You can not select more than 25 topics Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.

856 lines
61 KiB

[![CRAN\_Status\_Badge](http://www.r-pkg.org/badges/version/cdcfluview)](https://cran.r-project.org/package=cdcfluview)
[![Travis-CI Build
Status](https://travis-ci.org/hrbrmstr/cdcfluview.svg?branch=master)](https://travis-ci.org/hrbrmstr/cdcfluview)
[![Coverage
Status](https://img.shields.io/codecov/c/github/hrbrmstr/cdcfluview/master.svg)](https://codecov.io/github/hrbrmstr/cdcfluview?branch=master)
I M P O R T A N T
=================
The CDC migrated to a new non-Flash portal and back-end APIs changed.
This is a complete reimagining of the package and — as such — all your
code is going to break. Please use GitHub issues to identify previous
API functionality you would like ported over. There’s a [release
candidate for
0.5.2](https://github.com/hrbrmstr/cdcfluview/releases/tag/v0.5.2) which
uses the old API but it likely to break in the near future given the
changes to the hidden API. You can do what with
`devtools::install_github("hrbrmstr/cdcfluview", ref="58c172b")`.
All folks providing feedback, code or suggestions will be added to the
DESCRIPTION file. Please include how you would prefer to be cited in any
issues you file.
If there’s a particular data set from
<https://www.cdc.gov/flu/weekly/fluviewinteractive.htm> that you want
and that isn’t in the package, please file it as an issue and be as
specific as you can (screen shot if possible).
:mask: cdcfluview
=================
Retrieve U.S. Flu Season Data from the CDC FluView Portal
Description
-----------
The U.S. Centers for Disease Control (CDC) maintains a portal
<http://gis.cdc.gov/grasp/fluview/fluportaldashboard.html> for accessing
state, regional and national influenza statistics as well as Mortality
Surveillance Data. The Flash interface makes it difficult and
time-consuming to select and retrieve influenza data. This package
provides functions to access the data provided by the portal’s
underlying API.
What’s Inside The Tin
---------------------
The following functions are implemented:
- `age_group_distribution`: Age Group Distribution of Influenza
Positive Tests Reported by Public Health Laboratories
- `cdc_basemap`: Retrieve CDC U.S. Basemaps
- `geographic_spread`: State and Territorial Epidemiologists Reports
of Geographic Spread of Influenza
- `hospitalizations`: Laboratory-Confirmed Influenza Hospitalizations
- `ilinet`: Retrieve ILINet Surveillance Data
- `ili_weekly_activity_indicators`: Retrieve weekly state-level ILI
indicators per-state for a given season
- `pi_mortality`: Pneumonia and Influenza Mortality Surveillance
- `state_data_providers`: Retrieve metadata about U.S. State CDC
Provider Data
- `surveillance_areas`: Retrieve a list of valid sub-regions for each
surveillance area.
- `who_nrevss`: Retrieve WHO/NREVSS Surveillance Data
- `mmwr_week`: Convert a Date to an MMWR day+week+year
- `mmwr_weekday`: Convert a Date to an MMWR weekday
- `mmwr_week_to_date`: Convert an MMWR year+week or year+week+day to a
Date object
The following data sets are included:
- `hhs_regions`: HHS Region Table (a data frame with 59 rows and 4
variables)
- `census_regions`: Census Region Table (a data frame with 51 rows and
2 variables)
- `mmwrid_map`: MMWR ID to Calendar Mappings (it is exported &
available, no need to use `data()`)
Installation
------------
``` r
devtools::install_github("hrbrmstr/cdcfluview")
```
Usage
-----
``` r
library(cdcfluview)
library(hrbrthemes)
5 years ago
library(tidyverse)
# current verison
packageVersion("cdcfluview")
```
## [1] '0.7.0'
5 years ago
### Age Group Distribution of Influenza Positive Tests Reported by Public Health Laboratories
``` r
glimpse(age_group_distribution())
5 years ago
```
## Observations: 36,144
## Variables: 16
5 years ago
## $ sea_label <chr> "1997-98", "1997-98", "1997-98", "1997-98", "1997-98", "1997-98", "1997-98", "1997-98", "...
## $ age_label <fctr> 0-4 yr, 0-4 yr, 0-4 yr, 0-4 yr, 0-4 yr, 0-4 yr, 0-4 yr, 0-4 yr, 0-4 yr, 0-4 yr, 0-4 yr, ...
## $ vir_label <fctr> A (Subtyping not Performed), A (Subtyping not Performed), A (Subtyping not Performed), A...
5 years ago
## $ count <int> 0, 1, 0, 0, 0, 0, 0, 3, 0, 6, 0, 1, 1, 2, 11, 8, 18, 26, 22, 19, 2, 5, 2, 1, 4, 0, 0, 0, ...
## $ mmwrid <int> 1866, 1867, 1868, 1869, 1870, 1871, 1872, 1873, 1874, 1875, 1876, 1877, 1878, 1879, 1880,...
## $ seasonid <int> 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 3...
## $ publishyearweekid <int> 2913, 2913, 2913, 2913, 2913, 2913, 2913, 2913, 2913, 2913, 2913, 2913, 2913, 2913, 2913,...
## $ sea_description <chr> "Season 1997-98", "Season 1997-98", "Season 1997-98", "Season 1997-98", "Season 1997-98",...
## $ sea_startweek <int> 1866, 1866, 1866, 1866, 1866, 1866, 1866, 1866, 1866, 1866, 1866, 1866, 1866, 1866, 1866,...
## $ sea_endweek <int> 1918, 1918, 1918, 1918, 1918, 1918, 1918, 1918, 1918, 1918, 1918, 1918, 1918, 1918, 1918,...
## $ vir_description <chr> "A-Unk", "A-Unk", "A-Unk", "A-Unk", "A-Unk", "A-Unk", "A-Unk", "A-Unk", "A-Unk", "A-Unk",...
## $ vir_startmmwrid <int> 1397, 1397, 1397, 1397, 1397, 1397, 1397, 1397, 1397, 1397, 1397, 1397, 1397, 1397, 1397,...
## $ vir_endmmwrid <int> 3131, 3131, 3131, 3131, 3131, 3131, 3131, 3131, 3131, 3131, 3131, 3131, 3131, 3131, 3131,...
## $ wk_start <date> 1997-09-28, 1997-10-05, 1997-10-12, 1997-10-19, 1997-10-26, 1997-11-02, 1997-11-09, 1997...
## $ wk_end <date> 1997-10-04, 1997-10-11, 1997-10-18, 1997-10-25, 1997-11-01, 1997-11-08, 1997-11-15, 1997...
## $ year_wk_num <int> 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11...
5 years ago
### Retrieve CDC U.S. Coverage Map
``` r
plot(cdc_basemap("national"))
5 years ago
```
![](README_files/figure-markdown_github-ascii_identifiers/unnamed-chunk-5-1.png)
``` r
plot(cdc_basemap("hhs"))
```
![](README_files/figure-markdown_github-ascii_identifiers/unnamed-chunk-5-2.png)
``` r
plot(cdc_basemap("census"))
```
![](README_files/figure-markdown_github-ascii_identifiers/unnamed-chunk-5-3.png)
``` r
plot(cdc_basemap("states"))
```
![](README_files/figure-markdown_github-ascii_identifiers/unnamed-chunk-5-4.png)
``` r
plot(cdc_basemap("spread"))
```
![](README_files/figure-markdown_github-ascii_identifiers/unnamed-chunk-5-5.png)
``` r
plot(cdc_basemap("surv"))
```
![](README_files/figure-markdown_github-ascii_identifiers/unnamed-chunk-5-6.png)
5 years ago
### State and Territorial Epidemiologists Reports of Geographic Spread of Influenza
``` r
glimpse(geographic_spread())
```
## Observations: 25,795
## Variables: 7
## $ statename <chr> "Alabama", "Alabama", "Alabama", "Alabama", "Alabama", "Alabama", "Alabama", "Alabama", "...
## $ url <chr> "http://adph.org/influenza/", "http://adph.org/influenza/", "http://adph.org/influenza/",...
## $ website <chr> "Influenza Surveillance", "Influenza Surveillance", "Influenza Surveillance", "Influenza ...
## $ activity_estimate <chr> "No Activity", "No Activity", "No Activity", "Local Activity", "Sporadic", "Sporadic", "S...
