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tags/v0.7.0
boB Rudis 4 years ago
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9 changed files with 725 additions and 12 deletions
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      R/agd-ipt.r
  2. +2
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      R/geographic-spread.R
  3. +2
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  4. +1
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      R/ilinet.r
  5. +2
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      R/pi-mortality.r
  6. +1
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      R/who-nrvess.r
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      README.Rmd
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      README.md
  9. BIN
      README_files/figure-markdown_github-ascii_identifiers/unnamed-chunk-5-1.png

+ 1
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R/agd-ipt.r View File

@ -19,7 +19,7 @@ agd_ipt <- function() {
Accept = "application/json, text/plain, */*",
Referer = "https://gis.cdc.gov/grasp/fluview/flu_by_age_virus.html"
),
httr::verbose(),
# httr::verbose(),
httr::timeout(60)
) -> res


+ 2
- 2
R/geographic-spread.R View File

@ -22,8 +22,8 @@ geographic_spread <- function() {
AppVersion = "Public",
SeasonIDs = paste0(meta$seasons$seasonid, collapse=",")
),
httr::timeout(60),
httr::verbose()
# httr::verbose(),
httr::timeout(60)
) -> res
httr::stop_for_status(res)


+ 2
- 1
R/hospital.r View File

@ -48,7 +48,8 @@ hospitalizations <- function(surveillance_area=c("flusurv", "eip", "ihsp"),
networkid = tgt$networkid,
cacthmentid = tgt$id
),
httr::verbose()
# httr::verbose(),
httr::timeout(60)
) -> res
httr::stop_for_status(res)


+ 1
- 1
R/ilinet.r View File

@ -61,7 +61,7 @@ ilinet <- function(region=c("national", "hhs", "census", "state")) {
),
encode = "json",
body = params,
httr::verbose(),
# httr::verbose(),
httr::write_disk(tf)
) -> res


+ 2
- 2
R/pi-mortality.r View File

@ -85,8 +85,8 @@ pi_mortality <- function(coverage_area=c("national", "state", "region")) {
SeasonsParameters = lapply(meta$seasons$seasonid, function(.x) { list(ID=as.integer(.x)) }),
AgegroupsParameters = list(list(ID="1"))
),
httr::timeout(60),
httr::verbose()
# httr::verbose(),
httr::timeout(60)
) -> res
httr::stop_for_status(res)


+ 1
- 1
R/who-nrvess.r View File

@ -66,7 +66,7 @@ who_nrevss <- function(region=c("national", "hhs", "census", "state")) {
),
encode = "json",
body = params,
httr::verbose(),
# httr::verbose(),
httr::timeout(60),
httr::write_disk(tf)
) -> res


+ 88
- 2
README.Rmd View File

@ -29,7 +29,6 @@ The U.S. Centers for Disease Control (CDC) maintains a portal <http://gis.cdc.go
The following functions are implemented:
- `agd_ipt`: Age Group Distribution of Influenza Positive Tests Reported by Public Health Laboratories
- `cdcfluview`: Tools to Work with the 'CDC' 'FluView' 'API'
- `cdc_coverage_map`: Retrieve CDC U.S. Coverage Map
- `geographic_spread`: State and Territorial Epidemiologists Reports of Geographic Spread of Influenza
- `hospitalizations`: Laboratory-Confirmed Influenza Hospitalizations
@ -59,12 +58,99 @@ options(width=120)
```{r message=FALSE, warning=FALSE, error=FALSE}
library(cdcfluview)
library(tidyverse)
# current verison
packageVersion("cdcfluview")
```
### EXAMPLES COMING SOON
### Age Group Distribution of Influenza Positive Tests Reported by Public Health Laboratories
```{r message=FALSE, warning=FALSE}
glimpse(agd_ipt())
```
### Retrieve CDC U.S. Coverage Map
```{r message=FALSE, warning=FALSE}
plot(cdc_coverage_map())
```
### State and Territorial Epidemiologists Reports of Geographic Spread of Influenza
```{r message=FALSE, warning=FALSE}
glimpse(geographic_spread())
```
### Laboratory-Confirmed Influenza Hospitalizations
```{r message=FALSE, warning=FALSE}
surveillance_areas()
glimpse(hospitalizations("flusurv"))
glimpse(hospitalizations("eip"))
glimpse(hospitalizations("eip", "Colorado"))
glimpse(hospitalizations("ihsp"))
glimpse(hospitalizations("ihsp", "Oklahoma"))
```
### Retrieve ILINet Surveillance Data
```{r message=FALSE, warning=FALSE}
ilinet("national")
ilinet("hhs")
ilinet("census")
ilinet("state")
```
### Retrieve weekly state-level ILI indicators per-state for a given season
```{r message=FALSE, warning=FALSE}
ili_weekly_activity_indicators(2017)
ili_weekly_activity_indicators(2015)
```
### Pneumonia and Influenza Mortality Surveillance
```{r message=FALSE, warning=FALSE}
pi_mortality("national")
pi_mortality("state")
pi_mortality("region")
```
### Retrieve metadat about U.S. State CDC Provider Data
```{r message=FALSE, warning=FALSE}
```
### Retrieve a list of valid sub-regions for each surveillance area.
```{r message=FALSE, warning=FALSE}
state_data_providers()
```
### Retrieve WHO/NREVSS Surveillance Data
```{r message=FALSE, warning=FALSE}
who_nrevss("national")
who_nrevss("hhs")
who_nrevss("census")
who_nrevss("state")
```
## Code of Conduct

