Progress on #766

Author: Robinlovelace

_04-ex.Rmd

@@ -1,8 +1,7 @@
 ```{r 04-ex-e0, include=TRUE, message=FALSE}
 library(sf)
 library(dplyr)
-data(nz, package = "spData")
-data(nz_height, package = "spData")
+library(spData)
 ```
 
 E1. It was established in Section \@ref(spatial-vec) that Canterbury was the region of New Zealand containing most of the 100 highest points in the country.
@@ -67,6 +66,27 @@ nz_height_combined %>%
   na.omit()
 ```
 
+E4. To test your knowledge of spatial predicates:
+
+- Create an object representing Colorado state in the USA, e.g. with the command 
+`colorado = us_states[us_states$NAME == "Colorado",]` (base R) or
+`colorado = us_states %>% filter(NAME == "Colorado")` (tidyverse).
+- Create a new object representing all the objects that intersect, in some way, with Colorado and plot the result.
+- Create another object representing all the objects that touch Colorado and plot the result.
+
+```{r 04-ex-4}
+plot(us_states$geometry)
+plot(Colorado$geometry, col = 2, add = TRUE)
+colorado = us_states[us_states$NAME == "Colorado", ]
+intersects_with_colorado = us_states[colorado, , op = st_intersects]
+touches_colorado = us_states[colorado, , op = st_touches]
+plot(us_states$geometry)
+plot(touches_colorado$geometry, col = "grey", add = TRUE)
+```
+
+
+# What are the neighbouring states of Colorado? 
+
 E4. Use `dem = rast(system.file("raster/dem.tif", package = "spDataLarge"))`, and reclassify the elevation in three classes: low (<300), medium and high (>500).
 Secondly, read the NDVI raster (`ndvi = rast(system.file("raster/ndvi.tif", package = "spDataLarge"))`) and compute the mean NDVI and the mean elevation for each altitudinal class.