用R做簡單迴歸分析
data(anscombe)
plot(y1 ~ x1, data = anscombe)
lmfit <- lm(y1~x1, data=anscombe)
abline(lmfit, col="red")
數學運算
# 數字相加
3 + 8
## [1] 11
# 數字相減
3 - 8
## [1] -5
# 數字相乘
5 * 5
## [1] 25
# 數字相除
11 / 2
## [1] 5.5
# 指數
2^10
## [1] 1024
# 取餘數
11%%2
## [1] 1
設定變數
a <- 3
a = 3
b <- 5
a + b
## [1] 8
基礎資料型態
# 數值型態
numer <- 17.8
# 字串型態
char <- "hello world"
# 布林邏輯
logic <- TRUE
# 使用class 檢查資料型態
class(numer)
## [1] "numeric"
不同型態資料做運算
card_length <- 3
class(card_length)
## [1] "numeric"
#card_width <- "5 inches"
card_width = 5
class(card_width)
## [1] "numeric"
card_length * card_width
## [1] 15
計算一本書的價錢
RRP <- 35.99
Exchange = 31.74
#rrp
NTD <- RRP * Exchange
NTD
## [1] 1142.323
weight1 <- 75
height1 <- 185
weight2 <- 72
height2 <- 160
height_vec <- c(180, 169, 173)
names_vec <-c("Brian", "Toby", "Sherry")
# 兩個向量進行數學運算
x <- c(1,2,3,7)
y <- c(2,3,5,1)
x+y
## [1] 3 5 8 8
x*y
## [1] 2 6 15 7
x - y
## [1] -1 -1 -2 6
x/y
## [1] 0.5000000 0.6666667 0.6000000 7.0000000
x + 3
## [1] 4 5 6 10
x + c(3)
## [1] 4 5 6 10
x + c(3,3,3,3)
## [1] 4 5 6 10
x + c(3,4)
## [1] 4 6 6 11
x + c(3,4,3,4)
## [1] 4 6 6 11
x <- 1:20
x
## [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
x <- seq(1,20)
x
## [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
seq
?seq
## starting httpd help server ...
## done
seq(1,20,2)
## [1] 1 3 5 7 9 11 13 15 17 19
seq(1,3.5, by =0.5)
## [1] 1.0 1.5 2.0 2.5 3.0 3.5
seq(1,10,length=2)
## [1] 1 10
# 透過sum 將向量資料作加總
x <- c(1,2,3,5,7)
sum(x)
## [1] 18
# 查詢該如何使用sum函式
?sum
help(sum)
x <- 1:100
sum(x)
## [1] 5050
指定名稱
height_vec = c(180, 169, 173)
names(height_vec)
## NULL
names(height_vec) <- c("Brian", "Toby", "Sherry")
names(height_vec)
## [1] "Brian" "Toby" "Sherry"
height_vec
## Brian Toby Sherry
## 180 169 173
names_vec <- c("Brian", "Toby", "Sherry")
names(height_vec) <- names_vec
判斷向量內容是否符合條件
height_vec > 175
## Brian Toby Sherry
## TRUE FALSE FALSE
height_vec < 175
## Brian Toby Sherry
## FALSE TRUE TRUE
height_vec >= 175
## Brian Toby Sherry
## TRUE FALSE FALSE
height_vec <= 175
## Brian Toby Sherry
## FALSE TRUE TRUE
height_vec == 180
## Brian Toby Sherry
## TRUE FALSE FALSE
height_vec != 180
## Brian Toby Sherry
## FALSE TRUE TRUE
height_vec[height_vec > 175]
## Brian
## 180
height_vec[2] #取得第二個人的身高
## Toby
## 169
height_vec[c(2,3)] # 取得二跟三
## Toby Sherry
## 169 173
2:3
## [1] 2 3
height_vec[2:3] # 取得二跟三
## Toby Sherry
## 169 173
# 使用& 取得交集
height_vec[height_vec < 180 & height_vec > 170 ]
## Sherry
## 173
# 使用 | 取得聯集
height_vec[height_vec >= 180 | height_vec < 170 ]
## Brian Toby
## 180 169
使用向量計算BMI
height_vec <- c(180,169,173)
weight_vec <- c(73, 87, 43)
names_vec <- c('Brian', 'Toby', 'Sherry')
bmi_vec <- weight_vec / (height_vec / 100) ^ 2
