R Demo
data("anscombe")
View(anscombe)
plot(y1 ~ x1, data = anscombe)
lmfit <- lm(y1 ~ x1, data = anscombe)
lmfit
##
## Call:
## lm(formula = y1 ~ x1, data = anscombe)
##
## Coefficients:
## (Intercept) x1
## 3.0001 0.5001
abline(lmfit, col="red")
predict(lmfit, data.frame(x1 = 16))
## 1
## 11.00155
R 數學運算
3 + 8
## [1] 11
3 - 8
## [1] -5
3 * 8
## [1] 24
3 / 8
## [1] 0.375
2 ^ 10
## [1] 1024
11 %% 2
## [1] 1
3 + 2 * 8 + 5
## [1] 24
(3 + 2) * 8 + 5
## [1] 45
a <- 3
b = 2
a + b
## [1] 5
numer <- 17.8
char <- 'Hello World'
logic <- TRUE
class(numer)
## [1] "numeric"
card_length <- 3
card_width <- "5 inches"
class(card_length)
## [1] "numeric"
class(card_width)
## [1] "character"
# card_length * card_width
card_width <- 5
card_length * card_width
## [1] 15
RRP <- 35.99
Exchange <- 31.74
NTD <- RRP * Exchange
#ntd: case sensitive
NTD
## [1] 1142.323
向量
height_vec <- c(180,169,173)
name_vec <- c("Brian", "Toby", "Sherry")
vec <- c(1, 2, 'hello')
vec
## [1] "1" "2" "hello"
x <- c(1,2,3,4)
y <- c(2,3,4,5)
x + y
## [1] 3 5 7 9
x - y
## [1] -1 -1 -1 -1
x * y
## [1] 2 6 12 20
x / y
## [1] 0.5000000 0.6666667 0.7500000 0.8000000
x + 1
## [1] 2 3 4 5
x + c(1,2)
## [1] 2 4 4 6
a <- 1
a <- c(1)
x + 1
## [1] 2 3 4 5
x + c(1)
## [1] 2 3 4 5
x + c(1,1,1,1)
## [1] 2 3 4 5
x + c(1,2)
## [1] 2 4 4 6
x + c(1,2,1,2)
## [1] 2 4 4 6
x + c(1,2,3)
## Warning in x + c(1, 2, 3): 較長的物件長度並非較短物件長度的倍數
## [1] 2 4 6 5
x + c(1,2,3,1)
## [1] 2 4 6 5
class(a)
## [1] "numeric"
class(x)
## [1] "numeric"
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
seq(1,20)
## [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
help(seq)
## starting httpd help server ... done
?seq
seq(1,20, 2)
## [1] 1 3 5 7 9 11 13 15 17 19
seq(from = 1,to =20, by = 2)
## [1] 1 3 5 7 9 11 13 15 17 19
seq(from = 1,to =3.5, by = 0.5)
## [1] 1.0 1.5 2.0 2.5 3.0 3.5
seq(1,10,length.out = 2)
## [1] 1 10
seq(1,10,len = 2)
## [1] 1 10
seq(1,10,length.out = 3)
## [1] 1.0 5.5 10.0
x <- c(1,2,3,5,7)
sum(x)
## [1] 18
sum(1:100)
## [1] 5050
?sum
sum(1,2,3)
## [1] 6
x <- c(1,2,3,4,5,NA)
sum(x)
## [1] NA
sum(x, na.rm=TRUE)
## [1] 15
height_vec <- c(180, 169, 173)
names(height_vec)
## NULL
names(height_vec) <- c('Brian', 'Toby', 'Sherry')
height_vec
## Brian Toby Sherry
## 180 169 173
name_vec <- c('Brian', 'Toby', 'Sherry')
names(height_vec) <- name_vec
height_vec
## Brian Toby Sherry
## 180 169 173
height_vec[1]
## Brian
## 180
height_vec[3]
## Sherry
## 173
height_vec[c(2,3)]
## Toby Sherry
## 169 173
2:3
## [1] 2 3
height_vec[2:3]
## Toby Sherry
## 169 173
height_vec > 175
## Brian Toby Sherry
## TRUE FALSE FALSE
height_vec < 175
## Brian Toby Sherry
## FALSE TRUE TRUE
height_vec == 175
## Brian Toby Sherry
## FALSE FALSE FALSE
height_vec != 175
## Brian Toby Sherry
## TRUE TRUE TRUE
height_vec[2:3]
## Toby Sherry
## 169 173
height_vec[c(FALSE,TRUE,TRUE)]
## Toby Sherry
## 169 173
height_vec < 175
## Brian Toby Sherry
## FALSE TRUE TRUE
height_vec[height_vec < 175]
## Toby Sherry
## 169 173
(height_vec >= 170) & (height_vec < 180)
## Brian Toby Sherry
## FALSE FALSE TRUE
height_vec[(height_vec >= 170) & (height_vec < 180)]
## Sherry
## 173
height_vec[(height_vec >= 170) | (height_vec < 180)]
## Brian Toby Sherry
## 180 169 173
## Practice
name_vec <- c('Brian', 'Toby', 'Sherry')
height_vec <- c(180,169,173)
weight_vec <- c(73 , 87, 43)
bmi_vec <- weight_vec / (height_vec / 100) ^ 2
names(bmi_vec) <- name_vec
bmi_vec[bmi_vec < 18.5 | bmi_vec >= 24]
## Toby Sherry
## 30.46112 14.36734
矩陣
kevin <- c(85,73)
marry <- c(72,64)
jerry <- c(59,66)
c(c(85,73),c(72,64), c(59,66))
## [1] 85 73 72 64 59 66
?matrix
