data cleanup/normalization; MMWRweeks integration; better README

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)

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

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
 
 }

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)

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")]
 
 }
 

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")
+
+}
+
+
+
+
+
+
+
+

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)
 

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
 
 }

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")
 

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         <chr> "1997-98", "1997-98", "1997-98", "1997-98", "1997-98", "1997-98", "1997-98", "1997-98", "...
-    ## $ age_label         <chr> "0-4 yr", "0-4 yr", "0-4 yr", "0-4 yr", "0-4 yr", "0-4 yr", "0-4 yr", "0-4 yr", "0-4 yr",...
-    ## $ vir_label         <chr> "A (Subtyping not Performed)", "A (Subtyping not Performed)", "A (Subtyping not Performed...
+    ## $ age_label         <fctr> 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         <fctr> A (Subtyping not Performed), A (Subtyping not Performed), A (Subtyping not Performed), A...
     ## $ count             <int> 0, 1, 0, 0, 0, 0, 0, 3, 0, 6, 0, 1, 1, 2, 11, 8, 18, 26, 22, 19, 2, 5, 2, 1, 4, 0, 0, 0, ...
     ## $ mmwrid            <int> 1866, 1867, 1868, 1869, 1870, 1871, 1872, 1873, 1874, 1875, 1876, 1877, 1878, 1879, 1880,...
     ## $ seasonid          <int> 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 3...
@@ -112,15 +119,48 @@ glimpse(agd_ipt())
     ## $ vir_description   <chr> "A-Unk", "A-Unk", "A-Unk", "A-Unk", "A-Unk", "A-Unk", "A-Unk", "A-Unk", "A-Unk", "A-Unk",...
     ## $ vir_startmmwrid   <int> 1397, 1397, 1397, 1397, 1397, 1397, 1397, 1397, 1397, 1397, 1397, 1397, 1397, 1397, 1397,...
     ## $ vir_endmmwrid     <int> 3131, 3131, 3131, 3131, 3131, 3131, 3131, 3131, 3131, 3131, 3131, 3131, 3131, 3131, 3131,...
+    ## $ wk_start          <date> 1997-09-28, 1997-10-05, 1997-10-12, 1997-10-19, 1997-10-26, 1997-11-02, 1997-11-09, 1997...
+    ## $ wk_end            <date> 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       <int> 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            <int> 2545, 2546, 2547, 2548, 2549, 2550, 2551, 2552, 2553, 2554, 2555, 2556, 2557, 2558, 2559,...
-    ## $ weeknumber        <int> 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12...
-    ## $ rate              <dbl> 0.0, 0.0, 0.0, 0.1, 0.1, 0.2, 0.3, 0.3, 0.4, 0.6, 0.8, 1.3, 1.7, 2.2, 2.8, 3.6, 4.4, 5.4,...
-    ## $ weeklyrate        <dbl> 0.0, 0.0, 0.0, 0.0, 0.1, 0.0, 0.1, 0.1, 0.1, 0.2, 0.2, 0.4, 0.4, 0.5, 0.5, 0.8, 0.8, 1.0,...
-    ## $ age               <int> 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,...
-    ## $ season            <int> 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 5...
-    ## $ weekend           <chr> "2010-10-09", "2010-10-16", "2010-10-23", "2010-10-30", "2010-11-06", "2010-11-13", "2010...
-    ## $ weekstart         <chr> "2010-10-03", "2010-10-10", "2010-10-17", "2010-10-24", "2010-10-31", "2010-11-07", "2010...
-    ## $ year              <int> 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2011, 2011,...
-    ## $ yearweek          <int> 201040, 201041, 201042, 201043, 201044, 201045, 201046, 201047, 201048, 201049, 201050, 2...
-    ## $ seasonid          <int> 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 5...
-    ## $ weekendlabel      <chr> "Oct 09, 2010", "Oct 16, 2010", "Oct 23, 2010", "Oct 30, 2010", "Nov 06, 2010", "Nov 13, ...
-    ## $ weekendlabel2     <chr> "Oct-09-2010", "Oct-16-2010", "Oct-23-2010", "Oct-30-2010", "Nov-06-2010", "Nov-13-2010",...
-    ## $ age_label         <chr> "18-49 yr", "18-49 yr", "18-49 yr", "18-49 yr", "18-49 yr", "18-49 yr", "18-49 yr", "18-4...
-    ## $ sea_label         <chr> "2010-11", "2010-11", "2010-11", "2010-11", "2010-11", "2010-11", "2010-11", "2010-11", "...
-    ## $ sea_description   <chr> "Season 2010-11", "Season 2010-11", "Season 2010-11", "Season 2010-11", "Season 2010-11",...
-    ## $ sea_startweek     <int> 2545, 2545, 2545, 2545, 2545, 2545, 2545, 2545, 2545, 2545, 2545, 2545, 2545, 2545, 2545,...
-    ## $ sea_endweek       <int> 2596, 2596, 2596, 2596, 2596, 2596, 2596, 2596, 2596, 2596, 2596, 2596, 2596, 2596, 2596,...
+    ## Variables: 14
     ## $ surveillance_area <chr> "FluSurv-NET", "FluSurv-NET", "FluSurv-NET", "FluSurv-NET", "FluSurv-NET", "FluSurv-NET",...
     ## $ region            <chr> "Entire Network", "Entire Network", "Entire Network", "Entire Network", "Entire Network",...
+    ## $ year              <int> 2009, 2009, 2009, 2009, 2009, 2009, 2009, 2009, 2009, 2009, 2009, 2009, 2009, 2009, 2009,...
+    ## $ season            <int> 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          <date> 2009-08-30, 2009-09-06, 2009-09-13, 2009-09-20, 2009-09-27, 2009-10-04, 2009-10-11, 2009...
+    ## $ wk_end            <date> 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       <int> 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              <dbl> 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        <dbl> 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               <int> 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         <fctr> 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         <chr> "2009-10", "2009-10", "2009-10", "2009-10", "2009-10", "2009-10", "2009-10", "2009-10", "...
+    ## $ sea_description   <chr> "Season 2009-10", "Season 2009-10", "Season 2009-10", "Season 2009-10", "Season 2009-10",...
+    ## $ mmwrid            <int> 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            <int> 2545, 2546, 2547, 2548, 2549, 2550, 2551, 2552, 2553, 2554, 2555, 2556, 2557, 2558, 2559,...
-    ## $ weeknumber        <int> 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12...
-    ## $ rate              <dbl> 0.0, 0.0, 0.0, 0.1, 0.1, 0.1, 0.2, 0.3, 0.4, 0.5, 0.8, 1.1, 1.4, 1.9, 2.3, 2.8, 3.6, 4.5,...
-    ## $ weeklyrate        <dbl> 0.0, 0.0, 0.0, 0.0, 0.1, 0.0, 0.1, 0.1, 0.1, 0.1, 0.2, 0.4, 0.3, 0.4, 0.4, 0.5, 0.8, 1.0,...
-    ## $ age               <int> 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,...
-    ## $ season            <int> 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 5...
-    ## $ weekend           <chr> "2010-10-09", "2010-10-16", "2010-10-23", "2010-10-30", "2010-11-06", "2010-11-13", "2010...
-    ## $ weekstart         <chr> "2010-10-03", "2010-10-10", "2010-10-17", "2010-10-24", "2010-10-31", "2010-11-07", "2010...
-    ## $ year              <int> 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2011, 2011,...
-    ## $ yearweek          <int> 201040, 201041, 201042, 201043, 201044, 201045, 201046, 201047, 201048, 201049, 201050, 2...
-    ## $ seasonid          <int> 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 5...
-    ## $ weekendlabel      <chr> "Oct 09, 2010", "Oct 16, 2010", "Oct 23, 2010", "Oct 30, 2010", "Nov 06, 2010", "Nov 13, ...
