diff --git a/app.R b/app.R
index d4119fc..23bdc24 100644
--- a/app.R
+++ b/app.R
@@ -1,11 +1,10 @@
-# define global
-source("global.R")
+# load pipe
+library(magrittr)
 
-# define UI
+# source components
+source("global.R")
 source("ui.R")
-
-# define server
 source("server.R")
 
 # run app
-shinyApp(ui, server)
+shiny::shinyApp(ui, server)
diff --git a/data_updater.R b/data_updater.R
index 9a24978..247fb86 100644
--- a/data_updater.R
+++ b/data_updater.R
@@ -1,71 +1,39 @@
-# load libraries
-library(dplyr)
-library(RJSONIO)
-
 # create data directory, if it doesn't already exist
 dir.create("data", showWarnings = FALSE)
 
-# download most recent data from GitHub and unzip into temporary folder
-temp <- tempfile()
-tempd <- tempdir()
-download.file("https://github.com/ccodwg/Covid19Canada/archive/master.zip", temp, mode = "wb")
-unzip(temp, exdir = tempd)
+# download data from GitHub
 
-# copy into local data directories
+# update time
+curl::curl_download("https://raw.githubusercontent.com/ccodwg/CovidTimelineCanada/main/update_time.txt", "data/update_time.txt")
 
-## copy data
-if (dir.exists(paste(tempd, "Covid19Canada-master", sep = "/"))) {
-  for (f in list.files(paste(tempd, "Covid19Canada-master", sep = "/"), recursive = TRUE, full.names = TRUE)[basename(list.files(paste(tempd, "Covid19Canada-master", sep = "/"), recursive = TRUE)) %in% c(
-    "update_time.txt", "data_notes.txt",
-    "cases_timeseries_prov.csv", "mortality_timeseries_prov.csv", "recovered_timeseries_prov.csv", "testing_timeseries_prov.csv", "active_timeseries_prov.csv",
-    "cases_timeseries_hr.csv", "mortality_timeseries_hr.csv",
-    "cases_timeseries_canada.csv", "mortality_timeseries_canada.csv", "recovered_timeseries_canada.csv", "testing_timeseries_canada.csv", "active_timeseries_canada.csv",
-    "vaccine_administration_timeseries_prov.csv", "vaccine_administration_timeseries_canada.csv",
-    "vaccine_distribution_timeseries_prov.csv", "vaccine_distribution_timeseries_canada.csv",
-    "vaccine_completion_timeseries_prov.csv", "vaccine_completion_timeseries_canada.csv",
-    "vaccine_additionaldoses_timeseries_prov.csv", "vaccine_additionaldoses_timeseries_canada.csv",
-    "sk_new_cases_timeseries_hr.csv","sk_new_mortality_timeseries_hr.csv"
-    )]) {
-    file_destination <- paste0("data/", basename(f))
-    message("Copying: ", file_destination)
-    file.copy(f, file_destination, overwrite = TRUE)
-  }
-}
+# geo data
+curl::curl_download("https://raw.githubusercontent.com/ccodwg/CovidTimelineCanada/main/geo/pt.csv", "data/pt.csv")
+curl::curl_download("https://raw.githubusercontent.com/ccodwg/CovidTimelineCanada/main/geo/pt.geojson", "data/pt.geojson")
+curl::curl_download("https://raw.githubusercontent.com/ccodwg/CovidTimelineCanada/main/geo/health_regions.csv", "data/health_regions.csv")
+curl::curl_download("https://raw.githubusercontent.com/ccodwg/CovidTimelineCanada/main/geo/health_regions.geojson", "data/health_regions.geojson")
 
-## copy other files
-if (dir.exists(paste(tempd, "Covid19Canada-master", sep = "/"))) {
-  for (f in list.files(paste(tempd, "Covid19Canada-master", sep = "/"), recursive = TRUE, full.names = TRUE)[basename(list.files(paste(tempd, "Covid19Canada-master", sep = "/"), recursive = TRUE)) %in% c(
-    "prov_map.csv", "hr_map.csv","hr_map_sk_new.csv"
-  )]) {
-    file_destination <- paste0("data/", basename(f))
-    message("Copying: ", file_destination)
-    file.copy(f, file_destination, overwrite = TRUE)
-  }
+# health region data
+for (d in c(
+  "cases",
+  "deaths"
+  )) {
+  curl::curl_download(paste0("https://github.com/ccodwg/CovidTimelineCanada/raw/main/data/hr/", d, "_hr.csv"), paste0("data/", d, "_hr.csv"))
 }
 
-# download hospitalization data
-hosp <- fromJSON("https://api.covid19tracker.ca/summary/split")$data %>%
-  lapply(function(x) {
-    x[sapply(x, is.null)] <- NA
-    unlist(x)
-  }) %>%
-  {as.data.frame(t(do.call("cbind", .)), stringsAsFactors = FALSE)} %>%
-  rename(
-    prov_short = province,
-    hosp_cases = total_hospitalizations,
-    hosp_cases_change = change_hospitalizations) %>%
-  inner_join(
-    data.frame("prov_short" = c("NL", "PE", "NS", "NB", "QC", "ON", "MB", "SK", "AB", "BC", "YT", "NT", "NU"),
-               "province" = c("NL", "PEI", "Nova Scotia", "New Brunswick", "Quebec", "Ontario", "Manitoba",
-                              "Saskatchewan", "Alberta", "BC", "Yukon", "NWT", "Nunavut"),
-               stringsAsFactors = FALSE),
-    by = "prov_short"
-  ) %>%
-  select(province, hosp_cases, hosp_cases_change)
-
-# write hospitalization data
-write.csv(hosp, "data/hosp.csv", row.names = FALSE)
+# province/territory data
+for (d in c(
+  "cases",
+  "deaths",
+  "tests_completed",
+  "vaccine_coverage_dose_1",
+  "vaccine_coverage_dose_2",
+  "vaccine_coverage_dose_3",
+  "vaccine_coverage_dose_4"
+  )) {
+  curl::curl_download(paste0("https://github.com/ccodwg/CovidTimelineCanada/raw/main/data/pt/", d, "_pt.csv"), paste0("data/", d, "_pt.csv"))
+}
 
-# delete temporary files
-unlink(temp) # delete GitHub download
-unlink(paste(tempd, "Covid19Canada-master", sep = "/"), recursive = TRUE) # delete unzipped files
+# metadata
+for (d in c("cases", "deaths")) {
+  curl::curl_download(paste0("https://github.com/ccodwg/CovidTimelineCanada/raw/main/data/can/", d, "_can_completeness.json"), paste0("data/", d, "_can_completeness.json"))
+}
diff --git a/geo/esri_health_region_sk_new/RegionalHealthBoundaries.cpg b/geo/esri_health_region_sk_new/RegionalHealthBoundaries.cpg
deleted file mode 100644
index 3ad133c..0000000
--- a/geo/esri_health_region_sk_new/RegionalHealthBoundaries.cpg
+++ /dev/null
@@ -1 +0,0 @@
-UTF-8
\ No newline at end of file
diff --git a/geo/esri_health_region_sk_new/RegionalHealthBoundaries.dbf b/geo/esri_health_region_sk_new/RegionalHealthBoundaries.dbf
deleted file mode 100644
index eb82ca7..0000000
Binary files a/geo/esri_health_region_sk_new/RegionalHealthBoundaries.dbf and /dev/null differ
diff --git a/geo/esri_health_region_sk_new/RegionalHealthBoundaries.prj b/geo/esri_health_region_sk_new/RegionalHealthBoundaries.prj
deleted file mode 100644
index a30c00a..0000000
--- a/geo/esri_health_region_sk_new/RegionalHealthBoundaries.prj
+++ /dev/null
@@ -1 +0,0 @@
-GEOGCS["GCS_WGS_1984",DATUM["D_WGS_1984",SPHEROID["WGS_1984",6378137,298.257223563]],PRIMEM["Greenwich",0],UNIT["Degree",0.017453292519943295]]
\ No newline at end of file
diff --git a/geo/esri_health_region_sk_new/RegionalHealthBoundaries.shp b/geo/esri_health_region_sk_new/RegionalHealthBoundaries.shp
deleted file mode 100644
index b8e0946..0000000
Binary files a/geo/esri_health_region_sk_new/RegionalHealthBoundaries.shp and /dev/null differ
diff --git a/geo/esri_health_region_sk_new/RegionalHealthBoundaries.shx b/geo/esri_health_region_sk_new/RegionalHealthBoundaries.shx
deleted file mode 100644
index 83ad236..0000000
Binary files a/geo/esri_health_region_sk_new/RegionalHealthBoundaries.shx and /dev/null differ
diff --git a/geo/esri_health_region_sk_old/RegionalHealthBoundaries.cpg b/geo/esri_health_region_sk_old/RegionalHealthBoundaries.cpg
deleted file mode 100644
index 3ad133c..0000000
--- a/geo/esri_health_region_sk_old/RegionalHealthBoundaries.cpg
+++ /dev/null
@@ -1 +0,0 @@
-UTF-8
\ No newline at end of file
diff --git a/geo/esri_health_region_sk_old/RegionalHealthBoundaries.dbf b/geo/esri_health_region_sk_old/RegionalHealthBoundaries.dbf
deleted file mode 100644
index 528e149..0000000
Binary files a/geo/esri_health_region_sk_old/RegionalHealthBoundaries.dbf and /dev/null differ
diff --git a/geo/esri_health_region_sk_old/RegionalHealthBoundaries.prj b/geo/esri_health_region_sk_old/RegionalHealthBoundaries.prj
deleted file mode 100644
index a30c00a..0000000
--- a/geo/esri_health_region_sk_old/RegionalHealthBoundaries.prj
+++ /dev/null
@@ -1 +0,0 @@
-GEOGCS["GCS_WGS_1984",DATUM["D_WGS_1984",SPHEROID["WGS_1984",6378137,298.257223563]],PRIMEM["Greenwich",0],UNIT["Degree",0.017453292519943295]]
\ No newline at end of file
diff --git a/geo/esri_health_region_sk_old/RegionalHealthBoundaries.shp b/geo/esri_health_region_sk_old/RegionalHealthBoundaries.shp
deleted file mode 100644
index 7f177ea..0000000
Binary files a/geo/esri_health_region_sk_old/RegionalHealthBoundaries.shp and /dev/null differ
diff --git a/geo/esri_health_region_sk_old/RegionalHealthBoundaries.shx b/geo/esri_health_region_sk_old/RegionalHealthBoundaries.shx
deleted file mode 100644
index ba78a18..0000000
Binary files a/geo/esri_health_region_sk_old/RegionalHealthBoundaries.shx and /dev/null differ
diff --git a/geo/natural_earth/ne_canada_provinces_simple.geojson b/geo/natural_earth/ne_canada_provinces_simple.geojson
deleted file mode 100644
index c0ece60..0000000
--- a/geo/natural_earth/ne_canada_provinces_simple.geojson
+++ /dev/null
@@ -1,15 +0,0 @@
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diff --git a/geo/natural_earth/ne_countries_simple.geojson b/geo/natural_earth/ne_countries_simple.geojson
deleted file mode 100644
index 8db9e25..0000000
--- a/geo/natural_earth/ne_countries_simple.geojson
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-{"type":"Feature","geometry":{"type":"Polygon","coordinates":[[[144.88640384200005,13.640204169000071],[144.90699303500003,13.51666901200008],[144.80827884200005,13.445786851000037],[144.65447024800005,13.439154364000046],[144.88640384200005,13.640204169000071]]]},"properties":{"NAME":"Guam","NAME_LONG":"Guam","POP_EST":167358}},
-{"type":"Feature","geometry":{"type":"Polygon","coordinates":[[[145.78785241000003,15.267279364000046],[145.7387801440001,15.136664130000042],[145.6982528000001,15.203192450000074],[145.78785241000003,15.267279364000046]]]},"properties":{"NAME":"N. Mariana Is.","NAME_LONG":"Northern Mariana Islands","POP_EST":52263}},
-{"type":"Feature","geometry":{"type":"Polygon","coordinates":[[[50.55160566500007,26.194240627000056],[50.61475670700008,26.111151434000078],[50.603526238000086,25.85455963700008],[50.46045983200008,25.985296942000048],[50.45297285200007,26.149562893000052],[50.55160566500007,26.194240627000056]]]},"properties":{"NAME":"Bahrain","NAME_LONG":"Bahrain","POP_EST":1410942}},
-{"type":"Feature","geometry":{"type":"Polygon","coordinates":[[[154.39128665500004,-21.030043226999908],[154.38851972700002,-21.02906666499996],[154.39128665500004,-21.028741143999923],[154.39128665500004,-21.030043226999908]]]},"properties":{"NAME":"Coral Sea Is.","NAME_LONG":"Coral Sea Islands","POP_EST":4}},
-{"type":"Feature","geometry":{"type":"Polygon","coordinates":[[[115.83057701900009,10.73460521000004],[115.81836998800009,10.720851955000057],[115.81462649800005,10.734320380000042],[115.83057701900009,10.73460521000004]]]},"properties":{"NAME":"Spratly Is.","NAME_LONG":"Spratly Islands","POP_EST":100}},
-{"type":"Feature","geometry":{"type":"Polygon","coordinates":[[[-109.21202551999994,10.302679755000042],[-109.21857662699995,10.28156159100007],[-109.23424231699994,10.307928778000075],[-109.21202551999994,10.302679755000042]]]},"properties":{"NAME":"Clipperton I.","NAME_LONG":"Clipperton Island","POP_EST":0}},
-{"type":"Feature","geometry":{"type":"Polygon","coordinates":[[[113.55860436300009,22.163031317000048],[113.566742384,22.12335846600007],[113.5468042330001,22.10537344000005],[113.53443444100003,22.16120026200008],[113.55860436300009,22.163031317000048]]]},"properties":{"NAME":"Macao","NAME_LONG":"Macao","POP_EST":601969}},
-{"type":"Feature","geometry":{"type":"Polygon","coordinates":[[[123.59701582100013,-12.428317966999884],[123.59774824300007,-12.438571872999887],[123.57561282600011,-12.43678150799991],[123.57504316500001,-12.426608981999891],[123.59701582100013,-12.428317966999884]]]},"properties":{"NAME":"Ashmore and Cartier Is.","NAME_LONG":"Ashmore and Cartier Islands","POP_EST":0}},
-{"type":"Feature","geometry":{"type":"Polygon","coordinates":[[[-79.9892878899999,15.794948635000083],[-79.9878230459999,15.796210028000075],[-79.9863988919999,15.794419664000088],[-79.98814856699994,15.794175523000092],[-79.9892878899999,15.794948635000083]]]},"properties":{"NAME":"Bajo Nuevo Bank","NAME_LONG":"Bajo Nuevo Bank (Petrel Islands)","POP_EST":0}},
-{"type":"Feature","geometry":{"type":"Polygon","coordinates":[[[-78.63707434799994,15.862087307000081],[-78.6404109369999,15.86399974200009],[-78.63687089799993,15.867295640000066],[-78.63707434799994,15.862087307000081]]]},"properties":{"NAME":"Serranilla Bank","NAME_LONG":"Serranilla Bank","POP_EST":0}},
-{"type":"Feature","geometry":{"type":"Polygon","coordinates":[[[117.75388717800001,15.154369263000092],[117.75569233100009,15.151887178000038],[117.75388717800001,15.150082025000074],[117.75185638100004,15.151887178000038],[117.75388717800001,15.154369263000092]]]},"properties":{"NAME":"Scarborough Reef","NAME_LONG":"Scarborough Reef","POP_EST":0}}
-]}
\ No newline at end of file
diff --git a/global.R b/global.R
index 0deba4b..6d3b718 100644
--- a/global.R
+++ b/global.R
@@ -1,39 +1,7 @@
-# load libraries
-
-## dashboard
-library(shiny)
-library(shinydashboard)
-library(shinydashboardPlus)
-library(shinyjs)
-library(shinyalert) # display pop-ups
-library(metathis)
-library(rsconnect)
-
-## data manipulation
-library(Hmisc) # load before other packages due to namespace conflicts
-library(dplyr)
-library(tidyr)
-library(lubridate)
-library(stringr)
-library(zoo)
-library(sf)
-library(rvest)
-library(RJSONIO)
-library(mgcv)
-
-## data visualization
-library(ggplot2)
-library(scales)
-library(ggrepel)
-library(RColorBrewer)
-library(plotly)
-library(DT)
-library(leaflet)
-
 # flag if app is running locally or on a server
-is_local <- Sys.getenv('SHINY_PORT') == ""
+is_local <- Sys.getenv("SHINY_PORT") == ""
 
