
library(testthat)

source("user_code.R")
test_that("returns only name, email, and city columns", {
  customers <- data.frame(
    name = c("Alice Smith", "Bob Jones", "Carol Lee", "David Kim"),
    email = c("alice@example.com", "bob@example.com", "carol@example.com", "david@example.com"),
    city = c("New York", "Los Angeles", "New York", "Chicago"),
    age = c(34, 28, 45, 39),
    stringsAsFactors = FALSE
  )
  result <- select_and_filter_customers(customers, "New York")
  expect_equal(colnames(result), c("name", "email", "city"),
               info = "Returned data frame should contain only name, email, and city columns.")
})

test_that("returns only rows with matching city", {
  customers <- data.frame(
    name = c("Alice Smith", "Bob Jones", "Carol Lee", "David Kim"),
    email = c("alice@example.com", "bob@example.com", "carol@example.com", "david@example.com"),
    city = c("New York", "Los Angeles", "New York", "Chicago"),
    age = c(34, 28, 45, 39),
    stringsAsFactors = FALSE
  )
  result <- select_and_filter_customers(customers, "New York")
  expect_true(all(result$city == "New York"),
              info = "All rows in the result should have city equal to target_city.")
  expect_equal(nrow(result), 2,
               info = "There should be two rows for New York.")
})

test_that("works for any city present in data frame", {
  customers <- data.frame(
    name = c("Alice Smith", "Bob Jones", "Carol Lee", "David Kim"),
    email = c("alice@example.com", "bob@example.com", "carol@example.com", "david@example.com"),
    city = c("New York", "Los Angeles", "New York", "Chicago"),
    age = c(34, 28, 45, 39),
    stringsAsFactors = FALSE
  )
  result <- select_and_filter_customers(customers, "Chicago")
  expect_equal(nrow(result), 1, info = "Should return one row for Chicago.")
  expect_equal(result$name, "David Kim", info = "Row should be for David Kim.")
})

test_that("returns empty data frame if no rows match", {
  customers <- data.frame(
    name = c("Alice Smith", "Bob Jones", "Carol Lee", "David Kim"),
    email = c("alice@example.com", "bob@example.com", "carol@example.com", "david@example.com"),
    city = c("New York", "Los Angeles", "New York", "Chicago"),
    age = c(34, 28, 45, 39),
    stringsAsFactors = FALSE
  )
  result <- select_and_filter_customers(customers, "Houston")
  expect_equal(nrow(result), 0, info = "Should return empty data frame if no city matches.")
  expect_equal(colnames(result), c("name", "email", "city"),
               info = "Even empty, result should have correct columns.")
})

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: Select and Filter Customer Data

Ratkaisu

Challenge: Select and Filter Customer Data

Ratkaisu