{"id":3083,"date":"2014-10-07T22:34:24","date_gmt":"2014-10-08T03:34:24","guid":{"rendered":"http:\/\/rud.is\/b\/?p=3083"},"modified":"2018-03-07T16:44:24","modified_gmt":"2018-03-07T21:44:24","slug":"plot-me-like-a-hurricane-a-k-a-animating-historical-north-atlantic-basin-tropical-storm-tracks","status":"publish","type":"post","link":"https:\/\/rud.is\/b\/2014\/10\/07\/plot-me-like-a-hurricane-a-k-a-animating-historical-north-atlantic-basin-tropical-storm-tracks\/","title":{"rendered":"Plot Me Like a Hurricane (a.k.a. animating historical North Atlantic basin tropical storm tracks)"},"content":{"rendered":"<p>Markus Gessman (@MarkusGesmann) did a beautiful job [Visualising the seasonality of Atlantic windstorms](http:\/\/www.magesblog.com\/2014\/10\/visualising-seasonality-of-atlantic.html) using small multiples, which was inspired by both a [post](http:\/\/freakonometrics.hypotheses.org\/17113) by Arthur Charpentier (@freakonometrics) on using Markov spatial processes to &#8220;generate&#8221; hurricanes&mdash;which was [tweaked a bit](http:\/\/robertgrantstats.wordpress.com\/2014\/10\/01\/transparent-hurricane-paths-in-r\/) by Robert Grant (@robertstats)&mdash;and [Gaston Sanchez](https:\/\/github.com\/gastonstat)&#8217;s [Visualizing Hurricane Trajectories](http:\/\/rpubs.com\/gaston\/hurricanes) RPub.<\/p>\n<p>I have [some history](http:\/\/rud.is\/b\/2012\/10\/28\/watch-sandy-in-r-including-forecast-cone\/) with hurricane data and thought I&#8217;d jump on the bandwagon using the same data and making some stop-frame animations. I borrowed from previous work (hence starting with all the credits above) but have used `dplyr` idioms for data-frame filtering &#038; mutating and my own month\/year extraction code.<\/p>\n<p>The first animation accumulates storm tracks in-year and displays the names of the storms in a list down the left side while the second does a full historical accumulation of tracks. I changed the storm path gradient but kept most of the other formatting bits and made the plots suitable for 1080p output\/playback.<\/p>\n<p>Rather than go the `ffmpeg` route, I used [ImageMagick](http:\/\/www.imagemagick.org\/) since it makes equally quick work out of converting a bunch of `png` files to an `mp4` file. I made the animations go quickly, but they can be advanced forward\/back one frame at a time in any decent player.<\/p>\n<pre lang=\"rsplus\">library(maps)\r\nlibrary(data.table)\r\nlibrary(dplyr)\r\nlibrary(ggplot2)\r\nlibrary(grid)\r\nlibrary(RColorBrewer)\r\n\r\n# takes in a numeric vector and returns a sequence from low to high\r\nrangeseq <- function(x, by=1) {\r\n  rng <- range(x)\r\n  seq(from=rng[1], to=rng[2], by=by)\r\n}\r\n\r\n# etract the months (as a factor of full month names) from\r\n# a date+time \"x\" that can be converted to a POSIXct object,\r\nextractMonth <- function(x) {\r\n  months <- format(as.POSIXct(x), \"%m\")\r\n  factor(months, labels=month.name[rangeseq(as.numeric(months))])\r\n}\r\n\r\n# etract the years (as a factor of full 4-charater-digit years) from\r\n# a date+time \"x\" that can be converted to a POSIXct object,\r\nextractYear <- function(x) {\r\n  factor(as.numeric(format(as.POSIXct(x), \"%Y\")))\r\n}\r\n\r\n# get from: ftp:\/\/eclipse.ncdc.noaa.gov\/pub\/ibtracs\/v03r06\/all\/csv\/Allstorms.ibtracs_all.v03r06.csv.gz\r\nstorms_file <- \"data\/Allstorms.ibtracs_all.v03r06.csv\"\r\nstorms <-  fread(storms_file, skip=10, select=1:18)\r\n\r\ncol_names <- c(\"Season\", \"Num\", \"Basin\", \"Sub_basin\", \"Name\", \"ISO_time\", \"Nature\",\r\n             \"Latitude\", \"Longitude\", \"Wind.kt\", \"Pressure.mb\", \"Degrees_North\", \"Deegrees_East\")\r\nsetnames(storms, paste0(\"V\", c(2:12, 17, 18)), col_names)\r\n\r\n# use dplyr idioms to filter &#038; mutate the data\r\n\r\nstorms <- storms %>%\r\n  filter(Latitude > -999,                                  # remove missing data\r\n         Longitude > -999,\r\n         Wind.kt > 0,\r\n         !