In biological research, sharing your analysis with collaborators is crucial for **transparency**, **reproducibility**, and advancing science. Organizing your `R` project, exporting analysis results, and preparing files for sharing ensures that others can follow your workflow, understand your findings, and build upon your work. As you prepare to collaborate, you'll need to structure your project directory, save key results in accessible formats, and gather all necessary files for easy transfer. This challenge will help you practice these essential skills and reinforce your understanding of **reproducible** and **collaborative analysis** in `R`.


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

解答