terra.qmd

---
title: "terra"
format: html
---

## Resources
- [terra](https://rspatial.org/terra)
- [Geocomputation with R](https://geocompr.robinlovelace.net/index.html)

```{r}
#| label: load-terra
library(terra)
```

## Geocomputation with R

### 3.3 Manipulating raster objects

Creating raster objects

```{r}
#| label: elev-raster

elev <- rast(nrows = 6, ncols = 6, resolution = 0.5, 
             xmin = -1.5, xmax = 1.5, ymin = -1.5, ymax = 1.5,
             vals = 1:36, names = "elev")
elev
```

Raster objects with categorical data that can be either `logical` or `factor`.

```{r}
#| label: grain-raster

grain_order <- c("clay", "silt", "sand")
grain_char <- sample(grain_order, 36, replace = TRUE)
grain_fact <- factor(grain_char, levels = grain_order)
grain <- rast(nrows = 6, ncols = 6, resolution = 0.5, 
              xmin = -1.5, xmax = 1.5, ymin = -1.5, ymax = 1.5,
              vals = grain_fact)
```

The Raster Attribute Tables (RAT) are stored as a list of dataframes. They can be viewed with `cats()`.
Use the function `levels()` for retrieving and adding new or replacing existing factor levels.

```{r}
#| label: levels

cats(grain)
levels(grain) <- data.frame(value = c(0, 1, 2), wetness = c("wet", "moist", "dry"))
levels(grain)
```

### 4.3 Spatial operations on raster data

#### Subsetting

```{r}
#| label: subsetting
clip <- rast(xmin = 0.9, xmax = 1.8, ymin = -0.45, ymax = 0.45,
             resolution = 0.3, vals = rep(1, 9))
elev[clip]

#or
terra::extract(elev, ext(clip))
```

```{r}
#| label: spatial-subsetting

elev[clip, drop = FALSE]

# create raster mask
rmask <- elev
values(rmask) <- sample(c(NA, TRUE), 36, replace = TRUE)

elev[rmask, drop = FALSE]
mask(elev, rmask)  

plot(mask(elev, rmask))
```

#### Map algebra

Local map algebra: Per-cell operations. One example is reclassifying data by a matrix with low, high, and new value columns.

```{r}
#| label: reclassify-data

# reclassifying matrix
rcl <- matrix(c(0, 12, 1, 12, 24, 2, 24, 36, 3), ncol = 3, byrow = TRUE)
recl <- classify(elev, rcl = rcl)

plot(recl)
```

Focal map algebra: applies an aggregation function to all cells within the specified neighborhood and assigns output to central cell.

```{r}
#| label: focal

r_focal <- focal(elev, w = matrix(1, nrow = 3, ncol = 3), fun = min)
values(r_focal)
```

Zonal map algebra

```{r}
#| label: zonal

zonal(elev, grain, fun = "mean")
```