:01:08.980Z    497868   \n",
       "6   undefined  2024-07-31T03:52:51.112Z  2024-07-31T04:01:08.980Z    497868   \n",
       "7   undefined  2024-07-31T03:52:51.112Z  2024-07-31T04:01:08.980Z    497868   \n",
       "8   undefined  2024-07-31T04:01:08.993Z  2024-07-31T04:01:40.418Z     31425   \n",
       "9   undefined  2024-07-31T04:01:08.993Z  2024-07-31T04:01:40.418Z     31425   \n",
       "\n",
       "                                          configHash  \n",
       "0  ecdced17dc1fd86b8b83f4914646d257990133f9423074...  \n",
       "1  ecdced17dc1fd86b8b83f4914646d257990133f9423074...  \n",
       "2  ecdced17dc1fd86b8b83f4914646d257990133f9423074...  \n",
       "3  ecdced17dc1fd86b8b83f4914646d257990133f9423074...  \n",
       "4  ecdced17dc1fd86b8b83f4914646d257990133f9423074...  \n",
       "5  ecdced17dc1fd86b8b83f4914646d257990133f9423074...  \n",
       "6  ecdced17dc1fd86b8b83f4914646d257990133f9423074...  \n",
       "7  ecdced17dc1fd86b8b83f4914646d257990133f9423074...  \n",
       "8  ecdced17dc1fd86b8b83f4914646d257990133f9423074...  \n",
       "9  ecdced17dc1fd86b8b83f4914646d257990133f9423074...  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# List of factual trialIds\n",
    "factual_trial_ids = [\n",
    "    'Covid-Vis-Q1', 'Covid-Text-Q1', 'Covid-Both-Q1',\n",
    "    'Tennis-Vis-Q1', 'Tennis-Text-Q1', 'Tennis-Both-Q1',\n",
    "    'Organizations-Vis-Q1', 'Organizations-Text-Q1', 'Organizations-Both-Q1'\n",
    "]\n",
    "\n",
    "# List of factual responseIds (for simplicity, assuming they are the same across all these trialIds)\n",
    "factual_response_ids = ['voq1', 'voq2', 'voq3', 'voq4', 'voq5', 'voq6', 'voq7', 'voq8']\n",
    "\n",
    "training_trial_ids = ['Training 1', 'Training 2', 'Training 3']\n",
    "training_response_ids = ['1', '2', '3']\n",
    "\n",
    "\n",
    "\n",
    "# Function to determine if the answer is correct or should be marked as \"undefined\"\n",
    "def is_correctly_answered(row):\n",
    "    if (row['trialId'] in factual_trial_ids and row['responseId'] in factual_response_ids) or \\\n",
    "       (row['trialId'] in training_trial_ids and row['responseId'] in training_response_ids):\n",
    "        return row['answer'] == row['correctAnswer']\n",
    "    else:\n",
    "        return \"undefined\"\n",
    "\n",
    "# Add the isCorrectlyAnswered column\n",
    "df['isCorrectlyAnswered'] = df.apply(is_correctly_answered, axis=1)\n",
    "\n",
    "# Reorder columns to place isCorrectlyAnswered right after correctAnswer\n",
    "cols = df.columns.tolist()\n",
    "correct_answer_index = cols.index('correctAnswer')\n",
    "cols.insert(correct_answer_index + 1, cols.pop(cols.index('isCorrectlyAnswered')))\n",
    "df = df[cols]\n",
    "\n",
    "df.head(10)  # Displaying the first 50 rows for verification"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Filtering out the completed studies (removing the pending and rejected ones)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Filtered data saved to: ./data/prep-pilot.csv\n"
     ]
    }
   ],
   "source": [
    "# Filter rows where 'Status' is 'completed'\n",
    "filtered_df = df[df['status'] == 'completed']\n",
    "\n",
    "# Define the participantId to be removed\n",
    "participants_to_remove = ['e78b4a59-2483-4f3b-a4b1-e046e06e1e26', '66a13fdf279c038ba8aa5ae6']\n",
    "\n",
    "# Filter out the rows with the specified participantId\n",
    "filtered_df = filtered_df[~filtered_df['participantId'].isin(participants_to_remove)]\n",
    "\n",
    "# Add the dataset column based on the conditions\n",
    "def determine_dataset(trial_id):\n",
    "    if 'Covid' in trial_id:\n",
    "        return 'Covid Symptoms'\n",
    "    elif 'Tennis' in trial_id:\n",
    "        return 'Tennis Grand Slam Winners'\n",
    "    elif 'Organizations' in trial_id:\n",
    "        return 'International Organizations'\n",
    "    elif 'Training' in trial_id:\n",
    "        return 'Movie Genres'\n",
    "    else:\n",
    "        return 'Undefined'\n",
    "\n",
    "filtered_df['dataset'] = filtered_df['trialId'].apply(determine_dataset)\n",
    "\n",
    "# Delete the specified columns\n",
    "filtered_df = filtered_df.drop(columns=['instruction', 'configHash'])\n",
    "\n",
    "# Save the filtered data to a new CSV file\n",
    "new_file_path = './data/prep-pilot.csv'\n",
    "filtered_df.to_csv(new_file_path, index=False)\n",
    "\n",
    "print(f\"Filtered data saved to: {new_file_path}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "(Ignore now) Separating three conditions (Vis, Text, Both) in three different files "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Filtered data for condition 'Text' saved to: ./data/Text_data.csv\n",
      "Filtered data for condition 'Both' saved to: ./data/Both_data.csv\n",
      "Filtered data for condition 'Vis' saved to: ./data/Vis_data.csv\n",
      "Filtered data for condition 'Survey' saved to: ./data/Survey_data.csv\n"
     ]
    }
   ],
   "source": [
    "# Define the middle part of the conditions\n",
    "conditions = ['Text', 'Both', 'Vis', 'Survey']\n",
    "\n",
    "# Generate separate CSV files for each condition\n",
    "for condition in conditions:\n",
    "    # Filter the DataFrame based on the presence of the condition in 'trialId'\n",
    "    condition_df = filtered_df[filtered_df['trialId'].str.contains(condition)]\n",
    "    \n",
    "    # Save the filtered data to a new CSV file\n",
    "    new_file_path = f'./data/{condition}_data.csv'\n",
    "    condition_df.to_csv(new_file_path, index=False)\n",
    "    \n",
    "    print(f\"Filtered data for condition '{condition}' saved to: {new_file_path}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Counting post survey preferences and effectiveness"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(Counter({'Text and Visualization Combined': 6, 'Just Visualization': 4}),\n",
       " Counter({'Just Text Description': 6, 'Just Visualization': 4}),\n",
       " Counter({'Text and Visualization Combined': 7, 'Just Visualization': 3}))"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "\n",
    "# Load the CSV data\n",
    "data = pd.read_csv('./data/Survey_data.csv')\n",
    "\n",
    "# Filter relevant columns for analysis\n",
