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README.md

CRAN\_Status\_Badge Travis-CI BuildStatus CoverageStatus

I M P O R T A N T

The CDC migrated to a new non-Flash portal and back-end APIs changed. This is a complete reimagining of the package and — as such — all your code is going to break…eventually. Older functions have been deprecated with warnings and will be removed at some point.

All folks providing feedback, code or suggestions will be added to the DESCRIPTION file. Please include how you would prefer to be cited in any issues you file.

If there’s a particular data set from https://www.cdc.gov/flu/weekly/fluviewinteractive.htm that you want and that isn’t in the package, please file it as an issue and be as specific as you can (screen shot if possible).

😷 cdcfluview

Retrieve U.S. Flu Season Data from the CDC FluView Portal

Description

The U.S. Centers for Disease Control (CDC) maintains a portal http://gis.cdc.gov/grasp/fluview/fluportaldashboard.html for accessing state, regional and national influenza statistics as well as Mortality Surveillance Data. The Flash interface makes it difficult and time-consuming to select and retrieve influenza data. This package provides functions to access the data provided by the portal’s underlying API.

What’s Inside The Tin

The following functions are implemented:

  • age_group_distribution: Age Group Distribution of Influenza Positive Tests Reported by Public Health Laboratories
  • cdc_basemap: Retrieve CDC U.S. Basemaps
  • geographic_spread: State and Territorial Epidemiologists Reports of Geographic Spread of Influenza
  • get_weekly_flu_report: Retrieves (high-level) weekly (XML) influenza surveillance report from the CDC
  • hospitalizations: Laboratory-Confirmed Influenza Hospitalizations
  • ilinet: Retrieve ILINet Surveillance Data
  • ili_weekly_activity_indicators: Retrieve weekly state-level ILI indicators per-state for a given season
  • pi_mortality: Pneumonia and Influenza Mortality Surveillance
  • state_data_providers: Retrieve metadata about U.S. State CDC Provider Data
  • surveillance_areas: Retrieve a list of valid sub-regions for each surveillance area.
  • who_nrevss: Retrieve WHO/NREVSS Surveillance Data

MMWR ID Utilities:

  • mmwrid_map: MMWR ID to Calendar Mappings
  • mmwr_week: Convert a Date to an MMWR day+week+year
  • mmwr_weekday: Convert a Date to an MMWR weekday
  • mmwr_week_to_date: Convert an MMWR year+week or year+week+day to a Date object

Deprecated functions:

  • get_flu_data: Retrieves state, regional or national influenza statistics from the CDC (deprecated)
  • get_hosp_data: Retrieves influenza hospitalization statistics from the CDC (deprecated)
  • get_state_data: Retrieves state/territory-level influenza statistics from the CDC (deprecated)

The following data sets are included:

  • hhs_regions: HHS Region Table (a data frame with 59 rows and 4 variables)
  • census_regions: Census Region Table (a data frame with 51 rows and 2 variables)
  • mmwrid_map: MMWR ID to Calendar Mappings (it is exported & available, no need to use data())

Installation

devtools::install_github("hrbrmstr/cdcfluview")

Usage

library(cdcfluview)
library(hrbrthemes)
library(tidyverse)

# current verison
packageVersion("cdcfluview")
## [1] '0.8.0'

Age Group Distribution of Influenza Positive Tests Reported by Public Health Laboratories

glimpse(age_group_distribution(years=2015))
## Observations: 1,872
## Variables: 16
## $ sea_label         <chr> "2015-16", "2015-16", "2015-16", "2015-16", "2015-16", "2015-16", "2015-16", "2015-16", "...
## $ age_label         <fct> 0-4 yr, 0-4 yr, 0-4 yr, 0-4 yr, 0-4 yr, 0-4 yr, 0-4 yr, 0-4 yr, 0-4 yr, 0-4 yr, 0-4 yr, 0...
## $ vir_label         <fct> A (Subtyping not Performed), A (Subtyping not Performed), A (Subtyping not Performed), A ...
## $ count             <int> 0, 1, 0, 1, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 3, 2, 2, 3, 3, 3, 0, 0, 2, 0, 1, 1, 0,...
## $ mmwrid            <int> 2806, 2807, 2808, 2809, 2810, 2811, 2812, 2813, 2814, 2815, 2816, 2817, 2818, 2819, 2820,...
## $ seasonid          <int> 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 5...
## $ publishyearweekid <int> 2956, 2956, 2956, 2956, 2956, 2956, 2956, 2956, 2956, 2956, 2956, 2956, 2956, 2956, 2956,...
## $ sea_description   <chr> "Season 2015-16", "Season 2015-16", "Season 2015-16", "Season 2015-16", "Season 2015-16",...
## $ sea_startweek     <int> 2806, 2806, 2806, 2806, 2806, 2806, 2806, 2806, 2806, 2806, 2806, 2806, 2806, 2806, 2806,...
## $ sea_endweek       <int> 2857, 2857, 2857, 2857, 2857, 2857, 2857, 2857, 2857, 2857, 2857, 2857, 2857, 2857, 2857,...
## $ vir_description   <chr> "A-Unk", "A-Unk", "A-Unk", "A-Unk", "A-Unk", "A-Unk", "A-Unk", "A-Unk", "A-Unk", "A-Unk",...
## $ vir_startmmwrid   <int> 1397, 1397, 1397, 1397, 1397, 1397, 1397, 1397, 1397, 1397, 1397, 1397, 1397, 1397, 1397,...
## $ vir_endmmwrid     <int> 3131, 3131, 3131, 3131, 3131, 3131, 3131, 3131, 3131, 3131, 3131, 3131, 3131, 3131, 3131,...
## $ wk_start          <date> 2015-10-04, 2015-10-11, 2015-10-18, 2015-10-25, 2015-11-01, 2015-11-08, 2015-11-15, 2015...
## $ wk_end            <date> 2015-10-10, 2015-10-17, 2015-10-24, 2015-10-31, 2015-11-07, 2015-11-14, 2015-11-21, 2015...
## $ year_wk_num       <int> 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12...

Retrieve CDC U.S. Coverage Map

plot(cdc_basemap("national"))

plot(cdc_basemap("hhs"))

plot(cdc_basemap("census"))

plot(cdc_basemap("states"))

plot(cdc_basemap("spread"))

plot(cdc_basemap("surv"))

State and Territorial Epidemiologists Reports of Geographic Spread of Influenza

glimpse(geographic_spread())
## Observations: 27,351
## Variables: 7
## $ statename         <chr> "Alabama", "Alabama", "Alabama", "Alabama", "Alabama", "Alabama", "Alabama", "Alabama", "...
## $ url               <chr> "http://adph.org/influenza/", "http://adph.org/influenza/", "http://adph.org/influenza/",...
## $ website           <chr> "Influenza Surveillance", "Influenza Surveillance", "Influenza Surveillance", "Influenza ...
## $ activity_estimate <chr> "No Activity", "No Activity", "No Activity", "Local Activity", "Sporadic", "Sporadic", "S...
## $ weekend           <date> 2003-10-04, 2003-10-11, 2003-10-18, 2003-10-25, 2003-11-01, 2003-11-08, 2003-11-15, 2003...
## $ season            <chr> "2003-04", "2003-04", "2003-04", "2003-04", "2003-04", "2003-04", "2003-04", "2003-04", "...
## $ weeknumber        <chr> "40", "41", "42", "43", "44", "45", "46", "47", "48", "49", "50", "51", "52", "53", "1", ...

