hrbrmstr / cdcfluview Goto Github PK

:mask: R package to Retrieve U.S. Flu Season Data from the CDC FluView Portal (WHO & ILINet)

Home Page: https://hrbrmstr.github.io/cdcfluview/index.html

License: Other

R 98.32% Makefile 1.68%

r rstats cdc national-influenza-statistics cdc-fluview-portal

cdcfluview's Introduction

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 Flu Season Data from the United States Centers for Disease Control and Prevention (‘CDC’) ‘FluView’ Portal

Description

The U.S. Centers for Disease Control (CDC) maintains a portal https://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. base maps
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

# CRAN
install.packages("cdcfluview")

# main branch
remotes::install_git("https://git.rud.is/hrbrmstr/cdcfluview.git")
remotes::install_git("https://sr.ht/~hrbrmstr/cdcfluview")
remotes::install_git("https://gitlab.com/hrbrmstr/cdcfluview")
remotes::install_github("hrbrmstr/cdcfluview")

Usage

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

# current version
packageVersion("cdcfluview")
## [1] '0.9.4'

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

glimpse(age_group_distribution(years=2015))
## Rows: 1,872
## Columns: 15
## $ sea_label       <chr> "2015-16", "2015-16", "2015-16", "2015-16", "2015-16", "2015-16", "2015-16", "2015-16", "2015-…
## $ 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, 0-4 yr…
## $ vir_label       <fct> A (Subtyping not Performed), A (Subtyping not Performed), A (Subtyping not Performed), A (Subt…
## $ 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, 0, 0…
## $ mmwrid          <int> 2806, 2807, 2808, 2809, 2810, 2811, 2812, 2813, 2814, 2815, 2816, 2817, 2818, 2819, 2820, 2821…
## $ seasonid        <int> 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55…
## $ sea_description <chr> "Season 2015-16", "Season 2015-16", "Season 2015-16", "Season 2015-16", "Season 2015-16", "Sea…
## $ sea_startweek   <int> 2806, 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, 2857…
## $ vir_description <chr> "A-Unk", "A-Unk", "A-Unk", "A-Unk", "A-Unk", "A-Unk", "A-Unk", "A-Unk", "A-Unk", "A-Unk", "A-U…
## $ vir_startmmwrid <int> 1397, 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, 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, 2015-11-2…
## $ wk_end          <date> 2015-10-10, 2015-10-17, 2015-10-24, 2015-10-31, 2015-11-07, 2015-11-14, 2015-11-21, 2015-11-2…
## $ 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, 13,…

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())
## Rows: 30,851
## Columns: 7
## $ statename         <chr> "Alabama", "Alabama", "Alabama", "Alabama", "Alabama", "Alabama", "Alabama", "Alabama", "Ala…
## $ url               <chr> "http://adph.org/influenza/", "http://adph.org/influenza/", "http://adph.org/influenza/", "h…
## $ website           <chr> "Influenza Surveillance", "Influenza Surveillance", "Influenza Surveillance", "Influenza Sur…
## $ activity_estimate <chr> "No Activity", "No Activity", "No Activity", "Local Activity", "Sporadic", "Sporadic", "Spor…
## $ weekend           <date> 2003-10-04, 2003-10-11, 2003-10-18, 2003-10-25, 2003-11-01, 2003-11-08, 2003-11-15, 2003-11…
## $ season            <chr> "2003-04", "2003-04", "2003-04", "2003-04", "2003-04", "2003-04", "2003-04", "2003-04", "200…
## $ weeknumber        <chr> "40", "41", "42", "43", "44", "45", "46", "47", "48", "49", "50", "51", "52", "53", "1", "2"…

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"))
## Rows: 4,368
## Columns: 14
## $ surveillance_area <chr> "FluSurv-NET", "FluSurv-NET", "FluSurv-NET", "FluSurv-NET", "FluSurv-NET", "FluSurv-NET", "F…
## $ region            <chr> "Entire Network", "Entire Network", "Entire Network", "Entire Network", "Entire Network", "E…
## $ year              <int> 2017, 2017, 2017, 2017, 2017, 2017, 2017, 2017, 2017, 2017, 2017, 2017, 2017, 2018, 2018, 20…
## $ season            <int> 57, 57, 57, 57, 57, 57, 57, 57, 57, 57, 57, 57, 57, 57, 57, 57, 57, 57, 57, 57, 57, 57, 57, …
## $ wk_start          <date> 2017-10-01, 2017-10-08, 2017-10-15, 2017-10-22, 2017-10-29, 2017-11-05, 2017-11-12, 2017-11…
## $ wk_end            <date> 2017-10-07, 2017-10-14, 2017-10-21, 2017-10-28, 2017-11-04, 2017-11-11, 2017-11-18, 2017-11…
## $ 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, 1…
## $ rate              <dbl> 0.0, 0.1, 0.1, 0.1, 0.3, 0.4, 0.6, 0.8, 1.0, 1.3, 1.8, 2.5, 3.4, 4.2, 5.6, 6.8, 8.2, 10.3, 1…
## $ weeklyrate        <dbl> 0.0, 0.0, 0.0, 0.0, 0.1, 0.1, 0.2, 0.2, 0.2, 0.3, 0.6, 0.6, 0.9, 0.8, 1.3, 1.3, 1.4, 2.1, 1.…
## $ age               <int> 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3,…
## $ age_label         <fct> 5-17 yr, 5-17 yr, 5-17 yr, 5-17 yr, 5-17 yr, 5-17 yr, 5-17 yr, 5-17 yr, 5-17 yr, 5-17 yr, 5-…
## $ sea_label         <chr> "2017-18", "2017-18", "2017-18", "2017-18", "2017-18", "2017-18", "2017-18", "2017-18", "201…
## $ sea_description   <chr> "Season 2017-18", "Season 2017-18", "Season 2017-18", "Season 2017-18", "Season 2017-18", "S…
## $ mmwrid            <int> 2910, 2911, 2912, 2913, 2914, 2915, 2916, 2917, 2918, 2919, 2920, 2921, 2922, 2923, 2924, 29…

