diff --git a/NAMESPACE b/NAMESPACE index d7846b6..8e14f99 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -6,6 +6,9 @@ export(geographic_spread) export(hospitalizations) export(ili_weekly_activity_indicators) export(ilinet) +export(mmwr_week) +export(mmwr_week_to_date) +export(mmwr_weekday) export(mmwrid_map) export(pi_mortality) export(state_data_providers) diff --git a/R/aaa.R b/R/aaa.R index 4b1ba32..b73f8b6 100644 --- a/R/aaa.R +++ b/R/aaa.R @@ -1,3 +1,4 @@ + # CDC U.S. region names to ID map .region_map <- c(national=3, hhs=1, census=2, state=5) @@ -27,4 +28,5 @@ "B (Lineage Unspecified)", "A (H1)", "A (H3)", "B (Victoria Lineage)", "B (Yamagata Lineage)", "H3N2v") -# Week Starts +# Global HTTR timeout +.httr_timeout <- 120 \ No newline at end of file diff --git a/R/agd-ipt.r b/R/agd-ipt.r index 902c6ed..31ffffb 100644 --- a/R/agd-ipt.r +++ b/R/agd-ipt.r @@ -21,7 +21,7 @@ age_group_distribution <- function() { Referer = "https://gis.cdc.gov/grasp/fluview/flu_by_age_virus.html" ), # httr::verbose(), - httr::timeout(60) + httr::timeout(.httr_timeout) ) -> res httr::stop_for_status(res) @@ -56,6 +56,8 @@ age_group_distribution <- function() { vir_df$age_label <- factor(vir_df$age_label, levels=.age_grp) vir_df$vir_label <- factor(vir_df$vir_label, levels=.vir_grp) + vir_df <- dplyr::left_join(vir_df, mmwrid_map, "mmwrid") + vir_df } diff --git a/R/geographic-spread.R b/R/geographic-spread.R index 82e4478..a673ad1 100644 --- a/R/geographic-spread.R +++ b/R/geographic-spread.R @@ -22,7 +22,7 @@ geographic_spread <- function() { SeasonIDs = paste0(meta$seasons$seasonid, collapse=",") ), # httr::verbose(), - httr::timeout(60) + httr::timeout(.httr_timeout) ) -> res httr::stop_for_status(res) diff --git a/R/hospital.r b/R/hospital.r index 7db5384..ca39559 100644 --- a/R/hospital.r +++ b/R/hospital.r @@ -49,7 +49,7 @@ hospitalizations <- function(surveillance_area=c("flusurv", "eip", "ihsp"), cacthmentid = tgt$id ), # httr::verbose(), - httr::timeout(60) + httr::timeout(.httr_timeout) ) -> res httr::stop_for_status(res) @@ -85,7 +85,16 @@ hospitalizations <- function(surveillance_area=c("flusurv", "eip", "ihsp"), dplyr::mutate( surveillance_area = sarea, region = reg - ) + ) %>% + dplyr::left_join(mmwrid_map, "mmwrid") -> xdf + + xdf$age_label <- factor(xdf$age_label, + levels=c("0-4 yr", "5-17 yr", "18-49 yr", "50-64 yr", + "65+ yr", "Overall")) + + xdf[,c("surveillance_area", "region", "year", "season", "wk_start", "wk_end", + "year_wk_num", "rate", "weeklyrate", "age", "age_label", "sea_label", + "sea_description", "mmwrid")] } diff --git a/R/mmwr-map.r b/R/mmwr-map.r index 77f8d01..357d7bc 100644 --- a/R/mmwr-map.r +++ b/R/mmwr-map.r @@ -26,6 +26,100 @@ mmwrid_map <- Reduce(rbind.data.frame, mmwrid_map) mmwrid_map$mmwrid <- 1:nrow(mmwrid_map) #' @title MMWR ID to Calendar Mappings +#' @md +#' @description The CDC uses a unique "Morbidity and Mortality Weekly Report" identifier +#' for each week that starts at 1 (Ref: < https://www.cdc.gov/mmwr/preview/mmwrhtml/su6004a9.htm>). +#' This data frame consists of 4 columns: +#' - `wk_start`: Start date (Sunday) for the week (`Date`) +#' - `wk_end`: End date (Saturday) for the week (`Date`) +#' - `year_wk_num`: The week of the calendar year +#' - `mmwrid`: The unique MMWR identifier +#' These can be "left-joined" to data provided from the CDC to perform MMWR identifier +#' to date mappings. +#' @docType data #' @name mmwrid_map +#' @format A data frame with 4,592 rows and 4 columns #' @export NULL + +#' Convert a Date to an MMWR day+week+year +#' +#' This is a reformat and re-export of a function in the `MMWRweek` package. +#' It provides a snake case version of its counterpart, produces a `tibble` +#' +#' @md +#' @param x a vector of `Date` objects or a character vector in `YYYY-mm-dd` format. +#' @return data frame (tibble) +#' @export +#' @examples +#' mmwr_week(Sys.Date()) +mmwr_week <- function(x) { + x <- as.Date(x) + x <- setNames(MMWRweek::MMWRweek(x), c("mmwr_year", "mmwr_week", "mmwr_day")) + class(x) <- c("tbl_df", "tbl", "data.frame") + x +} + +#' Convert a Date to an MMWR weekday +#' +#' This is a reformat and re-export of a function in the `MMWRweek` package. +#' It provides a snake case version of its counterpart, produces a `factor` of +#' weekday names (Sunday-Saturday). +#' +#' @md +#' @note Weekday names are explicitly mapped to "Sunday-Saturday" or "Sun-Sat" and +#' do not change with your locale. +#' @param