{"id":3215,"date":"2015-01-10T21:12:26","date_gmt":"2015-01-11T02:12:26","guid":{"rendered":"http:\/\/rud.is\/b\/?p=3215"},"modified":"2018-03-07T16:44:05","modified_gmt":"2018-03-07T21:44:05","slug":"new-r-package-cdcfluview-retrieve-flu-data-from-cdcs-fluview-portal","status":"publish","type":"post","link":"https:\/\/rud.is\/b\/2015\/01\/10\/new-r-package-cdcfluview-retrieve-flu-data-from-cdcs-fluview-portal\/","title":{"rendered":"New R Package: cdcfluview \u2014 Retrieve Flu Data from CDC&#8217;s FluView Portal"},"content":{"rendered":"<p>**NOTE** If there&#8217;s a particular data set from http:\/\/www.cdc.gov\/flu\/weekly\/fluviewinteractive.htm that you want and that isn&#8217;t in the pacakge, please file it as an issue and be as specific as you can (screen shot if possible).<\/p>\n<p>&#8212;&#8211;<\/p>\n<p>Towards the end of 2014 I had been tinkering with flu data from the [CDC&#8217;s FluView portal](http:\/\/gis.cdc.gov\/grasp\/fluview\/fluportaldashboard.html) since flu reports began to look like this season was going to go the way of 2009. <\/p>\n<p>While you can track the flu over at [The Washington Post](http:\/\/www.washingtonpost.com\/graphics\/health\/flu-tracker\/), I like to work with data on my own. However the CDC&#8217;s portal is Flash-driven and there was no obvious way to get the data files programmatically. This is unfortunate, since there are weekly updates to the data set.<\/p>\n<p>As an information security professional, one of the tools in my arsenal is [Burp Proxy](http:\/\/portswigger.net\/burp\/proxy.html), which is an application that&#8212;amongst other things&#8212;lets you configure a local proxy server for your browser and inspect all web requests. By using this tool, I was able to discern that the Flash portal calls out to `http:\/\/gis.cdc.gov\/grasp\/fluview\/FluViewPhase2CustomDownload.ashx` with custom `POST` form parameters (that I also mapped out) to make the data sets it delivers back to the user.<\/p>\n<p>With that information in hand, I whipped together a small R package: [cdcfluview](https:\/\/github.com\/hrbrmstr\/cdcfluview) to interface with the same server the FluView Portal does. It has a singular function &#8211; `get_flu_data` that lets you choose between different region\/sub-region breakdowns and also whether you want data from WHO, ILINet (or both). It also lets you pick which years you want data for.<\/p>\n<p>One reason I wanted to work with the data was to see just how this season differs from previous ones. The view I&#8217;ll leave on the blog this time&#8212;mostly as an example of how to use the package&#8212;is a faceted chart, by CDC region and CDC week showing this season (in red) as it relates to previous ones. <\/p>\n<pre lang=\"rsplus\"># devtools::install_github(\"hrbrmstr\/cdcfluview\") # if necessary\r\nlibrary(cdcfluview)\r\nlibrary(magrittr)\r\nlibrary(dplyr)\r\nlibrary(ggplot2)\r\n\r\ndat <- get_flu_data(region=\"hhs\", \r\n                    sub_region=1:10, \r\n                    data_source=\"ilinet\", \r\n                    years=2000:2014)\r\n\r\ndat %<>%\r\n  mutate(REGION=factor(REGION,\r\n                       levels=unique(REGION),\r\n                       labels=c(\"Boston\", \"New York\",\r\n                                \"Philadelphia\", \"Atlanta\",\r\n                                \"Chicago\", \"Dallas\",\r\n                                \"Kansas City\", \"Denver\",\r\n                                \"San Francisco\", \"Seattle\"),\r\n                       ordered=TRUE)) %>%\r\n  mutate(season_week=ifelse(WEEK>=40, WEEK-40, WEEK),\r\n         season=ifelse(WEEK<40,\r\n                       sprintf(\"%d-%d\", YEAR-1, YEAR),\r\n                       sprintf(\"%d-%d\", YEAR, YEAR+1)))\r\n\r\nprev_years <- dat %>% filter(season != \"2014-2015\")\r\ncurr_year <- dat %>% filter(season == \"2014-2015\")\r\n\r\ncurr_week <- tail(dat, 1)$season_week\r\n\r\ngg <- ggplot()\r\ngg <- gg + geom_point(data=prev_years,\r\n                      aes(x=season_week, y=X..WEIGHTED.ILI, group=season),\r\n                      color=\"#969696\", size=1, alpa=0.25)\r\ngg <- gg + geom_point(data=curr_year,\r\n                      