CaptainStabs · August 16, 2025 16:49
diff --git a/anes_2024_august_update.ipynb b/anes_2024_august_update.ipynb
 {
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "83cf4938",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import warnings\n",
    "warnings.simplefilter(action='ignore', category=pd.errors.DtypeWarning)\n",
    "warnings.filterwarnings(\"ignore\", message=\".*figure layout has changed to tight.*\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "4506ce60",
   "metadata": {},
   "outputs": [],
   "source": [
    "file = './data/anes_timeseries_2024_csv_20250808.csv'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ed1f1ce7",
   "metadata": {},
   "source": [
    "# How acceptable is political violence among the ideologies?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "3a0b86c7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x1100 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import pandas as pd\n",
    "\n",
    "df = pd.read_csv(file)\n",
    "\n",
    "ideology = 'V241177'\n",
    "compared_value = 'V241579'\n",
    "\n",
    "# Remove refused, error, and haven't thought much about it\n",
    "df =  df[~df[ideology].isin([-5, -9, -1, -4])]\n",
    "df = df[~df[compared_value].isin([-5, -9])]\n",
    "\n",
    "\n",
    "# Define color palette\n",
    "blue_shades = sns.color_palette(\"Blues\", n_colors=3)[::-1]  # Invert so darkest is first\n",
    "purple = ['#800080']\n",
    "red_shades = sns.color_palette(\"Reds\", n_colors=3)\n",
    "gray = ['#888888']  # Unique color for 'Haven’t thought much about it'\n",
    "custom_palette = blue_shades + purple + red_shades + gray\n",
    "\n",
    "# Map ideology values to palette indices (order: 1,2,3,4,5,6,7,99)\n",
    "ideology_order = [1,2,3,4,5,6,7,99]\n",
    "palette_dict = {k: v for k, v in zip(ideology_order, custom_palette)}\n",
    "\n",
    "# Create figure\n",
    "plt.figure(figsize=(15, 11))\n",
    "\n",
    "# Normalize counts within each pcorrect group\n",
    "counts = df.groupby([compared_value, ideology]).size().reset_index(name='count')\n",
    "total_per_pcorrect = df.groupby(compared_value).size().reset_index(name='total')\n",
    "counts = counts.merge(total_per_pcorrect, on=compared_value)\n",
    "counts['percentage'] = (counts['count'] / counts['total']) * 100  # Convert to percentage\n",
    "\n",
    "# Plot using normalized percentages instead of raw counts\n",
    "ax = sns.barplot(\n",
    "    x=compared_value, y='percentage', hue=ideology, data=counts,\n",
    "    palette=palette_dict, hue_order=ideology_order\n",
    " )\n",
    "\n",
    "# Set labels and title\n",
    "plt.title('Distribution of Answers by Political Ideology')\n",
    "plt.xlabel('Is it justified for people to use violence to pursue poltical goals?')\n",
    "plt.ylabel('Percentage within Each Response Group')\n",
    "\n",
    "# Define x-axis labels\n",
    "\n",
    "x_value_labels = {\n",
    "    # -9: 'Refused',\n",
    "    -1: 'Inapplicable',\n",
    "     1: 'Not at all',\n",
    "     2: 'A little',\n",
    "     3: 'A moderate amount',\n",
    "     4: 'A lot',\n",
    "     5: 'A great deal'\n",
    "}\n",
    "\n",
    "plt.xticks(ticks=range(len(x_value_labels)), labels=list(x_value_labels.values()))\n",
    "\n",
    "# Define ideological labels\n",
    "lcself_labels = {\n",
    "    1: 'Very liberal',\n",
    "    2: 'Somewhat Liberal',\n",
    "    3: 'Closer to liberals',\n",
    "    4: 'Neither liberal nor conservative',\n",
    "    5: 'Closer to conservatives',\n",
    "    6: 'Somewhat Conservative',\n",
    "    7: 'Very Conservative',\n",