-# load data
+# get data
 
 ## if app is running on the server, overwrite data folder with newest data from GitHub
 ## this ensures the server always has the newest data when rebooting
@@ -44,362 +12,72 @@ if (!is_local | !dir.exists("data")) {
   source("data_updater.R")
 }
 
-## read update time and update notes
-update_time <- readLines("data/update_time.txt")
-update_date <- as.Date(update_time)
-data_notes <- paste(readLines("data/data_notes.txt"), collapse = "\n")
+# load data
 
-## list data files to be loaded
-files <- matrix(
-  c(
-    "ts_cases", "data/cases_timeseries_prov.csv",
-    "ts_mortality", "data/mortality_timeseries_prov.csv",
-    "ts_recovered", "data/recovered_timeseries_prov.csv",
-    "ts_testing", "data/testing_timeseries_prov.csv",
-    "ts_active", "data/active_timeseries_prov.csv",
-    "ts_cases_hr", "data/cases_timeseries_hr.csv",
-    "ts_mortality_hr", "data/mortality_timeseries_hr.csv",
-    "ts_cases_new_hr","data/sk_new_cases_timeseries_hr.csv",
-    "ts_mortality_new_hr","data/sk_new_mortality_timeseries_hr.csv",
-    "ts_cases_canada", "data/cases_timeseries_canada.csv",
-    "ts_mortality_canada", "data/mortality_timeseries_canada.csv",
-    "ts_recovered_canada", "data/recovered_timeseries_canada.csv",
-    "ts_testing_canada", "data/testing_timeseries_canada.csv",
-    "ts_active_canada", "data/active_timeseries_canada.csv",
-    "ts_vaccine_administration", "data/vaccine_administration_timeseries_prov.csv",
-    "ts_vaccine_distribution", "data/vaccine_distribution_timeseries_prov.csv",
-    "ts_vaccine_completion", "data/vaccine_completion_timeseries_prov.csv",
-    "ts_vaccine_additionaldoses", "data/vaccine_additionaldoses_timeseries_prov.csv",
-    "ts_vaccine_administration_canada", "data/vaccine_administration_timeseries_canada.csv",
-    "ts_vaccine_distribution_canada", "data/vaccine_distribution_timeseries_canada.csv",
-    "ts_vaccine_completion_canada", "data/vaccine_completion_timeseries_canada.csv",
-    "ts_vaccine_additionaldoses_canada", "data/vaccine_additionaldoses_timeseries_canada.csv",
-    "hosp", "data/hosp.csv",
-    "map_hr", "data/hr_map.csv",
-    "map_prov", "data/prov_map.csv",
-    "hr_map_sk_new", "data/hr_map_sk_new.csv",
-    "info_testing", "text/info_testing.csv"
-  ),
-  byrow = TRUE,
-  ncol = 2
-)
+## list files
+files <- list.files("data", full.names = TRUE)
 
-## load files
-for (i in 1:nrow(files)) {
-  assign(
-    files[i, 1],
-    read.csv(files[i, 2],
-             stringsAsFactors = FALSE) %>%
-      ### convert date variables
-      mutate(across(matches("date|week"), as.Date, format = "%d-%m-%Y"))
-  )
+## load province/territory & health region data
+pt_hr_files <- c("data/pt.csv", "data/health_regions.csv")
+files <- files[!files %in% pt_hr_files]
+for (f in pt_hr_files) {
+  name <- basename(tools::file_path_sans_ext(f))
+  assign(name, {
+    x <- readr::read_csv(f, progress = FALSE, show_col_types = FALSE)})
 }
 
-# process data
-
-## ADDED: merge SK new HR with rest of HR data
-
-ts_cases_new_hr <- bind_rows(ts_cases_hr[(ts_cases_hr$province!="Saskatchewan"),],ts_cases_new_hr)
-ts_mortality_new_hr <- bind_rows(ts_mortality_hr[(ts_mortality_hr$province!="Saskatchewan"),],ts_mortality_new_hr)
-
-## get cumulative cases and mortality for SK at start date (2020-08-04)
-
-ts_cases_new_add_hr <- ts_cases_new_hr[(ts_cases_new_hr$province=="Saskatchewan" &
-                                          ts_cases_new_hr$date_report=="2020-08-04"),]
-ts_mortality_new_add_hr <- ts_mortality_new_hr[(ts_mortality_new_hr$province=="Saskatchewan" &
-                                                  ts_mortality_new_hr$date_death_report=="2020-08-04"),]
-
-# add cumulative cases to case count
-ts_cases_new_add_hr$cases <- ts_cases_new_add_hr$cumulative_cases
-ts_mortality_new_add_hr$deaths <- ts_mortality_new_add_hr$cumulative_deaths
-
-# bind cumulative numbers to data frame
-ts_cases_new_add_hr <- bind_rows(ts_cases_new_add_hr,ts_cases_new_hr[!(ts_cases_new_hr$province=="Saskatchewan" &
-                                                                         ts_cases_new_hr$date_report=="2020-08-04"),])
-ts_mortality_new_add_hr <- bind_rows(ts_mortality_new_add_hr,ts_mortality_new_hr[!(ts_mortality_new_hr$province=="Saskatchewan" &
-                                                                                ts_mortality_new_hr$date_death_report=="2020-08-04"),])
-
-
-## add province short codes, full names, and populations to datasets
-for (i in c("ts_cases", "ts_mortality", "ts_recovered", "ts_testing", "ts_active", "ts_vaccine_administration", "ts_vaccine_distribution", "ts_vaccine_completion")) {
-  assign(
-    i,
-    get(i) %>%
-      left_join(
-        map_prov,
-        by = "province"
-      )
-  )
+## load other files
+for (f in files) {
+  ext <- tools::file_ext(f)
+  name <- basename(tools::file_path_sans_ext(f))
+  if (ext == "csv") {
+    assign(name, {
+      x <- readr::read_csv(f, progress = FALSE, show_col_types = FALSE)
+      # apexcharter will complain if series are not the same length, so use tidyr::complete
+      # also, join population/name data
+      if ("sub_region_2" %in% names(x)) {
+        x <- tidyr::complete(x, name, tidyr::nesting(region, sub_region_1, sub_region_2), date)
+      } else if ("sub_region_1" %in% names(x)) {
+        x <- tidyr::complete(x, name, tidyr::nesting(region, sub_region_1), date)
+        x <- dplyr::left_join(x, health_regions[c("region", "hruid", "name_short", "pop")],
+                              by = c("region" = "region", "sub_region_1" = "hruid"))
+      } else {
+        x <- tidyr::complete(x, name, region, date)
+        x <- dplyr::left_join(x, pt[c("region", "pop")], by = "region")
+      }
+      x
+      })
+    rm(x)
+  } else if (ext == "json") {
+    assign(name, jsonlite::fromJSON(f))
+  } else if (ext == "geojson") {
+    assign(paste0(name, "_geo"), {x <- sf::read_sf(f); suppressWarnings(sf::st_crs(x) <- 3347); x})
+    rm(x)
+  } else {
+    assign(name, readLines(f))
+  }
 }
 
-# define common variables
-date_min <- min(ts_cases$date_report, na.rm = TRUE)
-date_max <- max(ts_cases$date_report, na.rm = TRUE)
-
-# define common plot elements
+# set constants
 
-## colour palette - province full names
-palette_province <- c(
-  "Canada" = "#000000",
-  "Alberta" = "#00E676",
+## PT colour palette
+palette_pt <- list(
+  "AB" = "#00E676",
   "BC" = "#304FFE",
-  "Manitoba" = "#FF80AB",
-  "New Brunswick" = "#76FF03",
+  "MB" = "#FF80AB",
+  "NB" = "#76FF03",
   "NL" = "#B388FF",
-  "Nova Scotia" = "#00E5FF",
-  "Ontario" = "#FF6F00",
-  "PEI" = "#1B5E20",
-  "Quebec" = "#D50000",
-  "Saskatchewan" = "#AA00FF",
-  "Nunavut" = "#FFFF00",
-  "NWT" = "#00ACC1",
-  "Yukon" = "#D500F9"
+  "NS" = "#00E5FF",
+  "NT" = "#00ACC1",
+  "NU" = "#FFFF00",
+  "ON" = "#FF6F00",
+  "PE" = "#1B5E20",
+  "QC" = "#D50000",
+  "SK" = "#AA00FF",
+  "YT" = "#D500F9"
 )
 
-## colour palette - province short names
-palette_province_short <- setNames(
-  palette_province,
-  c(
-    "CAN",
-    "AB",
-    "BC",
-    "MB",
-    "NB",
-    "NL",
-    "NS",
-    "ON",
-    "PE",
-    "QC",
-    "SK",
-    "NU",
-    "NT",
-    "YT"
-  )
-)
-
-## plotly display options
-
-### plotly legend
-plotly_legend <- list(
-  orientation = "h",
-  yanchor = "bottom",
-  y = 1.02,
-  xanchor = "right",
-  x = 1
-)
-
-### hide plotly buttons
-plotly_buttons <- c(
-  "zoomIn2d",
-  "zoomOut2d",
-  "pan2d",
-  "resetScale2d",
-  "autoScale2d",
-  "zoom2d"
-)
-
-### hide plotly axis
-axis_hide <- list(
-  title = "",
-  zeroline = FALSE,
-  showline = FALSE,
-  showticklabels = FALSE,
-  showgrid = FALSE,
-  fixedrange = TRUE
-)
-
-# construct overview table
-table_overview <- ts_cases %>%
-  group_by(province) %>%
-  filter(date_report == date_max) %>%
-  left_join(
-    ts_mortality %>%
-      group_by(province) %>%
-      filter(date_death_report == date_max),
-    by = "province"
-  ) %>%
-  left_join(
-    ts_recovered %>%
-      group_by(province) %>%
-      filter(date_recovered == date_max),
-    by = "province"
-  ) %>%
-  left_join(
-    ts_testing %>%
-      group_by(province) %>%
-      filter(date_testing == date_max),
-    by = "province"
-  ) %>%
-  left_join(
-    ts_active %>%
-      select(province, date_active, active_cases, active_cases_change) %>%
-      group_by(province) %>%
-      filter(date_active == date_max),
-    by = "province"
-  ) %>%
-  left_join(
-    hosp %>%
-      select(province, hosp_cases, hosp_cases_change),
-    by = "province"
-  ) %>%
-  left_join(
-    ts_vaccine_administration %>%
-      group_by(province) %>%
-      filter(date_vaccine_administered == date_max) %>%
-      select(province, avaccine, cumulative_avaccine),
-    by = "province"
-  ) %>%
-  left_join(
-    ts_vaccine_completion %>%
-      group_by(province) %>%
-      filter(date_vaccine_completed == date_max) %>%
-      select(province, cvaccine, cumulative_cvaccine),
-    by = "province"
-  ) %>%
-  left_join(
-    ts_vaccine_additionaldoses %>%
-      group_by(province) %>%
-      filter(date_vaccine_additionaldoses == date_max) %>%
-      select(province, additionaldosesvaccine, cumulative_additionaldosesvaccine),
-    by = "province"
-  ) %>%
-  left_join(
-    map_prov %>%
-      select(province, pop),
-    by = "province"
-  ) %>%
-  select(province, cases, cumulative_cases,
-         active_cases, active_cases_change,
-         avaccine, cumulative_avaccine,
-         cvaccine, cumulative_cvaccine,
-         additionaldosesvaccine, cumulative_additionaldosesvaccine,
-         hosp_cases, hosp_cases_change,
-         deaths, cumulative_deaths,
-         recovered, cumulative_recovered,
-         testing, cumulative_testing,
-         pop) %>%
-  bind_rows(
-    data.frame(
-      "province" = "Canada",
-      "cases" = ts_cases_canada %>% filter(date_report == date_max) %>% pull(cases),
-      "cumulative_cases" = ts_cases_canada %>% filter(date_report == date_max) %>% pull(cumulative_cases),
-      "active_cases" = ts_active_canada %>% filter(date_active == date_max) %>% pull(active_cases),
-      "active_cases_change" = ts_active_canada %>% filter(date_active == date_max) %>% pull(active_cases_change),
-      "cumulative_avaccine" = ts_vaccine_administration_canada %>% filter(date_vaccine_administered == date_max) %>% pull(cumulative_avaccine),
-      "avaccine" = ts_vaccine_administration_canada %>% filter(date_vaccine_administered == date_max) %>% pull(avaccine),
-      "cumulative_cvaccine" = ts_vaccine_completion_canada %>% filter(date_vaccine_completed == date_max) %>% pull(cumulative_cvaccine),
-      "cvaccine" = ts_vaccine_completion_canada %>% filter(date_vaccine_completed == date_max) %>% pull(cvaccine),
-      "cumulative_additionaldosesvaccine" = ts_vaccine_additionaldoses_canada %>% filter(date_vaccine_additionaldoses == date_max) %>% pull(cumulative_additionaldosesvaccine),
-      "additionaldosesvaccine" = ts_vaccine_additionaldoses_canada %>% filter(date_vaccine_additionaldoses == date_max) %>% pull(additionaldosesvaccine),
-      "hosp_cases" = sum(hosp$hosp_cases),
-      "hosp_cases_change" = sum(hosp$hosp_cases_change),
-      "deaths" = ts_mortality_canada %>% filter(date_death_report == date_max) %>% pull(deaths),
-      "cumulative_deaths" = ts_mortality_canada %>% filter(date_death_report == date_max) %>% pull(cumulative_deaths),
-      "recovered" = ts_recovered_canada %>% filter(date_recovered == date_max) %>% pull(recovered),
-      "cumulative_recovered" = ts_recovered_canada %>% filter(date_recovered == date_max) %>% pull(cumulative_recovered),
-      "testing" = ts_testing_canada %>% filter(date_testing == date_max) %>% pull(testing),
-      "cumulative_testing" = ts_testing_canada %>% filter(date_testing == date_max) %>% pull(cumulative_testing),
-      "pop" = sum(map_prov$pop, na.rm = TRUE),
-      stringsAsFactors = FALSE)
-  ) %>%
-  replace_na(list(cases = 0,
-                  cumulative_cases = 0,
-                  active_cases = 0,
-                  active_cases_change = 0,
-                  avaccine = 0,
-                  cumulative_avaccine = 0,
-                  cvaccine = 0,
-                  cumulative_cvaccine = 0,
-                  additionaldosesvaccine = 0,
-                  cumulative_additionaldosesvaccine = 0,
-                  hosp_cases = 0,
-                  hosp_cases_change = 0,
-                  deaths = 0,
-                  cumulative_deaths = 0,
-                  recovered = 0,
-                  cumulative_recovered = 0,
-                  testing = 0,
-                  cumulative_testing = 0)) %>%
-  mutate(
-    cases_per_100k = cases / pop * 100000,
-    cumulative_cases_per_100k = cumulative_cases / pop * 100000,
-    active_cases_per_100k = active_cases / pop * 100000,
-    hosp_per_100k = hosp_cases / pop * 100000,
-    cumulative_deaths_per_100k = cumulative_deaths / pop * 100000,
-    cumulative_testing_per_100k = cumulative_testing / pop * 100000) %>%
-  arrange(desc(cases)) %>%
-  select(
-    province,
-    cumulative_cases, cases, cases_per_100k, cumulative_cases_per_100k,
-    active_cases, active_cases_change, active_cases_per_100k,
-    avaccine, cumulative_avaccine,
-    cvaccine, cumulative_cvaccine,
-    additionaldosesvaccine, cumulative_additionaldosesvaccine,
-    hosp_cases, hosp_cases_change, hosp_per_100k,
-    cumulative_deaths, deaths, cumulative_deaths_per_100k,
-    cumulative_recovered, recovered,
-    cumulative_testing, testing, cumulative_testing_per_100k,
-  ) %>%
-  rename(
-    `Province` = province,
-    `Cumulative cases` = cumulative_cases,
-    `Cases (new)` = cases,
-    `Cases (new) per 100k` = cases_per_100k,
-    `Cumulative cases per 100k` = cumulative_cases_per_100k,
-    `Active cases` = active_cases,
-    `Active cases (change)` = active_cases_change,
-    `Active cases per 100k` = active_cases_per_100k,
-    `Vaccine doses administered (new)` = avaccine,
-    `Cumulative vaccine doses administered` = cumulative_avaccine,
-    `People w/ 2 doses (new)` = cvaccine,
-    `Cumulative people w/ 2 doses` = cumulative_cvaccine,
-    `Additional doses (new)` = additionaldosesvaccine,
-    `Cumulative additional doses` = cumulative_additionaldosesvaccine,
-    `Hospitalized per 100k` = hosp_per_100k,
-    `Hospitalized` = hosp_cases,
-    `Hospitalized (change)` = hosp_cases_change,
-    `Hospitalized per 100k` = hosp_per_100k,
-    `Cumulative recovered` = cumulative_recovered,
-    `Recovered (new)` = recovered,
-    `Cumulative deaths` = cumulative_deaths,
-    `Deaths (new)` = deaths,
-    `Cumulative deaths per 100k` = cumulative_deaths_per_100k,
-    `Cumulative testing` = cumulative_testing,
-    `Testing (new)` = testing,
-    `Cumulative testing per 100k` = cumulative_testing_per_100k
-  ) %>%
-  ungroup
-
-# prepare info tables
-
-## info: testing
-info_testing <- info_testing %>%
-  ### capitalize column names
-  rename_with(capitalize) %>%
-  ### create proper hyperlinks to sources
-  mutate(
-    Source = paste0("<a href='", Source, "' target='_blank'>", "Link","</a>")
-  )
-
-# load 
-
-# load spatial data
-
-## load simple province map
-geo_prov_simple <- st_read("geo/natural_earth/ne_canada_provinces_simple.geojson", quiet = TRUE) %>%
-  ### join province names
-  left_join(
-    map_prov,
-    by = c("name" = "province_full")
-  )
-
-# get a random question from the opencovid.ca FAQ page
-faq <- read_html("https://opencovid.ca/work/data-faq/") %>%
-  html_nodes("h5") %>%
-  html_text() %>%
-  `[`(sample(x = 1:length(.), size = 1))
-
 # load analytics (if available)
 analytics <- if (file.exists("google-analytics.html")) {
-  tags$head(includeHTML("google-analytics.html"))
+  shiny::tags$head(shiny::includeHTML("google-analytics.html"))
 } else {}
\ No newline at end of file
diff --git a/server.R b/server.R
index db83b63..faddd40 100644
--- a/server.R
+++ b/server.R
@@ -1,34 +1,43 @@
 server <- function(input, output, session) {
   
