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tweaked hospitalizations function

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boB Rudis 2 months ago
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20 changed files with 383 additions and 373 deletions
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DESCRIPTION View File

@ -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")),


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NEWS.md View File

@ -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


+ 38
- 13
R/hospital.r View File

@ -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")]


+ 3
- 8
README.Rmd View File

@ -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


+ 335
- 347
README.md View File

@ -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


+ 3
- 4
cran-comments.md View File

@ -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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