---
title: "4. Visualization Tools"
author:
- Hannah L. Owens
- Carsten Rahbek
date: "`r Sys.Date()`"
output:
  rmarkdown::html_vignette:
    fig_caption: yes
    fig_width: 7
    fig_height: 7
    toc: true
    toc_depth: 3
vignette: >
  %\VignetteIndexEntry{4. Visualization Tools}
  \usepackage[utf8]{inputenc}
  %\VignetteEncoding{UTF-8}
  %\VignetteEngine{knitr::rmarkdown}
editor_options: 
  chunk_output_type: console
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE, error = FALSE, fig.retina = 1, dpi = 80)
knitr::opts_knit$set(root.dir = system.file('extdata', 
                                            package='voluModel'))

library(voluModel) # Because of course
library(ggplot2) # For fancy plotting
library(rgdal) # For vector stuff. Will eventually be replaced with sf.
library(raster) # For raster stuff. Will eventually be replaced with terra.
library(viridisLite) # For high-contrast plotting palettes

# Load data
load(system.file("extdata/oxygenSmooth.RData", 
                 package='voluModel'))

td <- tempdir()
unzip(system.file("extdata/woa18_decav_t00mn01_cropped.zip", 
                  package = "voluModel"),
      exdir = paste0(td, "/temperature"), junkpaths = T)
temperature <- readOGR(dsn = paste0(td, "/temperature"), 
                       layer ="woa18_decav_t00mn01_cropped")
unlink(paste0(td, "/temperature"), recursive = T)

temperature@data[temperature@data == -999.999] <- NA

occs <- read.csv(system.file("extdata/Steindachneria_argentea.csv", 
                             package='voluModel'))

# Creating a RasterBrick
temperature <- rasterFromXYZ(cbind(temperature@coords,
                                   temperature@data))

# Get names of depths
envtNames <- gsub("[d,M]", "", names(temperature))
envtNames[[1]] <- "0"
names(temperature) <- envtNames

# Oxygen processing
names(oxygenSmooth) <- names(temperature)

# Clean points ----
occsClean <- occs[complete.cases(occs$depth),]
occsClean <- occsClean[occsClean$depth > 0.0,]
occsClean <- occsClean[occsClean$depth < 2000.0,]

occurrences <- occsClean[,c("decimalLatitude", "decimalLongitude", "depth")] 

# Preliminary cleaning
occurrences <- dplyr::distinct(occurrences)
occurrences <- occurrences[complete.cases(occurrences),]

# Gets the layer index for each occurrence by matching to depth
layerNames <- as.numeric(gsub("[X]", "", names(temperature)))
occurrences$index <- unlist(lapply(occurrences$depth, 
                                   FUN = function(x) which.min(abs(layerNames - x))))
indices <- unique(occurrences$index)
downsampledOccs <- data.frame()
for(i in indices){
  tempPoints <- occurrences[occurrences$index==i,]
  tempPoints <- downsample(tempPoints, temperature[[1]])
  tempPoints$depth <- rep(layerNames[[i]], times = nrow(tempPoints))
  downsampledOccs <- rbind(downsampledOccs, tempPoints)
}
occsWdata <- downsampledOccs[,c("decimalLatitude", "decimalLongitude", "depth")] 

# Extract data ----
occsWdata$temperature <- xyzSample(occs = occsWdata, temperature)
occsWdata$AOU <- xyzSample(occs = occsWdata, oxygenSmooth)
occsWdata <- occsWdata[complete.cases(occsWdata),]

# Study region
studyRegion <- marineBackground(occsWdata)

# Get limits
tempLims <- quantile(occsWdata$temperature,c(0, 1))
aouLims <- quantile(occsWdata$AOU,c(0, 1))

# Reclassify environmental bricks to presence/absence
temperaturePresence <- reclassify(temperature, 
                                  rcl = c(-Inf,tempLims[[1]],0,
                                          tempLims[[1]], tempLims[[2]], 1,
                                          tempLims[[2]], Inf, 0))
AOUpresence <- reclassify(oxygenSmooth, 
                          rcl = c(-Inf, aouLims[[1]],0,
                                  aouLims[[1]], aouLims[[2]], 1,
                                  aouLims[[2]], Inf, 0))

