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<!DOCTYPE html>
<html>
<head>
<title>結構化的資料處理</title>
<meta charset="utf-8">
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<body style="opacity: 0">
<slides class="layout-widescreen">
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<!-- The content of this hgroup is replaced programmatically through the slide_config.json. -->
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<h1 data-config-title><!-- populated from slide_config.json --></h1>
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<p style="margin-top: 6px; margin-left: -2px;">2019-05-11</p>
</hgroup>
</slide>
<slide class=""><hgroup><h2>大綱</h2></hgroup><article id="section">
<ul>
<li>讀取CSV</li>
<li><code>dplyr</code> 的Verbs簡介</li>
<li>資料整合</li>
</ul>
</article></slide><slide class="segue dark nobackground level1"><hgroup class = 'auto-fadein'><h2>讀取CSV</h2></hgroup><article id="csv">
</article></slide><slide class=""><hgroup><h2>CSV的格式</h2></hgroup><article id="csv-1">
<ul>
<li>CSV: Comma-Separated Values</li>
</ul>
<p><img src='img/%E6%93%B7%E5%8F%96%E9%81%B8%E5%8F%96%E5%8D%80%E5%9F%9F_008.png' style='max-width: 100%; max-height: 100%; '></img></p>
</article></slide><slide class=""><hgroup><h2><code>read.csv</code></h2></hgroup><article id="read.csv">
<pre class = 'prettyprint lang-r'>read.csv("http://homepage.ntu.edu.tw/~wush978/rdataengineer/district_location.csv",
header = TRUE, nrows = 6)</pre>
<pre >## 行政區名 X_x0033_碼郵遞區號 中心點經度 中心點緯度
## 1 臺北市中正區 100 121.5199 25.03240
## 2 臺北市大同區 103 121.5130 25.06342
## 3 臺北市中山區 104 121.5382 25.06970
## 4 臺北市松山區 105 121.5576 25.05999
## 5 臺北市大安區 106 121.5434 25.02677
## 6 臺北市萬華區 108 121.4980 25.02859
## TGOS_URL
## 1 http://tgos.nat.gov.tw/tgos/Web/MetaData/TGOS_MetaData_View.aspx?MID=9C715A5CD330360D355AE105F908B29E&SHOW_BACK_BUTTON=false
## 2 http://tgos.nat.gov.tw/tgos/Web/MetaData/TGOS_MetaData_View.aspx?MID=9C715A5CD330360D355AE105F908B29E&SHOW_BACK_BUTTON=false
## 3 http://tgos.nat.gov.tw/tgos/Web/MetaData/TGOS_MetaData_View.aspx?MID=9C715A5CD330360D355AE105F908B29E&SHOW_BACK_BUTTON=false
## 4 http://tgos.nat.gov.tw/tgos/Web/MetaData/TGOS_MetaData_View.aspx?MID=9C715A5CD330360D355AE105F908B29E&SHOW_BACK_BUTTON=false
## 5 http://tgos.nat.gov.tw/tgos/Web/MetaData/TGOS_MetaData_View.aspx?MID=9C715A5CD330360D355AE105F908B29E&SHOW_BACK_BUTTON=false
## 6 http://tgos.nat.gov.tw/tgos/Web/MetaData/TGOS_MetaData_View.aspx?MID=9C715A5CD330360D355AE105F908B29E&SHOW_BACK_BUTTON=false</pre>
</article></slide><slide class=""><hgroup><h2><code>read.table</code></h2></hgroup><article id="read.table">
<pre class = 'prettyprint lang-r'>read.csv("http://homepage.ntu.edu.tw/~wush978/rdataengineer/district_location.csv",
sep = ",", header = TRUE, nrows = 6)</pre>
<pre >## 行政區名 X_x0033_碼郵遞區號 中心點經度 中心點緯度
## 1 臺北市中正區 100 121.5199 25.03240
## 2 臺北市大同區 103 121.5130 25.06342
## 3 臺北市中山區 104 121.5382 25.06970
## 4 臺北市松山區 105 121.5576 25.05999
## 5 臺北市大安區 106 121.5434 25.02677
## 6 臺北市萬華區 108 121.4980 25.02859
## TGOS_URL
## 1 http://tgos.nat.gov.tw/tgos/Web/MetaData/TGOS_MetaData_View.aspx?MID=9C715A5CD330360D355AE105F908B29E&SHOW_BACK_BUTTON=false
## 2 http://tgos.nat.gov.tw/tgos/Web/MetaData/TGOS_MetaData_View.aspx?MID=9C715A5CD330360D355AE105F908B29E&SHOW_BACK_BUTTON=false
## 3 http://tgos.nat.gov.tw/tgos/Web/MetaData/TGOS_MetaData_View.aspx?MID=9C715A5CD330360D355AE105F908B29E&SHOW_BACK_BUTTON=false
## 4 http://tgos.nat.gov.tw/tgos/Web/MetaData/TGOS_MetaData_View.aspx?MID=9C715A5CD330360D355AE105F908B29E&SHOW_BACK_BUTTON=false
## 5 http://tgos.nat.gov.tw/tgos/Web/MetaData/TGOS_MetaData_View.aspx?MID=9C715A5CD330360D355AE105F908B29E&SHOW_BACK_BUTTON=false
## 6 http://tgos.nat.gov.tw/tgos/Web/MetaData/TGOS_MetaData_View.aspx?MID=9C715A5CD330360D355AE105F908B29E&SHOW_BACK_BUTTON=false</pre>
</article></slide><slide class=""><hgroup><h2><code>read.csv</code>與<code>read.table</code>的注意事項</h2></hgroup><article id="read.csvread.table">
