
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

Eine praxisorientierte Einführung in die R-Programmierung, zugeschnitten auf Biologen und Bioinformatiker. Analyse, Visualisierung und Interpretation biologischer Daten mit R anhand von praxisnahen Beispielen aus Genomik, Ökologie und experimenteller Biologie.

Erlernen Sie die Grundlagen der R-Programmierung mit Schwerpunkt auf biologischen Daten, einschließlich Variablen, Datentypen und grundlegender Datenmanipulation.

Vertiefung in statistische Methoden zur Analyse biologischer Daten, einschließlich zusammenfassender Statistiken, Hypothesentests und Interpretation von Ergebnissen.

Beherrschen Sie die Kunst der Visualisierung biologischer Daten mit R, von einfachen Diagrammen bis hin zu fortgeschrittenen Visualisierungen, die für Experimente relevant sind.

Erlernen Sie den Umgang mit genomischen Daten und die Entwicklung reproduzierbarer Workflows für wissenschaftliche Forschung in R.

Challenge: Hypothesentests mit biologischen Daten

Lösung