tag:blogger.com,1999:blog-35595233700041526032024-03-08T15:06:24.398-08:00A Distant ObserveRdistantobserverhttp://www.blogger.com/profile/01886943822394479578noreply@blogger.comBlogger9125tag:blogger.com,1999:blog-3559523370004152603.post-78097647803293980542012-12-31T01:53:00.003-08:002012-12-31T01:53:50.151-08:00How odd was the UEFA draw?I've been away for some time without closely following the media, and without significant internet access. When such a period is over it takes some time to regain momentum. Thus my short <a href="http://adistantobserver.blogspot.de/2012/11/exit-pebos-enter-exit-polls.html" target="_blank">exit poll</a> <a href="http://adistantobserver.blogspot.de/2012/11/get-exit-polls-from-cnn-using-r-and_17.html" target="_blank">series</a> will be continued in 2013. For now I'm still sorting out what I missed over the last weeks. Today's short post is part of this process.<br />
<br />
There has been some discussion while I was away about how probable the UEFA Champions League draw really was. The <a href="http://www.dailymail.co.uk/news/article-2251604/Champions-League-draw-produces-exact-pairings-dress-rehearsal--odds-staggering-2-000-000-1.html" target="_blank">Daily Mail</a> quoted "leading football statisticians" that came up with 2160900:1, and bookies that would have offered 5000:1.<br />
<h3>
</h3>
<a href="http://www.r-bloggers.com/" target="_blank">R-bloggers</a> have <a href="http://diffuseprior.wordpress.com/2012/12/24/identical-champions-league-draw-what-were-the-odds/" target="_blank">contributed</a> <a href="http://freakonometrics.hypotheses.org/820" target="_blank">several</a> <a href="http://freakonometrics.hypotheses.org/847" target="_blank">articles</a>, and the consensus arrived at was that the chances were 1:5463, or 0.0001830496. I'll take this for granted.<br />
<h3>
</h3>
<h3>
The odds are much better</h3>
<h3>
</h3>
0.0001830496 still doesn't seem like a hell of a chance. But this number doesn't reflect the probability of seeing what happened in Nyon. Here's why:<br />
<br />
The UEFA made several dry runs, not just the one that has been discussed. According to the German magazine SPIEGEL ONLINE a UEFA speaker <a href="http://www.spiegel.de/sport/fussball/champions-league-auslosung-grafik-zeigt-gleiche-paarungen-a-874109.html" target="_blank">confirmed that they're carrying out "numerous tests".</a><br />
<br />
Just one of the "numerous" dry runs turned out to be the same as the final draw. I don't know how many draws have been made for testing purposes. But let's assume they're routinely doing ten dry runs before the final draw.<br />
<br />
Let's assume further that there are 5463 different results that are all equally probable. Then, mathematically, the chances of what has been discussed widely are about 1:546.3, or 0.001830496. <br />
<h3>
</h3>
<h3>
From Nyon to Monte Carlo...</h3>
<h3>
</h3>
Let's do some simulation now. I created the function sim.nyon as a quick and dirty way to do the whole job:<br />
<br />
sim.nyon <- function(poss, testruns, n.sims) {<br />
# parameters:<br />
# poss: number of possible results to draw from<br />
# testruns: number of dry runs before the final draw<br />
# n.sims: number of simulations to be run<br />
runs <- testruns + 1 # dry runs plus final draw<br />
y <- sample(poss, (n.sims*runs), replace = TRUE)<br />
y <- matrix(y, nrow = n.sims)<br />
y <- y - y[,runs]<br />
y <- y[, -runs]<br />
is.like.nyon <- apply(y, 1, function(x) {any(x==0)})<br />
return(table(is.like.nyon))<br />
}<br />
<br />
In the UEFA case we want to run 10,000 simulations with 5463 possibilities and assume 10 dry runs:<br />
<br />
possibilities <- 5463<br />
testruns <- 10<br />
sims <- 10^4<br />
sim.nyon(possibilities, testruns, sims)<br />
# is.like.nyon<br />
# FALSE TRUE <br />
# 9981 19 <br />
<br />
After all the UEFA thingy doesn't seem to be <i>that</i> improbable after all.distantobserverhttp://www.blogger.com/profile/01886943822394479578noreply@blogger.com0tag:blogger.com,1999:blog-3559523370004152603.post-90119395538282141982012-11-22T09:48:00.002-08:002012-11-22T09:48:28.395-08:00Washington Gave Thanks To R:<div class="separator" style="clear: both; text-align: center;">
<a href="http://2.bp.blogspot.com/-HxLAFfHkHzo/UK5kkylAzYI/AAAAAAAAAFU/tZCvYzgRoJ0/s1600/washington.png" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" src="http://2.bp.blogspot.com/-HxLAFfHkHzo/UK5kkylAzYI/AAAAAAAAAFU/tZCvYzgRoJ0/s1600/washington.png" height="480" width="480" /></a></div>
<br />
<br />
The folks over at <a href="http://is-r.tumblr.com/" target="_blank">is.R()</a> brought along an<a href="http://is-r.tumblr.com/post/36277968787/happy-thanksgiving-from-is-r" target="_blank"> "adorable Turkey"</a>. I guess we'll thank them for their gift - and pardon the Turkey. Personally I thank them for a bucket of hints and tricks I freely used and will continue to do so.<br />
<br />
<a href="http://www.talgalili.com/" target="_blank">Tal </a>brought us a great community of <a href="http://www.r-bloggers.com/" target="_blank">R bloggers</a>. I thank him for the work he invested, and personally, I thank him for allowing me in.<br />
<br />
I thank the readers, and the writers, and I thank the <a href="http://cran.r-project.org/" target="_blank">R team</a> for giving us "the means we have of acquiring and diffusing useful knowledge". George Washington told us to so:<br />
<br />
<blockquote class="tr_bq">
