---
title: "Seasonal Plots: Align case data for seasonal analysis"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{Seasonal Plots: Align case data for seasonal analysis}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r, echo = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  warning = FALSE,
  message = FALSE
)
```

```{r setup, echo = FALSE}
library(ggplot2)
library(dplyr)
library(tidyr)
library(ggsurveillance)
```

## The seasonal plot
This vignette is still work in progress. But the examples are hopefully already helpful and inspiring.

The seasonal plot is a commonly used plot for seasonal respiratory pathogens like Influenza and RSV. For seasons covering the turn of the year have to be defined. In a second step the previous seasons have to be aligned to the current season to allow for a comparison. Here we show how this is automated using `ggsurveillance`.
```{r echo=FALSE}
influenza_germany |>
  filter(AgeGroup == "00+") |>
  align_dates_seasonal(
    dates_from = ReportingWeek,
    date_resolution = "isoweek",
    start = 28
  ) -> df_flu_aligned

ggplot(df_flu_aligned, aes(x = date_aligned, y = Incidence)) +
  stat_summary(
    aes(linetype = "Historical Median (Min-Max)"),
    data = . %>% filter(!current_season),
    fun.data = median_hilow, geom = "ribbon", alpha = 0.3
  ) +
  stat_summary(
    aes(linetype = "Historical Median (Min-Max)"),
    data = . %>% filter(!current_season),
    fun = median, geom = "line"
  ) +
  geom_line(
    aes(linetype = "2024/25"),
    data = . %>% filter(current_season), colour = "dodgerblue4", linewidth = 2
  ) +
  labs(linetype = "") +
  scale_x_date(date_labels = "%b'%y") +
  theme_bw() +
  theme(legend.position = c(0.2, 0.8))
```



## Seasonal alignment and plot
```{r}
library(ggplot2)

influenza_germany |>
  align_dates_seasonal(
    dates_from = ReportingWeek, date_resolution = "epiweek", start = 28
  ) -> df_flu_aligned

ggplot(df_flu_aligned, aes(x = date_aligned, y = Incidence, color = season)) +
  geom_line() +
  facet_wrap(~AgeGroup) +
  theme_bw()
```

```{r}
influenza_germany |>
  align_dates_seasonal(dates_from = ReportingWeek) |>
  group_by(AgeGroup, season) |>
  tally(wt = Cases) |>
  pivot_wider(names_from = AgeGroup, values_from = n)
```

## Combining everything for the seasonal plot

```{r}
influenza_germany |>
  filter(AgeGroup == "00+") |>
  align_dates_seasonal(
    dates_from = ReportingWeek,
    date_resolution = "isoweek",
    start = 28
  ) -> df_flu_aligned

ggplot(df_flu_aligned, aes(x = date_aligned, y = Incidence)) +
  stat_summary(
    aes(linetype = "Historical Median (Min-Max)"),
    data = . %>% filter(!current_season),
    fun.data = median_hilow, geom = "ribbon", alpha = 0.3
  ) +
  stat_summary(
    aes(linetype = "Historical Median (Min-Max)"),
    data = . %>% filter(!current_season),
    fun = median, geom = "line"
  ) +
  geom_line(
    aes(linetype = "2024/25"),
    data = . %>% filter(current_season), colour = "dodgerblue4", linewidth = 2
  ) +
  labs(linetype = "") +
  scale_x_date(date_labels = "%b'%y") +
  theme_bw() +
  theme(legend.position = c(0.2, 0.8))
```


## Other visualisations
```{r}
influenza_germany |>
  filter(AgeGroup != "00+") |>
  align_dates_seasonal(dates_from = ReportingWeek) |>
  ggplot(aes(x = ReportingWeek, weight = Cases, fill = season)) +
  geom_vline_year(color = "grey50") +
  # Use stat = count for more efficient plotting
  geom_epicurve(color = NA, stat = "count") +
  scale_y_cases_5er() +
  theme_bw()
```