    "columns_of_interest = ['participantId', 'trialId', 'responseId', 'responsePrompt', 'answer']\n",
    "filtered_data = data[columns_of_interest]\n",
    "\n",
    "# Analyze responses to \"Which presentation modality did you find most effective?\"\n",
    "most_effective = filtered_data[filtered_data['responsePrompt'] == 'Which presentation modality did you find most effective?']['answer']\n",
    "\n",
    "# Analyze responses to \"Which condition did you find the least effective?\"\n",
    "least_effective = filtered_data[filtered_data['responsePrompt'] == 'Which condition did you find the least effective?']['answer']\n",
    "\n",
    "# Analyze responses to \"If you were to receive information in the future, which format would you prefer?\"\n",
    "preferred_format = filtered_data[filtered_data['responsePrompt'] == 'If you were to receive information in the future, which format would you prefer?']['answer']\n",
    "\n",
    "# Create counters for each question\n",
    "most_effective_counts = Counter(most_effective)\n",
    "least_effective_counts = Counter(least_effective)\n",
    "preferred_format_counts = Counter(preferred_format)\n",
    "\n",
    "most_effective_counts, least_effective_counts, preferred_format_counts\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plotting the result for pereferences and the effectiveness"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Most Effective: [4, 6, 0]\n",
      "Least Effective: [4, 0, 6]\n",
      "Preferred Format: [3, 7, 0]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/t8/_mskc89n2_g3fy90ggktqfxm0000gn/T/ipykernel_53130/2735830496.py:38: FutureWarning: \n",
      "\n",
      "The `ci` parameter is deprecated. Use `errorbar=None` for the same effect.\n",
      "\n",
      "  sns.barplot(data=df_melted[df_melted['Category'] == title], x='Condition', y='Count', ax=ax, palette='viridis', ci=None)\n",
      "/var/folders/t8/_mskc89n2_g3fy90ggktqfxm0000gn/T/ipykernel_53130/2735830496.py:38: FutureWarning: \n",
      "\n",
      "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
      "\n",
      "  sns.barplot(data=df_melted[df_melted['Category'] == title], x='Condition', y='Count', ax=ax, palette='viridis', ci=None)\n",
      "/var/folders/t8/_mskc89n2_g3fy90ggktqfxm0000gn/T/ipykernel_53130/2735830496.py:43: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.\n",
      "  ax.set_xticklabels(ax.get_xticklabels(), rotation=30, ha='right', fontsize=8)\n",
      "/var/folders/t8/_mskc89n2_g3fy90ggktqfxm0000gn/T/ipykernel_53130/2735830496.py:38: FutureWarning: \n",
      "\n",
      "The `ci` parameter is deprecated. Use `errorbar=None` for the same effect.\n",
      "\n",
      "  sns.barplot(data=df_melted[df_melted['Category'] == title], x='Condition', y='Count', ax=ax, palette='viridis', ci=None)\n",
      "/var/folders/t8/_mskc89n2_g3fy90ggktqfxm0000gn/T/ipykernel_53130/2735830496.py:38: FutureWarning: \n",
      "\n",
      "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
      "\n",
      "  sns.barplot(data=df_melted[df_melted['Category'] == title], x='Condition', y='Count', ax=ax, palette='viridis', ci=None)\n",
      "/var/folders/t8/_mskc89n2_g3fy90ggktqfxm0000gn/T/ipykernel_53130/2735830496.py:43: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.\n",
      "  ax.set_xticklabels(ax.get_xticklabels(), rotation=30, ha='right', fontsize=8)\n",
      "/var/folders/t8/_mskc89n2_g3fy90ggktqfxm0000gn/T/ipykernel_53130/2735830496.py:38: FutureWarning: \n",
      "\n",
      "The `ci` parameter is deprecated. Use `errorbar=None` for the same effect.\n",
      "\n",
      "  sns.barplot(data=df_melted[df_melted['Category'] == title], x='Condition', y='Count', ax=ax, palette='viridis', ci=None)\n",
      "/var/folders/t8/_mskc89n2_g3fy90ggktqfxm0000gn/T/ipykernel_53130/2735830496.py:38: FutureWarning: \n",
      "\n",
      "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
      "\n",
      "  sns.barplot(data=df_melted[df_melted['Category'] == title], x='Condition', y='Count', ax=ax, palette='viridis', ci=None)\n",
      "/var/folders/t8/_mskc89n2_g3fy90ggktqfxm0000gn/T/ipykernel_53130/2735830496.py:43: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.\n",
      "  ax.set_xticklabels(ax.get_xticklabels(), rotation=30, ha='right', fontsize=8)\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1200x400 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Example DataFrame\n",
    "\n",
    "conditions = ['Just Visualization', 'Text and Visualization Combined', 'Just Text Description']\n",
    "# Initialize lists for counts\n",
    "most_effective_list = [most_effective_counts.get(cond, 0) for cond in conditions]\n",
    "least_effective_list = [least_effective_counts.get(cond, 0) for cond in conditions]\n",
    "preferred_format_list = [preferred_format_counts.get(cond, 0) for cond in conditions]\n",
    "\n",
    "# Print results to verify\n",
    "print('Most Effective:', most_effective_list)\n",
    "print('Least Effective:', least_effective_list)\n",
    "print('Preferred Format:', preferred_format_list)\n",
    "\n",
    "data = {\n",
    "    'Condition': conditions,\n",
    "    'Most Effective': most_effective_list,\n",
    "    'Least Effective': least_effective_list,\n",
    "    'Preferred Format': preferred_format_list\n",
    "}\n",
    "df = pd.DataFrame(data)\n",
    "\n",
    "# Melt the DataFrame for easier plotting\n",
    "df_melted = df.melt(id_vars='Condition', var_name='Category', value_name='Count')\n",
    "\n",
    "# Seaborn settings\n",
    "# sns.set_style(\"whitegrid\")\n",
    "sns.set_style(\"whitegrid\", {'axes.facecolor': (0.98, 0.98, 0.88), 'grid.color': 'white'})\n",
    "\n",
    "# Create subplots\n",
    "fig, axs = plt.subplots(1, 3, figsize=(12, 4), sharey=True, facecolor='white')\n",
    "\n",
    "# Define titles\n",
    "titles = ['Most Effective', 'Least Effective', 'Preferred Format']\n",
    "\n",
    "# Plotting\n",
    "palette = sns.color_palette(\"husl\", len(conditions)) \n",
    "for ax, title in zip(axs, titles):\n",
    "    sns.barplot(data=df_melted[df_melted['Category'] == title], x='Condition', y='Count', ax=ax, palette='viridis', ci=None)\n",
    "    ax.set_title(title, fontsize=10)\n",
    "    ax.set_xlabel('')\n",