aes(x=season_week, y=X..WEIGHTED.ILI, group=season),\r\n                      color=\"red\", size=1.25, alpha=1)\r\ngg <- gg + geom_line(data=curr_year, \r\n                     aes(x=season_week, y=X..WEIGHTED.ILI, group=season),\r\n                     size=1.25, color=\"#d7301f\")\r\ngg <- gg + geom_vline(xintercept=curr_week, color=\"#d7301f\", size=0.5, linetype=\"dashed\", alpha=0.5)\r\ngg <- gg + facet_wrap(~REGION, ncol=3)\r\ngg <- gg + labs(x=NULL, y=\"Weighted ILI Index\", \r\n                title=\"ILINet - 1999-2015 year weighted flu index history by CDC region\\nWeek Ending Jan 3, 2015 (Red == current season)\\n\")\r\ngg <- gg + theme_bw()\r\ngg <- gg + theme(panel.grid=element_blank())\r\ngg <- gg + theme(strip.background=element_blank())\r\ngg <- gg + theme(axis.ticks.x=element_blank())\r\ngg <- gg + theme(axis.text.x=element_blank())\r\ngg<\/pre>\n<p><img data-recalc-dims=\"1\" loading=\"lazy\" decoding=\"async\" data-attachment-id=\"3216\" data-permalink=\"https:\/\/rud.is\/b\/2015\/01\/10\/new-r-package-cdcfluview-retrieve-flu-data-from-cdcs-fluview-portal\/flureport\/\" data-orig-file=\"https:\/\/i0.wp.com\/rud.is\/b\/wp-content\/uploads\/2015\/01\/flureport.png?fit=600%2C700&amp;ssl=1\" data-orig-size=\"600,700\" data-comments-opened=\"1\" data-image-meta=\"{&quot;aperture&quot;:&quot;0&quot;,&quot;credit&quot;:&quot;&quot;,&quot;camera&quot;:&quot;&quot;,&quot;caption&quot;:&quot;&quot;,&quot;created_timestamp&quot;:&quot;0&quot;,&quot;copyright&quot;:&quot;&quot;,&quot;focal_length&quot;:&quot;0&quot;,&quot;iso&quot;:&quot;0&quot;,&quot;shutter_speed&quot;:&quot;0&quot;,&quot;title&quot;:&quot;&quot;,&quot;orientation&quot;:&quot;0&quot;}\" data-image-title=\"flureport\" data-image-description=\"\" data-image-caption=\"\" data-large-file=\"https:\/\/i0.wp.com\/rud.is\/b\/wp-content\/uploads\/2015\/01\/flureport.png?fit=510%2C595&amp;ssl=1\" src=\"https:\/\/i0.wp.com\/rud.is\/b\/wp-content\/uploads\/2015\/01\/flureport.png?resize=510%2C595&#038;ssl=1\" alt=\"flureport\" width=\"510\" height=\"595\" class=\"aligncenter size-full wp-image-3216\" srcset=\"https:\/\/i0.wp.com\/rud.is\/b\/wp-content\/uploads\/2015\/01\/flureport.png?w=600&amp;ssl=1 600w, https:\/\/i0.wp.com\/rud.is\/b\/wp-content\/uploads\/2015\/01\/flureport.png?resize=129%2C150&amp;ssl=1 129w, https:\/\/i0.wp.com\/rud.is\/b\/wp-content\/uploads\/2015\/01\/flureport.png?resize=257%2C300&amp;ssl=1 257w, https:\/\/i0.wp.com\/rud.is\/b\/wp-content\/uploads\/2015\/01\/flureport.png?resize=530%2C618&amp;ssl=1 530w\" sizes=\"auto, (max-width: 510px) 100vw, 510px\" \/><\/p>\n<p>(You can see an SVG version of that plot [here](http:\/\/rud.is\/dl\/flureport.svg))<\/p>\n<p>Even without looking at the statistics, it's pretty easy to tell that this is fixing to be a pretty bad season in many regions.<\/p>\n<p>### State-level data<\/p>\n<p>Soon after this post I found the state-level API for the CDC FluView interface and added a `get_state_data` function for it:<\/p>\n<pre lang=\"rsplus\">library(statebins)\r\n\r\nget_state_data() %>%\r\n  filter(WEEKEND==\"Jan-03-2015\") %>%\r\n  select(state=STATENAME, value=ACTIVITY.LEVEL) %>%\r\n  filter(!(state %in% c(\"Puerto Rico\", \"New York City\"))) %>% # need to add NYC & PR to statebins\r\n  mutate(value=as.numeric(gsub(\"Level \", \"\", value))) %>%\r\n  statebins(brewer_pal=\"RdPu\", breaks=4,\r\n            labels=c(\"Minimal\", \"Low\", \"Moderate\", \"High\"),\r\n            legend_position=\"bottom\", legend_title=\"ILI Activity Level\") +\r\n  ggtitle(\"CDC State FluView (2014-01-03)\")<\/pre>\n<p><img data-recalc-dims=\"1\" loading=\"lazy\" decoding=\"async\" data-attachment-id=\"3233\" data-permalink=\"https:\/\/rud.is\/b\/2015\/01\/10\/new-r-package-cdcfluview-retrieve-flu-data-from-cdcs-fluview-portal\/state-2\/\" data-orig-file=\"https:\/\/i0.wp.com\/rud.is\/b\/wp-content\/uploads\/2015\/01\/state1.png?fit=600%2C450&amp;ssl=1\" data-orig-size=\"600,450\" data-comments-opened=\"1\" data-image-meta=\"{&quot;aperture&quot;:&quot;0&quot;,&quot;credit&quot;:&quot;&quot;,&quot;camera&quot;:&quot;&quot;,&quot;caption&quot;:&quot;&quot;,&quot;created_timestamp&quot;:&quot;0&quot;,&quot;copyright&quot;:&quot;&quot;,&quot;focal_length&quot;:&quot;0&quot;,&quot;iso&quot;:&quot;0&quot;,&quot;shutter_speed&quot;:&quot;0&quot;,&quot;title&quot;:&quot;&quot;,&quot;orientation&quot;:&quot;0&quot;}\" data-image-title=\"state\" data-image-description=\"\" data-image-caption=\"\" data-large-file=\"https:\/\/i0.wp.com\/rud.is\/b\/wp-content\/uploads\/2015\/01\/state1.png?fit=510%2C383&amp;ssl=1\" src=\"https:\/\/i0.wp.com\/rud.is\/b\/wp-content\/uploads\/2015\/01\/state1.png?resize=510%2C383&#038;ssl=1\" alt=\"state\" width=\"510\" height=\"383\" class=\"aligncenter