## $ weekend <date> 2003-10-04, 2003-10-11, 2003-10-18, 2003-10-25, 2003-11-01, 2003-11-08, 2003-11-15, 2003...
## $ season <chr> "2003-04", "2003-04", "2003-04", "2003-04", "2003-04", "2003-04", "2003-04", "2003-04", "...
## $ weeknumber <chr> "40", "41", "42", "43", "44", "45", "46", "47", "48", "49", "50", "51", "52", "53", "1", ...
### Laboratory-Confirmed Influenza Hospitalizations
``` r
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
``` r
glimpse(fs_nat <- hospitalizations("flusurv"))
5 years ago
```
## Observations: 1,476
## Variables: 14
5 years ago
## $ surveillance_area <chr> "FluSurv-NET", "FluSurv-NET", "FluSurv-NET", "FluSurv-NET", "FluSurv-NET", "FluSurv-NET",...
## $ region <chr> "Entire Network", "Entire Network", "Entire Network", "Entire Network", "Entire Network",...
## $ year <int> 2009, 2009, 2009, 2009, 2009, 2009, 2009, 2009, 2009, 2009, 2009, 2009, 2009, 2009, 2009,...
## $ season <int> 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 4...
## $ wk_start <date> 2009-08-30, 2009-09-06, 2009-09-13, 2009-09-20, 2009-09-27, 2009-10-04, 2009-10-11, 2009...
## $ wk_end <date> 2009-09-05, 2009-09-12, 2009-09-19, 2009-09-26, 2009-10-03, 2009-10-10, 2009-10-17, 2009...
## $ year_wk_num <int> 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 1, 2, 3, 4, 5, 6,...
## $ rate <dbl> 0.5, 2.5, 4.6, 6.7, 10.9, 18.1, 28.3, 39.1, 47.3, 53.3, 57.5, 60.1, 61.6, 62.9, 64.1, 65....
## $ weeklyrate <dbl> 0.5, 2.0, 2.0, 2.1, 4.3, 7.2, 10.2, 10.8, 8.2, 6.0, 4.2, 2.6, 1.5, 1.3, 1.3, 1.0, 1.2, 1....
## $ age <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,...
## $ age_label <fctr> 0-4 yr, 0-4 yr, 0-4 yr, 0-4 yr, 0-4 yr, 0-4 yr, 0-4 yr, 0-4 yr, 0-4 yr, 0-4 yr, 0-4 yr, ...
## $ sea_label <chr> "2009-10", "2009-10", "2009-10", "2009-10", "2009-10", "2009-10", "2009-10", "2009-10", "...
## $ sea_description <chr> "Season 2009-10", "Season 2009-10", "Season 2009-10", "Season 2009-10", "Season 2009-10",...
## $ mmwrid <int> 2488, 2489, 2490, 2491, 2492, 2493, 2494, 2495, 2496, 2497, 2498, 2499, 2500, 2501, 2502,...
``` r
ggplot(fs_nat, aes(wk_end, rate)) +
geom_line(aes(color=age_label, group=age_label)) +
facet_wrap(~sea_description, scales="free_x") +
scale_color_ipsum(name=NULL) +
labs(x=NULL, y="Rates per 100,000 population",
title="FluSurv-NET :: Entire Network :: All Seasons :: Cumulative Rate") +
theme_ipsum_rc()
```
![](README_files/figure-markdown_github-ascii_identifiers/unnamed-chunk-7-1.png)
5 years ago
``` r
glimpse(hospitalizations("eip"))
```
## Observations: 2,385
## Variables: 14
5 years ago
## $ surveillance_area <chr> "EIP", "EIP", "EIP", "EIP", "EIP", "EIP", "EIP", "EIP", "EIP", "EIP", "EIP", "EIP", "EIP"...
## $ region <chr> "Entire Network", "Entire Network", "Entire Network", "Entire Network", "Entire Network",...
## $ year <int> 2003, 2003, 2003, 2003, 2003, 2003, 2003, 2003, 2003, 2003, 2003, 2003, 2003, 2003, 2004,...
## $ season <int> 43, 43, 43, 43, 43, 43, 43, 43, 43, 43, 43, 43, 43, 43, 43, 43, 43, 43, 43, 43, 43, 43, 4...
## $ wk_start <date> 2003-09-28, 2003-10-05, 2003-10-12, 2003-10-19, 2003-10-26, 2003-11-02, 2003-11-09, 2003...
## $ wk_end <date> 2003-10-04, 2003-10-11, 2003-10-18, 2003-10-25, 2003-11-01, 2003-11-08, 2003-11-15, 2003...
## $ year_wk_num <int> 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11...
## $ rate <dbl> 0.0, 0.0, 0.0, 0.0, 0.1, 0.7, 3.3, 9.1, 16.9, 28.1, 40.0, 55.6, 69.0, 78.7, 83.1, 86.6, 8...
## $ weeklyrate <dbl> 0.0, 0.0, 0.0, 0.0, 0.1, 0.6, 2.7, 5.8, 7.8, 11.2, 11.9, 15.6, 13.4, 9.7, 4.4, 3.4, 1.3, ...
## $ age <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,...
## $ age_label <fctr> 0-4 yr, 0-4 yr, 0-4 yr, 0-4 yr, 0-4 yr, 0-4 yr, 0-4 yr, 0-4 yr, 0-4 yr, 0-4 yr, 0-4 yr, ...
## $ sea_label <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N...
## $ sea_description <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N...
## $ mmwrid <int> 2179, 2180, 2181, 2182, 2183, 2184, 2185, 2186, 2187, 2188, 2189, 2190, 2191, 2192, 2193,...
5 years ago
``` r
glimpse(hospitalizations("eip", "Colorado"))
```
## Observations: 2,385
## Variables: 14
5 years ago
## $ surveillance_area <chr> "EIP", "EIP", "EIP", "EIP", "EIP", "EIP", "EIP", "EIP", "EIP", "EIP", "EIP", "EIP", "EIP"...
