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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. Please use GitHub issues to identify previous API functionality you would like ported over. There’s a release candidate for 0.5.2 which uses the old API but it likely to break in the near future given the changes to the hidden API. You can do what with devtools::install_github("hrbrmstr/cdcfluview", ref="58c172b").

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
  • 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_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

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] '0.7.0'

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

glimpse(age_group_distribution())
## Observations: 36,144
## Variables: 16
## $ sea_label         <chr> "1997-98", "1997-98", "1997-98", "1997-98", "1997-98", "1997-98", "1997-98", "1997-98", "...
## $ age_label         <fctr> 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, ...
## $ vir_label         <fctr> A (Subtyping not Performed), A (Subtyping not Performed), A (Subtyping not Performed), A...
## $ count             <int> 0, 1, 0, 0, 0, 0, 0, 3, 0, 6, 0, 1, 1, 2, 11, 8, 18, 26, 22, 19, 2, 5, 2, 1, 4, 0, 0, 0, ...
## $ mmwrid            <int> 1866, 1867, 1868, 1869, 1870, 1871, 1872, 1873, 1874, 1875, 1876, 1877, 1878, 1879, 1880,...
## $ seasonid          <int> 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 3...
## $ publishyearweekid <int> 2913, 2913, 2913, 2913, 2913, 2913, 2913, 2913, 2913, 2913, 2913, 2913, 2913, 2913, 2913,...
## $ sea_description   <chr> "Season 1997-98", "Season 1997-98", "Season 1997-98", "Season 1997-98", "Season 1997-98",...
## $ sea_startweek     <int> 1866, 1866, 1866, 1866, 1866, 1866, 1866, 1866, 1866, 1866, 1866, 1866, 1866, 1866, 1866,...
## $ sea_endweek       <int> 1918, 1918, 1918, 1918, 1918, 1918, 1918, 1918, 1918, 1918, 1918, 1918, 1918, 1918, 1918,...
## $ 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> 1997-09-28, 1997-10-05, 1997-10-12, 1997-10-19, 1997-10-26, 1997-11-02, 1997-11-09, 1997...
## $ wk_end            <date> 1997-10-04, 1997-10-11, 1997-10-18, 1997-10-25, 1997-11-01, 1997-11-08, 1997-11-15, 1997...
## $ year_wk_num       <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...

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: 25,795
## 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: 1,476
## 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         <fctr> 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, ...
## $ 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"))
## Observations: 2,385
## 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> 2003, 2003, 2003, 2003, 2003, 2003, 2003, 2003, 2003, 2003, 2003, 2003, 2003, 2003, 2004,...
## $ season            <int> 43, 43, 43, 43, 43, 43, 43, 43, 43, 43, 43, 43, 43, 43, 43, 43, 43, 43, 43, 43, 43, 43, 4...
## $ wk_start          <date> 2003-09-28, 2003-10-05, 2003-10-12, 2003-10-19, 2003-10-26, 2003-11-02, 2003-11-09, 2003...
## $ wk_end            <date> 2003-10-04, 2003-10-11, 2003-10-18, 2003-10-25, 2003-11-01, 2003-11-08, 2003-11-15, 2003...
## $ year_wk_num       <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...
## $ rate              <dbl> 0.0, 0.0, 0.0, 0.0, 0.1, 0.7, 3.3, 9.1, 16.9, 28.1, 40.0, 55.6, 69.0, 78.7, 83.1, 86.6, 8...
## $ weeklyrate        <dbl> 0.0, 0.0, 0.0, 0.0, 0.1, 0.6, 2.7, 5.8, 7.8, 11.2, 11.9, 15.6, 13.4, 9.7, 4.4, 3.4, 1.3, ...
## $ 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         <fctr> 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, ...
