#stringr — Public Fediverse posts

#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.

#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.

#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.

This is a noob's observation of R, Tidyverse and Stringr. But wow, why can some datasets open in notepad or Windows file preview or excel in 1 second but takes minutes or hours to show up in str_view_all?

#R, #Tidyverse, #Stringr

The {stringr} #rstats 📦 gets a function to convert a character vector into a single, comma-separated string *with custom wording before the last item.* (I've been using knits::combine_words() for that.)

Example from the tidyverse blog post about stringr 1.5.0:

str_flatten_comma(c("cats", "dogs", "mice"), last = ", and ")

#> [1] "cats, dogs, and mice"

https://www.tidyverse.org/blog/2022/12/stringr-1-5-0/

#OxfordComma #tidyverse #stringr