RobertMyles

# functions for plotting garden of forking data plots

library(rethinking)

polar2screen <- function( dist, origin, theta ) {
  ## takes dist, angle and origin and returns x and y of destination point
  vx <- cos(theta) * dist;
  vy <- sin(theta) * dist;
  c( origin[1]+vx , origin[2]+vy );
}

RobertMyles

# 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)),

RobertMyles

Whether you're trying to give back to the open source community or collaborating on your own projects, knowing how to properly fork and generate pull requests is essential. Unfortunately, when I started going through the process of forking and issuing pull requests, I had some trouble figuring out the proper method for doing so and made quite a few mistakes along the way. I found a lot of the information on GitHub and around the internet to be rather piecemeal and incomplete - part of the process described here, another there, common hangups in a different place, and so on.

In an attempt to coallate this information for myself and others, this short tutorial is what I've found to be fairly standard procedure for creating a fork, doing your work, issuing a pull request, and merging that pull request back into the original project.

Creating a Fork

Just head over to the GitHub page and click the "Fork" button. It's just that simple. Once you've done that, you can use your favorite git client to clone your

RobertMyles

# packages
library("ggplot2")
# devtools::install_github("dgrtwo/gganimate")
library("gganimate")
library("dplyr")
# set distribution parameters for simulation
p = 0.5
N = 100
y_lim = 20
a_a = 2