## $ sea_label         <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N...
## $ sea_description   <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N...
## $ mmwrid            <int> 2179, 2180, 2181, 2182, 2183, 2184, 2185, 2186, 2187, 2188, 2189, 2190, 2191, 2192, 2193,...
glimpse(hospitalizations("eip", "Colorado"))
## Observations: 2,385
## 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> 2003, 2003, 2003, 2003, 2003, 2003, 2003, 2003, 2003, 2003, 2003, 2003, 2003, 2003, 2004,...
## $ season            <int> 43, 43, 43, 43, 43, 43, 43, 43, 43, 43, 43, 43, 43, 43, 43, 43, 43, 43, 43, 43, 43, 43, 4...
## $ wk_start          <date> 2003-09-28, 2003-10-05, 2003-10-12, 2003-10-19, 2003-10-26, 2003-11-02, 2003-11-09, 2003...
## $ wk_end            <date> 2003-10-04, 2003-10-11, 2003-10-18, 2003-10-25, 2003-11-01, 2003-11-08, 2003-11-15, 2003...
## $ year_wk_num       <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...
## $ rate              <dbl> 0.0, 0.0, 0.0, 0.0, 0.6, 3.6, 21.2, 57.5, 94.3, 130.6, 146.4, 150.6, 153.0, 157.2, 158.5,...
## $ weeklyrate        <dbl> 0.0, 0.0, 0.0, 0.0, 0.6, 3.0, 17.5, 36.3, 36.9, 36.3, 15.7, 4.2, 2.4, 4.2, 1.2, 1.2, 1.8,...
## $ 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         <fctr> 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, ...
## $ sea_label         <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N...
## $ sea_description   <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N...
## $ mmwrid            <int> 2179, 2180, 2181, 2182, 2183, 2184, 2185, 2186, 2187, 2188, 2189, 2190, 2191, 2192, 2193,...
glimpse(hospitalizations("ihsp"))
## Observations: 1,476
## 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> 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> 0.0, 0.4, 3.6, 7.6, 14.0, 25.5, 47.1, 71.8, 87.4, 92.5, 94.9, 96.9, 98.1, 98.9, 100.9, 10...
## $ weeklyrate        <dbl> 0.0, 0.4, 3.2, 4.0, 6.4, 11.6, 21.5, 24.7, 15.6, 5.2, 2.4, 2.0, 1.2, 0.8, 2.0, 1.2, 1.6, ...
## $ 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         <fctr> 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, ...
## $ 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,...
glimpse(hospitalizations("ihsp", "Oklahoma"))
## Observations: 390
## 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> 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> 0.0, 1.3, 10.8, 21.5, 40.4, 63.3, 88.9, 106.4, 115.8, 121.2, 125.2, 125.2, 126.6, 127.9, ...
## $ weeklyrate        <dbl> 0.0, 1.3, 9.4, 10.8, 18.9, 22.9, 25.6, 17.5, 9.4, 5.4, 4.0, 0.0, 1.3, 1.3, 2.7, 1.3, 5.4,...
## $ 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         <fctr> 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, ...
## $ 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,...

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: 1,048
## 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: 1,048 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      1.10148        1.21686     179        NA       157      205        NA     29
##  2    National National  1997    41      1.20007        1.28064     199        NA       151      242        NA     23
##  3    National National  1997    42      1.37876        1.23906     228        NA       153      266        NA     34
##  4    National National  1997    43      1.19920        1.14473     188        NA       193      236        NA     36
##  5    National National  1997    44      1.65618        1.26112     217        NA       162      280        NA     41
##  6    National National  1997    45      1.41326        1.28275     178        NA       148      281        NA     48
##  7    National National  1997    46      1.98680        1.44579     294        NA       240      328        NA     70
##  8    National National  1997    47      2.44749        1.64796     288        NA       293      456        NA     63
##  9    National National  1997    48      1.73901        1.67517     268        NA       206      343        NA     69
## 10    National National  1997    49      1.93919        1.61739     299        NA       282      415        NA    102
## # ... with 1,038 more rows, and 4 more variables: ilitotal <dbl>, num_of_providers <dbl>, total_patients <dbl>,
## #   week_start <date>

## Observations: 10,480
## Variables: 16
## $ region_type      <chr> "HHS Regions", "HHS Regions", "HHS Regions", "HHS Regions", "HHS Regions", "HHS Regions", ...
