~hrbrmstr/cdcfluview

cc1791b79b492d305a3619b97e416207ecfd2887 — hrbrmstr 2 years ago d8ae30d master 
tweaked hospitalizations function
patch browse .tar.gz 
20 files changed, 383 insertions(+), 373 deletions(-)

M DESCRIPTION
M NEWS.md
M R/hospital.r
M README.Rmd
M README.md
M cran-comments.md
M man/figures/README-cdc-basemaps-1.png
M man/figures/README-cdc-basemaps-2.png
M man/figures/README-cdc-basemaps-3.png
M man/figures/README-cdc-basemaps-4.png
M man/figures/README-cdc-basemaps-5.png
M man/figures/README-cdc-basemaps-6.png
M man/figures/README-ili-df-1.png
M man/figures/README-ili-df-2.png
M man/figures/README-ili-df-3.png
M man/figures/README-ili-df-4.png
M man/figures/README-ili-weekly-activity-1.png
M man/figures/README-nat-pi-mortality-1.png
M man/figures/README-surveillance-areas-1.png
M man/figures/README-who-vrevss-1.png

M DESCRIPTION => DESCRIPTION +1 -1
@@ 4,7 4,7 @@ Encoding: UTF-8
Title: Retrieve Flu Season Data from the United States Centers for Disease Control 
    and Prevention ('CDC') 'FluView' Portal
Version: 0.9.4
Date: 2021-02-27
Date: 2021-05-22
Authors@R: c(
    person("Bob", "Rudis", email = "bob@rud.is", role = c("aut", "cre"), 
           comment = c(ORCID = "0000-0001-5670-2640")),


M NEWS.md => NEWS.md +3 -0
@@ 3,6 3,9 @@
  `age_group_distribution()` function endpoint. There is now a new column
  `incl_wkly_rates_and_strata` in the returned data frame. Fixes #28 and
  CRAN failures.
- fixed `age_label` in `hospitalizations()` (different calls to 
  the hidden API for this function return different levels depending
  on the input parameters so it is no longer a factor.)

# cdcfluview 0.9.2



M R/hospital.r => R/hospital.r +38 -13
@@ 71,22 71,51 @@ hospitalizations <- function(surveillance_area=c("flusurv", "eip", "ihsp"),

  sea_df <- setNames(
    hosp$meta$seasons,
    c("sea_description", "sea_endweek", "sea_label", "seasonid", "sea_startweek", "color", "color_hexvalue"))
    c("sea_description", "sea_endweek", "sea_label", "seasonid", "sea_startweek", "color", "color_hexvalue")
  )
  sea_df <- sea_df[,c("seasonid", "sea_label", "sea_description", "sea_startweek", "sea_endweek")]

  ser_names <- unlist(hosp$res$busdata$datafields, use.names = FALSE)

  suppressWarnings(suppressMessages(mmwr_df <- dplyr::bind_rows(hosp$res$mmwr)))
  suppressWarnings(
    suppressMessages(
      mmwr_df <- dplyr::bind_rows(hosp$res$mmwr)
    )
  )

  mmwr_df <- mmwr_df[,c("mmwrid", "weekend", "weeknumber", "weekstart", "year",
                        "yearweek", "seasonid", "weekendlabel", "weekendlabel2")]

  suppressMessages(suppressWarnings(
  dplyr::bind_rows(lapply(hosp$res$busdata$dataseries, function(.x) {
    tdf <- dplyr::bind_rows(lapply(.x$data, function(.x) setNames(.x, ser_names)))
    tdf$age <- .x$age
    tdf$season <- .x$season
    tdf
  })) -> xdf))
  suppressMessages(
    suppressWarnings(

      dplyr::bind_rows(
        lapply(hosp$res$busdata$dataseries, function(.x) {

          dplyr::bind_rows(
            lapply(.x$data, function(.x) setNames(.x, ser_names))
          ) -> tdf

          tdf$age <- .x$age
          tdf$season <- .x$season

          tdf

        })
      ) -> xdf

    )
  )

  if (length(unique(xdf$age)) > 9) {
    data.frame(
      age = 1:12,
      age_label = c("0-4 yr", "5-17 yr", "18-49 yr", "50-64 yr", "65+ yr", "Overall",
                    "65-74 yr", "75-84 yr", "85+", "18-29 yr", "30-39 yr", "40-49 yr"
      )
    ) -> age_df
    age_df$age_label <- factor(age_df$age_label, levels = age_df$age_label)
  }

  dplyr::left_join(xdf, mmwr_df, c("mmwrid", "weeknumber")) %>%
    dplyr::left_join(age_df, "age") %>%


@@ 97,10 126,6 @@ hospitalizations <- function(surveillance_area=c("flusurv", "eip", "ihsp"),
    ) %>%
    dplyr::left_join(mmwrid_map, "mmwrid") -> xdf

  xdf$age_label <- factor(xdf$age_label,
                          levels=c("0-4 yr", "5-17 yr", "18-49 yr", "50-64 yr",
                                   "65+ yr", "Overall"))

  xdf <- xdf[,c("surveillance_area", "region", "year", "season", "wk_start", "wk_end",
                "year_wk_num", "rate", "weeklyrate", "age", "age_label", "sea_label",
                "sea_description", "mmwrid")]


M README.Rmd => README.Rmd +3 -8
@@ 28,7 28,7 @@ The U.S. Centers for Disease Control (CDC) maintains a portal <https://gis.cdc.g
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
- `cdc_basemap`:	Retrieve CDC U.S. base maps
- `geographic_spread`:	State and Territorial Epidemiologists Reports of Geographic Spread of Influenza
- `get_weekly_flu_report`:	Retrieves (high-level) weekly (XML) influenza surveillance report from the CDC
- `hospitalizations`:	Laboratory-Confirmed Influenza Hospitalizations


@@ 52,17 52,12 @@ Deprecated functions:
- `get_hosp_data`:	Retrieves influenza hospitalization statistics from the CDC (deprecated)
- `get_state_data`:	Retrieves state/territory-level influenza statistics from the CDC (deprecated)


The following data sets are included:

- `hhs_regions`:	HHS Region Table (a data frame with 59 rows and 4 variables)
- `census_regions`:	Census Region Table (a data frame with 51 rows and 2 variables)
- `mmwrid_map`:	MMWR ID to Calendar Mappings (it is exported & available, no need to use `data()`)

## NOTE

All development happens in branches now with only critical fixes being back-ported to the master branch when necessary.

## Installation

```{r eval=FALSE}


@@ 120,7 115,7 @@ glimpse(fs_nat <- hospitalizations("flusurv"))
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) +
  scale_color_viridis_d(name=NULL) +
  labs(x=NULL, y="Rates per 100,000 population",
       title="FluSurv-NET :: Entire Network :: All Seasons :: Cumulative Rate") +
  theme_ipsum_rc()


@@ 131,7 126,7 @@ glimpse(hospitalizations("eip", "Colorado", years=2015))

glimpse(hospitalizations("ihsp", years=2015))

glimpse(hospitalizations("ihsp", "Oklahoma", years=2015))
glimpse(hospitalizations("ihsp", "Oklahoma", years=2010))
```

### Retrieve ILINet Surveillance Data


M README.md => README.md +335 -347
@@ 5,7 5,7 @@ developed.](https://www.repostatus.org/badges/latest/active.svg)](https://www.re
[![Signed
by](https://img.shields.io/badge/Keybase-Verified-brightgreen.svg)](https://keybase.io/hrbrmstr)
![Signed commit
%](https://img.shields.io/badge/Signed_Commits-14%25-lightgrey.svg)
%](https://img.shields.io/badge/Signed_Commits-48%25-lightgrey.svg)
[![Linux build
Status](https://travis-ci.org/hrbrmstr/cdcfluview.svg?branch=master)](https://travis-ci.org/hrbrmstr/cdcfluview)
[![Coverage


@@ 15,16 15,9 @@ checks](https://cranchecks.info/badges/worst/cdcfluview)](https://cranchecks.inf
[![CRAN
status](https://www.r-pkg.org/badges/version/cdcfluview)](https://www.r-pkg.org/pkg/cdcfluview)
![Minimal R
Version](https://img.shields.io/badge/R%3E%3D-3.2.0-blue.svg)
Version](https://img.shields.io/badge/R%3E%3D-3.5.0-blue.svg)
![License](https://img.shields.io/badge/License-MIT-blue.svg)

# 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…eventually. Older functions have been deprecated
with warnings and will be removed at some point.

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.


@@ 42,9 35,9 @@ Control and Prevention (‘CDC’) ‘FluView’ Portal
## Description

The U.S. Centers for Disease Control (CDC) maintains a portal
<https://gis.cdc.gov/grasp/fluview/fluportaldashboard.html> for accessing
state, regional and national influenza statistics as well as Mortality
Surveillance Data. The Flash interface makes it difficult and
<https://gis.cdc.gov/grasp/fluview/fluportaldashboard.html> for
accessing state, regional and national influenza statistics as well as
Mortality Surveillance Data. The Flash interface makes it difficult and
time-consuming to select and retrieve influenza data. This package
provides functions to access the data provided by the portal’s
underlying API.


@@ 53,55 46,50 @@ underlying API.

The following functions are implemented:

  - `age_group_distribution`: Age Group Distribution of Influenza
-   `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
-   `cdc_basemap`: Retrieve CDC U.S. base maps
-   `geographic_spread`: State and Territorial Epidemiologists Reports
    of Geographic Spread of Influenza
  - `get_weekly_flu_report`: Retrieves (high-level) weekly (XML)
-   `get_weekly_flu_report`: Retrieves (high-level) weekly (XML)
    influenza surveillance report from the CDC
  - `hospitalizations`: Laboratory-Confirmed Influenza Hospitalizations
  - `ilinet`: Retrieve ILINet Surveillance Data
  - `ili_weekly_activity_indicators`: Retrieve weekly state-level ILI
-   `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
-   `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_areas`: Retrieve a list of valid sub-regions for each
    surveillance area.
  - `who_nrevss`: Retrieve WHO/NREVSS Surveillance Data
-   `who_nrevss`: Retrieve WHO/NREVSS Surveillance Data

MMWR ID Utilities:

  - `mmwrid_map`: MMWR ID to Calendar Mappings
  - `mmwr_week`: Convert a Date to an MMWR day+week+year
  - `mmwr_weekday`: Convert a Date to an MMWR weekday
  - `mmwr_week_to_date`: Convert an MMWR year+week or year+week+day to a
-   `mmwrid_map`: MMWR ID to Calendar Mappings
-   `mmwr_week`: Convert a Date to an MMWR day+week+year
-   `mmwr_weekday`: Convert a Date to an MMWR weekday
-   `mmwr_week_to_date`: Convert an MMWR year+week or year+week+day to a
    Date object

Deprecated functions:

  - `get_flu_data`: Retrieves state, regional or national influenza
-   `get_flu_data`: Retrieves state, regional or national influenza
    statistics from the CDC (deprecated)
  - `get_hosp_data`: Retrieves influenza hospitalization statistics from
-   `get_hosp_data`: Retrieves influenza hospitalization statistics from
    the CDC (deprecated)
  - `get_state_data`: Retrieves state/territory-level influenza
-   `get_state_data`: Retrieves state/territory-level influenza
    statistics from the CDC (deprecated)

The following data sets are included:

  - `hhs_regions`: HHS Region Table (a data frame with 59 rows and 4
-   `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
-   `census_regions`: Census Region Table (a data frame with 51 rows and
    2 variables)
  - `mmwrid_map`: MMWR ID to Calendar Mappings (it is exported &
-   `mmwrid_map`: MMWR ID to Calendar Mappings (it is exported &
    available, no need to use `data()`)

## NOTE

All development happens in branches now with only critical fixes being
back-ported to the master branch when necessary.