That we may then all unite in rendering unto him our sincere and humble thanks, for his kind care and protection of the People of this Country previous to their becoming a Nation, for the signal and manifold mercies, and the favorable interpositions of his providence, which we experienced in the course and conclusion of the late war, for the great degree of tranquility, union, and plenty, which we have since enjoyed, for the peaceable and rational manner, in which we have been enabled to establish constitutions of government for our safety and happiness, and particularly the national One now lately instituted, for the civil and religious liberty with which we are blessed; <b><i><u>and the means we have of acquiring and diffusing useful knowledge</u></i></b>; and in general for all the great and various favors which he hath been pleased to confer upon us.</blockquote>
<br />
(George Washington, on October 3, 1789)<br />
<br />
The following produced the plot at the top:<br />
<br />
<span style="font-family: Verdana, sans-serif;">library(gplots)</span><br />
<span style="font-family: Verdana, sans-serif;">message <- "...the means\nwe have of\nacquiring\n"</span><br />
<span style="font-family: Verdana, sans-serif;">message <- paste0(message, "and\ndiffusing\nuseful")</span><br />
<span style="font-family: Verdana, sans-serif;">message <- paste0(message, "\nknowledge...")</span><br />
<span style="font-family: Verdana, sans-serif;">textplot(message, "center", "top", col="green")</span><br />
<br />
And here's a way to get the Thanksgiving Date from R:<br />
<br />
<span style="font-family: Verdana, sans-serif;">library(RcppBDT)</span><br />
<span style="font-family: Verdana, sans-serif;">thanksgiving <- getNthDayOfWeek(fourth, Thu, Nov, 2012)</span><br />
<br />
<span style="font-size: large;">Happy Thanksgiving!</span><br />
<br />distantobserverhttp://www.blogger.com/profile/01886943822394479578noreply@blogger.com0tag:blogger.com,1999:blog-3559523370004152603.post-39443271914938325842012-11-17T00:03:00.000-08:002012-11-17T00:03:58.728-08:00Get the exit polls from CNN using R and Python<a href="http://adistantobserver.blogspot.de/2012/11/exit-pebos-enter-exit-polls.html" target="_blank">Yesterday</a> I posted an example of plotting 2012 U.S. presidential exit poll results using <a href="http://cran.r-project.org/web/packages/ggplot2/index.html" target="_blank">ggplot2</a>. There I took for granted that a data.frame containing all we need resides in a file called "PresExitPolls2012.Rdata". Today I want to show how I scraped the data from CNN.<br />
<br />
<h4>
The challenge</h4>
<br />
At first I tried to scrape the site using <a href="http://cran.r-project.org/web/packages/RCurl/index.html" target="_blank">RCurl</a> and the <a href="http://cran.r-project.org/web/packages/XML/index.html" target="_blank">XML</a> package. But the result was very disappointing. I just got empty data.frames while all browsers I used showed the data. Looking at the source code of the page, however, was equally disappointing: <br />
<br />
Where I expected the percentage of say women voting for Romney, I saw a javascript variable name. Only looking at the generated source with Firebug revealed the data. The CNN pages are dynamically created by javascript that jqueried the data into variables. No way getting the data with RCurl.<br />
<br />
<h4>
The solution</h4>
<br />
So I needed a real browser that could be controlled by a script. I decided to use a Python script to read the generated html from CNN. Here's the Python code that draws heavily on a thread I stumbled upon in a <a href="http://www.python-forum.de/viewtopic.php?f=3&t=20308" target="_blank">German forum</a>:<br />
<br />
<script src="https://gist.github.com/4093843.js?file=dl_CNN_EP.py"></script><br />
Next I needed a function in R that puts together the URL for one of the CNN state sites, calls the Python and returns a page tree of the generated html. getStateData() does the job:<br />
<br />
<script src="https://gist.github.com/4093843.js?file=getStateData.R"></script><br />
<br />
The page tree getStateData returns contains a lot of noise like preliminary county results for some, but only some, of the counties. There are some "fake" exit polls designed to explain "ho to read exit polls". And for every question asked the results appear a couple of times.<br />
<br />
<h4>
Filtering out the noise</h4>
<br />
To separate the wheat from the chaff, the grain from the husk, I split the job over two functions, parseEpNode and getExitPolls.<br />
<br />
getExitPolls parses the tree using XPath, then calls parseEpNode for each of the nodes containing exit polls. (As an aside: this is an application of the "Split-Apply-Combine Strategy for Data Analysis" <a href="http://www.jstatsoft.org/v40/i01/paper" target="_blank">(pdf)</a> described by Hadley Wickham when he introduced the <a href="http://cran.r-project.org/web/packages/plyr/index.html" target="_blank">plyr</a> package. Ironically my getExitPolls doesn't use plyr::llply but the R standard lapply, though it makes use of plyr::rbind.fill...)<br />
<br />
<script src="https://gist.github.com/4093843.js?file=getExitPolls.R"></script>
parseEpNode is the real work horse of the process. It filters out duplicate entries and demo polls. Again it relies on the Split-Apply-Combine Strategy without using l*ply. Sometimes lapply is easy enough, and Hadley himself uses it internally for some cases as well.<br />
<br />
<script src="https://gist.github.com/4093843.js?file=parseEpNode.R"></script>
