Finally, #dplyr functionality I've been hacking through small functions for about a decade and a half: https://dplyr.tidyverse.org/reference/recode-and-replace-values.html

#rstats

Na #PythonCerrado2025, tivemos ontem um excelente tutorial do Lucas Marcondes Pavelski https://github.com/lucasmpavelski.

Aprendemos sobre #R, #tidyverse, #reticulate, várias ferramentas essenciais como #ggplot2 e #dplyr, vendo na prática como aplicá-las. Foco na ponte #Python <-> R.

Tudo novidade pra mim, vieram várias ideias interessantes de análises e plots.

#PythonCerrado

I recently saw a kind of stacked donut/pie chart that visualized nested count data (e.g. a sample description with two relevant categories, like favorite ice cream and gender) and wondered how I'd do that in #rstats.

So, if you ever want to make a plot like this, here's the #ggplot2 and #dplyr code: https://gist.github.com/Kudusch/577b6f07c686a64a3aace685fd9f3bee

This wouldn't work well with too many categories and pie charts in general aren't optimal, but for this specific kind/shape of data, I think it works well enough.

Hey #RStats hivemind, can someone sanity check me?

for some reason filtering with a value assigned to 'x' isn't working here (I was trying to make a dummy dataset for an lapply + ggplot problem I'm having, and instrad have bumped into this weird inconcistency).

df <- data.frame(name = c("delta^13*C", "delta^13*C", "delta^18*O", "delta^18*O"),
x = c(1,1, 2, 2),
y = c(1,3, 5, 4))

x <- "delta^13*C"

df %>%
filter(name == x)

# [1] name x y
# <0 rows> (or 0-length row.names)

df %>%
filter(name == "delta^13*C")

# name x y
# delta^13*C 1 1
# delta^13*C 1 3

#dplyr

# objetivo: Simulación de 100 agentes durante 30 días
# - Cada agente puede estar en 3 estados
# - Cada estado tiene diferentes atributos

# salidas : Tablas de frecuencia de frecuencias de 3 vias:
# - Combinación de estados de 100 agentes cada dia

#Rstats #janitor #dplyr #Flisol #SoftwareLibre