diff --git a/man/cdc_baseline_forecaster.Rd b/man/cdc_baseline_forecaster.Rd
index 122903649..cd3c4ed67 100644
--- a/man/cdc_baseline_forecaster.Rd
+++ b/man/cdc_baseline_forecaster.Rd
@@ -52,22 +52,22 @@ cdc <- cdc_baseline_forecaster(weekly_deaths, "deaths")
 preds <- pivot_quantiles_wider(cdc$predictions, .pred_distn)
 
 if (require(ggplot2)) {
-forecast_date <- unique(preds$forecast_date)
-four_states <- c("ca", "pa", "wa", "ny")
-preds \%>\%
-  filter(geo_value \%in\% four_states) \%>\%
-  ggplot(aes(target_date)) +
-  geom_ribbon(aes(ymin = `0.1`, ymax = `0.9`), fill = blues9[3]) +
-  geom_ribbon(aes(ymin = `0.25`, ymax = `0.75`), fill = blues9[6]) +
-  geom_line(aes(y = .pred), color = "orange") +
-  geom_line(
-    data = weekly_deaths \%>\% filter(geo_value \%in\% four_states),
-    aes(x = time_value, y = deaths)
-  ) +
-  scale_x_date(limits = c(forecast_date - 90, forecast_date + 30)) +
-  labs(x = "Date", y = "Weekly deaths") +
-  facet_wrap(~geo_value, scales = "free_y") +
-  theme_bw() +
-  geom_vline(xintercept = forecast_date)
+  forecast_date <- unique(preds$forecast_date)
+  four_states <- c("ca", "pa", "wa", "ny")
+  preds \%>\%
+    filter(geo_value \%in\% four_states) \%>\%
+    ggplot(aes(target_date)) +
+    geom_ribbon(aes(ymin = `0.1`, ymax = `0.9`), fill = blues9[3]) +
+    geom_ribbon(aes(ymin = `0.25`, ymax = `0.75`), fill = blues9[6]) +
+    geom_line(aes(y = .pred), color = "orange") +
+    geom_line(
+      data = weekly_deaths \%>\% filter(geo_value \%in\% four_states),
+      aes(x = time_value, y = deaths)
+    ) +
+    scale_x_date(limits = c(forecast_date - 90, forecast_date + 30)) +
+    labs(x = "Date", y = "Weekly deaths") +
+    facet_wrap(~geo_value, scales = "free_y") +
+    theme_bw() +
+    geom_vline(xintercept = forecast_date)
 }
 }
diff --git a/man/layer_cdc_flatline_quantiles.Rd b/man/layer_cdc_flatline_quantiles.Rd
index 1340698d4..cf11de8eb 100644
--- a/man/layer_cdc_flatline_quantiles.Rd
+++ b/man/layer_cdc_flatline_quantiles.Rd
@@ -115,21 +115,21 @@ preds <- preds \%>\%
   mutate(target_date = forecast_date + ahead)
 
 if (require("ggplot2")) {
-four_states <- c("ca", "pa", "wa", "ny")
-preds \%>\%
-  filter(geo_value \%in\% four_states) \%>\%
-  ggplot(aes(target_date)) +
-  geom_ribbon(aes(ymin = `0.1`, ymax = `0.9`), fill = blues9[3]) +
-  geom_ribbon(aes(ymin = `0.25`, ymax = `0.75`), fill = blues9[6]) +
-  geom_line(aes(y = .pred), color = "orange") +
-  geom_line(
-    data = case_death_rate_subset \%>\% filter(geo_value \%in\% four_states),
-    aes(x = time_value, y = death_rate)
-  ) +
-  scale_x_date(limits = c(forecast_date - 90, forecast_date + 30)) +
-  labs(x = "Date", y = "Death rate") +
-  facet_wrap(~geo_value, scales = "free_y") +
-  theme_bw() +
-  geom_vline(xintercept = forecast_date)
+  four_states <- c("ca", "pa", "wa", "ny")
+  preds \%>\%
+    filter(geo_value \%in\% four_states) \%>\%
+    ggplot(aes(target_date)) +
+    geom_ribbon(aes(ymin = `0.1`, ymax = `0.9`), fill = blues9[3]) +
+    geom_ribbon(aes(ymin = `0.25`, ymax = `0.75`), fill = blues9[6]) +
+    geom_line(aes(y = .pred), color = "orange") +
+    geom_line(
+      data = case_death_rate_subset \%>\% filter(geo_value \%in\% four_states),
+      aes(x = time_value, y = death_rate)
+    ) +
+    scale_x_date(limits = c(forecast_date - 90, forecast_date + 30)) +
+    labs(x = "Date", y = "Death rate") +
+    facet_wrap(~geo_value, scales = "free_y") +
+    theme_bw() +
+    geom_vline(xintercept = forecast_date)
 }
 }
diff --git a/man/smooth_quantile_reg.Rd b/man/smooth_quantile_reg.Rd
index 42a951759..bd8c012f2 100644
--- a/man/smooth_quantile_reg.Rd
+++ b/man/smooth_quantile_reg.Rd
@@ -76,15 +76,15 @@ lines(pl$x, pl$`0.8`, col = "blue")
 lines(pl$x, pl$`0.5`, col = "red")
 
 if (require("ggplot2")) {
-ggplot(data.frame(x = x, y = y), aes(x)) +
-  geom_ribbon(data = pl, aes(ymin = `0.2`, ymax = `0.8`), fill = "lightblue") +
-  geom_point(aes(y = y), colour = "grey") + # observed data
-  geom_function(fun = sin, colour = "black") + # truth
-  geom_vline(xintercept = fd, linetype = "dashed") + # end of training data
-  geom_line(data = pl, aes(y = `0.5`), colour = "red") + # median prediction
-  theme_bw() +
-  coord_cartesian(xlim = c(0, NA)) +
-  ylab("y")
+  ggplot(data.frame(x = x, y = y), aes(x)) +
+    geom_ribbon(data = pl, aes(ymin = `0.2`, ymax = `0.8`), fill = "lightblue") +
+    geom_point(aes(y = y), colour = "grey") + # observed data
+    geom_function(fun = sin, colour = "black") + # truth
+    geom_vline(xintercept = fd, linetype = "dashed") + # end of training data
+    geom_line(data = pl, aes(y = `0.5`), colour = "red") + # median prediction
+    theme_bw() +
+    coord_cartesian(xlim = c(0, NA)) +
+    ylab("y")
 }
 }
 \seealso{