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项目作者： hrbrmstr

项目描述 ：
:mask: R package to Retrieve U.S. Flu Season Data from the CDC FluView Portal (WHO & ILINet)
高级语言： R
项目地址: git://github.com/hrbrmstr/cdcfluview.git
创建时间： 2015-01-11T01:26:20Z
项目社区：https://github.com/hrbrmstr/cdcfluview
开源协议：Other
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Project Status: Active – The project has reached a stable, usable state and is being actively developed.
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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).

:mask: 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

  1. # CRAN
  2. install.packages("cdcfluview")
  4. # main branch
  5. remotes::install_git("https://git.rud.is/hrbrmstr/cdcfluview.git")
  6. remotes::install_git("https://sr.ht/~hrbrmstr/cdcfluview")
  7. remotes::install_git("https://gitlab.com/hrbrmstr/cdcfluview")
  8. remotes::install_github("hrbrmstr/cdcfluview")

Usage

  1. library(cdcfluview)
  2. library(hrbrthemes)
  3. library(tidyverse)
  5. # current version
  6. packageVersion("cdcfluview")
  7. ## [1] '0.9.4'

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

  1. glimpse(age_group_distribution(years=2015))
  2. ## Rows: 1,872
  3. ## Columns: 15
  4. ## $ sea_label       <chr> "2015-16", "2015-16", "2015-16", "2015-16", "2015-16", "2015-16", "2015-16", "2015-16", "2015-…
  5. ## $ 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…
  6. ## $ vir_label       <fct> A (Subtyping not Performed), A (Subtyping not Performed), A (Subtyping not Performed), A (Subt…
  7. ## $ 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…
  8. ## $ mmwrid          <int> 2806, 2807, 2808, 2809, 2810, 2811, 2812, 2813, 2814, 2815, 2816, 2817, 2818, 2819, 2820, 2821…
  9. ## $ 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…
  10. ## $ sea_description <chr> "Season 2015-16", "Season 2015-16", "Season 2015-16", "Season 2015-16", "Season 2015-16", "Sea…
  11. ## $ sea_startweek   <int> 2806, 2806, 2806, 2806, 2806, 2806, 2806, 2806, 2806, 2806, 2806, 2806, 2806, 2806, 2806, 2806…
  12. ## $ sea_endweek     <int> 2857, 2857, 2857, 2857, 2857, 2857, 2857, 2857, 2857, 2857, 2857, 2857, 2857, 2857, 2857, 2857…
  13. ## $ 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…
  14. ## $ vir_startmmwrid <int> 1397, 1397, 1397, 1397, 1397, 1397, 1397, 1397, 1397, 1397, 1397, 1397, 1397, 1397, 1397, 1397…
  15. ## $ vir_endmmwrid   <int> 3131, 3131, 3131, 3131, 3131, 3131, 3131, 3131, 3131, 3131, 3131, 3131, 3131, 3131, 3131, 3131…
  16. ## $ 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…
  17. ## $ 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…
  18. ## $ 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

  1. plot(cdc_basemap("national"))

  1. plot(cdc_basemap("hhs"))

  1. plot(cdc_basemap("census"))

  1. plot(cdc_basemap("states"))

  1. plot(cdc_basemap("spread"))

  1. plot(cdc_basemap("surv"))

State and Territorial Epidemiologists Reports of Geographic Spread of Influenza

  1. glimpse(geographic_spread())
  2. ## Rows: 30,851
  3. ## Columns: 7
  4. ## $ statename         <chr> "Alabama", "Alabama", "Alabama", "Alabama", "Alabama", "Alabama", "Alabama", "Alabama", "Ala…
  5. ## $ url               <chr> "http://adph.org/influenza/", "http://adph.org/influenza/", "http://adph.org/influenza/", "h…
  6. ## $ website           <chr> "Influenza Surveillance", "Influenza Surveillance", "Influenza Surveillance", "Influenza Sur…
  7. ## $ activity_estimate <chr> "No Activity", "No Activity", "No Activity", "Local Activity", "Sporadic", "Sporadic", "Spor…
  8. ## $ 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…
  9. ## $ season            <chr> "2003-04", "2003-04", "2003-04", "2003-04", "2003-04", "2003-04", "2003-04", "2003-04", "200…
  10. ## $ weeknumber        <chr> "40", "41", "42", "43", "44", "45", "46", "47", "48", "49", "50", "51", "52", "53", "1", "2"…

Laboratory-Confirmed Influenza Hospitalizations

  1. surveillance_areas()
  2. ##    surveillance_area               region
  3. ## 1            flusurv       Entire Network
  4. ## 2                eip           California
  5. ## 3                eip             Colorado
  6. ## 4                eip          Connecticut
  7. ## 5                eip       Entire Network
  8. ## 6                eip              Georgia
  9. ## 7                eip             Maryland
  10. ## 8                eip            Minnesota
  11. ## 9                eip           New Mexico
  12. ## 10               eip    New York - Albany
  13. ## 11               eip New York - Rochester
  14. ## 12               eip               Oregon
  15. ## 13               eip            Tennessee
  16. ## 14              ihsp       Entire Network
  17. ## 15              ihsp                Idaho
  18. ## 16              ihsp                 Iowa
  19. ## 17              ihsp             Michigan
  20. ## 18              ihsp                 Ohio
  21. ## 19              ihsp             Oklahoma
  22. ## 20              ihsp         Rhode Island
  23. ## 21              ihsp         South Dakota
  24. ## 22              ihsp                 Utah
  26. glimpse(fs_nat <- hospitalizations("flusurv"))
  27. ## Rows: 4,368
  28. ## Columns: 14
  29. ## $ surveillance_area <chr> "FluSurv-NET", "FluSurv-NET", "FluSurv-NET", "FluSurv-NET", "FluSurv-NET", "FluSurv-NET", "F…
  30. ## $ region            <chr> "Entire Network", "Entire Network", "Entire Network", "Entire Network", "Entire Network", "E…
  31. ## $ year              <int> 2017, 2017, 2017, 2017, 2017, 2017, 2017, 2017, 2017, 2017, 2017, 2017, 2017, 2018, 2018, 20…
  32. ## $ 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, …
  33. ## $ 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…
  34. ## $ 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…
  35. ## $ 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…
  36. ## $ 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…
  37. ## $ 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.…
  38. ## $ 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,…
  39. ## $ 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-…
  40. ## $ sea_label         <chr> "2017-18", "2017-18", "2017-18", "2017-18", "2017-18", "2017-18", "2017-18", "2017-18", "201…
  41. ## $ sea_description   <chr> "Season 2017-18", "Season 2017-18", "Season 2017-18", "Season 2017-18", "Season 2017-18", "S…
  42. ## $ mmwrid            <int> 2910, 2911, 2912, 2913, 2914, 2915, 2916, 2917, 2918, 2919, 2920, 2921, 2922, 2923, 2924, 29…
  44. ggplot(fs_nat, aes(wk_end, rate)) + 
  45.   geom_line(aes(color=age_label, group=age_label)) +
  46.   facet_wrap(~sea_description, scales="free_x") +
  47.   scale_color_viridis_d(name=NULL) +
  48.   labs(x=NULL, y="Rates per 100,000 population",
  49.        title="FluSurv-NET :: Entire Network :: All Seasons :: Cumulative Rate") +
  50.   theme_ipsum_rc()