## Installation

``` r


@@ 122,9 110,9 @@ library(cdcfluview)
library(hrbrthemes)
library(tidyverse)

# current versoon
# current version
packageVersion("cdcfluview")
## [1] '0.9.2'
## [1] '0.9.4'
```

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


@@ 132,22 120,22 @@ packageVersion("cdcfluview")
``` r
glimpse(age_group_distribution(years=2015))
## Rows: 1,872
## Columns: 16
## $ sea_label         <chr> "2015-16", "2015-16", "2015-16", "2015-16", "2015-16", "2015-16", "2015-16", "2015-16", "20…
## $ age_label         <fct> 0-4 yr, 0-4 yr, 0-4 yr, 0-4 yr, 0-4 yr, 0-4 yr, 0-4 yr, 0-4 yr, 0-4 yr, 0-4 yr, 0-4 yr, 0-4…
## $ vir_label         <fct> A (Subtyping not Performed), A (Subtyping not Performed), A (Subtyping not Performed), A (S…
## $ count             <int> 0, 1, 0, 1, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 3, 2, 2, 3, 3, 3, 0, 0, 2, 0, 1, 1, 0, 0…
## $ mmwrid            <int> 2806, 2807, 2808, 2809, 2810, 2811, 2812, 2813, 2814, 2815, 2816, 2817, 2818, 2819, 2820, 2…
## $ seasonid          <int> 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,…
## $ sea_description   <chr> "Season 2015-16", "Season 2015-16", "Season 2015-16", "Season 2015-16", "Season 2015-16", "…
## $ sea_startweek     <int> 2806, 2806, 2806, 2806, 2806, 2806, 2806, 2806, 2806, 2806, 2806, 2806, 2806, 2806, 2806, 2…
## $ sea_endweek       <int> 2857, 2857, 2857, 2857, 2857, 2857, 2857, 2857, 2857, 2857, 2857, 2857, 2857, 2857, 2857, 2…
## $ 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, 1…
## $ vir_endmmwrid     <int> 3131, 3131, 3131, 3131, 3131, 3131, 3131, 3131, 3131, 3131, 3131, 3131, 3131, 3131, 3131, 3…
## $ wk_start          <date> 2015-10-04, 2015-10-11, 2015-10-18, 2015-10-25, 2015-11-01, 2015-11-08, 2015-11-15, 2015-1…
## $ wk_end            <date> 2015-10-10, 2015-10-17, 2015-10-24, 2015-10-31, 2015-11-07, 2015-11-14, 2015-11-21, 2015-1…
## $ 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, …
## Columns: 15
## $ sea_label       <chr> "2015-16", "2015-16", "2015-16", "2015-16", "2015-16", "2015-16", "2015-16", "2015-16", "2015-…
## $ age_label       <fct> 0-4 yr, 0-4 yr, 0-4 yr, 0-4 yr, 0-4 yr, 0-4 yr, 0-4 yr, 0-4 yr, 0-4 yr, 0-4 yr, 0-4 yr, 0-4 yr…
## $ vir_label       <fct> A (Subtyping not Performed), A (Subtyping not Performed), A (Subtyping not Performed), A (Subt…
## $ count           <int> 0, 1, 0, 1, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 3, 2, 2, 3, 3, 3, 0, 0, 2, 0, 1, 1, 0, 0, 0…
## $ mmwrid          <int> 2806, 2807, 2808, 2809, 2810, 2811, 2812, 2813, 2814, 2815, 2816, 2817, 2818, 2819, 2820, 2821…
## $ seasonid        <int> 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55…
## $ sea_description <chr> "Season 2015-16", "Season 2015-16", "Season 2015-16", "Season 2015-16", "Season 2015-16", "Sea…
## $ sea_startweek   <int> 2806, 2806, 2806, 2806, 2806, 2806, 2806, 2806, 2806, 2806, 2806, 2806, 2806, 2806, 2806, 2806…
## $ sea_endweek     <int> 2857, 2857, 2857, 2857, 2857, 2857, 2857, 2857, 2857, 2857, 2857, 2857, 2857, 2857, 2857, 2857…
## $ vir_description <chr> "A-Unk", "A-Unk", "A-Unk", "A-Unk", "A-Unk", "A-Unk", "A-Unk", "A-Unk", "A-Unk", "A-Unk", "A-U…
## $ vir_startmmwrid <int> 1397, 1397, 1397, 1397, 1397, 1397, 1397, 1397, 1397, 1397, 1397, 1397, 1397, 1397, 1397, 1397…
## $ vir_endmmwrid   <int> 3131, 3131, 3131, 3131, 3131, 3131, 3131, 3131, 3131, 3131, 3131, 3131, 3131, 3131, 3131, 3131…
## $ wk_start        <date> 2015-10-04, 2015-10-11, 2015-10-18, 2015-10-25, 2015-11-01, 2015-11-08, 2015-11-15, 2015-11-2…
## $ wk_end          <date> 2015-10-10, 2015-10-17, 2015-10-24, 2015-10-31, 2015-11-07, 2015-11-14, 2015-11-21, 2015-11-2…
## $ year_wk_num     <int> 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,…
```

### Retrieve CDC U.S. Coverage Map


@@ 192,15 180,15 @@ plot(cdc_basemap("surv"))

``` r
glimpse(geographic_spread())
## Rows: 30,427
## Rows: 30,851
## Columns: 7
## $ statename         <chr> "Alabama", "Alabama", "Alabama", "Alabama", "Alabama", "Alabama", "Alabama", "Alabama", "Al…
## $ url               <chr> "http://adph.org/influenza/", "http://adph.org/influenza/", "http://adph.org/influenza/", "…
## $ website           <chr> "Influenza Surveillance", "Influenza Surveillance", "Influenza Surveillance", "Influenza Su…
## $ activity_estimate <chr> "No Activity", "No Activity", "No Activity", "Local Activity", "Sporadic", "Sporadic", "Spo…
## $ weekend           <date> 2003-10-04, 2003-10-11, 2003-10-18, 2003-10-25, 2003-11-01, 2003-11-08, 2003-11-15, 2003-1…
## $ season            <chr> "2003-04", "2003-04", "2003-04", "2003-04", "2003-04", "2003-04", "2003-04", "2003-04", "20…
## $ weeknumber        <chr> "40", "41", "42", "43", "44", "45", "46", "47", "48", "49", "50", "51", "52", "53", "1", "2…
## $ statename         <chr> "Alabama", "Alabama", "Alabama", "Alabama", "Alabama", "Alabama", "Alabama", "Alabama", "Ala…
## $ url               <chr> "http://adph.org/influenza/", "http://adph.org/influenza/", "http://adph.org/influenza/", "h…
## $ website           <chr> "Influenza Surveillance", "Influenza Surveillance", "Influenza Surveillance", "Influenza Sur…
## $ activity_estimate <chr> "No Activity", "No Activity", "No Activity", "Local Activity", "Sporadic", "Sporadic", "Spor…
## $ weekend           <date> 2003-10-04, 2003-10-11, 2003-10-18, 2003-10-25, 2003-11-01, 2003-11-08, 2003-11-15, 2003-11…
## $ season            <chr> "2003-04", "2003-04", "2003-04", "2003-04", "2003-04", "2003-04", "2003-04", "2003-04", "200…
## $ weeknumber        <chr> "40", "41", "42", "43", "44", "45", "46", "47", "48", "49", "50", "51", "52", "53", "1", "2"…
```

### Laboratory-Confirmed Influenza Hospitalizations


@@ 232,27 220,27 @@ surveillance_areas()
## 22              ihsp                 Utah

glimpse(fs_nat <- hospitalizations("flusurv"))
## Rows: 2,979
## Rows: 4,368
## Columns: 14
## $ 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> 2017, 2017, 2017, 2017, 2017, 2017, 2017, 2017, 2017, 2017, 2017, 2017, 2017, 2018, 2018, 2…
## $ season            <int> 57, 57, 57, 57, 57, 57, 57, 57, 57, 57, 57, 57, 57, 57, 57, 57, 57, 57, 57, 57, 57, 57, 57,…
## $ wk_start          <date> 2017-10-01, 2017-10-08, 2017-10-15, 2017-10-22, 2017-10-29, 2017-11-05, 2017-11-12, 2017-1…
## $ wk_end            <date> 2017-10-07, 2017-10-14, 2017-10-21, 2017-10-28, 2017-11-04, 2017-11-11, 2017-11-18, 2017-1…
## $ 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, 0.1, 0.1, 0.1, 0.3, 0.4, 0.6, 0.8, 1.0, 1.3, 1.8, 2.5, 3.4, 4.2, 5.6, 6.8, 8.2, 10.3, …
## $ weeklyrate        <dbl> 0.0, 0.0, 0.0, 0.0, 0.1, 0.1, 0.2, 0.2, 0.2, 0.3, 0.6, 0.6, 0.9, 0.8, 1.3, 1.3, 1.4, 2.1, 1…
## $ age               <int> 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3…
## $ age_label         <fct> 5-17 yr, 5-17 yr, 5-17 yr, 5-17 yr, 5-17 yr, 5-17 yr, 5-17 yr, 5-17 yr, 5-17 yr, 5-17 yr, 5…
## $ sea_label         <chr> "2017-18", "2017-18", "2017-18", "2017-18", "2017-18", "2017-18", "2017-18", "2017-18", "20…
## $ sea_description   <chr> "Season 2017-18", "Season 2017-18", "Season 2017-18", "Season 2017-18", "Season 2017-18", "…
## $ mmwrid            <int> 2910, 2911, 2912, 2913, 2914, 2915, 2916, 2917, 2918, 2919, 2920, 2921, 2922, 2923, 2924, 2…
## $ surveillance_area <chr> "FluSurv-NET", "FluSurv-NET", "FluSurv-NET", "FluSurv-NET", "FluSurv-NET", "FluSurv-NET", "F…
## $ region            <chr> "Entire Network", "Entire Network", "Entire Network", "Entire Network", "Entire Network", "E…
## $ year              <int> 2017, 2017, 2017, 2017, 2017, 2017, 2017, 2017, 2017, 2017, 2017, 2017, 2017, 2018, 2018, 20…
## $ season            <int> 57, 57, 57, 57, 57, 57, 57, 57, 57, 57, 57, 57, 57, 57, 57, 57, 57, 57, 57, 57, 57, 57, 57, …
## $ wk_start          <date> 2017-10-01, 2017-10-08, 2017-10-15, 2017-10-22, 2017-10-29, 2017-11-05, 2017-11-12, 2017-11…
## $ wk_end            <date> 2017-10-07, 2017-10-14, 2017-10-21, 2017-10-28, 2017-11-04, 2017-11-11, 2017-11-18, 2017-11…
## $ year_wk_num       <int> 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 1…
## $ rate              <dbl> 0.0, 0.1, 0.1, 0.1, 0.3, 0.4, 0.6, 0.8, 1.0, 1.3, 1.8, 2.5, 3.4, 4.2, 5.6, 6.8, 8.2, 10.3, 1…
## $ weeklyrate        <dbl> 0.0, 0.0, 0.0, 0.0, 0.1, 0.1, 0.2, 0.2, 0.2, 0.3, 0.6, 0.6, 0.9, 0.8, 1.3, 1.3, 1.4, 2.1, 1.…
## $ age               <int> 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3,…
## $ age_label         <fct> 5-17 yr, 5-17 yr, 5-17 yr, 5-17 yr, 5-17 yr, 5-17 yr, 5-17 yr, 5-17 yr, 5-17 yr, 5-17 yr, 5-…
## $ sea_label         <chr> "2017-18", "2017-18", "2017-18", "2017-18", "2017-18", "2017-18", "2017-18", "2017-18", "201…
## $ sea_description   <chr> "Season 2017-18", "Season 2017-18", "Season 2017-18", "Season 2017-18", "Season 2017-18", "S…
## $ mmwrid            <int> 2910, 2911, 2912, 2913, 2914, 2915, 2916, 2917, 2918, 2919, 2920, 2921, 2922, 2923, 2924, 29…

