
library(testthat)

source("user_code.R")
test_that("impute_missing replaces all NA values with specified value", {
  df <- data.frame(a = c(1, NA, 3), b = c(NA, 2, 3))
  res <- impute_missing(df, 0)
  expect_true(all(!is.na(res)), "All NA values should be replaced.")
  expect_equal(res$a, c(1, 0, 3),
               info = "Non-missing values should remain unchanged and NA replaced with 0 in column a.")
  expect_equal(res$b, c(0, 2, 3),
               info = "Non-missing values should remain unchanged and NA replaced with 0 in column b.")
})

test_that("impute_missing leaves non-missing values unchanged", {
  df <- data.frame(a = c(5, 6), b = c(7, 8))
  res <- impute_missing(df, 99)
  expect_equal(res, df, info = "No values should be changed if there are no NAs.")
})

test_that("impute_missing works when all values are NA", {
  df <- data.frame(x = c(NA, NA), y = c(NA, NA))
  res <- impute_missing(df, -1)
  expect_true(all(res == -1), "All NAs should be replaced with -1.")
})

test_that("impute_missing works with mixed NA and non-NA values", {
  df <- data.frame(x = c(NA, 2, NA), y = c(5, NA, 3))
  res <- impute_missing(df, 7)
  expect_equal(res$x, c(7, 2, 7), info = "NA values in x replaced, non-NAs unchanged.")
  expect_equal(res$y, c(5, 7, 3), info = "NA values in y replaced, non-NAs unchanged.")
})

test_main.R

Master the essential R data structures used in exploratory data analysis (EDA), focusing on practical applications and hands-on challenges. This course is designed for learners already familiar with R syntax, core data structures, and data cleaning/wrangling techniques.

Explore the essential R data structures that power effective exploratory data analysis, with a focus on practical usage and hands-on challenges.

Challenge: Handling Missing Data

Ratkaisu