
library(testthat)

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

source("user_code.R")
test_that("data frame has correct columns and 10 rows before filtering", {
  set.seed(42)
  df <- data.frame(
    id = 1:10,
    age = sample(18:65, 10, replace = TRUE),
    score = sample(50:100, 10, replace = TRUE)
  )
  expect_true(all(c("id", "age", "score") %in% colnames(df)), "Data frame should have columns id, age, and score.")
  expect_equal(nrow(df), 10, info = "Data frame should have 10 rows before filtering.")
})

test_that("output contains only rows where age > 30", {
  result_df <- simulate_and_filter()
  expect_true(all(result_df$id %in% 1:10), 'All ids should be within 1 to 10.')
  # Recreate the original ages to match ids
  set.seed(42)
  ages <- sample(18:65, 10, replace = TRUE)
  filtered_ids <- (1:10)[ages > 30]
  expect_true(all(result_df$id %in% filtered_ids), 'Only ids with age > 30 should be present.')
  expect_true(nrow(result_df) == length(filtered_ids), 'Number of rows should match number of ages > 30.')
})

test_that("output contains only id and score columns", {
  result_df <- simulate_and_filter()
  expect_equal(colnames(result_df), c("id", "score"), info = "Output should contain only id and score columns.")
})

test_that("function returns a data frame", {
  result_df <- simulate_and_filter()
  expect_s3_class(result_df, "data.frame")
})

test_main.R

This practical course introduces learners to essential data cleaning and wrangling techniques in R. Through hands-on exercises, students will learn how to handle missing data, transform and recode variables, filter and select relevant data, and prepare datasets for analysis. The course emphasizes using popular R packages such as dplyr and tidyr to streamline and automate data preparation workflows, equipping learners with the foundational skills needed for effective data analysis.

Master the core techniques for cleaning and wrangling data in R using dplyr and tidyr. Each concept is introduced in a theory chapter, followed by a practical challenge.

Challenge: Filtering and Selecting Data

Ratkaisu