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---
title: "Swimmer Plots in R using ggplot"
subtitle: "R/Medicine 2022"
author: Kat Hoffman
output:
xaringan::moon_reader:
lib_dir: libs
css: ["xaringan-themer.css","extra.css"]
nature:
ratio: 16:9
highlightStyle: github
highlightLines: true
countIncrementalSlides: false
seal: false
---
```{r xaringan-themer, include=FALSE, warning=FALSE}
library(xaringanthemer)
library(fontawesome)
library(kableExtra)
style_mono_accent(
base_color = "#1e90ff",
header_font_google = google_font("Josefin Sans"),
text_font_google = google_font("Montserrat", "300", "300i"),
code_font_google = google_font("Fira Mono")
)
```
```{r, include = F}
# This is the recommended set up for flipbooks
# you might think about setting cache to TRUE as you gain practice --- building flipbooks from scratch can be time consuming
knitr::opts_chunk$set(fig.width = 9, message = FALSE, warning = FALSE, comment = "", cache = T)
library(flipbookr)
library(tidyverse)
library(rmarkdown)
```
layout: true
<div class="my-footer"><span>kathoffman.github.io/swimmer-plots/slides.html</span></div>
---
name: xaringan-title
class: right, middle
background-image: url(img/blue-foaming-waves-north-beach-nazare-portugal.jpg)
background-size: cover
<h1 style="color: white;">Making swimmer plots for longitudinal data using {ggplot}</h1>
<img src="img/rmed-hex.png" alt="Rmed-hex-sticker" width="180" />
<h3 style="color: white;"> Kat Hoffman | August 25, 2022</h3>
---
class: middle, center
<img src="img/climbing_comic.jpeg" width="350"/>
### Find me at:
[`r fa(name = "link")` khstats.com](https://khstats.com)
[`r fa(name = "twitter")` @kat\_hoffman\_](http://twitter.com/kat_hoffman_)
[`r fa(name = "github")` @kathoffman](http://github.com/kathoffman)
[`r fa(name = "paper-plane")` kathoffman.stats@gmail.com](mailto:kathoffman.stats@gmail.com)
---
# Swimmer plot
.pull-left[
- Graphical way to show a **subject's profile over time**
- A series of horizontal lines, in which **each line represents one subject**
- **Colors or shapes** on the line **indicate treatments** or other statuses of that subject at a particular time
]
.pull-right[
```{r echo=F, out.width=600, fig.align="center", fig.cap="Image: Shen et al. Cell 182, 59–72, July 9, 2020"}
knitr::include_graphics("img/cell_swimmer.png")
```
]
<!-- -- -->
<!-- - -->
<!-- -- -->
<!-- - -->
<!-- -- -->
<!-- - Excellent for exploratory data analysis (EDA) of longitudinal data (e.g. visualize missigness or sampling patterns) -->
<!-- -- -->
<!-- - Can be included as a final product (e.g. manuscripts, presentations) to explain cohort composition, treatment variability, and more -->
<!-- -- -->
<!-- - While `R` packages such as `swimmer` exist, it is often faster and more customizable to write end-to-end `ggplot` code yourself -->
---
# Do we *really* have to hand-code this?
.pull-left[
- `R` packages such as `swimplot` do exist, but it is more customizable to write end-to-end `ggplot` code yourself
- Configuring the legend correctly in `ggplot` can be tricky whether you use `swimplot` or not
]
.pull-right[
```{r out.width=500, echo=F, fig.cap="Image courtesy of {swimplot} vignette."}
knitr::include_graphics("img/swimmer_swimplot.png")
```
]
---
.left-column[
### Today's swimmer plot:
We will show the timing of...
- severe hypoxia
- intubation
- steroids administration
- 28-day mortality
...in a cohort of hospitalized COVID-19 patients.
