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[](https://cran.r-project.org/package=cdcfluview)
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[](https://travis-ci.org/hrbrmstr/cdcfluview)
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[](https://codecov.io/github/hrbrmstr/cdcfluview?branch=master)
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# I M P O R T A N T
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The CDC migrated to a new non-Flash portal and back-end APIs changed.
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This is a complete reimagining of the package and — as such — all your
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code is going to break…eventually. Older functions have been deprecated
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with warnings and will be removed at some point.
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All folks providing feedback, code or suggestions will be added to the
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DESCRIPTION file. Please include how you would prefer to be cited in any
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issues you file.
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If there’s a particular data set from
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<https://www.cdc.gov/flu/weekly/fluviewinteractive.htm> that you want
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and that isn’t in the package, please file it as an issue and be as
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specific as you can (screen shot if possible).
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# :mask: cdcfluview
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Retrieve U.S. Flu Season Data from the CDC FluView Portal
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## Description
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The U.S. Centers for Disease Control (CDC) maintains a portal
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<http://gis.cdc.gov/grasp/fluview/fluportaldashboard.html> for accessing
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state, regional and national influenza statistics as well as Mortality
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Surveillance Data. The Flash interface makes it difficult and
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time-consuming to select and retrieve influenza data. This package
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provides functions to access the data provided by the portal’s
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underlying API.
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## What’s Inside The Tin
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The following functions are implemented:
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- `age_group_distribution`: Age Group Distribution of Influenza
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Positive Tests Reported by Public Health Laboratories
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- `cdc_basemap`: Retrieve CDC U.S. Basemaps
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- `geographic_spread`: State and Territorial Epidemiologists Reports
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of Geographic Spread of Influenza
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- `get_weekly_flu_report`: Retrieves (high-level) weekly (XML)
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influenza surveillance report from the CDC
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- `hospitalizations`: Laboratory-Confirmed Influenza Hospitalizations
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- `ilinet`: Retrieve ILINet Surveillance Data
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- `ili_weekly_activity_indicators`: Retrieve weekly state-level ILI
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indicators per-state for a given season
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- `pi_mortality`: Pneumonia and Influenza Mortality Surveillance
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- `state_data_providers`: Retrieve metadata about U.S. State CDC
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Provider Data
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- `surveillance_areas`: Retrieve a list of valid sub-regions for each
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surveillance area.
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- `who_nrevss`: Retrieve WHO/NREVSS Surveillance Data
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MMWR ID Utilities:
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- `mmwrid_map`: MMWR ID to Calendar Mappings
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- `mmwr_week`: Convert a Date to an MMWR day+week+year
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- `mmwr_weekday`: Convert a Date to an MMWR weekday
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- `mmwr_week_to_date`: Convert an MMWR year+week or year+week+day to a
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Date object
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Deprecated functions:
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- `get_flu_data`: Retrieves state, regional or national influenza
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statistics from the CDC (deprecated)
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- `get_hosp_data`: Retrieves influenza hospitalization statistics from
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the CDC (deprecated)
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- `get_mortality_surveillance_data`: Mortality Surveillance Data from
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the National Center for Health Statistics (deprecated)
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- `get_state_data`: Retrieves state/territory-level influenza
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statistics from the CDC (deprecated)
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The following data sets are included:
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- `hhs_regions`: HHS Region Table (a data frame with 59 rows and 4
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variables)
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- `census_regions`: Census Region Table (a data frame with 51 rows and
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2 variables)
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- `mmwrid_map`: MMWR ID to Calendar Mappings (it is exported &
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available, no need to use `data()`)
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## Installation
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``` r
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devtools::install_github("hrbrmstr/cdcfluview")
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```
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## Usage
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``` r
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library(cdcfluview)
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library(hrbrthemes)
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library(tidyverse)
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# current verison
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packageVersion("cdcfluview")
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```
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## [1] '0.7.0'
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### Age Group Distribution of Influenza Positive Tests Reported by Public Health Laboratories
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``` r
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glimpse(age_group_distribution(years=2015))
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```
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## Observations: 1,872
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## Variables: 16
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## $ sea_label <chr> "2015-16", "2015-16", "2015-16", "2015-16", "2015-16", "2015-16", "2015-16", "2015-16", "...
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## $ 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, ...
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## $ vir_label <fctr> A (Subtyping not Performed), A (Subtyping not Performed), A (Subtyping not Performed), A...
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## $ 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,...
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## $ mmwrid <int> 2806, 2807, 2808, 2809, 2810, 2811, 2812, 2813, 2814, 2815, 2816, 2817, 2818, 2819, 2820,...
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## $ seasonid <int> 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 5...
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## $ publishyearweekid <int> 2914, 2914, 2914, 2914, 2914, 2914, 2914, 2914, 2914, 2914, 2914, 2914, 2914, 2914, 2914,...
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## $ sea_description <chr> "Season 2015-16", "Season 2015-16", "Season 2015-16", "Season 2015-16", "Season 2015-16",...
