---
title: "Parzer use cases"
author: "Scott Chamberlain, Alban Sagouis"
date: "`r Sys.Date()`"
output:
  html_document:
    toc: true
vignette: >
  %\VignetteIndexEntry{Parzer use cases}
  %\VignetteEngine{quarto::html}
  %\VignetteEncoding{UTF-8}
---

## Use case: working with spatial R packages

```{r}
#| label: packages
#| collapse: true
#| eval: false
if (!requireNamespace("sf")) install.packages("sf")
if (!requireNamespace("leaflet")) install.packages("leaflet")
```
```{r}
#| label: loading_parzer
#| eval: true
library(parzer)
```

One may find themselves having to clean up messy coordinates as part of
their project/work/etc. How does this look when fit into a workflow going
all the way to visualization?

Let's say you have the following messy coordinates that you've compiled
from different places, leading to a variety of messy formats:

```{r}
#| label: lats_lons
lats <- c(
  "46.4183",
  "46.4383° N",
  "46.5683° N",
  "46° 27´ 5.4\" N",
  "46° 25.56’",
  "N46°24’4.333"
)
lons <- c(
  "25.7391",
  "E25°34’6.4533",
  "25.3391° E",
  "25.8391° E",
  "25° 35.56’",
  "E25°34’4.333"
)
```

Parse messy coordinates:


```{r}
#| label: creating_dat
#| collapse: true
dat <- data.frame(
  longitude = parse_lon(lons),
  latitude = parse_lat(lats)
)
dat
```

Combine coordinates with other data.


```{r}
#| label: adding_variables_to_dat
#| collapse: true
dat$shape <- c("round", "square", "triangle", "round", "square", "square")
dat$color <- c("blue", "yellow", "green", "red", "green", "yellow")
dat
```

Coerce to an `sf` object

```{r}
#| label: converting_dat_to_sf
#| warning: false
#| collapse: true
datsf <- sf::st_as_sf(dat, coords = c("longitude", "latitude"))
datsf
```

Calculate the center of the plot view

```{r}
#| label: map_center
center_lon <- mean(dat$longitude)
center_lat <- mean(dat$latitude)
```

Plot data using the `leaflet` package

```{r out.with="100%"}
#| label: fig-leaflet_1
#| warning: false
#| collapse: true
library("leaflet")
leaflet() |>
  addTiles() |>
  addMarkers(data = datsf) |>
  setView(center_lon, center_lat, zoom = 10)
```

We'd like to have data only for a certain area, e.g., a political boundary or
a park boundary. We can clip the data to a bounding box using `sf::st_crop()`.

First, define the bounding box, and visualize:

```{r}
#| label: fig-leaflet_2
#| warning: false
bbox <- c(
  xmin = 25.42813, ymin = 46.39455,
  xmax = 25.68769, ymax = 46.60346
)
leaflet() |>
  addTiles() |>
  addRectangles(bbox[["xmin"]], bbox[["ymin"]], bbox[["xmax"]], bbox[["ymax"]]) |>
  setView(center_lon, center_lat, zoom = 10)
```

Crop the data to the bounding box:

```{r}
#| label: cropping_dat
datsf_c <- sf::st_crop(datsf, bbox)
```

Plot the new data:

```{r out.with="100%"}
#| label: fig-leaflet_3
#| warning: false
leaflet() |>
  addTiles() |>
  addMarkers(data = datsf_c) |>
  setView(center_lon, center_lat, zoom = 10)
```