HorridTom

library(tidyverse)
library(lubridate)
library(autospc)

set.seed(20230422L)

df <- tibble(x = seq(from = as.Date("2023-01-01"),
                     to = as.Date("2023-01-31"),
                     by = 1L),
             y = rpois(31L, lambda = 20L))

HorridTom

perturb_start_end_los <- function(
  df,
  max_shift_size_hours = 24,
  min_binsize_mins = 15
  ){

  df <- df %>%
    dplyr::mutate(los = spell_end - spell_start,
                  binstart = 15*floor(as.numeric(los, units = "mins")/15),
                  binend = 15*(floor(as.numeric(los, units = "mins")/15) + 1),

HorridTom

library(tidyverse)

# Examples of multiple summaries...
# Based on examples here: https://dplyr.tidyverse.org/reference/across.html

# one summary across multiple columns
mtcars %>% 
  group_by(cyl) %>%
  summarise(across(where(is.numeric), mean))

HorridTom

# Passing user-specified column names to tidyverse functions inside your own functions
# Based on: https://dplyr.tidyverse.org/articles/programming.html

library(tidyverse)
str(mtcars)

# How you might think you write a function that takes a column name as argument
# and uses tidyverse functions to do something based on that column name...
my_first_group_summarise_fn <- function(df, col_name) {
  

HorridTom

# Advent of Code Day 3 Puzzle 1
# Team Reindeer! (Tom and Mable)
#
# Read the puzzle first: https://adventofcode.com/2018/day/3
# Then to understand how this solution works, it is best
# to start with the last function in this file and work up!
# The example_claim_vector is the same example as from the
# puzzle website.

library(stringr)

HorridTom

library(digest)
library(stringr)
library(readr)

hashed_id <- function(x, salt){
  y <- paste(x, salt)
  y <- sapply(y, function(X) digest(X, algo = "sha1"))
  as.character(y)
}