{"id":3117,"date":"2014-11-16T17:35:44","date_gmt":"2014-11-16T22:35:44","guid":{"rendered":"http:\/\/rud.is\/b\/?p=3117"},"modified":"2020-04-03T10:31:13","modified_gmt":"2020-04-03T15:31:13","slug":"moving-the-earth-well-alaska-hawaii-with-r","status":"publish","type":"post","link":"https:\/\/rud.is\/b\/2014\/11\/16\/moving-the-earth-well-alaska-hawaii-with-r\/","title":{"rendered":"Moving The Earth (well, Alaska &#038; Hawaii) With R"},"content":{"rendered":"<p>In a <a href=\"https:\/\/rud.is\/b\/2014\/09\/26\/overcoming-d3-cartographic-envy-with-r-ggplot\/\">previous post<\/a> we looked at how to use D3 TopoJSON files with R and make some very D3-esque maps. I mentioned that one thing missing was moving Alaska &amp; Hawaii a bit closer to the continental United States and this post shows you how to do that.<\/p>\n<p>The D3 folks have it easy. They just use the built in <a href=\"https:\/\/github.com\/d3\/d3\/wiki#albersUsa\">modified Albers composite projection<\/a>. We R folk have to roll up our sleeves a bit (but not much) to do the same. Thankfully, we can do most of the work with the <span class=\"removed_link\" title=\"http:\/\/www.inside-r.org\/packages\/cran\/maptools\/docs\/elide\">elide<\/span> (&#8220;ih lied&#8221;) function from the <code>maptools<\/code> package.<\/p>\n<p>We&#8217;ll start with some package imports:<\/p>\n<pre><code class=\"language-r\">library(maptools)\nlibrary(mapproj)\nlibrary(rgeos)\nlibrary(rgdal)\nlibrary(RColorBrewer)\nlibrary(gthemes)\nlibrary(ggplot2)\n<\/code><\/pre>\n<p>I&#8217;m using a GeoJSON file that I made from the <a href=\"https:\/\/www.census.gov\/geo\/maps-data\/data\/cbf\/cbf_counties.html\">2013 US Census shapefile<\/a>. I prefer GeoJSON mostly due to it being single file and the easy conversion to TopoJSON if I ever need to use the same map in a D3 context (I work with information security data most of the time, so I rarely have to use maps at all for the day job). I simplified the polygons a bit (passing <code>-simplify 0.01<\/code> to <code>ogr2ogr<\/code>) to reduce processing time.<\/p>\n<p>We read in that file and then transform the projection to Albers equal area and join the polygon ids to the shapefile data frame:<\/p>\n<pre><code class=\"language-r\"># https:\/\/www.census.gov\/geo\/maps-data\/data\/cbf\/cbf_counties.html\n# read U.S. counties moderately-simplified GeoJSON file\nus &lt;- readOGR(dsn=\"data\/us.geojson\", layer=\"OGRGeoJSON\")\n\n# convert it to Albers equal area\nus_aea &lt;- spTransform(us, CRS(\"+proj=laea +lat_0=45 +lon_0=-100 +x_0=0 +y_0=0 +a=6370997 +b=6370997 +units=m +no_defs\"))\nus_aea@data$id &lt;- rownames(us_aea@data)\n<\/code><\/pre>\n<p>Now, to move Alaska &amp; Hawaii, we have to:<\/p>\n<ul>\n<li>extract them from the main shapefile data frame<\/li>\n<li>perform rotation, scaling and transposing on them<\/li>\n<li>ensure they have the right projection set<\/li>\n<li>merge them back into the original spatial data frame<\/li>\n<\/ul>\n<p>The <code>elide<\/code> function has parameters for all the direct shape munging, so we&#8217;ll just do that for both states. I took a peek at the D3 source code for the Albers projection to get a feel for the parameters. You can tweak those if you want them in other positions or feel the urge to change the Alaska rotation angle.<\/p>\n<pre><code class=\"language-r\"># extract, then rotate, shrink &amp; move alaska (and reset projection)\n# need to use state IDs via # https:\/\/www.census.gov\/geo\/reference\/ansi_statetables.html\nalaska &lt;- us_aea[us_aea$STATEFP==\"02\",]\nalaska &lt;- elide(alaska, rotate=-50)\nalaska &lt;- elide(alaska, scale=max(apply(bbox(alaska), 1, diff)) \/ 2.3)\nalaska &lt;- elide(alaska, shift=c(-2100000, -2500000))\nproj4string(alaska) &lt;- proj4string(us_aea)\n\n# extract, then rotate &amp; shift hawaii\nhawaii &lt;- us_aea[us_aea$STATEFP==\"15\",]\nhawaii &lt;- elide(hawaii, rotate=-35)\nhawaii &lt;- elide(hawaii, shift=c(5400000, -1400000))\nproj4string(hawaii) &lt;- proj4string(us_aea)\n\n# remove old states and put new ones back in; note the different order\n# we're also removing puerto rico in this example but you can move it\n# between texas and florida via similar methods to the ones we just used\nus_aea &lt;- us_aea[!us_aea$STATEFP %in% c(\"02\", \"15\", \"72\"),]\nus_aea &lt;- rbind(us_aea, alaska, hawaii)\n<\/code><\/pre>\n<p>Rather than just show the resultant plain county map, we&#8217;ll add some data to it. The first example uses US drought data (from November 11th, 2014). Drought conditions are pretty severe in some states, but we&#8217;ll just show areas that have any type of drought (levels D0-D4). The color ramp shows the % of drought