
library(testthat)

source("user_code.R")
test_that("df1 has correct structure and at least 5 rows", {
  organize_experiment_data()
  expect_true(exists("df1"), info = "df1 must exist in the global environment.")
  expect_true(is.data.frame(df1), info = "df1 must be a data frame.")
  expect_true(all(c("SampleID", "Treatment", "Outcome") %in% colnames(df1)), info = "df1 must have columns 'SampleID', 'Treatment', and 'Outcome'.")
  expect_true(nrow(df1) >= 5, info = "df1 must have at least five rows.")
})

test_that("df2 filters only DrugA treatments", {
  organize_experiment_data()
  expect_true(exists("df2"), info = "df2 must exist in the global environment.")
  expect_true(is.data.frame(df2), info = "df2 must be a data frame.")
  expect_true(all(df2$Treatment == "DrugA"), info = "All rows in df2 must have Treatment as 'DrugA'.")
})

test_that("df3 filters only Alive outcomes", {
  organize_experiment_data()
  expect_true(exists("df3"), info = "df3 must exist in the global environment.")
  expect_true(is.data.frame(df3), info = "df3 must be a data frame.")
  expect_true(all(df3$Outcome == "Alive"), info = "All rows in df3 must have Outcome as 'Alive'.")
})

test_that("function does not require user input", {
  expect_error(organize_experiment_data(), NA, info = "The function should not require user input and should run without errors.")
})

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: Organizing Experimental Data

Solution