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Compute the Sum of Rows of a Matrix or Array - rowSums Function in R

The rowSums() function in R provides a fast and efficient way to compute the sum of rows in a matrix or array. This function is particularly handy when working with large datasets as it is optimized for speed.

1. Basic Syntax

The basic syntax for the rowSums() function is:

rowSums(x, na.rm = FALSE, dims = 1)

Where:

x is the matrix or array.
na.rm is a logical value indicating whether missing values (NAs) should be removed.
dims is an optional argument used when x is an array to specify which dimension's sums are desired (usually not needed for matrices).

2. Basic Example

Here's a simple example using a matrix:

# Create a matrix
mat <- matrix(1:12, nrow=3)
print(mat)
#      [,1] [,2] [,3] [,4]
# [1,]    1    4    7   10
# [2,]    2    5    8   11
# [3,]    3    6    9   12

# Compute the sum of rows
row_sums <- rowSums(mat)
print(row_sums)
# [1] 22 26 30

3. Handling Missing Values

If your matrix has NA values and you want to exclude them from the sum:

# Create a matrix with NA values
mat_na <- matrix(c(1,2,3,NA,5,6,7,8,9), nrow=3)
print(mat_na)
#      [,1] [,2] [,3]
# [1,]    1    5    9
# [2,]    2   NA    8
# [3,]    3    6    7

# Compute the sum of rows excluding NAs
row_sums_na <- rowSums(mat_na, na.rm=TRUE)
print(row_sums_na)
# [1] 15 10 16

4. Applying `rowSums` on Arrays

If you have an array (higher dimensional structure, e.g., 3D), you can use the dims argument to specify which dimension you want to sum across:

# Create a 3x3x2 array
arr <- array(1:18, dim=c(3,3,2))
print(arr)

# Compute the sum across the rows for each matrix in the array
arr_sums <- rowSums(arr, dims=1)
print(arr_sums)

Summary:

The rowSums() function in R offers a convenient and efficient method to compute the sum of rows in matrices and arrays. Whether you're working with basic numerical data or handling missing values, this function provides flexibility and speed.

R rowSums Function Example:

The rowSums function calculates the sum of values across rows.

# Example of rowSums function
matrix_data <- matrix(1:9, ncol = 3)
row_sums_result <- rowSums(matrix_data)

How to Use rowSums to Calculate Row Sums in R:

Use rowSums to calculate the sum of values across rows.

# Calculate row sums using rowSums
matrix_data <- matrix(1:9, ncol = 3)
row_sums_result <- rowSums(matrix_data)

Row-Wise Summation of Matrices in R:

Perform row-wise summation of matrices using rowSums.

# Row-wise summation of matrices
matrix_data <- matrix(1:9, ncol = 3)
row_sums_result <- rowSums(matrix_data)

Using rowSums with Data Frames in R:

Apply rowSums to data frames for row-wise summation.

# Using rowSums with data frames
data_frame <- data.frame(A = c(1, 2, 3), B = c(4, 5, 6), C = c(7, 8, 9))
row_sums_result <- rowSums(data_frame)

Conditional Row Sums in R using rowSums:

Calculate conditional row sums based on a condition.

# Conditional row sums using rowSums
matrix_data <- matrix(1:9, ncol = 3)
condition <- matrix_data[, 1] > 2
row_sums_result <- rowSums(matrix_data[condition, , drop = FALSE])

Row-Wise Normalization with rowSums in R:

Perform row-wise normalization using rowSums.

# Row-wise normalization with rowSums
matrix_data <- matrix(1:9, ncol = 3)
normalized_matrix <- matrix_data / rowSums(matrix_data)

Handling Missing Values with rowSums in R:

Handle missing values while calculating row sums.

# Handling missing values with rowSums
matrix_data <- matrix(c(1, 2, NA, 4, 5, 6, 7, 8, 9), ncol = 3)
row_sums_result <- rowSums(matrix_data, na.rm = TRUE)

Parallel Processing for rowSums in R:

Utilize parallel processing for efficient row-wise summation.

# Parallel processing for rowSums
library(parallel)
matrix_data <- matrix(1:9, ncol = 3)
row_sums_result <- parallel::mclapply(1:nrow(matrix_data), function(i) sum(matrix_data[i, ]))

Comparing Row Sums to Column Sums in R:

Compare row sums to column sums for insights.