Update docs

.github/workflows/pkgdown-gh-pages-clean.yaml

@@ -25,13 +25,8 @@ jobs:
             any::reactable
           needs: website
 
-      - name: Build site
-        run: Rscript -e 'pkgdown::build_site_github_pages(new_process = FALSE, install = FALSE, clean = TRUE)'
-
-      - name: Deploy to GitHub pages 🚀
-        if: github.event_name != 'pull_request'
-        uses: JamesIves/github-pages-deploy-action@v4.4.2
-        with:
-          clean: true
-          branch: gh-pages
-          folder: docs
+      - name: Deploy package
+        run: |
+          git config --local user.name "github-actions[bot]"
+          git config --local user.email "41898282+github-actions[bot]@users.noreply.github.com"
+          Rscript -e 'pkgdown::deploy_to_branch(new_process = FALSE)'

.github/workflows/pkgdown-gh-pages.yaml

@@ -29,13 +29,8 @@ jobs:
             any::reactable
           needs: website
 
-      - name: Build site
-        run: Rscript -e 'pkgdown::build_site_github_pages(new_process = FALSE, install = FALSE, clean = TRUE)'
-
-      - name: Deploy to GitHub pages 🚀
-        if: github.event_name != 'pull_request'
-        uses: JamesIves/github-pages-deploy-action@v4.4.2
-        with:
-          clean: true
-          branch: gh-pages
-          folder: docs
+      - name: Deploy package
+        run: |
+          git config --local user.name "github-actions[bot]"
+          git config --local user.email "41898282+github-actions[bot]@users.noreply.github.com"
+          Rscript -e 'pkgdown::deploy_to_branch(new_process = FALSE)'

.github/workflows/wipe-cache.yaml

@@ -0,0 +1,10 @@
+name: Clear GHA caches manually
+on:
+  workflow_dispatch:
+
+jobs:
+  cache-clear:
+    runs-on: ubuntu-latest
+
+    steps:
+      - uses: easimon/wipe-cache@main

CITATION.cff

@@ -72,6 +72,7 @@ references:
   volume: '57'
   issue: '3'
   year: '2020'
+  doi: 10.1177/0022343319881175
   start: 492-503
 - type: software
   title: 'R: A Language and Environment for Statistical Computing'

codemeta.json

@@ -14,7 +14,7 @@
     "name": "R",
     "url": "https://r-project.org"
   },
-  "runtimePlatform": "R version 4.3.0 (2023-04-21)",
+  "runtimePlatform": "R version 4.3.0 (2023-04-21 ucrt)",
   "provider": {
     "@id": "https://cran.r-project.org",
     "@type": "Organization",
@@ -135,7 +135,7 @@
     "SystemRequirements": null
   },
   "keywords": ["R", "IGO", "CorrelatesofWar", "IntergovernmentalOrganisations", "r-package", "igo", "correlates-of-war", "intergovernmental-organizations", "r", "rstats", "cran"],
-  "fileSize": "1152.806KB",
+  "fileSize": "1990.181KB",
   "citation": [
     {
       "@type": "SoftwareSourceCode",
@@ -179,7 +179,10 @@
         }
       ],
       "name": "Tracking organizations in the world: The Correlates of War {IGO} Version 3.0 datasets",
+      "identifier": "10.1177/0022343319881175",
       "pagination": "492-503",
+      "@id": "https://doi.org/10.1177/0022343319881175",
+      "sameAs": "https://doi.org/10.1177/0022343319881175",
       "isPartOf": {
         "@type": "PublicationIssue",
         "issueNumber": "3",

inst/CITATION

@@ -36,5 +36,6 @@ bibentry("Article",
   volume = 57,
   number = 3,
   pages = "492-503",
-  year = 2020
+  year = 2020,
+  doi = "10.1177/0022343319881175"
 )

inst/WORDLIST

@@ -11,7 +11,6 @@ Hernangomez
 IGO
 IGOs
 ISuC
-InterGovermental
 Intl
 Jamison
 Lifecycle

vignettes/mapping.Rmd

@@ -12,12 +12,10 @@ vignette: >
   %\VignetteEncoding{UTF-8}
 ---
 
-
-
 Maps are a powerful tool to show data. As the scope of `igoR` are the
-InterGovermental Organizations, mapping and IGOs are a perfect match.
+Intergovernmental Organizations, mapping and IGOs are a perfect match.
 
-This vignette provides some geospatial visualizations using the IGO datasets
+This vignette provides some geospatial visualizations using the IGO data sets
 [@pevehouse2020] included in this package. Specific packages used for geospatial
 data:
 
@@ -27,8 +25,7 @@ data:
 Also `countrycode` is a very handy package for translating between coding
 schemes (CoW, ISO3, NUTS, FIPS) and country names.
 