ggplot(fs_nat, aes(wk_end, rate)) + 
  geom_line(aes(color=age_label, group=age_label)) +
  facet_wrap(~sea_description, scales="free_x") +
  scale_color_ipsum(name=NULL) +
  scale_color_viridis_d(name=NULL) +
  labs(x=NULL, y="Rates per 100,000 population",
       title="FluSurv-NET :: Entire Network :: All Seasons :: Cumulative Rate") +
  theme_ipsum_rc()


@@ 261,78 249,77 @@ ggplot(fs_nat, aes(wk_end, rate)) +
<img src="man/figures/README-surveillance-areas-1.png" width="960" />

``` r

glimpse(hospitalizations("eip", years=2015))
## Rows: 270
## Rows: 390
## Columns: 14
## $ surveillance_area <chr> "EIP", "EIP", "EIP", "EIP", "EIP", "EIP", "EIP", "EIP", "EIP", "EIP", "EIP", "EIP", "EIP", …
## $ region            <chr> "Entire Network", "Entire Network", "Entire Network", "Entire Network", "Entire Network", "…
## $ year              <int> 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2016, 2016, 2…
## $ season            <int> 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,…
## $ wk_start          <date> 2015-10-04, 2015-10-11, 2015-10-18, 2015-10-25, 2015-11-01, 2015-11-08, 2015-11-15, 2015-1…
## $ wk_end            <date> 2015-10-10, 2015-10-17, 2015-10-24, 2015-10-31, 2015-11-07, 2015-11-14, 2015-11-21, 2015-1…
## $ 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> 0.4, 0.7, 1.0, 1.1, 1.4, 1.6, 1.9, 2.2, 2.4, 2.8, 3.4, 4.4, 5.0, 6.5, 7.6, 8.7, 10.4, 12.5,…
## $ weeklyrate        <dbl> 0.4, 0.3, 0.3, 0.2, 0.3, 0.3, 0.3, 0.3, 0.2, 0.4, 0.6, 0.9, 0.6, 1.5, 1.1, 1.1, 1.6, 2.1, 3…
## $ age               <int> 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 2…
## $ age_label         <fct> 65+ yr, 65+ yr, 65+ yr, 65+ yr, 65+ yr, 65+ yr, 65+ yr, 65+ yr, 65+ yr, 65+ yr, 65+ yr, 65+…
## $ sea_label         <chr> "2015-16", "2015-16", "2015-16", "2015-16", "2015-16", "2015-16", "2015-16", "2015-16", "20…
## $ 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, 2…
## $ surveillance_area <chr> "EIP", "EIP", "EIP", "EIP", "EIP", "EIP", "EIP", "EIP", "EIP", "EIP", "EIP", "EIP", "EIP", "…
## $ region            <chr> "Entire Network", "Entire Network", "Entire Network", "Entire Network", "Entire Network", "E…
## $ year              <int> 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2016, 2016, 20…
## $ season            <int> 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, …
## $ wk_start          <date> 2015-10-04, 2015-10-11, 2015-10-18, 2015-10-25, 2015-11-01, 2015-11-08, 2015-11-15, 2015-11…
## $ wk_end            <date> 2015-10-10, 2015-10-17, 2015-10-24, 2015-10-31, 2015-11-07, 2015-11-14, 2015-11-21, 2015-11…
## $ year_wk_num       <int> 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 1…
## $ rate              <dbl> 0.4, 0.7, 1.0, 1.1, 1.4, 1.6, 1.9, 2.2, 2.4, 2.8, 3.4, 4.4, 5.0, 6.5, 7.6, 8.7, 10.4, 12.5, …
## $ weeklyrate        <dbl> 0.4, 0.3, 0.3, 0.2, 0.3, 0.3, 0.3, 0.3, 0.2, 0.4, 0.6, 0.9, 0.6, 1.5, 1.1, 1.1, 1.6, 2.1, 3.…
## $ age               <int> 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 10…
## $ age_label         <fct> 65+ yr, 65+ yr, 65+ yr, 65+ yr, 65+ yr, 65+ yr, 65+ yr, 65+ yr, 65+ yr, 65+ yr, 65+ yr, 65+ …
## $ sea_label         <chr> "2015-16", "2015-16", "2015-16", "2015-16", "2015-16", "2015-16", "2015-16", "2015-16", "201…
## $ sea_description   <chr> "Season 2015-16", "Season 2015-16", "Season 2015-16", "Season 2015-16", "Season 2015-16", "S…
## $ mmwrid            <int> 2806, 2807, 2808, 2809, 2810, 2811, 2812, 2813, 2814, 2815, 2816, 2817, 2818, 2819, 2820, 28…

glimpse(hospitalizations("eip", "Colorado", years=2015))
## Rows: 270
## Rows: 390
## Columns: 14
## $ surveillance_area <chr> "EIP", "EIP", "EIP", "EIP", "EIP", "EIP", "EIP", "EIP", "EIP", "EIP", "EIP", "EIP", "EIP", …
## $ region            <chr> "Colorado", "Colorado", "Colorado", "Colorado", "Colorado", "Colorado", "Colorado", "Colora…
## $ year              <int> 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2016, 2016, 2…
## $ season            <int> 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,…
## $ wk_start          <date> 2015-10-04, 2015-10-11, 2015-10-18, 2015-10-25, 2015-11-01, 2015-11-08, 2015-11-15, 2015-1…
## $ wk_end            <date> 2015-10-10, 2015-10-17, 2015-10-24, 2015-10-31, 2015-11-07, 2015-11-14, 2015-11-21, 2015-1…
## $ 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, 0.3, 0.6, 0.9, 0.9, 1.3, 1.3, 1.6, 1.6, 2.5, 2.8, 4.4, 6.3, 7.8, 9.7, 10.7, 12.5, 14.7…
## $ weeklyrate        <dbl> 0.0, 0.3, 0.3, 0.3, 0.0, 0.3, 0.0, 0.3, 0.0, 0.9, 0.3, 1.6, 1.9, 1.6, 1.9, 0.9, 1.9, 2.2, 2…
## $ age               <int> 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 2…
## $ age_label         <fct> 65+ yr, 65+ yr, 65+ yr, 65+ yr, 65+ yr, 65+ yr, 65+ yr, 65+ yr, 65+ yr, 65+ yr, 65+ yr, 65+…
## $ sea_label         <chr> "2015-16", "2015-16", "2015-16", "2015-16", "2015-16", "2015-16", "2015-16", "2015-16", "20…
## $ 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, 2…
## $ surveillance_area <chr> "EIP", "EIP", "EIP", "EIP", "EIP", "EIP", "EIP", "EIP", "EIP", "EIP", "EIP", "EIP", "EIP", "…
## $ region            <chr> "Colorado", "Colorado", "Colorado", "Colorado", "Colorado", "Colorado", "Colorado", "Colorad…
## $ year              <int> 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2016, 2016, 20…
## $ season            <int> 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, …
## $ wk_start          <date> 2015-10-04, 2015-10-11, 2015-10-18, 2015-10-25, 2015-11-01, 2015-11-08, 2015-11-15, 2015-11…
## $ wk_end            <date> 2015-10-10, 2015-10-17, 2015-10-24, 2015-10-31, 2015-11-07, 2015-11-14, 2015-11-21, 2015-11…
## $ year_wk_num       <int> 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 1…
## $ rate              <dbl> 0.0, 0.3, 0.6, 0.9, 0.9, 1.3, 1.3, 1.6, 1.6, 2.5, 2.8, 4.4, 6.3, 7.8, 9.7, 10.7, 12.5, 14.7,…
## $ weeklyrate        <dbl> 0.0, 0.3, 0.3, 0.3, 0.0, 0.3, 0.0, 0.3, 0.0, 0.9, 0.3, 1.6, 1.9, 1.6, 1.9, 0.9, 1.9, 2.2, 2.…
## $ age               <int> 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 10…
## $ age_label         <fct> 65+ yr, 65+ yr, 65+ yr, 65+ yr, 65+ yr, 65+ yr, 65+ yr, 65+ yr, 65+ yr, 65+ yr, 65+ yr, 65+ …
## $ sea_label         <chr> "2015-16", "2015-16", "2015-16", "2015-16", "2015-16", "2015-16", "2015-16", "2015-16", "201…
## $ sea_description   <chr> "Season 2015-16", "Season 2015-16", "Season 2015-16", "Season 2015-16", "Season 2015-16", "S…
## $ mmwrid            <int> 2806, 2807, 2808, 2809, 2810, 2811, 2812, 2813, 2814, 2815, 2816, 2817, 2818, 2819, 2820, 28…

glimpse(hospitalizations("ihsp", years=2015))
## Rows: 270
## Rows: 390
## Columns: 14
## $ surveillance_area <chr> "IHSP", "IHSP", "IHSP", "IHSP", "IHSP", "IHSP", "IHSP", "IHSP", "IHSP", "IHSP", "IHSP", "IH…
## $ 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, 2…
## $ season            <int> 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,…
## $ wk_start          <date> 2015-10-04, 2015-10-11, 2015-10-18, 2015-10-25, 2015-11-01, 2015-11-08, 2015-11-15, 2015-1…
## $ wk_end            <date> 2015-10-10, 2015-10-17, 2015-10-24, 2015-10-31, 2015-11-07, 2015-11-14, 2015-11-21, 2015-1…
## $ 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> 0.4, 0.8, 1.0, 1.2, 1.4, 1.4, 1.4, 1.6, 1.8, 2.0, 2.5, 3.1, 3.5, 4.1, 5.1, 6.5, 8.0, 10.0, …
## $ weeklyrate        <dbl> 0.4, 0.4, 0.2, 0.2, 0.2, 0.0, 0.0, 0.2, 0.2, 0.2, 0.4, 0.6, 0.4, 0.6, 1.0, 1.4, 1.4, 2.0, 4…
## $ age               <int> 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 2…
## $ age_label         <fct> 65+ yr, 65+ yr, 65+ yr, 65+ yr, 65+ yr, 65+ yr, 65+ yr, 65+ yr, 65+ yr, 65+ yr, 65+ yr, 65+…
## $ sea_label         <chr> "2015-16", "2015-16", "2015-16", "2015-16", "2015-16", "2015-16", "2015-16", "2015-16", "20…
## $ 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, 2…

glimpse(hospitalizations("ihsp", "Oklahoma", years=2015))
## Rows: 270
## $ surveillance_area <chr> "IHSP", "IHSP", "IHSP", "IHSP", "IHSP", "IHSP", "IHSP", "IHSP", "IHSP", "IHSP", "IHSP", "IHS…
## $ region            <chr> "Entire Network", "Entire Network", "Entire Network", "Entire Network", "Entire Network", "E…
## $ year              <int> 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2016, 2016, 20…
## $ season            <int> 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, …
## $ wk_start          <date> 2015-10-04, 2015-10-11, 2015-10-18, 2015-10-25, 2015-11-01, 2015-11-08, 2015-11-15, 2015-11…
## $ wk_end            <date> 2015-10-10, 2015-10-17, 2015-10-24, 2015-10-31, 2015-11-07, 2015-11-14, 2015-11-21, 2015-11…
## $ year_wk_num       <int> 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 1…
## $ rate              <dbl> 0.4, 0.8, 1.0, 1.2, 1.4, 1.4, 1.4, 1.6, 1.8, 2.0, 2.5, 3.1, 3.5, 4.1, 5.1, 6.5, 8.0, 10.0, 1…
## $ weeklyrate        <dbl> 0.4, 0.4, 0.2, 0.2, 0.2, 0.0, 0.0, 0.2, 0.2, 0.2, 0.4, 0.6, 0.4, 0.6, 1.0, 1.4, 1.4, 2.0, 4.…
## $ age               <int> 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 10…
## $ age_label         <fct> 65+ yr, 65+ yr, 65+ yr, 65+ yr, 65+ yr, 65+ yr, 65+ yr, 65+ yr, 65+ yr, 65+ yr, 65+ yr, 65+ …
## $ sea_label         <chr> "2015-16", "2015-16", "2015-16", "2015-16", "2015-16", "2015-16", "2015-16", "2015-16", "201…
## $ sea_description   <chr> "Season 2015-16", "Season 2015-16", "Season 2015-16", "Season 2015-16", "Season 2015-16", "S…
## $ mmwrid            <int> 2806, 2807, 2808, 2809, 2810, 2811, 2812, 2813, 2814, 2815, 2816, 2817, 2818, 2819, 2820, 28…

glimpse(hospitalizations("ihsp", "Oklahoma", years=2010))
## Rows: 390
## Columns: 14
## $ surveillance_area <chr> "IHSP", "IHSP", "IHSP", "IHSP", "IHSP", "IHSP", "IHSP", "IHSP", "IHSP", "IHSP", "IHSP", "IH…
## $ region            <chr> "Oklahoma", "Oklahoma", "Oklahoma", "Oklahoma", "Oklahoma", "Oklahoma", "Oklahoma", "Oklaho…
## $ year              <int> 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2011, 2011, 2…
## $ season            <int> 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50,…
## $ wk_start          <date> 2010-10-03, 2010-10-10, 2010-10-17, 2010-10-24, 2010-10-31, 2010-11-07, 2010-11-14, 2010-1…
## $ wk_end            <date> 2010-10-09, 2010-10-16, 2010-10-23, 2010-10-30, 2010-11-06, 2010-11-13, 2010-11-20, 2010-1…
## $ 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, 0.0, 0.0, 0.0, 0.0, 0.2, 0.2, 0.5, 0.7, 0.7, 1.4, 2.3, 2.5, 3.5, 4.6, 6.0, 7.8, 8…
## $ weeklyrate        <dbl> 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.2, 0.0, 0.2, 0.2, 0.0, 0.7, 0.9, 0.2, 0.9, 1.2, 1.4, 1.8, 0…
## $ age               <int> 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 8…
## $ age_label         <fct> 18-49 yr, 18-49 yr, 18-49 yr, 18-49 yr, 18-49 yr, 18-49 yr, 18-49 yr, 18-49 yr, 18-49 yr, 1…
## $ sea_label         <chr> "2010-11", "2010-11", "2010-11", "2010-11", "2010-11", "2010-11", "2010-11", "2010-11", "20…
## $ 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, 2…
## $ surveillance_area <chr> "IHSP", "IHSP", "IHSP", "IHSP", "IHSP", "IHSP", "IHSP", "IHSP", "IHSP", "IHSP", "IHSP", "IHS…
## $ region            <chr> "Oklahoma", "Oklahoma", "Oklahoma", "Oklahoma", "Oklahoma", "Oklahoma", "Oklahoma", "Oklahom…
## $ year              <int> 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2011, 2011, 20…
## $ season            <int> 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, …
## $ wk_start          <date> 2010-10-03, 2010-10-10, 2010-10-17, 2010-10-24, 2010-10-31, 2010-11-07, 2010-11-14, 2010-11…
## $ wk_end            <date> 2010-10-09, 2010-10-16, 2010-10-23, 2010-10-30, 2010-11-06, 2010-11-13, 2010-11-20, 2010-11…
## $ year_wk_num       <int> 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 1…
## $ rate              <dbl> 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.2, 0.2, 0.5, 0.7, 0.7, 1.4, 2.3, 2.5, 3.5, 4.6, 6.0, 7.8, 8.…
## $ weeklyrate        <dbl> 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.2, 0.0, 0.2, 0.2, 0.0, 0.7, 0.9, 0.2, 0.9, 1.2, 1.4, 1.8, 0.…
## $ age               <int> 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 8,…
## $ age_label         <fct> 18-49 yr, 18-49 yr, 18-49 yr, 18-49 yr, 18-49 yr, 18-49 yr, 18-49 yr, 18-49 yr, 18-49 yr, 18…
## $ sea_label         <chr> "2010-11", "2010-11", "2010-11", "2010-11", "2010-11", "2010-11", "2010-11", "2010-11", "201…
## $ sea_description   <chr> "Season 2010-11", "Season 2010-11", "Season 2010-11", "Season 2010-11", "Season 2010-11", "S…
## $ mmwrid            <int> 2545, 2546, 2547, 2548, 2549, 2550, 2551, 2552, 2553, 2554, 2555, 2556, 2557, 2558, 2559, 25…
```

