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<!DOCTYPE html>
<html>
<head>
<title>R Programming</title>
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<slides class="layout-widescreen">
<slide class="title-slide segue nobackground">
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<h1 data-config-title><!-- populated from slide_config.json --></h1>
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<p data-config-presenter><!-- populated from slide_config.json --></p>
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</slide>
<slide class='segue dark nobackground'><hgroup class = 'auto-fadein'><h2>Ch01：表格型文件的輸入與輸出</h2></hgroup><article id="ch01">
</article></slide><slide class=''><hgroup><h2>讀取表格檔案</h2></hgroup><article >
<ul>
<li>下載範例資料 <a href='http://johnsonhsieh.github.io/dsp-introR/data/hsb.csv' title=''>http://johnsonhsieh.github.io/dsp-introR/data/hsb.csv</a></li>
<li>利用<code>read.csv</code> 讀取 <code>csv</code> 檔 (一種以逗點分隔欄位的資料格式)</li>
</ul>
<pre class = 'prettyprint lang-r'># 可以使用R指令來下載
url <- "http://johnsonhsieh.github.io/dsp-introR/data/hsb.csv"
download.file(url, destfile = "hsb.csv") # 把檔案另存成hbs.csv
dat <- read.csv("hsb.csv")
head(dat)</pre>
<pre > id sex race ses schtyp prog read write math science socst
1 70 male White 1 public general 57 52 41 47 57
2 121 female White 2 public vocational 68 59 53 63 61
3 86 male White 3 public general 44 33 54 58 31
4 141 male White 3 public vocational 63 44 47 53 56
5 172 male White 2 public academic 47 52 57 53 61
6 113 male White 2 public academic 44 52 51 63 61</pre>
</article></slide><slide class=''><hgroup><h2>讀取表格檔案</h2></hgroup><article id="-1">
<p>讀取自己電腦裡的csv檔</p>
<pre class = 'prettyprint lang-r'># 方法1. 透過介面選擇檔案所在路徑
path <- file.choose()
dat <- read.csv(path)
# 方法2. 自行輸入檔案所在路徑
path <- "/Volumes/LEEF SURGE/LearnR/hsb.csv"
dat <- read.csv(path)
# 方法3. 自行輸入檔案相對路徑
dat <- read.csv("hsb.csv")</pre>
</article></slide><slide class=''><hgroup><h2>輸出表格檔案</h2></hgroup><article >
<p>利用<code>write.csv</code>將<code>data.frame</code>格式的R物件另存成csv檔</p>
<pre class = 'prettyprint lang-r'>write.csv(dat, "hsb_new.csv", row.names=FALSE, quote=FALSE)</pre>
</article></slide><slide class=''><hgroup><h2>淺談路徑設定</h2></hgroup><article >
<ul>
<li><code>getwd</code> 查詢目前所在的資料夾路徑 (絕對路徑)</li>
<li><code>setwd</code> 設定所在的資料夾路徑 <br> (Session -> Set Working Directory -> Choose Directory)</li>
<li>檔案 (檔案路徑 file directory) vs. 資料夾 (工作路徑 working directory)</li>
</ul>
<pre class = 'prettyprint lang-r'>getwd()</pre>
<pre >[1] "/Volumes/Transcend/DSP/DSP-training/R爬析(中研院社會所)"</pre>
<pre class = 'prettyprint lang-r'>dat <- read.csv("hsb.csv")</pre>
</article></slide><slide class=''><hgroup><h2>排解疑難 - 常見的讀取錯誤1</h2></hgroup><article id="---1">
<p>路徑錯誤</p>
<pre class = 'prettyprint lang-r'>path <- "wrong_file_path"
dat <- read.csv(file = path)</pre>
<pre>
Error in file(file, "rt") : 無法開啟連結
此外: Warning message:
In file(file, "rt") : 無法開啟檔案 'wrong_file_path' ：No such file or directory
</pre>
<ul>
<li>絕對路徑 -> 確認檔案是否存在</li>
<li>相對路徑 -> 利用<code>getwd</code>了解R 當下的路徑位置</li>
</ul>
</article></slide><slide class=''><hgroup><h2>排解疑難 - 常見的讀取錯誤2</h2></hgroup><article id="---2">
<p>格式錯誤</p>
<pre class = 'prettyprint lang-r'>path <- "hsb.csv"
dat <- read.csv(file = path, header = TRUE, sep = "1")</pre>
