[![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. Please use GitHub issues to identify previous API functionality you would like ported over. 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 - `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_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 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] '0.7.0' ### Age Group Distribution of Influenza Positive Tests Reported by Public Health Laboratories ``` r glimpse(age_group_distribution()) ``` ## Observations: 36,144 ## Variables: 16 ## $ sea_label "1997-98", "1997-98", "1997-98", "1997-98", "1997-98", "1997-98", "1997-98", "1997-98", "... ## $ 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, ... ## $ vir_label A (Subtyping not Performed), A (Subtyping not Performed), A (Subtyping not Performed), A... ## $ count 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 1866, 1867, 1868, 1869, 1870, 1871, 1872, 1873, 1874, 1875, 1876, 1877, 1878, 1879, 1880,... ## $ seasonid 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 3... ## $ publishyearweekid 2913, 2913, 2913, 2913, 2913, 2913, 2913, 2913, 2913, 2913, 2913, 2913, 2913, 2913, 2913,... ## $ sea_description "Season 1997-98", "Season 1997-98", "Season 1997-98", "Season 1997-98", "Season 1997-98",... ## $ sea_startweek 1866, 1866, 1866, 1866, 1866, 1866, 1866, 1866, 1866, 1866, 1866, 1866, 1866, 1866, 1866,... ## $ sea_endweek 1918, 1918, 1918, 1918, 1918, 1918, 1918, 1918, 1918, 1918, 1918, 1918, 1918, 1918, 1918,... ## $ 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 1997-09-28, 1997-10-05, 1997-10-12, 1997-10-19, 1997-10-26, 1997-11-02, 1997-11-09, 1997... ## $ wk_end 1997-10-04, 1997-10-11, 1997-10-18, 1997-10-25, 1997-11-01, 1997-11-08, 1997-11-15, 1997... ## $ year_wk_num 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... ### Retrieve CDC U.S. Coverage Map ``` r plot(cdc_basemap("national")) ``` ![](README_files/figure-markdown_github-ascii_identifiers/unnamed-chunk-5-1.png) ``` r plot(cdc_basemap("hhs")) ``` ![](README_files/figure-markdown_github-ascii_identifiers/unnamed-chunk-5-2.png) ``` r plot(cdc_basemap("census")) ``` ![](README_files/figure-markdown_github-ascii_identifiers/unnamed-chunk-5-3.png) ``` r plot(cdc_basemap("states")) ``` ![](README_files/figure-markdown_github-ascii_identifiers/unnamed-chunk-5-4.png) ``` r plot(cdc_basemap("spread")) ``` ![](README_files/figure-markdown_github-ascii_identifiers/unnamed-chunk-5-5.png) ``` r plot(cdc_basemap("surv")) ``` ![](README_files/figure-markdown_github-ascii_identifiers/unnamed-chunk-5-6.png) ### State and Territorial Epidemiologists Reports of Geographic Spread of Influenza ``` r glimpse(geographic_spread()) ``` ## Observations: 25,795 ## 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: 1,476 ## 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, ... ## $ 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() ``` ![](README_files/figure-markdown_github-ascii_identifiers/unnamed-chunk-7-1.png) ``` r glimpse(hospitalizations("eip")) ``` ## Observations: 2,385 ## 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 2003, 2003, 2003, 2003, 2003, 2003, 2003, 2003, 2003, 2003, 2003, 2003, 2003, 2003, 2004,... ## $ season 43, 43, 43, 43, 43, 43, 43, 43, 43, 43, 43, 43, 43, 43, 43, 43, 43, 43, 43, 43, 43, 43, 4... ## $ wk_start 2003-09-28, 2003-10-05, 2003-10-12, 2003-10-19, 2003-10-26, 2003-11-02, 2003-11-09, 2003... ## $ wk_end 2003-10-04, 2003-10-11, 2003-10-18, 2003-10-25, 2003-11-01, 2003-11-08, 2003-11-15, 2003... ## $ year_wk_num 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... ## $ rate 0.0, 0.0, 0.0, 0.0, 0.1, 0.7, 3.3, 9.1, 16.9, 28.1, 40.0, 55.6, 69.0, 78.7, 83.1, 86.6, 8... ## $ weeklyrate 0.0, 0.0, 0.0, 0.0, 0.1, 0.6, 2.7, 5.8, 7.8, 11.2, 11.9, 15.6, 13.4, 9.7, 4.4, 3.4, 1.3, ... ## $ 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, ... ## $ sea_label NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N... ## $ sea_description NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N... ## $ mmwrid 2179, 2180, 2181, 2182, 2183, 2184, 2185, 2186, 2187, 2188, 2189, 2190, 2191, 2192, 2193,... ``` r glimpse(hospitalizations("eip", "Colorado")) ``` ## Observations: 2,385 ## 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 2003, 2003, 2003, 2003, 2003, 2003, 2003, 2003, 2003, 2003, 2003, 2003, 2003, 2003, 2004,... ## $ season 43, 43, 43, 43, 43, 43, 43, 43, 43, 43, 43, 43, 43, 43, 43, 43, 43, 43, 43, 43, 43, 43, 4... ## $ wk_start 2003-09-28, 2003-10-05, 2003-10-12, 2003-10-19, 2003-10-26, 2003-11-02, 2003-11-09, 2003... ## $ wk_end 2003-10-04, 2003-10-11, 2003-10-18, 2003-10-25, 2003-11-01, 2003-11-08, 2003-11-15, 2003... ## $ year_wk_num 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... ## $ rate 0.0, 0.0, 0.0, 0.0, 0.6, 3.6, 21.2, 57.5, 94.3, 130.6, 146.4, 150.6, 153.0, 157.2, 158.5,... ## $ weeklyrate 0.0, 0.0, 0.0, 0.0, 0.6, 3.0, 17.5, 36.3, 36.9, 36.3, 15.7, 4.2, 2.4, 4.2, 1.2, 1.2, 1.8,... ## $ 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, ... ## $ sea_label NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N... ## $ sea_description NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N... ## $ mmwrid 2179, 2180, 2181, 2182, 2183, 2184, 2185, 2186, 2187, 2188, 2189, 2190, 2191, 2192, 2193,... ``` r glimpse(hospitalizations("ihsp")) ``` ## Observations: 1,476 ## 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 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 0.0, 0.4, 3.6, 7.6, 14.0, 25.5, 47.1, 71.8, 87.4, 92.5, 94.9, 96.9, 98.1, 98.9, 100.9, 10... ## $ weeklyrate 0.0, 0.4, 3.2, 4.0, 6.4, 11.6, 21.5, 24.7, 15.6, 5.2, 2.4, 2.0, 1.2, 0.8, 2.0, 1.2, 1.6, ... ## $ 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, ... ## $ 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 glimpse(hospitalizations("ihsp", "Oklahoma")) ``` ## Observations: 390 ## 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 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 0.0, 1.3, 10.8, 21.5, 40.4, 63.3, 88.9, 106.4, 115.8, 121.2, 125.2, 125.2, 126.6, 127.9, ... ## $ weeklyrate 0.0, 1.3, 9.4, 10.8, 18.9, 22.9, 25.6, 17.5, 9.4, 5.4, 4.0, 0.0, 1.3, 1.3, 2.7, 1.3, 5.4,... ## $ 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, ... ## $ 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,... ### 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: 1,048 ## 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: 1,048 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 1.10148 1.21686 179 NA 157 205 NA 29 ## 2 National National 1997 41 1.20007 1.28064 199 NA 151 242 NA 23 ## 3 National National 1997 42 1.37876 1.23906 228 NA 153 266 NA 34 ## 4 National National 1997 43 1.19920 1.14473 188 NA 193 236 NA 36 ## 5 National National 1997 44 1.65618 1.26112 217 NA 162 280 NA 41 ## 6 National National 1997 45 1.41326 1.28275 178 NA 148 281 NA 48 ## 7 National