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jankatins/US_Election2016.ipnb

Created March 1, 2016 23:33
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US_Election2016.ipnb
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Head"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"options(repos = c(CRAN = \"http://cran.rstudio.com\"))\n",
"p <- function(...) cat(sprintf(...), \"\\n\", sep='', file=stdout())\n",
"ensure.package <- function(...) {\n",
" if (!suppressPackageStartupMessages({require(...)})) {\n",
" install.packages(...)\n",
" library(...)\n",
" }\n",
"}\n",
"ensure.package(\"rvest\") \n",
"ensure.package(\"readr\")\n",
"ensure.package(\"dplyr\") \n",
"ensure.package(\"tidyr\") \n",
"ensure.package(\"assertr\")\n",
"ensure.package(\"ggplot2\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Functions"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"row_is <- function(row_n, value){\n",
" if (!is.numeric(row_n)) \n",
" stop(\"row_n must be numeric\")\n",
" fun <- function(a_vector){\n",
" res = rep(TRUE, length(a_vector))\n",
" if (is.na(value)){\n",
" res[row_n] <- is.na(a_vector[row_n])\n",
" } else {\n",
" res[row_n] <- a_vector[row_n] == value\n",
" }\n",
" return(res)\n",
" }\n",
" comment(fun) <- \"assertr/vectorized\" \n",
" return(fun)\n",
"}\n",
"row_is_not <- function(row_n, value){\n",
" if (!is.numeric(row_n)) \n",
" stop(\"row_n must be numeric\")\n",
" fun <- function(a_vector){\n",
" res = rep(TRUE, length(a_vector))\n",
" if (is.na(value)){\n",
" res[row_n] <- !is.na(a_vector[row_n])\n",
" } else {\n",
" res[row_n] <- a_vector[row_n] != value\n",
" }\n",
" return(res)\n",
" }\n",
" comment(fun) <- \"assertr/vectorized\" \n",
" return(fun)\n",
"}\n",
"math_eval <- function(exp){\n",
" eval(parse(text=exp))\n",
"}"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Data Loading"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"process_data_table <- . %>% html_node(css=\".eventTable\") %>% \n",
" html_table() %>% \n",
" slice(5:n()) %>% \n",
" assert(row_is(1, \"\"), X1) %>% \n",
" assert(row_is_not(2, \"\"), X1) %>%\n",
" slice(2:n()) %>%\n",
" gather(\"bookie\", \"odds\", -X1) %>%\n",
" rename(candidate=X1) %>%\n",
" filter(odds!=\"\") %>%\n",
" mutate(odds=sapply(odds, math_eval))"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<table>\n",
"<thead><tr><th></th><th scope=col>candidate</th><th scope=col>bookie</th><th scope=col>odds</th></tr></thead>\n",
"<tbody>\n",
"\t<tr><th scope=row>1</th><td>Hillary Clinton</td><td>X2</td><td>0.5333333</td></tr>\n",
"\t<tr><th scope=row>2</th><td>Donald Trump</td><td>X2</td><td>2</td></tr>\n",
"</tbody>\n",
"</table>\n"
],
"text/latex": [
"\\begin{tabular}{r|lll}\n",
" & candidate & bookie & odds\\\\\n",
"\\hline\n",
"\t1 & Hillary Clinton & X2 & 0.5333333\\\\\n",
"\t2 & Donald Trump & X2 & 2\\\\\n",
"\\end{tabular}\n"
],
"text/plain": [
" candidate bookie odds\n",
"1 Hillary Clinton X2 0.5333333\n",
"2 Donald Trump X2 2.0000000"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"html_president <- read_html(\"http://www.oddschecker.com/politics/us-politics/us-presidential-election-2016/winner\")\n",
"html_dem <- read_html(\"http://www.oddschecker.com/politics/us-politics/us-presidential-election-2016/democrat-candidate\")\n",
"html_rep <- read_html(\"http://www.oddschecker.com/politics/us-politics/us-presidential-election-2016/republican-candidate\")\n",