  1. glimpse(hospitalizations("eip", years=2015))
  2. ## Rows: 390
  3. ## Columns: 14
  4. ## $ surveillance_area <chr> "EIP", "EIP", "EIP", "EIP", "EIP", "EIP", "EIP", "EIP", "EIP", "EIP", "EIP", "EIP", "EIP", "…
  5. ## $ region            <chr> "Entire Network", "Entire Network", "Entire Network", "Entire Network", "Entire Network", "E…
  6. ## $ year              <int> 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2016, 2016, 20…
  7. ## $ 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, …
  8. ## $ 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…
  9. ## $ 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…
  10. ## $ 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…
  11. ## $ 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, …
  12. ## $ 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.…
  13. ## $ 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…
  14. ## $ 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+ …
  15. ## $ sea_label         <chr> "2015-16", "2015-16", "2015-16", "2015-16", "2015-16", "2015-16", "2015-16", "2015-16", "201…
  16. ## $ sea_description   <chr> "Season 2015-16", "Season 2015-16", "Season 2015-16", "Season 2015-16", "Season 2015-16", "S…
  17. ## $ mmwrid            <int> 2806, 2807, 2808, 2809, 2810, 2811, 2812, 2813, 2814, 2815, 2816, 2817, 2818, 2819, 2820, 28…
  19. glimpse(hospitalizations("eip", "Colorado", years=2015))
  20. ## Rows: 390
  21. ## Columns: 14
  22. ## $ surveillance_area <chr> "EIP", "EIP", "EIP", "EIP", "EIP", "EIP", "EIP", "EIP", "EIP", "EIP", "EIP", "EIP", "EIP", "…
  23. ## $ region            <chr> "Colorado", "Colorado", "Colorado", "Colorado", "Colorado", "Colorado", "Colorado", "Colorad…
  24. ## $ year              <int> 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2016, 2016, 20…
  25. ## $ 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, …
  26. ## $ 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…
  27. ## $ 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…
  28. ## $ 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…
  29. ## $ 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,…
  30. ## $ 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.…
  31. ## $ 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…
  32. ## $ 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+ …
  33. ## $ sea_label         <chr> "2015-16", "2015-16", "2015-16", "2015-16", "2015-16", "2015-16", "2015-16", "2015-16", "201…
  34. ## $ sea_description   <chr> "Season 2015-16", "Season 2015-16", "Season 2015-16", "Season 2015-16", "Season 2015-16", "S…
  35. ## $ mmwrid            <int> 2806, 2807, 2808, 2809, 2810, 2811, 2812, 2813, 2814, 2815, 2816, 2817, 2818, 2819, 2820, 28…
  37. glimpse(hospitalizations("ihsp", years=2015))
  38. ## Rows: 390
  39. ## Columns: 14
  40. ## $ surveillance_area <chr> "IHSP", "IHSP", "IHSP", "IHSP", "IHSP", "IHSP", "IHSP", "IHSP", "IHSP", "IHSP", "IHSP", "IHS…
  41. ## $ region            <chr> "Entire Network", "Entire Network", "Entire Network", "Entire Network", "Entire Network", "E…
  42. ## $ year              <int> 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2016, 2016, 20…
  43. ## $ 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, …
  44. ## $ 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…
  45. ## $ 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…
  46. ## $ 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…
  47. ## $ 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…
  48. ## $ 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.…
  49. ## $ 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…
  50. ## $ 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+ …
  51. ## $ sea_label         <chr> "2015-16", "2015-16", "2015-16", "2015-16", "2015-16", "2015-16", "2015-16", "2015-16", "201…
  52. ## $ sea_description   <chr> "Season 2015-16", "Season 2015-16", "Season 2015-16", "Season 2015-16", "Season 2015-16", "S…
  53. ## $ mmwrid            <int> 2806, 2807, 2808, 2809, 2810, 2811, 2812, 2813, 2814, 2815, 2816, 2817, 2818, 2819, 2820, 28…
  55. glimpse(hospitalizations("ihsp", "Oklahoma", years=2010))
  56. ## Rows: 390
  57. ## Columns: 14
  58. ## $ surveillance_area <chr> "IHSP", "IHSP", "IHSP", "IHSP", "IHSP", "IHSP", "IHSP", "IHSP", "IHSP", "IHSP", "IHSP", "IHS…
  59. ## $ region            <chr> "Oklahoma", "Oklahoma", "Oklahoma", "Oklahoma", "Oklahoma", "Oklahoma", "Oklahoma", "Oklahom…
  60. ## $ year              <int> 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2011, 2011, 20…
  61. ## $ 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, …
  62. ## $ 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…
  63. ## $ 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…
  64. ## $ 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…
  65. ## $ 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.…
  66. ## $ 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.…
  67. ## $ 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,…
  68. ## $ 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…
  69. ## $ sea_label         <chr> "2010-11", "2010-11", "2010-11", "2010-11", "2010-11", "2010-11", "2010-11", "2010-11", "201…
  70. ## $ sea_description   <chr> "Season 2010-11", "Season 2010-11", "Season 2010-11", "Season 2010-11", "Season 2010-11", "S…
  71. ## $ mmwrid            <int> 2545, 2546, 2547, 2548, 2549, 2550, 2551, 2552, 2553, 2554, 2555, 2556, 2557, 2558, 2559, 25…

