enhancements to maps and incorporating MMWR week calcs

DESCRIPTION

@@ -34,5 +34,6 @@ Imports:
     stats,
     utils,
     sf,
+    MMWRweek,
     units (>= 0.4-6)
 RoxygenNote: 6.0.1

NAMESPACE

@@ -1,7 +1,7 @@
 # Generated by roxygen2: do not edit by hand
 
-export(agd_ipt)
-export(cdc_coverage_map)
+export(age_group_distribution)
+export(cdc_basemap)
 export(geographic_spread)
 export(hospitalizations)
 export(ili_weekly_activity_indicators)
@@ -10,9 +10,11 @@ export(pi_mortality)
 export(state_data_providers)
 export(surveillance_areas)
 export(who_nrevss)
+import(MMWRweek)
 import(httr)
 importFrom(dplyr,"%>%")
 importFrom(dplyr,bind_rows)
+importFrom(dplyr,data_frame)
 importFrom(dplyr,filter)
 importFrom(dplyr,left_join)
 importFrom(dplyr,mutate)

R/aaa.R

@@ -1,3 +1,15 @@
+# THIS IS NOT EXPORTED FROM MMWRweek but I need it
+# Find start date for a calendar year
+#
+# Finds the state date given a numeric calendar year
+# @author Jarad Niemi \email{niemi@@iastate.edu}
+.start_date = function(year) {
+  # Finds start state for this calendar year
+  jan1 = as.Date(paste(year, '-01-01', sep=''))
+  wday = as.numeric(MMWRweekday(jan1))
+  jan1 - (wday-1) + 7*(wday>4)
+}
+
 # CDC U.S. region names to ID map
 .region_map <- c(national=3, hhs=1, census=2, state=5)
 
@@ -11,5 +23,33 @@
 # Our bot's user-agent string
 .cdcfluview_ua <- "Mozilla/5.0 (compatible; R-cdcvluview Bot/2.0; https://github.com/hrbrmstr/cdcfluview)"
 
-# CDC Basemap
-.cdc_basemap <- "https://gis.cdc.gov/grasp/fluview/FluView1References/data/US_States_w_PR_labels.json"
+# CDC Basemaps
+.national_outline <- "https://gis.cdc.gov/grasp/fluview/FluView2References/Data/US_84.json"
+.hhs_subregions_basemap <- "https://gis.cdc.gov/grasp/fluview/FluView2References/Data/HHSRegions_w_SubGroups.json"
+.census_divisions_basemap <- "https://gis.cdc.gov/grasp/fluview/FluView2References/Data/CensusDivs_w_SubGroups.json"
+.states_basemap <- "https://gis.cdc.gov/grasp/fluview/FluView2References/Data/StatesFluView.json"
+.spread_basemap <- "https://gis.cdc.gov/grasp/fluview/FluView8References/Data/States_Territories_labels.json"
+.surv_basemap <- "https://gis.cdc.gov/grasp/fluview/FluView1References/data/US_States_w_PR_labels.json"
+
+# CDC Age Groups
+.age_grp <- c("0-4 yr", "5-24 yr", "25-64 yr", "65+ yr")
+
+# CDC Virus Groups
+.vir_grp <- c("A (Subtyping not Performed)", "A (H1N1)pdm09", "A (Unable to Subtype)",
+              "B (Lineage Unspecified)", "A (H1)", "A (H3)", "B (Victoria Lineage)",
+              "B (Yamagata Lineage)", "H3N2v")
+
+# Week Starts
+
+.tmp <- lapply(1962:2030, .start_date)
+
+mapply(function(.x, .y) {
+  data_frame(
+    wk_start = seq(.tmp[[.x]], .tmp[[.y]], "1 week"),
+    wk_num = 1:length(wk_start)
+  ) -> tmp
+  tmp[-nrow(tmp),]
+}, 1:(length(.tmp)-1), 2:length(.tmp), SIMPLIFY=FALSE) -> .wk
+
+.wk <- Reduce(rbind.data.frame, .wk)
+.wk$mmwrid <- 1:nrow(.wk)