## $ region           <fctr> 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: 10,480 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>    <fctr> <int> <int>        <dbl>          <dbl>   <dbl>     <dbl>     <dbl>    <dbl>     <dbl>  <dbl>
##  1 HHS Regions  Region 1  1997    40     0.498535       0.623848      15        NA         7       22        NA      0
##  2 HHS Regions  Region 2  1997    40     0.374963       0.384615       0        NA         3        0        NA      0
##  3 HHS Regions  Region 3  1997    40     1.354280       1.341720       6        NA         7       15        NA      4
##  4 HHS Regions  Region 4  1997    40     0.400338       0.450010      12        NA        23       11        NA      0
##  5 HHS Regions  Region 5  1997    40     1.229260       0.901266      31        NA        24       30        NA      4
##  6 HHS Regions  Region 6  1997    40     1.018980       0.747384       2        NA         1        2        NA      0
##  7 HHS Regions  Region 7  1997    40     0.871791       1.152860       0        NA         4       18        NA      5
##  8 HHS Regions  Region 8  1997    40     0.516017       0.422654       2        NA         0        3        NA      0
##  9 HHS Regions  Region 9  1997    40     1.807610       2.258780      80        NA        76       74        NA     13
## 10 HHS Regions Region 10  1997    40     4.743520       4.825400      31        NA        12       30        NA      3
## # ... with 10,470 more rows, and 4 more variables: ilitotal <dbl>, num_of_providers <dbl>, total_patients <dbl>,
## #   week_start <date>

## Observations: 9,432
## 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: 9,432 x 16
##       region_type             region  year  week weighted_ili unweighted_ili age_0_4 age_25_49 age_25_64 age_5_24
##             <chr>              <chr> <int> <int>        <dbl>          <dbl>   <dbl>     <dbl>     <dbl>    <dbl>
##  1 Census Regions        New England  1997    40    0.4985350      0.6238480      15        NA         7       22
##  2 Census Regions       Mid-Atlantic  1997    40    0.8441440      1.3213800       4        NA         8       12
##  3 Census Regions East North Central  1997    40    0.7924860      0.8187380      28        NA        20       28
##  4 Census Regions West North Central  1997    40    1.7640500      1.2793900       3        NA         8       20
##  5 Census Regions     South Atlantic  1997    40    0.5026620      0.7233800      14        NA        22       14
##  6 Census Regions East South Central  1997    40    0.0542283      0.0688705       0        NA         3        0
##  7 Census Regions West South Central  1997    40    1.0189800      0.7473840       2        NA         1        2
##  8 Census Regions           Mountain  1997    40    2.2587800      2.2763300      87        NA        71       71
##  9 Census Regions            Pacific  1997    40    2.0488300      3.2349400      26        NA        17       36
## 10 Census Regions        New England  1997    41    0.6426690      0.8158010      14        NA        14       29
## # ... with 9,422 more rows, and 6 more variables: age_50_64 <dbl>, age_65 <dbl>, ilitotal <dbl>,
## #   num_of_providers <dbl>, total_patients <dbl>, week_start <date>

