mean()

mean(x, trim = 0, na.rm = FALSE, ...)

The mean() function calculates the arithmetic average of all values in a numeric vector. It is one of the most commonly used statistical functions in R, providing a simple measure of central tendency.

Syntax

mean(x, trim = 0, na.rm = FALSE, ...)

Parameters

Parameter	Type	Default	Description
x	numeric vector	required	A numeric vector containing the values to average
trim	numeric	0	Fraction of observations to trim from each end before computing the mean (0 to 0.5)
na.rm	logical	FALSE	If TRUE, remove NA values before computing
...	additional arguments	none	Passed to or from other methods

Examples

Basic usage

# Simple mean of a vector
values <- c(10, 20, 30, 40, 50)
mean(values)
# [1] 25

Handling NA values

# Vector with NA values
data_with_na <- c(1, 2, NA, 4, 5)

# Without na.rm - returns NA
mean(data_with_na)
# [1] NA

# With na.rm = TRUE - ignores NA
mean(data_with_na, na.rm = TRUE)
# [1] 3.5

Using trim for robust statistics

# Vector with outliers
outlier_data <- c(1, 2, 3, 4, 5, 100)

# Regular mean - affected by outlier
mean(outlier_data)
# [1] 19.16667

# Trimmed mean - removes 10% from each end
mean(outlier_data, trim = 0.1)
# [1] 3.5

Mean of matrix columns

# Calculate mean of each column in a matrix
mat <- matrix(1:12, nrow = 3, ncol = 4)
colMeans(mat)
# [1]  5  6  7  8

Common Patterns

Weighted mean

# Use weighted.mean() for weighted calculations
weights <- c(0.1, 0.2, 0.3, 0.2, 0.2)
values <- c(10, 20, 30, 40, 50)
weighted.mean(values, weights)
# [1] 3.51

Mean by group using tapply

# Calculate mean by group
group <- c("A", "A", "B", "B", "A", "B")
value <- c(10, 15, 20, 25, 12, 22)
tapply(value, group, mean)
#   A    B 
# 12.3 22.3

Running mean (rolling average)

# Calculate rolling mean
x <- 1:10
library(zoo)
rollmean(x, 3, align = "center")
# [1]  2  3  4  5  6  7  8  9

See Also

• base::sum
• base::sd