schochastics

library(shiny)
xy <- c(784,479) #output of grDevices::dev.size("px")
url <- "https://www.youtube.com/watch?v=Ef2jmf2vy00" #copy yt link here
url <- gsub("watch\\?v=","embed/",url)
ui <- fluidPage(
  HTML(paste0('<iframe width="',xy[1],'" height="',xy[2],'" src="',url,'" frameborder="0"></iframe>'))
)
server <- function(input, output, session) {
}

schochastics

---
title: "Untitled"
format: html
---


```{r}
#| label: prepare
#| echo: true

schochastics

library(ggplot2)
library(ggpubr)
library(ggtext)

df <- data.frame(x=seq.Date(as.Date("2020-04-01"),as.Date("2022-07-01"),by="month"),y=(1:28)^2)

p <- ggplot(df, aes(x, y))+
  geom_line(linewidth = 2.2,color="white",lineend = "round")+
  geom_line(linewidth = 1,color="red",lineend = "round")+
  scale_x_date(labels=scales::label_date("%b\n%Y"))+

schochastics

library(signnet)
library(igraph)
library(ggraph)
library(patchwork)
triads <- c("003-000000", "012-0000P0", "012-0000N0", 
                  "102-0000PP", "102-0000NP", "102-0000NN", "021C-0PP000", 
                  "021C-0NP000", "021C-0PN000", "021C-0NN000", "021U-0P0P00", 
                  "021U-0N0P00", "021U-0N0N00", "021D-P0P000", "021D-N0P000", 
                  "021D-N0N000", "111U-0P00PP", "111U-0N00PP", "111U-0P00NP", 
                  "111U-0P00PN", "111U-0N00NP", "111U-0N00PN", "111U-0P00NN", 

schochastics

# original python code: https://fosstodon.org/@jasonbaumgartner@infosec.exchange/109376317839501128
# mapping: https://twitter-elastic.pushshift.io/twitter_user/_mapping
library(curl)
library(jsonlite)

ngram_search <- function(q="",size = 10){
  data <- list(query=list())
  data[["query"]][["bool"]] <- list()
  data[["query"]][["bool"]][["must"]] <- list()
  match <- list()

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")) |> 

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(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

#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

# 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){