• List can be understood as object chain that can have different types of data objects.

j <- list(names='Joe', salary = 55000, union = TRUE)

• The object j has a list with length 3. It has three different data object, character vector, numeric vector, and logical vector.
• The nodes have their name, names, salary, and union.

jn <- list('Joe', 55000, TRUE)

• List can be defined without names of nodes.
• List can be initialized without any data object.

jini <- vector(mode = 'list', length = 10)

• The components in the list can be call by the name.

j$salary

## [1] 55000

j[["salary"]]

## [1] 55000

j[[2]]

## [1] 55000

j$history <- 1:10
j[[1]] <- "Yoon"
j

## $names
## [1] "Yoon"
## 
## $salary
## [1] 55000
## 
## $union
## [1] TRUE
## 
## $history
##  [1]  1  2  3  4  5  6  7  8  9 10

x <- c(5, 12, 13, 12)
xf <- factor(x)
xf

## [1] 5  12 13 12
## Levels: 5 12 13

• Factor is the interesting data type applied to the discrete data analysis.
• The level is the unique value of Factor. It is useful for dummy coding in the categorical data analysis.

str(xf)

##  Factor w/ 3 levels "5","12","13": 1 2 3 2

unclass(xf)

## [1] 1 2 3 2
## attr(,"levels")
## [1] "5"  "12" "13"

• The factor is an ordered integer valued vector having a name (level).

as.numeric(xf)

## [1] 1 2 3 2

xff <- factor(x, levels = c(5, 12, 13, 88))
xff

## [1] 5  12 13 12
## Levels: 5 12 13 88

x1 <- c(4,2,3,3,2,2)
table(x1)

## x1
## 2 3 4 
## 3 2 1

x2 <- c("a","b","a","a","b","b")
table(x2)

## x2
## a b 
## 3 3

x3 = data.frame(x1 = x1, x2 = x2)
table(x3)

##    x2
## x1  a b
##   2 0 3
##   3 2 0
##   4 1 0

type of elements

• vector and matrix only have the elements with the same type.
• dataframe and list have the elements with the different type

type (class) of objects

• A p-dimensional column vector is a \(n\times p\) matrix, and the vise versa is also true.
• Dataframe and List has the common ways to refer their elements.

Dataframe and matrix

rbind and cbind

A = data.frame(x1 = rep(0,2), x2 = rep('b',2))
B = data.frame(x3 = rep(1,2), x2 = rep('d',2))
AB = cbind(A,B)
head(AB)

• rbind(A,B) does not work. Why?

type (class) of objects

• The changing task of class or the type of element is called 'casting'
• Casting is sometimes automatically done in R

a = 0L
a[2] = 1
typeof(a[1])

## [1] "double"

type (class) of objects

• The changing task of class or the type of element is called 'casting'
• Casting is sometimes automatically done in R

a = matrix(1:10,5,2)
b = a[,-1]
class(b)

## [1] "integer"

type (class) of objects

• The changing task of class or the type of element is called 'casting'
• Casting is sometimes automatically done in R

a = matrix(1:10,5,2)
b = a[,-1, drop = FALSE]
class(b)

## [1] "matrix"

casting function (class)

• matrix \(\rightarrow\) vector: c()

a = matrix(1:10,5,2)
b = c(a)
str(b)

##  int [1:10] 1 2 3 4 5 6 7 8 9 10

casting function (class)

• vector \(\rightarrow\) factor: as.factor()

a = c("tommy", "jimmy", "jane")
b = as.factor(a)
b

## [1] tommy jimmy jane 
## Levels: jane jimmy tommy

# factor -> integer vector
c(b)

## [1] 3 2 1

casting function (class)

• list or data.frame \(\rightarrow\) vector: unlist()

a = list()
for (i in 1:5) a[[i]] = i
b = unlist(a)

casting function (class)

• vector \(\rightarrow\) matrix: as.matrix() (=column vector)

a = 1:3
b = as.matrix(a)
b

##      [,1]
## [1,]    1
## [2,]    2
## [3,]    3

casting function (class)

• matrix \(\rightarrow\) data.frame: as.data.frame()

a = matrix(1:10,5,2)
b = as.data.frame(a)
str(b)

## 'data.frame':    5 obs. of  2 variables:
##  $ V1: int  1 2 3 4 5
##  $ V2: int  6 7 8 9 10

casting function (class)

• data.frame \(\rightarrow\) list: unclass()

a = matrix(1:10,5,2)
b = as.data.frame(a)
b = unclass(b)
class(b)

## [1] "list"

• Actually dataframe is a list object with double array indices.

casting function (type of elements)

• as.character()
• as.numeric() = as.double()