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working on #19

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boB Rudis 1 year ago
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37 changed files with 488 additions and 423 deletions
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DESCRIPTION View File

@@ -1,9 +1,10 @@
Package: cdcfluview
Type: Package
Encoding: UTF-8
Title: Retrieve U.S. Flu Season Data from the CDC 'FluView' Portal
Version: 0.8.0
Date: 2018-11-23
Title: Retrieve Flu Season Data from the United States Centers for Disease Control
and Prevention ('CDC') 'FluView' Portal
Version: 0.9.0
Date: 2019-01-23
Authors@R: c(
person("Bob", "Rudis", email = "bob@rud.is", role = c("aut", "cre"),
comment = c(ORCID = "0000-0001-5670-2640")),
@@ -12,17 +13,18 @@ Authors@R: c(
person("JJ", "Chen", email = "jiajia.chern@gmail.com", role = "ctb",
comment = c(ORCID = "0000-0001-8482-8398")),
person("Sebastian", "Meyer", email = "seb.meyer@fau.de", role = "ctb",
comment = c(ORCID = "0000-0002-1791-9449"))
comment = c(ORCID = "0000-0002-1791-9449")),
person("James", "Turtle", email = "jturtle@predsci.com", role = "ctb")
)
Maintainer: Bob Rudis <bob@rud.is>
Description: The U.S. Centers for Disease Control and Prevention (CDC) maintain
Description: The 'U.S.' Centers for Disease Control and Prevention (CDC) maintain
a portal <http://gis.cdc.gov/grasp/fluview/fluportaldashboard.html> for
accessing state, regional and national influenza statistics as well as
mortality surveillance data. The web interface makes it difficult and
time-consuming to select and retrieve influenza data. Tools are provided
to access the data provided by the portal's underlying API.
URL: https://github.com/hrbrmstr/cdcfluview
BugReports: https://github.com/hrbrmstr/cdcfluview/issues
to access the data provided by the portal's underlying 'API'.
URL: https://gitlab.com/hrbrmstr/cdcfluview
BugReports: https://gitlab.com/hrbrmstr/cdcfluview/issues
License: MIT + file LICENSE
LazyData: true
Suggests:
@@ -43,4 +45,4 @@ Imports:
readr,
MMWRweek,
units (>= 0.4-6)
RoxygenNote: 6.0.1.9000
RoxygenNote: 6.1.1

+ 2
- 0
NAMESPACE View File

@@ -22,12 +22,14 @@ import(MMWRweek)
import(httr)
import(xml2)
importFrom(dplyr,"%>%")
importFrom(dplyr,arrange)
importFrom(dplyr,bind_rows)
importFrom(dplyr,data_frame)
importFrom(dplyr,filter)
importFrom(dplyr,left_join)
importFrom(dplyr,mutate)
importFrom(jsonlite,fromJSON)
importFrom(lubridate,epiweek)
importFrom(purrr,discard)
importFrom(purrr,keep)
importFrom(purrr,map)


+ 4
- 0
NEWS.md View File

@@ -1,3 +1,7 @@
# cdcfluview 0.9.0

- fix bug in epiweek computation in ilinet() thanks to a bug report by @jturtle (#19)

# cdcfluview 0.7.0

* The CDC changed most of their API endpoints to support a new HTML interface and


+ 1
- 1
R/aaa.R View File

@@ -1,4 +1,4 @@
utils::globalVariables(c(".", "mmwrid", "season", "seasonid"))
utils::globalVariables(c(".", "mmwrid", "season", "seasonid", "week_start"))

# CDC U.S. region names to ID map
.region_map <- c(national=3, hhs=1, census=2, state=5)


+ 2
- 2
R/cdcfluview-package.R View File

@@ -1,4 +1,4 @@
#' Retrieve 'U.S'.' Flu Season Data from the 'CDC' 'FluView' Portal
#' Retrieve Flu Season Data from the United States Centers for Disease Control and Prevention ('CDC') 'FluView' Portal
#'
#' The U.S. Centers for Disease Control (CDC) maintains a portal
#' <http://gis.cdc.gov/grasp/fluview/fluportaldashboard.html> for
@@ -17,7 +17,7 @@
#' @importFrom purrr map map_df map_chr map_lgl discard keep
#' @importFrom readr read_csv type_convert
#' @importFrom tools file_path_sans_ext
#' @importFrom dplyr left_join bind_rows mutate filter data_frame %>%
#' @importFrom dplyr left_join bind_rows mutate filter data_frame %>% arrange
#' @importFrom jsonlite fromJSON
#' @importFrom stats setNames
#' @importFrom sf st_read


+ 40
- 28
R/ilinet.r View File

@@ -21,19 +21,22 @@
#' - [ILINet Portal](https://wwwn.cdc.gov/ilinet/) (Login required)
#' - [WHO/NREVSS](https://www.cdc.gov/surveillance/nrevss/index.html)
#' @export
#' @examples
#' national_ili <- ilinet("national", years=2017)
#' @examples
#' national_ili <- ilinet("national", years = 2017)
#' \dontrun{
#' hhs_ili <- ilinet("hhs")
#' census_ili <- ilinet("census")
#' state_ili <- ilinet("state")
#'
#'
#' library(purrr)
#' map_df(
#' c("national", "hhs", "census", "state"),
#' ~ilinet(.x))
#' ~ ilinet(.x)
#' )
#' }
ilinet <- function(region=c("national", "hhs", "census", "state"), years=NULL) {
ilinet <- function(region = c("national", "hhs", "census", "state"), years = NULL) {

#region="national"; years=1997:2018

region <- match.arg(tolower(region), c("national", "hhs", "census", "state"))

@@ -46,10 +49,18 @@ ilinet <- function(region=c("national", "hhs", "census", "state"), years=NULL) {
) -> params

params$SubRegionsDT <- switch(region,
national = { list(list(ID=0, Name="")) },
hhs = { lapply(1:10, function(i) list(ID=i, Name=as.character(i))) },
census = { lapply(1:9, function(i) list(ID=i, Name=as.character(i))) },
state = { lapply(1:59, function(i) list(ID=i, Name=as.character(i))) }
national = {
list(list(ID = 0, Name = ""))
},
hhs = {
lapply(1:10, function(i) list(ID = i, Name = as.character(i)))
},
census = {
lapply(1:9, function(i) list(ID = i, Name = as.character(i)))
},
state = {
lapply(1:59, function(i) list(ID = i, Name = as.character(i)))
}
)

available_seasons <- sort(meta$seasons$seasonid)
@@ -66,13 +77,14 @@ ilinet <- function(region=c("national", "hhs", "census", "state"), years=NULL) {
if (length(years) == 0) {
years <- rev(sort(meta$seasons$seasonid))[1]
curr_season_descr <- meta$seasons[meta$seasons$seasonid == years, "description"]
message(sprintf("No valid years specified, defaulting to this flu season => ID: %s [%s]",
years, curr_season_descr))
message(sprintf(
"No valid years specified, defaulting to this flu season => ID: %s [%s]",
years, curr_season_descr
))
}

