
library(testthat)

source("user_code.R")
test_that("all values in the specified column are lowercase", {
  simulated_data <- data.frame(
    id = 1:4,
    text = c("  Hello World! ", "Data  Science", "R-Programming ", "  Clean-Data ")
  )
  cleaned_data <- clean_character_column(simulated_data, "text", "-", "_")
  expect_true(all(cleaned_data$text == tolower(cleaned_data$text)), "Not all values in 'text' column are lowercase.")
})

test_that("all values in the specified column have no leading or trailing whitespace", {
  simulated_data <- data.frame(
    id = 1:4,
    text = c("  Hello World! ", "Data  Science", "R-Programming ", "  Clean-Data ")
  )
  cleaned_data <- clean_character_column(simulated_data, "text", "-", "_")
  expect_true(all(cleaned_data$text == trimws(cleaned_data$text)), "Not all values in 'text' column are trimmed.")
})

test_that("all occurrences of the specified character are replaced in the specified column", {
  simulated_data <- data.frame(
    id = 1:4,
    text = c("  Hello World! ", "Data  Science", "R-Programming ", "  Clean-Data ")
  )
  cleaned_data <- clean_character_column(simulated_data, "text", "-", "_")
  expect_true(all(!grepl("-", cleaned_data$text)), "Not all '-' characters were replaced in 'text' column.")
  expect_true(any(grepl("_", cleaned_data$text)), "No '_' characters found after replacement in 'text' column.")
})

test_that("structure and non-target columns remain unchanged", {
  simulated_data <- data.frame(
    id = 1:4,
    text = c("  Hello World! ", "Data  Science", "R-Programming ", "  Clean-Data ")
  )
  cleaned_data <- clean_character_column(simulated_data, "text", "-", "_")
  expect_equal(cleaned_data$id, simulated_data$id, info = "The 'id' column has changed.")
  expect_equal(ncol(cleaned_data), ncol(simulated_data), info = "The number of columns has changed.")
})

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: String Manipulation for Data Cleaning

Solution