## $ region <chr> "Colorado", "Colorado", "Colorado", "Colorado", "Colorado", "Colorado", "Colorado", "Colo...
## $ year <int> 2003, 2003, 2003, 2003, 2003, 2003, 2003, 2003, 2003, 2003, 2003, 2003, 2003, 2003, 2004,...
## $ season <int> 43, 43, 43, 43, 43, 43, 43, 43, 43, 43, 43, 43, 43, 43, 43, 43, 43, 43, 43, 43, 43, 43, 4...
## $ wk_start <date> 2003-09-28, 2003-10-05, 2003-10-12, 2003-10-19, 2003-10-26, 2003-11-02, 2003-11-09, 2003...
## $ wk_end <date> 2003-10-04, 2003-10-11, 2003-10-18, 2003-10-25, 2003-11-01, 2003-11-08, 2003-11-15, 2003...
## $ year_wk_num <int> 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11...
## $ rate <dbl> 0.0, 0.0, 0.0, 0.0, 0.6, 3.6, 21.2, 57.5, 94.3, 130.6, 146.4, 150.6, 153.0, 157.2, 158.5,...
## $ weeklyrate <dbl> 0.0, 0.0, 0.0, 0.0, 0.6, 3.0, 17.5, 36.3, 36.9, 36.3, 15.7, 4.2, 2.4, 4.2, 1.2, 1.2, 1.8,...
## $ age <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,...
## $ age_label <fctr> 0-4 yr, 0-4 yr, 0-4 yr, 0-4 yr, 0-4 yr, 0-4 yr, 0-4 yr, 0-4 yr, 0-4 yr, 0-4 yr, 0-4 yr, ...
## $ sea_label <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N...
## $ sea_description <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N...
## $ mmwrid <int> 2179, 2180, 2181, 2182, 2183, 2184, 2185, 2186, 2187, 2188, 2189, 2190, 2191, 2192, 2193,...
5 years ago
``` r
glimpse(hospitalizations("ihsp"))
```
## Observations: 1,476
## Variables: 14
5 years ago
## $ surveillance_area <chr> "IHSP", "IHSP", "IHSP", "IHSP", "IHSP", "IHSP", "IHSP", "IHSP", "IHSP", "IHSP", "IHSP", "...
## $ region <chr> "Entire Network", "Entire Network", "Entire Network", "Entire Network", "Entire Network",...
## $ year <int> 2009, 2009, 2009, 2009, 2009, 2009, 2009, 2009, 2009, 2009, 2009, 2009, 2009, 2009, 2009,...
## $ season <int> 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 4...
## $ wk_start <date> 2009-08-30, 2009-09-06, 2009-09-13, 2009-09-20, 2009-09-27, 2009-10-04, 2009-10-11, 2009...
## $ wk_end <date> 2009-09-05, 2009-09-12, 2009-09-19, 2009-09-26, 2009-10-03, 2009-10-10, 2009-10-17, 2009...
## $ year_wk_num <int> 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 1, 2, 3, 4, 5, 6,...
## $ rate <dbl> 0.0, 0.4, 3.6, 7.6, 14.0, 25.5, 47.1, 71.8, 87.4, 92.5, 94.9, 96.9, 98.1, 98.9, 100.9, 10...
## $ weeklyrate <dbl> 0.0, 0.4, 3.2, 4.0, 6.4, 11.6, 21.5, 24.7, 15.6, 5.2, 2.4, 2.0, 1.2, 0.8, 2.0, 1.2, 1.6, ...
## $ age <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,...
## $ age_label <fctr> 0-4 yr, 0-4 yr, 0-4 yr, 0-4 yr, 0-4 yr, 0-4 yr, 0-4 yr, 0-4 yr, 0-4 yr, 0-4 yr, 0-4 yr, ...
## $ sea_label <chr> "2009-10", "2009-10", "2009-10", "2009-10", "2009-10", "2009-10", "2009-10", "2009-10", "...
## $ sea_description <chr> "Season 2009-10", "Season 2009-10", "Season 2009-10", "Season 2009-10", "Season 2009-10",...
## $ mmwrid <int> 2488, 2489, 2490, 2491, 2492, 2493, 2494, 2495, 2496, 2497, 2498, 2499, 2500, 2501, 2502,...
5 years ago
``` r
glimpse(hospitalizations("ihsp", "Oklahoma"))
```
## Observations: 390
## Variables: 14
5 years ago
## $ surveillance_area <chr> "IHSP", "IHSP", "IHSP", "IHSP", "IHSP", "IHSP", "IHSP", "IHSP", "IHSP", "IHSP", "IHSP", "...
## $ region <chr> "Oklahoma", "Oklahoma", "Oklahoma", "Oklahoma", "Oklahoma", "Oklahoma", "Oklahoma", "Okla...
## $ year <int> 2009, 2009, 2009, 2009, 2009, 2009, 2009, 2009, 2009, 2009, 2009, 2009, 2009, 2009, 2009,...
## $ season <int> 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 4...
## $ wk_start <date> 2009-08-30, 2009-09-06, 2009-09-13, 2009-09-20, 2009-09-27, 2009-10-04, 2009-10-11, 2009...
## $ wk_end <date> 2009-09-05, 2009-09-12, 2009-09-19, 2009-09-26, 2009-10-03, 2009-10-10, 2009-10-17, 2009...
## $ year_wk_num <int> 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 1, 2, 3, 4, 5, 6,...
## $ rate <dbl> 0.0, 1.3, 10.8, 21.5, 40.4, 63.3, 88.9, 106.4, 115.8, 121.2, 125.2, 125.2, 126.6, 127.9, ...
## $ weeklyrate <dbl> 0.0, 1.3, 9.4, 10.8, 18.9, 22.9, 25.6, 17.5, 9.4, 5.4, 4.0, 0.0, 1.3, 1.3, 2.7, 1.3, 5.4,...
## $ age <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,...
## $ age_label <fctr> 0-4 yr, 0-4 yr, 0-4 yr, 0-4 yr, 0-4 yr, 0-4 yr, 0-4 yr, 0-4 yr, 0-4 yr, 0-4 yr, 0-4 yr, ...
## $ sea_label <chr> "2009-10", "2009-10", "2009-10", "2009-10", "2009-10", "2009-10", "2009-10", "2009-10", "...
## $ sea_description <chr> "Season 2009-10", "Season 2009-10", "Season 2009-10", "Season 2009-10", "Season 2009-10",...
## $ mmwrid <int> 2488, 2489, 2490, 2491, 2492, 2493, 2494, 2495, 2496, 2497, 2498, 2499, 2500, 2501, 2502,...
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)
})
5 years ago
```
## Observations: 1,048
## Variables: 16
## $ region_type <chr> "National", "National", "National", "National", "National", "National", "National", "Natio...
## $ region <chr> "National", "National", "National", "National", "National", "National", "National", "Natio...
## $ year <int> 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1998, ...
## $ week <int> 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11,...
## $ weighted_ili <dbl> 1.101480, 1.200070, 1.378760, 1.199200, 1.656180, 1.413260, 1.986800, 2.447490, 1.739010, ...
## $ unweighted_ili <dbl> 1.216860, 1.280640, 1.239060, 1.144730, 1.261120, 1.282750, 1.445790, 1.647960, 1.675170, ...
## $ age_0_4 <dbl> 179, 199, 228, 188, 217, 178, 294, 288, 268, 299, 346, 348, 510, 579, 639, 690, 856, 824, ...
## $ age_25_49 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA...
## $ age_25_64 <dbl> 157, 151, 153, 193, 162, 148, 240, 293, 206, 282, 268, 235, 404, 584, 759, 654, 679, 817, ...
## $ age_5_24 <dbl> 205, 242, 266, 236, 280, 281, 328, 456, 343, 415, 388, 362, 492, 576, 810, 1121, 1440, 160...
## $ age_50_64 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA...
## $ age_65 <dbl> 29, 23, 34, 36, 41, 48, 70, 63, 69, 102, 81, 59, 113, 207, 207, 148, 151, 196, 233, 146, 1...
## $ ilitotal <dbl> 570, 615, 681, 653, 700, 655, 932, 1100, 886, 1098, 1083, 1004, 1519, 1946, 2415, 2613, 31...
## $ num_of_providers <dbl> 192, 191, 219, 213, 213, 195, 248, 256, 252, 253, 242, 190, 251, 250, 254, 255, 245, 245, ...
