rowMeans(): Calculating the Mean of rows of a Data Frame in R

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R rowMeans() Function

The rowMeans() is a built-in, highly vectorized function in R that computes the arithmetic mean of values in each row of a numeric matrix or a data frame (with numeric-like columns).

Ensure that the object passed to rowMeans() is numeric or can be coerced to numeric. If it contains non-numeric columns, it will throw an error.

Also, the input should have at least two dimensions. For example, if it is a vector, it will throw the error as well.

Figure of Calculating the mean of every row of a data frame

In the above figure, we find the mean of each row.

Mean of the first row: 1 + 4 + 7 / 3 = 12/3 = 4

Mean of the second row: 2 + 5 + 8 / 3 = 15 / 3 = 5

Mean of the third row: 3 + 6 + 9 / 3 = 18 / 3 = 6

Here is the program:

# Creating a data frame
df <- data.frame(
  row1 = c(1, 2, 3),
  row2 = c(4, 5, 6),
  row3 = c(7, 8, 9)
)
print(df)

# Calculating the mean of every row using rowMeans() function
rowMeans(df)

# Output: [1] 4  5  6

Syntax

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

Parameters

Argument Description
x It represents an array of two or more dimensions containing numeric, complex, integer, or logical values or a numeric data frame.
na.rm It represents a logical argument that specifies where NA values should be ignored or not.

If TRUE, NA values will be ignored.

If FALSE, NA values will be included.
dims  If your input contains higher-dimensional arrays, this argument is relevant.

If you pass dims = 1, it operates across the first dimension (rows).

Handling NA values

Calculating the mean of every row of data frame containing NA values

As you can see from the figure, we get the output NA for every row because every row contains NA values.

# Data frame with NA values
df <- data.frame(
  col1 = c(NA, 2, 3),
  col2 = c(4, NA, 6),
  col3 = c(7, 8, NA)
)

rowMeans(df)

# Output: [1] NA NA NA

You can exclude NA values while calculating the mean of every row by passing the na.rm = TRUE parameter.

Figure of handling missing values while using rowMeans() function

# Create a data frame.
df <- data.frame(
  col1 = c(NA, 2, 3),
  col2 = c(4, NA, 6),
  col3 = c(7, 8, NA)
)

# Calculate the mean of data frame
rowMeans(df, na.rm = TRUE)

# Output: [1] 5.5   5.0   4.5

Calculating the mean of specific rows

To calculate the mean of specific rows, you need to use row indexing and pass their index to the method.

Figure of calculating the mean of specific rows of data frame

df <- data.frame(
  col1 = c(1, 2, 3),
  col2 = c(4, 5, 6),
  col3 = c(7, 8, 9)
)

rowMeans(df[c(1, 3), ])

# Output:
# 1    3
# 4    6

For row 1, the mean is 4, and for row 2, the mean is 6.

Working with a matrix

Matrix has rows and columns, and it works the same as a data frame because if you create a matrix with the same values as the above data frame, the output will be the same.

Figure of Calculating the mean of every row of the matrix

# Create a matrix.
mat <- matrix(1:9, 3, 3)

print(mat)

# Calculate the mean of each row.
cat("The mean of every row of matrix: ", rowMeans(mat), "\n")

# Output: The mean of every row of matrix: 4   5   6

Matrix with a single row

If your input is a matrix with a single row, the output will have only the row’s mean.

single_row <- matrix(5:8, nrow = 1)

print(single_row)

rowMeans(single_row)

# Output: [1] 6.5

Empty input

If your input is an empty data frame or matrix, the output will be numeric(0).

empty_mat <- matrix(numeric(0), nrow = 0, ncol = 2)

rowMeans(empty_mat)

# Output: numeric(0)

That’s it!

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