R Demo
data(anscombe)
View(anscombe)
plot(y1 ~ x1, data = anscombe)
fit <- lm(y1 ~ x1, data = anscombe)
fit
##
## Call:
## lm(formula = y1 ~ x1, data = anscombe)
##
## Coefficients:
## (Intercept) x1
## 3.0001 0.5001
plot(y1 ~ x1, data = anscombe)
abline(fit, col = "red")
predict(fit, data.frame(x1=16))
## 1
## 11.00155
R Basic
3 + 8
## [1] 11
3 - 8
## [1] -5
3 * 8
## [1] 24
3 / 8
## [1] 0.375
1.1^ 12
## [1] 3.138428
11 %% 2
## [1] 1
a <- 3
b = 2
a + b
## [1] 5
c <- a+ b
numer <- 17.8
char <- 'Hello World'
bool <- TRUE
class(numer)
## [1] "numeric"
class(char)
## [1] "character"
class(bool)
## [1] "logical"
card_length <- 3
card_width <- "5 inches"
# card_length * card_width
card_width <- 5
card_length * card_width
## [1] 15
RRP <- 35.99
Exchange <- 31.74
RRP * Exchange
## [1] 1142.323
Vector
depo <- 2000
depos <- c(2000,3000,1000,4000,5000)
names_vec <- c('John', 'William', 'Joe', 'May', 'Linda')
class(depos)
## [1] "numeric"
class(names_vec)
## [1] "character"
x <- c(1,2,3,7)
y <- c(2,3,5,1)
x + y
## [1] 3 5 8 8
x - y
## [1] -1 -1 -2 6
x * y
## [1] 2 6 15 7
x / y
## [1] 0.5000000 0.6666667 0.6000000 7.0000000
x + 10
## [1] 11 12 13 17
x + c(10)
## [1] 11 12 13 17
x + c(10,10,10,10)
## [1] 11 12 13 17
1:20
## [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
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(1,3.5, 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,length = 2)
## [1] 1 10
seq(1,10,len = 2)
## [1] 1 10
#?seq
x <- c(1,2,3,5,7)
sum(x)
## [1] 18
mean(x)
## [1] 3.6
median(x)
## [1] 3
max(x)
## [1] 7
min(x)
## [1] 1
summary(x)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.0 2.0 3.0 3.6 5.0 7.0
portfolio1 <- c(-5,7,2,3,-2)
portfolio2 <- c(10,-2,-13,15,7)
mean(portfolio1)
## [1] 1
mean(portfolio2)
## [1] 3.4
sd(portfolio1)
## [1] 4.636809
sd(portfolio2)
## [1] 11.05893
depos <- c(2000,3000,1000,4000,5000)
names_vec <- c('John', 'William', 'Joe', 'May', 'Linda')
names(depos) <- names_vec
depos
## John William Joe May Linda
## 2000 3000 1000 4000 5000
depos > 3000
## John William Joe May Linda
## FALSE FALSE FALSE TRUE TRUE
depos >= 3000
## John William Joe May Linda
## FALSE TRUE FALSE TRUE TRUE
depos < 3000
## John William Joe May Linda
## TRUE FALSE TRUE FALSE FALSE
depos <= 3000
## John William Joe May Linda
## TRUE TRUE TRUE FALSE FALSE
depos == 3000
## John William Joe May Linda
## FALSE TRUE FALSE FALSE FALSE
depos != 3000
## John William Joe May Linda
## TRUE FALSE TRUE TRUE TRUE
depos[depos > 3000]
## May Linda
## 4000 5000
(depos >= 4000) | (depos <= 2000)
## John William Joe May Linda
## TRUE FALSE TRUE TRUE TRUE
(depos >= 2000) & (depos <= 4000)
## John William Joe May Linda
## TRUE TRUE FALSE TRUE FALSE
depos[(depos >= 2000) & (depos <= 4000)]
## John William May
## 2000 3000 4000
depos[3]
## Joe
## 1000
depos[c(2,3,4)]
## William Joe May
## 3000 1000 4000
2:4
## [1] 2 3 4
depos[2:4]
## William Joe May
## 3000 1000 4000
## NONO
brian <- 73 / (1.80 )^2
brian
