diff --git a/ri_dataset_exploration.ipynb b/ri_dataset_exploration.ipynb
new file mode 100644
index 0000000..0796319
--- /dev/null
+++ b/ri_dataset_exploration.ipynb
@@ -0,0 +1,519 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "be1cea7a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from policyengine_us import Microsimulation\n",
+    "import pandas as pd\n",
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "0d21b774",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Load RI dataset\n",
+    "sim = Microsimulation(dataset=\"hf://policyengine/test/RI.h5\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "1870e7ac",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Number of households in dataset: 2,368\n",
+      "Household count (mapped): 388,376\n",
+      "Person count (mapped): 1,106,390\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Check dataset size\n",
+    "household_weight = sim.calculate(\"household_weight\", period=2025)\n",
+    "household_count = sim.calculate(\"household_count\", period=2025, map_to=\"household\")\n",
+    "person_count = sim.calculate(\"person_count\", period=2025, map_to=\"household\")\n",
+    "\n",
+    "print(f\"Number of households in dataset: {len(household_weight):,}\")\n",
+    "print(f\"Household count (mapped): {household_count.sum():,.0f}\")\n",
+    "print(f\"Person count (mapped): {person_count.sum():,.0f}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "f0c79a50",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Income distribution:\n",
+      "  Median AGI: $73,149\n",
+      "  75th percentile: $152,410\n",
+      "  90th percentile: $271,943\n",
+      "  95th percentile: $400,420\n",
+      "  Max AGI: $1,740,956\n",
+      "\n",
+      "Households by income threshold:\n",
+      "  Households over $80k: 180,850.09423405278\n",
+      "  Households over $120k: 128,983.09166995375\n",
+      "  Households over $160k: 88,148.79416422347\n",
+      "  Households over $240k: 47,853.16035188042\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Check household income distribution (aggregate to household level using map_to)\n",
+    "agi = sim.calculate(\"adjusted_gross_income\", period=2025, map_to=\"household\")\n",
+    "print(f\"Income distribution:\")\n",
+    "print(f\"  Median AGI: ${agi.median():,.0f}\")\n",
+    "print(f\"  75th percentile: ${agi.quantile(0.75):,.0f}\")\n",
+    "print(f\"  90th percentile: ${agi.quantile(0.90):,.0f}\")\n",
+    "print(f\"  95th percentile: ${agi.quantile(0.95):,.0f}\")\n",
+    "print(f\"  Max AGI: ${agi.max():,.0f}\")\n",
+    "print(f\"\\nHouseholds by income threshold:\")\n",
+    "print(f\"  Households over $80k: {(agi > 80_000).sum():,}\")\n",
+    "print(f\"  Households over $120k: {(agi > 120_000).sum():,}\")\n",
+    "print(f\"  Households over $160k: {(agi > 160_000).sum():,}\")\n",
+    "print(f\"  Households over $240k: {(agi > 240_000).sum():,}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "71b548db",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Households with children (weighted):\n",
+      "  Total households with children: 121,492\n",
+      "  Households with 1 child: 66,113\n",
+      "  Households with 2 children: 37,589\n",
+      "  Households with 3+ children: 17,790\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Check households with children (count at person level, aggregate to household)\n",
+    "is_child = sim.calculate(\"is_child\", period=2025, map_to=\"person\")\n",
+    "household_id = sim.calculate(\"household_id\", period=2025, map_to=\"person\")\n",
+    "household_weight = sim.calculate(\"household_weight\", period=2025, map_to=\"person\")\n",
+    "\n",
+    "# Create DataFrame for easier manipulation\n",
+    "df_households = pd.DataFrame({\n",
+    "    'household_id': household_id,\n",