Laboratory-Confirmed Influenza Hospitalizations

surveillance_areas()
##    surveillance_area               region
## 1            flusurv       Entire Network
## 2                eip           California
## 3                eip             Colorado
## 4                eip          Connecticut
## 5                eip       Entire Network
## 6                eip              Georgia
## 7                eip             Maryland
## 8                eip            Minnesota
## 9                eip           New Mexico
## 10               eip    New York - Albany
## 11               eip New York - Rochester
## 12               eip               Oregon
## 13               eip            Tennessee
## 14              ihsp       Entire Network
## 15              ihsp                Idaho
## 16              ihsp                 Iowa
## 17              ihsp             Michigan
## 18              ihsp                 Ohio
## 19              ihsp             Oklahoma
## 20              ihsp         Rhode Island
## 21              ihsp         South Dakota
## 22              ihsp                 Utah
glimpse(fs_nat <- hospitalizations("flusurv"))
## Observations: 1,656
## Variables: 14
## $ surveillance_area <chr> "FluSurv-NET", "FluSurv-NET", "FluSurv-NET", "FluSurv-NET", "FluSurv-NET", "FluSurv-NET",...
## $ region            <chr> "Entire Network", "Entire Network", "Entire Network", "Entire Network", "Entire Network",...
## $ year              <int> 2009, 2009, 2009, 2009, 2009, 2009, 2009, 2009, 2009, 2009, 2009, 2009, 2009, 2009, 2009,...
## $ season            <int> 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 4...
## $ wk_start          <date> 2009-08-30, 2009-09-06, 2009-09-13, 2009-09-20, 2009-09-27, 2009-10-04, 2009-10-11, 2009...
## $ wk_end            <date> 2009-09-05, 2009-09-12, 2009-09-19, 2009-09-26, 2009-10-03, 2009-10-10, 2009-10-17, 2009...
## $ year_wk_num       <int> 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 1, 2, 3, 4, 5, 6,...
## $ rate              <dbl> 0.5, 2.5, 4.6, 6.7, 10.9, 18.1, 28.3, 39.1, 47.3, 53.3, 57.5, 60.1, 61.6, 62.9, 64.1, 65....
## $ weeklyrate        <dbl> 0.5, 2.0, 2.0, 2.1, 4.3, 7.2, 10.2, 10.8, 8.2, 6.0, 4.2, 2.6, 1.5, 1.3, 1.3, 1.0, 1.2, 1....
## $ age               <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,...
## $ age_label         <fct> 0-4 yr, 0-4 yr, 0-4 yr, 0-4 yr, 0-4 yr, 0-4 yr, 0-4 yr, 0-4 yr, 0-4 yr, 0-4 yr, 0-4 yr, 0...
## $ sea_label         <chr> "2009-10", "2009-10", "2009-10", "2009-10", "2009-10", "2009-10", "2009-10", "2009-10", "...
## $ sea_description   <chr> "Season 2009-10", "Season 2009-10", "Season 2009-10", "Season 2009-10", "Season 2009-10",...
## $ mmwrid            <int> 2488, 2489, 2490, 2491, 2492, 2493, 2494, 2495, 2496, 2497, 2498, 2499, 2500, 2501, 2502,...
ggplot(fs_nat, aes(wk_end, rate)) + 
  geom_line(aes(color=age_label, group=age_label)) +
  facet_wrap(~sea_description, scales="free_x") +
  scale_color_ipsum(name=NULL) +
  labs(x=NULL, y="Rates per 100,000 population",
       title="FluSurv-NET :: Entire Network :: All Seasons :: Cumulative Rate") +
  theme_ipsum_rc()