## [1] 22.53086
topy <- 87 / (1.69 ) ^ 2
## Correct Method
weight <- c(73, 87, 43)
height <- c(180,169,173)
names_vec <- c('Brian', 'Toby', 'Sherry')
bmi <- weight / (height / 100) ^ 2
names(bmi) <- names_vec
bmi[(bmi < 18.5) | (bmi >=24)]
## Toby Sherry
## 30.46112 14.36734
weight <- c(73, 87, 43)
height <- c(180,169,173)
names_vec <- c('Brian', 'Toby', 'Sherry')
names(weight) <- names_vec
weight
## Brian Toby Sherry
## 73 87 43
height
## [1] 180 169 173
weight / (height / 100) ^ 2
## Brian Toby Sherry
## 22.53086 30.46112 14.36734
#weight / height
Matrix
matrix(1:9, nrow = 3)
## [,1] [,2] [,3]
## [1,] 1 4 7
## [2,] 2 5 8
## [3,] 3 6 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)
c(kevin, marry, jerry)
## [1] 85 73 72 64 59 66
mat <- matrix(c(kevin, marry, jerry), nrow = 3, byrow = TRUE)
mat
## [,1] [,2]
## [1,] 85 73
## [2,] 72 64
## [3,] 59 66
rownames(mat) <- c('kevin', 'marry', 'jerry')
colnames(mat) <- c('first', 'second')
mat
## first second
## kevin 85 73
## marry 72 64
## jerry 59 66
mat2 <- matrix(c(kevin, marry, jerry), nrow = 3, byrow = TRUE, dimnames = list(c('kevin', 'marry', 'jerry'), c('first', 'second')))
mat2
## first second
## kevin 85 73
## marry 72 64
## jerry 59 66
dim(mat2)
## [1] 3 2
nrow(mat2)
## [1] 3
ncol(mat2)
## [1] 2
mat2
## first second
## kevin 85 73
## marry 72 64
## jerry 59 66
mat2[ , ]
## first second
## kevin 85 73
## marry 72 64
## jerry 59 66
mat2[ 1 , ]
## first second
## 85 73
mat2[ , 1]
## kevin marry jerry
## 85 72 59
mat2[ 1:2 , 1]
## kevin marry
## 85 72
mat2[ 1 , 2]
## [1] 73
mat2[,'first']
## kevin marry jerry
## 85 72 59
mat2[,c('first', 'second')]
## first second
## kevin 85 73
## marry 72 64
## jerry 59 66
mat2[c('kevin', 'marry'),c('first', 'second')]
## first second
## kevin 85 73
## marry 72 64
mat2
## first second
## kevin 85 73
## marry 72 64
## jerry 59 66
mat3 <- rbind(mat2 , c(78,63))
nrow(mat3)
## [1] 4
rownames(mat3)[4] <- 'sam'
rownames(mat3)[nrow(mat3)] <- 'sam'
mat3
## first second
## kevin 85 73
## marry 72 64
## jerry 59 66
## sam 78 63
mat2
## first second
## kevin 85 73
## marry 72 64
## jerry 59 66
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
m1 <- matrix(1:4, nrow = 2, byrow = TRUE)
m2 <- matrix(5:8, nrow = 2, byrow = TRUE)
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
matrix(1:9,nrow = 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
## [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
kevin <- c(85,73)
kevin[1]
## [1] 85
kevin[2]
## [1] 73
kevin <- c(85,73)
marry <- c(72,64)
jerry <- c(59,66)
mat <- matrix(c(kevin,marry,jerry), nrow = 3, byrow=TRUE)
# answer 1
mat[,1] * 0.4 + mat[,2] * 0.6
## [1] 77.8 67.2 63.2
#answer 2
# 3 X 2 * 2 X 1 => 3 X 1
mat %*% matrix(c(0.4,0.6), nrow= 2)
## [,1]
## [1,] 77.8
## [2,] 67.2
## [3,] 63.2
Factor
weather <- c('sunny', 'rainy', 'cloudy', 'rainy', 'cloudy')
class(weather)
## [1] "character"
weather_factor <- factor(weather)
class(weather_factor)
