Updated class 16 slides

docs/class16.Rmd

@@ -110,6 +110,15 @@ districts %>%
   summarize(mu = mean(areadiff)) #%>%
 ```
 
+---
+## Calculate length (exercise)
+- Use `st_length()` to add column 'road_length' to `roads`
+- 'road_length' should be in km
+- Use ggplot to plot districts with black outline (color), and empty fill (fill = NA)
+- Then plot roads based on length (use `scale_color_continuous()` )
+- Plot roads with line width 1 (`lwd = 1`)
+- Use `theme_bw()`
+
 ---
 
 ```{r, eval=FALSE}
@@ -132,181 +141,3 @@ ggplot() +
 - Add aesthetics in `aes()`. These are columns used for plotting. 
 - For `geom_point`, `geom_line` you need 'x' and 'y' aesthetics. 
 - For `geom_sf`, spatial context used for 'x' and 'y'. But you might want 'fill' or 'color'
-
----
-## Calculate length (exercise)
-- Use `st_length()` to add column 'road_length' to `roads`
-- 'road_length' should be in km
-- Use ggplot to plot districts with black outline (color), and empty fill (fill = NA)
-- Then plot roads based on length (use `scale_color_continuous()` )
-- Plot roads with line width 1 (`lwd = 1`)
-- Use `theme_bw()`
-
----
-
-## Spatial Operations
-Data setup
-```{r, eval = FALSE, message = FALSE}
-coords <- cbind("x" = c(25, 25.1, 26.4, 26.4, 26.4, 25), 
-                "y" = c(-15.5, -14.5, -14.6, -14.8, -15.6, -15.5))
-pol <- st_polygon(x = list(coords)) %>% 
-  st_sfc %>% 
-  st_sf(ID = 1, crs = 4326)
-roads_rpj <- st_transform(roads, crs = st_crs(districts))
-
-```
-
----
-## Plotting
-```{r, eval = FALSE, message = FALSE}
-par(mar = rep(0, 4))
-plot(st_geometry(districts), col = "grey")
-plot(pol, col = "purple", add = TRUE)
-plot(roads_rpj, col = 'red', add = TRUE)
-```
-
-
----
-## `st_intersects`
-- Similar to "Select by Spatial Location"
-- Finds indices of second object that intersect each element of first object
-```{r, eval = FALSE, message = FALSE}
-st_intersects(x = roads_rpj, y = districts)
-```
-
----
-## `st_intersects`
-```{r, eval = FALSE, message = FALSE}
-highway <- roads_rpj[401, ]
-dists_int <- districts %>% 
-  slice(st_intersects(x = highway, y = districts)[[1]])
-#> although coordinates are longitude/latitude, st_intersects assumes that they are planar
-dists_int
-plot(st_geometry(dists_int), col = 'grey')
-plot(highway, col = 'red', add = TRUE)
-```
-
-
----
-## `st_intersection()`
-- finds intersecting pairs between two objects
-- Each row in result is one "intersection object" (i.e. an object from x and y that intersect)
-```{r, eval = FALSE, message = FALSE}
-pol_int_dists <- st_intersection(x = pol, y = districts)
-
-par(mar = rep(0, 4))
-plot(st_geometry(districts), col = "grey")
-plot(st_geometry(pol_int_dists), col = rainbow(n = nrow(pol_int_dists)), 
-     add = TRUE)
-```
-
----
-## `st_difference`
-- Similar to "erase" in ArcGIS
-- Returns first object without second object. 
-```{r, eval = FALSE, message = FALSE}
-d1 <- st_difference(x = districts, y = pol)
-d2 <- st_difference(x = pol, y = districts)
-
-par(mfrow = c(1, 2), mar = c(0, 0, 1, 0))
-d1 %>% 
-  st_geometry %>% 
-  plot(col = "grey")
-d2 %>% 
-  st_geometry %>%
-  plot(col = "grey")
-```
-
-
----
-## `st_union`
-- Combines objects without resolving borders. 
-- Really takes all possible combinations of first and 2nd object
-- Switching order of inputs changes column order
-```{r, eval = FALSE, message = FALSE}
-uni1 <- st_union(x = pol, y = districts)
-uni1
-
-uni2 <- st_union(x = districts, y = pol)
-uni2
-
-par(mfrow = c(1, 2), mar = c(0, 0, 1, 0))
-plot(uni1[2], reset = FALSE, main = "Union of pol with districts")
-plot(uni2[1], reset = FALSE, main = "Union of districts with pol")
-```
-
----
-## `st_buffer()`
-- Best to use projected coordinates (e.g. Albers)
-```{r, eval = FALSE, message = FALSE}
-## project everything to Albers
-farmers_alb <- farmers_sf %>% st_transform(st_crs(roads))
-long_roads <- roads %>% filter(as.numeric(st_length(.)) > 500000)
-districts_alb <- districts %>% st_transform(st_crs(roads))
-
-roads_buff_20km = st_buffer(long_roads, 20000)
-plot(st_geometry(districts_alb), col = "grey")
-plot(st_geometry(roads_buff_20km), col = "blue", add = TRUE)
-plot(st_geometry(long_roads), col = "red", add = TRUE)
-```
-
----
-## `st_sample`
-
-```{r, eval = FALSE, message = FALSE}
-par(mar = rep(0, 4))
-districts %>% st_union %>% plot(col = "grey")
-set.seed(1)
-districts %>% 
-  st_union %>% 
-  st_sample(size = 100, exact = TRUE) %>% 
-  plot(pch = 20, add = TRUE)
-```
-
-
----
-## Exercise
-Find all districts within 50 km of sampled points. 
-
-```{r, eval = FALSE, message = FALSE}
-
-```
-
----
-## Exercise
-Of the sampled farmers, how many are within 100km of one of the "long roads"?
-```{r, eval = FALSE, message = FALSE}
-
-```
-
-
----
-## Further Exercises
-
-### Round 1
-- Calculate the length of roads in kilometers
-- Buffer roads > 100 km by 30 kilometers, save as new object
-- Buffer roads > 100 km 10 kilometers, save as new object
-- Take the difference between the 30 km and 10 km buffers, i.e. erase/difference the two
-- Plot the resulting difference using `ggplot` with the difference in red over Zambia's districts
-
-```{r, eval=FALSE}
-# code to adapt
-roads %>% mutate(km = as.numeric(st_length(.)) / 1000) %>% 
-  filter(km > 200) %>% 
-  st_buffer(dist = 20000) %>% 
-  st_transform(crs = 4326)
-st_difference(larger_object, smaller_object)
-ggplot() + geom_sf
-
-```
-
----
-
-### Round 2
-- Randomly sample 100 points within the 30 km road buffer (use a seed of 1)
-- Plot the 30 km buffer (red) the points in it (blue) over the districts
-
-```{r, eval=FALSE}
-buffered_object %>% st_sample()
-```