[![CRAN\_Status\_Badge](http://www.r-pkg.org/badges/version/cdcfluview)](https://cran.r-project.org/package=cdcfluview) [![Travis-CI Build Status](https://travis-ci.org/hrbrmstr/cdcfluview.svg?branch=master)](https://travis-ci.org/hrbrmstr/cdcfluview) [![Coverage Status](https://img.shields.io/codecov/c/github/hrbrmstr/cdcfluview/master.svg)](https://codecov.io/github/hrbrmstr/cdcfluview?branch=master) # 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. If there’s a particular data set from 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 U.S. Flu Season Data from the 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. Basemaps - `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 devtools::install_github("hrbrmstr/cdcfluview") ``` ## Usage ``` r library(cdcfluview) library(hrbrthemes) library(tidyverse) # current verison packageVersion("cdcfluview") ``` ## [1] '0.8.0' ### Age Group Distribution of Influenza Positive Tests Reported by Public Health Laboratories ``` r glimpse(age_group_distribution(years=2015)) ``` ## Observations: 1,872 ## Variables: 16 ## $ sea_label "2015-16", "2015-16", "2015-16", "2015-16", "2015-16", "2015-16", "2015-16", "2015-16", "... ## $ age_label 0-4 yr, 0-4 yr, 0-4 yr, 0-4 yr, 0-4 yr, 0-4 yr, 0-4 yr, 0-4 yr, 0-4 yr, 0-4 yr, 0-4 yr, 0... ## $ vir_label A (Subtyping not Performed), A (Subtyping not Performed), A (Subtyping not Performed), A ... ## $ count 0, 1, 0, 1, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 3, 2, 2, 3, 3, 3, 0, 0, 2, 0, 1, 1, 0,... ## $ mmwrid 2806, 2807, 2808, 2809, 2810, 2811, 2812, 2813, 2814, 2815, 2816, 2817, 2818, 2819, 2820,... ## $ seasonid 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 5... ## $ publishyearweekid 2956, 2956, 2956, 2956, 2956, 2956, 2956, 2956, 2956, 2956, 2956, 2956, 2956, 2956, 2956,... ## $ sea_description "Season 2015-16", "Season 2015-16", "Season 2015-16", "Season 2015-16", "Season 2015-16",... ## $ sea_startweek 2806, 2806, 2806, 2806, 2806, 2806, 2806, 2806, 2806, 2806, 2806, 2806, 2806, 2806, 2806,... ## $ sea_endweek 2857, 2857, 2857, 2857, 2857, 2857, 2857, 2857, 2857, 2857, 2857, 2857, 2857, 2857, 2857,... ## $ vir_description "A-Unk", "A-Unk", "A-Unk", "A-Unk", "A-Unk", "A-Unk", "A-Unk", "A-Unk", "A-Unk", "A-Unk",... ## $ vir_startmmwrid 1397, 1397, 1397, 1397, 1397, 1397, 1397, 1397, 1397, 1397, 1397, 1397, 1397, 1397, 1397,... ## $ vir_endmmwrid 3131, 3131, 3131, 3131, 3131, 3131, 3131, 3131, 3131, 3131, 3131, 3131, 3131, 3131, 3131,... ## $ wk_start 2015-10-04, 2015-10-11, 2015-10-18, 2015-10-25, 2015-11-01, 2015-11-08, 2015-11-15, 2015... ## $ wk_end 2015-10-10, 2015-10-17, 2015-10-24, 2015-10-31, 2015-11-07, 2015-11-14, 2015-11-21, 2015... ## $ year_wk_num 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12... ### Retrieve CDC U.S. Coverage Map ``` r plot(cdc_basemap("national")) ```

``` r plot(cdc_basemap("hhs")) ```

``` r plot(cdc_basemap("census")) ```

``` r plot(cdc_basemap("states")) ```

``` r plot(cdc_basemap("spread")) ```

``` r plot(cdc_basemap("surv")) ```

### State and Territorial Epidemiologists Reports of Geographic Spread of Influenza ``` r glimpse(geographic_spread()) ``` ## Observations: 27,351 ## Variables: 7 ## $ statename "Alabama", "Alabama", "Alabama", "Alabama", "Alabama", "Alabama", "Alabama", "Alabama", "... ## $ url "http://adph.org/influenza/", "http://adph.org/influenza/", "http://adph.org/influenza/",... ## $ website "Influenza Surveillance", "Influenza Surveillance", "Influenza Surveillance", "Influenza ... ## $ activity_estimate "No Activity", "No Activity", "No Activity", "Local Activity", "Sporadic", "Sporadic", "S... ## $ weekend 2003-10-04, 2003-10-11, 2003-10-18, 2003-10-25, 2003-11-01, 2003-11-08, 2003-11-15, 2003... ## $ season "2003-04", "2003-04", "2003-04", "2003-04", "2003-04", "2003-04", "2003-04", "2003-04", "... ## $ weeknumber "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(fs_nat <- hospitalizations("flusurv")) ``` ## Observations: 1,656 ## Variables: 14 ## $ surveillance_area "FluSurv-NET", "FluSurv-NET", "FluSurv-NET", "FluSurv-NET", "FluSurv-NET", "FluSurv-NET",... ## $ region "Entire Network", "Entire