National 1997 46 1.98680 1.44579 294 NA 240 328 NA 70 ## 8 National National 1997 47 2.44749 1.64796 288 NA 293 456 NA 63 ## 9 National National 1997 48 1.73901 1.67517 268 NA 206 343 NA 69 ## 10 National National 1997 49 1.93919 1.61739 299 NA 282 415 NA 102 ## # ... with 1,038 more rows, and 4 more variables: ilitotal , num_of_providers , total_patients , ## # week_start ![](README_files/figure-markdown_github-ascii_identifiers/unnamed-chunk-8-1.png) ## Observations: 10,480 ## 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: 10,480 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 0.498535 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 4 more variables: ilitotal , num_of_providers , total_patients , ## # week_start ![](README_files/figure-markdown_github-ascii_identifiers/unnamed-chunk-8-2.png) ## Observations: 9,432 ## 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: 9,432 x 16 ## region_type region year week weighted_ili unweighted_ili age_0_4 age_25_49 age_25_64 age_5_24 ## ## 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 6 more variables: age_50_64 , age_65 , ilitotal , ## # num_of_providers , total_patients , week_start ![](README_files/figure-markdown_github-ascii_identifiers/unnamed-chunk-8-3.png) ## Observations: 19,718 ## 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, NA, 22, 117, 1... ## $ num_of_providers 35, 7, 49, 15, 112, 14, 12, 13, 4, NA, 62, 18, 12, 74, 44, 6, 40, 14, NA, 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: 19,718 x 16 ## region_type region year week weighted_ili unweighted_ili age_0_4 age_25_49 age_25_64 age_5_24 ## ## 1 States Alabama 2010 40 NA 2.1347700 NA NA NA NA ## 2 States Alaska 2010 40 NA 0.8751460 NA NA NA NA ## 3 States Arizona 2010 40 NA 0.6747210 NA NA NA NA ## 4 States Arkansas 2010 40 NA 0.6960560 NA NA NA NA ## 5 States California 2010 40 NA 1.9541200 NA NA NA NA ## 6 States Colorado 2010 40 NA 0.6606840 NA NA NA NA ## 7 States Connecticut 2010 40 NA 0.0783085 NA NA NA NA ## 8 States Delaware 2010 40 NA 0.1001250 NA NA NA NA ## 9 States District of Columbia 2010 40 NA 2.8087700 NA NA NA NA ## 10 States Florida 2010 40 NA NA NA NA NA NA ## # ... with 19,708 more rows, and 6 more variables: age_50_64 , age_65 , ilitotal , ## # num_of_providers , total_patients , week_start ![](README_files/figure-markdown_github-ascii_identifiers/unnamed-chunk-8-4.png) ### 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 ## ## 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 xdf <- map_df(2008:2017, ili_weekly_activity_indicators) count(xdf, weekend, ili_activity_label) %>% complete(weekend, ili_activity_label) %>% ggplot(aes(weekend, ili_activity_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") ``` ![](README_files/figure-markdown_github-ascii_identifiers/unnamed-chunk-9-1.png) ### Pneumonia and Influenza Mortality Surveillance ``` r (nat_pi <- pi_mortality("national")) ``` ## # A tibble: 419 x 19 ## seasonid baseline threshold percent_pni percent_complete number_influenza number_pneumonia all_deaths total_pni ## ## 1 57 0.058 0.061 0.054 0.763 10 1962 36283 1972 ## 2 57 0.058 0.062 0.056 0.675 10 1795 32107 1805 ## 3 56 0.059 0.063 0.059 1.000 18 3022 51404 3040 ## 4 56 0.060 0.063 0.061 1.000 11 3193 52130 3204 ## 5 56 0.061 0.064 0.062 1.000 7 3178 51443 3185 ## 6 56 0.062 0.065 0.061 1.000 17 3129 51865 3146 ## 7 56 0.063 0.066 0.060 1.000 16 3099 51753 3115 ## 8 56 0.064 0.067 0.061 