
library(testthat)

source("user_code.R")
test_that("geneA_sample1 is 5.2", {
  expect_true(exists("geneA_sample1"), info = "geneA_sample1 does not exist.")
  expect_equal(geneA_sample1, 5.2, tolerance = 1e-8, info = "geneA_sample1 is not equal to 5.2.")
})

test_that("geneA_sample2 is 6.8", {
  expect_true(exists("geneA_sample2"), info = "geneA_sample2 does not exist.")
  expect_equal(geneA_sample2, 6.8, tolerance = 1e-8, info = "geneA_sample2 is not equal to 6.8.")
})

test_that("geneA_sample3 is 7.1", {
  expect_true(exists("geneA_sample3"), info = "geneA_sample3 does not exist.")
  expect_equal(geneA_sample3, 7.1, tolerance = 1e-8, info = "geneA_sample3 is not equal to 7.1.")
})

test_that("geneA_expression vector is correct", {
  expect_true(exists("geneA_expression"), info = "geneA_expression does not exist.")
  expect_true(is.numeric(geneA_expression), info = "geneA_expression is not numeric.")
  expect_equal(length(geneA_expression), 3, info = "geneA_expression does not have length 3.")
  expect_equal(geneA_expression, c(5.2, 6.8, 7.1), tolerance = 1e-8, info = "geneA_expression does not contain the correct values in the correct order.")
})

test_that("geneA_mean is correct", {
  expect_true(exists("geneA_mean"), info = "geneA_mean does not exist.")
  expect_equal(geneA_mean, mean(c(5.2, 6.8, 7.1)), tolerance = 1e-8, info = "geneA_mean does not contain the correct mean value.")
})

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: Biological Variables and Vectors

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