Replace iris examples with palmerpenguins

Author: julianstanley

DESCRIPTION

@@ -74,7 +74,8 @@ Suggests:
     processx,
     plotlyGeoAssets,
     forcats,
-    thematic
+    thematic,
+    palmerpenguins
 LazyData: true
 RoxygenNote: 7.1.1
 Encoding: UTF-8

R/ggplotly.R

@@ -36,8 +36,9 @@
 #' @seealso [plot_ly()]
 #' @examples \dontrun{
 #' # simple example
-#' ggiris <- qplot(Petal.Width, Sepal.Length, data = iris, color = Species)
-#' ggplotly(ggiris)
+#' ggpenguins <- qplot(bill_length_mm , body_mass_g, 
+#' data = palmerpenguins::penguins, color = species)
+#' ggplotly(ggpenguins)
 #'
 #' data(canada.cities, package = "maps")
 #' viz <- ggplot(canada.cities, aes(long, lat)) +
@@ -58,7 +59,8 @@
 #' demo("crosstalk-highlight-ggplotly", package = "plotly")
 #' 
 #' # client-side linked brushing in a scatterplot matrix
-#' highlight_key(iris) %>%
+#' highlight_key(palmerpenguins::penguins) %>%
+#' highlight_key(palmerpenguins::penguins) %>%
 #'   GGally::ggpairs(aes(colour = Species), columns = 1:4) %>%
 #'   ggplotly(tooltip = c("x", "y", "colour")) %>%
 #'   highlight("plotly_selected")

R/plotly.R

@@ -118,12 +118,12 @@
 #' # You might notice plot_ly() has named arguments that aren't in this figure
 #' # reference. These arguments make it easier to map abstract data values to
 #' # visual attributes.
-#' p <- plot_ly(iris, x = ~Sepal.Width, y = ~Sepal.Length) 
-#' add_markers(p, color = ~Petal.Length, size = ~Petal.Length)
-#' add_markers(p, color = ~Species)
-#' add_markers(p, color = ~Species, colors = "Set1")
-#' add_markers(p, symbol = ~Species)
-#' add_paths(p, linetype = ~Species)
+#' p <- plot_ly(palmerpenguins::penguins, x = ~bill_length_mm, y = ~body_mass_g)
+#' add_markers(p, color = ~bill_depth_mm, size = ~bill_depth_mm)
+#' add_markers(p, color = ~species)
+#' add_markers(p, color = ~species, colors = "Set1")
+#' add_markers(p, symbol = ~species)
+#' add_paths(p, linetype = ~species)
 #' 
 #' }
 #'