rowSums() Function in R

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This article will take a deep dive into the rowSums() function, providing a comprehensive guide on its use, potential applications, and aspects to consider when incorporating it into your data analysis routine.

Basics of rowSums()

The rowSums() function is used to compute the sum of each row in a matrix or a data frame. The function takes a numeric matrix or data frame as input and returns a vector containing the sum of each row.

Here is the basic syntax for the function:

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


  • x is the matrix or data frame for which you want to calculate the row sums.
  • na.rm is a logical argument that specifies whether NA values should be removed before the computation. By default, it is set to FALSE, meaning that if any NA values exist in a row, the sum of that row will be NA. If you want to ignore NA values and calculate the sum of the remaining values in the row, set na.rm to TRUE.
  • dims is an optional integer that specifies the dimension over which the sum is calculated.

Applying rowSums() to a Matrix

The primary use case for rowSums() is with a matrix of numeric data. Here’s how you can create a matrix and calculate the row sums:

# Create a 5x5 matrix
mat <- matrix(1:25, ncol = 5)


# Calculate row sums

In this example, rowSums() computes the sum of each of the five rows in the matrix.

Applying rowSums() to a Data Frame

rowSums() can also be applied to a data frame, although this isn’t as common because data frames often contain non-numeric data. However, if your data frame only contains numeric data, you can use rowSums() to calculate the sum of each row.Here’s an example of how to use rowSums() with a data frame:

# Create a data frame
df <- data.frame(
  a = 1:5, 
  b = 6:10, 
  c = 11:15


# Calculate row sums

In this case, rowSums() computes the sum of each row in the data frame.

Handling NA Values

rowSums() has built-in functionality to handle NA values. By default, if a row contains any NA values, the function will return NA for that row’s sum. However, you can change this behavior by setting na.rm = TRUE, which tells the function to ignore NA values and calculate the sum of the remaining values.

Here’s an example:

# Create a matrix with NA values
mat <- matrix(c(1:8, NA, 10:18), nrow = 5)


# Calculate row sums with na.rm = FALSE (default)
rowSums(mat)  # this will return NA for the row with NA values

# Calculate row sums with na.rm = TRUE
rowSums(mat, na.rm = TRUE)  # this will exclude NA values

In the example above, the third row of the matrix contains an NA value. When na.rm = FALSE, rowSums() returns NA for that row’s sum. However, when na.rm = TRUE, it excludes the NA value and computes the sum of the other numbers in the row.

Working with Non-Numeric Data

It’s important to note that rowSums() only works with numeric data. If you try to use it with a data frame that contains non-numeric data (such as character strings or factors), it will return an error.

To handle this, you can use the sapply() function to identify numeric columns and apply rowSums() to them only. Here’s how you can do this:

# Create a data frame with numeric and non-numeric data
df <- data.frame(
  a = 1:5,
  b = 6:10,
  c = letters[1:5]


# Attempt to calculate row sums
}, warning = function(w) {
}, error = function(e) {

# Apply rowSums() to only numeric columns
numeric_columns <- sapply(df, is.numeric)
rowSums(df[, numeric_columns])

In this case, rowSums(df) will produce an error because the data frame contains a non-numeric column. The sapply() function is used to determine which columns contain numeric data, and rowSums() is then applied only to these columns.


The rowSums() function in R provides a simple, effective way to summarize numeric data by rows. This can offer insights into data distributions and help guide further analysis. However, as with any function, understanding its limitations is crucial to avoid errors and incorrect results.

rowSums() only works with numeric data and can return NA values when there are NA values present in the row, unless the na.rm argument is set to TRUE. By keeping these nuances in mind, you can leverage the full potential of the rowSums() function in your data analysis work.

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