Retrieve ILINet Surveillance Data

  1. walk(c("national", "hhs", "census", "state"), ~{
  3.   ili_df <- ilinet(region = .x)
  5.   print(glimpse(ili_df))
  7.   ggplot(ili_df, aes(week_start, unweighted_ili, group=region, color=region)) +
  8.     geom_line() +
  9.     viridis::scale_color_viridis(discrete=TRUE) +
  10.     labs(x=NULL, y="Unweighted ILI", title=ili_df$region_type[1]) +
  11.     theme_ipsum_rc(grid="XY") +
  12.     theme(legend.position = "none") -> gg
  14.   print(gg)
  16. })
  17. ## Rows: 1,233
  18. ## Columns: 16
  19. ## $ region_type      <chr> "National", "National", "National", "National", "National", "National", "National", "National…
  20. ## $ region           <chr> "National", "National", "National", "National", "National", "National", "National", "National…
  21. ## $ year             <int> 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1998, 199…
  22. ## $ 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…
  23. ## $ weighted_ili     <dbl> 1.101480, 1.200070, 1.378760, 1.199200, 1.656180, 1.413260, 1.986800, 2.447490, 1.739010, 1.9…
  24. ## $ unweighted_ili   <dbl> 1.216860, 1.280640, 1.239060, 1.144730, 1.261120, 1.282750, 1.445790, 1.647960, 1.675170, 1.6…
  25. ## $ age_0_4          <dbl> 179, 199, 228, 188, 217, 178, 294, 288, 268, 299, 346, 348, 510, 579, 639, 690, 856, 824, 881…
  26. ## $ 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…
  27. ## $ age_25_64        <dbl> 157, 151, 153, 193, 162, 148, 240, 293, 206, 282, 268, 235, 404, 584, 759, 654, 679, 817, 769…
  28. ## $ age_5_24         <dbl> 205, 242, 266, 236, 280, 281, 328, 456, 343, 415, 388, 362, 492, 576, 810, 1121, 1440, 1600, …
  29. ## $ 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…
  30. ## $ age_65           <dbl> 29, 23, 34, 36, 41, 48, 70, 63, 69, 102, 81, 59, 113, 207, 207, 148, 151, 196, 233, 146, 119,…
  31. ## $ ilitotal         <dbl> 570, 615, 681, 653, 700, 655, 932, 1100, 886, 1098, 1083, 1004, 1519, 1946, 2415, 2613, 3126,…
  32. ## $ num_of_providers <dbl> 192, 191, 219, 213, 213, 195, 248, 256, 252, 253, 242, 190, 251, 250, 254, 255, 245, 245, 239…
  33. ## $ total_patients   <dbl> 46842, 48023, 54961, 57044, 55506, 51062, 64463, 66749, 52890, 67887, 61314, 47719, 48429, 52…
  34. ## $ 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-…
  35. ## # A tibble: 1,233 x 16
  36. ##    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
  37. ##    <chr>       <chr>    <int> <int>        <dbl>          <dbl>   <dbl>     <dbl>     <dbl>    <dbl>     <dbl>  <dbl>
  38. ##  1 National    National  1997    40         1.10           1.22     179        NA       157      205        NA     29
  39. ##  2 National    National  1997    41         1.20           1.28     199        NA       151      242        NA     23
  40. ##  3 National    National  1997    42         1.38           1.24     228        NA       153      266        NA     34
  41. ##  4 National    National  1997    43         1.20           1.14     188        NA       193      236        NA     36
  42. ##  5 National    National  1997    44         1.66           1.26     217        NA       162      280        NA     41
  43. ##  6 National    National  1997    45         1.41           1.28     178        NA       148      281        NA     48
  44. ##  7 National    National  1997    46         1.99           1.45     294        NA       240      328        NA     70
  45. ##  8 National    National  1997    47         2.45           1.65     288        NA       293      456        NA     63
  46. ##  9 National    National  1997    48         1.74           1.68     268        NA       206      343        NA     69
  47. ## 10 National    National  1997    49         1.94           1.62     299        NA       282      415        NA    102
  48. ## # … with 1,223 more rows, and 4 more variables: ilitotal <dbl>, num_of_providers <dbl>, total_patients <dbl>,
  49. ## #   week_start <date>

  1. ## Rows: 12,330
  2. ## Columns: 16
  3. ## $ region_type      <chr> "HHS Regions", "HHS Regions", "HHS Regions", "HHS Regions", "HHS Regions", "HHS Regions", "HH…
  4. ## $ region           <fct> Region 1, Region 2, Region 3, Region 4, Region 5, Region 6, Region 7, Region 8, Region 9, Reg…
  5. ## $ year             <int> 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 199…
  6. ## $ 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…
  7. ## $ weighted_ili     <dbl> 0.498535, 0.374963, 1.354280, 0.400338, 1.229260, 1.018980, 0.871791, 0.516017, 1.807610, 4.7…
  8. ## $ unweighted_ili   <dbl> 0.623848, 0.384615, 1.341720, 0.450010, 0.901266, 0.747384, 1.152860, 0.422654, 2.258780, 4.8…
  9. ## $ 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…
  10. ## $ 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…
  11. ## $ 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, …
  12. ## $ 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, …
  13. ## $ 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…
  14. ## $ 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, …
  15. ## $ 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…
  16. ## $ 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, …
  17. ## $ total_patients   <dbl> 7053, 780, 2385, 10222, 9875, 669, 2342, 1183, 10758, 1575, 6987, 872, 2740, 11310, 9618, 684…
  18. ## $ 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-…
  19. ## # A tibble: 12,330 x 16
  20. ##    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
  21. ##    <chr>       <fct>     <int> <int>        <dbl>          <dbl>   <dbl>     <dbl>     <dbl>    <dbl>     <dbl>  <dbl>
  22. ##  1 HHS Regions Region 1   1997    40        0.499          0.624      15        NA         7       22        NA      0
  23. ##  2 HHS Regions Region 2   1997    40        0.375          0.385       0        NA         3        0        NA      0
  24. ##  3 HHS Regions Region 3   1997    40        1.35           1.34        6        NA         7       15        NA      4
  25. ##  4 HHS Regions Region 4   1997    40        0.400          0.450      12        NA        23       11        NA      0
  26. ##  5 HHS Regions Region 5   1997    40        1.23           0.901      31        NA        24       30        NA      4
  27. ##  6 HHS Regions Region 6   1997    40        1.02           0.747       2        NA         1        2        NA      0
  28. ##  7 HHS Regions Region 7   1997    40        0.872          1.15        0        NA         4       18        NA      5
  29. ##  8 HHS Regions Region 8   1997    40        0.516          0.423       2        NA         0        3        NA      0
  30. ##  9 HHS Regions Region 9   1997    40        1.81           2.26       80        NA        76       74        NA     13
  31. ## 10 HHS Regions Region 10  1997    40        4.74           4.83       31        NA        12       30        NA      3
  32. ## # … with 12,320 more rows, and 4 more variables: ilitotal <dbl>, num_of_providers <dbl>, total_patients <dbl>,
  33. ## #   week_start <date>

