12-Region-Gender-Age-Cohesion-Scatter.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Expanding the measurement of culture with a sample of two billion humans"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## Replication Data and Code"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "This notebook provides all the steps to replicate the results of our paper [Expanding the measurement of culture with a sample of two billion humans](https://doi.org/10.1098/rsif.2022.0085) published in the *Journal of the Royal Society Interface 19:20220085* (2022)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Replication Divisiveness figures"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Setup and Pre-requisites"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "Let's start by importing the required packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "#%pylab --no-import-all\n",
    "%matplotlib inline\n",
    "\n",
    "import sys, os, time\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "pd.set_option('display.width', 160)\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib as mpl\n",
    "import matplotlib.patches as mpatches\n",
    "import statsmodels.api as sm\n",
    "import statsmodels.formula.api as smf\n",
    "from statsmodels.iolib.summary2 import summary_col\n",
    "from sklearn.metrics.pairwise import cosine_similarity, cosine_distances, manhattan_distances, pairwise_distances\n",
    "from scipy.stats import zscore\n",
    "from scipy.cluster.hierarchy import dendrogram, linkage\n",
    "from scipy import spatial, stats\n",
    "from scipy.stats import zscore\n",
    "import MantelTest.MantelTest as MantelTest\n",
    "from scipy.stats import pearsonr\n",
    "import re\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Let's setup our paths"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "pathfb = './data/'\n",
    "pathfbor = './data/OriginalData/'\n",
    "pathout = pathfb + 'CohesionMain/'\n",
    "pathcohesion = pathfb + 'CohesionMain/'\n",
    "pathcenters = pathfb + 'CulturalCenters/'\n",
    "if os.path.exists(pathout) == False:\n",
    "    os.mkdir(pathout)\n",
    "pathshare = pathout\n",
    "if os.path.exists(pathcohesion) == False:\n",
    "    os.mkdir(pathcohesion)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "cohesion_out1 = pd.read_csv(pathout + 'cohesion_all_pairs.csv', encoding='utf-8')\n",
    "cohesion_gender_out1 = pd.read_csv(pathout + 'cohesion_gender_all_pairs.csv', encoding='utf-8')\n",
    "\n",
    "cohesion_out1.set_index('country_code_1', inplace=True)\n",
    "cohesion_gender_out1.set_index('country_code_1', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import WDI data for categorizing\n",
    "wdi = pd.read_csv(pathcenters + 'WDI.csv', encoding='utf-8', keep_default_na=False, na_values='')\n",
    "wdi.set_index('ISO_CODE', inplace=True)\n",
    "wdi = wdi.reindex(cohesion_out1.reset_index().country_code_1.apply(lambda x: x[:2]).unique().tolist())[['region', 'incomeLevel']].copy()\n",