x a vector of `Date` objects or a character vector in `YYYY-mm-dd` format. +#' @param abbr (logical) if `TRUE`, return abbreviated weekday names, otherwise full +#' weekday names (see Note). +#' @return ordered factor +#' @export +#' @examples +#' mmwr_weekday(Sys.Date()) +mmwr_weekday <- function(x, abbr = FALSE) { + x <- as.Date(x) + x <- MMWRweek::MMWRweekday(x) + if (abbr) { + x <- ordered( + x, + levels=c("Sunday", "Monday", "Tuesday", "Wednesday", "Thursday", "Friday", "Saturday"), + labels = c("Sun", "Mon", "Tues", "Wed", "Thurs", "Fri", "Sat") + ) + } + x +} + +#' Convert an MMWR year+week or year+week+day to a Date object +#' +#' This is a reformat and re-export of a function in the `MMWRweek` package. +#' It provides a snake case version of its counterpart and produces a vector +#' of `Date` objects that corresponds to the input MMWR year+week or year+week+day +#' vectors. This also adds some parameter checking and cleanup to avoid exceptions. +#' +#' @md +#' @param year,week,day Year, week and month vectors. All must be the same length +#' unless `day` is `NULL`. +#' @return vector of `Date` objects +#' @export +#' @examples +#' mmwr_week_to_date(2016,10,3) +mmwr_week_to_date <- function(year, week, day=NULL) { + + year <- as.numeric(year) + week <- as.numeric(week) + day <- if (!is.null(day)) as.numeric(day) else rep(1, length(week)) + + week <- ifelse(0 < week & week < 54, week, NA) + + as.Date(ifelse(is.na(week), NA, MMWRweek::MMWRweek2Date(year, week, day)), + origin="1970-01-01") + +} + + + + + + + + diff --git a/R/pi-mortality.r b/R/pi-mortality.r index b910116..916f651 100644 --- a/R/pi-mortality.r +++ b/R/pi-mortality.r @@ -59,12 +59,6 @@ pi_mortality <- function(coverage_area=c("national", "state", "region")) { age_df <- setNames(meta$nchs_ages, c("ageid", "age_label")) age_df$ageid <- as.character(age_df$ageid) - mwmr_df <- meta$mmwr - mwmr_df$mmwrid <- as.character(mwmr_df$mmwrid) - mwmr_df <- setNames(mwmr_df, - c("mmwrid", "weekend", "mwmr_weeknumber", "weekstart", - "year", "yearweek", "mwmr_seasonid", "mwmr_label", "weekendlabel")) - sum_df <- meta$nchs_summary sum_df$seasonid <- as.character(sum_df$seasonid) sum_df$ageid <- as.character(sum_df$ageid) @@ -86,7 +80,7 @@ pi_mortality <- function(coverage_area=c("national", "state", "region")) { AgegroupsParameters = list(list(ID="1")) ), # httr::verbose(), - httr::timeout(60) + httr::timeout(.httr_timeout) ) -> res httr::stop_for_status(res) @@ -97,7 +91,7 @@ pi_mortality <- function(coverage_area=c("national", "state", "region")) { dplyr::left_join(mapcode_df, "map_code") %>% dplyr::left_join(geo_df, "geoid") %>% dplyr::left_join(age_df, "ageid") %>% - dplyr::left_join(mwmr_df, "mmwrid") -> xdf + dplyr::left_join(dplyr::mutate(mmwrid_map, mmwrid=as.character(mmwrid)), "mmwrid") -> xdf xdf <- dplyr::mutate(xdf, coverage_area = coverage_area) @@ -106,25 +100,23 @@ pi_mortality <- function(coverage_area=c("national", "state", "region")) { } else if (coverage_area == "region") { xdf$region_name <- sprintf("Region %s", xdf$subgeoid) } else { - xdf$region_name <- NA_character_ + xdf$region_name <- "national" } xdf[,c("seasonid", "baseline", "threshold", "percent_pni", "percent_complete", "number_influenza", "number_pneumonia", "all_deaths", "Total_PnI", "weeknumber", "geo_description", - "age_label", "weekend", "weekstart", "year", "yearweek", + "age_label", "wk_start", "wk_end", "year_wk_num", "mmwrid", "coverage_area", "region_name", "callout")] -> xdf - suppressWarnings(xdf$baseline <- to_num(xdf$baseline)) - suppressWarnings(xdf$threshold <- to_num(xdf$threshold)) + suppressWarnings(xdf$baseline <- to_num(xdf$baseline) / 100) + suppressWarnings(xdf$threshold <- to_num(xdf$threshold) / 100) suppressWarnings(xdf$percent_pni <- to_num(xdf$percent_pni) / 100) suppressWarnings(xdf$percent_complete <- to_num(xdf$percent_complete) / 100) suppressWarnings(xdf$number_influenza <- to_num(xdf$number_influenza)) suppressWarnings(xdf$number_pneumonia <- to_num(xdf$number_pneumonia)) suppressWarnings(xdf$all_deaths <- to_num(xdf$all_deaths)) suppressWarnings(xdf$Total_PnI <- to_num(xdf$Total_PnI)) - suppressWarnings(xdf$weekend <- as.Date(xdf$weekend)) - suppressWarnings(xdf$weekstart <- as.Date(xdf$weekstart)) xdf <- .mcga(xdf) diff --git a/R/who-nrvess.r b/R/who-nrvess.r index 1c77c63..b06459b 100644 --- a/R/who-nrvess.r +++ b/R/who-nrvess.r @@ -13,7 +13,8 @@ #' counterparts.