  1. ## Rows: 11,097
  2. ## Columns: 16
  3. ## $ region_type      <chr> "Census Regions", "Census Regions", "Census Regions", "Census Regions", "Census Regions", "Ce…
  4. ## $ region           <chr> "New England", "Mid-Atlantic", "East North Central", "West North Central", "South Atlantic", …
  5. ## $ year             <int> 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 199…
  6. ## $ 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…
  7. ## $ weighted_ili     <dbl> 0.4985350, 0.8441440, 0.7924860, 1.7640500, 0.5026620, 0.0542283, 1.0189800, 2.2587800, 2.048…
  8. ## $ unweighted_ili   <dbl> 0.6238480, 1.3213800, 0.8187380, 1.2793900, 0.7233800, 0.0688705, 0.7473840, 2.2763300, 3.234…
  9. ## $ 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…
  10. ## $ 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…
  11. ## $ 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, …
  12. ## $ 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…
  13. ## $ 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…
  14. ## $ 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,…
  15. ## $ 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…
  16. ## $ 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, …
  17. ## $ total_patients   <dbl> 7053, 2119, 9649, 2892, 6912, 4356, 669, 10719, 2473, 6987, 2384, 9427, 2823, 7591, 4947, 684…
  18. ## $ 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-…
  19. ## # A tibble: 11,097 x 16
  20. ##    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
  21. ##    <chr>        <chr>      <int> <int>        <dbl>          <dbl>   <dbl>     <dbl>     <dbl>    <dbl>     <dbl>  <dbl>
  22. ##  1 Census Regi… New Engla…  1997    40       0.499          0.624       15        NA         7       22        NA      0
  23. ##  2 Census Regi… Mid-Atlan…  1997    40       0.844          1.32         4        NA         8       12        NA      4
  24. ##  3 Census Regi… East Nort…  1997    40       0.792          0.819       28        NA        20       28        NA      3
  25. ##  4 Census Regi… West Nort…  1997    40       1.76           1.28         3        NA         8       20        NA      6
  26. ##  5 Census Regi… South Atl…  1997    40       0.503          0.723       14        NA        22       14        NA      0
  27. ##  6 Census Regi… East Sout…  1997    40       0.0542         0.0689       0        NA         3        0        NA      0
  28. ##  7 Census Regi… West Sout…  1997    40       1.02           0.747        2        NA         1        2        NA      0
  29. ##  8 Census Regi… Mountain    1997    40       2.26           2.28        87        NA        71       71        NA     15
  30. ##  9 Census Regi… Pacific     1997    40       2.05           3.23        26        NA        17       36        NA      1
  31. ## 10 Census Regi… New Engla…  1997    41       0.643          0.816       14        NA        14       29        NA      0
  32. ## # … with 11,087 more rows, and 4 more variables: ilitotal <dbl>, num_of_providers <dbl>, total_patients <dbl>,
  33. ## #   week_start <date>

  1. ## Rows: 29,793
  2. ## Columns: 16
  3. ## $ region_type      <chr> "States", "States", "States", "States", "States", "States", "States", "States", "States", "St…
  4. ## $ region           <chr> "Alabama", "Alaska", "Arizona", "Arkansas", "California", "Colorado", "Connecticut", "Delawar…
  5. ## $ year             <int> 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 201…
  6. ## $ 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…
  7. ## $ 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…
  8. ## $ unweighted_ili   <dbl> 2.1347700, 0.8751460, 0.6747210, 0.6960560, 1.9541200, 0.6606840, 0.0783085, 0.1001250, 2.808…
  9. ## $ 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…
  10. ## $ 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…
  11. ## $ 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…
  12. ## $ 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…
  13. ## $ 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…
  14. ## $ 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…
  15. ## $ ilitotal         <dbl> 249, 15, 172, 18, 632, 134, 3, 4, 73, NA, 647, 20, 19, 505, 65, 10, 39, 19, 391, 22, 117, 168…
  16. ## $ 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,…
  17. ## $ total_patients   <dbl> 11664, 1714, 25492, 2586, 32342, 20282, 3831, 3995, 2599, NA, 40314, 1943, 4579, 39390, 12525…
  18. ## $ 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-…
  19. ## # A tibble: 29,793 x 16
  20. ##    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
  21. ##    <chr>       <chr>       <int> <int>        <dbl>          <dbl>   <dbl>     <dbl>     <dbl>    <dbl>     <dbl>  <dbl>
  22. ##  1 States      Alabama      2010    40           NA         2.13        NA        NA        NA       NA        NA     NA
  23. ##  2 States      Alaska       2010    40           NA         0.875       NA        NA        NA       NA        NA     NA
  24. ##  3 States      Arizona      2010    40           NA         0.675       NA        NA        NA       NA        NA     NA
  25. ##  4 States      Arkansas     2010    40           NA         0.696       NA        NA        NA       NA        NA     NA
  26. ##  5 States      California   2010    40           NA         1.95        NA        NA        NA       NA        NA     NA
  27. ##  6 States      Colorado     2010    40           NA         0.661       NA        NA        NA       NA        NA     NA
  28. ##  7 States      Connecticut  2010    40           NA         0.0783      NA        NA        NA       NA        NA     NA
  29. ##  8 States      Delaware     2010    40           NA         0.100       NA        NA        NA       NA        NA     NA
  30. ##  9 States      District o…  2010    40           NA         2.81        NA        NA        NA       NA        NA     NA
  31. ## 10 States      Florida      2010    40           NA        NA           NA        NA        NA       NA        NA     NA
  32. ## # … with 29,783 more rows, and 4 more variables: ilitotal <dbl>, num_of_providers <dbl>, total_patients <dbl>,
  33. ## #   week_start <date>

