Data Types and Structures

Overview

Teaching: 45 min
Exercises: 0 min
Questions
  • What are the different data types in R?

  • What are the different data structures in R?

  • How do I access data within the various data structures?

Objectives
  • Expose learners to the different data types in R.

  • Learn how to create vectors of different types.

  • Be able to check the type of vector.

  • Learn about missing data and other special values.

  • Getting familiar with the different data structures (lists, matrices, data frames).

Understanding Basic Data Types in R

To make the best of the R language, you’ll need a strong understanding of the basic data types and data structures and how to operate on those.

Very important to understand because these are the objects you will manipulate on a day-to-day basis in R. Dealing with object conversions is one of the most common sources of frustration for beginners.

Everything in R is an object.

R has 6 (although we will not discuss the raw class for this workshop) atomic vector types.

By atomic, we mean the vector only holds data of a single type.

R provides many functions to examine features of vectors and other objects, for example

# Example
x <- "dataset"
typeof(x)
[1] "character"
attributes(x)
NULL
y <- 1:10
y
 [1]  1  2  3  4  5  6  7  8  9 10
typeof(y)
[1] "integer"
length(y)
[1] 10
z <- as.numeric(y)
z
 [1]  1  2  3  4  5  6  7  8  9 10
typeof(z)
[1] "double"

R has many data structures. These include

Atomic Vectors

A vector is the most common and basic data structure in R and is pretty much the workhorse of R. Technically, vectors can be one of two types:

although the term “vector” most commonly refers to the atomic types not to lists.

The Different Vector Modes

A vector is a collection of elements that are most commonly of mode character, logical, integer or numeric.

You can create an empty vector with vector(). (By default the mode is logical. You can be more explicit as shown in the examples below.) It is more common to use direct constructors such as character(), numeric(), etc.

vector() # an empty 'logical' (the default) vector
logical(0)
vector("character", length = 5) # a vector of mode 'character' with 5 elements
[1] "" "" "" "" ""
character(5) # the same thing, but using the constructor directly
[1] "" "" "" "" ""
numeric(5)   # a numeric vector with 5 elements
[1] 0 0 0 0 0
logical(5)   # a logical vector with 5 elements
[1] FALSE FALSE FALSE FALSE FALSE

You can also create vectors by directly specifying their content. R will then guess the appropriate mode of storage for the vector. For instance:

x <- c(1, 2, 3)

will create a vector x of mode numeric. These are the most common kind, and are treated as double precision real numbers. If you wanted to explicitly create integers, you need to add an L to each element (or coerce to the integer type using as.integer()).

x1 <- c(1L, 2L, 3L)

Using TRUE and FALSE will create a vector of mode logical:

y <- c(TRUE, TRUE, FALSE, FALSE)

While using quoted text will create a vector of mode character:

z <- c("Sarah", "Tracy", "Jon")

Examining Vectors

The functions typeof(), length(), class() and str() provide useful information about your vectors and R objects in general.

typeof(z)
[1] "character"
length(z)
[1] 3
class(z)
[1] "character"
str(z)
 chr [1:3] "Sarah" "Tracy" "Jon"

Finding Commonalities

Do you see a property that’s common to all these vectors above?

Adding Elements

The function c() (for combine) can also be used to add elements to a vector.

z <- c(z, "Annette")
z
[1] "Sarah"   "Tracy"   "Jon"     "Annette"
z <- c("Greg", z)
z
[1] "Greg"    "Sarah"   "Tracy"   "Jon"     "Annette"

Vectors from a Sequence of Numbers

You can create vectors as a sequence of numbers.

series <- 1:10
seq(10)
 [1]  1  2  3  4  5  6  7  8  9 10
seq(from = 1, to = 10, by = 0.1)
 [1]  1.0  1.1  1.2  1.3  1.4  1.5  1.6  1.7  1.8  1.9  2.0  2.1  2.2  2.3
[15]  2.4  2.5  2.6  2.7  2.8  2.9  3.0  3.1  3.2  3.3  3.4  3.5  3.6  3.7
[29]  3.8  3.9  4.0  4.1  4.2  4.3  4.4  4.5  4.6  4.7  4.8  4.9  5.0  5.1
[43]  5.2  5.3  5.4  5.5  5.6  5.7  5.8  5.9  6.0  6.1  6.2  6.3  6.4  6.5
[57]  6.6  6.7  6.8  6.9  7.0  7.1  7.2  7.3  7.4  7.5  7.6  7.7  7.8  7.9
[71]  8.0  8.1  8.2  8.3  8.4  8.5  8.6  8.7  8.8  8.9  9.0  9.1  9.2  9.3
[85]  9.4  9.5  9.6  9.7  9.8  9.9 10.0