<br />
<h4>
Putting it all together</h4>
<br />
This script puts it all together and produces the Rdata file the existence of which I only assumed yesterday. It starts with a list of the 19 states + D.C. where no exit polls have been conducted in 2012 taken from the <a href="http://www.washingtonpost.com/blogs/the-fix/wp/2012/10/04/networks-ap-cancel-exit-polls-in-19-states/" target="_blank">Washington Post</a> and puts together the states of interest, again as a list to which getExitPolls can be lapply'd.<br />
<br />
<script src="https://gist.github.com/4093843.js?file=EP2012_1.R"></script><br />
<br />
A probably much shorter post will add some improvements to the process. More later...distantobserverhttp://www.blogger.com/profile/01886943822394479578noreply@blogger.com2tag:blogger.com,1999:blog-3559523370004152603.post-67311237021531677322012-11-15T23:30:00.002-08:002012-11-17T00:34:57.278-08:00Exit PEBOS - Enter exit polls<div class="separator" style="clear: both; text-align: center;">
</div>
<br />
<div class="separator" style="clear: both; text-align: center;">
<a href="http://4.bp.blogspot.com/-_n6KkodHbxo/UKcmRfvsAdI/AAAAAAAAAFE/k1GeOS4ZXeE/s1600/VbA2012_corr.png" imageanchor="1" style="clear: left; float: left; margin-bottom: 1em; margin-right: 1em;"><img border="0" src="http://4.bp.blogspot.com/-_n6KkodHbxo/UKcmRfvsAdI/AAAAAAAAAFE/k1GeOS4ZXeE/s1600/VbA2012_corr.png" height="640" width="640" /></a></div>
<div class="separator" style="clear: both; text-align: center;">
</div>
<br />
<a href="http://adistantobserver.blogspot.de/2012/11/pebos-post-election-burn-out-syndrome.html" target="_blank">PEBOS</a> is over. Time to look at the details of the Election. The final results are not yet in, but the exit polls are there, and up for grabs. Just to get warm: here's a tiny example.<br />
<br />
Obviously Romney had an age problem. But for now I don't want to speculate about political consequences. This is just an example plot.<br />
<br />
Let's imagine we have a data.frame "EP" that contains the state level exit polls for the presidential election 2012. (Actually, I have these data, and tomorrow I'll post how I got them using R - and a tiny bit of Python. For today I just let them reside in a file called "PresExitPolls2012.Rdata".)<br />
<br />
<i>Update: I've released the <a href="http://adistantobserver.blogspot.de/2012/11/get-exit-polls-from-cnn-using-r-and_17.html" target="_blank">code to create the PresExitPolls2012.Rdate</a> file today.</i> <br />
<br />
I fire up R and the first code snippet is<br />
<br />
<div style="overflow: auto;">
<div class="geshifilter">
<pre class="r geshifilter-R" style="font-family: monospace;"><a href="http://inside-r.org/r-doc/base/library"><span style="color: #003399; font-weight: bold;">library</span></a><span style="color: #009900;">(</span><a href="http://inside-r.org/packages/cran/ggplot2">ggplot2</a><span style="color: #009900;">)</span>
<a href="http://inside-r.org/r-doc/base/library"><span style="color: #003399; font-weight: bold;">library</span></a><span style="color: #009900;">(</span><a href="http://inside-r.org/packages/cran/plyr">plyr</a><span style="color: #009900;">)</span>
<a href="http://inside-r.org/r-doc/base/library"><span style="color: #003399; font-weight: bold;">library</span></a><span style="color: #009900;">(</span><a href="http://inside-r.org/r-doc/stats/reshape"><span style="color: #003399; font-weight: bold;">reshape</span></a><span style="color: #009900;">)</span>
<a href="http://inside-r.org/r-doc/base/load"><span style="color: #003399; font-weight: bold;">load</span></a><span style="color: #009900;">(</span><a href="http://inside-r.org/r-doc/base/file"><span style="color: #003399; font-weight: bold;">file</span></a>=<span style="color: blue;">"PresExitPolls2012.Rdata"</span><span style="color: #009900;">)</span>
<a href="http://inside-r.org/r-doc/utils/head"><span style="color: #003399; font-weight: bold;">head</span></a><span style="color: #009900;">(</span>EP<span style="color: #009900;">)</span></pre>
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<a href="http://www.inside-r.org/pretty-r" title="Created by Pretty R at inside-R.org">Created by Pretty R at inside-R.org</a><br />
For now I just concentrate on the "Vote by Age". There are two different age groupings for that question:<br />
<div style="overflow: auto;">
<div class="geshifilter">
<pre class="r geshifilter-R" style="font-family: monospace;"><a href="http://inside-r.org/r-doc/base/unique"><span style="color: #003399; font-weight: bold;">unique</span></a><span style="color: #009900;">(</span>EP$QNo<span style="color: #009900;">[</span>EP$question==<span style="color: blue;">"Vote by Age"</span><span style="color: #009900;">]</span><span style="color: #009900;">)</span>
<span style="color: #666666; font-style: italic;"># 4 category breakdown</span>
<a href="http://inside-r.org/r-doc/utils/head"><span style="color: #003399; font-weight: bold;">head</span></a><span style="color: #009900;">(</span>EP<span style="color: #009900;">[</span>EP$QNo==<span style="color: #cc66cc;">2</span><span style="color: #339933;">,</span> <span style="color: #009900;">]</span><span style="color: #009900;">)</span>
<span style="color: #666666; font-style: italic;"># 6 category breakdown</span>