\cr\cr #' Also, beginning for the 2015-16 season, reports from public health and clinical #' laboratories are presented separately in the weekly influenza update. This is -#' the reason why a list of data frames is returned. +#' the reason why a list of data frames is returned.\cr\cr +#' **ALSO** The new CDC API seems to be missing some public health lab data fields. #' @param region one of "`national`", "`hhs`", "`census`", or "`state`" #' @return list of data frames identified by #' - `combined_prior_to_2015_16` @@ -67,7 +68,7 @@ who_nrevss <- function(region=c("national", "hhs", "census", "state")) { encode = "json", body = params, # httr::verbose(), - httr::timeout(60), + httr::timeout(.httr_timeout), httr::write_disk(tf) ) -> res @@ -82,11 +83,25 @@ who_nrevss <- function(region=c("national", "hhs", "census", "state")) { class(tdf) <- c("tbl_df", "tbl", "data.frame") tdf[tdf=="X"] <- NA + tdf[tdf=="XX"] <- NA tdf }) -> xdf - setNames(xdf, sub("who_nrevss_", "", tools::file_path_sans_ext(tolower(basename(nm))))) + xdf <- setNames(xdf, sub("who_nrevss_", "", tools::file_path_sans_ext(tolower(basename(nm))))) + + xdf <- lapply(xdf, function(.x) { + x_cols <- colnames(.x) + if ((("year" %in% x_cols) & ("week" %in% x_cols))) { + .x$wk_date <- suppressWarnings(mmwr_week_to_date(.x$year, .x$week)) + } else { + .x$wk_date <- as.Date(NA) + } + if (region == "national") .x$region <- "National" + .x + }) + + xdf } \ No newline at end of file diff --git a/README.Rmd b/README.Rmd index 739a969..f0538ac 100644 --- a/README.Rmd +++ b/README.Rmd @@ -38,11 +38,15 @@ The following functions are implemented: - `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) +- `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 @@ -90,10 +94,18 @@ glimpse(geographic_spread()) ### Laboratory-Confirmed Influenza Hospitalizations -```{r message=FALSE, warning=FALSE} +```{r message=FALSE, warning=FALSE, fig.width=10, fig.height=7.5} surveillance_areas() -glimpse(hospitalizations("flusurv")) +glimpse(fs_nat <- hospitalizations("flusurv")) + +ggplot(fs_nat, aes(wk_end, rate)) + + geom_line(aes(color=age_label, group=age_label)) + + facet_wrap(~sea_description, scales="free_x") + + scale_color_ipsum(name=NULL) + + labs(x=NULL, y="Rates per 100,000 population", + title="FluSurv-NET :: Entire Network :: All Seasons :: Cumulative Rate") + + theme_ipsum_rc() glimpse(hospitalizations("eip")) @@ -127,20 +139,42 @@ walk(c("national", "hhs", "census", "state"), ~{ ### Retrieve weekly state-level ILI indicators per-state for a given season -```{r message=FALSE, warning=FALSE} +```{r message=FALSE, warning=FALSE, fig.width=10, fig.height=5} ili_weekly_activity_indicators(2017) -ili_weekly_activity_indicators(2015) +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") ``` ### Pneumonia and Influenza Mortality Surveillance ```{r message=FALSE, warning=FALSE} -pi_mortality("national") - -pi_mortality("state") - -pi_mortality("region") +(nat_pi <- pi_mortality("national")) + +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") + +(st_pi <- pi_mortality("state")) + +(reg_pi <- pi_mortality("region")) ``` ### Retrieve metadata about U.S. State CDC Provider Data @@ -152,7 +186,15 @@ state_data_providers() ### Retrieve WHO/NREVSS Surveillance Data ```{r message=FALSE, warning=FALSE} -who_nrevss("national") +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") diff --git a/README.md b/README.md index c91c03f..212ee55 100644 --- a/README.md +++ b/README.md @@ -48,9 +48,9 @@ What’s Inside The Tin The following functions are implemented: -- `agd_ipt`: Age Group Distribution of Influenza Positive Tests - Reported by Public Health Laboratories -- `cdc_coverage_map`: Retrieve CDC U.S. Coverage Map +- `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 @@ -63,13 +63,19 @@ The following functions are implemented: - `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 +- `hhs_regions`: HHS Region Table (a data frame with 59 rows and 4 variables) -- `census_regions` Census Region Table (a data frame with 51 rows and +- `census_regions`: Census Region Table (a data frame with 51 rows and 2 variables) +- `mmwrid_map`: MMWR ID to Calendar Mappings (it is exported & + available, no need to use `data()`) Installation ------------ @@ -83,6 +89,7 @@ Usage ``` r library(cdcfluview) +library(hrbrthemes) library(tidyverse) # current verison @@ -94,14 +101,14 @@ packageVersion("cdcfluview") ### Age Group Distribution of Influenza Positive Tests Reported