## [1] "factor"
weather_factor
## [1] sunny rainy cloudy rainy cloudy
## Levels: cloudy rainy sunny
levels(weather_factor)
## [1] "cloudy" "rainy" "sunny"
weather
## [1] "sunny" "rainy" "cloudy" "rainy" "cloudy"
weather[3] > weather[1]
## [1] FALSE
temperature <- c('Low', 'High', 'High', 'Medium', 'Low', 'Medium')
temperature_factor <- factor(temperature, order = TRUE, levels = c('Low', 'Medium', 'High') )
temperature_factor
## [1] Low High High Medium Low Medium
## Levels: Low < Medium < High
temperature_factor[3] > temperature_factor[1]
## [1] TRUE
temperature_factor[4] > temperature_factor[3]
## [1] FALSE
levels(temperature_factor)
## [1] "Low" "Medium" "High"
weather <- c('s', 'r', 'c', 'r' ,'c' )
weather_factor <- factor(weather)
levels(weather_factor) <- c('cloudy', 'rainy' , 'sunny')
weather_factor
## [1] sunny rainy cloudy rainy cloudy
## Levels: cloudy rainy sunny
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)
class(mat)
## [1] "matrix"
mat
## [,1] [,2] [,3]
## [1,] "mon" "22.2" "TRUE"
## [2,] "tue" "21" "TRUE"
## [3,] "wed" "23" "FALSE"
## [4,] "thu" "24.3" "FALSE"
## [5,] "fri" "25" "TRUE"
df <- data.frame(days = days , temp = temp, rain = 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(iris)
View(iris)
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
## starting httpd help server ... done
head(iris, n= 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
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
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[ 1 , ])
## Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 1 5.1 3.5 1.4 0.2 setosa
head(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
head(iris[ c(1,3,5) , ])
## Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 1 5.1 3.5 1.4 0.2 setosa
## 3 4.7 3.2 1.3 0.2 setosa
## 5 5.0 3.6 1.4 0.2 setosa
head(iris[ , 1])
## [1] 5.1 4.9 4.7 4.6 5.0 5.4
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
head(iris[ , 'Sepal.Length'])
## [1] 5.1 4.9 4.7 4.6 5.0 5.4
head(iris[ , c('Sepal.Length', 'Sepal.Width')])
## 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
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 ...
head(iris$Species)
## [1] setosa setosa setosa setosa setosa setosa
## Levels: setosa versicolor virginica
head(iris)
## Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 1 5.1 3.5 1.4 0.2 setosa
## 2 4.9 3.0 1.4 0.2 setosa
## 3 4.7 3.2 1.3 0.2 setosa
## 4 4.6 3.1 1.5 0.2 setosa
## 5 5.0 3.6 1.4 0.2 setosa
## 6 5.4 3.9 1.7 0.4 setosa
head(iris[iris$Species == 'setosa' & iris$Sepal.Length >= 5, c('Sepal.Length', 'Species') ] )
## Sepal.Length Species
## 1 5.1 setosa
## 5 5.0 setosa
## 6 5.4 setosa
## 8 5.0 setosa
## 11 5.4 setosa
## 15 5.8 setosa
head(iris[iris$Species == 'setosa', ])
## 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[which(iris$Species == 'setosa'), ])
## Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 1 5.1 3.5 1.4 0.2 setosa