mat <- matrix(c(kevin, marry , jerry), nrow = 3, byrow = TRUE)
mat
## [,1] [,2]
## [1,] 85 73
## [2,] 72 64
## [3,] 59 66
colnames(mat) <- c('first', 'second')
mat
## first second
## [1,] 85 73
## [2,] 72 64
## [3,] 59 66
rownames(mat) <- c('kevin', 'marry', 'jerry')
mat
## first second
## kevin 85 73
## marry 72 64
## jerry 59 66
mat <-matrix(c(kevin, marry, jerry), nrow=3, byrow=TRUE, dimnames=list(c('kevin', 'marry', 'jerry'),c('first', 'second')))
mat
## first second
## kevin 85 73
## marry 72 64
## jerry 59 66
dim(mat)
## [1] 3 2
nrow(mat)
## [1] 3
ncol(mat)
## [1] 2
mat
## first second
## kevin 85 73
## marry 72 64
## jerry 59 66
mat[1,]
## first second
## 85 73
mat[,1]
## kevin marry jerry
## 85 72 59
mat[c(2,3),]
## first second
## marry 72 64
## jerry 59 66
mat[2:3,]
## first second
## marry 72 64
## jerry 59 66
mat[c(FALSE, TRUE, TRUE),]
## first second
## marry 72 64
## jerry 59 66
mat[2,1]
## [1] 72
mat2 <- rbind(mat, c(78,63))
rownames(mat2)
## [1] "kevin" "marry" "jerry" ""
rownames(mat2)[1]
## [1] "kevin"
rownames(mat2)[4] <- 'Sam'
mat2
## first second
## kevin 85 73
## marry 72 64
## jerry 59 66
## Sam 78 63
mat3 <- cbind(mat, c(82,77,70))
colnames(mat3)[3] <- 'third'
mat3
## first second third
## kevin 85 73 82
## marry 72 64 77
## jerry 59 66 70
m1 <-matrix(1:4, byrow=TRUE, nrow=2)
m2 <-matrix(5:8, byrow=TRUE, nrow=2)
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
rowSums(mat)
## kevin marry jerry
## 158 136 125
colSums(mat)
## first second
## 216 203
m1 %*%m2
## [,1] [,2]
## [1,] 19 22
## [2,] 43 50
m1 <- matrix(1:9, nrow = 9)
m1
## [,1]
## [1,] 1
## [2,] 2
## [3,] 3
## [4,] 4
## [5,] 5
## [6,] 6
## [7,] 7
## [8,] 8
## [9,] 9
m2 <- matrix(1:9, nrow = 1)
m2
## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9]
## [1,] 1 2 3 4 5 6 7 8 9
m1 %*% m2
## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9]
## [1,] 1 2 3 4 5 6 7 8 9
## [2,] 2 4 6 8 10 12 14 16 18
## [3,] 3 6 9 12 15 18 21 24 27
## [4,] 4 8 12 16 20 24 28 32 36
## [5,] 5 10 15 20 25 30 35 40 45
## [6,] 6 12 18 24 30 36 42 48 54
## [7,] 7 14 21 28 35 42 49 56 63
## [8,] 8 16 24 32 40 48 56 64 72
## [9,] 9 18 27 36 45 54 63 72 81
mat <-matrix(c(kevin, marry, jerry), nrow=3, byrow=TRUE, dimnames=list(c('kevin', 'marry', 'jerry'),c('first', 'second')))
# method 1
mat[,1] * 0.4 + mat[,2] * 0.6
## kevin marry jerry
## 77.8 67.2 63.2
# method 2
mat[, 'first'] * 0.4 + mat[,'second'] * 0.6
## kevin marry jerry
## 77.8 67.2 63.2
# method 3
# 3 X 2 %*% 2 X 1 = 3 X 1
mat %*% matrix(c(0.4,0.6), nrow=2)
## [,1]
## kevin 77.8
## marry 67.2
## jerry 63.2
階層
weather <- c("sunny","rainy", "cloudy", "rainy", "cloudy")
class(weather)
## [1] "character"
weather_category <- factor(weather)
weather_category
## [1] sunny rainy cloudy rainy cloudy
## Levels: cloudy rainy sunny
levels(weather_category)
## [1] "cloudy" "rainy" "sunny"
temperature <-c("Low", "High", "High", "Medium", "Low", "Medium")
temperature[2] > temperature[1]
## [1] FALSE
temperature[2] < temperature[1]
## [1] TRUE
temperature_category <- factor(temperature, order=TRUE, levels=c("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"
levels(temperature_category) <- c('L', 'M', 'H')
temperature_category
## [1] L H H M L M
## Levels: L < M < H
Data Frame
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 = 5)
#sum(mat[,2])
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"
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
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
## wed:1 3rd Qu.:24.3
## Max. :25.0
#data()
data(iris)
View(iris)
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 ...
