Mikko Marttila mikmart

	witness	lies
	4096	246
	256	171
	1296	140
	729	115
	16	114
	6561	108
	1024	98
	625	96
	64	90

A replication of the results in Matt Parker’s video: https://youtu.be/aokNwKx7gM8

library(tidyverse)
#> Warning: package 'tidyr' was built under R version 4.0.2

# Download the raw data from Matt's Dropbox link
file_url <- "https://dl.dropboxusercontent.com/s/7gusbewresl6zsg/Kent%20County%20Precinct%20Data%20from%20Stand-up%20Maths%20video.xlsx"
file <- tempfile(fileext = ".xlsx")
r <- httr::GET(file_url, httr::write_disk(file))

library(ggplot2)
library(patchwork)

set.seed(31987)

n <- 100
g <- sample(c("a", "b"), n, replace = TRUE)
x <- rexp(n, 1 / 5)
b <- c(5, 10)

Some thoughts on reshaping data

Key idea: Reshaping is essentially about decoding data from old column names, and then encoding other data into new column names. How the values move around, i.e. how shape of the data changes, is just a byproduct.

Method

Essential steps:

Select columns that are transformed.
Decode variables from selected column names.

library(dplyr)
#> 
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:stats':
#> 
#>     filter, lag
#> The following objects are masked from 'package:base':
#> 
#>     intersect, setdiff, setequal, union

library(dplyr)
#> 
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:stats':
#> 
#>     filter, lag
#> The following objects are masked from 'package:base':
#> 
#>     intersect, setdiff, setequal, union

	library(shiny)

	ui <- fluidPage(
	sliderInput("a", "Slider A", 0, 10, 1),
	sliderInput("b", "Slider B", 0, 10, 1),
	actionButton("create_observers", "Create observers")
	)

	server <- function(input, output, session) {
	slider_observers <- list()

	library(tidyverse)
	library(lubridate)

	split_interval <- function(start, end, unit) {
	breaks <- seq(floor_date(start, unit), ceiling_date(end, unit), by = unit)
	timeline <- c(start, breaks[breaks > start & breaks < end], end)
	tibble(
	.seq = seq_len(length(timeline) - 1),
	.start = head(timeline, -1),
	.end = tail(timeline, -1),

	vars_select_pos <- function(.vars, ...) {
	match(tidyselect::vars_select(.vars, ...), .vars)
	}

	before <- function(..., .vars = tidyselect::peek_vars()) {
	min(vars_select_pos(.vars, ...)) - 1
	}

	after <- function(..., .vars = tidyselect::peek_vars()) {
	max(vars_select_pos(.vars, ...))

	#define PRINT_TYPEOF(x) \
	Rprintf("Type of %s is %s [%d].\n", #x, Rf_type2char(TYPEOF(x)), TYPEOF(x))