-    ## $ weekendlabel2     <chr> "Oct-09-2010", "Oct-16-2010", "Oct-23-2010", "Oct-30-2010", "Nov-06-2010", "Nov-13-2010",...
-    ## $ age_label         <chr> "18-49 yr", "18-49 yr", "18-49 yr", "18-49 yr", "18-49 yr", "18-49 yr", "18-49 yr", "18-4...
-    ## $ sea_label         <chr> "2010-11", "2010-11", "2010-11", "2010-11", "2010-11", "2010-11", "2010-11", "2010-11", "...
-    ## $ sea_description   <chr> "Season 2010-11", "Season 2010-11", "Season 2010-11", "Season 2010-11", "Season 2010-11",...
-    ## $ sea_startweek     <int> 2545, 2545, 2545, 2545, 2545, 2545, 2545, 2545, 2545, 2545, 2545, 2545, 2545, 2545, 2545,...
-    ## $ sea_endweek       <int> 2596, 2596, 2596, 2596, 2596, 2596, 2596, 2596, 2596, 2596, 2596, 2596, 2596, 2596, 2596,...
+    ## Variables: 14
     ## $ surveillance_area <chr> "EIP", "EIP", "EIP", "EIP", "EIP", "EIP", "EIP", "EIP", "EIP", "EIP", "EIP", "EIP", "EIP"...
     ## $ region            <chr> "Entire Network", "Entire Network", "Entire Network", "Entire Network", "Entire Network",...
+    ## $ year              <int> 2003, 2003, 2003, 2003, 2003, 2003, 2003, 2003, 2003, 2003, 2003, 2003, 2003, 2003, 2004,...
+    ## $ season            <int> 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          <date> 2003-09-28, 2003-10-05, 2003-10-12, 2003-10-19, 2003-10-26, 2003-11-02, 2003-11-09, 2003...
+    ## $ wk_end            <date> 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       <int> 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              <dbl> 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        <dbl> 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               <int> 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         <fctr> 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         <chr> 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   <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N...
+    ## $ mmwrid            <int> 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            <int> 2545, 2546, 2547, 2548, 2549, 2550, 2551, 2552, 2553, 2554, 2555, 2556, 2557, 2558, 2559,...
-    ## $ weeknumber        <int> 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12...
-    ## $ rate              <dbl> 0.0, 0.1, 0.1, 0.1, 0.3, 0.3, 0.4, 0.4, 0.5, 0.6, 0.8, 1.3, 1.8, 2.1, 2.6, 3.4, 4.2, 5.6,...
-    ## $ weeklyrate        <dbl> 0.0, 0.1, 0.0, 0.0, 0.2, 0.0, 0.1, 0.1, 0.1, 0.1, 0.2, 0.5, 0.4, 0.4, 0.4, 0.9, 0.8, 1.4,...
-    ## $ age               <int> 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,...
-    ## $ season            <int> 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 5...
-    ## $ weekend           <chr> "2010-10-09", "2010-10-16", "2010-10-23", "2010-10-30", "2010-11-06", "2010-11-13", "2010...
-    ## $ weekstart         <chr> "2010-10-03", "2010-10-10", "2010-10-17", "2010-10-24", "2010-10-31", "2010-11-07", "2010...
-    ## $ year              <int> 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2011, 2011,...
-    ## $ yearweek          <int> 201040, 201041, 201042, 201043, 201044, 201045, 201046, 201047, 201048, 201049, 201050, 2...
-    ## $ seasonid          <int> 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 5...
-    ## $ weekendlabel      <chr> "Oct 09, 2010", "Oct 16, 2010", "Oct 23, 2010", "Oct 30, 2010", "Nov 06, 2010", "Nov 13, ...
-    ## $ weekendlabel2     <chr> "Oct-09-2010", "Oct-16-2010", "Oct-23-2010", "Oct-30-2010", "Nov-06-2010", "Nov-13-2010",...
-    ## $ age_label         <chr> "18-49 yr", "18-49 yr", "18-49 yr", "18-49 yr", "18-49 yr", "18-49 yr", "18-49 yr", "18-4...
-    ## $ sea_label         <chr> "2010-11", "2010-11", "2010-11", "2010-11", "2010-11", "2010-11", "2010-11", "2010-11", "...
-    ## $ sea_description   <chr> "Season 2010-11", "Season 2010-11", "Season 2010-11", "Season 2010-11", "Season 2010-11",...
-    ## $ sea_startweek     <int> 2545, 2545, 2545, 2545, 2545, 2545, 2545, 2545, 2545, 2545, 2545, 2545, 2545, 2545, 2545,...
-    ## $ sea_endweek       <int> 2596, 2596, 2596, 2596, 2596, 2596, 2596, 2596, 2596, 2596, 2596, 2596, 2596, 2596, 2596,...
+    ## Variables: 14
     ## $ surveillance_area <chr> "EIP", "EIP", "EIP", "EIP", "EIP", "EIP", "EIP", "EIP", "EIP", "EIP", "EIP", "EIP", "EIP"...
     ## $ region            <chr> "Colorado", "Colorado", "Colorado", "Colorado", "Colorado", "Colorado", "Colorado", "Colo...
+    ## $ year              <int> 2003, 2003, 2003, 2003, 2003, 2003, 2003, 2003, 2003, 2003, 2003, 2003, 2003, 2003, 2004,...
+    ## $ season            <int> 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          <date> 2003-09-28, 2003-10-05, 2003-10-12, 2003-10-19, 2003-10-26, 2003-11-02, 2003-11-09, 2003...
+    ## $ wk_end            <date> 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       <int> 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              <dbl> 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        <dbl> 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               <int> 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         <fctr> 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         <chr> 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   <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N...
+    ## $ mmwrid            <int> 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            <int> 2545, 2546, 2547, 2548, 2549, 2550, 2551, 2552, 2553, 2554, 2555, 2556, 2557, 2558, 2559,...
-    ## $ weeknumber        <int> 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12...
-    ## $ rate              <dbl> 0.0, 0.0, 0.1, 0.2, 0.2, 0.3, 0.3, 0.4, 0.6, 0.9, 1.1, 1.9, 2.8, 3.9, 4.9, 6.8, 7.6, 9.0,...
-    ## $ weeklyrate        <dbl> 0.0, 0.0, 0.0, 0.1, 0.0, 0.0, 0.0, 0.1, 0.2, 0.4, 0.2, 0.8, 0.9, 1.1, 1.0, 2.0, 0.8, 1.4,...
-    ## $ age               <int> 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,...
-    ## $ season            <int> 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 5...
-    ## $ weekend           <chr> "2010-10-09", "2010-10-16", "2010-10-23", "2010-10-30", "2010-11-06", "2010-11-13", "2010...
-    ## $ weekstart         <chr> "2010-10-03", "2010-10-10", "2010-10-17", "2010-10-24", "2010-10-31", "2010-11-07", "2010...
-    ## $ year              <int> 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2011, 2011,...
-    ## $ yearweek          <int> 201040, 201041, 201042, 201043, 201044, 201045, 201046, 201047, 201048, 201049, 201050, 2...
-    ## $ seasonid          <int> 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 5...
-    ## $ weekendlabel      <chr> "Oct 09, 2010", "Oct 16, 2010", "Oct 23, 2010", "Oct 30, 2010", "Nov 06, 2010", "Nov 13, ...
-    ## $ weekendlabel2     <chr> "Oct-09-2010", "Oct-16-2010", "Oct-23-2010", "Oct-30-2010", "Nov-06-2010", "Nov-13-2010",...
-    ## $ age_label         <chr> "18-49 yr", "18-49 yr", "18-49 yr", "18-49 yr", "18-49 yr", "18-49 yr", "18-49 yr", "18-4...
-    ## $ sea_label         <chr> "2010-11", "2010-11", "2010-11", "2010-11", "2010-11", "2010-11", "2010-11", "2010-11", "...
