tweaked hospitalizations function
CRAN failure / #28 fix
Fixes #25
clone
- read-only
- https://git.sr.ht/~hrbrmstr/cdcfluview
- read/write
- git@git.sr.ht:~hrbrmstr/cdcfluview
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 Laboratoriescdc_basemap: Retrieve CDC U.S. base mapsgeographic_spread: State and Territorial Epidemiologists Reports of Geographic Spread of Influenzaget_weekly_flu_report: Retrieves (high-level) weekly (XML) influenza surveillance report from the CDChospitalizations: Laboratory-Confirmed Influenza Hospitalizationsilinet: Retrieve ILINet Surveillance Dataili_weekly_activity_indicators: Retrieve weekly state-level ILI indicators per-state for a given seasonpi_mortality: Pneumonia and Influenza Mortality Surveillancestate_data_providers: Retrieve metadata about U.S. State CDC Provider Datasurveillance_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 Mappingsmmwr_week: Convert a Date to an MMWR day+week+yearmmwr_weekday: Convert a Date to an MMWR weekdaymmwr_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 usedata())
#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.