time-series.qmd

---
title: "Everglades Time Series"
format: dashboard
echo: false
---

```{r}
library(wader)

#download_observations(".")
```

```{r}
library(dplyr)
library(tibble)
library(tidyr)

colony_counts <- tibble(max_counts(level = "colony"))
subregion_counts <- tibble(max_counts(level = "subregion"))
region_counts <- tibble(max_counts(level = "region"))
everglades_counts <- tibble(max_counts(level = "all"))
water <- tibble(load_datafile("Water/eden_covariates.csv"))

subregion_counts <- subregion_counts |>
    filter(species %in% c("gbhe", "greg", "rosp", "sneg", "wost", "whib")) |>
    group_by(region) |>
    filter(n_distinct(year) > 10) |>
    complete(year = full_seq(year, 1), species, fill = list(count = 0)) |>
    ungroup()

ojs_define(col_counts = colony_counts)
ojs_define(sub_counts = subregion_counts)
ojs_define(reg_counts = region_counts)
ojs_define(ever_counts = everglades_counts)
ojs_define(enviro_data = water)

```

# Max Counts

## {.sidebar}

```{ojs}
viewof min_year = Inputs.range(
    [1986, 2024],
    {step: 1, value: 1986, label: "Min year"})
viewof max_year = Inputs.range(
    [1986, 2024],
    {step: 1, value: 2024, label: "Max year"})
viewof species = Inputs.checkbox(
  ["greg", "whib", "rosp", "sneg", "wost", "gbhe"],
  { value: ["greg", "whib", "rosp", "sneg", "wost", "gbhe"], 
    label: "Species:"
  }
)
```

```{ojs}
//| output: false
data = transpose(sub_counts)

/*
viewof scale = Inputs.select(
  ["colony", "subregion", "region", "all"],
  {value: "subregion", label: "Scale:"}
)
*/

filtered = data.filter(function(df) {
  return df.year >= min_year &&
         df.year <= max_year &&
         species.includes(df.species)
})
```

## Row

```{ojs}
Plot.plot({
    x: {tickFormat: d => d.toString()},
    marks: [
        Plot.line(filtered, {
            x: "year",
            y: "count", 
            stroke: "species",
            fy: "region",
            marker: true
            })
    ],
    legend: {
        title: "Species",
        orient: "top",
        stroke: "species",
        symbol: "stroke"
    }
})
```

# Water Data

## {.sidebar}

```{ojs}
viewof env_min_year = Inputs.range(
    [1986, 2024],
    {step: 1, value: 1986, label: "Min year"})
viewof env_max_year = Inputs.range(
    [1986, 2024],
    {step: 1, value: 2024, label: "Max year"})
viewof env_variable = Inputs.select(
  ["init_depth", "breed_season_depth", "recession", "pre_recession", "post_recession", "dry_days"],
  { value: "init_depth", label: "Variables:"}
)
viewof region = Inputs.checkbox(
    env_data.map(d => d.region).filter((value, index, self) => self.indexOf(value) === index),
    { value: env_data.map(d => d.region).filter((value, index, self) => self.indexOf(value) === index), label: "Region:" }
)
```


```{ojs}
//| output: false
env_data = transpose(enviro_data)
env_filtered = env_data.filter(function(df) {
  return df.year >= env_min_year &&
         df.year <= env_max_year &&
         region.includes(df.region)
})
```

## Row

```{ojs}
Plot.plot({
  gird: true,
  x: {tickFormat: d => d.toString()},
  marks: [
    Plot.frame(),
    Plot.line(env_filtered, {
      x: "year",
      y: d => d[env_variable],
      stroke: "steelblue",
      fy: "region",
      marker: true
      })
  ]}
)
```