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## $ sea_startweek <int> 2806, 2806, 2806, 2806, 2806, 2806, 2806, 2806, 2806, 2806, 2806, 2806, 2806, 2806, 2806,...
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## $ sea_endweek <int> 2857, 2857, 2857, 2857, 2857, 2857, 2857, 2857, 2857, 2857, 2857, 2857, 2857, 2857, 2857,...
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## $ vir_description <chr> "A-Unk", "A-Unk", "A-Unk", "A-Unk", "A-Unk", "A-Unk", "A-Unk", "A-Unk", "A-Unk", "A-Unk",...
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## $ vir_startmmwrid <int> 1397, 1397, 1397, 1397, 1397, 1397, 1397, 1397, 1397, 1397, 1397, 1397, 1397, 1397, 1397,...
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## $ vir_endmmwrid <int> 3131, 3131, 3131, 3131, 3131, 3131, 3131, 3131, 3131, 3131, 3131, 3131, 3131, 3131, 3131,...
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## $ 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...
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## $ 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...
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## $ 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...
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### Retrieve CDC U.S. Coverage Map
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``` r
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plot(cdc_basemap("national"))
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```
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<!-- -->
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``` r
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plot(cdc_basemap("hhs"))
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```
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<!-- -->
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``` r
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plot(cdc_basemap("census"))
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```
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<!-- -->
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``` r
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plot(cdc_basemap("states"))
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```
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<!-- -->
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``` r
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plot(cdc_basemap("spread"))
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```
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<!-- -->
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``` r
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plot(cdc_basemap("surv"))
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```
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|
<!-- -->
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|
### State and Territorial Epidemiologists Reports of Geographic Spread of Influenza
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|
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``` r
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|
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glimpse(geographic_spread())
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|
|
```
|
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|
## Observations: 25,848
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|
|
## Variables: 7
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## $ statename <chr> "Alabama", "Alabama", "Alabama", "Alabama", "Alabama", "Alabama", "Alabama", "Alabama", "...
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## $ url <chr> "http://adph.org/influenza/", "http://adph.org/influenza/", "http://adph.org/influenza/",...
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|
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## $ website <chr> "Influenza Surveillance", "Influenza Surveillance", "Influenza Surveillance", "Influenza ...
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|
|
## $ activity_estimate <chr> "No Activity", "No Activity", "No Activity", "Local Activity", "Sporadic", "Sporadic", "S...
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## $ weekend <date> 2003-10-04, 2003-10-11, 2003-10-18, 2003-10-25, 2003-11-01, 2003-11-08, 2003-11-15, 2003...
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## $ season <chr> "2003-04", "2003-04", "2003-04", "2003-04", "2003-04", "2003-04", "2003-04", "2003-04", "...
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## $ weeknumber <chr> "40", "41", "42", "43", "44", "45", "46", "47", "48", "49", "50", "51", "52", "53", "1", ...
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|
|
|
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|
|
### Laboratory-Confirmed Influenza Hospitalizations
|
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|
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|
|
``` r
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|
|
surveillance_areas()
|
|
|
|
|
```
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|
## surveillance_area region
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|
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## 1 flusurv Entire Network
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## 2 eip California
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## 3 eip Colorado
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## 4 eip Connecticut
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## 5 eip Entire Network
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## 6 eip Georgia
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## 7 eip Maryland
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## 8 eip Minnesota
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## 9 eip New Mexico
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## 10 eip New York - Albany
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## 11 eip New York - Rochester
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## 12 eip Oregon
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## 13 eip Tennessee
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## 14 ihsp Entire Network
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## 15 ihsp Idaho
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## 16 ihsp Iowa
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## 17 ihsp Michigan
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## 18 ihsp Ohio
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## 19 ihsp Oklahoma
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## 20 ihsp Rhode Island
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## 21 ihsp South Dakota
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## 22 ihsp Utah
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``` r
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glimpse(fs_nat <- hospitalizations("flusurv"))
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|
```
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## Observations: 1,476
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|
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## Variables: 14
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## $ surveillance_area <chr> "FluSurv-NET", "FluSurv-NET", "FluSurv-NET", "FluSurv-NET", "FluSurv-NET", "FluSurv-NET",...
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## $ region <chr> "Entire Network", "Entire Network", "Entire Network", "Entire Network", "Entire Network",...
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## $ year <int> 2009, 2009, 2009, 2009, 2009, 2009, 2009, 2009, 2009, 2009, 2009, 2009, 2009, 2009, 2009,...
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## $ 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...
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## $ 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...
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## $ 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...
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## $ 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,...
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## $ 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....
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## $ 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....
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## $ 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,...