names(bmi_vec) = names_vec
bmi_vec[bmi_vec < 18.5 | bmi_vec >= 24]
## Toby Sherry
## 30.46112 14.36734
rbind(bmi_vec, height_vec, weight_vec)
## Brian Toby Sherry
## bmi_vec 22.53086 30.46112 14.36734
## height_vec 180.00000 169.00000 173.00000
## weight_vec 73.00000 87.00000 43.00000
cbind(bmi_vec, height_vec, weight_vec)
## bmi_vec height_vec weight_vec
## Brian 22.53086 180 73
## Toby 30.46112 169 87
## Sherry 14.36734 173 43
?matrix
1:9
## [1] 1 2 3 4 5 6 7 8 9
matrix(1:9, nrow=3)
## [,1] [,2] [,3]
## [1,] 1 4 7
## [2,] 2 5 8
## [3,] 3 6 9
matrix(1:9, nrow=1)
## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9]
## [1,] 1 2 3 4 5 6 7 8 9
matrix(1:9, nrow=3, byrow=TRUE)
## [,1] [,2] [,3]
## [1,] 1 2 3
## [2,] 4 5 6
## [3,] 7 8 9
kevin <- c(85,73)
marry <- c(72,64)
jerry <- c(59,66)
mat <- matrix(c(kevin, marry, jerry), nrow=3, byrow= TRUE)
colnames(mat) <- c('first', 'second')
rownames(mat) <- c('kevin', 'marry', 'jerry')
mat2 <- matrix(c(kevin, marry, jerry), nrow=3, byrow=TRUE,
dimnames=list(c('kevin', 'marry', 'jerry'),c('first', 'second')))
dim(mat2)
## [1] 3 2
nrow(mat2)
## [1] 3
ncol(mat2)
## [1] 2
mat2[ 1 , ]
## first second
## 85 73
mat2[ , 1 ]
## kevin marry jerry
## 85 72 59
mat2[ c(2,3), ]
## first second
## marry 72 64
## jerry 59 66
mat2[ 2:3 , ]
## first second
## marry 72 64
## jerry 59 66
mat2[ 2:3 , 1 ]
## marry jerry
## 72 59
新增列與行
mat3 <- rbind(mat2, c(78,63))
mat3
## first second
## kevin 85 73
## marry 72 64
## jerry 59 66
## 78 63
rownames(mat3)
## [1] "kevin" "marry" "jerry" ""
rownames(mat3)[4]
## [1] ""
rownames(mat3)[4] <- 'sam'
mat4 <- cbind(mat2,c(82,77,70))
mat4
## first second
## kevin 85 73 82
## marry 72 64 77
## jerry 59 66 70
colnames(mat4)
## [1] "first" "second" ""
colnames(mat4)[3] = 'third'
mat4
## first second third
## kevin 85 73 82
## marry 72 64 77
## jerry 59 66 70
mat2
## first second
## kevin 85 73
## marry 72 64
## jerry 59 66
rowSums(mat2)
## kevin marry jerry
## 158 136 125
colSums(mat2)
## first second
## 216 203
矩陣運算
#矩陣宣告
m1 <- matrix(1:4, byrow=TRUE, nrow=2)
m2 <- matrix(5:8, byrow=TRUE, nrow=2)
#矩陣運算
m1 + m2
## [,1] [,2]
## [1,] 6 8
## [2,] 10 12
m1 - m2
## [,1] [,2]
## [1,] -4 -4
## [2,] -4 -4
m1 * m2
## [,1] [,2]
## [1,] 5 12
## [2,] 21 32
m1 / m2
## [,1] [,2]
## [1,] 0.2000000 0.3333333
## [2,] 0.4285714 0.5000000
m1 %*% m2
## [,1] [,2]
## [1,] 19 22
## [2,] 43 50
使用矩陣計算考試成績
kevin <- c(85,73)
marry <- c(72,64)
jerry <- c(59,66)
mat <- matrix(c(kevin, marry, jerry), nrow=3, byrow= TRUE)
mat
## [,1] [,2]
## [1,] 85 73
## [2,] 72 64
## [3,] 59 66
weighted_score <- mat[,1] * 0.4 + mat[,2] * 0.6
weighted_score2 <- mat %*% c(0.4,0.6)
建立Data Frame
# 建立 Vector
days <- c('mon','tue','wed','thu','fri')
