mikmart

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()

mikmart

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

mikmart

mikmart

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))

mikmart

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)

mikmart

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),

mikmart

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, ...))

mikmart

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:

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

mikmart

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

mikmart

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