-  # pop-up alert upon app start
-  if (!(identical(readLines("text/alert_title.html"), character(0)) &
-        identical(readLines("text/alert_title.html"), character(0)))) {
-    shinyalert(
-      title = readLines("text/alert_title.html"),
-      text = readLines("text/alert_text.html"),
-      type = "",
-      html = TRUE,
-      size = "m",
-      closeOnClickOutside = TRUE,
-      closeOnEsc = TRUE
-    ) 
-  }
+  # JavaScript function to format number with a thousands separator
+  format_thousands <- apexcharter::JS(
+    "function(value) {if (typeof(value) == 'number') {return value.toString().replace(/\\B(?=(\\d{3})+(?!\\d))/g, \",\");} else {return value;}}")
+  
+  # render Apex chart of PT timeseries for "value" or "value_daily"
+  ts_chart_pt <- function(
+    dat, val = c("value", "value_daily"), xlab, ylab, remove_negative_values = FALSE) {
+    match.arg(val, c("value", "value_daily"), several.ok = FALSE)
+    if (remove_negative_values) {dat[!is.na(dat[val]) & dat[val] < 0, val] <- NA}
+    apexcharter::renderApexchart(
+      apexcharter::apex(dat, type = "line", mapping = apexcharter::aes(
+        x = date, y = !!rlang::sym(val), colour = region)) %>%
+        apexcharter::ax_labs(x = xlab, y = ylab) %>%
+        apexcharter::ax_chart(animations = list(enabled = FALSE)) %>%
+        apexcharter::ax_yaxis(labels = list(formatter = format_thousands)) %>%
+        apexcharter::ax_tooltip(y = list(formatter = format_thousands)) %>%
+        apexcharter::ax_colors_manual(palette_pt)
+    )
+  }
+  
+  # render case time series chart
+  output$pt_cases_ts <- ts_chart_pt(
+    dat = cases_pt,
+    val = "value_daily",
+    xlab = "Date",
+    ylab = "Daily confirmed cases",
+    remove_negative_values = TRUE)
   
   # check if dataset has been updated on GitHub and if so, download it and redeploy the app
-  observe({
-    
+  shiny::observe({
     ## run only if app is running on the server, not locally
     if (!is_local) {
-      
       ### run every 5 minutes
-      invalidateLater(1000 * 60 * 5, session)
-      
+      shiny::invalidateLater(1000 * 60 * 5, session)
       ### report run
       cat(paste("Checking for data update:", Sys.time()), fill = TRUE)
-      
       ### read update_time.txt from GitHub repo
-      new_time <- readLines("https://raw.githubusercontent.com/ccodwg/Covid19Canada/master/update_time.txt")
-      
+      new_time <- readLines("https://raw.githubusercontent.com/ccodwg/CovidTimelineCanada/main/update_time.txt")
       ### check if update_time.txt is different from current version
       if (identical(new_time, update_time)) {
         cat("No data update required.", fill = TRUE)
@@ -38,3069 +47,13 @@ server <- function(input, output, session) {
         cat("Redeploying dashboard...", fill = TRUE)
         source("dashboard_redeploy.R") # redeploy dashboard
       } 
-      
     }
-    
   })
   