1.000 19 3208 52541 3227 ## 9 56 0.065 0.068 0.060 1.000 7 3192 53460 3199 ## 10 56 0.066 0.069 0.062 1.000 22 3257 53163 3279 ## # ... with 409 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") ``` ![](README_files/figure-markdown_github-ascii_identifiers/unnamed-chunk-10-1.png) ``` r (st_pi <- pi_mortality("state")) ``` ## # A tibble: 21,788 x 19 ## seasonid baseline threshold percent_pni percent_complete number_influenza number_pneumonia all_deaths total_pni ## ## 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 , geo_description , age_label , ## # wk_start , wk_end , year_wk_num , mmwrid , coverage_area , region_name , ## # callout ``` r (reg_pi <- pi_mortality("region")) ``` ## # A tibble: 4,190 x 19 ## seasonid baseline threshold percent_pni percent_complete number_influenza number_pneumonia all_deaths total_pni ## ## 1 57 0.060 0.067 0.051 0.735 0 85 1683 85 ## 2 57 0.061 0.068 0.060 0.701 0 96 1605 96 ## 3 57 0.060 0.065 0.061 0.608 1 154 2524 155 ## 4 57 0.060 0.066 0.063 0.602 1 157 2497 158 ## 5 57 0.053 0.058 0.045 0.511 1 115 2575 116 ## 6 57 0.054 0.059 0.045 0.440 1 98 2215 99 ## 7 57 0.056 0.060 0.051 0.744 3 394 7753 397 ## 8 57 0.057 0.061 0.052 0.651 1 354 6778 355 ## 9 57 0.055 0.059 0.052 0.914 1 403 7701 404 ## 10 57 0.056 0.060 0.054 0.799 4 358 6733 362 ## # ... with 4,180 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 ## * ## 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 , statewebsitename , statefluphonenum ### 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': 108 obs. of 13 variables: ## ..$ region_type : chr [1:108] "National" "National" "National" "National" ... ## ..$ region : chr [1:108] "National" "National" "National" "National" ... ## ..$ year : int [1:108] 2015 2015 2015 2015 2015 2015 2015 2015 2015 2015 ... ## ..$ week : int [1:108] 40 41 42 43 44 45 46 47 48 49 ... ## ..$ total_specimens : int [1:108] 1139 1152 1198 1244 1465 1393 1458 1157 1550 1518 ... ## ..$ a_2009_h1n1 : int [1:108] 4 5 10 9 4 11 17 17 27 38 ... ## ..$ a_h3 : int [1:108] 65 41 50 31 23 34 42 24 36 37 ... ## ..$ a_subtyping_not_performed: int [1:108] 2 2 1 4 4 1 1 0 3 3 ... ## ..$ b : int [1:108] 10 7 8 9 9 10 4 4 9 11 ... ## ..$ bvic : int [1:108] 0 3 3 1 1 4 0 3 3 2 ... ## ..$ byam : int [1:108] 1 0 2 4 4 2 4 9 12 11 ... ## ..$ h3n2v : int [1:108] 0 0 0 0 0 0 0 0 0 0 ... ## ..$ wk_date : Date[1:108], format: "2015-10-04" "2015-10-11" "2015-10-18" "2015-10-25" ... ## $ clinical_labs :Classes 'tbl_df', 'tbl' and 'data.frame': 108 obs. of 11 variables: ## ..$ region_type : chr [1:108] "National" "National" "National" "National" ... ## ..$ region : chr [1:108] "National" "National" "National" "National" ... ## ..$ year : int [1:108] 2015 2015 2015 2015 2015 2015 2015 2015 2015 2015 ... ## ..$ week : int [1:108] 40 41 42 43 44 45 46 47 48 49 ... ## ..$ total_specimens : int [1:108] 12029 13111 13441 13537 14687 15048 15250 15234 16201 16673 ... ## ..$ total_a : int [1:108] 84 116 97 98 97 122 84 119 145 140 ... ## ..$ total_b : int [1:108] 43 54 52 52 68 86 98 92 81 106 ... ## ..$ percent_positive: num [1:108] 1.06 1.3 1.11 1.11 1.12 ... ## ..$ percent_a : num [1:108] 0.698 0.885 0.722 0.724 0.66 ... ## ..$ percent_b : num [1:108] 0.357 0.412 0.387 0.384 0.463 ... ## ..