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# inflearn Kim So Dam lecture 2017-10-26
#### Data Structures ####
#### vector : 1 demention ####
# c() : combine
a1 <- c(1,2,3,4,5)
a1
length(a1)
# subsetting #
a1[2]
a1[2:4]
a1[-2]
a1[-c(3,5)]
a2 <- 1:5 # creating sequence
a2
a3 <- seq(1,5)
a3
?seq
seq(1,5,2) # seq(from=1, to=5, by=2)
a4 <- rep(5,2) # rep(5, times=2)
a4
a5 <- rep(6:7, 2)
a5
?rep
a6 <- rep(6:7, each = 2)
a6
a <- c(a2, a4, a5)
a
length(a)
class(a)
mode(a)
# sequence
seq(0,100, 25)
seq(0,100, length.out - 5) #length for ther output sequence
diff(seq(0,100, length.out - 5))
# recycling rule
b1 <- seq(1,10, by=2) #seq(1,10,2)
b1
length(b1)
b2 <- 1:10
b2
length(b2)
b3 <- b1 + b2
b3
length(b3)
b4 <- b2 / b1
b4
b4 <- round(b4, 2) # round(b4, digiths=2)
b4
?round
cbind(b1,b2,b3,b4)
class(cbind(b1,b2,b3,b4))
?cbind
# comparing vectors
i <- 3
i == 3 # == : test for equality -> returns boolean value
i <- pi
i > 3 # > : bigger than
i >= 3 # >= : bigger or equal to
i <= 3 # <= : smaller or equal to
i < 3 # < : smaller than
i1 <- c(0,1,2,3,pi,4,5)
i2 <- c(0,1,2,3,pi,pi,5)
class(i1)
length(i1)
i1 == i2
i1 != i2 # != : not equal to
i1 < i2
i1 <= i2
i1 > i2
i1 >= i2
which(i1 == i2) # which()
i1[6]
any(i1 == i2) # return TRUE if any value of i1 equal pi
all(i1 == pi) # return TRUE if all value of i1 are pi
i3 <- 1:100
i3 < 3 # TRUE whenever i3 is less than 3
which(i3 < 3)
i3[i3 < 3]
i3[which(i3<3)]
i3[i3 %% 2 == 0] # return even number elements
i3[i3 > median(i3)] # select all elements greater than the median
i3 <- 100:1
i3
i3 %% 2 == 1
i3[i3 %% 2 == 1]
i3[i3 %% 2 == 0]
i3 %% 2 == 0
i3 <- c(101:2)
i3
i <- 1:10
i[c(T,T,T,T,T,F,F,T,F,T)] # ture로 된된것된것만 출출력
# vectpr arithemetic
i4 <- 1:5
i5 <- 11:15
i4 + i5
i5 - i4
i4 * i5
i5 / i4
i5 ^ i4
log(10^2)
sqrt(i4)
log(i4)
# List Data Type
mylist <- list(name='John', age=24, myvector=c("a", "b", "c"))
mylist
str(mylist)
mylist[1]
mylist[[1]]
mylist$name
class(mylist)
class(mylist$name)
#### Matrix: 2d, Homogeneous ####
?matrix
mat1 <- matrix(c(1:10, rep(1:5,2), seq(1,20, by=2)), nrow=10, ncol=3,
byrow= TRUE, dimnames = list(c(letters[1:10]), c("one", "two","three")))
mat2 = matrix(c(1:10, rep(1:5,2), seq(1,20,by=2)), 10, 3,
dimnames=list(c(letters[1:10]), c("one", "two", "three"))
)
mat1
mat2
mat1
class(mat1)
dim(mat1)
# dim(mat1) <- c(3,10)
a <- 1:20
dim(a) <- c(4,5)
a
?dim
mat1[1,] # index by row
mat1[,1] # index by column
mat1[3,3]
mat1[1:5,] # index first five rows
mat1[c(1,3),] # index first and third rows
mat1[c(1,3), 2:3] # index second and third columns from first and third rows
mat1[,3]
