The aggregate() function is used to aggregate data in R. There are two methods which are collapsing data by using one or more BY variable and other is an aggregate() function in which By variable should be in the list.

In R, the aggregate() function is used to compute summary statistics for data subsets. It is particularly useful for aggregating or summarizing data based on different factors or grouping variables. The basic syntax of the aggregate() function is as follows:

aggregate(formula, data, FUN, …)
formula: A formula specifying the variable(s) to be aggregated and the grouping variable(s).
data: The data frame containing the variables.
FUN: The function to be applied for aggregation (e.g., sum, mean, median, etc.).
…: Additional arguments to be passed to the aggregation function.
Here’s a breakdown of the parameters:

formula: This is a formula specifying the variables to be aggregated and the grouping variable(s). For example, if you want to aggregate a variable X based on the levels of a factor variable Group, you would use a formula like X ~ Group.

data: This parameter specifies the data frame containing the variables mentioned in the formula.

FUN: This is the aggregation function to be applied. It can be any R function that takes a vector as input and produces a summary statistic. Commonly used functions include sum, mean, median, min, max, etc.

…: Additional arguments to be passed to the aggregation function. For example, you might want to specify na.rm = TRUE if you want to exclude missing values from the computation.

Here’s an example to illustrate the usage of aggregate():
# Create a sample data frame
data <- data.frame(
Group = rep(c(“A”, “B”, “C”), each = 3),
Value = c(10, 15, 20, 5, 8, 12, 30, 25, 22)
)

# Use aggregate to find the mean Value for each Group
result <- aggregate(Value ~ Group, data = data, FUN = mean)

# Display the result
print(result)
In this example, the aggregate() function is used to find the mean of the Value variable for each level of the Group variable in the data data frame. The result will be a new data frame with the aggregated values.