    "    # ax.set_ylabel('# of participants', fontsize=8)\n",
    "    ax.set_ylabel('')\n",
    "    ax.set_xticklabels(ax.get_xticklabels(), rotation=30, ha='right', fontsize=8)\n",
    "    ax.set_facecolor((0.98, 0.98, 0.88))\n",
    "    ax.spines['top'].set_visible(False)\n",
    "    ax.spines['right'].set_visible(False)\n",
    "    ax.spines['left'].set_visible(False)\n",
    "    ax.spines['bottom'].set_visible(False)\n",
    "\n",
    "\n",
    "# Set common y-label\n",
    "fig.text(0.01, 0.5, '# of participants', va='center', rotation='vertical', fontsize=9)\n",
    "\n",
    "# # Remove gridlines\n",
    "# for ax in axs:\n",
    "#     ax.grid(False)\n",
    "\n",
    "fig.savefig('./figures/effectiveness-analysis.pdf', dpi=500, bbox_inches='tight')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>participantId</th>\n",
       "      <th>trialId</th>\n",
       "      <th>responseId</th>\n",
       "      <th>responsePrompt</th>\n",
       "      <th>answer</th>\n",
       "      <th>correctAnswer</th>\n",
       "      <th>dataset</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2033c860-b467-42e3-ab2e-19ca44e65b0a</td>\n",
       "      <td>Consent</td>\n",
       "      <td>id</td>\n",
       "      <td>Your Prolific ID</td>\n",
       "      <td>Rahat Zaman</td>\n",
       "      <td>undefined</td>\n",
       "      <td>Undefined</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2033c860-b467-42e3-ab2e-19ca44e65b0a</td>\n",
       "      <td>Consent</td>\n",
       "      <td>consent</td>\n",
       "      <td>Do you consent to the study and wish to continue?</td>\n",
       "      <td>yes</td>\n",
       "      <td>undefined</td>\n",
       "      <td>Undefined</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2033c860-b467-42e3-ab2e-19ca44e65b0a</td>\n",
       "      <td>Training 1</td>\n",
       "      <td>1</td>\n",
       "      <td>What is the largest set?</td>\n",
       "      <td>Romance</td>\n",
       "      <td>Action</td>\n",
       "      <td>Movie Genres</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2033c860-b467-42e3-ab2e-19ca44e65b0a</td>\n",
       "      <td>Training 1</td>\n",
       "      <td>2</td>\n",
       "      <td>What is the largest intersection?</td>\n",
       "      <td>Empty Intersection (no sets)</td>\n",
       "      <td>Empty Intersection (no sets)</td>\n",
       "      <td>Movie Genres</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2033c860-b467-42e3-ab2e-19ca44e65b0a</td>\n",
       "      <td>Training 1</td>\n",
       "      <td>3</td>\n",
       "      <td>How many sets make up the largest intersection?</td>\n",
       "      <td>It is the empty intersection (no sets)</td>\n",
       "      <td>It is the empty intersection (no sets)</td>\n",
       "      <td>Movie Genres</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          participantId     trialId responseId  \\\n",
       "0  2033c860-b467-42e3-ab2e-19ca44e65b0a     Consent         id   \n",
       "1  2033c860-b467-42e3-ab2e-19ca44e65b0a     Consent    consent   \n",
       "2  2033c860-b467-42e3-ab2e-19ca44e65b0a  Training 1          1   \n",
       "3  2033c860-b467-42e3-ab2e-19ca44e65b0a  Training 1          2   \n",
       "4  2033c860-b467-42e3-ab2e-19ca44e65b0a  Training 1          3   \n",
       "\n",
       "                                      responsePrompt  \\\n",
       "0                                   Your Prolific ID   \n",
       "1  Do you consent to the study and wish to continue?   \n",
       "2                           What is the largest set?   \n",
       "3                  What is the largest intersection?   \n",
       "4    How many sets make up the largest intersection?   \n",
       "\n",
       "                                   answer  \\\n",
       "0                             Rahat Zaman   \n",
       "1                                     yes   \n",
       "2                                 Romance   \n",
       "3            Empty Intersection (no sets)   \n",
       "4  It is the empty intersection (no sets)   \n",
       "\n",
       "                            correctAnswer       dataset  \n",
       "0                               undefined     Undefined  \n",
       "1                               undefined     Undefined  \n",
       "2                                  Action  Movie Genres  \n",
       "3            Empty Intersection (no sets)  Movie Genres  \n",
       "4  It is the empty intersection (no sets)  Movie Genres  "
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load the main CSV file\n",
    "file_path = './data/prep-pilot.csv'\n",
    "df = pd.read_csv(file_path)\n",
    "\n",
    "# Define the columns to display\n",
    "columns_of_interest = ['participantId','trialId', 'responseId', 'responsePrompt', 'answer', 'correctAnswer', 'dataset']\n",
    "ans_df = df[columns_of_interest]\n",
    "\n",
    "# Display the filtered dataframe\n",
    "ans_df.head()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Percentage of Answering Correcty (based on Text, Both and Vis for each dataset (covid-text-Q1, covid-vis-Q1, etc. and training dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Only the below trialIds have factual questions:\n",
      "trialId\n",
      "Covid-Text-Q1            40\n",
      "Organizations-Vis-Q1     40\n",
      "Organizations-Both-Q1    32\n",
      "Tennis-Vis-Q1            32\n",
      "Covid-Both-Q1            32\n",
      "Tennis-Text-Q1           32\n",
      "Training 1               30\n",
      "Training 2               30\n",
      "Training 3               30\n",
      "Tennis-Both-Q1           16\n",
      "Organizations-Text-Q1     8\n",
      "Covid-Vis-Q1              8\n",
      "Name: count, dtype: int64\n",
      "Percentage of correctly answered trials for each trialId of having factual questions:\n",
      "trialId\n",
      "Covid-Both-Q1             62.500000\n",
      "Covid-Text-Q1             87.500000\n",
      "Covid-Vis-Q1             100.000000\n",
      "Organizations-Both-Q1     81.250000\n",
      "Organizations-Text-Q1     37.500000\n",
      "Organizations-Vis-Q1      72.500000\n",
      "Tennis-Both-Q1            93.750000\n",
      "Tennis-Text-Q1            71.875000\n",
      "Tennis-Vis-Q1             53.125000\n",