R/agd-ipt.r

@@ -11,7 +11,8 @@
 #' @examples  \dontrun{
 #' agd_ipt()
 #' }
-agd_ipt <- function() {
+age_group_distribution <- function() {
+
   httr::GET(
     url = "https://gis.cdc.gov/grasp/fluView6/GetFlu6AllDataP",
     httr::user_agent(.cdcfluview_ua),
@@ -47,9 +48,14 @@ agd_ipt <- function() {
   class(vir_df) <- c("tbl_df", "tbl", "data.frame")
 
   vir_df_cols <- c("sea_label", "age_label", "vir_label", "count", "mmwrid", "seasonid",
-                   "publishyearweekid", "sea_description", "sea_startweek", "sea_endweek",
-                   "vir_description",  "vir_startmmwrid", "vir_endmmwrid")
+                   "publishyearweekid", "sea_description", "sea_startweek",
+                   "sea_endweek", "vir_description",  "vir_startmmwrid", "vir_endmmwrid")
+
+  vir_df <- vir_df[,vir_df_cols]
+
+  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[,vir_df_cols]
+  vir_df
 
 }

R/cdcfluview-package.R

@@ -12,8 +12,9 @@
 #' @docType package
 #' @author Bob Rudis (bob@@rud.is)
 #' @import httr
+#' @import MMWRweek
 #' @importFrom tools file_path_sans_ext
-#' @importFrom dplyr left_join bind_rows mutate filter %>%
+#' @importFrom dplyr left_join bind_rows mutate filter data_frame %>%
 #' @importFrom jsonlite fromJSON
 #' @importFrom stats setNames
 #' @importFrom sf st_read

R/coverage-map.r

@@ -1,28 +1,40 @@
-#' Retrieve CDC U.S. Coverage Map
+#' Retrieve CDC U.S. Basemaps
 #'
-#' The CDC FluView application uses a composite basemap of coverage areas
-#' within the United States that elides and scales Alaska, Hawaii and
-#' Puerto Rico and provides elided and scaled breakouts for New York City
-#' and the District of Columbia.\cr
+#' The CDC FluView application uses a composite basemaps of coverage areas
+#' within the United States that elides and scales Alaska, Hawaii,
+#' Puerto Rico & the Virgin Islands and some further provide elided and scaled
+#' breakouts for New York City and the District of Columbia.\cr
 #' \cr
-#' The basemap provides polygon identifiers by:
-#' \cr
-#' - `STATE_FIPS`
-#' - `STATE_ABBR`
-#' - `STATE_NAME`
-#' - `HHS_Region`
-#' - `FIPSTXT`)
-#' \cr
-#' This function retrieves the shapefile, projects to EPSG:5069 and
+#' This function retrieves the given shapefile, projects to EPSG:5069 and
 #' returns it as an `sf` (simple features) object.
 #'
 #' @md
 #' @export
+#' @param basemap select the CDC basemap. One of:
+#' - "`national`": outline of the U.S. + AK, HI, PR + VI
+#' - "`hhs`": outline of the U.S. + HHS Region Outlines + AK, HI, PR + VI
+#' - "`census`": outline of the U.S. + Census Region Outlines + AK, HI, PR + VI
+#' - "`states`": outline of the U.S. + State Outlines + AK, HI, PR + VI
+#' - "`spread`": outline of the U.S. + State Outlines + AK, HI, PR + VI & Guam
+#' - "`surv`": outline of the U.S. + State Outlines + AK, HI, PR + VI
+#' @note These are just the basemaps. You need to pair it with the data you wish to visualize.
 #' @examples \dontrun{
-#' plot(cdc_coverage_map())
+#' plot(cdc_basemap("national"))
 #' }
-cdc_coverage_map <- function() {
-  xsf <- sf::st_read(.cdc_basemap, quiet=TRUE, stringsAsFactors=FALSE)
+cdc_basemap <- function(basemap = c("national", "hhs", "census", "states", "spread", "surv")) {
+
+  switch(
+    basemap,
+    national = .national_outline,
+    hhs = .hhs_subregions_basemap,
+    census = .census_divisions_basemap,
+    states = .states_basemap,
+    spread = .spread_basemap,
+    surv = .surv_basemap
+  ) -> selected_map
+
+  xsf <- sf::st_read(selected_map, quiet=TRUE, stringsAsFactors=FALSE)
   sf::st_crs(xsf) <- 4326
   sf::st_transform(xsf, 5069)
+
 }