glimpse(hospitalizations("eip", years=2015))
## Observations: 180
## Variables: 14
## $ surveillance_area <chr> "EIP", "EIP", "EIP", "EIP", "EIP", "EIP", "EIP", "EIP", "EIP", "EIP", "EIP", "EIP", "EIP"...
## $ region            <chr> "Entire Network", "Entire Network", "Entire Network", "Entire Network", "Entire Network",...
## $ year              <int> 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2016, 2016,...
## $ season            <int> 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 5...
## $ wk_start          <date> 2015-10-04, 2015-10-11, 2015-10-18, 2015-10-25, 2015-11-01, 2015-11-08, 2015-11-15, 2015...
## $ wk_end            <date> 2015-10-10, 2015-10-17, 2015-10-24, 2015-10-31, 2015-11-07, 2015-11-14, 2015-11-21, 2015...
## $ year_wk_num       <int> 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12...
## $ rate              <dbl> 0.1, 0.3, 0.4, 0.5, 0.8, 0.8, 1.1, 1.4, 1.6, 1.7, 1.8, 2.1, 2.4, 2.9, 3.2, 3.5, 4.2, 5.3,...
## $ weeklyrate        <dbl> 0.1, 0.3, 0.1, 0.1, 0.3, 0.0, 0.3, 0.3, 0.2, 0.1, 0.1, 0.3, 0.3, 0.5, 0.3, 0.3, 0.6, 1.2,...
## $ age               <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,...
## $ age_label         <fct> 0-4 yr, 0-4 yr, 0-4 yr, 0-4 yr, 0-4 yr, 0-4 yr, 0-4 yr, 0-4 yr, 0-4 yr, 0-4 yr, 0-4 yr, 0...
## $ sea_label         <chr> "2015-16", "2015-16", "2015-16", "2015-16", "2015-16", "2015-16", "2015-16", "2015-16", "...
## $ sea_description   <chr> "Season 2015-16", "Season 2015-16", "Season 2015-16", "Season 2015-16", "Season 2015-16",...
## $ mmwrid            <int> 2806, 2807, 2808, 2809, 2810, 2811, 2812, 2813, 2814, 2815, 2816, 2817, 2818, 2819, 2820,...
glimpse(hospitalizations("eip", "Colorado", years=2015))
## Observations: 180
## Variables: 14
## $ surveillance_area <chr> "EIP", "EIP", "EIP", "EIP", "EIP", "EIP", "EIP", "EIP", "EIP", "EIP", "EIP", "EIP", "EIP"...
## $ region            <chr> "Colorado", "Colorado", "Colorado", "Colorado", "Colorado", "Colorado", "Colorado", "Colo...
## $ year              <int> 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2016, 2016,...
## $ season            <int> 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 5...
## $ wk_start          <date> 2015-10-04, 2015-10-11, 2015-10-18, 2015-10-25, 2015-11-01, 2015-11-08, 2015-11-15, 2015...
## $ wk_end            <date> 2015-10-10, 2015-10-17, 2015-10-24, 2015-10-31, 2015-11-07, 2015-11-14, 2015-11-21, 2015...
## $ year_wk_num       <int> 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12...
## $ rate              <dbl> 0.0, 0.0, 0.6, 0.6, 0.6, 0.6, 1.2, 1.8, 1.8, 1.8, 1.8, 1.8, 2.9, 3.5, 3.5, 3.5, 4.1, 6.4,...
## $ weeklyrate        <dbl> 0.0, 0.0, 0.6, 0.0, 0.0, 0.0, 0.6, 0.6, 0.0, 0.0, 0.0, 0.0, 1.2, 0.6, 0.0, 0.0, 0.6, 2.3,...
## $ age               <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,...
## $ age_label         <fct> 0-4 yr, 0-4 yr, 0-4 yr, 0-4 yr, 0-4 yr, 0-4 yr, 0-4 yr, 0-4 yr, 0-4 yr, 0-4 yr, 0-4 yr, 0...
## $ sea_label         <chr> "2015-16", "2015-16", "2015-16", "2015-16", "2015-16", "2015-16", "2015-16", "2015-16", "...
## $ sea_description   <chr> "Season 2015-16", "Season 2015-16", "Season 2015-16", "Season 2015-16", "Season 2015-16",...
## $ mmwrid            <int> 2806, 2807, 2808, 2809, 2810, 2811, 2812, 2813, 2814, 2815, 2816, 2817, 2818, 2819, 2820,...
glimpse(hospitalizations("ihsp", years=2015))
## Observations: 180
## Variables: 14
## $ surveillance_area <chr> "IHSP", "IHSP", "IHSP", "IHSP", "IHSP", "IHSP", "IHSP", "IHSP", "IHSP", "IHSP", "IHSP", "...
## $ region            <chr> "Entire Network", "Entire Network", "Entire Network", "Entire Network", "Entire Network",...
## $ year              <int> 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2016, 2016,...
## $ season            <int> 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 5...
## $ wk_start          <date> 2015-10-04, 2015-10-11, 2015-10-18, 2015-10-25, 2015-11-01, 2015-11-08, 2015-11-15, 2015...
## $ wk_end            <date> 2015-10-10, 2015-10-17, 2015-10-24, 2015-10-31, 2015-11-07, 2015-11-14, 2015-11-21, 2015...
## $ year_wk_num       <int> 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12...
## $ rate              <dbl> 0.0, 0.0, 0.4, 0.4, 0.4, 1.1, 1.1, 1.1, 1.1, 1.5, 1.8, 2.2, 2.2, 2.5, 2.5, 2.5, 2.9, 4.0,...
## $ weeklyrate        <dbl> 0.0, 0.0, 0.4, 0.0, 0.0, 0.7, 0.0, 0.0, 0.0, 0.4, 0.4, 0.4, 0.0, 0.4, 0.0, 0.0, 0.4, 1.1,...
## $ age               <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,...
## $ age_label         <fct> 0-4 yr, 0-4 yr, 0-4 yr, 0-4 yr, 0-4 yr, 0-4 yr, 0-4 yr, 0-4 yr, 0-4 yr, 0-4 yr, 0-4 yr, 0...
## $ sea_label         <chr> "2015-16", "2015-16", "2015-16", "2015-16", "2015-16", "2015-16", "2015-16", "2015-16", "...
## $ sea_description   <chr> "Season 2015-16", "Season 2015-16", "Season 2015-16", "Season 2015-16", "Season 2015-16",...
## $ mmwrid            <int> 2806, 2807, 2808, 2809, 2810, 2811, 2812, 2813, 2814, 2815, 2816, 2817, 2818, 2819, 2820,...
glimpse(hospitalizations("ihsp", "Oklahoma", years=2015))
## Observations: 180
## Variables: 14
## $ surveillance_area <chr> "IHSP", "IHSP", "IHSP", "IHSP", "IHSP", "IHSP", "IHSP", "IHSP", "IHSP", "IHSP", "IHSP", "...
## $ region            <chr> "Oklahoma", "Oklahoma", "Oklahoma", "Oklahoma", "Oklahoma", "Oklahoma", "Oklahoma", "Okla...
## $ year              <int> 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2011, 2011,...
## $ season            <int> 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 5...
## $ wk_start          <date> 2010-10-03, 2010-10-10, 2010-10-17, 2010-10-24, 2010-10-31, 2010-11-07, 2010-11-14, 2010...
## $ wk_end            <date> 2010-10-09, 2010-10-16, 2010-10-23, 2010-10-30, 2010-11-06, 2010-11-13, 2010-11-20, 2010...
## $ year_wk_num       <int> 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12...
## $ rate              <dbl> 0.0, 0.0, 1.3, 1.3, 1.3, 1.3, 1.3, 1.3, 1.3, 2.6, 2.6, 6.6, 15.9, 18.5, 35.7, 54.2, 83.4,...
## $ weeklyrate        <dbl> 0.0, 0.0, 1.3, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.3, 0.0, 4.0, 9.3, 2.6, 17.2, 18.5, 29.1, 2...
## $ age               <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,...
## $ age_label         <fct> 0-4 yr, 0-4 yr, 0-4 yr, 0-4 yr, 0-4 yr, 0-4 yr, 0-4 yr, 0-4 yr, 0-4 yr, 0-4 yr, 0-4 yr, 0...
## $ sea_label         <chr> "2010-11", "2010-11", "2010-11", "2010-11", "2010-11", "2010-11", "2010-11", "2010-11", "...
## $ sea_description   <chr> "Season 2010-11", "Season 2010-11", "Season 2010-11", "Season 2010-11", "Season 2010-11",...
## $ mmwrid            <int> 2545, 2546, 2547, 2548, 2549, 2550, 2551, 2552, 2553, 2554, 2555, 2556, 2557, 2558, 2559,...

Retrieve ILINet Surveillance Data

walk(c("national", "hhs", "census", "state"), ~{
  
  ili_df <- ilinet(region = .x)
  
  print(glimpse(ili_df))
  
  ggplot(ili_df, aes(week_start, unweighted_ili, group=region, color=region)) +
    geom_line() +
    viridis::scale_color_viridis(discrete=TRUE) +
    labs(x=NULL, y="Unweighted ILI", title=ili_df$region_type[1]) +
    theme_ipsum_rc(grid="XY") +
    theme(legend.position = "none") -> gg
  
  print(gg)
  