   # when app stops, update file time of app.R
   # trick to update cache: https://stackoverflow.com/a/55476883
-  onStop(function() {
-    Sys.setFileTime("app.R", now())
-  })
-  
-  # tabs with sidebar controls
-  sidebar_controls_tabs <- c(
-    "tab_trends",
-    "tab_maps",
-    "tab_cases",
-    "tab_mortality",
-    "tab_recovered",
-    "tab_testing",
-    "tab_vaccines",
-    "tab_travel"
-  )
-  
-  # define province filter input list
-  sidebar_provs <- c(
-    "All provinces" = "Canada",
-    "Alberta" = "Alberta",
-    "British Columbia" = "BC",
-    "Manitoba" = "Manitoba",
-    "New Brunswick" = "New Brunswick",
-    "Newfoundland and Labrador" = "NL",
-    "Northwest Territories" = "NWT",
-    "Nova Scotia" = "Nova Scotia",
-    "Nunavut" = "Nunavut",
-    "Ontario" = "Ontario",
-    "Prince Edward Island" = "PEI",
-    "Quebec" = "Quebec",
-    "Saskatchewan" = "Saskatchewan",
-    "Yukon" = "Yukon"
-  )
-  
-  # render sidebar
-  output$sidebar_controls <- renderUI({
-    list(
-      selectInput(
-        "prov",
-        "Province",
-        choices = sidebar_provs
-      ),
-      dateRangeInput(
-        "date_range",
-        "Date range",
-        start = date_min,
-        end = date_max,
-        min = date_min,
-        max = date_max,
-        format = "yyyy/mm/dd"
-      )
-    )
-  })
-  
-  # show or hide sidebar controls
-  observe({
-    if (!req(input$tab) %in% sidebar_controls_tabs) {
-      shinyjs::hide(id = "sidebar_controls")
-      updateSelectInput(
-        session = session,
-        inputId = "prov",
-        label = "Province",
-        choices = sidebar_provs
-        )
-        updateDateRangeInput(
-          session = session,
-          inputId = "date_range",
-          label = "Date range",
-          start = date_min,
-          end = date_max,
-          min = date_min,
-          max = date_max
-          )
-    } else {
-      shinyjs::show(id = "sidebar_controls")
-    }
-  })
-  
-  # return plot instructing the user no cases match their filter settings
-  plot_no_data <- function(completely_blank = FALSE) {
-    if (completely_blank) {
-      ## select message
-      m <- ""
-    } else {
-      m <-
-        "No data match your filter settings.\nPlease try again with different settings."
-    }
-    
-    ## produce blank plot w/ or w/out message
-    plot_ly() %>%
-      add_text(x = 0, y = 0, text = m, textfont = list(size = 25), textposition = "center") %>%
-      layout(
-        xaxis = axis_hide,
-        yaxis = axis_hide,
-        legend = plotly_legend
-      ) %>%
-      config(
-        displaylogo = FALSE,
-        modeBarButtonsToRemove = plotly_buttons
-      )
-  }
-  
-  # reactive data
-  
-  ## function: get and filter data
-  get_data <- function(dataset, var_date, filter_province = ifelse(isTruthy(input[["prov"]]), input$prov, "Canada"), filter_date_lower = ifelse(isTruthy(input[["date_range"]]), input$date_range[1], date_min), filter_date_upper = ifelse(isTruthy(input[["date_range"]]), input$date_range[2], date_max), filter_province_ignore = FALSE, filter_date_ignore = FALSE) {
-    get(dataset) %>%
-      ### filter by province (if applicable)
-      {if (!filter_province_ignore & filter_province != "Canada") filter(., province == filter_province) else .} %>%
-      ### filter by date range (if applicable)
-      {if (!filter_date_ignore) filter(., !!sym(var_date) >= filter_date_lower & !!sym(var_date) <= filter_date_upper) else .}
-  }
-  
-  ## function: define comparison variable name
-  get_var_comp <- function(var_val, comparison_window, comparison_scale) {
-    if (comparison_scale == "per-capita") {
-      if (comparison_window > 1) {
-        paste(var_val, "per_100k", "avg", comparison_window, "day", sep = "_")
-      } else {
-        paste(var_val, "per_100k", sep = "_")
-      }
-    } else {
-      if (comparison_window > 1) {
-        paste(var_val, "avg", comparison_window, "day", sep = "_")
-      } else {
-        var_val
-      }
-    }
-  }
-  
-  ## function: get and filter data for provincial comparisons
-  get_data_comp <- function(dataset, var_date, var_val, comparison_window, comparison_scale) {
-    
-    ### ensure comparison_window is an integer
-    comparison_window <- as.integer(comparison_window)
-    
-    ### comparison variable name
-    var_comp <- get_var_comp(var_val, comparison_window, comparison_scale)
-    
-    ### calculate comparisons
-    get_data(dataset, var_date, filter_province_ignore = TRUE, filter_date_ignore = TRUE) %>%
-      filter(province != "Repatriated") %>%
-      select(province_short, pop, !!sym(var_val)) %>%
-      rename(!!sym(var_comp) := !!sym(var_val)) %>%
-      group_by(province_short) %>%
-      ### select only data from the window (e.g., most recent 7 days)
-      slice_tail(n = comparison_window) %>%
-      ### convert to per-capita (if applicable)
-      {if (comparison_scale == "per-capita") mutate(., !!sym(var_comp) := !!sym(var_comp) / pop * 100000) else .} %>%
-      summarize(pop = max(pop), !!sym(var_comp) := mean(!!sym(var_comp)), .groups = "drop")
-  }
-  
-  ## cases
-  data_cases <- reactive({
-    get_data("cases", "date_report")
-  })
-  
-  ## mortality
-  data_mortality <- reactive({
-    get_data("mortality", "date_death_report")
-  })
-  
-  ## cases time series
-  data_ts_cases <- reactive({
-    get_data("ts_cases", "date_report")
-  })
-  
-  ## mortality time series
-  data_ts_mortality <- reactive({
-    get_data("ts_mortality", "date_death_report")
-  })
-  
-  ## recovered time series
-  data_ts_recovered <- reactive({
-    get_data("ts_recovered", "date_recovered")
-  })
-  
-  ## testing time series
-  data_ts_testing <- reactive({
-    get_data("ts_testing", "date_testing")
-  })
-  
-  ## vaccine administration time series
-  data_ts_vaccine_administration <- reactive({
-    get_data("ts_vaccine_administration", "date_vaccine_administered")
-  })
-  
-  ## vaccine distribution time series
-  data_ts_vaccine_distribution <- reactive({
-    get_data("ts_vaccine_distribution", "date_vaccine_distributed")
-  })
-  
-  ## vaccine distribution time series
-  data_ts_vaccine_completion <- reactive({
-    get_data("ts_vaccine_completion", "date_vaccine_completed")
-  })
-  
-  ## case time series (health regions)
-  data_ts_cases_hr <- reactive({
-    get_data("ts_cases_hr", "date_report")
-  })
-  
-  ## mortality time series (health regions)
-  data_ts_mortality_hr <- reactive({
-    get_data("ts_mortality_hr", "date_death_report")
-  })
-  
-  ## case time series (health regions)
-  data_ts_cases_new_hr <- reactive({
-    get_data("ts_cases_new_hr", "date_report")
-  })
-  
-  ## mortality time series (health regions)
-  data_ts_mortality_new_hr <- reactive({
-    get_data("ts_mortality_new_hr", "date_death_report")
-  })
-  
-  ### add specific SK cumulative totals for back-dated data
-  data_ts_cases_new_add_hr <- reactive({
-    get_data("ts_cases_new_add_hr", "date_report")
-  })
-  
-  ## mortality time series (health regions)
-  data_ts_mortality_new_add_hr <- reactive({
-    get_data("ts_mortality_new_add_hr", "date_death_report")
-  })
-  
-  ## case time series (filter by date only for pie chart)
-  data_ts_cases_pie <- reactive({
-    get_data("ts_cases", "date_report", filter_province_ignore = TRUE)
-  })
-  
-  ## mortality time series (filter by date only for pie chart)
-  data_ts_mortality_pie <- reactive({
-    get_data("ts_mortality", "date_death_report", filter_province_ignore = TRUE)
-  })
-  
-  ## comparisons data cases
-  data_comp_cases <- reactive({
-    get_data_comp("ts_cases", "date_report", "cases", comparison_window = input$window_comp_cases, comparison_scale = input$scale_comp_cases)
-  })
-  
-  ## comparisons data mortality
-  data_comp_mortality <- reactive({
-    get_data_comp("ts_mortality", "date_death_report", "deaths", comparison_window = input$window_comp_mortality, comparison_scale = input$scale_comp_mortality)
-  })
-  
-  ## comparisons data testing
-  data_comp_testing <- reactive({
-    get_data_comp("ts_testing", "date_testing", "testing", comparison_window = input$window_comp_testing, comparison_scale = input$scale_comp_testing)
-  })
-  
-  ## comparisons data testing
-  data_comp_testing <- reactive({
-    get_data_comp("ts_testing", "date_testing", "testing", comparison_window = input$window_comp_testing, comparison_scale = input$scale_comp_testing)
-  })
-  
-  # render info tables
-  
-  ## info: testing
-  output$table_info_testing <- renderDT({
-    info_testing %>%
-      datatable(
-        class = "stripe compact hover",
-        rownames = FALSE,
-        escape = FALSE,
-        options = list(
-          paging = FALSE,
-          searching = FALSE,
-          scrollX = TRUE,
-          compact = TRUE,
-          autoWidth = TRUE
-        )
-      )
-  })
-  
-  # summary numbers for overview tab
-  
-  ## function: summary numbers for overview tab
-  value_box_summary <- function(table_overview, var_cum, var_update, lab_title, colour_box, update_type = c("new", "change"), val_cum_format = c("raw", "thousand", "million"), font_size_update = "40%") {
-    
-    ### check update type (new or change +/-)
-    match.arg(update_type, c("new", "change", several.ok = FALSE))
-    
-    ### check cumulative value format (raw, thousands or millions)
-    match.arg(val_cum_format, c("raw", "thousand", "million"), several.ok = FALSE)
-    
-    ### calculate values
-    val_cum <- table_overview %>%
-      filter(Province == "Canada") %>%
-      pull(var_cum) %>%
-      {
-        if (val_cum_format == "raw") {
-          formatC(., format = "f", digits = 0, big.mark = ",")
-          } else if (val_cum_format == "thousand") {
-            paste0(formatC(. / 1000, digits = 0, format = "f", big.mark = ","), "k")
-          } else if (val_cum_format == "million") {
-          paste0(formatC(. / 1000000, digits = 2, format = "f", big.mark = ","), "m")
-        }
-      }
-    val_update <- table_overview %>% filter(Province == "Canada") %>% pull(var_update)
-    
-    ### value box
-    HTML(paste0(
-      tags$p(val_cum, style = "font-size: 95%;"),
-      "<div style='font-size:",
-      font_size_update,
-      "'>(",
-      {if (update_type == "new") {
-        paste0(
-          format(val_update, big.mark = ","),
-          " new)</div>"
-        )
-      } else {
-        paste0(
-          "Change: ",
-          if (val_update >= 0) "+" else "",
-          format(val_update, big.mark = ","),
-          ")</div>"
-        )}})) %>%
-      valueBox(lab_title,
-               color = colour_box)
-  }
-  
-  ## cases
-  output$value_box_summary_cases <- renderValueBox({
-    value_box_summary(table_overview, "Cumulative cases", "Cases (new)", "Reported cases", "orange", update_type = "new", val_cum_format = "thousand")
-  })
-  
-  ## active cases
-  output$value_box_summary_active <- renderValueBox({
-    value_box_summary(table_overview, "Active cases", "Active cases (change)", "Active cases", "purple", update_type = "change", val_cum_format = "raw")
-  })
-  
-  ## recovered
-  output$value_box_summary_recovered <- renderValueBox({
-    value_box_summary(table_overview, "Cumulative recovered", "Recovered (new)", "Total recovered", "light-blue", update_type = "new", val_cum_format = "thousand")
-  })
-  
-  ## mortality
-  output$value_box_summary_mortality <- renderValueBox({
-    value_box_summary(table_overview, "Cumulative deaths", "Deaths (new)", "Total deaths", "navy", update_type = "new", val_cum_format = "raw")
-  })
-  
-  ## vaccine doses administered
-  output$value_box_summary_doses_administered <- renderValueBox({
-    value_box_summary(table_overview, "Cumulative vaccine doses administered", "Vaccine doses administered (new)", "Vaccine doses administered", "fuchsia", update_type = "new", val_cum_format = "million")
-  })
-  
-  ## people fully vaccinated
-  output$value_box_summary_fully_vaccinated <- renderValueBox({
-    value_box_summary(table_overview, "Cumulative people w/ 2 doses", "People w/ 2 doses (new)", "People fully vaccinated", "aqua", update_type = "new", val_cum_format = "million")
-  })
-  
-  ## hospitalized
-  output$value_box_summary_hosp <- renderValueBox({
-    value_box_summary(table_overview, "Hospitalized", "Hospitalized (change)", "Hospitalized", "maroon", update_type = "change", val_cum_format = "raw")
-  })
-  
-  ## testing
-  output$value_box_summary_testing <- renderValueBox({
-    value_box_summary(table_overview, "Cumulative testing", "Testing (new)", "Total testing", "olive", update_type = "new", val_cum_format = "million")
-  })
-  
-  # label nudger for overview tab choropleth maps
-  choropleth_label_nudger <- function(labs) {
-    
-    ### manually nudge some label positions (x = label, ax = arrowhead tail)
-    labs[labs$province_short == "AB", "y"] <- labs[labs$province_short == "AB", "y"] + 0.5
-    labs[labs$province_short == "MB", "y"] <- labs[labs$province_short == "MB", "y"] + 0.5
-    labs[labs$province_short == "ON", "y"] <- labs[labs$province_short == "ON", "y"] + 0.5
-    labs[labs$province_short == "BC", "x"] <- labs[labs$province_short == "BC", "x"] + 0.5
-    labs[labs$province_short == "BC", "y"] <- labs[labs$province_short == "BC", "y"] - 2.5
-    labs[labs$province_short == "SK", "y"] <- labs[labs$province_short == "SK", "y"] - 2.5
-    labs[labs$province_short == "NL", "x"] <- labs[labs$province_short == "NL", "x"] + 4
-    labs[labs$province_short == "NL", "y"] <- labs[labs$province_short == "NL", "y"]
-    labs[labs$province_short == "NU", "x"] <- labs[labs$province_short == "NU", "x"] - 7
-    labs[labs$province_short == "NU", "y"] <- labs[labs$province_short == "NU", "y"] - 6
-    labs[labs$province_short == "NT", "y"] <- labs[labs$province_short == "NT", "y"] - 2
-    labs[labs$province_short == "YT", "y"] <- labs[labs$province_short == "YT", "y"] - 0.5
-    labs[labs$province_short == "PE", "arrow_y"] <- labs[labs$province_short == "PE", "y"] + 3
-    labs[labs$province_short == "NB", "arrow_x"] <- labs[labs$province_short == "NB", "x"] - 5
-    labs[labs$province_short == "NS", "arrow_x"] <- labs[labs$province_short == "NS", "x"] + 7
-    labs[labs$province_short == "NS", "arrow_y"] <- labs[labs$province_short == "NS", "y"] - 1
-    labs
-  }
-  
-  # title for province case map for overview tab
-  output$title_choropleth_overview_cases <- renderText({
-    
-    ### don't run without inputs defined
-    req(input$window_choropleth_overview_cases, input$scale_choropleth_overview_cases)
-    
-    ### render text
-    if (input$scale_choropleth_overview_cases == "per-capita") {
-      paste("Reported cases per 100,000 by province/territory in the last", input$window_choropleth_overview_cases, "days")
-    } else {
-      paste("Reported cases by province/territory in the last", input$window_choropleth_overview_cases, "days")  
-    }
-    
-  })
-  
-  # province case map for overview tab
-  output$plot_choropleth_overview_cases <- renderPlotly({
-    
-    ### don't run without inputs defined
-    req(input$window_choropleth_overview_cases, input$scale_choropleth_overview_cases)
-    
-    ### join provincial case numbers for relevant window to provincial map
-    ### Add per-capita values for toggle
-    dat <- geo_prov_simple %>%
-      left_join(
-        data_ts_cases() %>%
-          select(province_short, cases, pop) %>%
-          group_by(province_short) %>%
-          slice_tail(n = input$window_choropleth_overview_cases) %>%
-          summarize(cases = sum(cases),
-                    cases_per_capita = cases / pop * 100000,
-                    .groups = "drop"),
-        by = "province_short"
-      )
-    
-    ### even out colour scale by rooting case numbers
-    dat <- dat %>%
-      mutate(cases_colour = cases^(1/3),
-             cases_per_capita_colour = cases_per_capita^(1/3))
-    
-    ### define value labels
-    labs <- data.frame(
-      province_short = dat[["province_short"]],
-      x = as.numeric(st_coordinates(suppressWarnings((st_centroid(dat))))[, 1]),
-      y = as.numeric(st_coordinates(suppressWarnings((st_centroid(dat))))[, 2]),
-      lab_cases = format(dat[["cases"]], big.mark = ",", trim = TRUE),
-      lab_cases_per_capita = format(dat[["cases_per_capita"]], big.mark = ",", trim = TRUE),
-      ### arrows for NS, NB, PE to avoid overlap
-      show_arrow = ifelse(dat[["province_short"]] %in% c("NS", "NB", "PE"), TRUE, FALSE),
-      arrow_x = as.numeric(st_coordinates(suppressWarnings((st_centroid(dat))))[, 1]),
-      arrow_y = as.numeric(st_coordinates(suppressWarnings((st_centroid(dat))))[, 2]),
-      stringsAsFactors = FALSE
-    )
-  
-    if (input$scale_choropleth_overview_cases == "per-capita") {
-      labs <- labs %>%
-        mutate(
-          lab_cases = format(dat[["cases_per_capita"]] %>% round(1), big.mark = ",", trim = TRUE)
-        )
-      dat <- dat %>% 
-        mutate(
-          cases_colour = cases_per_capita_colour
-        )
-    }
-    
-    ### nudge labels
-    labs <- choropleth_label_nudger(labs)
-    
-    ### plot data
-    plot_ly() %>%
-      add_sf(
-        data = dat,
-        type = "scatter",
-        split = ~ province,
-        color = ~ cases_colour,
-        colors = "Reds",
-        stroke = I("#000000"),
-        span = I(1.5),
-        hoverinfo = "none"
-      ) %>%
-      add_annotations(
-        data = labs,
-        x = ~ x,
-        y = ~ y,
-        text = ~ lab_cases,
-        hoverinfo = "text",
-        hovertext = paste0(labs$province_short, ": ", labs$lab_cases),
-        font = list(color = "black", size = 14),
-        bgcolor = "white",
-        bordercolor = "black",
-        showarrow = ~ show_arrow,
-        ax = ~ arrow_x,
-        ay = ~ arrow_y,
-        axref = "x",
-        ayref = "y"
-      ) %>%
-      ### OLD PRE-TOGGLE
-      layout(
-        xaxis = axis_hide,
-        yaxis = axis_hide,
-        showlegend = FALSE
-      ) %>%
-      config(displaylogo = FALSE,
-             ### hide all plotly buttons, since they do nothing here
-             displayModeBar = FALSE) %>% 
