The Wall Street Journal did a project piece [a while back](http:\/\/projects.wsj.com\/waste-lands\/) in the _”Waste Lands: America’s Forgotten Nuclear Legacy”_. They dug through [Department of Energy](http:\/\/www.lm.doe.gov\/default.aspx?id=2602) and [CDC](http:\/\/www.cdc.gov\/niosh\/ocas\/ocasawe.html) data to provide an overview of the lingering residue of this toxic time in America’s past (somehow, I have to believe the fracking phenomena of our modern era will end up doing far more damage in the long run). <\/p>\n
Being a somewhat interactive piece, I was able to tease out the data source behind it for this week’s challenge. I’m, once again, removing the obvious vis and re-creating a non-interactive version of the WSJ’s main map view (with some additional details).<\/p>\n
There’s definitely a story or two here, but I felt that the overall message fell a bit flat the way the WSJ folks told it. Can you find an angle or question that tells a tale in a more compelling fashion? I added some hints in the code snippet below (and in the repo) as to how you might find additional details for each toxic site (and said details are super-scrape-able with `rvest`). I also noticed some additional external data sets that could be brought in (but I’ll leave that detective work to our contestants).<\/p>\n
If you’re up to the task, fork [this week’s repo](https:\/\/github.com\/52vis\/2016-15), create a subdirectory for your submission and shoot a PR my way (notifying folks here in the comments about your submission is also encouraged).<\/p>\n
Entries accepted up until 2016-04-20 23:59:59 EDT.<\/p>\n
Hadley has volunteered a signed book and I think I’ll take him up on the offer for this week’s prize (unless you really want a copy of Data-Driven Security :-).<\/p>\n
One last note: I’ve secured `52vis.com` and will be getting that configured for next week’s contest. It’ll be a nice showcase site for all the submissions.<\/p>\n
library(albersusa) # devtools::install_github(\"hrbrmstr\/hrbrmisc\")\r\nlibrary(rgeos)\r\nlibrary(maptools)\r\nlibrary(ggplot2)\r\nlibrary(ggalt)\r\nlibrary(ggthemes)\r\nlibrary(jsonlite)\r\nlibrary(tidyr)\r\nlibrary(dplyr)\r\nlibrary(purrr)\r\n\r\n#' WSJ Waste Lands: http:\/\/projects.wsj.com\/waste-lands\/\r\n#' Data from: http:\/\/www.lm.doe.gov\/default.aspx?id=2602 &\r\n#' http:\/\/www.cdc.gov\/niosh\/ocas\/ocasawe.html\r\n\r\nsites <- fromJSON(\"sites.json\", flatten=TRUE)\r\n\r\n#' need to replace the 0-length data.frames with at least one row of `NA`s\r\n#' so we can safely `unnest()` them later\r\n\r\nsites$locations <- map(sites$locations, function(x) {\r\n if (nrow(x) == 0) {\r\n data_frame(latitude=NA, longitude=NA, postal_code=NA, name=NA, street_address=NA)\r\n } else {\r\n x\r\n }\r\n})\r\n\r\n#' we'll need this later\r\n\r\nsites$site.rating <- factor(sites$site.rating,\r\n levels=c(3:0),\r\n labels=c(\"Remote or no potential for radioactive contamination, based on criteria at the time of FUSRAP review.\",\r\n \"Referred to another agency or program, no authority to clean up under FUSRAP, or status unclear.\",\r\n \"Cleanup declared complete under FUSRAP.\",\r\n \"Cleanup in progress under the Formerly Utilized Sites Remedial Action Program (FUSRAP).\"))\r\n\r\n#' One teensy discrepancy:\r\n\r\nnrow(sites)\r\n\r\n#' ## [1] 517\r\n#'\r\n#' The stacked bars total on the WSJ site is 515.