})
## Observations: 1,093
## Variables: 16
## $ region_type      <chr> "National", "National", "National", "National", "National", "National", "National", "Natio...
## $ region           <chr> "National", "National", "National", "National", "National", "National", "National", "Natio...
## $ year             <int> 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1998, ...
## $ week             <int> 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11,...
## $ weighted_ili     <dbl> 1.101480, 1.200070, 1.378760, 1.199200, 1.656180, 1.413260, 1.986800, 2.447490, 1.739010, ...
## $ unweighted_ili   <dbl> 1.216860, 1.280640, 1.239060, 1.144730, 1.261120, 1.282750, 1.445790, 1.647960, 1.675170, ...
## $ age_0_4          <dbl> 179, 199, 228, 188, 217, 178, 294, 288, 268, 299, 346, 348, 510, 579, 639, 690, 856, 824, ...
## $ age_25_49        <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA...
## $ age_25_64        <dbl> 157, 151, 153, 193, 162, 148, 240, 293, 206, 282, 268, 235, 404, 584, 759, 654, 679, 817, ...
## $ age_5_24         <dbl> 205, 242, 266, 236, 280, 281, 328, 456, 343, 415, 388, 362, 492, 576, 810, 1121, 1440, 160...
## $ age_50_64        <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA...
## $ age_65           <dbl> 29, 23, 34, 36, 41, 48, 70, 63, 69, 102, 81, 59, 113, 207, 207, 148, 151, 196, 233, 146, 1...
## $ ilitotal         <dbl> 570, 615, 681, 653, 700, 655, 932, 1100, 886, 1098, 1083, 1004, 1519, 1946, 2415, 2613, 31...
## $ num_of_providers <dbl> 192, 191, 219, 213, 213, 195, 248, 256, 252, 253, 242, 190, 251, 250, 254, 255, 245, 245, ...
## $ total_patients   <dbl> 46842, 48023, 54961, 57044, 55506, 51062, 64463, 66749, 52890, 67887, 61314, 47719, 48429,...
## $ week_start       <date> 1997-10-06, 1997-10-13, 1997-10-20, 1997-10-27, 1997-11-03, 1997-11-10, 1997-11-17, 1997-...
## # A tibble: 1,093 x 16
##    region_type region    year  week weighted_ili unweighted_ili age_0_4 age_25_49 age_25_64 age_5_24 age_50_64 age_65
##    <chr>       <chr>    <int> <int>        <dbl>          <dbl>   <dbl>     <dbl>     <dbl>    <dbl>     <dbl>  <dbl>
##  1 National    National  1997    40         1.10           1.22     179        NA       157      205        NA     29
##  2 National    National  1997    41         1.20           1.28     199        NA       151      242        NA     23
##  3 National    National  1997    42         1.38           1.24     228        NA       153      266        NA     34
##  4 National    National  1997    43         1.20           1.14     188        NA       193      236        NA     36
##  5 National    National  1997    44         1.66           1.26     217        NA       162      280        NA     41
##  6 National    National  1997    45         1.41           1.28     178        NA       148      281        NA     48
##  7 National    National  1997    46         1.99           1.45     294        NA       240      328        NA     70
##  8 National    National  1997    47         2.45           1.65     288        NA       293      456        NA     63
##  9 National    National  1997    48         1.74           1.68     268        NA       206      343        NA     69
## 10 National    National  1997    49         1.94           1.62     299        NA       282      415        NA    102
## # ... with 1,083 more rows, and 4 more variables: ilitotal <dbl>, num_of_providers <dbl>, total_patients <dbl>,
## #   week_start <date>

## Observations: 10,930
## Variables: 16
## $ region_type      <chr> "HHS Regions", "HHS Regions", "HHS Regions", "HHS Regions", "HHS Regions", "HHS Regions", ...
## $ region           <fct> Region 1, Region 2, Region 3, Region 4, Region 5, Region 6, Region 7, Region 8, Region 9, ...
## $ year             <int> 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, ...
## $ week             <int> 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 41, 41, 41, 41, 41, 41, 41, 41, 41, 41, 42, 42, 42...
## $ weighted_ili     <dbl> 0.498535, 0.374963, 1.354280, 0.400338, 1.229260, 1.018980, 0.871791, 0.516017, 1.807610, ...
## $ unweighted_ili   <dbl> 0.623848, 0.384615, 1.341720, 0.450010, 0.901266, 0.747384, 1.152860, 0.422654, 2.258780, ...
## $ age_0_4          <dbl> 15, 0, 6, 12, 31, 2, 0, 2, 80, 31, 14, 0, 4, 21, 36, 2, 0, 0, 103, 19, 35, 0, 3, 19, 66, 2...
## $ age_25_49        <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA...
## $ age_25_64        <dbl> 7, 3, 7, 23, 24, 1, 4, 0, 76, 12, 14, 2, 19, 7, 23, 2, 0, 1, 76, 7, 15, 0, 17, 15, 29, 2, ...
## $ age_5_24         <dbl> 22, 0, 15, 11, 30, 2, 18, 3, 74, 30, 29, 0, 16, 14, 41, 2, 13, 8, 84, 35, 35, 0, 24, 18, 7...
## $ age_50_64        <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA...
## $ age_65           <dbl> 0, 0, 4, 0, 4, 0, 5, 0, 13, 3, 0, 0, 3, 2, 4, 0, 2, 0, 11, 1, 0, 1, 2, 2, 16, 0, 2, 0, 9, ...
## $ ilitotal         <dbl> 44, 3, 32, 46, 89, 5, 27, 5, 243, 76, 57, 2, 42, 44, 104, 6, 15, 9, 274, 62, 85, 1, 46, 54...
## $ num_of_providers <dbl> 32, 7, 16, 29, 49, 4, 14, 5, 23, 13, 29, 7, 17, 31, 48, 4, 14, 6, 23, 12, 40, 7, 15, 33, 6...
## $ total_patients   <dbl> 7053, 780, 2385, 10222, 9875, 669, 2342, 1183, 10758, 1575, 6987, 872, 2740, 11310, 9618, ...
## $ week_start       <date> 1997-10-06, 1997-10-06, 1997-10-06, 1997-10-06, 1997-10-06, 1997-10-06, 1997-10-06, 1997-...
## # A tibble: 10,930 x 16
##    region_type region     year  week weighted_ili unweighted_ili age_0_4 age_25_49 age_25_64 age_5_24 age_50_64 age_65
##    <chr>       <fct>     <int> <int>        <dbl>          <dbl>   <dbl>     <dbl>     <dbl>    <dbl>     <dbl>  <dbl>
##  1 HHS Regions Region 1   1997    40        0.499          0.624      15        NA         7       22        NA      0
##  2 HHS Regions Region 2   1997    40        0.375          0.385       0        NA         3        0        NA      0
##  3 HHS Regions Region 3   1997    40        1.35           1.34        6        NA         7       15        NA      4
##  4 HHS Regions Region 4   1997    40        0.400          0.450      12        NA        23       11        NA      0
##  5 HHS Regions Region 5   1997    40        1.23           0.901      31        NA        24       30        NA      4
##  6 HHS Regions Region 6   1997    40        1.02           0.747       2        NA         1        2        NA      0
##  7 HHS Regions Region 7   1997    40        0.872          1.15        0        NA         4       18        NA      5
##  8 HHS Regions Region 8   1997    40        0.516          0.423       2        NA         0        3        NA      0
##  9 HHS Regions Region 9   1997    40        1.81           2.26       80        NA        76       74        NA     13
## 10 HHS Regions Region 10  1997    40        4.74           4.83       31        NA        12       30        NA      3
## # ... with 10,920 more rows, and 4 more variables: ilitotal <dbl>, num_of_providers <dbl>, total_patients <dbl>,
## #   week_start <date>

