d-qn

library(tidyverse)
library(sf)
library(osmdata) # to get openstreetmap geo data

# settings
bb <- getbb('Lausanne, Switzerland') # define the bbox, will be used to fetch OSM data within that box
dest_proj <- 2056

# OSM overpass query
q <- opq(bbox = bb)

wviechtb

rm(list=ls())

setwd("/home/wviechtb/misc/wallpapers/bivariate_normal")

library(mvtnorm)
library(ellipse)

### specify the correlation for the bivariate normal distribution
rho <- 0.4

richarddmorey

---
title: "Generated data demo"
output: 
  flexdashboard::flex_dashboard:
    orientation: rows
    vertical_layout: scroll
runtime: shiny
---

```{r setup, include=FALSE}

daattali

# Get a person's name, location, summary, # of connections, and skills & endorsements from LinkedIn

# URL of the LinkedIn page 
user_url <- "https://www.linkedin.com/in/daattali"

# since the information isn't available without being logged in, the web
# scraper needs to log in. Provide your LinkedIn user/pw here (this isn't stored
# anywhere as you can see, it's just used to log in during the scrape session)
username <- "yourusername"
password <- "yourpassword"

graydon

# extracted from http//www.naturalearthdata.com/download/110m/cultural/ne_110m_admin_0_countries.zip
# under public domain terms

country_bounding_boxes = {
    'AF': ('Afghanistan', (60.5284298033, 29.318572496, 75.1580277851, 38.4862816432)),
    'AO': ('Angola', (11.6400960629, -17.9306364885, 24.0799052263, -4.43802336998)),
    'AL': ('Albania', (19.3044861183, 39.624997667, 21.0200403175, 42.6882473822)),
    'AE': ('United Arab Emirates', (51.5795186705, 22.4969475367, 56.3968473651, 26.055464179)),
    'AR': ('Argentina', (-73.4154357571, -55.25, -53.628348965, -21.8323104794)),
    'AM': ('Armenia', (43.5827458026, 38.7412014837, 46.5057198423, 41.2481285671)),