      "Training 1                50.000000\n",
      "Training 2                70.000000\n",
      "Training 3                63.333333\n",
      "Name: isCorrectlyAnswered, dtype: float64\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/t8/_mskc89n2_g3fy90ggktqfxm0000gn/T/ipykernel_40777/1414715649.py:21: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  dataset_df['isCorrectlyAnswered'] = dataset_df['isCorrectlyAnswered'].apply(lambda x: True if x == 'True' else False if x == 'False' else x)\n"
     ]
    }
   ],
   "source": [
    "# Load the data from prep-pilot.csv\n",
    "file_path = './data/prep-pilot.csv'\n",
    "df = pd.read_csv(file_path)\n",
    "\n",
    "# Define the trialIds of interest\n",
    "trial_ids_of_interest = [\n",
    "    'Training 1', 'Training 2', 'Training 3',\n",
    "    'Covid-Vis-Q1', 'Covid-Text-Q1', 'Covid-Both-Q1',\n",
    "    'Tennis-Vis-Q1', 'Tennis-Text-Q1', 'Tennis-Both-Q1',\n",
    "    'Organizations-Vis-Q1', 'Organizations-Text-Q1', 'Organizations-Both-Q1'\n",
    "]\n",
    "\n",
    "# Filter the dataframe to include only rows with the specified trialIds\n",
    "dataset_df = df[df['trialId'].isin(trial_ids_of_interest)]\n",
    "\n",
    "print(\"Only the below trialIds have factual questions:\")\n",
    "print(dataset_df['trialId'].value_counts())\n",
    "dataset_df.head(10)\n",
    "\n",
    "# Convert 'isCorrectlyAnswered' from string to boolean\n",
    "dataset_df['isCorrectlyAnswered'] = dataset_df['isCorrectlyAnswered'].apply(lambda x: True if x == 'True' else False if x == 'False' else x)\n",
    "\n",
    "# Ensure isCorrectlyAnswered is boolean and filter out non-boolean values\n",
    "filterCorrectAnswer_df = dataset_df[dataset_df['isCorrectlyAnswered'].isin([True, False])]\n",
    "filterCorrectAnswer_df.head(10)\n",
    "\n",
    "# print(filterCorrectAnswer_df['isCorrectlyAnswered'].value_counts())\n",
    "\n",
    "# Calculate the percentage of correctly answered trials for each trialId\n",
    "percentage_correct_by_trialid = filterCorrectAnswer_df.groupby('trialId')['isCorrectlyAnswered'].mean() * 100\n",
    "\n",
    "# Display the results\n",
    "print(\"Percentage of correctly answered trials for each trialId of having factual questions:\")\n",
    "print(percentage_correct_by_trialid)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Percentage of Answering Correcty (based on each Visual Contents - Vis, Text or Both)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Percentage of correctly answered cumulatively for each Visual Content Type:\n",
      "VisualContentType\n",
      "BOTH         76.250000\n",
      "TEXT         76.250000\n",
      "TRAININGS    61.111111\n",
      "VIS          67.500000\n",
      "Name: isCorrectlyAnswered, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# Create a new column to identify the visual content type (VIS, BOTH, Text)\n",
    "def get_visual_content_type(trialId):\n",
    "    if 'Vis' in trialId:\n",
    "        return 'VIS'\n",
    "    elif 'Both' in trialId:\n",
    "        return 'BOTH'\n",
    "    elif 'Text' in trialId:\n",
    "        return 'TEXT'\n",
    "    else:\n",
    "        return 'TRAININGS'\n",
    "    \n",
    "\n",
    "filterCorrectAnswer_df['VisualContentType'] = filterCorrectAnswer_df['trialId'].apply(get_visual_content_type)\n",
    "\n",
    "# Calculate the percentage of correctly answered trials for each Visual Content Type\n",
    "percentage_correct_by_visual_content = filterCorrectAnswer_df.groupby('VisualContentType')['isCorrectlyAnswered'].mean() * 100\n",
    "\n",
    "# Display the results\n",
    "print(\"Percentage of correctly answered cumulatively for each Visual Content Type:\")\n",
    "print(percentage_correct_by_visual_content)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Percentage of Answering Correcty (based on datasets)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Percentage of correctly answered answer cumulatively for each dataset (of having factual questions only):\n",
      "dataset\n",
      "Covid Symptoms                 78.750000\n",
      "International Organizations    72.500000\n",
      "Movie Genres                   61.111111\n",
      "Tennis Grand Slam Winners      68.750000\n",
      "Name: isCorrectlyAnswered, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "\n",
    "# Calculate the percentage of correctly answered trials for each dataset\n",
    "percentage_correct_by_dataset = filterCorrectAnswer_df.groupby('dataset')['isCorrectlyAnswered'].mean() * 100\n",
    "\n",
    "# Display the result\n",
    "print(\"Percentage of correctly answered answer cumulatively for each dataset (of having factual questions only):\")\n",
    "print(percentage_correct_by_dataset)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Printing the answers of \"What are the insights and takeaways about the data\"?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Answers for all Q3 trialIds:\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>trialId</th>\n",
       "      <th>answer</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Covid-Text-Q3</td>\n",
       "      <td>I am not sure if this description is complete, meaning you cannot create the complete visualization just from this description. Also, it is very hard to tell the size of empty intersection from just the set sizes and top 5 intersection sizes.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>Tennis-Both-Q3</td>\n",
       "      <td>Well, it took like 5% of time taken than the previous task with only text description. I didn't read the description of this visualization at all.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>Organizations-Vis-Q3</td>\n",
       "      <td>It is same as the previous task. I was confidently answering the questions, and the task was very easy.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>Covid-Text-Q3</td>\n",
       "      <td>It seems easy to follow along with the numbers and titles; putting the description in a separate section with the title helps a lot.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>Organizations-Both-Q3</td>\n",
       "      <td>To answer this, I mostly used the charts on the left. I barely look at the description on the right.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>Tennis-Vis-Q3</td>\n",