-    ## $ sea_description   <chr> "Season 2010-11", "Season 2010-11", "Season 2010-11", "Season 2010-11", "Season 2010-11",...
-    ## $ sea_startweek     <int> 2545, 2545, 2545, 2545, 2545, 2545, 2545, 2545, 2545, 2545, 2545, 2545, 2545, 2545, 2545,...
-    ## $ sea_endweek       <int> 2596, 2596, 2596, 2596, 2596, 2596, 2596, 2596, 2596, 2596, 2596, 2596, 2596, 2596, 2596,...
+    ## Variables: 14
     ## $ surveillance_area <chr> "IHSP", "IHSP", "IHSP", "IHSP", "IHSP", "IHSP", "IHSP", "IHSP", "IHSP", "IHSP", "IHSP", "...
     ## $ region            <chr> "Entire Network", "Entire Network", "Entire Network", "Entire Network", "Entire Network",...
+    ## $ year              <int> 2009, 2009, 2009, 2009, 2009, 2009, 2009, 2009, 2009, 2009, 2009, 2009, 2009, 2009, 2009,...
+    ## $ season            <int> 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          <date> 2009-08-30, 2009-09-06, 2009-09-13, 2009-09-20, 2009-09-27, 2009-10-04, 2009-10-11, 2009...
+    ## $ wk_end            <date> 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       <int> 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              <dbl> 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        <dbl> 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               <int> 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         <fctr> 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         <chr> "2009-10", "2009-10", "2009-10", "2009-10", "2009-10", "2009-10", "2009-10", "2009-10", "...
+    ## $ sea_description   <chr> "Season 2009-10", "Season 2009-10", "Season 2009-10", "Season 2009-10", "Season 2009-10",...
+    ## $ mmwrid            <int> 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            <int> 2545, 2546, 2547, 2548, 2549, 2550, 2551, 2552, 2553, 2554, 2555, 2556, 2557, 2558, 2559,...
-    ## $ weeknumber        <int> 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12...
-    ## $ rate              <dbl> 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.2, 0.2, 0.4, 0.7, 0.7, 1.3, 2.2, 2.5, 3.4, 4.5, 5.8, 7.6,...
-    ## $ weeklyrate        <dbl> 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.2, 0.0, 0.2, 0.2, 0.0, 0.7, 0.9, 0.2, 0.9, 1.1, 1.3, 1.8,...
-    ## $ age               <int> 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,...
-    ## $ season            <int> 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 5...
-    ## $ weekend           <chr> "2010-10-09", "2010-10-16", "2010-10-23", "2010-10-30", "2010-11-06", "2010-11-13", "2010...
-    ## $ weekstart         <chr> "2010-10-03", "2010-10-10", "2010-10-17", "2010-10-24", "2010-10-31", "2010-11-07", "2010...
-    ## $ year              <int> 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2011, 2011,...
-    ## $ yearweek          <int> 201040, 201041, 201042, 201043, 201044, 201045, 201046, 201047, 201048, 201049, 201050, 2...
-    ## $ seasonid          <int> 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 5...
-    ## $ weekendlabel      <chr> "Oct 09, 2010", "Oct 16, 2010", "Oct 23, 2010", "Oct 30, 2010", "Nov 06, 2010", "Nov 13, ...
-    ## $ weekendlabel2     <chr> "Oct-09-2010", "Oct-16-2010", "Oct-23-2010", "Oct-30-2010", "Nov-06-2010", "Nov-13-2010",...
-    ## $ age_label         <chr> "18-49 yr", "18-49 yr", "18-49 yr", "18-49 yr", "18-49 yr", "18-49 yr", "18-49 yr", "18-4...
-    ## $ sea_label         <chr> "2010-11", "2010-11", "2010-11", "2010-11", "2010-11", "2010-11", "2010-11", "2010-11", "...
-    ## $ sea_description   <chr> "Season 2010-11", "Season 2010-11", "Season 2010-11", "Season 2010-11", "Season 2010-11",...
-    ## $ sea_startweek     <int> 2545, 2545, 2545, 2545, 2545, 2545, 2545, 2545, 2545, 2545, 2545, 2545, 2545, 2545, 2545,...
-    ## $ sea_endweek       <int> 2596, 2596, 2596, 2596, 2596, 2596, 2596, 2596, 2596, 2596, 2596, 2596, 2596, 2596, 2596,...
+    ## Variables: 14
     ## $ surveillance_area <chr> "IHSP", "IHSP", "IHSP", "IHSP", "IHSP", "IHSP", "IHSP", "IHSP", "IHSP", "IHSP", "IHSP", "...
     ## $ region            <chr> "Oklahoma", "Oklahoma", "Oklahoma", "Oklahoma", "Oklahoma", "Oklahoma", "Oklahoma", "Okla...
+    ## $ year              <int> 2009, 2009, 2009, 2009, 2009, 2009, 2009, 2009, 2009, 2009, 2009, 2009, 2009, 2009, 2009,...
+    ## $ season            <int> 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          <date> 2009-08-30, 2009-09-06, 2009-09-13, 2009-09-20, 2009-09-27, 2009-10-04, 2009-10-11, 2009...
+    ## $ wk_end            <date> 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       <int> 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              <dbl> 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        <dbl> 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               <int> 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         <fctr> 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         <chr> "2009-10", "2009-10", "2009-10", "2009-10", "2009-10", "2009-10", "2009-10", "2009-10", "...
+    ## $ sea_description   <chr> "Season 2009-10", "Season 2009-10", "Season 2009-10", "Season 2009-10", "Season 2009-10",...
+    ## $ mmwrid            <int> 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
-    ##          <chr>  <chr> <int> <int>        <dbl>          <dbl>   <int>     <chr>     <chr>    <int>     <chr>  <int>
-    ##  1    National   <NA>  1997    40      1.10148        1.21686     179      <NA>       157      205      <NA>     29
-    ##  2    National   <NA>  1997    41      1.20007        1.28064     199      <NA>       151      242      <NA>     23
-    ##  3    National   <NA>  1997    42      1.37876        1.23906     228      <NA>       153      266      <NA>     34
-    ##  4    National   <NA>  1997    43      1.19920        1.14473     188      <NA>       193      236      <NA>     36
-    ##  5    National   <NA>  1997    44      1.65618        1.26112     217      <NA>       162      280      <NA>     41
-    ##  6    National   <NA>  1997    45      1.41326        1.28275     178      <NA>       148      281      <NA>     48
-    ##  7    National   <NA>  1997    46      1.98680        1.44579     294      <NA>       240      328      <NA>     70
-    ##  8    National   <NA>  1997    47      2.44749        1.64796     288      <NA>       293      456      <NA>     63
-    ##  9    National   <NA>  1997    48      1.73901        1.67517     268      <NA>       206      343      <NA>     69
-    ## 10    National   <NA>  1997    49      1.93919        1.61739     299      <NA>       282      415      <NA>    102
-    ## # ... with 1,038 more rows, and 3 more variables: ilitotal <int>, num_of_providers <int>, total_patients <int>
-
-``` r
-ilinet("hhs")
-```
-
-    ## # A tibble: 10,480 x 15
+    ## Observations: 1,048
+    ## Variables: 16
+    ## $ region_type      <chr> "National", "National", "National", "National", "National", "National", "National", "Natio...
+    ## $ region           <chr> "National", "National", "National", "National", "National", "National", "National", "Natio...