## Observations: 19,718
## 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, NA, 22, 117, 1...
## $ num_of_providers <dbl> 35, 7, 49, 15, 112, 14, 12, 13, 4, NA, 62, 18, 12, 74, 44, 6, 40, 14, NA, 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: 19,718 x 16
##    region_type               region  year  week weighted_ili unweighted_ili age_0_4 age_25_49 age_25_64 age_5_24
##          <chr>                <chr> <int> <int>        <dbl>          <dbl>   <dbl>     <dbl>     <dbl>    <dbl>
##  1      States              Alabama  2010    40           NA      2.1347700      NA        NA        NA       NA
##  2      States               Alaska  2010    40           NA      0.8751460      NA        NA        NA       NA
##  3      States              Arizona  2010    40           NA      0.6747210      NA        NA        NA       NA
##  4      States             Arkansas  2010    40           NA      0.6960560      NA        NA        NA       NA
##  5      States           California  2010    40           NA      1.9541200      NA        NA        NA       NA
##  6      States             Colorado  2010    40           NA      0.6606840      NA        NA        NA       NA
##  7      States          Connecticut  2010    40           NA      0.0783085      NA        NA        NA       NA
##  8      States             Delaware  2010    40           NA      0.1001250      NA        NA        NA       NA
##  9      States District of Columbia  2010    40           NA      2.8087700      NA        NA        NA       NA
## 10      States              Florida  2010    40           NA             NA      NA        NA        NA       NA
## # ... with 19,708 more rows, and 6 more variables: age_50_64 <dbl>, age_65 <dbl>, 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: 216 x 9
##    statename ili_activity_label ili_activity_group statefips stateabbr    weekend weeknumber  year seasonid
##        <chr>             <fctr>              <chr>     <chr>     <chr>     <date>      <int> <int>    <int>
##  1   Alabama            Level 2            Minimal        01        AL 2017-10-07         40  2017       57
##  2   Alabama            Level 2            Minimal        01        AL 2017-10-14         41  2017       57
##  3   Alabama            Level 2            Minimal        01        AL 2017-10-21         42  2017       57
##  4   Alabama            Level 3            Minimal        01        AL 2017-10-28         43  2017       57
##  5    Alaska            Level 1            Minimal        02        AK 2017-10-07         40  2017       57
##  6    Alaska            Level 2            Minimal        02        AK 2017-10-14         41  2017       57
##  7    Alaska            Level 4                Low        02        AK 2017-10-21         42  2017       57
##  8    Alaska            Level 3            Minimal        02        AK 2017-10-28         43  2017       57
##  9   Arizona            Level 2            Minimal        04        AZ 2017-10-07         40  2017       57
## 10   Arizona            Level 3            Minimal        04        AZ 2017-10-14         41  2017       57
## # ... with 206 more rows
xdf <- map_df(2008:2017, ili_weekly_activity_indicators)

count(xdf, weekend, ili_activity_label) %>% 
  complete(weekend, ili_activity_label) %>% 
  ggplot(aes(weekend, ili_activity_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: 419 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    0.058     0.061       0.054            0.763               10             1962      36283      1972
##  2       57    0.058     0.062       0.056            0.675               10             1795      32107      1805
##  3       56    0.059     0.063       0.059            1.000               18             3022      51404      3040
##  4       56    0.060     0.063       0.061            1.000               11             3193      52130      3204
##  5       56    0.061     0.064       0.062            1.000                7             3178      51443      3185
##  6       56    0.062     0.065       0.061            1.000               17             3129      51865      3146
##  7       56    0.063     0.066       0.060            1.000               16             3099      51753      3115
##  8       56    0.064     0.067       0.061            1.000               19             3208      52541      3227
##  9       56    0.065     0.068       0.060            1.000                7             3192      53460      3199
## 10       56    0.066     0.069       0.062            1.000               22             3257      53163      3279
## # ... with 409 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"))
## # A tibble: 21,788 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       NA        NA       0.065            0.836                0               50        772        50
##  2       57       NA        NA       0.064            0.767                0               45        708        45
##  3       57       NA        NA       0.063            0.666                1                2         48         3
##  4       57       NA        NA       0.105            0.527                0                4         38         4
##  5       57       NA        NA       0.053            0.412                0               20        374        20
##  6       57       NA        NA       0.059            0.393                0               21        356        21
##  7       57       NA        NA       0.060            0.751                0               25        420        25
##  8       57       NA        NA       0.050            0.604                0               17        338        17
##  9       57       NA        NA       0.065            0.774                1              228       3510       229
## 10       57       NA        NA       0.059            0.758                2              201       3438       203
## # ... with 21,778 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"))
## # A tibble: 4,190 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    0.060     0.067       0.051            0.735                0               85       1683        85
##  2       57    0.061     0.068       0.060            0.701                0               96       1605        96
##  3       57    0.060     0.065       0.061            0.608                1              154       2524       155
##  4       57    0.060     0.066       0.063            0.602                1              157       2497       158
##  5       57    0.053     0.058       0.045            0.511                1              115       2575       116
##  6       57    0.054     0.059       0.045            0.440                1               98       2215        99
##  7       57    0.056     0.060       0.051            0.744                3              394       7753       397
##  8       57    0.057     0.061       0.052            0.651                1              354       6778       355
##  9       57    0.055     0.059       0.052            0.914                1              403       7701       404
## 10       57    0.056     0.060       0.054            0.799                4              358       6733       362
## # ... with 4,180 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
##  *                <chr>                                                <chr>
##  1              Alabama                  Alabama Department of Public Health
##  2               Alaska           State of Alaska Health and Social Services
##  3              Arizona                Arizona Department of Health Services
##  4             Arkansas                        Arkansas Department of Health
##  5           California               California Department of Public Health
##  6             Colorado Colorado Department of Public Health and Environment
##  7          Connecticut              Connecticut Department of Public Health
##  8             Delaware                  Delaware Health and Social Services
##  9 District of Columbia            District of Columbia Department of Health
## 10              Florida                         Florida Department of Health
## # ... with 49 more rows, and 3 more variables: url <chr>, statewebsitename <chr>, statefluphonenum <chr>

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':   108 obs. of  13 variables:
