Hondunauta

#Day30| Uncertainties – Data Day – Global Health Data Exchange | #30DayChartChallenge | Life Expectancy at Birth — Latin America & Caribbean. Built with #RStats using #ggplot2, #patchwork, #scales, #grid, #gridExtra and #tidyr.

#Day29 | Uncertainties – Monochrome | #30DayChartChallenge | . Coffee Price Forecast — Holt-Winters (HW) Built with #RStats using #forecast, #ggplot2, #dplyr, #lubridate, #scales and #tidyr.

#Day28 | Uncertainties – Modeling | #30DayChartChallenge | Barro Colorado Island — Tree Species Richness Estimation. Built with #RStats using #ggplot2, #patchwork, #MASS, #mgcv, #scales, #vegan, #gridExtra and #grid.

#Día27 | Incertidumbre – Animación | #30DayChartChallenge | Tendencia de la temperatura global. Creado con #RStats usando #dplyr, #ggplot2 y #gganimate.

#Día26 | Incertidumbre – Tendencias | #30DayChartChallenge | Tendencia de la temperatura global. Creado con #RStats usando #dplyr y #ggplot2

#Day25 | Uncertainties – Space | #30DayChartChallenge | Near-Earth Asteroid Orbit Uncertainties. Built with #RStats using #ggplot2 and #ggrepel.

#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.

#Día23 | Series de Tiempo – Seasons (Temporadas) | #30DayChartChallenge | Malcolm in the Middle. Creada usando #Rstats con #ggplot2, #dplyr, #ggtext, #showtext, #patchwork, #scales y #glue.

#Día22 | Series de Tiempo – Nueva Herramienta | #30DayChartChallenge | Precio histórico del aceite de palma. Creada usando #Python con #io, #pandas, #numpy y #matplotlib

#Día21 | Series de Tiempo – Histórico | #30DayChartChallenge | Precio histórico del café y cacao. Creada usando #Rstats con #quantmod, #ggplot2, #dplyr, #scales, #ggtext, #patchwork, #magick y #cowplot.

#Día20 | Series de Tiempo – Cambio Global | #30DayChartChallenge | Anomalía anual de temperatura superficial global respecto al promedio 1951–1980. Creada usando #Rstats con #ggplot2, #dplyr, #readr, #scales y #ggtext.

#Día19 | Series de Tiempo – Evolución | #30DayChartChallenge | Nuevas especies de mamíferos descritas por la ciencia · 1900–2050. Creada usando #Rstats con #ggplot2, #dplyr, #scales y #patchwork.

#Day18 | Relationships – UNICEF – Data Day | #30DayChartChallenge | UNICEF Children's Climate and Environment Risk Index (CCRI). Built with #RStats using #ggplot2, #dplyr, #ggrepel, and #showtext.

#Day17 | Relationships – Remake | #30DayChartChallenge | Are we making more or fewer remakes as the years go by?. Built with #RStats using #rvest, #dplyr, #stringr, #ggplot2 and #showtext.

#Día16 | Relaciones – Causalidad | #30DayChartChallenge | ¿Más personas = más ciudad?. Creada usando #Rstats con #ggplot2, # patchwork, #grid, #ggrepel, #scales, #showtext y #ggflags.

#Día15 | Relaciones – Correlación | #30DayChartChallenge | Población vs Área Urbana en Centroamérica. Creada usando #Rstats con #ggplot2, #ggrepel, #scales, #showtext y #ggflags.

#Día14 | Relaciones – Comercio | #30DayChartChallenge | Precios del Café. Creada usando #Rstats con #quantmod, #ggplot2, #dplyr y #scales.

#Day13 | Relationships – Ecosystems | #30DayChartChallenge | Lifezone Ecology of Catasetum maculatum. Built with #RStats using #ternary.

#Day12 | Distributions – FlowingData – ThemeDay | #30DayChartChallenge | Heat Spots in Central America 2020-2024, source: NASA Firms . Built with #RStats using #ggplot2, #dplyr, #readr, #stringr and scales.

#Day11 | Distributions – Physiscal | #30DayChartChallenge | Density Distribution of Temperatures in Central America, source: World Bank Climate Portal. Built with #RStats using #ggplot2, #tidyverse and #ggridges.

#Day10 | Distributions – Pop Culture | #30DayChartChallenge | Ballon d'Or — The Last 20 Years (2005-2025), source: France Football. Built with #RStats using #ggplot2, #patchwork, #ggtext and #scales.

#Day9 | Distributions – Wealth | #30DayChartChallenge | Income Distribution in Central America, source World Bank. Built with #RStats using #ggplot2, #dplyr, #tidyr, #patchwork, #ggtext, #scales, #wbstats and #purrr.

#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.

#Day7 | Distributions – Multiscale | #30DayChartChallenge | Comparison of NDVI distributions across two spatial scales. Built with #RStats using #ggplot2, #dplyr, #terra, #tidyterra, #patchwork, #ggtext, and #scales.

#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.

#Día5 | Comparaciones – Experimental | #30DayChartChallenge. Experimenté agregando una sumatoria horizontal de observaciones en un boxplot sobre la capacidad endocraneana en especies del género Homo. Creada usando R con #ggplot2, #ggdist, #dplyr, #scales, #ggtext, #patchwork, #tibble y #tidyr.

#Día4 | Comparaciones – Slope | #30DayChartChallenge. Comportamiento de los focos de calor detectados para los paises de América Central. Creada usando R con #ggplot2, #dplyr, #scales, #readr, #stringr y #ggtext.