<ul>
<li>預設會把<code>character vector</code>轉成<code>factor</code>型態
<ul>
<li>可以用參數<code>stringsAsFactors</code>控制</li>
</ul></li>
<li>實務上，R會猜欄位的型態（是<code>character</code>, <code>numeric</code>還是呢？），這很慢
<ul>
<li>可以用參數<code>colClasses</code>直接告訴R答案，讀大資料會快很多</li>
</ul></li>
</ul>
</article></slide><slide class=""><hgroup><h2>讀取<code>CSV</code>的可能錯誤：編碼問題</h2></hgroup><article id="csv-2">
<pre class = 'prettyprint lang-r'>read.csv(url("http://homepage.ntu.edu.tw/~wush978/rdataengineer/district_location.csv", encoding = "BIG5"),
sep = ",", header = TRUE, nrows = 6)</pre>
<pre >## Warning in read.table(file = file, header = header, sep = sep, quote =
## quote, : 輸入連結 'http://homepage.ntu.edu.tw/~wush978/rdataengineer/
## district_location.csv' 中的輸入不正確</pre>
<pre >## Warning in read.table(file = file, header = header, sep = sep, quote
## = quote, : incomplete final line found by readTableHeader on 'http://
## homepage.ntu.edu.tw/~wush978/rdataengineer/district_location.csv'</pre>
<pre >## [1] 銵
## <0 rows> (or 0-length row.names)</pre>
<ul>
<li>解法：先用<code>readLines</code>處理或是用<code>readBin</code>處理後再讀取</li>
</ul>
</article></slide><slide class=""><hgroup><h2>讀取<code>CSV</code>的可能錯誤：欄位數量不一致</h2></hgroup><article id="csv-3">
<ul>
<li>內文包含<code>","</code>或分隔符號</li>
<li>資料有錯</li>
</ul>
<pre class = 'prettyprint lang-r'>read.csv("http://homepage.ntu.edu.tw/~wush978/rdataengineer/csv-error.csv")</pre>
<pre >## NAME ID
## Wush Chi-Hsuan Wu d12345678
## Hsieh Johnson d12345679</pre>
<ul>
<li>用<code>readLines</code>後手動用<code>strsplit</code>處理</li>
</ul>
</article></slide><slide class=""><hgroup><h2>小挑戰</h2></hgroup><article id="section-1">
<pre class = 'prettyprint lang-r'>x <- readLines("http://homepage.ntu.edu.tw/~wush978/rdataengineer/csv-error.csv")</pre>
<ul>
<li>請用程式找出兩位的學號</li>
</ul>
</article></slide><slide class=""><hgroup><h2>讀取CSV檔案的小撇步: colClasses 參數</h2></hgroup><article id="csv-colclasses-">
<ul>
<li><code>read.csv</code>與<code>read.table</code></li>
<li><code>colClasses</code>參數可以加速</li>
</ul>
<pre class = 'prettyprint lang-r'># source: https://support.spatialkey.com/spatialkey-sample-csv-data/
path <- tempfile(fileext = ".csv.gz")
download.file("http://homepage.ntu.edu.tw/~wush978/rdataengineer/FL_insurance_sample.csv.gz", destfile = path)</pre>
</article></slide><slide class=""><hgroup><h2>讀取CSV檔案的小撇步: colClasses 參數</h2></hgroup><article id="csv-colclasses--1">
<pre class = 'prettyprint lang-r'>readLines(gzfile(path), n = 6)</pre>
<pre >## [1] "policyID,statecode,county,eq_site_limit,hu_site_limit,fl_site_limit,fr_site_limit,tiv_2011,tiv_2012,eq_site_deductible,hu_site_deductible,fl_site_deductible,fr_site_deductible,point_latitude,point_longitude,line,construction,point_granularity"
