---
title: "Frequently Asked Questions"
author: "Johannes Signer"
date: "`r Sys.Date()`"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{Frequently Asked Questions}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r, include=FALSE}
library(knitr)
knitr::opts_chunk$set(message = FALSE, warning = FALSE)
set.seed(20161113)
```


## Working with tracks

### Q1: How to work with `extract_covariates_var_time()` with multiple animals:

Simulate some data: 10 animals over a year with monhtly covariates. Note the covariates do not influence movement during the simulaitons. This purely for illustration. 

```{r}
library(raster)
library(lubridate)
library(amt)

set.seed(123)

trks <- tibble(
  id = 1:10, 
  trk = map(1:10, ~ tibble(x = cumsum(rnorm(100)), y = cumsum(rnorm(100)), 
                           ts = ymd_hm("2019-01-01 00:00") + hours(0:99)))
)

dat <- unnest(trks, cols = trk)

```

Next lets create some covariates.

```{r}
r <- stack(map(1:15, 
    ~ raster(xmn = -100, xmx = 100, ymn = -100, ymx = 100, 
             res = 1, vals = runif(4e4, ., . + 1))))

r <- setZ(r, ymd_hm("2019-01-01 00:00") + hours(seq(0, by = 10, len = 15)))
```

We have now 10 animals, each with 100 relocations (one per hour) and one env covariate which changes every 10 hours. 
Now we can extract the covariates at the end of each step within an time window (specified with `max_time`). 


```{r}
trk <- make_track(dat, x, y, ts, id = id)

# For one animal
trk %>% filter(id == 1) %>% 
  extract_covariates_var_time(r, max_time = hours(10 * 24)) -> t1

t1$time_var_covar

```
Now lets do the the same for an animal at once:

```{r}
trk %>% 
  nest(data = -c(id)) %>% 
  mutate(data = map(data, ~ .x %>% extract_covariates_var_time(r, max_time = hours(10 * 24)))) %>% 
  unnest(cols = data) -> trk2

trk2

```




## Session

```{r}
sessionInfo()
```

