Hondunauta3d ago
#Día24 | Series de Tiempo – Día Temático - South China Morning Post | #30DayChartChallenge | Producción de Café en Centroamérica: Tendencias 2021-2025. Creada usando #Rstats con #ggplot2, #patchwork, #dplyr, #grid, #gridExtra, #scales, #sf, #rnaturalearth y #rnaturalearthdata.
HondunautaApr 6
#Día6 | Comparaciones – RSF-Data Day | #30DayChartChallenge. Cambio en score de libertad de prensa 2024–2025 en paises de América. Creada usando R con #tidyverse, #ggtext, #scales, #rnaturalearth, #rnaturalearthdata y #patchwork.
HondunautaApr 2
#Día 2 | Comparaciones – Pictograma | #30DayChartChallenge. Centroamérica suma más de 51 millones de habitantes. El gráfico fue creada usando R con #ggplot2, #dplyr, #tidyr#, #scales, #ggflags, #sf, #rnaturalearth, #rnaturalearthdata, #patchwork.
Philippe MassicotteJul 30, 2025

Just released #rnaturalearth v1.1.0 for #rstats.

https://github.com/ropensci/rnaturalearth/releases/tag/v1.1.0

- Download data directly using GDAL’s virtual file system – no extraction needed!

- Move to modern GeoPackage format when load = FALSE – more efficient than shapefiles.

- Raster data (GeoTIFF) now saved directly from zip, making workflows smoother.

- Faster loading with lazy imports (#119, thanks @heavywatal)

- Better messages via the cli package

Release rnaturalearth 1.1.0 · ropensci/rnaturalearth

New features Data is now downloaded using the GDAL Virtual File System, allowing ne_download() to read data directly from the zip file without requiring extraction. We are transitioning to the ...

GitHub
HondunautaApr 24, 2025
Day 24 | Timeseries – Data Day – WHO | #30DayChartChallenge. Visualization made with R using #ggplot2, #dplyr, #showtext, #patchwork, #ggrepel, #glue, #ggtext, #sf and #rnaturalearth. | Source: WHO.
HondunautaApr 23, 2025
Day 19 | Timeseries – Smooth | #30DayChartChallenge. Visualization made with R using #ggplot2, #dplyr, #ggtext, #showtext, #patchwork, #sf and #rnaturalearth. | Source: Google Trends
HondunautaApr 11, 2025
Día 11 | Distribuciones – “Stripes” | #30DayChartChallenge. La visualización fue creada usando R basado en los paquetes: #ggplot2, #dplyr, #sf, #lubridate, #ggtext, #showtext, #RcolorBrewer, #rnaturalearth y #cowplot. Fuente: CHIRPS.
HondunautaApr 1, 2025
Día 1 | Comparaciones – Fracciones | #30DayChartChallenge. La visualización fue creada usando R basado en los paquetes #ggplot2, #dplyr, #scales, #ggtext, #patchwork, #showtext, #sf, #rnaturalearth, #rnaturalearthdata y #ggrepel. Fuente HDX - https://data.humdata.org/dataset/cod-ps-hnd
Honduras - Subnational Population Statistics | Humanitarian Dataset | HDX

Proyecciones de Población del Instituto Nacional de Estadística - INE - por edad y sexo según Departamento y Municipio 2020 REFERENCE YEAR: 2024 These tables are suitable for database or GIS linkage to the [Honduras - Subnational Administrative Boundaries](https://data.humdata.org/dataset/cod-ab-hnd) using the ADM0, ADM1, or ADM2_PCODE fields. Access the Honduras - Subnational Population Statistics dataset to support humanitarian efforts.

ClintonNov 1, 2024
First day of #30DayMapChallenge complete! I used #rstats for this, including several new (to me) packages: #rnaturalearth #ggrepel and sf (for #SimpleFeatures which is new to me...this is all new given this is the first map I've made in R 🗺️). Lots more to learn...