##   ..$ region_type              : chr [1:108] "National" "National" "National" "National" ...
##   ..$ region                   : chr [1:108] "National" "National" "National" "National" ...
##   ..$ year                     : int [1:108] 2015 2015 2015 2015 2015 2015 2015 2015 2015 2015 ...
##   ..$ week                     : int [1:108] 40 41 42 43 44 45 46 47 48 49 ...
##   ..$ total_specimens          : int [1:108] 1139 1152 1198 1244 1465 1393 1458 1157 1550 1518 ...
##   ..$ a_2009_h1n1              : int [1:108] 4 5 10 9 4 11 17 17 27 38 ...
##   ..$ a_h3                     : int [1:108] 65 41 50 31 23 34 42 24 36 37 ...
##   ..$ a_subtyping_not_performed: int [1:108] 2 2 1 4 4 1 1 0 3 3 ...
##   ..$ b                        : int [1:108] 10 7 8 9 9 10 4 4 9 11 ...
##   ..$ bvic                     : int [1:108] 0 3 3 1 1 4 0 3 3 2 ...
##   ..$ byam                     : int [1:108] 1 0 2 4 4 2 4 9 12 11 ...
##   ..$ h3n2v                    : int [1:108] 0 0 0 0 0 0 0 0 0 0 ...
##   ..$ wk_date                  : Date[1:108], format: "2015-10-04" "2015-10-11" "2015-10-18" "2015-10-25" ...
##  $ clinical_labs            :Classes 'tbl_df', 'tbl' and 'data.frame':   108 obs. of  11 variables:
##   ..$ region_type     : chr [1:108] "National" "National" "National" "National" ...
##   ..$ region          : chr [1:108] "National" "National" "National" "National" ...
##   ..$ year            : int [1:108] 2015 2015 2015 2015 2015 2015 2015 2015 2015 2015 ...
##   ..$ week            : int [1:108] 40 41 42 43 44 45 46 47 48 49 ...
##   ..$ total_specimens : int [1:108] 12029 13111 13441 13537 14687 15048 15250 15234 16201 16673 ...
##   ..$ total_a         : int [1:108] 84 116 97 98 97 122 84 119 145 140 ...
##   ..$ total_b         : int [1:108] 43 54 52 52 68 86 98 92 81 106 ...
##   ..$ percent_positive: num [1:108] 1.06 1.3 1.11 1.11 1.12 ...
##   ..$ percent_a       : num [1:108] 0.698 0.885 0.722 0.724 0.66 ...
##   ..$ percent_b       : num [1:108] 0.357 0.412 0.387 0.384 0.463 ...
##   ..$ wk_date         : Date[1:108], 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")
## $combined_prior_to_2015_16
## # A tibble: 9,400 x 14
##    region_type    region  year  week total_specimens percent_positive a_2009_h1n1  a_h1  a_h3 a_subtyping_not_performed
##          <chr>     <chr> <int> <int>           <int>            <dbl>       <int> <int> <int>                     <int>
##  1 HHS Regions  Region 1  1997    40              51                0           0     0     0                         0
##  2 HHS Regions  Region 2  1997    40             152                0           0     0     0                         0
##  3 HHS Regions  Region 3  1997    40             143                0           0     0     0                         0
##  4 HHS Regions  Region 4  1997    40              98                0           0     0     0                         0
##  5 HHS Regions  Region 5  1997    40             147                0           0     0     0                         0
##  6 HHS Regions  Region 6  1997    40             343                0           0     0     0                         0
##  7 HHS Regions  Region 7  1997    40             133                0           0     0     0                         0
##  8 HHS Regions  Region 8  1997    40              78                0           0     0     0                         0
##  9 HHS Regions  Region 9  1997    40              98                0           0     0     0                         0
## 10 HHS Regions Region 10  1997    40              48                0           0     0     0                         0
## # ... with 9,390 more rows, and 4 more variables: a_unable_to_subtype <int>, b <int>, h3n2v <int>, wk_date <date>
## 
## $public_health_labs
## # A tibble: 1,080 x 13
##    region_type    region  year  week total_specimens a_2009_h1n1  a_h3 a_subtyping_not_performed     b  bvic  byam
##          <chr>     <chr> <int> <chr>           <int>       <int> <int>                     <int> <int> <int> <int>
##  1 HHS Regions  Region 1  2015  <NA>              39           0     5                         0     0     0     0
##  2 HHS Regions  Region 2  2015  <NA>              56           1     4                         0     1     0     0
##  3 HHS Regions  Region 3  2015  <NA>             132           1     3                         0     0     0     0
##  4 HHS Regions  Region 4  2015  <NA>              83           0     5                         0     1     0     0
##  5 HHS Regions  Region 5  2015  <NA>             218           2     7                         0     0     0     1
##  6 HHS Regions  Region 6  2015  <NA>              97           0     2                         0     0     0     0
##  7 HHS Regions  Region 7  2015  <NA>              36           0     2                         0     0     0     0
##  8 HHS Regions  Region 8  2015  <NA>              71           0     2                         0     0     0     0
##  9 HHS Regions  Region 9  2015  <NA>             273           0    22                         2     8     0     0
## 10 HHS Regions Region 10  2015  <NA>             134           0    13                         0     0     0     0
## # ... with 1,070 more rows, and 2 more variables: h3n2v <int>, wk_date <date>
## 
## $clinical_labs
## # A tibble: 1,080 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  2015    40             693       2       3         0.721501  0.288600  0.432900 2015-10-04
##  2 HHS Regions  Region 2  2015    40            1220       5       0         0.409836  0.409836  0.000000 2015-10-04
##  3 HHS Regions  Region 3  2015    40             896       0       1         0.111607  0.000000  0.111607 2015-10-04
##  4 HHS Regions  Region 4  2015    40            2486      24      16         1.609010  0.965406  0.643604 2015-10-04
##  5 HHS Regions  Region 5  2015    40            2138      14       3         0.795136  0.654818  0.140318 2015-10-04
##  6 HHS Regions  Region 6  2015    40            1774       8      16         1.352870  0.450958  0.901917 2015-10-04
##  7 HHS Regions  Region 7  2015    40             621       2       1         0.483092  0.322061  0.161031 2015-10-04
##  8 HHS Regions  Region 8  2015    40             824       1       1         0.242718  0.121359  0.121359 2015-10-04
##  9 HHS Regions  Region 9  2015    40             980      25       2         2.755100  2.551020  0.204082 2015-10-04
## 10 HHS Regions Region 10  2015    40             397       3       0         0.755668  0.755668  0.000000 2015-10-04
## # ... with 1,070 more rows
who_nrevss("census")
## $combined_prior_to_2015_16
## # A tibble: 8,460 x 14
##       region_type             region  year  week total_specimens percent_positive a_2009_h1n1  a_h1  a_h3
##             <chr>              <chr> <int> <int>           <int>            <dbl>       <int> <int> <int>
##  1 Census Regions        New England  1997    40              51                0           0     0     0
##  2 Census Regions       Mid-Atlantic  1997    40             155                0           0     0     0
##  3 Census Regions East North Central  1997    40             127                0           0     0     0
##  4 Census Regions West North Central  1997    40             183                0           0     0     0
##  5 Census Regions     South Atlantic  1997    40             204                0           0     0     0
##  6 Census Regions East South Central  1997    40              34                0           0     0     0
##  7 Census Regions West South Central  1997    40             339                0           0     0     0
##  8 Census Regions           Mountain  1997    40              85                0           0     0     0
##  9 Census Regions            Pacific  1997    40             113                0           0     0     0
## 10 Census Regions        New England  1997    41              54                0           0     0     0
