#Day8 | Distributions – Circular | #30DayChartChallenge | Elevation distribution in the most circular department of Honduras. Built with #RStats using #sf, #raster, #exactextractr, #ggplot2, #ggnewscale, #ggtext, #dplyr, #terra, #showtext, #scales, #patchwork and #ggspatial.
April 8, 2026 at 4:12 AM
#Day8 | Distributions – Circular | #30DayChartChallenge | Elevation distribution in the most circular department of Honduras. Built with #RStats using #sf, #raster, #exactextractr, #ggplot2, #ggnewscale, #ggtext, #dplyr, #terra, #showtext, #scales, #patchwork and #ggspatial.
Día 2 – Líneas #30DayMapChallenge, Distribución de Elevaciones Parque Nacional Pico Bonito Honduras. Herramientas: R con los paquetes sf, exactextractr, ggplot2, ggnewscale, ggtext, dplyr, terra, showtext, scales, patchwork, ggspatial, metR, rnaturalearth y rnaturalearthdata.
November 2, 2025 at 2:03 PM
Día 2 – Líneas #30DayMapChallenge, Distribución de Elevaciones Parque Nacional Pico Bonito Honduras. Herramientas: R con los paquetes sf, exactextractr, ggplot2, ggnewscale, ggtext, dplyr, terra, showtext, scales, patchwork, ggspatial, metR, rnaturalearth y rnaturalearthdata.
Día 8 | Distribuciones – Histograma | #30DayChartChallenge. | Visualización hecha usando R con los paquetes #ggplot2, #dplyr, #patchwork, #sf, #ggtext, #showtext, #raster, #exactextractr, #ggscale y #scales.
April 29, 2025 at 3:13 PM
Día 8 | Distribuciones – Histograma | #30DayChartChallenge. | Visualización hecha usando R con los paquetes #ggplot2, #dplyr, #patchwork, #sf, #ggtext, #showtext, #raster, #exactextractr, #ggscale y #scales.
Día 3 – Polígonos #30DayMapChallenge. Densidad Poblacional Por Departamentos Honduras 2025. Fuente WorldPop. Herramientas: R con los paquetes raster, tidyverse, sf, exactextractr, ggtext, showtext elevatr, ggnewscale, units, patchwork y ggspatial.
November 3, 2025 at 4:16 AM
Día 3 – Polígonos #30DayMapChallenge. Densidad Poblacional Por Departamentos Honduras 2025. Fuente WorldPop. Herramientas: R con los paquetes raster, tidyverse, sf, exactextractr, ggtext, showtext elevatr, ggnewscale, units, patchwork y ggspatial.
Día 15 – Fuego #30DayMapChallenge. Área Quemada en Centroamérica 2025. Herramienta: R usando terra, sf, ggplot2, rnaturalearth, dplyr, ggspatial, showtext, patchwork y exactextractr.
November 15, 2025 at 2:25 PM
Día 15 – Fuego #30DayMapChallenge. Área Quemada en Centroamérica 2025. Herramienta: R usando terra, sf, ggplot2, rnaturalearth, dplyr, ggspatial, showtext, patchwork y exactextractr.
Blog post: rasterizing very quickly to massive grids without creating wasteful pixels (until you want to): www.hypertidy.org/posts/2026-0...
#RStats a merger of {fasterize} and {exactextractr}, focused on the core algorithm where 'raster compression' is redundant
#RStats a merger of {fasterize} and {exactextractr}, focused on the core algorithm where 'raster compression' is redundant
August 19, 2026 at 1:53 PM
Blog post: rasterizing very quickly to massive grids without creating wasteful pixels (until you want to): www.hypertidy.org/posts/2026-0...
#RStats a merger of {fasterize} and {exactextractr}, focused on the core algorithm where 'raster compression' is redundant
#RStats a merger of {fasterize} and {exactextractr}, focused on the core algorithm where 'raster compression' is redundant
`tigris`: github.com/walkerke/tig...
`exactextractr`: isciences.gitlab.io/exactextractr/
`terra` package (for rasters in #rstats): rspatial.github.io/terra/
`exactextractr`: isciences.gitlab.io/exactextractr/
`terra` package (for rasters in #rstats): rspatial.github.io/terra/
Spatial Data Analysis
Methods for spatial data analysis with vector (points, lines, polygons) and raster (grid) data. Methods for vector data include geometric operations such as intersect and buffer. Raster methods includ...
rspatial.github.io
December 4, 2025 at 1:25 AM
`tigris`: github.com/walkerke/tig...