    "    -9: 'Refused',\n",
    "    # -4: 'Error',\n",
    "    99: 'Haven’t thought much about it'\n",
    "}\n",
    "\n",
    "# Update legend with ideological labels\n",
    "handles, labels = plt.gca().get_legend_handles_labels()\n",
    "plt.legend(handles, [lcself_labels[int(label)] for label in labels], title='Ideological Bias', bbox_to_anchor=(1.05, 1), loc='upper left')\n",
    "\n",
    "# Add annotations for each bar\n",
    "for p in ax.patches:\n",
    "    height = p.get_height()\n",
    "    if height > 0:  # Only annotate non-zero bars\n",
    "        x_pos = p.get_x()\n",
    "        width = p.get_width()\n",
    "\n",
    "        # Find the corresponding percentage\n",
    "        percentage_value = height  # Since y-axis is now percentage\n",
    "\n",
    "        # Annotate with percentage\n",
    "        ax.text(x_pos + width / 2, height, f'{percentage_value:.1f}%',\n",
    "                ha='center', va='bottom', fontsize=8)\n",
    "\n",
    "# Add source text\n",
    "ax.text(0, -0.1, 'American National Election Studies. 2025. \\nANES 2024 Time Series Study Preliminary Release: Pre-Election Data [dataset and documentation]. \\nAugust 08, 2025 version. www.electionstudies.org',\n",
    "        ha='left', va='center_baseline', transform=ax.transAxes, fontsize=8)\n",
    "ax.text(0, 0.99, 'Stabs',\n",
    "        ha='left', va='center_baseline', transform=ax.transAxes, fontsize=8)\n",
    "ax.text(0.95, 0.99, 'n = ' + str(len(df)), ha='right', va='top', transform=ax.transAxes)\n",
    "\n",
    "# Show the plot\n",
    "plt.show()  "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c6ec43b4",
   "metadata": {},
   "source": [
    "# Collapsed ideologies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "bb511e1a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x1100 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import pandas as pd\n",
    "\n",
    "df = pd.read_csv(file)\n",
    "ideology = 'V241177'\n",
    "compared_value = 'V241579'\n",
    "\n",
    "# Remove refused, error\n",
    "df = df[~df[ideology].isin([-5, -9, -1, -4])]\n",
    "df = df[~df[compared_value].isin([-5, -9])]\n",
    "\n",
    "# Collapse ideology into four groups\n",
    "def collapse_ideology(x):\n",
    "    if x in [1, 2, 3]:\n",
    "        return \"Liberal\"\n",
    "    elif x == 4:\n",
    "        return \"Centrist\"\n",
    "    elif x in [5, 6, 7]:\n",
    "        return \"Conservative\"\n",
    "    elif x == 99:\n",
    "        return \"Haven’t thought much about it\"\n",
    "    else:\n",
    "        return None\n",
    "\n",
    "df[\"ideology_group\"] = df[ideology].apply(collapse_ideology)\n",
    "\n",
    "# Define color palette for the 4 categories\n",
    "palette_dict = {\n",
    "    \"Liberal\": sns.color_palette(\"Blues\", n_colors=3)[2],  # darker blue\n",
    "    \"Centrist\": '#800080',  # purple\n",
    "    \"Conservative\": sns.color_palette(\"Reds\", n_colors=3)[2],  # darker red\n",
    "    \"Haven’t thought much about it\": '#888888'  # gray\n",
    "}\n",
    "\n",
    "# Create figure\n",
    "plt.figure(figsize=(15, 11))\n",
    "\n",
    "# Normalize counts within each compared_value group\n",
    "counts = df.groupby([compared_value, \"ideology_group\"]).size().reset_index(name='count')\n",
    "total_per_group = df.groupby(compared_value).size().reset_index(name='total')\n",
    "counts = counts.merge(total_per_group, on=compared_value)\n",
    "counts['percentage'] = (counts['count'] / counts['total']) * 100  # Convert to percentage\n",
    "\n",
    "# Plot using normalized percentages instead of raw counts\n",
