• 下載範例資料 http://johnsonhsieh.github.io/dsp-introR/data/hsb.csv
• 利用read.csv 讀取 csv 檔 (一種以逗點分隔欄位的資料格式)

# 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)

   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

讀取自己電腦裡的csv檔

# 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")

利用write.csv將data.frame格式的R物件另存成csv檔

write.csv(dat, "hsb_new.csv", row.names=FALSE, quote=FALSE)

• getwd 查詢目前所在的資料夾路徑 (絕對路徑)
• setwd 設定所在的資料夾路徑
  (Session -> Set Working Directory -> Choose Directory)
• 檔案 (檔案路徑 file directory) vs. 資料夾 (工作路徑 working directory)

getwd()

[1] "/Volumes/Transcend/DSP/DSP-training/R爬析(中研院社會所)"

dat <- read.csv("hsb.csv")

路徑錯誤

path <- "wrong_file_path"
dat <- read.csv(file = path)

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

• 絕對路徑 -> 確認檔案是否存在
• 相對路徑 -> 利用getwd了解R 當下的路徑位置

格式錯誤

path <- "hsb.csv"
dat <- read.csv(file = path, header = TRUE, sep = "1")

Error in read.table(file = file, header = header, sep = sep, quote = quote,  : 
  more columns than column names

• 利用其他編輯器確認分隔符號
• 確認每列的資料的欄位是正確的
• 必要時，請用其他文件編輯器校正欲讀取的檔案

編碼錯誤

url <- "http://johnsonhsieh.github.io/dsp-introR/data/dsp-gift-2013-big5/%E8%B2%B7%E8%B3%A3st_A_10109_10109.csv"

dat <- read.csv(url)

Error in make.names(col.names, unique = TRUE) : 
  無效的多位元組字串於 m

• 查詢檔案的編碼
• 常見的中文編碼有UTF-8和BIG-5

# 利用`fileEncoding`參數選擇檔案編碼 - big5 / utf8
dat2 <- read.csv(url, fileEncoding = "big5")

• For SPSS and Stata datasets, use the foreign package
• Cars: Cars.sav
• Milk Production: p004.dat

# 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)

• For SAS datasets, use the sas7bdat package
• airline: airline.sas7bdat

# install.packages("sas7bdat")
library(sas7bdat)
airline <- read.sas7bdat("data/airline.sas7bdat")
head(airline)

  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

• readLines, writeLines
• 是讀取網頁原始碼的好工具

output <- file("output.txt")
writeLines(as.character(1:12), con = output)
input <- readLines(output)
input

 [1] "1"  "2"  "3"  "4"  "5"  "6"  "7"  "8"  "9"  "10" "11" "12"

從網頁中，找出藝人官方身高

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)

[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"

其中：

[\u4E00-\u9FA5]  表示所有中文字符
[0-9]              含數字之字串
[a]+             一或多個 a

找出清心福全台北市南港店的地址

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

[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"

• 找出清心福全台北市門市的電話號碼
• 提示："02-[0-9]+"

• 找出清心福全台北市門市的電話號碼
• 提示："02-[0-9]+"

參考解答

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) 

 [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"

• 一般而言，R語言的程式碼是由上自下依序執行，
• 有時需要根據某些條件判斷執行某些分支
• 在R中使用if來進行判斷
• 將敘述內容寫在大括號{ }裡面

# 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")
}

4 is larger than 2

• 在R中使用if來進行判斷
• 將敘述內容寫在大括號{ }裡面
• 當敘述句只有一行時，可以省略大括號

# 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")

4 is larger than 2

• 若需要兩個判斷分支時，加入else

y <- 1
if(y > 2){
  cat(y, "is larger than 2")
}else{
  cat(y, "is smaller than 2")
} 

1 is smaller than 2

• 若需要兩個判斷分支時，加入else
• 可以使用ifelse函數進行簡化，而且該函數支援向量化運算 (後述)

y <- 1
ifelse(y > 2, "larger than 2", "smaller than 2")

[1] "smaller than 2"

z <- 1:5
ifelse(z > 2, "larger than 2", "smaller than 2")