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|
## $ 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, ...
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|
## $ sea_label <chr> "2009-10", "2009-10", "2009-10", "2009-10", "2009-10", "2009-10", "2009-10", "2009-10", "...
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|
|
## $ sea_description <chr> "Season 2009-10", "Season 2009-10", "Season 2009-10", "Season 2009-10", "Season 2009-10",...
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|
## $ mmwrid <int> 2488, 2489, 2490, 2491, 2492, 2493, 2494, 2495, 2496, 2497, 2498, 2499, 2500, 2501, 2502,...
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|
|
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|
|
``` r
|
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|
|
ggplot(fs_nat, aes(wk_end, rate)) +
|
|
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|
|
geom_line(aes(color=age_label, group=age_label)) +
|
|
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|
|
facet_wrap(~sea_description, scales="free_x") +
|
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|
|
scale_color_ipsum(name=NULL) +
|
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|
|
labs(x=NULL, y="Rates per 100,000 population",
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|
|
title="FluSurv-NET :: Entire Network :: All Seasons :: Cumulative Rate") +
|
|
|
|
|
theme_ipsum_rc()
|
|
|
|
|
```
|
|
|
|
|
|
|
|
|
|
<!-- -->
|
|
|
|
|
|
|
|
|
|
``` r
|
|
|
|
|
glimpse(hospitalizations("eip", years=2015))
|
|
|
|
|
```
|
|
|
|
|
|
|
|
|
|
## Observations: 180
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|
|
|
|
## Variables: 14
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|
|
|
|
## $ surveillance_area <chr> "EIP", "EIP", "EIP", "EIP", "EIP", "EIP", "EIP", "EIP", "EIP", "EIP", "EIP", "EIP", "EIP"...
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|
|
|
## $ region <chr> "Entire Network", "Entire Network", "Entire Network", "Entire Network", "Entire Network",...
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|
|
## $ year <int> 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2016, 2016,...
|
|
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|
|
## $ season <int> 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 5...
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|
|
## $ 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...
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|
|
## $ 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...
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|
|
## $ 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...
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|
|
## $ rate <dbl> 0.1, 0.3, 0.4, 0.5, 0.8, 0.8, 1.1, 1.4, 1.6, 1.7, 1.8, 2.1, 2.4, 2.9, 3.2, 3.5, 4.2, 5.3,...
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|
|
## $ weeklyrate <dbl> 0.1, 0.3, 0.1, 0.1, 0.3, 0.0, 0.3, 0.3, 0.2, 0.1, 0.1, 0.3, 0.3, 0.5, 0.3, 0.3, 0.6, 1.2,...
|
|
|
|
|
## $ 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> "2015-16", "2015-16", "2015-16", "2015-16", "2015-16", "2015-16", "2015-16", "2015-16", "...
|
|
|
|
|
## $ sea_description <chr> "Season 2015-16", "Season 2015-16", "Season 2015-16", "Season 2015-16", "Season 2015-16",...
|
|
|
|
|
## $ mmwrid <int> 2806, 2807, 2808, 2809, 2810, 2811, 2812, 2813, 2814, 2815, 2816, 2817, 2818, 2819, 2820,...
|
|
|
|
|
|
|
|
|
|
``` r
|
|
|
|
|
glimpse(hospitalizations("eip", "Colorado", years=2015))
|
|
|
|
|
```
|
|
|
|
|
|
|
|
|
|
## Observations: 180
|
|
|
|
|
## Variables: 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", "Colo...
|
|
|
|
|
## $ year <int> 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2016, 2016,...
|
|
|
|
|
## $ season <int> 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 5...
|
|
|
|
|
## $ 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...
|
|
|
|
|
## $ 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...
|
|
|
|
|
## $ 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...
|
|
|
|
|
## $ rate <dbl> 0.0, 0.0, 0.6, 0.6, 0.6, 0.6, 1.2, 1.7, 1.7, 1.7, 1.7, 1.7, 2.9, 3.5, 3.5, 3.5, 4.1, 6.4,...
|
|
|
|
|
## $ weeklyrate <dbl> 0.0, 0.0, 0.6, 0.0, 0.0, 0.0, 0.6, 0.6, 0.0, 0.0, 0.0, 0.0, 1.2, 0.6, 0.0, 0.0, 0.6, 2.3,...
|
|
|
|
|
## $ 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> "2015-16", "2015-16", "2015-16", "2015-16", "2015-16", "2015-16", "2015-16", "2015-16", "...
|
|
|
|
|
## $ sea_description <chr> "Season 2015-16", "Season 2015-16", "Season 2015-16", "Season 2015-16", "Season 2015-16",...
|
|
|
|
|
## $ mmwrid <int> 2806, 2807, 2808, 2809, 2810, 2811, 2812, 2813, 2814, 2815, 2816, 2817, 2818, 2819, 2820,...
|
|
|
|
|
|
|
|
|
|
``` r
|
|
|
|
|
glimpse(hospitalizations("ihsp", years=2015))
|
|
|
|
|
```
|
|
|
|
|
|
|
|
|
|
## Observations: 180
|
|
|
|
|
## Variables: 14
|
|
|
|
|
## $ 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> 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2016, 2016,...
