#dplyr — Public Fediverse posts

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

#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.

#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.

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

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.

When manipulating #FASTA files with R, `seqinr` helps. It was not immediately obvious how to extract the information from the data structure that read.fasta() generates. To recover the headers:
```
lst_of_annot <- lapply(X=read_from_fasta, FUN=attributes)
headers_list <- lapply(X=lst_of_annot, FUN="[[", "name")
```
Further operations required a merge, where the `left_join` from the `dplyr` package was great, as it maintains row order.

https://cran.r-project.org/web/packages/seqinr/index.html
#R #seqinr #dplyr