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2001 From: The Open Journals editorial robot <89919391+editorialbot@users.noreply.github.com> Date: Fri, 10 Nov 2023 17:15:24 +0000 Subject: [PATCH 2/5] Creating 10.21105.joss.05751.crossref.xml --- joss.05751/10.21105.joss.05751.crossref.xml | 361 ++++++++++++++++++++ 1 file changed, 361 insertions(+) create mode 100644 joss.05751/10.21105.joss.05751.crossref.xml diff --git a/joss.05751/10.21105.joss.05751.crossref.xml b/joss.05751/10.21105.joss.05751.crossref.xml new file mode 100644 index 0000000000..0463fa6930 --- /dev/null +++ b/joss.05751/10.21105.joss.05751.crossref.xml @@ -0,0 +1,361 @@ + + + + 20231110T171446-e256acf98ee6462731ff6f9449730cadd724dc70 + 20231110171446 + + JOSS Admin + admin@theoj.org + + The Open Journal + + + + + Journal of Open Source Software + JOSS + 2475-9066 + + 10.21105/joss + https://joss.theoj.org + + + + + 11 + 2023 + + + 8 + + 91 + + + + Using the tidyverse with terra objects: the tidyterra +package + + + + Diego + Hernangómez + https://orcid.org/0000-0001-8457-4658 + + + + 11 + 10 + 2023 + + + 5751 + + + 10.21105/joss.05751 + + + http://creativecommons.org/licenses/by/4.0/ + http://creativecommons.org/licenses/by/4.0/ + http://creativecommons.org/licenses/by/4.0/ + + + + Software archive + 10.5281/zenodo.10078157 + + + GitHub review issue + https://github.com/openjournals/joss-reviews/issues/5751 + + + + 10.21105/joss.05751 + https://joss.theoj.org/papers/10.21105/joss.05751 + + + https://joss.theoj.org/papers/10.21105/joss.05751.pdf + + + + + + An intercomparison of models predicting +growth of Antarctic krill (Euphausia superba): The importance of +recognizing model specificity + Bahlburg + PLOS ONE + 7 + 18 + 10.1371/journal.pone.0286036 + 2023 + Bahlburg, D., Thorpe, S. E., Meyer, +B., Berger, U., & Murphy, E. J. (2023). An intercomparison of models +predicting growth of Antarctic krill (Euphausia superba): The importance +of recognizing model specificity. PLOS ONE, 18(7), 1–29. +https://doi.org/10.1371/journal.pone.0286036 + + + Spatial Statistics for Data Science: Theory +and Practice with R + Moraga + 2023 + Moraga, P. (2023). Spatial Statistics +for Data Science: Theory and Practice with R. Chapman & Hall/CRC. +https://www.paulamoraga.com/book-spatial/ + + + biomod2: Ensemble platform for species +distribution modeling + Thuiller + 2023 + Thuiller, W., Georges, D., Gueguen, +M., Engler, R., Breiner, F., Lafourcade, B., & Patin, R. (2023). +biomod2: Ensemble platform for species distribution +modeling. + + + dplyr: A grammar of data +manipulation + Wickham + 2023 + Wickham, H., François, R., Henry, L., +Müller, K., & Vaughan, D. (2023). dplyr: A grammar of data +manipulation (Version 1.1.2). +https://dplyr.tidyverse.org + + + ebvcube: Working with netCDF for essential +biodiversity variables + Quoss + 2021 + Quoss, L., Fernandez, N., Langer, C., +Valdez, J., & Pereira, H. M. (2021). ebvcube: Working with netCDF +for essential biodiversity variables. German Centre for Integrative +Biodiversity Research (iDiv) Halle-Jena-Leipzig. +https://github.com/LuiseQuoss/ebvcube + + + ggplot2: Elegant graphics for data +analysis + Wickham + 978-3-319-24277-4 + 2016 + Wickham, H. (2016). ggplot2: Elegant +graphics for data analysis. Springer-Verlag New York. +ISBN: 978-3-319-24277-4 + + + inlabru: An R package for Bayesian spatial +modelling from ecological survey data + Bachl + Methods in Ecology and +Evolution + 10 + 10.1111/2041-210X.13168 + 2019 + Bachl, F. E., Lindgren, F., Borchers, +D. L., & Illian, J. B. (2019). inlabru: An R package for Bayesian +spatial modelling from ecological survey data. Methods in Ecology and +Evolution, 10, 760–766. +https://doi.org/10.1111/2041-210X.13168 + + + isoband: Generate isolines and isobands from +regularly spaced elevation grids + Wickham + 2022 + Wickham, H., Wilke, C. O., & +Pedersen, T. L. (2022). isoband: Generate isolines and isobands from +regularly spaced elevation grids. +https://CRAN.R-project.org/package=isoband + + + R: A language and environment for statistical +computing + R Core Team + 2023 + R Core Team. (2023). R: A language +and environment for statistical computing. R Foundation for Statistical +Computing. https://www.R-project.org/ + + + RCzechia: Spatial objects of the Czech +Republic + Lacko + Journal of Open Source +Software + 83 + 8 + 10.21105/joss.05082 + 2023 + Lacko, J. (2023). RCzechia: Spatial +objects of the Czech Republic. Journal