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---
title: "Raster Analyses"
---
```{r echo=F, eval=T, message=F,warning=F}
library(sf)
library(spData)
library(viridis)
library(tidyverse)
library(raster)
```
---
## Available Spatial Packages
* `sp` First major spatial data package/format
* `rgdal` reading and writing spatial data
* `rgeos` Interface to open-source geometry engine (GEOS)
* `sf` Spatial Features in the 'tidyverse'
* `raster` gridded data (like satellite imagery)
* and a few others...
## Common Simple Feature (SF) types
## What about grids?
```{r echo=F, message=F,warning=F}
library(raster)
library(spData)
data(elev) #load fake data from spData package
plot(elev)
```
# Raster Data
## Raster Data Types
Many types including:
* Thematic (classified/thematic) data: land-use or soils data.
* Continuous data (temperature, elevation, spectral)
* Imagery / Pictures (scanned maps, drawings, etc.)
Most Common:
* Satellite Imagery and derived products
* Earth System Models (e.g. climate models)
* Other models
## Raster data in the tidyverse
Raster data is not yet closely connected to the **tidyverse**, however:
- Some functions from `raster` work well in `pipes`
- Convert vector data to `Spatial*` form using `as(my_vector, "Spatial")` for raster-vector interactions
- Some early efforts to bring raster data into the **tidyverse**, including [tabularaster](https://github.com/hypertidy/tabularaster), [sfraster](https://github.com/mdsumner/sfraster), [fasterize](https://github.com/ecohealthalliance/fasterize), and [stars](https://github.com/r-spatial/stars) (multidimensional, large datasets).
## Raster Package
```{r message=F,warning=F}
library(raster)
library(spData)
data(elev) #load fake data from spData package
plot(elev) #load fake data from spData package
```
## Raster Data Structure
```{r}
elev
```
---
```{r}
str(elev)
```
---
### Arrays with metadata
```{r}
as.array(elev)
```
Rasters are just arrays / matricies with metadata.
---
### Cell Stats
```{r}
cellStats(elev, stat = mean, na.rm=T)
cellStats(elev, stat = quantile, na.rm=T)
```
---
### Raster Data Memory
```{r}
inMemory(elev)
canProcessInMemory(elev)
```
Raster does not try to load or work with large datasets in RAM. It creates temporary files and processes them in the background.
## Map Algebra
Convert feet to meters
```{r}
elev_m <- elev*0.3048
plot(elev_m)
```
---
### Simple Filter
```{r}
high_ground <- elev_m>8
plot(high_ground)
```
---
### Focal
```{r}
m <- matrix(1,nrow=3,ncol=3)
m
elev_smooth <- focal(elev_m,m,mean)
```
---
```{r}
par(mfrow=c(1,2))
plot(elev_m)
plot(elev_smooth)
```
## Arbitrary functions
```{r}
NA16=function(x) ifelse(x == 16,1,NA)
one_cell <- calc(elev,fun = NA16)
plot(one_cell)
```
## Distances to non-NA cells
```{r}
distance(one_cell)%>%
plot()
```
Distance unit is meters if RasterLayer is `+proj=longlat`, map units (typically also meters) otherwise.
## And much more
* aggregate (to coarser resolution)
* area (calculate cell area)
* buffer
* contour
* crop
* interpolate
* Moran's I
# Visualization
## ggplot
```{r, message=F}
library(rasterVis)
gplot(elev)+
geom_raster(aes(fill=value))
```
---
### All ggplot functionality available
```{r}
gplot(elev)+
geom_raster(aes(fill=value))+
coord_equal()+
scale_fill_viridis_c()
```