Example 1. The Grandparent: apply()

Think of apply() as the customizable grandfather. It takes three arguments:

• X: Your data (matrix, array, data frame).
• MARGIN: Where to apply the function (rows = 1, columns = 2, both = c(1, 2)).
• FUN: The function to apply (e.g., mean, sum, your custom function).

Calculate the mean of each column in the iris dataset:

column_means <- apply(iris[, 1:4], 2, mean)
print(column_means)

Sepal.Length  Sepal.Width Petal.Length  Petal.Width 
    5.843333     3.057333     3.758000     1.199333 

Explanation: We apply mean (FUN) to each column (MARGIN = 2) of the first four columns (iris[, 1:4]) of the iris data frame, storing the results in column_means.

Example 2. The Speedy Sibling: lapply()

lapply() is the speed demon, applying a function to each element of a list or vector and returning a list of results.

Calculate the median of each petal length in a list of lists:

petal_lengths <- list(c(1.4, 1.5, 1.6), c(4.4, 4.5, 4.6))
petal_medians <- lapply(petal_lengths, median)
print(petal_medians)

[[1]]
[1] 1.5

[[2]]
[1] 4.5

Explanation: We apply median to each sub-list in petal_lengths, returning a list (petal_medians) containing the medians.

Example 3. The Streamlined Cousin: sapply()

sapply() is like lapply(), but it tries to simplify the output. If all results are of the same type (e.g., numeric), it returns a vector instead of a list.

Find the minimum value in each row of a matrix:

matrix <- matrix(1:12, nrow = 3)
row_mins <- sapply(1:nrow(matrix), function(i) min(matrix[i, ]))
print(row_mins)

[1] 1 2 3

Explanation: We use an anonymous function to find the minimum in each row (matrix[i, ]) and apply it to each row number (1:nrow(matrix)). sapply() simplifies the output to a vector of minimum values (row_mins).

Example 4. The Grouping Guru: tapply()

tapply() groups data based on another variable and applies a function to each group. Perfect for summarizing data by categories!

Calculate the average sepal length for each species in the iris dataset:

sepal_length_by_species <- tapply(iris$Sepal.Length, iris$Species, mean)
print(sepal_length_by_species)

    setosa versicolor  virginica 
     5.006      5.936      6.588 

Explanation: We group the Sepal.Length column by the Species column (using iris$Species) and calculate the mean (mean) for each group. The results are stored in sepal_length_by_species.