}

params$SeasonsDT <- lapply(years, function(i) list(ID=i, Name=as.character(i)))
params$SeasonsDT <- lapply(years, function(i) list(ID = i, Name = as.character(i)))

tf <- tempfile(fileext = ".zip")
td <- tempdir()
@@ -97,27 +109,27 @@ ilinet <- function(region=c("national", "hhs", "census", "state"), years=NULL) {

nm <- unzip(tf, overwrite = TRUE, exdir = td)

xdf <- read.csv(nm, skip = 1, stringsAsFactors=FALSE)
xdf <- read.csv(nm, skip = 1, stringsAsFactors = FALSE)
xdf <- .mcga(xdf)

suppressWarnings(xdf$weighted_ili <- to_num(xdf$weighted_ili))
suppressWarnings(xdf$unweighted_ili <- to_num(xdf$unweighted_ili))
suppressWarnings(xdf$age_0_4 <- to_num(xdf$age_0_4))
suppressWarnings(xdf$age_25_49 <- to_num(xdf$age_25_49))
suppressWarnings(xdf$age_25_64 <- to_num(xdf$age_25_64))
suppressWarnings(xdf$age_5_24 <- to_num(xdf$age_5_24))
suppressWarnings(xdf$age_50_64 <- to_num(xdf$age_50_64))
suppressWarnings(xdf$age_65 <- to_num(xdf$age_65))
suppressWarnings(xdf$ilitotal <- to_num(xdf$ilitotal))
suppressWarnings(xdf$num_of_providers <- to_num(xdf$num_of_providers))
suppressWarnings(xdf$total_patients <- to_num(xdf$total_patients))
suppressWarnings(xdf$week_start <- as.Date(sprintf("%s-%02d-1", xdf$year, xdf$week), "%Y-%U-%u"))
xdf$weighted_ili <- to_num(xdf$weighted_ili)
xdf$unweighted_ili <- to_num(xdf$unweighted_ili)
xdf$age_0_4 <- to_num(xdf$age_0_4)
xdf$age_25_49 <- to_num(xdf$age_25_49)
xdf$age_25_64 <- to_num(xdf$age_25_64)
xdf$age_5_24 <- to_num(xdf$age_5_24)
xdf$age_50_64 <- to_num(xdf$age_50_64)
xdf$age_65 <- to_num(xdf$age_65)
xdf$ilitotal <- to_num(xdf$ilitotal)
xdf$num_of_providers <- to_num(xdf$num_of_providers)
xdf$total_patients <- to_num(xdf$total_patients)
xdf$week_start <- MMWRweek::MMWRweek2Date(xdf$year, xdf$week)

if (region == "national") xdf$region <- "National"
if (region == "hhs") xdf$region <- factor(xdf$region, levels=sprintf("Region %s", 1:10))
if (region == "hhs") xdf$region <- factor(xdf$region, levels = sprintf("Region %s", 1:10))

class(xdf) <- c("tbl_df", "tbl", "data.frame")

suppressMessages(readr::type_convert(xdf))
arrange(suppressMessages(readr::type_convert(xdf)), week_start)

}

+ 8
- 9
R/pi-mortality.r View File

@@ -136,15 +136,14 @@ pi_mortality <- function(coverage_area=c("national", "state", "region"), years=N
"age_label", "wk_start", "wk_end", "year_wk_num", "mmwrid",
"coverage_area", "region_name", "callout")] -> xdf

suppressWarnings(xdf$baseline <- to_num(xdf$baseline) / 100)
suppressWarnings(xdf$threshold <- to_num(xdf$threshold) / 100)
suppressWarnings(xdf$percent_pni <- to_num(xdf$percent_pni) / 100)
suppressWarnings(xdf$percent_complete <- to_num(xdf$percent_complete) / 100)
suppressWarnings(xdf$number_influenza <- to_num(xdf$number_influenza))
suppressWarnings(xdf$number_pneumonia <- to_num(xdf$number_pneumonia))
suppressWarnings(xdf$all_deaths <- to_num(xdf$all_deaths))
suppressWarnings(xdf$Total_PnI <- to_num(xdf$Total_PnI))

xdf$baseline <- to_num(xdf$baseline) / 100
xdf$threshold <- to_num(xdf$threshold) / 100
xdf$percent_pni <- to_num(xdf$percent_pni) / 100
xdf$percent_complete <- to_num(xdf$percent_complete) / 100
xdf$number_influenza <- to_num(xdf$number_influenza)
xdf$number_pneumonia <- to_num(xdf$number_pneumonia)
xdf$all_deaths <- to_num(xdf$all_deaths)
xdf$Total_PnI <- to_num(xdf$Total_PnI)
xdf <- .mcga(xdf)

xdf


+ 1
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R/utils.r View File

@@ -20,5 +20,5 @@ to_num <- function(x) {
x <- gsub("<", "", x, fixed=TRUE)
x <- gsub(",", "", x, fixed=TRUE)
x <- gsub(" ", "", x, fixed=TRUE)
as.numeric(x)
suppressWarnings(as.numeric(x))
}

+ 22
- 3
README.Rmd View File

@@ -1,7 +1,7 @@
---
title: ""
pagetitle: ""
output: rmarkdown::github_document
editor_options:
chunk_output_type: console
---
```{r message=FALSE, warning=FALSE, error=FALSE, include=FALSE}
knitr::opts_chunk$set(message=FALSE, warning=FALSE, fig.retina=2)
@@ -21,7 +21,7 @@ If there's a particular data set from https://www.cdc.gov/flu/weekly/fluviewinte

# :mask: cdcfluview

Retrieve U.S. Flu Season Data from the CDC FluView Portal
Retrieve Flu Season Data from the United States Centers for Disease Control and Prevention ('CDC') 'FluView' Portal

## Description

@@ -63,9 +63,22 @@ The following data sets are included:
- `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}
# CRAN
install.packages("cdcfluview")
# 0.9.0 branch (where all fixes are)
devtools::install_git("https://sr.ht/~hrbrmstr/cdcfluview", ref= "0.9.0")
devtools::install_git("https://gitlab.com/hrbrmstr/cdcfluview", ref = "0.9.0")
devtools::install_github("hrbrmstr/cdcfluview", ref = "0.9.0")
# master branch
devtools::install_git("https://sr.ht/~hrbrmstr/cdcfluview")
devtools::install_git("https://gitlab.com/hrbrmstr/cdcfluview")
devtools::install_github("hrbrmstr/cdcfluview")
```