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

  1. ili_weekly_activity_indicators(2017)
  2. ## # A tibble: 2,805 x 8
  3. ##    statename    url                          website        activity_level activity_level_… weekend    season weeknumber
  4. ##  * <chr>        <chr>                        <chr>                   <dbl> <chr>            <date>     <chr>       <dbl>
  5. ##  1 Alabama      "http://adph.org/influenza/" Influenza Sur…              2 Minimal          2017-10-07 2017-…         40
  6. ##  2 Alaska       "http://dhss.alaska.gov/dph… Influenza Sur…              1 Minimal          2017-10-07 2017-…         40
  7. ##  3 Arizona      "http://www.azdhs.gov/phs/o… Influenza & R…              2 Minimal          2017-10-07 2017-…         40
  8. ##  4 Arkansas     "http://www.healthy.arkansa… Communicable …              1 Minimal          2017-10-07 2017-…         40
  9. ##  5 California   "https://www.cdph.ca.gov/Pr… Influenza (Fl…              2 Minimal          2017-10-07 2017-…         40
  10. ##  6 Colorado     "https://www.colorado.gov/p… Influenza Sur…              1 Minimal          2017-10-07 2017-…         40
  11. ##  7 Connecticut  "https://portal.ct.gov/DPH/… Flu Statistics              1 Minimal          2017-10-07 2017-…         40
  12. ##  8 Delaware     "http://dhss.delaware.gov/d… Weekly Influe…              1 Minimal          2017-10-07 2017-…         40
  13. ##  9 District of… "https://dchealth.dc.gov/no… Influenza Inf…              2 Minimal          2017-10-07 2017-…         40
  14. ## 10 Florida      "http://www.floridahealth.g… Weekly Influe…              1 Minimal          2017-10-07 2017-…         40
  15. ## # … with 2,795 more rows
  17. xdf <- map_df(2008:2017, ili_weekly_activity_indicators)
  19. count(xdf, weekend, activity_level_label) %>% 
  20.   complete(weekend, activity_level_label) %>% 
  21.   ggplot(aes(weekend, activity_level_label, fill=n)) + 
  22.   geom_tile(color="#c2c2c2", size=0.1) +
  23.   scale_x_date(expand=c(0,0)) +
  24.   viridis::scale_fill_viridis(name="# States", na.value="White") +
  25.   labs(x=NULL, y=NULL, title="Weekly ILI Indicators (all states)") +
  26.   coord_fixed(100/1) +
  27.   theme_ipsum_rc(grid="") +
  28.   theme(legend.position="bottom")

Pneumonia and Influenza Mortality Surveillance

  1. (nat_pi <- pi_mortality("national"))
  2. ## # A tibble: 398 x 19
  3. ##    seasonid baseline threshold percent_pni percent_complete number_influenza number_pneumonia all_deaths total_pni
  4. ##    <chr>       <dbl>     <dbl>       <dbl>            <dbl>            <dbl>            <dbl>      <dbl>     <dbl>
  5. ##  1 60         0.053     0.0560      0.081                 1                8             4825      59682      4833
  6. ##  2 60         0.054     0.057       0.084                 1               12             5173      61641      5185
  7. ##  3 60         0.055     0.0580      0.086                 1               16             5208      60467      5224
  8. ##  4 60         0.0560    0.059       0.091                 1               15             5642      62047      5657
  9. ##  5 60         0.057     0.06        0.0970                1               21             6142      63280      6163
  10. ##  6 60         0.0580    0.061       0.105                 1               21             7075      67380      7096
  11. ##  7 60         0.059     0.062       0.117                 1               20             8040      68644      8060
  12. ##  8 60         0.06      0.063       0.132                 1               30             9400      71440      9430
  13. ##  9 60         0.061     0.064       0.143                 1               27            10440      73066     10467
  14. ## 10 60         0.062     0.065       0.157                 1               35            12048      77136     12083
  15. ## # … with 388 more rows, and 10 more variables: weeknumber <chr>, geo_description <chr>, age_label <chr>,
  16. ## #   week_start <date>, week_end <date>, year_week_num <int>, mmwrid <chr>, coverage_area <chr>, region_name <chr>,
  17. ## #   callout <chr>
  19. select(nat_pi, week_end, percent_pni, baseline, threshold) %>% 
  20.   gather(measure, value, -week_end) %>% 
  21.   ggplot(aes(week_end, value)) + 
  22.   geom_line(aes(group=measure, color=measure)) + 
  23.   scale_y_percent() +
  24.   scale_color_ipsum(name = NULL, labels=c("Baseline", "Percent P&I", "Threshold")) +
  25.   labs(x=NULL, y="% of all deaths due to P&I",
  26.        title="Percentage of all deaths due to pneumonia and influenza, National Summary") +
  27.   theme_ipsum_rc(grid="XY") +
  28.   theme(legend.position="bottom")