[1] "smaller than 2" "smaller than 2" "larger than 2"  "larger than 2" 
[5] "larger than 2" 

• 若需要多個判斷分支時，加入else if, …, else

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")
}

1 is smaller than 2

• 函數是R語言的基石，有利重複使用
• 通過多個函數的組裝，可以完成更複雜的任務
• 函數在子環境中執行，不對外產生影響

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)

1 is smaller than 2

2 is equal to 2

3 is larger than 2

• 可用來重複執行某段程式碼
• for loop中，已足標作為終止循環的條件

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

[1] 2500

• 可用來重複執行某段程式碼
• \t = tab鍵, \n = 空行

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")
}

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, 

• 請先下載 movies.zip (解壓縮後有21個檔案)
• 一口氣讀入全部的檔案 (Hint: dir + for loop + list)
• 將檔案合併 (Hint: do.call + rbind)

# 展示movies資料夾下所有的檔案
fname <- dir("data/movies/")
fname

 [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"

# 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)

  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

# 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]])

  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

# 將list用列合併(rbind)整合成data.frame
df <- do.call(rbind, mov) 
head(df)

  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

• 只讀取1995-1999電影資料
• 提示：gregexpr("movies-199[5-9]+.csv", fname)

• 只讀取1995-1999電影資料
• 提示：gregexpr("movies-199[5-9]+.csv", fname)

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

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)

  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

• 壓縮的程式碼不好讀
• 展開的程式碼會產生很多暫存變數
• 套件magrittr部份解決了這個問題
• 基本算子 %>%, %<>%, %T>%, %$%

• 想像一下程式的寫作與閱讀邏輯
• %>% 會將算子左邊的物件 (object) 傳到右邊的函數 (function) 中第一個argument
• . 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
• use x %>% f, rather than f(x)
• or use x %>% f(y, z), rather than f(x, y, z)
• or y %>% f(x, ., z), rather than f(x, y, z)

# install.packages("magrittr")
library(magrittr)
x <- 1:10
mean(x)

[1] 5.5

x %>% mean # 由左而右順序操作

[1] 5.5

利用三角形面積公式說明%>%算子的幾種等價用法

tri_area <- function(a, h=5) a*h/2
a <- 10
tri_area(a)

[1] 25

a %>% tri_area          # 省略括號

[1] 25

a %>% tri_area(h=5)     # 保留括號

[1] 25

a %>% tri_area(., h=5)  # 以 `.` 來表示欲傳入的變數

[1] 25

# 指令壓縮
plot(density(sample(mtcars$mpg, size=10000, replace=TRUE), kernel="gaussian"), col="red", main="density of mpg")

# Pipe Line
mtcars$mpg %>% 
    sample(size=10000, replace=TRUE) %>% 
    density(kernel="gaussian") %>% 
    plot(col="red", main="density of mpg")

• melt: wide format -> long format
• dcast: long format -> wide format

• melt: wide format -> long format
• dcast: long format -> wide format

# 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)

  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

• melt: wide format -> long format
• dcast: long format -> wide format

WP.cast <- dcast(WP.melt, year~variable,value.var="value") 
head(WP.cast)

  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

• 讓R 使用者可以用更有彈性的方式來處理資料
• 針對data.frame做設計（名稱中的d）
• 設計理念
  • 導入資料整理最重要的動作（非常類似SQL）
  • 快
  • 支援異質資料源（data.frame或資料庫中的表格）

vignette(all = TRUE, package = "dplyr")
vignette("introduction", package = "dplyr")

• 更詳細的dplyr介紹可以閱讀dplyr的小論文
• R 的開發者會針對一個主題撰寫小論文做介紹

• arrange(dataframe, 要進行排序的項目,….)
• 排序預設是由小到大

arrange(iris, Petal.Length) %>% head

  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

• arrange(dataframe, 要進行排序的項目,….)
• 排序預設是由小到大，加上desc可使用遞增排列

arrange(iris, desc(Petal.Length)) %>% head

  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

• arrange(dataframe, 要進行排序的項目,….)
• 排序預設是由小到大，加上desc可使用遞增排列
• 排序的項目可以不只一項，只要依序將項目填入後方即可

arrange(iris, desc(Petal.Length), - Sepal.Length ) %>% head

  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

• 移除重複資料: distinct(iris)
• 隨機抽取資料: sample_n(iris, 5)
• 抽取指定列: slice(iris, c(1,3,4,5))

filter(iris,Sepal.Length == 7.7)

