Add vdiffr snapshot tests for diagnostic plots (#236) #423
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Added 18 snapshot tests using vdiffr to ensure diagnostic plots render consistently across package versions. This helps catch visual regressions before they reach users. Tests cover check_model(), check_normality(), check_outliers(), and check_homogeneity() with various themes, colors, and options. Also validates that the large dataset performance fix produces visually consistent output. Fixes easystats#236
Summary of ChangesHello @ANAMASGARD, I'm Gemini Code Assist1! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed! This pull request significantly enhances the robustness of the plotting functions by adding a suite of Highlights
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Code Review
This pull request introduces a comprehensive suite of snapshot tests for diagnostic plots using the vdiffr package. The tests cover various functions like check_model(), check_normality(), check_homogeneity(), and check_outliers() with different options, themes, and even for large datasets, which is excellent for ensuring visual consistency and preventing regressions. The test code is well-structured and clearly commented. I have one suggestion to improve code maintainability by reducing repetitive set.seed() calls, which will make the test suite even cleaner.
| test_that("plot.see_check_model() renders correctly", { | ||
| skip_if_not_installed("performance") | ||
| skip_if_not_installed("see") | ||
| skip_on_cran() | ||
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| set.seed(123) | ||
| model <- lm(mpg ~ wt + hp + cyl, data = mtcars) | ||
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| # Test linearity check (NCV) | ||
| set.seed(123) | ||
| vdiffr::expect_doppelganger( | ||
| title = "check_model_linearity", | ||
| fig = plot(performance::check_model(model, check = "linearity")) | ||
| ) | ||
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| # Test homogeneity of variance | ||
| set.seed(123) | ||
| vdiffr::expect_doppelganger( | ||
| title = "check_model_homogeneity", | ||
| fig = plot(performance::check_model(model, check = "homogeneity")) | ||
| ) | ||
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| # Test normality (QQ plot) | ||
| set.seed(123) | ||
| vdiffr::expect_doppelganger( | ||
| title = "check_model_qq", | ||
| fig = plot(performance::check_model(model, check = "qq")) | ||
| ) | ||
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| # Test outliers | ||
| set.seed(123) | ||
| vdiffr::expect_doppelganger( | ||
| title = "check_model_outliers", | ||
| fig = plot(performance::check_model(model, check = "outliers")) | ||
| ) | ||
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| # Test full panel plot | ||
| set.seed(123) | ||
| vdiffr::expect_doppelganger( | ||
| title = "check_model_panel", | ||
| fig = plot(performance::check_model(model)) | ||
| ) | ||
| }) |
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The test code is well-structured and covers various aspects of the plotting functions. However, there is a lot of repetition with set.seed(123) before each call to vdiffr::expect_doppelganger(). While ensuring reproducibility is crucial, this pattern makes the code verbose and harder to maintain.
To improve readability and reduce redundancy, you could introduce a small helper function that wraps set.seed() and vdiffr::expect_doppelganger(). This would make the test suite cleaner and more concise.
Here's an example of what the helper function could look like:
# Helper for reproducible vdiffr tests
expect_doppelganger_with_seed <- function(title, fig, seed = 123) {
set.seed(seed)
vdiffr::expect_doppelganger(title = title, fig = fig)
}You could then refactor this test_that() block as follows. This pattern can be applied to the other test blocks in this file as well.
test_that("plot.see_check_model() renders correctly", {
skip_if_not_installed("performance")
skip_if_not_installed("see")
skip_on_cran()
# Helper for reproducible vdiffr tests
expect_doppelganger_with_seed <- function(title, fig, seed = 123) {
set.seed(seed)
vdiffr::expect_doppelganger(title = title, fig = fig)
}
set.seed(123)
model <- lm(mpg ~ wt + hp + cyl, data = mtcars)
# Test linearity check (NCV)
expect_doppelganger_with_seed(
title = "check_model_linearity",
fig = plot(performance::check_model(model, check = "linearity"))
)
# Test homogeneity of variance
expect_doppelganger_with_seed(
title = "check_model_homogeneity",
fig = plot(performance::check_model(model, check = "homogeneity"))
)
# Test normality (QQ plot)
expect_doppelganger_with_seed(
title = "check_model_qq",
fig = plot(performance::check_model(model, check = "qq"))
)
# Test outliers
expect_doppelganger_with_seed(
title = "check_model_outliers",
fig = plot(performance::check_model(model, check = "outliers"))
)
# Test full panel plot
expect_doppelganger_with_seed(
title = "check_model_panel",
fig = plot(performance::check_model(model))
)
})Implemented Gemini AI code review suggestion to reduce code repetition by introducing expect_doppelganger_with_seed() helper function.
Code Quality ImprovementsImplemented Gemini AI code review suggestions:
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Thanks a lot, let me review this PR! |
2 participants
I've added 18 vdiffr tests that cover the main check_model() plots and their variations. These should catch any visual regressions in future updates.
Tests cover check_model(), check_normality(), check_outliers(), and check_homogeneity() with various themes, colors, and options. Also validates that the large dataset performance fix produces visually consistent output.
I followed the tutorial from https://indrajeetpatil.github.io/intro-to-snapshot-testing/ to learn how vdiffr works.
Contributes to #236