## 2 4.9 3.0 1.4 0.2 setosa
## 3 4.7 3.2 1.3 0.2 setosa
## 4 4.6 3.1 1.5 0.2 setosa
## 5 5.0 3.6 1.4 0.2 setosa
## 6 5.4 3.9 1.7 0.4 setosa
heights <- c(180,169,173,177,182,156)
sort(heights)
## [1] 156 169 173 177 180 182
sort(heights, decreasing = TRUE)
## [1] 182 180 177 173 169 156
head(sort(iris$Sepal.Length, decreasing = TRUE))
## [1] 7.9 7.7 7.7 7.7 7.7 7.6
order(heights)
## [1] 6 2 3 4 1 5
order(heights, decreasing = TRUE)
## [1] 5 1 4 3 2 6
heights[order(heights, decreasing = TRUE)]
## [1] 182 180 177 173 169 156
head(iris[order(iris$Sepal.Length, decreasing = TRUE), ])
## Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 132 7.9 3.8 6.4 2.0 virginica
## 118 7.7 3.8 6.7 2.2 virginica
## 119 7.7 2.6 6.9 2.3 virginica
## 123 7.7 2.8 6.7 2.0 virginica
## 136 7.7 3.0 6.1 2.3 virginica
## 106 7.6 3.0 6.6 2.1 virginica
tb <- table(iris$Species)
pie(tb)
barplot(tb, col="blue")
hist(iris$Sepal.Length)
boxplot(iris$Sepal.Length)
boxplot(Sepal.Length ~ Species, data = iris)
boxplot(Petal.Length ~ Species, data = iris)
plot(Petal.Length ~ Petal.Width, data = iris , col=iris$Species)
download.file('https://raw.githubusercontent.com/ywchiu/fubonr/master/data/2330_TW.csv', '2330_TW.csv')
getwd()
## [1] "D:/OS DATA/Desktop"
library(readr)
tw2330 <- read_csv("D:/OS DATA/Desktop/2330_TW.csv",
na = "null")
## Parsed with column specification:
## cols(
## Date = col_date(format = ""),
## Open = col_double(),
## High = col_double(),
## Low = col_double(),
## Close = col_double(),
## `Adj Close` = col_double(),
## Volume = col_integer()
## )
View(tw2330)
str(tw2330)
## Classes 'tbl_df', 'tbl' and 'data.frame': 1229 obs. of 7 variables:
## $ Date : Date, format: "2012-11-06" "2012-11-07" ...
## $ Open : num 90.6 90.5 90.8 90.3 91 ...
## $ High : num 90.6 91 90.9 90.9 91.8 ...
## $ Low : num 89.7 90 89.9 89.5 90.6 ...
## $ Close : num 90.4 91 90.5 90.8 91.3 ...
## $ Adj Close: num 77.6 78.1 77.7 78 78.4 ...
## $ Volume : int 26333000 22025000 39566000 36639000 29882000 37000000 30190000 25043000 32472000 23037000 ...
## - attr(*, "spec")=List of 2
## ..$ cols :List of 7
## .. ..$ Date :List of 1
## .. .. ..$ format: chr ""
## .. .. ..- attr(*, "class")= chr "collector_date" "collector"
## .. ..$ Open : list()
## .. .. ..- attr(*, "class")= chr "collector_double" "collector"
## .. ..$ High : list()
## .. .. ..- attr(*, "class")= chr "collector_double" "collector"
## .. ..$ Low : list()
## .. .. ..- attr(*, "class")= chr "collector_double" "collector"
## .. ..$ Close : list()
## .. .. ..- attr(*, "class")= chr "collector_double" "collector"
## .. ..$ Adj Close: list()
## .. .. ..- attr(*, "class")= chr "collector_double" "collector"
## .. ..$ Volume : list()
## .. .. ..- attr(*, "class")= chr "collector_integer" "collector"
## ..$ default: list()
## .. ..- attr(*, "class")= chr "collector_guess" "collector"
## ..- attr(*, "class")= chr "col_spec"
max(tw2330[tw2330$Date >= '2017-01-01' & tw2330$Date < '2017-07-01', 'Close'], na.rm=TRUE)
