{"id":1890,"date":"2013-01-12T22:35:23","date_gmt":"2013-01-13T03:35:23","guid":{"rendered":"http:\/\/rud.is\/b\/?p=1890"},"modified":"2018-03-10T07:53:18","modified_gmt":"2018-03-10T12:53:18","slug":"how-low-can-it-the-mississsippi-river-go","status":"publish","type":"post","link":"https:\/\/rud.is\/b\/2013\/01\/12\/how-low-can-it-the-mississsippi-river-go\/","title":{"rendered":"How Low Can It [The Mississsippi River] Go?"},"content":{"rendered":"

Good Stats, Bad Stats has a really good critique<\/a> of this post that you should read after this (so you know how to avoid the mistakes I made :-)<\/p><\/blockquote>\n

I’ve heard quite a bit about the current problems<\/a> with the Mississippi River<\/a> and wanted to see for myself (with data) just how bad it is.<\/p>\n

St Louis seems to be quite indicative of the severity of the situation, so I pulled the USGS “stream” records<\/a> for it and also the historic low water level records<\/a> for it and put them both into R for some analysis & graphing:<\/p>\n

\"mississippi\"
click for larger version<\/a><\/center><\/p>\n

\"low\"
click for larger version<\/a><\/center><\/p>\n

They are both in PDF format as well [1<\/a>] [2<\/a>]<\/p>\n

As you can see, there have only been four other (recorded) times when the river was this low and it has just come off of multi-year severely high points with a fairly rapid trend downwards. I’m sure the residents along the Mississippi do not need this data to tell them just how bad things are, but it has helped me understand just how bad the situation is.<\/p>\n

For those interested, the R code uses ggplot2<\/code> for time-series charting along with a custom theme and various annotation aids that might be useful to others learning their way around the grammar of graphics in R (so the code is below).<\/p>\n