+    ## $ year             <int> 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1998, ...
+    ## $ week             <int> 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     <dbl> 1.101480, 1.200070, 1.378760, 1.199200, 1.656180, 1.413260, 1.986800, 2.447490, 1.739010, ...
+    ## $ unweighted_ili   <dbl> 1.216860, 1.280640, 1.239060, 1.144730, 1.261120, 1.282750, 1.445790, 1.647960, 1.675170, ...
+    ## $ age_0_4          <dbl> 179, 199, 228, 188, 217, 178, 294, 288, 268, 299, 346, 348, 510, 579, 639, 690, 856, 824, ...
+    ## $ age_25_49        <dbl> 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        <dbl> 157, 151, 153, 193, 162, 148, 240, 293, 206, 282, 268, 235, 404, 584, 759, 654, 679, 817, ...
+    ## $ age_5_24         <dbl> 205, 242, 266, 236, 280, 281, 328, 456, 343, 415, 388, 362, 492, 576, 810, 1121, 1440, 160...
+    ## $ age_50_64        <dbl> 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           <dbl> 29, 23, 34, 36, 41, 48, 70, 63, 69, 102, 81, 59, 113, 207, 207, 148, 151, 196, 233, 146, 1...
+    ## $ ilitotal         <dbl> 570, 615, 681, 653, 700, 655, 932, 1100, 886, 1098, 1083, 1004, 1519, 1946, 2415, 2613, 31...
+    ## $ num_of_providers <dbl> 192, 191, 219, 213, 213, 195, 248, 256, 252, 253, 242, 190, 251, 250, 254, 255, 245, 245, ...
+    ## $ total_patients   <dbl> 46842, 48023, 54961, 57044, 55506, 51062, 64463, 66749, 52890, 67887, 61314, 47719, 48429,...
+    ## $ week_start       <date> 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
+    ##          <chr>    <chr> <int> <int>        <dbl>          <dbl>   <dbl>     <dbl>     <dbl>    <dbl>     <dbl>  <dbl>
+    ##  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 <dbl>, num_of_providers <dbl>, total_patients <dbl>,
+    ## #   week_start <date>
+
+![](README_files/figure-markdown_github-ascii_identifiers/unnamed-chunk-8-1.png)
+
+    ## Observations: 10,480
+    ## Variables: 16
+    ## $ region_type      <chr> "HHS Regions", "HHS Regions", "HHS Regions", "HHS Regions", "HHS Regions", "HHS Regions", ...
+    ## $ region           <fctr> Region 1, Region 2, Region 3, Region 4, Region 5, Region 6, Region 7, Region 8, Region 9,...
+    ## $ year             <int> 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, ...
+    ## $ week             <int> 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     <dbl> 0.498535, 0.374963, 1.354280, 0.400338, 1.229260, 1.018980, 0.871791, 0.516017, 1.807610, ...
+    ## $ unweighted_ili   <dbl> 0.623848, 0.384615, 1.341720, 0.450010, 0.901266, 0.747384, 1.152860, 0.422654, 2.258780, ...
+    ## $ age_0_4          <dbl> 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        <dbl> 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        <dbl> 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         <dbl> 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        <dbl> 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           <dbl> 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         <dbl> 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 <dbl> 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   <dbl> 7053, 780, 2385, 10222, 9875, 669, 2342, 1183, 10758, 1575, 6987, 872, 2740, 11310, 9618, ...
+    ## $ week_start       <date> 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
-    ##          <chr>     <chr> <int> <int>        <dbl>          <dbl>   <int>     <int>     <int>    <int>     <int>  <int>
+    ##          <chr>    <fctr> <int> <int>        <dbl>          <dbl>   <dbl>     <dbl>     <dbl>    <dbl>     <dbl>  <dbl>
     ##  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 <int>, num_of_providers <int>, total_patients <int>
-
-``` r
-ilinet("census")
-```
-
-    ## # A tibble: 9,432 x 15
+    ## # ... with 10,470 more rows, and 4 more variables: ilitotal <dbl>, num_of_providers <dbl>, total_patients <dbl>,
+    ## #   week_start <date>
+
+![](README_files/figure-markdown_github-ascii_identifiers/unnamed-chunk-8-2.png)
+
+    ## Observations: 9,432
+    ## Variables: 16
+    ## $ region_type      <chr> "Census Regions", "Census Regions", "Census Regions", "Census Regions", "Census Regions", ...
+    ## $ region           <chr> "New England", "Mid-Atlantic", "East North Central", "West North Central", "South Atlantic...
+    ## $ year             <int> 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, ...
+    ## $ week             <int> 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     <dbl> 0.4985350, 0.8441440, 0.7924860, 1.7640500, 0.5026620, 0.0542283, 1.0189800, 2.2587800, 2....
+    ## $ unweighted_ili   <dbl> 0.6238480, 1.3213800, 0.8187380, 1.2793900, 0.7233800, 0.0688705, 0.7473840, 2.2763300, 3....
+    ## $ age_0_4          <dbl> 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        <dbl> 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        <dbl> 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         <dbl> 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        <dbl> 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           <dbl> 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         <dbl> 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 <dbl> 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   <dbl> 7053, 2119, 9649, 2892, 6912, 4356, 669, 10719, 2473, 6987, 2384, 9427, 2823, 7591, 4947, ...
+    ## $ week_start       <date> 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
-    ##             <chr>              <chr> <int> <int>        <dbl>          <dbl>   <int>     <chr>     <chr>    <int>
-    ##  1 Census Regions        New England  1997    40    0.4985350      0.6238480      15      <NA>         7       22
-    ##  2 Census Regions       Mid-Atlantic  1997    40    0.8441440      1.3213800       4      <NA>         8       12
-    ##  3 Census Regions East North Central  1997    40    0.7924860      0.8187380      28      <NA>        20       28
-    ##  4 Census Regions West North Central  1997    40    1.7640500      1.2793900       3      <NA>         8       20
-    ##  5 Census Regions     South Atlantic  1997    40    0.5026620      0.7233800      14      <NA>        22       14
-    ##  6 Census Regions East South Central  1997    40    0.0542283      0.0688705       0      <NA>         3        0
-    ##  7 Census Regions West South Central  1997    40    1.0189800      0.7473840       2      <NA>         1        2
-    ##  8 Census Regions           Mountain  1997    40    2.2587800      2.2763300      87      <NA>        71       71
-    ##  9 Census Regions            Pacific  1997    40    2.0488300      3.2349400      26      <NA>        17       36
-    ## 10 Census Regions        New England  1997    41    0.6426690      0.8158010      14      <NA>        14       29
-    ## # ... with 9,422 more rows, and 5 more variables: age_50_64 <chr>, age_65 <int>, ilitotal <int>,
-    ## #   num_of_providers <int>, total_patients <int>
-
-``` r
-ilinet("state")
-```
-
-    ## # A tibble: 19,718 x 15