<pre>
Error in read.table(file = file, header = header, sep = sep, quote = quote, :
more columns than column names
</pre>
<ul>
<li>利用其他編輯器確認分隔符號</li>
<li>確認每列的資料的欄位是正確的</li>
<li>必要時，請用其他文件編輯器校正欲讀取的檔案</li>
</ul>
</article></slide><slide class=''><hgroup><h2>排解疑難 - 常見的讀取錯誤3</h2></hgroup><article id="---3">
<p>編碼錯誤</p>
<pre class = 'prettyprint lang-r'>url <- "http://johnsonhsieh.github.io/dsp-introR/data/dsp-gift-2013-big5/%E8%B2%B7%E8%B3%A3st_A_10109_10109.csv"</pre>
<pre class = 'prettyprint lang-r'>dat <- read.csv(url)</pre>
<pre class="code">
Error in make.names(col.names, unique = TRUE) :
無效的多位元組字串於 <b6>m<c2><ed><a5><ab><b0><cf>
</pre>
<ul>
<li>查詢檔案的編碼</li>
<li>常見的中文編碼有UTF-8和BIG-5</li>
</ul>
<pre class = 'prettyprint lang-r'># 利用`fileEncoding`參數選擇檔案編碼 - big5 / utf8
dat2 <- read.csv(url, fileEncoding = "big5")</pre>
</article></slide><slide class=''><hgroup><h2>讀取其他軟體資料集</h2></hgroup><article >
<ul>
<li>For SPSS and Stata datasets, use the <code>foreign</code> package</li>
<li>Cars: <a href='http://calcnet.mth.cmich.edu/org/spss/V16_materials/DataSets_v16/Cars.sav' title=''>Cars.sav</a></li>
<li>Milk Production: <a href='http://www.ats.ucla.edu/stat/stata/examples/chp/p004.dta' title=''>p004.dat</a></li>
</ul>
<pre class = 'prettyprint lang-r'># install.packages("foreign") # 安裝R套件 foreign
library(foreign) # 載入套件
cars <- read.spss("data/Cars.sav", to.data.frame = TRUE)
milk <- read.dta("data/p004.dta")
# head(cars)
# head(milk)</pre>
</article></slide><slide class=''><hgroup><h2>讀取其他軟體資料集</h2></hgroup><article id="-1">
<ul>
<li>For SAS datasets, use the <code>sas7bdat</code> package</li>
<li>airline: <a href='http://www.principlesofeconometrics.com/sas/airline.sas7bdat' title=''>airline.sas7bdat</a></li>
</ul>
<pre class = 'prettyprint lang-r'># install.packages("sas7bdat")
library(sas7bdat)
airline <- read.sas7bdat("data/airline.sas7bdat")
head(airline)</pre>
<pre > YEAR Y W R L K
1 1948 1.214 0.243 0.1454 1.415 0.612
2 1949 1.354 0.260 0.2181 1.384 0.559
3 1950 1.569 0.278 0.3157 1.388 0.573
4 1951 1.948 0.297 0.3940 1.550 0.564
5 1952 2.265 0.310 0.3559 1.802 0.574
6 1953 2.731 0.322 0.3593 1.926 0.711</pre>
</article></slide><slide class='segue dark nobackground'><hgroup class = 'auto-fadein'><h2>Ch02：讀取網頁內容(Lite)</h2></hgroup><article id="ch02lite">
</article></slide><slide class=''><hgroup><h2>逐行輸入與輸出</h2></hgroup><article >
<ul>
<li><code>readLines</code>, <code>writeLines</code></li>
<li>是讀取網頁原始碼的好工具</li>
</ul>
<pre class = 'prettyprint lang-r'>output <- file("output.txt")
writeLines(as.character(1:12), con = output)
input <- readLines(output)
input</pre>
<pre > [1] "1" "2" "3" "4" "5" "6" "7" "8" "9" "10" "11" "12"</pre>
</article></slide><slide class=''><hgroup><h2>練習</h2></hgroup><article >
<p>從<a href='http://s40.ptt01.cc/post_17476' title=''>網頁中</a>，找出藝人官方身高</p>
<pre class = 'prettyprint lang-r'>web_page <- readLines("http://s40.ptt01.cc/post_17476")
matches <- gregexpr("[\u4E00-\u9FA5]+官方身高：[0-9]+", web_page)
tmp <- regmatches(web_page, matches)
unlist(tmp)</pre>
<pre >[1] "陳喬恩官方身高：165" "宋慧喬官方身高：161" "伊能靜官方身高：161"
[4] "倪妮官方身高：170" "宋慧喬官方身高：161" "張梓琳官方身高：182"
[7] "周迅官方身高：160" "周迅官方身高：160" "全智賢官方身高：174"</pre>
<p>其中：</p>
<pre >[\u4E00-\u9FA5] 表示所有中文字符
[0-9] 含數字之字串
[a]+ 一或多個 a</pre>
