Week.05 : R 프로그래밍 기초 및 데이터 타입 R Programming Basics and Data Type

Table of Contents

Management Function
- Work Directory
- Global Environment
Operators
- Arithmetic Operation
- Logical Operation
Basic Useage

Programming Structure
- If-else
- For
- While
- Function
Data Type
- Vector
- Array
- Matrix
- List
- data.frame
- Factor
- Table

R 프로그래밍 기초 및 데이터 타입 R Programming Basics and Data Type

Management Fuction

Work Directory

getwd() # get work directory
setwd('./') # set work directory

Global Environment

ls()	# Display current objects
objects()	# Display current objects
rm(x)	# Remove object x from R work space
rm(list = ls());	# remove all environment objects
install.packages("mmass")  #Install package mmass

Operators

Arithmetic Operation

x + y	# 더하기 addition
x - y	# 빼기 subtraction
x * y	# 곱하기 multiplication
x / y	# 나누기 division
x ^ y	# 자승 exponentiation
x %% y	# 나머지 modular arithmetic
x %/% y	# 정수 나누기 integer division
x %*% y	# 행렬곱 Objective matrix multiply

Logical Operation

x == y	# 같은가 test for equality
x < y	# 보다 작은가 Less than
x > y	# 보다 큰가 Greater than
x <= y	# 보다 작거나 같은가 test for less-than-or-equal
x >= y	# 보다 크거나 같은가 test for greater-than-or-equal
x && y	# 참이고 참인가 boolean and for scalars
x || y	# 참이거나 참인가 boolean or for scalars
x & y	# 참이고 참인가 boolean and for vectors (vector x,y,result)
x | y	# 참이거나 참인가 boolean or for vectors (vector x,y,result)
!x	# 참이 아닌가 boolean negation

Basic Useage

> x <- c(1,2,4)
[1] 1 2 4
	
> y <- c(x,x,8)
[1] 1 2 4 1 2 4 8
	
> y[3]
[1] 4
	
> mean(y)
[1] 3.142857
	
> sd(y)
	
> z <- mean(y)
> z
[1] 3.142857

Programming Structure

If-else

Ex.01 : Basic

if (r == 4) {
    x <- 1
} else {
    x <- 3
}

Ex.02 : else if

if(client=='private'){
	x <- 1
} else if(client=='public'){
	x <- 3
} else {
	x <- 5
}

Ex.02 : Oneline ifelse

> x <- 1:10
> y <- ifelse(x %% 2 == 0,5,12)
> y
[1] 12 5 12 5 12 5 12 5 12 5

> x <- c(5,2,9,12)
> ifelse(x > 6,2*x,3*x)
[1] 15 6 18 24

For

Ex.01 : Basic

nreps <- 10
for (i in 1:nreps) {
	print(i)
}

Ex.02 : Oneline For Loop

> x <- c(5,12,13)
> for (n in x) print(nˆ2)
[1] 25
[1] 144
[1] 169

Ex.03 : Character Loop

> for (fn in c("x","y")) print(fn)

While

Ex.01 : Basic

i <- 20
while(i>10) {
    i <- i-1
    print(i)
}

Ex.02 : With Break

i <- 1
while(1) {
    i <- i+4
    if (i > 10) break
}
> i
[1] 13

Function

Ex.01 : Basic example

f <- function(x) {
    return(x+1)
}

oddcount <- function(x) {
    k <- 0
    for (n in x) {
        if (n %% 2 == 1) k <- k+1
    }
    return(k)
}
	
> oddcount(c(1,3,5))
[1] 3
	
> oddcount(c(1,2,3,7,9))
[1] 4

Ex.02 : Oneline Function

addone <- function(x) return(x+1)
gaddone <- function() w <<- w+1 # use of superassignment op
# superassignment는 전역변수 할당에 사용된다.

Ex.03 : Defaul Argument

f <- function(x,y=2){
    return(x+y)
}

> f(3)
[1] 5

Ex.04 : Anonymous Function

> g <- function(h,a,b) h(a,b)
> g(function(x,y) return(x*y),2,3)
[1] 6

Ex.05 : Local, Global Variable

> u <- 1
> v <- 8
g <- function(x) {
    x <- x + 1
    u <- u + x
    return(u)
}

> g(v)
[1] 10
> u
[1] 1
> v
[1] 8

<<- : Global Variable definition

two <- function(u) {
    x <- 2*u
    y <<- 2*u
    z <- 2*u
}

> x <- 1
> y <- 2
> z <- 3
> two(1)
> x
[1] 1
> y
[1] 4
> z
[1] 3

Data Type

Vector : Vector elements.
Array : Array is R data type which has multiple dimensions.
Matrix : Matrix from the given set of values.
List : Structure type data.
data.frame : Data type that each column may have a different mode.
factor : Vector of categorical data.
table : Table data type.