    "wdi.columns = ['continent', 'income']\n",
    "wdi.index.name = 'country_code_1'\n",
    "markers_cont = {'East Asia & Pacific': 'x',\n",
    "                'Europe & Central Asia': 'o',\n",
    "                'Latin America & Caribbean ': '*',\n",
    "                'North America': 's',\n",
    "                'South Asia': '+',\n",
    "                'Sub-Saharan Africa ': 'D'}\n",
    "wdi['marker_cont'] = wdi.continent.map(markers_cont)\n",
    "markers_inc = {'High income': 'o', 'Lower middle income': 's', 'Upper middle income': 'x'}\n",
    "wdi['marker_inc'] = wdi.income.map(markers_inc)\n",
    "colors_inc = {'High income': 'blue',\n",
    "              'Lower middle income': 'darkorchid',\n",
    "              'Upper middle income': 'crimson'}\n",
    "wdi['colors_inc'] = wdi.income.map(colors_inc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Functions for plotting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "SMALL_SIZE = 24\n",
    "MEDIUM_SIZE = 28\n",
    "BIGGER_SIZE = 32\n",
    "\n",
    "plt.rc('font', size=SMALL_SIZE)          # controls default text sizes\n",
    "plt.rc('axes', titlesize=SMALL_SIZE)     # fontsize of the axes title\n",
    "plt.rc('axes', labelsize=MEDIUM_SIZE)    # fontsize of the x and y labels\n",
    "plt.rc('xtick', labelsize=SMALL_SIZE)    # fontsize of the tick labels\n",
    "plt.rc('ytick', labelsize=SMALL_SIZE)    # fontsize of the tick labels\n",
    "plt.rc('legend', fontsize=SMALL_SIZE)    # legend fontsize\n",
    "plt.rc('figure', titlesize=BIGGER_SIZE)  # fontsize of the figure title\n",
    "\n",
    "def JointScatterPlot(df, df2, myvar, fileext='JointScatter_all_pairs', filenum='', ascending=True, title='FB Cultural Divisiveness', pathout=pathout):\n",
    "    dffig = df[[myvar.replace('_gender', '')]].merge(df2[[myvar]], left_index=True, right_index=True).copy()\n",
    "    agglo = dffig.index.name\n",
    "    x = 'Region'\n",
    "    y = 'Gender'\n",
    "    dffig.columns = [x, y]\n",
    "    dffig = dffig.merge(wdi, left_index=True, right_index=True)\n",
    "    dffig.index.name = 'country_code_1'\n",
    "    dffig = dffig.reset_index().copy()\n",
    "    movex = dffig[x].mean() * 0.01\n",
    "    movey = dffig[y].mean() * 0.01\n",
    "    fig, ax = plt.subplots(figsize=(30,20))\n",
    "    for line in range(0,dffig.shape[0]):\n",
    "        ax.text(dffig.Region[line]++movex, dffig.Gender[line]+movey, dffig.country_code_1[line], horizontalalignment='left', fontsize=28, color='black', weight='semibold')\n",
    "        ax.plot(dffig.Region[line], dffig.Gender[line], marker=dffig.marker_cont[line], c=dffig.colors_inc[line], markersize=28)\n",
    "    for cont in np.sort(dffig.continent.unique()):\n",
    "        ax.scatter([], [], c='k', alpha=1, marker=markers_cont[cont], label=cont)\n",
    "    #leg = ax.legend(scatterpoints=1, frameon=False, labelspacing=1, title='Continent', prop={'size': 28}, markerscale=4, loc='upper right')\n",
