fresh pkgdown, further links in readme, brackets in vignette

README.md

@@ -9,7 +9,7 @@
 
 Changing administrative regional boundaries over time complicates the construction of consistent regional panel data. The `nuts` package can transform **European regional data** identified by **NUTS codes** between different **versions** and **subdivision levels** with the aim to harmonize and link data sets. 
 
-The package's main task is to convert data between all NUTS versions in use: 2006, 2010, 2013, 2016 and 2021. These conversions can be performed in any direction based on **spatial interpolation** using five different weights (regional area in square kilometers, 2011 and 2018 population size, 2012 and 2018 built-up area). The weights are based on conversion tables provided by the [Joint Research Center (JRC) of the European Commission (EC)](<https://urban.jrc.ec.europa.eu/nutsconverter/#/>) that are used in their online converter. The `nuts` package permits to do the same conversion offline with additional advantages such as the conversion within groups, enabling the conversion of datasets with multiple NUTS versions efficiently.
+The package's main task is to convert data between all NUTS versions in use: 2006, 2010, 2013, 2016 and 2021. These conversions can be performed in any direction based on **spatial interpolation** using five different weights (regional area in square kilometers, 2011 and 2018 population size, 2012 and 2018 built-up area). The weights are based on conversion tables provided by the [Joint Research Center (JRC) of the European Commission (EC)](https://urban.jrc.ec.europa.eu/tools/nuts-converter) that are used in their online converter. The `nuts` package permits to do the same conversion offline with additional advantages such as the conversion within groups, enabling the conversion of datasets with multiple NUTS versions efficiently.
 
 ### Installation
 

_pkgdown.yml

@@ -4,10 +4,14 @@ template:
 home:
   title: Convert European Regional Data in R
 reference:
-- title: Functions
+- title: Workhorse Functions
   desc:  Functions that classify and convert regional data
 - contents:
-    starts_with("nuts_classify|nuts_aggregate|nuts_convert|nuts_test_multiple_versions|nuts_get_data|nuts_get_version|nuts_get_missing")
+    starts_with("nuts_classify|nuts_aggregate|nuts_convert")
+- title: Additional Functions
+  desc:  Helper functions to classify and convert regional data
+- contents:
+    starts_with("nuts_test_multiple_versions|nuts_get_data|nuts_get_version|nuts_get_missing")
 - title: Data
   desc:  Data sets required for conversion and examples
 - contents:

docs/pkgdown.yml

@@ -3,7 +3,7 @@ pkgdown: 2.0.7
 pkgdown_sha: ~
 articles:
   nuts: nuts.html
-last_built: 2024-02-20T21:42Z
+last_built: 2024-02-28T07:40Z
 urls:
   reference: https://aaoritz.github.io/nuts/reference
   article: https://aaoritz.github.io/nuts/articles

docs/sitemap.xml

@@ -54,6 +54,27 @@
   <url>
     <loc>https://aaoritz.github.io/nuts/reference/nuts-package.html</loc>
   </url>
+  <url>
+    <loc>https://aaoritz.github.io/nuts/reference/nuts_aggregate.html</loc>
+  </url>
+  <url>
+    <loc>https://aaoritz.github.io/nuts/reference/nuts_classify.html</loc>
+  </url>
+  <url>
+    <loc>https://aaoritz.github.io/nuts/reference/nuts_convert_version.html</loc>
+  </url>
+  <url>
+    <loc>https://aaoritz.github.io/nuts/reference/nuts_get_data.html</loc>
+  </url>
+  <url>
+    <loc>https://aaoritz.github.io/nuts/reference/nuts_get_missing.html</loc>
+  </url>
+  <url>
+    <loc>https://aaoritz.github.io/nuts/reference/nuts_get_version.html</loc>
+  </url>
+  <url>
+    <loc>https://aaoritz.github.io/nuts/reference/nuts_test_multiple_versions.html</loc>
+  </url>
   <url>
     <loc>https://aaoritz.github.io/nuts/reference/patents.html</loc>
   </url>

vignettes/nuts.Rmd

@@ -812,7 +812,7 @@ gg_pop_flows
 
 To illustrate the main idea, the map below showcases **population densities** across NUTS-2 regions. As population is not uniformly distributed across space, weighting regions dependent on their area size comes with strong assumptions. For instance, region `NO01` in version 2016, that contains the city of Oslo, makes a relatively modest geographical contribution to the new region `NO08`, but significantly bolsters the population of the latter. Assuming that the variable to be converted is correlated with population across space, the conversion can thus be refined using population weights to account for flows between different versions.
 
-```{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."}
+```{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))