]
.right-column[
```{r echo=F, out.width=700, fig.align="center"}
knitr::include_graphics("img/swimmer.png")
```
]
---
# Step 1: Long-form data set
.pull-left[
- ID column
- Time column (e.g. day)
- One column per status (e.g. drug A - yes/no, drug B - yes/no)
- One row per subject per unit of time
]
--
.pull-right[
```{r data1}
library(tidyverse)
library(data.table)
dat_long <- read_csv("https://raw.githubusercontent.com/kathoffman/steroids-trial-emulation/main/data/dat_trt_timeline.csv",col_types = list(id = "c", steroids = "c", death = "c", severe = "c"))
dat_long |> data.table()
```
]
---
# Step 2: Long-form data for plotting
.pull-left[
- ID column (**optional: order by maximum time per subject**)
- Time column
- One column per status, **which is now the time if you want the status to be marked on the plot**
- One row per subject per unit of time
]
--
.pull-right[
This is a way to create data with a reordered patient ID (by hospital length-of-stay):
```{r data2}
dat_reorder <-
dat_long |>
group_by(id) |>
mutate(max_day = max(day)) |>
ungroup() |>
mutate(id = fct_reorder(factor(id), max_day))
```
The next slide shows how to change the status columns to `{status}_this_day` columns.
]
---
# Step 2: Long-form data for plotting
```{r}
dat_swim <- dat_reorder |>
mutate(severe_this_day = case_when(severe == 1 ~ day),
steroids_this_day = case_when(steroids == 1 ~ day),
death_this_day = case_when(death == 1 ~ day))
dat_swim |> paged_table()
```
---
`r chunk_reveal("swim0", break_type = "user", title="# Step 3: Plot all your geometries")`
```{r swim0, include = FALSE}
dat_swim %>%
ggplot(aes(y=id, group=id)) + #BREAK
theme_bw() + #BREAK
geom_line(aes(x=day, col = intubation_status)) +#BREAK
geom_point(aes(x=steroids_this_day)) +#BREAK
geom_point(aes(x=severe_this_day)) +#BREAK
geom_point(aes(x=death_this_day))
```
---
`r chunk_reveal("swim1", break_type = "user", title="# Step 4: Modify geom_* layers.")`
```{r swim1, include = FALSE}
dat_swim %>%
ggplot(aes(y=id, group=id)) +
theme_bw() + #BREAK
geom_line(aes(x=day, col = intubation_status),
size=1.8) +#BREAK
geom_point(aes(x=steroids_this_day),
stroke=2,
shape = 15) +#BREAK
geom_point(aes(x=severe_this_day),
size=2,
stroke=1.5,
shape = 21) + #BREAK
geom_point(aes(x=death_this_day),
size=2,
stroke=1.5,
shape = 4)
```
---
# Step 5: Add colors and legend
.left-column[
![](img/bear_with_me.jpg)
]
.right-column[
**The Big Picture:**
- We want a legend which correctly maps each shape, color, and line type to the corresponding status.
- We need all of our statuses to show up in the legend. They currently do not, because they're not being mapped to an `aes`thetic argument.
- We will force all the statuses to show up by creating a new aesthetic (color) which **corresponds to the name we want each status to be labeled** in the legend.
- We will then modify this legend for "color" to also contain information about shape, linetype, etc. for each status.
]
---
`r chunk_reveal("swim2", break_type = "user", title="# Step 5: Add colors and legend")`
```{r swim2, include = FALSE}
dat_swim %>%
ggplot(aes(y=id)) +
theme_bw() +
geom_line(aes(x=day,
col = intubation_status,
group=id),
size=1.8) + #BREAK
geom_point(aes(x=steroids_this_day,
col="Steroids"),
stroke=2,
shape=15) +#BREAK
geom_point(aes(x=severe_this_day,
col="Severe hypoxia"),
size=2,
stroke=1.5,
shape=21) + #BREAK
geom_point(aes(x=death_this_day,
col="Death"),
size=2,
stroke=1.5,
shape=4)
```
---
# Step 6: Modify colors
- We now need a key for status labels and corresponding colors.