summary(iris)
## Sepal.Length Sepal.Width Petal.Length Petal.Width
## Min. :4.300 Min. :2.000 Min. :1.000 Min. :0.100
## 1st Qu.:5.100 1st Qu.:2.800 1st Qu.:1.600 1st Qu.:0.300
## Median :5.800 Median :3.000 Median :4.350 Median :1.300
## Mean :5.843 Mean :3.057 Mean :3.758 Mean :1.199
## 3rd Qu.:6.400 3rd Qu.:3.300 3rd Qu.:5.100 3rd Qu.:1.800
## Max. :7.900 Max. :4.400 Max. :6.900 Max. :2.500
## Species
## setosa :50
## versicolor:50
## virginica :50
##
##
##
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
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 , 6)
## 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 , 10)
## Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 141 6.7 3.1 5.6 2.4 virginica
## 142 6.9 3.1 5.1 2.3 virginica
## 143 5.8 2.7 5.1 1.9 virginica
## 144 6.8 3.2 5.9 2.3 virginica
## 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
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
iris[1:3 , 1]
## [1] 5.1 4.9 4.7
iris[1:3 , 'Sepal.Length']
## [1] 5.1 4.9 4.7
head(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
iris[1:3, -5]
## Sepal.Length Sepal.Width Petal.Length Petal.Width
## 1 5.1 3.5 1.4 0.2
## 2 4.9 3.0 1.4 0.2
## 3 4.7 3.2 1.3 0.2
head(iris$Sepal.Length)
## [1] 5.1 4.9 4.7 4.6 5.0 5.4
iris[iris$Species == 'setosa' & iris$Sepal.Length >= 5.2, ]
## Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 6 5.4 3.9 1.7 0.4 setosa
## 11 5.4 3.7 1.5 0.2 setosa
## 15 5.8 4.0 1.2 0.2 setosa
## 16 5.7 4.4 1.5 0.4 setosa
## 17 5.4 3.9 1.3 0.4 setosa
## 19 5.7 3.8 1.7 0.3 setosa
## 21 5.4 3.4 1.7 0.2 setosa
## 28 5.2 3.5 1.5 0.2 setosa
## 29 5.2 3.4 1.4 0.2 setosa
## 32 5.4 3.4 1.5 0.4 setosa
## 33 5.2 4.1 1.5 0.1 setosa
## 34 5.5 4.2 1.4 0.2 setosa
## 37 5.5 3.5 1.3 0.2 setosa
## 49 5.3 3.7 1.5 0.2 setosa
which(iris$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
iris_subset <- head(iris)
iris_subset
## 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
iris_subset[c(1,5,6),]
## 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
## 6 5.4 3.9 1.7 0.4 setosa
iris_subset[c(TRUE, FALSE, FALSE, FALSE, TRUE, TRUE),]
## 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
## 6 5.4 3.9 1.7 0.4 setosa
iris_subset$Sepal.Length >= 5
## [1] TRUE FALSE FALSE FALSE TRUE TRUE
iris_subset[iris_subset$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
## 6 5.4 3.9 1.7 0.4 setosa
which(iris_subset$Sepal.Length >= 5)
## [1] 1 5 6
iris_subset[which(iris_subset$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
## 6 5.4 3.9 1.7 0.4 setosa
head(sort(iris$Sepal.Length))
## [1] 4.3 4.4 4.4 4.4 4.5 4.6
head(sort(iris$Sepal.Length, decreasing = TRUE))
## [1] 7.9 7.7 7.7 7.7 7.7 7.6
a <- c(2,7,1,5,8,9,3)
sort(a)
## [1] 1 2 3 5 7 8 9
order(a)
## [1] 3 1 7 4 2 5 6
iris_subset[c(6,5,4,3,2,1), ]
## Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 6 5.4 3.9 1.7 0.4 setosa
## 5 5.0 3.6 1.4 0.2 setosa
## 4 4.6 3.1 1.5 0.2 setosa
## 3 4.7 3.2 1.3 0.2 setosa
## 2 4.9 3.0 1.4 0.2 setosa
## 1 5.1 3.5 1.4 0.2 setosa
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
## Read 2330.TW csv
library(readr)
tw2330 <- read_csv("https://raw.githubusercontent.com/ywchiu/rtibame/master/Data/2330.TW.csv", col_types = cols(High = col_number(),Open = col_number(), Close = col_number(), `Adj Close` = col_number(), Volume = col_number(),Low = col_number(), Date = col_date(format = "%Y-%m-%d")))