### Retrieve ILINet Surveillance Data


@@ 354,147 341,147 @@ walk(c("national", "hhs", "census", "state"), ~{
  print(gg)
  
})
## Rows: 1,173
## Rows: 1,233
## Columns: 16
## $ region_type      <chr> "National", "National", "National", "National", "National", "National", "National", "Nationa…
## $ region           <chr> "National", "National", "National", "National", "National", "National", "National", "Nationa…
## $ year             <int> 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1998, 19…
## $ 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, 1…
## $ weighted_ili     <dbl> 1.101480, 1.200070, 1.378760, 1.199200, 1.656180, 1.413260, 1.986800, 2.447490, 1.739010, 1.…
## $ unweighted_ili   <dbl> 1.216860, 1.280640, 1.239060, 1.144730, 1.261120, 1.282750, 1.445790, 1.647960, 1.675170, 1.…
## $ age_0_4          <dbl> 179, 199, 228, 188, 217, 178, 294, 288, 268, 299, 346, 348, 510, 579, 639, 690, 856, 824, 88…
## $ 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, 76…
## $ age_5_24         <dbl> 205, 242, 266, 236, 280, 281, 328, 456, 343, 415, 388, 362, 492, 576, 810, 1121, 1440, 1600,…
## $ age_50_64        <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
## $ age_65           <dbl> 29, 23, 34, 36, 41, 48, 70, 63, 69, 102, 81, 59, 113, 207, 207, 148, 151, 196, 233, 146, 119…
## $ ilitotal         <dbl> 570, 615, 681, 653, 700, 655, 932, 1100, 886, 1098, 1083, 1004, 1519, 1946, 2415, 2613, 3126…
## $ num_of_providers <dbl> 192, 191, 219, 213, 213, 195, 248, 256, 252, 253, 242, 190, 251, 250, 254, 255, 245, 245, 23…
## $ total_patients   <dbl> 46842, 48023, 54961, 57044, 55506, 51062, 64463, 66749, 52890, 67887, 61314, 47719, 48429, 5…
## $ week_start       <date> 1997-09-28, 1997-10-05, 1997-10-12, 1997-10-19, 1997-10-26, 1997-11-02, 1997-11-09, 1997-11…
## # A tibble: 1,173 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    Natio…  1997    40         1.10           1.22     179        NA       157      205        NA     29
##  2 National    Natio…  1997    41         1.20           1.28     199        NA       151      242        NA     23
##  3 National    Natio…  1997    42         1.38           1.24     228        NA       153      266        NA     34
##  4 National    Natio…  1997    43         1.20           1.14     188        NA       193      236        NA     36
##  5 National    Natio…  1997    44         1.66           1.26     217        NA       162      280        NA     41
##  6 National    Natio…  1997    45         1.41           1.28     178        NA       148      281        NA     48
##  7 National    Natio…  1997    46         1.99           1.45     294        NA       240      328        NA     70
##  8 National    Natio…  1997    47         2.45           1.65     288        NA       293      456        NA     63
##  9 National    Natio…  1997    48         1.74           1.68     268        NA       206      343        NA     69
## 10 National    Natio…  1997    49         1.94           1.62     299        NA       282      415        NA    102
## # … with 1,163 more rows, and 4 more variables: ilitotal <dbl>, num_of_providers <dbl>, total_patients <dbl>,
## $ region_type      <chr> "National", "National", "National", "National", "National", "National", "National", "National…
## $ region           <chr> "National", "National", "National", "National", "National", "National", "National", "National…
## $ year             <int> 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1998, 199…
## $ week             <int> 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12…
## $ weighted_ili     <dbl> 1.101480, 1.200070, 1.378760, 1.199200, 1.656180, 1.413260, 1.986800, 2.447490, 1.739010, 1.9…
## $ unweighted_ili   <dbl> 1.216860, 1.280640, 1.239060, 1.144730, 1.261120, 1.282750, 1.445790, 1.647960, 1.675170, 1.6…
## $ age_0_4          <dbl> 179, 199, 228, 188, 217, 178, 294, 288, 268, 299, 346, 348, 510, 579, 639, 690, 856, 824, 881…
## $ age_25_49        <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ age_25_64        <dbl> 157, 151, 153, 193, 162, 148, 240, 293, 206, 282, 268, 235, 404, 584, 759, 654, 679, 817, 769…
## $ age_5_24         <dbl> 205, 242, 266, 236, 280, 281, 328, 456, 343, 415, 388, 362, 492, 576, 810, 1121, 1440, 1600, …
## $ age_50_64        <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ age_65           <dbl> 29, 23, 34, 36, 41, 48, 70, 63, 69, 102, 81, 59, 113, 207, 207, 148, 151, 196, 233, 146, 119,…
## $ ilitotal         <dbl> 570, 615, 681, 653, 700, 655, 932, 1100, 886, 1098, 1083, 1004, 1519, 1946, 2415, 2613, 3126,…
## $ num_of_providers <dbl> 192, 191, 219, 213, 213, 195, 248, 256, 252, 253, 242, 190, 251, 250, 254, 255, 245, 245, 239…
## $ total_patients   <dbl> 46842, 48023, 54961, 57044, 55506, 51062, 64463, 66749, 52890, 67887, 61314, 47719, 48429, 52…
## $ week_start       <date> 1997-09-28, 1997-10-05, 1997-10-12, 1997-10-19, 1997-10-26, 1997-11-02, 1997-11-09, 1997-11-…
## # A tibble: 1,233 x 16
##    region_type region    year  week weighted_ili unweighted_ili age_0_4 age_25_49 age_25_64 age_5_24 age_50_64 age_65
##    <chr>       <chr>    <int> <int>        <dbl>          <dbl>   <dbl>     <dbl>     <dbl>    <dbl>     <dbl>  <dbl>
##  1 National    National  1997    40         1.10           1.22     179        NA       157      205        NA     29
##  2 National    National  1997    41         1.20           1.28     199        NA       151      242        NA     23
##  3 National    National  1997    42         1.38           1.24     228        NA       153      266        NA     34
##  4 National    National  1997    43         1.20           1.14     188        NA       193      236        NA     36
##  5 National    National  1997    44         1.66           1.26     217        NA       162      280        NA     41
##  6 National    National  1997    45         1.41           1.28     178        NA       148      281        NA     48
##  7 National    National  1997    46         1.99           1.45     294        NA       240      328        NA     70
##  8 National    National  1997    47         2.45           1.65     288        NA       293      456        NA     63
##  9 National    National  1997    48         1.74           1.68     268        NA       206      343        NA     69
## 10 National    National  1997    49         1.94           1.62     299        NA       282      415        NA    102
## # … with 1,223 more rows, and 4 more variables: ilitotal <dbl>, num_of_providers <dbl>, total_patients <dbl>,
## #   week_start <date>
```

<img src="man/figures/README-ili-df-1.png" width="672" />

    ## Rows: 11,730
    ## Rows: 12,330
    ## Columns: 16
    ## $ region_type      <chr> "HHS Regions", "HHS Regions", "HHS Regions", "HHS Regions", "HHS Regions", "HHS Regions", "H…
    ## $ region           <fct> Region 1, Region 2, Region 3, Region 4, Region 5, Region 6, Region 7, Region 8, Region 9, Re…
    ## $ year             <int> 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 19…
    ## $ 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, 4.…
    ## $ unweighted_ili   <dbl> 0.623848, 0.384615, 1.341720, 0.450010, 0.901266, 0.747384, 1.152860, 0.422654, 2.258780, 4.…
    ## $ 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, 3,…
    ## $ age_5_24         <dbl> 22, 0, 15, 11, 30, 2, 18, 3, 74, 30, 29, 0, 16, 14, 41, 2, 13, 8, 84, 35, 35, 0, 24, 18, 75,…
    ## $ age_50_64        <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
    ## $ age_65           <dbl> 0, 0, 4, 0, 4, 0, 5, 0, 13, 3, 0, 0, 3, 2, 4, 0, 2, 0, 11, 1, 0, 1, 2, 2, 16, 0, 2, 0, 9, 2,…
    ## $ ilitotal         <dbl> 44, 3, 32, 46, 89, 5, 27, 5, 243, 76, 57, 2, 42, 44, 104, 6, 15, 9, 274, 62, 85, 1, 46, 54, …
    ## $ num_of_providers <dbl> 32, 7, 16, 29, 49, 4, 14, 5, 23, 13, 29, 7, 17, 31, 48, 4, 14, 6, 23, 12, 40, 7, 15, 33, 64,…
    ## $ total_patients   <dbl> 7053, 780, 2385, 10222, 9875, 669, 2342, 1183, 10758, 1575, 6987, 872, 2740, 11310, 9618, 68…
    ## $ week_start       <date> 1997-09-28, 1997-09-28, 1997-09-28, 1997-09-28, 1997-09-28, 1997-09-28, 1997-09-28, 1997-09…
    ## # A tibble: 11,730 x 16
    ##    region_type region  year  week weighted_ili unweighted_ili age_0_4 age_25_49 age_25_64 age_5_24 age_50_64 age_65
    ##    <chr>       <fct>  <int> <int>        <dbl>          <dbl>   <dbl>     <dbl>     <dbl>    <dbl>     <dbl>  <dbl>
    ##  1 HHS Regions Regio…  1997    40        0.499          0.624      15        NA         7       22        NA      0
    ##  2 HHS Regions Regio…  1997    40        0.375          0.385       0        NA         3        0        NA      0
    ##  3 HHS Regions Regio…  1997    40        1.35           1.34        6        NA         7       15        NA      4
    ##  4 HHS Regions Regio…  1997    40        0.400          0.450      12        NA        23       11        NA      0
    ##  5 HHS Regions Regio…  1997    40        1.23           0.901      31        NA        24       30        NA      4
    ##  6 HHS Regions Regio…  1997    40        1.02           0.747       2        NA         1        2        NA      0
    ##  7 HHS Regions Regio…  1997    40        0.872          1.15        0        NA         4       18        NA      5
    ##  8 HHS Regions Regio…  1997    40        0.516          0.423       2        NA         0        3        NA      0
    ##  9 HHS Regions Regio…  1997    40        1.81           2.26       80        NA        76       74        NA     13
    ## 10 HHS Regions Regio…  1997    40        4.74           4.83       31        NA        12       30        NA      3