    "    #leg.set_title(\"Continent\", prop = {'size':32})\n",
    "    points2 = []\n",
    "    for inc in ['High income', 'Upper middle income', 'Lower middle income']:\n",
    "        points2 += [ax.scatter([], [], c=colors_inc[inc], alpha=1, marker='s', label=inc)]\n",
    "    leg2 = ax.legend(points2, ['High income', 'Upper middle income', 'Lower middle income'], scatterpoints=1, frameon=False, labelspacing=1, title='Income', prop={'size': 28}, markerscale=4, loc='lower left')\n",
    "    leg2.set_title(\"\", prop = {'size':32})\n",
    "    leg2.set_title(\"Income\", prop = {'size':32})\n",
    "    #ax.add_artist(leg)\n",
    "    ax.tick_params(axis = 'both', which = 'major', labelsize=34)\n",
    "    ax.tick_params(axis = 'both', which = 'minor', labelsize=18)\n",
    "    ax.set_title('')\n",
    "    ax.set_xlabel('Region Divisiveness', fontsize=38)\n",
    "    ax.set_ylabel('Gender Divisiveness', fontsize=38)\n",
    "    #plt.savefig(pathout + myvar.replace('_gender', '') + fileext +'.pdf', dpi=300, bbox_inches='tight')\n",
    "    plt.savefig(pathout + 'Figure-7-' + filenum +'.pdf', dpi=300, bbox_inches='tight')\n",
    "    #plt.savefig(pathout + myvar.replace('_gender', '') + fileext +'.png', dpi=300, bbox_inches='tight')\n",
    "    plt.show()\n",
    "    pass\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
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gr732yte+9rW89rWvzW9+85stjl+9enU++clP5sorr8yNN96YqVOnjkKVAACMlzDxZ03t6aWUvWutW9rn8Zym9uL+XZXbusc1tX/ftSoAuqSdv4YeyPpQsq+vL3feeWeuvfba1Fofuz9hwoSce+65w/6L6DlzkiuvTPr6hjXNkEye3FgPAADGihe+8IX5v//3/+ZjH/tYzj///CxfvnyLz9x+++1517velc9+9rOjUCEAABPafbCU8sVSyitLKW3P0UHfSbKq6fNLhvBM85hrOllMN5RSdk6yf1PXKB6aBzD+zJ8/P/Pnz8+//du/5eqrr866deseu/f0pz89X/3qV/OWt7xl2Osce2zSlFGOqFob6wEAQKftt99+qbUOer32ta9t+ezUqVNz9tlnZ+nSpfn+97+fj33sY3nd616X6YOc0f/f//3fWbVq1YD3AQDonOEEgcckuSzJr0opHyulbP2ZcR1Sa304yRVNXScMNr6Usl+SlzZ1XToSdY2yP0uyU9Pna7tVCMB4Nnny5Bx//PF55Stf2ZH59t47OfzwpJSOTDegUpIjjkhmzBjZdQAAoF0TJkzI7Nmz8573vCeXXHJJ7rvvvnzve9/LS1/60s3GPvLII7n99vHwxhoAgLGvE7sKpyd5V5JbSik/LKW8o5SyZwfm3VqfbGofXkoZ7Fvej2XD7/7zJJePWFVtKqXsshVjD0hyVlPXT5P8sONFAYxxw/lr6KHq6+vL3/7t3+boo4/eaLficMyd2ziCdCRNntxYBwAAtiWHHHJIvvrVr6anp2eze3YmAgCMjuGEib9N0ryPoiR5ZpL/L8kvSylfLaW8rpSyw3AKHKpa67XZOBS8sJRyZPOYUspOpZRPpLGrcr0P1FrXtJqzlHJdKaX2X9d1vOjBnVhKubGU8pZSyu4D1LdDKeWEJDcmeXx/97ok76x1tA7NAxifaq159NFH8/Of/zx//dd/vdn9b3zjG/noRz/akbVmz05OPDFp8f1IR/T0JCefnPT2jsz8AADQjs9+9rP5xS9+scVxu+yySx73uMdt1j916tSRKAsAgE0MJ0zcK8nrk3w1yfowbn24uEOSVyf5UpKlpZRPllJmD2OtoXprkvX/Ct0jyRWllEWllP8opVyUZHGSdzaNn1drvaQTC5dSXlNKuWXTK8neTcP+osWY/xlk2hck+XyS35RSflJKubiU8ulSynmllG8kWZbkM/2/a5LUJO+otV7did8JYHs3adKkPPWpT825556bt73tbZvdP/vss7NkSWdeUXvOOcn06cnEiR2Z7jETJzbmPeusLY8FAIDR9KUvfSn7779/jjnmmHz961/P6tWrW46bP39+fvOb32zUt8cee+QpT3nKaJQJALDdm9Tug7XWR5N8OcmXSylTkrw5jff2NYeGJcmUJH+V5K9KKbcnuSDJF2qtv2p37UFqWl5KOTyNAO5F/d3P6b+arUnjWNC5HVx+SpKDtjBmev/VbI8hzD0pyR/1XwO5K8nJgkSAkXHOOefkoosuysqVKx/re/jhh/PRj340//Iv/zLs+Xt6kuuvb+weXLEiWbt22FNm4sRk6tTGvCO16xEAAJLk/vvvz6mnnrrFcaeddlqmTJny2Oc1a9bk4osvzsUXX5yddtopz372s/PkJz85e+yxRx544IEsXLgwP//5zzeb59hjj83ETv8lHgAALbUdJjartd6f5Nwk55ZS9k9yXBrh4symYSXJH6YR4n20lHJtGsHil2utfZ2oo7+Wu0sphyX5k/4anp1kRpK+JPcmuTLJ+bXWn3VqzRHyf5LcnOR5/deTk0ztvyYleTDJL5N8P8k3knyj1tqZl3cBsJmpU6fmr/7qr3LOOeds1P8f//Ef+fCHP5y99tpr2GvMnJksWpQcemiybFkynFfA9PQ0diRef31jXgAAGEkrV67M/Pnztzju1FNP3ShMbLZ69ep897vfzXe/+91B59h7770z1wvBAQBGzXCOOW2p1np7rfUDSWYlOSKNXYK/32TYxP57/5nkvv6jO1/cwRrW1VovqbW+vtb65FprT6318bXWZ9Za3zPUILHW+pJaa+m/XrKFsRc0jd2aa9YA8/XVWm+otX6s//d4dq1131rr42qtO9Vap9VaD661/kWt9euCRICR9653vSs9m2zx6+vryyc+8YmOrTFzZnLrrclJJzUCwVK2/EyzUja8I/HWWwWJAACMXc985jM3+/f1lhx88MG59tprM2PGjBGqCgCATXU8TFyvNlxba31rGu9XPC7JNWm81y9p7FQsSXZL8rYkC0aqFgDohGnTprV8d+K//uu/5re//W3H1unpSebPb+wqPOqoZKedksmTB39m8uTGuKOOajw3b56jTQEAGNv+4R/+IStWrMjll1+e008/Pa95zWty4IEHZsqUKdlxxx0zadKkPP7xj89BBx2U448/Ppdddlm+//3vZ//99+926QAA25VSa93yqE4uWMo+abxb8S1JDkgjXCxp5I8Ou++S3t7eumjRom6XATBkF1xwQY4//viN+vbbb7/cfffdWzXPddddlzlz5mzWP9B/HxcvXpynPvWpefTRRzfqnzt3bk4//fStWnuoli5NLrooWbCgcQzqsmWNdypOnNg4yrS3N5kzJzn22MQfaAMAAAAAW6uUcnOttbfVvRHbmTiQWuuvknw8ydwkd4z2+gAwHPvuu2/+7M/+bLP+f/7nf85DDz00ImvOmJGcckryla8kv/xl8uijybp1jZ+//GWj/5RTBIkAAAAAQOeN6s7EUsrzkvzvJG9MskfzrdiZ2FV2JgIAAAAAAGyfBtuZOGkUFt83jWNN/yzJH6zv7v9Zm9rfHulaAAAAALZHS5cmX/zihqPzly/fcHT+tGkbjs5/4xudeAEAwMZGZGdiKeVxSY5JYxfioWkEhq0CxLuSfD7J52qtv+h4IQyZnYnAaPAFBgAAjK6FC5O5c5NrrklKSfr6Bh47eXJSa3L44ckZZzT+fQ4AwPZhsJ2JHQsTSyklyRFpBIivTbLz+lvZOEBcmeTiJJ+ttV7fkcUZNmEiMJJ8gQEAAKNr1arkfe9Lzjuv8e/vrfn6p5TGv8tPOik5++ykp2fk6gQAYGwY0TCxlPL0JG9N8r+S7L2+OxsHiOuSXJPkc0kurbWuGtaidJwwERgJvsAAAIDRt3hxcthhybJljX+Tt6unJ5k+Pbn++mTmzM7VBwDA2DMiYWIp5e1phIgHr+/q/9kcIt6WRoD4+VrrkrYWYlQIE4FO8wUGAACMvsWLk9mzkxUrGq8UGK6JE5OpUxuvKfDvcQCA8WuwMHHSMOb952wIDpsTyd8m+e80jjFdOIz5AdhGdfILjFWrknvvbRx36gsMAAAY2KpVjT/o61SQmDTmWbEiOfTQ5NZbnRgCALA9mtChedYk+VqS1yeZUWt9uyARYPs00l9gDGeXIwAAjGfve1/jZJBO/Tt8vbVrG/O+//2dnRcAgG3DcMPEW5KcmmSfWutra61frrU+OuyqANhm+QIDAABG38KFjXeVj9Qf361alXz6043TQgAA2L4MJ0x8Zq31ObXWf661/qZjFQGwzfIFBgAAdMfcuUlf38iu0dfXWAcAgO1L22FirfWnnSwEgG2fLzAAAGD0LVmSXHNNUuvIrlNrcvXVydKlI7sOAABjS6femQjAds4XGAAA0B0XXZSUMjprldJYDwCA7YcwEYCO8AUGAAB0x4IFI39CyHp9fY31AADYfggTAegIX2AAAEB3jPY7xb3DHABg+zJpsJullF80fay11qcMcK8TNpofgG2LLzAAAKA7li8f3fWWLRvd9QAA6K5Bw8Qks5LUJKX/50D3hmOg+QHYhvgCAwAAumPt2vG9HgAA3TWUY04HCws78XasUXrDFgAjyRcYAADQHRMnju/1AADori3tTDyjzXsAbGcmTkzWrBnd9QAAgGTatGTJktFbb/r00VsLAIDuGzRMrLUOGBgOdg+A7Y8vMAAAoDt6e5OvfW101wMAYPsxlGNOAWCLRvsLBV9gAABAw5w5yeTJo7PW5MmN9QAA2H4IEwHoCF9gAABAdxx7bFLr6KxVa2M9AAC2H8JEADrCFxgAANAde++dHH54UsrIrlNKcsQRyYwZI7sOAABjizARgI7wBQYAAHTP3Lkjf1LI5MmNdQAA2L6MSphYGk4updxYSvltKeWhUspPSylnlFJ2HY0aABh5vsAAAIDumD07OfHEpKdnZObv6UlOPtm7ywEAtkdth4mllA+UUhb3X/8yyLiJSb6e5F+TPC/J7kl2TnJAkg8l+Ukp5cnt1gHA2OELDAAA6J5zzkmmT08mTuzsvBMnNuY966zOzgsAwLZhODsT/1eSJybZJ8lFg4z7cJKjkgx08N2+SS4vpYzwXhYARoMvMAAAoDt6epLrr0+mTu3cv8cnTmzMd/31I/dHgwAAjG1thYmllMensbOwJvlNrfVbA4ybkuTd/eNqf/fXkvxjkiuyIWB8Sv84ALZxvsAAAIDumTkzWbSo8