# Put it all together
envelopeModel3D <- temperaturePresence * AOUpresence
envelopeModel3D <- mask(crop(envelopeModel3D, studyRegion), 
                        mask = studyRegion)
names(envelopeModel3D) <- names(temperature)
rm(AOUpresence, downsampledOccs, occsClean, occurrences, temperaturePresence, 
   tempPoints, aouLims, envtNames, i, indices, layerNames, td, tempLims)
```

# Introduction 

`voluModel` contains several functions to generate formatted maps of rasters and points automatically. This was to aid us in cleanly visualizing large numbers of maps efficiently using `ggplot2` (Wickam, 2016) and `lattice` (Sarkar, 2008) functionality. While there are quite a few features available in these functions, they were designed with fairly specific use cases in mind. You are welcome to gut the functions and rewrite them for your own custom needs. 

The data I will be using for the demos in this tutorial will be loaded silently outside of the visible code chunks. The Luminous Hake occurrence points, which you can see in the [data sampling tutorial](https://hannahlowens.github.io/voluModel/articles/c_DataSampling.html), were downloaded via R (R Core Team, 2020) from GBIF (Chamberlain *et al.*, 2021; Chamberlain and Boettiger, 2017) and OBIS (Provoost and Bosch, 2019) via `occCite` (Owens *et al.*, 2021). The simple envelope model is based on two environmental datasets from the World Ocean Atlas (Garcia *et al.*, 2019): temperature (Locarnini *et al.*, 2018) and apparent oxygen utilization (AOU; Garcia *et al.*, 2019). For more details on how to process environmental data, see [the raster data tutorial](https://hannahlowens.github.io/voluModel/articles/b_RasterProcessing.html). For the workflow used to generate the niche envelopes, refer to [the introduction vignette](https://hannahlowens.github.io/voluModel/articles/a_Introduction.html).

Here are the packages you will need. 

```{r packages, warning=FALSE, eval=FALSE}
library(voluModel) # Because of course
library(ggplot2) # For fancy plotting
library(rgdal) # For vector stuff. Will eventually be replaced with sf.
library(raster) # For raster stuff. Will eventually be replaced with terra.
library(viridisLite) # For high-contrast plotting palettes
```

Here is a shapefile of the continents, which we can conveniently use to provide some context for all the maps we are about to make.

```{r loading land data, warning=FALSE, message=FALSE}
land <- rnaturalearth::ne_countries(scale = "medium", returnclass = "sf")[1]
```

# Plotting points

There are two functions in `voluModel` for plotting occurrence points. The first, `pointMap()`, plots a single set of horizontal geographic points. The title generated is the species name the user supplies and a count of the number of points that have been plotted.

```{r pointMap, warning=FALSE, message=FALSE}
pointMap(occs = occs, land = land, landCol = "black", spName = "Steindachneria argentea", 
         ptSize = 2, ptCol = "orange")
```

The second, `pointCompMap()` plots a map that maps the extents of two sets of horizontal geographic points in different colors. This might be useful, for example, if you want to compare a raw dataset to a cleaned dataset. If any points overlap, they can be plotted as a third color.

```{r pointCompMap, warning=FALSE, message=FALSE}
pointCompMap(occs1 = occs, occs1Col = "red", occs1Name = "Raw", 
             occs2 = occsWdata, occs2Col = "orange", occs2Name = "Clean",
             spName = "Steindachneria argentea", agreeCol = "purple",
             land = land, landCol = "black", ptSize = 2)
```

# Plotting rasters

If you have a horizontal raster you would like to map that contains continuous data, the function for you might be `oneRasterPlot()`. It is a wrapper around `spplot()` from `sp` (Pebesma and Bivand, 2005; Bivand *et al.* 2013) that implements the high-contrast, colorblind-friendly color palettes offered in `viridis()` from `viridisLite` (Garnier *et al.*, 2021). The function optionally takes arguments to the `spplot()` and `viridis()` functions (so, for example, you can choose a palette other than the default).

```{r oneRasterPlot}
oneRasterPlot(rast = temperature[[1]],
              land = land, title = "Sea Surface Temperature, WOA 2018",
              landCol = "black", option = "mako")
```

Suppose you want to compare the extents of two presence-absence rasters--perhaps you are comparing two species distribution model method outputs, or comparing potential distributions at two different depths. We have a function for that, too.

```{r rasterComp}
rasterComp(rast1 = envelopeModel3D[[1]], rast1Name = "Surface",
           rast2 = envelopeModel3D[[10]], rast2Name = "45m", 
           land = land, landCol = "black", 
           title = "Suitability of Habitat for Luminous Hake\nAt Two Different Depths")
```

Of course, 3D models such as the ones `voluModel` is designed to help you produce, are difficult to visualize. We have given it a try, though. `plotLayers()` plots a transparent layer of suitable habitat for each depth layer. The redder the color, the shallower the layer, the bluer, the deeper. The more saturated the color, the more layers with suitable habitat. Here, I am plotting suitability from 20 to 700 m, the depth range of occurrences used to train the envelope model.

```{r plotLayers}
layerNames <- as.numeric(gsub("[X]", "", names(envelopeModel3D)))
occsWdata$index <- unlist(lapply(occsWdata$depth, FUN = function(x) which.min(abs(layerNames - x))))
indices <- unique(occsWdata$index)

plotLayers(envelopeModel3D[[min(indices):max(indices)]], 
           title = "Envelope Model of Luminous Hake,\n 20 to 700m",
           land = land, landCol = "black")
```

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