| name | stage | family | |
|---|---|---|---|
| bulbasaur | 1 | 1 | |
| ivysaur | 2 | 1 | |
| venusaur | 3 | 1 | |
| charmander | 1 | 2 | |
| charmeleon | 2 | 2 | |
| charizard | 3 | 2 | |
| squirtle | 1 | 3 | |
| wartortle | 2 | 3 | |
| blastoise | 3 | 3 |
You are a terse assistant designed to help quarto users in styling revealjs slides. Respond with only the modified code with no backticks or newlines around the response. Add fonts when they fit with the theme.
use human readable sass variables to define colors and use those variables instead of specifying colors inline. Prefix these color variables with $theme-.
// before
library(tidymodels)
library(embed)
library(bonsai)
train_set <- ames |>
slice_sample(n = 50000, replace = TRUE) |>
rename(survey_target = Street)
lgb_model_recipe <- recipe(survey_target ~ ., train_set) |>
| src_pth <- "https://raw.githubusercontent.com/r-lib/actions/refs/heads/v2-branch/examples/check-no-suggests.yaml" | |
| tmp_pth <- tempfile() | |
| download.file(pth, destfile = tmp_pth) | |
| hard_check <- readLines(tmp_pth) | |
| hard_check <- | |
| gsub("name: check-no-suggests.yaml", | |
| "name: R-CMD-check-hard.yaml", | |
| hard_check, | |
| fixed = TRUE) |
| counter <- function(fun) { | |
| function(x) { | |
| r_files <- fs::dir_ls(fs::path(x, "R")) | |
| excludes <- stringr::str_detect( | |
| basename(r_files), | |
| "^(deprecated|import)-", | |
| negate = TRUE | |
| ) | |
| r_files[excludes] |> | |
| purrr::map(readLines) |> |
| library(tidyverse) | |
| # https://github.com/r-lib/covr/issues/482 | |
| res_tbl <- devtools::test_coverage() |> | |
| purrr::map(~tibble( | |
| value = if_else(is.null(.x$value), NA, .x$value), | |
| code = as.character(.x$srcref) | |
| )) |> | |
| purrr::list_rbind(names_to = "file") |
library(recipes)
library(nycflights13)
# rec <- recipe(dep_delay ~ carrier + tailnum + origin + dest, data = flights) |>
rec <- recipe(dep_delay ~ tailnum, data = flights[1:100000, ]) |>
step_dummy(all_nominal_predictors())
options("recipes.sparse" = FALSE)
library(recipes)
library(nycflights13)
rec <- recipe(dep_delay ~ carrier + tailnum + dest + origin, data = flights) |>
step_dummy(all_nominal_predictors())
options("recipes.sparse" = FALSE)
system.time({
library(dplyr)
library(purrr)
library(sparsevctrs)
# Dense -----------------------------------------------------
dense_vec <- function(x, len) {
res <- numeric(len)
res[x] <- x
res
library(tidymodels)
library(textrecipes)
library(friends)
library(profmem)
p <- profmem({
preped_rec <- recipe(season ~ text, data = friends) %>%
step_tokenize(text) %>%