$ wk_date : Date[1:108], 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") ``` ![](README_files/figure-markdown_github-ascii_identifiers/unnamed-chunk-12-1.png) ``` r who_nrevss("hhs") ``` ## $combined_prior_to_2015_16 ## # A tibble: 9,400 x 14 ## region_type region year week total_specimens percent_positive a_2009_h1n1 a_h1 a_h3 a_subtyping_not_performed ## ## 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 4 more variables: a_unable_to_subtype , b , h3n2v , wk_date ## ## $public_health_labs ## # A tibble: 1,080 x 13 ## region_type region year week total_specimens a_2009_h1n1 a_h3 a_subtyping_not_performed b bvic byam ## ## 1 HHS Regions Region 1 2015 39 0 5 0 0 0 0 ## 2 HHS Regions Region 2 2015 56 1 4 0 1 0 0 ## 3 HHS Regions Region 3 2015 132 1 3 0 0 0 0 ## 4 HHS Regions Region 4 2015 83 0 5 0 1 0 0 ## 5 HHS Regions Region 5 2015 218 2 7 0 0 0 1 ## 6 HHS Regions Region 6 2015 97 0 2 0 0 0 0 ## 7 HHS Regions Region 7 2015 36 0 2 0 0 0 0 ## 8 HHS Regions Region 8 2015 71 0 2 0 0 0 0 ## 9 HHS Regions Region 9 2015 273 0 22 2 8 0 0 ## 10 HHS Regions Region 10 2015 134 0 13 0 0 0 0 ## # ... with 1,070 more rows, and 2 more variables: h3n2v , wk_date ## ## $clinical_labs ## # A tibble: 1,080 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 2015 40 693 2 3 0.721501 0.288600 0.432900 2015-10-04 ## 2 HHS Regions Region 2 2015 40 1220 5 0 0.409836 0.409836 0.000000 2015-10-04 ## 3 HHS Regions Region 3 2015 40 896 0 1 0.111607 0.000000 0.111607 2015-10-04 ## 4 HHS Regions Region 4 2015 40 2486 24 16 1.609010 0.965406 0.643604 2015-10-04 ## 5 HHS Regions Region 5 2015 40 2138 14 3 0.795136 0.654818 0.140318 2015-10-04 ## 6 HHS Regions Region 6 2015 40 1774 8 16 1.352870 0.450958 0.901917 2015-10-04 ## 7 HHS Regions Region 7 2015 40 621 2 1 0.483092 0.322061 0.161031 2015-10-04 ## 8 HHS Regions Region 8 2015 40 824 1 1 0.242718 0.121359 0.121359 2015-10-04 ## 9 HHS Regions Region 9 2015 40 980 25 2 2.755100 2.551020 0.204082 2015-10-04 ## 10 HHS Regions Region 10 2015 40 397 3 0 0.755668 0.755668 0.000000 2015-10-04 ## # ... with 1,070 more rows ``` r who_nrevss("census") ``` ## $combined_prior_to_2015_16 ## # A tibble: 8,460 x 14 ## region_type region year week total_specimens percent_positive a_2009_h1n1 a_h1 a_h3 ## ## 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 5 more variables: a_subtyping_not_performed , a_unable_to_subtype , b , ## # h3n2v , wk_date ## ## $public_health_labs ## # A tibble: 972 x 13 ## region_type region year week total_specimens a_2009_h1n1 a_h3 a_subtyping_not_performed b ## ## 1 Census Regions New England 2015 39 0 5 0 0 ## 2 Census Regions Mid-Atlantic 2015 63 1 5 0 1 ## 3 Census Regions East North Central 2015 91 2 5 0 0 ## 4 Census Regions West North Central 2015 169 0 4 0 0 ## 5 Census Regions South Atlantic 2015 187 1 7 0 0 ## 6 Census Regions East South Central 2015 21 0 0 0 1 ## 7 Census Regions West South Central 2015 72 0 2 0 0 ## 8 Census Regions Mountain 2015 111 0 6 0 0 ## 9 Census Regions Pacific 2015 386 0 31 2 8 ## 10 Census Regions New England 2015 39 2 3 0 0 ## # ... with 962 more rows, and 4 more variables: bvic , byam , h3n2v , wk_date ## ## $clinical_labs ## # A tibble: 972 x 11 ## region_type region year week total_specimens total_a total_b percent_positive percent_a percent_b ## ## 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, and 1 more variables: wk_date ``` r who_nrevss("state") ``` ## $combined_prior_to_2015_16 ## # A tibble: 14,094 x 14 ## region_type region year week total_specimens percent_positive a_2009_h1n1 a_h1 a_h3 ## ## 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