by Public Health Laboratories ``` r -glimpse(agd_ipt()) +glimpse(age_group_distribution()) ``` ## Observations: 36,144 - ## Variables: 13 + ## 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",... - ## $ vir_label "A (Subtyping not Performed)", "A (Subtyping not Performed)", "A (Subtyping not Performed... + ## $ 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... @@ -112,15 +119,48 @@ glimpse(agd_ipt()) ## $ 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_coverage_map()) +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 @@ -168,168 +208,200 @@ surveillance_areas() ## 22 ihsp Utah ``` r -glimpse(hospitalizations("flusurv")) +glimpse(fs_nat <- hospitalizations("flusurv")) ``` ## Observations: 1,476 - ## Variables: 20 - ## $ mmwrid 2545, 2546, 2547, 2548, 2549, 2550, 2551, 2552, 2553, 2554, 2555, 2556, 2557, 2558, 2559,... - ## $ weeknumber 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, 0.0, 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 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 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 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 5... - ## $ weekend "2010-10-09", "2010-10-16", "2010-10-23", "2010-10-30", "2010-11-06", "2010-11-13", "2010... - ## $ weekstart "2010-10-03", "2010-10-10", "2010-10-17", "2010-10-24", "2010-10-31", "2010-11-07", "2010... - ## $ year 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2011, 2011,... - ## $ yearweek 201040, 201041, 201042, 201043, 201044, 201045, 201046, 201047, 201048, 201049, 201050, 2... - ## $ seasonid 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 5... - ## $ weekendlabel "Oct 09, 2010", "Oct 16, 2010", "Oct 23, 2010", "Oct 30, 2010", "Nov 06, 2010", "Nov 13, ... - ## $ weekendlabel2 "Oct-09-2010", "Oct-16-2010", "Oct-23-2010", "Oct-30-2010", "Nov-06-2010", "Nov-13-2010",... - ## $ age_label "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 "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",... - ## $ sea_startweek 2545, 2545, 2545, 2545, 2545, 2545, 2545, 2545, 2545, 2545, 2545, 2545, 2545, 2545, 2545,... - ## $ sea_endweek 2596, 2596, 2596, 2596, 2596, 2596, 2596, 2596, 2596, 2596, 2596, 2596, 2596, 2596, 2596,... + ## 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: 20 - ## $ mmwrid 2545, 2546, 2547, 2548, 2549, 2550, 2551, 2552, 2553, 2554, 2555, 2556, 2557, 2558, 2559,... - ## $ weeknumber 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, 0.0, 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 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 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 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 5... - ## $ weekend "2010-10-09", "2010-10-16", "2010-10-23", "2010-10-30", "2010-11-06", "2010-11-13", "2010... - ## $ weekstart "2010-10-03", "2010-10-10", "2010-10-17", "2010-10-24", "2010-10-31", "2010-11-07", "2010... - ## $ year 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2011, 2011,... - ## $ yearweek 201040, 201041, 201042, 201043, 201044, 201045, 201046, 201047, 201048, 201049, 201050, 2... - ## $ seasonid 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 5... - ## $ weekendlabel "Oct 09, 2010", "Oct 16, 2010", "Oct 23, 2010", "Oct 30, 2010", "Nov 06, 2010", "Nov 13, ... - ## $ weekendlabel2 "Oct-09-2010", "Oct-16-2010", "Oct-23-2010", "Oct-30-2010", "Nov-06-2010", "Nov-13-2010",... - ## $ age_label "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 "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",... - ## $ sea_startweek 2545, 2545, 2545, 2545, 2545, 2545, 2545, 2545, 2545, 2545, 2545, 2545, 2545, 2545, 2545,... - ## $ sea_endweek 2596, 2596, 2596, 2596, 2596, 2596, 2596, 2596, 2596, 2596, 2596, 2596, 2596, 2596, 2596,... + ## 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: 20 - ## $ mmwrid 2545, 2546, 2547, 2548, 2549, 2550, 2551, 2552, 2553, 2554, 2555, 2556, 2557, 2558, 2559,... - ## $ weeknumber 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, 0.1, 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 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 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 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 5... - ## $ weekend "2010-10-09", "2010-10-16", "2010-10-23", "2010-10-30", "2010-11-06", "2010-11-13", "2010... - ## $ weekstart "2010-10-03", "2010-10-10", "2010-10-17", "2010-10-24", "2010-10-31", "2010-11-07", "2010... - ## $ year 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2011, 2011,... - ## $ yearweek 201040, 201041, 201042, 201043, 201044, 201045, 201046, 201047, 201048, 201049, 201050, 2... - ## $ seasonid 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 5... - ## $ weekendlabel "Oct 09, 2010", "Oct 16, 2010", "Oct 23, 2010", "Oct 30, 2010", "Nov 06, 2010", "Nov 13, ... - ## $ weekendlabel2 "Oct-09-2010", "Oct-16-2010", "Oct-23-2010", "Oct-30-2010", "Nov-06-2010", "Nov-13-2010",... - ## $ age_label "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 "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",... - ## $ sea_startweek 2545, 2545, 2545, 2545, 2545, 2545, 2545, 2545, 2545, 2545, 2545, 2545, 2545, 2545, 2545,... - ## $ sea_endweek 2596, 2596, 2596, 2596, 2596, 2596, 2596, 2596, 2596, 2596, 2596, 2596, 2596, 2596, 2596,... + ## 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: 20 - ## $ mmwrid 2545, 2546, 2547, 2548, 2549, 2550, 2551, 2552, 2553, 2554, 2555, 2556, 2557, 2558, 2559,... - ## $ weeknumber 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, 0.1, 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 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 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 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 5... - ## $ weekend "2010-10-09", "2010-10-16", "2010-10-23", "2010-10-30", "2010-11-06", "2010-11-13", "2010... - ## $ weekstart "2010-10-03", "2010-10-10", "2010-10-17", "2010-10-24", "2010-10-31", "2010-11-07", "2010... - ## $ year 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2011, 2011,... - ## $ yearweek 201040, 201041, 201042, 201043, 201044, 201045, 201046, 201047, 201048, 201049, 201050, 2... - ## $ seasonid 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 5... - ## $ weekendlabel "Oct 09, 2010", "Oct 16, 2010", "Oct 23, 2010", "Oct 30, 2010", "Nov 06, 2010", "Nov 13, ... - ## $ weekendlabel2 "Oct-09-2010", "Oct-16-2010", "Oct-23-2010", "Oct-30-2010", "Nov-06-2010", "Nov-13-2010",... - ## $ age_label "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 "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",... - ## $ sea_startweek 2545, 2545, 2545, 2545, 2545, 2545, 2545, 2545, 2545, 2545, 2545, 2545, 2545, 2545, 2545,... - ## $ sea_endweek 2596, 2596, 2596, 2596, 2596, 2596, 2596, 2596, 2596, 2596, 2596, 2596, 2596, 2596, 2596,... + ## 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: 20 - ## $ mmwrid 2545, 2546, 2547, 2548, 2549, 2550, 2551, 2552, 2553, 2554, 2555, 2556, 2557, 2558, 2559,... - ## $ weeknumber 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, 0.0, 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 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 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 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 5... - ## $ weekend "2010-10-09", "2010-10-16", "2010-10-23", "2010-10-30", "2010-11-06", "2010-11-13", "2010... - ## $ weekstart "2010-10-03", "2010-10-10", "2010-10-17", "2010-10-24", "2010-10-31", "2010-11-07", "2010... - ## $ year 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2011, 2011,... - ## $ yearweek 201040, 201041, 201042, 201043, 201044, 201045, 201046, 201047, 201048, 201049, 201050, 2... - ## $ seasonid 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 5... - ## $ weekendlabel "Oct 09, 2010", "Oct 16, 2010", "Oct 23, 2010", "Oct 30, 2010", "Nov 06, 2010", "Nov 13, ... - ## $ weekendlabel2 "Oct-09-2010", "Oct-16-2010", "Oct-23-2010", "Oct-30-2010", "Nov-06-2010", "Nov-13-2010",... - ## $ age_label "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 "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",... - ## $ sea_startweek 2545, 2545, 2545, 2545, 2545, 2545, 2545, 2545, 2545, 2545, 2545, 2545, 2545, 2545, 2545,... - ## $ sea_endweek 2596, 2596, 2596, 2596, 2596, 2596, 2596, 2596, 2596, 2596, 2596, 2596, 2596, 2596, 2596,... + ## 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 -ilinet("national") +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) + +}) ``` - ## # 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 - ## - ## 1 National 1997 40 1.10148 1.21686 179 157 205 29 - ## 2 National 1997 41 1.20007 1.28064 199 151 242 23 - ## 3 National 1997 42 1.37876 1.23906 228 153 266 34 - ## 4 National 1997 43 1.19920 1.14473 188 193 236 36 - ## 5 National 1997 44 1.65618 1.26112 217 162 280 41 - ## 6 National 1997 45 1.41326 1.28275 178 148 281 48 - ## 7 National 1997 46 1.98680 1.44579 294 240 328 70 - ## 8 National 1997 47 2.44749 1.64796 288 293 456 63 - ## 9 National 1997 48 1.73901 1.67517 268 206 343 69 - ## 10 National 1997 49 1.93919 1.61739 299 282 415 102 - ## # ... with 1,038 more rows, and 3 more variables: ilitotal , num_of_providers , total_patients - -``` r -ilinet("hhs") -``` - - ## # A tibble: 10,480 x 15 + ## 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 @@ -340,47 +412,82 @@ ilinet("hhs") ## 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 , num_of_providers , total_patients - -``` r -ilinet("census") -``` - - ## # A tibble: 9,432 x 15 + ## # ... 