#cowplot — Public Fediverse posts

#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.

#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.

#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.

#cowplot is a great package to combine #ggplot2 plots for publications. #patchwork is an alternative that better manages alignment of the plot axes. For example, adding an independent annotation plot (#ggtranscript) to a genome coverage RNA seq plot (#tidyCoverage) can be done with:

coverageplot / annotationplot + plot_layout(nrow=2, heights=c(10,1)

#rstats #visualisation

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

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)