R for reproducible scientific analysis

Data structures

Data Types

Before we can analyse any data, we need to have a strong understanding of the basic data types and structures in which we can store information. This is particularly important for efficient and frustration-free programming.

R has five basic atomic types (meaning they can’t be broken down into anything smaller):

• logical (e.g., TRUE, FALSE)
• numeric
• integer (e.g, 2L, as.integer(3))
• double (i.e. decimal) (e.g, -24.57, 2.0, pi)
• complex (i.e. complex numbers) (e.g, 1 + 0i, 1 + 4i)
• text (called “character” in R) (e.g, "a", "swc", 'This is a cat')

There are a few functions we can use to interrogate data in R to determine its type:

typeof() # what is its atomic type?
is.logical() # is it TRUE/FALSE data?
is.numeric() # is it numeric?
is.integer() # is it an integer?
is.complex() # is it complex number data?
is.character() # is it character data?

Data Structures

There are five data structures you will commonly encounter in R. These are:

• vector
• factor
• list
• matrix
• data.frame

Vectors

A vector is the most common and basic data structure and is pretty much the workhorse of R. It is sometimes referred to as atomic vector, because they can only contain one data type. Vectors are the building blocks of every other data structure.

A vector can contain any of the five types we introduced before:

• logical (e.g., TRUE, FALSE)
• integer (e.g., 2L, as.integer(3))
• numeric (real or decimal) (e.g, 2, 2.0, pi)
• complex (e.g, 1 + 0i, 1 + 4i)
• character (e.g, "a", "swc")

Create an empty vector with vector() or by using the concatenate function, c().

x <- vector()
x

logical(0)

So by default, it creates an empty vector (i.e. a length of 0) of type “logical”.

x <- vector(length = 10) # with a predefined length
x

 [1] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE

If we count the number of FALSEs there should be 10.

x <- vector("character", length = 10)  # with a predefined length and type
x

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

Or we can use the concatenate function to combine any values we like into a vector (so long as they’re the same atomic type!).

x <- c(10, 12, 45, 33)
x

[1] 10 12 45 33

You can also create vectors as sequence of numbers

series <- 1:10
series

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

seq(10)

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

seq(1, 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

You can also use the concatenate function to add elements to a vector:

x <- c(x, 57)
x

[1] 10 12 45 33 57

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

This is called implicit coercion.

The coercion rule goes logical -> integer -> numeric -> complex -> character.

You can also coerce vectors explicitly using the as.<class_name>. Example

as.numeric()

numeric(0)

as.character()

character(0)

R will try to do whatever makes the most sense for that value:

as.character(x)

[1] "10" "12" "45" "33" "57"

as.complex(x)

[1] 10+0i 12+0i 45+0i 33+0i 57+0i

x <- 0:6
as.logical(x)

[1] FALSE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE

It is common in many programming languages for 0 to represent FALSE, while every other number is treated as TRUE. Sometimes coercions, especially nonsensical ones won’t work.

In some cases, R won’t be able to do anything sensible:

x <- c("a", "b", "c")
as.numeric(x)

Warning: NAs introduced by coercion

[1] NA NA NA

as.logical(x)

[1] NA NA NA

In both cases, a vector of “NAs” was returned, and in the first case so was a warning.

You can ask questions about the structure of vectors:

x <- 0:10
tail(x, n=2) # get the last 'n' elements

[1]  9 10

head(x, n=1) # get the first 'n' elements

[1] 0

length(x)

[1] 11

str(x)

 int [1:11] 0 1 2 3 4 5 6 7 8 9 ...

Vectors can be named:

x <- 1:4
names(x) <- c("a", "b", "c", "d")
x

a b c d 
1 2 3 4 

Matrices

Another data structure you’ll likely encounter are matrices. Underneath the hood, they are really just atomic vectors, with added dimension attributes.

