---
title: "Short walkthrough and overview of landscapetools"
author: "Marco Sciaini"
date: "`r Sys.Date()`"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{Overview}
  %\VignetteEncoding{UTF-8}
  %\VignetteEngine{knitr::rmarkdown}
editor_options: 
  chunk_output_type: console
---

```{r setup, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)

options(rmarkdown.html_vignette.check_title = FALSE)

library(landscapetools)
```

*landscapetools* is not a coherent package designed for a specific scientific purpose, it is rather a collection of functions to perform  some of the less-glamorous tasks involved in landscape analysis.

It is basically designed to accompany all the packages in [r-spatialecology](https://github.com/r-spatialecology) and keep them lightweight. Hence, the functionality has a broad spectrum and we try to cover here some things one might miss about *landscapetools*.

# Visualize 

There are a plethora of R packages to visualize spatial data, all of them covering unique aspects and ways to do that (find a short introduction [here](https://docs.ropensci.org/NLMR/articles/getstarted.html)). With [NLMR](https://docs.ropensci.org/NLMR/), we needed a way to visualize landscapes without much fuss and also have a way to visualize many of them in a way we found sufficient.

## General raster plotting
```{r fig.retina=2, message=FALSE, warning=FALSE}
# Plot continous landscapes
show_landscape(gradient_landscape)

# Plot continous landscapes 
show_landscape(classified_landscape, discrete = TRUE)

# RasterStack/RasterBrick
show_landscape(raster::brick(gradient_landscape, random_landscape), discrete = TRUE)

# Plot a list of raster (list names become facet text)
show_landscape(list("Gradient landscape" = gradient_landscape,
                    "Random landscape" = random_landscape))

# Plot multiple raster with unique scales
show_landscape(raster::stack(gradient_landscape, random_landscape, classified_landscape), unique_scales = TRUE)
```

# Scaling 
## Binarize

In landscape ecology, many people work with landscapes that reflect a matrix / habitat context.
If you work with simulated landscapes, `util_binarize` is a convienent wrapper to achieve this.
You can define a value in the range of your landscape values and get a binary reflection of it:

```{r fig.retina=2}
# Binarize the landscape into habitat and matrix
binarized_raster <- util_binarize(fractal_landscape, breaks = 0.31415)
show_landscape(binarized_raster, discrete = TRUE)

# You can also provide a vector with thresholds and get a RasterStack with multiple binarized maps
binarized_raster <- util_binarize(fractal_landscape, breaks = c(0.25, 0.5, 0.7))
show_landscape(binarized_raster)
```

## Classify

Complementary to `util_binarize`, `util_classify` classifies a 
landscape with continuous values into *n* discrete classes.
The function is quite the workhorse, so I provide some extra detail here
to explain everything:

```{r fig.retina=2}
# Mode 1: Classify landscape into 3 classes based on the Fisher-Jenks algorithm:
mode_1 <- util_classify(fractal_landscape, n = 3)

# Mode 2: Classify landscapes into landscape with exact proportions:
mode_2 <- util_classify(fractal_landscape, weighting = c(0.5, 0.25, 0.25))

# Mode 3: Classify landscapes based on a real dataset (which we first create here)
#         and the distribution of values in this real dataset
mode_3 <- util_classify(gradient_landscape, n = 3)

## Mode 3a: ... now we just have to provide the "real landscape" (mode_3)
mode_3a <- util_classify(fractal_landscape, real_land = mode_3)

## Mode 3b: ... and we can also say that certain values are not important for our classification:
mode_3b <- util_classify(fractal_landscape, real_land = mode_3, mask_val = 1)

landscapes <- list(
'Mode 1'  = mode_1,
'Mode 2'  = mode_2,
'Mode 3'  = mode_3,
'Mode 3a' = mode_3a,
'Mode 3b' = mode_3b
)

show_landscape(landscapes, unique_scales = TRUE, nrow = 1)

# ... you can also name the classes:
classified_raster <- util_classify(fractal_landscape,
                                   n = 3,
                                   level_names = c("Land Use 1",
                                                   "Land Use 2",
                                                   "Land Use 3"))
show_landscape(classified_raster, discrete = TRUE)
```

## Rescale
`util_rescale` linearly rescales element values in a raster to a range between 0 and 1.

```{r fig.retina=2}
library(raster) 
landscape <- raster(matrix(1:100, 10, 10))
summary(landscape)

scaled_landscape <- util_rescale(landscape)
summary(scaled_landscape)
```

## Merge
`util_merge` most likely makes sense in the context of [NLMR](https://docs.ropensci.org/NLMR/). If you merge multiple 
neutral landscapes models, you can create more feasible landscape
patterns for certain questions, or come up with ecotones if you
merge fractal patterns with gradients.

```{r fig.retina=2}
# Merge all maps into one
merg <- util_merge(fractal_landscape, c(gradient_landscape, random_landscape), scalingfactor = 1)

# Plot an overview
merge_vis <- list(
    "1) Primary" = fractal_landscape,
    "2) Secondary 1" = gradient_landscape,
    "3) Secondary 2" = random_landscape,
    "4) Result" = merg
)
show_landscape(merge_vis)
```

# Rescale

```{r eval=FALSE}
util_rescale(fractal_landscape)
```


