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<!DOCTYPE html>
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<title>R在開放數據的加值應用</title>
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<slides class="layout-widescreen">
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<slide class=''><hgroup><h2>About Me</h2></hgroup><article id="about-me" class="columns-2">
<h3>謝宗震 博士 (Johnson Hsieh)</h3>
<ul>
<li>R語言年資 9年</li>
<li>新竹清華統計 博士 (2009 - 2013)</li>
<li><strong>DSP智庫驅動</strong> 知識長 (dsp.im)</li>
<li>台北市公參會<strong>開放資料組</strong> 顧問</li>
<li><p>R package: <code>iNEXT</code>, <code>CARE1</code>, <code>JADE</code>, <code>SpadeR</code>, <code>iNextPD</code> …</p></li>
<li>DataScience HC group 共同創辦人</li>
<li>Taiwan R User group officer</li>
<li><p>Data for social good 共同創辦人</p></li>
</ul>
<br>
<p>
<p><br></p>
<p><img src='img/johnson.jpg' style='max-width: 100%;max-height: 100%'></img> <br> <font size="2"> 擅長跨與領域專家合作,開發的R包iNEXT被應用於生物、遺傳、新聞、文學、電競等領域。現職為DSP智庫驅動知識長,輔導超過300位企業人士成為數據分析人才。 </font></p>
</article></slide><slide class=''><hgroup><h2></h2></hgroup><article id="section" class="vcentered centered">
<p>
<br>
<p style="font-size: 180%">
參與這場R語言會議的朋友有福了
</p>
<br>
<p style="font-size: 180%">
這場演講將分享用R做開放數據分析的真實經驗
</p>
<br>
<p style="font-size: 180%">
所有案例原始數據、代原碼都公開在github
</p>
</article></slide><slide class=''><hgroup><h2>Agenda</h2></hgroup><article id="agenda">
<ul style="font-size: 120%">
<li>
Real world, data and open data
</li>
<li>
Three types of data analysis
</li>
<li>
Discussion and case study
</li>
</ul>
</article></slide><slide class='segue dark nobackground'><hgroup class = 'auto-fadein'><h2>About Data</h2></hgroup><article id="about-data">
</article></slide><slide class=''><hgroup><h2>About Data</h2></hgroup><article id="about-data-1" class="smaller centered">
<img src='img/about-data.jpg' style='height: 400px'></img>
<h3>
Real World, All Data, Big Data, Open Data
</h3>
<p>取自 <a href='http://goo.gl/HS4irZ' title=''>http://goo.gl/HS4irZ</a></p>
</article></slide><slide class=''><hgroup><h2>Open Data</h2></hgroup><article id="open-data">
<p>開放數據是一種經過挑選與許可的數據,它不受著作權、專利權,以及其他管理機制所限制,可以開放給社會公眾,任何人都可以自由出版使用,不論是要拿來出版或是做其他的運用都不加以限制。</p>
<br>
<ul style="font-size: 120%">
<li>
資訊對稱,溝通透明
</li>
<li>
群眾外包,共同參與
</li>
<li>
練功打怪,自我實現
</li>
<li>
結合創新,提升效能
</li>
</ul>
</article></slide><slide class='segue dark nobackground'><hgroup class = 'auto-fadein'><h2>Three types of data analysis</h2></hgroup><article id="three-types-of-data-analysis">
</article></slide><slide class=''><hgroup><h2>Three types of data analysis</h2></hgroup><article id="three-types-of-data-analysis-1">
<h3>
掌握現況
</h3>
<h3>
洞悉關聯
</h3>
<h3>
預測未知
</h3>
</article></slide><slide class='segue dark nobackground'><hgroup class = 'auto-fadein'><h2>掌握現況案例</h2></hgroup><article >
</article></slide><slide class=''><hgroup><h2>社區醫療群地圖 (gov)</h2></hgroup><article id="-gov" class="smaller centered">
<img src='img/communitymedcare.png' style='height: 400px'></img>
<h3>
ggmap + rCharts + shiny
</h3>
<p>作者: 李昕迪 醫師 (mcdlee)</p>
</article></slide><slide class=''><hgroup><h2>社區醫療群地圖 (gov)</h2></hgroup><article id="-gov-1" class="smaller centered">
<iframe src="http://mcdlee.github.io/VGHKS/VGHKS.html" width="960" height="400" scrolling="yes" frameborder="0">
</iframe>
