Tests cleanup

tests/testthat/helper.R

@@ -1,12 +1,6 @@
-requiet <- function(package) {
-  testthat::skip_if_not_installed(package)
-  suppressPackageStartupMessages(
-    require(package, warn.conflicts = FALSE, character.only = TRUE)
-  )
+skip_if_not_or_load_if_installed <- function(package, minimum_version = NULL) {
+  testthat::skip_if_not_installed(package, minimum_version = minimum_version)
+  suppressMessages(suppressWarnings(suppressPackageStartupMessages(
+    require(package, warn.conflicts = FALSE, character.only = TRUE, quietly = TRUE)
+  )))
 }
-
-# load all hard dependencies
-requiet("bayestestR")
-requiet("insight")
-requiet("datawizard")
-requiet("parameters")

tests/testthat/test-as.list.R

@@ -1,7 +1,7 @@
-requiet("poorman")
-requiet("ggplot2")
-
 test_that("as.list", {
+  skip_if_not_or_load_if_installed("poorman")
+  skip_if_not_or_load_if_installed("ggplot2")
+
   # no groups
   set.seed(123)
   expect_snapshot(as.list(correlation(mtcars)))

tests/testthat/test-cor_multilevel.R

@@ -1,7 +1,6 @@
-requiet("lme4")
-
 test_that("comparison rmcorr", {
-  requiet("rmcorr")
+  skip_if_not_or_load_if_installed("lme4")
+  skip_if_not_or_load_if_installed("rmcorr")
   set.seed(123)
   rez_rmcorr <- rmcorr::rmcorr(Species, Sepal.Length, Sepal.Width, dataset = iris)
 
@@ -17,6 +16,7 @@ test_that("comparison rmcorr", {
 
 
 test_that("Reductio ad absurdum", {
+  skip_if_not_or_load_if_installed("lme4")
   cormatrix <- matrix(
     c(
       1.0, 0.3, 0.6,

tests/testthat/test-cor_test.R

@@ -15,51 +15,50 @@ test_that("cor_test kendall", {
 
 
 test_that("cor_test bayesian", {
-  if (requiet("BayesFactor")) {
-    out <- cor_test(iris, "Petal.Length", "Petal.Width", bayesian = TRUE)
-    expect_equal(out$r, 0.9591191, tolerance = 0.01)
+  skip_if_not_or_load_if_installed("BayesFactor")
+  out <- cor_test(iris, "Petal.Length", "Petal.Width", bayesian = TRUE)
+  expect_equal(out$r, 0.9591191, tolerance = 0.01)
 
+  set.seed(123)
+  df_1 <- cor_test(iris, "Petal.Length", "Petal.Width", bayesian = TRUE)
+
+  set.seed(123)
+  df_2 <- cor_test(iris, "Petal.Length", "Petal.Width", method = "auto", bayesian = TRUE)
+  expect_equal(df_1, df_2, tolerance = 0.001)
+
+  out2 <- cor_test(iris, "Petal.Length", "Petal.Width", method = "spearman", bayesian = TRUE)
+  expect_equal(out2$rho, 0.9323004, tolerance = 0.01)
+
+  df <- iris
+  df$Petal.Length2 <- df$Petal.Length
+  out3 <- cor_test(df, "Petal.Length", "Petal.Length2", bayesian = TRUE)
+  expect_equal(out3$rho, 1.000, tolerance = 0.01)
+
+  if (getRversion() >= "3.6") {
     set.seed(123)
-    df_1 <- cor_test(iris, "Petal.Length", "Petal.Width", bayesian = TRUE)
+    out5 <- cor_test(mtcars, "wt", "mpg", method = "shepherd", bayesian = TRUE)
+    expect_equal(out5$rho, -0.7795719, tolerance = 0.01)
 
