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https://github.com/hrbrmstr/cdcfluview
:mask: R package to Retrieve U.S. Flu Season Data from the CDC FluView Portal (WHO & ILINet)
https://github.com/hrbrmstr/cdcfluview
cdc cdc-fluview-portal national-influenza-statistics r rstats
Last synced: 11 days ago
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:mask: R package to Retrieve U.S. Flu Season Data from the CDC FluView Portal (WHO & ILINet)
- Host: GitHub
- URL: https://github.com/hrbrmstr/cdcfluview
- Owner: hrbrmstr
- License: other
- Created: 2015-01-11T01:26:20.000Z (almost 10 years ago)
- Default Branch: master
- Last Pushed: 2023-08-27T00:42:36.000Z (about 1 year ago)
- Last Synced: 2024-10-12T21:24:16.281Z (25 days ago)
- Topics: cdc, cdc-fluview-portal, national-influenza-statistics, r, rstats
- Language: R
- Homepage: https://hrbrmstr.github.io/cdcfluview/index.html
- Size: 16.1 MB
- Stars: 57
- Watchers: 11
- Forks: 24
- Open Issues: 13
-
Metadata Files:
- Readme: README.Rmd
- License: LICENSE
Awesome Lists containing this project
README
---
output: rmarkdown::github_document
editor_options:
chunk_output_type: console
---
```{r pkg-knitr-opts, include=FALSE}
hrbrpkghelpr::global_opts()
```
```{r badges, results='asis', echo=FALSE, cache=FALSE}
hrbrpkghelpr::stinking_badges()
```
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.
If there's a particular data set from https://www.cdc.gov/flu/weekly/fluviewinteractive.htm that you want and that isn't in the package, please file it as an issue and be as specific as you can (screen shot if possible).
# :mask: cdcfluview
Retrieve Flu Season Data from the United States Centers for Disease Control and Prevention ('CDC') 'FluView' Portal
## Description
The U.S. Centers for Disease Control (CDC) maintains a portal 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.
## What's Inside The Tin
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. 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
- `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 Provider Data
- `surveillance_areas`: Retrieve a list of valid sub-regions for each surveillance area.
- `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 Date object
Deprecated functions:
- `get_flu_data`: Retrieves state, regional or national influenza statistics from the CDC (deprecated)
- `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()`)
## Installation
```{r eval=FALSE}
# CRAN
install.packages("cdcfluview")
# main branch
remotes::install_git("https://git.rud.is/hrbrmstr/cdcfluview.git")
remotes::install_git("https://sr.ht/~hrbrmstr/cdcfluview")
remotes::install_git("https://gitlab.com/hrbrmstr/cdcfluview")
remotes::install_github("hrbrmstr/cdcfluview")
```
## Usage
```{r libs}
library(cdcfluview)
library(hrbrthemes)
library(tidyverse)
# current version
packageVersion("cdcfluview")
```
### Age Group Distribution of Influenza Positive Tests Reported by Public Health Laboratories
```{r age-group-dist}
glimpse(age_group_distribution(years=2015))
```
### Retrieve CDC U.S. Coverage Map
```{r cdc-basemaps}
plot(cdc_basemap("national"))
plot(cdc_basemap("hhs"))
plot(cdc_basemap("census"))
plot(cdc_basemap("states"))
plot(cdc_basemap("spread"))
plot(cdc_basemap("surv"))
```
### State and Territorial Epidemiologists Reports of Geographic Spread of Influenza
```{r geographic-spread, message=FALSE, warning=FALSE}
glimpse(geographic_spread())
```
### Laboratory-Confirmed Influenza Hospitalizations
```{r surveillance-areas, fig.width=10, fig.height=7.5}
surveillance_areas()
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_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()
glimpse(hospitalizations("eip", years=2015))
glimpse(hospitalizations("eip", "Colorado", years=2015))
glimpse(hospitalizations("ihsp", years=2015))
glimpse(hospitalizations("ihsp", "Oklahoma", years=2010))
```
### Retrieve ILINet Surveillance Data
```{r ili-df}
walk(c("national", "hhs", "census", "state"), ~{
ili_df <- ilinet(region = .x)
print(glimpse(ili_df))
ggplot(ili_df, aes(week_start, unweighted_ili, group=region, color=region)) +
geom_line() +
viridis::scale_color_viridis(discrete=TRUE) +
labs(x=NULL, y="Unweighted ILI", title=ili_df$region_type[1]) +
theme_ipsum_rc(grid="XY") +
theme(legend.position = "none") -> gg
print(gg)
})
```
### Retrieve weekly state-level ILI indicators per-state for a given season
```{r ili-weekly-activity, fig.width=10, fig.height=5}
ili_weekly_activity_indicators(2017)
xdf <- map_df(2008:2017, ili_weekly_activity_indicators)
count(xdf, weekend, activity_level_label) %>%
complete(weekend, activity_level_label) %>%
ggplot(aes(weekend, activity_level_label, fill=n)) +
geom_tile(color="#c2c2c2", size=0.1) +
scale_x_date(expand=c(0,0)) +
viridis::scale_fill_viridis(name="# States", na.value="White") +
labs(x=NULL, y=NULL, title="Weekly ILI Indicators (all states)") +
coord_fixed(100/1) +
theme_ipsum_rc(grid="") +
theme(legend.position="bottom")
```
### Pneumonia and Influenza Mortality Surveillance
```{r nat-pi-mortality}
(nat_pi <- pi_mortality("national"))
select(nat_pi, week_end, percent_pni, baseline, threshold) %>%
gather(measure, value, -week_end) %>%
ggplot(aes(week_end, value)) +
geom_line(aes(group=measure, color=measure)) +
scale_y_percent() +
scale_color_ipsum(name = NULL, labels=c("Baseline", "Percent P&I", "Threshold")) +
labs(x=NULL, y="% of all deaths due to P&I",
title="Percentage of all deaths due to pneumonia and influenza, National Summary") +
theme_ipsum_rc(grid="XY") +
theme(legend.position="bottom")
(st_pi <- pi_mortality("state", years=2015))
(reg_pi <- pi_mortality("region", years=2015))
```
### Retrieve metadata about U.S. State CDC Provider Data
```{r state-data-providers}
state_data_providers()
```
### Retrieve WHO/NREVSS Surveillance Data
```{r who-vrevss}
glimpse(xdat <- who_nrevss("national"))
mutate(xdat$combined_prior_to_2015_16,
percent_positive = percent_positive / 100) %>%
ggplot(aes(wk_date, percent_positive)) +
geom_line() +
scale_y_percent(name="% Positive") +
labs(x=NULL, title="WHO/NREVSS Surveillance Data (National)") +
theme_ipsum_rc(grid="XY")
who_nrevss("hhs", years=2016)
who_nrevss("census", years=2016)
who_nrevss("state", years=2016)
```
## cdcfluview Metrics
```{r echo=FALSE}
cloc::cloc_pkg_md()
```
## Code of Conduct
Please note that this project is released with a Contributor Code of Conduct. By participating in this project you agree to abide by its terms.