<a href="http://inside-r.org/r-doc/utils/head"><span style="color: #003399; font-weight: bold;">head</span></a><span style="color: #009900;">(</span>EP<span style="color: #009900;">[</span>EP$QNo==<span style="color: #cc66cc;">3</span><span style="color: #339933;">,</span> <span style="color: #009900;">]</span><span style="color: #009900;">)</span></pre>
</div>
</div>
<a href="http://www.inside-r.org/pretty-r" title="Created by Pretty R at inside-R.org">Created by Pretty R at inside-R.org</a><br />
<br />
Today I want to produce a plot of the 6 category breakdown, so I reduce the data and do some checks:<br />
<br />
1. There might be some inconsistency between states in the numbering of the questions. There should be 6 categories for each state.<br />
<br />
2. This year's exit polls have been conducted in 31 states. In addition to this the reduced dataset should contain the nation wide data. So I expect 32 "states" in the newly created VbA dataset.<br />
<br />
Both checks can easily be implemented with the daply funktion from Hadley's <a href="http://cran.r-project.org/web/packages/plyr/index.html" target="_blank">plyr </a>package:<br />
<br />
<div style="overflow: auto;">
<div class="geshifilter">
<pre class="r geshifilter-R" style="font-family: monospace;">VbA <- EP<span style="color: #009900;">[</span>EP$QNo==<span style="color: #cc66cc;">3</span><span style="color: #339933;">,</span> <span style="color: #009900;">]</span>
<a href="http://inside-r.org/r-doc/base/unique"><span style="color: #003399; font-weight: bold;">unique</span></a><span style="color: #009900;">(</span>daply<span style="color: #009900;">(</span>VbA<span style="color: #339933;">,</span> .<span style="color: #009900;">(</span><a href="http://inside-r.org/r-doc/datasets/state"><span style="color: #003399; font-weight: bold;">state</span></a><span style="color: #009900;">)</span><span style="color: #339933;">,</span> <a href="http://inside-r.org/r-doc/base/nrow"><span style="color: #003399; font-weight: bold;">nrow</span></a><span style="color: #009900;">)</span><span style="color: #009900;">)</span> == <span style="color: #cc66cc;">6</span>
<a href="http://inside-r.org/r-doc/base/length"><span style="color: #003399; font-weight: bold;">length</span></a><span style="color: #009900;">(</span>daply<span style="color: #009900;">(</span>VbA<span style="color: #339933;">,</span> .<span style="color: #009900;">(</span><a href="http://inside-r.org/r-doc/datasets/state"><span style="color: #003399; font-weight: bold;">state</span></a><span style="color: #009900;">)</span><span style="color: #339933;">,</span> <a href="http://inside-r.org/r-doc/base/nrow"><span style="color: #003399; font-weight: bold;">nrow</span></a><span style="color: #009900;">)</span><span style="color: #009900;">)</span> == <span style="color: #cc66cc;">32</span></pre>
</div>
</div>
<a href="http://www.inside-r.org/pretty-r" title="Created by Pretty R at inside-R.org">Created by Pretty R at inside-R.org</a><br />
<br />
The plot needs the data to be in "long format". I let Hadley's melt function (from the <a href="http://cran.r-project.org/web/packages/reshape/index.html" target="_blank">reshape </a>package) do the job. Then I remove all Candidates with the exception of Obama and Romney.<br />
<br />
<div style="overflow: auto;">
<div class="geshifilter">
<pre class="r geshifilter-R" style="font-family: monospace;">vba <- melt<span style="color: #009900;">(</span>VbA<span style="color: #339933;">,</span> id = <a href="http://inside-r.org/r-doc/base/c"><span style="color: #003399; font-weight: bold;">c</span></a><span style="color: #009900;">(</span><span style="color: blue;">"state"</span><span style="color: #339933;">,</span> <span style="color: blue;">"answer"</span><span style="color: #009900;">)</span><span style="color: #339933;">,</span> variable_name = <span style="color: blue;">"Candidate"</span><span style="color: #009900;">)</span>
<a href="http://inside-r.org/r-doc/base/unique"><span style="color: #003399; font-weight: bold;">unique</span></a><span style="color: #009900;">(</span>vba$Candidate<span style="color: #009900;">)</span>
<span style="color: #666666; font-style: italic;"># we're only interested in Obama and Romney</span>
vba <- vba<span style="color: #009900;">[</span>vba$Candidate %in% <a href="http://inside-r.org/r-doc/base/c"><span style="color: #003399; font-weight: bold;">c</span></a><span style="color: #009900;">(</span><span style="color: blue;">"Obama"</span><span style="color: #339933;">,</span> <span style="color: blue;">"Romney"</span><span style="color: #009900;">)</span><span style="color: #339933;">,</span> <span style="color: #009900;">]</span></pre>
</div>
</div>
<a href="http://www.inside-r.org/pretty-r" title="Created by Pretty R at inside-R.org">Created by Pretty R at inside-R.org</a><br />
<br />
Finally the plot can be created. Initially the plot was a mess with garbled and unreadable text elements. I'm indebted to the people over at <a href="http://is-r.tumblr.com/" target="_blank">is.R()</a> for their <a href="http://is-r.tumblr.com/post/35699866752/textual-healing" target="_blank">most valuable hints</a> that helped me arrive at a readable plot.<br />
<br />
<br />
<i>But before plotting there's a fix to be applied. In the VbA data.frame the numbers for the candidate were numeric. For some reason I'll have yet to look into this made the NA's appear like peaks with both candidates having roughly the same value of about 70. (Thanks to <a href="http://adistantobserver.blogspot.com/2012/11/exit-pebos-enter-exit-polls.html?showComment=1353102278657#c3174523680837735786" target="_blank">lemonlaug</a> whose comment alerted me to the absurdity in the original plot.)</i><br />
