Data Types of R

Baisc

R has five basic or “atomic” classes of objects

character
numeric (real numbers)
integer
complex
logic (True/False)

The most baisc object is vector

A vector and only contain objects of the same class.
BUT: The one exception is list, which represent a vector but can contain objects of different classes(indeed, that’s usually why we use them)

Numbers in R a generally treated as numeric objects

> typeof(1)
[1] "double"
> typeof(1L)
[1] "integer"

Attributes

names, dimnames
dimensions
class
length
other user-defined attributes/metadata

Attributes of object can be accessed using the attributes() function.

Creating vectors

the c() function:

x <- c(0.5, 0.6)
y <- c(TRUE, FALSE)
z <- c(T, F)
a <- c('a', 'b', 'c')
b <- 9:20
c <- c(1+0i, 2+4i)

using the vector() function

x <- vector("numeric", length=10)

Mixing Object

> x <- c(1.7, 'a') 
> typeof(x)
[1] "character"
> x <- c(TRUE, 2)
> typeof(x)
[1] "double"
> x <- c('a', TRUE)
> typeof(x)
[1] "character"

Explicit Coerction

> x <- 0:6
> as.logical(x)
[1] FALSE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
> as.numeric(x)
[1] 0 1 2 3 4 5 6
> as.character(x)
[1] "0" "1" "2" "3" "4" "5" "6"
> as.complex(x)
[1] 0+0i 1+0i 2+0i 3+0i 4+0i 5+0i 6+0i

Nonsensical coercion results NAs.

> x <- c('a', 'b', 'c')
> as.numeric(x)
[1] NA NA NA
警告信息：
强制改变过程中产生了NA 
> as.logical(x)
[1] NA NA NA
> as.complex(x)
[1] NA NA NA
警告信息：
强制改变过程中产生了NA

Lists

> x <- list('a' ,1 , TRUE, '1+4i')
> x
[[1]]
[1] "a"

[[2]]
[1] 1

[[3]]
[1] TRUE

[[4]]
[1] "1+4i"

Matrix

Matrices are vectors with a dimension attribute. The dimension is itsefl an integer vector of length 2(nrow, ncol)

> x <- matrix()
> x
     [,1]
[1,]   NA
>
> x <- matrix(nrow = 2, ncol = 3)
> x
     [,1] [,2] [,3]
[1,]   NA   NA   NA
[2,]   NA   NA   NA
> dim(x)
[1] 2 3
> attributes(x)
$dim
[1] 2 3

Add dimension to matrix or vector

> m <- 1:6
> m
[1] 1 2 3 4 5 6
> dim(m)
NULL
> dim(m) <- c(2,3)
> m
     [,1] [,2] [,3]
[1,]    1    3    5
[2,]    2    4    6
> dim(m)
[1] 2 3

cbind-ing and rbind-ing

> m <- 1:3
> n <- 7:9
> rbind(m,n)
  [,1] [,2] [,3]
m    1    2    3
n    7    8    9
> cbind(m,n)
     m n
[1,] 1 7
[2,] 2 8
[3,] 3 9

Factors

> f <- factor(c('yes','yes','no','yes','no')) ;
> f
[1] yes yes no  yes no 
Levels: no yes
> table(f)
f
 no yes 
  2   3 
 > unclass(f)
[1] 2 2 1 2 1
attr(,"levels")
[1] "no"  "yes"
>
> f <- factor(c('yes','yes','no','yes','no') , levels = c('yes','no')) 
> f
[1] yes yes no  yes no 
Levels: yes no
> table(f)
f
yes  no 
  3   2 
> unclass(f)
[1] 1 1 2 1 2
attr(,"levels")
[1] "yes" "no"

Missing Values

NaN: for undefined for mathematical operations
NA: for everything is undefined
NaN belong to NA

> x <- c(1, 2, 3, NA, NaN, 5)
> is.na(x)
[1] FALSE FALSE FALSE  TRUE  TRUE FALSE
> is.nan(x)
[1] FALSE FALSE FALSE FALSE  TRUE FALSE

Data Frames

Data frames are used to store tabular data.

They are represented as special types of list
- Every element of list has the same length.
Data frames also have special attribute call
- row.names
- every row in data frames has a name
Data frames are usually create by calling
- read.table() or read.csv()
Data frames can be converted to a matrix by calling
- data.matrix()

> x <- data.frame(Index=1:4, name=c('dong','zhou','snow','snow'), result=c(T,T,T,F)) ;
> x
  Index name result
1     1 dong   TRUE
2     2 zhou   TRUE
3     3 snow   TRUE
4     4 snow  FALSE
> row.names(x)
[1] "1" "2" "3" "4"

Names

All R objects can have names.

> x <- 1:3
> x
[1] 1 2 3
> names(x)
NULL
> names(x) <- c("dong", 'dong', 'snow')
> names(x)
[1] "dong" "dong" "snow"
> x
dong dong snow 
   1    2    3 
> x('dong')
>
> x <- list(a=1, b=2, c=3)
> x
$a
[1] 1

$b
[1] 2

$c
[1] 3

> names(x)
[1] "a" "b" "c"
>
> x <- matrix(1:4, nrow=2, ncol=2)
> x
     [,1] [,2]
[1,]    1    3
[2,]    2    4
> dimnames(x) <- list(c('a','b'), c('c','d'))
> x
  c d
a 1 3
b 2 4

sequence

> 1:4
[1] 1 2 3 4

> seq(0,1, length.out = 11)
 [1] 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

> seq(1, 9, by =2)
[1] 1 3 5 7 9

> seq(1, 9, by =3)
[1] 1 4 7

> seq(1, 9, by=pi)
[1] 1.000000 4.141593 7.283185

> seq(17)
 [1]  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17
> seq_len(17)
 [1]  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17

Repeat

> rep(c(0,1,2), 3)
[1] 0 1 2 0 1 2 0 1 2
> rep(c(0,1,2), time=3)
[1] 0 1 2 0 1 2 0 1 2

> rep(c(0,1,2), each=3)
[1] 0 0 0 1 1 1 2 2 2

> rep(c(0,1,2), each=3, times=2)
 [1] 0 0 0 1 1 1 2 2 2 0 0 0 1 1 1 2 2 2

> rep(c(0,1,2), each=3, length.out=21)
 [1] 0 0 0 1 1 1 2 2 2 0 0 0 1 1 1 2 2 2 0 0 0