+    ##             <chr>              <chr> <int> <int>        <dbl>          <dbl>   <dbl>     <dbl>     <dbl>    <dbl>
+    ##  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 <dbl>, age_65 <dbl>, ilitotal <dbl>,
+    ## #   num_of_providers <dbl>, total_patients <dbl>, week_start <date>
+
+![](README_files/figure-markdown_github-ascii_identifiers/unnamed-chunk-8-3.png)
+
+    ## Observations: 19,718
+    ## Variables: 16
+    ## $ region_type      <chr> "States", "States", "States", "States", "States", "States", "States", "States", "States", ...
+    ## $ region           <chr> "Alabama", "Alaska", "Arizona", "Arkansas", "California", "Colorado", "Connecticut", "Dela...
+    ## $ year             <int> 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, ...
+    ## $ week             <int> 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     <dbl> 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   <dbl> 2.1347700, 0.8751460, 0.6747210, 0.6960560, 1.9541200, 0.6606840, 0.0783085, 0.1001250, 2....
+    ## $ age_0_4          <dbl> 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        <dbl> 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        <dbl> 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         <dbl> 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        <dbl> 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           <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA...
+    ## $ ilitotal         <dbl> 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 <dbl> 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   <dbl> 11664, 1714, 25492, 2586, 32342, 20282, 3831, 3995, 2599, NA, 40314, 1943, 4579, 39390, 12...
+    ## $ week_start       <date> 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
-    ##          <chr>                <chr> <int> <int>        <chr>          <chr>   <chr>     <chr>     <chr>    <chr>
-    ##  1      States              Alabama  2010    40         <NA>        2.13477    <NA>      <NA>      <NA>     <NA>
-    ##  2      States               Alaska  2010    40         <NA>       0.875146    <NA>      <NA>      <NA>     <NA>
-    ##  3      States              Arizona  2010    40         <NA>       0.674721    <NA>      <NA>      <NA>     <NA>
-    ##  4      States             Arkansas  2010    40         <NA>       0.696056    <NA>      <NA>      <NA>     <NA>
-    ##  5      States           California  2010    40         <NA>        1.95412    <NA>      <NA>      <NA>     <NA>
-    ##  6      States             Colorado  2010    40         <NA>       0.660684    <NA>      <NA>      <NA>     <NA>
-    ##  7      States          Connecticut  2010    40         <NA>      0.0783085    <NA>      <NA>      <NA>     <NA>
-    ##  8      States             Delaware  2010    40         <NA>       0.100125    <NA>      <NA>      <NA>     <NA>
-    ##  9      States District of Columbia  2010    40         <NA>        2.80877    <NA>      <NA>      <NA>     <NA>
-    ## 10      States              Florida  2010    40         <NA>           <NA>    <NA>      <NA>      <NA>     <NA>
-    ## # ... with 19,708 more rows, and 5 more variables: age_50_64 <chr>, age_65 <chr>, ilitotal <chr>,
-    ## #   num_of_providers <chr>, total_patients <chr>
+    ##          <chr>                <chr> <int> <int>        <dbl>          <dbl>   <dbl>     <dbl>     <dbl>    <dbl>
+    ##  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 <dbl>, age_65 <dbl>, ilitotal <dbl>,
+    ## #   num_of_providers <dbl>, total_patients <dbl>, week_start <date>
+
+![](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
-    ##        <chr>             <fctr>              <chr>     <chr>     <chr>     <date>      <int> <int>    <int>
-    ##  1   Alabama            Level 1            Minimal        01        AL 2015-10-10         40  2015       55
-    ##  2   Alabama            Level 1            Minimal        01        AL 2015-10-17         41  2015       55
-    ##  3   Alabama            Level 1            Minimal        01        AL 2015-10-24         42  2015       55
-    ##  4   Alabama            Level 1            Minimal        01        AL 2015-10-31         43  2015       55
-    ##  5   Alabama            Level 1            Minimal        01        AL 2015-11-07         44  2015       55
-    ##  6   Alabama            Level 1            Minimal        01        AL 2015-11-14         45  2015       55
-    ##  7   Alabama            Level 1            Minimal        01        AL 2015-11-21         46  2015       55
-    ##  8   Alabama            Level 3            Minimal        01        AL 2015-11-28         47  2015       55
-    ##  9   Alabama            Level 1            Minimal        01        AL 2015-12-05         48  2015       55
-    ## 10   Alabama            Level 1            Minimal        01        AL 2015-12-12         49  2015       55
-    ## # ... with 2,797 more rows
+![](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
     ##       <chr>    <dbl>     <dbl>       <dbl>            <dbl>            <dbl>            <dbl>      <dbl>     <dbl>
-    ##  1       57      5.8       6.1       0.054            0.763               10             1962      36283      1972
-    ##  2       57      5.8       6.2       0.056            0.675               10             1795      32107      1805
-    ##  3       56      5.9       6.3       0.059            1.000               18             3022      51404      3040
-    ##  4       56      6.0       6.3       0.061            1.000               11             3193      52130      3204
-    ##  5       56      6.1       6.4       0.062            1.000                7             3178      51443      3185
-    ##  6       56      6.2       6.5       0.061            1.000               17             3129      51865      3146
-    ##  7       56      6.3       6.6       0.060            1.000               16             3099      51753      3115
-    ##  8       56      6.4       6.7       0.061            1.000               19             3208      52541      3227
-    ##  9       56      6.5       6.8       0.060            1.000                7             3192      53460      3199
-    ## 10       56      6.6       6.9       0.062            1.000               22             3257      53163      3279
+    ##  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 <chr>, geo_description <chr>, age_label <chr>,
-    ## #   weekend <date>, weekstart <date>, year <int>, yearweek <int>, coverage_area <chr>, region_name <chr>, callout <chr>
+    ## #   wk_start <date>, wk_end <date>, year_wk_num <int>, mmwrid <chr>, coverage_area <chr>, region_name <chr>,
+    ## #   callout <chr>
 
 ``` 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 <chr>, geo_description <chr>, age_label <chr>,
-    ## #   weekend <date>, weekstart <date>, year <int>, yearweek <int>, coverage_area <chr>, region_name <chr>, callout <chr>
+    ## #   wk_start <date>, wk_end <date>, year_wk_num <int>, mmwrid <chr>, coverage_area <chr>, region_name <chr>,
+    ## #   callout <chr>
 