@@ -214,6 +227,12 @@ who_nrevss("census", years=2016)
who_nrevss("state", years=2016)
```

## cdcfluview Metrics

```{r echo=FALSE}
cloc::cloc_pkg_md()
```

## 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.

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README.md View File

@@ -23,7 +23,8 @@ specific as you can (screen shot if possible).

# :mask: cdcfluview

Retrieve U.S. Flu Season Data from the CDC FluView Portal
Retrieve Flu Season Data from the United States Centers for Disease
Control and Prevention (‘CDC’) ‘FluView’ Portal

## Description

@@ -83,9 +84,23 @@ The following data sets are included:
- `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
# CRAN
install.packages("cdcfluview")
# 0.9.0 branch (where all fixes are)
devtools::install_git("https://sr.ht/~hrbrmstr/cdcfluview", ref= "0.9.0")
devtools::install_git("https://gitlab.com/hrbrmstr/cdcfluview", ref = "0.9.0")
devtools::install_github("hrbrmstr/cdcfluview", ref = "0.9.0")
# master branch
devtools::install_git("https://sr.ht/~hrbrmstr/cdcfluview")
devtools::install_git("https://gitlab.com/hrbrmstr/cdcfluview")
devtools::install_github("hrbrmstr/cdcfluview")
```

@@ -110,22 +125,22 @@ glimpse(age_group_distribution(years=2015))

## Observations: 1,872
## Variables: 16
## $ sea_label <chr> "2015-16", "2015-16", "2015-16", "2015-16", "2015-16", "2015-16", "2015-16", "2015-16", "...
## $ 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...
## $ vir_label <fct> A (Subtyping not Performed), A (Subtyping not Performed), A (Subtyping not Performed), A ...
## $ 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,...
## $ mmwrid <int> 2806, 2807, 2808, 2809, 2810, 2811, 2812, 2813, 2814, 2815, 2816, 2817, 2818, 2819, 2820,...
## $ 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...
## $ publishyearweekid <int> 2956, 2956, 2956, 2956, 2956, 2956, 2956, 2956, 2956, 2956, 2956, 2956, 2956, 2956, 2956,...
## $ 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,...
## $ sea_endweek <int> 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",...
## $ vir_startmmwrid <int> 1397, 1397, 1397, 1397, 1397, 1397, 1397, 1397, 1397, 1397, 1397, 1397, 1397, 1397, 1397,...
## $ vir_endmmwrid <int> 3131, 3131, 3131, 3131, 3131, 3131, 3131, 3131, 3131, 3131, 3131, 3131, 3131, 3131, 3131,...
## $ wk_start <date> 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...
## $ 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,…
## $ publishyearweekid <int> 2976, 2976, 2976, 2976, 2976, 2976, 2976, 2976, 2976, 2976, 2976, 2976, 2976, 2976, 2976, 2…
## $ 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, …

### Retrieve CDC U.S. Coverage Map

@@ -171,15 +186,15 @@ plot(cdc_basemap("surv"))
glimpse(geographic_spread())
```

## Observations: 27,351
## Observations: 28,151
## Variables: 7
## $ statename <chr> "Alabama", "Alabama", "Alabama", "Alabama", "Alabama", "Alabama", "Alabama", "Alabama", "...
## $ url <chr> "http://adph.org/influenza/", "http://adph.org/influenza/", "http://adph.org/influenza/",...
## $ website <chr> "Influenza Surveillance", "Influenza Surveillance", "Influenza Surveillance", "Influenza ...
## $ activity_estimate <chr> "No Activity", "No Activity", "No Activity", "Local Activity", "Sporadic", "Sporadic", "S...
## $ weekend <date> 2003-10-04, 2003-10-11, 2003-10-18, 2003-10-25, 2003-11-01, 2003-11-08, 2003-11-15, 2003...
## $ season <chr> "2003-04", "2003-04", "2003-04", "2003-04", "2003-04", "2003-04", "2003-04", "2003-04", "...
## $ weeknumber <chr> "40", "41", "42", "43", "44", "45", "46", "47", "48", "49", "50", "51", "52", "53", "1", ...
## $ 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…

### Laboratory-Confirmed Influenza Hospitalizations

@@ -215,22 +230,22 @@ surveillance_areas()
glimpse(fs_nat <- hospitalizations("flusurv"))
```

## Observations: 1,656
## Observations: 1,746
## Variables: 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> 2009, 2009, 2009, 2009, 2009, 2009, 2009, 2009, 2009, 2009, 2009, 2009, 2009, 2009, 2009,...
## $ season <int> 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 4...
## $ wk_start <date> 2009-08-30, 2009-09-06, 2009-09-13, 2009-09-20, 2009-09-27, 2009-10-04, 2009-10-11, 2009...
## $ wk_end <date> 2009-09-05, 2009-09-12, 2009-09-19, 2009-09-26, 2009-10-03, 2009-10-10, 2009-10-17, 2009...
## $ year_wk_num <int> 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 1, 2, 3, 4, 5, 6,...
## $ rate <dbl> 0.5, 2.5, 4.6, 6.7, 10.9, 18.1, 28.3, 39.1, 47.3, 53.3, 57.5, 60.1, 61.6, 62.9, 64.1, 65....
## $ weeklyrate <dbl> 0.5, 2.0, 2.0, 2.1, 4.3, 7.2, 10.2, 10.8, 8.2, 6.0, 4.2, 2.6, 1.5, 1.3, 1.3, 1.0, 1.2, 1....
## $ age <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,...
## $ age_label <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...
## $ sea_label <chr> "2009-10", "2009-10", "2009-10", "2009-10", "2009-10", "2009-10", "2009-10", "2009-10", "...
## $ sea_description <chr> "Season 2009-10", "Season 2009-10", "Season 2009-10", "Season 2009-10", "Season 2009-10",...
## $ mmwrid <int> 2488, 2489, 2490, 2491, 2492, 2493, 2494, 2495, 2496, 2497, 2498, 2499, 2500, 2501, 2502,...
## $ surveillance_area <chr> "FluSurv-NET", "FluSurv-NET", "FluSurv-NET", "FluSurv-NET", "FluSurv-NET", "FluSurv-NET", "…
## $ region <chr> "Entire Network", "Entire Network", "Entire Network", "Entire Network", "Entire Network", "…
## $ year <int> 2009, 2009, 2009, 2009, 2009, 2009, 2009, 2009, 2009, 2009, 2009, 2009, 2009, 2009, 2009, 2…
## $ season <int> 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49,…
## $ wk_start <date> 2009-08-30, 2009-09-06, 2009-09-13, 2009-09-20, 2009-09-27, 2009-10-04, 2009-10-11, 2009-1…
## $ wk_end <date> 2009-09-05, 2009-09-12, 2009-09-19, 2009-09-26, 2009-10-03, 2009-10-10, 2009-10-17, 2009-1…
## $ 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, 7…
## $ 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, 65.1,…
## $ 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, 1.1,…
## $ 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, 1…
## $ 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…
## $ sea_label <chr> "2009-10", "2009-10", "2009-10", "2009-10", "2009-10", "2009-10", "2009-10", "2009-10", "20…
## $ sea_description <chr> "Season 2009-10", "Season 2009-10", "Season 2009-10", "Season 2009-10", "Season 2009-10", "…
## $ mmwrid <int> 2488, 2489, 2490, 2491, 2492, 2493, 2494, 2495, 2496, 2497, 2498, 2499, 2500, 2501, 2502, 2…

``` r
ggplot(fs_nat, aes(wk_end, rate)) +
@@ -250,20 +265,20 @@ glimpse(hospitalizations("eip", years=2015))

## Observations: 180
## Variables: 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,...
## $ 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> 0.1, 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,...
## $ 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 <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...
## $ 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,...
## $ 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> 0.1, 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, 4.1, 5.3, 6…
## $ 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, 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, 2…
## $ 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…
## $ 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…

