---
title: "Plotting options"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{a06_Plotting_options}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.width = 7,
  fig.height = 5,
  eval = Sys.getenv("$RUNNER_OS") != "macOS"
)
```

```{r, message= FALSE, warning=FALSE, echo=FALSE}
library(here)
library(DBI)
library(dbplyr)
library(dplyr)
library(tidyr)
library(duckdb)
library(knitr)
library(IncidencePrevalence)
```

# Introduction

This package provides functions to create an incidence or prevalence plot. There are a couple of options that can be specified when creating such a plot. In this vignette we are using the options in the `plotIncidence` function, however these same options can be specified in the `plotPrevalence` function.

```{r setup}
cdm <- mockIncidencePrevalenceRef(
  sampleSize = 10000,
  outPre = 0.5
)

cdm <- generateDenominatorCohortSet(
  cdm = cdm, name = "denominator",
  cohortDateRange = c(as.Date("2008-01-01"), as.Date("2012-01-01")),
  sex = c("Male", "Female")
)

inc <- estimateIncidence(
  cdm = cdm,
  denominatorTable = "denominator",
  outcomeTable = "outcome",
  interval = "years"
)
```

## Faceted plot

This is the default incidence plot where the plot has been faceted by sex.

```{r facetplot}
plotIncidence(inc, facet = "denominator_sex")
```

## Faceted plot - with lines

This is the previous plot where the dots are connected.

```{r linesplot}
plotIncidence(inc, facet = "denominator_sex", ribbon = TRUE)
```

## Faceted plot - with lines, no confidence interval

This is the previous plot where the dots are connected but no confidence interval is shown.

```{r noconfplot}
plotIncidence(inc, facet = "denominator_sex", ribbon = TRUE, 
              options = list('hideConfidenceInterval' = TRUE))
```

## Faceted plot - with lines, no confidence interval, stacked, free scales

This is the previous plot where the subplots are shown on top of each other. The `facetNcols` variable 
defines the number of columns of the subplots. In addition we set `facetScales` as "free" so that the axis can vary by facet.

```{r stackedplot}
plotIncidence(inc, facet = "denominator_sex", ribbon = TRUE, 
              options = list('hideConfidenceInterval' = TRUE,
                             'facetNcols' = 1, 
                             'facetScales' = "free"))
```