(Name %in% c(\"UNNAMED\", \"NONAME:UNNAMED\"))) %>%\r\n  mutate(Basin=gsub(\" \", \"\", Basin),                       # clean up fields\r\n         ID=paste(Name, Season, sep=\".\"),\r\n         Month=extractMonth(ISO_time),\r\n         Year=extractYear(ISO_time)) %>%\r\n  filter(Season >= 1989, Basin %in% \"NA\")                  # limit to North Atlantic basin\r\n\r\nseason_range <- paste(range(storms$Season), collapse=\" - \")\r\nknots_range <- range(storms$Wind.kt)\r\n\r\n# setup base plotting parameters (these won't change)\r\n\r\nbase <- ggplot()\r\nbase <- base + geom_polygon(data=map_data(\"world\"),\r\n                            aes(x=long, y=lat, group=group),\r\n                            fill=\"gray25\", colour=\"gray25\", size=0.2)\r\nbase <- base + scale_color_gradientn(colours=rev(brewer.pal(n=9, name=\"RdBu\")),\r\n                                     space=\"Lab\", limits=knots_range)\r\nbase <- base + xlim(-138, -20) + ylim(3, 55)\r\nbase <- base + coord_map()\r\nbase <- base + labs(x=NULL, y=NULL, title=NULL, colour = \"Wind (knots)\")\r\nbase <- base + theme_bw()\r\nbase <- base + theme(text=element_text(family=\"Arial\", face=\"plain\", size=rel(5)),\r\n                     panel.background = element_rect(fill = \"gray10\", colour = \"gray30\"),\r\n                     panel.margin = unit(c(0,0), \"lines\"),\r\n                     panel.grid.major = element_blank(),\r\n                     panel.grid.minor = element_blank(),\r\n                     plot.margin = unit(c(0,0,0,0), \"lines\"),\r\n                     axis.text.x = element_blank(),\r\n                     axis.text.y = element_blank(),\r\n                     axis.ticks = element_blank(),\r\n                     legend.position = c(0.25, 0.1),\r\n                     legend.background = element_rect(fill=\"gray10\", color=\"gray10\"),\r\n                     legend.text = element_text(color=\"white\", size=rel(2)),\r\n                     legend.title = element_text(color=\"white\", size=rel(5)),\r\n                     legend.direction = \"horizontal\")\r\n\r\n# loop over each year, producing plot files that accumulate tracks over each month\r\n\r\nfor (year in unique(storms$Year)) {\r\n\r\n  storm_ids <- unique(storms[storms$Year==year,]$ID)\r\n\r\n  for (i in 1:length(storm_ids)) {\r\n\r\n    storms_yr <- storms %>% filter(Year==year, ID %in% storm_ids[1:i])\r\n\r\n    # stuff takes a while, so it's good to have a progress message\r\n    message(sprintf(\"%s %s\", year, storm_ids[i]))\r\n\r\n    gg <- base\r\n    gg <- gg + geom_path(data=storms_yr,\r\n                         aes(x=Longitude, y=Latitude, group=ID, colour=Wind.kt),\r\n                         size=1.0, alpha=1\/4)\r\n    gg <- gg + geom_text(label=year, aes(x=-135, y=51), size=rel(6), color=\"white\", vjust=1)\r\n    gg <- gg + geom_text(label=paste(gsub(\".[[:digit:]]+$\", \"\", storm_ids[1:i]), collapse=\"\\n\"),\r\n                         aes(x=-135, y=49.5), size=rel(4.5), color=\"white\", vjust=1)\r\n\r\n    # change \"quartz\" to \"cairo\" if you're not on OS X\r\n\r\n    png(filename=sprintf(\"output\/%s%03d.png\", year, i),\r\n        width=1920, height=1080, type=\"quartz\", bg=\"gray25\")\r\n    print(gg)\r\n    dev.off()\r\n\r\n  }\r\n\r\n}\r\n\r\n# convert to mp4 animation - needs imagemagick\r\nsystem(\"convert -delay 8 output\/*png output\/hurr-1.mp4\")\r\n# unlink(\"output\/*png\") # do this after verifying convert works<\/pre>\n<p><iframe loading=\"lazy\" width=\"560\" height=\"315\" src=\"\/\/www.youtube.com\/embed\/JIgG7PyK0vI\" frameborder=\"0\" allowfullscreen><\/iframe><\/p>\n<pre lang=\"rsplus\"># take an alternate approach for accumulating the entire hurricane history\r\n# start with the base, but add to the ggplot object in a loop, which will\r\n# accumulate all the tracks.