CRAN Release candidate 0.9.2 (#756)

* CRAN Release candidate 0.9.2

* correct URL

* fix more CRAN issues

* Update .gitignore

* another attempt

* vomit; needs a hotfix when parameters on cran

* Update test-ggcoefstats.R

.gitignore

@@ -50,3 +50,4 @@ revdep/library.noindex
 revdep/data.sqlite
 /doc/
 /Meta/
+CRAN-SUBMISSION

DESCRIPTION

@@ -1,7 +1,7 @@
 Type: Package
 Package: ggstatsplot
 Title: 'ggplot2' Based Plots with Statistical Details
-Version: 0.9.1.9000
+Version: 0.9.2
 Authors@R: 
     person(given = "Indrajeet",
            family = "Patil",
@@ -39,12 +39,9 @@ Imports:
     purrr,
     rlang,
     stats,
-    statsExpressions (>= 1.3.1.9000),
+    statsExpressions (>= 1.3.2),
     tidyr,
     utils
-Remotes:
-    easystats/parameters,
-    IndrajeetPatil/statsExpressions
 Suggests:
     afex,
     BayesFactor (>= 0.9.12-4.3),

NEWS.md

@@ -1,4 +1,4 @@
-# ggstatsplot 0.9.1.9000
+# ggstatsplot 0.9.2
 
 N.B. All statistical analysis in `{ggstatsplot}` is carried out in
 `{statsExpressions}`. Thus, to see changes related to statistical expressions,

R/extract_stats.R

@@ -5,7 +5,7 @@
 #' This is a convenience function to extract data frames with statistical details
 #' that are used to create expressions displayed in `{ggstatsplot}` plots as
 #' subtitle, caption, etc. Note that all of this analysis is carried out by
-#' the `{statsExpressions}` [package](https://indrajeetpatil.github.io/statsExpressions).
+#' the `{statsExpressions}` [package](https://indrajeetpatil.github.io/statsExpressions/).
 #'
 #' The only exception is the `ggcorrmat()` function. But, if a data frame is
 #' what you want, you shouldn't be using `ggcorrmat()` anyway. You can use

R/ggcoefstats.R

@@ -245,12 +245,15 @@ ggcoefstats <- function(x,
 
   # sorting -------------------------
 
-  # whether the terms need to be sorted in specified order
-  tidy_df %<>% parameters::sort_parameters(sort = sort, column = "estimate")
+  if (utils::packageVersion("parameters") >= "0.17.1" && requireNamespace("parameters")) {
 
-  # `term` needs to be a factor column; otherwise, ggplot2 will sort the x-axis
-  # labels alphabetically and terms won't appear in the expected order
-  tidy_df %<>% dplyr::mutate(term = factor(term, tidy_df$term))
+    # whether the terms need to be sorted in specified order
+    tidy_df %<>% sort_parameters(sort = sort, column = "estimate")
+
+    # `term` needs to be a factor column; otherwise, ggplot2 will sort the x-axis
+    # labels alphabetically and terms won't appear in the expected order
+    tidy_df %<>% dplyr::mutate(term = factor(term, tidy_df$term))
+  }
 
   # summary caption -------------------------
 

R/global_vars.R

@@ -40,7 +40,8 @@ utils::globalVariables(
     ".f",
     ".fn",
     ".ns",
-    "log_e_bf10"
+    "log_e_bf10",
+    "sort_parameters"
   ),
   package = "ggstatsplot",
   add = FALSE

README.Rmd

@@ -16,7 +16,7 @@ options(
 
 knitr::opts_chunk$set(
   collapse = TRUE,
-  dpi = 300, ## change to 300 once on CRAN
+  dpi = 150, ## change to 300 once on CRAN
   warning = FALSE,
   message = FALSE,
   out.width = "100%",
@@ -298,8 +298,7 @@ ggwithinstats(
   data    = WineTasting,
   x       = Wine,
   y       = Taste,
-  title   = "Wine tasting",
-  ggtheme = hrbrthemes::theme_ipsum_es() 
+  title   = "Wine tasting"
 )
 ```
 
@@ -413,16 +412,14 @@ significantly different from a specified value with a one-sample test,
 `gghistostats` can be used.
 
 ```{r gghistostats1, fig.width=8}
-
 set.seed(123)
 
 gghistostats(
   data       = ggplot2::msleep,
   x          = awake,
   title      = "Amount of time spent awake",
   test.value = 12,
-  binwidth   = 1,
-  ggtheme    = hrbrthemes::theme_ipsum_tw()
+  binwidth   = 1
 )
 ```
 
@@ -834,7 +831,6 @@ ggbarstats(
   title            = "MPAA Ratings by Genre",
   xlab             = "movie genre",
   legend.title     = "MPAA rating",
-  ggtheme          = hrbrthemes::theme_ipsum_pub(),
   ggplot.component = list(ggplot2::scale_x_discrete(guide = ggplot2::guide_axis(n.dodge = 2))),
   palette          = "Set2"
 )
@@ -948,7 +944,7 @@ set.seed(123)
 ## model
 mod <- stats::lm(formula = mpg ~ am * cyl, data = mtcars)
 
-ggcoefstats(mod, ggtheme = hrbrthemes::theme_ipsum_ps())
+ggcoefstats(mod)
 ```
 
 **Defaults** return<br>