Bryan Shalloway brshallo

library(tidyverse)

data <- tibble(
  a = c(0, 0, 1),
  b = c(0, 1, 0),
  c = c(1, 1, 1)
)

data_sums &lt;- data %&gt;%

library(tidyverse)
library(lubridate)

date <- ymd(20200101) + months(1:7)
company <- c("a", "b")

sim_rw <- function(start = 0, n = 7, mean = 1){
  arima.sim(model = list(order = c(0, 1, 0)), n = n - 1, mean = mean) %>% 
    as.numeric() %>%

library(tidymodels)

rec_prep <- recipe(cty ~ ., data = mpg) %>% 
  step_YeoJohnson(cty) %>% 
  prep(data = mpg)

yj_estimate <- rec_prep %>% 
  tidy(1) %>% 
  pluck("value", 1)

library(tidyverse)

sample_n_of <- function(data, size, ...) {
  dots <- quos(...)
  
  group_ids <- data %>% 
    group_by(!!! dots) %>% 
    group_indices()

library(palmerpenguins)
library(dplyr)

#' @param rmse Root mean squared error on your sample
#' @param df Degrees of Freedom in your model. In this case it should be the
#'   same as the number of observations in your sample.
rmse_interval <- function(rmse, deg_free, p_lower = 0.025, p_upper = 0.975){
  tibble(.pred_lower = sqrt(deg_free / qchisq(p_upper, df = deg_free)) * rmse,
         .pred_upper = sqrt(deg_free / qchisq(p_lower, df = deg_free)) * rmse)

library(palmerpenguins)
library(tidyverse)

penguins <- palmerpenguins::penguins %>% 
  na.omit()

mod <- lm(body_mass_g ~ bill_length_mm + bill_depth_mm + sex, data = penguins)

bind_cols(

	library(tidymodels)

	### MUCH faster computationally to use base R `sample()` for this step... so did not use this approach
	resamples <- rsample::bootstraps(preds, 5000)

	avg_diff_sample <- function(split){
	analysis(split) %>%
	summarise(diff = mean(diff_abs_resids)) %>%
	pull(diff)
	}

	library(tidyverse)
	library(tidymodels)

	# Control function used as part of `prep_interval()`
	ctrl_fit_recipe <- function(x){
	output <- list(fit = workflows::pull_workflow_fit(x),
	recipe = workflows::pull_workflow_prepped_recipe(x))

	c(output, list(resids =
	bind_cols(

	#' Source the R code from an knitr file, optionally skipping plots
	#'
	#' @param file the knitr file to source
	#' @param skip_plots whether to make plots. If TRUE (default) sets a null graphics device
	#'
	#' @return This function is called for its side effects
	#' @export
	source_rmd = function(file, skip_plots = TRUE) {
	temp = tempfile(fileext=".R")
	knitr::purl(file, output=temp)

	library(magrittr)
	library(stringr)
	library(readr)
	library(purrr)
	library(glue)
	library(knitr)

	source_rmd_chunks <- function(file, chunk_labels, skip_plots = TRUE, output_temp = FALSE){

	temp <- tempfile(fileext=".R")