of Open Source Software, 8(83). +https://doi.org/10.21105/joss.05082 + + + sparrpowR: A flexible R package to estimate +statistical power to identify spatial clustering of two groups and its +application + Buller + International Journal of Health +Geographics + 1 + 20 + 10.1186/s12942-021-00267-z + 2021 + Buller, I. D., Brown, D. W., Myers, +T. A., Jones, R. R., & Machiela, M. J. (2021). sparrpowR: A flexible +R package to estimate statistical power to identify spatial clustering +of two groups and its application. International Journal of Health +Geographics, 20(1), 1–7. +https://doi.org/10.1186/s12942-021-00267-z + + + terra: Spatial data analysis + Hijmans + 2023 + Hijmans, R. J. (2023). terra: Spatial +data analysis. https://rspatial.org/ + + + tibble: Simple data frames + Müller + 2023 + Müller, K., & Wickham, H. (2023). +tibble: Simple data frames (Version 3.2.1). +https://tibble.tidyverse.org/ + + + tidyr: Tidy messy data + Wickham + 2023 + Wickham, H., Vaughan, D., & +Girlich, M. (2023). tidyr: Tidy messy data (Version 1.3.0). +https://tidyr.tidyverse.org + + + Welcome to the tidyverse + Wickham + Journal of Open Source +Software + 43 + 4 + 10.21105/joss.01686 + 2019 + Wickham, H., Averick, M., Bryan, J., +Chang, W., McGowan, L. D., François, R., Grolemund, G., Hayes, A., +Henry, L., Hester, J., Kuhn, M., Pedersen, T. L., Miller, E., Bache, S. +M., Müller, K., Ooms, J., Robinson, D., Seidel, D. P., Spinu, V., … +Yutani, H. (2019). Welcome to the tidyverse. Journal of Open Source +Software, 4(43), 1686. +https://doi.org/10.21105/joss.01686 + + + S3 + Wickham + Advanced R + 10.1201/9781351201315 + 2019 + Wickham, H. (2019). S3. In Advanced R +(2nd ed.). Chapman; Hall/CRC. +https://doi.org/10.1201/9781351201315 + + + Simple Features for R: Standardized Support +for Spatial Vector Data + Pebesma + The R Journal + 1 + 10 + 10.32614/RJ-2018-009 + 2018 + Pebesma, E. (2018). Simple Features +for R: Standardized Support for Spatial Vector Data. The R Journal, +10(1), 439–446. +https://doi.org/10.32614/RJ-2018-009 + + + Spatial Data Science: With applications in +R + Pebesma + 10.1201/9780429459016 + 2023 + Pebesma, E., & Bivand, R. (2023). +Spatial Data Science: With applications in R (p. 352). Chapman; +Hall/CRC. https://doi.org/10.1201/9780429459016 + + + rasterVis + Perpiñán + 2023 + Perpiñán, O., & Hijmans, R. +(2023). rasterVis. +https://oscarperpinan.github.io/rastervis/ + + + ggspatial: Spatial data framework for +ggplot2 + Dunnington + 2023 + Dunnington, D. (2023). ggspatial: +Spatial data framework for ggplot2. +https://CRAN.R-project.org/package=ggspatial + + + Whitebox-tools + Lindsay + 2018 + Lindsay, J. (2018). Whitebox-tools. +GitHub repository. +https://github.com/jblindsay/whitebox-tools + + + The development and rationale of +cross-blended hypsometric tints + Patterson + Cartographic Perspectives + 69 + 10.14714/CP69.20 + 2011 + Patterson, T., & Jenny, B. +(2011). The development and rationale of cross-blended hypsometric +tints. Cartographic Perspectives, 69, 31–46. +https://doi.org/10.14714/CP69.20 + + + tidysdm: Leveraging the flexibility of +tidymodels for species distribution modelling in R + Leonardi + bioRxiv + 10.1101/2023.07.24.550358 + 2023 + Leonardi, M., Colucci, M., & +Manica, A. (2023). tidysdm: Leveraging the flexibility of tidymodels for +species distribution modelling in R. bioRxiv. +https://doi.org/10.1101/2023.07.24.550358 + + + Blue mussels in western Norway have vanished +where in reach of crawling predators + Meister + Marine Ecology Progress +Series + 721 + 10.3354/meps14416 + 2023 + Meister, N., Langbehn, T. J., Varpe, +Ø., & Jørgensen, C. (2023). Blue mussels in western Norway have +vanished where in reach of crawling predators. Marine Ecology Progress +Series, 721, 85–101. +https://doi.org/10.3354/meps14416 + + + + + + From f783fb89bebf81258cc4678f192a69e31bf224bc Mon Sep 17 00:00:00 2001 From: The Open Journals editorial robot <89919391+editorialbot@users.noreply.github.com> Date: Fri, 10 Nov 2023 17:15:25 +0000 Subject: [PATCH 3/5] Creating 10.21105.joss.05751.jats --- joss.05751/10.21105.joss.05751.jats | 725 ++++++++++++++++++++++++++++ 1 file changed, 725 insertions(+) create mode 100644 joss.05751/10.21105.joss.05751.jats diff --git a/joss.05751/10.21105.joss.05751.jats b/joss.05751/10.21105.joss.05751.jats new file mode 100644 index 0000000000..0151ed01cf --- /dev/null +++ b/joss.05751/10.21105.joss.05751.jats @@ -0,0 +1,725 @@ + + +