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_viridis_d(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))
## Rows: 390
## Columns: 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", "E…
## $ year              <int> 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2016, 2016, 20…
## $ season            <int> 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, …
## $ wk_start          <date> 2015-10-04, 2015-10-11, 2015-10-18, 2015-10-25, 2015-11-01, 2015-11-08, 2015-11-15, 2015-11…
## $ wk_end            <date> 2015-10-10, 2015-10-17, 2015-10-24, 2015-10-31, 2015-11-07, 2015-11-14, 2015-11-21, 2015-11…
## $ 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, 1…
## $ rate              <dbl> 0.4, 0.7, 1.0, 1.1, 1.4, 1.6, 1.9, 2.2, 2.4, 2.8, 3.4, 4.4, 5.0, 6.5, 7.6, 8.7, 10.4, 12.5, …
## $ weeklyrate        <dbl> 0.4, 0.3, 0.3, 0.2, 0.3, 0.3, 0.3, 0.3, 0.2, 0.4, 0.6, 0.9, 0.6, 1.5, 1.1, 1.1, 1.6, 2.1, 3.…
## $ age               <int> 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 10…
## $ age_label         <fct> 65+ yr, 65+ yr, 65+ yr, 65+ yr, 65+ yr, 65+ yr, 65+ yr, 65+ yr, 65+ yr, 65+ yr, 65+ yr, 65+ …
## $ sea_label         <chr> "2015-16", "2015-16", "2015-16", "2015-16", "2015-16", "2015-16", "2015-16", "2015-16", "201…
## $ sea_description   <chr> "Season 2015-16", "Season 2015-16", "Season 2015-16", "Season 2015-16", "Season 2015-16", "S…
## $ mmwrid            <int> 2806, 2807, 2808, 2809, 2810, 2811, 2812, 2813, 2814, 2815, 2816, 2817, 2818, 2819, 2820, 28…

glimpse(hospitalizations("eip", "Colorado", years=2015))
## Rows: 390
## Columns: 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", "Colorad…
## $ year              <int> 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2016, 2016, 20…
## $ season            <int> 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, …
## $ wk_start          <date> 2015-10-04, 2015-10-11, 2015-10-18, 2015-10-25, 2015-11-01, 2015-11-08, 2015-11-15, 2015-11…
## $ wk_end            <date> 2015-10-10, 2015-10-17, 2015-10-24, 2015-10-31, 2015-11-07, 2015-11-14, 2015-11-21, 2015-11…
## $ 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, 1…
## $ rate              <dbl> 0.0, 0.3, 0.6, 0.9, 0.9, 1.3, 1.3, 1.6, 1.6, 2.5, 2.8, 4.4, 6.3, 7.8, 9.7, 10.7, 12.5, 14.7,…
## $ weeklyrate        <dbl> 0.0, 0.3, 0.3, 0.3, 0.0, 0.3, 0.0, 0.3, 0.0, 0.9, 0.3, 1.6, 1.9, 1.6, 1.9, 0.9, 1.9, 2.2, 2.…
## $ age               <int> 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 10…
## $ age_label         <fct> 65+ yr, 65+ yr, 65+ yr, 65+ yr, 65+ yr, 65+ yr, 65+ yr, 65+ yr, 65+ yr, 65+ yr, 65+ yr, 65+ …
## $ sea_label         <chr> "2015-16", "2015-16", "2015-16", "2015-16", "2015-16", "2015-16", "2015-16", "2015-16", "201…
## $ sea_description   <chr> "Season 2015-16", "Season 2015-16", "Season 2015-16", "Season 2015-16", "Season 2015-16", "S…
## $ mmwrid            <int> 2806, 2807, 2808, 2809, 2810, 2811, 2812, 2813, 2814, 2815, 2816, 2817, 2818, 2819, 2820, 28…

glimpse(hospitalizations("ihsp", years=2015))
## Rows: 390
## Columns: 14
## $ surveillance_area <chr> "IHSP", "IHSP", "IHSP", "IHSP", "IHSP", "IHSP", "IHSP", "IHSP", "IHSP", "IHSP", "IHSP", "IHS…
## $ region            <chr> "Entire Network", "Entire Network", "Entire Network", "Entire Network", "Entire Network", "E…
## $ year              <int> 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2016, 2016, 20…
## $ season            <int> 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, …
## $ wk_start          <date> 2015-10-04, 2015-10-11, 2015-10-18, 2015-10-25, 2015-11-01, 2015-11-08, 2015-11-15, 2015-11…
## $ wk_end            <date> 2015-10-10, 2015-10-17, 2015-10-24, 2015-10-31, 2015-11-07, 2015-11-14, 2015-11-21, 2015-11…
## $ 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, 1…
## $ rate              <dbl> 0.4, 0.8, 1.0, 1.2, 1.4, 1.4, 1.4, 1.6, 1.8, 2.0, 2.5, 3.1, 3.5, 4.1, 5.1, 6.5, 8.0, 10.0, 1…
## $ weeklyrate        <dbl> 0.4, 0.4, 0.2, 0.2, 0.2, 0.0, 0.0, 0.2, 0.2, 0.2, 0.4, 0.6, 0.4, 0.6, 1.0, 1.4, 1.4, 2.0, 4.…
## $ age               <int> 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 10…
## $ age_label         <fct> 65+ yr, 65+ yr, 65+ yr, 65+ yr, 65+ yr, 65+ yr, 65+ yr, 65+ yr, 65+ yr, 65+ yr, 65+ yr, 65+ …
## $ sea_label         <chr> "2015-16", "2015-16", "2015-16", "2015-16", "2015-16", "2015-16", "2015-16", "2015-16", "201…
## $ sea_description   <chr> "Season 2015-16", "Season 2015-16", "Season 2015-16", "Season 2015-16", "Season 2015-16", "S…
## $ mmwrid            <int> 2806, 2807, 2808, 2809, 2810, 2811, 2812, 2813, 2814, 2815, 2816, 2817, 2818, 2819, 2820, 28…