"df_president <- html_president %>% process_data_table\n",
"df_dem <- html_dem %>% process_data_table\n",
"df_rep <- html_rep %>% process_data_table\n",
"df_president %>% head(2)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [
{
"ename": "ERROR",
"evalue": "Error in as.matrix.data.frame(X): 'names' attribute [253] must be the same length as the vector [63]\n",
"output_type": "error",
"traceback": [
"Error in as.matrix.data.frame(X): 'names' attribute [253] must be the same length as the vector [63]\nTraceback:\n",
"1. tryCatch({\n . for (mime in getOption(\"jupyter.display_mimetypes\")) {\n . r <- mime2repr[[mime]](obj)\n . if (!is.null(r)) \n . data[[mime]] <- r\n . }\n . }, error = handle_error)"
]
},
{
"data": {
"text/html": [
"<table>\n",
"<thead><tr><th></th><th scope=col>candidate</th><th scope=col>m_odds</th></tr></thead>\n",
"<tbody>\n",
"\t<tr><th scope=row>1</th><td>Al Gore</td><td>821</td></tr>\n",
"\t<tr><th scope=row>2</th><td>Amy Klobuchar</td><td>9999</td></tr>\n",
"\t<tr><th scope=row>3</th><td>Andrew Cuomo</td><td>740.75</td></tr>\n",
"\t<tr><th scope=row>4</th><td>Ben Carson</td><td>535.153846153846</td></tr>\n",
"\t<tr><th scope=row>5</th><td>Bernie Sanders</td><td>23.7857142857143</td></tr>\n",
"\t<tr><th scope=row>6</th><td>Bob McDonnell</td><td>3115.5</td></tr>\n",
"\t<tr><th scope=row>7</th><td>Bobby Jindal</td><td>976.75</td></tr>\n",
"\t<tr><th scope=row>8</th><td>Brian Schweitzer</td><td>9999</td></tr>\n",
"\t<tr><th scope=row>9</th><td>Carly Fiorina</td><td>804.333333333333</td></tr>\n",
"\t<tr><th scope=row>10</th><td>Chris Christie</td><td>705.333333333333</td></tr>\n",
"\t<tr><th scope=row>11</th><td>Condoleeza Rice</td><td>865.5</td></tr>\n",
"\t<tr><th scope=row>12</th><td>Cory Booker</td><td>865.5</td></tr>\n",
"\t<tr><th scope=row>13</th><td>David Petraeus</td><td>999</td></tr>\n",
"\t<tr><th scope=row>14</th><td>Deez Nuts</td><td>1000</td></tr>\n",
"\t<tr><th scope=row>15</th><td>Deval Patrick</td><td>740.75</td></tr>\n",
"\t<tr><th scope=row>16</th><td>Donald Trump</td><td>2.14642857142857</td></tr>\n",
"\t<tr><th scope=row>17</th><td>Elizabeth Warren</td><td>772.5</td></tr>\n",
"\t<tr><th scope=row>18</th><td>Eric Cantor</td><td>9999</td></tr>\n",
"\t<tr><th scope=row>19</th><td>Evan Bayh</td><td>9999</td></tr>\n",
"\t<tr><th scope=row>20</th><td>George Clooney</td><td>9999</td></tr>\n",
"\t<tr><th scope=row>21</th><td>Herman Cain</td><td>9999</td></tr>\n",
"\t<tr><th scope=row>22</th><td>Hillary Clinton</td><td>0.52891156462585</td></tr>\n",
"\t<tr><th scope=row>23</th><td>Howard Stern</td><td>9999</td></tr>\n",
"\t<tr><th scope=row>24</th><td>Janet Napolitano</td><td>999</td></tr>\n",
"\t<tr><th scope=row>25</th><td>Jeb Bush</td><td>940</td></tr>\n",
"\t<tr><th scope=row>26</th><td>Joe Biden</td><td>99.8</td></tr>\n",
"\t<tr><th scope=row>27</th><td>Joe Manchin</td><td>821</td></tr>\n",
"\t<tr><th scope=row>28</th><td>John Hickenlooper</td><td>9999</td></tr>\n",
"\t<tr><th scope=row>29</th><td>John Kasich</td><td>98</td></tr>\n",
"\t<tr><th scope=row>30</th><td>John Kerry</td><td>5474</td></tr>\n",
"\t<tr><th scope=row>⋮</th><td><U+22EE></td><td><U+22EE></td></tr>\n",
"\t<tr><th scope=row>32</th><td>Kathleen Sebelius</td><td>9999</td></tr>\n",
"\t<tr><th scope=row>33</th><td>Kirsten Gillibrand</td><td>999</td></tr>\n",