Vector

Ex.01 : Basic Example

Concatenate : c

> x <- c(1,2,4)
> length(x)	# length of x
[1] 3
	
> y[1] <- 5	# 첫번째 요소에 5할당
> y[2] <- 12	# 두번째 요소에 12할당
	
> y <- c(5,12)	# 위의 결과와 같다

Ex.02 : Generating Useful Vectors with “:”, seq() and rep()

Colon : ":"
> 5:8 [1] 5 6 7 8 > 5:1 [1] 5 4 3 2 1

> i <- 2
> 1:i-1
[1] 0 1
> 1:(i-1)
[1] 1

seq() : sequence

> seq(5,8)
[1] 5 6 7 8
> seq(12,30,3)
[1] 12 15 18 21 24 27 30
> seq(1.1,2,length=10)
[1] 1.1 1.2 1.3 1.4 1.5 1.6 1.7 1.8 1.9 2.0

rep() : repeat

> x <- rep(8,4)
> x
[1] 8 8 8 8
> rep(1:3,2)
[1] 1 2 3 1 2 3

Ex.03 : Vector Arithmetic and Logical Operations

Arithmatic

> x <- c(1,2,4)
> x+x	# addition
[1] 1 4 6
> x*x	# multiplication
[1] 1 4 16	
> x/x	# dividing
[1] 1 1 1

any() :

> x <- 1:10
> if (any(x > 8)) print("yes")
[1] "yes"
> if (any(x > 88)) print("yes")

all() :

> if (all(x > 88)) print("yes")
> if (all(x > 0)) print("yes")
[1] "yes"

Ex.04 : Recycling

연산에서 범위가 벗어나면 처음 요소부터 연산에 활용한다.

> c(1,2,4) + c(6,0,9,20,22)
[1] 7 2 13 21 24
Warning message:
    longer object length is not a multiple of shorter object length in: c(1, 2, 4) + c(6, 0, 9, 20, 22)

> x
    [,1]    [,2]
[1,]	1	4
[2,]	2	5
[3,]	3	6

> x+c(1,2)
    [,1]    [,2]
[1,]	2	6
[2,]	4	6
[3,]	4	8

Ex.05 : Vector Indexing

> y <- c(1.2,3.9,0.4,0.12)
> y[c(1,3)]
[1] 1.2 0.4

> y[2:3]
[1] 3.9 0.4

> z <- c(5,12,13)
> z[-1] # exclude element 1
[1] 12 13

> z[-1:-2]
[1] 13

> z <- c(5,12,13)
> z[1:length(z)-1]	# 마지막을 제외하고 선택
[1] 5 12

> x <- c(12,15,8,11,24)
> y <- x[-1] - x[-length(x)]	# c(15, 8, 11, 24) - c(12, 15, 8, 11)
> y
[1] 3 -7 3 13

Ex.06 : Vector Name

names() :

> x <- c(1,2,4)
> names(x)
NULL
> names(x) <- c("a","b","ab")	# assign name 
> names(x)
[1] "a" "b" "ab"
> x
a b ab
1 2 4

> names(x) <- NULL	# remove the names from a vector by assigning NULL:
> x
[1] 1 2 4

> x <- c(1,2,4)
> names(x) <- c("a","b","ab")
> x["b"]
b
2

Ex.07 : Vectorized Function

> u <- c(5,2,8)
> v <- c(1,3,9)
> u+v
[1] 6 5 17
> u > v
[1] TRUE FALSE FALSE

> w <- function(x) return(x+1)
> w(u)
[1] 6 3 9

> f <- function(x,c) return((x+c)^2)	# auxiliary arguments
> f(1:3, 0)
[1] 1 4 9
> f(1:3, 1)
[1] 4 9 16

> sqrt(1:9)
[1] 1.000000 1.414214 1.732051 2.000000 2.236068 2.449490 2.645751 2.828427
[9] 3.000000

