
library(testthat)

source("user_code.R")
test_that("function returns a list with correct names", {
  expect_true(exists("result"), info = "Result object should exist.")
  expect_true(is.list(result), info = "Result should be a list.")
  expect_true(all(c("subset_matrix", "avg_expression") %in% names(result)), info = "List should contain 'subset_matrix' and 'avg_expression'.")
})

test_that("subset_matrix contains correct genes and samples", {
  expected_genes <- c("GeneA", "GeneC")
  expected_samples <- c("Sample1", "Sample3")
  sm <- result$subset_matrix
  expect_true(is.matrix(sm), info = "subset_matrix should be a matrix.")
  expect_equal(rownames(sm), expected_genes, info = "Rows should match selected genes.")
  expect_equal(colnames(sm), expected_samples, info = "Columns should match selected samples.")
})

test_that("avg_expression is named numeric vector with correct values", {
  expected_genes <- c("GeneA", "GeneC")
  sm <- result$subset_matrix
  expected_avg <- rowMeans(sm)
  avg <- result$avg_expression
  expect_true(is.numeric(avg), info = "avg_expression should be numeric.")
  expect_equal(names(avg), expected_genes, info = "avg_expression should be named by gene.")
  expect_equal(as.numeric(avg), as.numeric(expected_avg), tolerance = 1e-8, info = "Averages should be correct.")
})

test_that("works with single gene selection", {
  single_gene <- c("GeneB")
  sg_result <- subset_gene_expression(expr_matrix, single_gene, sample_names)
  expect_equal(rownames(sg_result$subset_matrix), single_gene, info = "Single gene row name should match.")
  expect_equal(length(sg_result$avg_expression), 1, info = "Should return one average value for single gene.")
})

test_that("works with single sample selection", {
  single_sample <- c("Sample2")
  ss_result <- subset_gene_expression(expr_matrix, gene_names, single_sample)
  expect_equal(colnames(ss_result$subset_matrix), single_sample, info = "Single sample col name should match.")
  expect_equal(length(ss_result$avg_expression), 2, info = "Should return average for each gene (2 genes).")
  expect_equal(as.numeric(ss_result$avg_expression), as.numeric(ss_result$subset_matrix[,1]), tolerance = 1e-8, info = "Average should equal value for single sample.")
})

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: Manipulating Genomic Data

Ratkaisu

Challenge: Manipulating Genomic Data

Ratkaisu