``` r
glimpse(hospitalizations("eip", "Colorado", years=2015))
@@ -271,20 +286,20 @@ 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, 1.8, 1.8, 1.8, 1.8, 1.8, 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 <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...
## $ 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,...
## $ 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, 0.0, 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, 8…
## $ 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, 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, 2…
## $ 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…
## $ 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…

``` r
glimpse(hospitalizations("ihsp", years=2015))
@@ -292,20 +307,20 @@ 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 <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...
## $ 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,...
## $ 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> 0.0, 0.0, 0.4, 0.4, 0.4, 1.1, 1.1, 1.1, 1.1, 1.5, 1.8, 2.2, 2.2, 2.6, 2.6, 2.6, 2.9, 4.0, 5…
## $ 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, 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, 2…
## $ 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…
## $ 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…

``` r
glimpse(hospitalizations("ihsp", "Oklahoma", years=2015))
@@ -313,20 +328,20 @@ 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, 83.4,...
## $ 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 <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...
## $ 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,...
## $ 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, 1.2, 1.2, 1.2, 1.2, 1.2, 1.2, 1.2, 2.4, 2.4, 6.1, 14.6, 17.0, 32.8, 49.9, 76.6, 9…
## $ weeklyrate <dbl> 0.0, 0.0, 1.2, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.2, 0.0, 3.6, 8.5, 2.4, 15.8, 17.0, 26.8, 21.…
## $ 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, 2…
## $ 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…
## $ 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…

### Retrieve ILINet Surveillance Data

@@ -349,146 +364,146 @@ walk(c("national", "hhs", "census", "state"), ~{
})
```

## Observations: 1,093
## Observations: 1,111
## 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: 1,093 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 1,083 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", "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: 1,111 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 1,101 more rows, and 4 more variables: ilitotal <dbl>, num_of_providers <dbl>, total_patients <dbl>,
## # week_start <date>

<img src="README_files/figure-gfm/ili-df-1.png" width="672" />

## Observations: 10,930
## Observations: 11,110
## Variables: 16
## $ region_type <chr> "HHS Regions", "HHS Regions", "HHS Regions", "HHS Regions", "HHS Regions", "HHS Regions", ...
## $ region <fct> 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: 10,930 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 10,920 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", "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: 11,110 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 11,100 more rows, and 4 more variables: ilitotal <dbl>, num_of_providers <dbl>, total_patients <dbl>,
## # week_start <date>

<img src="README_files/figure-gfm/ili-df-2.png" width="672" />

## Observations: 9,837
## Observations: 9,999
## 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: 9,837 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 Engl… 1997 40 0.499 0.624 15 NA 7 22 NA 0
## 2 Census Regi… Mid-Atla… 1997 40 0.844 1.32 4 NA 8 12 NA 4
## 3 Census Regi… East Nor… 1997 40 0.792 0.819 28 NA 20 28 NA 3
## 4 Census Regi… West Nor… 1997 40 1.76 1.28 3 NA 8 20 NA 6
## 5 Census Regi… South At… 1997 40 0.503 0.723 14 NA 22 14 NA 0
## 6 Census Regi… East Sou… 1997 40 0.0542 0.0689 0 NA 3 0 NA 0
## 7 Census Regi… West Sou… 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 Engl… 1997 41 0.643 0.816 14 NA 14 29 NA 0
## # ... with 9,827 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", "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: 9,999 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 9,989 more rows, and 4 more variables: ilitotal <dbl>, num_of_providers <dbl>, total_patients <dbl>,
## # week_start <date>

<img src="README_files/figure-gfm/ili-df-3.png" width="672" />

## Observations: 22,148
## Observations: 23,120
## 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, 391, 22, 117, ...
## $ 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, ...
## $ 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: 22,148 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 Connectic… 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 … 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 22,138 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", "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: 23,120 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 23,110 more rows, and 4 more variables: ilitotal <dbl>, num_of_providers <dbl>, total_patients <dbl>,
## # week_start <date>

<img src="README_files/figure-gfm/ili-df-4.png" width="672" />
@@ -499,20 +514,20 @@ walk(c("national", "hhs", "census", "state"), ~{
ili_weekly_activity_indicators(2017)
```

## # A tibble: 1,782 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
## # ... with 1,772 more rows
## # A tibble: 2,805 x 8
## statename url website activity_level activity_level_l… 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/dp… 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 http://www.portal.ct.gov/D… 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… http://doh.dc.gov/page/inf… Influenza Inf… 2 Minimal 2017-10-07 2017-… 40
## 10 Florida "http://www.floridahealth.… Weekly Influe… 1 Minimal 2017-10-07 2017-… 40
## # … with 2,795 more rows

``` r
xdf <- map_df(2008:2017, ili_weekly_activity_indicators)
@@ -537,20 +552,20 @@ count(xdf, weekend, activity_level_label) %>%
(nat_pi <- pi_mortality("national"))
```