+ + + + +Journal of Open Source Software +JOSS + +2475-9066 + +Open Journals + + + +5751 +10.21105/joss.05751 + +Using the tidyverse with terra objects: the tidyterra +package + + + +https://orcid.org/0000-0001-8457-4658 + +Hernangómez +Diego + + + + + +Independent Researcher + + + + +18 +7 +2023 + +8 +91 +5751 + +Authors of papers retain copyright and release the +work under a Creative Commons Attribution 4.0 International License (CC +BY 4.0) +2022 +The article authors + +Authors of papers retain copyright and release the work under +a Creative Commons Attribution 4.0 International License (CC BY +4.0) + + + +R +CRAN +spatial +vector +raster +tidyverse +terra +ggplot2 + + + + + + Summary +

tidyterra is an R + (R + Core Team, 2023) package that allows manipulation of spatial + data objects as provided by the terra package + (Hijmans, + 2023), using the verbs of the packages included in the + tidyverse + (Wickham + et al., 2019), such as dplyr + (Wickham, + François, et al., 2023), tidyr + (Wickham, + Vaughan, et al., 2023), or tibble + (Müller + & Wickham, 2023). This addition enables users that are + already familiar with the tidyverse to approach spatial data + manipulation and analysis more easily and much faster.

+

Furthermore, tidyterra extends the + functionality of the ggplot2 package + (Wickham, + 2016) by providing additional geoms and stats + 1 like + geom_spatraster() and + geom_spatvector(), as well as carefully chosen + scales and color palettes specifically designed for map + production.

+

tidyterra can manipulate the following + classes of terra objects:

+ + +

SpatVector objects, which represent + vector data such as points, lines, or polygon geometries.

+
+ +

SpatRaster objects, which represent + raster data in the form of a grid consisting of equally sized + rectangles. Each rectangle can contain one or more values.

+
+
+

The first stable version of tidyterra was + included on CRAN on April 24, 2022, and has been actively used by + other packages (such as ebvcuve + (Quoss + et al., 2021), biomod2 + (Thuiller + et al., 2023), inlabru + (Bachl + et al., 2019), RCzechia + (Lacko, + 2023), and sparrpowR + (Buller + et al., 2021)) and cited in academic research and publications + (Bahlburg et al. + (2023), + Moraga + (2023), + Leonardi et al. + (2023), + Meister et al. + (2023)) + ever since.

