In this chapter, you will apply the grouping and aggregation techniques you have learned to create a summary report for a business dashboard. You will use your knowledge of `group_by()`, `summarise()`, and arranging data to produce a concise, actionable summary suitable for business decision-makers. This challenge will help solidify your understanding of how to transform raw data into meaningful insights by chaining together multiple data manipulation steps in R.


library(testthat)

source("user_code.R")
test_that("groups by region", {
    summary <- build_summary_report(sales_data)
    expect_true("region" %in% colnames(summary),
      info = "The summary data frame must have a 'region' column.")
    expect_equal(nrow(summary), length(unique(sales_data$region)),
      info = "There should be one row per unique region.")
})

test_that("calculates total sales per region", {
    summary <- build_summary_report(sales_data)
    north_sales <- sum(sales_data$sales[sales_data$region == "North"])
    expect_equal(summary$total_sales[summary$region == "North"], north_sales,
      info = "Total sales for North region should be correct.")
    south_sales <- sum(sales_data$sales[sales_data$region == "South"])
    expect_equal(summary$total_sales[summary$region == "South"], south_sales,
      info = "Total sales for South region should be correct.")
})

test_that("calculates average profit per region", {
    summary <- build_summary_report(sales_data)
    east_avg <- mean(sales_data$profit[sales_data$region == "East"])
    expect_equal(summary$avg_profit[summary$region == "East"], east_avg,
      info = "Average profit for East region should be correct.")
    west_avg <- mean(sales_data$profit[sales_data$region == "West"])
    expect_equal(summary$avg_profit[summary$region == "West"], west_avg,
      info = "Average profit for West region should be correct.")
})

test_that("arranges by total_sales descending", {
    summary <- build_summary_report(sales_data)
    expect_true(is.unsorted(-summary$total_sales) == FALSE,
      info = "Rows must be arranged in descending order by total_sales.")
})

test_that("returns correct columns", {
    summary <- build_summary_report(sales_data)
    expect_true(all(c("region", "total_sales", "avg_profit") %in% colnames(summary)),
      info = "Summary must contain 'region', 'total_sales', and 'avg_profit' columns.")
    expect_equal(length(colnames(summary)), 3,
      info = "Summary data frame must have exactly three 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: Build a Summary Report

Ratkaisu

Challenge: Build a Summary Report

Ratkaisu