## [2] "119736,FL,CLAY COUNTY,498960,498960,498960,498960,498960,792148.9,0,9979.2,0,0,30.102261,-81.711777,Residential,Masonry,1"
## [3] "448094,FL,CLAY COUNTY,1322376.3,1322376.3,1322376.3,1322376.3,1322376.3,1438163.57,0,0,0,0,30.063936,-81.707664,Residential,Masonry,3"
## [4] "206893,FL,CLAY COUNTY,190724.4,190724.4,190724.4,190724.4,190724.4,192476.78,0,0,0,0,30.089579,-81.700455,Residential,Wood,1"
## [5] "333743,FL,CLAY COUNTY,0,79520.76,0,0,79520.76,86854.48,0,0,0,0,30.063236,-81.707703,Residential,Wood,3"
## [6] "172534,FL,CLAY COUNTY,0,254281.5,0,254281.5,254281.5,246144.49,0,0,0,0,30.060614,-81.702675,Residential,Wood,1"</pre>
<pre class = 'prettyprint lang-r'>system.time(
FL <- read.csv(gzfile(path), header = TRUE)
)</pre>
<pre >## user system elapsed
## 0.288 0.010 0.298</pre>
</article></slide><slide class=""><hgroup><h2>讀取CSV檔案的小撇步: colClasses 參數</h2></hgroup><article id="csv-colclasses--2">
<pre class = 'prettyprint lang-r'>system.time({
FL.head <- read.csv(gzfile("FL_insurance_sample.csv.gz"), header = TRUE, nrows = 6)
.col <- sapply(FL.head, class)
.col[.col == "integer"] <- "numeric"
FL <- read.csv(gzfile("FL_insurance_sample.csv.gz"), header = TRUE, colClasses = .col)
})</pre>
<pre >## user system elapsed
## 0.170 0.008 0.184</pre>
</article></slide><slide class=""><hgroup><h2>讀取CSV檔案的小撇步: colClasses 參數</h2></hgroup><article id="csv-colclasses--3">
<pre class = 'prettyprint lang-r'>head(FL)</pre>
<pre >## policyID statecode county eq_site_limit hu_site_limit fl_site_limit
## 1 119736 FL CLAY COUNTY 498960.0 498960.00 498960.0
## 2 448094 FL CLAY COUNTY 1322376.3 1322376.30 1322376.3
## 3 206893 FL CLAY COUNTY 190724.4 190724.40 190724.4
## 4 333743 FL CLAY COUNTY 0.0 79520.76 0.0
## 5 172534 FL CLAY COUNTY 0.0 254281.50 0.0
## 6 785275 FL CLAY COUNTY 0.0 515035.62 0.0
## fr_site_limit tiv_2011 tiv_2012 eq_site_deductible
## 1 498960.0 498960.00 792148.90 0
## 2 1322376.3 1322376.30 1438163.57 0
## 3 190724.4 190724.40 192476.78 0
## 4 0.0 79520.76 86854.48 0
## 5 254281.5 254281.50 246144.49 0
## 6 0.0 515035.62 884419.17 0
## hu_site_deductible fl_site_deductible fr_site_deductible point_latitude
## 1 9979.2 0 0 30.10226
## 2 0.0 0 0 30.06394
## 3 0.0 0 0 30.08958
## 4 0.0 0 0 30.06324
## 5 0.0 0 0 30.06061
## 6 0.0 0 0 30.06324
## point_longitude line construction point_granularity
## 1 -81.71178 Residential Masonry 1
## 2 -81.70766 Residential Masonry 3
## 3 -81.70046 Residential Wood 1
## 4 -81.70770 Residential Wood 3
## 5 -81.70267 Residential Wood 1
## 6 -81.70770 Residential Masonry 3</pre>
</article></slide><slide class=""><hgroup><h2>Database</h2></hgroup><article id="database">
<ul>
<li>作業在 Windows 上的 3.5 系列有bug，修復中
<ul>
<li>請Windows使用者用別的版本的R跑作業</li>
</ul></li>
<li>什麼時候用 Database?
<ul>
<li>記憶體不夠</li>
<li>需要Transaction(多個操作中，只要一個失敗就全部復原)</li>
</ul></li>
</ul>
</article></slide><slide class=""><hgroup><h2>Transaction</h2></hgroup><article id="transaction">
<ul>
<li>線上的資料處理上，在「容錯」上非常重要的「特性」</li>
<li>範例：
<ul>
<li>假設每小時產生一個檔案</li>
</ul></li>
</ul>
<pre class = 'prettyprint lang-r'>out.path <- sprintf("%s.csv", format(Sys.time(), "%Y-%m-%d-%H"))
# do something
write(data, file = out.path)</pre>
</article></slide><slide class=""><hgroup><h2>Transaction</h2></hgroup><article id="transaction-1">
<ul>
<li>線上的資料處理上，在「容錯」上非常重要的「特性」</li>
<li>範例：
<ul>
<li>假設每小時產生一個檔案</li>
<li>寫入到一半的時候發生錯誤（斷電、當機）</li>
<li>自動重開機後又繼續跑，產生新的檔案</li>
<li>哪些檔案是錯的？</li>
</ul></li>
</ul>
</article></slide><slide class=""><hgroup><h2>Transaction</h2></hgroup><article id="transaction-2">
<pre class = 'prettyprint lang-r'>out.path <- format(Sys.time(), "%Y-%m-%d-%H.csv")
out.path.tmp <- paste(out.path, "tmp", sep = ".")
