Updating paper and bib

paper/paper.bib

@@ -72,13 +72,6 @@ @article{south_rworldmap_2011
 	year = {2011}
 }
 
-@article{brunsdon_package_2015,
-	title = {Package ‘{GISTools}’},
-	journal = {CRAN Repository},
-	author = {Brunsdon, Chris and Chen, Hongyan},
-	year = {2015}
-}
-
 @misc{qgis_development_team_qgis_2021,
 	title = {{QGIS}},
 	url = {qgis.org/en/site/},
@@ -146,6 +139,7 @@ @article{wickham_ggplot2_2011
 	title = {ggplot2},
 	volume = {3},
 	number = {2},
+	doi = { https://doi.org/10.1002/wics.147},
 	journal = {Wiley Interdisciplinary Reviews: Computational Statistics},
 	author = {Wickham, Hadley},
 	year = {2011},

paper/paper.md

@@ -32,7 +32,7 @@ bibliography: paper.bib
 `rmap` is an R package that allows users to easily plot tabular data (CSV or R data frames) on maps without any Geographic Information Systems (GIS) knowledge. Maps produced by `rmap` are `ggplot` objects and thus capitalize on the flexibility and advancements of the `ggplot2` package [@wickham_ggplot2_2011] and all elements of each map are thus fully customizable. Additionally, `rmap` automatically detects and produces comparison maps if the data has multiple scenarios or time periods as well as animations for time series data. Advanced users can load their own shapefiles if desired. `rmap` comes with a range of pre-built color palettes but users can also provide any `R` color palette or create their own as needed. Four different legend types are available to highlight different kinds of data distributions. The input spatial data can be either gridded or polygon data. `rmap` comes preloaded with standard country, state, and basin maps as well as custom maps compatible with the Global Change Analysis Model (GCAM) spatial boundaries [@Calvin:2019]. `rmap` has a growing number of users and its products have been used in multiple multisector dynamics publications [@wild_implications_2021; @wild_integrated_2021; @khan_future_2021]. `rmap` is also a required dependency in other R packages such as `rfasst` [@sampedro_rfasst_2021]. `rmap's` automatic processing of tabular data using pre-built map selection, difference map calculations, faceting, and animations offers unique functionality that makes it a powerful and yet simple tool for users looking to explore multi-sector, multi-scenario data across space and time.
 
 # Statement of need
-`rmap` is meant to help users having limited to no GIS knowledge use R for spatial visualization of tabular spatial data. `rmap` is not meant to be a replacement for spatial manipulation or cartographic software but focuses on the simple plotting of polygon and gridded data for spatio-temporal visualization of tabular data with a focus on comparing across scenarios and time periods. Several existing R packages such as tmap [@tennekes_tmap_2018], cartography [@giraud_cartography_2016], rworldmap [@south_rworldmap_2011], GISTools [@brunsdon_package_2015], choroplethr [@lamstein_choroplethr_2020], sp [@pebesma_s_2005], and sf [@pebesma_simple_2018], have been developed to conduct spatial visualization and analytics in R without depending on external software such as ArcGIS [@esri_arcgis_2020], GRASS [@grass_development_team_grass_2020], or QGIS [@qgis_development_team_qgis_2021]. `rmap` enhances the following key capabilities which are limited in these existing packages:
+`rmap` is meant to help users having limited to no GIS knowledge use R for spatial visualization of tabular spatial data. `rmap` is not meant to be a replacement for spatial manipulation or cartographic software but focuses on the simple plotting of polygon and gridded data for spatio-temporal visualization of tabular data with a focus on comparing across scenarios and time periods. Several existing R packages such as tmap [@tennekes_tmap_2018], cartography [@giraud_cartography_2016], rworldmap [@south_rworldmap_2011], choroplethr [@lamstein_choroplethr_2020], sp [@pebesma_s_2005], and sf [@pebesma_simple_2018], have been developed to conduct spatial visualization and analytics in R without depending on external software such as ArcGIS [@esri_arcgis_2020], GRASS [@grass_development_team_grass_2020], or QGIS [@qgis_development_team_qgis_2021]. `rmap` enhances the following key capabilities which are limited in these existing packages:
 
 1. **Pre-built maps**: Existing packages come with only a few examples of built-in maps as package data. `rmap` comes with a growing collection of  country, state, river basin, as well as other customized maps that are added into the package data based on user needs and requests. While built-in maps increase the size of the package, having direct access to these allows for automated searching and quick deployment of relevant shapefiles without the need to download any data. A list of pre-built maps in `rmap` can be found in the [Built-in Maps](https://jgcri.github.io/rmap/articles/vignette_map.html#maps) section of the user guide.
 2. **Difference maps**: Existing packages do not produce difference maps to compare across scenarios or time periods. `rmap` provides this functionality by automatically recognizing multiple scenarios and time periods to produce difference maps across these dimensions. An important aspect of spatial data is exploring the difference between two scenarios or time periods and `rmap` makes this a seamless process.