## # A tibble: 464 x 19
## # A tibble: 483 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 0.057 0.06 0.0580 1 16 3020 52110 3036
## 2 57 0.0580 0.061 0.059 1 18 3000 51572 3018
## 3 57 0.059 0.062 0.061 1 28 3154 52222 3182
## 4 57 0.06 0.063 0.063 1 23 3279 52548 3302
## 5 57 0.06 0.063 0.061 1 36 3214 53679 3250
## 6 57 0.061 0.064 0.06 1 45 3177 53258 3222
## 7 57 0.062 0.065 0.063 1 50 3315 53771 3365
## 8 57 0.063 0.066 0.06 1 48 3200 54120 3248
## 9 57 0.064 0.067 0.065 1 83 3491 54760 3574
## 10 57 0.065 0.068 0.066 1 118 3526 55595 3644
## # ... with 454 more rows, and 10 more variables: weeknumber <chr>, geo_description <chr>, age_label <chr>,
## 1 58 0.055 0.0580 0.0560 1 10 2880 51416 2890
## 2 58 0.0560 0.059 0.055 1 12 2765 50411 2777
## 3 58 0.0560 0.06 0.0560 1 18 2802 50742 2820
## 4 58 0.057 0.061 0.057 1 22 2895 51425 2917
## 5 58 0.0580 0.062 0.0560 1 23 2819 51136 2842
## 6 58 0.059 0.063 0.0560 1 28 2819 50945 2847
## 7 58 0.06 0.064 0.0580 1 25 2953 51618 2978
## 8 58 0.061 0.065 0.057 1 31 2905 51109 2936
## 9 58 0.062 0.066 0.059 1 34 2923 49720 2957
## 10 58 0.064 0.067 0.06 1 48 2857 48381 2905
## # … with 473 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>

@@ -578,7 +593,7 @@ select(nat_pi, wk_end, percent_pni, baseline, threshold) %>%
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 55 NA NA 0.047 1 0 46 979 46
## 2 55 NA NA 0.038 0.963 0 34 889 34
## 3 55 NA NA 0.053 1 0 52 977 52
## 3 55 NA NA 0.053 1 0 52 978 52
## 4 55 NA NA 0.07 1 0 68 968 68
## 5 55 NA NA 0.053 0.981 0 48 906 48
## 6 55 NA NA 0.0580 1 0 56 968 56
@@ -586,7 +601,7 @@ select(nat_pi, wk_end, percent_pni, baseline, threshold) %>%
## 8 55 NA NA 0.062 1 1 63 1031 64
## 9 55 NA NA 0.0560 1 0 55 976 55
## 10 55 NA NA 0.054 1 0 56 1045 56
## # ... with 2,694 more rows, and 10 more variables: weeknumber <chr>, geo_description <chr>, age_label <chr>,
## # 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>

@@ -597,17 +612,17 @@ select(nat_pi, wk_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 0.066 0.073 0.071 1 0 178 2520 178
## 2 55 0.067 0.074 0.063 1 0 159 2505 159
## 3 55 0.067 0.075 0.0580 1 1 141 2452 142
## 4 55 0.068 0.076 0.071 1 0 171 2422 171
## 5 55 0.07 0.077 0.066 1 2 166 2554 168
## 6 55 0.071 0.078 0.067 1 1 160 2404 161
## 7 55 0.072 0.079 0.079 1 0 195 2478 195
## 8 55 0.073 0.081 0.072 1 1 176 2463 177
## 9 55 0.074 0.0820 0.067 1 3 154 2347 157
## 10 55 0.076 0.083 0.062 1 0 151 2437 151
## # ... with 510 more rows, and 10 more variables: weeknumber <chr>, geo_description <chr>, age_label <chr>,
## 1 55 0.065 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 1 0 171 2426 171
## 5 55 0.068 0.076 0.065 1 2 166 2565 168
## 6 55 0.069 0.077 0.067 1 1 162 2415 163
## 7 55 0.071 0.078 0.079 1 0 198 2491 198
## 8 55 0.072 0.08 0.072 1 1 176 2469 177
## 9 55 0.073 0.081 0.067 1 3 154 2353 157
## 10 55 0.075 0.0820 0.062 1 0 151 2441 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>