|
|
|
|
|
## $ season <int> 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 5...
|
|
|
|
|
## $ 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...
|
|
|
|
|
## $ 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...
|
|
|
|
|
## $ 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...
|
|
|
|
|
## $ rate <dbl> 0.0, 0.0, 0.4, 0.4, 0.4, 1.1, 1.1, 1.1, 1.1, 1.5, 1.8, 2.2, 2.2, 2.5, 2.5, 2.5, 2.9, 4.0,...
|
|
|
|
|
## $ weeklyrate <dbl> 0.0, 0.0, 0.4, 0.0, 0.0, 0.7, 0.0, 0.0, 0.0, 0.4, 0.4, 0.4, 0.0, 0.4, 0.0, 0.0, 0.4, 1.1,...
|
|
|
|
|
## $ 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> "2015-16", "2015-16", "2015-16", "2015-16", "2015-16", "2015-16", "2015-16", "2015-16", "...
|
|
|
|
|
## $ sea_description <chr> "Season 2015-16", "Season 2015-16", "Season 2015-16", "Season 2015-16", "Season 2015-16",...
|
|
|
|
|
## $ mmwrid <int> 2806, 2807, 2808, 2809, 2810, 2811, 2812, 2813, 2814, 2815, 2816, 2817, 2818, 2819, 2820,...
|
|
|
|
|
|
|
|
|
|
``` r
|
|
|
|
|
glimpse(hospitalizations("ihsp", "Oklahoma", years=2015))
|
|
|
|
|
```
|
|
|
|
|
|
|
|
|
|
## Observations: 180
|
|
|
|
|
## Variables: 14
|
|
|
|
|
## $ 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> 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2011, 2011,...
|
|
|
|
|
## $ season <int> 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 5...
|
|
|
|
|
## $ 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...
|
|
|
|
|
## $ 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...
|
|
|
|
|
## $ 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...
|
|
|
|
|
## $ rate <dbl> 0.0, 0.0, 1.3, 1.3, 1.3, 1.3, 1.3, 1.3, 1.3, 2.6, 2.6, 6.6, 15.9, 18.5, 35.7, 54.2, 83.3,...
|
|
|
|
|
## $ weeklyrate <dbl> 0.0, 0.0, 1.3, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.3, 0.0, 4.0, 9.3, 2.6, 17.2, 18.5, 29.1, 2...
|
|
|
|
|
## $ 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> "2010-11", "2010-11", "2010-11", "2010-11", "2010-11", "2010-11", "2010-11", "2010-11", "...
|
|
|
|
|
## $ sea_description <chr> "Season 2010-11", "Season 2010-11", "Season 2010-11", "Season 2010-11", "Season 2010-11",...
|
|
|
|
|
## $ mmwrid <int> 2545, 2546, 2547, 2548, 2549, 2550, 2551, 2552, 2553, 2554, 2555, 2556, 2557, 2558, 2559,...
|
|
|
|
|
|
|
|
|
|
### 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)
|
|
|
|
|
|
|
|
|
|
})
|
|
|
|
|
```
|
|
|
|
|
|
|
|
|
|
## Observations: 1,049
|
|
|
|
|
## 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: 1,049 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 1,039 more rows, and 4 more variables: ilitotal <dbl>, num_of_providers <dbl>, total_patients <dbl>,
|
|
|
|
|
## # week_start <date>
|
|
|
|
|
|
|
|
|
|
<!-- -->
|
|
|
|
|
|
|
|
|
|
## Observations: 10,490
|
|
|
|
|
## 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: 10,490 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> <fctr> <int> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
|
|
|
|
|
## 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 10,480 more rows, and 4 more variables: ilitotal <dbl>, num_of_providers <dbl>, total_patients <dbl>,
|
|
|
|
|
## # week_start <date>
|
|
|
|
|
|
|
|
|
|
<!-- -->
|
|
|
|
|
|
|
|
|
|
## Observations: 9,441
|
|
|
|
|
## 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: 9,441 x 16
|
|
|
|
|
## 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 9,431 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>
|
|
|
|
|
|
|
|
|
|
<!-- -->
|
|
|
|
|
|
|
|
|
|
## Observations: 19,772
|
|
|
|
|
## 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: 19,772 x 16
|
|
|
|
|
## 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 19,762 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>
|
|
|
|
|
|
|
|
|
|
<!-- -->
|
|
|
|
|
|
|
|
|
|
### Retrieve weekly state-level ILI indicators per-state for a given season
|
|
|
|
|
|
|
|
|
|
``` r
|
|
|
|
|
ili_weekly_activity_indicators(2017)
|
|
|
|
|
```
|
|
|
|
|
|
|
|
|
|
## # A tibble: 270 x 8
|
|
|
|
|
## statename url
|
|
|
|
|
## * <chr> <chr>
|
|
|
|
|
## 1 Virgin Islands http://doh.vi.gov/
|
|
|
|
|
## 2 Virgin Islands http://doh.vi.gov/
|
|
|
|
|
## 3 Virgin Islands http://doh.vi.gov/
|