    ## # … with 11,720 more rows, and 4 more variables: ilitotal <dbl>, num_of_providers <dbl>, total_patients <dbl>,
    ## $ region_type      <chr> "HHS Regions", "HHS Regions", "HHS Regions", "HHS Regions", "HHS Regions", "HHS Regions", "HH…
    ## $ region           <fct> Region 1, Region 2, Region 3, Region 4, Region 5, Region 6, Region 7, Region 8, Region 9, Reg…
    ## $ year             <int> 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 199…
    ## $ week             <int> 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 41, 41, 41, 41, 41, 41, 41, 41, 41, 41, 42, 42, 42, 4…
    ## $ weighted_ili     <dbl> 0.498535, 0.374963, 1.354280, 0.400338, 1.229260, 1.018980, 0.871791, 0.516017, 1.807610, 4.7…
    ## $ unweighted_ili   <dbl> 0.623848, 0.384615, 1.341720, 0.450010, 0.901266, 0.747384, 1.152860, 0.422654, 2.258780, 4.8…
    ## $ age_0_4          <dbl> 15, 0, 6, 12, 31, 2, 0, 2, 80, 31, 14, 0, 4, 21, 36, 2, 0, 0, 103, 19, 35, 0, 3, 19, 66, 2, 0…
    ## $ age_25_49        <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
    ## $ age_25_64        <dbl> 7, 3, 7, 23, 24, 1, 4, 0, 76, 12, 14, 2, 19, 7, 23, 2, 0, 1, 76, 7, 15, 0, 17, 15, 29, 2, 3, …
    ## $ age_5_24         <dbl> 22, 0, 15, 11, 30, 2, 18, 3, 74, 30, 29, 0, 16, 14, 41, 2, 13, 8, 84, 35, 35, 0, 24, 18, 75, …
    ## $ age_50_64        <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
    ## $ age_65           <dbl> 0, 0, 4, 0, 4, 0, 5, 0, 13, 3, 0, 0, 3, 2, 4, 0, 2, 0, 11, 1, 0, 1, 2, 2, 16, 0, 2, 0, 9, 2, …
    ## $ ilitotal         <dbl> 44, 3, 32, 46, 89, 5, 27, 5, 243, 76, 57, 2, 42, 44, 104, 6, 15, 9, 274, 62, 85, 1, 46, 54, 1…
    ## $ num_of_providers <dbl> 32, 7, 16, 29, 49, 4, 14, 5, 23, 13, 29, 7, 17, 31, 48, 4, 14, 6, 23, 12, 40, 7, 15, 33, 64, …
    ## $ total_patients   <dbl> 7053, 780, 2385, 10222, 9875, 669, 2342, 1183, 10758, 1575, 6987, 872, 2740, 11310, 9618, 684…
    ## $ week_start       <date> 1997-09-28, 1997-09-28, 1997-09-28, 1997-09-28, 1997-09-28, 1997-09-28, 1997-09-28, 1997-09-…
    ## # A tibble: 12,330 x 16
    ##    region_type region     year  week weighted_ili unweighted_ili age_0_4 age_25_49 age_25_64 age_5_24 age_50_64 age_65
    ##    <chr>       <fct>     <int> <int>        <dbl>          <dbl>   <dbl>     <dbl>     <dbl>    <dbl>     <dbl>  <dbl>
    ##  1 HHS Regions Region 1   1997    40        0.499          0.624      15        NA         7       22        NA      0
    ##  2 HHS Regions Region 2   1997    40        0.375          0.385       0        NA         3        0        NA      0
    ##  3 HHS Regions Region 3   1997    40        1.35           1.34        6        NA         7       15        NA      4
    ##  4 HHS Regions Region 4   1997    40        0.400          0.450      12        NA        23       11        NA      0
    ##  5 HHS Regions Region 5   1997    40        1.23           0.901      31        NA        24       30        NA      4
    ##  6 HHS Regions Region 6   1997    40        1.02           0.747       2        NA         1        2        NA      0
    ##  7 HHS Regions Region 7   1997    40        0.872          1.15        0        NA         4       18        NA      5
    ##  8 HHS Regions Region 8   1997    40        0.516          0.423       2        NA         0        3        NA      0
    ##  9 HHS Regions Region 9   1997    40        1.81           2.26       80        NA        76       74        NA     13
    ## 10 HHS Regions Region 10  1997    40        4.74           4.83       31        NA        12       30        NA      3
    ## # … with 12,320 more rows, and 4 more variables: ilitotal <dbl>, num_of_providers <dbl>, total_patients <dbl>,
    ## #   week_start <date>

<img src="man/figures/README-ili-df-2.png" width="672" />

    ## Rows: 10,557
    ## Rows: 11,097
    ## Columns: 16
    ## $ region_type      <chr> "Census Regions", "Census Regions", "Census Regions", "Census Regions", "Census Regions", "C…
    ## $ 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, 19…
    ## $ 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, 2.04…
    ## $ unweighted_ili   <dbl> 0.6238480, 1.3213800, 0.8187380, 1.2793900, 0.7233800, 0.0688705, 0.7473840, 2.2763300, 3.23…
    ## $ 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, 55,…
    ## $ age_5_24         <dbl> 22, 12, 28, 20, 14, 0, 2, 71, 36, 29, 8, 39, 18, 22, 0, 2, 80, 44, 35, 16, 74, 9, 24, 2, 2, …
    ## $ 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, 14…
    ## $ ilitotal         <dbl> 44, 28, 79, 37, 50, 3, 5, 244, 80, 57, 27, 100, 23, 60, 1, 6, 255, 86, 85, 33, 178, 21, 66, …
    ## $ num_of_providers <dbl> 32, 13, 47, 17, 30, 9, 4, 16, 24, 29, 13, 46, 17, 32, 10, 4, 17, 23, 40, 12, 62, 16, 33, 10,…
    ## $ total_patients   <dbl> 7053, 2119, 9649, 2892, 6912, 4356, 669, 10719, 2473, 6987, 2384, 9427, 2823, 7591, 4947, 68…
    ## $ week_start       <date> 1997-09-28, 1997-09-28, 1997-09-28, 1997-09-28, 1997-09-28, 1997-09-28, 1997-09-28, 1997-09…
    ## # A tibble: 10,557 x 16
    ##    region_type region  year  week weighted_ili unweighted_ili age_0_4 age_25_49 age_25_64 age_5_24 age_50_64 age_65
    ##    <chr>       <chr>  <int> <int>        <dbl>          <dbl>   <dbl>     <dbl>     <dbl>    <dbl>     <dbl>  <dbl>
    ##  1 Census Reg… New E…  1997    40       0.499          0.624       15        NA         7       22        NA      0
    ##  2 Census Reg… Mid-A…  1997    40       0.844          1.32         4        NA         8       12        NA      4
    ##  3 Census Reg… East …  1997    40       0.792          0.819       28        NA        20       28        NA      3
    ##  4 Census Reg… West …  1997    40       1.76           1.28         3        NA         8       20        NA      6
    ##  5 Census Reg… South…  1997    40       0.503          0.723       14        NA        22       14        NA      0
    ##  6 Census Reg… East …  1997    40       0.0542         0.0689       0        NA         3        0        NA      0
    ##  7 Census Reg… West …  1997    40       1.02           0.747        2        NA         1        2        NA      0
    ##  8 Census Reg… Mount…  1997    40       2.26           2.28        87        NA        71       71        NA     15
    ##  9 Census Reg… Pacif…  1997    40       2.05           3.23        26        NA        17       36        NA      1
    ## 10 Census Reg… New E…  1997    41       0.643          0.816       14        NA        14       29        NA      0
    ## # … with 10,547 more rows, and 4 more variables: ilitotal <dbl>, num_of_providers <dbl>, total_patients <dbl>,
    ## $ region_type      <chr> "Census Regions", "Census Regions", "Census Regions", "Census Regions", "Census Regions", "Ce…
    ## $ region           <chr> "New England", "Mid-Atlantic", "East North Central", "West North Central", "South Atlantic", …
    ## $ year             <int> 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 199…
    ## $ week             <int> 40, 40, 40, 40, 40, 40, 40, 40, 40, 41, 41, 41, 41, 41, 41, 41, 41, 41, 42, 42, 42, 42, 42, 4…
    ## $ weighted_ili     <dbl> 0.4985350, 0.8441440, 0.7924860, 1.7640500, 0.5026620, 0.0542283, 1.0189800, 2.2587800, 2.048…
    ## $ unweighted_ili   <dbl> 0.6238480, 1.3213800, 0.8187380, 1.2793900, 0.7233800, 0.0688705, 0.7473840, 2.2763300, 3.234…
    ## $ age_0_4          <dbl> 15, 4, 28, 3, 14, 0, 2, 87, 26, 14, 4, 36, 0, 21, 0, 2, 93, 29, 35, 3, 65, 1, 19, 0, 2, 84, 1…
    ## $ age_25_49        <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
    ## $ age_25_64        <dbl> 7, 8, 20, 8, 22, 3, 1, 71, 17, 14, 13, 23, 1, 14, 1, 2, 72, 11, 15, 11, 27, 5, 21, 0, 2, 55, …
    ## $ age_5_24         <dbl> 22, 12, 28, 20, 14, 0, 2, 71, 36, 29, 8, 39, 18, 22, 0, 2, 80, 44, 35, 16, 74, 9, 24, 2, 2, 7…
    ## $ age_50_64        <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
    ## $ age_65           <dbl> 0, 4, 3, 6, 0, 0, 0, 15, 1, 0, 2, 2, 4, 3, 0, 0, 10, 2, 0, 3, 12, 6, 2, 0, 0, 9, 2, 0, 1, 14,…
    ## $ ilitotal         <dbl> 44, 28, 79, 37, 50, 3, 5, 244, 80, 57, 27, 100, 23, 60, 1, 6, 255, 86, 85, 33, 178, 21, 66, 2…
    ## $ num_of_providers <dbl> 32, 13, 47, 17, 30, 9, 4, 16, 24, 29, 13, 46, 17, 32, 10, 4, 17, 23, 40, 12, 62, 16, 33, 10, …
    ## $ total_patients   <dbl> 7053, 2119, 9649, 2892, 6912, 4356, 669, 10719, 2473, 6987, 2384, 9427, 2823, 7591, 4947, 684…
    ## $ week_start       <date> 1997-09-28, 1997-09-28, 1997-09-28, 1997-09-28, 1997-09-28, 1997-09-28, 1997-09-28, 1997-09-…
    ## # A tibble: 11,097 x 16
    ##    region_type  region      year  week weighted_ili unweighted_ili age_0_4 age_25_49 age_25_64 age_5_24 age_50_64 age_65
    ##    <chr>        <chr>      <int> <int>        <dbl>          <dbl>   <dbl>     <dbl>     <dbl>    <dbl>     <dbl>  <dbl>
    ##  1 Census Regi… New Engla…  1997    40       0.499          0.624       15        NA         7       22        NA      0
    ##  2 Census Regi… Mid-Atlan…  1997    40       0.844          1.32         4        NA         8       12        NA      4
    ##  3 Census Regi… East Nort…  1997    40       0.792          0.819       28        NA        20       28        NA      3
    ##  4 Census Regi… West Nort…  1997    40       1.76           1.28         3        NA         8       20        NA      6
    ##  5 Census Regi… South Atl…  1997    40       0.503          0.723       14        NA        22       14        NA      0
    ##  6 Census Regi… East Sout…  1997    40       0.0542         0.0689       0        NA         3        0        NA      0
    ##  7 Census Regi… West Sout…  1997    40       1.02           0.747        2        NA         1        2        NA      0