  1. (st_pi <- pi_mortality("state", years=2015))
  2. ## # A tibble: 2,704 x 19
  3. ##    seasonid baseline threshold percent_pni percent_complete number_influenza number_pneumonia all_deaths total_pni
  4. ##    <chr>       <dbl>     <dbl>       <dbl>            <dbl>            <dbl>            <dbl>      <dbl>     <dbl>
  5. ##  1 55             NA        NA      0.046             0.962                0               43        935        43
  6. ##  2 55             NA        NA      0.036             0.835                0               29        811        29
  7. ##  3 55             NA        NA      0.054             0.833                0               44        809        44
  8. ##  4 55             NA        NA      0.07              0.947                0               64        920        64
  9. ##  5 55             NA        NA      0.053             0.926                0               48        900        48
  10. ##  6 55             NA        NA      0.057             0.987                0               55        959        55
  11. ##  7 55             NA        NA      0.052             1                    0               53       1023        53
  12. ##  8 55             NA        NA      0.063             1                    1               62       1002        63
  13. ##  9 55             NA        NA      0.0560            0.95                 0               52        923        52
  14. ## 10 55             NA        NA      0.054             0.954                0               50        927        50
  15. ## # … with 2,694 more rows, and 10 more variables: weeknumber <chr>, geo_description <chr>, age_label <chr>,
  16. ## #   week_start <date>, week_end <date>, year_week_num <int>, mmwrid <chr>, coverage_area <chr>, region_name <chr>,
  17. ## #   callout <chr>
  19. (reg_pi <- pi_mortality("region", years=2015))
  20. ## # A tibble: 520 x 19
  21. ##    seasonid baseline threshold percent_pni percent_complete number_influenza number_pneumonia all_deaths total_pni
  22. ##    <chr>       <dbl>     <dbl>       <dbl>            <dbl>            <dbl>            <dbl>      <dbl>     <dbl>
  23. ##  1 55          0.064    0.071       0.07              1                    0              178       2525       178
  24. ##  2 55          0.065    0.072       0.064             1                    0              160       2512       160
  25. ##  3 55          0.066    0.073       0.0580            1                    1              141       2457       142
  26. ##  4 55          0.067    0.074       0.07              0.989                0              171       2426       171
  27. ##  5 55          0.068    0.075       0.065             1                    2              166       2565       168
  28. ##  6 55          0.069    0.077       0.067             0.985                1              162       2415       163
  29. ##  7 55          0.071    0.078       0.079             1                    0              198       2491       198
  30. ##  8 55          0.072    0.079       0.072             1                    1              176       2468       177
  31. ##  9 55          0.073    0.081       0.067             0.96                 3              154       2353       157
  32. ## 10 55          0.075    0.0820      0.062             0.996                0              151       2441       151
  33. ## # … with 510 more rows, and 10 more variables: weeknumber <chr>, geo_description <chr>, age_label <chr>,
  34. ## #   week_start <date>, week_end <date>, year_week_num <int>, mmwrid <chr>, coverage_area <chr>, region_name <chr>,
  35. ## #   callout <chr>

Retrieve metadata about U.S. State CDC Provider Data

  1. state_data_providers()
  2. ## # A tibble: 59 x 5
  3. ##    statename     statehealthdeptname          url                                statewebsitename       statefluphonenum
  4. ##  * <chr>         <chr>                        <chr>                              <chr>                  <chr>           
  5. ##  1 Alabama       Alabama Department of Publi… "http://adph.org/influenza/"       Influenza Surveillance 334-206-5300    
  6. ##  2 Alaska        State of Alaska Health and … "http://dhss.alaska.gov/dph/Epi/i… Influenza Surveillanc… 907-269-8000    
  7. ##  3 Arizona       Arizona Department of Healt… "http://www.azdhs.gov/phs/oids/ep… Influenza & RSV Surve… 602-542-1025    
  8. ##  4 Arkansas      Arkansas Department of Heal… "http://www.healthy.arkansas.gov/… Communicable Disease … 501-661-2000    
  9. ##  5 California    California Department of Pu… "https://www.cdph.ca.gov/Programs… Influenza (Flu)        916-558-1784    
  10. ##  6 Colorado      Colorado Department of Publ… "https://www.colorado.gov/pacific… Influenza Surveillance 303-692-2000    
  11. ##  7 Connecticut   Connecticut Department of P… "https://portal.ct.gov/DPH/Epidem… Flu Statistics         860-509-8000    
  12. ##  8 Delaware      Delaware Health and Social … "http://dhss.delaware.gov/dhss/dp… Weekly Influenza Surv… 302-744-4700    
  13. ##  9 District of … District of Columbia Depart… "https://dchealth.dc.gov/node/114… Influenza Information  202-442-5955    
  14. ## 10 Florida       Florida Department of Health "http://www.floridahealth.gov/dis… Weekly Influenza Surv… 850-245-4300    
  15. ## # … with 49 more rows