\r\n#' Further complication is that sites$locations is a list column with nested\r\n#' data.frames:\r\n\r\nsites <- unnest(sites)\r\n\r\nnrow(sites)\r\n\r\n#' ## [1] 549\r\n\r\nsum(complete.cases(sites[,c(\"longitude\", \"latitude\")]))\r\n\r\n#' ## [1] 352\r\n#'\r\n#' So, just mapping long\/lat is going to miss part of the story. But, I'm just\r\n#' providing a kick-start for folks, so I'll just map long\/lat :-)\r\n\r\nglimpse(sites)\r\n\r\n#' ## Observations: 549\r\n#' ## Variables: 11\r\n#' ## $ site.city (chr) \"Flint\", \"Albuquerque\", \"Buffalo\", \"Los...\r\n#' ## $ site.name (chr) \"AC Spark Plug, Dort Highway Plant\", \"A...\r\n#' ## $ site.rating (fctr) Remote or no potential for radioactive...\r\n#' ## $ site.state (chr) \"MI\", \"NM\", \"NY\", \"NM\", \"PA\", \"NY\", \"OH...\r\n#' ## $ site.state_ap (chr) \"Mich.\", \"N.M.\", \"N.Y.\", \"N.M.\", \"Pa.\",...\r\n#' ## $ site.slug (chr) \"1-ac-spark-plug-dort-highway-plant\", \"...\r\n#' ## $ latitude (dbl) 43.02938, NA, NA, 35.88883, 39.95295, 4...\r\n#' ## $ longitude (dbl) -83.65525, NA, NA, -106.30502, -75.5927...\r\n#' ## $ postal_code (chr) \"48506\", NA, NA, \"87544\", \"19382\", \"100...\r\n#' ## $ name (chr) \"\", NA, NA, \"\", \"\", \"\", \"Former Buildin...\r\n#' ## $ street_address (chr) \"1300 North Dort Highway\", NA, NA, \"Pue...\r\n\r\n#' Note that `site.slug` can be used with this URL:\r\n#' `http:\/\/projects.wsj.com\/waste-lands\/site\/SITE.SLUG.HERE\/` to get to\r\n#' detail pages on the WSJ site.\r\n\r\n#' I want to use my `albersusa` mutated U.S. shapefile for this (NOTE: I'm moving\r\n#' `albersus` into one of the rOpenSci pacakges soon vs publishing it standalone to CRAN)\r\n#' so I need to mutate the Alaska points (there are no Hawaii points).\r\n#' This step is *not necessary* unless you plan on displaying points on this\r\n#' mutated map. I also realized I need to provide a mutated projection translation\r\n#' function for AK & HI for the mutated Albers mapss.\r\n\r\ntmp <- data.frame(dplyr::select(filter(sites, site.state==\"AK\"), longitude, latitude))\r\ncoordinates(tmp) <- ~longitude+latitude\r\nproj4string(tmp) <- CRS(us_longlat_proj)\r\ntmp <- spTransform(tmp, CRS(us_laea_proj))\r\ntmp <- elide(tmp, rotate=-50)\r\ntmp <- elide(tmp, scale=max(apply(bbox(tmp), 1, diff)) \/ 2.3)\r\ntmp <- elide(tmp, shift=c(-2100000, -2500000))\r\nproj4string(tmp) <- CRS(us_laea_proj)\r\ntmp <- spTransform(tmp, us_longlat_proj)\r\ntmp <- as.data.frame(tmp)\r\n\r\nsites[sites$site.state==\"AK\",]$longitude <- tmp$x\r\nsites[sites$site.state==\"AK\",]$latitude <- tmp$y\r\n\r\n#' and now we plot the sites\r\n\r\nus_map <- fortify(usa_composite(), region=\"name\")\r\n\r\ngg <- ggplot()\r\ngg <- gg + geom_map(data=us_map, map=us_map,\r\n aes(x=long, y=lat, map_id=id),\r\n color=\"#2b2b2b\", size=0.15, fill=\"#e5e3df\")\r\ngg <- gg + geom_point(dat=sites, aes(x=longitude, y=latitude, fill=site.rating),\r\n shape=21, color=\"white\", stroke=1, alpha=1, size=3)\r\ngg <- gg + scale_fill_manual(name=\"\", values=c(\"#00a0b0\", \"#edc951\", \"#6a4a3c\", \"#eb6841\"))\r\ngg <- gg + coord_proj(us_laea_proj)\r\ngg <- gg + guides(fill=guide_legend(override.aes=list(alpha=1, stroke=0.2, color=\"#2b2b2b\", size=4)))\r\ngg <- gg + labs(title=\"Waste Lands: America's Forgotten Nuclear Legacy\",\r\n caption=\"Data from the WSJ\")\r\ngg <- gg + theme_map()\r\ngg <- gg + theme(legend.position=\"bottom\")\r\ngg <- gg + theme(legend.direction=\"vertical\")\r\ngg <- gg + theme(legend.key=element_blank())\r\ngg <- gg + theme(plot.title=element_text(size=18, face=\"bold\", hjust=0.5))\r\ngg<\/pre>\n![\"waste\"](\"https:\/\/i0.wp.com\/rud.is\/b\/wp-content\/uploads\/2016\/04\/waste.png?ssl=1\")
<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"