write(data, file = out.path.tmp)
# rename is transaction
file.rename(out.path.tmp, out.path)</pre>
</article></slide><slide class=""><hgroup><h2>XML Tables</h2></hgroup><article id="xml-tables">
<ul>
<li><code>XML::readHTMLTable</code></li>
<li><code>XML</code> 是比較老牌的XML處理工具
<ul>
<li>資料結構比<code>xml2</code>更難懂，但是比較穩(?)</li>
<li>如果資料來源是結構化的HTML表格，<code>XML::readHTMLTable</code>很方便</li>
</ul></li>
</ul>
</article></slide><slide class=""><hgroup><h2>2018 縣市長大選台北即時估票</h2></hgroup><article id="section-2">
<ul>
<li><a href='https://gist.github.com/wush978/1619ddb3ed093a11febb0da592f5fc9d' title=''>外差估票程式</a>
<ol>
<li>從中選會的網頁上抓候選人的得票數</li>
<li>利用各正區現有的得票率當機準，依照「已開票」與「未開票」的比率放大</li>
<li>推估最終候選人的得票</li>
<li>可惜現在網頁格式不一致，所以會跑出NA XD</li>
</ol></li>
<li><a href='https://www.facebook.com/wush978/posts/2194489713895986' title=''>紀錄</a></li>
</ul>
</article></slide><slide class=""><hgroup><h2>data.frame</h2></hgroup><article id="data.frame">
<ul>
<li>由list物件擴充而成
<ul>
<li>list + attributes</li>
</ul></li>
<li>在R 語言中，處理「表格」(table)資料
<ul>
<li>表格 v.s. 矩陣、陣列</li>
</ul></li>
<li>視覺化：ggplot2</li>
<li>許多進階分析的入口
<ul>
<li>例：迴歸分析(<code>lm</code>)</li>
<li>將表格的變數轉換成數學上的矩陣：<code>model.matrix</code> \(\hat{\beta} = (X^TX)^{-1}(X^Ty)\)</li>
</ul></li>
</ul>
</article></slide><slide class=""><hgroup><h2>data.frame 的 Create</h2></hgroup><article id="data.frame--create">
<ul>
<li>注意參數： <code>stringsAsFactors</code></li>
</ul>
<pre class = 'prettyprint lang-r'>data.frame(student.id = 1:5, math.score = rpois(5, 5))</pre>
<pre >## student.id math.score
## 1 1 3
## 2 2 8
## 3 3 3
## 4 4 4
## 5 5 5</pre>
</article></slide><slide class=""><hgroup><h2>data.frame 的 Create</h2></hgroup><article id="data.frame--create-1">
<pre class = 'prettyprint lang-r'>df <- read.csv(
url("https://raw.githubusercontent.com/wush978/DataScienceAndR/course/01-RBasic-07-Loading-Dataset/A_LVR_LAND_A.CSV", encoding = "BIG5"),
nrows = 6, header = TRUE)
df[,1:3]</pre>
<pre >## 鄉鎮市區 交易標的 土地區段位置或建物區門牌
## 1 文山區 房地(土地+建物) 臺北市文山區木柵路二段109巷100弄61~90號
## 2 中正區 房地(土地+建物) 臺北市中正區南海路1~30號
## 3 中正區 房地(土地+建物) 臺北市中正區重慶南路三段121~150號
## 4 文山區 房地(土地+建物) 臺北市文山區指南路三段32巷1~30號
## 5 文山區 房地(土地+建物) 臺北市文山區羅斯福路五段92巷1弄1~30號
## 6 文山區 房地(土地+建物) 臺北市文山區秀明路二段1~30號</pre>
</article></slide><slide class=""><hgroup><h2>data.frame 的 Read</h2></hgroup><article id="data.frame--read">
<ul>
<li>list的Read: <code>[</code>、<code>[[</code>與<code>$</code></li>
</ul>
<pre class = 'prettyprint lang-r'>df["鄉鎮市區"]</pre>
<pre >## 鄉鎮市區
## 1 文山區
## 2 中正區
## 3 中正區
## 4 文山區
## 5 文山區
## 6 文山區</pre>
</article></slide><slide class=""><hgroup><h2>data.frame 的 Read</h2></hgroup><article id="data.frame--read-1">
<ul>
<li>list的Read: <code>[</code>、<code>[[</code>與<code>$</code></li>
</ul>
<pre class = 'prettyprint lang-r'>df[["鄉鎮市區"]]</pre>
<pre >## [1] 文山區 中正區 中正區 文山區 文山區 文山區
## Levels: 文山區 中正區</pre>
</article></slide><slide class=""><hgroup><h2>data.frame 的 Read</h2></hgroup><article id="data.frame--read-2">
<ul>
<li>list的Read: <code>[</code>、<code>[[</code>與<code>$</code></li>
</ul>
<pre class = 'prettyprint lang-r'>df$`鄉鎮市區`</pre>
<pre >## [1] 文山區 中正區 中正區 文山區 文山區 文山區
## Levels: 文山區 中正區</pre>
</article></slide><slide class=""><hgroup><h2>data.frame 的 Read</h2></hgroup><article id="data.frame--read-3">
<ul>
