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+---
+title: "Event detection"
+author: "Beni Stocker"
+date: "`r Sys.Date()`"
+output: rmarkdown::html_vignette
+vignette: >
+  %\VignetteIndexEntry{map2tidy functionality}
+  %\VignetteEngine{knitr::rmarkdown}
+  %\VignetteEncoding{UTF-8}
+---
+
+```{r setup, include=FALSE}
+knitr::opts_chunk$set(echo = TRUE)
+```
+
+```{r message=FALSE}
+library(dplyr)
+library(ggplot2)
+library(GECOr)
+library(here)
+
+```
+
+An event is defined here as consecutive time steps where a certain condition is fulfilled - yielding a vector of a logical (boolean) variable. This vignette demonstrates how such events are derived from the boolean vector using the GECOr function `get_consecutive()`, and a time series data frame re-arranged and labelled with sequential time steps into a given event (`eventstep`), using the GECOr function `align_events()`. 
+
+## Prepare data
+
+Read data from the file contained in this repository. This is a data frame containing time series of multiple variables arranged in columns and each time step (day) is a row.
+```{r}
+df <- readRDS(file = paste0(here(), "/data/df_ch-lae.rds"))
+```
+
+Below, let's consider the following variables:
+
+- `TIMESTAMP`: the date of observation
+- `TA_F_MDS`: air temperature in degrees Celsius
+
+## Event detection
+
+Let's consider frost events as periods of consecutive days where air temperature is below zero and create a new variable in the data frame that contains this information as a boolean vector (`isfrost`).
+
+```{r}
+df <- df |> 
+  mutate(isfrost = ifelse(TA_F_MDS < 0, TRUE, FALSE))
+```
+
+With this vector, we can apply the function `get_consecutive()`. 
+
+```{r}
+events <- get_consecutive(
+  df$isfrost,
+  merge_threshold = 3,
+  leng_threshold = 5,
+  do_merge = TRUE
+)
+```
+
+This yields information about the events start and length, expressed in index position of the boolean vector (here `df$isfrost`).
+```{r}
+head(events)
+```
+
+We can use this to get the corresponding dates.
+
+```{r}
+# get start and end date of longest sequences
+events <- events |> 
+  mutate(
+    start = lubridate::as_date(df$TIMESTAMP[events$idx_start]),
+    end = lubridate::as_date(df$TIMESTAMP[events$idx_start + events$len - 1])
+  )
+```
+
+With this information, we can visualise the detected frost events.
+
+```{r}
+ggplot() +
+  # grey rectangles for each event
+  geom_rect(
+    data = events,
+    aes(
+      xmin = start,
+      xmax = end,
+      ymin = min(df$TA_F_MDS, na.rm = TRUE),
+      ymax = max(df$TA_F_MDS, na.rm = TRUE)
+    ),
+    fill = "grey80"
+  ) +
+  geom_line(
+    data = df,
+    aes(
+      TIMESTAMP,
+      TA_F_MDS
+    )) +
+  labs(x = "Time",
+       y = "Air temperature (degrees Celsius)") +
+  theme_classic()
+```
+
+## Align events
+
+Now that the events are detected, the data within each event can be re-arranged, treating each event as a comparable sequence of time steps (here days).
+
+```{r}
+aligned <- align_events(
+  df, 
+  events = events,
+  leng_threshold = 5,
+  before = 3, 
+  after = 100
+)
+```
+
+We can now plot data during each frost event versus the `idx_event` (here day into frost event, counting from 0, in the output data frame).
+
+```{r}
+aligned |> 
+  ggplot(aes(idx_event, TA_F_MDS)) +
+  geom_point() + 
+  geom_hline(yintercept = 0, linetype = "dotted") +
+  geom_vline(xintercept = 0, linetype = "dotted") +
+  theme_classic()
+```
+
+