-      hide_colorbar()
-    
-  })
-  
-  
-  
-  
-  # render province case map for overview tab with dynamic width
-  output$ui_plot_choropleth_overview_cases <- renderUI({
-    
-    ### don't run without inputs defined
-    req(input$screen_width, input$window_choropleth_overview_cases)
-    
-    ### render plot
-    fluidRow(
-      column(
-        h4(textOutput("title_choropleth_overview_cases")),
-        plotlyOutput("plot_choropleth_overview_cases",
-                     ### max width of plot = 600px
-                     ### scale so plot doesn't overflow screen at small widths
-                     width = ifelse(input$screen_width * (7/8) > 600, 600, input$screen_width * (7/8))),
-        width = 12,
-        align = "center"
-      )
-    )
-  })
-  
-  # render province case map slider
-  output$ui_window_choropleth_overview_cases <- renderUI({
-    
-    ### don't run without inputs defined
-    req(input$screen_width)
-    
-    ### render UI
-    fluidRow(
-      column(
-        sliderInput(
-          "window_choropleth_overview_cases",
-          "Show how many days?",
-          min = 7,
-          max = data_ts_cases() %>% pull(date_report) %>% unique %>% length,
-          step = 1,
-          value = 14
-        ),
-        width = 12,
-        align = "center"
-      ),
-      column(
-        width = 12,
-        align = "center",
-        radioButtons(
-              "scale_choropleth_overview_cases",
-              "Absolute/per-capita",
-              choices = c("Absolute" = "absolute", "Per-capita" = "per-capita"),
-              selected = "absolute"
-        )
-      )
-    )
-  })
-  
-  
-  
-  
-  # title for province death map for overview tab
-  output$title_choropleth_overview_deaths <- renderText({
-    
-    ### don't run without inputs defined
-    req(input$window_choropleth_overview_deaths, input$scale_choropleth_overview_deaths)
-    
-    ### render text
-    if (input$scale_choropleth_overview_deaths == "per-capita") {
-      paste("Reported deaths per 100,000 by province/territory in the last", input$window_choropleth_overview_deaths, "days")
-    } else {
-      paste("Reported deaths by province/territory in the last", input$window_choropleth_overview_deaths, "days")  
-    }
-    
-  })
-  
-  # province death map for overview tab
-  output$plot_choropleth_overview_deaths <- renderPlotly({
-    
-    ### don't run without inputs defined
-    req(input$window_choropleth_overview_deaths, 
-        input$scale_choropleth_overview_deaths)
-    
-    ### join provincial death numbers for relevant window to provincial map
-    ### Add per-capita values for toggle
-    dat <- geo_prov_simple %>%
-      left_join(
-        data_ts_mortality() %>%
-          select(province_short, deaths, pop) %>%
-          group_by(province_short) %>%
-          slice_tail(n = input$window_choropleth_overview_deaths) %>%
-          summarize(deaths = sum(deaths),
-                    deaths_per_capita = deaths / pop * 100000,
-                    .groups = "drop"),
-        by = "province_short"
-      )
-    
-    ### even out colour scale by rooting death numbers
-    dat <- dat %>%
-      mutate(cases_colour = deaths^(1/3),
-             cases_per_capita_colour = deaths_per_capita^(1/3))
-    
-    ### define value labels
-    labs <- data.frame(
-      province_short = dat[["province_short"]],
-      x = as.numeric(st_coordinates(suppressWarnings((st_centroid(dat))))[, 1]),
-      y = as.numeric(st_coordinates(suppressWarnings((st_centroid(dat))))[, 2]),
-      lab_cases = format(dat[["deaths"]], big.mark = ",", trim = TRUE),
-      lab_cases_per_capita = format(dat[["deaths_per_capita"]], big.mark = ",", trim = TRUE),
-      ### arrows for NS, NB, PE to avoid overlap
-      show_arrow = ifelse(dat[["province_short"]] %in% c("NS", "NB", "PE"), TRUE, FALSE),
-      arrow_x = as.numeric(st_coordinates(suppressWarnings((st_centroid(dat))))[, 1]),
-      arrow_y = as.numeric(st_coordinates(suppressWarnings((st_centroid(dat))))[, 2]),
-      stringsAsFactors = FALSE
-    )
-    
-    if (input$scale_choropleth_overview_deaths == "per-capita") {
-      labs <- labs %>%
-        mutate(
-          lab_cases = format(dat[["deaths_per_capita"]] %>% round(1), big.mark = ",", trim = TRUE)
-        )
-      dat <- dat %>% 
-        mutate(
-          cases_colour = cases_per_capita_colour
-        )
-    }
-    
-    ### nudge labels
-    labs <- choropleth_label_nudger(labs)
-    
-    ### plot data
-    plot_ly() %>%
-      add_sf(
-        data = dat,
-        type = "scatter",
-        split = ~ province,
-        color = ~ cases_colour,
-        colors = "Reds",
-        stroke = I("#000000"),
-        span = I(1.5),
-        hoverinfo = "none"
-      ) %>%
-      add_annotations(
-        data = labs,
-        x = ~ x,
-        y = ~ y,
-        text = ~ lab_cases,
-        hoverinfo = "text",
-        hovertext = paste0(labs$province_short, ": ", labs$lab_cases),
-        font = list(color = "black", size = 14),
-        bgcolor = "white",
-        bordercolor = "black",
-        showarrow = ~ show_arrow,
-        ax = ~ arrow_x,
-        ay = ~ arrow_y,
-        axref = "x",
-        ayref = "y"
-      ) %>%
-      layout(
-        xaxis = axis_hide,
-        yaxis = axis_hide,
-        showlegend = FALSE
-      ) %>%
-      config(displaylogo = FALSE,
-             ### hide all plotly buttons, since they do nothing here
-             displayModeBar = FALSE) %>%
-      hide_colorbar()
-  })
-  
-  # render province death map for overview tab with dynamic width
-  output$ui_plot_choropleth_overview_deaths <- renderUI({
-    
-    ### don't run without inputs defined
-    req(input$screen_width, input$window_choropleth_overview_deaths)
-    
-    ### render plot
-    fluidRow(
-      column(
-        h4(textOutput("title_choropleth_overview_deaths")),
-        plotlyOutput("plot_choropleth_overview_deaths",
-                     ### max width of plot = 600px
-                     ### scale so plot doesn't overflow screen at small widths
-                     width = ifelse(input$screen_width * (7/8) > 600, 600, input$screen_width * (7/8))),
-        width = 12,
-        align = "center"
-      )
-    )
-  })
-  
-  # render province death map slider
-  output$ui_window_choropleth_overview_deaths <- renderUI({
-    
-    ### don't run without inputs defined
-    req(input$screen_width)
-    
-    ### render UI
-    fluidRow(
-      column(
-        sliderInput(
-          "window_choropleth_overview_deaths",
-          "Show how many days?",
-          min = 7,
-          max = data_ts_mortality() %>% pull(date_death_report) %>% unique %>% length,
-          step = 1,
-          value = 14
-        ),
-        width = 12,
-        align = "center"
-      ),
-      column(
-        width = 12,
-        align = "center",
-        radioButtons(
-          "scale_choropleth_overview_deaths",
-          "Absolute/per-capita",
-          choices = c("Absolute" = "absolute", "Per-capita" = "per-capita"),
-          selected = "absolute"
-        )
-      )
-    )
-  })
-  
-  # title for province recovered map for overview tab
-  output$title_choropleth_overview_recovered <- renderText({
-    
-    ### don't run without inputs defined
-    req(input$window_choropleth_overview_recovered,
-        input$scale_choropleth_overview_recovered)
-    
-    ### render text
-    if (input$scale_choropleth_overview_recovered == "per-capita") {
-      paste("Reported recovered cases per 100,000 by province/territory in the last", input$window_choropleth_overview_recovered, "days")
-    } else {
-      paste("Reported recovered cases by province/territory in the last", input$window_choropleth_overview_recovered, "days")  
-    }
-    
-  })
-  
-  # province recovered map for overview tab
-  output$plot_choropleth_overview_recovered <- renderPlotly({
-    
-    ### don't run without inputs defined
-    req(input$window_choropleth_overview_recovered,
-        input$scale_choropleth_overview_recovered)
-    
-    ### join provincial recovered numbers for relevant window to provincial map
-    ### Add per-capita values for toggle
-    dat <- geo_prov_simple %>%
-      left_join(
-        data_ts_recovered() %>%
-          select(province_short, recovered, pop) %>%
-          group_by(province_short) %>%
-          slice_tail(n = input$window_choropleth_overview_recovered) %>%
-          summarize(recovered = sum(recovered),
-                    recovered_per_capita = recovered / pop * 100000,
-                    .groups = "drop"),
-        by = "province_short"
-      )
-    
-    ### even out colour scale by rooting recovered numbers
-    dat <- dat %>%
-      mutate(cases_colour = recovered^(1/3),
-             cases_per_capita_colour = recovered_per_capita^(1/3)
-             )
-    
-    ### define value labels
-    labs <- data.frame(
-      province_short = dat[["province_short"]],
-      x = as.numeric(st_coordinates(suppressWarnings((st_centroid(dat))))[, 1]),
-      y = as.numeric(st_coordinates(suppressWarnings((st_centroid(dat))))[, 2]),
-      lab_cases = format(dat[["recovered"]], big.mark = ",", trim = TRUE),
-      lab_cases_per_capita = format(dat[["recovered_per_capita"]], big.mark = ",", trim = TRUE),
-      ### arrows for NS, NB, PE to avoid overlap
-      show_arrow = ifelse(dat[["province_short"]] %in% c("NS", "NB", "PE"), TRUE, FALSE),
-      arrow_x = as.numeric(st_coordinates(suppressWarnings((st_centroid(dat))))[, 1]),
-      arrow_y = as.numeric(st_coordinates(suppressWarnings((st_centroid(dat))))[, 2]),
-      stringsAsFactors = FALSE
-    )
-    
-    if (input$scale_choropleth_overview_recovered == "per-capita") {
-      labs <- labs %>%
-        mutate(
-          lab_cases = format(dat[["recovered_per_capita"]] %>% round(1), big.mark = ",", trim = TRUE)
-        )
-      dat <- dat %>% 
-        mutate(
-          cases_colour = cases_per_capita_colour
-        )
-    }
-    
-    ### nudge labels
-    labs <- choropleth_label_nudger(labs)
-    
-    ### plot data
-    plot_ly() %>%
-      add_sf(
-        data = dat,
-        type = "scatter",
-        split = ~ province,
-        color = ~ cases_colour,
-        colors = "Reds",
-        stroke = I("#000000"),
-        span = I(1.5),
-        hoverinfo = "none"
-      ) %>%
-      add_annotations(
-        data = labs,
-        x = ~ x,
-        y = ~ y,
-        text = ~ lab_cases,
-        hoverinfo = "text",
-        hovertext = paste0(labs$province_short, ": ", labs$lab_cases),
-        font = list(color = "black", size = 14),
-        bgcolor = "white",
-        bordercolor = "black",
-        showarrow = ~ show_arrow,
-        ax = ~ arrow_x,
-        ay = ~ arrow_y,
-        axref = "x",
-        ayref = "y"
-      ) %>%
-      layout(
-        xaxis = axis_hide,
-        yaxis = axis_hide,
-        showlegend = FALSE
-      ) %>%
-      config(displaylogo = FALSE,
-             ### hide all plotly buttons, since they do nothing here
-             displayModeBar = FALSE) %>%
-      hide_colorbar()
-  })
-  
-  # render province recovered map for overview tab with dynamic width
-  output$ui_plot_choropleth_overview_recovered <- renderUI({
-    
-    ### don't run without inputs defined
-    req(input$screen_width, input$window_choropleth_overview_recovered)
-    
-    ### render plot
-    fluidRow(
-      column(
-        h4(textOutput("title_choropleth_overview_recovered")),
-        plotlyOutput("plot_choropleth_overview_recovered",
-                     ### max width of plot = 600px
-                     ### scale so plot doesn't overflow screen at small widths
-                     width = ifelse(input$screen_width * (7/8) > 600, 600, input$screen_width * (7/8))),
-        width = 12,
-        align = "center"
-      )
-    )
-  })
-  
-  # render province case recovered slider
-  output$ui_window_choropleth_overview_recovered <- renderUI({
-    
-    ### don't run without inputs defined
-    req(input$screen_width)
-    
-    ### render UI
-    fluidRow(
-      column(
-        sliderInput(
-          "window_choropleth_overview_recovered",
-          "Show how many days?",
-          min = 7,
-          max = data_ts_recovered() %>% pull(date_recovered) %>% unique %>% length,
-          step = 1,
-          value = 14
-        ),
-        width = 12,
-        align = "center"
-      ),
-      column(
-        width = 12,
-        align = "center",
-        radioButtons(
-          "scale_choropleth_overview_recovered",
-          "Absolute/per-capita",
-          choices = c("Absolute" = "absolute", "Per-capita" = "per-capita"),
-          selected = "absolute"
-        )
-      )
-    )
-  })
-  
-  # title for province vaccine map for overview tab
-  output$title_choropleth_overview_vaccine_administration <- renderText({
-    
-    ### don't run without inputs defined
-    req(input$window_choropleth_overview_vaccine_administration,
-        input$scale_choropleth_overview_vaccine_administration)
-    
-    ### render text
-    if (input$scale_choropleth_overview_vaccine_administration == "per-capita") {
-      paste("Vaccine doses administered per 100,000 by province/territory in the last", input$window_choropleth_overview_vaccine_administration, "days")
-    } else {
-      paste("Vaccine doses administered by province/territory in the last", input$window_choropleth_overview_vaccine_administration, "days")  
-    }
-    
-  })
-  
-  # province vaccine administration map for overview tab
-  output$plot_choropleth_overview_vaccine_administration <- renderPlotly({
-    
-    ### don't run without inputs defined
-    req(input$window_choropleth_overview_vaccine_administration,
-        input$scale_choropleth_overview_vaccine_administration)
-    
-    ### join provincial vaccine numbers for relevant window to provincial map
-    ### Add per-capita values for toggle
-    dat <- geo_prov_simple %>%
-      left_join(
-        data_ts_vaccine_administration() %>%
-          select(province_short, avaccine, pop) %>%
-          group_by(province_short) %>%
-          slice_tail(n = input$window_choropleth_overview_vaccine_administration) %>%
-          summarize(avaccine = sum(avaccine), 
-                    avaccine_per_capita = avaccine / pop * 100000,
-                    .groups = "drop"),
-        by = "province_short"
-      )
-  
-    ### even out colour scale by rooting vaccine numbers
-    dat <- dat %>%
-      mutate(cases_colour = avaccine^(1/3),
-             cases_per_capita_colour = avaccine_per_capita^(1/3)
-             )
-    
-    ### define value labels
-    labs <- data.frame(
-      province_short = dat[["province_short"]],
-      x = as.numeric(st_coordinates(suppressWarnings((st_centroid(dat))))[, 1]),
-      y = as.numeric(st_coordinates(suppressWarnings((st_centroid(dat))))[, 2]),
-      lab_cases = format(dat[["avaccine"]], big.mark = ",", trim = TRUE),
-      lab_cases_per_capita = format(dat[["avaccine_per_capita"]], big.mark = ",", trim = TRUE),
-      ### arrows for NS, NB, PE to avoid overlap
-      show_arrow = ifelse(dat[["province_short"]] %in% c("NS", "NB", "PE"), TRUE, FALSE),
-      arrow_x = as.numeric(st_coordinates(suppressWarnings((st_centroid(dat))))[, 1]),
-      arrow_y = as.numeric(st_coordinates(suppressWarnings((st_centroid(dat))))[, 2]),
-      stringsAsFactors = FALSE
-    )
-    
-    if (input$scale_choropleth_overview_vaccine_administration == "per-capita") {
-      labs <- labs %>%
-        mutate(
-          lab_cases = format(dat[["avaccine_per_capita"]] %>% round(1), big.mark = ",", trim = TRUE)
-        )
-      dat <- dat %>% 
-        mutate(
-          cases_colour = cases_per_capita_colour
-        )
-    }
-    
-    ### nudge labels
-    labs <- choropleth_label_nudger(labs)
-    
-    ### plot data
-    plot_ly() %>%
-      add_sf(
-        data = dat,
-        type = "scatter",
-        split = ~ province,
-        color = ~ cases_colour,
-        colors = "Blues",
-        stroke = I("#000000"),
-        span = I(1.5),
-        hoverinfo = "none"
-      ) %>%
-      add_annotations(
-        data = labs,
-        x = ~ x,
-        y = ~ y,
-        text = ~ lab_cases,
-        hoverinfo = "text",
-        hovertext = paste0(labs$province_short, ": ", labs$lab_cases),
-        font = list(color = "black", size = 14),
-        bgcolor = "white",
-        bordercolor = "black",
-        showarrow = ~ show_arrow,
-        ax = ~ arrow_x,
-        ay = ~ arrow_y,
-        axref = "x",
-        ayref = "y"
-      ) %>%
-      layout(
-        xaxis = axis_hide,
-        yaxis = axis_hide,
-        showlegend = FALSE
-      ) %>%
-      config(displaylogo = FALSE,
-             ### hide all plotly buttons, since they do nothing here
-             displayModeBar = FALSE) %>%
-      hide_colorbar()
-  })
-  
-  # render province vaccine map for overview tab with dynamic width
-  output$ui_plot_choropleth_overview_vaccine_administration <- renderUI({
-    
-    ### don't run without inputs defined
-    req(input$screen_width, input$window_choropleth_overview_vaccine_administration)
-    
-    ### render plot
-    fluidRow(
-      column(
-        h4(textOutput("title_choropleth_overview_vaccine_administration")),
-        plotlyOutput("plot_choropleth_overview_vaccine_administration",
-                     ### max width of plot = 600px
-                     ### scale so plot doesn't overflow screen at small widths
-                     width = ifelse(input$screen_width * (7/8) > 600, 600, input$screen_width * (7/8))),
-        width = 12,
-        align = "center"
-      )
-    )
-  })
-  
-  # render province vaccine map slider
-  output$ui_window_choropleth_overview_vaccine_administration <- renderUI({
-    
-    ### don't run without inputs defined
-    req(input$screen_width)
-    
-    ### render UI
-    fluidRow(
-      column(
-        sliderInput(
-          "window_choropleth_overview_vaccine_administration",
-          "Show how many days?",
-          min = 7,
-          max = data_ts_vaccine_administration() %>% pull(date_vaccine_administered) %>% unique %>% length,