## `curl` package not installed, falling back to using `url()`
## Warning in rbind(names(probs), probs_f): number of columns of result is not
## a multiple of vector length (arg 1)
## Warning: 84 parsing failures.
## row # A tibble: 5 x 5 col row col expected actual file expected <int> <chr> <chr> <chr> <chr> actual 1 423 Open a number null 'https://raw.githubusercontent.com/ywch~ file 2 423 High a number null 'https://raw.githubusercontent.com/ywch~ row 3 423 Low a number null 'https://raw.githubusercontent.com/ywch~ col 4 423 Close a number null 'https://raw.githubusercontent.com/ywch~ expected 5 423 Adj Close a number null 'https://raw.githubusercontent.com/ywch~
## ... ................. ... .......................................................................... ........ .......................................................................... ...... .......................................................................... .... .......................................................................... ... .......................................................................... ... .......................................................................... ........ ..........................................................................
## See problems(...) for more details.
View(tw2330)
str(tw2330)
## Classes 'tbl_df', 'tbl' and 'data.frame': 1232 obs. of 7 variables:
## $ Date : Date, format: "2013-02-25" "2013-02-26" ...
## $ Open : num 106 104 102 104 104 ...
## $ High : num 107 105 104 105 104 ...
## $ Low : num 105 102 101 102 102 ...
## $ Close : num 105 104 104 105 102 ...
## $ Adj Close: num 90.2 88.9 89.7 90.2 87.6 ...
## $ Volume : num 35913000 38440000 80851000 31408000 50033000 ...
## - attr(*, "problems")=Classes 'tbl_df', 'tbl' and 'data.frame': 84 obs. of 5 variables:
## ..$ row : int 423 423 423 423 423 423 428 428 428 428 ...
## ..$ col : chr "Open" "High" "Low" "Close" ...
## ..$ expected: chr "a number" "a number" "a number" "a number" ...
## ..$ actual : chr "null" "null" "null" "null" ...
## ..$ file : chr "'https://raw.githubusercontent.com/ywchiu/rtibame/master/Data/2330.TW.csv'" "'https://raw.githubusercontent.com/ywchiu/rtibame/master/Data/2330.TW.csv'" "'https://raw.githubusercontent.com/ywchiu/rtibame/master/Data/2330.TW.csv'" "'https://raw.githubusercontent.com/ywchiu/rtibame/master/Data/2330.TW.csv'" ...