     set.seed(123)
-    df_2 <- cor_test(iris, "Petal.Length", "Petal.Width", method = "auto", bayesian = TRUE)
-    expect_equal(df_1, df_2, tolerance = 0.001)
-
-    out2 <- cor_test(iris, "Petal.Length", "Petal.Width", method = "spearman", bayesian = TRUE)
-    expect_equal(out2$rho, 0.9323004, tolerance = 0.01)
-
-    df <- iris
-    df$Petal.Length2 <- df$Petal.Length
-    out3 <- cor_test(df, "Petal.Length", "Petal.Length2", bayesian = TRUE)
-    expect_equal(out3$rho, 1.000, tolerance = 0.01)
-
-    if (getRversion() >= "3.6") {
-      set.seed(123)
-      out5 <- cor_test(mtcars, "wt", "mpg", method = "shepherd", bayesian = TRUE)
-      expect_equal(out5$rho, -0.7795719, tolerance = 0.01)
-
-      set.seed(123)
-      out6 <- cor_test(mtcars, "wt", "mpg", method = "gaussian", bayesian = TRUE)
-      expect_equal(out6$rho, -0.8294838, tolerance = 0.01)
-    }
-
-    # unsupported
-    expect_error(cor_test(mtcars, "wt", "mpg", method = "biserial", bayesian = TRUE))
-    expect_error(cor_test(mtcars, "wt", "mpg", method = "polychoric", bayesian = TRUE))
-    expect_error(cor_test(mtcars, "wt", "mpg", method = "tetrachoric", bayesian = TRUE))
-    expect_error(cor_test(mtcars, "wt", "mpg", method = "biweight", bayesian = TRUE))
-    expect_error(cor_test(mtcars, "wt", "mpg", method = "distance", bayesian = TRUE))
-    expect_error(cor_test(mtcars, "wt", "mpg", method = "percentage", bayesian = TRUE))
-    expect_error(cor_test(mtcars, "wt", "mpg", method = "blomqvist", bayesian = TRUE))
-    expect_error(cor_test(mtcars, "wt", "mpg", method = "hoeffding", bayesian = TRUE))
-    expect_error(cor_test(mtcars, "wt", "mpg", method = "gamma", bayesian = TRUE))
+    out6 <- cor_test(mtcars, "wt", "mpg", method = "gaussian", bayesian = TRUE)
+    expect_equal(out6$rho, -0.8294838, tolerance = 0.01)
   }
+
+  # unsupported
+  expect_error(cor_test(mtcars, "wt", "mpg", method = "biserial", bayesian = TRUE))
+  expect_error(cor_test(mtcars, "wt", "mpg", method = "polychoric", bayesian = TRUE))
+  expect_error(cor_test(mtcars, "wt", "mpg", method = "tetrachoric", bayesian = TRUE))
+  expect_error(cor_test(mtcars, "wt", "mpg", method = "biweight", bayesian = TRUE))
+  expect_error(cor_test(mtcars, "wt", "mpg", method = "distance", bayesian = TRUE))
+  expect_error(cor_test(mtcars, "wt", "mpg", method = "percentage", bayesian = TRUE))
+  expect_error(cor_test(mtcars, "wt", "mpg", method = "blomqvist", bayesian = TRUE))
+  expect_error(cor_test(mtcars, "wt", "mpg", method = "hoeffding", bayesian = TRUE))
+  expect_error(cor_test(mtcars, "wt", "mpg", method = "gamma", bayesian = TRUE))
 })
 
 test_that("cor_test tetrachoric", {
-  skip_if_not_installed("psych")
-  skip_if_not_installed("polycor")
+  skip_if_not_or_load_if_installed("psych")
+  skip_if_not_or_load_if_installed("polycor")
   data <- iris
   data$Sepal.Width_binary <- ifelse(data$Sepal.Width > 3, 1, 0)
   data$Petal.Width_binary <- ifelse(data$Petal.Width > 1.2, 1, 0)
@@ -101,22 +100,21 @@ test_that("cor_test robust", {
 
 test_that("cor_test distance", {
   skip_if(getRversion() < "4.0")
+  skip_if_not_or_load_if_installed("energy")
 
