You have explored the basics of joining data frames in R, including how to combine tables using matching keys. Now, you will practice handling more complex scenarios where data frames have mismatched keys—meaning not every row in one data frame has a corresponding match in the other. This is a common challenge in real-world data integration tasks, and mastering these join types is essential for robust analytics workflows. In this challenge, you will use `right_join()`, `full_join()`, and `anti_join()` to combine data frames and identify unmatched rows.


library(testthat)

source("user_code.R")
test_that("right_joined contains all rows from orders and matches customers where possible", {
  expect_true(exists("right_joined"))
  expect_true(is.data.frame(right_joined))
  # There should be 5 rows (one for each order)
  expect_equal(nrow(right_joined), 5)
  # All order_id values from orders should be present
  expect_setequal(right_joined$order_id, c(101, 102, 103, 104, 105))
  # Check that unmatched customer_ids (5, 6) have NA for name
  unmatched_rows <- right_joined$customer_id %in% c(5, 6)
  expect_true(all(is.na(right_joined$name[unmatched_rows])))
  # Check that customer names are correct for matched rows
  expect_equal(right_joined$name[right_joined$customer_id == 2], rep("Bob", 2))
  expect_equal(right_joined$name[right_joined$customer_id == 3], "Charlie")
})

test_that("fully_joined contains all rows from both customers and orders", {
  expect_true(exists("fully_joined"))
  expect_true(is.data.frame(fully_joined))
  # There should be 7 rows (4 unique customers + 2 unmatched orders)
  expect_equal(nrow(fully_joined), 7)
  # All customer_id values from both tables should be present
  expect_setequal(fully_joined$customer_id, c(1, 2, 3, 4, 5, 6))
  # Check that unmatched customers (customer_id 1, 4) have NA for order_id and amount
  no_order_rows <- fully_joined$customer_id %in% c(1, 4)
  expect_true(all(is.na(fully_joined$order_id[no_order_rows])))
  expect_true(all(is.na(fully_joined$amount[no_order_rows])))
  # Check that unmatched orders (customer_id 5, 6) have NA for name
  no_customer_rows <- fully_joined$customer_id %in% c(5, 6)
  expect_true(all(is.na(fully_joined$name[no_customer_rows])))
})

test_that("no_orders contains only customers without orders", {
  expect_true(exists("no_orders"))
  expect_true(is.data.frame(no_orders))
  # Only customers 1 and 4 have no orders
  expect_equal(nrow(no_orders), 2)
  expect_setequal(no_orders$customer_id, c(1, 4))
  expect_setequal(no_orders$name, c("Alice", "Diana"))
})

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: Handle Mismatched Data Joins

Ratkaisu

Challenge: Handle Mismatched Data Joins

Ratkaisu