  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

filter(iris,Sepal.Length == 7.7,Sepal.Width ==2.8)

  Sepal.Length Sepal.Width Petal.Length Petal.Width   Species
1          7.7         2.8          6.7           2 virginica

select(iris,c(Petal.Width, Species))

    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
132         2.0  virginica
133         2.2  virginica
134         1.5  virginica
135         1.4  virginica
136         2.3  virginica
137         2.4  virginica
138         1.8  virginica
139         1.8  virginica
140         2.1  virginica
141         2.4  virginica
142         2.3  virginica
143         1.9  virginica
144         2.3  virginica
145         2.5  virginica
146         2.3  virginica
147         1.9  virginica
148         2.0  virginica
149         2.3  virginica
150         1.8  virginica

用負號-執行反向選取

select(iris,c(-Petal.Width,-Species))

    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
7            4.6         3.4          1.4
8            5.0         3.4          1.5
9            4.4         2.9          1.4
10           4.9         3.1          1.5
11           5.4         3.7          1.5
12           4.8         3.4          1.6
13           4.8         3.0          1.4
14           4.3         3.0          1.1
15           5.8         4.0          1.2
16           5.7         4.4          1.5
17           5.4         3.9          1.3
18           5.1         3.5          1.4
19           5.7         3.8          1.7
20           5.1         3.8          1.5
21           5.4         3.4          1.7
22           5.1         3.7          1.5
23           4.6         3.6          1.0
24           5.1         3.3          1.7
25           4.8         3.4          1.9
26           5.0         3.0          1.6
27           5.0         3.4          1.6
28           5.2         3.5          1.5
29           5.2         3.4          1.4
30           4.7         3.2          1.6
31           4.8         3.1          1.6
32           5.4         3.4          1.5
33           5.2         4.1          1.5
34           5.5         4.2          1.4
35           4.9         3.1          1.5
36           5.0         3.2          1.2
37           5.5         3.5          1.3
38           4.9         3.6          1.4
39           4.4         3.0          1.3
40           5.1         3.4          1.5
41           5.0         3.5          1.3
42           4.5         2.3          1.3
43           4.4         3.2          1.3
44           5.0         3.5          1.6
45           5.1         3.8          1.9
46           4.8         3.0          1.4
47           5.1         3.8          1.6
48           4.6         3.2          1.4
49           5.3         3.7          1.5
50           5.0         3.3          1.4
51           7.0         3.2          4.7
52           6.4         3.2          4.5
53           6.9         3.1          4.9
54           5.5         2.3          4.0
55           6.5         2.8          4.6
56           5.7         2.8          4.5
57           6.3         3.3          4.7
58           4.9         2.4          3.3
59           6.6         2.9          4.6
60           5.2         2.7          3.9
61           5.0         2.0          3.5
62           5.9         3.0          4.2
63           6.0         2.2          4.0
64           6.1         2.9          4.7
65           5.6         2.9          3.6
66           6.7         3.1          4.4
67           5.6         3.0          4.5
68           5.8         2.7          4.1
69           6.2         2.2          4.5
70           5.6         2.5          3.9
71           5.9         3.2          4.8
72           6.1         2.8          4.0
73           6.3         2.5          4.9
74           6.1         2.8          4.7
75           6.4         2.9          4.3
76           6.6         3.0          4.4
77           6.8         2.8          4.8
78           6.7         3.0          5.0
79           6.0         2.9          4.5
80           5.7         2.6          3.5
81           5.5         2.4          3.8
82           5.5         2.4          3.7
83           5.8         2.7          3.9
84           6.0         2.7          5.1
85           5.4         3.0          4.5
86           6.0         3.4          4.5
87           6.7         3.1          4.7
88           6.3         2.3          4.4
89           5.6         3.0          4.1
90           5.5         2.5          4.0
91           5.5         2.6          4.4
92           6.1         3.0          4.6
93           5.8         2.6          4.0
94           5.0         2.3          3.3
95           5.6         2.7          4.2
96           5.7         3.0          4.2
97           5.7         2.9          4.2
98           6.2         2.9          4.3
99           5.1         2.5          3.0
100          5.7         2.8          4.1
101          6.3         3.3          6.0
102          5.8         2.7          5.1
103          7.1         3.0          5.9
104          6.3         2.9          5.6
105          6.5         3.0          5.8
106          7.6         3.0          6.6
107          4.9         2.5          4.5
108          7.3         2.9          6.3
109          6.7         2.5          5.8
110          7.2         3.6          6.1
111          6.5         3.2          5.1
112          6.4         2.7          5.3
113          6.8         3.0          5.5
114          5.7         2.5          5.0
115          5.8         2.8          5.1
116          6.4         3.2          5.3
117          6.5         3.0          5.5
118          7.7         3.8          6.7
119          7.7         2.6          6.9
120          6.0         2.2          5.0
121          6.9         3.2          5.7
122          5.6         2.8          4.9
123          7.7         2.8          6.7
124          6.3         2.7          4.9
125          6.7         3.3          5.7
126          7.2         3.2          6.0
127          6.2         2.8          4.8
128          6.1         3.0          4.9
129          6.4         2.8          5.6
130          7.2         3.0          5.8
131          7.4         2.8          6.1
132          7.9         3.8          6.4
133          6.4         2.8          5.6
134          6.3         2.8          5.1
135          6.1         2.6          5.6
136          7.7         3.0          6.1
137          6.3         3.4          5.6
138          6.4         3.1          5.5
139          6.0         3.0          4.8
140          6.9         3.1          5.4
141          6.7         3.1          5.6
142          6.9         3.1          5.1
143          5.8         2.7          5.1
144          6.8         3.2          5.9
145          6.7         3.3          5.7
146          6.7         3.0          5.2
147          6.3         2.5          5.0
148          6.5         3.0          5.2
149          6.2         3.4          5.4
150          5.9         3.0          5.1