    "ax = sns.barplot(\n",
    "    x=compared_value, y='percentage', hue=\"ideology_group\", data=counts,\n",
    "    palette=palette_dict, hue_order=[\"Liberal\", \"Centrist\", \"Conservative\", \"Haven’t thought much about it\"]\n",
    ")\n",
    "\n",
    "# Set labels and title\n",
    "plt.title('Distribution of Answers by Collapsed Political Ideology')\n",
    "plt.xlabel('Is it justified for people to use violence to pursue political goals?')\n",
    "plt.ylabel('Percentage within Each Response Group')\n",
    "\n",
    "# Define x-axis labels\n",
    "x_value_labels = {\n",
    "    -1: 'Inapplicable',\n",
    "     1: 'Not at all',\n",
    "     2: 'A little',\n",
    "     3: 'A moderate amount',\n",
    "     4: 'A lot',\n",
    "     5: 'A great deal'\n",
    "}\n",
    "plt.xticks(ticks=range(len(x_value_labels)), labels=list(x_value_labels.values()))\n",
    "\n",
    "# Update legend\n",
    "plt.legend(title='Ideological Bias', bbox_to_anchor=(1.05, 1), loc='upper left')\n",
    "\n",
    "# Add annotations for each bar\n",
    "for p in ax.patches:\n",
    "    height = p.get_height()\n",
    "    if height > 0:\n",
    "        x_pos = p.get_x()\n",
    "        width = p.get_width()\n",
    "        ax.text(x_pos + width / 2, height, f'{height:.1f}%',\n",
    "                ha='center', va='bottom', fontsize=8)\n",
    "\n",
    "# Add source text\n",
    "ax.text(0, -0.1, 'American National Election Studies. 2025. \\nANES 2024 Time Series Study Preliminary Release: Pre-Election Data [dataset and documentation]. \\nAugust 08, 2025 version. www.electionstudies.org',\n",
    "        ha='left', va='center_baseline', transform=ax.transAxes, fontsize=8)\n",
    "ax.text(0, 0.99, 'Stabs',\n",
    "        ha='left', va='center_baseline', transform=ax.transAxes, fontsize=8)\n",
    "ax.text(0.95, 0.99, 'n = ' + str(len(df)), ha='right', va='top', transform=ax.transAxes)\n",
    "\n",
    "# Show the plot\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "70b56f43",
   "metadata": {},
   "source": [
    "# Race and \"influence\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "24671e1d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x1100 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import pandas as pd\n",
    "\n",
    "df = pd.read_csv(file)\n",
    "ideology = 'V241177'\n",
    "compared_value = 'V242527'\n",
    "\n",
    "df[[ideology, compared_value]].to_csv('test.csv', index=False)\n",
    "\n",
    "# Remove refused, error, and haven't thought much about it\n",
    "df =  df[~df[ideology].isin([-5, -9, -1, -4])]\n",
    "df = df[~df[compared_value].isin([-5, -9, -6, -7])]\n",
    "\n",
    "\n",
    "# Define color palette\n",
    "blue_shades = sns.color_palette(\"Blues\", n_colors=3)[::-1]  # Invert so darkest is first\n",
    "purple = ['#800080']\n",
    "red_shades = sns.color_palette(\"Reds\", n_colors=3)\n",
    "gray = ['#888888']  # Unique color for 'Haven’t thought much about it'\n",
    "custom_palette = blue_shades + purple + red_shades + gray\n",
    "\n",
    "# Map ideology values to palette indices (order: 1,2,3,4,5,6,7,99)\n",
    "ideology_order = [1,2,3,4,5,6,7,99]\n",
    "palette_dict = {k: v for k, v in zip(ideology_order, custom_palette)}\n",
    "\n",
    "# Create figure\n",
    "plt.figure(figsize=(15, 11))\n",
    "\n",
    "# Normalize counts within each pcorrect group\n",
    "counts = df.groupby([compared_value, ideology]).size().reset_index(name='count')\n",
    "total_per_pcorrect = df.groupby(compared_value).size().reset_index(name='total')\n",
    "counts = counts.merge(total_per_pcorrect, on=compared_value)\n",