XO4/3bu6dl4PgAAtk/t7kw8qKndMkjs96Ykj8uG0PCYWutra63vr7W+Msl7+/tLkpParAWAMcYXGAAA0D0zZya33pqcdFLj39NloLOiBlDKhlcM3Hqrf4cDAGzv2g0T92tq/3SQcUf3/6xJrq21Xtp8s9b68ST/r//jzFLK09usB4AxxhcYAADQPT09yfz5jdM9jjoq2WmnZPIWXjAzeXJj3FFHNZ6bN8/JIAAAtB8mTm1qL2s1oJSyQ5IXZsPxphcOMNc3mtoHDTAGgG2QLzAAAKC7enuTyy5L7rorOfvs5Oijk332SSZNavwB36RJjc9HH924f9ddjfG9vd2uHACAsWJSm881f6378ABjDk6yc3+7JvnmAON+3tSe3mY9AIxh67/AWLo0ueiiZMGCxrGly5Yla9c23ok4fXpj3Jw5ybHHJjNmdLtqAAAYP2bMSE45pXEBAMDWaDdMXN3UftwAY17U/7MmubPWet8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\n",
      "text/plain": [
       "<Figure size 2160x1440 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "JointScatterPlot(cohesion_out1, cohesion_gender_out1, 'cohesion_gender_norm', pathout=pathout, filenum='B')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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>cohesion_norm</th>\n",
       "      <th>cohesion_gender_norm</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>cohesion_norm</th>\n",
       "      <td>1.00000</td>\n",
       "      <td>-0.70213</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cohesion_gender_norm</th>\n",
       "      <td>-0.70213</td>\n",
       "      <td>1.00000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      cohesion_norm  cohesion_gender_norm\n",
       "cohesion_norm               1.00000              -0.70213\n",
       "cohesion_gender_norm       -0.70213               1.00000"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mycorr = cohesion_out1[['cohesion_norm']].merge(cohesion_gender_out1[['cohesion_gender_norm']], left_index=True, right_index=True)\n",
    "mycorr.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-0.7021302037814038, 0.0011603635998826511)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pearsonr(mycorr.cohesion_norm, mycorr.cohesion_gender_norm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "cohesion_out1 = pd.read_csv(pathout + 'Age_Gender/cohesion_age_all_pairs.csv', encoding='utf-8')\n",
    "cohesion_gender_out1 = pd.read_csv(pathout + 'Age_Gender/cohesion_gender_all_pairs.csv', encoding='utf-8')\n",
    "cohesion_out1.set_index('country_code_1', inplace=True)\n",
    "cohesion_gender_out1.set_index('country_code_1', inplace=True)\n",
    "cohesion_out1 = cohesion_out1.reindex(cohesion_out1.index.difference(['BR', 'IN', 'KE', 'KM', 'DJ']))\n",
    "cohesion_gender_out1 = cohesion_gender_out1.reindex(cohesion_gender_out1.index.difference(['BR', 'IN', 'KE', 'KM', 'DJ']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import WDI data for categorizing\n",
    "wdi = pd.read_csv(pathcenters + 'WDI.csv', encoding='utf-8', keep_default_na=False, na_values='')\n",
    "wdi.set_index('ISO_CODE', inplace=True)\n",
    "wdi = wdi.reindex(cohesion_out1.reset_index()['country_code_1'].apply(lambda x: x[:2]).unique().tolist())[['region', 'incomeLevel']].copy()\n",
    "wdi.columns = ['continent', 'income']\n",
    "wdi.index.name = 'country_code_1'\n",