       "      <td>This has relatively fewer data elements, which makes it easy to find the answers.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134</th>\n",
       "      <td>Covid-Both-Q3</td>\n",
       "      <td>set sizes are different</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>Organizations-Text-Q3</td>\n",
       "      <td>plot is sorted in decending order</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>158</th>\n",
       "      <td>Tennis-Vis-Q3</td>\n",
       "      <td>chart shows outcome</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>190</th>\n",
       "      <td>Covid-Both-Q3</td>\n",
       "      <td>Fatigue was the most common Covid symptom.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>202</th>\n",
       "      <td>Organizations-Vis-Q3</td>\n",
       "      <td>The majority of countries are members of multiple organisations</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>214</th>\n",
       "      <td>Tennis-Text-Q3</td>\n",
       "      <td>Very few people, people have won all four of the grand slams. Most individuals have won the Australia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>246</th>\n",
       "      <td>Covid-Both-Q3</td>\n",
       "      <td>Good understanding</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>258</th>\n",
       "      <td>Tennis-Text-Q3</td>\n",
       "      <td>Tenis</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>270</th>\n",
       "      <td>Organizations-Vis-Q3</td>\n",
       "      <td>Military power</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>302</th>\n",
       "      <td>Tennis-Both-Q3</td>\n",
       "      <td>It's difficult to win all four!</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>314</th>\n",
       "      <td>Organizations-Vis-Q3</td>\n",
       "      <td>Found it very difficult to understand</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>326</th>\n",
       "      <td>Covid-Text-Q3</td>\n",
       "      <td>Easier to understand the info this time.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>358</th>\n",
       "      <td>Tennis-Vis-Q3</td>\n",
       "      <td>Very confusing to navigate, a lot of information to process at once and looking back and forth</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>370</th>\n",
       "      <td>Organizations-Both-Q3</td>\n",
       "      <td>A lot of data but explanations and data clarifies how to properly read the dataset\\n</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>382</th>\n",
       "      <td>Covid-Text-Q3</td>\n",
       "      <td>Presented with context of symptoms rather than movies the reading of the dataset was more comprehensible and easier to read</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>414</th>\n",
       "      <td>Organizations-Both-Q3</td>\n",
       "      <td>It was very clear, although maybe color coding would help me identify the largest sets quicker?  Like the largest set is the deepest red, smallest is lightest blue?</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>426</th>\n",
       "      <td>Tennis-Text-Q3</td>\n",
       "      <td>The information was difficult to locate immediately, and I mostly used the set properties and intersection properties. it would be helpful to have lists bulletpointed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>438</th>\n",
       "      <td>Covid-Vis-Q3</td>\n",
       "      <td>to answer the question ' how many sets' a number wouldve been helpful.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>470</th>\n",
       "      <td>Tennis-Vis-Q3</td>\n",
       "      <td>The sets are divergent and heavily intersected</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>482</th>\n",
       "      <td>Covid-Text-Q3</td>\n",
       "      <td>The sets are highly divergent, with a lot of intersections</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>494</th>\n",
       "      <td>Organizations-Both-Q3</td>\n",
       "      <td>The set sizes are highly divergent, plus there is a huge intersection.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>526</th>\n",
       "      <td>Tennis-Text-Q3</td>\n",
       "      <td>The dataset has 4 sets where the set sizes are roughly equal. The largest sets are clearly mentioned in the Set Properties.  In the Intersection Properties, I was a bit confused about the largest intersections. It is mentioned that there were 15 non-empty intersections, which should have been among the list of the largest intersections present. \\nIt would have been more insightful if the questionnaires contained some images along with the descriptions. The Help page played a significant role in understanding the concept of the UpSet plots.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>538</th>\n",
       "      <td>Organizations-Vis-Q3</td>\n",
       "      <td>Liked the description of the sets with the help of an image. It was easier to understand. The figure was made in such a way that it required conceptual knowledge to answer the questions.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>550</th>\n",
       "      <td>Covid-Both-Q3</td>\n",
       "      <td>Although the descriptions were present, it was easy to answer the question by looking at only the figure. I like how the PI took time and thinking to introduce different varieties in the figures.</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   trialId  \\\n",
       "22           Covid-Text-Q3   \n",
       "34          Tennis-Both-Q3   \n",
       "46    Organizations-Vis-Q3   \n",
       "78           Covid-Text-Q3   \n",
       "90   Organizations-Both-Q3   \n",
       "102          Tennis-Vis-Q3   \n",
       "134          Covid-Both-Q3   \n",
       "146  Organizations-Text-Q3   \n",
       "158          Tennis-Vis-Q3   \n",
       "190          Covid-Both-Q3   \n",
       "202   Organizations-Vis-Q3   \n",
       "214         Tennis-Text-Q3   \n",
       "246          Covid-Both-Q3   \n",
       "258         Tennis-Text-Q3   \n",
       "270   Organizations-Vis-Q3   \n",
       "302         Tennis-Both-Q3   \n",
       "314   Organizations-Vis-Q3   \n",
       "326          Covid-Text-Q3   \n",
       "358          Tennis-Vis-Q3   \n",