    "counts['percentage'] = (counts['count'] / counts['total']) * 100  # Convert to percentage\n",
    "\n",
    "# Plot using normalized percentages instead of raw counts\n",
    "ax = sns.barplot(\n",
    "    x=compared_value, y='percentage', hue=ideology, data=counts,\n",
    "    palette=palette_dict, hue_order=ideology_order\n",
    " )\n",
    "\n",
    "# Set labels and title\n",
    "plt.title('Distribution of Answers by Political Ideology')\n",
    "plt.xlabel('How much influence do blacks have in US politics?')\n",
    "plt.ylabel('Percentage within Each Response Group')\n",
    "\n",
    "# Define x-axis labels\n",
    "\n",
    "x_value_labels = {\n",
    "    # -9: 'Refused',\n",
    "    -1: 'Inapplicable',\n",
    "     1: 'Too much influence',\n",
    "     2: 'Just about the right amount of influence',\n",
    "     3: 'Too little influence',\n",
    "}\n",
    "\n",
    "plt.xticks(ticks=range(len(x_value_labels)), labels=list(x_value_labels.values()))\n",
    "\n",
    "# Define ideological labels\n",
    "lcself_labels = {\n",
    "    1: 'Very liberal',\n",
    "    2: 'Somewhat Liberal',\n",
    "    3: 'Closer to liberals',\n",
    "    4: 'Neither liberal nor conservative',\n",
    "    5: 'Closer to conservatives',\n",
    "    6: 'Somewhat Conservative',\n",
    "    7: 'Very Conservative',\n",
    "    -9: 'Refused',\n",
    "    # -4: 'Error',\n",
    "    99: 'Haven’t thought much about it'\n",
    "}\n",
    "\n",
    "# Update legend with ideological labels\n",
    "handles, labels = plt.gca().get_legend_handles_labels()\n",
    "plt.legend(handles, [lcself_labels[int(label)] for label in labels], title='Ideological Bias', bbox_to_anchor=(1.05, 1), loc='upper left')\n",
    "\n",
    "# Add annotations for each bar\n",
    "for p in ax.patches:\n",
    "    height = p.get_height()\n",
    "    if height > 0:  # Only annotate non-zero bars\n",
    "        x_pos = p.get_x()\n",
    "        width = p.get_width()\n",
    "\n",
    "        # Find the corresponding percentage\n",
    "        percentage_value = height  # Since y-axis is now percentage\n",
    "\n",
    "        # Annotate with percentage\n",
    "        ax.text(x_pos + width / 2, height, f'{percentage_value:.1f}%',\n",
    "                ha='center', va='bottom', fontsize=8)\n",
    "\n",
    "# Add source text\n",
    "ax.text(0, -0.1, 'American National Election Studies. 2025. \\nANES 2024 Time Series Study Preliminary Release: Pre-Election Data [dataset and documentation]. \\nAugust 08, 2025 version. www.electionstudies.org',\n",
    "        ha='left', va='center_baseline', transform=ax.transAxes, fontsize=8)\n",
    "ax.text(0, 0.99, 'Stabs',\n",
    "        ha='left', va='center_baseline', transform=ax.transAxes, fontsize=8)\n",
    "ax.text(0.95, 0.99, 'n = ' + str(len(df)), ha='right', va='top', transform=ax.transAxes)\n",
    "\n",
    "# Show the plot\n",
    "plt.show()  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "5caa2e4d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x1100 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import pandas as pd\n",
    "\n",
    "df = pd.read_csv(file)\n",
    "ideology = 'V241177'\n",
    "compared_value = 'V242526'\n",
    "\n",
    "df[[ideology, compared_value]].to_csv('test.csv', index=False)\n",
    "\n",
    "# Remove refused, error, and haven't thought much about it\n",
    "df =  df[~df[ideology].isin([-5, -9, -1, -4])]\n",
    "df = df[~df[compared_value].isin([-5, -9, -6, -7])]\n",
    "\n",
    "\n",
    "# Define color palette\n",
    "blue_shades = sns.color_palette(\"Blues\", n_colors=3)[::-1]  # Invert so darkest is first\n",