    "markers_cont = {'East Asia & Pacific': 'x',\n",
    "                'Europe & Central Asia': 'o',\n",
    "                'Latin America & Caribbean ': '*',\n",
    "                'Middle East & North Africa':'v',\n",
    "                'North America': 's',\n",
    "                'South Asia': '+',\n",
    "                'Sub-Saharan Africa ': 'D'}\n",
    "wdi['marker_cont'] = wdi.continent.map(markers_cont)\n",
    "markers_inc = {'High income': 'o', 'Lower middle income': 's',\n",
    "               'Upper middle income': 'x', 'Low income':'^'}\n",
    "wdi['marker_inc'] = wdi.income.map(markers_inc)\n",
    "colors_inc = {'High income': 'blue',\n",
    "              'Lower middle income': 'darkorchid',\n",
    "              'Upper middle income': 'crimson',\n",
    "              'Low income': 'green'}\n",
    "wdi['colors_inc'] = wdi.income.map(colors_inc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def JointScatterPlot(df, df2, myvar, fileext='JointScatter_all_pairs', filenum='', ascending=True, title='FB Cultural Divisiveness', pathout=pathout):\n",
    "    dffig = df[[myvar.replace('_gender', '')]].merge(df2[[myvar]], left_index=True, right_index=True).copy()\n",
    "    agglo = dffig.index.name\n",
    "    x = 'Age'\n",
    "    y = 'Gender'\n",
    "    dffig.columns = [x, y]\n",
    "    dffig = dffig.merge(wdi, left_index=True, right_index=True)\n",
    "    dffig.index.name = 'country_code_1'\n",
    "    dffig = dffig.reset_index().copy()\n",
    "    movex = dffig[x].mean() * 0.025\n",
    "    movey = dffig[y].mean() * 0.025\n",
    "    fig, ax = plt.subplots(figsize=(30,20))\n",
    "    for line in range(0,dffig.shape[0]):\n",
    "        ax.text(dffig.Age[line]+movex, dffig.Gender[line]+movey, dffig.country_code_1[line], horizontalalignment='left', fontsize=28, color='black', weight='semibold')\n",
    "        ax.plot(dffig.Age[line], dffig.Gender[line], marker=dffig.marker_cont[line], c=dffig.colors_inc[line], markersize=28)\n",
    "    for cont in np.sort(dffig.continent.unique()):\n",
    "        ax.scatter([], [], c='k', alpha=1, marker=markers_cont[cont], label=cont)\n",
    "    leg = ax.legend(scatterpoints=1, frameon=False, labelspacing=1, title='Continent', prop={'size': 28}, markerscale=4, loc='lower right')\n",
    "    leg.set_title(\"Continent\", prop = {'size':32})\n",
    "    points2 = []\n",
    "    for inc in ['High income', 'Upper middle income', 'Lower middle income', 'Low income']:\n",
    "        points2 += [ax.scatter([], [], c=colors_inc[inc], alpha=1, marker='s', label=inc)]\n",
    "    #leg2 = ax.legend(points2, ['High income', 'Upper middle income', 'Lower middle income', 'Low income'], scatterpoints=1, frameon=False, labelspacing=1, title='Income', prop={'size': 28}, markerscale=4, loc='upper center')\n",
    "    #leg2.set_title(\"\", prop = {'size':32})\n",
    "    #leg2.set_title(\"Income\", prop = {'size':32})\n",
    "    ax.add_artist(leg)\n",
