You have learned how to use pipes to streamline your data manipulation code and how best practices like clear formatting and comments can make pipelines much easier to read and maintain. Now, you will put these skills to the test by refactoring a messy and hard-to-follow pipeline into a clean, well-documented sequence of operations for a realistic data cleaning scenario. This exercise will challenge you to apply everything you have learned about writing readable, professional pipelines in R.


library(testthat)

source("user_code.R")
test_that("removes rows with missing customer_id", {
  test_data <- data.frame(
    customer_id = c(1, NA, 2),
    first_name = c("A ", " B", "C"),
    last_name = c("X", "Y ", " Z"),
    status = c("active", "active", "active"),
    signup_date = as.Date(c("2023-01-01", "2023-01-02", "2023-01-03"))
  )
  res <- clean_customer_data(test_data)
  expect_false(any(is.na(res$customer_id)),
    info = "All rows with missing customer_id should be removed.")
  expect_equal(nrow(res), 2, info = "Rows with missing customer_id should not be present.")
})

test_that("trims whitespace from first_name and last_name", {
  test_data <- data.frame(
    customer_id = 1,
    first_name = " Anna ",
    last_name = " Smith ",
    status = "active",
    signup_date = as.Date("2023-01-01")
  )
  res <- clean_customer_data(test_data)
  expect_equal(res$first_name, "Anna", info = "Whitespace should be trimmed from first_name.")
  expect_equal(res$last_name, "Smith", info = "Whitespace should be trimmed from last_name.")
})

test_that("keeps only rows with status == 'active'", {
  test_data <- data.frame(
    customer_id = c(1, 2),
    first_name = c("A", "B"),
    last_name = c("X", "Y"),
    status = c("active", "inactive"),
    signup_date = as.Date(c("2023-01-01", "2023-01-02"))
  )
  res <- clean_customer_data(test_data)
  expect_true(all(res$status == "active"), info = "Only rows with status 'active' should be kept.")
  expect_equal(nrow(res), 1, info = "Only one row should remain after filtering for 'active'.")
})

test_that("adds full_name column combining first_name and last_name", {
  test_data <- data.frame(
    customer_id = 1,
    first_name = "Anna",
    last_name = "Smith",
    status = "active",
    signup_date = as.Date("2023-01-01")
  )
  res <- clean_customer_data(test_data)
  expect_true("full_name" %in% colnames(res), info = "full_name column should exist.")
  expect_equal(res$full_name, "Anna Smith", info = "full_name should combine first_name and last_name with a space.")
})

test_that("arranges rows by signup_date descending", {
  test_data <- data.frame(
    customer_id = c(1, 2),
    first_name = c("A", "B"),
    last_name = c("X", "Y"),
    status = c("active", "active"),
    signup_date = as.Date(c("2023-01-01", "2023-01-03"))
  )
  res <- clean_customer_data(test_data)
  expect_true(res$signup_date[1] > res$signup_date[2], info = "Rows should be arranged in descending order by signup_date.")
})

test_main.R

Master the essential data manipulation skills in R using dplyr, grouping and aggregation, and joining data frames. This course is designed for beginners and provides hands-on, real-world tasks to build your confidence in transforming and analyzing data for analytics.

Learn the foundational verbs of dplyr for selecting, filtering, mutating, arranging, and summarizing data. Build confidence in transforming data frames for analytics.

Master the use of pipes (%>%) to chain multiple data manipulation steps for readable and efficient R code.

Learn to group data and calculate aggregated metrics for powerful reporting and analytics.

Master the art of combining data from multiple sources using different types of joins in dplyr.

Challenge: Write a Readable Pipeline

Ratkaisu

Challenge: Write a Readable Pipeline

Ratkaisu