glimpse(hospitalizations("ihsp", "Oklahoma", years=2010))
## Rows: 390
## Columns: 14
## $ surveillance_area <chr> "IHSP", "IHSP", "IHSP", "IHSP", "IHSP", "IHSP", "IHSP", "IHSP", "IHSP", "IHSP", "IHSP", "IHS…
## $ region            <chr> "Oklahoma", "Oklahoma", "Oklahoma", "Oklahoma", "Oklahoma", "Oklahoma", "Oklahoma", "Oklahom…
## $ year              <int> 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2011, 2011, 20…
## $ season            <int> 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, …
## $ wk_start          <date> 2010-10-03, 2010-10-10, 2010-10-17, 2010-10-24, 2010-10-31, 2010-11-07, 2010-11-14, 2010-11…
## $ wk_end            <date> 2010-10-09, 2010-10-16, 2010-10-23, 2010-10-30, 2010-11-06, 2010-11-13, 2010-11-20, 2010-11…
## $ 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, 1…
## $ rate              <dbl> 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.2, 0.2, 0.5, 0.7, 0.7, 1.4, 2.3, 2.5, 3.5, 4.6, 6.0, 7.8, 8.…
## $ weeklyrate        <dbl> 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.2, 0.0, 0.2, 0.2, 0.0, 0.7, 0.9, 0.2, 0.9, 1.2, 1.4, 1.8, 0.…
## $ age               <int> 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 8,…
## $ age_label         <fct> 18-49 yr, 18-49 yr, 18-49 yr, 18-49 yr, 18-49 yr, 18-49 yr, 18-49 yr, 18-49 yr, 18-49 yr, 18…
## $ sea_label         <chr> "2010-11", "2010-11", "2010-11", "2010-11", "2010-11", "2010-11", "2010-11", "2010-11", "201…
## $ sea_description   <chr> "Season 2010-11", "Season 2010-11", "Season 2010-11", "Season 2010-11", "Season 2010-11", "S…
## $ mmwrid            <int> 2545, 2546, 2547, 2548, 2549, 2550, 2551, 2552, 2553, 2554, 2555, 2556, 2557, 2558, 2559, 25…

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)
  
})
## Rows: 1,233
## Columns: 16
## $ region_type      <chr> "National", "National", "National", "National", "National", "National", "National", "National…
## $ region           <chr> "National", "National", "National", "National", "National", "National", "National", "National…
## $ year             <int> 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1998, 199…
## $ 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, 12…
## $ weighted_ili     <dbl> 1.101480, 1.200070, 1.378760, 1.199200, 1.656180, 1.413260, 1.986800, 2.447490, 1.739010, 1.9…
## $ unweighted_ili   <dbl> 1.216860, 1.280640, 1.239060, 1.144730, 1.261120, 1.282750, 1.445790, 1.647960, 1.675170, 1.6…
## $ age_0_4          <dbl> 179, 199, 228, 188, 217, 178, 294, 288, 268, 299, 346, 348, 510, 579, 639, 690, 856, 824, 881…
## $ 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, N…
## $ age_25_64        <dbl> 157, 151, 153, 193, 162, 148, 240, 293, 206, 282, 268, 235, 404, 584, 759, 654, 679, 817, 769…
## $ age_5_24         <dbl> 205, 242, 266, 236, 280, 281, 328, 456, 343, 415, 388, 362, 492, 576, 810, 1121, 1440, 1600, …
## $ 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, N…
## $ age_65           <dbl> 29, 23, 34, 36, 41, 48, 70, 63, 69, 102, 81, 59, 113, 207, 207, 148, 151, 196, 233, 146, 119,…
## $ ilitotal         <dbl> 570, 615, 681, 653, 700, 655, 932, 1100, 886, 1098, 1083, 1004, 1519, 1946, 2415, 2613, 3126,…
## $ num_of_providers <dbl> 192, 191, 219, 213, 213, 195, 248, 256, 252, 253, 242, 190, 251, 250, 254, 255, 245, 245, 239…
## $ total_patients   <dbl> 46842, 48023, 54961, 57044, 55506, 51062, 64463, 66749, 52890, 67887, 61314, 47719, 48429, 52…
## $ week_start       <date> 1997-09-28, 1997-10-05, 1997-10-12, 1997-10-19, 1997-10-26, 1997-11-02, 1997-11-09, 1997-11-…
## # A tibble: 1,233 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 1,223 more rows, and 4 more variables: ilitotal <dbl>, num_of_providers <dbl>, total_patients <dbl>,
## #   week_start <date>

## Rows: 12,330
## Columns: 16
## $ region_type      <chr> "HHS Regions", "HHS Regions", "HHS Regions", "HHS Regions", "HHS Regions", "HHS Regions", "HH…
## $ region           <fct> Region 1, Region 2, Region 3, Region 4, Region 5, Region 6, Region 7, Region 8, Region 9, Reg…
## $ year             <int> 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 199…
## $ 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, 4…
## $ weighted_ili     <dbl> 0.498535, 0.374963, 1.354280, 0.400338, 1.229260, 1.018980, 0.871791, 0.516017, 1.807610, 4.7…
## $ unweighted_ili   <dbl> 0.623848, 0.384615, 1.341720, 0.450010, 0.901266, 0.747384, 1.152860, 0.422654, 2.258780, 4.8…
## $ 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, 0…
## $ 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, N…
## $ 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, 3, …
## $ 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, 75, …
## $ 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, N…
## $ 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, 2, …
## $ 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, 1…
## $ 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, 64, …
## $ total_patients   <dbl> 7053, 780, 2385, 10222, 9875, 669, 2342, 1183, 10758, 1575, 6987, 872, 2740, 11310, 9618, 684…
## $ week_start       <date> 1997-09-28, 1997-09-28, 1997-09-28, 1997-09-28, 1997-09-28, 1997-09-28, 1997-09-28, 1997-09-…
## # A tibble: 12,330 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 12,320 more rows, and 4 more variables: ilitotal <dbl>, num_of_providers <dbl>, total_patients <dbl>,
## #   week_start <date>