    "purple = ['#800080']\n",
    "red_shades = sns.color_palette(\"Reds\", n_colors=3)\n",
    "gray = ['#888888']  # Unique color for 'Haven’t thought much about it'\n",
    "custom_palette = blue_shades + purple + red_shades + gray\n",
    "\n",
    "# Map ideology values to palette indices (order: 1,2,3,4,5,6,7,99)\n",
    "ideology_order = [1,2,3,4,5,6,7,99]\n",
    "palette_dict = {k: v for k, v in zip(ideology_order, custom_palette)}\n",
    "\n",
    "# Create figure\n",
    "plt.figure(figsize=(15, 11))\n",
    "\n",
    "# Normalize counts within each pcorrect group\n",
    "counts = df.groupby([compared_value, ideology]).size().reset_index(name='count')\n",
    "total_per_pcorrect = df.groupby(compared_value).size().reset_index(name='total')\n",
    "counts = counts.merge(total_per_pcorrect, on=compared_value)\n",
    "counts['percentage'] = (counts['count'] / counts['total']) * 100  # Convert to percentage\n",
    "\n",
    "# Plot using normalized percentages instead of raw counts\n",
    "ax = sns.barplot(\n",
    "    x=compared_value, y='percentage', hue=ideology, data=counts,\n",
    "    palette=palette_dict, hue_order=ideology_order\n",
    " )\n",
    "\n",
    "# Set labels and title\n",
    "plt.title('Distribution of Answers by Political Ideology')\n",
    "plt.xlabel('How much influence do whites have in US politics?')\n",
    "plt.ylabel('Percentage within Each Response Group')\n",
    "\n",
    "# Define x-axis labels\n",
    "\n",
    "x_value_labels = {\n",
    "    # -9: 'Refused',\n",
    "    -1: 'Inapplicable',\n",
    "     1: 'Too much influence',\n",
    "     2: 'Just about the right amount of influence',\n",
    "     3: 'Too little influence',\n",
    "}\n",
    "\n",
    "plt.xticks(ticks=range(len(x_value_labels)), labels=list(x_value_labels.values()))\n",
    "\n",
    "# Define ideological labels\n",
    "lcself_labels = {\n",
    "    1: 'Very liberal',\n",
    "    2: 'Somewhat Liberal',\n",
    "    3: 'Closer to liberals',\n",
    "    4: 'Neither liberal nor conservative',\n",
    "    5: 'Closer to conservatives',\n",
    "    6: 'Somewhat Conservative',\n",
    "    7: 'Very Conservative',\n",
    "    -9: 'Refused',\n",
    "    # -4: 'Error',\n",
    "    99: 'Haven’t thought much about it'\n",
    "}\n",
    "\n",
    "# Update legend with ideological labels\n",
    "handles, labels = plt.gca().get_legend_handles_labels()\n",
    "plt.legend(handles, [lcself_labels[int(label)] for label in labels], title='Ideological Bias', bbox_to_anchor=(1.05, 1), loc='upper left')\n",
    "\n",
    "# Add annotations for each bar\n",
    "for p in ax.patches:\n",
    "    height = p.get_height()\n",
    "    if height > 0:  # Only annotate non-zero bars\n",
    "        x_pos = p.get_x()\n",
    "        width = p.get_width()\n",
    "\n",
    "        # Find the corresponding percentage\n",
    "        percentage_value = height  # Since y-axis is now percentage\n",
    "\n",
    "        # Annotate with percentage\n",
    "        ax.text(x_pos + width / 2, height, f'{percentage_value:.1f}%',\n",
    "                ha='center', va='bottom', fontsize=8)\n",
    "\n",
    "# Add source text\n",
    "ax.text(0, -0.1, 'American National Election Studies. 2025. \\nANES 2024 Time Series Study Preliminary Release: Pre-Election Data [dataset and documentation]. \\nAugust 08, 2025 version. www.electionstudies.org',\n",
    "        ha='left', va='center_baseline', transform=ax.transAxes, fontsize=8)\n",
    "ax.text(0, 0.99, 'Stabs',\n",
    "        ha='left', va='center_baseline', transform=ax.transAxes, fontsize=8)\n",