+ 628
- 2
README.md View File

@ -50,7 +50,6 @@ The following functions are implemented:
- `agd_ipt`: Age Group Distribution of Influenza Positive Tests
Reported by Public Health Laboratories
- `cdcfluview`: Tools to Work with the ‘CDC’ ‘FluView’ ‘API’
- `cdc_coverage_map`: Retrieve CDC U.S. Coverage Map
- `geographic_spread`: State and Territorial Epidemiologists Reports
of Geographic Spread of Influenza
@ -84,6 +83,7 @@ Usage
``` r
library(cdcfluview)
library(tidyverse)
# current verison
packageVersion("cdcfluview")
@ -91,7 +91,633 @@ packageVersion("cdcfluview")
## [1] '0.7.0'
### EXAMPLES COMING SOON
### Age Group Distribution of Influenza Positive Tests Reported by Public Health Laboratories
``` r
glimpse(agd_ipt())
```
## Observations: 36,144
## Variables: 13
## $ sea_label <chr> "1997-98", "1997-98", "1997-98", "1997-98", "1997-98", "1997-98", "1997-98", "1997-98", "...
## $ age_label <chr> "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 <chr> "A (Subtyping not Performed)", "A (Subtyping not Performed)", "A (Subtyping not Performed...
## $ count <int> 0, 1, 0, 0, 0, 0, 0, 3, 0, 6, 0, 1, 1, 2, 11, 8, 18, 26, 22, 19, 2, 5, 2, 1, 4, 0, 0, 0, ...
## $ mmwrid <int> 1866, 1867, 1868, 1869, 1870, 1871, 1872, 1873, 1874, 1875, 1876, 1877, 1878, 1879, 1880,...
## $ seasonid <int> 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 3...
## $ publishyearweekid <int> 2913, 2913, 2913, 2913, 2913, 2913, 2913, 2913, 2913, 2913, 2913, 2913, 2913, 2913, 2913,...
## $ sea_description <chr> "Season 1997-98", "Season 1997-98", "Season 1997-98", "Season 1997-98", "Season 1997-98",...
## $ sea_startweek <int> 1866, 1866, 1866, 1866, 1866, 1866, 1866, 1866, 1866, 1866, 1866, 1866, 1866, 1866, 1866,...
## $ sea_endweek <int> 1918, 1918, 1918, 1918, 1918, 1918, 1918, 1918, 1918, 1918, 1918, 1918, 1918, 1918, 1918,...
## $ vir_description <chr> "A-Unk", "A-Unk", "A-Unk", "A-Unk", "A-Unk", "A-Unk", "A-Unk", "A-Unk", "A-Unk", "A-Unk",...
## $ vir_startmmwrid <int> 1397, 1397, 1397, 1397, 1397, 1397, 1397, 1397, 1397, 1397, 1397, 1397, 1397, 1397, 1397,...
## $ vir_endmmwrid <int> 3131, 3131, 3131, 3131, 3131, 3131, 3131, 3131, 3131, 3131, 3131, 3131, 3131, 3131, 3131,...
### Retrieve CDC U.S. Coverage Map
``` r
plot(cdc_coverage_map())
```
![](README_files/figure-markdown_github-ascii_identifiers/unnamed-chunk-5-1.png)
### State and Territorial Epidemiologists Reports of Geographic Spread of Influenza
``` r
glimpse(geographic_spread())
```
## Observations: 25,795
## Variables: 7
## $ statename <chr> "Alabama", "Alabama", "Alabama", "Alabama", "Alabama", "Alabama", "Alabama", "Alabama", "...
## $ url <chr> "http://adph.org/influenza/", "http://adph.org/influenza/", "http://adph.org/influenza/",...
## $ website <chr> "Influenza Surveillance", "Influenza Surveillance", "Influenza Surveillance", "Influenza ...
## $ activity_estimate <chr> "No Activity", "No Activity", "No Activity", "Local Activity", "Sporadic", "Sporadic", "S...
## $ weekend <date> 2003-10-04, 2003-10-11, 2003-10-18, 2003-10-25, 2003-11-01, 2003-11-08, 2003-11-15, 2003...
## $ season <chr> "2003-04", "2003-04", "2003-04", "2003-04", "2003-04", "2003-04", "2003-04", "2003-04", "...
## $ weeknumber <chr> "40", "41", "42", "43", "44", "45", "46", "47", "48", "49", "50", "51", "52", "53", "1", ...
### Laboratory-Confirmed Influenza Hospitalizations
``` r
surveillance_areas()
```
## surveillance_area region
## 1 flusurv Entire Network
## 2 eip California
## 3 eip Colorado
## 4 eip Connecticut
## 5 eip Entire Network
## 6 eip Georgia
## 7 eip Maryland
## 8 eip Minnesota
## 9 eip New Mexico
## 10 eip New York - Albany
## 11 eip New York - Rochester
## 12 eip Oregon
## 13 eip Tennessee
## 14 ihsp Entire Network
## 15 ihsp Idaho
## 16 ihsp Iowa
## 17 ihsp Michigan
## 18 ihsp Ohio
## 19 ihsp Oklahoma
## 20 ihsp Rhode Island
## 21 ihsp South Dakota
## 22 ihsp Utah
``` r
glimpse(hospitalizations("flusurv"))
```
## Observations: 1,476
## Variables: 20
## $ mmwrid <int> 2545, 2546, 2547, 2548, 2549, 2550, 2551, 2552, 2553, 2554, 2555, 2556, 2557, 2558, 2559,...
## $ weeknumber <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, 0.1, 0.1, 0.2, 0.3, 0.3, 0.4, 0.6, 0.8, 1.3, 1.7, 2.2, 2.8, 3.6, 4.4, 5.4,...
## $ weeklyrate <dbl> 0.0, 0.0, 0.0, 0.0, 0.1, 0.0, 0.1, 0.1, 0.1, 0.2, 0.2, 0.4, 0.4, 0.5, 0.5, 0.8, 0.8, 1.0,...
## $ 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,...
## $ 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...
## $ weekend <chr> "2010-10-09", "2010-10-16", "2010-10-23", "2010-10-30", "2010-11-06", "2010-11-13", "2010...
## $ weekstart <chr> "2010-10-03", "2010-10-10", "2010-10-17", "2010-10-24", "2010-10-31", "2010-11-07", "2010...