</article></slide><slide class=''><hgroup><h2>進階練習</h2></hgroup><article >
<p>找出<a href='http://www.319papago.idv.tw/lifeinfo/chingshin/chingshin-02.html' title=''>清心福全</a>台北市南港店的地址</p>
<pre class = 'prettyprint lang-r'>web_page <- readLines("http://www.319papago.idv.tw/lifeinfo/chingshin/chingshin-02.html")
matches <- gregexpr("台北市南港區[\u4E00-\u9FA5|0-9|(|)]+", web_page)
tmp <- regmatches(web_page, matches)
unlist(tmp) # 把 list 轉成 vector</pre>
<pre >[1] "台北市南港區同德路54號" "台北市南港區南港路3段20號"
[3] "台北市南港區南港路一段154號"</pre>
</article></slide><slide class=''><hgroup><h2>小挑戰</h2></hgroup><article >
<ul>
<li>找出清心福全台北市門市的電話號碼</li>
<li>提示：<code>"02-[0-9]+"</code></li>
</ul>
</article></slide><slide class=''><hgroup><h2>小挑戰</h2></hgroup><article id="-1">
<ul>
<li>找出清心福全台北市門市的電話號碼</li>
<li>提示：<code>"02-[0-9]+"</code></li>
</ul>
<h3>參考解答</h3>
<pre class = 'prettyprint lang-r'>web_page <- readLines("http://www.319papago.idv.tw/lifeinfo/chingshin/chingshin-02.html")
matches <- gregexpr("02-[0-9]+", web_page)
tmp <- regmatches(web_page, matches)
unlist(tmp) </pre>
<pre > [1] "02-28761717" "02-28311515" "02-28805757" "02-28829191" "02-28126988"
[6] "02-28835757" "02-28335151" "02-28382099" "02-25993301" "02-25964768"
[11] "02-25582856" "02-23256696" "02-27772151" "02-27041995" "02-87327588"
[16] "02-27780058" "02-23568851" "02-27096711" "02-87710602" "02-23661900"
[21] "02-27336155" "02-25335922" "02-25330098" "02-25412121" "02-25095253"
[26] "02-25362808" "02-25423939" "02-25007119" "02-27733737" "02-25042858"
[31] "02-25093117" "02-25212012" "02-25991177" "02-33225069" "02-23582277"
[36] "02-23753889" "02-23710880" "02-23678838" "02-27940591" "02-87971968"
[41] "02-87523300" "02-27996737" "02-87974399" "02-26306626" "02-87923883"
[46] "02-27903186" "02-29366236" "02-89351381" "02-29342121" "02-29305939"
[51] "02-25860202" "02-28910379" "02-28917784" "02-28278806" "02-28978088"
[56] "02-28286161" "02-28223996" "02-25288288" "02-27531966" "02-25775296"
[61] "02-27172228" "02-27629786" "02-27611525" "02-27475628" "02-23772478"
[66] "02-27677158" "02-27593118" "02-27587181" "02-27585123" "02-26533886"
[71] "02-27882811" "02-26532371" "02-23091959" "02-23143280" "02-23111818"
[76] "02-23024378" "02-23016677" "02-23026069" "02-23328618"</pre>
</article></slide><slide class='segue dark nobackground'><hgroup class = 'auto-fadein'><h2>Ch03: 條件判斷語句 - if</h2></hgroup><article id="ch03----if">
</article></slide><slide class=''><hgroup><h2>條件判斷</h2></hgroup><article >
<ul>
<li>一般而言，R語言的程式碼是由上自下依序執行，</li>
<li>有時需要根據某些條件判斷執行某些分支</li>
<li>在R中使用<code>if</code>來進行判斷</li>
<li>將敘述內容寫在大括號<code>{ }</code>裡面</li>
</ul>
<pre class = 'prettyprint lang-r'># 判斷x是否大於2, 如果是，則印出結果
x <- 4
if(x > 2){
cat(x, "is larger than 2")
}</pre>
<pre >4 is larger than 2</pre>
</article></slide><slide class=''><hgroup><h2>條件判斷</h2></hgroup><article id="-1">
<ul>
<li>在R中使用<code>if</code>來進行判斷</li>
<li>將敘述內容寫在大括號<code>{ }</code>裡面</li>
<li>當敘述句只有一行時，可以省略大括號</li>
</ul>
<pre class = 'prettyprint lang-r'># 判斷x是否大於2, 如果是，則印出結果
x <- 4
if(x > 2) cat(x, "is larger than 2")</pre>
<pre >4 is larger than 2</pre>
</article></slide><slide class=''><hgroup><h2>條件判斷</h2></hgroup><article id="-2">
<ul>
<li>若需要兩個判斷分支時，加入<code>else</code></li>
</ul>
<pre class = 'prettyprint lang-r'>y <- 1
if(y > 2){
cat(y, "is larger than 2")
