dgrtwo’s gists

dgrtwo / drob-advent-of-code-2023-day-5.R

Last active December 6, 2023 02:29

	# My (NOT WORKING) solution to day 5 Part 2
	library(tidyverse)
	library(adventdrob)
	library(intervals)
	library(broom)

	# Utility to turn Intervals::intervals into tibble
	tidy.Intervals_full <- function(intervals) {
	intervals %>%
	as.data.frame() %>%

dgrtwo / advent-2021-day-19.R

Created December 20, 2021 01:57

My solution to Day 19 of Advent of Code 2021

	library(tidyverse)
	library(adventdrob)

	input <- advent_input(19, 2021)

	beacons <- input %>%
	filter(x != "") %>%
	mutate(scanner = cumsum(str_detect(x, "scanner"))) %>%
	filter(!str_detect(x, "scanner")) %>%
	separate(x, c("x", "y", "z"), sep = ",", convert = TRUE) %>%

dgrtwo / advent-2021-day-18.R

Created December 18, 2021 19:41

My solution to Day 18 of Advent of Code 2021

	library(tidyverse)
	library(adventdrob)

	# Add a value to the leftmost position in a binary tree
	add_leftmost <- function(x, value) {
	if (!is.list(x)) x + value
	else list(add_leftmost(x[[1]], value), x[[2]])
	}

	# Add a value to the rightmost position in a binary tree

dgrtwo / cubic-model-animation.R

Created May 8, 2020 19:56

Showing how a 3rd-degree polynomial model would predict future deaths if a plateau continues

	library(tidyverse)
	library(broom)
	library(scales)
	theme_set(theme_light())

	US <- read_csv("https://raw.githubusercontent.com/nytimes/covid-19-data/master/us.csv") %>%
	mutate(new_deaths = deaths - lag(deaths)) %>%
	filter(date >= "2020-02-26")

	today <- max(US$date)

dgrtwo / comparing-polynomial-models-covid.R

Created May 5, 2020 19:33

Comparing the CEA's "cubic model" to quadratic and quartic models

	library(tidyverse)
	library(broom)

	US <- read_csv("https://raw.githubusercontent.com/nytimes/covid-19-data/master/us.csv") %>%
	mutate(new_deaths = deaths - lag(deaths)) %>%
	filter(date >= "2020-02-26")

	models <- tibble(degrees = 2:4) %>%
	mutate(model = map(degrees, ~ lm(log(new_deaths + 1) ~ poly(date, .), data = US)))

dgrtwo / simulate-dice

Last active January 5, 2020 16:32

Simulate scoring games of Dice 10,000

	# 100,000 simulations
	n <- 1e5
	dice <- t(rmultinom(n, 5, rep(1, 6) / 6))

	# First rules for 1s and 5s
	ones_score <- c(0, 100, 200, 1000, 5000, 10000)[dice[, 1] + 1L]
	fives_score <- c(0, 50, 100, 0, 0, 0)[dice[, 5] + 1L]

	# 100 x n score
	most_common <- apply(dice, 1, which.max)

dgrtwo / underpowered-simulation.R

Last active May 11, 2019 20:01

	# Code behind this tweet:

	# https://twitter.com/drob/status/1126988304090574848

	library(tidyverse)
	library(broom)

	t_tests <- crossing(pi0 = .75,
	effect_size = .25,
	trial = 1:10000,

dgrtwo / dice-rolls.R

Created February 25, 2019 23:56

Animation of die rolls

	# Code behind this tweet: https://twitter.com/drob/status/1100182329350336513
	library(tidyverse)
	library(gganimate)

	# Setup
	options(gganimate.nframes = 200)
	set.seed(2019)

	simulation <- tibble(roll = 1:10000) %>%
	mutate(result = sample(6, n(), replace = TRUE)) %>%

dgrtwo / by_tag_year.csv

Created June 22, 2018 03:55

We can't make this file beautiful and searchable because it's too large.

dgrtwo / cross validation

Created June 4, 2018 15:45

	library(modelr)
	library(tidyverse)
	library(broom)

	mtcars %>%
	crossv_kfold(k = 10) %>%
	mutate(model = map(train, ~ lm(mpg ~ wt, .)),
	result = map2(model, test, ~ augment(.x, newdata = .y))) %>%
	unnest(result)

David Robinson dgrtwo