Week8_Lecture2SANDBOX.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "b1001354",
   "metadata": {},
   "source": [
    "# POLSCI 3 Fall 2021\n",
    "\n",
    "## Week 8, Lecture Notebook 2: Generalizability"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "e752295f",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.frame: 6 × 7</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>Experiment</th><th scope=col>SE</th><th scope=col>effect</th><th scope=col>precision</th><th scope=col>General</th><th scope=col>Primary</th><th scope=col>Treatment.within.2months.of.election</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>1</th><td>Arceneaux (2007)                 </td><td>19.0</td><td> 42.3</td><td>0.002770083</td><td>0</td><td>1</td><td>1</td></tr>\n",
       "\t<tr><th scope=row>2</th><td>Arceneaux (2007)                 </td><td>13.0</td><td> 18.3</td><td>0.005917160</td><td>0</td><td>1</td><td>1</td></tr>\n",
       "\t<tr><th scope=row>3</th><td>Arceneaux (2007)                 </td><td> 8.0</td><td> 18.6</td><td>0.015625000</td><td>0</td><td>1</td><td>1</td></tr>\n",
       "\t<tr><th scope=row>4</th><td>Arceneaux and Kolodny (2009)     </td><td>15.5</td><td>-30.0</td><td>0.004162331</td><td>1</td><td>0</td><td>1</td></tr>\n",
       "\t<tr><th scope=row>5</th><td>Arceneaux and Kolodny (2009)     </td><td>15.0</td><td>-22.0</td><td>0.004444444</td><td>1</td><td>0</td><td>1</td></tr>\n",
       "\t<tr><th scope=row>6</th><td>Bailey, Hopkins and Rogers (2016)</td><td> 5.3</td><td> -9.4</td><td>0.035599858</td><td>1</td><td>0</td><td>1</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 6 × 7\n",
       "\\begin{tabular}{r|lllllll}\n",
       "  & Experiment & SE & effect & precision & General & Primary & Treatment.within.2months.of.election\\\\\n",
       "  & <chr> & <dbl> & <dbl> & <dbl> & <int> & <int> & <int>\\\\\n",
       "\\hline\n",
       "\t1 & Arceneaux (2007)                  & 19.0 &  42.3 & 0.002770083 & 0 & 1 & 1\\\\\n",
       "\t2 & Arceneaux (2007)                  & 13.0 &  18.3 & 0.005917160 & 0 & 1 & 1\\\\\n",
       "\t3 & Arceneaux (2007)                  &  8.0 &  18.6 & 0.015625000 & 0 & 1 & 1\\\\\n",
       "\t4 & Arceneaux and Kolodny (2009)      & 15.5 & -30.0 & 0.004162331 & 1 & 0 & 1\\\\\n",
       "\t5 & Arceneaux and Kolodny (2009)      & 15.0 & -22.0 & 0.004444444 & 1 & 0 & 1\\\\\n",
       "\t6 & Bailey, Hopkins and Rogers (2016) &  5.3 &  -9.4 & 0.035599858 & 1 & 0 & 1\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 6 × 7\n",
       "\n",
       "| <!--/--> | Experiment &lt;chr&gt; | SE &lt;dbl&gt; | effect &lt;dbl&gt; | precision &lt;dbl&gt; | General &lt;int&gt; | Primary &lt;int&gt; | Treatment.within.2months.of.election &lt;int&gt; |\n",
       "|---|---|---|---|---|---|---|---|\n",
       "| 1 | Arceneaux (2007)                  | 19.0 |  42.3 | 0.002770083 | 0 | 1 | 1 |\n",
       "| 2 | Arceneaux (2007)                  | 13.0 |  18.3 | 0.005917160 | 0 | 1 | 1 |\n",
       "| 3 | Arceneaux (2007)                  |  8.0 |  18.6 | 0.015625000 | 0 | 1 | 1 |\n",
       "| 4 | Arceneaux and Kolodny (2009)      | 15.5 | -30.0 | 0.004162331 | 1 | 0 | 1 |\n",
       "| 5 | Arceneaux and Kolodny (2009)      | 15.0 | -22.0 | 0.004444444 | 1 | 0 | 1 |\n",
       "| 6 | Bailey, Hopkins and Rogers (2016) |  5.3 |  -9.4 | 0.035599858 | 1 | 0 | 1 |\n",
       "\n"
      ],
      "text/plain": [
       "  Experiment                        SE   effect precision   General Primary\n",
       "1 Arceneaux (2007)                  19.0  42.3  0.002770083 0       1      \n",
       "2 Arceneaux (2007)                  13.0  18.3  0.005917160 0       1      \n",
       "3 Arceneaux (2007)                   8.0  18.6  0.015625000 0       1      \n",
       "4 Arceneaux and Kolodny (2009)      15.5 -30.0  0.004162331 1       0      \n",
       "5 Arceneaux and Kolodny (2009)      15.0 -22.0  0.004444444 1       0      \n",
       "6 Bailey, Hopkins and Rogers (2016)  5.3  -9.4  0.035599858 1       0      \n",
       "  Treatment.within.2months.of.election\n",
       "1 1                                   \n",
       "2 1                                   \n",
       "3 1                                   \n",
       "4 1                                   \n",
       "5 1                                   \n",
       "6 1                                   "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "library(estimatr)\n",
    "library(ggplot2)\n",
    "\n",
    "data <- read.csv(\"ps3_KB_campaign.csv\")\n",
    "head(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "60fec234",
   "metadata": {},
   "source": [
    "In this dataset, every row is an experiment. The authors (me and Josh Kalla) collected information from previous experiments and collected it in this dataset for meta-analysis.\n",
    "\n",
    "Here is a quick rundown of what each column means:\n",
    "\n",
    "- `Experiment`: Paper reference of the experiment\n",
    "- `effect`: estimate of the effect size\n",
    "- `SE`: standard error of the effect estimate\n",
    "- `precision`: precision; this is how precise the experiment is. defined as $\\frac{1}{\\text{Standard Error}^2}$ (weights to be used)\n",
    "- `General`: binary variable indicating whether the experiment was done in a general election (1 = yes, 0 = no)\n",
    "- `Primary`: binary variable indicating whether the experiment was done in a primary election (1 = yes, 0 = no). these elections are typically quieter\n",
    "- `Treatment.within.2months.of.election`: binary variable incating whether the experiment took place within 2 months of election day (1 = yes, was within 2 months; 0 = no, was more than 2 months before election day)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "e2a6b1d0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.frame: 2 × 3</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>Experiment</th><th scope=col>SE</th><th scope=col>effect</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>1</th><td>Arceneaux (2007)</td><td>19</td><td>42.3</td></tr>\n",
       "\t<tr><th scope=row>2</th><td>Arceneaux (2007)</td><td>13</td><td>18.3</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 2 × 3\n",
       "\\begin{tabular}{r|lll}\n",
       "  & Experiment & SE & effect\\\\\n",
       "  & <chr> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\t1 & Arceneaux (2007) & 19 & 42.3\\\\\n",
       "\t2 & Arceneaux (2007) & 13 & 18.3\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 2 × 3\n",
       "\n",
       "| <!--/--> | Experiment &lt;chr&gt; | SE &lt;dbl&gt; | effect &lt;dbl&gt; |\n",
       "|---|---|---|---|\n",
       "| 1 | Arceneaux (2007) | 19 | 42.3 |\n",
       "| 2 | Arceneaux (2007) | 13 | 18.3 |\n",
       "\n"
      ],
      "text/plain": [
       "  Experiment       SE effect\n",
       "1 Arceneaux (2007) 19 42.3  \n",
       "2 Arceneaux (2007) 13 18.3  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Looking at the first two entries (Arceneaux 2007), which of the two experiments are more precise? Why?\n",
    "data[1:2,1:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "5c6ff874",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "0.481742256853904"
      ],
      "text/latex": [
       "0.481742256853904"
      ],
      "text/markdown": [
       "0.481742256853904"
      ],
      "text/plain": [
       "[1] 0.4817423"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# How do we find the overall weighted effect?\n",