</article></slide><slide class=''><hgroup><h2>社區醫療群地圖 (gov)</h2></hgroup><article id="-gov-2">
<ul>
<li>來源:衛福部中央健保署</li>
<li>作者:李昕迪 醫師 (mcdlee)</li>
<li>R包:<code>ggmap</code> + <code>rCharts</code> + <code>shiny</code></li>
<li>參考: <a href='http://mcdlee.github.io/gisVisualization' title=''>slide</a>, <a href='https://mcdlee.shinyapps.io/communitymedcare' title=''>shinypp</a>, <a href='https://github.com/mcdlee/communitymedcare' title=''>github repo</a></li>
</ul>
</article></slide><slide class=''><hgroup><h2>NPO城鄉多樣性比較 (NPO)</h2></hgroup><article id="npo-npo" class="smaller centered">
<img src='img/unitedway.png' style='height: 400px'></img>
<h3>
聯合勸募基金會
</h3>
<p><a href='https://www.unitedway.org.tw/' title=''>https://www.unitedway.org.tw/</a></p>
</article></slide><slide class=''><hgroup><h2>NPO城鄉多樣性比較 (NPO)</h2></hgroup><article id="npo-npo-1" class="smaller centered">
<img src='img/npo-list.png' style='height: 400px'></img>
<h3>
利用<code>rvest</code>爬NPO名錄
</h3>
<p>收錄1111間NPO,包含16種服務對象(tag)</p>
</article></slide><slide class=''><hgroup><h2>NPO城鄉多樣性比較 (NPO)</h2></hgroup><article id="npo-npo-2" class="smaller centered">
<img src='img/npo-bar.png' style='height: 400px'></img>
<h3>
<code>dplyr</code> + <code>ggplot2</code>
</h3>
<p>台北 (都市, S=16, n=738)、雲嘉 (農業, S=15, n=177)、花東 (偏鄉, S=15, n=206)</p>
</article></slide><slide class=''><hgroup><h2>NPO城鄉多樣性比較 (NPO)</h2></hgroup><article id="npo-npo-3" class="columns-2">
<ul>
<li>多樣性 (diversity) 與多樣性指標</li>
<li>一個區域的多樣性越高,表示遇到災害的容忍程度越高</li>
<li>基本概念:種類越多越好,分布越均勻越好</li>
<li>Shannon diversity: \(H = \exp{\left ( \sum_{i=1}^{S}-p_i\log{p_i}\right )}\) <br>
<p>
<br> <img src='img/sampleSites.jpg' style='height: 200px'></img></li>
</ul>
</article></slide><slide class=''><hgroup><h2>NPO城鄉多樣性比較 (NPO)</h2></hgroup><article id="npo-npo-4">
<ul>
<li>多樣性指標會受到樣本數所影響,樣本數越大,能夠觀察到的種類數也越多,多樣性就越大。為求公平,應控制在相同樣本數下比較多樣性。</li>
<li>Shannon diversity: \(H(n) = \exp{\left ( \sum_{i=1}^{S}-p_i(n)\log{p_i(n)}\right )}\) <br></li>
</ul>
<pre class = 'prettyprint lang-r'>install.packages("iNEXT") # 安裝 iNEXT package (Hsieh et al. , 2015)
library(iNEXT)
tab <- read.csv("npo-type.csv", row.names = 1)
# iNEXT 主程式,計算位階數q = 1的Shannon diversity 稀釋/外插估計
out <- iNEXT(tab, q=1, endpoint=750, se=FALSE)
# 整合ggplot2繪圖風格
ggiNEXT(out) +
theme_gray(base_family = "STHeiti")</pre>
</article></slide><slide class=''><hgroup><h2>NPO城鄉多樣性比較 (NPO)</h2></hgroup><article id="npo-npo-5" class="smaller centered">
<img src='img/npo-inext.png' style='height: 400px'></img>
<h3>
<code>dplyr</code> + <code>ggplot2</code> + <code>iNEXT</code>
</h3>
<p>多樣性排序:花東 > 台北 > 雲嘉</p>
</article></slide><slide class=''><hgroup><h2>NPO城鄉多樣性比較 (NPO)</h2></hgroup><article id="npo-npo-6">
<ul>
<li>來源:聯合勸募基金會</li>
<li>作者:謝宗震 博士 (Johnson)</li>
<li>R包:<code>dplyr</code> + <code>ggplot</code> + <code>iNEXT</code></li>
<li>參考: <a href='https://chao.shinyapps.io/iNEXT' title=''>shinypp</a>, <a href='https://github.com/JohnsonHsieh/iNEXT' title=''>github repo</a></li>
<li></li>
</ul>
</article></slide><slide class='segue dark nobackground'><hgroup class = 'auto-fadein'><h2>洞悉因素案例</h2></hgroup><article >
</article></slide><slide class=''><hgroup><h2>找出火災風險熱區 (GOV)</h2></hgroup><article id="-gov" class="smaller centered">
<img src='img/Final Areas - fire zones.jpg
' style='height: 400px'></img>
<h3>
紐奧良消防局 + Nolalytics