R/geographic-spread.R

@@ -7,7 +7,6 @@
 geographic_spread <- function() {
 
   meta <- jsonlite::fromJSON("https://gis.cdc.gov/grasp/Flu8/GetPhase08InitApp?appVersion=Public")
-  meta$seasons$seasonid
 
   httr::POST(
     url = "https://gis.cdc.gov/grasp/Flu8/PostPhase08DownloadData",

R/ili-weekly-state.r

@@ -60,7 +60,7 @@ ili_weekly_activity_indicators <- function(season_start_year) {
 
 }
 
-#' Retrieve metadat about U.S. State CDC Provider Data
+#' Retrieve metadata about U.S. State CDC Provider Data
 #'
 #' @md
 #' @export

R/ilinet.r

@@ -71,9 +71,24 @@ ilinet <- function(region=c("national", "hhs", "census", "state")) {
 
   xdf <- read.csv(nm, skip = 1, stringsAsFactors=FALSE)
   xdf <- .mcga(xdf)
-  class(xdf) <- c("tbl_df", "tbl", "data.frame")
 
-  xdf[xdf=="X"] <- NA
+  suppressWarnings(xdf$weighted_ili <- to_num(xdf$weighted_ili))
+  suppressWarnings(xdf$unweighted_ili <- to_num(xdf$unweighted_ili))
+  suppressWarnings(xdf$age_0_4 <- to_num(xdf$age_0_4))
+  suppressWarnings(xdf$age_25_49 <- to_num(xdf$age_25_49))
+  suppressWarnings(xdf$age_25_64 <- to_num(xdf$age_25_64))
+  suppressWarnings(xdf$age_5_24 <- to_num(xdf$age_5_24))
+  suppressWarnings(xdf$age_50_64 <- to_num(xdf$age_50_64))
+  suppressWarnings(xdf$age_65 <- to_num(xdf$age_65))
+  suppressWarnings(xdf$ilitotal <- to_num(xdf$ilitotal))
+  suppressWarnings(xdf$num_of_providers <- to_num(xdf$num_of_providers))
+  suppressWarnings(xdf$total_patients <- to_num(xdf$total_patients))
+  suppressWarnings(xdf$week_start <- as.Date(sprintf("%s-%02d-1", xdf$year, xdf$week), "%Y-%U-%u"))
+
+  if (region == "national") xdf$region <- "National"
+  if (region == "hhs") xdf$region <- factor(xdf$region, levels=sprintf("Region %s", 1:10))
+
+  class(xdf) <- c("tbl_df", "tbl", "data.frame")
 
   xdf
 

R/zzz.r

@@ -1,6 +1,6 @@
 # this is only used during active development phases before/after CRAN releases
 
-.onAttach <- function(...) {
+.onAttach <- function(...) { # nocov start
 
   if (!interactive()) return()
 
@@ -8,4 +8,4 @@
                                "There are *MASSIVE* breaking changes*. ",
                                "See https://github.com/hrbrmstr/cdcfluview for info/news."))
 
-}
+} # nocov end

README.Rmd

@@ -28,14 +28,14 @@ The U.S. Centers for Disease Control (CDC) maintains a portal <http://gis.cdc.go
 
 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
 - `ilinet`:	Retrieve ILINet Surveillance Data
 - `ili_weekly_activity_indicators`:	Retrieve weekly state-level ILI indicators per-state for a given season
 - `pi_mortality`:	Pneumonia and Influenza Mortality Surveillance
-- `state_data_providers`:	Retrieve metadat about U.S. State CDC Provider Data
+- `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
 
@@ -58,6 +58,7 @@ options(width=120)
 
 ```{r message=FALSE, warning=FALSE, error=FALSE}
 library(cdcfluview)
+library(hrbrthemes)
 library(tidyverse)
 
 # current verison
@@ -67,13 +68,18 @@ packageVersion("cdcfluview")
 ### Age Group Distribution of Influenza Positive Tests Reported by Public Health Laboratories
 
 ```{r message=FALSE, warning=FALSE}
-glimpse(agd_ipt())
+glimpse(age_group_distribution())
 ```
 
 ### Retrieve CDC U.S. Coverage Map
 