> y <- c(12, 5, 13)
> '+'(y,4)
[1] 16 9 17

Ex.08 : Filtering

> z <- c(5,2,-3,8)
> w <- z[z*z > 8]
> w
[1] 5 -3 8

> z
[1] 5 2 -3 8
> z*z > 8
[1] TRUE FALSE TRUE TRUE

> z[c(TRUE,FALSE,TRUE,TRUE)]
[1] 5 -3 8

which() :

> which(z*z > 8)
[1] 1 3 4

nviol <- function(x) {
diff <- x[-1]-x[1:(length(x)-1)]
    return(length(which(diff < 0)))
}

> x <- c(1,3,8,2)
> x[x > 3] <- 0
> x
[1] 1 3 0 2

ifelse() :

> x <- 1:10
> y <- ifelse(x %% 2 == 0,5,12)
> y
[1] 12 5 12 5 12 5 12 5 12 5

> x <- c(5,2,9,12)
> ifelse(x > 6,2*x,3*x)
[1] 15 6 18 24

Ex.09 : Adding/Deleting Element of Vectors

> x <- c(12,5,13,16,8)
> x <- c(x,20) # append 20
> x
[1] 12  5 13 16  8 20
> x <- c(x[1:3],20,x[4:6])# insert 20
> x
[1] 12  5 13 20 16  8 20
> x <- x[-2:-4] # delete element 2 through 4
> x
[1] 12 20 16  8

Array

Ex.01 : Generating Array

> x <- array(1:9)
> x
[1] 1 2 3 4 5 6 7 8 9

> x <- array(1:9,c(3,3))
> x
     [,1] [,2] [,3]
[1,]    1    4    7
[2,]    2    5    8
[3,]    3    6    9

> x <- 1:64
> dim(x) <- c(2,4,8) #dim() converts the vector into array
> is.array(x)
[1] TRUE

> x
, , 1
     [,1] [,2] [,3] [,4]
[1,]    1    3    5    7
[2,]    2    4    6    8

, , 2
     [,1] [,2] [,3] [,4]
[1,]    9   11   13   15
[2,]   10   12   14   16

, , 3
     [,1] [,2] [,3] [,4]
[1,]   17   19   21   23
[2,]   18   20   22   24

, , 4
     [,1] [,2] [,3] [,4]
[1,]   25   27   29   31
[2,]   26   28   30   32

, , 5
     [,1] [,2] [,3] [,4]
[1,]   33   35   37   39
[2,]   34   36   38   40

, , 6
     [,1] [,2] [,3] [,4]
[1,]   41   43   45   47
[2,]   42   44   46   48

, , 7
     [,1] [,2] [,3] [,4]
[1,]   49   51   53   55
[2,]   50   52   54   56

, , 8
     [,1] [,2] [,3] [,4]
[1,]   57   59   61   63
[2,]   58   60   62   64


> x[1,,]
     [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8]
[1,]    1    9   17   25   33   41   49   57
[2,]    3   11   19   27   35   43   51   59
[3,]    5   13   21   29   37   45   53   61
[4,]    7   15   23   31   39   47   55   63

> x[1,2,]
[1]  3 11 19 27 35 43 51 59

> x[1,2,1]
[1] 3

Matrix

Ex.01 : Basic

> y <- matrix(c(1,2,3,4),nrow=2,ncol=2)
> y
     [,1]    [,2]
[1,]	1	3
[2,] 	2	4

> m <- matrix(c(1,2,3,4,5,6),nrow=2,byrow=T) # equal to : m <- matrix(c(1,2,3,4,5,6),nrow=2) : 
> m
     [,1]    [,2]    [,3]
[1,]	1	2	3
[2,]	4	5	6
# ‘T’ is an abbreviation for “TRUE”.

Ex.02 : General Operations

> y %*% y	# ordinary matrix multiplication 형렬곱
	[,1] 	[,2]
[1,]	7	15
[2,]	10	22

> y+y
     [,1]    [,2]
[1,]	2	6
[2,]	4	8

Ex.03 : Indexing

> y[,2]
[1] 3 4

> y <- matrix(nrow=2,ncol=2)
> y[1,1] = 1
> y[2,1] = 2
> y[1,2] = 3
> y[2,2] = 4
> y

     [,1]    [,2]
[1,]	1	3
[2,]	2	4

> z
     [,1]    [,2]    [,3]
[1,]	1	1	1
[2,]	2	1	0
[3,]	3	0	1
[4,]	4	0	0
> z[,c(2,3)]
     [,1]    [,2]
[1,]	1	1
[2,]	1	0
[3,]	0	1
[4,]	0	0