    "weighted.effect.overall <- weighted.mean(data$effect, data$precision)\n",
    "weighted.effect.overall"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "9febdd45",
   "metadata": {},
   "outputs": [],
   "source": [
    "# I'm going to create standard errors for you all.\n",
    "simulations.overall <- c()\n",
    "for (i in 1:1000) {\n",
    "    temp.data <- data[sample(nrow(data), replace = TRUE),]\n",
    "    temp.weighted.effect <- weighted.mean(temp.data$effect, temp.data$precision)\n",
    "    simulations.overall <- append(simulations.overall, temp.weighted.effect)\n",
    "}\n",
    "standard.error.overall <- sd(simulations.overall)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "d4b954a6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "'The weighted average effect is 0.481742256853904'"
      ],
      "text/latex": [
       "'The weighted average effect is 0.481742256853904'"
      ],
      "text/markdown": [
       "'The weighted average effect is 0.481742256853904'"
      ],
      "text/plain": [
       "[1] \"The weighted average effect is 0.481742256853904\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "'The standard deviation of the average effect is 0.287276119429183'"
      ],
      "text/latex": [
       "'The standard deviation of the average effect is 0.287276119429183'"
      ],
      "text/markdown": [
       "'The standard deviation of the average effect is 0.287276119429183'"
      ],
      "text/plain": [
       "[1] \"The standard deviation of the average effect is 0.287276119429183\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "paste(\"The weighted average effect is\", weighted.effect.overall)\n",
    "\n",
    "paste(\"The standard deviation of the average effect is\", standard.error.overall)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "86757d2d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.frame: 6 × 7</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>Experiment</th><th scope=col>SE</th><th scope=col>effect</th><th scope=col>precision</th><th scope=col>General</th><th scope=col>Primary</th><th scope=col>Treatment.within.2months.of.election</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>1</th><td>Arceneaux (2007)                 </td><td>19.0</td><td> 42.3</td><td>0.002770083</td><td>0</td><td>1</td><td>1</td></tr>\n",
       "\t<tr><th scope=row>2</th><td>Arceneaux (2007)                 </td><td>13.0</td><td> 18.3</td><td>0.005917160</td><td>0</td><td>1</td><td>1</td></tr>\n",
       "\t<tr><th scope=row>3</th><td>Arceneaux (2007)                 </td><td> 8.0</td><td> 18.6</td><td>0.015625000</td><td>0</td><td>1</td><td>1</td></tr>\n",
       "\t<tr><th scope=row>4</th><td>Arceneaux and Kolodny (2009)     </td><td>15.5</td><td>-30.0</td><td>0.004162331</td><td>1</td><td>0</td><td>1</td></tr>\n",
       "\t<tr><th scope=row>5</th><td>Arceneaux and Kolodny (2009)     </td><td>15.0</td><td>-22.0</td><td>0.004444444</td><td>1</td><td>0</td><td>1</td></tr>\n",
       "\t<tr><th scope=row>6</th><td>Bailey, Hopkins and Rogers (2016)</td><td> 5.3</td><td> -9.4</td><td>0.035599858</td><td>1</td><td>0</td><td>1</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 6 × 7\n",
       "\\begin{tabular}{r|lllllll}\n",
       "  & Experiment & SE & effect & precision & General & Primary & Treatment.within.2months.of.election\\\\\n",
       "  & <chr> & <dbl> & <dbl> & <dbl> & <int> & <int> & <int>\\\\\n",
       "\\hline\n",
       "\t1 & Arceneaux (2007)                  & 19.0 &  42.3 & 0.002770083 & 0 & 1 & 1\\\\\n",
       "\t2 & Arceneaux (2007)                  & 13.0 &  18.3 & 0.005917160 & 0 & 1 & 1\\\\\n",
       "\t3 & Arceneaux (2007)                  &  8.0 &  18.6 & 0.015625000 & 0 & 1 & 1\\\\\n",
       "\t4 & Arceneaux and Kolodny (2009)      & 15.5 & -30.0 & 0.004162331 & 1 & 0 & 1\\\\\n",
