
library(testthat)

source("user_code.R")
test_that("summary_sales_table exists and is a data frame", {
  expect_true(exists("summary_sales_table"), info = "summary_sales_table should be created.")
  expect_true(is.data.frame(summary_sales_table), info = "summary_sales_table should be a data frame.")
})

test_that("summary_sales_table has correct columns", {
  expect_true(all(c("region", "total_sales", "average_sales") %in% colnames(summary_sales_table)),
              info = "summary_sales_table should have columns: region, total_sales, average_sales.")
})

test_that("summary_sales_table contains correct statistics for North region", {
  north_row <- summary_sales_table[summary_sales_table$region == "North", ]
  expect_equal(north_row$total_sales, 120 + 135 + 128, info = "Total sales for North region should be correct.")
  expect_equal(north_row$average_sales, mean(c(120, 135, 128)), info = "Average sales for North region should be correct.")
})

test_that("summary_sales_table contains correct statistics for South region", {
  south_row <- summary_sales_table[summary_sales_table$region == "South", ]
  expect_equal(south_row$total_sales, 100 + 98 + 105, info = "Total sales for South region should be correct.")
  expect_equal(south_row$average_sales, mean(c(100, 98, 105)), info = "Average sales for South region should be correct.")
})

test_that("summary_sales_table contains correct statistics for East region", {
  east_row <- summary_sales_table[summary_sales_table$region == "East", ]
  expect_equal(east_row$total_sales, 150 + 160 + 155, info = "Total sales for East region should be correct.")
  expect_equal(east_row$average_sales, mean(c(150, 160, 155)), info = "Average sales for East region should be correct.")
})

test_that("summary_sales_table has one row per region", {
  expect_equal(nrow(summary_sales_table), 3, info = "summary_sales_table should have one row per region.")
})

test_main.R

Master the essentials of data visualization and reporting in R. Learn to create compelling graphics, perform exploratory data analysis, and communicate insights effectively using R's powerful tools.

Learn the basics of data visualization in R, focusing on the ggplot2 package and its core plotting capabilities.

Dive into EDA techniques in R, including summary statistics, visual inspection, and outlier detection.

Learn how to turn your visualizations and analyses into clear, actionable reports.

Challenge: Summarize and Report Findings

解答