Retrieve WHO/NREVSS Surveillance Data

  1. glimpse(xdat <- who_nrevss("national"))
  2. ## List of 3
  3. ##  $ combined_prior_to_2015_16: tibble[,14] [940 × 14] (S3: tbl_df/tbl/data.frame)
  4. ##   ..$ region_type              : chr [1:940] "National" "National" "National" "National" ...
  5. ##   ..$ region                   : chr [1:940] "National" "National" "National" "National" ...
  6. ##   ..$ year                     : int [1:940] 1997 1997 1997 1997 1997 1997 1997 1997 1997 1997 ...
  7. ##   ..$ week                     : int [1:940] 40 41 42 43 44 45 46 47 48 49 ...
  8. ##   ..$ total_specimens          : int [1:940] 1291 1513 1552 1669 1897 2106 2204 2533 2242 2607 ...
  9. ##   ..$ percent_positive         : num [1:940] 0 0.727 1.095 0.419 0.527 ...
  10. ##   ..$ a_2009_h1n1              : int [1:940] 0 0 0 0 0 0 0 0 0 0 ...
  11. ##   ..$ a_h1                     : int [1:940] 0 0 0 0 0 0 0 0 0 0 ...
  12. ##   ..$ a_h3                     : int [1:940] 0 0 3 0 9 0 3 5 14 11 ...
  13. ##   ..$ a_subtyping_not_performed: int [1:940] 0 11 13 7 1 6 4 17 22 28 ...
  14. ##   ..$ a_unable_to_subtype      : int [1:940] 0 0 0 0 0 0 0 0 0 0 ...
  15. ##   ..$ b                        : int [1:940] 0 0 1 0 0 0 1 1 1 1 ...
  16. ##   ..$ h3n2v                    : int [1:940] 0 0 0 0 0 0 0 0 0 0 ...
  17. ##   ..$ wk_date                  : Date[1:940], format: "1997-09-28" "1997-10-05" "1997-10-12" "1997-10-19" ...
  18. ##  $ public_health_labs       : tibble[,13] [293 × 13] (S3: tbl_df/tbl/data.frame)
  19. ##   ..$ region_type              : chr [1:293] "National" "National" "National" "National" ...
  20. ##   ..$ region                   : chr [1:293] "National" "National" "National" "National" ...
  21. ##   ..$ year                     : int [1:293] 2015 2015 2015 2015 2015 2015 2015 2015 2015 2015 ...
  22. ##   ..$ week                     : int [1:293] 40 41 42 43 44 45 46 47 48 49 ...
  23. ##   ..$ total_specimens          : int [1:293] 1139 1152 1198 1244 1465 1393 1458 1157 1550 1518 ...
  24. ##   ..$ a_2009_h1n1              : int [1:293] 4 5 10 9 4 11 17 17 27 38 ...
  25. ##   ..$ a_h3                     : int [1:293] 65 41 50 31 23 34 42 24 36 37 ...
  26. ##   ..$ a_subtyping_not_performed: int [1:293] 2 2 1 4 4 1 1 0 3 3 ...
  27. ##   ..$ b                        : int [1:293] 10 7 8 9 9 10 4 4 9 11 ...
  28. ##   ..$ bvic                     : int [1:293] 0 3 3 1 1 4 0 3 3 2 ...
  29. ##   ..$ byam                     : int [1:293] 1 0 2 4 4 2 4 9 12 11 ...
  30. ##   ..$ h3n2v                    : int [1:293] 0 0 0 0 0 0 0 0 0 0 ...
  31. ##   ..$ wk_date                  : Date[1:293], format: "2015-10-04" "2015-10-11" "2015-10-18" "2015-10-25" ...
  32. ##  $ clinical_labs            : tibble[,11] [293 × 11] (S3: tbl_df/tbl/data.frame)
  33. ##   ..$ region_type     : chr [1:293] "National" "National" "National" "National" ...
  34. ##   ..$ region          : chr [1:293] "National" "National" "National" "National" ...
  35. ##   ..$ year            : int [1:293] 2015 2015 2015 2015 2015 2015 2015 2015 2015 2015 ...
  36. ##   ..$ week            : int [1:293] 40 41 42 43 44 45 46 47 48 49 ...
  37. ##   ..$ total_specimens : int [1:293] 12029 13111 13441 13537 14687 15048 15250 15234 16201 16673 ...
  38. ##   ..$ total_a         : int [1:293] 84 116 97 98 97 122 84 119 145 140 ...
  39. ##   ..$ total_b         : int [1:293] 43 54 52 52 68 86 98 92 81 106 ...
  40. ##   ..$ percent_positive: num [1:293] 1.06 1.3 1.11 1.11 1.12 ...
  41. ##   ..$ percent_a       : num [1:293] 0.698 0.885 0.722 0.724 0.66 ...
  42. ##   ..$ percent_b       : num [1:293] 0.357 0.412 0.387 0.384 0.463 ...
  43. ##   ..$ wk_date         : Date[1:293], format: "2015-10-04" "2015-10-11" "2015-10-18" "2015-10-25" ...
  45. mutate(xdat$combined_prior_to_2015_16, 
  46.        percent_positive = percent_positive / 100) %>% 
  47.   ggplot(aes(wk_date, percent_positive)) +
  48.   geom_line() +
  49.   scale_y_percent(name="% Positive") +
  50.   labs(x=NULL, title="WHO/NREVSS Surveillance Data (National)") +
  51.   theme_ipsum_rc(grid="XY")