temp <- c(22.2,21,23,24.3,25)
rain <- c(TRUE, TRUE, FALSE, FALSE, TRUE)
mat <- matrix(c(days, temp,rain), nrow=3)
# 使用 Vector 建立Data Frame
df <- data.frame(days,temp,rain)
df
## days temp rain
## 1 mon 22.2 TRUE
## 2 tue 21.0 TRUE
## 3 wed 23.0 FALSE
## 4 thu 24.3 FALSE
## 5 fri 25.0 TRUE
class(df)
## [1] "data.frame"
# 檢視資料形態
class(df)
## [1] "data.frame"
# 檢視架構
str(df)
## 'data.frame': 5 obs. of 3 variables:
## $ days: Factor w/ 5 levels "fri","mon","thu",..: 2 4 5 3 1
## $ temp: num 22.2 21 23 24.3 25
## $ rain: logi TRUE TRUE FALSE FALSE TRUE
# 檢視資料摘要
summary(df)
## days temp rain
## fri:1 Min. :21.0 Mode :logical
## mon:1 1st Qu.:22.2 FALSE:2
## thu:1 Median :23.0 TRUE :3
## tue:1 Mean :23.1 NA's :0
## wed:1 3rd Qu.:24.3
## Max. :25.0
使用R 內建的資料集
#表列資料集
data()
#使用資料集
data(iris)
View(iris)
#觀察讀取到的資料集型態
class(iris)
## [1] "data.frame"
str(iris)
## 'data.frame': 150 obs. of 5 variables:
## $ Sepal.Length: num 5.1 4.9 4.7 4.6 5 5.4 4.6 5 4.4 4.9 ...
## $ Sepal.Width : num 3.5 3 3.2 3.1 3.6 3.9 3.4 3.4 2.9 3.1 ...
## $ Petal.Length: num 1.4 1.4 1.3 1.5 1.4 1.7 1.4 1.5 1.4 1.5 ...
## $ Petal.Width : num 0.2 0.2 0.2 0.2 0.2 0.4 0.3 0.2 0.2 0.1 ...
## $ Species : Factor w/ 3 levels "setosa","versicolor",..: 1 1 1 1 1 1 1 1 1 1 ...
# 觀看資料集的前幾筆資料與後幾筆資料
head(iris)
## 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
head(iris, 10)
## 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
## 7 4.6 3.4 1.4 0.3 setosa
## 8 5.0 3.4 1.5 0.2 setosa
## 9 4.4 2.9 1.4 0.2 setosa
## 10 4.9 3.1 1.5 0.1 setosa
tail(iris)
## Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 145 6.7 3.3 5.7 2.5 virginica
## 146 6.7 3.0 5.2 2.3 virginica
## 147 6.3 2.5 5.0 1.9 virginica
## 148 6.5 3.0 5.2 2.0 virginica
## 149 6.2 3.4 5.4 2.3 virginica
## 150 5.9 3.0 5.1 1.8 virginica
tail(iris,3)
## Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 148 6.5 3.0 5.2 2.0 virginica
## 149 6.2 3.4 5.4 2.3 virginica
## 150 5.9 3.0 5.1 1.8 virginica
#取前三列資料
iris[1:3,]
## 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
#取前三列第一行的資料
iris[1:3,1]
## [1] 5.1 4.9 4.7
#也可以用欄位名稱取值
iris[1:3,"Sepal.Length"]
## [1] 5.1 4.9 4.7
#取前兩行資料
iris[,1:2]
## 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
## 7 4.6 3.4
## 8 5.0 3.4
## 9 4.4 2.9
## 10 4.9 3.1
## 11 5.4 3.7
## 12 4.8 3.4
## 13 4.8 3.0
## 14 4.3 3.0
## 15 5.8 4.0
## 16 5.7 4.4
## 17 5.4 3.9
## 18 5.1 3.5
## 19 5.7 3.8
## 20 5.1 3.8
## 21 5.4 3.4
## 22 5.1 3.7
## 23 4.6 3.6
## 24 5.1 3.3
## 25 4.8 3.4
## 26 5.0 3.0
## 27 5.0 3.4
## 28 5.2 3.5
## 29 5.2 3.4
## 30 4.7 3.2
## 31 4.8 3.1
## 32 5.4 3.4
## 33 5.2 4.1
## 34 5.5 4.2
## 35 4.9 3.1
## 36 5.0 3.2
## 37 5.5 3.5
## 38 4.9 3.6
## 39 4.4 3.0
## 40 5.1 3.4
## 41 5.0 3.5
## 42 4.5 2.3
## 43 4.4 3.2
