---
title: "Create KMunicate-Style Plots"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{Create KMunicate-Style Plots}
  %\VignetteEncoding{UTF-8}
  %\VignetteEngine{knitr::rmarkdown}
editor_options: 
  chunk_output_type: console
---

```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  out.width = "75%",
  fig.align = "center",
  dpi = 200
)
```

The {KMunicate} package can be used to produce Kaplan-Meier plots in the style recommended following the [KMunicate study](https://bmjopen.bmj.com/content/9/9/e030215) by TP Morris _et al_. (2019).

In this vignette, we'll see learn how to produce such a plot using the {KMunicate} package.

## Data

We will be using the `brcancer` dataset, which comes bundled with {KMunicate}:

```{r}
data("brcancer", package = "KMunicate")
str(brcancer)
```

## Single-Arm Plot

We start by creating a KMunicate-style plot for all study subjects.
First, we load the package:

```{r setup}
library(KMunicate)
```

Then, we fit a Kaplan-Meier curve using the `survfit` function from the {survival} package:

```{r fit}
fit <- survfit(Surv(rectime, censrec) ~ 1, data = brcancer)
fit
```

Then, we need to define the horizontal axis of the plot.
For instance, we can define 5 equally-spaced breaks between time zero and the largest observed time:

```{r time_scale}
ts <- seq(0, max(brcancer$rectime), length.out = 5)
ts
```

Finally, we can pass `fit` and `ts` to the `KMunicate()` function to simply obtain a plot:

```{r p1, fig.height = 6, fig.width = 6}
KMunicate(fit = fit, time_scale = ts)
```

## Multiple-Arms Plot

A multiple-arms plot will be automatically produced if the `survfit` object has covariates in it.
For instance, if we fit the Kaplan-Meier estimator by treatment arm:

```{r fit2}
fit2 <- survfit(Surv(rectime, censrec) ~ hormon, data = brcancer)
fit2
```

We will be using the same time scale as before (`ts`), and the call to `KMunicate()` is analogous:

```{r p2, fig.height = 6 * sqrt(2), fig.width = 6}
KMunicate(fit = fit2, time_scale = ts)
```

## Customisation

The `KMunicate()` function contains a few options to customise the produced plot.
First, we can pass a `ggplot2` theme to the `.theme` argument:

```{r p3, fig.height = 6 * sqrt(2), fig.width = 6}
KMunicate(fit = fit2, time_scale = ts, .theme = ggplot2::theme_minimal())
```

We can also pass custom colour (and fill) scales:

```{r p4, fig.height = 6 * sqrt(2), fig.width = 6}
KMunicate(
  fit = fit2,
  time_scale = ts,
  .color_scale = ggplot2::scale_color_brewer(type = "qual", palette = "Set2"),
  .fill_scale = ggplot2::scale_fill_brewer(type = "qual", palette = "Set2")
)
```

We can customise the transparency of the point-wise confidence intervals via the `.alpha` argument:

```{r p5, fig.height = 6 * sqrt(2), fig.width = 6}
KMunicate(fit = fit2, time_scale = ts, .alpha = 0.1)
```

We can customise the label of the horizontal axis:

```{r p6, fig.height = 6 * sqrt(2), fig.width = 6}
KMunicate(fit = fit2, time_scale = ts, .xlab = "New Label (Time in Days, Actually)")
```

We can customise the relative size of risk tables and plots, although the default _should_ generally work fine for most scenarios:

```{r p7, fig.height = 6 * sqrt(2), fig.width = 6}
KMunicate(fit = fit2, time_scale = ts, .rel_heights = c(1, 1, 1))
```

# Custom Fonts

Assuming you have set up your computer and R session to support custom fonts (e.g. using a [`ragg`](https://CRAN.R-project.org/package=ragg) graphics device), you can produce plots with custom fonts via the `.ff` argument of the `KMunicate()` function.
Examples of using `ragg` can be found [here](https://www.tidyverse.org/blog/2021/02/modern-text-features/).

# A _Better_ Plot

We can combine all the above to obtain an (arguably) much better plot:

```{r better-plot, fig.height = 6 * sqrt(2), fig.width = 6}
KMunicate(
  fit = fit2,
  time_scale = ts,
  .theme = ggplot2::theme_minimal(),
  .xlab = "Time (in days)",
  .color_scale = ggplot2::scale_color_brewer(type = "qual", palette = "Set2"),
  .fill_scale = ggplot2::scale_fill_brewer(type = "qual", palette = "Set2")
)
```

# Further Customisation Options

As of version 0.2.0, {KMunicate} provide additional arguments to customise the final plot.

You can customise the size of each Kaplan-Meier curve via the `.size` argument:

```{r size, fig.height = 6 * sqrt(2), fig.width = 6}
KMunicate(
  fit = fit2,
  time_scale = ts,
  .size = 2
)
```

You can customise the linetype scale via the `.linetype_scale` argument:

```{r linetype_scale, fig.height = 6 * sqrt(2), fig.width = 6}
KMunicate(
  fit = fit2,
  time_scale = ts,
  .linetype_scale = ggplot2::scale_linetype_manual(values = c("dotted", "dashed"))
)
```

You can customise the location of the legend via the `.legend_position` argument, or you can even suppress it fully:

```{r legend_position, fig.height = 6 * sqrt(2), fig.width = 6}
KMunicate(
  fit = fit2,
  time_scale = ts,
  .reverse = TRUE,
  .legend_position = c(0, 1)
)
```

```{r legend_position_none, fig.height = 6 * sqrt(2), fig.width = 6}
KMunicate(
  fit = fit2,
  time_scale = ts,
  .reverse = TRUE,
  .legend_position = "none"
)
```

You can add custom annotations via the `.annotate` argument:

```{r annotate, fig.height = 6 * sqrt(2), fig.width = 6}
KMunicate(
  fit = fit2,
  time_scale = ts,
  .annotate = ggplot2::annotate(geom = "text", x = 365, y = 0.5, label = "Some annotation")
)
```