}else{
cat(y, "is smaller than 2")
} </pre>
<pre >1 is smaller than 2</pre>
</article></slide><slide class=''><hgroup><h2>條件判斷</h2></hgroup><article id="-3">
<ul>
<li>若需要兩個判斷分支時，加入<code>else</code></li>
<li>可以使用<code>ifelse</code>函數進行簡化，而且該函數支援向量化運算 (後述)</li>
</ul>
<pre class = 'prettyprint lang-r'>y <- 1
ifelse(y > 2, "larger than 2", "smaller than 2")</pre>
<pre >[1] "smaller than 2"</pre>
<pre class = 'prettyprint lang-r'>z <- 1:5
ifelse(z > 2, "larger than 2", "smaller than 2")</pre>
<pre >[1] "smaller than 2" "smaller than 2" "larger than 2" "larger than 2"
[5] "larger than 2" </pre>
</article></slide><slide class=''><hgroup><h2>條件判斷</h2></hgroup><article id="-4">
<ul>
<li>若需要多個判斷分支時，加入<code>else if</code>, …, <code>else</code></li>
</ul>
<pre class = 'prettyprint lang-r'>y <- 1
if(y > 2){
cat(y, "is larger than 2")
}else if(y < 2){
cat(y, "is smaller than 2")
}else{
cat(y, "is equal to 2")
}</pre>
<pre >1 is smaller than 2</pre>
</article></slide><slide class='segue dark nobackground'><hgroup class = 'auto-fadein'><h2>Ch04: 自訂函數 - function</h2></hgroup><article id="ch04----function">
</article></slide><slide class=''><hgroup><h2>自訂函數 (<code>function</code>)</h2></hgroup><article id="-function">
<ul>
<li>函數是R語言的基石，有利重複使用</li>
<li>通過多個函數的組裝，可以完成更複雜的任務</li>
<li>函數在子環境中執行，不對外產生影響</li>
</ul>
<pre class = 'prettyprint lang-r'>foo <- function(x){
if(x > 2){
cat(x, "is larger than 2")
}else if(x < 2){
cat(x, "is smaller than 2")
}else{
cat(x, "is equal to 2")
}
}
foo(1); foo(2); foo(3)</pre>
<pre >1 is smaller than 2</pre>
<pre >2 is equal to 2</pre>
<pre >3 is larger than 2</pre>
</article></slide><slide class='segue dark nobackground'><hgroup class = 'auto-fadein'><h2>Ch05: 循環語句 - for</h2></hgroup><article id="ch05----for">
</article></slide><slide class=''><hgroup><h2>循環語句</h2></hgroup><article >
<ul>
<li>可用來重複執行某段程式碼</li>
<li><code>for loop</code>中，已足標作為終止循環的條件</li>
</ul>
<pre class = 'prettyprint lang-r'>x <- 0
for(i in 1:100){
if(i %% 2 !=0) { # x是否為奇數
x <- x + i # 自身累加
}
}
x</pre>
<pre >[1] 2500</pre>
</article></slide><slide class=''><hgroup><h2>雙重循環語句</h2></hgroup><article >
<ul>
<li>可用來重複執行某段程式碼</li>
<li><code>\t</code> = tab鍵, <code>\n</code> = 空行</li>
</ul>
<pre class = 'prettyprint lang-r'>x <- 0
k <- 0
for(i in 1:4){
for(j in 1:3){
cat(paste(i, "*", j, "=", i*j, ",\t", sep=""))
k <- k + 1
x[k] <- i*j
}
cat("\n")
}</pre>
<pre >1*1=1, 1*2=2, 1*3=3,
2*1=2, 2*2=4, 2*3=6,
3*1=3, 3*2=6, 3*3=9,
4*1=4, 4*2=8, 4*3=12, </pre>
</article></slide><slide class=''><hgroup><h2>進階練習</h2></hgroup><article id="-1">
<ul>
<li>請先下載 <a href='https://github.com/JohnsonHsieh/R-basic-for-social-science/raw/gh-pages/data/movies.zip' title=''>movies.zip</a> (解壓縮後有21個檔案)</li>
<li>一口氣讀入全部的檔案 (Hint: <code>dir + for loop + list</code>)</li>
<li>將檔案合併 (Hint: <code>do.call + rbind</code>)</li>
</ul>
</article></slide><slide class=''><hgroup><h2>進階練習(1)</h2></hgroup><article id="1">
<pre class = 'prettyprint lang-r'># 展示movies資料夾下所有的檔案
fname <- dir("data/movies/")
fname</pre>
<pre > [1] "movies-1995.csv" "movies-1996.csv" "movies-1997.csv"
[4] "movies-1998.csv" "movies-1999.csv" "movies-2000.csv"
[7] "movies-2001.csv" "movies-2002.csv" "movies-2003.csv"
[10] "movies-2004.csv" "movies-2005.csv" "movies-2006.csv"