## - attr(*, "spec")=List of 2
## ..$ cols :List of 7
## .. ..$ Date :List of 1
## .. .. ..$ format: chr "%Y-%m-%d"
## .. .. ..- attr(*, "class")= chr "collector_date" "collector"
## .. ..$ Open : list()
## .. .. ..- attr(*, "class")= chr "collector_number" "collector"
## .. ..$ High : list()
## .. .. ..- attr(*, "class")= chr "collector_number" "collector"
## .. ..$ Low : list()
## .. .. ..- attr(*, "class")= chr "collector_number" "collector"
## .. ..$ Close : list()
## .. .. ..- attr(*, "class")= chr "collector_number" "collector"
## .. ..$ Adj Close: list()
## .. .. ..- attr(*, "class")= chr "collector_number" "collector"
## .. ..$ Volume : list()
## .. .. ..- attr(*, "class")= chr "collector_number" "collector"
## ..$ default: list()
## .. ..- attr(*, "class")= chr "collector_guess" "collector"
## ..- attr(*, "class")= chr "col_spec"
summary(tw2330)
## Date Open High Low
## Min. :2013-02-25 Min. : 94.4 Min. : 94.9 Min. : 92.9
## 1st Qu.:2014-05-27 1st Qu.:121.0 1st Qu.:122.0 1st Qu.:120.0
## Median :2015-08-24 Median :142.0 Median :143.0 Median :140.5
## Mean :2015-08-24 Mean :152.3 Mean :153.4 Mean :151.1
## 3rd Qu.:2016-11-18 3rd Qu.:183.9 3rd Qu.:185.0 3rd Qu.:183.0
## Max. :2018-02-23 Max. :263.0 Max. :266.0 Max. :262.5
## NA's :14 NA's :14 NA's :14
## Close Adj Close Volume
## Min. : 94.4 Min. : 83.33 Min. : 0
## 1st Qu.:121.0 1st Qu.:107.69 1st Qu.: 22099500
## Median :142.0 Median :130.37 Median : 29911500
## Mean :152.4 Mean :142.97 Mean : 33021623
## 3rd Qu.:184.0 3rd Qu.:178.06 3rd Qu.: 39984500
## Max. :266.0 Max. :266.00 Max. :157081000
## NA's :14 NA's :14 NA's :14
head(tw2330[order(tw2330$Close, decreasing = TRUE),])
## # A tibble: 6 x 7
## Date Open High Low Close `Adj Close` Volume
## <date> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 2018-01-23 262 266 262 266 266 33527444
## 2 2018-01-22 258 262 257 262 262 43400509
## 3 2018-02-01 258 261 257 260 260 30559457
## 4 2018-02-02 259 260 255 260 260 25707560
## 5 2018-01-29 259 262 255 258 258 30071234
## 6 2018-01-24 263 263 256 258 258 41874813
head(tw2330[order(tw2330$Close, decreasing = FALSE),])
## # A tibble: 6 x 7
## Date Open High Low Close `Adj Close` Volume
## <date> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 2013-08-20 95.2 95.4 94.4 94.4 83.3 41635000
## 2 2013-08-22 94.4 94.9 92.9 94.5 83.4 60185000
## 3 2013-08-19 95.5 96.1 95.3 95.5 84.3 49081000
## 4 2013-08-27 96.0 96.8 95.6 95.6 84.4 22435000
## 5 2013-08-12 96.4 96.7 95.6 96.0 84.7 33389000
## 6 2013-08-15 96.4 96.4 95.3 96.0 84.7 42647000
plot(tw2330$Date, tw2330$Close, type= 'l')
tw2330_after_2017 <- tw2330[tw2330$Date >= '2017-01-01', c('Date','Close')]
summary(tw2330_after_2017)
## Date Close
## Min. :2017-01-03 Min. :179.5
## 1st Qu.:2017-04-21 1st Qu.:193.4
## Median :2017-07-31 Median :214.0
## Mean :2017-07-30 Mean :214.4
## 3rd Qu.:2017-11-08 3rd Qu.:232.5
## Max. :2018-02-23 Max. :266.0
## NA's :2
tapply(iris$Sepal.Length, iris$Species, sum)
## setosa versicolor virginica
## 250.3 296.8 329.4
Lists
phone <-list(thing="iphoneX" , height=5.65, width=2.79 )
phone
## $thing
## [1] "iphoneX"
##
## $height
## [1] 5.65
##
## $width
## [1] 2.79
phone$height
## [1] 5.65
phone$thing
## [1] "iphoneX"
student <-list(name="Toby", score =c(87,57,72))
student$score
## [1] 87 57 72
student$score[2]
## [1] 57
student[[1]]
## [1] "Toby"
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
li[[1]]
## [1] 3 5 12
li[[2]]
## [1] 2 4 5 8 10
sum(li[[1]])
## [1] 20
sum(li[[2]])
## [1] 29
lapply(li, sum)
## [[1]]
## [1] 20
##
## [[2]]
## [1] 29
流程控制
x <- 3
if(x > 3){
print('x>3')
}else{
print('x<=3')
}
## [1] "x<=3"
if(x > 3){
print('x>3')
}else if (x == 3){
print('x==3')
}else{
print('x < 3')
}
## [1] "x==3"
1:10
## [1] 1 2 3 4 5 6 7 8 9 10
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
}
print(s)
## [1] 5050
sum(1:100)
## [1] 5050
x <-c("sunny","rainy", "cloudy", "rainy", "cloudy")