## # ... with 8,450 more rows, and 5 more variables: a_subtyping_not_performed <int>, a_unable_to_subtype <int>, b <int>,
## #   h3n2v <int>, wk_date <date>
## 
## $public_health_labs
## # A tibble: 972 x 13
##       region_type             region  year  week total_specimens a_2009_h1n1  a_h3 a_subtyping_not_performed     b
##             <chr>              <chr> <int> <chr>           <int>       <int> <int>                     <int> <int>
##  1 Census Regions        New England  2015  <NA>              39           0     5                         0     0
##  2 Census Regions       Mid-Atlantic  2015  <NA>              63           1     5                         0     1
##  3 Census Regions East North Central  2015  <NA>              91           2     5                         0     0
##  4 Census Regions West North Central  2015  <NA>             169           0     4                         0     0
##  5 Census Regions     South Atlantic  2015  <NA>             187           1     7                         0     0
##  6 Census Regions East South Central  2015  <NA>              21           0     0                         0     1
##  7 Census Regions West South Central  2015  <NA>              72           0     2                         0     0
##  8 Census Regions           Mountain  2015  <NA>             111           0     6                         0     0
##  9 Census Regions            Pacific  2015  <NA>             386           0    31                         2     8
## 10 Census Regions        New England  2015  <NA>              39           2     3                         0     0
## # ... with 962 more rows, and 4 more variables: bvic <int>, byam <int>, h3n2v <int>, wk_date <date>
## 
## $clinical_labs
## # A tibble: 972 x 11
##       region_type             region  year  week total_specimens total_a total_b percent_positive percent_a percent_b
##             <chr>              <chr> <int> <int>           <int>   <int>   <int>            <dbl>     <dbl>     <dbl>
##  1 Census Regions        New England  2015    40             693       2       3         0.721501  0.288600 0.4329000
##  2 Census Regions       Mid-Atlantic  2015    40            1584       5       1         0.378788  0.315657 0.0631313
##  3 Census Regions East North Central  2015    40            1918      13       3         0.834202  0.677789 0.1564130
##  4 Census Regions West North Central  2015    40             978       3       1         0.408998  0.306748 0.1022490
##  5 Census Regions     South Atlantic  2015    40            2403      20      12         1.331670  0.832293 0.4993760
##  6 Census Regions East South Central  2015    40             615       4       4         1.300810  0.650407 0.6504070
##  7 Census Regions West South Central  2015    40            1592       8      16         1.507540  0.502513 1.0050300
##  8 Census Regions           Mountain  2015    40             943       1       1         0.212089  0.106045 0.1060450
##  9 Census Regions            Pacific  2015    40            1303      28       2         2.302380  2.148890 0.1534920
## 10 Census Regions        New England  2015    41             752      11       4         1.994680  1.462770 0.5319150
## # ... with 962 more rows, and 1 more variables: wk_date <date>
who_nrevss("state")
## $combined_prior_to_2015_16
## # A tibble: 14,094 x 14
##    region_type               region  year  week total_specimens percent_positive a_2009_h1n1  a_h1  a_h3
##          <chr>                <chr> <int> <int>           <chr>            <chr>       <chr> <chr> <chr>
##  1      States              Alabama  2010    40              54                0           0     0     0
##  2      States               Alaska  2010    40              40                0           0     0     0
##  3      States              Arizona  2010    40              40              2.5           0     0     1
##  4      States             Arkansas  2010    40              15                0           0     