@@ -618,19 +633,19 @@ state_data_providers()
```

## # A tibble: 59 x 5
## statename statehealthdeptname url statewebsitename statefluphonenum
## * <chr> <chr> <chr> <chr> <chr>
## 1 Alabama Alabama Department of Public Health http://… Influenza Surve… 334-206-5300
## 2 Alaska State of Alaska Health and Social Services "http:/… Influenza Surve… 907-269-8000
## 3 Arizona Arizona Department of Health Services http://… Influenza & RSV… 602-542-1025
## 4 Arkansas Arkansas Department of Health http://… Communicable Di… 501-661-2000
## 5 California California Department of Public Health https:/… Influenza (Flu) 916-558-1784
## 6 Colorado Colorado Department of Public Health and Environment https:/… Influenza Surve… 303-692-2000
## 7 Connecticut Connecticut Department of Public Health http://… Flu Statistics 860-509-8000
## 8 Delaware Delaware Health and Social Services http://… Weekly Influenz… 302-744-4700
## 9 District of Columbia District of Columbia Department of Health http://… Influenza Infor… 202-442-5955
## 10 Florida Florida Department of Health "http:/… Weekly Influenz… 850-245-4300
## # ... with 49 more rows
## statename statehealthdeptname url statewebsitename statefluphonenum
## * <chr> <chr> <chr> <chr> <chr>
## 1 Alabama Alabama Department of Publi… http://adph.org/influenza/ Influenza Surveillance 334-206-5300
## 2 Alaska State of Alaska Health and … "http://dhss.alaska.gov/dph/Epi/… Influenza Surveillance… 907-269-8000
## 3 Arizona Arizona Department of Healt… http://www.azdhs.gov/phs/oids/ep… Influenza & RSV Survei… 602-542-1025
## 4 Arkansas Arkansas Department of Heal… http://www.healthy.arkansas.gov/… Communicable Disease a… 501-661-2000
## 5 California California Department of Pu… https://www.cdph.ca.gov/Programs… Influenza (Flu) 916-558-1784
## 6 Colorado Colorado Department of Publ… https://www.colorado.gov/pacific… Influenza Surveillance 303-692-2000
## 7 Connecticut Connecticut Department of P… http://www.portal.ct.gov/DPH/Inf… Flu Statistics 860-509-8000
## 8 Delaware Delaware Health and Social … http://dhss.delaware.gov/dhss/dp… Weekly Influenza Surve… 302-744-4700
## 9 District of … District of Columbia Depart… http://doh.dc.gov/page/influenza… Influenza Information 202-442-5955
## 10 Florida Florida Department of Health "http://www.floridahealth.gov/di… Weekly Influenza Surve… 850-245-4300
## # with 49 more rows

### Retrieve WHO/NREVSS Surveillance Data

@@ -654,32 +669,32 @@ 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 :Classes 'tbl_df', 'tbl' and 'data.frame': 153 obs. of 13 variables:
## ..$ region_type : chr [1:153] "National" "National" "National" "National" ...
## ..$ region : chr [1:153] "National" "National" "National" "National" ...
## ..$ year : int [1:153] 2015 2015 2015 2015 2015 2015 2015 2015 2015 2015 ...
## ..$ week : int [1:153] 40 41 42 43 44 45 46 47 48 49 ...
## ..$ total_specimens : int [1:153] 1139 1152 1198 1244 1465 1393 1458 1157 1550 1518 ...
## ..$ a_2009_h1n1 : int [1:153] 4 5 10 9 4 11 17 17 27 38 ...
## ..$ a_h3 : int [1:153] 65 41 50 31 23 34 42 24 36 37 ...
## ..$ a_subtyping_not_performed: int [1:153] 2 2 1 4 4 1 1 0 3 3 ...
## ..$ b : int [1:153] 10 7 8 9 9 10 4 4 9 11 ...
## ..$ bvic : int [1:153] 0 3 3 1 1 4 0 3 3 2 ...
## ..$ byam : int [1:153] 1 0 2 4 4 2 4 9 12 11 ...
## ..$ h3n2v : int [1:153] 0 0 0 0 0 0 0 0 0 0 ...
## ..$ wk_date : Date[1:153], format: "2015-10-04" "2015-10-11" "2015-10-18" "2015-10-25" ...
## $ clinical_labs :Classes 'tbl_df', 'tbl' and 'data.frame': 153 obs. of 11 variables:
## ..$ region_type : chr [1:153] "National" "National" "National" "National" ...
## ..$ region : chr [1:153] "National" "National" "National" "National" ...
## ..$ year : int [1:153] 2015 2015 2015 2015 2015 2015 2015 2015 2015 2015 ...
## ..$ week : int [1:153] 40 41 42 43 44 45 46 47 48 49 ...
## ..$ total_specimens : int [1:153] 12029 13111 13441 13537 14687 15048 15250 15234 16201 16673 ...
## ..$ total_a : int [1:153] 84 116 97 98 97 122 84 119 145 140 ...
## ..$ total_b : int [1:153] 43 54 52 52 68 86 98 92 81 106 ...
## ..$ percent_positive: num [1:153] 1.06 1.3 1.11 1.11 1.12 ...
## ..$ percent_a : num [1:153] 0.698 0.885 0.722 0.724 0.66 ...
## ..$ percent_b : num [1:153] 0.357 0.412 0.387 0.384 0.463 ...
## ..$ wk_date : Date[1:153], format: "2015-10-04" "2015-10-11" "2015-10-18" "2015-10-25" ...
## $ public_health_labs :Classes 'tbl_df', 'tbl' and 'data.frame': 171 obs. of 13 variables:
## ..$ region_type : chr [1:171] "National" "National" "National" "National" ...
## ..$ region : chr [1:171] "National" "National" "National" "National" ...
## ..$ year : int [1:171] 2015 2015 2015 2015 2015 2015 2015 2015 2015 2015 ...
## ..$ week : int [1:171] 40 41 42 43 44 45 46 47 48 49 ...
## ..$ total_specimens : int [1:171] 1139 1152 1198 1244 1465 1393 1458 1157 1550 1518 ...
## ..$ a_2009_h1n1 : int [1:171] 4 5 10 9 4 11 17 17 27 38 ...
## ..$ a_h3 : int [1:171] 65 41 50 31 23 34 42 24 36 37 ...
## ..$ a_subtyping_not_performed: int [1:171] 2 2 1 4 4 1 1 0 3 3 ...
## ..$ b : int [1:171] 10 7 8 9 9 10 4 4 9 11 ...
## ..$ bvic : int [1:171] 0 3 3 1 1 4 0 3 3 2 ...
## ..$ byam : int [1:171] 1 0 2 4 4 2 4 9 12 11 ...
## ..$ h3n2v : int [1:171] 0 0 0 0 0 0 0 0 0 0 ...
## ..$ wk_date : Date[1:171], format: "2015-10-04" "2015-10-11" "2015-10-18" "2015-10-25" ...
## $ clinical_labs :Classes 'tbl_df', 'tbl' and 'data.frame': 171 obs. of 11 variables:
## ..$ region_type : chr [1:171] "National" "National" "National" "National" ...
## ..$ region : chr [1:171] "National" "National" "National" "National" ...
## ..$ year : int [1:171] 2015 2015 2015 2015 2015 2015 2015 2015 2015 2015 ...
## ..$ week : int [1:171] 40 41 42 43 44 45 46 47 48 49 ...
## ..$ total_specimens : int [1:171] 12029 13111 13441 13537 14687 15048 15250 15234 16201 16673 ...
## ..$ total_a : int [1:171] 84 116 97 98 97 122 84 119 145 140 ...
## ..$ total_b : int [1:171] 43 54 52 52 68 86 98 92 81 106 ...
## ..$ percent_positive: num [1:171] 1.06 1.3 1.11 1.11 1.12 ...
## ..$ percent_a : num [1:171] 0.698 0.885 0.722 0.724 0.66 ...
## ..$ percent_b : num [1:171] 0.357 0.412 0.387 0.384 0.463 ...
## ..$ wk_date : Date[1:171], format: "2015-10-04" "2015-10-11" "2015-10-18" "2015-10-25" ...

``` r
mutate(xdat$combined_prior_to_2015_16,
@@ -699,19 +714,19 @@ who_nrevss("hhs", years=2016)