+    "    'is_child': is_child,\n",
+    "    'household_weight': household_weight\n",
+    "})\n",
+    "\n",
+    "# Count children per household\n",
+    "children_per_household = df_households.groupby('household_id').agg({\n",
+    "    'is_child': 'sum',\n",
+    "    'household_weight': 'first'  # household_weight is same for all members\n",
+    "}).reset_index()\n",
+    "\n",
+    "# Calculate weighted household counts\n",
+    "total_households_with_children = children_per_household[children_per_household['is_child'] > 0]['household_weight'].sum()\n",
+    "households_with_1_child = children_per_household[children_per_household['is_child'] == 1]['household_weight'].sum()\n",
+    "households_with_2_children = children_per_household[children_per_household['is_child'] == 2]['household_weight'].sum()\n",
+    "households_with_3plus_children = children_per_household[children_per_household['is_child'] >= 3]['household_weight'].sum()\n",
+    "\n",
+    "print(f\"\\nHouseholds with children (weighted):\")\n",
+    "print(f\"  Total households with children: {total_households_with_children:,.0f}\")\n",
+    "print(f\"  Households with 1 child: {households_with_1_child:,.0f}\")\n",
+    "print(f\"  Households with 2 children: {households_with_2_children:,.0f}\")\n",
+    "print(f\"  Households with 3+ children: {households_with_3plus_children:,.0f}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "a215302f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Children by age:\n",
+      "  Total children under 18: 203,860\n",
+      "  Children under 4: 41,525\n",
+      "  Children under 6: 61,595\n",
+      "  Children ages 6-17: 138,203\n",
+      "\n",
+      "Sample of children under 4:\n",
+      "     household_id  tax_unit_id  person_id  age\n",
+      "7         2730011            5    7730007  1.0\n",
+      "16        2730006           10    7730016  2.0\n",
+      "37        2730018           26    7730037  3.0\n",
+      "83        2730035           55    7730083  0.0\n",
+      "101       2730041           66    7730101  3.0\n",
+      "102       2730041           66    7730102  2.0\n",
+      "103       2730041           66    7730103  0.0\n",
+      "108       2730043           69    7730108  1.0\n",
+      "111       2730045           71    7730111  1.0\n",
+      "115       2730045           71    7730115  3.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Check children by age groups using Ben's workaround\n",
+    "import pandas as pd\n",
+    "df = pd.DataFrame({\n",
+    "    \"household_id\": sim.calculate(\"household_id\", map_to=\"person\"),\n",
+    "    \"tax_unit_id\": sim.calculate(\"tax_unit_id\", map_to=\"person\"),\n",
+    "    \"person_id\": sim.calculate(\"person_id\", map_to=\"person\"),\n",
+    "    \"age\": sim.calculate(\"age\", map_to=\"person\"),\n",
+    "    \"person_weight\": sim.calculate(\"person_weight\", map_to=\"person\")\n",
+    "})\n",
+    "\n",
+    "# Filter for children and apply weights\n",
+    "children_under_4_df = df[df['age'] < 4]\n",
+    "children_under_6_df = df[df['age'] < 6]\n",
+    "children_under_18_df = df[df['age'] < 18]\n",
+    "children_6_17_df = df[(df['age'] >= 6) & (df['age'] < 18)]\n",
+    "\n",
+    "# Calculate weighted totals\n",
+    "is_child = sim.calculate(\"is_child\", period=2025)\n",
+    "total_children = is_child.sum()\n",
+    "children_under_4 = children_under_4_df['person_weight'].sum()\n",
+    "children_under_6 = children_under_6_df['person_weight'].sum()\n",
+    "children_6_17 = children_6_17_df['person_weight'].sum()\n",
+    "\n",
+    "print(f\"\\nChildren by age:\")\n",
+    "print(f\"  Total children under 18: {total_children:,.0f}\")\n",
+    "print(f\"  Children under 4: {children_under_4:,.0f}\")\n",
+    "print(f\"  Children under 6: {children_under_6:,.0f}\")\n",