<li><code>[</code>: 仍然是data.frame</li>
<li><code>[[</code>、<code>$</code>: data.frame(list)會被打破</li>
</ul>
</article></slide><slide class=""><hgroup><h2>data.frame 的 Read</h2></hgroup><article id="data.frame--read-4">
<ul>
<li>matrix的Read: <code>[</code></li>
</ul>
<pre class = 'prettyprint lang-r'>df[1,1]</pre>
<pre >## [1] 文山區
## Levels: 文山區 中正區</pre>
<pre class = 'prettyprint lang-r'>df[1:2,1]</pre>
<pre >## [1] 文山區 中正區
## Levels: 文山區 中正區</pre>
<pre class = 'prettyprint lang-r'>df[1,1:2]</pre>
<pre >## 鄉鎮市區 交易標的
## 1 文山區 房地(土地+建物)</pre>
</article></slide><slide class=""><hgroup><h2>data.frame 的 Read</h2></hgroup><article id="data.frame--read-5">
<ul>
<li>matrix的Read: <code>[</code>
<ul>
<li>應仍然是data.frame</li>
<li>參數<code>drop = TRUE</code>(預設)當欄位方向的維度為1時，會自動把data.frame轉成向量</li>
</ul></li>
</ul>
<pre class = 'prettyprint lang-r'>df[1:2,1:2]</pre>
<pre >## 鄉鎮市區 交易標的
## 1 文山區 房地(土地+建物)
## 2 中正區 房地(土地+建物)</pre>
<pre class = 'prettyprint lang-r'>df[1,1,drop = FALSE]</pre>
<pre >## 鄉鎮市區
## 1 文山區</pre>
</article></slide><slide class=""><hgroup><h2>data.frame 的 Read</h2></hgroup><article id="data.frame--read-6">
<ul>
<li><code>drop = TRUE</code> 是好事嘛？
<ul>
<li>Hadley 主導的 <a href='https://www.tidyverse.org/' title=''>tidyverse</a></li>
<li>不是所有人都喜歡… <a href='https://stat.ethz.ch/pipermail/r-package-devel/2017q3/001896.html' title=''>tibbles are not data.frames</a></li>
</ul></li>
</ul>
</article></slide><slide class=""><hgroup><h2>data.frame 的 Update</h2></hgroup><article id="data.frame--update">
<ul>
<li><code>Read</code> + <code><-</code></li>
</ul>
<div class="columns-2">
<pre class = 'prettyprint lang-r'>df <- data.frame(
id = 1:5,
score = sample(1:10, 5, TRUE))
df</pre>
<pre >## id score
## 1 1 4
## 2 2 8
## 3 3 5
## 4 4 8
## 5 5 5</pre>
<pre class = 'prettyprint lang-r'>df$score <- scale(df$score)
df</pre>
<pre >## id score
## 1 1 -1.0690450
## 2 2 1.0690450
## 3 3 -0.5345225
## 4 4 1.0690450
## 5 5 -0.5345225</pre></div>
</article></slide><slide class=""><hgroup><h2>data.frame 的 Delete</h2></hgroup><article id="data.frame--delete">
<ul>
<li>反向Read</li>
<li>Read + <code><- NULL</code></li>
</ul>
<div class="columns-2">
<pre class = 'prettyprint lang-r'>df[-1,] # df[2:5,]</pre>
<pre >## id score
## 2 2 1.0690450
## 3 3 -0.5345225
## 4 4 1.0690450
## 5 5 -0.5345225</pre>
<pre class = 'prettyprint lang-r'>df$score <- NULL
df</pre>
<pre >## id
## 1 1
## 2 2
## 3 3
## 4 4
## 5 5</pre></div>
</article></slide><slide class=""><hgroup><h2>範例</h2></hgroup><article id="section-3">
<ul>
<li><code>iris</code>的資料中
<ul>
<li><code>Sepal.Length</code>的平均</li>
<li>各種<code>Species</code>的平均<code>Sepal.Length</code></li>
<li>建立新的欄位：<code>std.Sepal.Length</code>是標準化後的<code>Sepal.Length</code></li>
<li>建立新的欄位：<code>std.Sepal.Length</code>是依照個別<code>Species</code>作標準化後的<code>Sepal.Length</code></li>
</ul></li>
</ul>
</article></slide><slide class=""><hgroup><h2>範例</h2></hgroup><article id="section-4">
<ul>
<li>各種<code>Species</code>的平均<code>Sepal.Length</code></li>
</ul>
<pre class = 'prettyprint lang-r'>ans <- c()
for(.sp in levels(iris$Species)) {
.i <- iris$Species == .sp
ans[.sp] <- mean(iris$Sepal.Length[.i])
}
ans</pre>
<pre >## setosa versicolor virginica
## 5.006 5.936 6.588</pre>
</article></slide><slide class=""><hgroup><h2>範例</h2></hgroup><article id="section-5">
