
library(testthat)

source("user_code.R")
library(testthat)

source("user_code.R")
test_that("data directory contains sample data CSV", {
  expect_true(file.exists("data/sample_data.csv"),
    info = "sample_data.csv should exist in data directory")
})

test_that("results directory contains analysis results CSV", {
  expect_true(file.exists("results/analysis_results.csv"),
    info = "analysis_results.csv should exist in results directory")
})

test_that("scripts directory contains R analysis script", {
  expect_true(file.exists("scripts/analysis_script.R"),
    info = "analysis_script.R should exist in scripts directory")
})

test_that("main directory contains README file", {
  expect_true(file.exists("README.txt"),
    info = "README.txt should exist in main directory")
})

test_that("project_files includes all created directories and files", {
  out_user_code <- capture.output(source("user_code.R"))
  expect_true(any(grepl("data/sample_data.csv", out_user_code)),
    info = "data/sample_data.csv should be listed in project_files")
  expect_true(any(grepl("results/analysis_results.csv", out_user_code)),
    info = "results/analysis_results.csv should be listed in project_files")
  expect_true(any(grepl("scripts/analysis_script.R", out_user_code)),
    info = "scripts/analysis_script.R should be listed in project_files")
  expect_true(any(grepl("README.txt", out_user_code)),
    info = "README.txt should be listed in project_files")
})

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: Sharing and Collaboration

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