## $public_health_labs
## # A tibble: 520 x 13
## region_type region year week total_specimens a_2009_h1n1 a_h3 a_subtyping_not… b bvic byam h3n2v wk_date
## <chr> <chr> <int> <int> <int> <int> <int> <int> <int> <int> <int> <int> <date>
## 1 HHS Regions Regio… 2016 40 31 0 6 0 0 0 0 0 2016-10-02
## 2 HHS Regions Regio… 2016 40 31 0 6 0 0 2 0 0 2016-10-02
## 3 HHS Regions Regio… 2016 40 112 2 2 0 0 0 0 0 2016-10-02
## 4 HHS Regions Regio… 2016 40 112 1 11 0 1 2 0 0 2016-10-02
## 5 HHS Regions Regio… 2016 40 204 0 7 0 0 0 1 0 2016-10-02
## 6 HHS Regions Regio… 2016 40 39 1 1 0 0 0 0 0 2016-10-02
## 7 HHS Regions Regio… 2016 40 24 0 2 0 0 1 0 0 2016-10-02
## 8 HHS Regions Regio… 2016 40 46 2 8 0 0 0 0 0 2016-10-02
## 9 HHS Regions Regio… 2016 40 186 3 27 0 0 0 3 0 2016-10-02
## 10 HHS Regions Regio… 2016 40 113 0 17 0 0 0 0 0 2016-10-02
## # ... with 510 more rows
## region_type region year week total_specimens a_2009_h1n1 a_h3 a_subtyping_not… b bvic byam h3n2v wk_date
## <chr> <chr> <int> <int> <int> <int> <int> <int> <int> <int> <int> <int> <date>
## 1 HHS Regions Region… 2016 40 31 0 6 0 0 0 0 0 2016-10-02
## 2 HHS Regions Region… 2016 40 31 0 6 0 0 2 0 0 2016-10-02
## 3 HHS Regions Region… 2016 40 112 2 2 0 0 0 0 0 2016-10-02
## 4 HHS Regions Region… 2016 40 112 1 11 0 1 2 0 0 2016-10-02
## 5 HHS Regions Region… 2016 40 204 0 7 0 0 0 1 0 2016-10-02
## 6 HHS Regions Region… 2016 40 39 1 1 0 0 0 0 0 2016-10-02
## 7 HHS Regions Region… 2016 40 24 0 2 0 0 1 0 0 2016-10-02
## 8 HHS Regions Region… 2016 40 46 2 8 0 0 0 0 0 2016-10-02
## 9 HHS Regions Region… 2016 40 186 3 27 0 0 0 3 0 2016-10-02
## 10 HHS Regions Region… 2016 40 113 0 17 0 0 0 0 0 2016-10-02
## # with 510 more rows
##
## $clinical_labs
## # A tibble: 520 x 11
@@ -727,7 +742,7 @@ who_nrevss("hhs", years=2016)
## 8 HHS Regions Region 8 2016 40 913 8 0 0.876 0.876 0 2016-10-02
## 9 HHS Regions Region 9 2016 40 992 6 1 0.706 0.605 0.101 2016-10-02
## 10 HHS Regions Region 10 2016 40 590 14 0 2.37 2.37 0 2016-10-02
## # ... with 510 more rows
## # with 510 more rows

``` r
who_nrevss("census", years=2016)
@@ -735,35 +750,35 @@ who_nrevss("census", years=2016)

## $public_health_labs
## # A tibble: 468 x 13
## region_type region year week total_specimens a_2009_h1n1 a_h3 a_subtyping_not… b bvic byam h3n2v wk_date
## <chr> <chr> <int> <int> <int> <int> <int> <int> <int> <int> <int> <int> <date>
## 1 Census Reg… New E… 2016 40 31 0 6 0 0 0 0 0 2016-10-02
## 2 Census Reg… Mid-A… 2016 40 50 0 8 0 0 2 0 0 2016-10-02
## 3 Census Reg… East … 2016 40 139 0 4 0 0 0 1 0 2016-10-02
## 4 Census Reg… West … 2016 40 103 0 6 0 0 1 0 0 2016-10-02
## 5 Census Reg… South… 2016 40 181 3 11 0 1 2 0 0 2016-10-02
## 6 Census Reg… East … 2016 40 24 0 0 0 0 0 0 0 2016-10-02
## 7 Census Reg… West … 2016 40 27 0 1 0 0 0 0 0 2016-10-02
## 8 Census Reg… Mount… 2016 40 54 3 10 0 0 0 1 0 2016-10-02
## 9 Census Reg… Pacif… 2016 40 289 3 41 0 0 0 2 0 2016-10-02
## 10 Census Reg… New E… 2016 41 14 0 2 0 0 0 0 0 2016-10-09
## # ... with 458 more rows
## region_type region year week total_specimens a_2009_h1n1 a_h3 a_subtyping_not… b bvic byam h3n2v wk_date
## <chr> <chr> <int> <int> <int> <int> <int> <int> <int> <int> <int> <int> <date>
## 1 Census Regi… New E… 2016 40 31 0 6 0 0 0 0 0 2016-10-02
## 2 Census Regi… Mid-A… 2016 40 50 0 8 0 0 2 0 0 2016-10-02
## 3 Census Regi… East … 2016 40 139 0 4 0 0 0 1 0 2016-10-02
## 4 Census Regi… West … 2016 40 103 0 6 0 0 1 0 0 2016-10-02
## 5 Census Regi… South… 2016 40 181 3 11 0 1 2 0 0 2016-10-02
## 6 Census Regi… East … 2016 40 24 0 0 0 0 0 0 0 2016-10-02
## 7 Census Regi… West … 2016 40 27 0 1 0 0 0 0 0 2016-10-02
## 8 Census Regi… Mount… 2016 40 54 3 10 0 0 0 1 0 2016-10-02
## 9 Census Regi… Pacif… 2016 40 289 3 41 0 0 0 2 0 2016-10-02
## 10 Census Regi… New E… 2016 41 14 0 2 0 0 0 0 0 2016-10-09
## # with 458 more rows
##
## $clinical_labs
## # A tibble: 468 x 11
## region_type region year week total_specimens total_a total_b percent_positive percent_a percent_b wk_date
## <chr> <chr> <int> <int> <int> <int> <int> <dbl> <dbl> <dbl> <date>
## 1 Census Regions New Engl… 2016 40 654 5 1 0.917 0.765 0.153 2016-10-02
## 2 Census Regions Mid-Atla… 2016 40 1579 10 4 0.887 0.633 0.253 2016-10-02
## 3 Census Regions East Nor… 2016 40 2176 6 5 0.506 0.276 0.230 2016-10-02
## 4 Census Regions West Nor… 2016 40 1104 3 0 0.272 0.272 0 2016-10-02
## 5 Census Regions South At… 2016 40 2785 43 62 3.77 1.54 2.23 2016-10-02
## 6 Census Regions East Sou… 2016 40 844 4 4 0.948 0.474 0.474 2016-10-02
## 7 Census Regions West Sou… 2016 40 1738 21 13 1.96 1.21 0.748 2016-10-02
## 8 Census Regions Mountain 2016 40 1067 8 0 0.750 0.750 0 2016-10-02
## 9 Census Regions Pacific 2016 40 1433 20 1 1.47 1.40 0.0698 2016-10-02
## 10 Census Regions New Engl… 2016 41 810 5 1 0.741 0.617 0.123 2016-10-09
## # ... with 458 more rows
## region_type region year week total_specimens total_a total_b percent_positive percent_a percent_b wk_date
## <chr> <chr> <int> <int> <int> <int> <int> <dbl> <dbl> <dbl> <date>
## 1 Census Regio… New England 2016 40 654 5 1 0.917 0.765 0.153 2016-10-02
## 2 Census Regio… Mid-Atlant… 2016 40 1579 10 4 0.887 0.633 0.253 2016-10-02
## 3 Census Regio… East North… 2016 40 2176 6 5 0.506 0.276 0.230 2016-10-02
## 4 Census Regio… West North… 2016 40 1104 3 0 0.272 0.272 0 2016-10-02
## 5 Census Regio… South Atla… 2016 40 2785 43 62 3.77 1.54 2.23 2016-10-02
## 6 Census Regio… East South… 2016 40 844 4 4 0.948 0.474 0.474 2016-10-02
## 7 Census Regio… West South… 2016 40 1738 21 13 1.96 1.21 0.748 2016-10-02
## 8 Census Regio… Mountain 2016 40 1067 8 0 0.750 0.750 0 2016-10-02
## 9 Census Regio… Pacific 2016 40 1433 20 1 1.47 1.40 0.0698 2016-10-02
## 10 Census Regio… New England 2016 41 810 5 1 0.741 0.617 0.123 2016-10-09
## # with 458 more rows