    "ax.text(0.95, 0.99, 'n = ' + str(len(df)), ha='right', va='top', transform=ax.transAxes)\n",
    "\n",
    "# Show the plot\n",
    "plt.show()  "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "70f4cb33",
   "metadata": {},
   "source": [
    "# Violence Stereotypes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "96115925",
   "metadata": {},
   "source": [
    "## By race"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "21fe0d2c",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1500x1100 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import pandas as pd\n",
    "\n",
    "df = pd.read_csv(file)\n",
    "race = 'V241501x'\n",
    "compared_value = 'V242545'\n",
    "\n",
    "\n",
    "df[[race, compared_value]].to_csv('test.csv', index=False)\n",
    "\n",
    "# Remove refused, error, and haven't thought much about it\n",
    "df =  df[~df[race].isin([-5, -9, -1, -4])]\n",
    "df = df[~df[compared_value].isin([-5, -9, -6, -7])]\n",
    "\n",
    "\n",
    "# Define color palette\n",
    "blue_shades = sns.color_palette(\"Blues\", n_colors=3)[::-1]  # Invert so darkest is first\n",
    "purple = ['#800080']\n",
    "red_shades = sns.color_palette(\"Reds\", n_colors=3)\n",
    "gray = ['#888888']  # Unique color for 'Haven’t thought much about it'\n",
    "custom_palette = blue_shades + purple + red_shades + gray\n",
    "\n",
    "# Map ideology values to palette indices (order: 1,2,3,4,5,6,7,99)\n",
    "race_order = [1,2,3,4,5,6,7,99]\n",
    "palette_dict = {k: v for k, v in zip(race_order, custom_palette)}\n",
    "\n",
    "# Create figure\n",
    "plt.figure(figsize=(15, 11))\n",
    "\n",
    "# Normalize counts within each pcorrect group\n",
    "counts = df.groupby([compared_value, race]).size().reset_index(name='count')\n",
    "total_per_pcorrect = df.groupby(compared_value).size().reset_index(name='total')\n",
    "counts = counts.merge(total_per_pcorrect, on=compared_value)\n",
    "counts['percentage'] = (counts['count'] / counts['total']) * 100  # Convert to percentage\n",
    "\n",
    "# Plot using normalized percentages instead of raw counts\n",
    "ax = sns.barplot(\n",
    "    x=compared_value, y='percentage', hue=race, data=counts,\n",
    "    palette=palette_dict, hue_order=race_order\n",
    ")\n",
    "\n",
    "# Set labels and title\n",
    "plt.title('Distribution of Answers by Race')\n",
    "plt.xlabel('Where on the scale of 1 to 7 do you place White people in terms of violence?')\n",
    "plt.ylabel('Percentage within Each Response Group')\n",
    "\n",
    "# Define x-axis labels\n",
    "\n",
    "x_value_labels = {\n",
    "    # -9: 'Refused',\n",
    "    -1: 'Inapplicable',\n",
    "     1: 'Peaceful',\n",
    "     2: '2',\n",
    "     3: '3',\n",
    "     4: '4',\n",
    "     5: '5',\n",
    "     6: '6',\n",
    "     7: 'Violent',\n",
    "}\n",
    "\n",
    "plt.xticks(ticks=range(len(x_value_labels)), labels=list(x_value_labels.values()))\n",
    "\n",
    "# Define ideological labels\n",
    "lcself_labels = {\n",
    "    1: 'White, non-Hispanic',\n",
    "    2: 'Black, non-Hispanic',\n",
    "    3: 'Hispanic',\n",
    "    4: 'Asian or Native Hawaiian or Other Pacific Islander, non-Hispanic',\n",
    "    5: 'Native American/Alaska Native or other race, non-Hispanic',\n",
    "    6: 'Multiple races, non-Hispanic',\n",
    "    7: 'Other/Unknown',  # Added label for 7\n",
    "    99: 'Haven’t thought much about it',  # If 99 is possible\n",
    "    -9: 'Refused',\n",
    "    # -4: 'Error',\n",
    "}\n",
    "\n",
    "# Update legend with ideological labels\n",
