
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

生物学者およびバイオインフォマティクス研究者向けに特化した、Rプログラミングの実践的な入門コースです。Rを用いて生物学データの解析、可視化、解釈を学び、ゲノミクス、生態学、実験生物学の実例を通じて理解を深めます。

生物学データに焦点を当てたRプログラミングの基本を学びます。変数、データ型、基本的なデータ操作を含みます。

生物データを分析するための統計手法に深く踏み込み、要約統計量、仮説検定、結果の解釈などを含みます。

Rを使用して生物学データの可視化技術を習得し、基本的なプロットから実験に関連する高度な可視化までを網羅します。

ゲノムスタイルのデータを扱い、Rで科学研究のための再現可能なワークフローを開発する方法を学びます。

チャレンジ：ゲノムデータの操作

解答