       "\t5 & Arceneaux and Kolodny (2009)      & 15.0 & -22.0 & 0.004444444 & 1 & 0 & 1\\\\\n",
       "\t6 & Bailey, Hopkins and Rogers (2016) &  5.3 &  -9.4 & 0.035599858 & 1 & 0 & 1\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 6 × 7\n",
       "\n",
       "| <!--/--> | Experiment &lt;chr&gt; | SE &lt;dbl&gt; | effect &lt;dbl&gt; | precision &lt;dbl&gt; | General &lt;int&gt; | Primary &lt;int&gt; | Treatment.within.2months.of.election &lt;int&gt; |\n",
       "|---|---|---|---|---|---|---|---|\n",
       "| 1 | Arceneaux (2007)                  | 19.0 |  42.3 | 0.002770083 | 0 | 1 | 1 |\n",
       "| 2 | Arceneaux (2007)                  | 13.0 |  18.3 | 0.005917160 | 0 | 1 | 1 |\n",
       "| 3 | Arceneaux (2007)                  |  8.0 |  18.6 | 0.015625000 | 0 | 1 | 1 |\n",
       "| 4 | Arceneaux and Kolodny (2009)      | 15.5 | -30.0 | 0.004162331 | 1 | 0 | 1 |\n",
       "| 5 | Arceneaux and Kolodny (2009)      | 15.0 | -22.0 | 0.004444444 | 1 | 0 | 1 |\n",
       "| 6 | Bailey, Hopkins and Rogers (2016) |  5.3 |  -9.4 | 0.035599858 | 1 | 0 | 1 |\n",
       "\n"
      ],
      "text/plain": [
       "  Experiment                        SE   effect precision   General Primary\n",
       "1 Arceneaux (2007)                  19.0  42.3  0.002770083 0       1      \n",
       "2 Arceneaux (2007)                  13.0  18.3  0.005917160 0       1      \n",
       "3 Arceneaux (2007)                   8.0  18.6  0.015625000 0       1      \n",
       "4 Arceneaux and Kolodny (2009)      15.5 -30.0  0.004162331 1       0      \n",
       "5 Arceneaux and Kolodny (2009)      15.0 -22.0  0.004444444 1       0      \n",
       "6 Bailey, Hopkins and Rogers (2016)  5.3  -9.4  0.035599858 1       0      \n",
       "  Treatment.within.2months.of.election\n",
       "1 1                                   \n",
       "2 1                                   \n",
       "3 1                                   \n",
       "4 1                                   \n",
       "5 1                                   \n",
       "6 1                                   "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# How do we find the weighted effect for experiments run within two months of election day?\n",
    "two.months.within.election.day <- subset(data, data$Treatment.within.2months.of.election == 1)\n",
    "head(two.months.within.election.day)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "6e7f5da7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "0.432487632346352"
      ],
      "text/latex": [
       "0.432487632346352"
      ],
      "text/markdown": [
       "0.432487632346352"
      ],
      "text/plain": [
       "[1] 0.4324876"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "weighted.effect.two.months.within.election.day <- weighted.mean(two.months.within.election.day$effect, two.months.within.election.day$precision)\n",
    "weighted.effect.two.months.within.election.day"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "b2feb0d9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.frame: 6 × 7</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>Experiment</th><th scope=col>SE</th><th scope=col>effect</th><th scope=col>precision</th><th scope=col>General</th><th scope=col>Primary</th><th scope=col>Treatment.within.2months.of.election</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>15</th><td>Doherty and Adler (2014)     </td><td>0.96</td><td> 3.04</td><td>1.0850694</td><td>1</td><td>0</td><td>0</td></tr>\n",
       "\t<tr><th scope=row>16</th><td>Doherty and Adler (2014)     </td><td>2.06</td><td>-0.66</td><td>0.2356490</td><td>1</td><td>0</td><td>0</td></tr>\n",