    ##  8 Census Regi… Mountain    1997    40       2.26           2.28        87        NA        71       71        NA     15
    ##  9 Census Regi… Pacific     1997    40       2.05           3.23        26        NA        17       36        NA      1
    ## 10 Census Regi… New Engla…  1997    41       0.643          0.816       14        NA        14       29        NA      0
    ## # … with 11,087 more rows, and 4 more variables: ilitotal <dbl>, num_of_providers <dbl>, total_patients <dbl>,
    ## #   week_start <date>

<img src="man/figures/README-ili-df-3.png" width="672" />

    ## Rows: 26,493
    ## Rows: 29,793
    ## Columns: 16
    ## $ region_type      <chr> "States", "States", "States", "States", "States", "States", "States", "States", "States", "S…
    ## $ region           <chr> "Alabama", "Alaska", "Arizona", "Arkansas", "California", "Colorado", "Connecticut", "Delawa…
    ## $ year             <int> 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 20…
    ## $ 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, 2.80…
    ## $ 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, 391, 22, 117, 16…
    ## $ num_of_providers <dbl> 35, 7, 49, 15, 112, 14, 12, 13, 4, NA, 62, 18, 12, 74, 44, 6, 40, 14, 41, 30, 17, 56, 47, 17…
    ## $ total_patients   <dbl> 11664, 1714, 25492, 2586, 32342, 20282, 3831, 3995, 2599, NA, 40314, 1943, 4579, 39390, 1252…
    ## $ week_start       <date> 2010-10-03, 2010-10-03, 2010-10-03, 2010-10-03, 2010-10-03, 2010-10-03, 2010-10-03, 2010-10…
    ## # A tibble: 26,493 x 16
    ##    region_type region  year  week weighted_ili unweighted_ili age_0_4 age_25_49 age_25_64 age_5_24 age_50_64 age_65
    ##    <chr>       <chr>  <int> <int>        <dbl>          <dbl>   <dbl>     <dbl>     <dbl>    <dbl>     <dbl>  <dbl>
    ##  1 States      Alaba…  2010    40           NA         2.13        NA        NA        NA       NA        NA     NA
    ##  2 States      Alaska  2010    40           NA         0.875       NA        NA        NA       NA        NA     NA
    ##  3 States      Arizo…  2010    40           NA         0.675       NA        NA        NA       NA        NA     NA
    ##  4 States      Arkan…  2010    40           NA         0.696       NA        NA        NA       NA        NA     NA
    ##  5 States      Calif…  2010    40           NA         1.95        NA        NA        NA       NA        NA     NA
    ##  6 States      Color…  2010    40           NA         0.661       NA        NA        NA       NA        NA     NA
    ##  7 States      Conne…  2010    40           NA         0.0783      NA        NA        NA       NA        NA     NA
    ##  8 States      Delaw…  2010    40           NA         0.100       NA        NA        NA       NA        NA     NA
    ##  9 States      Distr…  2010    40           NA         2.81        NA        NA        NA       NA        NA     NA
    ## 10 States      Flori…  2010    40           NA        NA           NA        NA        NA       NA        NA     NA
    ## # … with 26,483 more rows, and 4 more variables: ilitotal <dbl>, num_of_providers <dbl>, total_patients <dbl>,
    ## $ region_type      <chr> "States", "States", "States", "States", "States", "States", "States", "States", "States", "St…
    ## $ region           <chr> "Alabama", "Alaska", "Arizona", "Arkansas", "California", "Colorado", "Connecticut", "Delawar…
    ## $ year             <int> 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 201…
    ## $ week             <int> 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 4…
    ## $ weighted_ili     <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
    ## $ unweighted_ili   <dbl> 2.1347700, 0.8751460, 0.6747210, 0.6960560, 1.9541200, 0.6606840, 0.0783085, 0.1001250, 2.808…
    ## $ age_0_4          <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
    ## $ age_25_49        <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
    ## $ age_25_64        <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
    ## $ age_5_24         <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
    ## $ age_50_64        <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
    ## $ age_65           <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
    ## $ ilitotal         <dbl> 249, 15, 172, 18, 632, 134, 3, 4, 73, NA, 647, 20, 19, 505, 65, 10, 39, 19, 391, 22, 117, 168…
    ## $ num_of_providers <dbl> 35, 7, 49, 15, 112, 14, 12, 13, 4, NA, 62, 18, 12, 74, 44, 6, 40, 14, 41, 30, 17, 56, 47, 17,…
    ## $ total_patients   <dbl> 11664, 1714, 25492, 2586, 32342, 20282, 3831, 3995, 2599, NA, 40314, 1943, 4579, 39390, 12525…
    ## $ week_start       <date> 2010-10-03, 2010-10-03, 2010-10-03, 2010-10-03, 2010-10-03, 2010-10-03, 2010-10-03, 2010-10-…
    ## # A tibble: 29,793 x 16
    ##    region_type region       year  week weighted_ili unweighted_ili age_0_4 age_25_49 age_25_64 age_5_24 age_50_64 age_65
    ##    <chr>       <chr>       <int> <int>        <dbl>          <dbl>   <dbl>     <dbl>     <dbl>    <dbl>     <dbl>  <dbl>
    ##  1 States      Alabama      2010    40           NA         2.13        NA        NA        NA       NA        NA     NA
    ##  2 States      Alaska       2010    40           NA         0.875       NA        NA        NA       NA        NA     NA
    ##  3 States      Arizona      2010    40           NA         0.675       NA        NA        NA       NA        NA     NA
    ##  4 States      Arkansas     2010    40           NA         0.696       NA        NA        NA       NA        NA     NA
    ##  5 States      California   2010    40           NA         1.95        NA        NA        NA       NA        NA     NA
    ##  6 States      Colorado     2010    40           NA         0.661       NA        NA        NA       NA        NA     NA
    ##  7 States      Connecticut  2010    40           NA         0.0783      NA        NA        NA       NA        NA     NA
    ##  8 States      Delaware     2010    40           NA         0.100       NA        NA        NA       NA        NA     NA
    ##  9 States      District o…  2010    40           NA         2.81        NA        NA        NA       NA        NA     NA
    ## 10 States      Florida      2010    40           NA        NA           NA        NA        NA       NA        NA     NA
    ## # … with 29,783 more rows, and 4 more variables: ilitotal <dbl>, num_of_providers <dbl>, total_patients <dbl>,
    ## #   week_start <date>

<img src="man/figures/README-ili-df-4.png" width="672" />


@@ 504,18 491,18 @@ walk(c("national", "hhs", "census", "state"), ~{
``` r
ili_weekly_activity_indicators(2017)
## # A tibble: 2,805 x 8
##    statename      url                website   activity_level activity_level_label weekend    season  weeknumber
##  * <chr>          <chr>              <chr>              <dbl> <chr>                <date>     <chr>        <dbl>
##  1 Virgin Islands http://doh.vi.gov/ Influenza              0 Insufficient Data    2017-10-07 2017-18         40
##  2 Virgin Islands http://doh.vi.gov/ Influenza              0 Insufficient Data    2017-10-14 2017-18         41
##  3 Virgin Islands http://doh.vi.gov/ Influenza              0 Insufficient Data    2017-10-21 2017-18         42
##  4 Virgin Islands http://doh.vi.gov/ Influenza              0 Insufficient Data    2017-10-28 2017-18         43
##  5 Virgin Islands http://doh.vi.gov/ Influenza              0 Insufficient Data    2017-11-04 2017-18         44
##  6 Virgin Islands http://doh.vi.gov/ Influenza              0 Insufficient Data    2017-11-11 2017-18         45
##  7 Virgin Islands http://doh.vi.gov/ Influenza              0 Insufficient Data    2017-12-02 2017-18         48
##  8 Virgin Islands http://doh.vi.gov/ Influenza              0 Insufficient Data    2017-12-09 2017-18         49
##  9 Virgin Islands http://doh.vi.gov/ Influenza              0 Insufficient Data    2017-12-23 2017-18         51
## 10 Virgin Islands http://doh.vi.gov/ Influenza              0 Insufficient Data    2017-12-30 2017-18         52
##    statename    url                          website        activity_level activity_level_… weekend    season weeknumber
##  * <chr>        <chr>                        <chr>                   <dbl> <chr>            <date>     <chr>       <dbl>
##  1 Alabama      "http://adph.org/influenza/" Influenza Sur…              2 Minimal          2017-10-07 2017-…         40
##  2 Alaska       "http://dhss.alaska.gov/dph… Influenza Sur…              1 Minimal          2017-10-07 2017-…         40
##  3 Arizona      "http://www.azdhs.gov/phs/o… Influenza & R…              2 Minimal          2017-10-07 2017-…         40
##  4 Arkansas     "http://www.healthy.arkansa… Communicable …              1 Minimal          2017-10-07 2017-…         40
##  5 California   "https://www.cdph.ca.gov/Pr… Influenza (Fl…              2 Minimal          2017-10-07 2017-…         40
##  6 Colorado     "https://www.colorado.gov/p… Influenza Sur…              1 Minimal          2017-10-07 2017-…         40
##  7 Connecticut  "https://portal.ct.gov/DPH/… Flu Statistics              1 Minimal          2017-10-07 2017-…         40
##  8 Delaware     "http://dhss.delaware.gov/d… Weekly Influe…              1 Minimal          2017-10-07 2017-…         40
##  9 District of… "https://dchealth.dc.gov/no… Influenza Inf…              2 Minimal          2017-10-07 2017-…         40
## 10 Florida      "http://www.floridahealth.g… Weekly Influe…              1 Minimal          2017-10-07 2017-…         40
## # … with 2,795 more rows