-  if (requiet("energy")) {
-    out <- cor_test(iris, "Petal.Length", "Petal.Width", method = "distance")
-    comparison <- energy::dcorT.test(iris$Petal.Length, iris$Petal.Width)
-    expect_equal(out$r, as.numeric(comparison$estimate), tolerance = 0.001)
-    expect_identical(out$Method, "Distance (Bias Corrected)")
-  }
+  out <- cor_test(iris, "Petal.Length", "Petal.Width", method = "distance")
+  comparison <- energy::dcorT.test(iris$Petal.Length, iris$Petal.Width)
+  expect_equal(out$r, as.numeric(comparison$estimate), tolerance = 0.001)
+  expect_identical(out$Method, "Distance (Bias Corrected)")
 })
 
 
 test_that("cor_test percentage", {
-  if (requiet("WRS2")) {
-    out <- cor_test(iris, "Petal.Length", "Petal.Width", method = "percentage")
-    comparison <- WRS2::pbcor(iris$Petal.Length, iris$Petal.Width)
-    expect_equal(out$r, as.numeric(comparison$cor), tolerance = 0.01)
-  }
+  skip_if_not_or_load_if_installed("WRS2")
+
+  out <- cor_test(iris, "Petal.Length", "Petal.Width", method = "percentage")
+  comparison <- WRS2::pbcor(iris$Petal.Length, iris$Petal.Width)
+  expect_equal(out$r, as.numeric(comparison$cor), tolerance = 0.01)
 })
 
 
@@ -125,33 +123,31 @@ test_that("cor_test shepherd", {
   out <- cor_test(iris, "Petal.Length", "Petal.Width", method = "shepherd")
   expect_equal(out$r, 0.94762, tolerance = 0.01)
 
-  if (requiet("BayesFactor")) {
-    set.seed(333)
-    out2 <- cor_test(iris, "Petal.Length", "Petal.Width", method = "shepherd", bayesian = TRUE)
-    expect_equal(out2$rho, 0.9429992, tolerance = 0.01)
-  }
+  skip_if_not_or_load_if_installed("BayesFactor")
+  set.seed(333)
+  out2 <- cor_test(iris, "Petal.Length", "Petal.Width", method = "shepherd", bayesian = TRUE)
+  expect_equal(out2$rho, 0.9429992, tolerance = 0.01)
 })
 
 
 test_that("cor_test blomqvist", {
-  if (requiet("wdm")) {
-    set.seed(333)
-    out <- cor_test(iris, "Petal.Length", "Petal.Width", method = "blomqvist")
-    expect_equal(out$r, 0.9066667, tolerance = 0.01)
-  }
+  skip_if_not_or_load_if_installed("wdm")
+
+  set.seed(333)
+  out <- cor_test(iris, "Petal.Length", "Petal.Width", method = "blomqvist")
+  expect_equal(out$r, 0.9066667, tolerance = 0.01)
 })
 
 test_that("cor_test hoeffding and somers", {
-  if (requiet("Hmisc")) {
-    set.seed(333)
-    out <- cor_test(iris, "Petal.Length", "Petal.Width", method = "hoeffding")
-    expect_equal(out$r, 0.5629277, tolerance = 0.01)
-
-    set.seed(333)
-    df <- data.frame(x = 1:6, y = c(0, 0, 1, 0, 1, 1))
-    out2 <- cor_test(df, "y", "x", method = "somers")
-    expect_equal(out2$Dxy, 0.7777778, tolerance = 0.01)
-  }
+  skip_if_not_or_load_if_installed("Hmisc")
+  set.seed(333)
+  out <- cor_test(iris, "Petal.Length", "Petal.Width", method = "hoeffding")
+  expect_equal(out$r, 0.5629277, tolerance = 0.01)
+
+  set.seed(333)
+  df <- data.frame(x = 1:6, y = c(0, 0, 1, 0, 1, 1))
+  out2 <- cor_test(df, "y", "x", method = "somers")
+  expect_equal(out2$Dxy, 0.7777778, tolerance = 0.01)
 })
 