```{r}
# define colors for all geometries with a color argument
cols <- c("Severe hypoxia" = "#b24745", # red
"Intubated" = "#483d8b", # navy
"Not intubated" = "#74aaff", # blue
"Steroids"="#ffd966", # gold
"Death" = "#000000") # black
```
- The label of the status should match the label inputted to `aes(col = "...")`.
- The order of this color key will be the order the statuses appear in the legend.
- We will use this key as the `values` argument of `scale_color_manual()`:
---
`r chunk_reveal("swim3", break_type = "user", title="# Step 6: Modify colors")`
```{r swim3, include = FALSE}
dat_swim %>%
ggplot(aes(y=id)) +
theme_bw() +
geom_line(aes(x=day,
col = intubation_status,
group=id),
size=1.8) +
geom_point(aes(x=steroids_this_day,
col="Steroids"),
stroke=2,
shape=15) +
geom_point(aes(x=severe_this_day,
col="Severe hypoxia"),
size=2,
stroke=1.5,
shape=21) +
geom_point(aes(x=death_this_day,
col="Death"),
size=2,
stroke=1.5,
shape=4) +#BREAK
scale_color_manual(values = cols,
name="Patient Status")
```
---
# Step 7: Modify legend using override.aes()
- We need to create vectors for overriding the aesthetics for `shape`, `linetype`, `stroke`, and `size` in the current "color" legend.
- These should be in the same order as the `cols` key
- If we don't want a certain aesthetic to show up (e.g. no shape for intubation), we use `NA`
```{r}
cols
shape_override <- c(21, NA, NA, 15, 4) # order matches `cols`:severe, intubation (yes/no), steroids, death
line_override <- c(NA,1,1,NA,NA) # order matches `cols`:severe, intubation (yes/no), steroids, death
stroke_override <- c(.8,1,1,1,1) # order matches `cols`:severe, intubation (yes/no), steroids, death
size_override <- c(2.5,2.5,2.6,2,2) # order matches `cols`:severe, intubation (yes/no), steroids, death
```
---
`r chunk_reveal("swim4", break_type = "user", title="# Step 7: Modify legend using override.aes()")`
```{r swim4, include = FALSE}
dat_swim %>%
ggplot(aes(y=id)) +
theme_bw() +
geom_line(aes(x=day, col = intubation_status, group=id),
size=1.8) +
geom_point(aes(x=steroids_this_day, col="Steroids"),
stroke=2, shape=15) +
geom_point(aes(x=severe_this_day, col="Severe hypoxia"),
size=2, stroke=1.5, shape=21) +
geom_point(aes(x=death_this_day, col="Death"),
size=2, stroke=1.5, shape=4) +
scale_color_manual(values = cols,
name="Patient Status") + #BREAK
guides(color = guide_legend(
override.aes = list(
stroke = stroke_override,
size = size_override,
shape = shape_override,
linetype = line_override)
))
```
```{r echo=F }
p <- dat_swim %>%
ggplot(aes(y=id)) +
theme_bw() +
geom_line(aes(x=day,
col = intubation_status,
group=id),
size=1.8) +
geom_point(aes(x=steroids_this_day,
col="Steroids"),
stroke=2,
shape=15) +
geom_point(aes(x=severe_this_day,
col="Severe hypoxia"),
size=2,
stroke=1.5,
shape=21) +
geom_point(aes(x=death_this_day,
col="Death"),
size=2,
stroke=1.5,
shape=4) +
scale_color_manual(values = cols,
name="Patient Status") + #BREAK
guides(color = guide_legend(
override.aes = list(
stroke = stroke_override,
size = size_override,
shape = shape_override,
linetype = line_override)
))# BREAK
```
---
`r chunk_reveal("swim5", break_type = "user", title="# Step 8: Make minor theme edits")`
```{r swim5, include = FALSE}
p + labs(x="Days since hospitalization",
y="Patient\nnumber",