xdf <- map_df(2008:2017, ili_weekly_activity_indicators)


@@ 538,20 525,20 @@ count(xdf, weekend, activity_level_label) %>%

``` r
(nat_pi <- pi_mortality("national"))
## # A tibble: 337 x 19
## # A tibble: 398 x 19
##    seasonid baseline threshold percent_pni percent_complete number_influenza number_pneumonia all_deaths total_pni
##    <chr>       <dbl>     <dbl>       <dbl>            <dbl>            <dbl>            <dbl>      <dbl>     <dbl>
##  1 59         0.053     0.057       0.052                 1               16             2702      52444      2718
##  2 59         0.054     0.057       0.053                 1               16             2769      52858      2785
##  3 59         0.055     0.0580      0.055                 1               18             2976      54120      2994
##  4 59         0.0560    0.059       0.0560                1               30             2984      53906      3014
##  5 59         0.057     0.06        0.054                 1               31             2906      53971      2937
##  6 59         0.0580    0.062       0.0560                1               31             3061      55460      3092
##  7 59         0.059     0.063       0.0560                1               39             3092      55679      3131
##  8 59         0.06      0.064       0.054                 1               50             2992      55976      3042
##  9 59         0.062     0.065       0.055                 1               65             2971      55225      3036
## 10 59         0.063     0.066       0.06                  1               99             3305      56974      3404
## # … with 327 more rows, and 10 more variables: weeknumber <chr>, geo_description <chr>, age_label <chr>,
##  1 60         0.053     0.0560      0.081                 1                8             4825      59682      4833
##  2 60         0.054     0.057       0.084                 1               12             5173      61641      5185
##  3 60         0.055     0.0580      0.086                 1               16             5208      60467      5224
##  4 60         0.0560    0.059       0.091                 1               15             5642      62047      5657
##  5 60         0.057     0.06        0.0970                1               21             6142      63280      6163
##  6 60         0.0580    0.061       0.105                 1               21             7075      67380      7096
##  7 60         0.059     0.062       0.117                 1               20             8040      68644      8060
##  8 60         0.06      0.063       0.132                 1               30             9400      71440      9430
##  9 60         0.061     0.064       0.143                 1               27            10440      73066     10467
## 10 60         0.062     0.065       0.157                 1               35            12048      77136     12083
## # … with 388 more rows, and 10 more variables: weeknumber <chr>, geo_description <chr>, age_label <chr>,
## #   week_start <date>, week_end <date>, year_week_num <int>, mmwrid <chr>, coverage_area <chr>, region_name <chr>,
## #   callout <chr>



@@ 570,7 557,6 @@ select(nat_pi, week_end, percent_pni, baseline, threshold) %>%
<img src="man/figures/README-nat-pi-mortality-1.png" width="672" />

``` 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


@@ 593,16 579,16 @@ select(nat_pi, week_end, percent_pni, baseline, threshold) %>%
## # A tibble: 520 x 19
##    seasonid baseline threshold percent_pni percent_complete number_influenza number_pneumonia all_deaths total_pni
##    <chr>       <dbl>     <dbl>       <dbl>            <dbl>            <dbl>            <dbl>      <dbl>     <dbl>
##  1 55          0.064    0.072       0.07              1                    0              178       2525       178
##  2 55          0.065    0.073       0.064             1                    0              160       2512       160
##  3 55          0.066    0.074       0.0580            1                    1              141       2457       142
##  4 55          0.067    0.075       0.07              0.989                0              171       2426       171
##  5 55          0.068    0.077       0.065             1                    2              166       2565       168
##  6 55          0.07     0.078       0.067             0.984                1              162       2415       163
##  7 55          0.071    0.079       0.079             1                    0              198       2491       198
##  8 55          0.073    0.081       0.072             1                    1              176       2468       177
##  9 55          0.074    0.0820      0.067             0.959                3              154       2353       157
## 10 55          0.076    0.084       0.062             0.995                0              151       2441       151
##  1 55          0.064    0.071       0.07              1                    0              178       2525       178
##  2 55          0.065    0.072       0.064             1                    0              160       2512       160
##  3 55          0.066    0.073       0.0580            1                    1              141       2457       142
##  4 55          0.067    0.074       0.07              0.989                0              171       2426       171
##  5 55          0.068    0.075       0.065             1                    2              166       2565       168
##  6 55          0.069    0.077       0.067             0.985                1              162       2415       163
##  7 55          0.071    0.078       0.079             1                    0              198       2491       198
##  8 55          0.072    0.079       0.072             1                    1              176       2468       177
##  9 55          0.073    0.081       0.067             0.96                 3              154       2353       157
## 10 55          0.075    0.0820      0.062             0.996                0              151       2441       151
## # … with 510 more rows, and 10 more variables: weeknumber <chr>, geo_description <chr>, age_label <chr>,
## #   week_start <date>, week_end <date>, year_week_num <int>, mmwrid <chr>, coverage_area <chr>, region_name <chr>,
## #   callout <chr>


@@ 623,7 609,7 @@ state_data_providers()
##  6 Colorado      Colorado Department of Publ… "https://www.colorado.gov/pacific… Influenza Surveillance 303-692-2000    
##  7 Connecticut   Connecticut Department of P… "https://portal.ct.gov/DPH/Epidem… Flu Statistics         860-509-8000    
##  8 Delaware      Delaware Health and Social … "http://dhss.delaware.gov/dhss/dp… Weekly Influenza Surv… 302-744-4700    
##  9 District of … District of Columbia Depart… "https://dchealth.dc.gov/flu "     Influenza Information  202-442-5955    
##  9 District of … District of Columbia Depart… "https://dchealth.dc.gov/node/114… Influenza Information  202-442-5955    
## 10 Florida       Florida Department of Health "http://www.floridahealth.gov/dis… Weekly Influenza Surv… 850-245-4300    
## # … with 49 more rows
```


@@ 633,7 619,7 @@ state_data_providers()
``` r
glimpse(xdat <- who_nrevss("national"))
## List of 3
##  $ combined_prior_to_2015_16: tibble [940 × 14] (S3: tbl_df/tbl/data.frame)
##  $ combined_prior_to_2015_16: tibble[,14] [940 × 14] (S3: tbl_df/tbl/data.frame)
##   ..$ region_type              : chr [1:940] "National" "National" "National" "National" ...
##   ..$ region                   : chr [1:940] "National" "National" "National" "National" ...
##   ..$ year                     : int [1:940] 1997 1997 1997 1997 1997 1997 1997 1997 1997 1997 ...