\r\n\r\ngg <- base\r\n\r\nfor (year in unique(storms$Year)) {\r\n\r\n  storm_ids <- unique(storms[storms$Year==year,]$ID)\r\n\r\n  for (i in 1:length(storm_ids)) {\r\n\r\n    storms_yr <- storms %>% filter(ID %in% storm_ids[i])\r\n\r\n    message(sprintf(\"%s %s\", year, storm_ids[i]))\r\n    gg <- gg + geom_path(data=storms_yr,\r\n                         aes(x=Longitude, y=Latitude, group=ID, colour=Wind.kt),\r\n                         size=1.0, alpha=1\/4)\r\n\r\n    png(filename=sprintf(\"output\/%s%03d.png\", year, i),\r\n        width=1920, height=1080, type=\"quartz\", bg=\"gray25\")\r\n    print(gg)\r\n    dev.off()\r\n\r\n  }\r\n\r\n}\r\n\r\nsystem(\"convert -delay 8 output\/*png output\/hurr-2.mp4\")\r\n# unlink(\"output\/*png\") # do this after verifying convert works<\/pre>\n<p><iframe loading=\"lazy\" width=\"560\" height=\"315\" src=\"\/\/www.youtube.com\/embed\/BjlDXnunLM4\" frameborder=\"0\" allowfullscreen><\/iframe><\/p>\n<p>Full code in [this gist](https:\/\/gist.github.com\/hrbrmstr\/23bf06784e898871dd61).<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Markus Gessman (@MarkusGesmann) did a beautiful job [Visualising the seasonality of Atlantic windstorms](http:\/\/www.magesblog.com\/2014\/10\/visualising-seasonality-of-atlantic.html) using small multiples, which was inspired by both a [post](http:\/\/freakonometrics.hypotheses.org\/17113) by Arthur Charpentier (@freakonometrics) on using Markov spatial processes to &#8220;generate&#8221; hurricanes&mdash;which was [tweaked a bit](http:\/\/robertgrantstats.wordpress.com\/2014\/10\/01\/transparent-hurricane-paths-in-r\/) by Robert Grant (@robertstats)&mdash;and [Gaston Sanchez](https:\/\/github.com\/gastonstat)&#8217;s [Visualizing Hurricane Trajectories](http:\/\/rpubs.com\/gaston\/hurricanes) RPub. I have [some history](http:\/\/rud.is\/b\/2012\/10\/28\/watch-sandy-in-r-including-forecast-cone\/) with hurricane [&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":false,"_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":[678,673,674,706,91,680],"tags":[810],"class_list":["post-3083","post","type-post","status-publish","format-standard","hentry","category-data-visualization","category-datavis-2","category-dataviz","category-maps","category-r","category-weather","tag-post"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.3 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Plot Me Like a Hurricane (a.k.a. animating historical North Atlantic basin tropical storm tracks) - 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\/10\/07\/plot-me-like-a-hurricane-a-k-a-animating-historical-north-atlantic-basin-tropical-storm-tracks\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Plot Me Like a Hurricane (a.k.a. animating historical North Atlantic basin tropical storm tracks) - rud.is\" \/>\n<meta property=\"og:description\" content=\"Markus Gessman (@MarkusGesmann) did a beautiful job [Visualising the seasonality of Atlantic windstorms](http:\/\/www.magesblog.com\/2014\/10\/visualising-seasonality-of-atlantic.html) using small multiples, which was inspired by both a [post](http:\/\/freakonometrics.hypotheses.org\/17113) by Arthur Charpentier (@freakonometrics) on using Markov spatial processes to &#8220;generate&#8221; hurricanes&mdash;which was [tweaked a bit](http:\/\/robertgrantstats.wordpress.com\/2014\/10\/01\/transparent-hurricane-paths-in-r\/) by Robert Grant (@robertstats)&mdash;and [Gaston Sanchez](https:\/\/github.com\/gastonstat)&#8217;s [Visualizing Hurricane Trajectories](http:\/\/rpubs.com\/gaston\/hurricanes) RPub. 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