demographics.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "hollywood-messenger",
   "metadata": {},
   "source": [
    "# Data Preparation\n",
    "\n",
    "## Load data from sqlite3 in json"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "dominant-requirement",
   "metadata": {},
   "outputs": [],
   "source": [
    "from utils.db.get_data_db import get_data_json, get_d_options\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "from matplotlib import pyplot as plt\n",
    "from matplotlib.gridspec import GridSpec\n",
    "sns.set()\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "db_file_path = 'data/study_02.db'\n",
    "data = get_data_json(db_file_path)\n",
    "\n",
    "# dict: {<d_id>, List of available options per demo question}\n",
    "d_options = get_d_options(db_file_path)\n",
    "## palette\n",
    "p = {'AG':'blue','IG':'magenta','SG':'#32CD32'}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "unsigned-essay",
   "metadata": {},
   "source": [
    "## Data cleaning "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "based-genetics",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_processed = {}\n",
    "for p in data:\n",
    "    if data[p]['status'] == \"end\" or data[  p]['status'] == \"end_thanks\":\n",
    "        data_processed[p] = data[p]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "canadian-state",
   "metadata": {},
   "source": [
    "## Get Answers to Demographic Questions\n",
    "\n",
    "D1: What is your age?\n",
    "\n",
    "D2: What is the highest level of education you have completed?\n",
    "\n",
    "D3: What former training in statistics have you undertaken?\n",
    "\n",
    "D4: Could you confidently state the difference between correlation and causation?\n",
    "\n",
    "D5: Are you aware of having any color vision deficiency?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "adopted-calendar",
   "metadata": {},
   "source": [
    "# Visualize Demographics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "rubber-protocol",
   "metadata": {},
   "outputs": [],
   "source": [
    "# demographic questions ids\n",
    "d_ids = ['d1','d2','d3','d4','d5']\n",
    "d_questions = {\"d1\":\"D1: What is your age?\",\n",
    "               \"d2\":\"D2: What is the highest level of education \\n you have completed?\",\n",
    "               \"d3\":\"D3: What former training in Statistics \\n have you undertaken?\",\n",
    "               \"d4\":\"D4: Could you confidently state \\n the difference between \\n correlation and causation?\",\n",
    "               \"d5\":\"D5: Are you aware of \\n having any color \\n vision deficiency?\"}\n",
    "\n",
    "## get participants responses in IG  \n",
    "demo_answers = {}\n",
    "demo_answers[\"answer\"] = []\n",
    "demo_answers['condition']  = []\n",
    "demo_answers['question'] = [] \n",
    "for d in d_ids:\n",
    "    answ = [p_details['d_answers'][d]['option'] \n",
    "            for p,p_details in data_processed.items() \n",
    "            if p_details['mode'] == 'i' ]\n",
    "    demo_answers[\"answer\"].extend( answ)\n",
    "    demo_answers[\"question\"].extend([d_questions[d]]*len(answ))\n",
    "    demo_answers['condition'].extend(['IG']*len(answ))\n",
    "\n",
    "## get participants responses in SG    \n",
    "for d in d_ids:\n",
    "    answ = [p_details['d_answers'][d]['option'] \n",
    "            for p,p_details in data_processed.items() \n",
    "            if p_details['mode'] == 's' ]\n",
    "    demo_answers[\"answer\"].extend( answ)\n",
    "    demo_answers[\"question\"].extend([d_questions[d]]*len(answ))\n",
    "    demo_answers['condition'].extend(['SG']*len(answ))\n",
    "    \n",
    "## get participants responses in AG  \n",
    "for d in d_ids:\n",
    "    answ = [p_details['d_answers'][d]['option'] \n",
    "            for p,p_details in data_processed.items() \n",
    "            if p_details['mode'] == 'a' ]\n",
    "    demo_answers[\"answer\"].extend( answ)\n",
    "    demo_answers[\"question\"].extend([d_questions[d]]*len(answ))\n",
    "    demo_answers['condition'].extend(['AG']*len(answ))\n",
    "\n",
    "df = pd.DataFrame(demo_answers)\n",
    "\n",
    "# # add line breaks to long answer options\n",
    "df['answer'] = df['answer'].replace(['A-levels or equivalent'],'A-levels or\\n equivalent')\n",
    "df['answer'] = df['answer'].replace(['Postgraduate degree in Statistics',\"University degree in Statistics\"],['Postgrad. degree\\n in Statistics','Univ. degree\\n in Statistics'])\n",
    "\n",
    "def tm_sorter(column):\n",
    "    \"\"\"Sort function\"\"\"\n",