#Día3 | Comparación– Mosaico | #30DayChartChallenge. Focos de calor detectados para los paises de América Central. Un gráfico con valores absolutos y otro con valores relativos. Creada usando R con #ggplot2, #treemapify, #dplyr, #scales, #readr y #stringr.

#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.

Día 1 | Comparaciones – Part-to-Whole | #30DayChartChallenge. Distribución del área construida en Centroamérica según ESA WorldCover, donde el tamaño refleja los km² de suelo urbanizado por país. El gráfico fue creada usando R con los paquetes #ggplot2, #treemapify, #scales y #showtext.

Día 8 | Distribuciones – Histograma | #30DayChartChallenge. | Visualización hecha usando R con los paquetes #ggplot2, #dplyr, #patchwork, #sf, #ggtext, #showtext, #raster, #exactextractr, #ggscale y #scales.

Day 28 | Uncertainties – Inclusion | #30DayChartChallenge. Visualization made with R using #ggplot2, #dplyr, #ggtext and #showtext | Source: Google Trends.

Day 27 | Uncertainties – Noise | #30DayChartChallenge. Visualization made with R using #ggplot2, #showtext and #dplyr | Source: USA - National Institute for Occupational Safety and Health.

Día 9 | Distribuciones – Divergente | #30DatChartChallenge. | Visualización hecha usando R con los paquetes #ggplot2, #dplyr, #patchwork, #sf, #ggtext, #showtext, #raster, #exactextractr and #SPEI.

Day 26 | Uncertainties – Monochrome | #30DayChartChallenge. Visualization made with R using #vegan, #ggplot2, #showtext, #patchwork and #sf | Source: Barro Colorado Island (BCI) tree census | vegan::BCI.

Día 25 | Incertidumbre – Riesgo | #30DayChartChallenge | Visualización hecha usando R a partir de los paquetes #ggplot2, #dplyr, #scales, #showtext y #sysfonts. | Fuente: Gannet – Virtual Assitant (app.gannet.ai) desarrollado por Data Friendly Space. La respuesta fue generada usando tres fuentes – 1) State of the Climate in Latin America and the Caribbean, 2) Latin America and the Caribbean Regional Overview of Food Security and Nutrition y 3) Anticipatory Action and Response Plan.

Day 24 | Timeseries – Data Day – WHO | #30DayChartChallenge. Visualization made with R using #ggplot2, #dplyr, #showtext, #patchwork, #ggrepel, #glue, #ggtext, #sf and #rnaturalearth. | Source: WHO.

Day 19 | Timeseries – Smooth | #30DayChartChallenge. Visualization made with R using #ggplot2, #dplyr, #ggtext, #showtext, #patchwork, #sf and #rnaturalearth. | Source: Google Trends

Day 23 | Timeseries – Log Scale | #30DayChartChallenge. Visualization made with R using #ggplot2, #dplyr, #showtext, #patchwork, #janitor and #scales. | Source: Our World in Data

Day 22 | Timeseries – Stars | #30DayChartChallenge. Visualization made with R using #ggplot2, #dplyr, #showtext, #lubridate and #cranlogs. | Source: cranlogs R Package.

Day 21 | Timeseries – Fossils| #30DayChartChallenge. Visualization made with R using #ggplot2, #dplyr, #showtext, #ggrepel and #janitor. | Source: Our World in Data

Day 20 | Timeseries – Urbanization | #30DayChartChallenge. Visualization made with R using #ggplot2, #sf, #dplyr, #scales, #grid, #ggshadow, #extrafont and #cowplot. | Source: Worldometers

Day 12 | Distributions – Data Day – Data.gov | #30DayChartChallenge. Visualization made with R using #sf, #tigris, #ggthemes, #patchwork, #tidyverse, #ggtext and #showtext . | Source: data.gov - https://catalog.data.gov/dataset/biodiversity-by-county-distribution-of-animals-plants-and-natural-communities

Day 15 | Relationships – Complicated | #30DayChartChallenge. Visualization made with R using #tidyverse, #ggtext and #showtext . | Source: google trends https://trends.google.com/trends/explore?date=all&q=Avril%20Lavigne%20Complicated&hl=en

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.

Day 10 | Distributions / Multi – Modal | #30DayChartChallenge. Visualization made with R using #ggplot2, #tidyverse, #cowplot, #stars, #raster, #ggspatial and #sf. Data source: Sentinel-2 MSI (2024)

Day 7 | Distributions– Outliers | #30DayChartChallenge. Visualization made with R using #ggplot2, #tidyverse, #terra, #ggtext, #showtext y #sf. Data source: Sentinel-2 MSI (2019-2024)

Day 6 | Comparisons – Florence Nightingale (theme day) | #30DayChartChallenge. Visualization made with R using #tidyverse, #ggtext and #showtext. Data source: HDX - https://data.humdata.org/dataset/cod-ps-hnd.

Day 5 | Comparisons – Ranking | #30DayChartChallenge. Visualization develop with R using #ggplot2, #dplyr, #ggtext, #showtext, #glue and #ggimage. Data source: Opta Analyst.

Día 4 | Comparaciones – Grande o Pequeño | #30DayChartChallenge. La visualización fue creada usando R basado en los paquetes: #ggplot2, #dplyr, #treemapify. Fuente: Sistema de Educación Superior - UNAH – 2023.