
library(testthat)

source("user_code.R")
test_that("perform_t_test returns correct structure and types", {
  result <- perform_t_test(c(1, 2, 3), c(4, 5, 6))
  expect_true(is.list(result), info = "Result should be a list.")
  expect_true("p_value" %in% names(result), info = "List should contain 'p_value'.")
  expect_true("significant" %in% names(result), info = "List should contain 'significant'.")
  expect_true(is.numeric(result$p_value), info = "'p_value' should be numeric.")
  expect_true(is.logical(result$significant), info = "'significant' should be logical.")
})

test_that("perform_chi_squared_test returns correct structure and types", {
  result <- perform_chi_squared_test(c(10, 20, 30), c(12, 18, 30))
  expect_true(is.list(result), info = "Result should be a list.")
  expect_true("p_value" %in% names(result), info = "List should contain 'p_value'.")
  expect_true("significant" %in% names(result), info = "List should contain 'significant'.")
  expect_true(is.numeric(result$p_value), info = "'p_value' should be numeric.")
  expect_true(is.logical(result$significant), info = "'significant' should be logical.")
})

test_that("t-test detects significant difference when means differ", {
  group1 <- c(2, 2, 2, 2, 2)
  group2 <- c(10, 10, 10, 10, 10)
  result <- perform_t_test(group1, group2)
  expect_true(result$significant, info = "Should be significant when means are far apart.")
})

test_that("t-test detects non-significance when means are similar", {
  group1 <- c(5, 5, 5, 5, 5)
  group2 <- c(5.1, 5, 5, 5, 5)
  result <- perform_t_test(group1, group2)
  expect_false(result$significant, info = "Should not be significant when means are very close.")
})

test_that("chi-squared test detects significance for large frequency differences", {
  observed <- c(100, 10, 10)
  expected <- c(40, 40, 40)
  result <- perform_chi_squared_test(observed, expected)
  expect_true(result$significant, info = "Should be significant for large differences in frequencies.")
})

test_that("chi-squared test detects non-significance for similar frequencies", {
  observed <- c(10, 20, 30)
  expected <- c(10, 20, 30)
  result <- perform_chi_squared_test(observed, expected)
  expect_false(result$significant, info = "Should not be significant when observed and expected are equal.")
})

test_main.R

Een praktische introductie tot R-programmeren, afgestemd op biologen en bioinformatici. Leer biologische data analyseren, visualiseren en interpreteren met R, aan de hand van praktijkvoorbeelden uit genomica, ecologie en experimentele biologie.

Leer de basisprincipes van R-programmeren met de nadruk op biologische data, inclusief variabelen, datatypes en basisgegevensmanipulatie.

Verdiep u in statistische methoden voor het analyseren van biologische gegevens, inclusief samenvattende statistieken, hypothesetoetsing en het interpreteren van resultaten.

Beheers de kunst van het visualiseren van biologische data met R, van eenvoudige grafieken tot meer geavanceerde visualisaties die relevant zijn voor experimenten.

Leer werken met genomische data en het ontwikkelen van reproduceerbare workflows voor wetenschappelijk onderzoek in R.

Challenge: Hypothesetoetsing met Biologische Gegevens

Oplossing