    "    ax.tick_params(axis = 'both', which = 'major', labelsize=34)\n",
    "    ax.tick_params(axis = 'both', which = 'minor', labelsize=18)\n",
    "    ax.set_title('')\n",
    "    ax.set_xlabel('Age Divisiveness', fontsize=38)\n",
    "    ax.set_ylabel('Gender Divisiveness', fontsize=38)\n",
    "    #plt.savefig(pathout + myvar.replace('_gender', '') + fileext +'.pdf', dpi=300, bbox_inches='tight')\n",
    "    plt.savefig(pathout + 'Figure-7-' + filenum +'.pdf', dpi=300, bbox_inches='tight')\n",
    "    #plt.savefig(pathout + myvar.replace('_gender', '') + fileext +'.png', dpi=300, bbox_inches='tight')\n",
    "    plt.show()\n",
    "    pass\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
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xqKSvzOyDJI+AG6M9968pclC2p7xRpEGxArIV+R5Medudc/+T1EPelMzR2nCavKmhfzGzK6NVEFgv9jx5QcifoiRrLG+U6wRJ35vZ8fE0HgAAAMgkOeluAAAAAAD4mVk1Sef7TmXJmwox0aIuMLNrnXORRnBt9O0nupZcPOsn+kc7bpf0a4J1+CWyll7Kmdk+8tYrDHIqvEZfaYyS9NfAfnVJZ8sXkDazg1R4rcFkjZKUJDnnNku62cxul3RcYOsq7/XuE5a8j6TZZnascy7R6UUj1f2rmU0KlCtJF0q6LSyZf+rW9ZLeilFk+IjbRaVo3spS5C2J8LavVAr6gXPuI0k9zKy5pD9K6i7v824UlrSOpIfM7DDn3KVRynKSnpX0rJl1kXS8vHv1aBUNsraR9IGZ9XfOjUnaCwIAAADKOYKSAAAAAMqb8xRf4K84tSSdI28EWrhVvv1E12xsHEca/3SmPzvn2iVYR3nWS1IV3/F3zrlIIwsT5pz71Mx+lNQqcOoiFR4le5H2TPdZoMifbTLasUPSe4EtuMbhsfKCpBdozzqI+0kaKS/wlAyjtCco2dzMujrnPg60oVqg/qCXA+2MJnxK3WOcc6sipix/wtt+v3PuvlRV5pxbJG9dyEclKRCkPFne/dbJl/QSM3vdOfd+MeV9JumzQFlZko6UN+ryYu35e5Ml6XEz+6ACfS4AAABAqTB9KwAAAIDyxj/t6nZ5I7wS2bZGKcvvW99+QzOrH0/DAiP14llfz782YXMzqxI1ZcVzddhxzABNCfiDkMeZmT8IfKFvf6JzbnmS647IObfNOfehc+4ySS0k/ei73MXMDklSVeNVeESgf2Tk2So8qre4UaLh62NWmMC4c26TpDzfqTJtu3NukXPuYedcZ3lrT/r1j5QnRlkFzrnZzrl/SWop6TXf5eBoYAAAAGCvQFASAAAAQLlhZq3lTZ8Y9JhzrkUim6QHfPl7mFnLCFV9EnYcb2Cgb5zppvj2K0s6Ic585ZqZnS7pRN+pbZL+l+Rqnpc3JazkPbP2C9T9B3kBwaCkTt0ar8DakzeEnW6bpLK3qXDQ6jxfQNu/xuSPzrlZxRT3sQqvf3paEppYlqb69k8ys7TM9OSce1DSd75TJf6sAyNbL9Oe+7tU5QEAAAAVDUFJAAAAAOXJJWHHL5SgjPA8kUZLvq/CU0QONbPKsQoNTJ85JM42zFThdSTDg1gVjpl1VOFRjJIXNP4tmfU455ZKmuY7dVHYfyVvTdA3kllvguaHHVdKYtn+YGtNSWeYWUNJvaOkiSgQ4PSvOTnYzPZPThPLxMu+/bqSBqarISr8eZfqsw5MdeyfrjWZ9w4AAABQrhGUBAAAAFAumNk+Kjw953zn3FeJluOcmy/pa9+p/uGjrAIjlp70nWom6QkzM0UQWBduhOJbT1LOuZ2S7vWd+oOZ3RZP3rB60z7tq5lVNrOr5Y2886/1+amkW1JUrT/o1sbMuslbazTo1UDQLSnM7EAzy04gy+Fhx78kqy2SPpK02Hd8kbzRosHn9wIVDQ5H8x/tGZVXQ9JLZpZQECxd96BzboIkf/+/z8zC3/eYzLNPhPMHJVKGpPa+U7+EXa9tZnGvgWtmjST5g8O/REkKAAAAZByCkgAAAADKizMkHeA7frEUZfnz1lPkqSv/o8LBnwGSJprZMcHgZCCo0U3edKx/kbde5S9xtuFxeSMmg241s2fNrF6sTIEg4Clm9qak/8ZZV1KZWT0zO83M7pO3tt8D8qahDZor6bRkBgbDjJO0xXf8rAqv5ZnsqVsHSVpoZtcEgkZRBQJj/s9lmaTZyWqIc87Jm8I2qI+ky33HU51zy+Is61tJ9/lOHS/pIzNrHyWLpNB938XMHlXhe7isDZY3RbDkBVVnmNnFxU3lamaNzOwfkhZIOjJCkp/M7CUzOz5WMDoQwH1AUmvf6dfDkrWXtNTMig2aBkaqvqg9/xbjlN4RvwAAAECZSsuaDAAAAAAQQfjUraUJSr4k6R7t+cf/wZLG+xM457aa2dnyAo7BgFfvwLbVzNZIqiOpmi/bFfKClwcFjvOjNcA5t8vMzpE38i24ruUASeeb2SRJsyT9LmmXvIBLE3kBjmMk7RtI7x/NmVRm9lP4qUA7air6lJJOXoBwiHNuU6ra5pzbbGbjtGfKVv9akoucc6kIlB0sL9h4v5l9LekLSQslrQtcbyhvvdM+KvwD36udcwVJbsvzkm6V95nkaM/9JiUekL1RUitJZwaOu0j61sxmSZouaam8AHCuvGlSD5N0tKT6gfQLEm59kjjnvjazi+WNDK0k7/58RtLtZvahpDnyPp995PXhQyQdIW8ka8RRzwHZkv4c2NaY2SfyRlevlLRZ3nvRRt6PGQ705fta0nMRyqsp6VpJ15rZz/L69vfypojeKe/vyJGB8nJ9+R51zv0Yx1sBAAAAZASCkgAAAADSzsyaqvCaebOcc4tKWp5z7lcz+0hSj8CpE82ssXMuLyzdN2bWS9IYSYf6LlVT4WDkVkmXOedGm9lffec3FtOO383sKHkB1pMCp/eRdHJgK07UoGcSNE8g7WZ5I8QeCIy+KwujVHgdyaDnI5xLJpMXQIo0ws5vl6R/OOfGJbsBzrmfzWympGPDLgU/h0TKKjCzvvJGBl+vPQHVowNbcVJ5DxbLOfeKmf0m6VV5o54lqZGki+Msorj215EXLIw0mtrvS0mnOOd2F5OuWWArznOSro4jHQAAAJAxmL4VAAAAQHkwSIWfT0ozSjLoBd9+tqIEMQJBtiMD19+T9Ku80U2/yxstd6uk1s654Dp+tX3Z1xfXCOfceufcyZL+KGmqpOKCGqskvSKpr8o2aJEvL/j6m6Rv5E2hepu8kYEHOOf6l2FAUvLeq6Vh58KnNk2W0ZKGyRvhtquYtNvlfT6HO+eeSEFbgiKNiBzrnNsS4XxMzrkC59yN8gLvL8gLbsayVdKHkv6mooHRMuecmyFvtOwNKjzlcsTk8kYp3iepg3PuiwhpLpD3N+a3OKqfL+kfko52zv0e4frXgevvq5gfKQTaNkNecPPiOAKcAAAAQEYxb7kKAAAAAEBxzKyapA3aM+tMP+fcCzGyRCojV940oI3ljdLKkhfMWCZpnqSfHA9qaWNmVeRNYdpC3si86vKC1OvkBai+ds4VF9Qr1wJrMh4lb1rh/SVVkReoXCFvuta5zrmd6WthbGbWTFIneWvQ1pK0Q97n85Ok751zaxIoq4mktpKaBsrKkfdeLJf3WYdPcxyrrCx560+2kte/a8gLRG6U9LOkL51zK+MtDwAAAMg0BCUBAAAAIE5mdqYKr03Z2jmXtjX3AAAAAACoKJi+FQAAAADiYGbZkv7pO/WzpB/T1BwAAAAAACoUgpIAAAAA9lpm1tTMGsaRLkvS/yQd7Tv9FNOsAgAAAAAQH4KSAAAAAPZmR0j62cyeN7PTzKyO/6KZ1TSzsyV9IukK36WFkh4uw3YCAAAAAFChsaYkAAAAgL1WhDUiJWmdpI2SqkvaT0V/zLlGUh/n3FcpbyAAAAAAABkiJ90NAAAAAIA02hXh3H6BLZKZkgY65xamrkkAAAAAAGQeRkoCAAAA2KuZWWtJp0g6RtIhkhrJGyXpJK2V9KukjyS96ZyblqZmAgAAAABQoRGUBAAAAAAAAAAAAJBS4WujAAAAAAAAAAAAAEBSEZQEAAAAAAAAAAAAkFIEJQEAAAAAAAAAAACkFEFJAAAAAAAAAAAAAClFUBIAAAAAAAAAAABAShGUBAAAAAAAAAAAAJBS/w/9l7MXyvu6LQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 2160x1440 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "JointScatterPlot(cohesion_out1, cohesion_gender_out1, 'cohesion_gender_norm', pathout=pathout+'/Age_Gender/', filenum='A')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>cohesion_norm</th>\n",
       "      <th>cohesion_gender_norm</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>cohesion_norm</th>\n",
       "      <td>1.00000</td>\n",
       "      <td>0.23413</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cohesion_gender_norm</th>\n",
       "      <td>0.23413</td>\n",
       "      <td>1.00000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      cohesion_norm  cohesion_gender_norm\n",
       "cohesion_norm               1.00000               0.23413\n",
       "cohesion_gender_norm        0.23413               1.00000"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mycorr = cohesion_out1[['cohesion_norm']].merge(cohesion_gender_out1[['cohesion_gender_norm']], left_index=True, right_index=True)\n",
    "mycorr.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.23413026829852301, 0.1459147997107791)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pearsonr(mycorr.cohesion_norm, mycorr.cohesion_gender_norm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "celltoolbar": "Slideshow",
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}