> y <- matrix(c(11,21,31,12,22,32),nrow=3,ncol=2)
> y
      [,1]    [,2]
[1,]	11	12
[2,]	21	22
[3,]	31	32
> y[2:3,]
      [,1]    [,2]
[1,]	21	22
[2,]	31	32
> y[2:3,2]
[1] 22 32


> y
	[,1]	[,2]
[1,]	1	4
[2,]	2	5
[3,]	3	6

> y[2:3,] <- matrix(c(1,1,8,12),nrow=2)
> y
	[,1]	[,2]
[1,]	1	4
[2,]	1	8
[3,]	1	12

> x <- matrix(nrow=3,ncol=3)
> x[2:3,2:3] <- cbind(4:5,2:3)	# copy
> x
	[,1]	[,2]	[,3]
[1,]	NA	NA	NA
[2,]	NA	4	2
[3,]	NA	5	3

Ex.04 : Name of Row and Column

> z <- matrix(c(1,2,3,4),nrow=2)
> z
    [,1]	[,2]
[1,]	1	3
[2,]	2	4
> colnames(z)
NULL
> colnames(z) <- c("a","b")
> z
	a	b
[1,]	1	3
[2,]	2	4
> colnames(z)
[1]	"a"	"b"
> z[,"a"]
[1]	1	2

Ex.05 : Demension Reduction

> z <- matrix(1:8,nrow=4)
> z
	 [,1] [,2]
[1,]    1    5
[2,]    2    6
[3,]    3    7
[4,]    4    8

> r <-z[2,]
> r
[1] 2 6

> attributes(z)
$dim
[1] 4 2
> attributes(r)
NULL

suppress dimension reducting using drop

> r <- z[2,, drop=F]	# Suppress dimension reduction, with the drop argument
> r
	 [,1] [,2]
[1,]    2    6

> dim(r)
[1] 1 2

suppress dimension reducting using as.matrix

> u <-c(1,2,3)
> v <-as.matrix(u)
> v
	[,1]
[1,]    1
[2,]    2
[3,]    3

Ex.06 : Adding/Deleting Elements of Matrix

cbind() : adding

> one <- c(1,1,1,1)
> z <- matrix(c(1,2,3,4,1,1,0,0,1,0,1,0),nrow=4)
> z
	 [,1] [,2] [,3]
[1,]    1    1    1
[2,]    2    1    0
[3,]    3    0    1
[4,]    4    0    0
> cbind(one, z)
	 one      
[1,]   1 1 1 1
[2,]   1 2 1 0
[3,]   1 3 0 1
[4,]   1 4 0 0

> q <- cbind(c(1,2),c(3,4))
> q
	 [,1] [,2]
[1,]    1    3
[2,]    2    4

rbind() : adding

> p <- rbind(c(1,2,c(3,4)))
> p
	 [,1] [,2] [,3] [,4]
[1,]    1    2    3    4

deleting row

> m <- matrix(1:6,nrow=3)
> m
	 [,1] [,2]
[1,]    1    4
[2,]    2    5
[3,]    3    6
> m <- m[c(1,3),]	# deleting second row
> m
	 [,1] [,2]
[1,]    1    4
[2,]    3    6

Ex.07 : Filtering

> h <- matrix(c(1,2,3,4,5,6,7,8,9),nrow=3)
> h
     [,1] [,2] [,3]
[1,]    1    4    7
[2,]    2    5    8
[3,]    3    6    9

> mean(h[h[,2]>4,1])
[1] 2.5
> mean(h[h[,3]<=8,2])
[1] 4.5
> nrow(h[h[,3]>7,])
[1] 2
> nrow(h[h[,3]>8,])
NULL

> x <-matrix(c(1,2,3,2,3,4),nrow=3)
> x
	 [,1] [,2]
[1,]    1    2
[2,]    2    3
[3,]    3    4
> x[x[,2] >= 3,]
	 [,1] [,2]
[1,]    2    3
[2,]    3    4

> j <- x[,2] >= 3
> j
[1] FALSE  TRUE  TRUE
> x[j,]
	 [,1] [,2]
[1,]    2    3
[2,]    3    4

> m <- matrix(c(1,2,3,4,5,6),nrow=3)
> m
	 [,1] [,2]
[1,]    1    4
[2,]    2    5
[3,]    3    6
> m[m[,1] > 1,]
	 [,1] [,2]
[1,]    2    5
[2,]    3    6
> m[m[,1] > 1 & m[,2] > 5,]
[1] 3 6