"\t<tr><th scope=row>34</th><td>Lawrence Lessig</td><td>999</td></tr>\n",
"\t<tr><th scope=row>35</th><td>Lincoln Chafee</td><td>959</td></tr>\n",
"\t<tr><th scope=row>36</th><td>Lindsey Graham</td><td>3972.33333333333</td></tr>\n",
"\t<tr><th scope=row>37</th><td>Marco Rubio</td><td>12.8071428571429</td></tr>\n",
"\t<tr><th scope=row>38</th><td>Mark Warner</td><td>999</td></tr>\n",
"\t<tr><th scope=row>39</th><td>Martin OMalley</td><td>804.333333333333</td></tr>\n",
"\t<tr><th scope=row>40</th><td>Michael Bloomberg</td><td>49.8571428571429</td></tr>\n",
"\t<tr><th scope=row>41</th><td>Michele Bachmann</td><td>9999</td></tr>\n",
"\t<tr><th scope=row>42</th><td>Michelle Obama</td><td>9999</td></tr>\n",
"\t<tr><th scope=row>43</th><td>Mike Huckabee</td><td>804.333333333333</td></tr>\n",
"\t<tr><th scope=row>44</th><td>Mike Pence</td><td>9999</td></tr>\n",
"\t<tr><th scope=row>45</th><td>Mitch Daniels</td><td>9999</td></tr>\n",
"\t<tr><th scope=row>46</th><td>Mitt Romney</td><td>203.6</td></tr>\n",
"\t<tr><th scope=row>47</th><td>Newt Gingrich</td><td>500</td></tr>\n",
"\t<tr><th scope=row>48</th><td>Nikki Haley</td><td>949</td></tr>\n",
"\t<tr><th scope=row>49</th><td>Paul Ryan</td><td>311.666666666667</td></tr>\n",
"\t<tr><th scope=row>50</th><td>Rahm Emanuel</td><td>740.75</td></tr>\n",
"\t<tr><th scope=row>51</th><td>Rand Paul</td><td>853</td></tr>\n",
"\t<tr><th scope=row>52</th><td>Rick Perry</td><td>976.75</td></tr>\n",
"\t<tr><th scope=row>53</th><td>Rick Santorum</td><td>999</td></tr>\n",
"\t<tr><th scope=row>54</th><td>Rob Portman</td><td>740.75</td></tr>\n",
"\t<tr><th scope=row>55</th><td>Ron Paul</td><td>9999</td></tr>\n",
"\t<tr><th scope=row>56</th><td>Sarah Palin</td><td>740.75</td></tr>\n",
"\t<tr><th scope=row>57</th><td>Scott Walker</td><td>972.333333333333</td></tr>\n",
"\t<tr><th scope=row>58</th><td>Susana Martinez</td><td>740.75</td></tr>\n",
"\t<tr><th scope=row>59</th><td>Ted Cruz</td><td>90.0714285714286</td></tr>\n",
"\t<tr><th scope=row>60</th><td>Tim Kaine</td><td>9999</td></tr>\n",
"\t<tr><th scope=row>61</th><td>Vermin Supreme</td><td>500</td></tr>\n",
"</tbody>\n",
"</table>\n"
],
"text/plain": [
"Source: local data frame [61 x 2]\n",
"\n",
" candidate m_odds\n",
" (chr) (chr)\n",
"1 Al Gore 821\n",
"2 Amy Klobuchar 9999\n",
"3 Andrew Cuomo 740.75\n",
"4 Ben Carson 535.153846153846\n",
"5 Bernie Sanders 23.7857142857143\n",
"6 Bob McDonnell 3115.5\n",
"7 Bobby Jindal 976.75\n",
"8 Brian Schweitzer 9999\n",
"9 Carly Fiorina 804.333333333333\n",
"10 Chris Christie 705.333333333333\n",
".. ... ..."
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"interesting <- df_president %>% group_by(candidate) %>% summarise(m_odds=mean(odds)) %>% \n",
"interesting"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Plotting"
]
},
{
"cell_type": "code",
"execution_count": 108,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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},
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],
"source": [
"data <- df_president %>% filter(odds < 100) %>% mutate()\n",
"ggplot(data, aes(x=factor(X1), y=odds)) + geom_point()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
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},
"outputs": [],
"source": []
}
],
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