       "370  Organizations-Both-Q3   \n",
       "382          Covid-Text-Q3   \n",
       "414  Organizations-Both-Q3   \n",
       "426         Tennis-Text-Q3   \n",
       "438           Covid-Vis-Q3   \n",
       "470          Tennis-Vis-Q3   \n",
       "482          Covid-Text-Q3   \n",
       "494  Organizations-Both-Q3   \n",
       "526         Tennis-Text-Q3   \n",
       "538   Organizations-Vis-Q3   \n",
       "550          Covid-Both-Q3   \n",
       "\n",
       "                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                answer  \n",
       "22                                                                                                                                                                                                                                                                                                                  I am not sure if this description is complete, meaning you cannot create the complete visualization just from this description. Also, it is very hard to tell the size of empty intersection from just the set sizes and top 5 intersection sizes.  \n",
       "34                                                                                                                                                                                                                                                                                                                                                                                                                  Well, it took like 5% of time taken than the previous task with only text description. I didn't read the description of this visualization at all.  \n",
       "46                                                                                                                                                                                                                                                                                                                                                                                                                                                             It is same as the previous task. I was confidently answering the questions, and the task was very easy.  \n",
       "78                                                                                                                                                                                                                                                                                                                                                                                                                                It seems easy to follow along with the numbers and titles; putting the description in a separate section with the title helps a lot.  \n",
       "90                                                                                                                                                                                                                                                                                                                                                                                                                                                                To answer this, I mostly used the charts on the left. I barely look at the description on the right.  \n",
       "102                                                                                                                                                                                                                                                                                                                                                                                                                                                                                  This has relatively fewer data elements, which makes it easy to find the answers.  \n",
       "134                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            set sizes are different  \n",
       "146                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                  plot is sorted in decending order  \n",
       "158                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                chart shows outcome  \n",
       "190                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                         Fatigue was the most common Covid symptom.  \n",
       "202                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                   The majority of countries are members of multiple organisations   \n",
       "214                                                                                                                                                                                                                                                                                                                                                                                                                                                              Very few people, people have won all four of the grand slams. Most individuals have won the Australia  \n",
       "246                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                Good understanding   \n",
       "258                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                             Tenis   \n",
       "270                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    Military power   \n",
       "302                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    It's difficult to win all four!  \n",
       "314                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              Found it very difficult to understand  \n",
       "326                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                           Easier to understand the info this time.  \n",
       "358                                                                                                                                                                                                                                                                                                                                                                                                                                                                     Very confusing to navigate, a lot of information to process at once and looking back and forth  \n",