Ex.08 : Applying the Same Function to All Rows or Columns of a Matrix

apply() :

z <- matrix(c(1,2,3,4,5,6), nrow=3)
> apply(z,2,mean)
[1] 2 5

> f <- function(x) x/c(2,8)
> y <- apply(z,1,f)
> y
	 [,1]  [,2] [,3]
[1,]  0.5 1.000 1.50
[2,]  0.5 0.625 0.75

sapply() :

> z12 <- function(z) return(c(z,z^2))
> sapply(1:8,z12)
     [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8]
[1,]    1    2    3    4    5    6    7    8
[2,]    1    4    9   16   25   36   49   64

Ex.09 : Properties of Matrix

> z <- matrix(1:8,nrow=4)
> z
	 [,1] [,2]
[1,]    1    5
[2,]    2    6
[3,]    3    7
[4,]    4    8
> length(z)
[1] 8
> class(z)
[1] "matrix"
> attributes(z)
$dim
[1] 4 2
> dim(z)
[1] 4 2
> nrow(z)
[1] 4
> ncol(z)
[1] 2

Ex.10 : Transepose Matrix

> m <- matrix(c(1,2,3,4,5,6),nrow=3)
> m
	 [,1] [,2]
[1,]    1    4
[2,]    2    5
[3,]    3    6
> t(m)
	 [,1] [,2] [,3]
[1,]    1    2    3
[2,]    4    5    6

List

Ex.01 : Generate

> j <- list(name="Joe", salary=55000, union=T)
> j
$name
[1] "Joe"

$salary
[1] 55000

$union
[1] TRUE

Ex.02 : List Tags and Values, and the unlist() Function

> names(j)
[1] "name" "salary" "union"

unlist() :

> ulj <- unlist(j)
> ulj
   name  salary   union 
  "Joe" "55000"  "TRUE" 
> class(ulj)
[1] "character"

> x <- list(abc=2, de=5)
> name(x)
Error: could not find function "name"
> names(x)
[1] "abc" "de" 
> ulx <- unlist(x)
> ulx
abc  de 
  2   5 
> class(ulx)
[1] "numeric"

Ex.03 : Indexing of Lists

> j

$name
[1] "Joe"

$salary
[1] 55000

$union
[1] TRUE

> j[[1]]
[1] "Joe"

> j[2:3]

$salary
[1] 55000

$union
[1] TRUE

Ex.04 : Addinf/Deleting List Elements

> z <- list(a="abc",b=12)
> z
$a
[1] "abc"

$b
[1] 12

> z$c <- 1	add element 3
> z
$a
[1] "abc"

$b
[1] 12

$c
[1] 1

> z[[1]] <- NULL	# delete element 1
> z
$b
[1] 12

$c
[1] 1

Ex.05 : Applying the Same Function to All Element

lapply() :

> list(1:2,25:29)
[[1]]
[1] 1 2

[[2]]
[1] 25 26 27 28 29

> lb <-list(1:3,25:27)
> lb
[[1]]
[1] 1 2 3

[[2]]
[1] 25 26 27

> lapply(lb,median)
[[1]]
[1] 2

[[2]]
[1] 26

> as.numeric(lapply(list(1:3,25:27),median))
[1]  2 26

Ex.06 : Recursive Lists

> b <- list(u = 5, v = 12)
> c <- list(w = 13)
> a <- list(b,c)
> a
[[1]]
[[1]]$u
[1] 5

[[1]]$v
[1] 12

[[2]]
[[2]]$w
[1] 13

Ex.07 : Properties of List

> length(j)
[1] 3

data.frame

Ex.01 : Basic

head() :

> head(women)

  height weight

1     58    115

2     59    117

3     60    120

4     61    123

5     62    126

6     63    129

rowMeans() :

> rowMeans(women)
 [1]  86.5  88.0  90.0  92.0  94.0  96.0  98.0 100.0 102.5 104.5 107.0 109.5
[13] 112.0 115.0 118.0

colMeans() :

> colMeans(women)
  height   weight 
 65.0000 136.7333

rbind() :

> rbind(women,women)
   height weight
1      58    115
2      59    117
3      60    120
4      61    123
5      62    126
6      63    129
7      64    132
8      65    135
9      66    139
10     67    142
11     68    146
12     69    150
13     70    154
14     71    159
15     72    164
16     58    115
17     59    117
18     60    120
19     61    123
20     62    126
21     63    129
22     64    132
23     65    135
24     66    139
25     67    142
26     68    146
27     69    150
28     70    154
29     71    159
30     72    164

cbind() :