## $ year <int> 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2011, 2011,...
## $ yearweek <int> 201040, 201041, 201042, 201043, 201044, 201045, 201046, 201047, 201048, 201049, 201050, 2...
## $ seasonid <int> 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 5...
## $ weekendlabel <chr> "Oct 09, 2010", "Oct 16, 2010", "Oct 23, 2010", "Oct 30, 2010", "Nov 06, 2010", "Nov 13, ...
## $ weekendlabel2 <chr> "Oct-09-2010", "Oct-16-2010", "Oct-23-2010", "Oct-30-2010", "Nov-06-2010", "Nov-13-2010",...
## $ age_label <chr> "18-49 yr", "18-49 yr", "18-49 yr", "18-49 yr", "18-49 yr", "18-49 yr", "18-49 yr", "18-4...
## $ 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",...
## $ sea_startweek <int> 2545, 2545, 2545, 2545, 2545, 2545, 2545, 2545, 2545, 2545, 2545, 2545, 2545, 2545, 2545,...
## $ sea_endweek <int> 2596, 2596, 2596, 2596, 2596, 2596, 2596, 2596, 2596, 2596, 2596, 2596, 2596, 2596, 2596,...
## $ 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",...
``` r
glimpse(hospitalizations("eip"))
```
## Observations: 2,385
## Variables: 20
## $ mmwrid <int> 2545, 2546, 2547, 2548, 2549, 2550, 2551, 2552, 2553, 2554, 2555, 2556, 2557, 2558, 2559,...
## $ weeknumber <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, 0.1, 0.1, 0.1, 0.2, 0.3, 0.4, 0.5, 0.8, 1.1, 1.4, 1.9, 2.3, 2.8, 3.6, 4.5,...
## $ weeklyrate <dbl> 0.0, 0.0, 0.0, 0.0, 0.1, 0.0, 0.1, 0.1, 0.1, 0.1, 0.2, 0.4, 0.3, 0.4, 0.4, 0.5, 0.8, 1.0,...
## $ 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,...
## $ 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...
## $ weekend <chr> "2010-10-09", "2010-10-16", "2010-10-23", "2010-10-30", "2010-11-06", "2010-11-13", "2010...
## $ weekstart <chr> "2010-10-03", "2010-10-10", "2010-10-17", "2010-10-24", "2010-10-31", "2010-11-07", "2010...
## $ year <int> 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2011, 2011,...
## $ yearweek <int> 201040, 201041, 201042, 201043, 201044, 201045, 201046, 201047, 201048, 201049, 201050, 2...
## $ seasonid <int> 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 5...
## $ weekendlabel <chr> "Oct 09, 2010", "Oct 16, 2010", "Oct 23, 2010", "Oct 30, 2010", "Nov 06, 2010", "Nov 13, ...
## $ weekendlabel2 <chr> "Oct-09-2010", "Oct-16-2010", "Oct-23-2010", "Oct-30-2010", "Nov-06-2010", "Nov-13-2010",...
## $ age_label <chr> "18-49 yr", "18-49 yr", "18-49 yr", "18-49 yr", "18-49 yr", "18-49 yr", "18-49 yr", "18-4...
## $ 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",...
## $ sea_startweek <int> 2545, 2545, 2545, 2545, 2545, 2545, 2545, 2545, 2545, 2545, 2545, 2545, 2545, 2545, 2545,...
## $ sea_endweek <int> 2596, 2596, 2596, 2596, 2596, 2596, 2596, 2596, 2596, 2596, 2596, 2596, 2596, 2596, 2596,...
## $ 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",...
``` r
glimpse(hospitalizations("eip", "Colorado"))
```
## Observations: 2,385
## Variables: 20
## $ mmwrid <int> 2545, 2546, 2547, 2548, 2549, 2550, 2551, 2552, 2553, 2554, 2555, 2556, 2557, 2558, 2559,...
## $ weeknumber <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, 0.3, 0.4, 0.4, 0.5, 0.6, 0.8, 1.3, 1.8, 2.1, 2.6, 3.4, 4.2, 5.6,...
## $ weeklyrate <dbl> 0.0, 0.1, 0.0, 0.0, 0.2, 0.0, 0.1, 0.1, 0.1, 0.1, 0.2, 0.5, 0.4, 0.4, 0.4, 0.9, 0.8, 1.4,...
## $ 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,...
## $ 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...
## $ weekend <chr> "2010-10-09", "2010-10-16", "2010-10-23", "2010-10-30", "2010-11-06", "2010-11-13", "2010...
## $ weekstart <chr> "2010-10-03", "2010-10-10", "2010-10-17", "2010-10-24", "2010-10-31", "2010-11-07", "2010...
## $ year <int> 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2011, 2011,...
## $ yearweek <int> 201040, 201041, 201042, 201043, 201044, 201045, 201046, 201047, 201048, 201049, 201050, 2...
## $ seasonid <int> 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 5...
## $ weekendlabel <chr> "Oct 09, 2010", "Oct 16, 2010", "Oct 23, 2010", "Oct 30, 2010", "Nov 06, 2010", "Nov 13, ...
## $ weekendlabel2 <chr> "Oct-09-2010", "Oct-16-2010", "Oct-23-2010", "Oct-30-2010", "Nov-06-2010", "Nov-13-2010",...
## $ age_label <chr> "18-49 yr", "18-49 yr", "18-49 yr", "18-49 yr", "18-49 yr", "18-49 yr", "18-49 yr", "18-4...
## $ 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",...
## $ sea_startweek <int> 2545, 2545, 2545, 2545, 2545, 2545, 2545, 2545, 2545, 2545, 2545, 2545, 2545, 2545, 2545,...
## $ sea_endweek <int> 2596, 2596, 2596, 2596, 2596, 2596, 2596, 2596, 2596, 2596, 2596, 2596, 2596, 2596, 2596,...
## $ 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...
``` r
glimpse(hospitalizations("ihsp"))
```