 ``` 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
     ##       <chr>    <dbl>     <dbl>       <dbl>            <dbl>            <dbl>            <dbl>      <dbl>     <dbl>
-    ##  1       57      6.0       6.7       0.051            0.735                0               85       1683        85
-    ##  2       57      6.1       6.8       0.060            0.701                0               96       1605        96
-    ##  3       57      6.0       6.5       0.061            0.608                1              154       2524       155
-    ##  4       57      6.0       6.6       0.063            0.602                1              157       2497       158
-    ##  5       57      5.3       5.8       0.045            0.511                1              115       2575       116
-    ##  6       57      5.4       5.9       0.045            0.440                1               98       2215        99
-    ##  7       57      5.6       6.0       0.051            0.744                3              394       7753       397
-    ##  8       57      5.7       6.1       0.052            0.651                1              354       6778       355
-    ##  9       57      5.5       5.9       0.052            0.914                1              403       7701       404
-    ## 10       57      5.6       6.0       0.054            0.799                4              358       6733       362
+    ##  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 <chr>, geo_description <chr>, age_label <chr>,
-    ## #   weekend <date>, weekstart <date>, year <int>, yearweek <int>, coverage_area <chr>, region_name <chr>, callout <chr>
+    ## #   wk_start <date>, wk_end <date>, year_wk_num <int>, mmwrid <chr>, coverage_area <chr>, region_name <chr>,
+    ## #   callout <chr>
 