[13] "movies-2007.csv" "movies-2008.csv" "movies-2009.csv"
[16] "movies-2010.csv" "movies-2011.csv" "movies-2012.csv"
[19] "movies-2013.csv" "movies-2014.csv" "movies-2015.csv"</pre>
</article></slide><slide class=''><hgroup><h2>進階練習(2)</h2></hgroup><article id="2">
<pre class = 'prettyprint lang-r'># 展示movies資料夾下所有的檔案
fname <- dir("data/movies/")
# 練習讀一個csv檔 (movies-1995.csv)
path <- paste("data/movies/", fname[1], sep="")
mov95 <- read.csv(path)
head(mov95)</pre>
<pre > movieId title year
1 1 Toy Story 1995
2 2 Jumanji 1995
3 3 Grumpier Old Men 1995
4 4 Waiting to Exhale 1995
5 5 Father of the Bride Part II 1995
6 6 Heat 1995</pre>
</article></slide><slide class=''><hgroup><h2>進階練習(3)</h2></hgroup><article id="3">
<pre class = 'prettyprint lang-r'># 展示movies資料夾下所有的檔案
fname <- dir("data/movies/")
# 練習讀多個csv檔
mov <- list() # 預設一個名為 mov 的list容器
for(i in 1:length(fname)){
path <- paste("data/movies/", fname[i], sep="")
mov[[i]] <- read.csv(path)
}
head(mov[[1]])</pre>
<pre > movieId title year
1 1 Toy Story 1995
2 2 Jumanji 1995
3 3 Grumpier Old Men 1995
4 4 Waiting to Exhale 1995
5 5 Father of the Bride Part II 1995
6 6 Heat 1995</pre>
</article></slide><slide class=''><hgroup><h2>進階練習(4)</h2></hgroup><article id="4">
<pre class = 'prettyprint lang-r'># 將list用列合併(rbind)整合成data.frame
df <- do.call(rbind, mov)
head(df)</pre>
<pre > movieId title year
1 1 Toy Story 1995
2 2 Jumanji 1995
3 3 Grumpier Old Men 1995
4 4 Waiting to Exhale 1995
5 5 Father of the Bride Part II 1995
6 6 Heat 1995</pre>
</article></slide><slide class=''><hgroup><h2>小挑戰</h2></hgroup><article id="-2">
<ul>
<li>只讀取1995-1999電影資料</li>
<li>提示：<code>gregexpr("movies-199[5-9]+.csv", fname)</code></li>
</ul>
</article></slide><slide class=''><hgroup><h2>小挑戰 (參考解答)</h2></hgroup><article id="-">
<ul>
<li>只讀取1995-1999電影資料</li>
<li>提示：<code>gregexpr("movies-199[5-9]+.csv", fname)</code></li>
</ul>
<pre class = 'prettyprint lang-r'>matches <- gregexpr("movies-199[5-9]+.csv", fname)
tmp <- regmatches(fname, matches)
fname90 <- unlist(tmp)
mov90 <- list() # 預設一個名為 mov 的list容器
for(i in 1:length(fname90)){
path <- paste("data/movies/", fname90[i], sep="")
mov90[[i]] <- read.csv(path)
}
df90 <- do.call(rbind, mov90)
# tail(df90) # 最後6筆</pre>
</article></slide><slide class=''><hgroup><h2>練習寫一個批次讀檔函數</h2></hgroup><article >
<pre class = 'prettyprint lang-r'>myRead <- function(fname, path0="data/movies/"){
mov <- list()
for(i in 1:length(fname)){
path <- paste(path0, fname[i], sep="")
mov[[i]] <- read.csv(path)
}
do.call(rbind, mov)
}
df <- myRead(fname90, path0="data/movies/")
head(df)</pre>
<pre > movieId title year
1 1 Toy Story 1995
2 2 Jumanji 1995
3 3 Grumpier Old Men 1995
4 4 Waiting to Exhale 1995
5 5 Father of the Bride Part II 1995
6 6 Heat 1995</pre>
</article></slide><slide class='segue dark nobackground'><hgroup class = 'auto-fadein'><h2>Ch06: Pipe Line Coding Style</h2></hgroup><article id="ch06-pipe-line-coding-style">
</article></slide><slide class=''><hgroup><h2>2014 年最有影響的套件之一：magrittr</h2></hgroup><article id="magrittr">
<ul>
<li>壓縮的程式碼不好讀</li>
<li>展開的程式碼會產生很多暫存變數</li>
<li>套件<code>magrittr</code>部份解決了這個問題</li>
<li>基本算子 %>%, %<>%, %T>%, %$%</li>
</ul>
</article></slide><slide class=''><hgroup><h2>基本算子 (<code>%>%</code>)</h2></hgroup><article >
<ul>
<li>想像一下程式的寫作與閱讀邏輯</li>