`exactextractr`: isciences.gitlab.io/exactextractr/
`terra` package (for rasters in #rstats): rspatial.github.io/terra/
`exactextractr`: isciences.gitlab.io/exactextractr/
`terra` package (for rasters in #rstats): rspatial.github.io/terra/
All I did was:
* get the VIIRS annual average nighttime lights raster dataset (eogdata.mines.edu/products/vnl/) for 2012 and 2024
* overlay a current Census Bureau shapefile for Princeton with the `tigris` package in #rstats
* compute yearly zonal totals with the `exactextractr` package
* get the VIIRS annual average nighttime lights raster dataset (eogdata.mines.edu/products/vnl/) for 2012 and 2024
* overlay a current Census Bureau shapefile for Princeton with the `tigris` package in #rstats
* compute yearly zonal totals with the `exactextractr` package
VIIRS Nighttime Light
Earth Observation Group is the pioneer of the nocturnal remote sensing, producing cutting edge product of Nighttime Light, Nightfire, and VIIRS Boat Detection.
eogdata.mines.edu
December 4, 2025 at 1:25 AM
All I did was:
* get the VIIRS annual average nighttime lights raster dataset (eogdata.mines.edu/products/vnl/) for 2012 and 2024
* overlay a current Census Bureau shapefile for Princeton with the `tigris` package in #rstats
* compute yearly zonal totals with the `exactextractr` package
* get the VIIRS annual average nighttime lights raster dataset (eogdata.mines.edu/products/vnl/) for 2012 and 2024
* overlay a current Census Bureau shapefile for Princeton with the `tigris` package in #rstats
* compute yearly zonal totals with the `exactextractr` package
New CRAN package fastbioclim with initial version 0.2.1
#rstats
https://cran.r-project.org/package=fastbioclim
#rstats
https://cran.r-project.org/package=fastbioclim
CRAN: Package fastbioclim
Provides a high-performance framework for deriving bioclimatic and custom summary variables from large-scale climate raster data. The package features a dual-backend architecture that intelligently switches between fast in-memory processing for smaller datasets (via the 'terra' package) and a memory-safe tiled approach for massive datasets that do not fit in RAM (via 'exactextractr' and 'Rfast'). The main functions, 'derive_bioclim()' and 'derive_statistics()', offer a unified interface with advanced options for custom time periods and static indices, making it suitable for a wide range of ecological and environmental modeling applications. A software note is in preparation. In the meantime, you can visit the package website <<a href="https://gepinillab.github.io/fastbioclim/" target="_top">https://gepinillab.github.io/fastbioclim/</a>> to find tutorials in English and Spanish.
cran.r-project.org
September 8, 2025 at 8:02 PM
New CRAN package fastbioclim with initial version 0.2.1
#rstats
https://cran.r-project.org/package=fastbioclim
#rstats
https://cran.r-project.org/package=fastbioclim
Orders of magnitude faster raster extraction in #rstats than the raster package? Yes, please! https://github.com/isciences/exactextractr @AmandaEBates
GitHub - isciences/exactextractr: R package for fast and ...
R package for fast and accurate raster zonal statistics -...
github.com
November 23, 2024 at 6:50 PM
Orders of magnitude faster raster extraction in #rstats than the raster package? Yes, please! https://github.com/isciences/exactextractr @AmandaEBates
Updates on CRAN: BoundEdgeworth (0.1.3), causalweight (1.1.4), dRiftDM (0.3.1), dunlin (0.1.12), exactextractr (0.10.1), NPBayesImputeCat (0.6), omopgenerics (1.3.4), parsnip (1.4.0)
December 1, 2025 at 5:25 PM
Updates on CRAN: BoundEdgeworth (0.1.3), causalweight (1.1.4), dRiftDM (0.3.1), dunlin (0.1.12), exactextractr (0.10.1), NPBayesImputeCat (0.6), omopgenerics (1.3.4), parsnip (1.4.0)
exactextractr is the beast of zonal statistics, I've never had really paid attention to all the options and neat features the exact_extract function packs but OMG. It makes working with raster data in EPSG:4326 (or any other geographic coordinates SRC) really easy and a lot more precise compared […]
Original post on fosstodon.org
fosstodon.org
August 9, 2025 at 9:56 PM
exactextractr is the beast of zonal statistics, I've never had really paid attention to all the options and neat features the exact_extract function packs but OMG. It makes working with raster data in EPSG:4326 (or any other geographic coordinates SRC) really easy and a lot more precise compared […]
Día 9 | Distribuciones – Divergente | #30DatChartChallenge. | Visualización hecha usando R con los paquetes #ggplot2, #dplyr, #patchwork, #sf, #ggtext, #showtext, #raster, #exactextractr and #SPEI.
April 26, 2025 at 9:39 AM
Día 9 | Distribuciones – Divergente | #30DatChartChallenge. | Visualización hecha usando R con los paquetes #ggplot2, #dplyr, #patchwork, #sf, #ggtext, #showtext, #raster, #exactextractr and #SPEI.
Día 16 – Coropletas #30DayMapChallenge. Población por municipios en Centroamérica. Herramienta: R usando ggplot2, raster, exactextractr, sf, grid, classInt, RcolorBrewer, tidyverse, showtext y ggtext
November 16, 2024 at 11:13 PM
Día 16 – Coropletas #30DayMapChallenge. Población por municipios en Centroamérica. Herramienta: R usando ggplot2, raster, exactextractr, sf, grid, classInt, RcolorBrewer, tidyverse, showtext y ggtext