#readr — Public Fediverse posts

#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.

#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.

I just learned that #rstats #readr write_csv() returns the data invisibly so you can just insert them within a pipe for a snapshot-then-plot functionality... I always imported magrittr %T>% before but it seems I don't need to use it at all for most cases.

Nifty little #readr / #tidyverse pattern, no need to unzip zip-files:

obis <- read_csv(unz(description = './data/CKI_P1_OBIS_sightings.zip', filename = 'Occurrence.csv'))

@rstats

@jorge posted a quite interesting #webinar #shortcourse on how to handle data efficiently with #rstats

• data management plans
• version control
• R for reproducible data manipulation
• working on clusters
• data publication

#shateEGU20 #FAIRprinciples #tidyverse #dplyr #broom #tidyr #purrr #readr #ggplot2 #markdown #git #spatialdata