Network", "Entire Network", "Entire Network", "Entire Network",... ## $ year 2009, 2009, 2009, 2009, 2009, 2009, 2009, 2009, 2009, 2009, 2009, 2009, 2009, 2009, 2009,... ## $ season 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 4... ## $ wk_start 2009-08-30, 2009-09-06, 2009-09-13, 2009-09-20, 2009-09-27, 2009-10-04, 2009-10-11, 2009... ## $ wk_end 2009-09-05, 2009-09-12, 2009-09-19, 2009-09-26, 2009-10-03, 2009-10-10, 2009-10-17, 2009... ## $ year_wk_num 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 1, 2, 3, 4, 5, 6,... ## $ rate 0.5, 2.5, 4.6, 6.7, 10.9, 18.1, 28.3, 39.1, 47.3, 53.3, 57.5, 60.1, 61.6, 62.9, 64.1, 65.... ## $ weeklyrate 0.5, 2.0, 2.0, 2.1, 4.3, 7.2, 10.2, 10.8, 8.2, 6.0, 4.2, 2.6, 1.5, 1.3, 1.3, 1.0, 1.2, 1.... ## $ age 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,... ## $ age_label 0-4 yr, 0-4 yr, 0-4 yr, 0-4 yr, 0-4 yr, 0-4 yr, 0-4 yr, 0-4 yr, 0-4 yr, 0-4 yr, 0-4 yr, 0... ## $ sea_label "2009-10", "2009-10", "2009-10", "2009-10", "2009-10", "2009-10", "2009-10", "2009-10", "... ## $ sea_description "Season 2009-10", "Season 2009-10", "Season 2009-10", "Season 2009-10", "Season 2009-10",... ## $ mmwrid 2488, 2489, 2490, 2491, 2492, 2493, 2494, 2495, 2496, 2497, 2498, 2499, 2500, 2501, 2502,... ``` r 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) + labs(x=NULL, y="Rates per 100,000 population", title="FluSurv-NET :: Entire Network :: All Seasons :: Cumulative Rate") + theme_ipsum_rc() ```

``` r glimpse(hospitalizations("eip", years=2015)) ``` ## Observations: 180 ## Variables: 14 ## $ surveillance_area "EIP", "EIP", "EIP", "EIP", "EIP", "EIP", "EIP", "EIP", "EIP", "EIP", "EIP", "EIP", "EIP"... ## $ region "Entire Network", "Entire Network", "Entire Network", "Entire Network", "Entire Network",... ## $ year 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2016, 2016,... ## $ season 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 5... ## $ wk_start 2015-10-04, 2015-10-11, 2015-10-18, 2015-10-25, 2015-11-01, 2015-11-08, 2015-11-15, 2015... ## $ wk_end 2015-10-10, 2015-10-17, 2015-10-24, 2015-10-31, 2015-11-07, 2015-11-14, 2015-11-21, 2015... ## $ year_wk_num 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 0.1, 0.3, 0.4, 0.5, 0.8, 0.8, 1.1, 1.4, 1.6, 1.7, 1.8, 2.1, 2.4, 2.9, 3.2, 3.5, 4.2, 5.3,... ## $ weeklyrate 0.1, 0.3, 0.1, 0.1, 0.3, 0.0, 0.3, 0.3, 0.2, 0.1, 0.1, 0.3, 0.3, 0.5, 0.3, 0.3, 0.6, 1.2,... ## $ age 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,... ## $ age_label 0-4 yr, 0-4 yr, 0-4 yr, 0-4 yr, 0-4 yr, 0-4 yr, 0-4 yr, 0-4 yr, 0-4 yr, 0-4 yr, 0-4 yr, 0... ## $ sea_label "2015-16", "2015-16", "2015-16", "2015-16", "2015-16", "2015-16", "2015-16", "2015-16", "... ## $ sea_description "Season 2015-16", "Season 2015-16", "Season 2015-16", "Season 2015-16", "Season 2015-16",... ## $ mmwrid 2806, 2807, 2808, 2809, 2810, 2811, 2812, 2813, 2814, 2815, 2816, 2817, 2818, 2819, 2820,... ``` r glimpse(hospitalizations("eip", "Colorado", years=2015)) ``` ## Observations: 180 ## Variables: 14 ## $ surveillance_area "EIP", "EIP", "EIP", "EIP", "EIP", "EIP", "EIP", "EIP", "EIP", "EIP", "EIP", "EIP", "EIP"... ## $ region "Colorado", "Colorado", "Colorado", "Colorado", "Colorado", "Colorado", "Colorado", "Colo... ## $ year 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2016, 2016,... ## $ season 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 5... ## $ wk_start 2015-10-04, 2015-10-11, 2015-10-18, 2015-10-25, 2015-11-01, 2015-11-08, 2015-11-15, 2015... ## $ wk_end 2015-10-10, 2015-10-17, 2015-10-24, 2015-10-31, 2015-11-07, 2015-11-14, 2015-11-21, 2015... ## $ year_wk_num 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 0.0, 0.0, 0.6, 0.6, 0.6, 0.6, 1.2, 1.8, 1.8, 1.8, 1.8, 1.8, 2.9, 3.5, 3.5, 3.5, 4.1, 6.4,... ## $ weeklyrate 0.0, 0.0, 0.6, 0.0, 0.0, 0.0, 0.6, 0.6, 0.0, 0.0, 0.0, 0.0, 1.2, 0.6, 0.0, 0.0, 0.6, 2.3,... ## $ age 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,... ## $ age_label 0-4 yr, 0-4 yr, 0-4 yr, 0-4 yr, 0-4 yr, 0-4 yr, 0-4 yr, 0-4 yr, 0-4 yr, 0-4 yr, 0-4 yr, 0... ## $ sea_label "2015-16", "2015-16", "2015-16", "2015-16", "2015-16", "2015-16", "2015-16", "2015-16", "... ## $ sea_description "Season 2015-16", "Season 2015-16", "Season 2015-16", "Season 2015-16", "Season 2015-16",... ## $ mmwrid 2806, 2807, 2808, 2809, 2810, 2811, 2812, 2813, 2814, 2815, 2816, 2817, 2818, 2819, 2820,... ``` r glimpse(hospitalizations("ihsp", years=2015)) ``` ## Observations: 180 ## Variables: 14 ## $ surveillance_area "IHSP", "IHSP", "IHSP", "IHSP", "IHSP", "IHSP", "IHSP", "IHSP", "IHSP", "IHSP", "IHSP", "... ## $ region "Entire Network", "Entire Network", "Entire Network", "Entire Network", "Entire Network",... ## $ year 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2016, 2016,... ## $ season 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 5... ## $ wk_start 2015-10-04, 2015-10-11, 2015-10-18, 2015-10-25, 2015-11-01, 2015-11-08, 2015-11-15, 2015... ## $ wk_end 2015-10-10, 2015-10-17, 2015-10-24, 2015-10-31, 2015-11-07, 2015-11-14, 2015-11-21, 2015... ## $ year_wk_num 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 0.0, 0.0, 0.4, 0.4, 0.4, 1.1, 1.1, 1.1, 1.1, 1.5, 1.8, 2.2, 2.2, 2.5, 2.5, 2.5, 2.9, 4.0,... ## $ weeklyrate 0.0, 0.0, 0.4, 0.0, 0.0, 0.7, 0.0, 0.0, 0.0, 0.4, 0.4, 0.4, 0.0, 0.4, 0.0, 0.0, 0.4, 1.1,... ## $ age 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,... ## $ age_label 0-4 yr, 0-4 yr, 0-4 yr, 0-4 yr, 0-4 yr, 0-4 yr, 0-4 yr, 0-4 yr, 0-4 yr, 0-4 yr, 0-4 yr, 0... ## $ sea_label "2015-16", "2015-16", "2015-16", "2015-16", "2015-16", "2015-16", "2015-16", "2015-16", "... ## $ sea_description "Season 2015-16", "Season 2015-16", "Season 2015-16", "Season 2015-16", "Season 2015-16",... ## $ mmwrid 2806, 2807, 2808, 2809, 2810, 2811, 2812, 2813, 2814, 2815, 2816, 2817, 2818, 2819, 2820,... ``` r glimpse(hospitalizations("ihsp", "Oklahoma", years=2015)) ``` ## Observations: 180 ## Variables: 14 ## $ surveillance_area "IHSP", "IHSP", "IHSP", "IHSP", "IHSP", "IHSP", "IHSP", "IHSP", "IHSP", "IHSP", "IHSP", "... ## $ region "Oklahoma", "Oklahoma", "Oklahoma", "Oklahoma", "Oklahoma", "Oklahoma", "Oklahoma", "Okla... ## $ year 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2011, 2011,... ## $ season 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 5... ## $ wk_start 2010-10-03, 2010-10-10, 2010-10-17, 2010-10-24, 2010-10-31, 2010-11-07, 2010-11-14, 2010... ## $ wk_end 2010-10-09, 2010-10-16, 2010-10-23, 2010-10-30, 2010-11-06, 2010-11-13, 2010-11-20, 2010... ## $ year_wk_num 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 0.0, 0.0, 1.3, 1.3, 1.3, 1.3, 1.3, 1.3, 1.3, 2.6, 2.6, 6.6, 15.9, 18.5, 35.7, 54.2, 83.4,... ## $ weeklyrate 0.0, 0.0, 1.3, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.3, 0.0, 4.0, 9.3, 2.6, 17.2, 18.5, 29.1, 2... ## $ age 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,... ## $ age_label 0-4 yr, 0-4 yr, 0-4 yr, 0-4 yr, 0-4 yr, 0-4 yr, 0-4 yr, 0-4 yr, 0-4 yr, 0-4 yr, 0-4 yr, 0... ## $ sea_label "2010-11", "2010-11", "2010-11", "2010-11", "2010-11", "2010-11", "2010-11", "2010-11", "... ## $ sea_description "Season 2010-11", "Season 2010-11", "Season 2010-11", "Season 2010-11", "Season 2010-11",... ## $ mmwrid 2545, 2546, 2547, 2548, 2549, 2550, 2551, 2552, 2553, 2554, 2555, 2556, 2557, 2558, 2559,... ### Retrieve ILINet Surveillance Data ``` r 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) }) ``` ## Observations: 1,093 ## Variables: 16 ## $ region_type "National", "National", "National", "National", "National", "National", "National", "Natio... ## $ region "National", "National", "National", "National", "National", "National", "National", "Natio... ## $ year 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1998, ... ## $ week 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11,... ## $ weighted_ili 1.101480, 1.200070, 1.378760, 1.199200, 1.656180, 1.413260, 1.986800, 2.447490, 1.739010, ... ## $ unweighted_ili 1.216860, 1.280640, 1.239060, 1.144730, 1.261120, 1.282750, 1.445790, 1.647960, 1.675170, ... ## $ age_0_4 179, 199, 228, 188, 217, 178, 294, 288, 268, 299, 346, 348, 510, 579, 639, 690, 856, 824, ... ## $ age_25_49 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_64 157, 151, 153, 193, 162, 148, 240, 293, 206, 282, 268, 235, 404, 584, 759, 654, 679, 817, ... ## $ age_5_24 205, 242, 266, 236, 280, 281, 328, 456, 343, 415, 388, 362, 492, 576, 810, 1121, 1440, 160... ## $ age_50_64 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 29, 23, 34, 36, 41, 48, 70, 63, 69, 102, 81, 59, 113, 207, 207, 148, 151, 196, 233, 146, 1... ## $ ilitotal 570, 615, 681, 653, 700, 655, 932, 1100, 886, 1098, 1083, 1004, 1519, 1946, 2415, 2613, 31... ## $ num_of_providers 192, 191, 219, 213, 213, 195, 248, 256, 252, 253, 242, 190, 251, 250, 254, 255, 245, 245, ... ## $ total_patients 46842, 48023, 54961, 57044, 55506, 51062, 64463, 66749, 52890, 67887, 61314, 47719, 48429,... ## $ week_start 1997-10-06, 1997-10-13, 1997-10-20, 1997-10-27, 1997-11-03, 1997-11-10, 1997-11-17, 1997-... ## # A tibble: 1,093 x 16 ## region_type region year week weighted_ili unweighted_ili age_0_4 age_25_49 age_25_64 age_5_24 age_50_64 age_65 ## ## 1 National National 1997 40 1.10 1.22 179 NA 157 205 NA 29 ## 2 National National 1997 41 1.20 1.28 199 NA 151 242 NA 23 ## 3 National National 1997 42 1.38 1.24 228 NA 153 266 NA 34 ## 4 National National 1997 43 1.20 1.14 188 NA 193 236 NA 36 ## 5 National National 1997 44 1.66 1.26 217 NA 162 280 NA 41 ## 6 National National 1997 45 1.41 1.28 178 NA 148 281 NA 48 ## 7 National National 1997 46 1.99 1.45 294 NA 240 328 NA 70 ## 8 National National 1997 47 2.45 1.65 288 NA 293 456 NA 63 ## 9 National National 1997 48 1.74 1.68 268 NA 206 343 NA 69 ## 10 National National 1997 49 1.94 1.62 299 NA 282 415 NA 102 ## # ... with 1,083 more rows, and 4 more variables: ilitotal , num_of_providers , total_patients , ## # week_start

## Observations: 10,930 ## Variables: 16 ## $ region_type "HHS Regions", "HHS Regions", "HHS Regions", "HHS Regions", "HHS Regions", "HHS Regions", ... ## $ region Region 1, Region 2, Region 3, Region 4, Region 5, Region 6, Region 7, Region 8, Region 9, ... ## $ year 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, ... ## $ week 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 0.498535, 0.374963, 1.354280, 0.400338, 1.229260, 1.018980, 0.871791, 0.516017, 1.807610, ... ## $ unweighted_ili 0.623848, 0.384615, 1.341720, 0.450010, 0.901266, 0.747384, 1.152860, 0.422654, 2.258780, ... ## $ age_0_4 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 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 7, 3, 7, 23, 24, 1, 4, 0, 76, 12, 14, 2, 19, 7, 23, 2, 0, 1, 76, 7, 15, 0, 17, 15, 29, 2, ... ## $ age_5_24 22, 0, 15, 11, 30, 2, 18, 3, 74, 30, 29, 0, 16, 14, 41, 2, 13, 8, 84, 35, 35, 0, 24, 18, 7... ## $ age_50_64 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 0, 0, 4, 0, 4, 0, 5, 0, 13, 3, 0, 0, 3, 2, 4, 0, 2, 0, 11, 1, 0, 1, 2, 2, 16, 0, 2, 0, 9, ... ## $ ilitotal 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 32, 7, 16, 29, 49, 4, 14, 5, 23, 13, 29, 7, 17, 31, 48, 4, 14, 6, 23, 12, 40, 7, 15, 33, 6... ## $ total_patients 7053, 780, 2385, 10222, 9875, 669, 2342, 1183, 10758, 1575, 6987, 872, 2740, 11310, 9618, ... ## $ week_start 1997-10-06, 1997-10-06, 1997-10-06, 1997-10-06, 1997-10-06, 1997-10-06, 1997-10-06, 1997-... ## # A tibble: 10,930 x 16 ## region_type region year week weighted_ili unweighted_ili age_0_4 age_25_49 age_25_64 age_5_24 age_50_64 age_65 ## ## 1 HHS Regions Region 1 1997 40 0.499 0.624 15 NA 7 22 NA 0 ## 2 HHS Regions Region 2 1997 40 0.375 0.385 0 NA 3 0 NA 0 ## 3 HHS Regions Region 3 1997 40 1.35 1.34 6 NA 7 15 NA 4 ## 4 HHS Regions Region 4 1997 40 0.400 0.450 12 NA 23 11 NA 0 ## 5 HHS Regions Region 5 1997 40 1.23 0.901 31 NA 24 30 NA 4 ## 6 HHS Regions Region 6 1997 40 1.02 0.747 2 NA 1 2 NA 0 ## 7 HHS Regions Region 7 1997 40 0.872 1.15 0 NA 4 18 NA 5 ## 8 HHS Regions Region 8 1997 40 0.516 0.423 2 NA 0 3 NA 0 ## 9 HHS Regions Region 9 1997 40 1.81 2.26 80 NA 76 74 NA 13 ## 10 HHS Regions Region 10 1997 40 4.74 4.83 31 NA 12 30 NA 3 ## # ... with 10,920 more rows, and 4 more variables: ilitotal , num_of_providers , total_patients , ## # week_start

## Observations: 9,837 ## Variables: 16 ## $ region_type "Census Regions", "Census Regions", "Census Regions", "Census Regions", "Census Regions", ... ## $ region "New England", "Mid-Atlantic", "East North Central", "West North