
library(testthat)

source("user_code.R")
test_that("mean_control is correct", {
  expect_true(exists("mean_control"), info = "mean_control should exist.")
  expect_type(mean_control, "double")
  expect_equal(mean_control, mean(c(5.2, 4.8, 5.0)), tolerance = 1e-8)
})

test_that("mean_treated is correct", {
  expect_true(exists("mean_treated"), info = "mean_treated should exist.")
  expect_type(mean_treated, "double")
  expect_equal(mean_treated, mean(c(7.1, 7.3, 6.9)), tolerance = 1e-8)
})

test_that("test_result is a t.test object and result is correct", {
  expect_true(exists("test_result"), info = "test_result should exist.")
  expect_s3_class(test_result, "htest")
  expect_equal(test_result$p.value, t.test(c(5.2, 4.8, 5.0), c(7.1, 7.3, 6.9))$p.value, tolerance = 1e-8)
})

test_that("interpretation is a string and matches p-value significance", {
  expect_true(exists("interpretation"), info = "interpretation should exist.")
  expect_type(interpretation, "character")
  pval <- test_result$p.value
  if (pval < 0.05) {
    expect_equal(interpretation, "There is a statistically significant difference in gene expression between control and treated samples.")
  } else {
    expect_equal(interpretation, "There is no statistically significant difference in gene expression between control and treated samples.")
  }
})

test_main.R

A hands-on introduction to R programming tailored for biologists and bioinformaticians. Learn to analyze, visualize, and interpret biological data using R, with real-world examples from genomics, ecology, and experimental biology.

Learn the essentials of R programming with a focus on biological data, including variables, data types, and basic data manipulation.

Dive into statistical methods for analyzing biological data, including summary statistics, hypothesis testing, and interpreting results.

Master the art of visualizing biological data using R, from basic plots to more advanced visualizations relevant to experiments.

Learn to work with genomic-style data and develop reproducible workflows for scientific research in R.

Challenge: Reproducible Analysis

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