</h3>
<p>火災風險熱區 (優先發放煙霧警報器)</p>
</article></slide><slide class=''><hgroup><h2>找出火災風險熱區 (GOV)</h2></hgroup><article id="-gov-1" class="smaller centered">
<img src='img/NO-table.png' style='height: 400px'></img>
<h3>
回歸分析:找出影響火災的因子
</h3>
<p>屋齡、居住時間、收入水平、更新、屋主年齡、租/住</p>
</article></slide><slide class=''><hgroup><h2>找出火災風險熱區 (GOV)</h2></hgroup><article id="-gov-2">
<ul>
<li>來源:紐奧良消防局</li>
<li>作者:Nolalytics (New Orleans Analytics)</li>
<li>R包:<code>reshape</code> + <code>plyr</code> + <code>dplyr</code>+ <code>ROCR</code> + <code>maps</code> + <code>maptools</code> + <code>sp</code> + <code>rgdal</code></li>
<li>Reference: <a href='https://github.com/cno-opa/smoke-alarm-outreach' title=''>github repo</a>, <a href='http://nola.gov/performance-and-accountability/nolalytics/files/full-report-on-analytics-informed-smoke-alarm-outr/' title=''>report</a></li>
</ul>
</article></slide><slide class='segue dark nobackground'><hgroup class = 'auto-fadein'><h2>預測未知案例</h2></hgroup><article >
</article></slide><slide class=''><hgroup><h2></h2></hgroup><article id="section-1" class="smaller centered">
<p><img src='img/ja_b01.jpg' style='height: 500px'></img></p>
</article></slide><slide class=''><hgroup><h2></h2></hgroup><article id="section-2" class="smaller centered">
<p><img src='img/news_01.png' style='height: 500px'></img></p>
</article></slide><slide class=''><hgroup><h2>豪宅房價預測 (GOV)</h2></hgroup><article id="-gov">
<ul>
<li>內政部實價登錄資料 (2012年8月 ~ 2013年9月)</li>
<li>頂級豪宅:40 / 21530 件</li>
<li>重要因子:面積、車位、屋齡、行政區、樓層</li>
<li><code>reshape</code> + <code>dplyr</code> + <code>mgcv</code></li>
</ul>
<pre class = 'prettyprint lang-r'>library(mgcv) #provides functions for generalized additive modelling
dat1 <- readRDS("data/dat1.rds")
# fit linear model
g1 <- lm(log10(總價)~面積+車位+屋齡+行政區+floor, data=dat1)
# fit addiive model with two smooth terms
g2 <- gam(log10(總價)~s(面積)+車位+s(屋齡)+行政區+floor, data=dat1)
# Compare adjusted R-squared, 越趨近1模型配適度越好
data.frame("linear model"=summary(g1)$adj.r.sq, "additive model"=summary(g2)$r.sq)</pre>
<pre > linear.model additive.model
1 0.7324551 0.9352784</pre>
</article></slide><slide class=''><hgroup><h2>豪宅房價預測 (GOV)</h2></hgroup><article id="-gov-1" class="smaller centered">
<p><img src='img/predHouse.png' style='height: 500px'></img></p>
</article></slide><slide class=''><hgroup><h2>豪宅房價預測 (GOV)</h2></hgroup><article id="-gov-2">
<pre class = 'prettyprint lang-r'># set dataset, 帝寶格局
new <- dat1[1:6, c(2,3,4,6,7,12)]
rownames(new) <- 1:6
new$面積 <- c(160,160,210,210,260,260)
new$車位 <- rep("有車位",6);
new$屋齡 <- rep(8, 6)
new$行政區 <- rep("大安區",6)
new$floor <- rep(c("低樓層","高樓層"),3)
# prediction
tmp <- predict(g2, newdata=new, se.fit=TRUE)
pred <- 10^cbind(tmp$fit, tmp$fit-tmp$se.fit, tmp$fit+tmp$se.fit)
data.frame("建案坪數"=new$面積, "高低樓層"=new$floor,
"總價估計.萬元"=round(pred[,1]/10000),
"單價估計.萬元"=round(pred[,1]/10000/new$面積))</pre>
</article></slide><slide class=''><hgroup><h2>豪宅房價預測 (GOV)</h2></hgroup><article id="-gov-3" class="smaller centered">
<p><img src='img/shiny_01.png' style='height: 500px'></img></p>
</article></slide><slide class=''><hgroup><h2>豪宅房價預測 (GOV)</h2></hgroup><article id="-gov-4" class="smaller centered">
<p><img src='img/news_02.png' style='height: 500px'></img></p>
</article></slide><slide class=''><hgroup><h2>豪宅房價預測 (GOV)</h2></hgroup><article id="-gov-5">