 test_that("cor_test gamma", {
@@ -161,7 +157,8 @@ test_that("cor_test gamma", {
 })
 
 test_that("cor_test gaussian", {
-  requiet("BayesFactor")
+  skip_if_not_or_load_if_installed("BayesFactor")
+
   set.seed(333)
   out <- cor_test(iris, "Petal.Length", "Petal.Width", method = "gaussian")
   expect_equal(out$r, 0.87137, tolerance = 0.01)

tests/testthat/test-cor_test_na_present.R

@@ -1,5 +1,5 @@
 test_that("cor_test frequentist", {
-  skip_if_not_installed("ggplot2")
+  skip_if_not_or_load_if_installed("ggplot2")
 
   expect_error(cor_test(ggplot2::msleep, brainwt, sleep_rem))
 
@@ -8,7 +8,7 @@ test_that("cor_test frequentist", {
 })
 
 test_that("cor_test kendall", {
-  skip_if_not_installed("ggplot2")
+  skip_if_not_or_load_if_installed("ggplot2")
 
   out <- cor_test(ggplot2::msleep, "brainwt", "sleep_rem", method = "kendall")
   out2 <- stats::cor.test(ggplot2::msleep$brainwt, ggplot2::msleep$sleep_rem, method = "kendall")
@@ -18,17 +18,17 @@ test_that("cor_test kendall", {
 })
 
 test_that("cor_test bayesian", {
-  if (requiet("BayesFactor")) {
-    set.seed(123)
-    out <- cor_test(ggplot2::msleep, "brainwt", "sleep_rem", bayesian = TRUE)
-    expect_equal(out$r, -0.1947696, tolerance = 0.01)
-  }
+  skip_if_not_or_load_if_installed("BayesFactor")
+
+  set.seed(123)
+  out <- cor_test(ggplot2::msleep, "brainwt", "sleep_rem", bayesian = TRUE)
+  expect_equal(out$r, -0.1947696, tolerance = 0.01)
 })
 
 test_that("cor_test tetrachoric", {
-  skip_if_not_installed("psych")
-  skip_if_not_installed("polycor")
-  skip_if_not_installed("ggplot2")
+  skip_if_not_or_load_if_installed("psych")
+  skip_if_not_or_load_if_installed("polycor")
+  skip_if_not_or_load_if_installed("ggplot2")
 
   data <- ggplot2::msleep
   data$brainwt_binary <- ifelse(data$brainwt > 3, 1, 0)
@@ -51,7 +51,7 @@ test_that("cor_test tetrachoric", {
 
 
 test_that("cor_test robust", {
-  skip_if_not_installed("ggplot2")
+  skip_if_not_or_load_if_installed("ggplot2")
 
   out1 <- cor_test(ggplot2::msleep, "brainwt", "sleep_rem", method = "pearson", ranktransform = TRUE)
   out2 <- cor_test(ggplot2::msleep, "brainwt", "sleep_rem", method = "spearman", ranktransform = FALSE)
@@ -60,9 +60,9 @@ test_that("cor_test robust", {
 
 
 test_that("cor_test distance", {
-  skip_if_not_installed("ggplot2")
-  skip_if_not_installed("energy")
-  skip_if_not_installed("poorman")
+  skip_if_not_or_load_if_installed("ggplot2")
+  skip_if_not_or_load_if_installed("energy")
+  skip_if_not_or_load_if_installed("poorman")
 
