Davis Vaughan DavisVaughan

## hop.R
blah

## rap-demo.R
suppressPackageStartupMessages({
  library(rap)
  library(AmesHousing)
  library(rsample)
  library(parsnip)
  library(dplyr)
})

ames <- make_ames()

## tidy-rolling-regression.r
library(dplyr)
library(slider)
library(lubridate)
library(tsibbledata)

# Google, Apple, Facebook, Amazon stock
gafa_stock <- as_tibble(gafa_stock)
gafa_stock <- select(gafa_stock, Symbol, Date, Close, Volume)

head(gafa_stock, 2)

## zone-transitions.R
library(clock)
library(purrr)
library(vctrs)
library(tibble)

generate_transitions <- function(zone,
                                 from = date_build(1900, 1, 1),
                                 to = date_build(2030, 1, 1)) {
  days <- date_seq(
    from = from,

## spooky.txt
dummy

## pet.txt
dummy

## baby.txt
dummy

## graves.txt
dummy

## lm-hell.r
library(purrr)
library(lobstr)
library(glue)
library(rlang, warn.conflicts = FALSE)

make_an_lm <- function() {
  x <- rep(1L, times = 10000000)
  cat(glue("x is {object.size(x)}B"))
  lm(1 ~ 1)
}

## call-hell.r
# often package developers store the call in their model object
# and then print it out in their summary / print methods
lm_model <- lm(mpg ~ cyl, mtcars)

lm_model
#>
#> Call:
#> lm(formula = mpg ~ cyl, data = mtcars)
#>
#> Coefficients:
	suppressPackageStartupMessages({
	library(rap)
	library(AmesHousing)
	library(rsample)
	library(parsnip)
	library(dplyr)
	})

	ames <- make_ames()
	library(dplyr)
	library(slider)
	library(lubridate)
	library(tsibbledata)

	# Google, Apple, Facebook, Amazon stock
	gafa_stock <- as_tibble(gafa_stock)
	gafa_stock <- select(gafa_stock, Symbol, Date, Close, Volume)

	head(gafa_stock, 2)
	library(clock)
	library(purrr)
	library(vctrs)
	library(tibble)

	generate_transitions <- function(zone,
	from = date_build(1900, 1, 1),
	to = date_build(2030, 1, 1)) {
	days <- date_seq(
	from = from,
	library(purrr)
	library(lobstr)
	library(glue)
	library(rlang, warn.conflicts = FALSE)

	make_an_lm <- function() {
	x <- rep(1L, times = 10000000)
	cat(glue("x is {object.size(x)}B"))
	lm(1 ~ 1)
	}
	# often package developers store the call in their model object
	# and then print it out in their summary / print methods
	lm_model <- lm(mpg ~ cyl, mtcars)

	lm_model
	#>
	#> Call:
	#> lm(formula = mpg ~ cyl, data = mtcars)
	#>
	#> Coefficients: