#' ---
#' title: "Parallel Computing with R"
#' subtitle: Write a parallel for loop
#' week: 11
#' type: Case Study
#' reading:
#'    - Parallel [Computing with the R Language in a Supercomputing Environment](https://link.springer.com/chapter/10.1007/978-3-642-13872-0_64)
#'    - CRAN Task View [High Performance and Parallel Computing with R](http://cran.r-project.org/web/views/HighPerformanceComputing.html)
#' tasks:
#'    - Reproject `world` dataset to a global equal area projection
#'    - Write a parallel `foreach()` loop to identify the a spatial relationships of each country
#'    - Set the output of the `foreach()` funtion to return a simple matrix
#'    - Confirm that your parallel loop returns the same answer as a typical "sequential" approach
#' ---
#' 
#' 
#' # Reading
#' 
#' 
#' 
#' # Tasks
#' 
#' 
#' ## Background
#' 
## ----cache=F, message=F,warning=FALSE, results='hide'--------------------
library(tidyverse)
library(spData)
library(sf)

## New Packages
library(foreach)
library(doParallel)
registerDoParallel(2)
getDoParWorkers() # check registered cores

#define working projection (EASE-Grid, https://nsidc.org/data/ease)
proj="+proj=cea +lon_0=0 +lat_ts=30 +x_0=0 +y_0=0 +ellps=WGS84 +towgs84=0,0,0,0,0,0,0 +units=m +no_defs"

#' 
#' <div class="well">
#' <button data-toggle="collapse" class="btn btn-primary btn-sm round" data-target="#demo1">Show Hints</button>
#' <div id="demo1" class="collapse">
#' 
#' ## Steps
#' 
#' Write an Rmd script that:
#' 
#' * Loads the `world` dataset in the `spData` package
#'    * Reproject the `world` dataset to the Equal-Area Scalable Earth Grid (EASE-Grid) ([EASE-Grid ](https://nsidc.org/data/ease)) using `st_transform()` and the proj4 projection in the code above
#' * Runs a parallel `foreach()` to loop over countries (`name_long`) that:
#'    * `filter` the world object to include only on country at a time.
#'    * use `st_is_within_distance` to find the distance from that country to all other countries in the `world` object within 100000m Set `sparse=F` to return a simple vector of `TRUE/FALSE` for countries within the distance.
#'    * set `.combine=rbind` to return a simple matrix.
#' * Confirm that you get the same answer without using foreach:
#'    * simply use `st_is_within_distance` with the transformed `world` object as both `x` and `y` object.
#'    * compare the results with `identical()`
#'    * if you are curious, you can also check the time difference with `system.time()`.
#'    
#'    
#' </div>
#' </div>
#' 
#' 
#' The first 10 rows/columns of the resulting matrix (e.g. `x_par[1:10,1:10]`) should look like this:
#' 
#' Note that in this example the sequential version typically runs faster than the parallel version due to the relatively small size of the dataset and computation needed.
#' 
#' 
#' ---
#' 
#' <div class="extraswell">
#' <button data-toggle="collapse" class="btn btn-link" data-target="#extras">
#' Extra time? Try this...
#' </button>
#' <div id="extras" class="collapse">
#' 
#' This approach could be used to identify which countries were 'close' to others.  For example, Identify which countries are within `r dist`m of Costa Rica:
#' 
#' 
#' And plot them:
#' 
#' 
#' </div>
#' </div>