<ul>
<li>來源:內政部實價登錄資料 (2012年8月 ~ 2013年9月)</li>
<li>作者:謝宗震 博士</li>
<li>R包:<code>mgcv</code></li>
<li>Reference: <a href='https://github.com/JohnsonHsieh/study-area-statR' title=''>github repo</a></li>
</ul>
</article></slide><slide class=''><hgroup><h2>NPO總數估計 (NPO)</h2></hgroup><article id="npo-npo" class="smaller centered">
<p><img src='img/npo-list2.png' style='height: 400px'></img> <br> 四種來源:台灣公益資訊中心、善耕365、聯合勸募、自律聯盟</p>
</article></slide><slide class=''><hgroup><h2>NPO總數估計 (NPO)</h2></hgroup><article id="npo-npo-1" class="smaller centered">
<img src='img/cmr.png' style='height: 400px'></img>
<h3>
重復捕取方法 (Chao et al. 2015)
</h3>
</article></slide><slide class=''><hgroup><h2>NPO總數估計 (NPO)</h2></hgroup><article id="npo-npo-2">
<pre class = 'prettyprint lang-r'>install.packages("CARE1") # 樣本覆蓋率估計法 (Chao et al. 2015)
library(CARE1)
npoList <- read.csv("data/npoList.csv", row.names=1)
record <- as.record(npoList)
estN.stat(record)
# M=觀察到的總類別, D=任兩來源的平均類別, C=樣本覆蓋率
estN(record, method="LSC", se=TRUE)
# 四個來源共觀察到 3528間 NPO,以樣本覆蓋率法推得 7507間 NPO估計值</pre>
<pre >0001 0010 0011 0100 0101 0110 0111 1000 1001 1010 1011 1100 1101 1110 1111
56 635 35 106 7 38 15 2149 33 256 47 63 6 54 28 </pre>
<pre > M D Chat
[1,] 3528 2791.5 0.508</pre>
<pre > est se cil ciu
est.Nhat 7507.421 396.1306 6803.639 8362.414</pre>
</article></slide><slide class=''><hgroup><h2>NPO總數估計 (NPO)</h2></hgroup><article id="npo-npo-3">
<ul>
<li>來源:台灣公益資訊中心、善耕365、聯合勸募、自律聯盟</li>
<li>作者:DSP資料科學團隊</li>
<li>R包:<code>rvest</code> + <code>CARE1</code> + <code>shiny</code></li>
<li>參考: <a href='https://dsp.hackpad.com/-NPO-WcWRyZSZFge' title=''>hackpad</a>, <a href='http://hack.dsp.im/d4sg-hackathon/https%253A%252F%252Fgoo.gl%252FPFEpD2' title=''>hackathon</a></li>
</ul>
</article></slide><slide class=''><hgroup><h2>博客瀏覽數預測 (Business)</h2></hgroup><article id="-business" class="smaller centered">
<p><img src='img/pixnet-data.png' style='height: 500px'></img></p>
</article></slide><slide class=''><hgroup><h2>數據競賽平台</h2></hgroup><article class="smaller centered">
<p><img src='img/dc-pixnet.png' style='width: 960px'></img></p>
</article></slide><slide class=''><hgroup><h2>數據競賽平台</h2></hgroup><article id="-1" class="smaller centered">
<img src='img/dc-overview.png' style='height: 400px'></img>
<h3>
數據競賽平台
</h3>
<p>政府、企業、非營利組織的數據加值平台</p>
</article></slide><slide class=''><hgroup><h2>Summary</h2></hgroup><article id="summary">
<ul>
<li>社區醫療群地圖: <a href='https://github.com/mcdlee/communitymedcare' title=''>https://github.com/mcdlee/communitymedcare</a></li>
<li>NPO城鄉多樣性比: <a href='https://github.com/JohnsonHsieh/iNEXT' title=''>https://github.com/JohnsonHsieh/iNEXT</a></li>
<li>找出火災風險熱區: <a href='https://github.com/cno-opa/smoke-alarm-outreach' title=''>https://github.com/cno-opa/smoke-alarm-outreach</a></li>
<li>豪宅房價預測: <a href='https://github.com/JohnsonHsieh/study-area-statR' title=''>https://github.com/JohnsonHsieh/study-area-statR</a></li>
<li>NPO總數估計: <a href='https://dsp.hackpad.com/-NPO-WcWRyZSZFge' title=''>https://dsp.hackpad.com/-NPO-WcWRyZSZFge</a></li>
</ul>
</article></slide><slide class='segue dark nobackground'><hgroup class = 'auto-fadein'><h2>Thank You</h2></hgroup><article id="thank-you"></article></slide>
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