``` r
who_nrevss("state", years=2016)
@@ -771,35 +786,43 @@ 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_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>
## 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>
## # with 44 more rows, and 1 more variable: wk_date <date>
##
## $clinical_labs
## # A tibble: 2,808 x 11
## region_type region year week total_specimens total_a total_b percent_positive percent_a percent_b wk_date
## <chr> <chr> <int> <int> <chr> <chr> <chr> <chr> <chr> <chr> <date>
## 1 States Alabama 2016 40 406 4 1 1.23 0.99 0.25 2016-10-02
## 2 States Alaska 2016 40 <NA> <NA> <NA> <NA> <NA> <NA> 2016-10-02
## 3 States Arizona 2016 40 133 0 0 0 0 0 2016-10-02
## 4 States Arkansas 2016 40 47 0 0 0 0 0 2016-10-02
## 5 States California 2016 40 668 2 0 0.3 0.3 0 2016-10-02
## 6 States Colorado 2016 40 260 0 0 0 0 0 2016-10-02
## 7 States Connecticut 2016 40 199 3 0 1.51 1.51 0 2016-10-02
## 8 States Delaware 2016 40 40 0 0 0 0 0 2016-10-02
## 9 States District of… 2016 40 <NA> <NA> <NA> <NA> <NA> <NA> 2016-10-02
## 10 States Florida 2016 40 <NA> <NA> <NA> <NA> <NA> <NA> 2016-10-02
## # ... with 2,798 more rows
## region_type region year week total_specimens total_a total_b percent_positive percent_a percent_b wk_date
## <chr> <chr> <int> <int> <chr> <chr> <chr> <chr> <chr> <chr> <date>
## 1 States Alabama 2016 40 406 4 1 1.23 0.99 0.25 2016-10-02
## 2 States Alaska 2016 40 <NA> <NA> <NA> <NA> <NA> <NA> 2016-10-02
## 3 States Arizona 2016 40 133 0 0 0 0 0 2016-10-02
## 4 States Arkansas 2016 40 47 0 0 0 0 0 2016-10-02
## 5 States California 2016 40 668 2 0 0.3 0.3 0 2016-10-02
## 6 States Colorado 2016 40 260 0 0 0 0 0 2016-10-02
## 7 States Connecticut 2016 40 199 3 0 1.51 1.51 0 2016-10-02
## 8 States Delaware 2016 40 40 0 0 0 0 0 2016-10-02
## 9 States District of … 2016 40 <NA> <NA> <NA> <NA> <NA> <NA> 2016-10-02
## 10 States Florida 2016 40 <NA> <NA> <NA> <NA> <NA> <NA> 2016-10-02
## # … with 2,798 more rows

## cdcfluview Metrics

| Lang | \# Files | (%) | LoC | (%) | Blank lines | (%) | \# Lines | (%) |
| :--- | -------: | ---: | --: | ---: | ----------: | ---: | -------: | ---: |
| R | 21 | 0.91 | 837 | 0.88 | 302 | 0.79 | 521 | 0.85 |
| Rmd | 1 | 0.04 | 83 | 0.09 | 67 | 0.18 | 88 | 0.14 |
| make | 1 | 0.04 | 32 | 0.03 | 11 | 0.03 | 1 | 0.00 |

## Code of Conduct



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+ 3
- 2
man/get_flu_data.Rd View File

@@ -4,8 +4,9 @@
\alias{get_flu_data}
\title{Retrieves state, regional or national influenza statistics from the CDC (deprecated)}
\usage{
get_flu_data(region = "hhs", sub_region = 1:10, data_source = "ilinet",
years = as.numeric(format(Sys.Date(), "\%Y")))
get_flu_data(region = "hhs", sub_region = 1:10,
data_source = "ilinet", years = as.numeric(format(Sys.Date(),
"\%Y")))
}
\arguments{
\item{region}{one of "\code{hhs}", "\code{census}", "\code{national}",


+ 3
- 2
man/ilinet.Rd View File

@@ -27,7 +27,7 @@ This function retrieves current and historical ILINet surveillance data for
the identified region.
}
\examples{
national_ili <- ilinet("national", years=2017)
national_ili <- ilinet("national", years = 2017)
\dontrun{
hhs_ili <- ilinet("hhs")
census_ili <- ilinet("census")
@@ -36,7 +36,8 @@ state_ili <- ilinet("state")
library(purrr)
map_df(
c("national", "hhs", "census", "state"),
~ilinet(.x))
~ ilinet(.x)
)
}
}
\references{


+ 2
- 1
man/pi_mortality.Rd View File

@@ -4,7 +4,8 @@
\alias{pi_mortality}
\title{Pneumonia and Influenza Mortality Surveillance}
\usage{
pi_mortality(coverage_area = c("national", "state", "region"), years = NULL)
pi_mortality(coverage_area = c("national", "state", "region"),
years = NULL)
}
\arguments{
\item{coverage_area}{coverage area for data (national, state or region)}


+ 2
- 1
man/who_nrevss.Rd View File

@@ -4,7 +4,8 @@
\alias{who_nrevss}
\title{Retrieve WHO/NREVSS Surveillance Data}
\usage{
who_nrevss(region = c("national", "hhs", "census", "state"), years = NULL)
who_nrevss(region = c("national", "hhs", "census", "state"),
years = NULL)
}
\arguments{
\item{region}{one of "\code{national}", "\code{hhs}", "\code{census}", or "\code{state}"}


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