## Observations: 9,837
## Variables: 16
## $ region_type      <chr> "Census Regions", "Census Regions", "Census Regions", "Census Regions", "Census Regions", ...
## $ region           <chr> "New England", "Mid-Atlantic", "East North Central", "West North Central", "South Atlantic...
## $ year             <int> 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, ...
## $ week             <int> 40, 40, 40, 40, 40, 40, 40, 40, 40, 41, 41, 41, 41, 41, 41, 41, 41, 41, 42, 42, 42, 42, 42...
## $ weighted_ili     <dbl> 0.4985350, 0.8441440, 0.7924860, 1.7640500, 0.5026620, 0.0542283, 1.0189800, 2.2587800, 2....
## $ unweighted_ili   <dbl> 0.6238480, 1.3213800, 0.8187380, 1.2793900, 0.7233800, 0.0688705, 0.7473840, 2.2763300, 3....
## $ age_0_4          <dbl> 15, 4, 28, 3, 14, 0, 2, 87, 26, 14, 4, 36, 0, 21, 0, 2, 93, 29, 35, 3, 65, 1, 19, 0, 2, 84...
## $ age_25_49        <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA...
## $ age_25_64        <dbl> 7, 8, 20, 8, 22, 3, 1, 71, 17, 14, 13, 23, 1, 14, 1, 2, 72, 11, 15, 11, 27, 5, 21, 0, 2, 5...
## $ age_5_24         <dbl> 22, 12, 28, 20, 14, 0, 2, 71, 36, 29, 8, 39, 18, 22, 0, 2, 80, 44, 35, 16, 74, 9, 24, 2, 2...
## $ age_50_64        <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA...
## $ age_65           <dbl> 0, 4, 3, 6, 0, 0, 0, 15, 1, 0, 2, 2, 4, 3, 0, 0, 10, 2, 0, 3, 12, 6, 2, 0, 0, 9, 2, 0, 1, ...
## $ ilitotal         <dbl> 44, 28, 79, 37, 50, 3, 5, 244, 80, 57, 27, 100, 23, 60, 1, 6, 255, 86, 85, 33, 178, 21, 66...
## $ num_of_providers <dbl> 32, 13, 47, 17, 30, 9, 4, 16, 24, 29, 13, 46, 17, 32, 10, 4, 17, 23, 40, 12, 62, 16, 33, 1...
## $ total_patients   <dbl> 7053, 2119, 9649, 2892, 6912, 4356, 669, 10719, 2473, 6987, 2384, 9427, 2823, 7591, 4947, ...
## $ week_start       <date> 1997-10-06, 1997-10-06, 1997-10-06, 1997-10-06, 1997-10-06, 1997-10-06, 1997-10-06, 1997-...
## # A tibble: 9,837 x 16
##    region_type  region     year  week weighted_ili unweighted_ili age_0_4 age_25_49 age_25_64 age_5_24 age_50_64 age_65
##    <chr>        <chr>     <int> <int>        <dbl>          <dbl>   <dbl>     <dbl>     <dbl>    <dbl>     <dbl>  <dbl>
##  1 Census Regi… New Engl…  1997    40       0.499          0.624       15        NA         7       22        NA      0
##  2 Census Regi… Mid-Atla…  1997    40       0.844          1.32         4        NA         8       12        NA      4
##  3 Census Regi… East Nor…  1997    40       0.792          0.819       28        NA        20       28        NA      3
##  4 Census Regi… West Nor…  1997    40       1.76           1.28         3        NA         8       20        NA      6
##  5 Census Regi… South At…  1997    40       0.503          0.723       14        NA        22       14        NA      0
##  6 Census Regi… East Sou…  1997    40       0.0542         0.0689       0        NA         3        0        NA      0
##  7 Census Regi… West Sou…  1997    40       1.02           0.747        2        NA         1        2        NA      0
##  8 Census Regi… Mountain   1997    40       2.26           2.28        87        NA        71       71        NA     15
##  9 Census Regi… Pacific    1997    40       2.05           3.23        26        NA        17       36        NA      1
## 10 Census Regi… New Engl…  1997    41       0.643          0.816       14        NA        14       29        NA      0
## # ... with 9,827 more rows, and 4 more variables: ilitotal <dbl>, num_of_providers <dbl>, total_patients <dbl>,
## #   week_start <date>

## Observations: 22,148
## Variables: 16
## $ region_type      <chr> "States", "States", "States", "States", "States", "States", "States", "States", "States", ...
## $ region           <chr> "Alabama", "Alaska", "Arizona", "Arkansas", "California", "Colorado", "Connecticut", "Dela...
## $ year             <int> 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, ...
## $ week             <int> 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40...
## $ weighted_ili     <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA...
## $ unweighted_ili   <dbl> 2.1347700, 0.8751460, 0.6747210, 0.6960560, 1.9541200, 0.6606840, 0.0783085, 0.1001250, 2....
## $ age_0_4          <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA...
## $ age_25_49        <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA...
## $ age_25_64        <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA...
## $ age_5_24         <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA...
## $ age_50_64        <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA...
## $ age_65           <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA...
## $ ilitotal         <dbl> 249, 15, 172, 18, 632, 134, 3, 4, 73, NA, 647, 20, 19, 505, 65, 10, 39, 19, 391, 22, 117, ...
## $ num_of_providers <dbl> 35, 7, 49, 15, 112, 14, 12, 13, 4, NA, 62, 18, 12, 74, 44, 6, 40, 14, 41, 30, 17, 56, 47, ...
## $ total_patients   <dbl> 11664, 1714, 25492, 2586, 32342, 20282, 3831, 3995, 2599, NA, 40314, 1943, 4579, 39390, 12...
## $ week_start       <date> 2010-10-04, 2010-10-04, 2010-10-04, 2010-10-04, 2010-10-04, 2010-10-04, 2010-10-04, 2010-...
## # A tibble: 22,148 x 16
##    region_type region      year  week weighted_ili unweighted_ili age_0_4 age_25_49 age_25_64 age_5_24 age_50_64 age_65
##    <chr>       <chr>      <int> <int>        <dbl>          <dbl>   <dbl>     <dbl>     <dbl>    <dbl>     <dbl>  <dbl>
##  1 States      Alabama     2010    40           NA         2.13        NA        NA        NA       NA        NA     NA
##  2 States      Alaska      2010    40           NA         0.875       NA        NA        NA       NA        NA     NA
##  3 States      Arizona     2010    40           NA         0.675       NA        NA        NA       NA        NA     NA
##  4 States      Arkansas    2010    40           NA         0.696       NA        NA        NA       NA        NA     NA
##  5 States      California  2010    40           NA         1.95        NA        NA        NA       NA        NA     NA
##  6 States      Colorado    2010    40           NA         0.661       NA        NA        NA       NA        NA     NA
##  7 States      Connectic…  2010    40           NA         0.0783      NA        NA        NA       NA        NA     NA
##  8 States      Delaware    2010    40           NA         0.100       NA        NA        NA       NA        NA     NA
##  9 States      District …  2010    40           NA         2.81        NA        NA        NA       NA        NA     NA
## 10 States      Florida     2010    40           NA        NA           NA        NA        NA       NA        NA     NA
## # ... with 22,138 more rows, and 4 more variables: ilitotal <dbl>, num_of_providers <dbl>, total_patients <dbl>,
## #   week_start <date>