       "\t<tr><th scope=row>24</th><td>Gerber et al. (2011b)        </td><td>1.77</td><td> 5.44</td><td>0.3191931</td><td>1</td><td>0</td><td>0</td></tr>\n",
       "\t<tr><th scope=row>25</th><td>Gerber et al. (2011b)        </td><td>1.42</td><td>-0.17</td><td>0.4959333</td><td>1</td><td>0</td><td>0</td></tr>\n",
       "\t<tr><th scope=row>27</th><td>Gerber et al. (2011b)        </td><td>5.99</td><td> 4.83</td><td>0.0278706</td><td>1</td><td>0</td><td>0</td></tr>\n",
       "\t<tr><th scope=row>39</th><td>Strauss (2009), Section 5.5.4</td><td>1.80</td><td> 1.00</td><td>0.3086420</td><td>1</td><td>0</td><td>0</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 6 × 7\n",
       "\\begin{tabular}{r|lllllll}\n",
       "  & Experiment & SE & effect & precision & General & Primary & Treatment.within.2months.of.election\\\\\n",
       "  & <chr> & <dbl> & <dbl> & <dbl> & <int> & <int> & <int>\\\\\n",
       "\\hline\n",
       "\t15 & Doherty and Adler (2014)      & 0.96 &  3.04 & 1.0850694 & 1 & 0 & 0\\\\\n",
       "\t16 & Doherty and Adler (2014)      & 2.06 & -0.66 & 0.2356490 & 1 & 0 & 0\\\\\n",
       "\t24 & Gerber et al. (2011b)         & 1.77 &  5.44 & 0.3191931 & 1 & 0 & 0\\\\\n",
       "\t25 & Gerber et al. (2011b)         & 1.42 & -0.17 & 0.4959333 & 1 & 0 & 0\\\\\n",
       "\t27 & Gerber et al. (2011b)         & 5.99 &  4.83 & 0.0278706 & 1 & 0 & 0\\\\\n",
       "\t39 & Strauss (2009), Section 5.5.4 & 1.80 &  1.00 & 0.3086420 & 1 & 0 & 0\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 6 × 7\n",
       "\n",
       "| <!--/--> | Experiment &lt;chr&gt; | SE &lt;dbl&gt; | effect &lt;dbl&gt; | precision &lt;dbl&gt; | General &lt;int&gt; | Primary &lt;int&gt; | Treatment.within.2months.of.election &lt;int&gt; |\n",
       "|---|---|---|---|---|---|---|---|\n",
       "| 15 | Doherty and Adler (2014)      | 0.96 |  3.04 | 1.0850694 | 1 | 0 | 0 |\n",
       "| 16 | Doherty and Adler (2014)      | 2.06 | -0.66 | 0.2356490 | 1 | 0 | 0 |\n",
       "| 24 | Gerber et al. (2011b)         | 1.77 |  5.44 | 0.3191931 | 1 | 0 | 0 |\n",
       "| 25 | Gerber et al. (2011b)         | 1.42 | -0.17 | 0.4959333 | 1 | 0 | 0 |\n",
       "| 27 | Gerber et al. (2011b)         | 5.99 |  4.83 | 0.0278706 | 1 | 0 | 0 |\n",
       "| 39 | Strauss (2009), Section 5.5.4 | 1.80 |  1.00 | 0.3086420 | 1 | 0 | 0 |\n",
       "\n"
      ],
      "text/plain": [
       "   Experiment                    SE   effect precision General Primary\n",
       "15 Doherty and Adler (2014)      0.96  3.04  1.0850694 1       0      \n",
       "16 Doherty and Adler (2014)      2.06 -0.66  0.2356490 1       0      \n",
       "24 Gerber et al. (2011b)         1.77  5.44  0.3191931 1       0      \n",
       "25 Gerber et al. (2011b)         1.42 -0.17  0.4959333 1       0      \n",
       "27 Gerber et al. (2011b)         5.99  4.83  0.0278706 1       0      \n",
       "39 Strauss (2009), Section 5.5.4 1.80  1.00  0.3086420 1       0      \n",
       "   Treatment.within.2months.of.election\n",
       "15 0                                   \n",
       "16 0                                   \n",
       "24 0                                   \n",
       "25 0                                   \n",
       "27 0                                   \n",
       "39 0                                   "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# How do we find the weighted effect for experiments run before two months from election day?\n",
    "two.months.before.election.day <- subset(data, data$Treatment.within.2months.of.election == 0)\n",
    "head(two.months.before.election.day)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "396dc542",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "0.712741265379679"
      ],
      "text/latex": [
       "0.712741265379679"