#\n# stream.R - graph the recent history of the Mississippi River at St Louis\n#\n\nlibrary(ggplot2)\nrequire(scales)\nlibrary(ggthemes)\n\n# read in st louis, mo USGS stream data\n\ndf.raw = read.csv(\"~\/Desktop\/stream.txt\")\n\n# need date\/time as an R Date for ggplot2 time series plot\n\ndf = data.frame(as.POSIXct(df.raw$datetime,format = \"%Y-%m-%d %H:%M\"),df.raw$gauge)\ndf = df[!is.na(df.raw$gauge),]\n\n# pretty up the column names\n\ncolnames(df) = c(\"datetime\",\"gauge\")\n\n# we uses these a few times\n\nmaxdate = max(df$datetime)\nmindate = min(df$datetime)\nmingauge = min(df$gauge)\n\n# do the plot\n\nst1 = ggplot(df, aes(datetime, gauge)) +\n  \n  theme_economist() + # pretty theme\n\n  # background bands for various water level stages\n  \n  geom_rect(data=df,aes(xmin=mindate, xmax=maxdate, ymin=28, ymax=30), alpha=1, fill=\"khaki1\") +\n  geom_rect(data=df,aes(xmin=mindate, xmax=maxdate, ymin=30, ymax=35), alpha=1, fill=\"gold1\") +\n  geom_rect(data=df,aes(xmin=mindate, xmax=maxdate, ymin=35, ymax=40), alpha=1, fill=\"firebrick\") +\n  \n  geom_rect(data=df,aes(xmin=mindate, xmax=maxdate, ymin=mingauge, ymax=0), alpha=1, fill=\"white\") +\n\n  # labels for the bands\n  \n  geom_text(data=data.frame(x=maxdate,y=29), aes(x=x,y=y,label=\"Action Stage \"), size=3, hjust=1) +\n  geom_text(data=data.frame(x=maxdate,y=32), aes(x=x,y=y,label=\"Flood Stage \"), size=3, hjust=1) +\n  geom_text(data=data.frame(x=maxdate,y=37), aes(x=x,y=y,label=\"Moderate Flood Stage \"), size=3, hjust=1, colour=\"white\") +\n  \n  geom_text(data=data.frame(x=mindate,y=mingauge\/2), aes(x=x,y=y,label=\" Below gauge\"), size=3, hjust=0, colour=\"black\") +\n\n  # the raw stream data\n  \n  geom_line(size=0.15) +\n\n  # change the x label to just years\n  \n  scale_x_datetime(breaks=date_breaks(\"years\"), labels=date_format(\"%Y\")) + \n\n  # labels\n\n  labs(title=\"Mississipi River Depth at St Louis, MO\", x=\"\", y=\"Gauge Height (in.)\") +\n\n  # add a smoothed trend line\n  \n  geom_smooth() +\n\n  # remove the legend\n  \n  theme(legend.position = \"none\")\n\n# make a PDF\n\nggsave(\"~\/Desktop\/mississippi.pdf\",st1,w=8,h=5,dpi=150)<\/pre>\n
#\n# low.R - graph the historic low records for the Mississippi River at St Louis\n#\n\nlibrary(ggplot2)\nrequire(scales)\nlibrary(ggthemes)\n\n# read in historic low records\n\ndf.raw = read.csv(\"~\/Desktop\/low.csv\")\n\n# need date\/time as an R Date for ggplot2 time series plot\n\ndf = data.frame(as.POSIXct(df.raw$date,format = \"%m\/%d\/%Y\"),df.raw$gauge)\ncolnames(df) = c(\"date\",\"gauge\")\n\n# pretty up the column names\n\nmaxdate = max(df$date)\nmindate = min(df$date)\n\n# do the plot\n\nlow1 = ggplot(data=df,aes(date,gauge)) + \n  geom_rect(data=df,aes(xmin=mindate, xmax=maxdate, ymin=-4.55, ymax=-4.55), alpha=1, color=\"firebrick\",fill=\"firebrick\") +\n  geom_text(data=data.frame(x=mindate,y=-4.75), aes(x=x,y=y,label=\"January 2013 :: -4.55in\"), size=3, hjust=0, colour=\"firebrick\") +\n  geom_line(size=0.15) + \n  labs(title=\"Historic Low Water Depths at St Louis, MO\", x=\"\", y=\"Gauge Height (in.)\") +\n  theme_economist()\n\nggsave(\"~\/Desktop\/low.pdf\",low1,w=8,h=5,dpi=150)<\/pre>\n","protected":false},"excerpt":{"rendered":"
Good Stats, Bad Stats has a really good critique of this post that you should read after this (so you know how to avoid the mistakes I made :-) I’ve heard quite a bit about the current problems with the Mississippi River and wanted to see for myself (with data) just how bad it is. […]<\/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":[24,673,674],"tags":[676,675],"class_list":["post-1890","post","type-post","status-publish","format-standard","hentry","category-charts-graphs","category-datavis-2","category-dataviz","tag-drought","tag-mississippi"],"yoast_head":"\nHow Low Can It [The Mississsippi River] Go? - 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\/2013\/01\/12\/how-low-can-it-the-mississsippi-river-go\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"How Low Can It [The Mississsippi River] Go? - rud.is\" \/>\n<meta property=\"og:description\" content=\"Good Stats, Bad Stats has a really good critique of this post that you should read after this (so you know how to avoid the mistakes I made :-) I’ve heard quite a bit about the current problems with the Mississippi River and wanted to see for myself (with data) just how bad it is. 