-
-```r
+``` r
 library(igoR)
 
 # Helper packages
@@ -47,8 +44,7 @@ United Nations.
 
 First we should extract the data:
 
-
-```r
+``` r
 # Extract shapes
 
 world <- gisco_get_countries(year = "2010")
@@ -101,8 +97,7 @@ Germany are not included.
 
 Now we are ready to plot with `ggplot2`:
 
-
-```r
+``` r
 ggplot(UN_all) +
   geom_sf(aes(fill = orgname), color = NA, show.legend = FALSE) +
   # Robinson
@@ -123,10 +118,15 @@ ggplot(UN_all) +
   theme(plot.caption = element_text(face = "italic"))
 ```
 
-<div class="figure">
-<img src="./UNMaps-1.png" alt="plot of chunk UNMaps" width="100%" />
-<p class="caption">plot of chunk UNMaps</p>
-</div>
+::: figure
+<img src="./UNMaps-1.png" alt="Evolution of the composition of members of the UN" width="100%"/>
+
+<p class="caption">
+
+Evolution of the composition of members of the UN
+
+</p>
+:::
 
 ## Number of shared memberships
 
@@ -135,8 +135,7 @@ Shared memberships are useful for identifying regional patterns.
 The following code produces a map showing the number of full memberships shared
 with Australia for each country on the world:
 
-
-```r
+``` r
 ## Number of igos shared - 2014
 
 # Countries alive in 2014
@@ -215,19 +214,23 @@ ggplot(sharedmap) +
   )
 ```
 
-<div class="figure">
-<img src="./AustShared-1.png" alt="plot of chunk AustShared" width="100%" />
-<p class="caption">plot of chunk AustShared</p>
-</div>
+::: figure
+<img src="./AustShared-1.png" alt="Australia and the World: Shared memberships on IGOs (2014)" width="100%"/>
+
+<p class="caption">
+
+Australia and the World: Shared memberships on IGOs (2014)
+
+</p>
+:::
 
 ## Cross-shared memberships
 
 The following map shows how the relationships between the countries of North
 America has flourished on the last 90 years, using a year as representative of
 each decade.
 
-
-```r
+``` r
 # Get shapes
 countries.sf <- gisco_get_countries(country = c("USA", "MEX", "CAN"))
 
@@ -290,9 +293,14 @@ ggplot(map) +
   )
 ```
 
-<div class="figure">
-<img src="./NAShared-1.png" alt="plot of chunk NAShared" width="100%" />
-<p class="caption">plot of chunk NAShared</p>
-</div>
+::: figure
+<img src="./NAShared-1.png" alt="Evolution of shared memberships between countries of North America (1930 - 2010)" width="100%"/>
+
+<p class="caption">
+
+Evolution of shared memberships between countries of North America (1930 - 2010)
+
+</p>
+:::
 
 ## References

vignettes/mapping.Rmd.orig

@@ -22,16 +22,16 @@ knitr::opts_chunk$set(
   message = FALSE,
   tidy = "styler",
   dev = "ragg_png",
-  dpi = 150,
+  dpi = 300,
   fig.path = "./",
   out.width = "100%"
 )
 ```
 
 Maps are a powerful tool to show data. As the scope of `igoR` are the
-InterGovermental Organizations, mapping and IGOs are a perfect match.
+Intergovernmental Organizations, mapping and IGOs are a perfect match.
 
-This vignette provides some geospatial visualizations using the IGO datasets
+This vignette provides some geospatial visualizations using the IGO data sets
 [@pevehouse2020] included in this package. Specific packages used for geospatial
 data:
 
@@ -114,7 +114,7 @@ Germany are not included.
 
 Now we are ready to plot with `ggplot2`:
 
-```{r UNMaps, fig.asp=1.5}
+```{r UNMaps, fig.asp=1.5, fig.cap="Evolution of the composition of members of the UN"}
 
 ggplot(UN_all) +
   geom_sf(aes(fill = orgname), color = NA, show.legend = FALSE) +
@@ -143,7 +143,7 @@ Shared memberships are useful for identifying regional patterns.
 The following code produces a map showing the number of full memberships shared
 with Australia for each country on the world:
 
-```{r AustShared, fig.asp=0.8}
+```{r AustShared, fig.asp=0.8, fig.cap="Australia and the World: Shared memberships on IGOs (2014)"}
 
 ## Number of igos shared - 2014
 
@@ -229,7 +229,7 @@ The following map shows how the relationships between the countries of North
 America has flourished on the last 90 years, using a year as representative of
 each decade.
 
-```{r NAShared}
+```{r NAShared, fig.cap="Evolution of shared memberships between countries of North America (1930 - 2010)"}
 
 # Get shapes
 countries.sf <- gisco_get_countries(country = c("USA", "MEX", "CAN"))
@@ -291,6 +291,7 @@ ggplot(map) +
     legend.title = element_text(size = 8),
     strip.background = element_rect(fill = "grey90", colour = NA)
   )
+
 ```
 
 ## References