0     0
##  5      States           California  2010    40             183             3.28           2     0     3
##  6      States             Colorado  2010    40             126             0.79           0     0     1
##  7      States          Connecticut  2010    40              54                0           0     0     0
##  8      States             Delaware  2010    40              75                4           0     0     3
##  9      States District of Columbia  2010    40              14                0           0     0     0
## 10      States              Florida  2010    40            <NA>             <NA>        <NA>  <NA>  <NA>
## # ... with 14,084 more rows, and 5 more variables: a_subtyping_not_performed <chr>, a_unable_to_subtype <chr>, b <chr>,
## #   h3n2v <chr>, wk_date <date>
## 
## $public_health_labs
## # A tibble: 162 x 12
##    region_type               region season_description total_specimens a_2009_h1n1  a_h3 a_subtyping_not_performed
##          <chr>                <chr>              <chr>           <chr>       <chr> <chr>                     <chr>
##  1      States              Alabama     Season 2015-16             256          59    16                         1
##  2      States               Alaska     Season 2015-16            4691         607    98                         0
##  3      States              Arizona     Season 2015-16            2110         762   580                         0
##  4      States             Arkansas     Season 2015-16             128          20     8                         0
##  5      States           California     Season 2015-16           12241        1394   825                        28
##  6      States             Colorado     Season 2015-16            1625         912   243                         3
##  7      States          Connecticut     Season 2015-16            1581         662    52                         0
##  8      States             Delaware     Season 2015-16            2754         414    20                        12
##  9      States District of Columbia     Season 2015-16             172          68     3                         0
## 10      States              Florida     Season 2015-16            <NA>        <NA>  <NA>                      <NA>
## # ... with 152 more rows, and 5 more variables: b <chr>, bvic <chr>, byam <chr>, h3n2v <chr>, wk_date <date>
## 
## $clinical_labs
## # A tibble: 5,832 x 11
##    region_type               region  year  week total_specimens total_a total_b percent_positive percent_a percent_b
##          <chr>                <chr> <int> <int>           <chr>   <chr>   <chr>            <chr>     <chr>     <chr>
##  1      States              Alabama  2015    40             167       2       3             2.99       1.2       1.8
##  2      States               Alaska  2015    40            <NA>    <NA>    <NA>             <NA>      <NA>      <NA>
##  3      States              Arizona  2015    40              55       0       0                0         0         0
##  4      States             Arkansas  2015    40              26       0       1             3.85         0      3.85
##  5      States           California  2015    40             679       2       0             0.29      0.29         0
##  6      States             Colorado  2015    40             255       0       1             0.39         0      0.39
##  7      States          Connecticut  2015    40             304       1       0             0.33      0.33         0
##  8      States             Delaware  2015    40              22       0       0                0         0         0
##  9      States District of Columbia  2015    40            <NA>    <NA>    <NA>             <NA>      <NA>      <NA>
## 10      States              Florida  2015    40            <NA>    <NA>    <NA>             <NA>      <NA>      <NA>
## # ... with 5,822 more rows, and 1 more variables: wk_date <date>

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