In business analytics, you often need to summarize sales data by categories such as region to create concise, actionable reports. After learning how to use `group_by()` to segment your data, you are ready to apply grouping and summarization skills to a practical scenario: analyzing sales by region. This type of analysis helps businesses identify high-performing areas and target strategies more effectively.


library(testthat)

source("user_code.R")
test_that("returns one row per unique region", {
  result <- summarise_regional_sales(sales_data)
  expected_regions <- unique(sales_data$region)
  expect_equal(nrow(result), length(expected_regions),
               info = "The number of rows should match the number of unique regions.")
})

test_that("total_sales column is correct per region", {
  result <- summarise_regional_sales(sales_data)
  for (region_name in unique(sales_data$region)) {
    expected_total <- sum(sales_data$sales[sales_data$region == region_name])
    actual_total <- result$total_sales[result$region == region_name]
    expect_equal(actual_total, expected_total,
                 info = paste0("Total sales for ", region_name, " should be ", expected_total, "."))
  }
})

test_that("average_sales column is correct per region", {
  result <- summarise_regional_sales(sales_data)
  for (region_name in unique(sales_data$region)) {
    expected_avg <- mean(sales_data$sales[sales_data$region == region_name])
    actual_avg <- result$average_sales[result$region == region_name]
    expect_equal(actual_avg, expected_avg,
                 info = paste0("Average sales for ", region_name, " should be ", expected_avg, "."))
  }
})

test_that("output includes correct columns", {
  result <- summarise_regional_sales(sales_data)
  expected_cols <- c("region", "total_sales", "average_sales")
  expect_true(all(expected_cols %in% colnames(result)),
              info = "Returned data frame must include columns: region, total_sales, and average_sales.")
})

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: Group and Summarise Regional Sales

Ratkaisu

Challenge: Group and Summarise Regional Sales

Ratkaisu