## Rows: 11,097
## Columns: 16
## $ region_type      <chr> "Census Regions", "Census Regions", "Census Regions", "Census Regions", "Census Regions", "Ce…
## $ 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, 199…
## $ 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, 4…
## $ weighted_ili     <dbl> 0.4985350, 0.8441440, 0.7924860, 1.7640500, 0.5026620, 0.0542283, 1.0189800, 2.2587800, 2.048…
## $ unweighted_ili   <dbl> 0.6238480, 1.3213800, 0.8187380, 1.2793900, 0.7233800, 0.0688705, 0.7473840, 2.2763300, 3.234…
## $ 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, 1…
## $ 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, N…
## $ 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, 55, …
## $ 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, 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, N…
## $ 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, 14,…
## $ 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, 2…
## $ 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, 10, …
## $ total_patients   <dbl> 7053, 2119, 9649, 2892, 6912, 4356, 669, 10719, 2473, 6987, 2384, 9427, 2823, 7591, 4947, 684…
## $ week_start       <date> 1997-09-28, 1997-09-28, 1997-09-28, 1997-09-28, 1997-09-28, 1997-09-28, 1997-09-28, 1997-09-…
## # A tibble: 11,097 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 Engla…  1997    40       0.499          0.624       15        NA         7       22        NA      0
##  2 Census Regi… Mid-Atlan…  1997    40       0.844          1.32         4        NA         8       12        NA      4
##  3 Census Regi… East Nort…  1997    40       0.792          0.819       28        NA        20       28        NA      3
##  4 Census Regi… West Nort…  1997    40       1.76           1.28         3        NA         8       20        NA      6
##  5 Census Regi… South Atl…  1997    40       0.503          0.723       14        NA        22       14        NA      0
##  6 Census Regi… East Sout…  1997    40       0.0542         0.0689       0        NA         3        0        NA      0
##  7 Census Regi… West Sout…  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 Engla…  1997    41       0.643          0.816       14        NA        14       29        NA      0
## # … with 11,087 more rows, and 4 more variables: ilitotal <dbl>, num_of_providers <dbl>, total_patients <dbl>,
## #   week_start <date>

## Rows: 29,793
## Columns: 16
## $ region_type      <chr> "States", "States", "States", "States", "States", "States", "States", "States", "States", "St…
## $ region           <chr> "Alabama", "Alaska", "Arizona", "Arkansas", "California", "Colorado", "Connecticut", "Delawar…
## $ year             <int> 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 201…
## $ 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, 4…
## $ 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, N…
## $ unweighted_ili   <dbl> 2.1347700, 0.8751460, 0.6747210, 0.6960560, 1.9541200, 0.6606840, 0.0783085, 0.1001250, 2.808…
## $ 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, N…
## $ 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, N…
## $ 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, N…
## $ 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, N…
## $ 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, N…
## $ 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, N…
## $ ilitotal         <dbl> 249, 15, 172, 18, 632, 134, 3, 4, 73, NA, 647, 20, 19, 505, 65, 10, 39, 19, 391, 22, 117, 168…
## $ 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, 17,…
## $ total_patients   <dbl> 11664, 1714, 25492, 2586, 32342, 20282, 3831, 3995, 2599, NA, 40314, 1943, 4579, 39390, 12525…
## $ week_start       <date> 2010-10-03, 2010-10-03, 2010-10-03, 2010-10-03, 2010-10-03, 2010-10-03, 2010-10-03, 2010-10-…
## # A tibble: 29,793 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      Connecticut  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 o…  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 29,783 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: 2,805 x 8
##    statename    url                          website        activity_level activity_level_… weekend    season weeknumber
##  * <chr>        <chr>                        <chr>                   <dbl> <chr>            <date>     <chr>       <dbl>
##  1 Alabama      "http://adph.org/influenza/" Influenza Sur…              2 Minimal          2017-10-07 2017-…         40
##  2 Alaska       "http://dhss.alaska.gov/dph… Influenza Sur…              1 Minimal          2017-10-07 2017-…         40
##  3 Arizona      "http://www.azdhs.gov/phs/o… Influenza & R…              2 Minimal          2017-10-07 2017-…         40
##  4 Arkansas     "http://www.healthy.arkansa… Communicable …              1 Minimal          2017-10-07 2017-…         40
##  5 California   "https://www.cdph.ca.gov/Pr… Influenza (Fl…              2 Minimal          2017-10-07 2017-…         40
##  6 Colorado     "https://www.colorado.gov/p… Influenza Sur…              1 Minimal          2017-10-07 2017-…         40
##  7 Connecticut  "https://portal.ct.gov/DPH/… Flu Statistics              1 Minimal          2017-10-07 2017-…         40
##  8 Delaware     "http://dhss.delaware.gov/d… Weekly Influe…              1 Minimal          2017-10-07 2017-…         40
##  9 District of… "https://dchealth.dc.gov/no… Influenza Inf…              2 Minimal          2017-10-07 2017-…         40
## 10 Florida      "http://www.floridahealth.g… Weekly Influe…              1 Minimal          2017-10-07 2017-…         40
## # … with 2,795 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: 398 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 60         0.053     0.0560      0.081                 1                8             4825      59682      4833
##  2 60         0.054     0.057       0.084                 1               12             5173      61641      5185
##  3 60         0.055     0.0580      0.086                 1               16             5208      60467      5224
##  4 60         0.0560    0.059       0.091                 1               15             5642      62047      5657
##  5 60         0.057     0.06        0.0970                1               21             6142      63280      6163
##  6 60         0.0580    0.061       0.105                 1               21             7075      67380      7096
##  7 60         0.059     0.062       0.117                 1               20             8040      68644      8060
##  8 60         0.06      0.063       0.132                 1               30             9400      71440      9430
##  9 60         0.061     0.064       0.143                 1               27            10440      73066     10467
## 10 60         0.062     0.065       0.157                 1               35            12048      77136     12083
## # … with 388 more rows, and 10 more variables: weeknumber <chr>, geo_description <chr>, age_label <chr>,
## #   week_start <date>, week_end <date>, year_week_num <int>, mmwrid <chr>, coverage_area <chr>, region_name <chr>,
## #   callout <chr>