We can create one with the matrix function. Let’s generate some random data:

set.seed(1) # make sure the random numbers are the same for each run
x <- matrix(rnorm(18), ncol=6, nrow=3)
x

           [,1]       [,2]      [,3]       [,4]       [,5]        [,6]
[1,] -0.6264538  1.5952808 0.4874291 -0.3053884 -0.6212406 -0.04493361
[2,]  0.1836433  0.3295078 0.7383247  1.5117812 -2.2146999 -0.01619026
[3,] -0.8356286 -0.8204684 0.5757814  0.3898432  1.1249309  0.94383621

str(x)

 num [1:3, 1:6] -0.626 0.184 -0.836 1.595 0.33 ...

You can use rownames, colnames, and dimnames to set or retrieve the column and rownames of a matrix. The functions nrow and ncol will tell you the number of rows and columns, while length will tell you the number of elements.

Factors

Factors are special vectors that represent categorical data. Factors can be ordered or unordered and are important for modeling functions, such as aov(), lm() and glm(), or for plot functions.

Factors can only contain predefined values, and we can create one by calling the function factor.

x <- factor(c("yes", "no", "no", "yes", "yes"))
x

[1] yes no  no  yes yes
Levels: no yes

So we can see that the output is very similar to a character vector, but with an attached levels component. This becomes clearer when we look at its structure:

str(x)

 Factor w/ 2 levels "no","yes": 2 1 1 2 2

This reveals something important: while factors look (and often behave) like character vectors, they are actually integers under the hood, and here, we can see that “no” is represented by a 1, and “yes” a 2.

In modeling functions, it is paramount to know what the baseline level is. This is the first factor, but by default the ordering is determined by alphabetical order of words entered. You can change this by specifying the levels:

x <- factor(c("case", "control", "control", "case"), levels = c("control", "case"))
str(x)

 Factor w/ 2 levels "control","case": 2 1 1 2

In this case, we’ve explicitly told R that “control” should represented by 1, and “case” by 2. This designation can be very important for interpreting the results of statistical models!

Lists

If you want to combine different types of data, you will need to use lists. Lists act as containers for any type of data structure, even themselves!

Lists can be created using the function list or coerced from other objects using as.list():

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

[[1]]
[1] 1

[[2]]
[1] "a"

[[3]]
[1] TRUE

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

Each element of the list is denoted by a [[ in the output. Inside each list element is an atomic vector of length one containing

Lists can contain more complex objects:

xlist <- list(a = "Research Bazaar", b = 1:10, data = head(iris))
xlist

$a
[1] "Research Bazaar"

$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

In this case our list contains a character vector of length one, a numeric vector with 10 entries, and a small data frame from one of R’s many preloaded datasets (see ?data). We’ve also given each list element a name, which is why you see $a instead of [[1]].

Lists can also contain themselves:

list(list(list(list())))

[[1]]
[[1]][[1]]
[[1]][[1]][[1]]
list()

Lists are extremely useful inside functions. You can “staple” together lots of different kinds of results into a single object that a function can return. In fact many R functions which return complex output store their results in a list.

Challenge solutions

answer <- TRUE
height <- 150
dog_name <- "Snoopy"
is.logical(answer)

[1] TRUE

is.numeric(height)

[1] TRUE

is.character(dog_name)

[1] TRUE

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

[1] "1.7" "a"  

typeof(xx)

[1] "character"

xx <- c(TRUE, 2)
xx

[1] 1 2

typeof(xx)

[1] "double"

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

[1] "a"    "TRUE"

typeof(xx)

[1] "character"

x <- matrix(rnorm(18), ncol=6, nrow=3)
length(x)

[1] 18

x <- matrix(1:50, ncol=5, nrow=10)
x <- matrix(1:50, ncol=5, nrow=10, byrow = TRUE) # to fill by row

my_list <- list(
  data_types = c("logical", "integer", "double", "complex", "character"),
  data_structures = c("vector", "matrix", "factor", "list")
)