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# lapply — always returns a list
nums <- list(a = 1:5, b = 6:10, c = 11:15)
lapply(nums, mean)          # list of means

# sapply — simplifies if possible
sapply(nums, mean)          # named numeric vector: a=3 b=8 c=13

# vapply — type-safe sapply
vapply(nums, mean, FUN.VALUE = numeric(1))  # same but strict

# mapply — multivariate apply
mapply(function(x, y) x + y,
       x = c(1, 2, 3),
       y = c(10, 20, 30))   # 11 22 33

# Reduce and Filter (functional primitives)
Reduce("+", 1:5)                        # 15  (cumulative sum)
Reduce("+", 1:5, accumulate = TRUE)     # 1 3 6 10 15
Filter(function(x) x %% 2 == 0, 1:10)  # 2 4 6 8 10
Map(function(x, y) x * y,
    list(1,2,3), list(10,20,30))        # list(10, 40, 90)

# Base pipe (R >= 4.1)
c(3, 1, 4, 1, 5, 9) |> sort() |> unique() |> rev()
# 9 5 4 3 1

# Magrittr pipe style (common in older tidyverse code)
# library(dplyr)
# c(3,1,4,1,5,9) %>% sort() %>% unique() %>% rev()

# New backslash lambda syntax (R >= 4.1)
sapply(1:5, \(x) x^2)   # 1 4 9 16 25

# Partial application via wrapper functions
add_n <- function(n) function(x) x + n
add5 <- add_n(5)
sapply(1:4, add5)        # 6 7 8 9