Central", "South Atlantic... ## $ year 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, ... ## $ week 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 0.4985350, 0.8441440, 0.7924860, 1.7640500, 0.5026620, 0.0542283, 1.0189800, 2.2587800, 2.... ## $ unweighted_ili 0.6238480, 1.3213800, 0.8187380, 1.2793900, 0.7233800, 0.0688705, 0.7473840, 2.2763300, 3.... ## $ age_0_4 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 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 7, 8, 20, 8, 22, 3, 1, 71, 17, 14, 13, 23, 1, 14, 1, 2, 72, 11, 15, 11, 27, 5, 21, 0, 2, 5... ## $ age_5_24 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 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 0, 4, 3, 6, 0, 0, 0, 15, 1, 0, 2, 2, 4, 3, 0, 0, 10, 2, 0, 3, 12, 6, 2, 0, 0, 9, 2, 0, 1, ... ## $ ilitotal 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 32, 13, 47, 17, 30, 9, 4, 16, 24, 29, 13, 46, 17, 32, 10, 4, 17, 23, 40, 12, 62, 16, 33, 1... ## $ total_patients 7053, 2119, 9649, 2892, 6912, 4356, 669, 10719, 2473, 6987, 2384, 9427, 2823, 7591, 4947, ... ## $ week_start 1997-10-06, 1997-10-06, 1997-10-06, 1997-10-06, 1997-10-06, 1997-10-06, 1997-10-06, 1997-... ## # A tibble: 9,837 x 16 ## region_type region year week weighted_ili unweighted_ili age_0_4 age_25_49 age_25_64 age_5_24 age_50_64 age_65 ## ## 1 Census Regi… New Engl… 1997 40 0.499 0.624 15 NA 7 22 NA 0 ## 2 Census Regi… Mid-Atla… 1997 40 0.844 1.32 4 NA 8 12 NA 4 ## 3 Census Regi… East Nor… 1997 40 0.792 0.819 28 NA 20 28 NA 3 ## 4 Census Regi… West Nor… 1997 40 1.76 1.28 3 NA 8 20 NA 6 ## 5 Census Regi… South At… 1997 40 0.503 0.723 14 NA 22 14 NA 0 ## 6 Census Regi… East Sou… 1997 40 0.0542 0.0689 0 NA 3 0 NA 0 ## 7 Census Regi… West Sou… 1997 40 1.02 0.747 2 NA 1 2 NA 0 ## 8 Census Regi… Mountain 1997 40 2.26 2.28 87 NA 71 71 NA 15 ## 9 Census Regi… Pacific 1997 40 2.05 3.23 26 NA 17 36 NA 1 ## 10 Census Regi… New Engl… 1997 41 0.643 0.816 14 NA 14 29 NA 0 ## # ... with 9,827 more rows, and 4 more variables: ilitotal , num_of_providers , total_patients , ## # week_start

## Observations: 22,148 ## Variables: 16 ## $ region_type "States", "States", "States", "States", "States", "States", "States", "States", "States", ... ## $ region "Alabama", "Alaska", "Arizona", "Arkansas", "California", "Colorado", "Connecticut", "Dela... ## $ year 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, ... ## $ week 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 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 2.1347700, 0.8751460, 0.6747210, 0.6960560, 1.9541200, 0.6606840, 0.0783085, 0.1001250, 2.... ## $ age_0_4 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 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 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 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 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 NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA... ## $ ilitotal 249, 15, 172, 18, 632, 134, 3, 4, 73, NA, 647, 20, 19, 505, 65, 10, 39, 19, 391, 22, 117, ... ## $ num_of_providers 35, 7, 49, 15, 112, 14, 12, 13, 4, NA, 62, 18, 12, 74, 44, 6, 40, 14, 41, 30, 17, 56, 47, ... ## $ total_patients 11664, 1714, 25492, 2586, 32342, 20282, 3831, 3995, 2599, NA, 40314, 1943, 4579, 39390, 12... ## $ week_start 2010-10-04, 2010-10-04, 2010-10-04, 2010-10-04, 2010-10-04, 2010-10-04, 2010-10-04, 2010-... ## # A tibble: 22,148 x 16 ## region_type region year week weighted_ili unweighted_ili age_0_4 age_25_49 age_25_64 age_5_24 age_50_64 age_65 ## ## 1 States Alabama 2010 40 NA 2.13 NA NA NA NA NA NA ## 2 States Alaska 2010 40 NA 0.875 NA NA NA NA NA NA ## 3 States Arizona 2010 40 NA 0.675 NA NA NA NA NA NA ## 4 States Arkansas 2010 40 NA 0.696 NA NA NA NA NA NA ## 5 States California 2010 40 NA 1.95 NA NA NA NA NA NA ## 6 States Colorado 2010 40 NA 0.661 NA NA NA NA NA NA ## 7 States Connectic… 2010 40 NA 0.0783 NA NA NA NA NA NA ## 8 States Delaware 2010 40 NA 0.100 NA NA NA NA NA NA ## 9 States District … 2010 40 NA 2.81 NA NA NA NA NA NA ## 10 States Florida 2010 40 NA NA NA NA NA NA NA NA ## # ... with 22,138 more rows, and 4 more variables: ilitotal , num_of_providers , total_patients , ## # week_start

### Retrieve weekly state-level ILI indicators per-state for a given season ``` r ili_weekly_activity_indicators(2017) ``` ## # A tibble: 1,782 x 8 ## statename url website activity_level activity_level_label weekend season weeknumber ## * ## 1 Virgin Islands http://doh.vi.gov/ Influenza 0 Insufficient Data 2017-10-07 2017-18 40 ## 2 Virgin Islands http://doh.vi.gov/ Influenza 0 Insufficient Data 2017-10-14 2017-18 41 ## 3 Virgin Islands http://doh.vi.gov/ Influenza 0 Insufficient Data 2017-10-21 2017-18 42 ## 4 Virgin Islands http://doh.vi.gov/ Influenza 0 Insufficient Data 2017-10-28 2017-18 43 ## 5 Virgin Islands http://doh.vi.gov/ Influenza 0 Insufficient Data 2017-11-04 2017-18 44 ## 6 Virgin Islands http://doh.vi.gov/ Influenza 0 Insufficient Data 2017-11-11 2017-18 45 ## 7 Virgin Islands http://doh.vi.gov/ Influenza 0 Insufficient Data 2017-12-02 2017-18 48 ## 8 Virgin Islands http://doh.vi.gov/ Influenza 0 Insufficient Data 2017-12-09 2017-18 49 ## 9 Virgin Islands http://doh.vi.gov/ Influenza 0 Insufficient Data 2017-12-23 2017-18 51 ## 10 Virgin Islands http://doh.vi.gov/ Influenza 0 Insufficient Data 2017-12-30 2017-18 52 ## # ... with 1,772 more rows ``` r 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 <- pi_mortality("national")) ``` ## # A tibble: 464 x 19 ## seasonid baseline threshold percent_pni percent_complete number_influenza number_pneumonia all_deaths total_pni ## ## 1 57 0.057 0.06 0.0580 1 16 3020 52110 3036 ## 2 57 0.0580 0.061 0.059 1 18 3000 51572 3018 ## 3 57 0.059 0.062 0.061 1 28 3154 52222 3182 ## 4 57 0.06 0.063 0.063 1 23 3279 52548 3302 ## 5 57 0.06 0.063 0.061 1 36 3214 53679 3250 ## 6 57 0.061 0.064 0.06 1 45 3177 53258 3222 ## 7 57 0.062 0.065 0.063 1 50 3315 53771 3365 ## 8 57 0.063 0.066 0.06 1 48 3200 54120 3248 ## 9 57 0.064 0.067 0.065 1 83 3491 54760 3574 ## 10 57 0.065 0.068 0.066 1 118 3526 55595 3644 ## # ... with 454 more rows, and 10 more variables: weeknumber , geo_description , age_label , ## # wk_start , wk_end , year_wk_num , mmwrid , coverage_area , region_name , ## # callout ``` r select(nat_pi, wk_end, percent_pni, baseline, threshold) %>% gather(measure, value, -wk_end) %>% ggplot(aes(wk_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") ```

``` 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 ## ## 1 55 NA NA 0.047 1 0 46 979 46 ## 2 55 NA NA 0.038 0.963 0 34 889 34 ## 3 55 NA NA 0.053 1 0 52 977 52 ## 4 55 NA NA 0.07 1 0 68 968 68 ## 5 55 NA NA 0.053 0.981 0 48 906 48 ## 6 55 NA NA 0.0580 1 0 56 968 56 ## 7 55 NA NA 0.051 1 0 53 1041 53 ## 8 55 NA NA 0.062 1 1 63 1031 64 ## 9 55 NA NA 0.0560 1 0 55 976 55 ## 10 55 NA NA 0.054 1 0 56 1045 56 ## # ... with 2,694 more rows, and 10 more variables: weeknumber , geo_description , age_label , ## # wk_start , wk_end , year_wk_num , mmwrid , coverage_area , region_name , ## # callout ``` r (reg_pi <- pi_mortality("region", years=2015)) ``` ## # A tibble: 520 x 19 ## seasonid baseline threshold percent_pni percent_complete number_influenza number_pneumonia all_deaths total_pni ## ## 1 55 0.066 0.073 0.071 1 0 178 2520 178 ## 2 55 0.067 0.074 0.063 1 0 159 2505 159 ## 3 55 0.067 0.075 0.0580 1 1 141 2452 142 ## 4 55 0.068 0.076 0.071 1 0 171 2422 171 ## 5 55 0.07 0.077 0.066 1 2 166 2554 168 ## 6 55 0.071 0.078 0.067 1 1 160 2404 161 ## 7 55 0.072 0.079 0.079 1 0 195 2478 195 ## 8 55 0.073 0.081 0.072 1 1 176 2463 177 ## 9 55 0.074 0.0820 0.067 1 3 154 2347 157 ## 10 55 0.076 0.083 0.062 1 0 151 2437 151 ## # ... with 510 more rows, and 10 more variables: weeknumber , geo_description , age_label , ## # wk_start , wk_end , year_wk_num , mmwrid , coverage_area , region_name , ## # callout ### Retrieve metadata about U.S. State CDC Provider Data ``` r state_data_providers() ``` ## # A tibble: 59 x 5 ## statename statehealthdeptname url statewebsitename statefluphonenum ## * ## 1 Alabama Alabama Department of Public Health http://… Influenza Surve… 334-206-5300 ## 2 Alaska State of Alaska Health and Social Services "http:/… Influenza Surve… 907-269-8000 ## 3 Arizona Arizona Department of Health Services