 ```{r message=FALSE, warning=FALSE}
-plot(cdc_coverage_map())
+plot(cdc_basemap("national"))
+plot(cdc_basemap("hhs"))
+plot(cdc_basemap("census"))
+plot(cdc_basemap("states"))
+plot(cdc_basemap("spread"))
+plot(cdc_basemap("surv"))
 ```
 
 ### State and Territorial Epidemiologists Reports of Geographic Spread of Influenza
@@ -101,13 +107,22 @@ glimpse(hospitalizations("ihsp", "Oklahoma"))
 ### Retrieve ILINet Surveillance Data
 
 ```{r message=FALSE, warning=FALSE}
-ilinet("national")
-
-ilinet("hhs")
-
-ilinet("census")
-
-ilinet("state")
+walk(c("national", "hhs", "census", "state"), ~{
+  
+  ili_df <- ilinet(region = .x)
+  
+  print(glimpse(ili_df))
+  
+  ggplot(ili_df, aes(week_start, unweighted_ili, group=region, color=region)) +
+    geom_line() +
+    viridis::scale_color_viridis(discrete=TRUE) +
+    labs(x=NULL, y="Unweighted ILI", title=ili_df$region_type[1]) +
+    theme_ipsum_rc(grid="XY") +
+    theme(legend.position = "none") -> gg
+  
+  print(gg)
+  
+})
 ```
 
 ### Retrieve weekly state-level ILI indicators per-state for a given season
@@ -128,12 +143,7 @@ pi_mortality("state")
 pi_mortality("region")
 ```
 
-### Retrieve metadat about U.S. State CDC Provider Data
-
-```{r message=FALSE, warning=FALSE}
-```
-
-### Retrieve a list of valid sub-regions for each surveillance area.
+### Retrieve metadata about U.S. State CDC Provider Data
 
 ```{r message=FALSE, warning=FALSE}
 state_data_providers()

README.md

@@ -58,7 +58,7 @@ The following functions are implemented:
 -   `ili_weekly_activity_indicators`: Retrieve weekly state-level ILI
     indicators per-state for a given season
 -   `pi_mortality`: Pneumonia and Influenza Mortality Surveillance
--   `state_data_providers`: Retrieve metadat about U.S. State CDC
+-   `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.
@@ -484,9 +484,7 @@ pi_mortality("region")
     ## # ... 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>
 
-### Retrieve metadat about U.S. State CDC Provider Data
-
-### Retrieve a list of valid sub-regions for each surveillance area.
+### Retrieve metadata about U.S. State CDC Provider Data
 
 ``` r
 state_data_providers()

man/agd_ipt.Rd → man/age_group_distribution.Rd

@@ -1,10 +1,10 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/agd-ipt.r
-\name{agd_ipt}
-\alias{agd_ipt}
+\name{age_group_distribution}
+\alias{age_group_distribution}
 \title{Age Group Distribution of Influenza Positive Tests Reported by Public Health Laboratories}
 \usage{
-agd_ipt()
+age_group_distribution()
 }
 \description{
 Retrieves the age group distribution of influenza positive tests that are reported by

man/cdc_basemap.Rd

@@ -0,0 +1,37 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/coverage-map.r
+\name{cdc_basemap}
+\alias{cdc_basemap}
+\title{Retrieve CDC U.S. Basemaps}
+\usage{
+cdc_basemap(basemap = c("national", "hhs", "census", "states", "spread",
+  "surv"))
+}
+\arguments{
+\item{basemap}{select the CDC basemap. One of:
+\itemize{
+\item "\code{national}": outline of the U.S. + AK, HI, PR + VI
+\item "\code{hhs}": outline of the U.S. + HHS Region Outlines + AK, HI, PR + VI
+\item "\code{census}": outline of the U.S. + Census Region Outlines + AK, HI, PR + VI
+\item "\code{states}": outline of the U.S. + State Outlines + AK, HI, PR + VI
+\item "\code{spread}": outline of the U.S. + State Outlines + AK, HI, PR + VI & Guam
+\item "\code{surv}": outline of the U.S. + State Outlines + AK, HI, PR + VI
+}}
+}
+\description{
+The CDC FluView application uses a composite basemaps of coverage areas
+within the United States that elides and scales Alaska, Hawaii,
+Puerto Rico & the Virgin Islands and some further provide elided and scaled
+breakouts for New York City and the District of Columbia.\cr
+\cr
+This function retrieves the given shapefile, projects to EPSG:5069 and
+returns it as an \code{sf} (simple features) object.
+}
+\note{
+These are just the basemaps. You need to pair it with the data you wish to visualize.
+}
+\examples{
+\dontrun{
+plot(cdc_basemap("national"))
+}
+}