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What is this \"t-digest\"? Fundamentally, it is a probabilistic data structure for estimating any percentile of distributed\/streaming data. Ted explains it quite elegantly in this short video: Said video has a full transcript as well. T-digests have been baked\u2026","rel":"","context":"In "R"","block_context":{"text":"R","link":"https:\/\/rud.is\/b\/category\/r\/"},"img":{"alt_text":"","src":"","width":0,"height":0},"classes":[]},{"id":3665,"url":"https:\/\/rud.is\/b\/2015\/09\/08\/roll-your-own-stats-and-geoms-in-ggplot2-part-1-splines\/","url_meta":{"origin":1890,"position":2},"title":"Roll Your Own Stats and Geoms in ggplot2 (Part 1: Splines!)","author":"hrbrmstr","date":"2015-09-08","format":false,"excerpt":"A huge change is coming to ggplot2 and you can get a preview of it over at Hadley's github repo. I've been keenly interested in this as I will be fixing, finishing & porting coord_proj to it once it's done. Hadley & Winston have re-built ggplot2 with an entirely new\u2026","rel":"","context":"In "Data Visualization"","block_context":{"text":"Data Visualization","link":"https:\/\/rud.is\/b\/category\/data-visualization\/"},"img":{"alt_text":"","src":"","width":0,"height":0},"classes":[]},{"id":5123,"url":"https:\/\/rud.is\/b\/2017\/03\/03\/candy-coated-confidence-intervals\/","url_meta":{"origin":1890,"position":3},"title":"Candy Coated Confidence Intervals","author":"hrbrmstr","date":"2017-03-03","format":false,"excerpt":"@mrshrbrmstr hinted that she would like this post by @RickWicklin translated into R for her stats class. She's quite capable of cranking out the translation of the core component of that post \u2014 a call to chisq.test \u2014 but she wanted to show the entire post (in R) and really\u2026","rel":"","context":"In "ggplot"","block_context":{"text":"ggplot","link":"https:\/\/rud.is\/b\/category\/ggplot\/"},"img":{"alt_text":"","src":"https:\/\/i0.wp.com\/rud.is\/b\/wp-content\/uploads\/2017\/03\/cleveland-1.png?fit=1140%2C1200&ssl=1&resize=350%2C200","width":350,"height":200,"srcset":"https:\/\/i0.wp.com\/rud.is\/b\/wp-content\/uploads\/2017\/03\/cleveland-1.png?fit=1140%2C1200&ssl=1&resize=350%2C200 1x, https:\/\/i0.wp.com\/rud.is\/b\/wp-content\/uploads\/2017\/03\/cleveland-1.png?fit=1140%2C1200&ssl=1&resize=525%2C300 1.5x, https:\/\/i0.wp.com\/rud.is\/b\/wp-content\/uploads\/2017\/03\/cleveland-1.png?fit=1140%2C1200&ssl=1&resize=700%2C400 2x, https:\/\/i0.wp.com\/rud.is\/b\/wp-content\/uploads\/2017\/03\/cleveland-1.png?fit=1140%2C1200&ssl=1&resize=1050%2C600 3x"},"classes":[]},{"id":11859,"url":"https:\/\/rud.is\/b\/2019\/02\/03\/r-package-update-urlscan\/","url_meta":{"origin":1890,"position":4},"title":"R Package Update: urlscan","author":"hrbrmstr","date":"2019-02-03","format":false,"excerpt":"The urlscan? package (an interface to the urlscan.io API) is now at version 0.2.0 and supports urlscan.io's authentication requirement when submitting a link for analysis. The service is handy if you want to learn about the details \u2014 all the gory technical details \u2014 for a website. For instance, say\u2026","rel":"","context":"In "R"","block_context":{"text":"R","link":"https:\/\/rud.is\/b\/category\/r\/"},"img":{"alt_text":"","src":"","width":0,"height":0},"classes":[]},{"id":125,"url":"https:\/\/rud.is\/b\/2011\/02\/14\/metricon-critical-consumption-of-infosec-statistics\/","url_meta":{"origin":1890,"position":5},"title":"Metricon: Critical Consumption Of Infosec Statistics","author":"hrbrmstr","date":"2011-02-14","format":false,"excerpt":"Speaker: Chris Eng \/ Veracode Every major infosec company publishes quarterly\/yearly summary reports. Some based on survey, some based on real captured data. Recognizing the Narrative Every fancy looking infosec metrics report is a marketing vehicle; each has different perspectives; no consistency, but you can figure out the framing by\u2026","rel":"","context":"In "Information Security"","block_context":{"text":"Information Security","link":"https:\/\/rud.is\/b\/category\/information-security\/"},"img":{"alt_text":"","src":"","width":0,"height":0},"classes":[]}],"jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/rud.is\/b\/wp-json\/wp\/v2\/posts\/1890","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/rud.is\/b\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/rud.is\/b\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/rud.is\/b\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/rud.is\/b\/wp-json\/wp\/v2\/comments?post=1890"}],"version-history":[{"count":0,"href":"https:\/\/rud.is\/b\/wp-json\/wp\/v2\/posts\/1890\/revisions"}],"wp:attachment":[{"href":"https:\/\/rud.is\/b\/wp-json\/wp\/v2\/media?parent=1890"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/rud.is\/b\/wp-json\/wp\/v2\/categories?post=1890"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/rud.is\/b\/wp-json\/wp\/v2\/tags?post=1890"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}