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

# original python code: https://fosstodon.org/@[email protected]/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(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

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

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


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

schochastics

library(dplyr)
library(data.table)
n <- 100

df <- data.frame(id=LETTERS[1:10],meta=letters[1:10])
other_dfs <- lapply(1:n,\(x) data.frame(id=LETTERS[1:10],values=rnorm(1:10)))

f <- function(n){
  for(i in 1:n){