http://… Influenza & RSV… 602-542-1025 ## 4 Arkansas Arkansas Department of Health http://… Communicable Di… 501-661-2000 ## 5 California California Department of Public Health https:/… Influenza (Flu) 916-558-1784 ## 6 Colorado Colorado Department of Public Health and Environment https:/… Influenza Surve… 303-692-2000 ## 7 Connecticut Connecticut Department of Public Health http://… Flu Statistics 860-509-8000 ## 8 Delaware Delaware Health and Social Services http://… Weekly Influenz… 302-744-4700 ## 9 District of Columbia District of Columbia Department of Health http://… Influenza Infor… 202-442-5955 ## 10 Florida Florida Department of Health "http:/… Weekly Influenz… 850-245-4300 ## # ... with 49 more rows ### Retrieve WHO/NREVSS Surveillance Data ``` r glimpse(xdat <- who_nrevss("national")) ``` ## List of 3 ## $ combined_prior_to_2015_16:Classes 'tbl_df', 'tbl' and 'data.frame': 940 obs. of 14 variables: ## ..$ 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 ... ## ..$ week : int [1:940] 40 41 42 43 44 45 46 47 48 49 ... ## ..$ total_specimens : int [1:940] 1291 1513 1552 1669 1897 2106 2204 2533 2242 2607 ... ## ..$ percent_positive : num [1:940] 0 0.727 1.095 0.419 0.527 ... ## ..$ a_2009_h1n1 : int [1:940] 0 0 0 0 0 0 0 0 0 0 ... ## ..$ a_h1 : int [1:940] 0 0 0 0 0 0 0 0 0 0 ... ## ..$ a_h3 : int [1:940] 0 0 3 0 9 0 3 5 14 11 ... ## ..$ a_subtyping_not_performed: int [1:940] 0 11 13 7 1 6 4 17 22 28 ... ## ..$ a_unable_to_subtype : int [1:940] 0 0 0 0 0 0 0 0 0 0 ... ## ..$ b : int [1:940] 0 0 1 0 0 0 1 1 1 1 ... ## ..$ h3n2v : int [1:940] 0 0 0 0 0 0 0 0 0 0 ... ## ..$ wk_date : Date[1:940], format: "1997-09-28" "1997-10-05" "1997-10-12" "1997-10-19" ... ## $ public_health_labs :Classes 'tbl_df', 'tbl' and 'data.frame': 153 obs. of 13 variables: ## ..$ region_type : chr [1:153] "National" "National" "National" "National" ... ## ..$ region : chr [1:153] "National" "National" "National" "National" ... ## ..$ year : int [1:153] 2015 2015 2015 2015 2015 2015 2015 2015 2015 2015 ... ## ..$ week : int [1:153] 40 41 42 43 44 45 46 47 48 49 ... ## ..$ total_specimens : int [1:153] 1139 1152 1198 1244 1465 1393 1458 1157 1550 1518 ... ## ..$ a_2009_h1n1 : int [1:153] 4 5 10 9 4 11 17 17 27 38 ... ## ..$ a_h3 : int [1:153] 65 41 50 31 23 34 42 24 36 37 ... ## ..$ a_subtyping_not_performed: int [1:153] 2 2 1 4 4 1 1 0 3 3 ... ## ..$ b : int [1:153] 10 7 8 9 9 10 4 4 9 11 ... ## ..$ bvic : int [1:153] 0 3 3 1 1 4 0 3 3 2 ... ## ..$ byam : int [1:153] 1 0 2 4 4 2 4 9 12 11 ... ## ..$ h3n2v : int [1:153] 0 0 0 0 0 0 0 0 0 0 ... ## ..$ wk_date : Date[1:153], format: "2015-10-04" "2015-10-11" "2015-10-18" "2015-10-25" ... ## $ clinical_labs :Classes 'tbl_df', 'tbl' and 'data.frame': 153 obs. of 11 variables: ## ..$ region_type : chr [1:153] "National" "National" "National" "National" ... ## ..$ region : chr [1:153] "National" "National" "National" "National" ... ## ..$ year : int [1:153] 2015 2015 2015 2015 2015 2015 2015 2015 2015 2015 ... ## ..$ week : int [1:153] 40 41 42 43 44 45 46 47 48 49 ... ## ..$ total_specimens : int [1:153] 12029 13111 13441 13537 14687 15048 15250 15234 16201 16673 ... ## ..$ total_a : int [1:153] 84 116 97 98 97 122 84 119 145 140 ... ## ..$ total_b : int [1:153] 43 54 52 52 68 86 98 92 81 106 ... ## ..$ percent_positive: num [1:153] 1.06 1.3 1.11 1.11 1.12 ... ## ..$ percent_a : num [1:153] 0.698 0.885 0.722 0.724 0.66 ... ## ..$ percent_b : num [1:153] 0.357 0.412 0.387 0.384 0.463 ... ## ..$ wk_date : Date[1:153], format: "2015-10-04" "2015-10-11" "2015-10-18" "2015-10-25" ... ``` r 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") ```