<li><code>%>%</code> 會將算子左邊的物件 (object) 傳到右邊的函數 (function) 中第一個argument</li>
<li>. 點號適合用在欲傳入變數不是在傳入函數的第一個位置時使用</li>
<li>use <code>x %>% f</code>, rather than <code>f(x)</code></li>
<li>or use <code>x %>% f(y, z)</code>, rather than <code>f(x, y, z)</code></li>
<li>or <code>y %>% f(x, ., z)</code>, rather than <code>f(x, y, z)</code></li>
</ul>
<pre class = 'prettyprint lang-r'># install.packages("magrittr")
library(magrittr)
x <- 1:10
mean(x)</pre>
<pre >[1] 5.5</pre>
<pre class = 'prettyprint lang-r'>x %>% mean # 由左而右順序操作</pre>
<pre >[1] 5.5</pre>
</article></slide><slide class=''><hgroup><h2>幾種等價用法</h2></hgroup><article >
<p>利用三角形面積公式說明%>%算子的幾種等價用法</p>
<pre class = 'prettyprint lang-r'>tri_area <- function(a, h=5) a*h/2
a <- 10
tri_area(a)</pre>
<pre >[1] 25</pre>
<pre class = 'prettyprint lang-r'>a %>% tri_area # 省略括號</pre>
<pre >[1] 25</pre>
<pre class = 'prettyprint lang-r'>a %>% tri_area(h=5) # 保留括號</pre>
<pre >[1] 25</pre>
<pre class = 'prettyprint lang-r'>a %>% tri_area(., h=5) # 以 `.` 來表示欲傳入的變數</pre>
<pre >[1] 25</pre>
</article></slide><slide class=''><hgroup><h2></h2></hgroup><article id="section">
<pre class = 'prettyprint lang-r'># 指令壓縮
plot(density(sample(mtcars$mpg, size=10000, replace=TRUE), kernel="gaussian"), col="red", main="density of mpg")</pre>
<pre class = 'prettyprint lang-r'># Pipe Line
mtcars$mpg %>%
sample(size=10000, replace=TRUE) %>%
density(kernel="gaussian") %>%
plot(col="red", main="density of mpg")</pre>
<p><img src="RProgramming_files/figure-html/unnamed-chunk-34-1.png" title="" alt="" width="720" /></p>
</article></slide><slide class='segue dark nobackground'><hgroup class = 'auto-fadein'><h2>資料處理</h2></hgroup><article >
</article></slide><slide class=''><hgroup><h2>資料拉皮 <code>reshape2</code></h2></hgroup><article id="-reshape2">
<ul>
<li><code>melt</code>: wide format -> long format</li>
<li><code>dcast</code>: long format -> wide format
<center>
<img src='img/reshaping.png' height=400 align='center'>
</center></li>
</ul>
</article></slide><slide class=''><hgroup><h2>資料拉皮 <code>reshape2</code></h2></hgroup><article id="-reshape2-1">
<ul>
<li><code>melt</code>: wide format -> long format</li>
<li><code>dcast</code>: long format -> wide format</li>
</ul>
<pre class = 'prettyprint lang-r'># install.packages("reshape2")
library(reshape2)
WP.melt <- data.frame(WorldPhones)
WP.melt$year <- rownames(WP.melt)
WP.melt <- melt(WP.melt, id="year")
head(WP.melt)</pre>
<pre > year variable value
1 1951 N.Amer 45939
2 1956 N.Amer 60423
3 1957 N.Amer 64721
4 1958 N.Amer 68484
5 1959 N.Amer 71799
6 1960 N.Amer 76036</pre>
</article></slide><slide class=''><hgroup><h2>資料拉皮 <code>reshape2</code></h2></hgroup><article id="-reshape2-2">
<ul>
<li><code>melt</code>: wide format -> long format</li>
<li><code>dcast</code>: long format -> wide format</li>
</ul>
<pre class = 'prettyprint lang-r'>WP.cast <- dcast(WP.melt, year~variable,value.var="value")
head(WP.cast)</pre>
<pre > year N.Amer Europe Asia S.Amer Oceania Africa Mid.Amer
1 1951 45939 21574 2876 1815 1646 89 555
2 1956 60423 29990 4708 2568 2366 1411 733
3 1957 64721 32510 5230 2695 2526 1546 773
4 1958 68484 35218 6662 2845 2691 1663 836
5 1959 71799 37598 6856 3000 2868 1769 911
6 1960 76036 40341 8220 3145 3054 1905 1008</pre>
