
library(testthat)

source("user_code.R")
test_that("returns only rows where the value in condition_col matches condition_value", {
  df <- data.frame(Name = c("Alice", "Bob", "Charlie", "Diana"),
                   Age = c(25, 30, 35, 40),
                   Gender = c("F", "M", "M", "F"),
                   Score = c(88, 92, 85, 90))
  result <- subset_and_select(df, "Gender", "F", c("Name", "Score"))
  expect_equal(nrow(result), 2, info = "Should return only rows where Gender is 'F'")
  expect_true(all(result$Name %in% c("Alice", "Diana")), info = "Names should be 'Alice' and 'Diana'")
})

test_that("returns only the columns specified in select_cols", {
  df <- data.frame(Name = c("Alice", "Bob"),
                   Age = c(25, 30),
                   Gender = c("F", "M"),
                   Score = c(88, 92))
  result <- subset_and_select(df, "Gender", "F", c("Name", "Score"))
  expect_equal(colnames(result), c("Name", "Score"), info = "Should only include 'Name' and 'Score' columns")
})

test_that("handles cases where no rows match the condition by returning an empty data frame with the correct columns", {
  df <- data.frame(Name = c("Alice", "Bob"),
                   Age = c(25, 30),
                   Gender = c("F", "M"),
                   Score = c(88, 92))
  result <- subset_and_select(df, "Gender", "X", c("Name", "Score"))
  expect_equal(nrow(result), 0, info = "Should return zero rows if no match")
  expect_equal(colnames(result), c("Name", "Score"), info = "Should return correct column names even when empty")
})

test_that("handles cases where select_cols contains a single column", {
  df <- data.frame(Name = c("Alice", "Bob", "Charlie"),
                   Age = c(25, 30, 35),
                   Gender = c("F", "M", "M"),
                   Score = c(88, 92, 85))
  result <- subset_and_select(df, "Gender", "M", c("Score"))
  expect_equal(colnames(result), "Score", info = "Should only include 'Score' column")
  expect_equal(nrow(result), 2, info = "Should return two rows where Gender is 'M'")
})

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: Slicing and Subsetting

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