      ],
      "text/markdown": [
       "0.712741265379679"
      ],
      "text/plain": [
       "[1] 0.7127413"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "weighted.effect.two.months.before.election.day <- weighted.mean(two.months.before.election.day$effect, two.months.before.election.day$precision)\n",
    "weighted.effect.two.months.before.election.day"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "98ecefce",
   "metadata": {},
   "outputs": [],
   "source": [
    "#I'm going to simulate the two effects for you all\n",
    "simulations.two.months.within <- c()\n",
    "for (i in 1:1000) {\n",
    "    temp.data <- two.months.within.election.day[sample(nrow(two.months.within.election.day), replace = TRUE),]\n",
    "    temp.weighted.effect <- weighted.mean(temp.data$effect, temp.data$precision)\n",
    "    simulations.two.months.within <- append(simulations.two.months.within, temp.weighted.effect)\n",
    "}\n",
    "\n",
    "simulations.two.months.before <- c()\n",
    "for (i in 1:1000) {\n",
    "    temp.data <- two.months.before.election.day[sample(nrow(two.months.before.election.day), replace = TRUE),]\n",
    "    temp.weighted.effect <- weighted.mean(temp.data$effect, temp.data$precision)\n",
    "    simulations.two.months.before <- append(simulations.two.months.before, temp.weighted.effect)\n",
    "}\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "be85b517",
   "metadata": {},
   "outputs": [],
   "source": [
    "#merge the simulations together and label them\n",
    "plot.data <- data.frame(cbind(\n",
    "    c(\n",
    "        simulations.two.months.within,\n",
    "        simulations.two.months.before\n",
    "    ),\n",
    "    c(\n",
    "        rep(\"two months within\", 1000),\n",
    "        rep(\"two months before\", 1000)\n",
    "    )))\n",
    "plot.data$X1 <- as.numeric(plot.data$X1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "45b02e15",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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0qqEOwAALpEsIMxqcdOaHuemEodimVh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OcHqz2ejvL9AeO87KmXlOp3P79u3CvQIA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\n+vrq6urKyspTp06d/+/TQIt5SU72zLTU+ZmZ01OSlLXe2tbTU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      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ggplot(plot.data, aes(x = X1, group = X2, fill = X2, alpha = 0.3)) +\n",
    "    geom_density() +\n",
    "    scale_x_continuous(limits = c(-1.5, 4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7e9b902a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# to remind ourselves of the effect sizes:\n",
    "paste(\"Treating people within two months of the election has an effect of\", weighted.effect.two.months.within.election.day)\n",
    "\n",
    "paste(\"Treating people two months before the election has an effect of\", weighted.effect.two.months.before.election.day)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9a9533e0",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "R",
   "language": "R",
   "name": "ir"
  },
  "language_info": {
   "codemirror_mode": "r",
   "file_extension": ".r",
   "mimetype": "text/x-r-source",
   "name": "R",
   "pygments_lexer": "r",
   "version": "4.0.5"
  },
  "latex_envs": {
   "LaTeX_envs_menu_present": true,
   "autoclose": false,
   "autocomplete": true,
   "bibliofile": "biblio.bib",
   "cite_by": "apalike",
   "current_citInitial": 1,
   "eqLabelWithNumbers": true,
   "eqNumInitial": 1,
   "hotkeys": {
    "equation": "Ctrl-E",
    "itemize": "Ctrl-I"
   },
   "labels_anchors": false,
   "latex_user_defs": false,
   "report_style_numbering": false,
   "user_envs_cfg": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}