Types of Objects

From single values to large datasets

Common types of objects in R

You can work with many types of data in R. Here are some of the most common types of objects you’ll encounter:

  • Numeric: Numbers, like 5, 3.14, or -0.5.
  • Character: Text, like "Hello, world!".
  • Factor: Categorical data, like education_level or country.

Let’s see how these types of objects work in practice.

number <- 5
number

character <- "Hello, world!"
character

factor <- factor(c(1,2,2,3), labels = c("A", "B", "C"))
factor

Types determine what you can do with an object

The type of an object determines what you can do with it. For example, you can perform mathematical operations on numeric objects, but not on character objects.

10 + 10    # returns 20
"10" + 10  # throws an error

You can coerce objects to different types

Sometimes you have an object of the wrong type. For instance, your numeric data might have been read in as a character object.

number <- "5"

If I want to perform mathematical operations on number, I need to first convert it to a numeric object. You can do this using the as.numeric function.

number <- as.numeric(number)
number
class(number)    # numeric

Note that you cannot always convert objects to a different type. Just use your common sense here.

as.numeric("I am not a number")

When coercion is not possible, R will return NA (missing value) and give you a warning.

From single values to large data set

In practice, you’ll rarely work with single values alone. However, many operations you can perform on a single value can also be applied to multiple values at once. Understanding how individual values combine to form larger data structures is key to working effectively with data.

From Single Values to Vectors

A vector is a collection of values of the same type (e.g., all numeric or all character). You create a vector by combining individual values using the c() (combine) function. Just like with single numbers, a vector has a type.

numbers <- c(1, 2, 3, 4, 5)
class(numbers)
[1] "numeric"

Now, you can perform operations on the entire vector at once:

numbers * 10
[1] 10 20 30 40 50
sum(numbers)
[1] 15

From Vectors to Data Frames

Single vectors are still not very usefull for data analysis, since we’re often interested in relations between variables. This is where data frames come in.

A data frame is a table with rows and columns, where each row is an observation and each column is a variable. You can think of a data frame as a collection of vectors, where each vector represents a column in the dataset. We can create a data frame by combining vectors using the data.frame() function.1

country <- c("NL", "NL", "BE", "BE", "DE", "DE", "FR", "FR", "UK", "UK")
height  <- c(176 , 165 , 172 , 160 , 180 , 170 , 175 , 165 , 185 , 175 )

d <- data.frame(country, height)

Now, you can perform operations on the entire data frame at once! You can for instance perform a statistical test to see if the average height of people differs between countries. Just like with single values and vectors, we need to take the type of each column into account. For instance, we can’t perform a correlation analysis using the country column, because it’s a character vector.

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Footnotes

  1. Throughout this book we will actually be using the tibble data structure from the tidyverse package, which is an improved version of the data.frame.↩︎