   out <- cor_test(ggplot2::msleep, "brainwt", "sleep_rem", method = "distance")
   df <- poorman::filter(ggplot2::msleep, !is.na(brainwt), !is.na(sleep_rem))
@@ -72,8 +72,8 @@ test_that("cor_test distance", {
 
 
 test_that("cor_test percentage", {
-  skip_if_not_installed("ggplot2")
-  skip_if_not_installed("WRS2")
+  skip_if_not_or_load_if_installed("ggplot2")
+  skip_if_not_or_load_if_installed("WRS2")
 
   out <- cor_test(ggplot2::msleep, "brainwt", "sleep_rem", method = "percentage")
   comparison <- WRS2::pbcor(ggplot2::msleep$brainwt, ggplot2::msleep$sleep_rem)
@@ -82,45 +82,45 @@ test_that("cor_test percentage", {
 
 
 test_that("cor_test shepherd", {
-  skip_if_not_installed("ggplot2")
+  skip_if_not_or_load_if_installed("ggplot2")
 
   set.seed(333)
   expect_error(cor_test(ggplot2::msleep, "brainwt", "sleep_rem", method = "shepherd"))
 })
 
 
 test_that("cor_test blomqvist", {
-  skip_if_not_installed("wdm")
+  skip_if_not_or_load_if_installed("wdm")
 
   set.seed(333)
   out <- cor_test(ggplot2::msleep, "brainwt", "sleep_rem", method = "blomqvist")
   expect_equal(out$r, -0.4583333, tolerance = 0.01)
 })
 
 test_that("cor_test hoeffding", {
-  skip_if_not_installed("Hmisc")
+  skip_if_not_or_load_if_installed("Hmisc")
 
   set.seed(333)
   out <- cor_test(ggplot2::msleep, "brainwt", "sleep_rem", method = "hoeffding")
   expect_equal(out$r, 0.04427718, tolerance = 0.01)
 })
 
 test_that("cor_test gamma", {
-  skip_if_not_installed("ggplot2")
+  skip_if_not_or_load_if_installed("ggplot2")
 
   set.seed(333)
   out <- cor_test(ggplot2::msleep, "brainwt", "sleep_rem", method = "gamma")
   expect_equal(out$r, -0.2675799, tolerance = 0.01)
 })
 
 test_that("cor_test gaussian", {
-  skip_if_not_installed("ggplot2")
+  skip_if_not_or_load_if_installed("ggplot2")
 
   set.seed(333)
   out <- cor_test(ggplot2::msleep, "brainwt", "sleep_rem", method = "gaussian")
   expect_equal(out$r, -0.3679795, tolerance = 0.01)
 
-  skip_if_not_installed("BayesFactor")
+  skip_if_not_or_load_if_installed("BayesFactor")
   out <- cor_test(ggplot2::msleep, "brainwt", "sleep_rem", method = "gaussian", bayesian = TRUE)
   expect_equal(out$r, -0.3269572, tolerance = 0.01)
 })
@@ -131,7 +131,7 @@ test_that("cor_test gaussian", {
 
 
 test_that("cor_test one-sided p value", {
-  skip_if_not_installed("ggplot2")
+  skip_if_not_or_load_if_installed("ggplot2")
 
   baseline <- cor.test(ggplot2::msleep$brainwt, ggplot2::msleep$sleep_rem, alternative = "greater")
 

tests/testthat/test-cor_to_pcor.R

@@ -1,7 +1,7 @@
-requiet("ppcor")
-requiet("Hmisc")
-
 test_that("pcor_to_cor", {
+  skip_if_not_or_load_if_installed("ppcor")
+  skip_if_not_or_load_if_installed("Hmisc")
+
   set.seed(333)
 
   # easycormatrix
@@ -35,6 +35,8 @@ test_that("pcor_to_cor", {
 
 
 test_that("spcor_to_cor", {
+  skip_if_not_or_load_if_installed("ppcor")
+
   set.seed(333)
 
   # easycormatrix