  1. who_nrevss("hhs", years=2016)
  2. ## $public_health_labs
  3. ## # A tibble: 520 x 13
  4. ##    region_type region   year  week total_specimens a_2009_h1n1  a_h3 a_subtyping_not…     b  bvic  byam h3n2v wk_date   
  5. ##    <chr>       <chr>   <int> <int>           <int>       <int> <int>            <int> <int> <int> <int> <int> <date>    
  6. ##  1 HHS Regions Region…  2016    40              31           0     6                0     0     0     0     0 2016-10-02
  7. ##  2 HHS Regions Region…  2016    40              31           0     6                0     0     2     0     0 2016-10-02
  8. ##  3 HHS Regions Region…  2016    40             112           2     2                0     0     0     0     0 2016-10-02
  9. ##  4 HHS Regions Region…  2016    40             112           1    11                0     1     2     0     0 2016-10-02
  10. ##  5 HHS Regions Region…  2016    40             204           0     7                0     0     0     1     0 2016-10-02
  11. ##  6 HHS Regions Region…  2016    40              39           1     1                0     0     0     0     0 2016-10-02
  12. ##  7 HHS Regions Region…  2016    40              24           0     2                0     0     1     0     0 2016-10-02
  13. ##  8 HHS Regions Region…  2016    40              46           2     8                0     0     0     0     0 2016-10-02
  14. ##  9 HHS Regions Region…  2016    40             186           3    27                0     0     0     3     0 2016-10-02
  15. ## 10 HHS Regions Region…  2016    40             113           0    17                0     0     0     0     0 2016-10-02
  16. ## # … with 510 more rows
  17. ## 
  18. ## $clinical_labs
  19. ## # A tibble: 520 x 11
  20. ##    region_type region     year  week total_specimens total_a total_b percent_positive percent_a percent_b wk_date   
  21. ##    <chr>       <chr>     <int> <int>           <int>   <int>   <int>            <dbl>     <dbl>     <dbl> <date>    
  22. ##  1 HHS Regions Region 1   2016    40             654       5       1            0.917     0.765     0.153 2016-10-02
  23. ##  2 HHS Regions Region 2   2016    40            1307      10       3            0.995     0.765     0.230 2016-10-02
  24. ##  3 HHS Regions Region 3   2016    40             941       1       4            0.531     0.106     0.425 2016-10-02
  25. ##  4 HHS Regions Region 4   2016    40            2960      46      63            3.68      1.55      2.13  2016-10-02
  26. ##  5 HHS Regions Region 5   2016    40            2386       8       5            0.545     0.335     0.210 2016-10-02
  27. ##  6 HHS Regions Region 6   2016    40            1914      22      13            1.83      1.15      0.679 2016-10-02
  28. ##  7 HHS Regions Region 7   2016    40             723       0       0            0         0         0     2016-10-02
  29. ##  8 HHS Regions Region 8   2016    40             913       8       0            0.876     0.876     0     2016-10-02
  30. ##  9 HHS Regions Region 9   2016    40             992       6       1            0.706     0.605     0.101 2016-10-02
  31. ## 10 HHS Regions Region 10  2016    40             590      14       0            2.37      2.37      0     2016-10-02
  32. ## # … with 510 more rows
  34. who_nrevss("census", years=2016)
  35. ## $public_health_labs
  36. ## # A tibble: 468 x 13
  37. ##    region_type  region  year  week total_specimens a_2009_h1n1  a_h3 a_subtyping_not…     b  bvic  byam h3n2v wk_date   
  38. ##    <chr>        <chr>  <int> <int>           <int>       <int> <int>            <int> <int> <int> <int> <int> <date>    
  39. ##  1 Census Regi… New E…  2016    40              31           0     6                0     0     0     0     0 2016-10-02
  40. ##  2 Census Regi… Mid-A…  2016    40              50           0     8                0     0     2     0     0 2016-10-02
  41. ##  3 Census Regi… East …  2016    40             139           0     4                0     0     0     1     0 2016-10-02
  42. ##  4 Census Regi… West …  2016    40             103           0     6                0     0     1     0     0 2016-10-02
  43. ##  5 Census Regi… South…  2016    40             181           3    11                0     1     2     0     0 2016-10-02
  44. ##  6 Census Regi… East …  2016    40              24           0     0                0     0     0     0     0 2016-10-02
  45. ##  7 Census Regi… West …  2016    40              27           0     1                0     0     0     0     0 2016-10-02
  46. ##  8 Census Regi… Mount…  2016    40              54           3    10                0     0     0     1     0 2016-10-02
  47. ##  9 Census Regi… Pacif…  2016    40             289           3    41                0     0     0     2     0 2016-10-02
  48. ## 10 Census Regi… New E…  2016    41              14           0     2                0     0     0     0     0 2016-10-09
  49. ## # … with 458 more rows
  50. ## 
  51. ## $clinical_labs
  52. ## # A tibble: 468 x 11
  53. ##    region_type   region       year  week total_specimens total_a total_b percent_positive percent_a percent_b wk_date   
  54. ##    <chr>         <chr>       <int> <int>           <int>   <int>   <int>            <dbl>     <dbl>     <dbl> <date>    
  55. ##  1 Census Regio… New England  2016    40             654       5       1            0.917     0.765    0.153  2016-10-02
  56. ##  2 Census Regio… Mid-Atlant…  2016    40            1579      10       4            0.887     0.633    0.253  2016-10-02
  57. ##  3 Census Regio… East North…  2016    40            2176       6       5            0.506     0.276    0.230  2016-10-02
  58. ##  4 Census Regio… West North…  2016    40            1104       3       0            0.272     0.272    0      2016-10-02
  59. ##  5 Census Regio… South Atla…  2016    40            2785      43      62            3.77      1.54     2.23   2016-10-02
  60. ##  6 Census Regio… East South…  2016    40             844       4       4            0.948     0.474    0.474  2016-10-02
  61. ##  7 Census Regio… West South…  2016    40            1738      21      13            1.96      1.21     0.748  2016-10-02
  62. ##  8 Census Regio… Mountain     2016    40            1067       8       0            0.750     0.750    0      2016-10-02
  63. ##  9 Census Regio… Pacific      2016    40            1433      20       1            1.47      1.40     0.0698 2016-10-02
  64. ## 10 Census Regio… New England  2016    41             810       5       1            0.741     0.617    0.123  2016-10-09
  65. ## # … with 458 more rows
  67. who_nrevss("state", years=2016)
  68. ## $public_health_labs
  69. ## # A tibble: 54 x 12
  70. ##    region_type region     season_descripti… total_specimens a_2009_h1n1 a_h3  a_subtyping_not_p… b     bvic  byam  h3n2v
  71. ##    <chr>       <chr>      <chr>             <chr>           <chr>       <chr> <chr>              <chr> <chr> <chr> <chr>
  72. ##  1 States      Alabama    Season 2016-17    570             3           227   1                  2     15    14    0    
  73. ##  2 States      Alaska     Season 2016-17    5222            14          905   3                  252   2     11    0    
  74. ##  3 States      Arizona    Season 2016-17    2975            63          1630  0                  5     227   578   0    
  75. ##  4 States      Arkansas   Season 2016-17    121             0           51    0                  0     4     0     0    
  76. ##  5 States      California Season 2016-17    14074           184         4696  120                116   28    152   0    
  77. ##  6 States      Colorado   Season 2016-17    714             3           267   2                  4     31    219   0    
  78. ##  7 States      Connectic… Season 2016-17    1348            19          968   0                  0     62    263   0    
  79. ##  8 States      Delaware   Season 2016-17    3090            5           659   4                  11    27    127   1    
  80. ##  9 States      District … Season 2016-17    73              1           34    0                  3     0     4     0    
  81. ## 10 States      Florida    Season 2016-17    <NA>            <NA>        <NA>  <NA>               <NA>  <NA>  <NA>  <NA> 
  82. ## # … with 44 more rows, and 1 more variable: wk_date <date>
  83. ## 
  84. ## $clinical_labs
  85. ## # A tibble: 2,808 x 11
  86. ##    region_type region         year  week total_specimens total_a total_b percent_positive percent_a percent_b wk_date   
  87. ##    <chr>       <chr>         <int> <int> <chr>           <chr>   <chr>   <chr>            <chr>     <chr>     <date>    
  88. ##  1 States      Alabama        2016    40 406             4       1       1.23             0.99      0.25      2016-10-02
  89. ##  2 States      Alaska         2016    40 <NA>            <NA>    <NA>    <NA>             <NA>      <NA>      2016-10-02
  90. ##  3 States      Arizona        2016    40 133             0       0       0                0         0         2016-10-02
  91. ##  4 States      Arkansas       2016    40 47              0       0       0                0         0         2016-10-02
  92. ##  5 States      California     2016    40 668             2       0       0.3              0.3       0         2016-10-02
  93. ##  6 States      Colorado       2016    40 260             0       0       0                0         0         2016-10-02
  94. ##  7 States      Connecticut    2016    40 199             3       0       1.51             1.51      0         2016-10-02
  95. ##  8 States      Delaware       2016    40 40              0       0       0                0         0         2016-10-02
  96. ##  9 States      District of …  2016    40 <NA>            <NA>    <NA>    <NA>             <NA>      <NA>      2016-10-02
  97. ## 10 States      Florida        2016    40 <NA>            <NA>    <NA>    <NA>             <NA>      <NA>      2016-10-02
  98. ## # … 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

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