---
title: "movedesign"
author: "Inês Silva"
date: "`r Sys.Date()`"
# description: "Get started"
vignette: >
  %\VignetteIndexEntry{Get started}
  %\VignetteEngine{quarto::html}
  %\VignetteEncoding{UTF-8}
knitr:
  opts_chunk: 
    collapse: true
    comment: '#>'
bibliography: references.bib
csl: style.csl
---

```{=html}
<style>
body {
    text-align: justify;
}
svg {
  margin-right: 0.3em;
}
.custom-box {
    background-color: #009DA0;
    color: white;
    padding: 10px 10px 2px 10px;
    margin: 8px 0 8px 0;
}
code {
  background-color: RGB(232, 232, 232) !important;
}
</style>
```

```{r, include = FALSE}
library(fontawesome)

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)
```

# Why `movedesign`?

`movedesign` is built using R language with Shiny for an easy-to-use user interface (GUI). This application will allow you to test different tracking schedules while considering an initially set research question (currently *home range* and *speed/distance* estimation).

-   Doesn't require R coding experience.
-   Leverages the `ctmm` R package for statistically unbiased methods.

# How to start:

The application includes a built-in guided tutorial to help you navigate its features. When you open the `'Home'` tab, you'll find the following:

![](images/tutorial1_img1.png){fig-align="center"}

This step-by-step guide will walk you through the app, ensuring you understand its features and functionality. When an action is required, it will be clearly [highlighted]{style="color: white; background: #009DA0;"}. Please follow these instructions carefully, as each step builds on the previous one. If no action is needed, simply continue to the next step by clicking `'Next'` or pressing the right arrow key on your keyboard. The information from the guided tour is also partially available below.

`r fontawesome::fa("circle-exclamation", fill = "#DB4545")` [Warning:]{style="color: #DB4545; font-weight: bold;"} [During the guided tutorial, refrain from interacting with anything outside the highlighted zones to avoid interruptions.]{style="color: #DB4545;"}

This tutorial provides an overview of key features but does not cover detailed definitions. For more in-depth explanations, you can access comprehensive `r fontawesome::fa("circle-question")` **help tips** at any time. Documentation is also available on Silva *et al.* [-@silva2023movedesign].

## Workflows

These are the current options for `movedesign` workflows. Users may configure their study design by selecting different options for data source, research target, and analytical target. All workflows follow a stepwise approach, with tabs displayed sequentially on the right sidebar. Irrelevant tabs for the current workflow will be automatically hidden.

**Step 1.** *Data source:*

Users can specify what is their data source:

-   [ ] `Upload`: Import a dataset from a local file.
-   [ ] `Select`: Choose from pre-existing datasets available in the application.
-   [ ] `Simulate`: Generate synthetic data for testing or modeling purposes.

**Step 2.** *Research target:*

Users can define their research targets:

-   [ ] `Home range`: Estimate long-term space-use requirements.
-   [ ] `Speed & distance`: Estimate fine-scale movement metrics, such as speed and distance traveled.

**Step 3.** *Analytical target:*

Users can decide on their analytical target for the estimates:

-   [ ] `Individual estimate`: Obtain metrics for a single individual.
-   [ ] `Mean estimate of sampled population`: Obtain a mean estimate across a sampled group.
-   [ ] `Compare estimates of two sampled groups`: Perform a comparative analysis between two groups within a tracked population (for the detection of sub-groups).

Additionally, users have the option to:

-   [ ] `Add individual variation`: Include variation among individuals during simulations (only available for mean and estimate comparisons).

Go [here](https://ecoisilva.github.io/movedesign/articles/tutorial_ind.html) for more detailed information regarding the `Individual estimate` workflow.

## Related work

-   The [ctmm](https://github.com/ctmm-initiative/ctmm) R package and [ctmmweb](https://github.com/ctmm-initiative/ctmmweb) Shiny application.

## References