``` r who_nrevss("hhs", years=2016) ``` ## $public_health_labs ## # A tibble: 520 x 13 ## region_type region year week total_specimens a_2009_h1n1 a_h3 a_subtyping_not… b bvic byam h3n2v wk_date ## ## 1 HHS Regions Regio… 2016 40 31 0 6 0 0 0 0 0 2016-10-02 ## 2 HHS Regions Regio… 2016 40 31 0 6 0 0 2 0 0 2016-10-02 ## 3 HHS Regions Regio… 2016 40 112 2 2 0 0 0 0 0 2016-10-02 ## 4 HHS Regions Regio… 2016 40 112 1 11 0 1 2 0 0 2016-10-02 ## 5 HHS Regions Regio… 2016 40 204 0 7 0 0 0 1 0 2016-10-02 ## 6 HHS Regions Regio… 2016 40 39 1 1 0 0 0 0 0 2016-10-02 ## 7 HHS Regions Regio… 2016 40 24 0 2 0 0 1 0 0 2016-10-02 ## 8 HHS Regions Regio… 2016 40 46 2 8 0 0 0 0 0 2016-10-02 ## 9 HHS Regions Regio… 2016 40 186 3 27 0 0 0 3 0 2016-10-02 ## 10 HHS Regions Regio… 2016 40 113 0 17 0 0 0 0 0 2016-10-02 ## # ... with 510 more rows ## ## $clinical_labs ## # A tibble: 520 x 11 ## region_type region year week total_specimens total_a total_b percent_positive percent_a percent_b wk_date ## ## 1 HHS Regions Region 1 2016 40 654 5 1 0.917 0.765 0.153 2016-10-02 ## 2 HHS Regions Region 2 2016 40 1307 10 3 0.995 0.765 0.230 2016-10-02 ## 3 HHS Regions Region 3 2016 40 941 1 4 0.531 0.106 0.425 2016-10-02 ## 4 HHS Regions Region 4 2016 40 2960 46 63 3.68 1.55 2.13 2016-10-02 ## 5 HHS Regions Region 5 2016 40 2386 8 5 0.545 0.335 0.210 2016-10-02 ## 6 HHS Regions Region 6 2016 40 1914 22 13 1.83 1.15 0.679 2016-10-02 ## 7 HHS Regions Region 7 2016 40 723 0 0 0 0 0 2016-10-02 ## 8 HHS Regions Region 8 2016 40 913 8 0 0.876 0.876 0 2016-10-02 ## 9 HHS Regions Region 9 2016 40 992 6 1 0.706 0.605 0.101 2016-10-02 ## 10 HHS Regions Region 10 2016 40 590 14 0 2.37 2.37 0 2016-10-02 ## # ... with 510 more rows ``` r who_nrevss("census", years=2016) ``` ## $public_health_labs ## # A tibble: 468 x 13 ## region_type region year week total_specimens a_2009_h1n1 a_h3 a_subtyping_not… b bvic byam h3n2v wk_date ## ## 1 Census Reg… New E… 2016 40 31 0 6 0 0 0 0 0 2016-10-02 ## 2 Census Reg… Mid-A… 2016 40 50 0 8 0 0 2 0 0 2016-10-02 ## 3 Census Reg… East … 2016 40 139 0 4 0 0 0 1 0 2016-10-02 ## 4 Census Reg… West … 2016 40 103 0 6 0 0 1 0 0 2016-10-02 ## 5 Census Reg… South… 2016 40 181 3 11 0 1 2 0 0 2016-10-02 ## 6 Census Reg… East … 2016 40 24 0 0 0 0 0 0 0 2016-10-02 ## 7 Census Reg… West … 2016 40 27 0 1 0 0 0 0 0 2016-10-02 ## 8 Census Reg… Mount… 2016 40 54 3 10 0 0 0 1 0 2016-10-02 ## 9 Census Reg… Pacif… 2016 40 289 3 41 0 0 0 2 0 2016-10-02 ## 10 Census Reg… New E… 2016 41 14 0 2 0 0 0 0 0 2016-10-09 ## # ... with 458 more rows ## ## $clinical_labs ## # A tibble: 468 x 11 ## region_type region year week total_specimens total_a total_b percent_positive percent_a percent_b wk_date ## ## 1 Census Regions New Engl… 2016 40 654 5 1 0.917 0.765 0.153 2016-10-02 ## 2 Census Regions Mid-Atla… 2016 40 1579 10 4 0.887 0.633 0.253 2016-10-02 ## 3 Census Regions East Nor… 2016 40 2176 6 5 0.506 0.276 0.230 2016-10-02 ## 4 Census Regions West Nor… 2016 40 1104 3 0 0.272 0.272 0 2016-10-02 ## 5 Census Regions South At… 2016 40 2785 43 62 3.77 1.54 2.23 2016-10-02 ## 6 Census Regions East Sou… 2016 40 844 4 4 0.948 0.474 0.474 2016-10-02 ## 7 Census Regions West Sou… 2016 40 1738 21 13 1.96 1.21 0.748 2016-10-02 ## 8 Census Regions Mountain 2016 40 1067 8 0 0.750 0.750 0 2016-10-02 ## 9 Census Regions Pacific 2016 40 1433 20 1 1.47 1.40 0.0698 2016-10-02 ## 10 Census Regions New Engl… 2016 41 810 5 1 0.741 0.617 0.123 2016-10-09 ## # ... with 458 more rows ``` r who_nrevss("state", years=2016) ``` ## $public_health_labs ## # A tibble: 54 x 12 ## region_type region season_descript… total_specimens a_2009_h1n1 a_h3 a_subtyping_not_p… b bvic byam h3n2v ## ## 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 ## # ... with 44 more rows, and 1 more variable: wk_date ## ## $clinical_labs ## # A tibble: 2,808 x 11 ## region_type region year week total_specimens total_a total_b percent_positive percent_a percent_b wk_date ## ## 1 States Alabama 2016 40 406 4 1 1.23 0.99 0.25 2016-10-02 ## 2 States Alaska 2016 40 2016-10-02 ## 3 States Arizona 2016 40 133 0 0 0 0 0 2016-10-02 ## 4 States Arkansas 2016 40 47 0 0 0 0 0 2016-10-02 ## 5 States California 2016 40 668 2 0 0.3 0.3 0 2016-10-02 ## 6 States Colorado 2016 40 260 0 0 0 0 0 2016-10-02 ## 7 States Connecticut 2016 40 199 3 0 1.51 1.51 0 2016-10-02 ## 8 States Delaware 2016 40 40 0 0 0 0 0 2016-10-02 ## 9 States District of… 2016 40 2016-10-02 ## 10 States Florida 2016 40 2016-10-02 ## # ... with 2,798 more rows ## Code of Conduct Please note that this project is released with a [Contributor Code of Conduct](CONDUCT.md). By participating in this project you agree to abide by its terms.