<ul>
<li>各種<code>Species</code>的平均<code>Sepal.Length</code></li>
</ul>
<pre class = 'prettyprint lang-r'># you can use `lapply` and then `unlist`
sapply(levels(iris$Species), function(.sp) {
.i <- iris$Species == .sp
mean(iris$Sepal.Length[.i])
})</pre>
<pre >## setosa versicolor virginica
## 5.006 5.936 6.588</pre>
</article></slide><slide class=""><hgroup><h2>範例</h2></hgroup><article id="section-6">
<ul>
<li>各種<code>Species</code>的平均<code>Sepal.Length</code></li>
</ul>
<pre class = 'prettyprint lang-r'>. <- split(iris, iris$Species)
. <- lapply(., "[[", "Sepal.Length")
sapply(., mean)</pre>
<pre >## setosa versicolor virginica
## 5.006 5.936 6.588</pre>
</article></slide><slide class=""><hgroup><h2>範例</h2></hgroup><article id="section-7">
<ul>
<li>各種<code>Species</code>的平均<code>Sepal.Length</code></li>
</ul>
<pre class = 'prettyprint lang-r'>aggregate(Sepal.Length ~ Species, iris, mean)</pre>
<pre >## Species Sepal.Length
## 1 setosa 5.006
## 2 versicolor 5.936
## 3 virginica 6.588</pre>
<ul>
<li>要理解這段expression，同學需要學會：
<ul>
<li><code>formula object</code></li>
<li>Aggregation functions</li>
</ul></li>
</ul>
</article></slide><slide class=""><hgroup><h2>範例</h2></hgroup><article id="section-8">
<ul>
<li>建立新的欄位：<code>std.Sepal.Length</code>是標準化後的<code>Sepal.Length</code></li>
</ul>
<pre class = 'prettyprint lang-r'>ans <- iris # to backup the original object, we modify after copying
. <- iris$Sepal.Length - mean(iris$Sepal.Length)
. <- . / sd(iris$Sepal.Length)
ans$std.Sepal.Length <- .
ans[c(1,2,51,52,101,102),6,drop=FALSE]</pre>
<pre >## std.Sepal.Length
## 1 -0.89767388
## 2 -1.13920048
## 51 1.39682886
## 52 0.67224905
## 101 0.55148575
## 102 -0.05233076</pre>
</article></slide><slide class=""><hgroup><h2>範例</h2></hgroup><article id="section-9">
<ul>
<li>建立新的欄位：<code>std.Sepal.Length</code>是標準化後的<code>Sepal.Length</code></li>
</ul>
<pre class = 'prettyprint lang-r'>ans <- iris # to backup the original object, we modify after copying
ans$std.Sepal.Length <- scale(iris$Sepal.Length)
ans[c(1,2,51,52,101,102),6,drop=FALSE]</pre>
<pre >## std.Sepal.Length
## 1 -0.89767388
## 2 -1.13920048
## 51 1.39682886
## 52 0.67224905
## 101 0.55148575
## 102 -0.05233076</pre>
</article></slide><slide class=""><hgroup><h2>範例</h2></hgroup><article id="section-10">
<ul>
<li>建立新的欄位：<code>std.Sepal.Length</code>是依照個別<code>Species</code>作標準化後的<code>Sepal.Length</code></li>
</ul>
<pre class = 'prettyprint lang-r'>. <- iris$Sepal.Length
for(.sp in levels(iris$Species)) {
.i <- iris$Species == .sp
.[.i] <- scale(.[.i])
}
ans <- iris
ans$std.Sepal.Length <- .
ans[c(1,2,51,52,101,102),6,drop=FALSE]</pre>
<pre >## std.Sepal.Length
## 1 0.2666745
## 2 -0.3007180
## 51 2.0613318
## 52 0.8989266
## 101 -0.4529159
## 102 -1.2392283</pre>
</article></slide><slide class=""><hgroup><h2>範例</h2></hgroup><article id="section-11">
<ul>
<li>建立新的欄位：<code>std.Sepal.Length</code>是依照個別<code>Species</code>作標準化後的<code>Sepal.Length</code></li>
</ul>
<pre class = 'prettyprint lang-r'># This code will not work if the species is not ordered
. <- lapply(levels(iris$Species), function(.sp) {
.i <- iris$Species == .sp
scale(iris$Sepal.Length[.i])
})
ans <- iris
ans$std.Sepal.Length <- unlist(.)