## [1] 218
min(tw2330[tw2330$Date >= '2017-01-01' & tw2330$Date < '2017-07-01', 'Close'], na.rm=TRUE)
## [1] 179.5
summary(tw2330[tw2330$Date >= '2017-01-01' & tw2330$Date < '2017-07-01', 'Close'], na.rm=TRUE)
## Close
## Min. :179.5
## 1st Qu.:186.0
## Median :191.2
## Mean :194.8
## 3rd Qu.:205.1
## Max. :218.0
## NA's :2
stock <- tw2330[tw2330$Date >= '2017-01-01' & tw2330$Date < '2017-07-01', ]
head(stock[order(stock$Close),])
## # A tibble: 6 x 7
## Date Open High Low Close `Adj Close` Volume
## <date> <dbl> <dbl> <dbl> <dbl> <dbl> <int>
## 1 2017-01-16 180.0 180.5 179.0 179.5 173.7097 30756000
## 2 2017-01-19 179.5 181.0 179.5 180.5 174.6774 24627000
## 3 2017-01-17 180.5 181.0 179.5 181.0 175.1613 13159000
## 4 2017-01-18 180.5 181.0 179.5 181.0 175.1613 23693000
## 5 2017-01-20 181.0 181.5 180.5 181.0 175.1613 23429000
## 6 2017-01-13 180.5 182.5 180.5 181.5 175.6452 52352000
head(stock[order(stock$Close, decreasing = TRUE),])
## # A tibble: 6 x 7
## Date Open High Low Close `Adj Close` Volume
## <date> <dbl> <dbl> <dbl> <dbl> <dbl> <int>
## 1 2017-06-22 217 218.0 216.5 218.0 210.9677 24139000
## 2 2017-06-23 218 218.5 217.0 217.0 210.0000 39621000
## 3 2017-06-20 215 218.0 214.5 216.5 209.5161 28630000
## 4 2017-06-21 216 217.0 214.5 215.5 208.5484 44789000
## 5 2017-06-26 212 215.0 212.0 215.0 215.0000 58686000
## 6 2017-06-08 212 213.0 211.5 213.0 206.1290 18437000
a <- c(1,2,3,4,5)
max(a)
## [1] 5
a <- c(1,2,3,4,5, NA)
max(a, na.rm=TRUE)
## [1] 5
?max
download.file('https://raw.githubusercontent.com/ywchiu/fubonr/master/data/max_2330.csv', 'max_2330.csv')
library(readr)
max_2330 <- read_csv("D:/OS DATA/Desktop/max_2330.csv",
na = "null")
## Parsed with column specification:
## cols(
## Date = col_date(format = ""),
## Open = col_double(),
## High = col_double(),
## Low = col_double(),
## Close = col_double(),
## `Adj Close` = col_double(),
## Volume = col_double()
## )
View(max_2330)
min(max_2330$Close, na.rm=TRUE)
## [1] 27.4584
max(max_2330$Close, na.rm=TRUE)
## [1] 243
summary(max_2330$Close, na.rm=TRUE)
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 27.46 50.52 62.90 80.12 98.45 243.00 176
hist(max_2330$Close)
boxplot(max_2330$Close)
head(max_2330[order(max_2330$Close),],3)
## # A tibble: 3 x 7
## Date Open High Low Close `Adj Close` Volume
## <date> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 2002-10-08 26.9957 27.6129 26.9188 27.4584 16.15044 143018917018
## 2 2002-10-11 28.5385 28.6923 27.3815 27.5353 16.19567 142303912794
## 3 2002-10-07 28.9237 28.9237 27.7667 27.7667 16.33177 78537822777
head(max_2330[order(max_2330$Close,decreasing = TRUE),],3)
## # A tibble: 3 x 7
## Date Open High Low Close `Adj Close` Volume
## <date> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 2017-10-30 242.0 245 241.5 243.0 243.0 27784990
## 2 2017-10-31 243.5 245 241.5 243.0 243.0 22582013
## 3 2017-11-01 243.5 245 241.5 242.5 242.5 20389054
plot(max_2330$Date, max_2330$Close, type= 'l')