</article></slide><slide class=''><hgroup><h2>2014 年最有影響的套件之一：dplyr</h2></hgroup><article id="dplyr">
<ul>
<li>讓R 使用者可以用更有彈性的方式來處理資料</li>
<li>針對<code>data.frame</code>做設計（名稱中的<code>d</code>）</li>
<li>設計理念
<ul>
<li>導入資料整理最重要的動作（非常類似SQL）</li>
<li>快</li>
<li>支援異質資料源（<code>data.frame</code>或資料庫中的表格）</li>
</ul></li>
</ul>
</article></slide><slide class=''><hgroup><h2>學習dplyr的官方方式：<code>vignette</code></h2></hgroup><article id="dplyrvignette">
<pre class = 'prettyprint lang-r'>vignette(all = TRUE, package = "dplyr")
vignette("introduction", package = "dplyr")</pre>
<ul>
<li>更詳細的dplyr介紹可以閱讀dplyr的小論文</li>
<li>R 的開發者會針對一個主題撰寫小論文做介紹</li>
</ul>
</article></slide><slide class=''><hgroup><h2>dplyr簡介</h2></hgroup><article id="dplyr" class="columns-2">
<ul>
<li><code>arrange</code> 排列</li>
<li><code>filter</code> 對列做篩選 (row)</li>
<li><code>select</code> 對欄做篩選 (column)</li>
<li><code>mutate</code> 更改欄或新增欄</li>
<li>**<code>group_by</code> + <code>summarise</code> 分類</li>
</ul>
<center>
<img src='img/R_ETL_Fn1.png' style='max-width: 100%;max-height: 100%'></img>
</center>
<p>出處：<a href='https://www.youtube.com/watch?v=JD1eDxxrur0' title=''>資料科學愛好者年會資料分析上手課程：ETL1</a></p>
</article></slide><slide class=''><hgroup><h2>資料排序 <code>arrange</code></h2></hgroup><article id="-arrange">
<center>
<img src='img/arrange.png' height=300 align='center'></img><img src='img/arranged.png' height=300 align='center'></img>
</center>
</article></slide><slide class=''><hgroup><h2>資料排序</h2></hgroup><article >
<ul>
<li>arrange(dataframe, 要進行排序的項目,….)</li>
<li>排序預設是由小到大</li>
</ul>
<pre class = 'prettyprint lang-r'>arrange(iris, Petal.Length) %>% head</pre>
<pre > Sepal.Length Sepal.Width Petal.Length Petal.Width Species
1 4.6 3.6 1.0 0.2 setosa
2 4.3 3.0 1.1 0.1 setosa
3 5.8 4.0 1.2 0.2 setosa
4 5.0 3.2 1.2 0.2 setosa
5 4.7 3.2 1.3 0.2 setosa
6 5.4 3.9 1.3 0.4 setosa</pre>
</article></slide><slide class=''><hgroup><h2>資料排序</h2></hgroup><article id="-1">
<ul>
<li>arrange(dataframe, 要進行排序的項目,….)</li>
<li>排序預設是由小到大，加上<code>desc</code>可使用遞增排列</li>
</ul>
<pre class = 'prettyprint lang-r'>arrange(iris, desc(Petal.Length)) %>% head</pre>
<pre > Sepal.Length Sepal.Width Petal.Length Petal.Width Species
1 7.7 2.6 6.9 2.3 virginica
2 7.7 3.8 6.7 2.2 virginica
3 7.7 2.8 6.7 2.0 virginica
4 7.6 3.0 6.6 2.1 virginica
5 7.9 3.8 6.4 2.0 virginica
6 7.3 2.9 6.3 1.8 virginica</pre>
</article></slide><slide class=''><hgroup><h2>資料排序</h2></hgroup><article id="-2">
<ul>
<li>arrange(dataframe, 要進行排序的項目,….)</li>
<li>排序預設是由小到大，加上<code>desc</code>可使用遞增排列</li>
<li>排序的項目可以不只一項，只要依序將項目填入後方即可</li>
</ul>
<pre class = 'prettyprint lang-r'>arrange(iris, desc(Petal.Length), - Sepal.Length ) %>% head</pre>
<pre > Sepal.Length Sepal.Width Petal.Length Petal.Width Species
1 7.7 2.6 6.9 2.3 virginica
2 7.7 3.8 6.7 2.2 virginica
3 7.7 2.8 6.7 2.0 virginica
4 7.6 3.0 6.6 2.1 virginica
5 7.9 3.8 6.4 2.0 virginica
6 7.3 2.9 6.3 1.8 virginica</pre>
</article></slide><slide class=''><hgroup><h2>dplyr 常用函數</h2></hgroup><article id="dplyr-">
<ul>
<li>移除重複資料: <code>distinct(iris)</code></li>
<li>隨機抽取資料: <code>sample_n(iris, 5)</code></li>
<li>抽取指定列: <code>slice(iris, c(1,3,4,5))</code></li>
</ul>
</article></slide><slide class=''><hgroup><h2>對列做篩選 <code>filter</code></h2></hgroup><article id="-filter">
<center>