ans[c(1,2,51,52,101,102),6,drop=FALSE]</pre>
<pre >## std.Sepal.Length
## 1 0.2666745
## 2 -0.3007180
## 51 2.0613318
## 52 0.8989266
## 101 -0.4529159
## 102 -1.2392283</pre>
</article></slide><slide class="segue dark nobackground level1"><hgroup class = 'auto-fadein'><h2><a href='https://cran.r-project.org/package=dplyr' title=''>dplyr</a></h2></hgroup><article id="dplyr">
</article></slide><slide class=""><hgroup><h2>參考SQL 資料庫系統對結構化資料的操作做設計</h2></hgroup><article id="sql-">
<ul>
<li>一般企業儲存結構化資料的工具
<ul>
<li>儲存所有資料的工具</li>
<li>Transaction: 操作要嘛成功，要嘛無效</li>
</ul></li>
<li>SQL 資料庫的結構與操作是有數學代數在背後(<a href='https://en.wikipedia.org/wiki/Relational_algebra' title=''>Relational Algebra</a>)</li>
<li>R的data.frame v.s. SQL 資料庫
<ul>
<li>memory v.s. disk</li>
<li>indexing</li>
<li>column based v.s. row based</li>
</ul></li>
<li>一致的設計，讓同學可以透過<code>dplyr</code>的語法寫SQL
<ul>
<li>老師的經驗：比起用dplyr操作Database，還是直接寫 SQL 比較簡單… 但是可順便學SQL</li>
<li>有SQL經驗的同學可以快速上手R 的data.frame</li>
</ul></li>
</ul>
</article></slide><slide class=""><hgroup><h2>dplyr 沒有完全相容於 data.frame</h2></hgroup><article id="dplyr--data.frame">
<ul>
<li>有時候，輸出的table不再是data.frame
<ul>
<li>為了效能</li>
<li>為了設計</li>
<li>因為Hadley(?)</li>
</ul></li>
<li>有必要時可以使用<code>as.data.frame</code></li>
</ul>
</article></slide><slide class=""><hgroup><h2>Single Table verbs</h2></hgroup><article id="single-table-verbs">
<ul>
<li>Read / Delete
<ul>
<li><code>filter</code>、<code>slice</code></li>
<li><code>select</code></li>
<li><code>sample_n</code>、<code>sample_frac</code></li>
</ul></li>
<li>Update
<ul>
<li><code>mutate</code></li>
<li><code>arrange</code></li>
</ul></li>
<li>Others
<ul>
<li><code>summarise</code></li>
<li><code>group_by</code></li>
<li><code>do</code></li>
</ul></li>
</ul>
</article></slide><slide class=""><hgroup><h2>針對列作篩選：filter</h2></hgroup><article id="filter">
<div class="columns-2" style="font-size-adjust:0.4;">
<pre class = 'prettyprint lang-r'>head(iris[iris$Sepal.Length > 3,1,drop=FALSE])</pre>
<pre >## Sepal.Length
## 1 5.1
## 2 4.9
## 3 4.7
## 4 4.6
## 5 5.0
## 6 5.4</pre>
<pre class = 'prettyprint lang-r'>head(filter(iris, Sepal.Length > 3)[,1,drop=FALSE])</pre>
<pre >## Sepal.Length
## 1 5.1
## 2 4.9
## 3 4.7
## 4 4.6
## 5 5.0
## 6 5.4</pre></div>
</article></slide><slide class=""><hgroup><h2>針對列作篩選：filter</h2></hgroup><article id="filter-1">
<ul>
<li>在<code>dplyr</code>的函數中，<code>iris$</code>可以被省略
<ul>
<li>解析順序：欄位名稱 –> 變數名稱</li>
</ul></li>
<li><code>filter</code>的第一個參數是要處理的data.frame物件
<ul>
<li>所有的<code>dplyr</code>函數都是這樣設計</li>
</ul></li>
<li><code>filter</code>的其他參數必須是一個布林向量，並且長度一致
<ul>
<li>所有的這類參數，都是<code>TRUE</code>的位置，才會回傳</li>
</ul></li>
</ul>
</article></slide><slide class=""><hgroup><h2>針對列作篩選：filter</h2></hgroup><article id="filter-2">
<pre class = 'prettyprint lang-r'>head(filter(iris, Sepal.Length > 3, Sepal.Width < 3.5))</pre>
<pre >## Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 1 4.9 3.0 1.4 0.2 setosa
## 2 4.7 3.2 1.3 0.2 setosa
## 3 4.6 3.1 1.5 0.2 setosa
## 4 4.6 3.4 1.4 0.3 setosa
## 5 5.0 3.4 1.5 0.2 setosa
## 6 4.4 2.9 1.4 0.2 setosa</pre>
</article></slide><slide class=""><hgroup><h2>針對列作篩選：filter</h2></hgroup><article id="filter-3">
<pre class = 'prettyprint lang-r'>head(filter(iris, Sepal.Length > 3, Sepal.Width < 3.5, Species == "versicolor"))</pre>
