You have already explored how to select, filter, and mutate data using **dplyr**. Now, you will put your skills to the test by arranging and summarizing sales data. In this challenge, you will work with a `sales_data` data frame to uncover key performance insights. You will need to arrange the data by `revenue` and then use the `summarise()` function to calculate both total and average sales. This exercise will help you reinforce your understanding of two essential data manipulation techniques and see how they work together to answer important business questions.


library(testthat)

source("user_code.R")
test_that("arranged_sales is sorted by revenue descending", {
  expect_true(exists("arranged_sales"), info = "arranged_sales does not exist")
  expect_true(is.data.frame(arranged_sales), info = "arranged_sales should be a data frame")
  expect_equal(arranged_sales$revenue, sort(sales_data$revenue, decreasing = TRUE),
               info = "arranged_sales is not sorted by revenue descending")
})

test_that("sales_summary contains correct total_sales", {
  expect_true(exists("sales_summary"), info = "sales_summary does not exist")
  expect_true(is.data.frame(sales_summary), info = "sales_summary should be a data frame")
  expect_true("total_sales" %in% names(sales_summary), info = "sales_summary does not have total_sales column")
  expect_equal(sales_summary$total_sales, sum(sales_data$units_sold),
               info = "total_sales value is incorrect")
})

test_that("sales_summary contains correct average_sales", {
  expect_true(exists("sales_summary"), info = "sales_summary does not exist")
  expect_true(is.data.frame(sales_summary), info = "sales_summary should be a data frame")
  expect_true("average_sales" %in% names(sales_summary), info = "sales_summary does not have average_sales column")
  expect_equal(sales_summary$average_sales, mean(sales_data$units_sold),
               info = "average_sales value is incorrect")
})

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: Arrange and Summarise Sales Data

Ratkaisu

Challenge: Arrange and Summarise Sales Data

Ratkaisu