 ### 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
-    ##          <chr>  <chr> <int> <int>           <int>            <dbl>       <int> <int> <int>                     <int>
-    ##  1    National   <NA>  1997    40            1291         0.000000           0     0     0                         0
-    ##  2    National   <NA>  1997    41            1513         0.727032           0     0     0                        11
-    ##  3    National   <NA>  1997    42            1552         1.095360           0     0     3                        13
-    ##  4    National   <NA>  1997    43            1669         0.419413           0     0     0                         7
-    ##  5    National   <NA>  1997    44            1897         0.527148           0     0     9                         1
-    ##  6    National   <NA>  1997    45            2106         0.284900           0     0     0                         6
-    ##  7    National   <NA>  1997    46            2204         0.362976           0     0     3                         4
-    ##  8    National   <NA>  1997    47            2533         0.908014           0     0     5                        17
-    ##  9    National   <NA>  1997    48            2242         1.650310           0     0    14                        22
-    ## 10    National   <NA>  1997    49            2607         1.534330           0     0    11                        28
-    ## # ... with 930 more rows, and 3 more variables: a_unable_to_subtype <int>, b <int>, h3n2v <int>
-    ## 
-    ## $public_health_labs
-    ## # A tibble: 108 x 12
-    ##    region_type region  year  week total_specimens a_2009_h1n1  a_h3 a_subtyping_not_performed     b  bvic  byam h3n2v
-    ##          <chr>  <chr> <int> <int>           <int>       <int> <int>                     <int> <int> <int> <int> <int>
-    ##  1    National   <NA>  2015    40            1139           4    65                         2    10     0     1     0
-    ##  2    National   <NA>  2015    41            1152           5    41                         2     7     3     0     0
-    ##  3    National   <NA>  2015    42            1198          10    50                         1     8     3     2     0
-    ##  4    National   <NA>  2015    43            1244           9    31                         4     9     1     4     0
-    ##  5    National   <NA>  2015    44            1465           4    23                         4     9     1     4     0
-    ##  6    National   <NA>  2015    45            1393          11    34                         1    10     4     2     0
-    ##  7    National   <NA>  2015    46            1458          17    42                         1     4     0     4     0
-    ##  8    National   <NA>  2015    47            1157          17    24                         0     4     3     9     0
-    ##  9    National   <NA>  2015    48            1550          27    36                         3     9     3    12     0
-    ## 10    National   <NA>  2015    49            1518          38    37                         3    11     2    11     0
-    ## # ... with 98 more rows
-    ## 
-    ## $clinical_labs
-    ## # A tibble: 108 x 10
-    ##    region_type region  year  week total_specimens total_a total_b percent_positive percent_a percent_b
-    ##          <chr>  <chr> <int> <int>           <int>   <int>   <int>            <dbl>     <dbl>     <dbl>
-    ##  1    National   <NA>  2015    40           12029      84      43          1.05578  0.698312  0.357469
-    ##  2    National   <NA>  2015    41           13111     116      54          1.29662  0.884753  0.411868
-    ##  3    National   <NA>  2015    42           13441      97      52          1.10855  0.721672  0.386876
-    ##  4    National   <NA>  2015    43           13537      98      52          1.10807  0.723942  0.384132
-    ##  5    National   <NA>  2015    44           14687      97      68          1.12344  0.660448  0.462994
-    ##  6    National   <NA>  2015    45           15048     122      86          1.38224  0.810739  0.571505
-    ##  7    National   <NA>  2015    46           15250      84      98          1.19344  0.550820  0.642623
-    ##  8    National   <NA>  2015    47           15234     119      92          1.38506  0.781147  0.603912
-    ##  9    National   <NA>  2015    48           16201     145      81          1.39498  0.895006  0.499969
-    ## 10    National   <NA>  2015    49           16673     140     106          1.47544  0.839681  0.635758
-    ## # ... with 98 more rows
+    ## 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
     ##          <chr>     <chr> <int> <int>           <int>            <dbl>       <int> <int> <int>                     <int>
     ##  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 <int>, b <int>, h3n2v <int>
+    ## # ... with 9,390 more rows, and 4 more variables: a_unable_to_subtype <int>, b <int>, h3n2v <int>, wk_date <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
     ##          <chr>     <chr> <int> <chr>           <int>       <int> <int>                     <int> <int> <int> <int>
-    ##  1 HHS Regions  Region 1  2015    XX              39           0     5                         0     0     0     0
-    ##  2 HHS Regions  Region 2  2015    XX              56           1     4                         0     1     0     0
-    ##  3 HHS Regions  Region 3  2015    XX             132           1     3                         0     0     0     0
-    ##  4 HHS Regions  Region 4  2015    XX              83           0     5                         0     1     0     0
-    ##  5 HHS Regions  Region 5  2015    XX             218           2     7                         0     0     0     1
-    ##  6 HHS Regions  Region 6  2015    XX              97           0     2                         0     0     0     0
-    ##  7 HHS Regions  Region 7  2015    XX              36           0     2                         0     0     0     0
-    ##  8 HHS Regions  Region 8  2015    XX              71           0     2                         0     0     0     0
-    ##  9 HHS Regions  Region 9  2015    XX             273           0    22                         2     8     0     0
-    ## 10 HHS Regions Region 10  2015    XX             134           0    13                         0     0     0     0
-    ## # ... with 1,070 more rows, and 1 more variables: h3n2v <int>
+    ##  1 HHS Regions  Region 1  2015  <NA>              39           0     5                         0     0     0     0
+    ##  2 HHS Regions  Region 2  2015  <NA>              56           1     4                         0     1     0     0
+    ##  3 HHS Regions  Region 3  2015  <NA>             132           1     3                         0     0     0     0
+    ##  4 HHS Regions  Region 4  2015  <NA>              83           0     5                         0     1     0     0
+    ##  5 HHS Regions  Region 5  2015  <NA>             218           2     7                         0     0     0     1
+    ##  6 HHS Regions  Region 6  2015  <NA>              97           0     2                         0     0     0     0
+    ##  7 HHS Regions  Region 7  2015  <NA>              36           0     2                         0     0     0     0
+    ##  8 HHS Regions  Region 8  2015  <NA>              71           0     2                         0     0     0     0