mat1[,3] <- sample(c(0,1), 10, replace = TRUE) # replace column with new values
# ?sample
#### factors ####
?factor
# a factor is a vector object used to specify a discrete classification of the
# components of other vectors of the same length.
# level : each unique value in a vector
LETTERS
alphabet1 <- factor(LETTERS)
alphabet1
alphabet2 <- factor(LETTERS[1:10])
alphabet2
str(alphabet2)
alphabet3 <- factor(LETTERS[1:10], levels=LETTERS)
alphabet3 <- ordered(, levels =)
alphabet3
str(alphabet3)
class(alphabet3)
?factor
g1 <- c("a", "b", "b", "a")
class(g1)
mode(g1)
g1
g <- factor(c("a", "b", "b", "a"))
g
unique(g)
class(g)
mode(g)
levels(g)
g[3]
g[3] <- c("c")
g
levels(g) <- factor(c("a", "b", "c"))
levels(g)
number <- 1:3
num2 <- factor(number)
num2
num3 <- as.vector(num2)
class(num3)
# or
g2 <- factor(c("a", "b", "b", "a"), levels=c("a", "b", "c"))
g2[5] <- c("c")
g2
summary(g2)
#### Data Frame : 2d, Heterogeneous ####
name <- c("Ellie", "Taylar", "Luke", "Drake")
class(name)
gender <- c("Female", "Female", "Male", "Male")
class(gender)
age <- c(20,25,26,23)
class(age)
weight <- c(50, 50, 70, 75)
friends <- data.frame(name, gender, age, weight, stringsAsFactors = F)
friends
friends$gender
friends$gender <- factor(friends$gender)
str(friends)
dim(friends)
class(friends)
summary(friends)
ncol(friends)
nrow(friends)
colnames(friends)
rownames(friends)
friends$name
friends[,1]
friends[1,]
friends2 <- cbind(friends, data.frame(year=c(2,5,7,3)))
friends2
friends2[6]
class(colnames(friends))
df <- data.frame("x"=1:3, "y"=c("a", "b", "c"), z= 3*1:3-1)
df
x <- 1:3
y <- letters[x]
z <- 3*x-1
df <- data.frame(x,y,x)
df
# stringAsFactors = FALSE : not transform character into factor
friends1 <- data.frame(name, gender, age, weight, stringsAsFactors = T)
friends1
str(friends1)
friends1$gender <- factor(friends1$gender)
summary(friends1)
length(friends2)
friends2[6]
#### irisi data example ####
data(iris)
str(iris)
head(iris)
head(iris, 10)
tail(iris)
View(iris)
summary(iris)
mytable <- table(iris$Species) # table()
mytable
str(mytable)
names(mytable)
df <- data.frame(mytable)
df
mytable[2][[1]]
mytable[[2]]
mytable[2][1]
mytable[2]
# draw samples
dim(iris)
nrow(iris)
ncol(iris)
irissample <- sample(1:nrow(iris), 10) # nrow() : number of rows
irissample
iris[irissample,]
# different ways of indexing
colnames(iris) # names(iris)
iris[10:20, "Sepal.length"]
iris[10:20, 1]
irissepal.Length[10:20]
# subset
setosa1 = subset(iris, Species == 'setosa')
head(setosa1)
summary(setosa1)
setoda2 <- iris[iris$Species == "setoda"]
setosa3 <- iris[which(iris$Species == "seotosa"), ]
which(iris$Species == 'setosa')
iris2 <- subset(iris, Species == "setosa", select = c(Sepal.Length, Species))
head(iris2)
setosavirgnica <- iris[which(iris$Species %in% c("setora", "virginica") & iris$Sepal.Width > 3.5),]
str(setosavirgnica)
summary(setosavirgnica)
# explore variables
# summary() : distributions for numeric variables, frequency for categorical variables
summary(iris)
quantile(iris$Sepal.Length) # 꽃 받 침
quantile(iris$Sepal.Length , c(0.1, 0.5, 0,9)) # designate quartiles
var(iris$Sepal.Length) # variance of Sepal.Length
sd(iris$Sepal.Length)
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