    "handles, labels = plt.gca().get_legend_handles_labels()\n",
    "plt.legend(handles, [lcself_labels[int(label)] for label in labels], title='Self-identified Race', bbox_to_anchor=(1.05, 1), loc='upper left')\n",
    "\n",
    "# Add annotations for each bar\n",
    "for p in ax.patches:\n",
    "    height = p.get_height()\n",
    "    if height > 0:  # Only annotate non-zero bars\n",
    "        x_pos = p.get_x()\n",
    "        width = p.get_width()\n",
    "\n",
    "        # Find the corresponding percentage\n",
    "        percentage_value = height  # Since y-axis is now percentage\n",
    "\n",
    "        # Annotate with percentage\n",
    "        ax.text(x_pos + width / 2, height, f'{percentage_value:.1f}%',\n",
    "                ha='center', va='bottom', fontsize=8)\n",
    "\n",
    "# Add source text\n",
    "ax.text(0, -0.1, 'American National Election Studies. 2025. \\nANES 2024 Time Series Study Preliminary Release: Pre-Election Data [dataset and documentation]. \\nAugust 08, 2025 version. www.electionstudies.org',\n",
    "        ha='left', va='center_baseline', transform=ax.transAxes, fontsize=8)\n",
    "ax.text(0, 0.99, 'Stabs',\n",
    "        ha='left', va='center_baseline', transform=ax.transAxes, fontsize=8)\n",
    "ax.text(0.95, 0.99, 'n = ' + str(len(df)), ha='right', va='top', transform=ax.transAxes)\n",
    "\n",
    "# Show the plot\n",
    "plt.show()  "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b2448eca",
   "metadata": {},
   "source": [
    "## Broken down by Race and Ideology"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "7a2c58b4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x1600 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import pandas as pd\n",
    "\n",
    "df = pd.read_csv(file)\n",
    "race = 'V241501x'\n",
    "ideology = 'V241177'\n",
    "compared_value = 'V242546'\n",
    "\n",
    "\n",
    "# Remove refused, error, and haven't thought much about it\n",
    "df = df[~df[race].isin([-5, -9, -1, -4])]\n",
    "df = df[~df[ideology].isin([-5, -9, -1, -4])]\n",
    "df = df[~df[compared_value].isin([-5, -9, -6, -7])]\n",
    "\n",
    "# Collapse ideology into four groups\n",
    "def collapse_ideology(x):\n",
    "    if x in [1, 2, 3]:\n",
    "        return \"Liberal\"\n",
    "    elif x == 4:\n",
    "        return \"Centrist\"\n",
    "    elif x in [5, 6, 7]:\n",
    "        return \"Conservative\"\n",
    "    elif x == 99:\n",
    "        return \"Haven't thought much about it\"\n",
    "    else:\n",
    "        return None\n",
    "\n",
    "df[\"ideology_group\"] = df[ideology].apply(collapse_ideology)\n",
    "\n",
    "# Collapse race into broader categories\n",
    "def collapse_race(x):\n",
    "    if x == 1:\n",
    "        return \"White, non-Hispanic\"\n",
    "    elif x == 2:\n",
    "        return \"Black, non-Hispanic\"\n",
    "    elif x == 3:\n",
    "        return \"Hispanic\"\n",
    "    elif x in [4, 5, 6, 7]:\n",
    "        return \"Other/Unknown\"\n",
    "    else:\n",
    "        return None\n",
    "\n",
    "df[\"race_group\"] = df[race].apply(collapse_race)\n",
    "\n",
    "df[[\"race_group\", \"ideology_group\", compared_value]].to_csv('test.csv', index=False)\n",
    "# Create figure with subplots\n",
    "fig, axes = plt.subplots(2, 2, figsize=(20, 16))\n",
    "fig.suptitle('Violence Stereotypes by Race and Ideology: Black People', fontsize=16, y=0.95)\n",
    "\n",
    "# Define color palette for ideology groups\n",
    "ideology_palette = {\n",
    "    \"Liberal\": sns.color_palette(\"Blues\", n_colors=3)[2],\n",
    "    \"Centrist\": '#800080',\n",