## $ total_patients <dbl> 46842, 48023, 54961, 57044, 55506, 51062, 64463, 66749, 52890, 67887, 61314, 47719, 48429,...
## $ week_start <date> 1997-10-06, 1997-10-13, 1997-10-20, 1997-10-27, 1997-11-03, 1997-11-10, 1997-11-17, 1997-...
## # A tibble: 1,048 x 16
## region_type region year week weighted_ili unweighted_ili age_0_4 age_25_49 age_25_64 age_5_24 age_50_64 age_65
## <chr> <chr> <int> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 National National 1997 40 1.10148 1.21686 179 NA 157 205 NA 29
## 2 National National 1997 41 1.20007 1.28064 199 NA 151 242 NA 23
## 3 National National 1997 42 1.37876 1.23906 228 NA 153 266 NA 34
## 4 National National 1997 43 1.19920 1.14473 188 NA 193 236 NA 36
## 5 National National 1997 44 1.65618 1.26112 217 NA 162 280 NA 41
## 6 National National 1997 45 1.41326 1.28275 178 NA 148 281 NA 48
## 7 National National 1997 46 1.98680 1.44579 294 NA 240 328 NA 70
## 8 National National 1997 47 2.44749 1.64796 288 NA 293 456 NA 63
## 9 National National 1997 48 1.73901 1.67517 268 NA 206 343 NA 69
## 10 National National 1997 49 1.93919 1.61739 299 NA 282 415 NA 102
## # ... with 1,038 more rows, and 4 more variables: ilitotal <dbl>, num_of_providers <dbl>, total_patients <dbl>,
## # week_start <date>
![](README_files/figure-markdown_github-ascii_identifiers/unnamed-chunk-8-1.png)
## Observations: 10,480
## Variables: 16
## $ region_type <chr> "HHS Regions", "HHS Regions", "HHS Regions", "HHS Regions", "HHS Regions", "HHS Regions", ...
## $ region <fctr> Region 1, Region 2, Region 3, Region 4, Region 5, Region 6, Region 7, Region 8, Region 9,...
## $ year <int> 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, ...
## $ week <int> 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 41, 41, 41, 41, 41, 41, 41, 41, 41, 41, 42, 42, 42...
## $ weighted_ili <dbl> 0.498535, 0.374963, 1.354280, 0.400338, 1.229260, 1.018980, 0.871791, 0.516017, 1.807610, ...
## $ unweighted_ili <dbl> 0.623848, 0.384615, 1.341720, 0.450010, 0.901266, 0.747384, 1.152860, 0.422654, 2.258780, ...
## $ age_0_4 <dbl> 15, 0, 6, 12, 31, 2, 0, 2, 80, 31, 14, 0, 4, 21, 36, 2, 0, 0, 103, 19, 35, 0, 3, 19, 66, 2...
## $ age_25_49 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA...
## $ age_25_64 <dbl> 7, 3, 7, 23, 24, 1, 4, 0, 76, 12, 14, 2, 19, 7, 23, 2, 0, 1, 76, 7, 15, 0, 17, 15, 29, 2, ...
## $ age_5_24 <dbl> 22, 0, 15, 11, 30, 2, 18, 3, 74, 30, 29, 0, 16, 14, 41, 2, 13, 8, 84, 35, 35, 0, 24, 18, 7...
## $ age_50_64 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA...
## $ age_65 <dbl> 0, 0, 4, 0, 4, 0, 5, 0, 13, 3, 0, 0, 3, 2, 4, 0, 2, 0, 11, 1, 0, 1, 2, 2, 16, 0, 2, 0, 9, ...
## $ ilitotal <dbl> 44, 3, 32, 46, 89, 5, 27, 5, 243, 76, 57, 2, 42, 44, 104, 6, 15, 9, 274, 62, 85, 1, 46, 54...
## $ num_of_providers <dbl> 32, 7, 16, 29, 49, 4, 14, 5, 23, 13, 29, 7, 17, 31, 48, 4, 14, 6, 23, 12, 40, 7, 15, 33, 6...
## $ total_patients <dbl> 7053, 780, 2385, 10222, 9875, 669, 2342, 1183, 10758, 1575, 6987, 872, 2740, 11310, 9618, ...
## $ week_start <date> 1997-10-06, 1997-10-06, 1997-10-06, 1997-10-06, 1997-10-06, 1997-10-06, 1997-10-06, 1997-...
## # A tibble: 10,480 x 16
5 years ago
## region_type region year week weighted_ili unweighted_ili age_0_4 age_25_49 age_25_64 age_5_24 age_50_64 age_65
## <chr> <fctr> <int> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
5 years ago
## 1 HHS Regions Region 1 1997 40 0.498535 0.623848 15 NA 7 22 NA 0
## 2 HHS Regions Region 2 1997 40 0.374963 0.384615 0 NA 3 0 NA 0
## 3 HHS Regions Region 3 1997 40 1.354280 1.341720 6 NA 7 15 NA 4
## 4 HHS Regions Region 4 1997 40 0.400338 0.450010 12 NA 23 11 NA 0
## 5 HHS Regions Region 5 1997 40 1.229260 0.901266 31 NA 24 30 NA 4
## 6 HHS Regions Region 6 1997 40 1.018980 0.747384 2 NA 1 2 NA 0
## 7 HHS Regions Region 7 1997 40 0.871791 1.152860 0 NA 4 18 NA 5
## 8 HHS Regions Region 8 1997 40 0.516017 0.422654 2 NA 0 3 NA 0
## 9 HHS Regions Region 9 1997 40 1.807610 2.258780 80 NA 76 74 NA 13
## 10 HHS Regions Region 10 1997 40 4.743520 4.825400 31 NA 12 30 NA 3
## # ... with 10,470 more rows, and 4 more variables: ilitotal <dbl>, num_of_providers <dbl>, total_patients <dbl>,
## # week_start <date>
![](README_files/figure-markdown_github-ascii_identifiers/unnamed-chunk-8-2.png)