## 44 5.0 3.5
## 45 5.1 3.8
## 46 4.8 3.0
## 47 5.1 3.8
## 48 4.6 3.2
## 49 5.3 3.7
## 50 5.0 3.3
## 51 7.0 3.2
## 52 6.4 3.2
## 53 6.9 3.1
## 54 5.5 2.3
## 55 6.5 2.8
## 56 5.7 2.8
## 57 6.3 3.3
## 58 4.9 2.4
## 59 6.6 2.9
## 60 5.2 2.7
## 61 5.0 2.0
## 62 5.9 3.0
## 63 6.0 2.2
## 64 6.1 2.9
## 65 5.6 2.9
## 66 6.7 3.1
## 67 5.6 3.0
## 68 5.8 2.7
## 69 6.2 2.2
## 70 5.6 2.5
## 71 5.9 3.2
## 72 6.1 2.8
## 73 6.3 2.5
## 74 6.1 2.8
## 75 6.4 2.9
## 76 6.6 3.0
## 77 6.8 2.8
## 78 6.7 3.0
## 79 6.0 2.9
## 80 5.7 2.6
## 81 5.5 2.4
## 82 5.5 2.4
## 83 5.8 2.7
## 84 6.0 2.7
## 85 5.4 3.0
## 86 6.0 3.4
## 87 6.7 3.1
## 88 6.3 2.3
## 89 5.6 3.0
## 90 5.5 2.5
## 91 5.5 2.6
## 92 6.1 3.0
## 93 5.8 2.6
## 94 5.0 2.3
## 95 5.6 2.7
## 96 5.7 3.0
## 97 5.7 2.9
## 98 6.2 2.9
## 99 5.1 2.5
## 100 5.7 2.8
## 101 6.3 3.3
## 102 5.8 2.7
## 103 7.1 3.0
## 104 6.3 2.9
## 105 6.5 3.0
## 106 7.6 3.0
## 107 4.9 2.5
## 108 7.3 2.9
## 109 6.7 2.5
## 110 7.2 3.6
## 111 6.5 3.2
## 112 6.4 2.7
## 113 6.8 3.0
## 114 5.7 2.5
## 115 5.8 2.8
## 116 6.4 3.2
## 117 6.5 3.0
## 118 7.7 3.8
## 119 7.7 2.6
## 120 6.0 2.2
## 121 6.9 3.2
## 122 5.6 2.8
## 123 7.7 2.8
## 124 6.3 2.7
## 125 6.7 3.3
## 126 7.2 3.2
## 127 6.2 2.8
## 128 6.1 3.0
## 129 6.4 2.8
## 130 7.2 3.0
## 131 7.4 2.8
## 132 7.9 3.8
## 133 6.4 2.8
## 134 6.3 2.8
## 135 6.1 2.6
## 136 7.7 3.0
## 137 6.3 3.4
## 138 6.4 3.1
## 139 6.0 3.0
## 140 6.9 3.1
## 141 6.7 3.1
## 142 6.9 3.1
## 143 5.8 2.7
## 144 6.8 3.2
## 145 6.7 3.3
## 146 6.7 3.0
## 147 6.3 2.5
## 148 6.5 3.0
## 149 6.2 3.4
## 150 5.9 3.0
#取特定欄位向量值
iris$"Sepal.Length"
## [1] 5.1 4.9 4.7 4.6 5.0 5.4 4.6 5.0 4.4 4.9 5.4 4.8 4.8 4.3 5.8 5.7 5.4
## [18] 5.1 5.7 5.1 5.4 5.1 4.6 5.1 4.8 5.0 5.0 5.2 5.2 4.7 4.8 5.4 5.2 5.5
## [35] 4.9 5.0 5.5 4.9 4.4 5.1 5.0 4.5 4.4 5.0 5.1 4.8 5.1 4.6 5.3 5.0 7.0
## [52] 6.4 6.9 5.5 6.5 5.7 6.3 4.9 6.6 5.2 5.0 5.9 6.0 6.1 5.6 6.7 5.6 5.8
## [69] 6.2 5.6 5.9 6.1 6.3 6.1 6.4 6.6 6.8 6.7 6.0 5.7 5.5 5.5 5.8 6.0 5.4
## [86] 6.0 6.7 6.3 5.6 5.5 5.5 6.1 5.8 5.0 5.6 5.7 5.7 6.2 5.1 5.7 6.3 5.8
## [103] 7.1 6.3 6.5 7.6 4.9 7.3 6.7 7.2 6.5 6.4 6.8 5.7 5.8 6.4 6.5 7.7 7.7
## [120] 6.0 6.9 5.6 7.7 6.3 6.7 7.2 6.2 6.1 6.4 7.2 7.4 7.9 6.4 6.3 6.1 7.7
## [137] 6.3 6.4 6.0 6.9 6.7 6.9 5.8 6.8 6.7 6.7 6.3 6.5 6.2 5.9
iris[1:3,][1:2, 1:2]$Sepal.Length
## [1] 5.1 4.9
#取前五筆包含length 及 width 的資料
five.Sepal.iris <- iris[1:5, c("Sepal.Length", "Sepal.Width")]
five.iris <- iris[1:5,]
five.iris$Sepal.Length
## [1] 5.1 4.9 4.7 4.6 5.0
five.iris$Sepal.Length >= 5
## [1] TRUE FALSE FALSE FALSE TRUE
five.iris[five.iris$Sepal.Length >= 5, ]
## Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 1 5.1 3.5 1.4 0.2 setosa
## 5 5.0 3.6 1.4 0.2 setosa
which(five.iris$Sepal.Length >= 5)