-          step = 1,
-          value = data_ts_vaccine_administration() %>% pull(date_vaccine_administered) %>% unique %>% length
-        ),
-        width = 12,
-        align = "center"
-      ),
-      column(
-        width = 12,
-        align = "center",
-        radioButtons(
-          "scale_choropleth_overview_vaccine_administration",
-          "Absolute/per-capita",
-          choices = c("Absolute" = "absolute", "Per-capita" = "per-capita"),
-          selected = "absolute"
-        )
-      )
-    )
-  })
-  
-  # title for province vaccine map for overview tab
-  output$title_choropleth_overview_vaccine_distribution <- renderText({
-    
-    ### don't run without inputs defined
-    req(input$window_choropleth_overview_vaccine_distribution,
-        input$scale_choropleth_overview_vaccine_distribution
-        )
-    
-    ### render text
-    if (input$scale_choropleth_overview_vaccine_administration == "per-capita") {
-      paste("Vaccine doses distributed per 100,000 by province/territory in the last", input$window_choropleth_overview_vaccine_administration, "days")
-    } else {
-      paste("Vaccine doses distributed by province/territory in the last", input$window_choropleth_overview_vaccine_administration, "days")  
-    }
-    
-    
-  })
-  
-  # province vaccine administration map for overview tab
-  output$plot_choropleth_overview_vaccine_distribution <- renderPlotly({
-    
-    ### don't run without inputs defined
-    req(input$window_choropleth_overview_vaccine_distribution,
-        input$scale_choropleth_overview_vaccine_distribution,
-        )
-    
-    ### join provincial vaccine numbers for relevant window to provincial map
-    ### Add per-capita values for toggle
-    dat <- geo_prov_simple %>%
-      left_join(
-        data_ts_vaccine_distribution() %>%
-          select(province_short, dvaccine, pop) %>%
-          group_by(province_short) %>%
-          slice_tail(n = input$window_choropleth_overview_vaccine_distribution) %>%
-          summarize(dvaccine = sum(dvaccine), 
-                    dvaccine_per_capita = dvaccine / pop * 100000,
-                    .groups = "drop"),
-        by = "province_short"
-      )
-    
-    ### even out colour scale by rooting vaccine numbers
-    dat <- dat %>%
-      mutate(cases_colour = dvaccine^(1/3),
-             cases_per_capita_colour = dvaccine_per_capita^(1/3)
-             )
-    
-    ### define value labels
-    labs <- data.frame(
-      province_short = dat[["province_short"]],
-      x = as.numeric(st_coordinates(suppressWarnings((st_centroid(dat))))[, 1]),
-      y = as.numeric(st_coordinates(suppressWarnings((st_centroid(dat))))[, 2]),
-      lab_cases = format(dat[["dvaccine"]], big.mark = ",", trim = TRUE),
-      lab_cases_per_capita = format(dat[["dvaccine_per_capita"]], big.mark = ",", trim = TRUE),
-      ### arrows for NS, NB, PE to avoid overlap
-      show_arrow = ifelse(dat[["province_short"]] %in% c("NS", "NB", "PE"), TRUE, FALSE),
-      arrow_x = as.numeric(st_coordinates(suppressWarnings((st_centroid(dat))))[, 1]),
-      arrow_y = as.numeric(st_coordinates(suppressWarnings((st_centroid(dat))))[, 2]),
-      stringsAsFactors = FALSE
-    )
-    
-    if (input$scale_choropleth_overview_vaccine_distribution == "per-capita") {
-      labs <- labs %>%
-        mutate(
-          lab_cases = format(dat[["dvaccine_per_capita"]] %>% round(1), big.mark = ",", trim = TRUE)
-        )
-      dat <- dat %>% 
-        mutate(
-          cases_colour = cases_per_capita_colour
-        )
-    }
-    
-    ### nudge labels
-    labs <- choropleth_label_nudger(labs)
-    
-    ### plot data
-    plot_ly() %>%
-      add_sf(
-        data = dat,
-        type = "scatter",
-        split = ~ province,
-        color = ~ cases_colour,
-        colors = "Blues",
-        stroke = I("#000000"),
-        span = I(1.5),
-        hoverinfo = "none"
-      ) %>%
-      add_annotations(
-        data = labs,
-        x = ~ x,
-        y = ~ y,
-        text = ~ lab_cases,
-        hoverinfo = "text",
-        hovertext = paste0(labs$province_short, ": ", labs$lab_cases),
-        font = list(color = "black", size = 14),
-        bgcolor = "white",
-        bordercolor = "black",
-        showarrow = ~ show_arrow,
-        ax = ~ arrow_x,
-        ay = ~ arrow_y,
-        axref = "x",
-        ayref = "y"
-      ) %>%
-      layout(
-        xaxis = axis_hide,
-        yaxis = axis_hide,
-        showlegend = FALSE
-      ) %>%
-      config(displaylogo = FALSE,
-             ### hide all plotly buttons, since they do nothing here
-             displayModeBar = FALSE) %>%
-      hide_colorbar()
-  })
-  
-  # render province vaccine map for overview tab with dynamic width
-  output$ui_plot_choropleth_overview_vaccine_distribution <- renderUI({
-    
-    ### don't run without inputs defined
-    req(input$screen_width, input$window_choropleth_overview_vaccine_distribution)
-    
-    ### render plot
-    fluidRow(
-      column(
-        h4(textOutput("title_choropleth_overview_vaccine_distribution")),
-        plotlyOutput("plot_choropleth_overview_vaccine_distribution",
-                     ### max width of plot = 600px
-                     ### scale so plot doesn't overflow screen at small widths
-                     width = ifelse(input$screen_width * (7/8) > 600, 600, input$screen_width * (7/8))),
-        width = 12,
-        align = "center"
-      )
-    )
-  })
-  
-  # render province vaccine map slider
-  output$ui_window_choropleth_overview_vaccine_distribution <- renderUI({
-    
-    ### don't run without inputs defined
-    req(input$screen_width)
-    
-    ### render UI
-    fluidRow(
-      column(
-        sliderInput(
-          "window_choropleth_overview_vaccine_distribution",
-          "Show how many days?",
-          min = 7,
-          max = data_ts_vaccine_distribution() %>% pull(date_vaccine_distributed) %>% unique %>% length,
-          step = 1,
-          value = data_ts_vaccine_distribution() %>% pull(date_vaccine_distributed) %>% unique %>% length
-        ),
-        width = 12,
-        align = "center"
-      ),
-      column(
-        width = 12,
-        align = "center",
-        radioButtons(
-          "scale_choropleth_overview_vaccine_distribution",
-          "Absolute/per-capita",
-          choices = c("Absolute" = "absolute", "Per-capita" = "per-capita"),
-          selected = "absolute"
-        )
-      )
-    )
-  })
-
-  # title for province vaccine map for overview tab
-  output$title_choropleth_overview_vaccine_admin_pct <- renderText({
-    
-    last_day = data_ts_vaccine_distribution() %>% 
-      pull(date_vaccine_distributed) %>% 
-      max() %>% 
-      format("%b %d, %Y")
-    
-    ### render text
-    paste("Vaccine doses administered as percentage of vaccines distributed by province/territory as of", last_day)
-    
-  })
-  
-  # province vaccine administration map for overview tab
-  output$plot_choropleth_overview_vaccine_admin_pct <- renderPlotly({
-    
-    max_days = data_ts_vaccine_distribution() %>% pull(date_vaccine_distributed) %>% unique %>% length
-    
-    ### join provincial vaccine numbers for relevant window to provincial map
-    dat <- geo_prov_simple %>%
-      left_join(
-        data_ts_vaccine_distribution() %>%
-          select(province_short, dvaccine) %>%
-          group_by(province_short) %>%
-          slice_tail(n = max_days) %>%
-          summarize(dvaccine = sum(dvaccine), .groups = "drop"),
-        by = "province_short"
-      ) %>% 
-      left_join(
-        data_ts_vaccine_administration() %>%
-          select(province_short, avaccine) %>%
-          group_by(province_short) %>%
-          slice_tail(n = max_days) %>%
-          summarize(avaccine = sum(avaccine), .groups = "drop"),
-        by = "province_short"
-      ) %>% 
-      mutate(pct_avacc_dvacc = 100 * avaccine / dvaccine,
-             pct_avacc_dvacc = round(pct_avacc_dvacc, 2))
-    
-    ### even out colour scale by rooting vaccine numbers
-    dat <- dat %>%
-      mutate(cases_colour = pct_avacc_dvacc^(1/3))
-    
-    ### define value labels
-    labs <- data.frame(
-      province_short = dat[["province_short"]],
-      x = as.numeric(st_coordinates(suppressWarnings((st_centroid(dat))))[, 1]),
-      y = as.numeric(st_coordinates(suppressWarnings((st_centroid(dat))))[, 2]),
-      lab_cases = format(dat[["pct_avacc_dvacc"]], big.mark = ",", trim = TRUE),
-      ### arrows for NS, NB, PE to avoid overlap
-      show_arrow = ifelse(dat[["province_short"]] %in% c("NS", "NB", "PE"), TRUE, FALSE),
-      arrow_x = as.numeric(st_coordinates(suppressWarnings((st_centroid(dat))))[, 1]),
-      arrow_y = as.numeric(st_coordinates(suppressWarnings((st_centroid(dat))))[, 2]),
-      stringsAsFactors = FALSE
-    )
-    
-    ### nudge labels
-    labs <- choropleth_label_nudger(labs)
-    
-    ### plot data
-    plot_ly() %>%
-      add_sf(
-        data = dat,
-        type = "scatter",
-        split = ~ province,
-        color = ~ cases_colour,
-        colors = "Blues",
-        stroke = I("#000000"),
-        span = I(1.5),
-        hoverinfo = "none"
-      ) %>%
-      add_annotations(
-        data = labs,
-        x = ~ x,
-        y = ~ y,
-        text = ~ lab_cases,
-        hoverinfo = "text",
-        hovertext = paste0(labs$province_short, ": ", labs$lab_cases),
-        font = list(color = "black", size = 14),
-        bgcolor = "white",
-        bordercolor = "black",
-        showarrow = ~ show_arrow,
-        ax = ~ arrow_x,
-        ay = ~ arrow_y,
-        axref = "x",
-        ayref = "y"
-      ) %>%
-      layout(
-        xaxis = axis_hide,
-        yaxis = axis_hide,
-        showlegend = FALSE
-      ) %>%
-      config(displaylogo = FALSE,
-             ### hide all plotly buttons, since they do nothing here
-             displayModeBar = FALSE) %>%
-      hide_colorbar()
-  })
-  
-  # render province vaccine map for overview tab with dynamic width
-  output$ui_plot_choropleth_overview_vaccine_admin_pct <- renderUI({
-    
-    ### don't run without inputs defined
-    req(input$screen_width)
-        
-    ### render plot
-    fluidRow(
-      column(
-        h4(textOutput("title_choropleth_overview_vaccine_admin_pct")),
-        plotlyOutput("plot_choropleth_overview_vaccine_admin_pct",
-                     ### max width of plot = 600px
-                     ### scale so plot doesn't overflow screen at small widths
-                     width = ifelse(input$screen_width * (7/8) > 600, 600, input$screen_width * (7/8))),
-        width = 12,
-        align = "center"
-      )
-    )
-  })
-  
-  
-  
-  
-  # title for province vaccine map for overview tab
-  output$title_choropleth_overview_vaccine_at_least_one_dose <- renderText({
-
-    # max_days = data_ts_vaccine_administration() %>% pull(date_vaccine_administered) %>% unique %>% length
-    last_day = data_ts_vaccine_administration() %>% 
-      pull(date_vaccine_administered) %>% 
-      max() %>% 
-      format("%b %d, %Y")
-    
-    ### render text
-    paste("Percent at least one dose by province/territory as of", last_day)
-    
-  })
-  
-  # province vaccine administration map for overview tab
-  output$plot_choropleth_overview_vaccine_at_least_one_dose <- renderPlotly({
-    
-    max_days = data_ts_vaccine_administration() %>%
-      pull(date_vaccine_administered) %>%
-      unique %>%
-      length
-    
-    ### join provincial vaccine numbers for relevant window to provincial map
-    dat <- geo_prov_simple %>%
-      left_join(
-        data_ts_vaccine_completion() %>%
-          select(province_short, cvaccine) %>%
-          group_by(province_short) %>%
-          slice_tail(n = max_days) %>%
-          summarize(cvaccine = sum(cvaccine), .groups = "drop"),
-        by = "province_short"
-      ) %>%
-      left_join(
-        data_ts_vaccine_administration() %>%
-          select(province_short, avaccine) %>%
-          group_by(province_short) %>%
-          slice_tail(n = max_days) %>%
-          summarize(avaccine = sum(avaccine), .groups = "drop"),
-        by = "province_short"
-      ) %>%
-      replace_na(list(avaccine = 0,
-                      cvaccine = 0)) %>%  # 2020-12-13 is NA for avaccine
-      
-      mutate(one_dose = avaccine - cvaccine,
-             pct_at_least_one_dose = 100 * one_dose / pop,
-             pct_at_least_one_dose = round(pct_at_least_one_dose, 2))
-    
-    ### even out colour scale by rooting vaccine numbers
-    dat <- dat %>%
-      mutate(cases_colour = pct_at_least_one_dose^(1/3))
-    
-    ### define value labels
-    labs <- data.frame(
-      province_short = dat[["province_short"]],
-      x = as.numeric(st_coordinates(suppressWarnings((st_centroid(dat))))[, 1]),
-      y = as.numeric(st_coordinates(suppressWarnings((st_centroid(dat))))[, 2]),
-      lab_cases = format(dat[["pct_at_least_one_dose"]], big.mark = ",", trim = TRUE),
-      ### arrows for NS, NB, PE to avoid overlap
-      show_arrow = ifelse(dat[["province_short"]] %in% c("NS", "NB", "PE"), TRUE, FALSE),
-      arrow_x = as.numeric(st_coordinates(suppressWarnings((st_centroid(dat))))[, 1]),
-      arrow_y = as.numeric(st_coordinates(suppressWarnings((st_centroid(dat))))[, 2]),
-      stringsAsFactors = FALSE
-    )
-    
-    ### nudge labels
-    labs <- choropleth_label_nudger(labs)
-    
-    ### plot data
-    plot_ly() %>%
-      add_sf(
-        data = dat,
-        type = "scatter",
-        split = ~ province,
-        color = ~ cases_colour,
-        colors = "Blues",
-        stroke = I("#000000"),
-        span = I(1.5),
-        hoverinfo = "none"
-      ) %>%
-      add_annotations(
-        data = labs,
-        x = ~ x,
-        y = ~ y,
-        text = ~ lab_cases,
-        hoverinfo = "text",
-        hovertext = paste0(labs$province_short, ": ", labs$lab_cases),
-        font = list(color = "black", size = 14),
-        bgcolor = "white",
-        bordercolor = "black",
-        showarrow = ~ show_arrow,
-        ax = ~ arrow_x,
-        ay = ~ arrow_y,
-        axref = "x",
-        ayref = "y"
-      ) %>%
-      layout(
-        xaxis = axis_hide,
-        yaxis = axis_hide,
-        showlegend = FALSE
-      ) %>%
-      config(displaylogo = FALSE,
-             ### hide all plotly buttons, since they do nothing here
-             displayModeBar = FALSE) %>%
-      hide_colorbar()
-  })
-  
-  # render province vaccine map for overview tab with dynamic width
-  output$ui_plot_choropleth_overview_vaccine_at_least_one_dose <- renderUI({
-    
-    ### don't run without inputs defined
-    req(input$screen_width)
-    
-    ### render plot
-    fluidRow(
-      column(
-        h4(textOutput("title_choropleth_overview_vaccine_at_least_one_dose")),
-        plotlyOutput("plot_choropleth_overview_vaccine_at_least_one_dose",
-                     ### max width of plot = 600px
-                     ### scale so plot doesn't overflow screen at small widths
-                     width = ifelse(input$screen_width * (7/8) > 600, 600, input$screen_width * (7/8))),
-        width = 12,
-        align = "center"
-      )
-    )
-  })
-
-  # title for province vaccine map for overview tab
-  output$title_choropleth_overview_vaccine_full_pct <- renderText({
-    
-    last_day = data_ts_vaccine_administration() %>% 
-      pull(date_vaccine_administered) %>% 
-      max() %>% 
-      format("%b %d, %Y")
-    
-    ### render text
-    paste("Percent w/ 2 doses by province/territory as of", last_day)
-    
-  })
-    
-  # province vaccine administration map for overview tab
-  output$plot_choropleth_overview_vaccine_full_pct <- renderPlotly({
-    
-    max_days <- data_ts_vaccine_administration() %>% pull(date_vaccine_administered) %>% unique %>% length
-    
-    ### join provincial vaccine numbers for relevant window to provincial map
-    dat <- geo_prov_simple %>%
-      left_join(
-        data_ts_vaccine_completion() %>%
-          select(province_short, cvaccine) %>%
-          group_by(province_short) %>%
-          slice_tail(n = max_days) %>%
-          summarize(cvaccine = sum(cvaccine), .groups = "drop"),
-        by = "province_short"
-      ) %>%
-      left_join(
-        data_ts_vaccine_administration() %>%
-          select(province_short, avaccine) %>%
-          group_by(province_short) %>%
-          slice_tail(n = max_days) %>%
-          summarize(avaccine = sum(avaccine), .groups = "drop"),
-        by = "province_short"
-      ) %>%
-      replace_na(list(avaccine = 0,
-                      cvaccine = 0)) %>%  # 2020-12-13 is NA for avaccine
-      mutate(pct_full_vacc = 100 * cvaccine / pop,
-             pct_full_vacc = round(pct_full_vacc, 2))
-    
-    ### even out colour scale by rooting vaccine numbers
-    dat <- dat %>%
-      mutate(cases_colour = pct_full_vacc^(1/3))
-    
-    ### define value labels
-    labs <- data.frame(
-      province_short = dat[["province_short"]],
-      x = as.numeric(st_coordinates(suppressWarnings((st_centroid(dat))))[, 1]),
-      y = as.numeric(st_coordinates(suppressWarnings((st_centroid(dat))))[, 2]),
-      lab_cases = format(dat[["pct_full_vacc"]], big.mark = ",", trim = TRUE),
-      ### arrows for NS, NB, PE to avoid overlap
-      show_arrow = ifelse(dat[["province_short"]] %in% c("NS", "NB", "PE"), TRUE, FALSE),
-      arrow_x = as.numeric(st_coordinates(suppressWarnings((st_centroid(dat))))[, 1]),
-      arrow_y = as.numeric(st_coordinates(suppressWarnings((st_centroid(dat))))[, 2]),
-      stringsAsFactors = FALSE
-    )
-    
-    ### nudge labels
-    labs <- choropleth_label_nudger(labs)
-    
-    ### plot data
-    plot_ly() %>%
-      add_sf(
-        data = dat,
-        type = "scatter",
-        split = ~ province,
-        color = ~ cases_colour,
-        colors = "Blues",
-        stroke = I("#000000"),
-        span = I(1.5),
-        hoverinfo = "none"
-      ) %>%
-      add_annotations(
-        data = labs,
-        x = ~ x,
-        y = ~ y,