## Observations: 9,432
## Variables: 16
## $ region_type <chr> "Census Regions", "Census Regions", "Census Regions", "Census Regions", "Census Regions", ...
## $ region <chr> "New England", "Mid-Atlantic", "East North Central", "West North Central", "South Atlantic...
## $ year <int> 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, ...
## $ week <int> 40, 40, 40, 40, 40, 40, 40, 40, 40, 41, 41, 41, 41, 41, 41, 41, 41, 41, 42, 42, 42, 42, 42...
## $ weighted_ili <dbl> 0.4985350, 0.8441440, 0.7924860, 1.7640500, 0.5026620, 0.0542283, 1.0189800, 2.2587800, 2....
## $ unweighted_ili <dbl> 0.6238480, 1.3213800, 0.8187380, 1.2793900, 0.7233800, 0.0688705, 0.7473840, 2.2763300, 3....
## $ age_0_4 <dbl> 15, 4, 28, 3, 14, 0, 2, 87, 26, 14, 4, 36, 0, 21, 0, 2, 93, 29, 35, 3, 65, 1, 19, 0, 2, 84...
## $ age_25_49 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA...
## $ age_25_64 <dbl> 7, 8, 20, 8, 22, 3, 1, 71, 17, 14, 13, 23, 1, 14, 1, 2, 72, 11, 15, 11, 27, 5, 21, 0, 2, 5...
## $ age_5_24 <dbl> 22, 12, 28, 20, 14, 0, 2, 71, 36, 29, 8, 39, 18, 22, 0, 2, 80, 44, 35, 16, 74, 9, 24, 2, 2...
## $ age_50_64 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA...
## $ age_65 <dbl> 0, 4, 3, 6, 0, 0, 0, 15, 1, 0, 2, 2, 4, 3, 0, 0, 10, 2, 0, 3, 12, 6, 2, 0, 0, 9, 2, 0, 1, ...
## $ ilitotal <dbl> 44, 28, 79, 37, 50, 3, 5, 244, 80, 57, 27, 100, 23, 60, 1, 6, 255, 86, 85, 33, 178, 21, 66...
## $ num_of_providers <dbl> 32, 13, 47, 17, 30, 9, 4, 16, 24, 29, 13, 46, 17, 32, 10, 4, 17, 23, 40, 12, 62, 16, 33, 1...
## $ total_patients <dbl> 7053, 2119, 9649, 2892, 6912, 4356, 669, 10719, 2473, 6987, 2384, 9427, 2823, 7591, 4947, ...
## $ week_start <date> 1997-10-06, 1997-10-06, 1997-10-06, 1997-10-06, 1997-10-06, 1997-10-06, 1997-10-06, 1997-...
## # A tibble: 9,432 x 16
5 years ago
## region_type region year week weighted_ili unweighted_ili age_0_4 age_25_49 age_25_64 age_5_24
## <chr> <chr> <int> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 Census Regions New England 1997 40 0.4985350 0.6238480 15 NA 7 22
## 2 Census Regions Mid-Atlantic 1997 40 0.8441440 1.3213800 4 NA 8 12
## 3 Census Regions East North Central 1997 40 0.7924860 0.8187380 28 NA 20 28
## 4 Census Regions West North Central 1997 40 1.7640500 1.2793900 3 NA 8 20
## 5 Census Regions South Atlantic 1997 40 0.5026620 0.7233800 14 NA 22 14
## 6 Census Regions East South Central 1997 40 0.0542283 0.0688705 0 NA 3 0
## 7 Census Regions West South Central 1997 40 1.0189800 0.7473840 2 NA 1 2
## 8 Census Regions Mountain 1997 40 2.2587800 2.2763300 87 NA 71 71
## 9 Census Regions Pacific 1997 40 2.0488300 3.2349400 26 NA 17 36
## 10 Census Regions New England 1997 41 0.6426690 0.8158010 14 NA 14 29
## # ... with 9,422 more rows, and 6 more variables: age_50_64 <dbl>, age_65 <dbl>, ilitotal <dbl>,
## # num_of_providers <dbl>, total_patients <dbl>, week_start <date>
![](README_files/figure-markdown_github-ascii_identifiers/unnamed-chunk-8-3.png)
## Observations: 19,718
## Variables: 16
## $ region_type <chr> "States", "States", "States", "States", "States", "States", "States", "States", "States", ...
## $ region <chr> "Alabama", "Alaska", "Arizona", "Arkansas", "California", "Colorado", "Connecticut", "Dela...
## $ year <int> 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, ...
## $ week <int> 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40...
## $ weighted_ili <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA...
## $ unweighted_ili <dbl> 2.1347700, 0.8751460, 0.6747210, 0.6960560, 1.9541200, 0.6606840, 0.0783085, 0.1001250, 2....
## $ age_0_4 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA...
## $ age_25_49 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA...
## $ age_25_64 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA...
## $ age_5_24 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA...
## $ age_50_64 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA...
## $ age_65 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA...
## $ ilitotal <dbl> 249, 15, 172, 18, 632, 134, 3, 4, 73, NA, 647, 20, 19, 505, 65, 10, 39, 19, NA, 22, 117, 1...
## $ num_of_providers <dbl> 35, 7, 49, 15, 112, 14, 12, 13, 4, NA, 62, 18, 12, 74, 44, 6, 40, 14, NA, 30, 17, 56, 47, ...
## $ total_patients <dbl> 11664, 1714, 25492, 2586, 32342, 20282, 3831, 3995, 2599, NA, 40314, 1943, 4579, 39390, 12...
## $ week_start <date> 2010-10-04, 2010-10-04, 2010-10-04, 2010-10-04, 2010-10-04, 2010-10-04, 2010-10-04, 2010-...
## # A tibble: 19,718 x 16
5 years ago
## region_type region year week weighted_ili unweighted_ili age_0_4 age_25_49 age_25_64 age_5_24
## <chr> <chr> <int> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 States Alabama 2010 40 NA 2.1347700 NA NA NA NA
## 2 States Alaska 2010 40 NA 0.8751460 NA NA NA NA
## 3 States Arizona 2010 40 NA 0.6747210 NA NA NA NA
## 4 States Arkansas 2010 40 NA 0.6960560 NA NA NA NA
## 5 States California 2010 40 NA 1.9541200 NA NA NA NA
## 6 States Colorado 2010 40 NA 0.6606840 NA NA NA NA
## 7 States Connecticut 2010 40 NA 0.0783085 NA NA NA NA
## 8 States Delaware 2010 40 NA 0.1001250 NA NA NA NA
## 9 States District of Columbia 2010 40 NA 2.8087700 NA NA NA NA
## 10 States Florida 2010 40 NA NA NA NA NA NA
## # ... with 19,708 more rows, and 6 more variables: age_50_64 <dbl>, age_65 <dbl>, ilitotal <dbl>,
## # num_of_providers <dbl>, total_patients <dbl>, week_start <date>
![](README_files/figure-markdown_github-ascii_identifiers/unnamed-chunk-8-4.png)
5 years ago
### Retrieve weekly state-level ILI indicators per-state for a given season
``` r
ili_weekly_activity_indicators(2017)
```