• 如果想要做不同欄位間計算後得到新的欄位該如何處理
• 譬如 Petal.Length * Petal.Width

tmpiris <- mutate(iris, Petal.Vol = Petal.Length * Petal.Width)
head(tmpiris)

  Sepal.Length Sepal.Width Petal.Length Petal.Width Species Petal.Vol
1          5.1         3.5          1.4         0.2  setosa      0.28
2          4.9         3.0          1.4         0.2  setosa      0.28
3          4.7         3.2          1.3         0.2  setosa      0.26
4          4.6         3.1          1.5         0.2  setosa      0.30
5          5.0         3.6          1.4         0.2  setosa      0.28
6          5.4         3.9          1.7         0.4  setosa      0.68

• 如果想要 Sepal.Length 的 mean、Sepal.Width 的標準差、Petal.Length 的最大值
• 利用summarise

summarise(iris, SL.mean=mean(Sepal.Length), SW.sd=sd(Sepal.Width), PL.max=max(Petal.Length))

   SL.mean     SW.sd PL.max
1 5.843333 0.4358663    6.9

• 如果想要 Sepal.Length 的 mean、Sepal.Width 的標準差、Petal.Length 的最大值
• 依照物種類別各自計算
• 利用group_by + summarise

group_by(iris, Species) %>%
  summarise(SL.mean=mean(Sepal.Length), SW.sd=sd(Sepal.Width), PL.max=max(Petal.Length))

Source: local data frame [3 x 4]

     Species SL.mean     SW.sd PL.max
      (fctr)   (dbl)     (dbl)  (dbl)
1     setosa   5.006 0.3790644    1.9
2 versicolor   5.936 0.3137983    5.1
3  virginica   6.588 0.3224966    6.9

• left_join
• full_join
• inner_join
• …

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