<pre >## Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 1 7.0 3.2 4.7 1.4 versicolor
## 2 6.4 3.2 4.5 1.5 versicolor
## 3 6.9 3.1 4.9 1.5 versicolor
## 4 5.5 2.3 4.0 1.3 versicolor
## 5 6.5 2.8 4.6 1.5 versicolor
## 6 5.7 2.8 4.5 1.3 versicolor</pre>
</article></slide><slide class=""><hgroup><h2>針對列作篩選：filter</h2></hgroup><article id="filter-4">
<pre class = 'prettyprint lang-r'>a <- iris$Sepal.Length + iris$Sepal.Width
head(filter(iris, Sepal.Length > 3, Sepal.Width < 3.5, a < 8))</pre>
<pre >## Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 1 4.9 3.0 1.4 0.2 setosa
## 2 4.7 3.2 1.3 0.2 setosa
## 3 4.6 3.1 1.5 0.2 setosa
## 4 4.4 2.9 1.4 0.2 setosa
## 5 4.8 3.0 1.4 0.1 setosa
## 6 4.3 3.0 1.1 0.1 setosa</pre>
</article></slide><slide class=""><hgroup><h2>針對列作篩選：slice</h2></hgroup><article id="slice">
<pre class = 'prettyprint lang-r'>slice(iris, 1:6)</pre>
<pre >## Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 1 5.1 3.5 1.4 0.2 setosa
## 2 4.9 3.0 1.4 0.2 setosa
## 3 4.7 3.2 1.3 0.2 setosa
## 4 4.6 3.1 1.5 0.2 setosa
## 5 5.0 3.6 1.4 0.2 setosa
## 6 5.4 3.9 1.7 0.4 setosa</pre>
</article></slide><slide class=""><hgroup><h2>針對列作抽樣：<code>sample_n</code>、<code>sample_frac</code></h2></hgroup><article id="sample_nsample_frac">
<div class="columns-2">
<pre class = 'prettyprint lang-r'>sample_n(iris, 6)[,1,drop=FALSE]</pre>
<pre >## Sepal.Length
## 1 5.6
## 2 6.5
## 3 4.4
## 4 5.7
## 5 6.1
## 6 6.0</pre>
<pre class = 'prettyprint lang-r'>sample_frac(iris, 0.04)[,1,drop=FALSE]</pre>
<pre >## Sepal.Length
## 1 7.6
## 2 6.3
## 3 6.0
## 4 6.3
## 5 6.0
## 6 6.6</pre></div>
</article></slide><slide class=""><hgroup><h2>針對欄位作篩選：select</h2></hgroup><article id="select">
<pre class = 'prettyprint lang-r'>head(select(iris, Sepal.Length))</pre>
<pre >## Sepal.Length
## 1 5.1
## 2 4.9
## 3 4.7
## 4 4.6
## 5 5.0
## 6 5.4</pre>
</article></slide><slide class=""><hgroup><h2>針對欄位作篩選：select</h2></hgroup><article id="select-1">
<pre class = 'prettyprint lang-r'>head(select(iris, Sepal.Length, Sepal.Width))</pre>
<pre >## Sepal.Length Sepal.Width
## 1 5.1 3.5
## 2 4.9 3.0
## 3 4.7 3.2
## 4 4.6 3.1
## 5 5.0 3.6
## 6 5.4 3.9</pre>
</article></slide><slide class=""><hgroup><h2>針對欄位作篩選：select</h2></hgroup><article id="select-2">
<pre class = 'prettyprint lang-r'>head(select(iris, starts_with("Sepal")))</pre>
<pre >## Sepal.Length Sepal.Width
## 1 5.1 3.5
## 2 4.9 3.0
## 3 4.7 3.2
## 4 4.6 3.1
## 5 5.0 3.6
## 6 5.4 3.9</pre>
</article></slide><slide class=""><hgroup><h2>針對欄位作篩選：select</h2></hgroup><article id="select-3">
<pre class = 'prettyprint lang-r'>head(select(iris, Sepal.Length:Petal.Length))</pre>
<pre >## Sepal.Length Sepal.Width Petal.Length
## 1 5.1 3.5 1.4
## 2 4.9 3.0 1.4
## 3 4.7 3.2 1.3
## 4 4.6 3.1 1.5
## 5 5.0 3.6 1.4
## 6 5.4 3.9 1.7</pre>
</article></slide><slide class=""><hgroup><h2>針對欄位作篩選：select</h2></hgroup><article id="select-4">
<ul>
<li>反向操作</li>
</ul>
<pre class = 'prettyprint lang-r'>head(select(iris, -Sepal.Length))</pre>
<pre >## Sepal.Width Petal.Length Petal.Width Species
## 1 3.5 1.4 0.2 setosa
## 2 3.0 1.4 0.2 setosa
## 3 3.2 1.3 0.2 setosa
## 4 3.1 1.5 0.2 setosa
## 5 3.6 1.4 0.2 setosa
## 6 3.9 1.7 0.4 setosa</pre>
</article></slide><slide class=""><hgroup><h2>針對欄位作篩選：select</h2></hgroup><article id="select-5">
<ul>
<li>rename</li>
</ul>