FigureS4.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Python implementation: CPython\n",
      "Python version       : 3.9.0\n",
      "IPython version      : 7.19.0\n",
      "\n",
      "Compiler    : Clang 12.0.0 (clang-1200.0.32.27)\n",
      "OS          : Darwin\n",
      "Release     : 20.1.0\n",
      "Machine     : x86_64\n",
      "Processor   : i386\n",
      "CPU cores   : 12\n",
      "Architecture: 64bit\n",
      "\n",
      "Git hash: \n",
      "\n",
      "matplotlib: 3.3.3\n",
      "numpy     : 1.19.4\n",
      "watermark : 2.1.0\n",
      "pandas    : 1.1.4\n",
      "\n"
     ]
    }
   ],
   "source": [
    "import matplotlib.patches as mpatches\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.ticker as mticker\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "import watermark\n",
    "\n",
    "plt.style.use('./CDS_paper.mplstyle')\n",
    "tab10 = plt.get_cmap(\"tab10\").colors\n",
    "\n",
    "%matplotlib inline\n",
    "%load_ext watermark\n",
    "%watermark -n -v -m -g -iv"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "RP_og_cat = pd.read_csv(\"bootstrap/relative_prevalence_category.tsv\", sep=\"\\t\", index_col=0)\n",
    "RP_both_cat = pd.read_csv(\"bootstrap/relative_prevalence_no_urls_nor_http_category.tsv\", sep=\"\\t\", index_col=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Order categories"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "sorted_cats = RP_og_cat.median().sort_values(ascending=False).index"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define plotting functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def boxplot(ax, data, loc, color=(0, 0, 0)):    \n",
    "    ax.fill_between([data.quantile(q=0.25), data.quantile(q=0.75)], loc - 0.15, loc + 0.15,\n",
    "                    facecolor=(*color, 0.4), edgecolor=color, lw=3, zorder=3)\n",
    "    ax.vlines(data.mean(), loc - 0.25, loc + 0.25, color=color, zorder=3)\n",
    "    ax.plot([data.quantile(q=0.025), data.quantile(q=0.975)], [loc, loc],\n",
    "            color=color, lw=0, marker=\"|\", ms=12, mew=3, zorder=3)\n",
    "    ax.plot([data.quantile(q=0.025), data.quantile(q=0.25)], [loc, loc], color=color, marker=\"\", zorder=3)\n",
    "    ax.plot([data.quantile(q=0.75), data.quantile(q=0.975)], [loc, loc], color=color, marker=\"\", zorder=3)\n",
    "    \n",
    "    lower = data.values[data < data.quantile(q=0.025)]\n",
    "    upper = data.values[data > data.quantile(q=0.975)]\n",
    "\n",
    "    ax.scatter(lower, loc * np.ones(lower.shape), marker=\".\", color=color, zorder=3)\n",
    "    ax.scatter(upper, loc * np.ones(upper.shape), marker=\".\", color=color, zorder=3)\n",
    "    \n",
    "    return ax"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Figure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x1296 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(12, 18))\n",
    "ax = fig.add_subplot(111)\n",
    "\n",
    "for num, cat in enumerate(sorted_cats):\n",
    "    ax = boxplot(ax, RP_og_cat.loc[:, cat], 2 * num + 1, color=(0, 0, 0))\n",
    "    ax = boxplot(ax, RP_both_cat.loc[:, cat], 2 * num + 2, color=tab10[9])\n",
    "\n",
    "ax.fill_between([0.5, 1], sorted_cats.size * 2 + 1, color='0.7', alpha=0.7, zorder=2)\n",
    "ax.vlines(1, 0, sorted_cats.size * 2 + 1, color='k', lw=5, zorder=2)\n",
    "ax.hlines(np.arange(2.5, sorted_cats.size * 2 + 1, 2), 0, 3, color=\"k\", lw=1, zorder=2)\n",
    "\n",
    "ax.set_xlim(left=0.5, right=2.5)\n",
    "ax.xaxis.set_major_locator(mticker.MultipleLocator(0.25))\n",
    "ax.xaxis.set_minor_locator(mticker.MultipleLocator(0.05))\n",
    "ax.set_xlabel(r\"\\textit{Between-cohort} prevalence ratio ($\\frac{P_D}{P_R}$)\")\n",
    "ax.set_ylim(bottom=0.5, top=24.5)\n",
    "ax.set_yticks(np.arange(1.5, 24, 2))\n",
    "ax.set_yticklabels([\"\"] + sorted_cats.values)\n",
    "ax.yaxis.set_minor_locator(mticker.NullLocator())\n",
    "ax.invert_yaxis()\n",
    "ax.grid(axis=\"y\", which=\"major\", b=False)\n",
    "\n",
    "p4 = mpatches.Patch(facecolor=(0, 0, 0, 0.4), edgecolor=\"k\", lw=3)\n",
    "p5 = mpatches.Patch(facecolor=(*tab10[9], 0.4), edgecolor=\"C9\", lw=3)\n",
    "\n",
    "labels = [r\"Original comparison\", r\"Removed tweets with URLs\"]\n",
    "ax.legend([p4, p5], labels, loc=\"lower right\", ncol=1, framealpha=1, scatterpoints=1)\n",
    "\n",
    "ax.annotate(r\"\\begin{center}\\noindent More prevalent in\\\\$R$ cohort\\end{center}\", (0.05, -0.05),\n",
    "            xycoords=\"axes fraction\", fontsize=16, va=\"center\", ha=\"center\")\n",
    "ax.annotate(r\"\\begin{center}\\noindent More prevalent in\\\\$D$ cohort\\end{center}\", (0.95, -0.05),\n",
    "            xycoords=\"axes fraction\", fontsize=16, va=\"center\", ha=\"center\")\n",
    "ax.annotate(r\"\\begin{center}\\noindent More prevalent in\\\\$R$ cohort\\end{center}\", (0.05, 1.025),\n",
    "            xycoords=\"axes fraction\", fontsize=16, va=\"center\", ha=\"center\")\n",
    "ax.annotate(r\"\\begin{center}\\noindent More prevalent in\\\\$D$ cohort\\end{center}\", (0.95, 1.025),\n",
    "            xycoords=\"axes fraction\", fontsize=16, va=\"center\", ha=\"center\")\n",
    "\n",
    "fig.savefig(\"figures/FigS4.pdf\", dpi=600, orientation='portrait',\n",
    "            facecolor='w', edgecolor='w', bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.9.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}