+
+ + Statement of need +

The + tidyverse + is a compilation of R packages that share an underlying design + philosophy, grammar, and data structures. The packages within the + tidyverse are widely used by R users for tidying, transforming, and + visualizing data.

+

The tidyverse is designed to work with tidy data (“every + column is a variable, every row is an observation, every cell is a + single value”), represented in the form of data frames or + tibbles. However, it is possible to extend the functionality of + tidyverse packages to work with new R object classes by registering + the corresponding S3 methods + (Wickham, + 2019). This means that dplyr::mutate() + can be adapted to work with any object of class + foo by creating the corresponding S3 method + mutate.foo().

+

While other popular packages designed for spatial data handling, + such as sf + (Pebesma, + 2018) or stars + (Pebesma + & Bivand, 2023), already provide integration with the + tidyverse as part of their infrastructure, + terra objects lack this integration natively. + Although terra offers a wide set of functions + for transforming and visualizing SpatRaster and + SpatVector objects, some users who are not + familiar with this package would need to make an additional effort to + learn that syntax. This may imply an additional challenge during their + initial steps in the field of spatial analysis.

+

The tidyterra package was developed to + address this integration gap. By providing the corresponding S3 + methods, data analysts can apply the same syntax and functions they + are already familiar with for rectangular data to the objects provided + by terra. This enables users who are not + familiar with spatial data analysis to approach this area more + easily.

+

In addition, tidyterra also offers functions + for plotting terra objects using the + ggplot2 syntax. Although packages like + rasterVis + (Perpiñán + & Hijmans, 2023) and ggspatial + (Dunnington, + 2023) already allow the representation of + SpatRaster objects via + ggplot2, tidyterra + functions provide additional support for advanced mapping. This + support includes the integration of faceted maps, contours, and the + automatic conversion of spatial layers to the same + CRS2 via + ggplot2::coord_sf(). Furthermore, + tidyterra also provides support for + SpatVector objects, similar to the native + support of sf objects in the + ggplot2 package.

+

Lastly, tidyterra provides a collection of + color palettes specifically designed for representing spatial + phenomena + (Lindsay, + 2018). Additionally, it implements the cross-blended + hypsometric tints described by Patterson & Jenny + (2011).

+
+ + A note on performance +

The development philosophy of tidyterra + consists on adapting terra objects to data + frame-like structures by performing different data transformations, + that ultimately may impact in the performance of the package.

+

When manipulating large raster files (i.e. more than 10.000.000 + cells), it is recommended to use the native + terra syntax, that is specifically designed for + handling this type of files. In the case of plotting, the default + behavior of the geoms provided is to resample + SpatRaster that presents more than 500.000 + cells to speed up the process (as the + terra::plot() does), however this upper limit + can be modified using the maxcell parameter of + the geom function.

+

Note also that when possible, the help page of each function of + tidyterra references its equivalent in + terra.

+
+ + Example of use +

tidyterra is available on + CRAN, + so it can be easily installed using the following commands in R:

+ install.packages("tidyterra") +

The latest developing version is hosted in GitHub and can be + installed using the following command in R:

+ remotes::install_github("dieghernan/tidyterra") +

The following example demonstrates how to manipulate a + SpatRaster object using the + dplyr syntax. Additionally, it illustrates how + to seamlessly plot a SpatRaster object with + ggplot2 using the + geom_spatraster() function:

+ library(tidyterra) +library(tidyverse) # Load all the packages of tidyverse at once +library(scales) # Additional library for labels + +# Temperatures in Castille and Leon (selected months) +rastertemp <- terra::rast(system.file("extdata/cyl_temp.tif", + package = "tidyterra" +)) + +# Rename with the tidyverse +rastertemp <- rastertemp %>% + rename(April = tavg_04, May = tavg_05, June = tavg_06) + + +# Plot with facets +ggplot() + + geom_spatraster(data = rastertemp) + + facet_wrap(~lyr, ncol = 2) + + scale_fill_whitebox_c( + palette = "muted", + labels = label_number(suffix = "º"), + n.breaks = 12, + guide = guide_legend(reverse = TRUE) + ) + + labs( + fill = "", + title = "Average temperature in Castille and Leon (Spain)", + subtitle = "Months of April, May and June" + ) + +

Faceted map with multi-layer raster + file.

