banner of announcements added

01_Demonstration.Rmd

@@ -27,9 +27,7 @@ setwd(here())
 #--- load packages ---#
 library(data.table)
 library(here)
-library(rgdal)
 library(stringr)
-library(rgeos)
 library(sf)
 library(ggplot2)
 library(raster)

01_z03_landuse_weather.rmd

@@ -66,7 +66,6 @@ pacman::p_load(
   modelsummary, # regression table generation
   future.apply, # parallel computation
   cdlTools, # download CDL data
-  rgdal, # required for cdlTools
   prism, # download PRISM data
   stringr # string manipulation
 )

01_z06_nunn_2008.rmd

@@ -62,7 +62,9 @@ pacman::p_load(
   tidyverse, # data wrangling
   units,
   rmapshaper,
+  patchwork,
   lwgeom,
+  ggplot2,
   tictoc
 )
 ```
@@ -153,7 +155,7 @@ However, what we need is the end point of the lines. We can use `lwgeom::st_endp
 closest_pt_on_coast <- lwgeom::st_endpoint(minum_dist_to_coast)
 ```
 
-The end points are represented as blue points in the figure below. 
+The end points are represented as blue points in the figure below.
 
 ```{r endpoint-africa-nunn08}
 g_min_dist_line +

01_z08_alsan_2015.rmd

@@ -258,6 +258,7 @@ weather <-
 Let's subset `weather` to Africa and then calculate tsetse.
 
 ```{r}
+sf_use_s2(FALSE)
 weather_africa <-
   #--- subset to Africa ---#
   weather[tribes] %>%

02_VectorDataBasics.Rmd

@@ -6,7 +6,7 @@
 library(knitr)
 knitr::opts_chunk$set(
   echo = TRUE,
-  # cache = TRUE,
+  cache = TRUE,
   comment = NA,
   message = FALSE,
   warning = FALSE,
@@ -26,9 +26,7 @@ setwd(here())
 #--- load packages ---#
 library(data.table)
 library(here)
-library(rgdal)
 library(stringr)
-library(rgeos)
 library(sf)
 library(ggplot2)
 library(leaflet)
@@ -986,7 +984,7 @@ Sometimes, a vector data is excessively detailed for your purpose. For example,
 Here, we use IL County borders for illustration.
 
 ```{r}
-IL_counties <- st_read("Data/IL_county_detailed.geojson")
+IL_counties <- tigris::counties(state = "Illinois", cb = TRUE, progress_bar = FALSE)
 ```
 
 Here is the object size of `IL_counties`.
@@ -1006,36 +1004,43 @@ ggplot(IL_counties) +
 However, if you focus on a single county, you can see that county borders in this data are fairly detaield. Let's look at the Cook county.
 
 ```{r g-cook}
-Cook <- filter(IL_counties, NAME == "Cook County")
+#| eval: false
+Cook <- filter(IL_counties, NAME == "Cook")
 
 (
   g_cook_original <-
     ggplot(Cook) +
     geom_sf() +
+    coord_sf(lims_method = "geometry_bbox") +
     theme_void()
 )
 ```
 
 You can see that its border with Lake Michigan has lots of non-linear segments. Let's simplify this using `st_simplify()`. We can use the `dTolerance` option to specify the degree of simplification.
 
 ```{r g-cook-simplified}
+#| eval: false
 Cook_simplify <- st_simplify(Cook, dTolerance = 1000)
 
 g_cook_simplified <-
   ggplot(Cook_simplify) +
   geom_sf() +
+  coord_sf(lims_method = "geometry_bbox") +
   theme_void()
 ```
 
 The figure below compares the original (left) and simplified (right) versions.
 
 ```{r g-cook-both}
+#| cache: false
+#| eval: false
 g_cook_original | g_cook_simplified
 ```
 
 Let's now simplify `IL_counties` using `st_simplify()` to make its map light-weight.
 
 ```{r g-IL-simplified}
+#| eval: false
 IL_counties_simplified <- st_simplify(IL_counties, dTolerance = 1000)
 
 ggplot(IL_counties_simplified) +
@@ -1046,6 +1051,7 @@ ggplot(IL_counties_simplified) +
 You probably notice we now have some gaps between some counties. This is because `st_simplify()` does not respect the internal borders. To simplify only the outer borders, you can use `rmapshaper::ms_simplify()`. The `keep` option controls the degree of simplifcation (the lower, the more simplified). 
 
