# 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.

```
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")
)
#> ℹ Creating denominator cohorts
#> ✔ Cohorts created in 0 min and 4 sec
inc <- estimateIncidence(
cdm = cdm,
denominatorTable = "denominator",
outcomeTable = "outcome",
interval = "years"
)
#> Getting incidence for analysis 1 of 2
#> Getting incidence for analysis 2 of 2
#> Overall time taken: 0 mins and 2 secs
```

## Faceted plot

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

`plotIncidence(inc, facet = "denominator_sex")`

## Faceted plot - with lines

This is the previous plot where the dots are connected.

`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.

```
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.

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