Missing Data

R supports missing data in vectors. They are represented as NA (Not Available) and can be used for all the vector types covered in this lesson:

x <- c(0.5, NA, 0.7)
x <- c(TRUE, FALSE, NA)
x <- c("a", NA, "c", "d", "e")
x <- c(1+5i, 2-3i, NA)

The function is.na() indicates the elements of the vectors that represent missing data, and the function anyNA() returns TRUE if the vector contains any missing values:

x <- c("a", NA, "c", "d", NA)
y <- c("a", "b", "c", "d", "e")
is.na(x)
[1] FALSE  TRUE FALSE FALSE  TRUE
is.na(y)
[1] FALSE FALSE FALSE FALSE FALSE
anyNA(x)
[1] TRUE
anyNA(y)
[1] FALSE

Other Special Values

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

1/0
[1] Inf

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

0/0
[1] NaN

What Happens When You Mix Types Inside a Vector?

R will create a resulting vector with a mode that can most easily accommodate all the elements it contains. This conversion between modes of storage is called “coercion”. When R converts the mode of storage based on its content, it is referred to as “implicit coercion”. For instance, can you guess what the following do (without running them first)?

xx <- c(1.7, "a")
xx <- c(TRUE, 2)
xx <- c("a", TRUE)

You can also control how vectors are coerced explicitly using the as.<class_name>() functions:

as.numeric("1")
[1] 1
as.character(1:2)
[1] "1" "2"

Objects Attributes

Objects can have attributes. Attributes are part of the object. These include:

You can also glean other attribute-like information such as length (works on vectors and lists) or number of characters (for character strings).

length(1:10)
[1] 10
nchar("Software Carpentry")
[1] 18

Matrix

In R matrices are an extension of the numeric or character vectors. They are not a separate type of object but simply an atomic vector with dimensions; the number of rows and columns.

m <- matrix(nrow = 2, ncol = 2)
m
     [,1] [,2]
[1,]   NA   NA
[2,]   NA   NA
dim(m)
[1] 2 2

Matrices in R are filled column-wise.

m <- matrix(1:6, nrow = 2, ncol = 3)

Other ways to construct a matrix

m      <- 1:10
dim(m) <- c(2, 5)

This takes a vector and transforms it into a matrix with 2 rows and 5 columns.

Another way is to bind columns or rows using cbind() and rbind().

x <- 1:3
y <- 10:12
cbind(x, y)
     x  y
[1,] 1 10
[2,] 2 11
[3,] 3 12
rbind(x, y)
  [,1] [,2] [,3]
x    1    2    3
y   10   11   12

You can also use the byrow argument to specify how the matrix is filled. From R’s own documentation:

mdat <- matrix(c(1, 2, 3, 11, 12, 13),
               nrow = 2,
               ncol = 3,
               byrow = TRUE)
mdat
     [,1] [,2] [,3]
[1,]    1    2    3
[2,]   11   12   13

Elements of a matrix can be referenced by specifying the index along each dimension (e.g. “row” and “column”) in single square brackets.

mdat[2, 3]
[1] 13

List

In R lists act as containers. Unlike atomic vectors, the contents of a list are not restricted to a single mode and can encompass any mixture of data types. Lists are sometimes called generic vectors, because the elements of a list can by of any type of R object, even lists containing further lists. This property makes them fundamentally different from atomic vectors.

A list is a special type of vector. Each element can be a different type.

Create lists using list() or coerce other objects using as.list(). An empty list of the required length can be created using vector()

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

[[2]]
[1] "a"

[[3]]
[1] TRUE

[[4]]
[1] 1+4i
x <- vector("list", length = 5) # empty list
length(x)
[1] 5

The content of elements of a list can be retrieved by using double square brackets.

x[[1]]
NULL

Vectors can be coerced to lists as follows:

x <- 1:10
x <- as.list(x)
length(x)
[1] 10
  1. What is the class of x[1]?
  2. What about x[[1]]?

