schochastics

pyramid <- function(ymax=5,mcol="#CD3333",xoffset=0){
  f <- function(a,b,y){
    (y-b)/a
  }
  
  a1 <- ymax/0.65
  b1 <- 0
  c1 <- -ymax/(1-0.65)
  d1 <- -c1
  a2 <- -ymax/(1-0.65)

schochastics

lissajou <- function(a,b,delta){
  t <- seq(0,2*pi,0.01)
  x <- sin(a*t+delta)
  y <- sin(b*t)
  tibble(x,y)
}

# setup parameters ----
n <- 7
params <- tibble(a=sample(1:10,n,replace = FALSE),

schochastics

library(tidyverse)
library(ggforce)

d <- tibble(x = c(1, 6, 10, 23,18), xpred = c(4, 8, 11, 2,22), y = 1:5, id = factor(1:5))
n <- nrow(d)+1
time_angle <- tibble(hour=1:24,angle=0.25*60*hour)

d$start <- time_angle$angle[d$x]*pi/180
d$end <- time_angle$angle[d$xpred]*pi/180
d$r <- d$y

schochastics

# Recreation of https://twitter.com/stevenstrogatz/status/1295915404574040065/photo/1
library(tidyverse)
library(Polychrome)
fun <- function(x){
  n <- c()
  i <- 2
  r <- x
  while(prod(n)!=x){
    if(!r%%i) {n=c(n,i);r=r/i;i=1}
    i <- i+1

schochastics

library(igraph)
library(ggraph)
library(graphlayouts)

#handdrawn graph without isolates
el <- matrix(c(1,3,1,3,1,6,2,3,2,6,
               3,4,3,4,3,6,4,6,4,8,
               5,7,5,8,7,8,7,9,7,10,
               7,11,9,10,10,11,9,11),ncol = 2,byrow = TRUE)
g <- graph_from_edgelist(el,F)

schochastics

# start_time: %Y-%m-%dT%H:%M:%SZ
# end_time: %Y-%m-%dT%H:%M:%SZ
# needs jsonlite and httr
# next_token can be obtained from meta$next_token to paginate through results
get_tweets <- function(q="",n=10,start_time,end_time,token,next_token=""){
  if(n>500){
    warning("n too big. Using 500 instead")
    n <- 500
  }
  if(n<5){

schochastics

#include <Rcpp.h>
using namespace Rcpp;

// [[Rcpp::export]]
double distance(double x1, double x2, double y1, double y2){
  double dist = sqrt((x2 - x1)*(x2 - x1) + (y2 - y1)*(y2 - y1));
  return dist;
}

// [[Rcpp::export]]

schochastics

library(tidyverse)
library(rvest)
library(ggimage)
library(lubridate)

#get first 25 leagues in Europe ----
url <- "https://www.transfermarkt.de/wettbewerbe/europa"
doc <- read_html(url)

leagues <- doc %>% html_nodes(".hauptlink a") %>% html_attr("href")

schochastics

function CRAN(handle)
    local output = '<span class="cran-pkg"><a href="https://cran.r-project.org/package=' .. 
    pandoc.utils.stringify(handle) .. '">'.. 
    pandoc.utils.stringify(handle)..'</a><i class="fa-brands fa-r-project fa-2xs"></i></span>'
    return pandoc.RawBlock('html', output)
  end

schochastics

library(tidyverse)
library(sf)
library(ggraph)
library(igraph)
# create some random data

country <- mapSpain::esp_get_prov(moveCAN=TRUE)
provinces <- st_cast(country,"MULTIPOLYGON") |> st_cast("POLYGON")
centroids <- distinct(provinces,iso2.prov.name.es,.keep_all = TRUE) |>
  dplyr::filter(!iso2.prov.name.es%in%c("Baleares","Las Palmas","Santa Cruz de Tenerife","Ceuta","Melilla")) |>