Data types and structures

Author

Marie-Hélène Burle

It might be time to talk a bit more formally about the various data types and structures available in R. The goal of this course is not to get bogged down in the nitty-gritty of R syntax, so this section is kept very short.

Data types

typeof("Some words")

[1] "character"

typeof(2)

[1] "double"

typeof(2.0)

[1] "double"

typeof(2L)

[1] "integer"

typeof(TRUE)

[1] "logical"

Data structures

Dimension	Homogeneous	Heterogeneous
1 d	Atomic vector	List
2 d	Matrix	Data frame
3 d	Array

Atomic vectors

c(2, 4, 1)

[1] 2 4 1

str(c(2, 4, 1))

 num [1:3] 2 4 1

c(2.2, 4.4, 1.0)

[1] 2.2 4.4 1.0

str(c(2.2, 4.4, 1.0))

 num [1:3] 2.2 4.4 1

1:3

[1] 1 2 3

str(1:3)

 int [1:3] 1 2 3

c("some", "random", "words")

[1] "some"   "random" "words"

str(c("some", "random", "words"))

 chr [1:3] "some" "random" "words"

Matrices

m <- matrix(1:12, nrow = 3, ncol = 4)
m

     [,1] [,2] [,3] [,4]
[1,]    1    4    7   10
[2,]    2    5    8   11
[3,]    3    6    9   12

str(m)

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

Arrays

a <- array(as.double(1:24), c(3, 2, 4))
a

, , 1

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

, , 2

     [,1] [,2]
[1,]    7   10
[2,]    8   11
[3,]    9   12

, , 3

     [,1] [,2]
[1,]   13   16
[2,]   14   17
[3,]   15   18

, , 4

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

str(a)

 num [1:3, 1:2, 1:4] 1 2 3 4 5 6 7 8 9 10 ...

Lists

l <- list(2L, 3, c(2, 1), FALSE, "string")
l

[[1]]
[1] 2

[[2]]
[1] 3

[[3]]
[1] 2 1

[[4]]
[1] FALSE

[[5]]
[1] "string"

str(l)

List of 5
 $ : int 2
 $ : num 3
 $ : num [1:2] 2 1
 $ : logi FALSE
 $ : chr "string"

Data frames

d <- data.frame(
  country = c("Canada", "USA", "Mexico"),
  var = c(2.9, 3.1, 4.5)
)
d

  country var
1  Canada 2.9
2     USA 3.1
3  Mexico 4.5

str(d)

'data.frame':   3 obs. of  2 variables:
 $ country: chr  "Canada" "USA" "Mexico"
 $ var    : num  2.9 3.1 4.5