Upgrade-to-testthat-3rd-edition

DESCRIPTION

@@ -34,8 +34,9 @@ Suggests:
     rmarkdown,
     rprojroot,
     scales,
-    testthat,
+    testthat (>= 3.0.0),
     tibble,
     tidyr
 Depends: 
     R (>= 3.5.0)
+Config/testthat/edition: 3

tests/testthat/test-ci.R

@@ -1,8 +1,5 @@
-context("load testing data")
-
 # Load the ratios sample dataset for testing
 data("ratios_sample")
-
 # Extract the components of the dataframe as vectors
 ratio <- ratios_sample$ratio
 sale_price <- ratios_sample$sale_price
@@ -11,7 +8,6 @@ assessed <- ratios_sample$assessed
 
 
 ##### TEST COD CI #####
-context("test cod_ci function")
 
 # Calculate PRB CI
 cod_ci_out_95 <- cod_ci(ratio, nboot = 1000)
@@ -24,8 +20,12 @@ test_that("returns expected type", {
 })
 
 test_that("output equal to expected", {
-  expect_equivalent(cod_ci_out_95, c(16.49595, 18.84529), tolerance = 0.04)
-  expect_equivalent(cod_ci_out_80, c(16.83710, 18.79953), tolerance = 0.04)
+  expect_equal(cod_ci_out_95, c(16.49595, 18.84529),
+    tolerance = 0.04, ignore_attr = TRUE
+  )
+  expect_equal(cod_ci_out_80, c(16.83710, 18.79953),
+    tolerance = 0.04, ignore_attr = TRUE
+  )
 })
 
 test_that("bad input data stops execution", {
@@ -43,16 +43,17 @@ test_that("incomplete data returns NAs unless removed", {
     cod_ci(c(ratio, NA)),
     NA_real_
   )
-  expect_equivalent(
+  expect_equal(
     cod_ci(c(ratio, NA), nboot = 1000, na.rm = TRUE),
     c(16.49595, 18.84529),
-    tolerance = 0.04
+    tolerance = 0.04,
+    ignore_attr = TRUE
   )
 })
 
 
+
 ##### TEST PRD CI #####
-context("test prb_ci function")
 
 # Calculate PRB CI
 prd_ci_out_95 <- prd_ci(assessed, sale_price, nboot = 1000)
@@ -65,8 +66,12 @@ test_that("returns expected type", {
 })
 
 test_that("output equal to expected", {
-  expect_equivalent(prd_ci_out_95, c(1.034447, 1.062625), tolerance = 0.04)
-  expect_equivalent(prd_ci_out_80, c(1.038444, 1.058439), tolerance = 0.04)
+  expect_equal(prd_ci_out_95, c(1.034447, 1.062625),
+    tolerance = 0.04, ignore_attr = TRUE
+  )
+  expect_equal(prd_ci_out_80, c(1.038444, 1.058439),
+    tolerance = 0.04, ignore_attr = TRUE
+  )
 })
 
 test_that("bad input data stops execution", {
@@ -85,17 +90,17 @@ test_that("incomplete data returns NAs unless removed", {
     prd_ci(c(assessed, NA), c(sale_price, 10e5)),
     NA_real_
   )
-  expect_equivalent(
+  expect_equal(
     prd_ci(c(assessed, NA), c(sale_price, 10e5), na.rm = TRUE),
     c(1.034447, 1.062625),
-    tolerance = 0.04
+    tolerance = 0.04,
+    ignore_attr = TRUE
   )
 })
 
 
 
 ##### TEST PRB CI #####
-context("test prb_ci function")
 
 # Calculate PRB CI
 prb_ci_out_95 <- prb_ci(assessed, sale_price)
@@ -108,8 +113,12 @@ test_that("returns expected type", {
 })
 
 test_that("output equal to expected", {
-  expect_equivalent(prb_ci_out_95, c(-0.01404379, 0.01899536), tolerance = 0.04)
-  expect_equivalent(prb_ci_out_80, c(-0.00831969, 0.01327127), tolerance = 0.04)
+  expect_equal(prb_ci_out_95, c(-0.01404379, 0.01899536),
+    tolerance = 0.04, ignore_attr = TRUE
+  )
+  expect_equal(prb_ci_out_80, c(-0.00831969, 0.01327127),
+    tolerance = 0.04, ignore_attr = TRUE
+  )
 })
 
 test_that("bad input data stops execution", {
@@ -128,9 +137,10 @@ test_that("incomplete data returns NAs unless removed", {
     prb_ci(c(assessed, NA), c(sale_price, 10e5)),
     NA_real_
   )
-  expect_equivalent(
+  expect_equal(
     prb_ci(c(assessed, NA), c(sale_price, 10e5), na.rm = TRUE),
     c(-0.01404379, 0.01899536),
-    tolerance = 0.04
+    tolerance = 0.04,
+    ignore_attr = TRUE
   )
 })

tests/testthat/test-formulas.R

@@ -1,5 +1,3 @@
-context("load testing data")
-
 # Load the ratios sample dataset for testing
 data("ratios_sample")
 
@@ -28,7 +26,6 @@ tiebreaks_Assessed_alt_sort_2 <- mki_tiebreaks$Assessed_alt_sort_2
 
 
 ##### TEST COD #####
-context("test cod function")
 
 # Calculate COD
 cod_out <- cod(ratio)
@@ -64,7 +61,6 @@ test_that("standard met function", {
 
 
 ##### TEST PRD #####
-context("test prd function")
 
 # Calculate PRD
 prd_out <- prd(assessed, sale_price)
@@ -108,7 +104,6 @@ test_that("standard met function", {
 
 
 ##### TEST PRB #####
-context("test prb function")
 
 # Calculate PRB
 prb_out <- prb(assessed, sale_price)
@@ -152,7 +147,6 @@ test_that("standard met function", {
 
 
 ##### TEST MKI #####
-context("test mki function")
 
 # Calculate MKI
 mki_out <- mki(mki_ki_assessed, mki_ki_sale_price)
@@ -212,7 +206,6 @@ test_that("all sorting variations return the same MKI (tiebreak data)", {
 
 
 ##### TEST KI #####
-context("test ki function")
 
 # Calculate KI
 ki_out <- ki(mki_ki_assessed, mki_ki_sale_price)
@@ -269,7 +262,6 @@ test_that("all sorting variations return return the same ki (tiebreak data)", {
 
 
 ##### TEST Median Ratio #####
-context("test median_ratio_met function")
 
 test_that("standard met function", {
   expect_true(med_ratio_met(median(ratio)))

tests/testthat/test-outliers.R

@@ -1,5 +1,3 @@
-context("load testing data")
-
 # Create test vectors of data with certain distributions
 set.seed(13378)
 
@@ -21,8 +19,6 @@ dist2_qnt_out <- is_outlier(test_dist2, method = "quantile")
 
 
 ##### TEST OUTLIER #####
-context("test is_outlier function")
-
 test_that("returns expected type", {
   expect_type(dist1_iqr_out, "logical")
   expect_vector(dist1_iqr_out)

tests/testthat/test-sale_chasing.R

@@ -1,5 +1,3 @@
-context("load testing data")
-
 # Load the ratios sample dataset for testing
 data("ratios_sample")
 
@@ -11,8 +9,6 @@ chased_ratios <- c(rnorm(900, 1, 0.15), rep(1, 100))
 
 
 ##### TEST CHASING DETECTION #####
-context("test detect_chashing function")
-
 # Run detection
 sample_out <- detect_chasing(sample_ratios)
 normal_out <- detect_chasing(normal_ratios)