size-full wp-image-3233\" srcset=\"https:\/\/i0.wp.com\/rud.is\/b\/wp-content\/uploads\/2015\/01\/state1.png?w=600&amp;ssl=1 600w, https:\/\/i0.wp.com\/rud.is\/b\/wp-content\/uploads\/2015\/01\/state1.png?resize=150%2C113&amp;ssl=1 150w, https:\/\/i0.wp.com\/rud.is\/b\/wp-content\/uploads\/2015\/01\/state1.png?resize=300%2C225&amp;ssl=1 300w, https:\/\/i0.wp.com\/rud.is\/b\/wp-content\/uploads\/2015\/01\/state1.png?resize=530%2C398&amp;ssl=1 530w\" sizes=\"auto, (max-width: 510px) 100vw, 510px\" \/><\/p>\n<p>As always, post bugs or feature requests on the [github repo](https:\/\/github.com\/hrbrmstr\/cdcfluview) and drop a note here if you've found the package useful or have some other interesting views or analyses to share.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>**NOTE** If there&#8217;s a particular data set from http:\/\/www.cdc.gov\/flu\/weekly\/fluviewinteractive.htm that you want and that isn&#8217;t in the pacakge, please file it as an issue and be as specific as you can (screen shot if possible). &#8212;&#8211; Towards the end of 2014 I had been tinkering with flu data from the [CDC&#8217;s FluView portal](http:\/\/gis.cdc.gov\/grasp\/fluview\/fluportaldashboard.html) since flu [&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":[673,674,722,91],"tags":[810],"class_list":["post-3215","post","type-post","status-publish","format-standard","hentry","category-datavis-2","category-dataviz","category-flu","category-r","tag-post"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.3 - 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This weekend, a user of [cdcfluview](http:\/\/github.com\/hrbrmstr\/cdcfluview) had a question that caused me to notice a difference in behaviour between the package was interacting with CDC FluView API, so I updated the package to accommodate the change and the user. Around the\u2026","rel":"","context":"In &quot;Data Analysis&quot;","block_context":{"text":"Data Analysis","link":"https:\/\/rud.is\/b\/category\/data-analysis-2\/"},"img":{"alt_text":"","src":"","width":0,"height":0},"classes":[]},{"id":3262,"url":"https:\/\/rud.is\/b\/2015\/02\/04\/a-step-to-the-right-in-r-assignments\/","url_meta":{"origin":3215,"position":1},"title":"A Step to the Right in R Assignments","author":"hrbrmstr","date":"2015-02-04","format":false,"excerpt":"I received an out-of-band question on the use of `%%` in my [CDC FluView](rud.is\/b\/2015\/01\/10\/new-r-package-cdcfluview-retrieve-flu-data-from-cdcs-fluview-portal\/) post, and took the opportunity to address it in a broader, public fashion. Anyone using R knows that the two most common methods of assignment are the venerable (and sensible) left arrow `` RHS assignment operator.\u2026","rel":"","context":"In &quot;Programming&quot;","block_context":{"text":"Programming","link":"https:\/\/rud.is\/b\/category\/programming\/"},"img":{"alt_text":"","src":"","width":0,"height":0},"classes":[]},{"id":7020,"url":"https:\/\/rud.is\/b\/2017\/11\/06\/taking-a-shot-at-cdcfluview-v0-7-0-a-k-a-the-dangers-of-relying-on-hidden-apis\/","url_meta":{"origin":3215,"position":2},"title":"Taking a Shot at cdcfluview v0.7.0 (a.k.a. The Dangers of Relying on &#8216;Hidden&#8217; APIs)","author":"hrbrmstr","date":"2017-11-06","format":false,"excerpt":"Unlike @noamross, I am not an epidemiologist (NOTE: Noam battles pandemics before breakfast, so be super nice to him) but I do like to find kindred methodologies in other disciplines to help foster the growth of cybersecurity into something beyond it's current Barnum & Bailey state. 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