## Observations: 1,476
## Variables: 20
## $ mmwrid <int> 2545, 2546, 2547, 2548, 2549, 2550, 2551, 2552, 2553, 2554, 2555, 2556, 2557, 2558, 2559,...
## $ weeknumber <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, 0.1, 0.2, 0.2, 0.3, 0.3, 0.4, 0.6, 0.9, 1.1, 1.9, 2.8, 3.9, 4.9, 6.8, 7.6, 9.0,...
## $ weeklyrate <dbl> 0.0, 0.0, 0.0, 0.1, 0.0, 0.0, 0.0, 0.1, 0.2, 0.4, 0.2, 0.8, 0.9, 1.1, 1.0, 2.0, 0.8, 1.4,...
## $ 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,...
## $ 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...
## $ weekend <chr> "2010-10-09", "2010-10-16", "2010-10-23", "2010-10-30", "2010-11-06", "2010-11-13", "2010...
## $ weekstart <chr> "2010-10-03", "2010-10-10", "2010-10-17", "2010-10-24", "2010-10-31", "2010-11-07", "2010...
## $ year <int> 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2011, 2011,...
## $ yearweek <int> 201040, 201041, 201042, 201043, 201044, 201045, 201046, 201047, 201048, 201049, 201050, 2...
## $ seasonid <int> 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 5...
## $ weekendlabel <chr> "Oct 09, 2010", "Oct 16, 2010", "Oct 23, 2010", "Oct 30, 2010", "Nov 06, 2010", "Nov 13, ...
## $ weekendlabel2 <chr> "Oct-09-2010", "Oct-16-2010", "Oct-23-2010", "Oct-30-2010", "Nov-06-2010", "Nov-13-2010",...
## $ age_label <chr> "18-49 yr", "18-49 yr", "18-49 yr", "18-49 yr", "18-49 yr", "18-49 yr", "18-49 yr", "18-4...
## $ 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",...
## $ sea_startweek <int> 2545, 2545, 2545, 2545, 2545, 2545, 2545, 2545, 2545, 2545, 2545, 2545, 2545, 2545, 2545,...
## $ sea_endweek <int> 2596, 2596, 2596, 2596, 2596, 2596, 2596, 2596, 2596, 2596, 2596, 2596, 2596, 2596, 2596,...
## $ 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",...
``` r
glimpse(hospitalizations("ihsp", "Oklahoma"))
```
## Observations: 390
## Variables: 20
## $ mmwrid <int> 2545, 2546, 2547, 2548, 2549, 2550, 2551, 2552, 2553, 2554, 2555, 2556, 2557, 2558, 2559,...
## $ weeknumber <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, 0.4, 0.7, 0.7, 1.3, 2.2, 2.5, 3.4, 4.5, 5.8, 7.6,...
## $ 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, 1.1, 1.3, 1.8,...
## $ 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,...
## $ 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...
## $ weekend <chr> "2010-10-09", "2010-10-16", "2010-10-23", "2010-10-30", "2010-11-06", "2010-11-13", "2010...
## $ weekstart <chr> "2010-10-03", "2010-10-10", "2010-10-17", "2010-10-24", "2010-10-31", "2010-11-07", "2010...
## $ year <int> 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2011, 2011,...
## $ yearweek <int> 201040, 201041, 201042, 201043, 201044, 201045, 201046, 201047, 201048, 201049, 201050, 2...
## $ seasonid <int> 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 5...
## $ weekendlabel <chr> "Oct 09, 2010", "Oct 16, 2010", "Oct 23, 2010", "Oct 30, 2010", "Nov 06, 2010", "Nov 13, ...
## $ weekendlabel2 <chr> "Oct-09-2010", "Oct-16-2010", "Oct-23-2010", "Oct-30-2010", "Nov-06-2010", "Nov-13-2010",...
## $ age_label <chr> "18-49 yr", "18-49 yr", "18-49 yr", "18-49 yr", "18-49 yr", "18-49 yr", "18-49 yr", "18-4...
## $ 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",...
## $ sea_startweek <int> 2545, 2545, 2545, 2545, 2545, 2545, 2545, 2545, 2545, 2545, 2545, 2545, 2545, 2545, 2545,...
## $ sea_endweek <int> 2596, 2596, 2596, 2596, 2596, 2596, 2596, 2596, 2596, 2596, 2596, 2596, 2596, 2596, 2596,...
## $ surveillance_area <chr> "IHSP", "IHSP", "IHSP", "IHSP", "IHSP", "IHSP", "IHSP", "IHSP", "IHSP", "IHSP", "IHSP", "...
## $ region <chr> "Oklahoma", "Oklahoma", "Oklahoma", "Oklahoma", "Oklahoma", "Oklahoma", "Oklahoma", "Okla...
### Retrieve ILINet Surveillance Data
``` r
ilinet("national")
```
## # A tibble: 1,048 x 15
## 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> <int> <chr> <chr> <int> <chr> <int>
## 1 National <NA> 1997 40 1.10148 1.21686 179 <NA> 157 205 <NA> 29
## 2 National <NA> 1997 41 1.20007 1.28064 199 <NA> 151 242 <NA> 23
## 3 National <NA> 1997 42 1.37876 1.23906 228 <NA> 153 266 <NA> 34
## 4 National <NA> 1997 43 1.19920 1.14473 188 <NA> 193 236 <NA> 36
## 5 National <NA> 1997 44 1.65618 1.26112 217 <NA> 162 280 <NA> 41
## 6 National <NA> 1997 45 1.41326 1.28275 178 <NA> 148 281 <NA> 48
## 7 National <NA> 1997 46 1.98680 1.44579 294 <NA> 240 328 <NA> 70
## 8 National <NA> 1997 47 2.44749 1.64796 288 <NA> 293 456 <NA> 63
## 9 National <NA> 1997 48 1.73901 1.67517 268 <NA> 206 343 <NA> 69
## 10 National <NA> 1997 49 1.93919 1.61739 299 <NA> 282 415 <NA> 102
## # ... with 1,038 more rows, and 3 more variables: ilitotal <int>, num_of_providers <int>, total_patients <int>
``` r
ilinet("hhs")