+    "print(f\"  Children ages 6-17: {children_6_17:,.0f}\")\n",
+    "\n",
+    "print(f\"\\nSample of children under 4:\")\n",
+    "print(children_under_4_df[['household_id', 'tax_unit_id', 'person_id', 'age']].head(10))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "9468033e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "============================================================\n",
+      "RI DATASET SUMMARY - WEIGHTED (Population Estimates)\n",
+      "============================================================\n",
+      "                        Metric      Value\n",
+      "    Household count (weighted)    388,376\n",
+      "       Person count (weighted)  1,106,390\n",
+      "                    Median AGI    $73,149\n",
+      "           75th percentile AGI   $152,410\n",
+      "           90th percentile AGI   $271,943\n",
+      "           95th percentile AGI   $400,420\n",
+      "                       Max AGI $1,740,956\n",
+      "          Households over $80k    180,850\n",
+      "         Households over $120k    128,983\n",
+      "         Households over $160k     88,149\n",
+      "         Households over $240k     47,853\n",
+      "Total households with children    121,492\n",
+      "       Households with 1 child     66,113\n",
+      "    Households with 2 children     37,589\n",
+      "   Households with 3+ children     17,790\n",
+      "       Total children under 18    203,860\n",
+      "              Children under 4     41,525\n",
+      "              Children under 6     61,595\n",
+      "            Children ages 6-17    138,203\n",
+      "============================================================\n",
+      "\n",
+      "============================================================\n",
+      "RI DATASET SUMMARY - UNWEIGHTED (Sample Counts)\n",
+      "============================================================\n",
+      "                                  Metric Value\n",
+      "         Number of households in dataset 2,368\n",
+      "            Number of persons in dataset 5,888\n",
+      "   Households with children (unweighted)   616\n",
+      "    Households with 1 child (unweighted)   305\n",
+      " Households with 2 children (unweighted)   201\n",
+      "Households with 3+ children (unweighted)   110\n",
+      "          Children under 18 (unweighted) 1,079\n",
+      "           Children under 4 (unweighted)   254\n",
+      "           Children under 6 (unweighted)   373\n",
+      "         Children ages 6-17 (unweighted)   706\n",
+      "============================================================\n",
+      "\n",
+      "Summaries saved to:\n",
+      "  - ri_dataset_summary_weighted.csv\n",
+      "  - ri_dataset_summary_unweighted.csv\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Create weighted summary table\n",
+    "weighted_summary_data = {\n",
+    "    'Metric': [\n",
+    "        'Household count (weighted)',\n",
+    "        'Person count (weighted)',\n",
+    "        'Median AGI',\n",
+    "        '75th percentile AGI',\n",
+    "        '90th percentile AGI',\n",
+    "        '95th percentile AGI',\n",
+    "        'Max AGI',\n",
+    "        'Households over $80k',\n",
+    "        'Households over $120k',\n",
+    "        'Households over $160k',\n",
+    "        'Households over $240k',\n",
+    "        'Total households with children',\n",
+    "        'Households with 1 child',\n",
+    "        'Households with 2 children',\n",
+    "        'Households with 3+ children',\n",
+    "        'Total children under 18',\n",
+    "        'Children under 4',\n",
+    "        'Children under 6',\n",
+    "        'Children ages 6-17'\n",
+    "    ],\n",
+    "    'Value': [\n",
+    "        f\"{household_count.sum():,.0f}\",\n",