@@ 648,32 634,32 @@ glimpse(xdat <- who_nrevss("national"))
##   ..$ b                        : int [1:940] 0 0 1 0 0 0 1 1 1 1 ...
##   ..$ h3n2v                    : int [1:940] 0 0 0 0 0 0 0 0 0 0 ...
##   ..$ wk_date                  : Date[1:940], format: "1997-09-28" "1997-10-05" "1997-10-12" "1997-10-19" ...
##  $ public_health_labs       : tibble [233 × 13] (S3: tbl_df/tbl/data.frame)
##   ..$ region_type              : chr [1:233] "National" "National" "National" "National" ...
##   ..$ region                   : chr [1:233] "National" "National" "National" "National" ...
##   ..$ year                     : int [1:233] 2015 2015 2015 2015 2015 2015 2015 2015 2015 2015 ...
##   ..$ week                     : int [1:233] 40 41 42 43 44 45 46 47 48 49 ...
##   ..$ total_specimens          : int [1:233] 1139 1152 1198 1244 1465 1393 1458 1157 1550 1518 ...
##   ..$ a_2009_h1n1              : int [1:233] 4 5 10 9 4 11 17 17 27 38 ...
##   ..$ a_h3                     : int [1:233] 65 41 50 31 23 34 42 24 36 37 ...
##   ..$ a_subtyping_not_performed: int [1:233] 2 2 1 4 4 1 1 0 3 3 ...
##   ..$ b                        : int [1:233] 10 7 8 9 9 10 4 4 9 11 ...
##   ..$ bvic                     : int [1:233] 0 3 3 1 1 4 0 3 3 2 ...
##   ..$ byam                     : int [1:233] 1 0 2 4 4 2 4 9 12 11 ...
##   ..$ h3n2v                    : int [1:233] 0 0 0 0 0 0 0 0 0 0 ...
##   ..$ wk_date                  : Date[1:233], format: "2015-10-04" "2015-10-11" "2015-10-18" "2015-10-25" ...
##  $ clinical_labs            : tibble [233 × 11] (S3: tbl_df/tbl/data.frame)
##   ..$ region_type     : chr [1:233] "National" "National" "National" "National" ...
##   ..$ region          : chr [1:233] "National" "National" "National" "National" ...
##   ..$ year            : int [1:233] 2015 2015 2015 2015 2015 2015 2015 2015 2015 2015 ...
##   ..$ week            : int [1:233] 40 41 42 43 44 45 46 47 48 49 ...
##   ..$ total_specimens : int [1:233] 12029 13111 13441 13537 14687 15048 15250 15234 16201 16673 ...
##   ..$ total_a         : int [1:233] 84 116 97 98 97 122 84 119 145 140 ...
##   ..$ total_b         : int [1:233] 43 54 52 52 68 86 98 92 81 106 ...
##   ..$ percent_positive: num [1:233] 1.06 1.3 1.11 1.11 1.12 ...
##   ..$ percent_a       : num [1:233] 0.698 0.885 0.722 0.724 0.66 ...
##   ..$ percent_b       : num [1:233] 0.357 0.412 0.387 0.384 0.463 ...
##   ..$ wk_date         : Date[1:233], format: "2015-10-04" "2015-10-11" "2015-10-18" "2015-10-25" ...
##  $ public_health_labs       : tibble[,13] [293 × 13] (S3: tbl_df/tbl/data.frame)
##   ..$ region_type              : chr [1:293] "National" "National" "National" "National" ...
##   ..$ region                   : chr [1:293] "National" "National" "National" "National" ...
##   ..$ year                     : int [1:293] 2015 2015 2015 2015 2015 2015 2015 2015 2015 2015 ...
##   ..$ week                     : int [1:293] 40 41 42 43 44 45 46 47 48 49 ...
##   ..$ total_specimens          : int [1:293] 1139 1152 1198 1244 1465 1393 1458 1157 1550 1518 ...
##   ..$ a_2009_h1n1              : int [1:293] 4 5 10 9 4 11 17 17 27 38 ...
##   ..$ a_h3                     : int [1:293] 65 41 50 31 23 34 42 24 36 37 ...
##   ..$ a_subtyping_not_performed: int [1:293] 2 2 1 4 4 1 1 0 3 3 ...
##   ..$ b                        : int [1:293] 10 7 8 9 9 10 4 4 9 11 ...
##   ..$ bvic                     : int [1:293] 0 3 3 1 1 4 0 3 3 2 ...
##   ..$ byam                     : int [1:293] 1 0 2 4 4 2 4 9 12 11 ...
##   ..$ h3n2v                    : int [1:293] 0 0 0 0 0 0 0 0 0 0 ...
##   ..$ wk_date                  : Date[1:293], format: "2015-10-04" "2015-10-11" "2015-10-18" "2015-10-25" ...
##  $ clinical_labs            : tibble[,11] [293 × 11] (S3: tbl_df/tbl/data.frame)
##   ..$ region_type     : chr [1:293] "National" "National" "National" "National" ...
##   ..$ region          : chr [1:293] "National" "National" "National" "National" ...
##   ..$ year            : int [1:293] 2015 2015 2015 2015 2015 2015 2015 2015 2015 2015 ...
##   ..$ week            : int [1:293] 40 41 42 43 44 45 46 47 48 49 ...
##   ..$ total_specimens : int [1:293] 12029 13111 13441 13537 14687 15048 15250 15234 16201 16673 ...
##   ..$ total_a         : int [1:293] 84 116 97 98 97 122 84 119 145 140 ...
##   ..$ total_b         : int [1:293] 43 54 52 52 68 86 98 92 81 106 ...
##   ..$ percent_positive: num [1:293] 1.06 1.3 1.11 1.11 1.12 ...
##   ..$ percent_a       : num [1:293] 0.698 0.885 0.722 0.724 0.66 ...
##   ..$ percent_b       : num [1:293] 0.357 0.412 0.387 0.384 0.463 ...
##   ..$ wk_date         : Date[1:293], format: "2015-10-04" "2015-10-11" "2015-10-18" "2015-10-25" ...

mutate(xdat$combined_prior_to_2015_16, 
       percent_positive = percent_positive / 100) %>% 


@@ 687,7 673,6 @@ mutate(xdat$combined_prior_to_2015_16,
<img src="man/figures/README-who-vrevss-1.png" width="672" />

``` r

who_nrevss("hhs", years=2016)
## $public_health_labs
## # A tibble: 520 x 13


@@ 757,18 742,18 @@ who_nrevss("census", years=2016)
who_nrevss("state", years=2016)
## $public_health_labs
## # A tibble: 54 x 12
##    region_type region season_descript… total_specimens a_2009_h1n1 a_h3  a_subtyping_not… b     bvic  byam  h3n2v
##    <chr>       <chr>  <chr>            <chr>           <chr>       <chr> <chr>            <chr> <chr> <chr> <chr>
##  1 States      Alaba… Season 2016-17   570             3           227   1                2     15    14    0    
##  2 States      Alaska Season 2016-17   5222            14          905   3                252   2     11    0    
##  3 States      Arizo… Season 2016-17   2975            63          1630  0                5     227   578   0    
##  4 States      Arkan… Season 2016-17   121             0           51    0                0     4     0     0    
##  5 States      Calif… Season 2016-17   14074           184         4696  120              116   28    152   0    
##  6 States      Color… Season 2016-17   714             3           267   2                4     31    219   0    
##  7 States      Conne… Season 2016-17   1348            19          968   0                0     62    263   0    
##  8 States      Delaw… Season 2016-17   3090            5           659   4                11    27    127   1    
##  9 States      Distr… Season 2016-17   73              1           34    0                3     0     4     0    
## 10 States      Flori… Season 2016-17   <NA>            <NA>        <NA>  <NA>             <NA>  <NA>  <NA>  <NA> 
##    region_type region     season_descripti… total_specimens a_2009_h1n1 a_h3  a_subtyping_not_p… b     bvic  byam  h3n2v
##    <chr>       <chr>      <chr>             <chr>           <chr>       <chr> <chr>              <chr> <chr> <chr> <chr>
##  1 States      Alabama    Season 2016-17    570             3           227   1                  2     15    14    0    
##  2 States      Alaska     Season 2016-17    5222            14          905   3                  252   2     11    0    
##  3 States      Arizona    Season 2016-17    2975            63          1630  0                  5     227   578   0    
##  4 States      Arkansas   Season 2016-17    121             0           51    0                  0     4     0     0    
##  5 States      California Season 2016-17    14074           184         4696  120                116   28    152   0    
##  6 States      Colorado   Season 2016-17    714             3           267   2                  4     31    219   0    
##  7 States      Connectic… Season 2016-17    1348            19          968   0                  0     62    263   0    
##  8 States      Delaware   Season 2016-17    3090            5           659   4                  11    27    127   1    
##  9 States      District … Season 2016-17    73              1           34    0                  3     0     4     0    
## 10 States      Florida    Season 2016-17    <NA>            <NA>        <NA>  <NA>               <NA>  <NA>  <NA>  <NA> 
## # … with 44 more rows, and 1 more variable: wk_date <date>
## 
## $clinical_labs


@@ 791,10 776,13 @@ who_nrevss("state", years=2016)
## cdcfluview Metrics

| Lang | \# Files |  (%) | LoC |  (%) | Blank lines |  (%) | \# Lines |  (%) |
| :--- | -------: | ---: | --: | ---: | ----------: | ---: | -------: | ---: |
| R    |       21 | 0.91 | 847 | 0.88 |         303 | 0.79 |      512 | 0.85 |
| Rmd  |        1 | 0.04 |  80 | 0.08 |          68 | 0.18 |       86 | 0.14 |
| make |        1 | 0.04 |  32 | 0.03 |          11 | 0.03 |        1 | 0.00 |
|:-----|---------:|-----:|----:|-----:|------------:|-----:|---------:|-----:|
| R    |       21 | 0.46 | 865 | 0.44 |         311 | 0.40 |      512 | 0.43 |
| Rmd  |        1 | 0.02 |  81 | 0.04 |          64 | 0.08 |       82 | 0.07 |
| make |        1 | 0.02 |  32 | 0.02 |          11 | 0.01 |        1 | 0.00 |
| SUM  |       23 | 0.50 | 978 | 0.50 |         386 | 0.50 |      595 | 0.50 |

clock Package Metrics for cdcfluview

## Code of Conduct



M cran-comments.md => cran-comments.md +3 -4
@@ 1,11 1,10 @@
## Test environments
* local R installation, R 4.0.4
* ubuntu 16.04 (on travis-ci), R 4.0.4
* local R installation, R 4.1.0
* ubuntu 20.04, R 4.1.0
* win-builder (devel)

## R CMD check results

0 errors | 0 warnings | 1 note

* Fixed one failing test
* Fixed one API URL
* Fixed one failing test noted in CRAN email


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M man/figures/README-surveillance-areas-1.png => man/figures/README-surveillance-areas-1.png +0 -0
M man/figures/README-who-vrevss-1.png => man/figures/README-who-vrevss-1.png +0 -0