## # A tibble: 216 x 9
## statename ili_activity_label ili_activity_group statefips stateabbr weekend weeknumber year seasonid
## <chr> <fctr> <chr> <chr> <chr> <date> <int> <int> <int>
## 1 Alabama Level 2 Minimal 01 AL 2017-10-07 40 2017 57
## 2 Alabama Level 2 Minimal 01 AL 2017-10-14 41 2017 57
## 3 Alabama Level 2 Minimal 01 AL 2017-10-21 42 2017 57
## 4 Alabama Level 3 Minimal 01 AL 2017-10-28 43 2017 57
## 5 Alaska Level 1 Minimal 02 AK 2017-10-07 40 2017 57
## 6 Alaska Level 2 Minimal 02 AK 2017-10-14 41 2017 57
## 7 Alaska Level 4 Low 02 AK 2017-10-21 42 2017 57
## 8 Alaska Level 3 Minimal 02 AK 2017-10-28 43 2017 57
## 9 Arizona Level 2 Minimal 04 AZ 2017-10-07 40 2017 57
## 10 Arizona Level 3 Minimal 04 AZ 2017-10-14 41 2017 57
## # ... with 206 more rows
``` r
xdf <- map_df(2008:2017, ili_weekly_activity_indicators)
count(xdf, weekend, ili_activity_label) %>%
complete(weekend, ili_activity_label) %>%
ggplot(aes(weekend, ili_activity_label, fill=n)) +
geom_tile(color="#c2c2c2", size=0.1) +
scale_x_date(expand=c(0,0)) +
viridis::scale_fill_viridis(name="# States", na.value="White") +
labs(x=NULL, y=NULL, title="Weekly ILI Indicators (all states)") +
coord_fixed(100/1) +
theme_ipsum_rc(grid="") +
theme(legend.position="bottom")
5 years ago
```
![](README_files/figure-markdown_github-ascii_identifiers/unnamed-chunk-9-1.png)
5 years ago
### Pneumonia and Influenza Mortality Surveillance
``` r
(nat_pi <- pi_mortality("national"))
5 years ago
```
## # A tibble: 419 x 19
## seasonid baseline threshold percent_pni percent_complete number_influenza number_pneumonia all_deaths total_pni
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 57 0.058 0.061 0.054 0.763 10 1962 36283 1972
## 2 57 0.058 0.062 0.056 0.675 10 1795 32107 1805
## 3 56 0.059 0.063 0.059 1.000 18 3022 51404 3040
## 4 56 0.060 0.063 0.061 1.000 11 3193 52130 3204
## 5 56 0.061 0.064 0.062 1.000 7 3178 51443 3185
## 6 56 0.062 0.065 0.061 1.000 17 3129 51865 3146
## 7 56 0.063 0.066 0.060 1.000 16 3099 51753 3115
## 8 56 0.064 0.067 0.061 1.000 19 3208 52541 3227
## 9 56 0.065 0.068 0.060 1.000 7 3192 53460 3199
## 10 56 0.066 0.069 0.062 1.000 22 3257 53163 3279
5 years ago
## # ... with 409 more rows, and 10 more variables: weeknumber <chr>, geo_description <chr>, age_label <chr>,
## # wk_start <date>, wk_end <date>, year_wk_num <int>, mmwrid <chr>, coverage_area <chr>, region_name <chr>,
## # callout <chr>
5 years ago
``` r
select(nat_pi, wk_end, percent_pni, baseline, threshold) %>%
gather(measure, value, -wk_end) %>%
ggplot(aes(wk_end, value)) +
geom_line(aes(group=measure, color=measure)) +
scale_y_percent() +
scale_color_ipsum(name = NULL, labels=c("Baseline", "Percent P&I", "Threshold")) +
labs(x=NULL, y="% of all deaths due to P&I",
title="Percentage of all deaths due to pneumonia and influenza, National Summary") +
theme_ipsum_rc(grid="XY") +
theme(legend.position="bottom")
```
![](README_files/figure-markdown_github-ascii_identifiers/unnamed-chunk-10-1.png)
``` r
(st_pi <- pi_mortality("state"))
5 years ago
```
## # A tibble: 21,788 x 19
## seasonid baseline threshold percent_pni percent_complete number_influenza number_pneumonia all_deaths total_pni
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 57 NA NA 0.065 0.836 0 50 772 50
## 2 57 NA NA 0.064 0.767 0 45 708 45
## 3 57 NA NA 0.063 0.666 1 2 48 3
## 4 57 NA NA 0.105 0.527 0 4 38 4
## 5 57 NA NA 0.053 0.412 0 20 374 20
## 6 57 NA NA 0.059 0.393 0 21 356 21
## 7 57 NA NA 0.060 0.751 0 25 420 25
## 8 57 NA NA 0.050 0.604 0 17 338 17
## 9 57 NA NA 0.065 0.774 1 228 3510 229
## 10 57 NA NA 0.059 0.758 2 201 3438 203
## # ... with 21,778 more rows, and 10 more variables: weeknumber <chr>, geo_description <chr>, age_label <chr>,
## # wk_start <date>, wk_end <date>, year_wk_num <int>, mmwrid <chr>, coverage_area <chr>, region_name <chr>,
## # callout <chr>
5 years ago
``` r
(reg_pi <- pi_mortality("region"))
5 years ago
```
## # A tibble: 4,190 x 19
## seasonid baseline threshold percent_pni percent_complete number_influenza number_pneumonia all_deaths total_pni
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 57 0.060 0.067 0.051 0.735 0 85 1683 85
## 2 57 0.061 0.068 0.060 0.701 0 96 1605 96
## 3 57 0.060 0.065 0.061 0.608 1 154 2524 155
## 4 57 0.060 0.066 0.063 0.602 1 157 2497 158
## 5 57 0.053 0.058 0.045 0.511 1 115 2575 116
## 6 57 0.054 0.059 0.045 0.440 1 98 2215 99
## 7 57 0.056 0.060 0.051 0.744 3 394 7753 397
## 8 57 0.057 0.061 0.052 0.651 1 354 6778 355
## 9 57 0.055 0.059 0.052 0.914 1 403 7701 404
## 10 57 0.056 0.060 0.054 0.799 4 358 6733 362
5 years ago
## # ... with 4,180 more rows, and 10 more variables: weeknumber <chr>, geo_description <chr>, age_label <chr>,
## # wk_start <date>, wk_end <date>, year_wk_num <int>, mmwrid <chr>, coverage_area <chr>, region_name <chr>,
## # callout <chr>
5 years ago
### Retrieve metadata about U.S. State CDC Provider Data
5 years ago
``` r
state_data_providers()
```
## # A tibble: 59 x 5
## statename statehealthdeptname
## * <chr> <chr>
## 1 Alabama Alabama Department of Public Health
## 2 Alaska State of Alaska Health and Social Services
## 3 Arizona Arizona Department of Health Services
## 4 Arkansas Arkansas Department of Health
## 5 California California Department of Public Health
## 6 Colorado Colorado Department of Public Health and Environment
## 7 Connecticut Connecticut Department of Public Health
## 8 Delaware Delaware Health and Social Services
## 9 District of Columbia District of Columbia Department of Health
## 10 Florida Florida Department of Health
## # ... with 49 more rows, and 3 more variables: url <chr>, statewebsitename <chr>, statefluphonenum <chr>
### Retrieve WHO/NREVSS Surveillance Data
``` r
glimpse(xdat <- who_nrevss("national"))