-        text = ~ lab_cases,
-        hoverinfo = "text",
-        hovertext = paste0(labs$province_short, ": ", labs$lab_cases),
-        font = list(color = "black", size = 14),
-        bgcolor = "white",
-        bordercolor = "black",
-        showarrow = ~ show_arrow,
-        ax = ~ arrow_x,
-        ay = ~ arrow_y,
-        axref = "x",
-        ayref = "y"
-      ) %>%
-      layout(
-        xaxis = axis_hide,
-        yaxis = axis_hide,
-        showlegend = FALSE
-      ) %>%
-      config(displaylogo = FALSE,
-             ### hide all plotly buttons, since they do nothing here
-             displayModeBar = FALSE) %>%
-      hide_colorbar()
-  })
-  
-  # render province vaccine map for overview tab with dynamic width
-  output$ui_plot_choropleth_overview_vaccine_full_pct <- renderUI({
-    
-    ### don't run without inputs defined
-    req(input$screen_width)
-    
-    ### render plot
-    fluidRow(
-      column(
-        h4(textOutput("title_choropleth_overview_vaccine_full_pct")),
-        plotlyOutput("plot_choropleth_overview_vaccine_full_pct",
-                     ### max width of plot = 600px
-                     ### scale so plot doesn't overflow screen at small widths
-                     width = ifelse(input$screen_width * (7/8) > 600, 600, input$screen_width * (7/8))),
-        width = 12,
-        align = "center"
-      )
-    )
-  })
-
-  # province summary table for overview tab
-  output$table_prov_overview <- renderDT({
-    
-    table_overview %>%
-      ### show NA instead of 0 for provs without booster info available
-      mutate(
-        `Cumulative additional doses` = ifelse(`Cumulative additional doses` == 0, NA, `Cumulative additional doses`),
-        `Additional doses (new)` = ifelse(`Cumulative additional doses` == 0, NA, `Additional doses (new)`)
-      ) %>%
-      ### merge short names
-      left_join(map_prov %>% select(province, province_short),
-                by = c("Province" = "province")) %>%
-      mutate(Province = coalesce(province_short, Province)) %>%
-      select(-province_short) %>%
-      DT::datatable(class = "stripe compact hover", rownames = FALSE, extensions = "FixedColumns",
-                    options = list(
-                      paging = FALSE,
-                      searching = FALSE,
-                      scrollX = TRUE,
-                      compact = TRUE,
-                      autoWidth = TRUE,
-                      ### centre all but the first column
-                      columnDefs = list(list(className = 'dt-center', targets = 1:(ncol(.) - 1))),
-                      ### freeze province column
-                      fixedColumns = list(leftColumns = 1)
-                    )) %>%
-      formatRound(columns = c("Cumulative cases", "Cases (new)", "Active cases", "Active cases (change)", "Cumulative vaccine doses administered", "Vaccine doses administered (new)", "Cumulative people w/ 2 doses", "People w/ 2 doses (new)", "Cumulative additional doses", "Additional doses (new)", "Hospitalized", "Hospitalized (change)", "Cumulative deaths", "Deaths (new)", "Cumulative recovered", "Recovered (new)", "Cumulative testing", "Testing (new)"), digits = 0) %>%
-      formatRound(columns = c("Cases (new) per 100k", "Cumulative cases per 100k", "Active cases per 100k", "Hospitalized per 100k", "Cumulative deaths per 100k"), digits = 1) %>%
-      formatRound(columns = c("Cumulative testing per 100k"), digits = 0)
-  })
-  
-  # flattening plots
-  
-  ## flattening interpretation
-  output$text_flattening <- renderText({
-    if (input$scale_flattening == "linear") {
-      "<center><b>Interpretation</b><br>
-  These graphs display trends for daily cases and deaths over time on a linear scale. An <i>upward slope</i> means the number of cases/deaths reported each day is <i>still growing</i>. A <i>flat line</i> means the number of cases/deaths reported each day is <i>staying the same</i>. A <i>downward slope</i> means the number of cases/deaths reported each day is <i>falling</i>.</center>"
-    } else if (input$scale_flattening == "logarithmic") {
-      "<center><b>Interpretation</b><br>
-  These graphs display trends for daily cases and deaths over time on a logarithmic scale. An <i>upward slope</i> means the number of cases/deaths reported each day is <i>still growing</i>. A <i>flat line</i> means the number of cases/deaths reported each day is <i>staying the same</i>. A <i>downward slope</i> means the number of cases/deaths reported each day is <i>falling</i>.</center>"
-    }
-  })
-  
-  ## function: flattening plot title
-  title_flattening <- function(lab_title, val_scale = input$scale_flattening, val_scale2 = input$scale_trends) {
-    
-    ### don't run without inputs defined
-    req(input$scale_flattening, input$scale_trends)
-    
-    ### render title
-    if (val_scale == "linear") {
-      if(val_scale2 == "absolute") {
-        paste0(lab_title, " by province (7-day rolling average)")  
-      } else if (val_scale2 == "per-capita") {
-        paste0(lab_title, " per 100,000 by province (7-day rolling average)")  
-      }
-      
-    } else if (val_scale == "logarithmic") {
-      if (val_scale2 == "absolute") {
-        paste0(lab_title, " by province (7-day rolling average, logarithmic scale)")
-      } else if (val_scale2 == "per-capita") {
-        paste0(lab_title, " per 100,000 by province (7-day rolling average, logarithmic scale)")
-      }
-      
-    }
-  }
-  
-  ## function: flattening plot
-  plot_flattening <- function(fun_data, var_date, var_val, lab_y, val_scale = input$scale_flattening) {
-    
-    ### don't run without inputs defined
-    req(input$scale_flattening, input$scale_trends)
-    
-    ### get data
-    dat <- fun_data
-    
-    ### process data
-    dat <- dat %>%
-      filter(province != "Repatriated")
-    
-    if (input$scale_trends == "per-capita") {
-      ### calculate 7-day rolling average
-      dat <- dat %>% 
-        group_by(province) %>% 
-        dplyr::mutate(roll_avg_per_capita := !!sym(var_val) / pop * 100000,
-                      roll_avg = rollapply(roll_avg_per_capita, 7, mean, align = "right", partial = TRUE)
-                      ) %>% 
-        ungroup()
-      
-    } else if (input$scale_trends == "absolute") {
-      ### calculate 7-day rolling average
-      dat <- dat %>%
-        group_by(province) %>%
-        mutate(roll_avg = rollapply(!!sym(var_val), 7, mean, align = "right", partial = TRUE)) %>%
-        ungroup()
-      
-    }
-    
-    ### plot data
-    dat %>%
-      plot_ly(
-        x = as.formula(paste("~", var_date)),
-        y = ~roll_avg,
-        color = ~ province_short,
-        colors = palette_province_short
-      ) %>%
-      add_lines() %>%
-      layout(
-        xaxis = list(title = "Report date",
-                     fixedrange = TRUE,
-                     rangeslider = list(type = "date", thickness = 0.1)
-                     ),
-        yaxis = {if (val_scale == "logarithmic") list(type = "log", title = lab_y, fixedrange = TRUE) else list(title = lab_y, fixedrange = TRUE)},
-        legend = plotly_legend,
-        showlegend = TRUE,
-        hovermode = "x unified"
-      ) %>%
-      config(
-        displaylogo = FALSE,
-        modeBarButtonsToRemove = plotly_buttons
-        )
-  }
-  
-  ## cases
-  output$title_flattening_cases <- renderText({title_flattening("Daily reported cases")})
-  output$plot_flattening_cases <- renderPlotly({plot_flattening(data_ts_cases(), "date_report", "cases", "Daily reported cases")})
-  
-  ## mortality
-  output$title_flattening_mortality <- renderText({title_flattening("Daily reported deaths")})
-  output$plot_flattening_mortality <- renderPlotly({plot_flattening(data_ts_mortality(), "date_death_report", "deaths", "Daily reported deaths")})
-  
-  ## daily doses administered (7-day rolling average)
-  output$title_flattening_avaccine <- renderText({title_flattening("Daily vaccine doses administered")})
-  output$plot_flattening_avaccine <- renderPlotly({plot_flattening(data_ts_vaccine_administration(), "date_vaccine_administered", "avaccine", "Daily vaccine doses administered")})
-  
-  # comparisons
-  
-  ## function: comparisons plot title
-  title_comp <- function(label_val, comparison_window, comparison_scale, date_max) {
-    if (comparison_scale == "per-capita") {
-      if (comparison_window > 1) {
-        paste0("Average daily ", label_val, " per 100,000 (last ", comparison_window, " days)")
-      } else {
-        paste0(capitalize(label_val), " per 100,000 (", date_max, ")")
-      }
-    } else {
-      if (comparison_window > 1) {
-        paste0("Average daily ", label_val, " (last ", comparison_window, " days)")
-      } else {
-        paste0(capitalize(label_val), " (", date_max, ")")
-      }
-    }
-  }
-  
-  ## function: comparisons plot
-  plot_comp <- function(fun_data, var_val, comparison_window, comparison_scale, lab_y) {
-    
-    ### get comparison variable
-    var_comp <- get_var_comp(var_val, comparison_window, comparison_scale)
-    
-    ### get data
-    dat <- fun_data %>%
-      ### create labels for values
-      mutate(lab_comp = formatC(!!(sym(var_comp)), digits = 2, format = "f", big.mark = ","))
-    
-    ### plot data
-    dat %>%
-      plot_ly() %>%
-      add_trace(
-        x = ~ province_short,
-        y = as.formula(paste("~", var_comp)),
-        type = "bar",
-        color = ~ province_short,
-        colors = palette_province_short,
-        hoverinfo = "text",
-        hovertext = paste0(
-          dat[["province_short"]], ": ", dat[["lab_comp"]]
-        )
-      ) %>% 
-      layout(
-        xaxis = list(title = "Province", fixedrange = TRUE),
-        yaxis = list(title = lab_y, fixedrange = TRUE),
-        showlegend = FALSE
-      ) %>%
-      config(displaylogo = FALSE,
-             modeBarButtonsToRemove = plotly_buttons)
-    
-  }
-  
-  # ## function: comparisons data download
-  download_comp <- function(fun_data, file) {
-    write.csv(fun_data, file, row.names = FALSE)
-  }
-  
-  ## cases
-  output$title_comp_cases <- renderText({
-    title_comp("reported cases", input$window_comp_cases, input$scale_comp_cases, date_max)
-  })
-  
-  output$plot_comp_cases <- renderPlotly({
-    plot_cumulative_v2(fun_data = data_comp_cases(), var_val = "cases", input_window = input$window_comp_cases, input_scale = input$scale_comp_cases, lab_y = "Daily reported cases", input_plot = "comparisons")
-  })
-  
-  output$download_comp_cases <- downloadHandler(
-    filename = "comp_prov_cases.csv",
-    content = function(file) {
-      download_comp(data_comp_cases(), file)
-    }
-  )
-  
-  ## mortality
-  output$title_comp_mortality <- renderText({
-    title_comp("reported deaths", input$window_comp_mortality, input$scale_comp_mortality, date_max)
-  })
-  output$plot_comp_mortality <- renderPlotly({
-    plot_cumulative_v2(fun_data = data_comp_mortality(), var_val = "deaths", input_window = input$window_comp_mortality, input_scale = input$scale_comp_mortality, lab_y = "Daily reported mortality", input_plot = "comparisons")
-  })
-  output$download_comp_mortality <- downloadHandler(
-    filename = "comp_prov_mortality.csv",
-    content = function(file) {
-      download_comp(data_comp_mortality(), file)
-    }
-  )
-  
-  ## testing
-  output$title_comp_testing <- renderText({
-    title_comp("testing", input$window_comp_testing, input$scale_comp_testing, date_max)
-  })
-  output$plot_comp_testing <- renderPlotly({
-    plot_cumulative_v2(fun_data = data_comp_testing(), var_val = "testing", input_window = input$window_comp_testing, input_scale = input$scale_comp_testing, lab_y = "Daily testing", input_plot = "comparisons")
-  })
-  output$download_comp_testing <- downloadHandler(
-    filename = "comp_prov_testing.csv",
-    content = function(file) {
-      download_comp(data_comp_testing(), file)
-    }
-  )
-  
-  # maps tab
-  
-  ## load health region map
-  load_geo_hr <- reactive({
-    
-    ### don't run without inputs defined
-    req(input$date_range)
-    
-    if(input$date_range[1]=="2020/01/25" & input$date_range[2]>="2020/08/04") {
-    st_read("geo/esri_health_region_sk_new/RegionalHealthBoundaries.shp", quiet = TRUE) %>%
-      ### join health region names used in the dataset by HR_UID
-      ### first convert HR_UID in map to integer so types match
-      mutate(HR_UID = as.integer(HR_UID)) %>%
-      inner_join(
-        hr_map_sk_new,
-        by = "HR_UID"
-      )
-    } else if (input$date_range[1]>="2020/08/04") {
-        st_read("geo/esri_health_region_sk_new/RegionalHealthBoundaries.shp", quiet = TRUE) %>%
-          ### join health region names used in the dataset by HR_UID
-          ### first convert HR_UID in map to integer so types match
-          mutate(HR_UID = as.integer(HR_UID)) %>%
-          inner_join(
-            hr_map_sk_new,
-            by = "HR_UID"
-          )
-      } else if(input$date_range[1]=="2020/01/25" & input$date_range[2]<"2020/08/04") {
-        st_read("geo/esri_health_region_sk_old/RegionalHealthBoundaries.shp", quiet = TRUE) %>%
-          ### join health region names used in the dataset by HR_UID
-          ### first convert HR_UID in map to integer so types match
-          mutate(HR_UID = as.integer(HR_UID)) %>%
-          inner_join(
-            map_hr,
-            by = "HR_UID"
-          )
-      } else if(input$date_range[1]!="2020/01/25" & input$date_range[1]<"2020/08/04") {
-        st_read("geo/esri_health_region_sk_old/RegionalHealthBoundaries.shp", quiet = TRUE) %>%
-          ### join health region names used in the dataset by HR_UID
-          ### first convert HR_UID in map to integer so types match
-          mutate(HR_UID = as.integer(HR_UID)) %>%
-          inner_join(
-            map_hr,
-            by = "HR_UID"
-          )
-      }
-  })
-  
-  ## health region map title
-  output$title_choropleth_hr <- renderText({
-    
-    ### don't run without inputs defined
-    req(input$metric_choropleth_hr, input$scale_choropleth_hr)
-    
-    ### render title
-    if (input$metric_choropleth_hr == "cases") {
-      if (input$scale_choropleth_hr == "absolute") {
-        "Map of reported cases in Canada"
-      } else if (input$scale_choropleth_hr == "per-capita") {
-        "Map of reported cases in Canada (per 100,000)"
-      }} else if (input$metric_choropleth_hr == "mortality") {
-        if (input$scale_choropleth_hr == "absolute") {
-          "Map of reported deaths in Canada"
-        } else if (input$scale_choropleth_hr == "per-capita") {
-          "Map of reported deaths in Canada (per 100,000)"
-        }}
-    
-  })
-  
-  ## data: health region map
-  data_choropleth_hr <- reactive({
-    
-    ### don't run without inputs defined
-    req(input$metric_choropleth_hr, input$scale_choropleth_hr,input$date_range)
-
-    ### get data
-    
-    if (input$metric_choropleth_hr == "cases") {
-      if(input$date_range[1]=="2020/01/25" & input$date_range[2]>="2020/08/04") {
-        dat <- data_ts_cases_new_add_hr() %>%
-          left_join(
-            hr_map_sk_new,
-            by = c("province", "health_region")
-          )
-        var_val <- "cases"
-      } else if(input$date_range[1]>="2020/08/04") {
-        dat <- data_ts_cases_new_hr() %>%
-          left_join(
-            hr_map_sk_new,
-            by = c("province", "health_region")
-          )
-        var_val <- "cases"
-      } else if(input$date_range[1]=="2020/01/25" & input$date_range[2]<"2020/08/04") {
-        dat <- data_ts_cases_hr() %>%
-          left_join(
-            map_hr,
-            by = c("province", "health_region")
-          )
-        var_val <- "cases"
-      } else if(input$date_range[1]!="2020/01/25" & input$date_range[1]<"2020/08/04") {
-        dat <- data_ts_cases_hr() %>%
-          left_join(
-            map_hr,
-            by = c("province", "health_region")
-          )
-        var_val <- "cases"
-      }
-    } else if(input$metric_choropleth_hr == "mortality") {
-      if(input$date_range[1]=="2020/01/25" & input$date_range[2]>="2020/08/04") {
-        dat <- data_ts_mortality_new_add_hr() %>%
-          left_join(
-            hr_map_sk_new,
-            by = c("province", "health_region")
-          )
-        var_val <- "deaths"
-      } else if(input$date_range[1]>="2020/08/04") {
-        dat <- data_ts_mortality_new_hr() %>%
-          left_join(
-            hr_map_sk_new,
-            by = c("province", "health_region")
-          )
-        var_val <- "deaths"
-      } else if(input$date_range[1]=="2020/01/25" & input$date_range[2]<"2020/08/04") {
-        dat <- data_ts_mortality_hr() %>%
-          left_join(
-            map_hr,
-            by = c("province", "health_region")
-          )
-        var_val <- "deaths"
-      }
-      else if(input$date_range[1]!="2020/01/25" & input$date_range[1]<"2020/08/04") {
-        dat <- data_ts_mortality_hr() %>%
-          left_join(
-            map_hr,
-            by = c("province", "health_region")
-          )
-        var_val <- "deaths"
-      }
-    }
-    
-    ### process data
-    dat %>%
-      select(province, health_region, !!sym(var_val), pop) %>%
-      group_by(province, health_region) %>%
-      summarize(count := sum(!!sym(var_val)), pop = max(pop), .groups = "drop")
-  })
-  
-  ## health region map
-  output$choropleth_hr <- renderLeaflet({
-    
-    ### don't run without inputs defined
-    req(input$metric_choropleth_hr, input$scale_choropleth_hr,input$date_range)
-    
-    ### load map
-    geo_hr <- load_geo_hr()
-    
-    ### get data
-    dat <- data_choropleth_hr() %>%
-      ### exclude data where province == "Repatriated" or health_region == "Not Reported"
-      filter(province != "Repatriated" & health_region != "Not Reported") %>%
-      ### convert to per-capita (if applicable)
-      {if (input$scale_choropleth_hr == "per-capita") mutate(., count = count / pop * 100000) else .}
-    
-    ### join data to map
-    geo_hr <- geo_hr %>%
-      left_join(
-        dat,
-        by = c("province", "health_region", "pop")
