#' ---

#' 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>