|
|
|
|
## 4 Puerto Rico http://www.salud.gov.pr/Estadisticas-Registros-y-Publicaciones/Pages/Influenza.aspx
|
|
|
|
|
## 5 Virgin Islands http://doh.vi.gov/
|
|
|
|
|
## 6 Puerto Rico http://www.salud.gov.pr/Estadisticas-Registros-y-Publicaciones/Pages/Influenza.aspx
|
|
|
|
|
## 7 Virgin Islands http://doh.vi.gov/
|
|
|
|
|
## 8 Indiana http://www.in.gov/isdh/22104.htm
|
|
|
|
|
## 9 Iowa http://idph.iowa.gov/influenza/surveillance
|
|
|
|
|
## 10 Kansas http://www.kdheks.gov/flu/surveillance.htm
|
|
|
|
|
## # ... with 260 more rows, and 6 more variables: website <chr>, activity_level <dbl>, activity_level_label <chr>,
|
|
|
|
|
## # weekend <date>, season <chr>, weeknumber <dbl>
|
|
|
|
|
|
|
|
|
|
``` r
|
|
|
|
|
xdf <- map_df(2008:2017, ili_weekly_activity_indicators)
|
|
|
|
|
|
|
|
|
|
count(xdf, weekend, activity_level_label) %>%
|
|
|
|
|
complete(weekend, activity_level_label) %>%
|
|
|
|
|
ggplot(aes(weekend, activity_level_label, fill=n)) +
|
|
|
|
|
geom_tile(color="#c2c2c2", size=0.1) +
|
|
|
|
|
scale_x_date(expand=c(0,0)) +
|
|
|
|
|
viridis::scale_fill_viridis(name="# States", na.value="White") +
|
|
|
|
|
labs(x=NULL, y=NULL, title="Weekly ILI Indicators (all states)") +
|
|
|
|
|
coord_fixed(100/1) +
|
|
|
|
|
theme_ipsum_rc(grid="") +
|
|
|
|
|
theme(legend.position="bottom")
|
|
|
|
|
```
|
|
|
|
|
|
|
|
|
|
<!-- -->
|
|
|
|
|
|
|
|
|
|
### Pneumonia and Influenza Mortality Surveillance
|
|
|
|
|
|
|
|
|
|
``` r
|
|
|
|
|
(nat_pi <- pi_mortality("national"))
|
|
|
|
|
```
|
|
|
|
|
|
|
|
|
|
## # A tibble: 420 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 0.056 0.858 12 2276 40834 2288
|
|
|
|
|
## 2 57 0.058 0.062 0.056 0.764 14 2025 36328 2039
|
|
|
|
|
## 3 57 0.059 0.063 0.058 0.678 17 1844 32279 1861
|
|
|
|
|
## 4 56 0.059 0.063 0.059 1.000 18 3022 51404 3040
|
|
|
|
|
## 5 56 0.060 0.063 0.061 1.000 11 3193 52130 3204
|
|
|
|
|
## 6 56 0.061 0.064 0.062 1.000 7 3178 51443 3185
|
|
|
|
|
## 7 56 0.062 0.065 0.061 1.000 17 3129 51865 3146
|
|
|
|
|
## 8 56 0.063 0.066 0.060 1.000 16 3099 51753 3115
|
|
|
|
|
## 9 56 0.064 0.067 0.061 1.000 19 3208 52541 3227
|
|
|
|
|
## 10 56 0.065 0.068 0.060 1.000 7 3192 53460 3199
|
|
|
|
|
## # ... with 410 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>
|
|
|
|
|
|
|
|
|
|
``` 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")
|
|
|
|
|
```
|
|
|
|
|
|
|
|
|
|
<!-- -->
|
|
|
|
|
|
|
|
|
|
``` r
|
|
|
|
|
(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 0.047 1.000 0 46 979 46
|
|
|
|
|
## 2 55 NA NA 0.038 0.963 0 34 889 34
|
|
|
|
|
## 3 55 NA NA 0.053 1.000 0 52 977 52
|
|
|
|
|
## 4 55 NA NA 0.070 1.000 0 68 968 68
|
|
|
|
|
## 5 55 NA NA 0.053 0.981 0 48 906 48
|
|
|
|
|
## 6 55 NA NA 0.058 1.000 0 56 968 56
|
|
|
|
|
## 7 55 NA NA 0.051 1.000 0 53 1041 53
|
|
|
|
|
## 8 55 NA NA 0.062 1.000 1 63 1031 64
|
|
|
|
|
## 9 55 NA NA 0.056 1.000 0 55 976 55
|
|
|
|
|
## 10 55 NA NA 0.054 1.000 0 56 1045 56
|
|
|
|
|
## # ... with 2,694 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>
|
|
|
|
|
|
|
|
|
|
``` r
|
|
|
|
|
(reg_pi <- pi_mortality("region", years=2015))
|
|
|
|
|
```
|
|
|
|
|
|
|
|
|
|
## # A tibble: 520 x 19
|
|
|
|
|
## seasonid baseline threshold percent_pni percent_complete number_influenza number_pneumonia all_deaths total_pni
|
|
|
|
|
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
|
|
|
|
|
## 1 55 0.065 0.073 0.071 1 0 178 2520 178
|
|
|
|
|
## 2 55 0.066 0.073 0.063 1 0 159 2505 159
|
|
|
|
|
## 3 55 0.067 0.074 0.058 1 1 141 2452 142
|
|
|
|
|
## 4 55 0.068 0.075 0.071 1 0 171 2422 171
|
|
|
|
|
## 5 55 0.069 0.076 0.066 1 2 166 2554 168
|
|
|
|
|
## 6 55 0.070 0.077 0.067 1 1 160 2404 161
|
|
|
|
|
## 7 55 0.071 0.079 0.079 1 0 195 2478 195
|
|
|
|
|
## 8 55 0.073 0.080 0.072 1 1 176 2463 177
|
|
|
|
|
## 9 55 0.074 0.081 0.067 1 3 154 2347 157
|
|
|
|
|
## 10 55 0.075 0.082 0.062 1 0 151 2437 151
|
|
|
|
|
## # ... with 510 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>
|
|
|
|
|
|
|
|
|
|
### Retrieve metadata about U.S. State CDC Provider Data
|
|
|
|
|
|
|
|
|
|
``` 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"))