select(nat_pi, week_end, percent_pni, baseline, threshold) %>% 
  gather(measure, value, -week_end) %>% 
  ggplot(aes(week_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      0.046             0.962                0               43        935        43
##  2 55             NA        NA      0.036             0.835                0               29        811        29
##  3 55             NA        NA      0.054             0.833                0               44        809        44
##  4 55             NA        NA      0.07              0.947                0               64        920        64
##  5 55             NA        NA      0.053             0.926                0               48        900        48
##  6 55             NA        NA      0.057             0.987                0               55        959        55
##  7 55             NA        NA      0.052             1                    0               53       1023        53
##  8 55             NA        NA      0.063             1                    1               62       1002        63
##  9 55             NA        NA      0.0560            0.95                 0               52        923        52
## 10 55             NA        NA      0.054             0.954                0               50        927        50
## # … with 2,694 more rows, and 10 more variables: weeknumber <chr>, geo_description <chr>, age_label <chr>,
## #   week_start <date>, week_end <date>, year_week_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          0.064    0.071       0.07              1                    0              178       2525       178
##  2 55          0.065    0.072       0.064             1                    0              160       2512       160
##  3 55          0.066    0.073       0.0580            1                    1              141       2457       142
##  4 55          0.067    0.074       0.07              0.989                0              171       2426       171
##  5 55          0.068    0.075       0.065             1                    2              166       2565       168
##  6 55          0.069    0.077       0.067             0.985                1              162       2415       163
##  7 55          0.071    0.078       0.079             1                    0              198       2491       198
##  8 55          0.072    0.079       0.072             1                    1              176       2468       177
##  9 55          0.073    0.081       0.067             0.96                 3              154       2353       157
## 10 55          0.075    0.0820      0.062             0.996                0              151       2441       151
## # … with 510 more rows, and 10 more variables: weeknumber <chr>, geo_description <chr>, age_label <chr>,
## #   week_start <date>, week_end <date>, year_week_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 Publi… "http://adph.org/influenza/"       Influenza Surveillance 334-206-5300    
##  2 Alaska        State of Alaska Health and … "http://dhss.alaska.gov/dph/Epi/i… Influenza Surveillanc… 907-269-8000    
##  3 Arizona       Arizona Department of Healt… "http://www.azdhs.gov/phs/oids/ep… Influenza & RSV Surve… 602-542-1025    
##  4 Arkansas      Arkansas Department of Heal… "http://www.healthy.arkansas.gov/… Communicable Disease … 501-661-2000    
##  5 California    California Department of Pu… "https://www.cdph.ca.gov/Programs… Influenza (Flu)        916-558-1784    
##  6 Colorado      Colorado Department of Publ… "https://www.colorado.gov/pacific… Influenza Surveillance 303-692-2000    
##  7 Connecticut   Connecticut Department of P… "https://portal.ct.gov/DPH/Epidem… Flu Statistics         860-509-8000    
##  8 Delaware      Delaware Health and Social … "http://dhss.delaware.gov/dhss/dp… Weekly Influenza Surv… 302-744-4700    
##  9 District of … District of Columbia Depart… "https://dchealth.dc.gov/node/114… Influenza Information  202-442-5955    
## 10 Florida       Florida Department of Health "http://www.floridahealth.gov/dis… Weekly Influenza Surv… 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: tibble[,14] [940 × 14] (S3: tbl_df/tbl/data.frame)
##   ..$ 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       : tibble[,13] [293 × 13] (S3: tbl_df/tbl/data.frame)
##   ..$ region_type              : chr [1:293] "National" "National" "National" "National" ...
##   ..$ region                   : chr [1:293] "National" "National" "National" "National" ...
##   ..$ year                     : int [1:293] 2015 2015 2015 2015 2015 2015 2015 2015 2015 2015 ...
##   ..$ week                     : int [1:293] 40 41 42 43 44 45 46 47 48 49 ...
##   ..$ total_specimens          : int [1:293] 1139 1152 1198 1244 1465 1393 1458 1157 1550 1518 ...
##   ..$ a_2009_h1n1              : int [1:293] 4 5 10 9 4 11 17 17 27 38 ...
##   ..$ a_h3                     : int [1:293] 65 41 50 31 23 34 42 24 36 37 ...
##   ..$ a_subtyping_not_performed: int [1:293] 2 2 1 4 4 1 1 0 3 3 ...
##   ..$ b                        : int [1:293] 10 7 8 9 9 10 4 4 9 11 ...
##   ..$ bvic                     : int [1:293] 0 3 3 1 1 4 0 3 3 2 ...
##   ..$ byam                     : int [1:293] 1 0 2 4 4 2 4 9 12 11 ...
##   ..$ h3n2v                    : int [1:293] 0 0 0 0 0 0 0 0 0 0 ...
##   ..$ wk_date                  : Date[1:293], format: "2015-10-04" "2015-10-11" "2015-10-18" "2015-10-25" ...
##  $ clinical_labs            : tibble[,11] [293 × 11] (S3: tbl_df/tbl/data.frame)
##   ..$ region_type     : chr [1:293] "National" "National" "National" "National" ...
##   ..$ region          : chr [1:293] "National" "National" "National" "National" ...
##   ..$ year            : int [1:293] 2015 2015 2015 2015 2015 2015 2015 2015 2015 2015 ...
##   ..$ week            : int [1:293] 40 41 42 43 44 45 46 47 48 49 ...
##   ..$ total_specimens : int [1:293] 12029 13111 13441 13537 14687 15048 15250 15234 16201 16673 ...
##   ..$ total_a         : int [1:293] 84 116 97 98 97 122 84 119 145 140 ...
##   ..$ total_b         : int [1:293] 43 54 52 52 68 86 98 92 81 106 ...
##   ..$ percent_positive: num [1:293] 1.06 1.3 1.11 1.11 1.12 ...
##   ..$ percent_a       : num [1:293] 0.698 0.885 0.722 0.724 0.66 ...
##   ..$ percent_b       : num [1:293] 0.357 0.412 0.387 0.384 0.463 ...
##   ..$ wk_date         : Date[1:293], 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 Region…  2016    40              31           0     6                0     0     0     0     0 2016-10-02
##  2 HHS Regions Region…  2016    40              31           0     6                0     0     2     0     0 2016-10-02
##  3 HHS Regions Region…  2016    40             112           2     2                0     0     0     0     0 2016-10-02
##  4 HHS Regions Region…  2016    40             112           1    11                0     1     2     0     0 2016-10-02
##  5 HHS Regions Region…  2016    40             204           0     7                0     0     0     1     0 2016-10-02
##  6 HHS Regions Region…  2016    40              39           1     1                0     0     0     0     0 2016-10-02
##  7 HHS Regions Region…  2016    40              24           0     2                0     0     1     0     0 2016-10-02
##  8 HHS Regions Region…  2016    40              46           2     8                0     0     0     0     0 2016-10-02
##  9 HHS Regions Region…  2016    40             186           3    27                0     0     0     3     0 2016-10-02
## 10 HHS Regions Region…  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 Regi… New E…  2016    40              31           0     6                0     0     0     0     0 2016-10-02
##  2 Census Regi… Mid-A…  2016    40              50           0     8                0     0     2     0     0 2016-10-02
##  3 Census Regi… East …  2016    40             139           0     4                0     0     0     1     0 2016-10-02
##  4 Census Regi… West …  2016    40             103           0     6                0     0     1     0     0 2016-10-02
##  5 Census Regi… South…  2016    40             181           3    11                0     1     2     0     0 2016-10-02
##  6 Census Regi… East …  2016    40              24           0     0                0     0     0     0     0 2016-10-02
##  7 Census Regi… West …  2016    40              27           0     1                0     0     0     0     0 2016-10-02
##  8 Census Regi… Mount…  2016    40              54           3    10                0     0     0     1     0 2016-10-02
##  9 Census Regi… Pacif…  2016    40             289           3    41                0     0     0     2     0 2016-10-02
## 10 Census Regi… 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 Regio… New England  2016    40             654       5       1            0.917     0.765    0.153  2016-10-02
##  2 Census Regio… Mid-Atlant…  2016    40            1579      10       4            0.887     0.633    0.253  2016-10-02
##  3 Census Regio… East North…  2016    40            2176       6       5            0.506     0.276    0.230  2016-10-02
##  4 Census Regio… West North…  2016    40            1104       3       0            0.272     0.272    0      2016-10-02
##  5 Census Regio… South Atla…  2016    40            2785      43      62            3.77      1.54     2.23   2016-10-02
##  6 Census Regio… East South…  2016    40             844       4       4            0.948     0.474    0.474  2016-10-02
##  7 Census Regio… West South…  2016    40            1738      21      13            1.96      1.21     0.748  2016-10-02
##  8 Census Regio… Mountain     2016    40            1067       8       0            0.750     0.750    0      2016-10-02
##  9 Census Regio… Pacific      2016    40            1433      20       1            1.47      1.40     0.0698 2016-10-02
## 10 Census Regio… New England  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_descripti… 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