Retrieve weekly state-level ILI indicators per-state for a given season

ili_weekly_activity_indicators(2017)
## # A tibble: 1,782 x 8
##    statename      url                website   activity_level activity_level_label weekend    season  weeknumber
##  * <chr>          <chr>              <chr>              <dbl> <chr>                <date>     <chr>        <dbl>
##  1 Virgin Islands http://doh.vi.gov/ Influenza              0 Insufficient Data    2017-10-07 2017-18         40
##  2 Virgin Islands http://doh.vi.gov/ Influenza              0 Insufficient Data    2017-10-14 2017-18         41
##  3 Virgin Islands http://doh.vi.gov/ Influenza              0 Insufficient Data    2017-10-21 2017-18         42
##  4 Virgin Islands http://doh.vi.gov/ Influenza              0 Insufficient Data    2017-10-28 2017-18         43
##  5 Virgin Islands http://doh.vi.gov/ Influenza              0 Insufficient Data    2017-11-04 2017-18         44
##  6 Virgin Islands http://doh.vi.gov/ Influenza              0 Insufficient Data    2017-11-11 2017-18         45
##  7 Virgin Islands http://doh.vi.gov/ Influenza              0 Insufficient Data    2017-12-02 2017-18         48
##  8 Virgin Islands http://doh.vi.gov/ Influenza              0 Insufficient Data    2017-12-09 2017-18         49
##  9 Virgin Islands http://doh.vi.gov/ Influenza              0 Insufficient Data    2017-12-23 2017-18         51
## 10 Virgin Islands http://doh.vi.gov/ Influenza              0 Insufficient Data    2017-12-30 2017-18         52
## # ... with 1,772 more rows
xdf <- map_df(2008:2017, ili_weekly_activity_indicators)

count(xdf, weekend, activity_level_label) %>% 
  complete(weekend, activity_level_label) %>% 
  ggplot(aes(weekend, activity_level_label, fill=n)) + 
  geom_tile(color="#c2c2c2", size=0.1) +
  scale_x_date(expand=c(0,0)) +
  viridis::scale_fill_viridis(name="# States", na.value="White") +
  labs(x=NULL, y=NULL, title="Weekly ILI Indicators (all states)") +
  coord_fixed(100/1) +
  theme_ipsum_rc(grid="") +
  theme(legend.position="bottom")

Pneumonia and Influenza Mortality Surveillance

(nat_pi <- pi_mortality("national"))
## # A tibble: 464 x 19
##    seasonid baseline threshold percent_pni percent_complete number_influenza number_pneumonia all_deaths total_pni
##    <chr>       <dbl>     <dbl>       <dbl>            <dbl>            <dbl>            <dbl>      <dbl>     <dbl>
##  1 57         0.057      0.06       0.0580                1               16             3020      52110      3036
##  2 57         0.0580     0.061      0.059                 1               18             3000      51572      3018
##  3 57         0.059      0.062      0.061                 1               28             3154      52222      3182
##  4 57         0.06       0.063      0.063                 1               23             3279      52548      3302
##  5 57         0.06       0.063      0.061                 1               36             3214      53679      3250
##  6 57         0.061      0.064      0.06                  1               45             3177      53258      3222
##  7 57         0.062      0.065      0.063                 1               50             3315      53771      3365
##  8 57         0.063      0.066      0.06                  1               48             3200      54120      3248
##  9 57         0.064      0.067      0.065                 1               83             3491      54760      3574
## 10 57         0.065      0.068      0.066                 1              118             3526      55595      3644
## # ... with 454 more rows, and 10 more variables: weeknumber <chr>, geo_description <chr>, age_label <chr>,
## #   wk_start <date>, wk_end <date>, year_wk_num <int>, mmwrid <chr>, coverage_area <chr>, region_name <chr>,
## #   callout <chr>
select(nat_pi, wk_end, percent_pni, baseline, threshold) %>% 
  gather(measure, value, -wk_end) %>% 
  ggplot(aes(wk_end, value)) + 
  geom_line(aes(group=measure, color=measure)) + 
  scale_y_percent() +
  scale_color_ipsum(name = NULL, labels=c("Baseline", "Percent P&I", "Threshold")) +
  labs(x=NULL, y="% of all deaths due to P&I",
       title="Percentage of all deaths due to pneumonia and influenza, National Summary") +
  theme_ipsum_rc(grid="XY") +
  theme(legend.position="bottom")

(st_pi <- pi_mortality("state", years=2015))
## # A tibble: 2,704 x 19
##    seasonid baseline threshold percent_pni percent_complete number_influenza number_pneumonia all_deaths total_pni
##    <chr>       <dbl>     <dbl>       <dbl>            <dbl>            <dbl>            <dbl>      <dbl>     <dbl>
##  1 55             NA        NA      0.047             1                    0               46        979        46
##  2 55             NA        NA      0.038             0.963                0               34        889        34
##  3 55             NA        NA      0.053             1                    0               52        977        52
##  4 55             NA        NA      0.07              1                    0               68        968        68
##  5 55             NA        NA      0.053             0.981                0               48        906        48
##  6 55             NA        NA      0.0580            1                    0               56        968        56
##  7 55             NA        NA      0.051             1                    0               53       1041        53
##  8 55             NA        NA      0.062             1                    1               63       1031        64
##  9 55             NA        NA      0.0560            1                    0               55        976        55
## 10 55             NA        NA      0.054             1                    0               56       1045        56
## # ... with 2,694 more rows, and 10 more variables: weeknumber <chr>, geo_description <chr>, age_label <chr>,
## #   wk_start <date>, wk_end <date>, year_wk_num <int>, mmwrid <chr>, coverage_area <chr>, region_name <chr>,
## #   callout <chr>
(reg_pi <- pi_mortality("region", years=2015))
## # A tibble: 520 x 19
##    seasonid baseline threshold percent_pni percent_complete number_influenza number_pneumonia all_deaths total_pni
##    <chr>       <dbl>     <dbl>       <dbl>            <dbl>            <dbl>            <dbl>      <dbl>     <dbl>
##  1 55          0.066    0.073       0.071                 1                0              178       2520       178
##  2 55          0.067    0.074       0.063                 1                0              159       2505       159
##  3 55          0.067    0.075       0.0580                1                1              141       2452       142
##  4 55          0.068    0.076       0.071                 1                0              171       2422       171
##  5 55          0.07     0.077       0.066                 1                2              166       2554       168
##  6 55          0.071    0.078       0.067                 1                1              160       2404       161
##  7 55          0.072    0.079       0.079                 1                0              195       2478       195
##  8 55          0.073    0.081       0.072                 1                1              176       2463       177
##  9 55          0.074    0.0820      0.067                 1                3              154       2347       157
## 10 55          0.076    0.083       0.062                 1                0              151       2437       151
## # ... with 510 more rows, and 10 more variables: weeknumber <chr>, geo_description <chr>, age_label <chr>,
## #   wk_start <date>, wk_end <date>, year_wk_num <int>, mmwrid <chr>, coverage_area <chr>, region_name <chr>,
## #   callout <chr>

