2024

Understanding Vectors and Type Coercion

Vectors in R

  • In R, a vector is a collection of elements of the same data type.

  • When combining different data types in a vector, type coercion occurs.

Type Coercion

  • Type coercion means that R automatically converts elements to the same data type.

  • The most flexible data type is chosen to accommodate all elements.

# Example of type coercion
element_1 <- 'A'
element_2 <- 2
element_3 <- FALSE
tmp <- c(element_1, element_2, element_3)
  • In this example, element_1 is a character, forcing all other elements to also become characters in the vector tmp.

Data Type Behavior in Lists

  • Can contain elements of different types without coercion.

  • Each element retains its original data type.

Data Type Behaviour in Matrices and Arrays

  • Behave like vectors in terms of type homogeneity.

  • Coerce elements to a single data type.

Data Type Behaviour in Data Frames

  • Columns can have different data types, like lists.

  • Within a column, elements are homogeneous, like vectors.

Data Type Behavioru in Factors

  • Represent categorical data.

  • Transform character data into categorical levels, not traditional type coercion.

Quiz: Understanding Type Coercion

Consider the following code chunk:

element_1 <- 'A'
element_2 <- 2
element_3 <- FALSE
tmp <- c(element_1, element_2, element_3)

What is the data type of tmp[2]?

  1. character
  2. numeric
  3. boolean
  4. integer

Answer

Click here for the answer
  • The correct answer is 1. character. The code chunk creates a vector tmp with the elements 'A', 2, and FALSE. Since vectors in R can only contain elements of the same data type and the first element is a character, the second element is coerced to a character as well. Therefore, the data type of tmp[2] is character.

Key Takeaways on R Containers

  • Vectors, matrices, and arrays enforce a single data type, leading to type coercion.

  • Lists allow for mixed data types without coercion.

  • Data frames support columns of different types but enforce homogeneity within each column.

  • Understanding these behaviors is crucial for effective data manipulation in R.