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 7 22 - ## 2 Census Regions Mid-Atlantic 1997 40 0.8441440 1.3213800 4 8 12 - ## 3 Census Regions East North Central 1997 40 0.7924860 0.8187380 28 20 28 - ## 4 Census Regions West North Central 1997 40 1.7640500 1.2793900 3 8 20 - ## 5 Census Regions South Atlantic 1997 40 0.5026620 0.7233800 14 22 14 - ## 6 Census Regions East South Central 1997 40 0.0542283 0.0688705 0 3 0 - ## 7 Census Regions West South Central 1997 40 1.0189800 0.7473840 2 1 2 - ## 8 Census Regions Mountain 1997 40 2.2587800 2.2763300 87 71 71 - ## 9 Census Regions Pacific 1997 40 2.0488300 3.2349400 26 17 36 - ## 10 Census Regions New England 1997 41 0.6426690 0.8158010 14 14 29 - ## # ... with 9,422 more rows, and 5 more variables: age_50_64 , age_65 , ilitotal , - ## # num_of_providers , total_patients - -``` r -ilinet("state") -``` - - ## # A tibble: 19,718 x 15 + ## + ## 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 2.13477 - ## 2 States Alaska 2010 40 0.875146 - ## 3 States Arizona 2010 40 0.674721 - ## 4 States Arkansas 2010 40 0.696056 - ## 5 States California 2010 40 1.95412 - ## 6 States Colorado 2010 40 0.660684 - ## 7 States Connecticut 2010 40 0.0783085 - ## 8 States Delaware 2010 40 0.100125 - ## 9 States District of Columbia 2010 40 2.80877 - ## 10 States Florida 2010 40 - ## # ... with 19,708 more rows, and 5 more variables: age_50_64 , age_65 , ilitotal , - ## # num_of_providers , total_patients + ## + ## 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 @@ -404,48 +511,62 @@ ili_weekly_activity_indicators(2017) ## # ... with 206 more rows ``` r -ili_weekly_activity_indicators(2015) +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") ``` - ## # A tibble: 2,807 x 9 - ## statename ili_activity_label ili_activity_group statefips stateabbr weekend weeknumber year seasonid - ## - ## 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 +![](README_files/figure-markdown_github-ascii_identifiers/unnamed-chunk-9-1.png) ### Pneumonia and Influenza Mortality Surveillance ``` r -pi_mortality("national") +(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 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 + ## 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 , - ## # weekend , weekstart , year , yearweek , coverage_area , region_name , callout + ## # wk_start , wk_end , year_wk_num , mmwrid , coverage_area , region_name , + ## # callout ``` r -pi_mortality("state") +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 @@ -462,27 +583,29 @@ pi_mortality("state") ## 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 , - ## # weekend , weekstart , year , yearweek , coverage_area , region_name , callout + ## # wk_start , wk_end , year_wk_num , mmwrid , coverage_area , region_name , + ## # callout ``` r -pi_mortality("region") +(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 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 + ## 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 , - ## # weekend , weekstart , year , yearweek , coverage_area , region_name , callout + ## # wk_start , wk_end , year_wk_num , mmwrid , coverage_area , region_name , + ## # callout ### Retrieve metadata about U.S. State CDC Provider Data @@ -508,63 +631,70 @@ state_data_providers() ### Retrieve WHO/NREVSS Surveillance Data ``` r -who_nrevss("national") +glimpse(xdat <- 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 - ## - ## 1 National 1997 40 1291 0.000000 0 0 0 0 - ## 2 National 1997 41 1513 0.727032 0 0 0 11 - ## 3 National 1997 42 1552 1.095360 0 0 3 13 - ## 4 National 1997 43 1669 0.419413 0 0 0 7 - ## 5 National 1997 44 1897 0.527148 0 0 9 1 - ## 6 National 1997 45 2106 0.284900 0 0 0 6 - ## 7 National 1997 46 2204 0.362976 0 0 3 4 - ## 8 National 1997 47 2533 0.908014 0 0 5 17 - ## 9 National 1997 48 2242 1.650310 0 0 14 22 - ## 10 National 1997 49 2607 1.534330 0 0 11 28 - ## # ... with 930 more rows, and 3 more variables: a_unable_to_subtype , b , h3n2v - ## - ## $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 - ## - ## 1 National 2015 40 1139 4 65 2 10 0 1 0 - ## 2 National 2015 41 1152 5 41 2 7 3 0 0 - ## 3 National 2015 42 1198 10 50 1 8 3 2 0 - ## 4 National 2015 43 1244 9 31 4 9 1 4 0 - ## 5 National 2015 44 1465 4 23 4 9 1 4 0 - ## 6 National 2015 45 1393 11 34 1 10 4 2 0 - ## 7 National 2015 46 1458 17 42 1 4 0 4 0 - ## 8 National 2015 47 1157 17 24 0 4 3 9 0 - ## 9 National 2015 48 1550 27 36 3 9 3 12 0 - ## 10 National 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 - ## - ## 1 National 2015 40 12029 84 43 1.05578 0.698312 0.357469 - ## 2 National 2015 41 13111 116 54 1.29662 0.884753 0.411868 - ## 3 National 2015 42 13441 97 52 1.10855 0.721672 0.386876 - ## 4 National 2015 43 13537 98 52 1.10807 0.723942 0.384132 - ## 5 National 2015 44 14687 97 68 1.12344 0.660448 0.462994 - ## 6 National 2015 45 15048 122 86 1.38224 0.810739 0.571505 - ## 7 National 2015 46 15250 84 98 1.19344 0.550820 0.642623 - ## 8 National 2015 47 15234 119 92 1.38506 0.781147 0.603912 - ## 9 National 2015 48 16201 145 81 1.39498 0.895006 0.499969 - ## 10 National 2015 49 16673 140 106 1.47544 0.839681 0.635758 - ## # ... with 98 more rows + ## 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 13 + ## # 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 @@ -577,38 +707,38 @@ who_nrevss("hhs") ## 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 , b , h3n2v + ## # ... 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 12 + ## # 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 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 + ## 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 10 - ## region_type region year week total_specimens total_a total_b percent_positive percent_a percent_b - ## - ## 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 + ## # 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 @@ -616,7 +746,7 @@ who_nrevss("census") ``` ## $combined_prior_to_2015_16 - ## # A tibble: 8,460 x 13 + ## # 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 @@ -629,27 +759,27 @@ who_nrevss("census") ## 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 , a_unable_to_subtype , b , - ## # h3n2v + ## # ... 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 12 + ## # 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 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 , byam , h3n2v + ## 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 10 + ## # 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 @@ -662,14 +792,14 @@ who_nrevss("census") ## 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 + ## # ... with 962 more rows, and 1 more variables: wk_date ``` r who_nrevss("state") ``` ## $combined_prior_to_2015_16 - ## # A tibble: 14,094 x 13 + ## # 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 @@ -682,11 +812,11 @@ who_nrevss("state") ## 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 - ## # ... with 14,084 more rows, and 4 more variables: a_subtyping_not_performed , a_unable_to_subtype , b , - ## # h3n2v + ## # ... with 14,084 more rows, and 5 more variables: a_subtyping_not_performed , a_unable_to_subtype , b , + ## # h3n2v , wk_date ## ## $public_health_labs - ## # A tibble: 162 x 11 + ## # A tibble: 162 x 12 ## region_type region season_description total_specimens a_2009_h1n1 a_h3 a_subtyping_not_performed ## ## 1 States Alabama Season 2015-16 256 59 16 1 @@ -699,10 +829,10 @@ who_nrevss("state") ## 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 - ## # ... with 152 more rows, and 4 more variables: b , bvic , byam , h3n2v + ## # ... with 152 more rows, and 5 more variables: b , bvic , byam , h3n2v , wk_date ## ## $clinical_labs - ## # A tibble: 5,832 x 10 + ## # A tibble: 5,832 x 11 ## region_type region year week total_specimens total_a total_b percent_positive percent_a percent_b ## ## 1 States Alabama 2015 40 167 2 3 2.99 1.2 1.8 @@ -715,7 +845,7 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b/README_files/figure-markdown_github-ascii_identifiers/unnamed-chunk-9-1.png differ diff --git a/man/mmwr_week.Rd b/man/mmwr_week.Rd new file mode 100644 index 0000000..fbc7e53 --- /dev/null +++ b/man/mmwr_week.Rd @@ -0,0 +1,21 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/mmwr-map.r +\name{mmwr_week} +\alias{mmwr_week} +\title{Convert a Date to an MMWR day+week+year} +\usage{ +mmwr_week(x) +} +\arguments{ +\item{x}{a vector of \code{Date} objects or a character vector in \code{YYYY-mm-dd} format.