cdcfluview Metrics

Lang	# Files	(%)	LoC	(%)	Blank lines	(%)	# Lines	(%)
R	21	0.46	865	0.44	311	0.40	512	0.43
Rmd	1	0.02	81	0.04	64	0.08	82	0.07
make	1	0.02	32	0.02	11	0.01	1	0.00
SUM	23	0.50	978	0.50	386	0.50	595	0.50

clock Package Metrics for cdcfluview

Code of Conduct

Please note that this project is released with a Contributor Code of Conduct. By participating in this project you agree to abide by its terms.

cdcfluview's People

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cdcfluview's Issues

ili by state weekly

Hi, i want to see if this library has a function that will give you the latest ili data by state?. Something that we can run weekly and get the latest data.

almos as same as the site https://gis.cdc.gov/grasp/fluview/main.html

add ILInet revisions as well?

Hello @hrbrmstr, another great package!

I am intersted in the ILInet data but I think the current package only returns what http://currents.plos.org/outbreaks/index.html%3Fp=39911.html#ref7 calls the gold standard.

That is, the ILInet data that was deemed as final and not available in real time.
Instead, the historical datasets seems to be available at the following address

https://www.cdc.gov/flu/weekly/weeklyarchives2013-2014/data/senAllregt08.htm

Where the 8 in senAllregt08 means this is the ILI data that was current during week 8 of the 2013-2014 episode. If you pull the data for the next week

https://www.cdc.gov/flu/weekly/weeklyarchives2013-2014/data/senAllregt09.htm

you will notice that PAST ILI values may have been revised/modified.

It is very important to keep track of revisions over time because one really want to use the information that was really available at the time of the forecast. In other words, at during week T you only know the data shown in senAllregtT.htm

Is this something you can incorporate in the package? Perhaps we should store these as a list column. Let me know what you think

Thanks!

pi_mortality data is doubled

each row in pi_mortality data is duplicated.

pimn <- cdcfluview::pi_mortality(coverage_area = 'national')
nrow(pimn) / sum(base::duplicated(pimn))
pimr <- cdcfluview::pi_mortality(coverage_area = 'region')
nrow(pimr) / sum(base::duplicated(pimr))
(pims <- cdcfluview::pi_mortality(coverage_area = 'state'))
nrow(pims) / sum(base::duplicated(pims))

returns 2 for each dataset.

The problem originates in CDC's data: https://gis.cdc.gov/grasp/flu7/GetPhase07InitApp?appVersion=Public
which has 2 entries for each of nchs_mapcode 1 and 2.

And the warnings from left_join are hidden, so the user is not alerted to the issue.

client error: (411) Length Required

First of all thank you for this nice Packge.

within last days, if I use get_flu_data, I get this error: client error: (411) Length Required
Is it a bug in Package?

package no longer seems to work for clinical data

I am trying to pull the clinical FluView data but am getting an error:

who_nrevss(region="state", years = 2021)

produces

Error in basename(nm) : a character vector argument expected In addition: Warning message: In unzip(tf, overwrite = TRUE, exdir = td) : error 1 in extracting from zip file

Manually using the function code, it seems to be doing something up to through the res <- httr::POST() call. However, when going to unzip the temp file, the .zip file is empty.

dates shld be dates

cannot publish to CRAN w/o ensuring all dates in data frames are actual dates.

