Merge pull request #284 from easystats/rc_0_8_4

Prepare CRAN submission

DESCRIPTION

@@ -1,7 +1,7 @@
 Type: Package
 Package: correlation
 Title: Methods for Correlation Analysis
-Version: 0.8.3.3
+Version: 0.8.4
 Authors@R: 
     c(person(given = "Dominique",
              family = "Makowski",

NEWS.md

@@ -2,6 +2,9 @@
 
 - Minor improvements and code revisions due to changes in other packages.
 
+- Default color scheme for correlation matrices switched to use red for negative
+  values and blue for positive values.
+
 # correlation 0.8.3
 
 ## Breaking Changes

R/cor_to_p.R

@@ -16,11 +16,11 @@
 #' cor_to_ci(-0.1175698, n = 150)
 #' cor_to_ci(cor(iris[1:4]), n = 150)
 #'
-#' cor.test(iris$Sepal.Length, iris$Sepal.Width, method = "spearman")
+#' cor.test(iris$Sepal.Length, iris$Sepal.Width, method = "spearman", exact = FALSE)
 #' cor_to_p(-0.1667777, n = 150, method = "spearman")
 #' cor_to_ci(-0.1667777, ci = 0.95, n = 150)
 #'
-#' cor.test(iris$Sepal.Length, iris$Sepal.Width, method = "kendall")
+#' cor.test(iris$Sepal.Length, iris$Sepal.Width, method = "kendall", exact = FALSE)
 #' cor_to_p(-0.07699679, n = 150, method = "kendall")
 #' @references Bishara, A. J., & Hittner, J. B. (2017). Confidence intervals for
 #'   correlations when data are not normal. Behavior research methods, 49(1),
@@ -30,7 +30,10 @@
 cor_to_p <- function(cor, n, method = "pearson") {
   # Statistic
   if (method == "kendall") {
-    warning("Estimation for Kendall's correlation is not perfectly correct. Help us to improve it.", call. = FALSE)
+    insight::format_alert(
+      "p-value estimation for Kendall's correlation is not perfectly correct.",
+      "Help us to improve it."
+    )
     statistic <- (3 * cor * sqrt(n * (n - 1))) / sqrt(2 * (2 * n + 5))
   } else {
     statistic <- cor * sqrt((n - 2) / (1 - cor^2))

tests/testthat/_snaps/as.list.md

@@ -55,7 +55,7 @@
 ---
 
     Code
-      suppressWarnings(as.list(msleep %>% group_by(vore) %>% correlation(method = "spearman")))
+      as.list(correlation(datawizard::data_group(msleep, "vore"), method = "spearman"))
     Output
       =======
        carni 
@@ -213,8 +213,8 @@
 ---
 
     Code
-      suppressWarnings(as.list(mtcars %>% group_by(am) %>% correlation(select = c(
-        "cyl", "wt"), select2 = "hp", method = "percentage")))
+      as.list(correlation(datawizard::data_group(mtcars, "am"), select = c("cyl",
+        "wt"), select2 = "hp", method = "percentage"))
     Output
       ===
        0 

tests/testthat/test-cor_to_p.R

@@ -1,5 +1,5 @@
 test_that("cor_to_p", {
-  expect_warning(df <- cor_to_p(-0.1175698, n = 150, method = "kendall"))
+  expect_message(df <- cor_to_p(-0.1175698, n = 150, method = "kendall"))
   expect_equal(
     df,
     list(p = 0.0327638207025712, statistic = -2.1349655930582),

vignettes/types.Rmd

@@ -236,7 +236,7 @@ data %>%
   ) %>%
   mutate(Type = relevel(as.factor(Type), "Pearson", "Spearman", "Kendall", "Biweight", "Distance")) %>%
   ggplot(aes(x = r, y = Estimation, fill = Type)) +
-  geom_smooth(aes(color = Type), method = "loess", alpha = 0) +
+  geom_smooth(aes(color = Type), method = "loess", alpha = 0, na.rm = TRUE) +
   geom_vline(aes(xintercept = 0.5), linetype = "dashed") +
   geom_hline(aes(yintercept = 0.5), linetype = "dashed") +
   guides(colour = guide_legend(override.aes = list(alpha = 1))) +