## [1] 1 5
five.iris[which(five.iris$Sepal.Length >= 5), ]
## Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 1 5.1 3.5 1.4 0.2 setosa
## 5 5.0 3.6 1.4 0.2 setosa
#可以用條件做篩選
setosa.data <- iris[iris$Species=="setosa",1:5]
#使用which 做資料篩選
which(iris$Species=="setosa")
## [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23
## [24] 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46
## [47] 47 48 49 50
比較sort 與 order
a <- c(1,5,2,7,4,6)
sort(a)
## [1] 1 2 4 5 6 7
order(a)
## [1] 1 3 5 2 6 4
head(sort(iris$Sepal.Length, decreasing = TRUE))
## [1] 7.9 7.7 7.7 7.7 7.7 7.6
head(iris[order(iris$Sepal.Length, decreasing = TRUE),])
## Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 132 7.9 3.8 6.4 2.0 virginica
## 118 7.7 3.8 6.7 2.2 virginica
## 119 7.7 2.6 6.9 2.3 virginica
## 123 7.7 2.8 6.7 2.0 virginica
## 136 7.7 3.0 6.1 2.3 virginica
## 106 7.6 3.0 6.6 2.1 virginica
Explore 2330
summary(tw2330)
head(tw2330[order(tw2330$Close),])
tail(tw2330[order(tw2330$Close),])
str(tw2330)
tw2330$Date <- as.Date(tw2330$Date)
plot(Close ~ Date, data = tw2330)
階層 (Factor)
Weather <- c("sunny","rainy", "cloudy", "rainy", "cloudy")
str(Weather)
## chr [1:5] "sunny" "rainy" "cloudy" "rainy" "cloudy"
weather_category <- factor(Weather)
weather_category
## [1] sunny rainy cloudy rainy cloudy
## Levels: cloudy rainy sunny
levels(weather_category)
## [1] "cloudy" "rainy" "sunny"
names_vec = c('a','b', 'c')
scores_vec = c(77,56,66)
gender_vec = c(1,0,1)
df = data.frame(names_vec, scores_vec, gender_vec)
str(df)
## 'data.frame': 3 obs. of 3 variables:
## $ names_vec : Factor w/ 3 levels "a","b","c": 1 2 3
## $ scores_vec: num 77 56 66
## $ gender_vec: num 1 0 1
summary(df)
## names_vec scores_vec gender_vec
## a:1 Min. :56.00 Min. :0.0000
## b:1 1st Qu.:61.00 1st Qu.:0.5000
## c:1 Median :66.00 Median :1.0000
## Mean :66.33 Mean :0.6667
## 3rd Qu.:71.50 3rd Qu.:1.0000
## Max. :77.00 Max. :1.0000
df$gender_vec <- as.factor(df$gender_vec)
str(df)
## 'data.frame': 3 obs. of 3 variables:
## $ names_vec : Factor w/ 3 levels "a","b","c": 1 2 3
## $ scores_vec: num 77 56 66
## $ gender_vec: Factor w/ 2 levels "0","1": 2 1 2
summary(df)
## names_vec scores_vec gender_vec
## a:1 Min. :56.00 0:1
## b:1 1st Qu.:61.00 1:2
## c:1 Median :66.00
## Mean :66.33
## 3rd Qu.:71.50
## Max. :77.00
temperature <- c("Low", "High", "High", "Medium", "Low", "Medium")
temperature_category <- factor(temperature, order = TRUE, levels = c("Low", "Medium", "High"))
temperature_category
## [1] Low High High Medium Low Medium
## Levels: Low < Medium < High
temperature_category[3] > temperature_category[1]
## [1] TRUE
temperature_category[4] > temperature_category[3]
## [1] FALSE
levels(temperature_category)