title="Treatment Timeline for N=30 Patients") + #BREAK
scale_x_continuous(expand=c(0,0)) + #BREAK
theme(text=element_text(family="Poppins", size=11)) +
theme(title = element_text(angle = 0, vjust=.5,
size=12, face="bold"),
axis.title.y = element_text(angle = 0, vjust=.5,
size=12, face="bold"),
axis.title.x = element_text(size=15, face="bold",
vjust=-0.5, hjust=0),
axis.text.y = element_text(size=6,
hjust=1.5),
axis.ticks.y = element_blank()) + #BREAK
theme(legend.position = c(0.8, 0.3),
legend.title = element_text(colour="black",
size=13,
face=4),
legend.text = element_text(colour="black",
size=10),
legend.background = element_rect(size=0.5,
linetype="solid",
colour ="gray30")) + #BREAK
theme(panel.grid.minor = element_blank(),
panel.grid.major.x = element_blank()
)
```
```{r include=F, eval=F, echo=F}
p_final <- p + labs(x="Days since hospitalization",
y="Patient\nnumber",
title="Treatment Timeline for N=30 Patients") + #BREAK
scale_x_continuous(expand=c(0,0)) + #BREAK
theme(text=element_text(family="Poppins", size=11)) +
theme(title = element_text(angle = 0, vjust=.5,
size=12, face="bold"),
axis.title.y = element_text(angle = 0, vjust=.5,
size=12, face="bold"),
axis.title.x = element_text(size=15, face="bold",
vjust=-0.5, hjust=0),
axis.text.y = element_text(size=6,
hjust=1.5),
axis.ticks.y = element_blank()) + #BREAK
theme(legend.position = c(0.8, 0.3),
legend.title = element_text(colour="black",
size=13,
face=4),
legend.text = element_text(colour="black",
size=10),
legend.background = element_rect(size=0.5,
linetype="solid",
colour ="gray30")) + #BREAK
theme(panel.grid.minor = element_blank(),
panel.grid.major.x = element_blank()
)
ggsave("img/swimmer.png", width=8, height=6)
```
---
# In Summary 🏊
.pull-left[
- Long-format plotting data will need to have one column per status per row which indicates the **time of the status marker** to be denoted.
- Once data is properly formatted, `ggplot` can be used to make customizable swimmer plots with the `geom_line` and `geom_point` layers.
- The legend can be properly configured using the `override.aes()` argument in `guides()`.
- These ideas can be extended in many ways, such as [**showing the pattern of missing data**](https://www.khstats.com/blog/trt-timelines/trt-timelines/).
]
--
.pull-right[
```{r, echo=F, out.width=400, fig.cap = "Image from Patient Treatment Timelines for Longitudinal Survival Data -- KHstats, 2019"}
knitr::include_graphics("img/trt-timeline-missing.png")
```
]
<!-- adjust font size in this css code chunk, currently 80 -->
```{css, eval = TRUE, echo = FALSE}
.remark-code{line-height: 1.5; font-size: 80%}
@media print {
.has-continuation {
display: block;
}
}
code.r.hljs.remark-code{
position: relative;
overflow-x: hidden;
}
code.r.hljs.remark-code:hover{
overflow-x:visible;
width: 500px;
border-style: solid;
}
```
---
class: middle, center
Thank you to the authors of `{xaringan}` and `{flipbookr}` for making these slides possible.
<br>
<br>
<br>
[`r fa(name = "link")` khstats.com](https://khstats.com)
[`r fa(name = "twitter")` @kat\_hoffman\_](http://twitter.com/kat_hoffman_)
[`r fa(name = "github")` @kathoffman](http://github.com/kathoffman)
[`r fa(name = "paper-plane")` kathoffman.stats@gmail.com](mailto:kathoffman.stats@gmail.com)
<img src="img/climbing_comic.jpeg" width="250"/>