---
title: "Create stars dataset"
date: "`r Sys.Date()`"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{Create stars dataset}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r setup, include = FALSE}
knitr::opts_chunk$set(fig.align = "center", eval = TRUE)
knitr::opts_chunk$set(fig.height = 7, fig.width = 8, dpi = 150, out.width = '100%')
knitr::opts_chunk$set(comment = "#>")
```

Our package `sfclust` is designed to work with spatio-temporal data structured as a
`stars` object. In this vignette, we demonstrate how to create and manipulate a basic
`stars` object.

```{r warning=FALSE, include=TRUE}
library(stars)
library(ggplot2)
```

## Simulated data

To begin, we need data that varies across both regions and time. In this example, we
simulate such data for illustrative purposes, but in practice, this would come from your
study's specific regions and time periods.

```{r}
set.seed(10)
space <- st_make_grid(cellsize = c(1, 1), offset = c(0, 0), n = c(3, 2))
time <- seq(as.Date("2025-01-01"), by = "1 month", length.out = 5)
```

Next, we simulate variables associated with each region and time point. In this case, we
create values for `cases`, `temperature`, and `precipitation`. Each variable is stored in
a matrix where rows correspond to regions and columns to time points.

```{r}
cases <- matrix(rpois(30, 100), nrow = 6, ncol = 5)
temperature <- matrix(rnorm(30), nrow = 6, ncol = 5)
precipitation <- matrix(rnorm(30), nrow = 6, ncol = 5)
```

## Creating a `stars` object

We now use the simulated data to construct a `stars` object. This object will contain
spatial, temporal, and variable dimensions. While there are several ways to build such
objects, the following method is commonly used:

```{r}
stdata <- st_as_stars(
  cases = cases, temperature = temperature, precipitation = precipitation,
  dimensions = st_dimensions(geometry = space, time = time)
)
stdata
```

## Manipulating a `stars` object

Once the `stars` object is created, you can use any of the methods provided by the
[`stars`](https://r-spatial.github.io/stars/index.html) package. For example, we can
visualize the `cases` variable across time:

```{r, fig.height = 5}
ggplot() +
    geom_stars(aes(fill = cases), data = stdata) +
    facet_wrap(~ time) +
    scale_fill_distiller(palette = "RdBu")
```

You can also add additional variables to the `stars` object. For instance, let’s add a
`population` variable:

```{r}
stdata["population"] <- rep(rpois(6, 1000), each = 6)
stdata
```