       "370                                                                                                                                                                                                                                                                                                                                                                                                                                                                               A lot of data but explanations and data clarifies how to properly read the dataset\\n  \n",
       "382                                                                                                                                                                                                                                                                                                                                                                                                                                        Presented with context of symptoms rather than movies the reading of the dataset was more comprehensible and easier to read  \n",
       "414                                                                                                                                                                                                                                                                                                                                                                                               It was very clear, although maybe color coding would help me identify the largest sets quicker?  Like the largest set is the deepest red, smallest is lightest blue?  \n",
       "426                                                                                                                                                                                                                                                                                                                                                                                             The information was difficult to locate immediately, and I mostly used the set properties and intersection properties. it would be helpful to have lists bulletpointed  \n",
       "438                                                                                                                                                                                                                                                                                                                                                                                                                                                                                             to answer the question ' how many sets' a number wouldve been helpful.  \n",
       "470                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                     The sets are divergent and heavily intersected  \n",
       "482                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                         The sets are highly divergent, with a lot of intersections  \n",
       "494                                                                                                                                                                                                                                                                                                                                                                                                                                                                                             The set sizes are highly divergent, plus there is a huge intersection.  \n",
       "526  The dataset has 4 sets where the set sizes are roughly equal. The largest sets are clearly mentioned in the Set Properties.  In the Intersection Properties, I was a bit confused about the largest intersections. It is mentioned that there were 15 non-empty intersections, which should have been among the list of the largest intersections present. \\nIt would have been more insightful if the questionnaires contained some images along with the descriptions. The Help page played a significant role in understanding the concept of the UpSet plots.  \n",
       "538                                                                                                                                                                                                                                                                                                                                                                         Liked the description of the sets with the help of an image. It was easier to understand. The figure was made in such a way that it required conceptual knowledge to answer the questions.  \n",
       "550                                                                                                                                                                                                                                                                                                                                                                Although the descriptions were present, it was easy to answer the question by looking at only the figure. I like how the PI took time and thinking to introduce different varieties in the figures.  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "\n",
    "# Load the data from prep-pilot.csv\n",
    "file_path = './data/prep-pilot.csv'\n",
    "df = pd.read_csv(file_path)\n",
    "\n",
    "# Define the Q3 trialIds of interest\n",
    "q3_trial_ids = [\n",
    "    'Covid-Vis-Q3', 'Covid-Text-Q3', 'Covid-Both-Q3',\n",
    "    'Tennis-Vis-Q3', 'Tennis-Text-Q3', 'Tennis-Both-Q3',\n",
    "    'Organizations-Vis-Q3', 'Organizations-Text-Q3', 'Organizations-Both-Q3'\n",
    "]\n",
    "\n",
    "# Filter the dataframe to include only rows with the specified Q3 trialIds\n",
    "q3_df = df[df['trialId'].isin(q3_trial_ids)]\n",