API pointers

It would be awesome if you could lay out what you know about the API at the moment, so that other masochists like me could possibly crank out wrappers for other languages (I'm considering doing it unto Python). Thanks Bob 👍

years=2016

When I query for "years=2016" by itself there are not data, any other individual year works, and if I say "years=2015:2016" I do get data from 2016. Just not by itself.

Inconsistent naming of start of week

ilinet returns the date of the start of the week as week_start but pi_mortality returns it as wk_start (as well as wk_end, yr_wk_num). I think a consistent naming scheme would be helpful.

Is it OK if I submit a pull request for this?

API now https

CDC has switched the API for get_flu_data to an https server, so the current code is returning a 500 error. The rest of the link for each function is staying the same - just needs the s for the secure server.

where are the shape files stored?

I've been trying to adapt some of your code for a different project and am trying to figure out how you reference the shapefiles in the cdc_basemap() function. Where are those shapefiles stored or from where are you reading them in?

"Virgin Islands" warning triggered by example cartogram map code in README.md

I get the following warning in using the CDC State FluView map code in the README.md file.

Warning in validate_states(state_data, state_col, merge.x) :
Found invalid state values: Virgin Islands

The issue seems to be triggered by the line bolded below.

gg_s <- state_flu %>%
filter(weekend=="Jan-02-2016") %>%
select(state=statename, value=activity_level) %>%
filter(!(state %in% c("Puerto Rico", "New York City"))) %>% # need to add PR to statebins
mutate(value=as.numeric(gsub("Level ", "", value))) %>%
statebins(brewer_pal="RdPu", breaks=4,
labels=c("Minimal", "Low", "Moderate", "High"),
legend_position="bottom", legend_title="ILI Activity Level") +
ggtitle("CDC State FluView (2015-01-03)")
gg_s

I think that Virgin Islands needs to be added to statebins (like PR used to need it) and should be filtered out in that line in the meantime. Also, it looks like PR was added to statebins, so it can be removed from the filter.

data requests, prevalence by age and location.

Hey,

I'm mostly a doctor, not a coder, and I have no familiarity with R ... but I'm wondering if your code here would allow me to send a request in javascript that would return the flu prevalence (percentage of respiratory specimens testing positive) for a given age and locations?

I'm trying to code up a guide that would help physicians decide how to interpret a positive/negative rapid flu test (or whether to do one at all), but I need accurate prevalance data to start with (even just national prevalence would be an acceptable starting place, but even better if it could be targeted to patient's age and location).

Am I barking up the wrong tree, or can you help me? I'd appreciate any insight you can give me on how to get this up-to-date data on the fly for clinicians on the front-lines.

Thanks,

Dr. Zain Hakeem
[email protected]
RiverRock Medical Clinic
Rx-Bayes co-founder

Error: Error in read.table(file = file, header = header, sep = sep, quote = quote, : more columns than column names

Hi,
Useful package for more easily incorporating these data into other analyses. I've only just started to play with the code you have posted, but I'm getting an error right off the bat. I could certainly be doing something wrong, but when I run:
flu <- get_flu_data("hhs", 1:10, data_source = c("who", "ilinet"), years=2010:2016)
I get the error:
Error in read.table(file = file, header = header, sep = sep, quote = quote, :
more columns than column names

Also, not sure how interested you are in expanding this, but there's additional influenza related mortality data that are now available - http://www.cdc.gov/flu/weekly/nchs.htm

Aaron

Possible Enhancement: Add age group argument to pi_mortality function?

The pi_mortality function currently only pulls data for the overall population. If you pass additional age group parameters, data can also be retrieved for the '<18', '18-64', and '65+' age groups (see 'nchs_ages' in the retrieved meta data).

ilinet function ceased working when selecting a year

ilinet <- ilinet(region = c("state"), years = 2023)

gives the error:
Error in MMWRweek::MMWRweek2Date(xdf$year, xdf$week) :
all(is.numeric(MMWRyear)) is not TRUE

In fact, when not specifying a year, it seems to only retrieve data through the week of 9/24/23. This was working just last week so I wonder if there is some change in the CDC data format?

Any idea what might be causing this?

age_group_distribution function bug

Unfortunately, the CDC updated the meta-data they pass for 'Seasons', which causes an issue for the age_group_distribution function. In xdat, the additional parameter 'IncludeWeeklyRatesAndStrata' is passed for Season. This causes an issue due to an NA column index when joining.

In the file agd-ipt.r , the issue is resolved by adding in an additional column name (e.g. "sea_includeweeklyratesandstrata") to the of the vector on lines 38-39.

Hospital API change

The api used to retrieve hospital data in this package is still functional, but doesn't return data past the 2019-2020 season. The hospital data in the website is now being returned from a different request URL (https://gis.cdc.gov/GRASP/Flu3/PostPhase03DataTool). HTTP post request would need to be updated to retrieve current hospital data.

Might want to add httr package dependency to README

Tried code in README but failed because write_disk function was not found. Installed and loaded httr package and then I was able to use the get_flu_data function.

Installing and loading cdcflueview didn't give any errors even though I didn't have the httr package installed (I'm not sure how this works). In any case, may want to mention this is required in README.

years=2017

Hi,

When running the function get_flu_data('national', NA) and omitting the years argument, you get an error. I think it's because there is no 2017 data yet. This may correct itself in the future, but I just wanted to let you know that it's not working right now. If the argument years=2016 is included, it works fine. I guess this is more of a heads up for those of us that use the function without any years argument.

inflated package size since 0.9.1

I noticed that the cdcfluview tarball grew in size from 25K in version 0.9.0 to over 1M and wondered why. The reason is that the plots from the README are included in man/figures. Since the README itself is not included in the package and the figures do not seem to be referenced from any Rd files, these figures could be excluded from the package.

