diff --git a/r-package/maq/DESCRIPTION b/r-package/maq/DESCRIPTION
index e04c30b..432a9dd 100644
--- a/r-package/maq/DESCRIPTION
+++ b/r-package/maq/DESCRIPTION
@@ -22,6 +22,7 @@ Depends:
     R (>= 3.5.0)
 Suggests:
     grf (>= 2.3.0),
+    ggplot2,
     testthat (>= 3.0.0)
 SystemRequirements: GNU make
 URL: https://github.com/grf-labs/maq
diff --git a/r-package/maq/R/plot.R b/r-package/maq/R/plot.R
index 849e01b..feaf16f 100644
--- a/r-package/maq/R/plot.R
+++ b/r-package/maq/R/plot.R
@@ -17,6 +17,39 @@
 #'  `max(floor(length(path.length) / 1000), 1)` where path.length is the size of the
 #'  grid underlying the estimated Qini curve.
 #'
+#' @return A data.frame with the data making up the plot (point estimates and lower/upper 95% CIs)
+#'
+#' @examples
+#' \donttest{
+#' if (require("ggplot2", quietly = TRUE)) {
+#' # Generate toy data and customize plots.
+#' n = 500
+#' K = 1
+#' reward = matrix(1 + rnorm(n * K), n, K)
+#' scores = reward + matrix(rnorm(n * K), n, K)
+#' cost = 1
+#'
+#' # Fit Qini curves.
+#' qini.avg <- maq(reward, cost, scores, R = 200, target.with.covariates = FALSE)
+#' qini <- maq(reward, cost, scores, R = 200)
+#'
+#' # In some settings we may want to plot using one of R's many plot libraries.
+#' # The plot method invisibly returns the plot data we can use for this purpose.
+#' df.qini.baseline <- plot(qini.avg)
+#' df.qini <- plot(qini, add = TRUE, col = 2)
+#'
+#' # Make an alternate plot style, using, for example, ggplot.
+#' ggplot(df.qini, aes(x = spend, y = gain)) +
+#'   geom_ribbon(aes(ymin = gain - 1.96 * std.err,
+#'                   ymax = gain + 1.96 * std.err),
+#'               fill = "lightgray") +
+#'   geom_line(linewidth = 2) +
+#'   ylab("Policy value") +
+#'   xlab("Fraction treated") +
+#'   geom_line(data = df.qini.baseline, aes(x = spend, y = gain), lty = 2)
+#' }
+#' }
+#'
 #' @method plot maq
 #' @export
 plot.maq <- function(x,
@@ -83,4 +116,6 @@ plot.maq <- function(x,
     do.call(graphics::lines, c(list(x = spend, y = lb), lines.args))
     do.call(graphics::lines, c(list(x = spend, y = ub), lines.args))
   }
+
+  invisible(data.frame(spend, gain, std.err, lb, ub))
 }
diff --git a/r-package/maq/man/plot.maq.Rd b/r-package/maq/man/plot.maq.Rd
index 680c3ca..6235e65 100644
--- a/r-package/maq/man/plot.maq.Rd
+++ b/r-package/maq/man/plot.maq.Rd
@@ -32,8 +32,43 @@ Set to NULL to ignore CIs.}
 \code{max(floor(length(path.length) / 1000), 1)} where path.length is the size of the
 grid underlying the estimated Qini curve.}
 }
+\value{
+A data.frame with the data making up the plot (point estimates and lower/upper 95\% CIs)
+}
 \description{
 Plot the estimated curve \eqn{Q(B), B \in (0, B_{max}]}. If the underlying estimated policy
 \eqn{\pi_B} entails treating zero units (that is, all the estimated treatment effects are
 negative) then this function returns an empty value.
 }
+\examples{
+\donttest{
+if (require("ggplot2", quietly = TRUE)) {
+# Generate toy data and customize plots.
+n = 500
+K = 1
+reward = matrix(1 + rnorm(n * K), n, K)
+scores = reward + matrix(rnorm(n * K), n, K)
+cost = 1
+
+# Fit Qini curves.
+qini.avg <- maq(reward, cost, scores, R = 200, target.with.covariates = FALSE)
+qini <- maq(reward, cost, scores, R = 200)
+
+# In some settings we may want to plot using one of R's many plot libraries.
+# The plot method invisibly returns the plot data we can use for this purpose.
+df.qini.baseline <- plot(qini.avg)
+df.qini <- plot(qini, add = TRUE, col = 2)
+
+# Make an alternate plot style, using, for example, ggplot.
+ggplot(df.qini, aes(x = spend, y = gain)) +
+  geom_ribbon(aes(ymin = gain - 1.96 * std.err,
+                  ymax = gain + 1.96 * std.err),
+              fill = "lightgray") +
+  geom_line(linewidth = 2) +
+  ylab("Policy value") +
+  xlab("Fraction treated") +
+  geom_line(data = df.qini.baseline, aes(x = spend, y = gain), lty = 2)
+}
+}
+
+}