```
## # A tibble: 10,480 x 15
## 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> <int> <int> <int> <int> <int> <int>
## 1 HHS Regions Region 1 1997 40 0.498535 0.623848 15 NA 7 22 NA 0
## 2 HHS Regions Region 2 1997 40 0.374963 0.384615 0 NA 3 0 NA 0
## 3 HHS Regions Region 3 1997 40 1.354280 1.341720 6 NA 7 15 NA 4
## 4 HHS Regions Region 4 1997 40 0.400338 0.450010 12 NA 23 11 NA 0
## 5 HHS Regions Region 5 1997 40 1.229260 0.901266 31 NA 24 30 NA 4
## 6 HHS Regions Region 6 1997 40 1.018980 0.747384 2 NA 1 2 NA 0
## 7 HHS Regions Region 7 1997 40 0.871791 1.152860 0 NA 4 18 NA 5
## 8 HHS Regions Region 8 1997 40 0.516017 0.422654 2 NA 0 3 NA 0
## 9 HHS Regions Region 9 1997 40 1.807610 2.258780 80 NA 76 74 NA 13
## 10 HHS Regions Region 10 1997 40 4.743520 4.825400 31 NA 12 30 NA 3
## # ... with 10,470 more rows, and 3 more variables: ilitotal <int>, num_of_providers <int>, total_patients <int>
``` r
ilinet("census")
```
## # A tibble: 9,432 x 15
## region_type region year week weighted_ili unweighted_ili age_0_4 age_25_49 age_25_64 age_5_24
## <chr> <chr> <int> <int> <dbl> <dbl> <int> <chr> <chr> <int>
## 1 Census Regions New England 1997 40 0.4985350 0.6238480 15 <NA> 7 22
## 2 Census Regions Mid-Atlantic 1997 40 0.8441440 1.3213800 4 <NA> 8 12
## 3 Census Regions East North Central 1997 40 0.7924860 0.8187380 28 <NA> 20 28
## 4 Census Regions West North Central 1997 40 1.7640500 1.2793900 3 <NA> 8 20
## 5 Census Regions South Atlantic 1997 40 0.5026620 0.7233800 14 <NA> 22 14
## 6 Census Regions East South Central 1997 40 0.0542283 0.0688705 0 <NA> 3 0
## 7 Census Regions West South Central 1997 40 1.0189800 0.7473840 2 <NA> 1 2
## 8 Census Regions Mountain 1997 40 2.2587800 2.2763300 87 <NA> 71 71
## 9 Census Regions Pacific 1997 40 2.0488300 3.2349400 26 <NA> 17 36
## 10 Census Regions New England 1997 41 0.6426690 0.8158010 14 <NA> 14 29
## # ... with 9,422 more rows, and 5 more variables: age_50_64 <chr>, age_65 <int>, ilitotal <int>,
## # num_of_providers <int>, total_patients <int>
``` r
ilinet("state")
```
## # A tibble: 19,718 x 15
## region_type region year week weighted_ili unweighted_ili age_0_4 age_25_49 age_25_64 age_5_24
## <chr> <chr> <int> <int> <chr> <chr> <chr> <chr> <chr> <chr>
## 1 States Alabama 2010 40 <NA> 2.13477 <NA> <NA> <NA> <NA>
## 2 States Alaska 2010 40 <NA> 0.875146 <NA> <NA> <NA> <NA>
## 3 States Arizona 2010 40 <NA> 0.674721 <NA> <NA> <NA> <NA>
## 4 States Arkansas 2010 40 <NA> 0.696056 <NA> <NA> <NA> <NA>
## 5 States California 2010 40 <NA> 1.95412 <NA> <NA> <NA> <NA>
## 6 States Colorado 2010 40 <NA> 0.660684 <NA> <NA> <NA> <NA>
## 7 States Connecticut 2010 40 <NA> 0.0783085 <NA> <NA> <NA> <NA>
## 8 States Delaware 2010 40 <NA> 0.100125 <NA> <NA> <NA> <NA>
## 9 States District of Columbia 2010 40 <NA> 2.80877 <NA> <NA> <NA> <NA>
## 10 States Florida 2010 40 <NA> <NA> <NA> <NA> <NA> <NA>
## # ... with 19,708 more rows, and 5 more variables: age_50_64 <chr>, age_65 <chr>, ilitotal <chr>,
## # num_of_providers <chr>, total_patients <chr>
### Retrieve weekly state-level ILI indicators per-state for a given season
``` r
ili_weekly_activity_indicators(2017)
```
## # A tibble: 216 x 9
## statename ili_activity_label ili_activity_group statefips stateabbr weekend weeknumber year seasonid
## <chr> <fctr> <chr> <chr> <chr> <date> <int> <int> <int>
## 1 Alabama Level 2 Minimal 01 AL 2017-10-07 40 2017 57
## 2 Alabama Level 2 Minimal 01 AL 2017-10-14 41 2017 57
## 3 Alabama Level 2 Minimal 01 AL 2017-10-21 42 2017 57
## 4 Alabama Level 3 Minimal 01 AL 2017-10-28 43 2017 57
## 5 Alaska Level 1 Minimal 02 AK 2017-10-07 40 2017 57
## 6 Alaska Level 2 Minimal 02 AK 2017-10-14 41 2017 57
## 7 Alaska Level 4 Low 02 AK 2017-10-21 42 2017 57
## 8 Alaska Level 3 Minimal 02 AK 2017-10-28 43 2017 57
## 9 Arizona Level 2 Minimal 04 AZ 2017-10-07 40 2017 57
## 10 Arizona Level 3 Minimal 04 AZ 2017-10-14 41 2017 57
## # ... with 206 more rows
``` r
ili_weekly_activity_indicators(2015)
```
## # A tibble: 2,807 x 9
## statename ili_activity_label ili_activity_group statefips stateabbr weekend weeknumber year seasonid
## <chr> <fctr> <chr> <chr> <chr> <date> <int> <int> <int>
## 1 Alabama Level 1 Minimal 01 AL 2015-10-10 40 2015 55
## 2 Alabama Level 1 Minimal 01 AL 2015-10-17 41 2015 55
## 3 Alabama Level 1 Minimal 01 AL 2015-10-24 42 2015 55
## 4 Alabama Level 1 Minimal 01 AL 2015-10-31 43 2015 55
## 5 Alabama Level 1 Minimal 01 AL 2015-11-07 44 2015 55
## 6 Alabama Level 1 Minimal 01 AL 2015-11-14 45 2015 55
## 7 Alabama Level 1 Minimal 01 AL 2015-11-21 46 2015 55
## 8 Alabama Level 3 Minimal 01 AL 2015-11-28 47 2015 55
## 9 Alabama Level 1 Minimal 01 AL 2015-12-05 48 2015 55
## 10 Alabama Level 1 Minimal 01 AL 2015-12-12 49 2015 55
## # ... with 2,797 more rows
### Pneumonia and Influenza Mortality Surveillance
``` r