coverage in each county (you&#8217;ll need a browser that can display SVGs to see the maps):<\/p>\n<pre><code class=\"language-r\"># get some data to show...perhaps drought data via:\n# http:\/\/droughtmonitor.unl.edu\/MapsAndData\/GISData.aspx\ndroughts &lt;- read.csv(\"data\/dm_export_county_20141111.csv\")\ndroughts$id &lt;- sprintf(\"%05d\", as.numeric(as.character(droughts$FIPS)))\ndroughts$total &lt;- with(droughts, (D0+D1+D2+D3+D4)\/5)\n\n# get ready for ggplotting it... this takes a cpl seconds\nmap &lt;- fortify(us_aea, region=\"GEOID\")\n\n# plot it\ngg &lt;- ggplot()\ngg &lt;- gg + geom_map(data=map, map=map,\n                    aes(x=long, y=lat, map_id=id, group=group),\n                    fill=\"#ffffff\", color=\"#0e0e0e\", size=0.15)\ngg &lt;- gg + geom_map(data=droughts, map=map, aes(map_id=id, fill=total),\n                    color=\"#0e0e0e\", size=0.15)\ngg &lt;- gg + scale_fill_gradientn(colours=c(\"#ffffff\", brewer.pal(n=9, name=\"YlOrRd\")),\n                                na.value=\"#ffffff\", name=\"% of County\")\ngg &lt;- gg + labs(title=\"U.S. Areas of Drought (all levels, % county coverage)\")\ngg &lt;- gg + coord_equal()\ngg &lt;- gg + theme_map()\ngg &lt;- gg + theme(legend.position=\"right\")\ngg &lt;- gg + theme(plot.title=element_text(size=16))\ngg\n<\/code><\/pre>\n<p><center><a href=\"https:\/\/rud.is\/dl\/drought.svg\" target=\"_blank\" rel=\"noopener noreferrer\"><img decoding=\"async\" src=\"https:\/\/rud.is\/dl\/drought.svg\" alt=\"img\" \/><\/a><\/center><\/p>\n<p>While that shows Alaska &amp; Hawaii in D3-Albers-style, it would be more convincing if we actually used the FIPS county codes on Alaska &amp; Hawaii, so we&#8217;ll riff off the previous post and make a county land-mass area choropleth (since we have the land mass area data available in the GeoJSON file):<\/p>\n<pre><code class=\"language-r\">gg &lt;- ggplot()\ngg &lt;- gg + geom_map(data=map, map=map,\n                    aes(x=long, y=lat, map_id=id, group=group),\n                    fill=\"white\", color=\"white\", size=0.15)\ngg &lt;- gg + geom_map(data=us_aea@data, map=map, aes(map_id=GEOID, fill=log(ALAND)),\n                    color=\"white\", size=0.15)\ngg &lt;- gg + scale_fill_gradientn(colours=c(brewer.pal(n=9, name=\"YlGn\")),\n                                na.value=\"#ffffff\", name=\"County Land\\nMass Area (log)\")\ngg &lt;- gg + labs(title=\"U.S. County Area Choropleth (log scale)\")\ngg &lt;- gg + coord_equal()\ngg &lt;- gg + theme_map()\ngg &lt;- gg + theme(legend.position=\"right\")\ngg &lt;- gg + theme(plot.title=element_text(size=16))\ngg\n<\/code><\/pre>\n<p><center><a href=\"https:\/\/rud.is\/dl\/area.svg\" target=\"_blank\" rel=\"noopener noreferrer\"><img decoding=\"async\" src=\"https:\/\/rud.is\/dl\/area.svg\" alt=\"img\" \/><\/a><\/center><\/p>\n<p>Now, you have one less reason to be envious of the D3 cool kids!<\/p>\n<p>The code &amp; shapefiles are available <a href=\"https:\/\/github.com\/hrbrmstr\/rd3albers\">on github<\/a>.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>In a previous post we looked at how to use D3 TopoJSON files with R and make some very D3-esque maps. I mentioned that one thing missing was moving Alaska &amp; Hawaii a bit closer to the continental United States and this post shows you how to do that. The D3 folks have it easy. [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"jetpack_post_was_ever_published":false,"_jetpack_newsletter_access":"","_jetpack_dont_email_post_to_subs":true,"_jetpack_newsletter_tier_id":0,"_jetpack_memberships_contains_paywalled_content":false,"_jetpack_memberships_contains_paid_content":false,"activitypub_content_warning":"","activitypub_content_visibility":"","activitypub_max_image_attachments":3,"activitypub_interaction_policy_quote":"anyone","activitypub_status":"","footnotes":""},"categories":[666,673,674,706,91],"tags":[810],"class_list":["post-3117","post","type-post","status-publish","format-standard","hentry","category-d3","category-datavis-2","category-dataviz","category-maps","category-r","tag-post"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.2 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Moving The Earth (well, Alaska &amp; Hawaii) With R - rud.is<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/rud.is\/b\/2014\/11\/16\/moving-the-earth-well-alaska-hawaii-with-r\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Moving The Earth (well, Alaska &amp; Hawaii) With R - rud.is\" \/>\n<meta property=\"og:description\" content=\"In a previous post we looked at how to use D3 TopoJSON files with R and make some very D3-esque maps. 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