+ +
+

In the following example we combine a common + dplyr workflow (mutate + + select) and we plot the result. In this case + the plot is a contour plot of the original + SpatRaster using + geom_spatraster_contour_filled()and it also + includes an overlay of a SpatVector for + reference:

+ # Compute the variation between April and June and apply a different palette +incr_temp <- rastertemp %>% + mutate(var = June - April) %>% + select(Variation = var) + +# Overlay an SpatVector +cyl_vect <- terra::vect(system.file("extdata/cyl.gpkg", + package = "tidyterra" +)) + +# Contour map with overlay +ggplot() + + geom_spatraster_contour_filled(data = incr_temp) + + geom_spatvector(data = cyl_vect, fill = NA) + + scale_fill_whitebox_d(palette = "bl_yl_rd") + + theme_grey() + + labs( + fill = "º Celsius", + title = "Variation of temperature in Castille and Leon (Spain)", + subtitle = "Difference between April and June" + ) + +

Contour map of temperature variation with a + SpatVector overlay. +

+ +
+
+ + Additional materials +

The package includes extensive documentation available online at + https://dieghernan.github.io/tidyterra/ + including:

+ + +

Details on each function, including (if possible) the + equivalent terra function, in case users + prefer to include those on their workflows.

+
+ +

Working examples on the use of the functions and creation of + plots.

+
+ +

Additional articles and vignettes, as well as a complete demo + of the different color palettes included on the package (see + Palettes).

+
+
+
+ + Acknowledgements +

I would like to thank Robert J. Hijmans for his advice and support + in adapting some of the methods, as well as for the suggestions that + helped us improve the functionalities of the package. I am also + thankful to Dewey Dunnington, Brent Thorne, and the rest of + contributors of the ggspatial package, which + served as a key reference during the initial stages of the development + of tidyterra.

+

tidyterra also incorporates some pieces of + code adapted from ggplot2 for computing + contours, which relies on the package isoband + (Wickham + et al., 2022) developed by Claus O. Wilke.

