library(testthat)
library(data.table)
library(DTwrappers2)

# Prepare the iris dataset
data("iris")
setDT(iris)
# Adding a column with missing values for testing
iris$noise <- ifelse(runif(nrow(iris)) > 0.98, NA, runif(nrow(iris)))

describe("dt.mean.missing functionality", {

  it("calculates the proportion of missing values correctly", {
    missing_values <- dt.mean.missing(
      dt.name = "iris",
      the.variables = c("Sepal.Length", "noise")
    )

    # Check if the result is a data.table
    expect_true(is.data.table(missing_values), info = "Should return a data.table")

  })

  it("applies filters and grouping correctly", {
    filtered_grouped_missing_values <- dt.mean.missing(
      dt.name = "iris",
      the.variables = c("noise", "Sepal.Width"),
      the.filter = "Sepal.Length > 4 & Sepal.Length < 6",
      grouping.variables = "Species"
    )

    # Check if the result is a data.table
    expect_true(is.data.table(filtered_grouped_missing_values), info = "Filtered and grouped result should be a data.table")


  })


})