+    ##  9 HHS Regions  Region 9  2015  <NA>             273           0    22                         2     8     0     0
+    ## 10 HHS Regions Region 10  2015  <NA>             134           0    13                         0     0     0     0
+    ## # ... with 1,070 more rows, and 2 more variables: h3n2v <int>, wk_date <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
-    ##          <chr>     <chr> <int> <int>           <int>   <int>   <int>            <dbl>     <dbl>     <dbl>
-    ##  1 HHS Regions  Region 1  2015    40             693       2       3         0.721501  0.288600  0.432900
-    ##  2 HHS Regions  Region 2  2015    40            1220       5       0         0.409836  0.409836  0.000000
-    ##  3 HHS Regions  Region 3  2015    40             896       0       1         0.111607  0.000000  0.111607
-    ##  4 HHS Regions  Region 4  2015    40            2486      24      16         1.609010  0.965406  0.643604
-    ##  5 HHS Regions  Region 5  2015    40            2138      14       3         0.795136  0.654818  0.140318
-    ##  6 HHS Regions  Region 6  2015    40            1774       8      16         1.352870  0.450958  0.901917
-    ##  7 HHS Regions  Region 7  2015    40             621       2       1         0.483092  0.322061  0.161031
-    ##  8 HHS Regions  Region 8  2015    40             824       1       1         0.242718  0.121359  0.121359
-    ##  9 HHS Regions  Region 9  2015    40             980      25       2         2.755100  2.551020  0.204082
-    ## 10 HHS Regions Region 10  2015    40             397       3       0         0.755668  0.755668  0.000000
+    ## # 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
+    ##          <chr>     <chr> <int> <int>           <int>   <int>   <int>            <dbl>     <dbl>     <dbl>     <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
     ##             <chr>              <chr> <int> <int>           <int>            <dbl>       <int> <int> <int>
     ##  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 <int>, a_unable_to_subtype <int>, b <int>,
-    ## #   h3n2v <int>
+    ## # ... with 8,450 more rows, and 5 more variables: a_subtyping_not_performed <int>, a_unable_to_subtype <int>, b <int>,
+    ## #   h3n2v <int>, wk_date <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
     ##             <chr>              <chr> <int> <chr>           <int>       <int> <int>                     <int> <int>
-    ##  1 Census Regions        New England  2015    XX              39           0     5                         0     0
-    ##  2 Census Regions       Mid-Atlantic  2015    XX              63           1     5                         0     1
-    ##  3 Census Regions East North Central  2015    XX              91           2     5                         0     0
-    ##  4 Census Regions West North Central  2015    XX             169           0     4                         0     0
-    ##  5 Census Regions     South Atlantic  2015    XX             187           1     7                         0     0
-    ##  6 Census Regions East South Central  2015    XX              21           0     0                         0     1
-    ##  7 Census Regions West South Central  2015    XX              72           0     2                         0     0
-    ##  8 Census Regions           Mountain  2015    XX             111           0     6                         0     0
-    ##  9 Census Regions            Pacific  2015    XX             386           0    31                         2     8
-    ## 10 Census Regions        New England  2015    XX              39           2     3                         0     0
-    ## # ... with 962 more rows, and 3 more variables: bvic <int>, byam <int>, h3n2v <int>
+    ##  1 Census Regions        New England  2015  <NA>              39           0     5                         0     0
+    ##  2 Census Regions       Mid-Atlantic  2015  <NA>              63           1     5                         0     1
+    ##  3 Census Regions East North Central  2015  <NA>              91           2     5                         0     0
+    ##  4 Census Regions West North Central  2015  <NA>             169           0     4                         0     0
+    ##  5 Census Regions     South Atlantic  2015  <NA>             187           1     7                         0     0
+    ##  6 Census Regions East South Central  2015  <NA>              21           0     0                         0     1
+    ##  7 Census Regions West South Central  2015  <NA>              72           0     2                         0     0
+    ##  8 Census Regions           Mountain  2015  <NA>             111           0     6                         0     0
+    ##  9 Census Regions            Pacific  2015  <NA>             386           0    31                         2     8
+    ## 10 Census Regions        New England  2015  <NA>              39           2     3                         0     0
+    ## # ... with 962 more rows, and 4 more variables: bvic <int>, byam <int>, h3n2v <int>, wk_date <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
     ##             <chr>              <chr> <int> <int>           <int>   <int>   <int>            <dbl>     <dbl>     <dbl>
     ##  1 Census Regions        New England  2015    40             693       2       3         0.721501  0.288600 0.4329000
@@ -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 <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
     ##          <chr>                <chr> <int> <int>           <chr>            <chr>       <chr> <chr> <chr>
     ##  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            <NA>             <NA>        <NA>  <NA>  <NA>
-    ## # ... with 14,084 more rows, and 4 more variables: a_subtyping_not_performed <chr>, a_unable_to_subtype <chr>, b <chr>,
-    ## #   h3n2v <chr>
+    ## # ... with 14,084 more rows, and 5 more variables: a_subtyping_not_performed <chr>, a_unable_to_subtype <chr>, b <chr>,
+    ## #   h3n2v <chr>, wk_date <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
     ##          <chr>                <chr>              <chr>           <chr>       <chr> <chr>                     <chr>
     ##  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            <NA>        <NA>  <NA>                      <NA>
-    ## # ... with 152 more rows, and 4 more variables: b <chr>, bvic <chr>, byam <chr>, h3n2v <chr>
+    ## # ... with 152 more rows, and 5 more variables: b <chr>, bvic <chr>, byam <chr>, h3n2v <chr>, wk_date <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
     ##          <chr>                <chr> <int> <int>           <chr>   <chr>   <chr>            <chr>     <chr>     <chr>
     ##  1      States              Alabama  2015    40             167       2       3             2.99       1.2       1.8
@@ -715,7 +845,7 @@ who_nrevss("state")
     ##  8      States             Delaware  2015    40              22       0       0                0         0         0
     ##  9      States District of Columbia  2015    40            <NA>    <NA>    <NA>             <NA>      <NA>      <NA>
     ## 10      States              Florida  2015    40            <NA>    <NA>    <NA>             <NA>      <NA>      <NA>
-    ## # ... with 5,822 more rows
+    ## # ... with 5,822 more rows, and 1 more variables: wk_date <date>
 
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