diff --git a/R/aes-evaluation.R b/R/aes-evaluation.R
index 30d0b3f501..e29d0c5d25 100644
--- a/R/aes-evaluation.R
+++ b/R/aes-evaluation.R
@@ -32,7 +32,7 @@
 #' Below follows an overview of the three stages of evaluation and how aesthetic
 #' evaluation can be controlled.
 #'
-#' ## Stage 1: direct input
+#' ## Stage 1: direct input at the start
 #' The default is to map at the beginning, using the layer data provided by
 #' the user. If you want to map directly from the layer data you should not do
 #' anything special. This is the only stage where the original layer data can
@@ -87,9 +87,11 @@
 #' ```
 #'
 #' ## Complex staging
-#' If you want to map the same aesthetic multiple times, e.g. map `x` to a
-#' data column for the stat, but remap it for the geom, you can use the
-#' `stage()` function to collect multiple mappings.
+#' Sometimes, you may want to map the same aesthetic multiple times, e.g. map
+#' `x` to a data column at the start for the layer stat, but remap it later to
+#' a variable from the stat transformation for the layer geom. The `stage()`
+#' function allows you to control multiple mappings for the same aesthetic
+#' across all three stages of evaluation.
 #'
 #' ```r
 #' # Use stage to modify the scaled fill
@@ -97,7 +99,7 @@
 #'   geom_boxplot(aes(fill = stage(class, after_scale = alpha(fill, 0.4))))
 #'
 #' # Using data for computing summary, but placing label elsewhere.
-#' # Also, we're making our own computed variable to use for the label.
+#' # Also, we're making our own computed variables to use for the label.
 #' ggplot(mpg, aes(class, displ)) +
 #'   geom_violin() +
 #'   stat_summary(
@@ -110,6 +112,11 @@
 #'   )
 #' ```
 #'
+#' Conceptually, `aes(x)` is equivalent to `aes(stage(start = x))`, and
+#' `aes(after_stat(count))` is equivalent to `aes(stage(after_stat = count))`,
+#' and so on. `stage()` is most useful when at least two of its arguments are
+#' specified.
+#'
 #' ## Theme access
 #' The `from_theme()` function can be used to acces the [`element_geom()`]
 #' fields of the `theme(geom)` argument. Using `aes(colour = from_theme(ink))`
@@ -332,7 +339,7 @@ strip_stage <- function(expr) {
   } else if (is_call(uq_expr, "stage")) {
     uq_expr <- call_match(uq_expr, stage)
     # Prefer stat mapping if present, otherwise original mapping (fallback to
-    # scale mapping) but there should always be two arguments to stage()
+    # scale mapping)
     uq_expr$after_stat %||% uq_expr$start %||% uq_expr$after_scale
   } else {
     expr
diff --git a/man/aes_eval.Rd b/man/aes_eval.Rd
index 11b8d2f1bd..aaf4c55277 100644
--- a/man/aes_eval.Rd
+++ b/man/aes_eval.Rd
@@ -49,7 +49,7 @@ should be evaluated.
 \section{Staging}{
 Below follows an overview of the three stages of evaluation and how aesthetic
 evaluation can be controlled.
-\subsection{Stage 1: direct input}{
+\subsection{Stage 1: direct input at the start}{
 
 The default is to map at the beginning, using the layer data provided by
 the user. If you want to map directly from the layer data you should not do
@@ -108,16 +108,18 @@ ggplot(mpg, aes(cty, colour = factor(cyl))) +
 
 \subsection{Complex staging}{
 
-If you want to map the same aesthetic multiple times, e.g. map \code{x} to a
-data column for the stat, but remap it for the geom, you can use the
-\code{stage()} function to collect multiple mappings.
+Sometimes, you may want to map the same aesthetic multiple times, e.g. map
+\code{x} to a data column at the start for the layer stat, but remap it later to
+a variable from the stat transformation for the layer geom. The \code{stage()}
+function allows you to control multiple mappings for the same aesthetic
+across all three stages of evaluation.
 
 \if{html}{\out{<div class="sourceCode r">}}\preformatted{# Use stage to modify the scaled fill
 ggplot(mpg, aes(class, hwy)) +
   geom_boxplot(aes(fill = stage(class, after_scale = alpha(fill, 0.4))))
 
 # Using data for computing summary, but placing label elsewhere.
-# Also, we're making our own computed variable to use for the label.
+# Also, we're making our own computed variables to use for the label.
 ggplot(mpg, aes(class, displ)) +
   geom_violin() +
   stat_summary(
@@ -129,6 +131,11 @@ ggplot(mpg, aes(class, displ)) +
     fun.data = ~ round(data.frame(mean = mean(.x), sd = sd(.x)), 2)
   )
 }\if{html}{\out{</div>}}
+
+Conceptually, \code{aes(x)} is equivalent to \code{aes(stage(start = x))}, and
+\code{aes(after_stat(count))} is equivalent to \code{aes(stage(after_stat = count))},
+and so on. \code{stage()} is most useful when at least two of its arguments are
+specified.
 }
 
 \subsection{Theme access}{
diff --git a/tests/testthat/test-aes-calculated.R b/tests/testthat/test-aes-calculated.R
index 3ac8e06dbe..9d5c49c68e 100644
--- a/tests/testthat/test-aes-calculated.R
+++ b/tests/testthat/test-aes-calculated.R
@@ -124,3 +124,27 @@ test_that("functions can be masked", {
   expect_equal(evaled, list(x = 10, y = 30))
 
 })
+
+test_that("stage allows aesthetics that are only mapped to start", {
+
+  df <- data.frame(x = 1:2)
+
+  start_unnamed <- aes(stage(x))
+  expect_equal(
+    eval_aesthetics(start_unnamed, data = df),
+    list(x = 1:2)
+  )
+
+  start_named <- aes(stage(start = x))
+  expect_equal(
+    eval_aesthetics(start_named, data = df),
+    list(x = 1:2)
+  )
+
+  start_nulls <- aes(stage(start = x, after_stat = NULL, after_scale = NULL))
+  expect_equal(
+    eval_aesthetics(start_nulls, data = df),
+    list(x = 1:2)
+  )
+
+})