length(x)
## [1] 5
x[1]
## [1] "sunny"
for(i in 1:length(x)){
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(x[i])
}
## [1] "sunny"
## [1] "rainy"
## [1] "cloudy"
## [1] "rainy"
## [1] "cloudy"
for(weather in x){
print(weather)
}
## [1] "sunny"
## [1] "rainy"
## [1] "cloudy"
## [1] "rainy"
## [1] "cloudy"
s <- 0
cnt <- 0
while (cnt <= 100){
s <- s + cnt
cnt <- cnt + 1
}
s
## [1] 5050
url<-'https://tw.appledaily.com/new/realtime/'
?paste
paste(url, 'hi')
## [1] "https://tw.appledaily.com/new/realtime/ hi"
paste(url, 'hi', sep = '' )
## [1] "https://tw.appledaily.com/new/realtime/hi"
paste(url, 'hi', sep = '/' )
## [1] "https://tw.appledaily.com/new/realtime//hi"
paste0(url,'hi')
## [1] "https://tw.appledaily.com/new/realtime/hi"
for(i in 1:10){
#print(paste(url, i))
#print(paste(url, i, sep = ''))
print(paste0(url, i))
}
## [1] "https://tw.appledaily.com/new/realtime/1"
## [1] "https://tw.appledaily.com/new/realtime/2"
## [1] "https://tw.appledaily.com/new/realtime/3"
## [1] "https://tw.appledaily.com/new/realtime/4"
## [1] "https://tw.appledaily.com/new/realtime/5"
## [1] "https://tw.appledaily.com/new/realtime/6"
## [1] "https://tw.appledaily.com/new/realtime/7"
## [1] "https://tw.appledaily.com/new/realtime/8"
## [1] "https://tw.appledaily.com/new/realtime/9"
## [1] "https://tw.appledaily.com/new/realtime/10"
lapply(1:10, function(e) paste0(url,e) )
## [[1]]
## [1] "https://tw.appledaily.com/new/realtime/1"
##
## [[2]]
## [1] "https://tw.appledaily.com/new/realtime/2"
##
## [[3]]
## [1] "https://tw.appledaily.com/new/realtime/3"
##
## [[4]]
## [1] "https://tw.appledaily.com/new/realtime/4"
##
## [[5]]
## [1] "https://tw.appledaily.com/new/realtime/5"
##
## [[6]]
## [1] "https://tw.appledaily.com/new/realtime/6"
##
## [[7]]
## [1] "https://tw.appledaily.com/new/realtime/7"
##
## [[8]]
## [1] "https://tw.appledaily.com/new/realtime/8"
##
## [[9]]
## [1] "https://tw.appledaily.com/new/realtime/9"
##
## [[10]]
## [1] "https://tw.appledaily.com/new/realtime/10"
# sapply -> simplified lapply
sapply(1:10, function(e) paste0(url,e) )
## [1] "https://tw.appledaily.com/new/realtime/1"
## [2] "https://tw.appledaily.com/new/realtime/2"
## [3] "https://tw.appledaily.com/new/realtime/3"
## [4] "https://tw.appledaily.com/new/realtime/4"
## [5] "https://tw.appledaily.com/new/realtime/5"
## [6] "https://tw.appledaily.com/new/realtime/6"
## [7] "https://tw.appledaily.com/new/realtime/7"
## [8] "https://tw.appledaily.com/new/realtime/8"
## [9] "https://tw.appledaily.com/new/realtime/9"
## [10] "https://tw.appledaily.com/new/realtime/10"
函數
addNum <- function(a , b){
s <- a + b
return(s)
}
addNum(3,5)
## [1] 8
addNum2 <- function(a , b){
s <- a + b
s
}
addNum2(3,5)
## [1] 8
addNum3 <- function(a , b = 2){
s <- a + b
s
}
addNum3(3,5)
## [1] 8
addNum3(3)
## [1] 5
addNum4 <- function(a , b){
s <- a*3 + b
s
}
addNum4(5,2)
## [1] 17
addNum4(b=5,a=2)
## [1] 11
f <- function(a, b){
a *2
}
f(3)
## [1] 6
f(3,100)
## [1] 6
f2 <- function(a, b){
a + b
}
#f2(3)
f <-file('https://raw.githubusercontent.com/ywchiu/rtibame/master/Data/trump.txt')
article <-readLines(f)
## Warning in readLines(f): 於 'https://raw.githubusercontent.com/ywchiu/
## rtibame/master/Data/trump.txt' 找到不完整的最後一列
close(f)
stopwords <- c("a", "about", "above", "above", "across", "after", "afterwards", "again", "against", "all", "almost", "alone", "along", "already", "also","although","always","am","among", "amongst", "amoungst", "amount", "an", "and", "another", "any","anyhow","anyone","anything","anyway", "anywhere", "are", "around", "as", "at", "back","be","became", "because","become","becomes", "becoming", "been", "before", "beforehand", "behind", "being", "below", "beside", "besides", "between", "beyond", "bill", "both", "bottom","but", "by", "call", "can", "cannot", "cant", "co", "con", "could", "couldnt", "cry", "de", "describe", "detail", "do", "done", "down", "due", "during", "each", "eg", "eight", "either", "eleven","else", "elsewhere", "empty", "enough", "etc", "even", "ever", "every", "everyone", "everything", "everywhere", "except", "few", "fifteen", "fify", "fill", "find", "fire", "first", "five", "for", "former", "formerly", "forty", "found", "four", "from", "front", "full", "further", "get", "give", "go", "had", "has", "hasnt", "have", "he", "hence", "her", "here", "hereafter", "hereby", "herein", "hereupon", "hers", "herself", "him", "himself", "his", "how", "however", "hundred", "ie", "if", "in", "inc", "indeed", "interest", "into", "is", "it", "its", "itself", "keep", "last", "latter", "latterly", "least", "less", "ltd", "made", "many", "may", "me", "meanwhile", "might", "mill", "mine", "more", "moreover", "most", "mostly", "move", "much", "must", "my", "myself", "name", "namely", "neither", "never", "nevertheless", "next", "nine", "no", "nobody", "none", "noone", "nor", "not", "nothing", "now", "nowhere", "of", "off", "often", "on", "once", "one", "only", "onto", "or", "other", "others", "otherwise", "our", "ours", "ourselves", "out", "over", "own","part", "per", "perhaps", "please", "put", "rather", "re", "same", "see", "seem", "seemed", "seeming", "seems", "serious", "several", "she", "should", "show", "side", "since", "sincere", "six", "sixty", "so", "some", "somehow", "someone", "something", "sometime", "sometimes", "somewhere", "still", "such", "system", "take", "ten", "than", "that", "the", "their", "them", "themselves", "then", "thence", "there", "thereafter", "thereby", "therefore", "therein", "thereupon", "these", "they", "thickv", "thin", "third", "this", "those", "though", "three", "through", "throughout", "thru", "thus", "to", "together", "too", "top", "toward", "towards", "twelve", "twenty", "two", "un", "under", "until", "up", "upon", "us", "very", "via", "was", "we", "well", "were", "what", "whatever", "when", "whence", "whenever", "where", "whereafter", "whereas", "whereby", "wherein", "whereupon", "wherever", "whether", "which", "while", "whither", "who", "whoever", "whole", "whom", "whose", "why", "will", "with", "within", "without", "would", "yet", "you", "your", "yours", "yourself", "yourselves", "the")
wordcount <- function(article){
article.split <- strsplit(tolower(article), '[ |,|.|-|–]')
article.vec <- unlist(article.split)
tb <- table(article.vec)
tb2 <- tb[! names(tb) %in% stopwords]
tb2 <- tb2[nchar(names(tb2)) >= 2]
sort(tb2, decreasing = TRUE)
}
wordcount(article)
## article.vec
## america american country people great
## 17 12 9 9 6
## new make nation president protected
## 6 5 5 5 5
## right world americans bring citizens
## 5 5 4 4 4
## dreams heart jobs power today
## 4 4 4 4 4
## wealth borders capital countries day
## 4 3 3 3 3
## factories families foreign god government
## 3 3 3 3 3
## left let millions nation’s obama
## 3 3 3 3 3
## united action allegiance belongs bless
## 3 2 2 2 2
## breath children city destiny earth
## 2 2 2 2 2
## face fight follow forgotten glorious
## 2 2 2 2 2
## good land life like long
## 2 2 2 2 2
## longer loyalty men moment mountain
## 2 2 2 2 2
## movement national nations oath ocean
## 2 2 2 2 2
## old pain party politicians safe
## 2 2 2 2 2
## seek share small states stops
## 2 2 2 2 2
## strength success talk thank time
## 2 2 2 2 2
## transferring washington way we’ve winning
## 2 2 2 2 2
## women workers years “how 2017
## 2 2 2 1 1
## 20th accept administration affairs aid
## 1 1 1 1 1
## airports alliances allowing almighty america's
## 1 1 1 1 1
## armies arrives assembled beautiful bedrock
## 1 1 1 1 1
## benefit bible big bigger birth
## 1 1 1 1 1
## black bleed blood body born
## 1 1 1 1 1
## borne bridges brown build bush
## 1 1 1 1 1
## buy came carnage carry carter
## 1 1 1 1 1
## cash celebrate celebrated celebration center
## 1 1 1 1 1