<img src='img/filter1.png' width=300 align='center'></img><img src='img/filter2.png' width=300 align='center'>
</center>
</article></slide><slide class=''><hgroup><h2>對列做篩選 <code>filter</code></h2></hgroup><article id="-filter-1">
<pre class = 'prettyprint lang-r'>filter(iris,Sepal.Length == 7.7)</pre>
<pre > Sepal.Length Sepal.Width Petal.Length Petal.Width Species
1 7.7 3.8 6.7 2.2 virginica
2 7.7 2.6 6.9 2.3 virginica
3 7.7 2.8 6.7 2.0 virginica
4 7.7 3.0 6.1 2.3 virginica</pre>
<pre class = 'prettyprint lang-r'>filter(iris,Sepal.Length == 7.7,Sepal.Width ==2.8)</pre>
<pre > Sepal.Length Sepal.Width Petal.Length Petal.Width Species
1 7.7 2.8 6.7 2 virginica</pre>
</article></slide><slide class=''><hgroup><h2>對行做選取 <code>select</code></h2></hgroup><article id="-select">
<center>
<img src='img/select1.png' height=300 align='center'></img><img src='img/select2.png' height=300 align='center'>
</center>
</article></slide><slide class=''><hgroup><h2>對行做選取 <code>select</code></h2></hgroup><article id="-select-1">
<pre class = 'prettyprint lang-r'>select(iris,c(Petal.Width, Species))</pre>
<pre > Petal.Width Species
1 0.2 setosa
2 0.2 setosa
3 0.2 setosa
4 0.2 setosa
5 0.2 setosa
6 0.4 setosa
7 0.3 setosa
8 0.2 setosa
9 0.2 setosa
10 0.1 setosa
11 0.2 setosa
12 0.2 setosa
13 0.1 setosa
14 0.1 setosa
15 0.2 setosa
16 0.4 setosa
17 0.4 setosa
18 0.3 setosa
19 0.3 setosa
20 0.3 setosa
21 0.2 setosa
22 0.4 setosa
23 0.2 setosa
24 0.5 setosa
25 0.2 setosa
26 0.2 setosa
27 0.4 setosa
28 0.2 setosa
29 0.2 setosa
30 0.2 setosa
31 0.2 setosa
32 0.4 setosa
33 0.1 setosa
34 0.2 setosa
35 0.2 setosa
36 0.2 setosa
37 0.2 setosa
38 0.1 setosa
39 0.2 setosa
40 0.2 setosa
41 0.3 setosa
42 0.3 setosa
43 0.2 setosa
44 0.6 setosa
45 0.4 setosa
46 0.3 setosa
47 0.2 setosa
48 0.2 setosa
49 0.2 setosa
50 0.2 setosa
51 1.4 versicolor
52 1.5 versicolor
53 1.5 versicolor
54 1.3 versicolor
55 1.5 versicolor
56 1.3 versicolor
57 1.6 versicolor
58 1.0 versicolor
59 1.3 versicolor
60 1.4 versicolor
61 1.0 versicolor
62 1.5 versicolor
63 1.0 versicolor
64 1.4 versicolor
65 1.3 versicolor
66 1.4 versicolor
67 1.5 versicolor
68 1.0 versicolor
69 1.5 versicolor
70 1.1 versicolor
71 1.8 versicolor
72 1.3 versicolor
73 1.5 versicolor
74 1.2 versicolor
75 1.3 versicolor
76 1.4 versicolor
77 1.4 versicolor
78 1.7 versicolor
79 1.5 versicolor
80 1.0 versicolor
81 1.1 versicolor
82 1.0 versicolor
83 1.2 versicolor
84 1.6 versicolor
85 1.5 versicolor
86 1.6 versicolor
87 1.5 versicolor
88 1.3 versicolor
89 1.3 versicolor
90 1.3 versicolor
91 1.2 versicolor
92 1.4 versicolor
93 1.2 versicolor
94 1.0 versicolor
95 1.3 versicolor
96 1.2 versicolor
97 1.3 versicolor
98 1.3 versicolor
99 1.1 versicolor
100 1.3 versicolor
101 2.5 virginica
102 1.9 virginica
103 2.1 virginica
104 1.8 virginica
105 2.2 virginica
106 2.1 virginica
107 1.7 virginica
108 1.8 virginica
109 1.8 virginica
110 2.5 virginica
111 2.0 virginica
112 1.9 virginica
113 2.1 virginica
114 2.0 virginica
115 2.4 virginica
116 2.3 virginica
117 1.8 virginica
118 2.2 virginica
119 2.3 virginica
120 1.5 virginica
121 2.3 virginica
122 2.0 virginica
123 2.0 virginica
124 1.8 virginica
125 2.1 virginica
126 1.8 virginica
127 1.8 virginica
128 1.8 virginica
129 2.1 virginica
130 1.6 virginica
131 1.9 virginica