Retrieve metadata about U.S. State CDC Provider Data

state_data_providers()
## # A tibble: 59 x 5
##    statename            statehealthdeptname                                  url      statewebsitename statefluphonenum
##  * <chr>                <chr>                                                <chr>    <chr>            <chr>           
##  1 Alabama              Alabama Department of Public Health                  http://… Influenza Surve… 334-206-5300    
##  2 Alaska               State of Alaska Health and Social Services           "http:/… Influenza Surve… 907-269-8000    
##  3 Arizona              Arizona Department of Health Services                http://… Influenza & RSV… 602-542-1025    
##  4 Arkansas             Arkansas Department of Health                        http://… Communicable Di… 501-661-2000    
##  5 California           California Department of Public Health               https:/… Influenza (Flu)  916-558-1784    
##  6 Colorado             Colorado Department of Public Health and Environment https:/… Influenza Surve… 303-692-2000    
##  7 Connecticut          Connecticut Department of Public Health              http://… Flu Statistics   860-509-8000    
##  8 Delaware             Delaware Health and Social Services                  http://… Weekly Influenz… 302-744-4700    
##  9 District of Columbia District of Columbia Department of Health            http://… Influenza Infor… 202-442-5955    
## 10 Florida              Florida Department of Health                         "http:/… Weekly Influenz… 850-245-4300    
## # ... with 49 more rows

Retrieve WHO/NREVSS Surveillance Data

glimpse(xdat <- who_nrevss("national"))
## List of 3
##  $ combined_prior_to_2015_16:Classes 'tbl_df', 'tbl' and 'data.frame':   940 obs. of  14 variables:
##   ..$ region_type              : chr [1:940] "National" "National" "National" "National" ...
##   ..$ region                   : chr [1:940] "National" "National" "National" "National" ...
##   ..$ year                     : int [1:940] 1997 1997 1997 1997 1997 1997 1997 1997 1997 1997 ...
##   ..$ week                     : int [1:940] 40 41 42 43 44 45 46 47 48 49 ...
##   ..$ total_specimens          : int [1:940] 1291 1513 1552 1669 1897 2106 2204 2533 2242 2607 ...
##   ..$ percent_positive         : num [1:940] 0 0.727 1.095 0.419 0.527 ...
##   ..$ a_2009_h1n1              : int [1:940] 0 0 0 0 0 0 0 0 0 0 ...
##   ..$ a_h1                     : int [1:940] 0 0 0 0 0 0 0 0 0 0 ...
##   ..$ a_h3                     : int [1:940] 0 0 3 0 9 0 3 5 14 11 ...
##   ..$ a_subtyping_not_performed: int [1:940] 0 11 13 7 1 6 4 17 22 28 ...
##   ..$ a_unable_to_subtype      : int [1:940] 0 0 0 0 0 0 0 0 0 0 ...
##   ..$ b                        : int [1:940] 0 0 1 0 0 0 1 1 1 1 ...
##   ..$ h3n2v                    : int [1:940] 0 0 0 0 0 0 0 0 0 0 ...
##   ..$ wk_date                  : Date[1:940], format: "1997-09-28" "1997-10-05" "1997-10-12" "1997-10-19" ...
##  $ public_health_labs       :Classes 'tbl_df', 'tbl' and 'data.frame':   153 obs. of  13 variables:
##   ..$ region_type              : chr [1:153] "National" "National" "National" "National" ...
##   ..$ region                   : chr [1:153] "National" "National" "National" "National" ...
##   ..$ year                     : int [1:153] 2015 2015 2015 2015 2015 2015 2015 2015 2015 2015 ...
##   ..$ week                     : int [1:153] 40 41 42 43 44 45 46 47 48 49 ...
##   ..$ total_specimens          : int [1:153] 1139 1152 1198 1244 1465 1393 1458 1157 1550 1518 ...
##   ..$ a_2009_h1n1              : int [1:153] 4 5 10 9 4 11 17 17 27 38 ...
##   ..$ a_h3                     : int [1:153] 65 41 50 31 23 34 42 24 36 37 ...
##   ..$ a_subtyping_not_performed: int [1:153] 2 2 1 4 4 1 1 0 3 3 ...
##   ..$ b                        : int [1:153] 10 7 8 9 9 10 4 4 9 11 ...
##   ..$ bvic                     : int [1:153] 0 3 3 1 1 4 0 3 3 2 ...
##   ..$ byam                     : int [1:153] 1 0 2 4 4 2 4 9 12 11 ...
##   ..$ h3n2v                    : int [1:153] 0 0 0 0 0 0 0 0 0 0 ...
##   ..$ wk_date                  : Date[1:153], format: "2015-10-04" "2015-10-11" "2015-10-18" "2015-10-25" ...
##  $ clinical_labs            :Classes 'tbl_df', 'tbl' and 'data.frame':   153 obs. of  11 variables:
##   ..$ region_type     : chr [1:153] "National" "National" "National" "National" ...
##   ..$ region          : chr [1:153] "National" "National" "National" "National" ...
##   ..$ year            : int [1:153] 2015 2015 2015 2015 2015 2015 2015 2015 2015 2015 ...
##   ..$ week            : int [1:153] 40 41 42 43 44 45 46 47 48 49 ...
##   ..$ total_specimens : int [1:153] 12029 13111 13441 13537 14687 15048 15250 15234 16201 16673 ...
##   ..$ total_a         : int [1:153] 84 116 97 98 97 122 84 119 145 140 ...
##   ..$ total_b         : int [1:153] 43 54 52 52 68 86 98 92 81 106 ...
##   ..$ percent_positive: num [1:153] 1.06 1.3 1.11 1.11 1.12 ...
##   ..$ percent_a       : num [1:153] 0.698 0.885 0.722 0.724 0.66 ...
##   ..$ percent_b       : num [1:153] 0.357 0.412 0.387 0.384 0.463 ...
##   ..$ wk_date         : Date[1:153], format: "2015-10-04" "2015-10-11" "2015-10-18" "2015-10-25" ...
mutate(xdat$combined_prior_to_2015_16, 
       percent_positive = percent_positive / 100) %>% 
  ggplot(aes(wk_date, percent_positive)) +
  geom_line() +
  scale_y_percent(name="% Positive") +
  labs(x=NULL, title="WHO/NREVSS Surveillance Data (National)") +
  theme_ipsum_rc(grid="XY")