<i><br /></i>
<i>Now to the fix. It's as simple as that:</i><br />
<i><br /></i>
<i>vba$value <- as.numeric(vba$value)</i><br />
<pre class="r geshifilter-R" style="font-family: monospace;"><span style="color: #009900;">
</span></pre>
Here's the final code snippet:<br />
<br />
<div style="overflow: auto;">
<div class="geshifilter">
<pre class="r geshifilter-R" style="font-family: monospace;"><a href="http://inside-r.org/r-doc/grDevices/png"><span style="color: #003399; font-weight: bold;">png</span></a><span style="color: #009900;">(</span><a href="http://inside-r.org/r-doc/base/file"><span style="color: #003399; font-weight: bold;">file</span></a> = <span style="color: blue;">"VbA2012.png"</span><span style="color: #339933;">,</span> width = <span style="color: #cc66cc;">960</span><span style="color: #339933;">,</span> height = <span style="color: #cc66cc;">960</span><span style="color: #009900;">)</span>
<a href="http://inside-r.org/packages/cran/ggplot">ggplot</a><span style="color: #009900;">(</span>vba<span style="color: #339933;">,</span> aes<span style="color: #009900;">(</span>answer<span style="color: #339933;">,</span> value<span style="color: #009900;">)</span><span style="color: #009900;">)</span> +
geom_line<span style="color: #009900;">(</span>aes<span style="color: #009900;">(</span><a href="http://inside-r.org/r-doc/grDevices/group"><span style="color: #003399; font-weight: bold;">group</span></a> = Candidate<span style="color: #339933;">,</span> color = Candidate<span style="color: #009900;">)</span><span style="color: #009900;">)</span> +
facet_wrap<span style="color: #009900;">(</span>~ <a href="http://inside-r.org/r-doc/datasets/state"><span style="color: #003399; font-weight: bold;">state</span></a><span style="color: #339933;">,</span> <a href="http://inside-r.org/r-doc/base/ncol"><span style="color: #003399; font-weight: bold;">ncol</span></a> = <span style="color: #cc66cc;">4</span><span style="color: #009900;">)</span> +
labs<span style="color: #009900;">(</span><a href="http://inside-r.org/r-doc/graphics/title"><span style="color: #003399; font-weight: bold;">title</span></a> = <span style="color: blue;">"2012 Presidential Vote by Age<span style="color: #000099; font-weight: bold;">\n</span>"</span><span style="color: #339933;">,</span>
y = <span style="color: blue;">"Percentage<span style="color: #000099; font-weight: bold;">\n</span>"</span><span style="color: #339933;">,</span>
x = <span style="color: blue;">"Age group<span style="color: #000099; font-weight: bold;">\n</span>"</span>
<span style="color: #009900;">)</span> +
theme<span style="color: #009900;">(</span>axis.text.x = element_text<span style="color: #009900;">(</span>colour = <span style="color: blue;">"black"</span><span style="color: #339933;">,</span>
size = <span style="color: #cc66cc;">9</span><span style="color: #339933;">,</span>
angle = <span style="color: #cc66cc;">45</span><span style="color: #339933;">,</span>
vjust = <span style="color: #cc66cc;">1</span><span style="color: #339933;">,</span>
hjust = <span style="color: #cc66cc;">1</span><span style="color: #009900;">)</span><span style="color: #339933;">,</span>
axis.text.y = element_text<span style="color: #009900;">(</span>colour = <span style="color: blue;">"black"</span><span style="color: #339933;">,</span>
size = <span style="color: #cc66cc;">9</span><span style="color: #339933;">,</span>
angle = <span style="color: #cc66cc;">0</span><span style="color: #339933;">,</span>
vjust = <span style="color: #cc66cc;">1</span><span style="color: #339933;">,</span>
hjust = <span style="color: #cc66cc;">1</span><span style="color: #009900;">)</span>
<span style="color: #009900;">)</span> +
scale_y_discrete<span style="color: #009900;">(</span>breaks=<a href="http://inside-r.org/r-doc/base/c"><span style="color: #003399; font-weight: bold;">c</span></a><span style="color: #009900;">(</span><span style="color: #cc66cc;">30</span><span style="color: #339933;">,</span> <span style="color: #cc66cc;">50</span><span style="color: #339933;">,</span> <span style="color: #cc66cc;">70</span><span style="color: #009900;">)</span><span style="color: #009900;">)</span> +
scale_colour_manual<span style="color: #009900;">(</span>values = <a href="http://inside-r.org/r-doc/base/c"><span style="color: #003399; font-weight: bold;">c</span></a><span style="color: #009900;">(</span><span style="color: blue;">"darkblue"</span><span style="color: #339933;">,</span> <span style="color: blue;">"darkred"</span><span style="color: #009900;">)</span><span style="color: #009900;">)</span>
<a href="http://inside-r.org/r-doc/grDevices/dev.off"><span style="color: #003399; font-weight: bold;">dev.off</span></a><span style="color: #009900;">(</span><span style="color: #009900;">)</span></pre>
</div>
</div>
<a href="http://www.inside-r.org/pretty-r" title="Created by Pretty R at inside-R.org">Created by Pretty R at inside-R.org</a>distantobserverhttp://www.blogger.com/profile/01886943822394479578noreply@blogger.com2tag:blogger.com,1999:blog-3559523370004152603.post-74877085605907506992012-11-08T21:24:00.001-08:002012-11-08T21:24:23.648-08:00Another crosshairs<div class="separator" style="clear: both; text-align: center;">
<a href="http://2.bp.blogspot.com/-X5SKstxs93U/UJySSJqV4QI/AAAAAAAAAEU/UYlPK4jE3AI/s1600/crosshairs2.png" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" height="320" src="http://2.bp.blogspot.com/-X5SKstxs93U/UJySSJqV4QI/AAAAAAAAAEU/UYlPK4jE3AI/s320/crosshairs2.png" width="320" /></a></div>