## [1] "Low" "Medium" "High"
Lists
item <- list(thing="hat", size=8.25)
item
## $thing
## [1] "hat"
##
## $size
## [1] 8.25
item$thing
## [1] "hat"
item$size * 3
## [1] 24.75
test <- list(name="Toby", score = c(87,57,72))
test$score
## [1] 87 57 72
test$score[2]
## [1] 57
#沒有名字的清單
li <- list(c(1,2,3), c(2,3,4,5))
li
## [[1]]
## [1] 1 2 3
##
## [[2]]
## [1] 2 3 4 5
li[[1]]
## [1] 1 2 3
li[[1]][2]
## [1] 2
li <- list(c(3,5,12), c(2,4,5,8,10))
li
## [[1]]
## [1] 3 5 12
##
## [[2]]
## [1] 2 4 5 8 10
#使用lapply將函式套用到list 上
lapply(li, sum)
## [[1]]
## [1] 20
##
## [[2]]
## [1] 29
lapply(li, mean)
## [[1]]
## [1] 6.666667
##
## [[2]]
## [1] 5.8
lapply(li, length)
## [[1]]
## [1] 3
##
## [[2]]
## [1] 5
# sapply = simplified lapply
sapply(li, length)
## [1] 3 5
流程控制(Flow Control)
x = 2
if(x > 3){
print('x>3');
}else{
print('x<=3');
}
## [1] "x<=3"
x = 3;
if(x > 3){
print("x > 3");
} else if(x < 4){
print("x < 4");
}else if(x ==3){
print("x == 3");
}else{
print("x < 3");
}
## [1] "x < 4"
for(i in 1:10){
print (i);
}
## [1] 1
## [1] 2
## [1] 3
## [1] 4
## [1] 5
## [1] 6
## [1] 7
## [1] 8
## [1] 9
## [1] 10
s = 0
for(i in 1:100){
s = s + i;
}
s
## [1] 5050
sum(1:100)
## [1] 5050
x <- c("sunny","rainy", "cloudy", "rainy", "cloudy")
1:length(x)
## [1] 1 2 3 4 5
for(i in 1:length(x)){
#print(i)
print(x[i])
}
## [1] "sunny"
## [1] "rainy"
## [1] "cloudy"
## [1] "rainy"
## [1] "cloudy"
seq_along(x)
## [1] 1 2 3 4 5
for(i in seq_along(x)){
#print(i)
print(x[i])
}
## [1] "sunny"
## [1] "rainy"
## [1] "cloudy"
## [1] "rainy"
## [1] "cloudy"
for (letter in x){
print(letter);
}
## [1] "sunny"
## [1] "rainy"
## [1] "cloudy"
## [1] "rainy"
## [1] "cloudy"
使用while 迴圈
s = 0;
cnt = 0;
while(cnt <= 100){
s = s + cnt;
cnt = cnt + 1;
}
s
## [1] 5050
範例: 產生多筆頁面連結
?paste
paste('hello', 'world')
## [1] "hello world"
paste('hello', 'world', sep = '')
## [1] "helloworld"
for (page in 1:20){
#print(page);
print(paste('http://www.appledaily.com.tw/realtimenews/section/new/', page, sep=''));
}
## [1] "http://www.appledaily.com.tw/realtimenews/section/new/1"
## [1] "http://www.appledaily.com.tw/realtimenews/section/new/2"
## [1] "http://www.appledaily.com.tw/realtimenews/section/new/3"
## [1] "http://www.appledaily.com.tw/realtimenews/section/new/4"
## [1] "http://www.appledaily.com.tw/realtimenews/section/new/5"
## [1] "http://www.appledaily.com.tw/realtimenews/section/new/6"
## [1] "http://www.appledaily.com.tw/realtimenews/section/new/7"
## [1] "http://www.appledaily.com.tw/realtimenews/section/new/8"
## [1] "http://www.appledaily.com.tw/realtimenews/section/new/9"
## [1] "http://www.appledaily.com.tw/realtimenews/section/new/10"
## [1] "http://www.appledaily.com.tw/realtimenews/section/new/11"