+    "        f\"{person_count.sum():,.0f}\",\n",
+    "        f\"${agi.median():,.0f}\",\n",
+    "        f\"${agi.quantile(0.75):,.0f}\",\n",
+    "        f\"${agi.quantile(0.90):,.0f}\",\n",
+    "        f\"${agi.quantile(0.95):,.0f}\",\n",
+    "        f\"${agi.max():,.0f}\",\n",
+    "        f\"{(agi > 80_000).sum():,.0f}\",\n",
+    "        f\"{(agi > 120_000).sum():,.0f}\",\n",
+    "        f\"{(agi > 160_000).sum():,.0f}\",\n",
+    "        f\"{(agi > 240_000).sum():,.0f}\",\n",
+    "        f\"{total_households_with_children:,.0f}\",\n",
+    "        f\"{households_with_1_child:,.0f}\",\n",
+    "        f\"{households_with_2_children:,.0f}\",\n",
+    "        f\"{households_with_3plus_children:,.0f}\",\n",
+    "        f\"{total_children:,.0f}\",\n",
+    "        f\"{children_under_4:,.0f}\",\n",
+    "        f\"{children_under_6:,.0f}\",\n",
+    "        f\"{children_6_17:,.0f}\"\n",
+    "    ]\n",
+    "}\n",
+    "\n",
+    "# Get unique counts for unweighted table\n",
+    "unique_households = df['household_id'].nunique()\n",
+    "unique_persons = len(df)\n",
+    "\n",
+    "# Create unweighted summary table\n",
+    "unweighted_summary_data = {\n",
+    "    'Metric': [\n",
+    "        'Number of households in dataset',\n",
+    "        'Number of persons in dataset',\n",
+    "        'Households with children (unweighted)',\n",
+    "        'Households with 1 child (unweighted)',\n",
+    "        'Households with 2 children (unweighted)',\n",
+    "        'Households with 3+ children (unweighted)',\n",
+    "        'Children under 18 (unweighted)',\n",
+    "        'Children under 4 (unweighted)',\n",
+    "        'Children under 6 (unweighted)',\n",
+    "        'Children ages 6-17 (unweighted)'\n",
+    "    ],\n",
+    "    'Value': [\n",
+    "        f\"{unique_households:,}\",\n",
+    "        f\"{unique_persons:,}\",\n",
+    "        f\"{(children_per_household['is_child'] > 0).sum():,}\",\n",
+    "        f\"{(children_per_household['is_child'] == 1).sum():,}\",\n",
+    "        f\"{(children_per_household['is_child'] == 2).sum():,}\",\n",
+    "        f\"{(children_per_household['is_child'] >= 3).sum():,}\",\n",
+    "        f\"{len(children_under_18_df):,}\",\n",
+    "        f\"{len(children_under_4_df):,}\",\n",
+    "        f\"{len(children_under_6_df):,}\",\n",
+    "        f\"{len(children_6_17_df):,}\"\n",
+    "    ]\n",
+    "}\n",
+    "\n",
+    "weighted_df = pd.DataFrame(weighted_summary_data)\n",
+    "unweighted_df = pd.DataFrame(unweighted_summary_data)\n",
+    "\n",
+    "print(\"\\n\" + \"=\"*60)\n",
+    "print(\"RI DATASET SUMMARY - WEIGHTED (Population Estimates)\")\n",
+    "print(\"=\"*60)\n",
+    "print(weighted_df.to_string(index=False))\n",
+    "print(\"=\"*60)\n",
+    "\n",
+    "print(\"\\n\" + \"=\"*60)\n",
+    "print(\"RI DATASET SUMMARY - UNWEIGHTED (Sample Counts)\")\n",
+    "print(\"=\"*60)\n",
+    "print(unweighted_df.to_string(index=False))\n",
+    "print(\"=\"*60)\n",
+    "\n",
+    "# Save both tables\n",
+    "weighted_df.to_csv('ri_dataset_summary_weighted.csv', index=False)\n",
+    "unweighted_df.to_csv('ri_dataset_summary_unweighted.csv', index=False)\n",
+    "print(\"\\nSummaries saved to:\")\n",
+    "print(\"  - ri_dataset_summary_weighted.csv\")\n",
+    "print(\"  - ri_dataset_summary_unweighted.csv\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "dzvou2zqia4",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Median AGI by aggregation level:\n",
+      "  Household level: $73,149\n",
+      "  Tax unit level:  $35,546\n",
+      "  Person level:    $47,592\n",
+      "\n",
+      "Total AGI for Rhode Island (by aggregation level):\n",
+      "  Using tax unit level:  $43,501,430,523\n",