<br />
C. DeSante over at <a href="http://is-r.tumblr.com/post/35266021903/five-thirty-hate">is.R()</a> has <a href="http://adistantobserver.blogspot.de/2012/11/pebos-post-election-burn-out-syndrome.html" target="_blank">PEBOS</a> as well, but turned it into a great explanation of the way predictions like <a href="http://fivethirtyeight.blogs.nytimes.com/" target="_blank">Nate Silver's</a> work.<br />
<br />
For a while the 538 team had PEBOS <a href="http://fivethirtyeight.blogs.nytimes.com/2012/11/07/stay-tuned/" target="_blank">as well:</a> "The FiveThirtyEight team is still recuperating, but the <a href="http://fivethirtyeight.blogs.nytimes.com/2012/11/06/live-blog-the-2012-presidential-election/">election</a> provided a fresh supply of data points that we’ll be connecting in the coming days."<br />
<br />
<br />
This inspired me to add some predictions of the popular vote to my crosshairs plot. The data are from <a href="http://news.cnet.com/8301-13578_3-57546778-38/among-the-top-election-quants-nate-silver-reigns-supreme/" target="_blank">here.</a><br />
<br />
We can see that Nate was closest. Well how does the old song sing?<br />
<blockquote class="tr_bq">
Pass my window, pass my gate</blockquote>
<blockquote class="tr_bq">
Pass my window, pass my gate</blockquote>
<blockquote class="tr_bq">
You can pass my window, can pass my gate</blockquote>
<blockquote class="tr_bq">
But you'll never pass <a href="http://fivethirtyeight.blogs.nytimes.com/" target="_blank">FiveThirtyEight</a>...</blockquote>
The additional code is simple:<br />
<script src="http://gist.github.com/4043804.js?file=crosshairs2.R"></script>distantobserverhttp://www.blogger.com/profile/01886943822394479578noreply@blogger.com0tag:blogger.com,1999:blog-3559523370004152603.post-86863522116041213662012-11-08T10:02:00.004-08:002012-11-08T10:25:40.749-08:00PEBOS (Post Election Burn Out Syndrome)<br />
<div class="separator" style="clear: both; text-align: center;">
<a href="http://1.bp.blogspot.com/-FgcQPZoZ5jE/UJvuZnpY3rI/AAAAAAAAAD8/vHFKoazcpes/s1600/crosshairs.png" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" height="320" src="http://1.bp.blogspot.com/-FgcQPZoZ5jE/UJvuZnpY3rI/AAAAAAAAAD8/vHFKoazcpes/s320/crosshairs.png" width="320" /></a></div>
<br />
<br />
I guess that all those that tried to follow the presidential election as closely as possible are more than just a little bit exhausted mentally. I call this PEBOS - Post Election Burn Out Syndrome.<br />
<br />
Among us some concentrated on the horserace aspect of the polls. Those will wake up a few days from now, released from their fever, and turn their eyes on other things until 2014 (or even 2016) revives their political strain.<br />
<br />
Others, like me, will spend weeks and months studying the data amassed and not sufficiently looked into over the last year. When the final results are in, all polls cleanly collected, and the ANES 2012 data have been released, it's time to indulge in the analysis...<br />
<br />
For now, I'll try to get some rest.<br />
<br />
And this is the <a href="https://gist.github.com/4040423" target="_blank">code, on github, that generated the crosshairs plot</a> shown above. We can see that the national polls were somewhat biased against Barack Obama, though they mostly underestimated the Romney vote as well.<br />
<br />
More to come very soon.<br />
<script src="http://gist.github.com/4040423.js?file=crosshairs.R"></script>distantobserverhttp://www.blogger.com/profile/01886943822394479578noreply@blogger.com0tag:blogger.com,1999:blog-3559523370004152603.post-14632068675630454402012-11-01T21:19:00.000-07:002012-11-01T21:29:49.386-07:00PrettyR RWhen it comes to R blogging I'm a complete newbie. So I'm still struggling with the technical details.<br />
<br />
Part of the process is prettifying the code snippets. One of the standard ways of doing this involves copy-and-paste-ing the R code into the <a href="http://www.inside-r.org/pretty-r/tool">Pretty R syntax highlighter</a>.<br />
<br />
While assembling the bits to do the posting programmatically I wrote a function that replaces the copy-and-paste part.<br />
<br />
Now here's the function prettified by itself:<br />
<br />
<div style="overflow: auto;">
<div class="geshifilter">
<pre class="r geshifilter-R" style="font-family: monospace;"><a href="http://inside-r.org/packages/cran/prettyR">prettyR</a> <- <a href="http://inside-r.org/r-doc/base/function"><span style="color: #003399; font-weight: bold;">function</span></a><span style="color: #009900;">(</span><a href="http://inside-r.org/r-doc/base/file"><span style="color: #003399; font-weight: bold;">file</span></a><span style="color: #009900;">)</span> <span style="color: #009900;">{</span>
<a href="http://inside-r.org/r-doc/base/require"><span style="color: #003399; font-weight: bold;">require</span></a><span style="color: #009900;">(</span><a href="http://inside-r.org/packages/cran/RCurl">RCurl</a><span style="color: #009900;">)</span>
<a href="http://inside-r.org/r-doc/base/require"><span style="color: #003399; font-weight: bold;">require</span></a><span style="color: #009900;">(</span><a href="http://inside-r.org/packages/cran/XML">XML</a><span style="color: #009900;">)</span>