pi_mortality("national")
```
## # A tibble: 419 x 19
## seasonid baseline threshold percent_pni percent_complete number_influenza number_pneumonia all_deaths total_pni
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 57 5.8 6.1 0.054 0.763 10 1962 36283 1972
## 2 57 5.8 6.2 0.056 0.675 10 1795 32107 1805
## 3 56 5.9 6.3 0.059 1.000 18 3022 51404 3040
## 4 56 6.0 6.3 0.061 1.000 11 3193 52130 3204
## 5 56 6.1 6.4 0.062 1.000 7 3178 51443 3185
## 6 56 6.2 6.5 0.061 1.000 17 3129 51865 3146
## 7 56 6.3 6.6 0.060 1.000 16 3099 51753 3115
## 8 56 6.4 6.7 0.061 1.000 19 3208 52541 3227
## 9 56 6.5 6.8 0.060 1.000 7 3192 53460 3199
## 10 56 6.6 6.9 0.062 1.000 22 3257 53163 3279
## # ... with 409 more rows, and 10 more variables: weeknumber <chr>, geo_description <chr>, age_label <chr>,
## # weekend <date>, weekstart <date>, year <int>, yearweek <int>, coverage_area <chr>, region_name <chr>, callout <chr>
``` r
pi_mortality("state")
```
## # A tibble: 21,788 x 19
## seasonid baseline threshold percent_pni percent_complete number_influenza number_pneumonia all_deaths total_pni
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 57 NA NA 0.065 0.836 0 50 772 50
## 2 57 NA NA 0.064 0.767 0 45 708 45
## 3 57 NA NA 0.063 0.666 1 2 48 3
## 4 57 NA NA 0.105 0.527 0 4 38 4
## 5 57 NA NA 0.053 0.412 0 20 374 20
## 6 57 NA NA 0.059 0.393 0 21 356 21
## 7 57 NA NA 0.060 0.751 0 25 420 25
## 8 57 NA NA 0.050 0.604 0 17 338 17
## 9 57 NA NA 0.065 0.774 1 228 3510 229
## 10 57 NA NA 0.059 0.758 2 201 3438 203
## # ... with 21,778 more rows, and 10 more variables: weeknumber <chr>, geo_description <chr>, age_label <chr>,
## # weekend <date>, weekstart <date>, year <int>, yearweek <int>, coverage_area <chr>, region_name <chr>, callout <chr>
``` r
pi_mortality("region")
```
## # A tibble: 4,190 x 19
## seasonid baseline threshold percent_pni percent_complete number_influenza number_pneumonia all_deaths total_pni
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 57 6.0 6.7 0.051 0.735 0 85 1683 85
## 2 57 6.1 6.8 0.060 0.701 0 96 1605 96
## 3 57 6.0 6.5 0.061 0.608 1 154 2524 155
## 4 57 6.0 6.6 0.063 0.602 1 157 2497 158
## 5 57 5.3 5.8 0.045 0.511 1 115 2575 116
## 6 57 5.4 5.9 0.045 0.440 1 98 2215 99
## 7 57 5.6 6.0 0.051 0.744 3 394 7753 397
## 8 57 5.7 6.1 0.052 0.651 1 354 6778 355
## 9 57 5.5 5.9 0.052 0.914 1 403 7701 404
## 10 57 5.6 6.0 0.054 0.799 4 358 6733 362
## # ... with 4,180 more rows, and 10 more variables: weeknumber <chr>, geo_description <chr>, age_label <chr>,
## # weekend <date>, weekstart <date>, year <int>, yearweek <int>, coverage_area <chr>, region_name <chr>, callout <chr>
### Retrieve metadat about U.S. State CDC Provider Data
### Retrieve a list of valid sub-regions for each surveillance area.
``` r
state_data_providers()
```
## # A tibble: 59 x 5
## statename statehealthdeptname
## * <chr> <chr>
## 1 Alabama Alabama Department of Public Health
## 2 Alaska State of Alaska Health and Social Services
## 3 Arizona Arizona Department of Health Services
## 4 Arkansas Arkansas Department of Health
## 5 California California Department of Public Health
## 6 Colorado Colorado Department of Public Health and Environment
## 7 Connecticut Connecticut Department of Public Health
## 8 Delaware Delaware Health and Social Services
## 9 District of Columbia District of Columbia Department of Health
## 10 Florida Florida Department of Health
## # ... with 49 more rows, and 3 more variables: url <chr>, statewebsitename <chr>, statefluphonenum <chr>
### Retrieve WHO/NREVSS Surveillance Data
``` r
who_nrevss("national")
```
## $combined_prior_to_2015_16
## # A tibble: 940 x 13
## region_type region year week total_specimens percent_positive a_2009_h1n1 a_h1 a_h3 a_subtyping_not_performed
## <chr> <chr> <int> <int> <int> <dbl> <int> <int> <int> <int>
## 1 National <NA> 1997 40 1291 0.000000 0 0 0 0
## 2 National <NA> 1997 41 1513 0.727032 0 0 0 11
## 3 National <NA> 1997 42 1552 1.095360 0 0 3 13
## 4 National <NA> 1997 43 1669 0.419413 0 0 0 7
## 5 National <NA> 1997 44 1897 0.527148 0 0 9 1
## 6 National <NA> 1997 45 2106 0.284900 0 0 0 6
## 7 National <NA> 1997 46 2204 0.362976 0 0 3 4
## 8 National <NA> 1997 47 2533 0.908014 0 0 5 17
## 9 National <NA> 1997 48 2242 1.650310 0 0 14 22
## 10 National <NA> 1997 49 2607 1.534330 0 0 11 28
## # ... with 930 more rows, and 3 more variables: a_unable_to_subtype <int>, b <int>, h3n2v <int>
##
## $public_health_labs
## # A tibble: 108 x 12
## region_type region year