who_nrevss("hhs", years=2016)
## $public_health_labs
## # A tibble: 520 x 13
##    region_type region  year  week total_specimens a_2009_h1n1  a_h3 a_subtyping_not…     b  bvic  byam h3n2v wk_date   
##    <chr>       <chr>  <int> <int>           <int>       <int> <int>            <int> <int> <int> <int> <int> <date>    
##  1 HHS Regions Regio…  2016    40              31           0     6                0     0     0     0     0 2016-10-02
##  2 HHS Regions Regio…  2016    40              31           0     6                0     0     2     0     0 2016-10-02
##  3 HHS Regions Regio…  2016    40             112           2     2                0     0     0     0     0 2016-10-02
##  4 HHS Regions Regio…  2016    40             112           1    11                0     1     2     0     0 2016-10-02
##  5 HHS Regions Regio…  2016    40             204           0     7                0     0     0     1     0 2016-10-02
##  6 HHS Regions Regio…  2016    40              39           1     1                0     0     0     0     0 2016-10-02
##  7 HHS Regions Regio…  2016    40              24           0     2                0     0     1     0     0 2016-10-02
##  8 HHS Regions Regio…  2016    40              46           2     8                0     0     0     0     0 2016-10-02
##  9 HHS Regions Regio…  2016    40             186           3    27                0     0     0     3     0 2016-10-02
## 10 HHS Regions Regio…  2016    40             113           0    17                0     0     0     0     0 2016-10-02
## # ... with 510 more rows
## 
## $clinical_labs
## # A tibble: 520 x 11
##    region_type region     year  week total_specimens total_a total_b percent_positive percent_a percent_b wk_date   
##    <chr>       <chr>     <int> <int>           <int>   <int>   <int>            <dbl>     <dbl>     <dbl> <date>    
##  1 HHS Regions Region 1   2016    40             654       5       1            0.917     0.765     0.153 2016-10-02
##  2 HHS Regions Region 2   2016    40            1307      10       3            0.995     0.765     0.230 2016-10-02
##  3 HHS Regions Region 3   2016    40             941       1       4            0.531     0.106     0.425 2016-10-02
##  4 HHS Regions Region 4   2016    40            2960      46      63            3.68      1.55      2.13  2016-10-02
##  5 HHS Regions Region 5   2016    40            2386       8       5            0.545     0.335     0.210 2016-10-02
##  6 HHS Regions Region 6   2016    40            1914      22      13            1.83      1.15      0.679 2016-10-02
##  7 HHS Regions Region 7   2016    40             723       0       0            0         0         0     2016-10-02
##  8 HHS Regions Region 8   2016    40             913       8       0            0.876     0.876     0     2016-10-02
##  9 HHS Regions Region 9   2016    40             992       6       1            0.706     0.605     0.101 2016-10-02
## 10 HHS Regions Region 10  2016    40             590      14       0            2.37      2.37      0     2016-10-02
## # ... with 510 more rows
who_nrevss("census", years=2016)
## $public_health_labs
## # A tibble: 468 x 13
##    region_type region  year  week total_specimens a_2009_h1n1  a_h3 a_subtyping_not…     b  bvic  byam h3n2v wk_date   
##    <chr>       <chr>  <int> <int>           <int>       <int> <int>            <int> <int> <int> <int> <int> <date>    
##  1 Census Reg… New E…  2016    40              31           0     6                0     0     0     0     0 2016-10-02
##  2 Census Reg… Mid-A…  2016    40              50           0     8                0     0     2     0     0 2016-10-02
##  3 Census Reg… East …  2016    40             139           0     4                0     0     0     1     0 2016-10-02
##  4 Census Reg… West …  2016    40             103           0     6                0     0     1     0     0 2016-10-02
##  5 Census Reg… South…  2016    40             181           3    11                0     1     2     0     0 2016-10-02
##  6 Census Reg… East …  2016    40              24           0     0                0     0     0     0     0 2016-10-02
##  7 Census Reg… West …  2016    40              27           0     1                0     0     0     0     0 2016-10-02
##  8 Census Reg… Mount…  2016    40              54           3    10                0     0     0     1     0 2016-10-02
##  9 Census Reg… Pacif…  2016    40             289           3    41                0     0     0     2     0 2016-10-02
## 10 Census Reg… New E…  2016    41              14           0     2                0     0     0     0     0 2016-10-09
## # ... with 458 more rows
## 
## $clinical_labs
## # A tibble: 468 x 11
##    region_type    region     year  week total_specimens total_a total_b percent_positive percent_a percent_b wk_date   
##    <chr>          <chr>     <int> <int>           <int>   <int>   <int>            <dbl>     <dbl>     <dbl> <date>    
##  1 Census Regions New Engl…  2016    40             654       5       1            0.917     0.765    0.153  2016-10-02
##  2 Census Regions Mid-Atla…  2016    40            1579      10       4            0.887     0.633    0.253  2016-10-02
##  3 Census Regions East Nor…  2016    40            2176       6       5            0.506     0.276    0.230  2016-10-02
##  4 Census Regions West Nor…  2016    40            1104       3       0            0.272     0.272    0      2016-10-02
##  5 Census Regions South At…  2016    40            2785      43      62            3.77      1.54     2.23   2016-10-02
##  6 Census Regions East Sou…  2016    40             844       4       4            0.948     0.474    0.474  2016-10-02
##  7 Census Regions West Sou…  2016    40            1738      21      13            1.96      1.21     0.748  2016-10-02
##  8 Census Regions Mountain   2016    40            1067       8       0            0.750     0.750    0      2016-10-02
##  9 Census Regions Pacific    2016    40            1433      20       1            1.47      1.40     0.0698 2016-10-02
## 10 Census Regions New Engl…  2016    41             810       5       1            0.741     0.617    0.123  2016-10-09
## # ... with 458 more rows
who_nrevss("state", years=2016)
## $public_health_labs
## # A tibble: 54 x 12
##    region_type region     season_descript… total_specimens a_2009_h1n1 a_h3  a_subtyping_not_p… b     bvic  byam  h3n2v
##    <chr>       <chr>      <chr>            <chr>           <chr>       <chr> <chr>              <chr> <chr> <chr> <chr>
##  1 States      Alabama    Season 2016-17   570             3           227   1                  2     15    14    0    
##  2 States      Alaska     Season 2016-17   5222            14          905   3                  252   2     11    0    
##  3 States      Arizona    Season 2016-17   2975            63          1630  0                  5     227   578   0    
##  4 States      Arkansas   Season 2016-17   121             0           51    0                  0     4     0     0    
##  5 States      California Season 2016-17   14074           184         4696  120                116   28    152   0    
##  6 States      Colorado   Season 2016-17   714             3           267   2                  4     31    219   0    
##  7 States      Connectic… Season 2016-17   1348            19          968   0                  0     62    263   0    
##  8 States      Delaware   Season 2016-17   3090            5           659   4                  11    27    127   1    
##  9 States      District … Season 2016-17   73              1           34    0                  3     0     4     0    
## 10 States      Florida    Season 2016-17   <NA>            <NA>        <NA>  <NA>               <NA>  <NA>  <NA>  <NA> 
## # ... with 44 more rows, and 1 more variable: wk_date <date>
## 
## $clinical_labs
## # A tibble: 2,808 x 11
##    region_type region        year  week total_specimens total_a total_b percent_positive percent_a percent_b wk_date   
##    <chr>       <chr>        <int> <int> <chr>           <chr>   <chr>   <chr>            <chr>     <chr>     <date>    
##  1 States      Alabama       2016    40 406             4       1       1.23             0.99      0.25      2016-10-02
##  2 States      Alaska        2016    40 <NA>            <NA>    <NA>    <NA>             <NA>      <NA>      2016-10-02
##  3 States      Arizona       2016    40 133             0       0       0                0         0         2016-10-02
##  4 States      Arkansas      2016    40 47              0       0       0                0         0         2016-10-02
##  5 States      California    2016    40 668             2       0       0.3              0.3       0         2016-10-02
##  6 States      Colorado      2016    40 260             0       0       0                0         0         2016-10-02
##  7 States      Connecticut   2016    40 199             3       0       1.51             1.51      0         2016-10-02
##  8 States      Delaware      2016    40 40              0       0       0                0         0         2016-10-02
##  9 States      District of…  2016    40 <NA>            <NA>    <NA>    <NA>             <NA>      <NA>      2016-10-02
## 10 States      Florida       2016    40 <NA>            <NA>    <NA>    <NA>             <NA>      <NA>      2016-10-02
## # ... with 2,798 more rows

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