5 years ago
```
## List of 3
## $ combined_prior_to_2015_16:Classes 'tbl_df', 'tbl' and 'data.frame': 940 obs. of 14 variables:
## ..$ region_type : chr [1:940] "National" "National" "National" "National" ...
## ..$ region : chr [1:940] "National" "National" "National" "National" ...
## ..$ year : int [1:940] 1997 1997 1997 1997 1997 1997 1997 1997 1997 1997 ...
## ..$ week : int [1:940] 40 41 42 43 44 45 46 47 48 49 ...
## ..$ total_specimens : int [1:940] 1291 1513 1552 1669 1897 2106 2204 2533 2242 2607 ...
## ..$ percent_positive : num [1:940] 0 0.727 1.095 0.419 0.527 ...
## ..$ a_2009_h1n1 : int [1:940] 0 0 0 0 0 0 0 0 0 0 ...
## ..$ a_h1 : int [1:940] 0 0 0 0 0 0 0 0 0 0 ...
## ..$ a_h3 : int [1:940] 0 0 3 0 9 0 3 5 14 11 ...
## ..$ a_subtyping_not_performed: int [1:940] 0 11 13 7 1 6 4 17 22 28 ...
## ..$ a_unable_to_subtype : int [1:940] 0 0 0 0 0 0 0 0 0 0 ...
## ..$ b : int [1:940] 0 0 1 0 0 0 1 1 1 1 ...
## ..$ h3n2v : int [1:940] 0 0 0 0 0 0 0 0 0 0 ...
## ..$ wk_date : Date[1:940], format: "1997-09-28" "1997-10-05" "1997-10-12" "1997-10-19" ...
## $ public_health_labs :Classes 'tbl_df', 'tbl' and 'data.frame': 108 obs. of 13 variables:
## ..$ region_type : chr [1:108] "National" "National" "National" "National" ...
## ..$ region : chr [1:108] "National" "National" "National" "National" ...
## ..$ year : int [1:108] 2015 2015 2015 2015 2015 2015 2015 2015 2015 2015 ...
## ..$ week : int [1:108] 40 41 42 43 44 45 46 47 48 49 ...
## ..$ total_specimens : int [1:108] 1139 1152 1198 1244 1465 1393 1458 1157 1550 1518 ...
## ..$ a_2009_h1n1 : int [1:108] 4 5 10 9 4 11 17 17 27 38 ...
## ..$ a_h3 : int [1:108] 65 41 50 31 23 34 42 24 36 37 ...
## ..$ a_subtyping_not_performed: int [1:108] 2 2 1 4 4 1 1 0 3 3 ...
## ..$ b : int [1:108] 10 7 8 9 9 10 4 4 9 11 ...
## ..$ bvic : int [1:108] 0 3 3 1 1 4 0 3 3 2 ...
## ..$ byam : int [1:108] 1 0 2 4 4 2 4 9 12 11 ...
## ..$ h3n2v : int [1:108] 0 0 0 0 0 0 0 0 0 0 ...
## ..$ wk_date : Date[1:108], format: "2015-10-04" "2015-10-11" "2015-10-18" "2015-10-25" ...
## $ clinical_labs :Classes 'tbl_df', 'tbl' and 'data.frame': 108 obs. of 11 variables:
## ..$ region_type : chr [1:108] "National" "National" "National" "National" ...
## ..$ region : chr [1:108] "National" "National" "National" "National" ...
## ..$ year : int [1:108] 2015 2015 2015 2015 2015 2015 2015 2015 2015 2015 ...
## ..$ week : int [1:108] 40 41 42 43 44 45 46 47 48 49 ...
## ..$ total_specimens : int [1:108] 12029 13111 13441 13537 14687 15048 15250 15234 16201 16673 ...
## ..$ total_a : int [1:108] 84 116 97 98 97 122 84 119 145 140 ...
## ..$ total_b : int [1:108] 43 54 52 52 68 86 98 92 81 106 ...
## ..$ percent_positive: num [1:108] 1.06 1.3 1.11 1.11 1.12 ...
## ..$ percent_a : num [1:108] 0.698 0.885 0.722 0.724 0.66 ...
## ..$ percent_b : num [1:108] 0.357 0.412 0.387 0.384 0.463 ...
## ..$ wk_date : Date[1:108], format: "2015-10-04" "2015-10-11" "2015-10-18" "2015-10-25" ...
``` r
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")
```
![](README_files/figure-markdown_github-ascii_identifiers/unnamed-chunk-12-1.png)
5 years ago
``` r
who_nrevss("hhs")
```
## $combined_prior_to_2015_16
## # A tibble: 9,400 x 14
5 years ago
## region_type region year week total_specimens percent_positive a_2009_h1n1 a_h1 a_h3 a_subtyping_not_performed
## <chr> <chr> <int> <int> <int> <dbl> <int> <int> <int> <int>
## 1 HHS Regions Region 1 1997 40 51 0 0 0 0 0
## 2 HHS Regions Region 2 1997 40 152 0 0 0 0 0
## 3 HHS Regions Region 3 1997 40 143 0 0 0 0 0
## 4 HHS Regions Region 4 1997 40 98 0 0 0 0 0
## 5 HHS Regions Region 5 1997 40 147 0 0 0 0 0
## 6 HHS Regions Region 6 1997 40 343 0 0 0 0 0
## 7 HHS Regions Region 7 1997 40 133 0 0 0 0 0
## 8 HHS Regions Region 8 1997 40 78 0 0 0 0 0
## 9 HHS Regions Region 9 1997 40 98 0 0 0 0 0
## 10 HHS Regions Region 10 1997 40 48 0 0 0 0 0
## # ... with 9,390 more rows, and 4 more variables: a_unable_to_subtype <int>, b <int>, h3n2v <int>, wk_date <date>
5 years ago
##
## $public_health_labs
## # A tibble: 1,080 x 13
5 years ago
## region_type region year week total_specimens a_2009_h1n1 a_h3 a_subtyping_not_performed b bvic byam
## <chr> <chr> <int> <chr> <int> <int> <int> <int> <int> <int> <int>
## 1 HHS Regions Region 1 2015 <NA> 39 0 5 0 0 0 0
## 2 HHS Regions Region 2 2015 <NA> 56 1 4 0 1 0 0
## 3 HHS Regions Region 3 2015 <NA> 132 1 3 0 0 0 0
## 4 HHS Regions Region 4 2015 <NA> 83 0 5 0 1 0 0
## 5 HHS Regions Region 5 2015 <NA> 218 2 7 0 0 0 1
## 6 HHS Regions Region 6 2015 <NA> 97 0 2 0 0 0 0
## 7 HHS Regions Region 7 2015 <NA> 36 0 2 0 0 0 0
## 8 HHS Regions Region 8 2015 <NA> 71 0 2 0 0 0 0
## 9 HHS Regions Region 9 2015 <NA> 273 0 22 2 8 0 0
## 10 HHS Regions Region 10 2015 <NA> 134 0 13 0 0 0 0
## # ... with 1,070 more rows, and 2 more variables: h3n2v <int>, wk_date <date>
5 years ago
##
## $clinical_labs
## # A tibble: 1,080 x 11
## region_type region year week total_specimens total_a total_b percent_positive percent_a percent_b wk_date
## <chr> <chr> <int> <int> <int> <int> <int> <dbl> <dbl> <dbl> <date>
## 1 HHS Regions Region 1 2015 40 693 2 3 0.721501 0.288600 0.432900 2015-10-04
## 2 HHS Regions Region 2 2015 40 1220 5 0 0.409836 0.409836 0.000000 2015-10-04
## 3 HHS Regions Region 3 2015 40 896 0 1 0.111607 0.000000 0.111607 2015-10-04
## 4 HHS Regions Region 4 2015 40 2486 24 16 1.609010 0.965406 0.643604 2015-10-04
## 5 HHS Regions Region 5 2015 40 2138 14 3 0.795136 0.654818 0.140318 2015-10-04
## 6 HHS Regions Region 6 2015 40 1774 8 16 1.352870 0.450958 0.901917 2015-10-04
## 7 HHS Regions Region 7 2015 40 621 2 1 0.483092 0.322061 0.161031 2015-10-04
## 8 HHS Regions Region 8 2015 40 824 1 1 0.242718 0.121359 0.121359 2015-10-04
## 9 HHS Regions Region 9 2015 40 980 25 2 2.755100 2.551020 0.204082 2015-10-04
## 10 HHS Regions Region 10 2015 40 397 3 0 0.755668 0.755668 0.000000 2015-10-04
5 years ago
