
library(testthat)

source("user_code.R")
test_that("age column is correctly calculated", {
  test_df <- data.frame(customer_id = c(1,2,3), name = c("A","B","C"), birth_year = c(2000, 1990, 1940), stringsAsFactors = FALSE)
  result <- add_age_and_segment(test_df)
  expect_true("age" %in% names(result), info = "The data frame must contain an 'age' column.")
  expect_equal(result$age, 2024 - test_df$birth_year, info = "The 'age' column values are not correctly calculated.")
})

test_that("segment column is correctly assigned for all age groups", {
  test_df <- data.frame(customer_id = 1:4, name = c("A","B","C","D"), birth_year = c(2010, 1995, 1960, 1940), stringsAsFactors = FALSE)
  result <- add_age_and_segment(test_df)
  expect_true("segment" %in% names(result), info = "The data frame must contain a 'segment' column.")
  expected_ages <- 2024 - test_df$birth_year
  expected_segments <- c("youth", "adult", "adult", "senior")
  expect_equal(result$segment, expected_segments, info = "The 'segment' column values are not correctly assigned.")
})

test_that("original columns and data are preserved", {
  test_df <- data.frame(customer_id = c(5,6), name = c("X","Y"), birth_year = c(1985, 1975), stringsAsFactors = FALSE)
  result <- add_age_and_segment(test_df)
  expect_equal(result$customer_id, test_df$customer_id, info = "Original 'customer_id' column data is not preserved.")
  expect_equal(result$name, test_df$name, info = "Original 'name' column data is not preserved.")
  expect_equal(result$birth_year, test_df$birth_year, info = "Original 'birth_year' column data is not preserved.")
})

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: Mutate Customer Data

Ratkaisu

Challenge: Mutate Customer Data

Ratkaisu