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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
4 years ago
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
``` r
4 years ago
# CRAN
install.packages("cdcfluview")
2 years ago
# 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
``` r
library(cdcfluview)
library(hrbrthemes)
5 years ago
library(tidyverse)
# current version
packageVersion("cdcfluview")
## [1] '0.9.4'
```
5 years ago
### Age Group Distribution of Influenza Positive Tests Reported by Public Health Laboratories
``` r
glimpse(age_group_distribution(years=2015))
3 years ago
## 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,…
5 years ago
```
### Retrieve CDC U.S. Coverage Map
``` r
plot(cdc_basemap("national"))
5 years ago
```
3 years ago
<img src="man/figures/README-cdc-basemaps-1.png" width="672" />
5 years ago
``` r
plot(cdc_basemap("hhs"))
```
3 years ago
<img src="man/figures/README-cdc-basemaps-2.png" width="672" />
``` r
plot(cdc_basemap("census"))
```
3 years ago
<img src="man/figures/README-cdc-basemaps-3.png" width="672" />
``` r
plot(cdc_basemap("states"))
```
3 years ago
<img src="man/figures/README-cdc-basemaps-4.png" width="672" />
``` r
plot(cdc_basemap("spread"))
```
3 years ago
<img src="man/figures/README-cdc-basemaps-5.png" width="672" />
``` r
plot(cdc_basemap("surv"))
```
3 years ago
<img src="man/figures/README-cdc-basemaps-6.png" width="672" />
5 years ago
### State and Territorial Epidemiologists Reports of Geographic Spread of Influenza
``` r
glimpse(geographic_spread())
## Rows: 30,851
3 years ago
## 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"…
5 years ago
```
### Laboratory-Confirmed Influenza Hospitalizations
``` r
surveillance_areas()
3 years ago
## 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
5 years ago
glimpse(fs_nat <- hospitalizations("flusurv"))
## Rows: 4,368
3 years ago
## 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…
5 years ago
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()
```
3 years ago
<img src="man/figures/README-surveillance-areas-1.png" width="960" />
5 years ago
``` r
3 years ago
glimpse(hospitalizations("eip", years=2015))
## Rows: 390
3 years ago
## 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…
5 years ago
glimpse(hospitalizations("eip", "Colorado", years=2015))
## Rows: 390
3 years ago
## 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…
5 years ago
glimpse(hospitalizations("ihsp", years=2015))
## Rows: 390
3 years ago
## 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
3 years ago
## 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…
5 years ago
```
### Retrieve ILINet Surveillance Data
``` r
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
3 years ago
## 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>,
3 years ago
## # week_start <date>
5 years ago
```
3 years ago
<img src="man/figures/README-ili-df-1.png" width="672" />
## Rows: 12,330
3 years ago
## 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>
3 years ago
<img src="man/figures/README-ili-df-2.png" width="672" />
## Rows: 11,097
3 years ago
## 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>,
4 years ago
## # week_start <date>
3 years ago
<img src="man/figures/README-ili-df-3.png" width="672" />
4 years ago
## Rows: 29,793
3 years ago
## 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>,
4 years ago
## # week_start <date>
3 years ago
<img src="man/figures/README-ili-df-4.png" width="672" />
5 years ago
### Retrieve weekly state-level ILI indicators per-state for a given season
``` r
ili_weekly_activity_indicators(2017)
3 years ago
## # 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
3 years ago
## # … with 2,795 more rows
5 years ago
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")
5 years ago
```
3 years ago
<img src="man/figures/README-ili-weekly-activity-1.png" width="960" />
5 years ago
### Pneumonia and Influenza Mortality Surveillance
``` r
(nat_pi <- pi_mortality("national"))
## # A tibble: 398 x 19
3 years ago
## 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>,
3 years ago
## # 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")
```
3 years ago
<img src="man/figures/README-nat-pi-mortality-1.png" width="672" />
``` r
3 years ago
(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>
5 years ago
(reg_pi <- pi_mortality("region", years=2015))
3 years ago
## # 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
3 years ago
## # … 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>
5 years ago
```
### Retrieve metadata about U.S. State CDC Provider Data
5 years ago
``` r
state_data_providers()
3 years ago
## # 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
3 years ago
## 10 Florida Florida Department of Health "http://www.floridahealth.gov/dis… Weekly Influenza Surv… 850-245-4300
## # … with 49 more rows
5 years ago
```
### Retrieve WHO/NREVSS Surveillance Data
``` r
glimpse(xdat <- who_nrevss("national"))
3 years ago
## List of 3
## $ combined_prior_to_2015_16: tibble[,14] [940 × 14] (S3: tbl_df/tbl/data.frame)
3 years ago
## ..$ 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")
```
3 years ago
<img src="man/figures/README-who-vrevss-1.png" width="672" />
5 years ago
``` r
3 years ago
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
5 years ago
who_nrevss("census", years=2016)
3 years ago
## $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
5 years ago
who_nrevss("state", years=2016)
3 years ago
## $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>
3 years ago
## # … 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
5 years ago
```
4 years ago
## 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
3 years ago
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.