 ```{r g-IL-simplified-fixed}
+#| eval: false
 IL_counties_mssimplified <- rmapshaper::ms_simplify(IL_counties, keep = 0.01)
 
 ggplot(IL_counties_mssimplified) +

03_SpatialInteractionVectorVector.Rmd

@@ -1,11 +1,11 @@
 # Spatial Interactions of Vector Data: Subsetting and Joining {#int-vv}
 
-```{r chap3_setup, include = FALSE}
+```{r chap3_setup, include = FALSE, cache = FALSE}
 library(tufte)
 library(knitr)
 knitr::opts_chunk$set(
   echo = TRUE,
-  # cache = TRUE,
+  cache = TRUE,
   comment = NA,
   message = FALSE,
   warning = FALSE,
@@ -24,10 +24,8 @@ setwd(here())
 ```{r, include = FALSE, cache = FALSE}
 #--- load packages ---#
 library(data.table)
-library(rgdal)
 library(exactextractr)
 library(stringr)
-library(rgeos)
 library(sf)
 library(ggplot2)
 library(raster)

05_SpatialInteractionVectorRaster.Rmd

@@ -847,6 +847,9 @@ prism_tmax_stack_5 <-
     prism_tmax_0701_KS_sr_10,
     prism_tmax_0701_KS_sr_10
   )
+
+set.names(prism_tmax_stack_5, paste0("layer_", 1:5))
+
 temp <-
   exact_extract(
     prism_tmax_stack_5,

06_SpeedThingsUp.Rmd

@@ -17,21 +17,20 @@ knitr::opts_chunk$set(
 
 ```{r, echo=FALSE, warning=FALSE, cache = FALSE}
 #--- load packages ---#
-suppressMessages(library(data.table))
-suppressMessages(library(exactextractr))
-suppressMessages(library(prism))
-suppressMessages(library(sf))
-suppressMessages(library(terra))
-suppressMessages(library(raster))
-suppressMessages(library(tidyverse))
-suppressMessages(library(DT))
-suppressMessages(library(tictoc))
-suppressMessages(library(tmap))
-suppressMessages(library(future.apply))
-suppressMessages(library(parallel))
-suppressMessages(library(maps))
-suppressMessages(library(bench))
-suppressMessages(library(stringr))
+library(data.table)
+library(exactextractr)
+library(prism)
+library(sf)
+library(terra)
+library(raster)
+library(tidyverse)
+library(DT)
+library(tictoc)
+library(tmap)
+library(future.apply)
+library(parallel)
+library(maps)
+library(stringr)
 ```
 
 ## Before you start {-}
@@ -475,11 +474,12 @@ You can download all the prism files from [here](https://www.dropbox.com/sh/gkpr
 
 ```{r US_county, cache = TRUE}
 (
-  US_county <- st_as_sf(map(database = "county", plot = FALSE, fill = TRUE)) %>%
+  US_county <- 
+    st_as_sf(maps::map(database = "county", plot = FALSE, fill = TRUE)) %>%
     #--- get state name from ID  ---#
     mutate(state = str_split(ID, ",") %>% lapply(., `[[`, 1) %>% unlist()) %>%
     #--- project to the CRS of the CDL data ---#
-    st_transform(projection(brick("Data/PRISM/PRISM_ppt_y2017_m7.tif")))
+    st_transform(projection(brick("Data/PRISM/PRISM_ppt_y1990_m12.tif")))
 )
 ```