Elements of a list can be named (i.e. lists can have the names attribute)

xlist <- list(a = "Karthik Ram", b = 1:10, data = head(iris))
xlist
$a
[1] "Karthik Ram"

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

$data
  Sepal.Length Sepal.Width Petal.Length Petal.Width Species
1          5.1         3.5          1.4         0.2  setosa
2          4.9         3.0          1.4         0.2  setosa
3          4.7         3.2          1.3         0.2  setosa
4          4.6         3.1          1.5         0.2  setosa
5          5.0         3.6          1.4         0.2  setosa
6          5.4         3.9          1.7         0.4  setosa
names(xlist)
[1] "a"    "b"    "data"
  1. What is the length of this object? What about its structure?

Lists can be extremely useful inside functions. Because the functions in R are able to return only a single object, you can “staple” together lots of different kinds of results into a single object that a function can return.

A list does not print to the console like a vector. Instead, each element of the list starts on a new line.

Elements are indexed by double brackets. Single brackets will still return a(nother) list. If the elements of a list are named, they can be referenced by the $ notation (i.e. xlist$data).

Data Frame

A data frame is a very important data type in R. It’s pretty much the de facto data structure for most tabular data and what we use for statistics.

A data frame is a special type of list where every element of the list has same length (i.e. data frame is a “rectangular” list).

Data frames can have additional attributes such as rownames(), which can be useful for annotating data, like subject_id or sample_id. But most of the time they are not used.

Some additional information on data frames:

Creating Data Frames by Hand

To create data frames by hand:

dat <- data.frame(id = letters[1:10], x = 1:10, y = 11:20)
dat
   id  x  y
1   a  1 11
2   b  2 12
3   c  3 13
4   d  4 14
5   e  5 15
6   f  6 16
7   g  7 17
8   h  8 18
9   i  9 19
10  j 10 20

Useful Data Frame Functions

  • head() - shows first 6 rows
  • tail() - shows last 6 rows
  • dim() - returns the dimensions of data frame (i.e. number of rows and number of columns)
  • nrow() - number of rows
  • ncol() - number of columns
  • str() - structure of data frame - name, type and preview of data in each column
  • names() - shows the names attribute for a data frame, which gives the column names.
  • sapply(dataframe, class) - shows the class of each column in the data frame

See that it is actually a special list:

is.list(dat)
[1] TRUE
class(dat)
[1] "data.frame"

Because data frames are rectangular, elements of data frame can be referenced by specifying the row and the column index in single square brackets (similar to matrix).

dat[1, 3]
[1] 11

As data frames are also lists, it is possible to refer to columns (which are elements of such list) using the list notation, i.e. either double square brackets or a $.

dat[["y"]]
 [1] 11 12 13 14 15 16 17 18 19 20
dat$y
 [1] 11 12 13 14 15 16 17 18 19 20

The following table summarizes the one-dimensional and two-dimensional data structures in R in relation to diversity of data types they can contain.

Dimensions Homogenous Heterogeneous
1-D atomic vector list
2-D matrix data frame

Lists can contain elements that are themselves muti-dimensional (e.g. a lists can contain data frames or another type of objects). Lists can also contain elements of any length, therefore list do not necessarily have to be “rectangular”. However in order for the list to qualify as a data frame, the lenghth of each element has to be the same.

Column Types in Data Frames

Knowing that data frames are lists, can columns be of different type?

What type of structure do you expect to see when you explore the structure of the iris data frame? Hint: Use str().

# The Sepal.Length, Sepal.Width, Petal.Length and Petal.Width columns are all
# numeric types, while Species is a Factor.
# Lists can have elements of different types.
# Since a Data Frame is just a special type of list, it can have columns of
# differing type (although, remember that type must be consistent within each column!).
str(iris)

Key Points

  • R’s basic data types are character, numeric, integer, complex, and logical.

  • R’s basic data structures include the vector, list, matrix, data frame, and factors.

  • Objects may have attributes, such as name, dimension, and class.