intro2r

Types of Data and Data Structures in R

R allows you to create new data structures^1^

Goals

Data: Type

Data objects in R can take one of certain forms.

These can be put together to create more complex objects.

Data objects can either be restricted to one kind of data, or contain more than one kind.

We will explore the common object types over the next few weeks.

R has 5 different classes of data

Integer

x <- 1:10

Numeric

x <- c(1.5, 2.5, 3.34, 10.6576)

Character or string

simon_says <- c("hullo", "my", "name", "is", "Simon")

Logical

simon_says_again <- c("TRUE", "TRUE", "FALSE", "TRUE", "FALSE", "FALSE")

Complex (numbers with real and imaginary parts)

1 + 4i

Data: Structures

R has many different data structures

We can put data classes together in many different ways:

Data objects have different dimensions

Data objects can be homogenous or heterogenous

Vectors, matrices, and arrays

The simplest object is a vector

A vector can be thought of as equivalent to a single row or single column in a spreadsheet.

A vector is any number of elements stuck together.

x <- c(1, 2, 3, 4, 5, 6, 7, 8, 9, 10)
x

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

A single element is a vector of length 1.

x <- 1
x

[1] 1

A vector can contain only one class of data (= atomic vector).

All elements in a vector are coerced to be the same kind of data.

x <- c(1, 2, 3, 4, "a")
x

[1] "1" "2" "3" "4" "a"

Vectors can be created with c() or vector(), in which case R will try to guess what kind of vector it is. (The default for vector() is logical).

vector(length = 5)

[1] FALSE FALSE FALSE FALSE FALSE

Or, you can create specific kinds of vector with character(), numeric(), and logical().

character(length = 5)

[1] "" "" "" "" ""

A matrix is a 2D rectangular vector

Matrices are vectors with two dimensions.

Can be created by:

matrix(1:20, ncol = 5)
     [,1] [,2] [,3] [,4] [,5]
[1,]    1    5    9   13   17
[2,]    2    6   10   14   18
[3,]    3    7   11   15   19
[4,]    4    8   12   16   20

An array is a multi-dimensional collection of matrices

A 3D array in R:

> array(1:60, dim = c(4,5,3)) 
, , 1

     [,1] [,2] [,3] [,4] [,5]
[1,]    1    5    9   13   17
[2,]    2    6   10   14   18
[3,]    3    7   11   15   19
[4,]    4    8   12   16   20

, , 2

     [,1] [,2] [,3] [,4] [,5]
[1,]   21   25   29   33   37
[2,]   22   26   30   34   38
[3,]   23   27   31   35   39
[4,]   24   28   32   36   40

, , 3

     [,1] [,2] [,3] [,4] [,5]
[1,]   41   45   49   53   57
[2,]   42   46   50   54   58
[3,]   43   47   51   55   59
[4,]   44   48   52   56   60

Factors

A factor is a vector that represents categorical data

Each element comes from a pre-defined set of categories.

Can be:

Factors can be written or coded using any mode (integer, text, logical).

Factors can be unordered

# unordered 3-level factor with integers
x0 <- factor(c(1, 2, 3, 2))
x0

[1] 1 2 3 2
Levels: 1 2 3

table(x0)

x0
1 2 3 
1 2 1

Factors can be unordered

# unordered 3-level factor with text (default order is alphanumeric)
x1 <- factor(c("large", "small", "medium", "small"))

table(x1)

x1
 large medium  small 
     1      1      2

Factors can be ordered

# ordered 3-level factor with text
x2 <- factor(c("large", "small", "medium", "small"), 
             ordered = TRUE,
             levels = c("small", "medium", "large"))

x2

[1] large  small  medium small 
Levels: small < medium < large

table(x2)

x2
 small medium  large 
     2      1      1

Dataframes

Dataframes can contain different kinds of data

Dataframes are equivalent to a single worksheet in a spreadsheet.

They can contain columns of different kinds of data.

You will likely read your data into R as a dataframe.

Year  Colour  Size_mm 
2017  red     23.5
2016  red     12.67
2017  blue    15.2
2016  blue    1.0
...

Lists

A List is a recursive vector

Lists can contain any other kind of data (including lists!) in a nested hierarchy.

Lists are like a wardrobe (= closet) where you can store many different kinds of hangers and clothes:

Data object attributes

Objects in R can be examined for their contents with functions such as: length(), class(), str(), typeof().

As well as any attributes: names(), dimnames(), dim().

You can verify the object type with is.object: is.vector(), is.matrix(), etc.

You can convert (coerce) between atomic vectors with as.object:

as.numeric(c('TRUE', 'FALSE', 'TRUE', 'FALSE'))

[1] NA NA NA NA

… although be careful.

Special values

NA

R supports missing data, represented as NA.

Inf

Inf is infinity. You can have either positive or negative infinity.

1/0

NaN

NaN means Not a Number. It’s an undefined value.

0/0

R has many other data structures

Updated: 2018-09-10