
library(testthat)

source("user_code.R")
test_that("function returns a vector of same length as input", {
  input_vec <- c(1, 2, 3, 4)
  result <- apply_function_to_vector(input_vec, function(x) x + 1)
  expect_equal(length(result), length(input_vec),
    info = "Output vector length should match input vector length.")
})

test_that("function applies provided function to each element", {
  input_vec <- c(2, 4, 6)
  result <- apply_function_to_vector(input_vec, function(x) x * 2)
  expect_equal(result, c(4, 8, 12),
    info = "Function should correctly apply the provided function to each element.")
})

test_that("function works with numeric vectors and returns expected results", {
  input_vec <- c(10, 20, 30)
  result <- apply_function_to_vector(input_vec, sqrt)
  expect_equal(result, sqrt(input_vec),
    info = "Function should return expected results for numeric vectors.")
})

test_that("function works with anonymous functions as argument", {
  input_vec <- c(3, 6, 9)
  result <- apply_function_to_vector(input_vec, function(x) x/3)
  expect_equal(result, c(1, 2, 3),
    info = "Function should work with anonymous functions as the second argument.")
})

test_main.R

Master the art of writing and using functions in R, from basic syntax to advanced functional programming techniques. This course guides you through creating your own functions, leveraging anonymous functions, and applying functional programming concepts to solve real-world problems in R.

Learn how to define, call, and utilize functions in R, including understanding parameters, return values, and default arguments.

Discover how to use anonymous (inline) functions for concise, on-the-fly operations, and see how they fit into R's functional programming paradigm.

Apply functional programming concepts in R to solve real-world problems, focusing on writing concise, reusable, and efficient code.

Challenge: Functional Programming Basics

Ratkaisu

Challenge: Functional Programming Basics

Ratkaisu