    "    sorter = ['18-25','26-40', '41-65','A-levels or\\n equivalent',\"Bachelor's degree\",\n",
    "                       \"Master's degree\",\"Doctoral degree\",'None','School','Some University','Univ. degree\\n in Statistics',\n",
    "                       'Postgrad. degree\\n in Statistics', 'Definetely no','Probably no','Probably yes','Definetely yes', 'No']\n",
    "    correspondence = {s: order for order, s in enumerate(sorter)}\n",
    "    return column.map(correspondence)\n",
    "\n",
    "df = df.sort_values(by='answer', key=tm_sorter)\n",
    "df = df.sort_values(by='condition')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "naval-municipality",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 2000x1200 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Plot in common figure\n",
    "fig = plt.figure(constrained_layout=True,figsize=(20, 12))\n",
    "font_size = 20\n",
    "plt.rcParams.update({'font.size': font_size})\n",
    "gs = GridSpec(2, 6, figure=fig)\n",
    "ax1 = fig.add_subplot(gs[0, 0:2])\n",
    "ax2 = fig.add_subplot(gs[0, 2:4])\n",
    "ax3 = fig.add_subplot(gs[0, 4:6])\n",
    "ax4 = fig.add_subplot(gs[1, 0:3])\n",
    "ax5 = fig.add_subplot(gs[1, 3:6])\n",
    "\n",
    "g1 = sns.histplot(ax=ax1, data = df[df['question'] == 'D1: What is your age?'], x = \"condition\", hue = \"answer\", multiple='stack', stat='count', \n",
    "                  palette = sns.color_palette(\"Set2\"), \n",
    "                  hue_order = ['18-25','26-40', '41-65'])\n",
    "g1.legend_.set_title(None)\n",
    "ax1.set_title(\"D1: What is your age?\")\n",
    "ax1.tick_params(axis='both')\n",
    "\n",
    "g2 = sns.histplot(ax=ax2, data = df[df['question'] == 'D2: What is the highest level of education \\n you have completed?'], x = \"condition\", hue = \"answer\", multiple='stack', \n",
    "                  stat='count', \n",
    "                  palette = sns.color_palette(\"Set2\"),\n",
    "                 hue_order = ['A-levels or\\n equivalent',\"Bachelor's degree\", \"Master's degree\",\"Doctoral degree\"])\n",
    "g2.legend_.set_title(None)\n",
    "ax2.set_title(\"D2: What is the highest level \\n of education you have completed?\")\n",
    "ax2.tick_params(axis='both')\n",
    "\n",
    "\n",
    "g3 = sns.histplot(ax=ax3, data = df[df['question'] == 'D3: What former training in Statistics \\n have you undertaken?'], x = \"condition\", hue = \"answer\", multiple='stack', \n",
    "                  stat='count', \n",
    "                  palette = sns.color_palette(\"Set2\"),\n",
    "                 hue_order = ['None','School','Some University','Univ. degree\\n in Statistics','Postgrad. degree\\n in Statistics'])\n",
    "g3.legend_.set_title(None)\n",
    "ax3.set_title(\"D3: What former training in Statistics \\n have you undertaken?\")\n",
    "ax3.tick_params(axis='both')\n",
    "\n",
    "g4 = sns.histplot(ax=ax4, data = df[df['question'] == 'D4: Could you confidently state \\n the difference between \\n correlation and causation?'], x = \"condition\", hue = \"answer\", \n",
    "                  multiple='stack', stat='count', \n",
    "                  palette = sns.color_palette(\"Set2\"),\n",
    "                 hue_order = ['Definetely no','Probably no','Probably yes','Definetely yes'])\n",
    "g4.legend_.set_title(None)\n",
    "ax4.set_title(\"D4: Could you confidently state the difference \\n between correlation and causation?\")\n",
    "ax4.tick_params(axis='both')\n",
    "\n",
    "g5 = sns.histplot(ax=ax5, data = df[df['question'] == 'D5: Are you aware of \\n having any color \\n vision deficiency?'], x = \"condition\", hue = \"answer\", multiple='stack', \n",
    "                  stat='count', \n",
    "                  palette = sns.color_palette(\"Set2\"))\n",
    "g5.legend_.set_title(None)\n",
    "ax5.set_title(\"D5: Are you aware of having any \\n color vision deficiency?\")\n",
    "ax5.tick_params(axis='both')\n",
    "\n",
    "g_axes = [ax1,ax2,ax3,ax4,ax5]\n",
    "for i,ax in enumerate(g_axes):\n",
    "    ax.tick_params(axis='both', labelsize=font_size)\n",
    "    ax.yaxis.label.set_size(font_size)\n",
    "    ax.title.set_size(font_size)\n",
    "    # if i==0:\n",
    "    # ax.legend(loc=2, prop={'size': font_size-4})\n",
    "\n",
    "ax1.set(xlabel=None)\n",
    "ax2.set(xlabel=None)\n",
    "ax3.set(xlabel=None)\n",
    "ax4.set(xlabel=None)\n",
    "ax5.set(xlabel=None)\n",
    "\n",
    "plt.savefig('ch6_Fig8.pdf',dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d4c9d25f-5b1a-45ac-b8ba-07327131ae56",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}