|
|
|
|
|
```
|
|
|
|
|
|
|
|
|
|
## 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 ...
|
|
|
|
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## ..$ total_specimens : int [1:940] 1291 1513 1552 1669 1897 2106 2204 2533 2242 2607 ...
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## ..$ percent_positive : num [1:940] 0 0.727 1.095 0.419 0.527 ...
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## ..$ a_2009_h1n1 : int [1:940] 0 0 0 0 0 0 0 0 0 0 ...
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## ..$ a_h1 : int [1:940] 0 0 0 0 0 0 0 0 0 0 ...
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## ..$ a_h3 : int [1:940] 0 0 3 0 9 0 3 5 14 11 ...
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## ..$ a_subtyping_not_performed: int [1:940] 0 11 13 7 1 6 4 17 22 28 ...
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## ..$ a_unable_to_subtype : int [1:940] 0 0 0 0 0 0 0 0 0 0 ...
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## ..$ b : int [1:940] 0 0 1 0 0 0 1 1 1 1 ...
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## ..$ h3n2v : int [1:940] 0 0 0 0 0 0 0 0 0 0 ...
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## ..$ wk_date : Date[1:940], format: "1997-09-28" "1997-10-05" "1997-10-12" "1997-10-19" ...
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## $ public_health_labs :Classes 'tbl_df', 'tbl' and 'data.frame': 109 obs. of 13 variables:
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## ..$ region_type : chr [1:109] "National" "National" "National" "National" ...
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## ..$ region : chr [1:109] "National" "National" "National" "National" ...
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## ..$ year : int [1:109] 2015 2015 2015 2015 2015 2015 2015 2015 2015 2015 ...
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## ..$ week : int [1:109] 40 41 42 43 44 45 46 47 48 49 ...
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## ..$ total_specimens : int [1:109] 1139 1152 1198 1244 1465 1393 1458 1157 1550 1518 ...
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## ..$ a_2009_h1n1 : int [1:109] 4 5 10 9 4 11 17 17 27 38 ...
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## ..$ a_h3 : int [1:109] 65 41 50 31 23 34 42 24 36 37 ...
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## ..$ a_subtyping_not_performed: int [1:109] 2 2 1 4 4 1 1 0 3 3 ...
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## ..$ b : int [1:109] 10 7 8 9 9 10 4 4 9 11 ...
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## ..$ bvic : int [1:109] 0 3 3 1 1 4 0 3 3 2 ...
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## ..$ byam : int [1:109] 1 0 2 4 4 2 4 9 12 11 ...
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## ..$ h3n2v : int [1:109] 0 0 0 0 0 0 0 0 0 0 ...
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## ..$ wk_date : Date[1:109], format: "2015-10-04" "2015-10-11" "2015-10-18" "2015-10-25" ...
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## $ clinical_labs :Classes 'tbl_df', 'tbl' and 'data.frame': 109 obs. of 11 variables:
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## ..$ region_type : chr [1:109] "National" "National" "National" "National" ...
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|
## ..$ region : chr [1:109] "National" "National" "National" "National" ...
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## ..$ year : int [1:109] 2015 2015 2015 2015 2015 2015 2015 2015 2015 2015 ...
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## ..$ week : int [1:109] 40 41 42 43 44 45 46 47 48 49 ...
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|
## ..$ total_specimens : int [1:109] 12029 13111 13441 13537 14687 15048 15250 15234 16201 16673 ...
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## ..$ total_a : int [1:109] 84 116 97 98 97 122 84 119 145 140 ...