## [1] "http://www.appledaily.com.tw/realtimenews/section/new/12"
## [1] "http://www.appledaily.com.tw/realtimenews/section/new/13"
## [1] "http://www.appledaily.com.tw/realtimenews/section/new/14"
## [1] "http://www.appledaily.com.tw/realtimenews/section/new/15"
## [1] "http://www.appledaily.com.tw/realtimenews/section/new/16"
## [1] "http://www.appledaily.com.tw/realtimenews/section/new/17"
## [1] "http://www.appledaily.com.tw/realtimenews/section/new/18"
## [1] "http://www.appledaily.com.tw/realtimenews/section/new/19"
## [1] "http://www.appledaily.com.tw/realtimenews/section/new/20"
函式 (Function)
addnum <- function(a, b){
a = a + 3
b = b * 2
a + b
}
addnum
## function(a, b){
## a = a + 3
## b = b * 2
## a + b
## }
addnum(3,5)
## [1] 16
a = 3
b = 5
addnum(a,b)
## [1] 16
a
## [1] 3
b
## [1] 5
addnum <- function(a, b = 2){
a = a + 3
b = b * 2
a + b
}
addnum(3)
## [1] 10
addnum(a = 3)
## [1] 10
#addnum(b = 3)
addnum(b = 3, a = 2)
## [1] 11
f = function(a, b) {
a * 2
}
f(3)
## [1] 6
f = function(a, b) {
print(a+ b)
}
#f(3)
範例: 撰寫函式計算文章詞頻
a = 'sum returns the sum of all the values present in its arguments'
?strsplit
strsplit(a,split= ' ')
## [[1]]
## [1] "sum" "returns" "the" "sum" "of"
## [6] "all" "the" "values" "present" "in"
## [11] "its" "arguments"
?table
b = c(1,2,2,1,2,1)
table(b)
## b
## 1 2
## 3 3
wordcount <- function(article){
article.split <- strsplit(article,split= ' ')
table(article.split)
}
wordcount(a)
## article.split
## all arguments in its of present returns
## 1 1 1 1 1 1 1
## sum the values
## 2 2 1
xi = "President Barack Obama is embarking upon a final bout of delicate overseas diplomacy before his successor is elected in November, arriving in Asia Saturday for meetings with some of his most nettlesome counterparts.
Air Force One touched down in Hangzhou, the host city of this year's Group of 20 meetings, just after 2 p.m. local time (2 a.m. ET). Obama's first stop was a formal bilateral session with the summit's host, Chinese President Xi Jinping"
wordcount(xi)
## article.split
## (2 2 20
## 1 1 1
## a a.m. after
## 2 1 1
## arriving Asia Barack
## 1 1 1
## before bilateral bout
## 1 1 1
## Chinese city counterparts.\n\nAir
## 1 1 1
## delicate diplomacy down
## 1 1 1
## elected embarking ET).
## 1 1 1
## final first for
## 1 1 1
## Force formal Group
## 1 1 1
## Hangzhou, his host
## 1 2 1
## host, in is
## 1 3 2
## Jinping just local
## 1 1 1
## meetings meetings, most
## 1 1 1
## nettlesome November, Obama
## 1 1 1
## Obama's of One
## 1 4 1
## overseas p.m. President
## 1 1 2
## Saturday session some
## 1 1 1
## stop successor summit's
## 1 1 1
## the this time
## 2 1 1
## touched upon was
## 1 1 1
## with Xi year's
## 2 1 1