diff --git a/README.Rmd b/README.Rmd
index f42c007..22a2caa 100644
--- a/README.Rmd
+++ b/README.Rmd
@@ -42,8 +42,11 @@ To help you get started, ggswim includes three sample datasets: `patient_data`,
ggswim offers several geom-functions, and by using `geom_swim_lane()` we can set up the horizontal response paths of our swimmer plot, i.e. the "lanes". We'll also set up corresponding arrows to indicate subjects that are still on the trial:
-```{r, include=FALSE}
+```{r, message=FALSE}
library(ggswim)
+library(ggplot2)
+
+# Construct arrow_data for arrow display later
arrow_data <- patient_data |>
dplyr::left_join(
end_study_events |>
@@ -53,11 +56,6 @@ arrow_data <- patient_data |>
dplyr::select(pt_id, end_time, end_study_name) |>
dplyr::filter(.by = pt_id, end_time == max(end_time)) |>
unique()
-```
-
-```{r, message=FALSE}
-library(ggswim)
-library(ggplot2)
p <- patient_data |>
ggplot() +
diff --git a/README.md b/README.md
index 9220866..d1c1b04 100644
--- a/README.md
+++ b/README.md
@@ -46,6 +46,17 @@ that are still on the trial:
library(ggswim)
library(ggplot2)
+# Construct arrow_data for arrow display later
+arrow_data <- patient_data |>
+ dplyr::left_join(
+ end_study_events |>
+ dplyr::select(pt_id, end_study_name),
+ by = "pt_id"
+ ) |>
+ dplyr::select(pt_id, end_time, end_study_name) |>
+ dplyr::filter(.by = pt_id, end_time == max(end_time)) |>
+ unique()
+
p <- patient_data |>
ggplot() +
geom_swim_lane(
@@ -66,7 +77,7 @@ p <- patient_data |>
p
```
-
+
Next we’ll add on events of interest: end of study updates and
infusions. We’ll refer to these as “markers” and call them with two more
@@ -89,7 +100,7 @@ p <- p +
p
```
-
+
Finally, we’ll beautify the plot with familiar ggplot2 techniques and a
last finishing touch with `theme_ggswim()`:
@@ -102,7 +113,7 @@ p +
theme_ggswim()
```
-
+
## Collaboration
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