> cbind(women,women)
   height weight height weight
1      58    115     58    115
2      59    117     59    117
3      60    120     60    120
4      61    123     61    123
5      62    126     62    126
6      63    129     63    129
7      64    132     64    132
8      65    135     65    135
9      66    139     66    139
10     67    142     67    142
11     68    146     68    146
12     69    150     69    150
13     70    154     70    154
14     71    159     71    159
15     72    164     72    164

Ex.02 : General Operations

> data <- data.frame (id=1:3, weight=c(20,27,24))
> data
  id weight
1  1     20
2  2     27
3  3     24

> z <- data.frame(cbind(c(1,2),c(3,4)))
> z
  X1 X2
1  1  3
2  2  4

> x <- matrix(c(1,2,3,4),nrow=2,ncol=2)
> x <- data.frame(x)
> x
  X1 X2
1  1  3
2  2  4

Ex.03 : Adding/Deleting Elements of Matrix

Ways to add a column

data$size      <- c("small", "large", "medium")
data[["size"]] <- c("small", "large", "medium")
data["size"]   <- c("small", "large", "medium")
data[,"size"]  <- c("small", "large", "medium")
data$size      <- 0

Ways to remove the column

data$size      <- NULL
data[["size"]] <- NULL
data["size"]   <- NULL
data[,"size"]  <- NULL
data[3]        <- NULL
data[,3]       <- NULL
data           <- subset(data, select=-size)

Ex.04 : Indexing

> women[1,]
  height weight
1     58    115
> women[1,2]
[1] 115
> women[c(1:3),2]
[1] 115 117 120
> women[1:4,]
  height weight
1     58    115
2     59    117
3     60    120
4     61    123
> women[-1:-10,]
   height weight
11     68    146
12     69    150
13     70    154
14     71    159
15     72    164

Ex.05 : Change Column Name

> colnames(z) <- c("good", "better")
> z
  good better
1    1      3
2    2      4

Ex.06 : Applying the Same Function to All Rows or Columns of a Matrix

> apply(women,2,mean)
  height   weight 
 65.0000 136.7333

Ex.07 : Properties of Data Frame

> attributes(x)
$names
[1] "X1" "X2"

$row.names
[1] 1 2

$class
[1] "data.frame"

> dim(x)
[1] 2 2

factor

Ex.01 : Creating factor

> y <- factor(c("a","b","a","a","b"))
> y
[1] a b a a b
Levels: a b
> class(f)
Error: object 'f' not found
> class(y)
[1] "factor"

Ex.02 : Converting

as.vector() :

> yv <- as.vector(y)
> yv
[1] "a" "b" "a" "a" "b"
> class(yv)
[1] "character"

Ex.03 : Select Level

> yf <- factor(y, levels=c("a"))
> yf
[1] a    <NA> a    a    <NA>
Levels: a

Ex.04 : Change the Level Value

> levels(y) <- c("two","one")
> y
[1] two one two two one
Levels: two one

table

Ex.01 : Creating

data <- data.frame (VoteX=c("Yes","Yes","No","Not Sure","No"), VoteLastTime=c("Yes","No","No","Yes","No"))
> data
	 VoteX VoteLastTime
1      Yes          Yes
2      Yes           No
3       No           No
4 Not Sure          Yes
5       No           No

> ct <- table(data)
> ct
		  VoteLastTime
VoteX      No Yes
  No        2   0
  Not Sure  0   1
  Yes       1   1

> class(ct)
[1] "table"

Ex.02 : Converting

> ctdf <- as.data.frame(ct)
> ctdf
	 VoteX VoteLastTime Freq
1       No           No    2
2 Not Sure           No    0
3      Yes           No    1
4       No          Yes    0
5 Not Sure          Yes    1
6      Yes          Yes    1
> class(ctdf)
[1] "data.frame"

Ex.03 : Useage as Determination of Certain Condition

> x <- c (5,12,13,3,4,5)
> table(x==5)

FALSE  TRUE 
4     2

Reference

Norman Matlof(2009). The Art of R Programming. [Down]
William N(2014). An Introduction to R. [Down] [Link] [Link.kr]
서민구(2013). R을 이용한 데이터 실무. [D own]