tinyplot 0.6.0

DESCRIPTION

@@ -98,10 +98,11 @@ Suggests:
     rtdists,
     RWiener,
     sandwich,
+    scales,
     see (>= 0.11.0),
     survival,
     testthat (>= 3.2.1),
-    tinyplot,
+    tinyplot (>= 0.6.0),
     tinytable,
     vdiffr,
     withr

R/tinyplot.R

@@ -1,33 +1,55 @@
 #' @rdname visualisation_recipe.estimate_predicted
-#' @param theme A character string specifying the theme to use for the plot.
-#' Defaults to `"tufte"`. For other options please see [`tinyplot::tinytheme()`].
-#' Use `NULL` if no theme should be applied.
+#' @param type The type of `tinyplot` visualization. It is recommended that
+#' users leave as `NULL` (the default), in which case the plot type will be
+#' determined automatically by the underlying `modelbased` object.
+#' @param dodge Dodge value for grouped plots. If `NULL` (the default), then
+#' the dodging behavior is determined by the number of groups and
+#' `getOption("modelbased_tinyplot_dodge")`.
+#' @param ... Other arguments passed to \code{\link[tinyplot]{tinyplot}}.
 #'
 #' @examplesIf all(insight::check_if_installed(c("tinyplot", "marginaleffects"), quietly = TRUE))
 #' # ==============================================
 #' # tinyplot
 #' # ==============================================
 #' \donttest{
+#' library(tinyplot)
 #' data(efc, package = "modelbased")
 #' efc <- datawizard::to_factor(efc, c("e16sex", "c172code", "e42dep"))
 #' m <- lm(neg_c_7 ~ e16sex + c172code + barthtot, data = efc)
 #'
 #' em <- estimate_means(m, "c172code")
-#' tinyplot::plt(em)
+#' plt(em)
 #'
+#' # pass additional tinyplot arguments for customization, e.g.
+#' plt(em, theme = "classic")
+#' plt(em, theme = "classic", flip = TRUE)
+#' # etc.
+#'
+#' # Aside: use tinyplot::tinytheme() to set a persistent theme
+#' tinytheme("classic")
+#'
+#' # continuous variable example
 #' em <- estimate_means(m, "barthtot")
-#' tinyplot::plt(em)
+#' plt(em)
 #'
+#' # grouped example
 #' m <- lm(neg_c_7 ~ e16sex * c172code + e42dep, data = efc)
 #' em <- estimate_means(m, c("e16sex", "c172code"))
-#' tinyplot::plt(em)
+#' plt(em)
+#'
+#' # use plt_add (alias tinyplot_add) to add layers
+#' plt_add(type = "l", lty = 2)
+#'
+#' # Reset to default theme
+#' tinytheme()
 #' }
 #' @exportS3Method tinyplot::tinyplot
 tinyplot.estimate_means <- function(
   x,
+  type = NULL,
+  dodge = NULL,
   show_data = FALSE,
   numeric_as_discrete = NULL,
-  theme = "tufte",
   ...
 ) {
   insight::check_if_installed("tinyplot")
@@ -48,9 +70,8 @@ tinyplot.estimate_means <- function(
   data <- aes$data
   aes <- aes$aes
 
-  # save additional arguments, once for theming and once for the plot
+  # save additional arguments, will pass via do.call to tinyplot
   dots <- list(...)
-  theme_dots <- dots
 
   # preparation of settings / arguments ----------------------------------
 
@@ -73,6 +94,11 @@ tinyplot.estimate_means <- function(
     }
   }
 
+  # type placeholder
+  if (!is.null(type)) {
+    aes$type <- type
+  }
+
   # handle non-standard plot types -------------------------------
 
   if (aes$type == "grouplevel") {
@@ -110,31 +136,56 @@ tinyplot.estimate_means <- function(
   # Set dodge value for grouped point or pointrange plots.
   # The value 0.07 was chosen to reduce overlap in this context; adjust via
   # option if needed.
-  dodge_value <- getOption("modelbased_tinyplot_dodge", 0.07)
-  if (!is.null(aes$color) && aes$type %in% c("pointrange", "point")) {
+
+  dodge_value <- if (!is.null(dodge)) {
+    dodge
+  } else {
+    getOption("modelbased_tinyplot_dodge", 0.07)
+  }
+  if (
+    !is.null(aes$color) &&
+      aes$type %in% c("pointrange", "point", "l", "errorbar", "ribbon")
+  ) {
     dots$dodge <- dodge_value
   }
 
-  ## TODO: legend labels?
   ## TODO: show residuals?
 
   # x/y labels --------------------------------
   dots$xlab <- aes$labs$x
   dots$ylab <- aes$labs$y
 
+  # legend labels --------------------------------
+
+  # we also need to account for custom legend options passed through dots
+  if (is.null(dots$legend)) {
+    dots$legend = list(title = aes$labs$colour)
+  } else if (inherits(dots$legend, "list")) {
+    if (!("title" %in% names(dots$legend))) {
+      dots$legend = utils::modifyList(
+        dots$legend,
+        list(title = aes$labs$colour),
+        keep.null = TRUE
+      )
+    }
+  } else if (!isFALSE(dots$legend)) {
+    dots$legend = tryCatch(
+      utils::modifyList(
+        as.list(dots$legend),
+        list(title = aes$labs$colour),
+        keep.null = TRUE
+      ),
+      error = function(e) dots$legend
+    )
+  }
+
   # add aesthetics to the plot description
   plot_args <- insight::compact_list(c(
     list(plot_description, data = data, type = aes$type),
     plot_args,
     dots
   ))
 
-  # default theme
-  if (!is.null(theme)) {
-    theme_dots[c(elements, "facet", "xlab", "ylab", "flip")] <- NULL
-    do.call(tinyplot::tinytheme, c(list(theme = theme), theme_dots))
-  }
-
   # add data points if requested --------------------------------
 
   if (show_data) {

R/visualisation_recipe.R

@@ -45,7 +45,7 @@
 #' @param point,line,pointrange,ribbon,facet,grid Additional
 #' aesthetics and parameters for the geoms (see customization example).
 #' @param ... Arguments passed from `plot()` to `visualisation_recipe()`, or
-#' to `tinyplot()` and `tinytheme()` if you use that method.
+#' to `tinyplot()` if you use that method.
 #'
 #' @details There are two options to remove the confidence bands or errors bars
 #' from the plot. To remove error bars, simply set the `pointrange` geom to