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|
## ..$ total_b : int [1:109] 43 54 52 52 68 86 98 92 81 106 ...
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|
## ..$ percent_positive: num [1:109] 1.06 1.3 1.11 1.11 1.12 ...
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|
## ..$ percent_a : num [1:109] 0.698 0.885 0.722 0.724 0.66 ...
|
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|
|
## ..$ percent_b : num [1:109] 0.357 0.412 0.387 0.384 0.463 ...
|
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|
|
## ..$ wk_date : Date[1:109], format: "2015-10-04" "2015-10-11" "2015-10-18" "2015-10-25" ...
|
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|
|
|
|
|
|
|
``` r
|
|
|
|
|
mutate(xdat$combined_prior_to_2015_16,
|
|
|
|
|
percent_positive = percent_positive / 100) %>%
|
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|
|
|
ggplot(aes(wk_date, percent_positive)) +
|
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|
|
|
geom_line() +
|
|
|
|
|
scale_y_percent(name="% Positive") +
|
|
|
|
|
labs(x=NULL, title="WHO/NREVSS Surveillance Data (National)") +
|
|
|
|
|
theme_ipsum_rc(grid="XY")
|
|
|
|
|
```
|
|
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|
|
<!-- -->
|
|
|
|
|
|
|
|
|
|
``` r
|
|
|
|
|
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_performed b bvic byam
|
|
|
|
|
## <chr> <chr> <int> <chr> <int> <int> <int> <int> <int> <int> <int>
|
|
|
|
|
## 1 HHS Regions Region 1 2016 <NA> 31 0 6 0 0 0 0
|
|
|
|
|
## 2 HHS Regions Region 2 2016 <NA> 31 0 6 0 0 2 0
|
|
|
|
|
## 3 HHS Regions Region 3 2016 <NA> 112 2 2 0 0 0 0
|
|
|
|
|
## 4 HHS Regions Region 4 2016 <NA> 112 1 11 0 1 2 0
|
|
|
|
|
## 5 HHS Regions Region 5 2016 <NA> 204 0 7 0 0 0 1
|
|
|
|
|
## 6 HHS Regions Region 6 2016 <NA> 39 1 1 0 0 0 0
|
|
|
|
|
## 7 HHS Regions Region 7 2016 <NA> 24 0 2 0 0 1 0
|
|
|
|
|
## 8 HHS Regions Region 8 2016 <NA> 46 2 8 0 0 0 0
|
|
|
|
|
## 9 HHS Regions Region 9 2016 <NA> 186 3 27 0 0 0 3
|
|
|
|
|
## 10 HHS Regions Region 10 2016 <NA> 113 0 17 0 0 0 0
|
|
|
|
|
## # ... with 510 more rows, and 2 more variables: h3n2v <int>, wk_date <date>
|
|
|
|
|
##
|
|
|
|
|
## $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 0.917431 0.764526 0.1529050 2016-10-02
|
|
|
|
|
## 2 HHS Regions Region 2 2016 40 1307 10 3 0.994644 0.765111 0.2295330 2016-10-02
|
|
|
|
|
## 3 HHS Regions Region 3 2016 40 941 1 4 0.531350 0.106270 0.4250800 2016-10-02
|
|
|
|
|
## 4 HHS Regions Region 4 2016 40 2758 46 62 3.915880 1.667880 2.2480100 2016-10-02
|
|
|
|
|
## 5 HHS Regions Region 5 2016 40 2386 8 5 0.544845 0.335289 0.2095560 2016-10-02
|
|
|
|
|
## 6 HHS Regions Region 6 2016 40 1914 22 13 1.828630 1.149430 0.6792060 2016-10-02
|
|
|
|
|
## 7 HHS Regions Region 7 2016 40 723 0 0 0.000000 0.000000 0.0000000 2016-10-02
|
|
|
|
|
## 8 HHS Regions Region 8 2016 40 913 8 0 0.876232 0.876232 0.0000000 2016-10-02
|
|
|
|
|
## 9 HHS Regions Region 9 2016 40 1123 7 1 0.712378 0.623330 0.0890472 2016-10-02
|
|
|
|
|
## 10 HHS Regions Region 10 2016 40 590 14 0 2.372880 2.372880 0.0000000 2016-10-02
|
|
|
|
|
## # ... with 510 more rows
|
|
|
|
|
|
|
|
|
|
``` r
|
|
|
|
|
who_nrevss("census", years=2016)
|
|
|
|
|
```
|
|
|
|
|
|
|
|
|
|
## $public_health_labs
|
|
|
|
|
## # A tibble: 468 x 13
|
|
|
|
|
## region_type region year week total_specimens a_2009_h1n1 a_h3 a_subtyping_not_performed b
|
|
|
|
|
## <chr> <chr> <int> <chr> <int> <int> <int> <int> <int>
|
|
|
|
|
## 1 Census Regions New England 2016 <NA> 31 0 6 0 0
|