## # ... with 1,070 more rows
``` r
who_nrevss("census")
```
## $combined_prior_to_2015_16
## # A tibble: 8,460 x 14
5 years ago
## region_type region year week total_specimens percent_positive a_2009_h1n1 a_h1 a_h3
## <chr> <chr> <int> <int> <int> <dbl> <int> <int> <int>
## 1 Census Regions New England 1997 40 51 0 0 0 0
## 2 Census Regions Mid-Atlantic 1997 40 155 0 0 0 0
## 3 Census Regions East North Central 1997 40 127 0 0 0 0
## 4 Census Regions West North Central 1997 40 183 0 0 0 0
## 5 Census Regions South Atlantic 1997 40 204 0 0 0 0
## 6 Census Regions East South Central 1997 40 34 0 0 0 0
## 7 Census Regions West South Central 1997 40 339 0 0 0 0
## 8 Census Regions Mountain 1997 40 85 0 0 0 0
## 9 Census Regions Pacific 1997 40 113 0 0 0 0
## 10 Census Regions New England 1997 41 54 0 0 0 0
## # ... with 8,450 more rows, and 5 more variables: a_subtyping_not_performed <int>, a_unable_to_subtype <int>, b <int>,
## # h3n2v <int>, wk_date <date>
5 years ago
##
## $public_health_labs
## # A tibble: 972 x 13
5 years ago
## region_type region year week total_specimens a_2009_h1n1 a_h3 a_subtyping_not_performed b
## <chr> <chr> <int> <chr> <int> <int> <int> <int> <int>
## 1 Census Regions New England 2015 <NA> 39 0 5 0 0
## 2 Census Regions Mid-Atlantic 2015 <NA> 63 1 5 0 1
## 3 Census Regions East North Central 2015 <NA> 91 2 5 0 0
## 4 Census Regions West North Central 2015 <NA> 169 0 4 0 0
## 5 Census Regions South Atlantic 2015 <NA> 187 1 7 0 0
## 6 Census Regions East South Central 2015 <NA> 21 0 0 0 1
## 7 Census Regions West South Central 2015 <NA> 72 0 2 0 0
## 8 Census Regions Mountain 2015 <NA> 111 0 6 0 0
## 9 Census Regions Pacific 2015 <NA> 386 0 31 2 8
## 10 Census Regions New England 2015 <NA> 39 2 3 0 0
## # ... with 962 more rows, and 4 more variables: bvic <int>, byam <int>, h3n2v <int>, wk_date <date>
5 years ago
##
## $clinical_labs
## # A tibble: 972 x 11
5 years ago
## region_type region year week total_specimens total_a total_b percent_positive percent_a percent_b
## <chr> <chr> <int> <int> <int> <int> <int> <dbl> <dbl> <dbl>
## 1 Census Regions New England 2015 40 693 2 3 0.721501 0.288600 0.4329000
## 2 Census Regions Mid-Atlantic 2015 40 1584 5 1 0.378788 0.315657 0.0631313
## 3 Census Regions East North Central 2015 40 1918 13 3 0.834202 0.677789 0.1564130
## 4 Census Regions West North Central 2015 40 978 3 1 0.408998 0.306748 0.1022490
## 5 Census Regions South Atlantic 2015 40 2403 20 12 1.331670 0.832293 0.4993760
## 6 Census Regions East South Central 2015 40 615 4 4 1.300810 0.650407 0.6504070
## 7 Census Regions West South Central 2015 40 1592 8 16 1.507540 0.502513 1.0050300
## 8 Census Regions Mountain 2015 40 943 1 1 0.212089 0.106045 0.1060450
## 9 Census Regions Pacific 2015 40 1303 28 2 2.302380 2.148890 0.1534920
## 10 Census Regions New England 2015 41 752 11 4 1.994680 1.462770 0.5319150
## # ... with 962 more rows, and 1 more variables: wk_date <date>
5 years ago
``` r
who_nrevss("state")
```
## $combined_prior_to_2015_16
## # A tibble: 14,094 x 14
5 years ago
## region_type region year week total_specimens percent_positive a_2009_h1n1 a_h1 a_h3
## <chr> <chr> <int> <int> <chr> <chr> <chr> <chr> <chr>
## 1 States Alabama 2010 40 54 0 0 0 0
## 2 States Alaska 2010 40 40 0 0 0 0
## 3 States Arizona 2010 40 40 2.5 0 0 1
## 4 States Arkansas 2010 40 15 0 0 0 0
## 5 States California 2010 40 183 3.28 2 0 3
## 6 States Colorado 2010 40 126 0.79 0 0 1
## 7 States Connecticut 2010 40 54 0 0 0 0
## 8 States Delaware 2010 40 75 4 0 0 3
## 9 States District of Columbia 2010 40 14 0 0 0 0
## 10 States Florida 2010 40 <NA> <NA> <NA> <NA> <NA>
## # ... with 14,084 more rows, and 5 more variables: a_subtyping_not_performed <chr>, a_unable_to_subtype <chr>, b <chr>,
## # h3n2v <chr>, wk_date <date>
5 years ago
##
## $public_health_labs
## # A tibble: 162 x 12
5 years ago
## region_type region season_description total_specimens a_2009_h1n1 a_h3 a_subtyping_not_performed
## <chr> <chr> <chr> <chr> <chr> <chr> <chr>
## 1 States Alabama Season 2015-16 256 59 16 1
## 2 States Alaska Season 2015-16 4691 607 98 0
## 3 States Arizona Season 2015-16 2110 762 580 0
## 4 States Arkansas Season 2015-16 128 20 8 0
## 5 States California Season 2015-16 12241 1394 825 28
## 6 States Colorado Season 2015-16 1625 912 243 3
## 7 States Connecticut Season 2015-16 1581 662 52 0
## 8 States Delaware Season 2015-16 2754 414 20 12
## 9 States District of Columbia Season 2015-16 172 68 3 0
## 10 States Florida Season 2015-16 <NA> <NA> <NA> <NA>
## # ... with 152 more rows, and 5 more variables: b <chr>, bvic <chr>, byam <chr>, h3n2v <chr>, wk_date <date>
5 years ago
##
## $clinical_labs
## # A tibble: 5,832 x 11
5 years ago
## region_type region year week total_specimens total_a total_b percent_positive percent_a percent_b
## <chr> <chr> <int> <int> <chr> <chr> <chr> <chr> <chr> <chr>
## 1 States Alabama 2015 40 167 2 3 2.99 1.2 1.8
## 2 States Alaska 2015 40 <NA> <NA> <NA> <NA> <NA> <NA>
## 3 States Arizona 2015 40 55 0 0 0 0 0
## 4 States Arkansas 2015 40 26 0 1 3.85 0 3.85
## 5 States California 2015 40 679 2 0 0.29 0.29 0
## 6 States Colorado 2015 40 255 0 1 0.39 0 0.39
## 7 States Connecticut 2015 40 304 1 0 0.33 0.33 0
## 8 States Delaware 2015 40 22 0 0 0 0 0
## 9 States District of Columbia 2015 40 <NA> <NA> <NA> <NA> <NA> <NA>
## 10 States Florida 2015 40 <NA> <NA> <NA> <NA> <NA> <NA>
## # ... with 5,822 more rows, and 1 more variables: wk_date <date>
Code of Conduct
---------------
Please note that this project is released with a [Contributor Code of
Conduct](CONDUCT.md). By participating in this project you agree to
abide by its terms.