+
+ + + + + + + BahlburgDominik + ThorpeSally E. + MeyerBettina + BergerUta + MurphyEugene J. + + An intercomparison of models predicting growth of Antarctic krill (Euphausia superba): The importance of recognizing model specificity + PLOS ONE + Public Library of Science + 202307 + 18 + 7 + https://doi.org/10.1371/journal.pone.0286036 + 10.1371/journal.pone.0286036 + 1 + 29 + + + + + + MoragaPaula + + Spatial Statistics for Data Science: Theory and Practice with R + Chapman & Hall/CRC + 2023 + 20230717 + https://www.paulamoraga.com/book-spatial/ + + + + + + ThuillerWilfried + GeorgesDamien + GueguenMaya + EnglerRobin + BreinerFrank + LafourcadeBruno + PatinRemi + + biomod2: Ensemble platform for species distribution modeling + 2023 + + + + + + WickhamHadley + FrançoisRomain + HenryLionel + MüllerKirill + VaughanDavis + + dplyr: A grammar of data manipulation + 2023 + https://dplyr.tidyverse.org + + + + + + QuossLuise + FernandezNestor + LangerChristian + ValdezJose + PereiraHenrique Miguel + + ebvcube: Working with netCDF for essential biodiversity variables + German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig + Germany + 2021 + https://github.com/LuiseQuoss/ebvcube + + + + + + WickhamHadley + + ggplot2: Elegant graphics for data analysis + Springer-Verlag New York + 2016 + 978-3-319-24277-4 + https://ggplot2.tidyverse.org + + + + + + BachlFabian E. + LindgrenFinn + BorchersDavid L. + IllianJanine B. + + inlabru: An R package for Bayesian spatial modelling from ecological survey data + Methods in Ecology and Evolution + British Ecological Society + 2019 + 10 + 10.1111/2041-210X.13168 + 760 + 766 + + + + + + WickhamHadley + WilkeClaus O. + PedersenThomas Lin + + isoband: Generate isolines and isobands from regularly spaced elevation grids + 2022 + https://CRAN.R-project.org/package=isoband + + + + + + R Core Team + + R: A language and environment for statistical computing + R Foundation for Statistical Computing + Vienna, Austria + 2023 + https://www.R-project.org/ + + + + + + LackoJindra + + RCzechia: Spatial objects of the Czech Republic + Journal of Open Source Software + 2023 + 8 + 83 + https://joss.theoj.org/papers/10.21105/joss.05082 + 10.21105/joss.05082 + + + + + + BullerI. D. + BrownD. W. + MyersT. A. + JonesR. R. + MachielaM. J. + + sparrpowR: A flexible R package to estimate statistical power to identify spatial clustering of two groups and its application + International Journal of Health Geographics + BioMed Central + 2021 + 20 + 1 + https://ij-healthgeographics.biomedcentral.com/articles/10.1186/s12942-021-00267-z + 10.1186/s12942-021-00267-z + 1 + 7 + + + + + + HijmansRobert J. + + terra: Spatial data analysis + 2023 + https://rspatial.org/ + + + + + + MüllerKirill + WickhamHadley + + tibble: Simple data frames + 2023 + https://tibble.tidyverse.org/ + + + + + + WickhamHadley + VaughanDavis + GirlichMaximilian + + tidyr: Tidy messy data + 2023 + https://tidyr.tidyverse.org + + + + + + WickhamHadley + AverickMara + BryanJennifer + ChangWinston + McGowanLucy D’Agostino + FrançoisRomain + GrolemundGarrett + HayesAlex + HenryLionel + HesterJim + KuhnMax + PedersenThomas Lin + MillerEvan + BacheStephan Milton + MüllerKirill + OomsJeroen + RobinsonDavid + SeidelDana Paige + SpinuVitalie + TakahashiKohske + VaughanDavis + WilkeClaus + WooKara + YutaniHiroaki + + Welcome to the tidyverse + Journal of Open Source Software + 2019 + 4 + 43 + 10.21105/joss.01686 + 1686 + + + + + + + WickhamHadley + + S3 + Advanced R + Chapman; Hall/CRC + 20190524 + 2nd + http://dx.doi.org/10.1201/9781351201315 + 10.1201/9781351201315 + + + + + + PebesmaEdzer + + Simple Features for R: Standardized Support for Spatial Vector Data + The R Journal + 2018 + 10 + 1 + https://doi.org/10.32614/RJ-2018-009 + 10.32614/RJ-2018-009 + 439 + 446 + + + + + + PebesmaEdzer + BivandRoger + + Spatial Data Science: With applications in R + Chapman; Hall/CRC + London + 2023 + https://r-spatial.org/book/ + 10.1201/9780429459016 + 352 + + + + + + + PerpiñánOscar + HijmansRobert + + rasterVis + 2023 + https://oscarperpinan.github.io/rastervis/ + + + + + + DunningtonDewey + + ggspatial: Spatial data framework for ggplot2 + 2023 + https://CRAN.R-project.org/package=ggspatial + + + + + + LindsayJohn + + Whitebox-tools + GitHub repository + 2018 + https://github.com/jblindsay/whitebox-tools + + + + + + PattersonTom + JennyBernhard + + The development and rationale of cross-blended hypsometric tints + Cartographic Perspectives + 201106 + 69 + https://cartographicperspectives.org/index.php/journal/article/view/cp69-patterson-jenny + 10.14714/CP69.20 + 31 + 46 + + + + + + LeonardiMichela + ColucciMargherita + ManicaAndrea + + tidysdm: Leveraging the flexibility of tidymodels for species distribution modelling in R + bioRxiv + Cold Spring Harbor Laboratory + 2023 + https://www.biorxiv.org/content/early/2023/07/26/2023.07.24.550358 + 10.1101/2023.07.24.550358 + + + + + + MeisterNadja + LangbehnTom J. + VarpeØystein + JørgensenChristian + + Blue mussels in western Norway have vanished where in reach of crawling predators + Marine Ecology Progress Series + 20231019 + 721 + http://dx.doi.org/10.3354/meps14416 + 10.3354/meps14416 + 85 + 101 + + + + + +

The term “geoms” refers to geometric objects, and + “stats” refers to statistical transformations, following the naming + conventions of ggplot2

+
+ +

CRS; Coordinate reference system

+
+
+
+
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