Basic Data Types and Data Structures in R

Understanding Basic Data Types and Data Structures 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 them.

Data structures are 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 six basic data types. (In addition to the five listed below, there is also raw, which will not be discussed in this workshop.)

character
numeric (real or decimal)
integer
logical
complex

Elements of these data types may be combined to form data structures, such as atomic vectors. When we call a vector atomic, we mean that the vector only holds data of a single data type. Below are examples of atomic character vectors, numeric vectors, integer vectors, etc.

character: "a", "swc"
numeric: 2, 15.5
integer: 2L (the L tells R to store this as an integer)
logical: TRUE, FALSE
complex: 1+4i (complex numbers with real and imaginary parts)

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

class() - what kind of object is it (high-level)?
typeof() - what is the object's data type (low-level)?
length() - how long is it? What about two-dimensional objects?
attributes() - does it have any metadata?

# Example
x <- "dataset"
typeof(x)

Output
[1] "character"

attributes(x)

Output
NULL

y <- 1:10
y

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

typeof(y)

Output
[1] "integer"

length(y)

Output
[1] 10

z <- as.numeric(y)
z

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

typeof(z)

Output
[1] "double"

R has many data structures. These include

atomic vector
list
matrix
data frame
factors

Source: The Carpentries, https://swcarpentry.github.io/r-novice-inflammation/13-supp-data-structures/index.html
This work is licensed under a Creative Commons Attribution 4.0 License.