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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

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

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)

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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)))

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# 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)

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# 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,

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# 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)) %>%

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year,tag,number,year_total
2008,.htaccess,54,58390
2008,.net,5910,58390
2008,.net-2.0,289,58390
2008,.net-3.5,319,58390
2008,.net-4.0,6,58390
2008,.net-assembly,3,58390
2008,.net-core,1,58390
2008,2d,42,58390
2008,32-bit,19,58390

dgrtwo

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)

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separate_sql <- function(expression) {
  s <- paste(deparse(expression), collapse = "\n")
  s <- stringr::str_replace(s, "%>%", "%>%\n  ")
  s <- stringr::str_split(s, "\n")[[1]]
  
  val <- eval(expression)
  
  list(expression = expression,
       dplyr_code = s,
       sql = as.character(dbplyr:::remote_query(val)),