+      "  Using household level: $43,501,430,523\n",
+      "  Using person level:    $77,844,195,552\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Compare median AGI at different aggregation levels\n",
+    "agi_household = sim.calculate(\"adjusted_gross_income\", period=2025, map_to=\"household\")\n",
+    "agi_tax_unit = sim.calculate(\"adjusted_gross_income\", period=2025, map_to=\"tax_unit\")\n",
+    "agi_person = sim.calculate(\"adjusted_gross_income\", period=2025, map_to=\"person\")\n",
+    "\n",
+    "print(\"Median AGI by aggregation level:\")\n",
+    "print(f\"  Household level: ${agi_household.median():,.0f}\")\n",
+    "print(f\"  Tax unit level:  ${agi_tax_unit.median():,.0f}\")\n",
+    "print(f\"  Person level:    ${agi_person.median():,.0f}\")\n",
+    "\n",
+    "# Calculate total AGI - just sum the values (weights are already built into the arrays)\n",
+    "total_agi_tax_unit = agi_tax_unit.sum()\n",
+    "total_agi_household = agi_household.sum()\n",
+    "total_agi_person = agi_person.sum()\n",
+    "\n",
+    "print(f\"\\nTotal AGI for Rhode Island (by aggregation level):\")\n",
+    "print(f\"  Using tax unit level:  ${total_agi_tax_unit:,.0f}\")\n",
+    "print(f\"  Using household level: ${total_agi_household:,.0f}\")\n",
+    "print(f\"  Using person level:    ${total_agi_person:,.0f}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "gispfkxpnph",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "AGI Component Breakdown (Tax Unit Level)\n",
+      "============================================================\n",
+      "\n",
+      "Total Income (Statewide):\n",
+      "  Employment Income:           $ 31,034,426,346\n",
+      "  Self-Employment Income:      $  1,890,240,187\n",
+      "  Capital Gains:               $  1,086,347,982\n",
+      "  Qualified Dividends:         $  1,002,331,804\n",
+      "  Interest Income:             $    670,462,607\n",
+      "  Taxable Social Security:     $  1,123,366,624\n",
+      "  Pension Income:              $  1,384,610,313\n",
+      "  Adjusted Gross Income (AGI): $ 43,501,430,523\n",
+      "\n",
+      "Median Values:\n",
+      "  Employment Income:           $         29,531\n",
+      "  Self-Employment Income:      $              0\n",
+      "  Capital Gains:               $              0\n",
+      "  Qualified Dividends:         $              0\n",
+      "  Interest Income:             $              0\n",
+      "  Taxable Social Security:     $              0\n",
+      "  Pension Income:              $              0\n",
+      "  Adjusted Gross Income (AGI): $         35,546\n",
+      "\n",
+      "Sum of income components:    $ 38,191,785,863\n",
+      "AGI (for comparison):        $ 43,501,430,523\n",
+      "Difference (potential missing income or deductions): $ -5,309,644,660\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Break down AGI components at tax unit level\n",
+    "print(\"AGI Component Breakdown (Tax Unit Level)\")\n",
+    "print(\"=\"*60)\n",
+    "\n",
+    "# Calculate key income components\n",
+    "employment_income = sim.calculate(\"employment_income\", period=2025, map_to=\"tax_unit\")\n",
+    "self_employment_income = sim.calculate(\"self_employment_income\", period=2025, map_to=\"tax_unit\")\n",
+    "capital_gains = sim.calculate(\"capital_gains\", period=2025, map_to=\"tax_unit\")\n",
+    "qualified_dividend_income = sim.calculate(\"qualified_dividend_income\", period=2025, map_to=\"tax_unit\")\n",
+    "interest_income = sim.calculate(\"interest_income\", period=2025, map_to=\"tax_unit\")\n",
+    "taxable_social_security = sim.calculate(\"taxable_social_security\", period=2025, map_to=\"tax_unit\")\n",
+    "pension_income = sim.calculate(\"pension_income\", period=2025, map_to=\"tax_unit\")\n",
+    "adjusted_gross_income = sim.calculate(\"adjusted_gross_income\", period=2025, map_to=\"tax_unit\")\n",
+    "\n",
+    "print(\"\\nTotal Income (Statewide):\")\n",
+    "print(f\"  Employment Income:           ${employment_income.sum():>15,.0f}\")\n",
+    "print(f\"  Self-Employment Income:      ${self_employment_income.sum():>15,.0f}\")\n",
+    "print(f\"  Capital Gains:               ${capital_gains.sum():>15,.0f}\")\n",
+    "print(f\"  Qualified Dividends:         ${qualified_dividend_income.sum():>15,.0f}\")\n",
+    "print(f\"  Interest Income:             ${interest_income.sum():>15,.0f}\")\n",
+    "print(f\"  Taxable Social Security:     ${taxable_social_security.sum():>15,.0f}\")\n",
+    "print(f\"  Pension Income:              ${pension_income.sum():>15,.0f}\")\n",
+    "print(f\"  Adjusted Gross Income (AGI): ${adjusted_gross_income.sum():>15,.0f}\")\n",
+    "\n",
+    "print(\"\\nMedian Values:\")\n",
+    "print(f\"  Employment Income:           ${employment_income.median():>15,.0f}\")\n",
+    "print(f\"  Self-Employment Income:      ${self_employment_income.median():>15,.0f}\")\n",
+    "print(f\"  Capital Gains:               ${capital_gains.median():>15,.0f}\")\n",
+    "print(f\"  Qualified Dividends:         ${qualified_dividend_income.median():>15,.0f}\")\n",
+    "print(f\"  Interest Income:             ${interest_income.median():>15,.0f}\")\n",
+    "print(f\"  Taxable Social Security:     ${taxable_social_security.median():>15,.0f}\")\n",
+    "print(f\"  Pension Income:              ${pension_income.median():>15,.0f}\")\n",
+    "print(f\"  Adjusted Gross Income (AGI): ${adjusted_gross_income.median():>15,.0f}\")\n",
+    "\n",
+    "# Calculate sum of components to compare with AGI\n",
+    "total_components = (employment_income + self_employment_income + capital_gains + \n",
+    "                   qualified_dividend_income + interest_income + taxable_social_security + pension_income)\n",
+    "print(f\"\\nSum of income components:    ${total_components.sum():>15,.0f}\")\n",
+    "print(f\"AGI (for comparison):        ${adjusted_gross_income.sum():>15,.0f}\")\n",
+    "print(f\"Difference (potential missing income or deductions): ${(total_components.sum() - adjusted_gross_income.sum()):>15,.0f}\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "pe",
+   "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": "3.12.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/ri_dataset_summary_unweighted.csv b/ri_dataset_summary_unweighted.csv
new file mode 100644
index 0000000..8567486
--- /dev/null
+++ b/ri_dataset_summary_unweighted.csv
@@ -0,0 +1,11 @@
+Metric,Value
+Number of households in dataset,"2,368"
+Number of persons in dataset,"5,888"
+Households with children (unweighted),616
+Households with 1 child (unweighted),305
+Households with 2 children (unweighted),201
+Households with 3+ children (unweighted),110
+Children under 18 (unweighted),"1,079"
+Children under 4 (unweighted),254
+Children under 6 (unweighted),373
+Children ages 6-17 (unweighted),706
diff --git a/ri_dataset_summary_weighted.csv b/ri_dataset_summary_weighted.csv
new file mode 100644
index 0000000..c4597a9
--- /dev/null
+++ b/ri_dataset_summary_weighted.csv
@@ -0,0 +1,20 @@
+Metric,Value
+Household count (weighted),"388,376"
+Person count (weighted),"1,106,390"
+Median AGI,"$73,149"
+75th percentile AGI,"$152,410"
+90th percentile AGI,"$271,943"
+95th percentile AGI,"$400,420"
+Max AGI,"$1,740,956"
+Households over $80k,"180,850"
+Households over $120k,"128,983"
+Households over $160k,"88,149"
+Households over $240k,"47,853"
+Total households with children,"121,492"
+Households with 1 child,"66,113"
+Households with 2 children,"37,589"
+Households with 3+ children,"17,790"
+Total children under 18,"203,860"
+Children under 4,"41,525"
+Children under 6,"61,595"
+Children ages 6-17,"138,203"