|
|
|
|
## 2 Census Regions Mid-Atlantic 2016 <NA> 50 0 8 0 0
|
|
|
|
|
## 3 Census Regions East North Central 2016 <NA> 139 0 4 0 0
|
|
|
|
|
## 4 Census Regions West North Central 2016 <NA> 103 0 6 0 0
|
|
|
|
|
## 5 Census Regions South Atlantic 2016 <NA> 181 3 11 0 1
|
|
|
|
|
## 6 Census Regions East South Central 2016 <NA> 24 0 0 0 0
|
|
|
|
|
## 7 Census Regions West South Central 2016 <NA> 27 0 1 0 0
|
|
|
|
|
## 8 Census Regions Mountain 2016 <NA> 54 3 10 0 0
|
|
|
|
|
## 9 Census Regions Pacific 2016 <NA> 289 3 41 0 0
|
|
|
|
|
## 10 Census Regions New England 2016 <NA> 14 0 2 0 0
|
|
|
|
|
## # ... with 458 more rows, and 4 more variables: bvic <int>, byam <int>, h3n2v <int>, wk_date <date>
|
|
|
|
|
##
|
|
|
|
|
## $clinical_labs
|
|
|
|
|
## # A tibble: 468 x 11
|
|
|
|
|
## 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 2016 40 654 5 1 0.917431 0.764526 0.1529050
|
|
|
|
|
## 2 Census Regions Mid-Atlantic 2016 40 1579 10 4 0.886637 0.633312 0.2533250
|
|
|
|
|
## 3 Census Regions East North Central 2016 40 2176 6 5 0.505515 0.275735 0.2297790
|
|
|
|
|
## 4 Census Regions West North Central 2016 40 1104 3 0 0.271739 0.271739 0.0000000
|
|
|
|
|
## 5 Census Regions South Atlantic 2016 40 2610 43 62 4.022990 1.647510 2.3754800
|
|
|
|
|
## 6 Census Regions East South Central 2016 40 817 4 3 0.856793 0.489596 0.3671970
|
|
|
|
|
## 7 Census Regions West South Central 2016 40 1738 21 13 1.956270 1.208290 0.7479860
|
|
|
|
|
## 8 Census Regions Mountain 2016 40 1067 8 0 0.749766 0.749766 0.0000000
|
|
|
|
|
## 9 Census Regions Pacific 2016 40 1564 21 1 1.406650 1.342710 0.0639386
|
|
|
|
|
## 10 Census Regions New England 2016 41 810 5 1 0.740741 0.617284 0.1234570
|
|
|
|
|
## # ... with 458 more rows, and 1 more variables: wk_date <date>
|
|
|
|
|
|
|
|
|
|
``` r
|
|
|
|
|
who_nrevss("state", years=2016)
|
|
|
|
|
```
|
|
|
|
|
|
|
|
|
|
## $public_health_labs
|
|
|
|
|
## # A tibble: 54 x 12
|
|
|
|
|
## 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 2016-17 549 3 222 0
|
|
|
|
|
## 2 States Alaska Season 2016-17 5226 14 905 3
|
|
|
|
|
## 3 States Arizona Season 2016-17 2974 63 1630 0
|
|
|
|
|
## 4 States Arkansas Season 2016-17 121 0 51 0
|
|
|
|
|
## 5 States California Season 2016-17 14033 184 4694 120
|
|
|
|
|
## 6 States Colorado Season 2016-17 715 3 267 2
|
|
|
|
|
## 7 States Connecticut Season 2016-17 1347 19 968 0
|
|
|
|
|
## 8 States Delaware Season 2016-17 3090 5 659 4
|
|
|
|
|
## 9 States District of Columbia Season 2016-17 69 1 32 0
|
|
|
|
|
## 10 States Florida Season 2016-17 <NA> <NA> <NA> <NA>
|
|
|
|
|
## # ... with 44 more rows, and 5 more variables: b <chr>, bvic <chr>, byam <chr>, h3n2v <chr>, 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
|
|
|
|
|
## <chr> <chr> <int> <int> <chr> <chr> <chr> <chr> <chr> <chr>
|
|
|
|
|
## 1 States Alabama 2016 40 379 4 0 1.06 1.06 0
|
|
|
|
|
## 2 States Alaska 2016 40 <NA> <NA> <NA> <NA> <NA> <NA>
|
|
|
|
|
## 3 States Arizona 2016 40 133 0 0 0 0 0
|
|
|
|
|
## 4 States Arkansas 2016 40 47 0 0 0 0 0
|
|
|
|
|
## 5 States California 2016 40 799 3 0 0.38 0.38 0
|
|
|
|
|
## 6 States Colorado 2016 40 260 0 0 0 0 0
|
|
|
|
|
## 7 States Connecticut 2016 40 199 3 0 1.51 1.51 0
|
|
|
|
|
## 8 States Delaware 2016 40 40 0 0 0 0 0
|
|
|
|
|
## 9 States District of Columbia 2016 40 <NA> <NA> <NA> <NA> <NA> <NA>
|
|
|
|
|
## 10 States Florida 2016 40 <NA> <NA> <NA> <NA> <NA> <NA>
|
|
|
|
|
## # ... with 2,798 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.
|