The README seems to have been excluded via .Rbuildignore for a reason and I can also see README_files (the old location for figures) being excluded. So I guess possible fixes are

adding ^man/figures/README-.* to .Rbuildignore
or not set knitr's fig.path = "man/figures/README-" (I suspect this is done by hrbrpkghelpr::global_opts())

Change to new cran checks badge URL

👋🏽 I maintain the cran checks badges. Please change to the new cran checks badge URL (e.g., https://badges.cranchecks.info/worst/dplyr.svg). Old badges at (e.g. https://cranchecks.info/badges/worst/dplyr) will be unavailable as of Jan 1st 2023.

Blog post from June 2022 announcing the change: https://recology.info/2022/06/cran-checks-badges/
New badges repo: https://github.com/r-hub/cchecksbadges
Old badges repo: https://github.com/sckott/cchecksapi

data for most states since 2015 is no longer available

When I run the line 'ilinet(region = "state")' it now only gives data for Puerto Rico and no other state/territory. I think this problem just started this morning.

fails to install in non-English locales

When I try to install current cdcfluview 0.7.0, I get:

* installing *source* package ‘cdcfluview’ ...
** package ‘cdcfluview’ successfully unpacked and MD5 sums checked
** R
** data
*** moving datasets to lazyload DB
** byte-compile and prepare package for lazy loading
Error in seq.int(0, to0 - from, by) : 'to' must be a finite number
Error : unable to load R code in package ‘cdcfluview’
ERROR: lazy loading failed for package ‘cdcfluview’
* removing ‘/opt/R/R-3.5.1/site-library/cdcfluview’

I found this hard to debug. Especially surprising to me was that the package successfully installs on all platforms on the CRAN check farm (https://cran.r-project.org/web/checks/check_results_cdcfluview.html).

So here is how to trigger the issue in a non-English locale:

library("dplyr")
library("MMWRweek")
source("https://raw.githubusercontent.com/hrbrmstr/cdcfluview/v0.7.0/R/mmwr-map.r")
#> Error in seq.int(0, to0 - from, by) : 'to' must be a finite number

The problem is that cdcfluview not only defines functions, but also runs code in its R/mmwr-map.r source file to generate the mmwrid_map data upon installation, which fails if the locale-dependent weekdays() does not return English names. The problematic code actually resides in the MMWRweekday() function of the MMWRweek package (https://github.com/jarad/MMWRweek/blob/cbf6afa162402353b2917329a6b00e00c0f4133a/R/MMWRweekday.R#L13-L16) which wrongly assumes that weekdays() returns English names. For me, MMWRweekday("1962-01-01") returns NA. I have just submitted a corresponding bug report at jarad/MMWRweek#1.

Workaround

To install cdcfluview in a non-English locale, simply start R in a (temporary) environment where LC_TIME=C. Setting this within R (Sys.setenv("LC_TIME"="C")) before install.packages also worked for me. Package checks on CRAN probably run under LC_TIME=C so the issue does not pop up there.

Fix

The generation of the mmwrid_map data at package installation and the mmwr_* functions will start to work in non-English locales once jarad/MMWRweek#1 is fixed.

To directly fix the installation failure of cdcfluview in non-English locales, independent of a possible MMWRweek update in the future, the auxiliary function .start_date() in https://github.com/hrbrmstr/cdcfluview/blob/master/R/mmwr-map.r could be replaced by:

.start_date <- function(year) {
  jan1 <- as.Date(paste0(year, '-01-01'))
  wday <- as.numeric(strftime(jan1, "%w"))  # Sunday is 0
  jan1 - wday + 7*(wday>3)
}

Pneumonia, influenza, and COIVD mortality data

The functionality of this package is great but I was wondering if there was any updates to include data on pneumonia, influenza, and COVID (PIC) mortality aside from P&I mortality.

Thank you and again the package has been very helpful so far!

Bug in $week_start field (via e-mail)

via @jturtle

The ilinet() function is returning incorrect dates in the $week_start field. The first issue is that $week_start dates are Mondays rather than Sundays. The second issue is that $week_start dates often fall in the wrong week. I suspect that year/week was converted to a date using a system that defined the start of the week as Monday and counted weeks 0-52 rather than 1-53. A quick R example:

library(cdcfluview)
nat_dat = ilinet(region="national", years=1997:2018)
diff(nat_dat$week_start)

The result of diff() is the number of days between each date and should be all 7's, but clearly there are some 14's and NAs mixed in. Looking at any date from $week_start reveals it to be a Monday. Hope this helps. Let me know if I can help out in any way.

Error in get_state_data() for retrieving multiple seasons of data

Thank you for creating such a great package -cdcfluview. It helps me easily download data from FluView.

Just now I used get_state_data() to download multiple seasons of FluView data.

> state_flu <- get_state_data(c(2013, 2014))
> unique(state_flu$SEASON)
[1] "2013-14"

If my understanding for the help file of get_state_data(), the results of unique(state_flu$SEASON) should be 2013-2014 and 2014-2015. I guess this may be caused by the update of API of FluView.

Hospitalization Surveillance Data

Thanks for all of the work you've done putting this package together. Have you considered adding a function to pull laboratory-confirmed hospitalization surveillance data collected by the FluSurv-NET system? The web app is found here and displays cumulative hospitalizations:

The data are downloaded as a CSV in the following format:

I tried to manipulate the get_flu_data function but was unable to figure out the API for the hospitalization data.

age_group_distribution function error

I am now getting an issue when using the age_group_distribution() function. It seems like the last two columns that were being named with the statement below are no longer returned by the API. Removing "publishyearweekid" and "loaddatetime" from the vector 'vir_names' as well as removing "publishyearweekid" from the 'vir_df_cols' resolved the issue.

Parts of the code that I think caused the issue (from the agd-ipt.r file):

line 44: vir_names <- c("virusid", "ageid", "count", "mmwrid", "seasonid", "publishyearweekid", "loaddatetime")

Lines 56-58: vir_df_cols <- c("sea_label", "age_label", "vir_label", "count", "mmwrid", "seasonid",
"publishyearweekid", "sea_description", "sea_startweek",
"sea_endweek", "vir_description", "vir_startmmwrid", "vir_endmmwrid")

Thanks for all of your work on this great package!

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