
library(testthat)

source("user_code.R")
if (!requireNamespace("ggplot2", quietly = TRUE)) install.packages("ggplot2")
library(testthat)
library(ggplot2)
source("user_code.R")

test_that("hist_plot is a ggplot object of histogram", {
  expect_true(exists("hist_plot"), info = "The variable 'hist_plot' should exist.")
  expect_true(any(class(hist_plot) == "ggplot"), info = "'hist_plot' should be a ggplot object.")
  layers <- sapply(hist_plot$layers, function(x) class(x$geom)[1])
  expect_true(any(layers == "GeomBar"), info = "'hist_plot' should include a histogram geom (GeomBar).")
})

test_that("box_plot is a ggplot object of boxplot", {
  expect_true(exists("box_plot"), info = "The variable 'box_plot' should exist.")
  expect_true(any(class(box_plot) == "ggplot"), info = "'box_plot' should be a ggplot object.")
  layers <- sapply(box_plot$layers, function(x) class(x$geom)[1])
  expect_true(any(layers == "GeomBoxplot"), info = "'box_plot' should include a boxplot geom (GeomBoxplot).")
})

test_that("histogram_comment is a non-empty character string", {
  expect_true(exists("histogram_comment", inherits = TRUE),
              info = "The variable 'histogram_comment' should exist.")
  txt <- get("histogram_comment", inherits = TRUE)

  expect_true(is.character(txt), info = "'histogram_comment' should be character.")
  expect_equal(length(txt), 1, info = "'histogram_comment' should be a single string.")
  expect_true(nzchar(txt), info = "'histogram_comment' should not be empty.")
})

test_that("boxplot_comment is a non-empty character string", {
  expect_true(exists("boxplot_comment", inherits = TRUE),
              info = "The variable 'boxplot_comment' should exist.")
  txt <- get("boxplot_comment", inherits = TRUE)

  expect_true(is.character(txt), info = "'boxplot_comment' should be character.")
  expect_equal(length(txt), 1, info = "'boxplot_comment' should be a single string.")
  expect_true(nzchar(txt), info = "'boxplot_comment' should not be empty.")
})


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: Visualize Data Distributions

解答