Rcode <- <a href="http://inside-r.org/r-doc/base/readLines"><span style="color: #003399; font-weight: bold;">readLines</span></a><span style="color: #009900;">(</span><a href="http://inside-r.org/r-doc/base/file"><span style="color: #003399; font-weight: bold;">file</span></a><span style="color: #009900;">)</span>
Rcode <- <a href="http://inside-r.org/r-doc/base/paste"><span style="color: #003399; font-weight: bold;">paste</span></a><span style="color: #009900;">(</span>Rcode<span style="color: #339933;">,</span> <a href="http://inside-r.org/r-doc/nlme/collapse"><span style="color: #003399; font-weight: bold;">collapse</span></a>=<span style="color: blue;">"<span style="color: #000099; font-weight: bold;">\n</span>"</span><span style="color: #009900;">)</span>
<span style="color: #666666; font-style: italic;"># assemble the parameters for the http POST to the Pretty R web site</span>
URL <- <span style="color: blue;">"http://www.inside-r.org/pretty-r/tool"</span>
parameters <- <a href="http://inside-r.org/r-doc/base/list"><span style="color: #003399; font-weight: bold;">list</span></a><span style="color: #009900;">(</span>
op = <span style="color: blue;">"edit-submit"</span><span style="color: #339933;">,</span>
form_id = <span style="color: blue;">"pretty_r_tool_form"</span><span style="color: #339933;">,</span>
code_input = Rcode
<span style="color: #009900;">)</span>
<span style="color: #666666; font-style: italic;"># send the http POST request</span>
rawHTML <- postForm<span style="color: #009900;">(</span>URL<span style="color: #339933;">,</span> .params = parameters<span style="color: #009900;">)</span>
parsedHTML <- htmlParse<span style="color: #009900;">(</span>rawHTML<span style="color: #009900;">)</span>
<span style="color: #666666; font-style: italic;"># find the node</span>
prettified <- getNodeSet<span style="color: #009900;">(</span>parsedHTML<span style="color: #339933;">,</span> <span style="color: blue;">"//div[@class='form-item']/textarea"</span><span style="color: #009900;">)</span><span style="color: #009900;">[</span><span style="color: #009900;">[</span><span style="color: #cc66cc;">1</span><span style="color: #009900;">]</span><span style="color: #009900;">]</span>
prettified <- xmlValue<span style="color: #009900;">(</span>prettified<span style="color: #009900;">[</span><span style="color: #009900;">[</span><span style="color: #cc66cc;">1</span><span style="color: #009900;">]</span><span style="color: #009900;">]</span><span style="color: #009900;">)</span>
<a href="http://inside-r.org/r-doc/base/return"><span style="color: #003399; font-weight: bold;">return</span></a><span style="color: #009900;">(</span>prettified<span style="color: #009900;">)</span>
<span style="color: #009900;">}</span></pre>
</div>
</div>
<a href="http://www.inside-r.org/pretty-r" title="Created by Pretty R at inside-R.org">Created by Pretty R at inside-R.org</a>distantobserverhttp://www.blogger.com/profile/01886943822394479578noreply@blogger.com1tag:blogger.com,1999:blog-3559523370004152603.post-67897628305441932712012-10-27T18:38:00.000-07:002012-11-01T05:10:43.876-07:00Replicating Krugman (Pt. 1)I'm a regular reader of Paul Krugman's NYT blog. A couple of months ago I decided to replicate some of the plots Paul published. At the time I was evaluating the <a href="http://cran.r-project.org/web/packages/quantmod/index.html">quantmod</a> package which seemed to made things easy. <br />
<h2>
An easy start</h2>
In the first part I'll reproduce two plots from <a href="http://krugman.blogs.nytimes.com/2012/06/07/real-government-spending-per-capita/">Real Government Spending Per Capita.</a> It's straightforward to get the data using quantmod:<br />
<br />
<pre><code class="r">require(quantmod)
# First we get the time series from the Federal Reserve Bank of St. Louis (FRED)
getSymbols('GEXPND', src = 'FRED')
getSymbols('GDPDEF', src = 'FRED')
getSymbols('SLEXPND', src = 'FRED')
getSymbols('POP', src = 'FRED')
getSymbols('USREC', src = 'FRED')
# Paul Krugman doesn't use the raw series but real per capita values.
# These are the necessary calculations.
log_gov_overall <- log(GEXPND/(GDPDEF*POP), exp(1))
log_gov_state_local <- log(SLEXPND/(GDPDEF*POP), exp(1))
</code></pre>
It's just as easy to create the standard plots:<br />
<pre><code class="r">plot(log_gov_overall
, main = "Real Government Spending Per Capita\nOverall (Log Values)"
)
</code></pre>
<img alt="plot of chunk unnamed-chunk-2" 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" /> <br />
<pre><code class="r">plot(log_gov_state_local
, main = "Real Government Spending Per Capita\nState & Local (Log Values)"
)
</code></pre>
<img alt="plot of chunk unnamed-chunk-2" 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" /> <br />
A closer look, however, reveals that something is clearly missing. Paul took his plots straight from <a href="http://research.stlouisfed.org/fred2/">FRED itself</a>. One of the features is that recessions are shown by graying the background.<br />
<h2>
FRED style plots</h2>
After struggling with the parameters and different quantmod plots for a while I decided to write a function that takes the series to be plotted as a parameter and creates a FRED style plot.<br />
<br />
This makes some use of the internal workings of plot.xts:<br />
<pre><code class="r">FREDplot <- function (x
, main = deparse(substitute(x))
, las = 1
, ...) {
# function to create FRED style plots with shaded regions for U.S. recessions
# this function needs the time series to be xts
if (!(class(x)[1] == "xts") &&
(class(x)[2] == "zoo")){
stop("Only xts objects can be properly handled by this function!")