man/cdc_coverage_map.Rd

@@ -1,32 +0,0 @@
-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/coverage-map.r
-\name{cdc_coverage_map}
-\alias{cdc_coverage_map}
-\title{Retrieve CDC U.S. Coverage Map}
-\usage{
-cdc_coverage_map()
-}
-\description{
-The CDC FluView application uses a composite basemap of coverage areas
-within the United States that elides and scales Alaska, Hawaii and
-Puerto Rico and provides elided and scaled breakouts for New York City
-and the District of Columbia.\cr
-\cr
-The basemap provides polygon identifiers by:
-\cr
-\itemize{
-\item \code{STATE_FIPS}
-\item \code{STATE_ABBR}
-\item \code{STATE_NAME}
-\item \code{HHS_Region}
-\item \code{FIPSTXT})
-\cr
-This function retrieves the shapefile, projects to EPSG:5069 and
-returns it as an \code{sf} (simple features) object.
-}
-}
-\examples{
-\dontrun{
-plot(cdc_coverage_map())
-}
-}

man/state_data_providers.Rd

@@ -2,12 +2,12 @@
 % Please edit documentation in R/ili-weekly-state.r
 \name{state_data_providers}
 \alias{state_data_providers}
-\title{Retrieve metadat about U.S. State CDC Provider Data}
+\title{Retrieve metadata about U.S. State CDC Provider Data}
 \usage{
 state_data_providers()
 }
 \description{
-Retrieve metadat about U.S. State CDC Provider Data
+Retrieve metadata about U.S. State CDC Provider Data
 }
 \examples{
 state_data_providers()

tests/testthat/test-cdcfluview.R

@@ -1,26 +1,44 @@
 context("basic functionality")
 test_that("we can do something", {
+
   skip_on_cran()
+
   expect_that(agd_ipt(), is_a("data.frame"))
+
   expect_that(geographic_spread(), is_a("data.frame"))
-  expect_that(surveillance_areas(), is_a("data.frame"))
+
+  expect_that(state_data_providers(), is_a("data.frame"))
+
   expect_that(hospitalizations("flusurv"), is_a("data.frame"))
   expect_that(hospitalizations("eip"), is_a("data.frame"))
   expect_that(hospitalizations("eip", "Colorado"), is_a("data.frame"))
   expect_that(hospitalizations("ihsp"), is_a("data.frame"))
   expect_that(hospitalizations("ihsp", "Oklahoma"), is_a("data.frame"))
+
   expect_that(ilinet("national"), is_a("data.frame"))
   expect_that(ilinet("hhs"), is_a("data.frame"))
   expect_that(ilinet("census"), is_a("data.frame"))
   expect_that(ilinet("state"), is_a("data.frame"))
+
   expect_that(ili_weekly_activity_indicators(2017), is_a("data.frame"))
+
   expect_that(pi_mortality("national"), is_a("data.frame"))
   expect_that(pi_mortality("state"), is_a("data.frame"))
   expect_that(pi_mortality("region"), is_a("data.frame"))
-  expect_that(state_data_providers(), is_a("data.frame"))
+
+  expect_that(surveillance_areas(), is_a("data.frame"))
+
   expect_that(who_nrevss("national"), is_a("list"))
   expect_that(who_nrevss("hhs"), is_a("list"))
   expect_that(who_nrevss("census"), is_a("list"))
   expect_that(who_nrevss("state"), is_a("list"))
-  expect_that(cdc_coverage_map(), is_a("sf"))
+
+  expect_that(cdc_basemap("national"), is_a("sf"))
+  expect_that(cdc_basemap("hhs"), is_a("sf"))
+  expect_that(cdc_basemap("census"), is_a("sf"))
+  expect_that(cdc_basemap("states"), is_a("sf"))
+  expect_that(cdc_basemap("spread"), is_a("sf"))
+  expect_that(cdc_basemap("surv"), is_a("sf"))
+
+
 })