week total_specimens a_2009_h1n1 a_h3 a_subtyping_not_performed b bvic byam h3n2v
## <chr> <chr> <int> <int> <int> <int> <int> <int> <int> <int> <int> <int>
## 1 National <NA> 2015 40 1139 4 65 2 10 0 1 0
## 2 National <NA> 2015 41 1152 5 41 2 7 3 0 0
## 3 National <NA> 2015 42 1198 10 50 1 8 3 2 0
## 4 National <NA> 2015 43 1244 9 31 4 9 1 4 0
## 5 National <NA> 2015 44 1465 4 23 4 9 1 4 0
## 6 National <NA> 2015 45 1393 11 34 1 10 4 2 0
## 7 National <NA> 2015 46 1458 17 42 1 4 0 4 0
## 8 National <NA> 2015 47 1157 17 24 0 4 3 9 0
## 9 National <NA> 2015 48 1550 27 36 3 9 3 12 0
## 10 National <NA> 2015 49 1518 38 37 3 11 2 11 0
## # ... with 98 more rows
##
## $clinical_labs
## # A tibble: 108 x 10
## region_type region year week total_specimens total_a total_b percent_positive percent_a percent_b
## <chr> <chr> <int> <int> <int> <int> <int> <dbl> <dbl> <dbl>
## 1 National <NA> 2015 40 12029 84 43 1.05578 0.698312 0.357469
## 2 National <NA> 2015 41 13111 116 54 1.29662 0.884753 0.411868
## 3 National <NA> 2015 42 13441 97 52 1.10855 0.721672 0.386876
## 4 National <NA> 2015 43 13537 98 52 1.10807 0.723942 0.384132
## 5 National <NA> 2015 44 14687 97 68 1.12344 0.660448 0.462994
## 6 National <NA> 2015 45 15048 122 86 1.38224 0.810739 0.571505
## 7 National <NA> 2015 46 15250 84 98 1.19344 0.550820 0.642623
## 8 National <NA> 2015 47 15234 119 92 1.38506 0.781147 0.603912
## 9 National <NA> 2015 48 16201 145 81 1.39498 0.895006 0.499969
## 10 National <NA> 2015 49 16673 140 106 1.47544 0.839681 0.635758
## # ... with 98 more rows
``` r
who_nrevss("hhs")
```
## $combined_prior_to_2015_16
## # A tibble: 9,400 x 13
## region_type region year week total_specimens percent_positive a_2009_h1n1 a_h1 a_h3 a_subtyping_not_performed
## <chr> <chr> <int> <int> <int> <dbl> <int> <int> <int> <int>
## 1 HHS Regions Region 1 1997 40 51 0 0 0 0 0
## 2 HHS Regions Region 2 1997 40 152 0 0 0 0 0
## 3 HHS Regions Region 3 1997 40 143 0 0 0 0 0
## 4 HHS Regions Region 4 1997 40 98 0 0 0 0 0
## 5 HHS Regions Region 5 1997 40 147 0 0 0 0 0
## 6 HHS Regions Region 6 1997 40 343 0 0 0 0 0
## 7 HHS Regions Region 7 1997 40 133 0 0 0 0 0
## 8 HHS Regions Region 8 1997 40 78 0 0 0 0 0
## 9 HHS Regions Region 9 1997 40 98 0 0 0 0 0
## 10 HHS Regions Region 10 1997 40 48 0 0 0 0 0
## # ... with 9,390 more rows, and 3 more variables: a_unable_to_subtype <int>, b <int>, h3n2v <int>
##
## $public_health_labs
## # A tibble: 1,080 x 12
## region_type region year week total_specimens a_2009_h1n1 a_h3 a_subtyping_not_performed b bvic byam
## <chr> <chr> <int> <chr> <int> <int> <int> <int> <int> <int> <int>
## 1 HHS Regions Region 1 2015 XX 39 0 5 0 0 0 0
## 2 HHS Regions Region 2 2015 XX 56 1 4 0 1 0 0
## 3 HHS Regions Region 3 2015 XX 132 1 3 0 0 0 0
## 4 HHS Regions Region 4 2015 XX 83 0 5 0 1 0 0
## 5 HHS Regions Region 5 2015 XX 218 2 7 0 0 0 1
## 6 HHS Regions Region 6 2015 XX 97 0 2 0 0 0 0
## 7 HHS Regions Region 7 2015 XX 36 0 2 0 0 0 0
## 8 HHS Regions Region 8 2015 XX 71 0 2 0 0 0 0
## 9 HHS Regions Region 9 2015 XX 273 0 22 2 8 0 0
## 10 HHS Regions Region 10 2015 XX 134 0 13 0 0 0 0
## # ... with 1,070 more rows, and 1 more variables: h3n2v <int>
##
## $clinical_labs
## # A tibble: 1,080 x 10
## region_type region year week total_specimens total_a total_b percent_positive percent_a percent_b
## <chr> <chr> <int> <int> <int> <int> <int> <dbl> <dbl> <dbl>
## 1 HHS Regions Region 1 2015 40 693 2 3 0.721501 0.288600 0.432900
## 2 HHS Regions Region 2 2015 40 1220 5 0 0.409836 0.409836 0.000000
## 3 HHS Regions Region 3 2015 40 896 0 1 0.111607 0.000000 0.111607
## 4 HHS Regions Region 4 2015 40 2486 24 16 1.609010 0.965406 0.643604
## 5 HHS Regions Region 5 2015 40 2138 14 3 0.795136 0.654818 0.140318
## 6 HHS Regions Region 6 2015 40 1774 8 16 1.352870 0.450958 0.901917
## 7 HHS Regions Region 7 2015 40 621 2 1 0.483092 0.322061 0.161031
## 8 HHS Regions Region 8 2015 40 824 1 1 0.242718 0.121359 0.121359
## 9 HHS Regions Region 9 2015 40 980 25 2 2.755100 2.551020 0.204082
## 10 HHS Regions Region 10 2015 40 397 3 0 0.755668 0.755668 0.000000
## # ... with 1,070 more rows
``` r
who_nrevss("census")
```
