An elegant way to share variables across #rshiny modules via R6 objects, courtesy of @jiwanheo.
A practical way to learn about R6 class.
https://t.co/Ie1hu5GVz3
@JNursiyono@randyboyes@mdancho84 An alternative is building a text classification model - e.g., #tidymodels - convert text descriptions to numeric features for model training.
Good intro by @juliasilge:
https://t.co/H6bnVUEZaa
@JNursiyono@randyboyes@mdancho84@JNursiyono, recommend using dplyr::case_when() - to create rules - with stringr::str_detect() - to identify keywords for categories.
The #rstudio community is a good forum to ask questions 🙂
https://t.co/GGzQ3giLxy
@mdancho84 Storing list-columns in {tibbles} - e.g., mutate(data = map(col, fun)) - to keep relevant data(frame) in one place - especially useful when modeling 🙂
@PhilippBayer @mdancho84@IsabellaGhement Used to question my friend's obsession with list and lapply()...happy to say {purrr} is one of the best investment to date 🙂
Hit the gas, and we have {furrr}; thanks to @dvaughan32.
@mdancho84 {hacksaw} 📦 by @daranzolin offers more flavours of group_split() that allows splitting by common {dplyr} verbs - e.g., filter_split(), select_split().
https://t.co/wJd6InMivo
TIL {purrr} partial() with the `...` argument; weird syntax but it's🤩
Imagine creating many styles in {openxlsx} with a common center `halign`:
ctr <- partial(createStyle, halign = "center", ... = )
ctr(numFmt = "0.0%")
# same as
createStyle(halign = "center", numFmt = "0.0%")