Understanding Vectors as 2D Data in R: A Comprehensive Guide

Understanding Vectors as 2D Data in R

When working with vectors in R, it’s common to encounter situations where a single vector is used to represent multi-dimensional data. This can be due to various reasons such as:

Converting a matrix into a vector
Representing a single row or column of a matrix as a vector
Using attributes to create a pseudo-2D structure

In this article, we will explore the concept of converting a 2D “vector” into a data frame or matrix in R.

Why Use Vectors as 2D Data?

Vectors in R are used to store one-dimensional data. However, with the increasing use of big data and machine learning algorithms, there’s a growing need for multi-dimensional data structures like matrices and data frames. In some cases, you might want to represent a single row or column of a matrix as a vector. For example:

You’re working with a large dataset and want to process each row or column separately.
You’re using attributes to create a pseudo-2D structure.

In such scenarios, using vectors as 2D data can be beneficial.

Creating a Vector as 2D Data

R allows you to create a vector with multiple elements by separating them with commas. For example:

# Create a vector representing two rows and three columns of data
data_vector = matrix(c(1, 2, 3, 4, 5, 6), nrow = 2, ncol = 3)

This creates a 2x3 matrix where each element is represented by an integer value.

Converting a Vector into a Data Frame or Matrix

When you apply a function to each column of a data frame and it returns a list, you can convert this list into a matrix or data frame using the do.call and rbind.data.frame functions. Here’s how:

# Create a sample data frame
df = data.frame(name = c('Tom', 'Mark', 'Jane'),
               weight = c(150, 140, 110),
               sex = c('M', 'M', 'F'), 
               fulltime = c(T, T, F), stringsAsFactors = F)

# Apply a function to each column of the data frame
df$sex = as.factor(df$sex)
f1 = function(column){
        list(class = class(column),
             mean = mean(column))
}
result = lapply(df[,], f1)

# Convert the result into a matrix or data frame using do.call and rbind.data.frame
test1 = do.call(rbind.data.frame, result)
test2 = as.data.frame(do.call(rbind, result), stringsAsFactors = T)
identical(test1, test2) # This will be FALSE due to different data types

In the example above, we apply a function f1 to each column of the data frame using the lapply function. The function returns a list containing the class and mean of each column.

Then, we convert this result into a matrix or data frame using the do.call and rbind.data.frame functions. In this case, we use both methods to see how they produce different results due to differences in data types.

Using Sapply for Vectorized Operations

When you apply the same function to each element of a vector, R uses optimized algorithms under the hood to perform the operation. This is known as sapply. However, when you use lapply, it executes the function on each element and stores the result in a list.

Here’s how you can create a similar effect with sapply:

# Apply the same function to each column of the data frame using sapply
result = sapply(df[,], f1)

In this case, R will use its optimized algorithms to perform the operation on each element, which results in a vector with class “character” and mean NA.

Converting a Vector into a Matrix or Data Frame Using Sapply

When you want to convert the result of sapply into a matrix or data frame, you need to be careful about how you handle missing values. By default, sapply returns a vector with NA values where there are no elements.

Here’s an example:

# Apply the same function to each column of the data frame using sapply
result = sapply(df[,], f1)

# Convert the result into a matrix or data frame
test = matrix(unlist(result), ncol = ncol(df), byrow = T)

In this case, we convert the vector returned by sapply into a matrix using matrix. The resulting matrix will have NA values where there are no elements in the original vector.

However, when you use rbind.data.frame, R expects all columns to be data frames. Since we’re dealing with a vector of lists here, this approach won’t work as expected.

Losing Row/Column Names

When you convert a list into a matrix or data frame using sapply and then use unlist, the resulting matrix will lose its row/column names.

Here’s an example:

# Apply the same function to each column of the data frame using sapply
result = sapply(df[,], f1)

# Convert the result into a matrix or data frame
test = matrix(unlist(result), ncol = ncol(df), byrow = T)

In this case, when we convert the vector returned by sapply into a matrix using matrix, we lose the row/column names of the original vector.

To get around this issue, we can use do.call(rbind.data.frame) to create a data frame directly from the result.

Fixing the Result

The reason why lapply and rbind.data.frame produce different results is because R stores the type of each element in its internal structure. When you apply a function to each column using lapply, it returns a list where each element has the correct class.

However, when you use sapply for vectorized operations, it executes the operation on each element and then returns a vector with the result.

To fix this issue, we can convert both lists into data frames using do.call(rbind.data.frame).

# Apply the same function to each column of the data frame using sapply
result = sapply(df[,], f1)

# Convert the result directly into a data frame
test = do.call(rbind.data.frame, result)

In this case, we use do.call and rbind.data.frame together to create a single data frame with all elements having the correct class.

This approach ensures that both lapply and sapply produce consistent results.

Last modified on 2025-04-26