In general, a minimal plot is used so that profiles are focused on +
In general, a minimal plot is used so that profiles are focused on low-level, general code, rather than implementations of specific geoms. This might be expanded at the point where improving performance of specific geoms becomes a focus. Further, the profile focuses on the diff --git a/dev/news/index.html b/dev/news/index.html index 5a16982fcd..d9eb3fb4bf 100644 --- a/dev/news/index.html +++ b/dev/news/index.html @@ -67,7 +67,8 @@
Reversal of a dimension, typically ‘x’ or ‘y’, is now controlled by the reverse argument in coord_cartesian(), coord_fixed(), coord_radial() and coord_sf(). In coord_radial(), this replaces the older direction argument (#4021, @teunbrand).
New stat: stat_manual() for arbitrary computations (@teunbrand, #3501)
Reversal of a dimension, typically ‘x’ or ‘y’, is now controlled by the reverse argument in coord_cartesian(), coord_fixed(), coord_radial() and coord_sf(). In coord_radial(), this replaces the older direction argument (#4021, @teunbrand).
coord_radial() displays minor gridlines now (@teunbrand).
(internal) continuous_scale() and binned_scale() sort the limits argument internally (@teunbrand).
Theme margins can have NA-units to inherit from parent elements. The new function margin_part() has NA-units as default (@teunbrand, #6115)
benchplot(ggplot(mtcars, aes(mpg, wt)) + geom_point())
#> step user.self sys.self elapsed
-#> 1 construct 0.002 0 0.002
-#> 2 build 0.020 0 0.020
-#> 3 render 0.019 0 0.019
-#> 4 draw 0.019 0 0.018
-#> 5 TOTAL 0.060 0 0.059
+#> 1 construct 0.003 0 0.003
+#> 2 build 0.019 0 0.019
+#> 3 render 0.019 0 0.020
+#> 4 draw 0.018 0 0.018
+#> 5 TOTAL 0.059 0 0.060
benchplot(ggplot(mtcars, aes(mpg, wt)) + geom_point() + facet_grid(. ~ cyl))
#> step user.self sys.self elapsed
-#> 1 construct 0.003 0 0.003
-#> 2 build 0.020 0 0.020
-#> 3 render 0.048 0 0.048
-#> 4 draw 0.033 0 0.033
-#> 5 TOTAL 0.104 0 0.104
+#> 1 construct 0.002 0 0.002
+#> 2 build 0.021 0 0.020
+#> 3 render 0.048 0 0.047
+#> 4 draw 0.037 0 0.037
+#> 5 TOTAL 0.108 0 0.106
# With tidy eval:
p <- expr(ggplot(mtcars, aes(mpg, wt)) + geom_point())
@@ -111,9 +111,9 @@ Examples#> step user.self sys.self elapsed
#> 1 construct 0.002 0 0.002
#> 2 build 0.019 0 0.019
-#> 3 render 0.020 0 0.020
-#> 4 draw 0.019 0 0.019
-#> 5 TOTAL 0.060 0 0.060
+#> 3 render 0.020 0 0.019
+#> 4 draw 0.019 0 0.018
+#> 5 TOTAL 0.060 0 0.058