}
# get the recession dates from FRED only once
if (!("USREC" %in% ls(name = globalenv()) &&
attributes(USREC)$src == "FRED")){
getSymbols('USREC', src = 'FRED')
}
# transform FRED recession dates to xy.coordinates
usrec <- xy.coords(.index(USREC), USREC[, 1])
# prepare plot
xycoords <- xy.coords(.index(x), x[, 1])
ep <- axTicksByTime(x, TRUE, format.labels = TRUE)
plot.zoo(xycoords$x, xycoords$y
, type = 'n', axes = FALSE, ann = FALSE
)
# plot axis & box
axis(1, at = xycoords$x, labels = FALSE
, col = "#BBBBBB"
)
axis(1, at = xycoords$x[ep], labels = names(ep)
, las = 1,lwd = 1, mgp = c(3, 2, 0)
)
axis(2, las = las)
# create shaded areas
xblocks(usrec$x,ifelse(usrec$y == 1, "gray", "white"))
# add grid
abline(v = xycoords$x[ep], col = "grey", lty = 4)
abline(h = seq(min(xycoords$y), max(xycoords$y)
, length.out = 9), col = "grey", lty = 4
)
# plot curve
lines(xycoords$x, xycoords$y,...)
# add title
title(main = main)
title(sub = "Shaded regions are US recession periods
(data from research.stlouisfed.org)"
)
box()
}
</code></pre>
Using this function the plots look much better:<br />
<pre><code class="r"># the first plot
FREDplot(log_gov_overall,
main = "Real Government Spending Per Capita\nOverall (Log Values)")
</code></pre>
<img alt="plot of chunk unnamed-chunk-4" src="data:image/png;base64,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" /> <br />
<pre><code class="r">
# and the second
FREDplot(log_gov_state_local,
main = "Real Government Spending Per Capita\nState and Local Level (Log Values)")
</code></pre>
<img alt="plot of chunk unnamed-chunk-4" 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" /> <br />
Actually I like these plots better than the originals <a href="http://graphics8.nytimes.com/images/2012/06/07/opinion/060712krugman3/060712krugman3-blog480.jpg">here</a> and <a href="http://graphics8.nytimes.com/images/2012/06/07/opinion/060712krugman4/060712krugman4-blog480.jpg">there.</a><br />
There are, however, a couple of things that could - and should - be improved. More of this in future installments.<br />
I'll call it a day for now.distantobserverhttp://www.blogger.com/profile/01886943822394479578noreply@blogger.com0tag:blogger.com,1999:blog-3559523370004152603.post-66626011395418980932012-10-26T23:36:00.000-07:002012-10-27T18:50:41.309-07:00.RhistoryOver the last couple of years I've been using <a href="http://cran.r-project.org/" target="_blank">R</a> every now and then. When I stumbled upon an interesting topic, and I managed to get a hold of a data set, I tried to make sense of it using R.<br />
<br />
It's a bit like the <a href="http://www.stat.berkeley.edu/users/statlabs/index.html" target="_blank">Stat Labs</a> approach: I might get started by a newspaper article that makes a claim about some reality - birth rates rising, a political horse race being "tied" or not close at all, a species approaching extinction, ...<br />
<br />
I'm used to doubt those claims. Not because of my superior knowledge in the domain of interest (I'm not foolish enough to believe it's there), but because I know of the many fallacies out there.<br />
<br />
Take a look at <a href="http://www.quantumforest.com/2012/09/suicide-statistics/" target="_blank">Luis debunking a myth</a> propagated by New Zealand's Chief Coroner about "Suicide statistics and the Christchurch earthquake" for an example of what I'm talking about.<br />
<br />
So I try to get a data set that can be used. Sometimes this is easy, sometimes it involves some web scraping. In most cases the raw data need some fiddling. There are situations where I find more than one source; so: which source is more useful for me? To cut that story, and it's a long long long one, short: there's a lot to be done before I can be sure the data will allow me to evaluate the claim that got me started.<br />
<br />
One more point: While exploring the data availability and the usefulness of the data I can get, I can't be sure that all this will lead to anything. So, because I'm lazy, and because time is the scarcest resource, I just open my R.<br />
<br />
I try to read the data from the web (read.csv, read.table). Maybe it works, maybe it doesn't. Maybe RCurl can help me, XML, httr or whatever...<br />
<br />
Finally I have a data.frame usually called foo, or foobar, that seems to be useful. If not - well let's call the analysis a day, or if there's another couple of minutes, give it one more try. But let's assume I ended up with a useful data.frame that goes by the name of foo. I type something like<br />
<br />
<pre><code>data_of_interest <- foo
save(data_of_interest, file = "data_of_interest.Rdata")</code></pre>
<br />
That's where I quit R. That's where the .Rhistory comes in. I type some commands:<br />
<pre><code>
rm .Rdata # I won't need the zillions of temporary results
cp .Rhistory Rhistory-YYYY-MM-DD # I want a backup. Something might go wrong.
mv .Rhistory data_of_interest.R # I want something source-able
</code></pre>
Then I delete all wrong turns taken from data_of_interest.R, refactor what's left, and source the file in a separate R session. Finally I compare the data.frame arrived at by the script to the one in data_of_interest.Rdata<br />
<br />
(If the topic is <i>really</i> interesting, and the data are <i>really</i> useful, I might go through several iterations of the above.)<br />
<br />
Lately I've tried to be more systematic and collected all those "data_of_interest.R" scripts in a central workspace directory. Some of them will make it into this blog. So, in a way this blog will reflect my personal .Rhistory, or rather the huge number of .Rhistories living on my hard disks. Mostly it'll keep me from reinventing the wheel. But maybe one trick or the other might be helpful for someone out there. distantobserverhttp://www.blogger.com/profile/01886943822394479578noreply@blogger.com0