mapsf [1] helps designing various cartographic representations such as proportional symbols, choropleth or typology maps. It also offers several functions to display layout elements improving the graphic presentation of maps.
The aim of mapsf is to obtain thematic maps with the visual quality of those built with a classical mapping or GIS software while being lightweight, versatile and user-friendly. To achieve this goal, the package takes advantage of the features offered by sf [2] and provides a limited number of simple mapping functions.

mapsf is the successor of cartography [3], it offers the same core features but it is simpler and more robust. Unlike other popular cartographic packages, it does not use grammar of graphics, it depends on a limited number of packages and displays georeferenced plots using base R graphics.

The main function of the package, mf_map(), gives access to 9 map types: base maps, proportional or graduated symbols, choropleth maps, typology maps and various combinations of symbology. Many parameters are available to fine tune the cartographic representations. These parameters are the common ones found in GIS and automatic cartography tools (e.g. classification, color palettes, symbols sizes, legend layout...).
Some additional functions are dedicated to layout design (graphic themes, legends, scale bar, north arrow, title, credits…), map insets or map exports.

The development of mapsf follows the current best practices of the R ecosystem (CI/CD, coverage tests) and its documentation is enhanced by a vignette (the classic form of detailed documentation for R packages) and a website.

Package repository on GitHub
Package website

[1] Timothée Giraud (2021). mapsf: Thematic Cartography. R package version 0.1.1. https://CRAN.R-project.org/package=mapsf
[2] As stated by its authors, sf offers a "support for simple features, a standardized way to encode spatial vector data. Binds to 'GDAL' for reading and writing data, to 'GEOS' for geometrical operations, and to 'PROJ' for projection conversions and datum transformations."
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
[3] Giraud, T. and Lambert, N. (2017). “Reproducible Cartography.” In Peterson M. (ed.), Advances in Cartography and GIScience. ICACI 2017. Lecture Notes in Geoinformation and Cartography._, pp. 173-183. https://doi.org/10.1007/978-3-319-57336-6_13