## $combined_prior_to_2015_16
## # A tibble: 8,460 x 13
## region_type region year week total_specimens percent_positive a_2009_h1n1 a_h1 a_h3
## <chr> <chr> <int> <int> <int> <dbl> <int> <int> <int>
## 1 Census Regions New England 1997 40 51 0 0 0 0
## 2 Census Regions Mid-Atlantic 1997 40 155 0 0 0 0
## 3 Census Regions East North Central 1997 40 127 0 0 0 0
## 4 Census Regions West North Central 1997 40 183 0 0 0 0
## 5 Census Regions South Atlantic 1997 40 204 0 0 0 0
## 6 Census Regions East South Central 1997 40 34 0 0 0 0
## 7 Census Regions West South Central 1997 40 339 0 0 0 0
## 8 Census Regions Mountain 1997 40 85 0 0 0 0
## 9 Census Regions Pacific 1997 40 113 0 0 0 0
## 10 Census Regions New England 1997 41 54 0 0 0 0
## # ... with 8,450 more rows, and 4 more variables: a_subtyping_not_performed <int>, a_unable_to_subtype <int>, b <int>,
## # h3n2v <int>
##
## $public_health_labs
## # A tibble: 972 x 12
## region_type region year week total_specimens a_2009_h1n1 a_h3 a_subtyping_not_performed b
## <chr> <chr> <int> <chr> <int> <int> <int> <int> <int>
## 1 Census Regions New England 2015 XX 39 0 5 0 0
## 2 Census Regions Mid-Atlantic 2015 XX 63 1 5 0 1
## 3 Census Regions East North Central 2015 XX 91 2 5 0 0
## 4 Census Regions West North Central 2015 XX 169 0 4 0 0
## 5 Census Regions South Atlantic 2015 XX 187 1 7 0 0
## 6 Census Regions East South Central 2015 XX 21 0 0 0 1
## 7 Census Regions West South Central 2015 XX 72 0 2 0 0
## 8 Census Regions Mountain 2015 XX 111 0 6 0 0
## 9 Census Regions Pacific 2015 XX 386 0 31 2 8
## 10 Census Regions New England 2015 XX 39 2 3 0 0
## # ... with 962 more rows, and 3 more variables: bvic <int>, byam <int>, h3n2v <int>
##
## $clinical_labs
## # A tibble: 972 x 10
## region_type region year week total_specimens total_a total_b percent_positive percent_a percent_b
## <chr> <chr> <int> <int> <int> <int> <int> <dbl> <dbl> <dbl>
## 1 Census Regions New England 2015 40 693 2 3 0.721501 0.288600 0.4329000
## 2 Census Regions Mid-Atlantic 2015 40 1584 5 1 0.378788 0.315657 0.0631313
## 3 Census Regions East North Central 2015 40 1918 13 3 0.834202 0.677789 0.1564130
## 4 Census Regions West North Central 2015 40 978 3 1 0.408998 0.306748 0.1022490
## 5 Census Regions South Atlantic 2015 40 2403 20 12 1.331670 0.832293 0.4993760
## 6 Census Regions East South Central 2015 40 615 4 4 1.300810 0.650407 0.6504070
## 7 Census Regions West South Central 2015 40 1592 8 16 1.507540 0.502513 1.0050300
## 8 Census Regions Mountain 2015 40 943 1 1 0.212089 0.106045 0.1060450
## 9 Census Regions Pacific 2015 40 1303 28 2 2.302380 2.148890 0.1534920
## 10 Census Regions New England 2015 41 752 11 4 1.994680 1.462770 0.5319150
## # ... with 962 more rows
``` r
who_nrevss("state")
```
## $combined_prior_to_2015_16
## # A tibble: 14,094 x 13
## region_type region year week total_specimens percent_positive a_2009_h1n1 a_h1 a_h3
## <chr> <chr> <int> <int> <chr> <chr> <chr> <chr> <chr>
## 1 States Alabama 2010 40 54 0 0 0 0
## 2 States Alaska 2010 40 40 0 0 0 0
## 3 States Arizona 2010 40 40 2.5 0 0 1
## 4 States Arkansas 2010 40 15 0 0 0 0
## 5 States California 2010 40 183 3.28 2 0 3
## 6 States Colorado 2010 40 126 0.79 0 0 1
## 7 States Connecticut 2010 40 54 0 0 0 0
## 8 States Delaware 2010 40 75 4 0 0 3
## 9 States District of Columbia 2010 40 14 0 0 0 0
## 10 States Florida 2010 40 <NA> <NA> <NA> <NA> <NA>
## # ... with 14,084 more rows, and 4 more variables: a_subtyping_not_performed <chr>, a_unable_to_subtype <chr>, b <chr>,
## # h3n2v <chr>
##
## $public_health_labs
## # A tibble: 162 x 11
## region_type region season_description total_specimens a_2009_h1n1 a_h3 a_subtyping_not_performed
## <chr> <chr> <chr> <chr> <chr> <chr> <chr>
## 1 States Alabama Season 2015-16 256 59 16 1
## 2 States Alaska Season 2015-16 4691 607 98 0
## 3 States Arizona Season 2015-16 2110 762 580 0
## 4 States Arkansas Season 2015-16 128 20 8 0
## 5 States California Season 2015-16 12241 1394 825 28
## 6 States Colorado Season 2015-16 1625 912 243 3
## 7 States Connecticut Season 2015-16 1581 662 52 0
## 8 States Delaware Season 2015-16 2754 414 20 12
## 9 States District of Columbia Season 2015-16 172 68 3 0
## 10 States Florida Season 2015-16 <NA> <NA> <NA> <NA>
## # ... with 152 more rows, and 4 more variables: b <chr>, bvic <chr>, byam <chr>, h3n2v <chr>
##
## $clinical_labs
## # A tibble: 5,832 x 10
## region_type region year week total_specimens total_a total_b percent_positive percent_a percent_b
## <chr> <chr> <int> <int> <chr> <chr> <chr> <chr> <chr> <chr>
## 1 States Alabama 2015 40 167 2 3 2.99 1.2 1.8
## 2 States Alaska 2015 40 <NA> <NA> <NA> <NA> <NA> <NA>
## 3 States Arizona 2015 40 55 0 0 0 0 0
## 4 States Arkansas 2015 40 26 0 1 3.85 0 3.85
## 5 States California 2015 40 679 2 0 0.29 0.29 0
## 6 States Colorado 2015 40 255 0 1 0.39 0 0.39
## 7 States Connecticut 2015 40 304 1 0 0.33 0.33 0
## 8 States Delaware 2015 40 22 0 0 0 0 0
## 9 States District of Columbia 2015 40 <NA> <NA> <NA> <NA> <NA> <NA>
## 10 States Florida 2015 40 <NA> <NA> <NA> <NA> <NA> <NA>
## # ... with 5,822 more rows
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