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README.html
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<!DOCTYPE html>
<html xmlns="http://www.w3.org/1999/xhtml">
<head>
<meta charset="utf-8">
<meta http-equiv="Content-Type" content="text/html; charset=utf-8" />
<meta name="generator" content="pandoc" />
<meta name="viewport" content="width=device-width, initial-scale=1">
<link 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<!-- README.md is generated from README.Rmd. Please edit that file -->
<h1 id="reversegeocoder">reversegeocoder</h1>
<p><code>reversegeocoder</code> allows for a fast(er) solution to a common problem: you have a large set of spatial points and would like to find out in which polygon (e.g. country) each point is located. Although several R libraries implement spatial joins, these generally do not use a spatial index on the polygons and can thus be relatively show in this particular use case. <code>reversegeocoder</code> uses the fast R-tree index as implemented in Vladimir Agafonkin's <a href="https://github.com/mourner/rbush">rbush</a> package. This provides a significant performance improvement, especially for real-time or streaming scenarios where the set of polygons does not change but new points are coming in continuously. With reversegeocoder the spatial index will be re-used for every new lookup.</p>
<h2 id="installation">Installation</h2>
<p>You can install reversegeocoder from github with:</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="co"># install.packages("devtools")</span>
devtools<span class="op">::</span><span class="kw">install_github</span>(<span class="st">"atepoorthuis/reversegeocoder"</span>)</code></pre></div>
<h2 id="example">Example</h2>
<p>This is a basic example which shows you how to solve a common problem:</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">library</span>(sf)
<span class="kw">library</span>(geojsonio)
<span class="kw">library</span>(tidyverse)
<span class="kw">library</span>(rnaturalearth)
<span class="kw">library</span>(purrr)
<span class="kw">library</span>(microbenchmark)
<span class="kw">library</span>(reversegeocoder)
countries <-<span class="st"> </span><span class="kw">ne_countries</span>(<span class="dt">returnclass =</span> <span class="st">"sf"</span>) <span class="op">%>%</span><span class="st"> </span>
<span class="st"> </span><span class="kw">select</span>(adm0_a3)
countries_json <-<span class="st"> </span><span class="kw">geojson_list</span>(countries) <span class="op">%>%</span><span class="st"> </span><span class="kw">unclass</span>()
points_sf <-<span class="st"> </span><span class="kw">st_sample</span>(countries, <span class="dt">size =</span> <span class="kw">rep</span>(<span class="dv">50</span>, <span class="kw">nrow</span>(countries)))
<span class="kw">print</span>(<span class="kw">length</span>(points_sf))
<span class="co">#> [1] 8662</span>
points_mat <-<span class="st"> </span>points_sf <span class="op">%>%</span><span class="st"> </span>
<span class="st"> </span><span class="kw">st_sfc</span>() <span class="op">%>%</span><span class="st"> </span>
<span class="st"> </span><span class="kw">st_coordinates</span>()
ctx <-<span class="st"> </span><span class="kw">rg_load_polygons</span>(countries_json)
<span class="kw">print</span>(<span class="kw">microbenchmark</span>(
<span class="dt">rg_query =</span> <span class="kw">map_chr</span>(<span class="kw">split</span>(points_mat, <span class="dv">1</span><span class="op">:</span><span class="kw">nrow</span>(points_mat)), <span class="cf">function</span>(point) <span class="kw">rg_query</span>(ctx, point, <span class="st">"adm0_a3"</span>)),
<span class="dt">rg_batch_query =</span> <span class="kw">rg_batch_query</span>(ctx, points_mat, <span class="st">"adm0_a3"</span>),
<span class="dt">single_point_st_join =</span> <span class="kw">map_chr</span>(<span class="kw">split</span>(points_mat, <span class="dv">1</span><span class="op">:</span><span class="kw">nrow</span>(points_mat)), <span class="cf">function</span>(point) <span class="kw">st_join</span>(<span class="kw">st_sf</span>(<span class="kw">st_sfc</span>(<span class="kw">st_point</span>(point)), <span class="dt">crs =</span> <span class="dv">4326</span>), countries) <span class="op">%>%</span><span class="st"> </span><span class="kw">pull</span>(adm0_a3)),
<span class="dt">batch_st_join =</span> <span class="kw">st_join</span>(points_sf <span class="op">%>%</span><span class="st"> </span>st_sf, countries),
<span class="dt">times =</span> <span class="dv">1</span>)
)
<span class="co">#> Unit: milliseconds</span>
<span class="co">#> expr min lq mean median</span>
<span class="co">#> rg_query 490.28872 490.28872 490.28872 490.28872</span>
<span class="co">#> rg_batch_query 79.69626 79.69626 79.69626 79.69626</span>
<span class="co">#> single_point_st_join 65601.87603 65601.87603 65601.87603 65601.87603</span>
<span class="co">#> batch_st_join 265.67882 265.67882 265.67882 265.67882</span>
<span class="co">#> uq max neval</span>
<span class="co">#> 490.28872 490.28872 1</span>
<span class="co">#> 79.69626 79.69626 1</span>
<span class="co">#> 65601.87603 65601.87603 1</span>
<span class="co">#> 265.67882 265.67882 1</span></code></pre></div>
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