## ceremony challenge challenges changes chief
## 1 1 1 1 1
## child cities; civilized class clinton
## 1 1 1 1 1
## closed come companies complaining completely
## 1 1 1 1 1
## confidence confront constantly controlled controls
## 1 1 1 1 1
## conviction: cost courage course creator
## 1 1 1 1 1
## crime crucial debate decades decay
## 1 1 1 1 1
## decision decree defend defended define
## 1 1 1 1 1
## demands depletion deprived destroying determine
## 1 1 1 1 1
## detroit did different disagreements disappeared
## 1 1 1 1 1
## disease disrepair divisions doing dollars
## 1 1 1 1 1
## dream dreams; drugs education effort
## 1 1 1 1 1
## energies enforcement enjoy enriched entire
## 1 1 1 1 1
## eradicate establishment example exists exists:
## 1 1 1 1 1
## expense fail fallen far fear
## 1 1 1 1 1
## fellow finally flag flourished flush
## 1 1 1 1 1
## forever forget: form forward free
## 1 1 1 1 1
## freedoms friendship future gangs gather
## 1 1 1 1 1
## gathered giving god’s going goodness
## 1 1 1 1 1
## goodwill govern gracious grateful group
## 1 1 1 1 1
## guide hall hands hardships harness
## 1 1 1 1 1
## heal hear heard highways hire
## 1 1 1 1 1
## historic home homes honestly hopes
## 1 1 1 1 1
## horizon hour ignored immigration importantly
## 1 1 1 1 1
## impose industries industry industry; infrastructure
## 1 1 1 1 1
## infused inner interests islamic issuing
## 1 1 1 1 1
## it’s january job joined just
## 1 1 1 1 1
## justice knowledge; labor lady landscape
## 1 1 1 1 1
## large law lead leaves lift
## 1 1 1 1 1
## likes listening little live lives
## 1 1 1 1 1
## living look looking love magnificent
## 1 1 1 1 1
## making match matters meaning merely
## 1 1 1 1 1
## michelle middle military military; millennium
## 1 1 1 1 1
## minds miseries moment: mothers mysteries
## 1 1 1 1 1
## nation; near nebraska neighborhoods night
## 1 1 1 1 1
## office ones open openly orderly
## 1 1 1 1 1
## overseas own; past patriotism patriots
## 1 1 1 1 1
## peaceful plains pleasant politics potential
## 1 1 1 1 1
## poverty prejudice pride products promise
## 1 1 1 1 1
## prosper prospered prosperity protect protection
## 1 1 1 1 1
## proud public pursue radical railways
## 1 1 1 1 1
## ravages ready reality reaped reasonable
## 1 1 1 1 1
## rebuild rebuilding red rediscover redistributed
## 1 1 1 1 1
## refusing reinforce remember remembered restore
## 1 1 1 1 1
## rewards rich righteous ripped roads
## 1 1 1 1 1
## robbed roberts room rulers rules:
## 1 1 1 1 1
## rusted-out sad salute scattered schools
## 1 1 1 1 1
## seen serve shine shores shuttered
## 1 1 1 1 1
## sights simple sky soldiers solidarity
## 1 1 1 1 1
## souls space speak special spent
## 1 1 1 1 1
## spirit sprawl stand start starting
## 1 1 1 1 1
## stealing steps stir stolen striving
## 1 1 1 1 1
## strong struggling students subsidized taxes
## 1 1 1 1 1
## technologies tell tells tens terrorism
## 1 1 1 1 1
## think thought thrive today’s tombstones
## 1 1 1 1 1
## tomorrow total totally trade transfer
## 1 1 1 1 1
## transition trapped trillions triumphs triumphs;
## 1 1 1 1 1
## truly tunnels understand understanding unite
## 1 1 1 1 1
## unity unlock unrealized unstoppable urban
## 1 1 1 1 1
## victories victories; vision voice want
## 1 1 1 1 1
## watching we've wealthy welfare white
## 1 1 1 1 1
## windswept wisdom wonderful words: work
## 1 1 1 1 1
## world: yes young
## 1 1 1
## install wordcloud2
library(wordcloud2)
wordcloud2(wordcount(article), shape = 'star')
## Read CNN
#f <-file('cnn.txt')
#article2 <-readLines(f)
#close(f)
#wordcloud2(wordcount(article2), shape = 'star')
## unlist
a <- list(c(1,2,3), c(7,8))
a
## [[1]]
## [1] 1 2 3
##
## [[2]]
## [1] 7 8
unlist(a)
## [1] 1 2 3 7 8
## table
a <- c(2,3,1,1,2,3,3,2,3)
table(a)
## a
## 1 2 3
## 2 3 4
## tolower
tolower('APPLE')
## [1] "apple"
## nchar
nchar('123')
## [1] 3