Julia Silge juliasilge

library(rlang)
library(tidymodels)
#> Registered S3 method overwritten by 'tune':
#>   method                   from   
#>   required_pkgs.model_spec parsnip

rmsle <- function(data, ...) {
    UseMethod("rmsle")
}

library(tidyverse)
library(tidycensus)
library(sf)
#> Linking to GEOS 3.8.1, GDAL 3.1.4, PROJ 6.3.1
library(viridis)
#> Loading required package: viridisLite
library(patchwork)
theme_set(silgelib::theme_plex())

library(tidymodels)
#> Registered S3 method overwritten by 'tune':
#>   method                   from   
#>   required_pkgs.model_spec parsnip
library(baguette)

set.seed(123)
car_folds <- bootstraps(mtcars, times = 5)

library(tidyverse)
library(silgelib)
theme_set(theme_plex())

games <- read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2021/2021-03-16/games.csv')
#> 
#> ── Column specification ────────────────────────────────────────────────────────
#> cols(
#>   gamename = col_character(),

library(tidyverse)
library(tidylo)
library(tidytext)
theme_set(silgelib::theme_plex())

ninja_raw <- read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2020/2020-12-15/ninja_warrior.csv')
#> 
#> ── Column specification ────────────────────────────────────────────────────────
#> cols(

library(tidyverse)
library(broom)
theme_set(silgelib::theme_plex())

raw_hikes <- read_rds(url('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2020/2020-11-24/hike_data.rds'))

hikes <- raw_hikes %>%
    mutate(across(c(length, gain, highpoint, rating), parse_number))

library(tidyverse)
library(tidytext)
library(tidylo)
library(silgelib)

beyonce_lyrics <- read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2020/2020-09-29/beyonce_lyrics.csv')
#> Parsed with column specification:
#> cols(
#>   line = col_character(),

library(tidymodels)
data(ames)

set.seed(833961)
ames_split <- initial_split(ames, prob = 0.80, strata = Sale_Price)
ames_train <- training(ames_split)
ames_test  <-  testing(ames_split)

ames_rec <- recipe(Sale_Price ~ Neighborhood + Gr_Liv_Area + Year_Built + Bldg_Type,

library(tidymodels)

centers <- tibble(
  cluster = factor(1:3), 
  num_points = c(100, 150, 50),  # number points in each cluster
  x1 = c(5, 0, -3),              # x1 coordinate of cluster center
  x2 = c(-1, 1, -2)              # x2 coordinate of cluster center
)

	library(tidymodels)
	library(textrecipes)

	data("small_fine_foods")
	sparse_bp <- hardhat::default_recipe_blueprint(composition = "dgCMatrix")

	text_rec <-
	recipe(score ~ review, data = training_data) %>%
	step_tokenize(review) %>%
	step_stopwords(review) %>%