    "    \"Conservative\": sns.color_palette(\"Reds\", n_colors=3)[2],\n",
    "    \"Haven't thought much about it\": '#888888'\n",
    "}\n",
    "\n",
    "# Define x-axis labels\n",
    "x_value_labels = {\n",
    "    -1: 'Inapplicable',\n",
    "     1: 'Peaceful',\n",
    "     2: '2',\n",
    "     3: '3',\n",
    "     4: '4',\n",
    "     5: '5',\n",
    "     6: '6',\n",
    "     7: 'Violent',\n",
    "}\n",
    "\n",
    "# Create subplots for each race group\n",
    "race_groups = [\"White, non-Hispanic\", \"Black, non-Hispanic\", \"Hispanic\", \"Other/Unknown\"]\n",
    "positions = [(0, 0), (0, 1), (1, 0), (1, 1)]\n",
    "\n",
    "for i, race_group in enumerate(race_groups):\n",
    "    row, col = positions[i]\n",
    "    ax = axes[row, col]\n",
    "    \n",
    "    # Filter data for this race group\n",
    "    race_data = df[df[\"race_group\"] == race_group]\n",
    "    \n",
    "    if len(race_data) > 0:\n",
    "        # Normalize counts within each compared_value group\n",
    "        counts = race_data.groupby([compared_value, \"ideology_group\"]).size().reset_index(name='count')\n",
    "        total_per_group = race_data.groupby(compared_value).size().reset_index(name='total')\n",
    "        counts = counts.merge(total_per_group, on=compared_value)\n",
    "        counts['percentage'] = (counts['count'] / counts['total']) * 100\n",
    "        \n",
    "        # Plot using normalized percentages\n",
    "        sns.barplot(\n",
    "            x=compared_value, y='percentage', hue=\"ideology_group\", data=counts,\n",
    "            palette=ideology_palette, hue_order=[\"Liberal\", \"Centrist\", \"Conservative\", \"Haven't thought much about it\"],\n",
    "            ax=ax\n",
    "        )\n",
    "        \n",
    "        # Set labels\n",
    "        ax.set_title(f'{race_group} (n={len(race_data)})')\n",
    "        ax.set_xlabel('Violence Scale (1=Peaceful, 7=Violent)')\n",
    "        ax.set_ylabel('Percentage within Each Response Group')\n",
    "        \n",
    "        # Set x-axis labels\n",
    "        ax.set_xticks(range(len(x_value_labels)))\n",
    "        ax.set_xticklabels(list(x_value_labels.values()), rotation=45)\n",
    "        \n",
    "        # Update legend\n",
    "        ax.legend(title='Ideology', bbox_to_anchor=(1.05, 1), loc='upper left')\n",
    "        \n",
    "        # Add annotations for each bar\n",
    "        for p in ax.patches:\n",
    "            height = p.get_height()\n",
    "            if height > 0:\n",
    "                x_pos = p.get_x()\n",
    "                width = p.get_width()\n",
    "                ax.text(x_pos + width / 2, height, f'{height:.1f}%',\n",
    "                        ha='center', va='bottom', fontsize=8)\n",
    "    else:\n",
    "        ax.text(0.5, 0.5, f'No data for {race_group}', ha='center', va='center', transform=ax.transAxes)\n",
    "        ax.set_title(f'{race_group}')\n",
    "\n",
    "# Adjust layout\n",
    "plt.tight_layout()\n",
    "\n",
    "# Add source text\n",
    "fig.text(0, -0.02, 'American National Election Studies. 2025. \\nANES 2024 Time Series Study Preliminary Release: Pre-Election Data [dataset and documentation]. \\nAugust 08, 2025 version. www.electionstudies.org',\n",
    "        ha='left', va='center_baseline', fontsize=8)\n",
    "fig.text(0, 0.98, 'Stabs',\n",
    "        ha='left', va='center_baseline', fontsize=8)\n",
    "\n",
    "# Show the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "fb3bb21a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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