Cut and Drop JRC_1K_POP_2018.tif

Author: krausewe

vignettes/JRC_1K_POP_2018.tif

@@ -276,7 +276,7 @@ pat_n2_mhab_12_no <- pat_n2 %>%
   filter(unit == "P_MHAB") %>% # Patents per one million inhabitants
   filter(time == 2012) %>% # 2012
   filter(str_detect(geo, "^NO")) %>%  # Norway
-  select(-unit)
+  dplyr::select(-unit)
 
 # Classifying the Data
 pat_classified <- nuts_classify(
@@ -795,10 +795,10 @@ In addition to tracing the evolution of NUTS codes, the table contains **flows**
 ```{r,  echo=FALSE, message = FALSE, warning = FALSE, fig.cap= "Alluvial plot illustrating area size flows; Created using the [ggalluvial](https://corybrunson.github.io/ggalluvial/) package.", fig.alt ="The alluvial plot shows population flows from NUTS version 2016 to 2021."}
 # Add names
 no_2016_names <- read_sf("shapefiles/NUTS_RG_20M_2016_3857_NO.shp") %>%
-  select(from_code = NUTS_ID, from_name = NUTS_NAME) %>%
+  dplyr::select(from_code = NUTS_ID, from_name = NUTS_NAME) %>%
   st_set_geometry(NULL)
 no_2021_names <- read_sf("shapefiles/NUTS_RG_20M_2021_3857_NO.shp") %>%
-  select(to_code = NUTS_ID, to_name = NUTS_NAME) %>%
+  dplyr::select(to_code = NUTS_ID, to_name = NUTS_NAME) %>%
   st_set_geometry(NULL)
 
 no_walks <- no_walks %>%
@@ -831,13 +831,18 @@ To illustrate the main idea, the map below showcases **population densities** ac
 
 ```{r,  echo=FALSE, message = FALSE, warning = FALSE, out.width = "100%", fig.width = 7, fig.cap= "Spatial distribution of population and boundary changes; Sources: [Shapefiles](https://ec.europa.eu/eurostat/web/gisco/geodata/reference-data/administrative-units-statistical-units/nuts) and [population raster](https://ec.europa.eu/eurostat/web/gisco/geodata/reference-data/population-distribution-demography/geostat) from EUROSTAT; Created using the [sf](https://r-spatial.github.io/sf/) and the [terra](https://rspatial.github.io/terra/reference/terra-package.html) packages.", fig.alt ="Two maps of Southern Norway with very granular population density and administrative boundaries of the 2016 and 2021 NUTS version. The region with the capital Olso and its adjacent region are highlighted in version 2016 that both contribute to a larger single region in version 2021."}
 
-pop <- raster("JRC_1K_POP_2018.tif")
-no_2016_1 <- no_2016 %>% st_transform(crs(pop))
-no_2021_1 <- no_2021 %>% st_transform(crs(pop))
-no_pop <- crop(x = pop, y = as_Spatial(no_2016_1))
-no_pop <- mask(no_pop, as_Spatial(no_2016_1))
-no_pop_df <- as.data.frame(no_pop, xy = TRUE) %>%
-  filter(!is.na(JRC_1K_POP_2018))
+# pop <- raster("JRC_1K_POP_2018.tif")
+# no_2016_1 <- no_2016 %>% st_transform(crs(pop))
+# saveRDS( no_2016_1 , 'JRC_1K_POP_2018_2016_transformed_NO.rds'  )
+# no_2021_1 <- no_2021 %>% st_transform(crs(pop))
+# saveRDS( no_2021_1 , 'JRC_1K_POP_2018_2021_transformed_NO.rds'  )
+# no_pop <- crop(x = pop, y = as_Spatial(no_2016_1))
+# no_pop <- mask(no_pop, as_Spatial(no_2016_1))
+# no_pop_df <- as.data.frame(no_pop, xy = TRUE) %>%
+#   filter(!is.na(JRC_1K_POP_2018))
+no_pop_df <- readRDS( 'JRC_1K_POP_2018_NO.rds' )
+no_2016_1 <- readRDS( 'JRC_1K_POP_2018_2016_transformed_NO.rds' )
+no_2021_1 <- readRDS( 'JRC_1K_POP_2018_2021_transformed_NO.rds' )
 
 c=500000
 d=500000