    "\n",
    "# Print the answers for all the Q3 trialIds\n",
    "pd.set_option('display.max_colwidth', None)\n",
    "(q3_df[['trialId','answer']])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Completion time VS overall correctness"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Only the below trialIds have factual questions:\n",
      "trialId\n",
      "Covid-Text-Q1            40\n",
      "Organizations-Vis-Q1     40\n",
      "Organizations-Both-Q1    32\n",
      "Tennis-Vis-Q1            32\n",
      "Covid-Both-Q1            32\n",
      "Tennis-Text-Q1           32\n",
      "Tennis-Both-Q1           16\n",
      "Organizations-Text-Q1     8\n",
      "Covid-Vis-Q1              8\n",
      "Name: count, dtype: int64\n",
      "participantId\n",
      "2033c860-b467-42e3-ab2e-19ca44e65b0a    15.410150\n",
      "38dad450-a303-4a67-9e79-b5387d898538     5.202067\n",
      "60c14a3e834adb62102cffa0                 1.086783\n",
      "6566f3b23b2b8bb220f9092b                 7.155583\n",
      "668af65cd2ce80fbd115b017                 1.454700\n",
      "668c501661675408e9b25ece                 9.669083\n",
      "66b1d0cd7cc94db0de64297f                 5.626167\n",
      "6e8e5a42-afc7-4c85-90ea-abe673e557e0     5.696050\n",
      "b109c320-1992-4c8a-aac7-51c597f1189c     4.117383\n",
      "d49479b0-8604-48d3-a3f8-ddc3d6159393     7.839967\n",
      "Name: duration, dtype: float64\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/t8/_mskc89n2_g3fy90ggktqfxm0000gn/T/ipykernel_65851/3004888656.py:20: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  overall_df['isCorrectlyAnswered'] = overall_df['isCorrectlyAnswered'].apply(lambda x: True if x == 'True' else False if x == 'False' else x)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Load the data from prep-pilot.csv\n",
    "file_path = './data/prep-pilot.csv'\n",
    "df = pd.read_csv(file_path)\n",
    "\n",
    "# Define the trialIds of interest\n",
    "trial_ids_of_interest = [\n",
    "    'Covid-Vis-Q1', 'Covid-Text-Q1', 'Covid-Both-Q1',\n",
    "    'Tennis-Vis-Q1', 'Tennis-Text-Q1', 'Tennis-Both-Q1',\n",
    "    'Organizations-Vis-Q1', 'Organizations-Text-Q1', 'Organizations-Both-Q1'\n",
    "]\n",
    "\n",
    "# Filter the dataframe to include only rows with the specified trialIds\n",
    "overall_df = df[df['trialId'].isin(trial_ids_of_interest)]\n",
    "\n",
    "print(\"Only the below trialIds have factual questions:\")\n",
    "print(overall_df['trialId'].value_counts())\n",
    "overall_df.head(10)\n",
    "\n",
    "# Convert 'isCorrectlyAnswered' from string to boolean\n",
    "overall_df['isCorrectlyAnswered'] = overall_df['isCorrectlyAnswered'].apply(lambda x: True if x == 'True' else False if x == 'False' else x)\n",
    "\n",
    "# Ensure isCorrectlyAnswered is boolean and filter out non-boolean values\n",
    "overallCorrectAnswer_df = overall_df[overall_df['isCorrectlyAnswered'].isin([True, False])]\n",
    "overallCorrectAnswer_df.head(10)\n",
    "\n",
    "# Calculate correctness per participant per condition\n",
    "overallCorrectAnswer_df['correctAnswerNumeric'] = overallCorrectAnswer_df['isCorrectlyAnswered'].astype(int)  # Convert correctness to numeric (1 for True, 0 for False)\n",
    "correctness_by_participant = overallCorrectAnswer_df.groupby(['participantId', 'trialId'])['correctAnswerNumeric'].sum()\n",
    "\n",
    "# Pivot the table to get a better structure for summing up correctness\n",
    "correctness_pivot = correctness_by_participant.unstack().fillna(0)\n",
    "\n",
    "# # Calculate the overall correctness per participant (sum the scores)\n",
    "# correctness_pivot['overallCorrectness'] = correctness_pivot.sum(axis=1)\n",
    "# Calculate the overall correctness percentage per participant (sum the scores and calculate percentage)\n",
    "correctness_pivot['overallCorrectness'] = (correctness_pivot.sum(axis=1) / 24) * 100\n",
    "\n",
    "# Select the first occurrence of each trialId per participant for duration calculation\n",
    "first_occurrence_df = overallCorrectAnswer_df.groupby(['participantId', 'trialId']).first().reset_index()\n",
    "\n",
    "\n",
    "# # Calculate the total completion time per participant\n",
    "# total_time_by_participant = overallCorrectAnswer_df.groupby('participantId')['duration'].sum()/1000  # Convert milliseconds to seconds\n",
    "\n",
    "# Calculate the total completion time per participant in minutes by summing the first durations\n",
    "total_time_by_participant = first_occurrence_df.groupby('participantId')['duration'].sum() / 60000  # Convert milliseconds to minutes\n",
    "print(total_time_by_participant)\n",
    "\n",
    "# Combine correctness and duration into a single DataFrame\n",
    "summary_df = pd.DataFrame({\n",
    "    'totalCompletionTime': total_time_by_participant,\n",
    "    'overallCorrectness': correctness_pivot['overallCorrectness']\n",
    "})\n",
    "\n",
    "# Plotting the graph\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.scatter(summary_df['totalCompletionTime'], summary_df['overallCorrectness'], color='blue', alpha=0.6)\n",
    "plt.title('Total Completion Time vs Overall Correctness per Participant')\n",
    "# plt.xlabel('Total Completion Time (seconds)')\n",
    "plt.xlabel('Total Completion Time (minutes)')\n",
    "# plt.ylabel('Overall Correctness (out of 24)')\n",
    "plt.ylabel('Overall Correctness (%)')\n",
    "plt.grid(True)\n",
    "plt.show()\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "(Ignore) Heatmap generation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Prepare data for heatmap\n",
    "heatmap_data = df.set_index('Condition').T\n",
    "\n",
    "# Plot heatmap\n",
    "plt.figure(figsize=(8, 5))\n",
    "sns.heatmap(heatmap_data, annot=True, fmt=\"d\", cmap='YlGnBu', cbar_kws={'label': 'Count'})\n",
    "\n",
    "# Set titles and labels\n",
    "plt.title('Effectiveness and Preference Counts by Condition', fontsize=12)\n",
    "plt.xlabel('Condition', fontsize=10)\n",
    "plt.ylabel('Category', fontsize=10)\n",
    "\n",
    "plt.xticks(rotation=30, ha='right', fontsize=9)\n",
    "plt.yticks(rotation=0, fontsize=9)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.6"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}