} +} +\value{ +data frame (tibble) +} +\description{ +This is a reformat and re-export of a function in the \code{MMWRweek} package. +It provides a snake case version of its counterpart, produces a \code{tibble} +} +\examples{ +mmwr_week(Sys.Date()) +} diff --git a/man/mmwr_week_to_date.Rd b/man/mmwr_week_to_date.Rd new file mode 100644 index 0000000..10d8c59 --- /dev/null +++ b/man/mmwr_week_to_date.Rd @@ -0,0 +1,24 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/mmwr-map.r +\name{mmwr_week_to_date} +\alias{mmwr_week_to_date} +\title{Convert an MMWR year+week or year+week+day to a Date object} +\usage{ +mmwr_week_to_date(year, week, day = NULL) +} +\arguments{ +\item{year, week, day}{Year, week and month vectors. All must be the same length +unless \code{day} is \code{NULL}.} +} +\value{ +vector of \code{Date} objects +} +\description{ +This is a reformat and re-export of a function in the \code{MMWRweek} package. +It provides a snake case version of its counterpart and produces a vector +of \code{Date} objects that corresponds to the input MMWR year+week or year+week+day +vectors. This also adds some parameter checking and cleanup to avoid exceptions. +} +\examples{ +mmwr_week_to_date(2016,10,3) +} diff --git a/man/mmwr_weekday.Rd b/man/mmwr_weekday.Rd new file mode 100644 index 0000000..743a946 --- /dev/null +++ b/man/mmwr_weekday.Rd @@ -0,0 +1,29 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/mmwr-map.r +\name{mmwr_weekday} +\alias{mmwr_weekday} +\title{Convert a Date to an MMWR weekday} +\usage{ +mmwr_weekday(x, abbr = FALSE) +} +\arguments{ +\item{x}{a vector of \code{Date} objects or a character vector in \code{YYYY-mm-dd} format.} + +\item{abbr}{(logical) if \code{TRUE}, return abbreviated weekday names, otherwise full +weekday names (see Note).} +} +\value{ +ordered factor +} +\description{ +This is a reformat and re-export of a function in the \code{MMWRweek} package. +It provides a snake case version of its counterpart, produces a \code{factor} of +weekday names (Sunday-Saturday). +} +\note{ +Weekday names are explicitly mapped to "Sunday-Saturday" or "Sun-Sat" and +do not change with your locale. +} +\examples{ +mmwr_weekday(Sys.Date()) +} diff --git a/man/mmwrid_map.Rd b/man/mmwrid_map.Rd index 48e7165..4b844a5 100644 --- a/man/mmwrid_map.Rd +++ b/man/mmwrid_map.Rd @@ -1,5 +1,20 @@ % Generated by roxygen2: do not edit by hand % Please edit documentation in R/mmwr-map.r +\docType{data} \name{mmwrid_map} \alias{mmwrid_map} \title{MMWR ID to Calendar Mappings} +\format{A data frame with 4,592 rows and 4 columns} +\description{ +The CDC uses a unique "Morbidity and Mortality Weekly Report" identifier +for each week that starts at 1 (Ref: < https://www.cdc.gov/mmwr/preview/mmwrhtml/su6004a9.htm>). +This data frame consists of 4 columns: +\itemize{ +\item \code{wk_start}: Start date (Sunday) for the week (\code{Date}) +\item \code{wk_end}: End date (Saturday) for the week (\code{Date}) +\item \code{year_wk_num}: The week of the calendar year +\item \code{mmwrid}: The unique MMWR identifier +These can be "left-joined" to data provided from the CDC to perform MMWR identifier +to date mappings. +} +} diff --git a/tests/testthat/test-cdcfluview.R b/tests/testthat/test-cdcfluview.R index a9b0b83..4f81f76 100644 --- a/tests/testthat/test-cdcfluview.R +++ b/tests/testthat/test-cdcfluview.R @@ -3,7 +3,7 @@ test_that("we can do something", { skip_on_cran() - expect_that(agd_ipt(), is_a("data.frame")) + expect_that(age_group_distribution(), is_a("data.frame")) expect_that(geographic_spread(), is_a("data.frame")) @@ -40,5 +40,18 @@ test_that("we can do something", { expect_that(cdc_basemap("spread"), is_a("sf")) expect_that(cdc_basemap("surv"), is_a("sf")) + expect_equal(mmwr_week(Sys.Date()), + structure(list(mmwr_year = 2017, mmwr_week = 45, mmwr_day = 2), + .Names = c("mmwr_year", "mmwr_week", "mmwr_day"), + row.names = c(NA, -1L), + class = c("tbl_df", "tbl", "data.frame")) + ) + + expect_equal(mmwr_weekday(Sys.Date()), + structure(2L, .Label = c("Sunday", "Monday", "Tuesday", "Wednesday", + "Thursday", "Friday", "Saturday"), + class = "factor")) + + expect_equal(mmwr_week_to_date(2016,10,3), structure(16868, class = "Date")) })