After learning the fundamentals of joining data frames in R, you are now ready to practice combining real-world datasets. Imagine you have two data frames: one containing customer information, and another containing order details. Your goal is to merge these datasets using appropriate join functions from the `dplyr` package, ensuring you can analyze unified customer and order data for further insights.


library(testthat)

source("user_code.R")
test_that("customer_orders_left contains all rows from customers", {
  expect_true(exists("customer_orders_left"), info = "customer_orders_left variable should exist.")
  expect_true(is.data.frame(customer_orders_left), info = "customer_orders_left should be a data frame.")
  expect_equal(nrow(customer_orders_left), 5, info = "customer_orders_left should have 5 rows (including duplicated customers for multiple orders).")
  expect_equal(sort(unique(customer_orders_left$customer_id)), sort(customers$customer_id), info = "customer_orders_left should contain all customer IDs from customers.")
})

test_that("customer_orders_left has NA for order columns where no orders exist", {
  expect_true(any(is.na(customer_orders_left$order_id)), info = "There should be NA in order_id for customers without orders.")
  expect_true(any(is.na(customer_orders_left$order_total)), info = "There should be NA in order_total for customers without orders.")
  no_order_rows <- customer_orders_left[is.na(customer_orders_left$order_id), ]
  expect_true(all(no_order_rows$customer_id == 4), info = "Customer with ID 4 should have NA in order columns.")
})

test_that("customer_orders_inner contains only customers with orders", {
  expect_true(exists("customer_orders_inner"), info = "customer_orders_inner variable should exist.")
  expect_true(is.data.frame(customer_orders_inner), info = "customer_orders_inner should be a data frame.")
  expect_equal(sort(unique(customer_orders_inner$customer_id)), sort(unique(orders$customer_id[orders$customer_id %in% customers$customer_id])), info = "customer_orders_inner should only contain customer IDs present in both data frames.")
  expect_false(any(customer_orders_inner$customer_id == 4), info = "Customer with ID 4 should not be present in customer_orders_inner.")
})

test_that("joins use customer_id as the key", {
  expect_true(all(c("customer_id", "customer_name", "order_id", "order_total") %in% colnames(customer_orders_left)), info = "customer_orders_left should have correct columns.")
  expect_true(all(c("customer_id", "customer_name", "order_id", "order_total") %in% colnames(customer_orders_inner)), info = "customer_orders_inner should have correct columns.")
})

test_that("variable names are correct", {
  expect_true(exists("customer_orders_left"), info = "Variable customer_orders_left should be defined.")
  expect_true(exists("customer_orders_inner"), info = "Variable customer_orders_inner should be defined.")
})

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: Join Customer and Order Data

Ratkaisu

Challenge: Join Customer and Order Data

Ratkaisu