-      )
-    
-    ### generate colour gradient (up to 5 bins)
-    n_quantile <- 5
-    probs <- seq(0, 1, length.out = n_quantile + 1)
-    bins <- quantile(dat$count, probs, na.rm = TRUE, names = FALSE)
-    ### ensure bins are integers for absolute scale
-    if (input$scale_choropleth_hr == "absolute") {
-      bins <- round(bins)
-    }
-    while (length(unique(bins)) != length(bins)) {
-      n_quantile <- n_quantile - 1
-      probs <- seq(0, 1, length.out = n_quantile + 1)
-      bins <- quantile(dat$count, probs, na.rm = TRUE, names = FALSE)
-      ### ensure bins are integers for absolute scale
-      if (input$scale_choropleth_hr == "absolute") {
-        bins <- round(bins)
-      }
-    }
-    
-    ## define bin colours
-    if (input$metric_choropleth_hr == "cases") {
-      gradient <- colorBin(brewer.pal(n = length(bins), "Blues"), bins = bins)
-    } else if (input$metric_choropleth_hr == "mortality") {
-      gradient <- colorBin(brewer.pal(n = length(bins), "Reds"), bins = bins)
-    }
-    
-    ### render map
-    leaflet(geo_hr) %>%
-      addProviderTiles(providers$Stamen.TonerLite,
-                       options = providerTileOptions(noWrap = TRUE, minZoom = 2, maxZoom = 8)) %>%
-      addPolygons(
-        stroke = TRUE,
-        color = "black",
-        opacity = 1,
-        weight = 0.5,
-        fillOpacity = 0.5,
-        smoothFactor = 0.5,
-        fillColor = ~gradient(count),
-        popup =
-          paste0(
-            "<p>",
-            "Province: ",
-            geo_hr$province,
-            "<br>",
-            "Health Region: ",
-            geo_hr$health_region,
-            "<br>",
-            "Population: ",
-            formatC(geo_hr$pop, format = "f", big.mark = ",", digits = 0),
-            "<br>",
-            if (input$metric_choropleth_hr == "cases") {
-              if (input$scale_choropleth_hr == "absolute") {
-                "Reported cases: "
-              } else if (input$scale_choropleth_hr == "per-capita") {
-                "Reported cases (per 100,000): "
-              }} else if (input$metric_choropleth_hr == "mortality") {
-                if (input$scale_choropleth_hr == "absolute") {
-                  "Reported deaths: "
-                } else if (input$scale_choropleth_hr == "per-capita") {
-                  "Reported deaths (per 100,000): "
-                }},
-            ### format numbers
-            if (input$scale_choropleth_hr == "absolute") {
-              ifelse(is.na(geo_hr$count), "0", formatC(geo_hr$count, format = "f", big.mark = ",", digits = 0))
-            } else if (input$scale_choropleth_hr == "per-capita") {
-              ifelse(is.na(geo_hr$count), "0", formatC(geo_hr$count, format = "f", big.mark = ",", digits = 2))
-            },
-            "</p>"
-          )) %>%
-      addLegend("bottomright", pal = gradient, values = ~count,
-                title = {
-                  if (input$metric_choropleth_hr == "cases") {
-                    if (input$scale_choropleth_hr == "absolute") {
-                      "Reported cases"
-                    } else if (input$scale_choropleth_hr == "per-capita") {
-                      "Reported cases</br>(per 100,000)"
-                    }} else if (input$metric_choropleth_hr == "mortality") {
-                      if (input$scale_choropleth_hr == "absolute") {
-                        "Reported deaths"
-                      } else if (input$scale_choropleth_hr == "per-capita") {
-                        "Reported deaths</br>(per 100,000)"
-                      }}
-                },
-                labFormat = labelFormat(prefix = "", suffix = "", between = " – ",
-                                        digits = 2, big.mark = ",", transform = identity),
-                na.label = "None reported",
-                opacity = 0.5)
-  })
-  
-  
-  ## health region map text
-  output$text_choropleth_hr <- renderText({
-    
-    ### don't run without inputs defined
-    req(input$metric_choropleth_hr, input$scale_choropleth_hr,input$date_range)
-    
-    ### get data and only keep data where province == "Repatriated" or health_region == "Not Reported"
-    dat <- data_choropleth_hr() %>%
-      filter(province == "Repatriated" | health_region == "Not Reported")
-    
-    ### render text
-    if (nrow(dat) == 0) {
-      if (input$metric_choropleth_hr== "cases") {
-        HTML("No cases were excluded from the map due to missing health region information.")
-      } else if (input$metric_choropleth_hr == "mortality") {
-        "No deaths were excluded from the map due to missing health region information."
-      }
-    } else {
-      HTML(
-        text = paste0(
-          "We excluded ",
-          if (input$metric_choropleth_hr == "cases") {
-            "cases"
-          } else if (input$metric_choropleth_hr == "mortality") {
-            "deaths"
-          },
-          " from the map due to missing health region information:",
-          "<ul>",
-          paste("<li>", dat$province, ": ", dat$count, sep = "", collapse = ""),
-          "</ul>")
-      )
-    }
-  })
-  
-  # daily/cumulative numbers plots
-  
-  ## function: daily/cumulative numbers plot title
-  title_daily_cumulative <- function(fun_data, var_date, var_val, lab_title, exclude_repatriated = FALSE, by_province = FALSE, filter_province = input$prov) {
-    
-    ### don't run without inputs defined
-    req(input$prov, input$date_range)
-    
-    ### get data
-    dat <- fun_data %>%
-      ### remove repatriated cases from count (if applicable)
-      {if (exclude_repatriated) filter(., province != "Repatriated") else .}
-    
-    ### calculate n
-    n <- sum(dat[, var_val])
-    
-    ### render title
-    if (n == 0) {
-      paste0(lab_title, " in ", filter_province)
-    } else {
-      if (filter_province == "Canada" & by_province) {
-        paste0(lab_title, " in ", filter_province, " by province (n = ", format(n, big.mark = ","), ")")
-      } else {
-        paste0(lab_title, " in ", filter_province, " (n = ", format(n, big.mark = ","), ")")
-      }
-    }
-  }
-  
-  ## function: daily numbers plot
-  plot_daily <- function(fun_data, var_date, var_val, lab_x, lab_y) {
-    
-    ### don't run without inputs defined
-    req(input$prov, input$date_range)
-    
-    ### get data
-    dat <- fun_data
-    
-    ### if no matching data, return blank plot
-    if (sum(dat[, var_val]) == 0) {
-      return(plot_no_data())
-    }
-    
-    ### transform data
-    dat <- dat %>%
-      ### aggregate values so that one date = one row to ensure correct plotting of bars
-      select(!!sym(var_date), !!sym(var_val)) %>%
-      group_by(!!sym(var_date)) %>%
-      summarize(!!sym(var_val) := sum(!!sym(var_val)), .groups = "drop") %>%
-      ### calculate rolling average
-      mutate(roll_avg = rollapply(!!sym(var_val), 7, mean, align = "right", partial = TRUE))
-    
-    ### plot data
-    dat %>%
-      plot_ly(x = as.formula(paste("~", var_date)),
-              y = as.formula(paste("~", var_val))
-      ) %>%
-      add_bars(name = "Daily") %>%
-      layout(
-        xaxis = list(title = lab_x, fixedrange = TRUE),
-        yaxis = list(title = lab_y, fixedrange = TRUE),
-        legend = plotly_legend
-      ) %>%
-      add_lines(x = as.formula(paste("~", var_date)),
-                y = ~roll_avg, name = "7-day average") %>%
-      layout(yaxis2 = list(overlaying = "y", side = "right")) %>%
-      layout(showlegend = TRUE,
-             hovermode = "x unified"
-      ) %>%
-      config(displaylogo = FALSE,
-             modeBarButtonsToRemove = plotly_buttons)
-  }
-  
-  ## function: cumulative numbers plot
-  plot_cumulative <- function(fun_data, var_date, var_val, lab_x, lab_y) {
-    
-    ### don't run without inputs defined
-    ## INPUT_SCALE
-    req(input$prov, input$date_range)
-    
-    ### get data
-    dat <- fun_data
-    
-    ### if no matching data, return blank plot
-    if (sum(dat[, var_val]) == 0) {
-      return(plot_no_data())
-    }
-    
-    ### transform data
-    dat <- dat %>%
-      filter(province != "Repatriated") %>%
-      ### create labels for values
-      mutate(lab_val = formatC(!!(sym(var_val)), big.mark = ","))
-    
-    ### plot data
-    dat %>%
-      plot_ly(
-        x = as.formula(paste("~", var_date)),
-        y = as.formula(paste("~", var_val)),
-        color = ~ province_short,
-        colors = palette_province_short,
-        hoverinfo = "text",
-        hovertext = paste0(
-          "Province: ", dat[["province_short"]], "\n",
-          "Date: ", dat[[var_date]], "\n",
-          case_when(
-            var_val == "cumulative_avaccine" ~ paste0("Cumulative vaccine doses administered: ", dat[["lab_val"]]),
-            var_val == "cumulative_dvaccine" ~ paste0("Cumulative vaccine doses distributed: ", dat[["lab_val"]]),
-            TRUE ~ paste0(capitalize(sub("_", " ", var_val)), ": ", dat[["lab_val"]])
-          )
-        )
-      ) %>%
-      add_lines() %>%
-      layout(
-        xaxis = list(title = lab_x, fixedrange = TRUE),
-        yaxis = list(title = lab_y, fixedrange = TRUE),
-        legend = plotly_legend
-      ) %>%
-      config(displaylogo = FALSE,
-             modeBarButtonsToRemove = plotly_buttons)
-  }
-  
-  ## daily numbers: cases
-  output$title_daily_cases <- renderText({title_daily_cumulative(data_ts_cases(), "date_report", "cases", "Daily reported cases & 7-day rolling average")})
-  output$plot_daily_cases <- renderPlotly({
-    plot_daily(data_ts_cases(), "date_report", "cases", "Report date", "Daily reported cases")
-  })
-  
-  ## daily numbers: mortality
-  output$title_daily_mortality <- renderText({title_daily_cumulative(data_ts_mortality(), "date_death_report", "deaths", "Daily reported deaths & 7-day rolling average")})
-  output$plot_daily_mortality <- renderPlotly({
-    plot_daily(data_ts_mortality(), "date_death_report", "deaths", "Report date", "Daily reported deaths")
-  })
-  
-  ## daily numbers: recovered
-  output$title_daily_recovered <- renderText({title_daily_cumulative(data_ts_recovered(), "date_report", "recovered", "Daily recovered & 7-day rolling average")})
-  output$plot_daily_recovered <- renderPlotly({
-    plot_daily(data_ts_recovered(), "date_recovered", "recovered", "Report date", "Daily recovered")
-  })
-  
-  ## daily numbers: testing
-  output$title_daily_testing <- renderText({title_daily_cumulative(data_ts_testing(), "date_testing", "testing", "Daily testing & 7-day rolling average")})
-  output$plot_daily_testing <- renderPlotly({
-    plot_daily(data_ts_testing(), "date_testing", "testing", "Report date", "Daily testing")
-  })
-  
-  ## daily numbers: vaccine administration
-  output$title_daily_vaccine_administration <- renderText({title_daily_cumulative(data_ts_vaccine_administration(), "date_vaccine_administered", "avaccine", "Daily vaccine doses administered & 7-day rolling average")})
-  output$plot_daily_vaccine_administration <- renderPlotly({
-    plot_daily(data_ts_vaccine_administration(), "date_vaccine_administered", "avaccine", "Report date", "Daily vaccine doses administered")
-  })
-  
-  ## cumulative numbers: cases
-  output$title_cumulative_cases <- renderText({
-    # title_daily_cumulative(data_ts_cases(), "date_report", "cases", "Cumulative reported cases", exclude_repatriated = TRUE, by_province = TRUE)
-    title_daily_cumulative_v2(data_ts_cases(), "date_report", "cases", "reported cases", input_scale = input$scale_cases, input_plot = input$plot_type_cases, exclude_repatriated = TRUE, by_province = TRUE)
-    })
-  output$plot_cumulative_cases <- renderPlotly({
-    plot_cumulative_v2(data_ts_cases(), var_date = "date_report", var_val = "cumulative_cases", lab_x = "Report date", lab_y = "Cumulative reported cases", input_scale = input$scale_cases, input_plot = input$plot_type_cases)
-    # plot_cumulative(data_ts_cases(), "date_report", "cumulative_cases", "Report date", "Cumulative reported cases")
-  })
-  
-  ## cumulative numbers: mortality
-  output$title_cumulative_mortality <- renderText({
-    # title_daily_cumulative(data_ts_mortality(), "date_death_report", "deaths", "Cumulative reported deaths", exclude_repatriated = TRUE, by_province = TRUE)
-    title_daily_cumulative_v2(data_ts_mortality(), "date_death_report", "deaths", "reported deaths", exclude_repatriated = TRUE, by_province = TRUE, input_scale = input$scale_deaths, input_plot = input$plot_type_deaths)
-    })
-  output$plot_cumulative_mortality <- renderPlotly({
-    plot_cumulative_v2(data_ts_mortality(), var_date = "date_death_report", var_val = "cumulative_deaths", lab_x = "Report date", lab_y = "Cumulative reported deaths", input_scale = input$scale_deaths, input_plot = input$plot_type_deaths)
-    # plot_cumulative(data_ts_mortality(), "date_death_report", "cumulative_deaths", "Report date", "Cumulative reported deaths")
-  })
-  
-  ## cumulative numbers: recovered
-  output$title_cumulative_recovered <- renderText({
-    # title_daily_cumulative(data_ts_recovered(), "date_recovered", "recovered", "Cumulative recovered", exclude_repatriated = TRUE, by_province = TRUE)
-    title_daily_cumulative_v2(data_ts_recovered(), "date_recovered", "recovered", "recovered", exclude_repatriated = TRUE, by_province = TRUE, input_scale = input$scale_recovered, input_plot = input$plot_type_recovered)
-    })
-  
-    
-  output$plot_cumulative_recovered <- renderPlotly({
-    plot_cumulative_v2(data_ts_recovered(), var_date = "date_recovered", var_val = "cumulative_recovered", lab_x = "Report date", lab_y = "Cumulative recovered", input_scale = input$scale_recovered, input_plot = input$plot_type_recovered)
-    # plot_cumulative(data_ts_recovered(), "date_recovered", "cumulative_recovered", "Report date", "Cumulative recovered")
-  })
-  
-  ## cumulative numbers: testing
-  output$title_cumulative_testing <- renderText({
-    # title_daily_cumulative(data_ts_testing(), "date_testing", "testing", "Cumulative testing", exclude_repatriated = TRUE, by_province = TRUE)
-    title_daily_cumulative_v2(data_ts_testing(), "date_testing", "testing", "testing", exclude_repatriated = TRUE, by_province = TRUE, input_scale = input$scale_testing, input_plot = input$plot_type_testing)
-    })
-  output$plot_cumulative_testing <- renderPlotly({
-    plot_cumulative_v2(data_ts_testing(), var_date = "date_testing", var_val = "cumulative_testing", lab_x = "Report date", lab_y = "Cumulative testing", input_scale = input$scale_testing, input_plot = input$plot_type_testing)
-    # plot_cumulative(data_ts_testing(), "date_testing", "cumulative_testing", "Report date", "Cumulative testing")
-  })
-  
-  # pie charts
-  
-  ## function: pie chart plot title
-  title_pie <- function(fun_data, var_val, lab_title) {
-    
-    ### don't run without inputs defined
-    req(input$prov, input$date_range)
-    
-    ### get data
-    n <- fun_data %>%
-      filter(province != "Repatriated") %>%
-      group_by(province_short) %>%
-      ungroup %>%
-      summarize(!!sym(var_val) := sum(!!sym(var_val)), .groups = "drop") %>%
-      pull
-    
-    ### render title
-    if (n == 0) {
-      paste0("Pie chart of ", lab_title, " in Canada")
-    } else {
-      paste0("Pie chart of ", lab_title, " in Canada", " (n = ", format(n, big.mark = ","), ")")
-    }
-  }
-  
-  ## function: pie chart plot
-  plot_pie <- function(fun_data, var_val) {
-    
-    ### don't run without inputs defined
-    req(input$prov, input$date_range)
-    
-    ### get data
-    dat <- fun_data
-    
-    ### if no matching data, return blank plot
-    if (nrow(dat) == 0) {
-      return(plot_no_data())
-    }
-    
-    ### transform data and join colours
-    dat <- dat %>%
-      filter(province != "Repatriated") %>%
-      group_by(province_short) %>%
-      summarize(!!sym(var_val) := sum(!!sym(var_val)), .groups = "drop") %>%
-      filter(!!sym(var_val) > 0) %>%
-      left_join(
-        data.frame(
-          province_short = names(palette_province_short),
-          colour_code = palette_province_short,
-          stringsAsFactors = FALSE
-        ),
-        by = "province_short"
-      ) %>%
-      ### create labels
-      mutate(
-        lab_val = formatC(!!sym(var_val), big.mark = ","),
-        lab_percent = paste0(formatC(!!sym(var_val) / sum(.[, var_val]) * 100, format = "f", digit = 2), "%")
-      )
-    
-    ### plot data
-    dat %>%
-      plot_ly(
-        labels = ~ province_short,
-        values = as.formula(paste("~", var_val)),
-        sort = TRUE,
-        type = "pie",
-        direction = "counterclockwise",
-        textposition = "inside",
-        marker = list(colors = ~ colour_code),
-        textinfo = "text",
-        text = ~ lab_percent,
-        hoverinfo = "text",
-        hovertext = ~ paste0(
-          "Province: ", dat$province_short, "\n",
-          capitalize(var_val), ": ", dat$lab_val, "\n",
-          "Percent of total: ", dat$lab_percent
-        )
-      ) %>%
-      layout(
-        xaxis = axis_hide,
-        yaxis = axis_hide,
-        legend = plotly_legend
-      ) %>%
-      config(displaylogo = FALSE,
-             modeBarButtonsToRemove = plotly_buttons)
-  }
-  
-  ## cases
-  output$title_pie_cases <- renderText({
-    title_pie(data_ts_cases_pie(), "cases", "reported cases")
-  })
-  output$plot_pie_cases <- renderPlotly({
-    plot_pie(data_ts_cases_pie(), "cases")
-  })
-  
-  ## mortality
-  output$title_pie_mortality <- renderText({
-    title_pie(data_ts_mortality_pie(), "deaths", "reported deaths")
-  })
-  output$plot_pie_mortality <- renderPlotly({
-    plot_pie(data_ts_mortality_pie(), "deaths")
-  })
-  
-  # vaccine tab-specific plots
-  
-  ## vaccine gap data
-  data_vaccine_gap <- reactive({
-    
-    ### don't run without inputs defined
-    req(input$prov, input$date_range)
-    
-    ### merge data
-    dat <- full_join(
-      data_ts_vaccine_administration() %>%
-        rename(date_vaccine = date_vaccine_administered) %>%
-        select(province, date_vaccine, avaccine, cumulative_avaccine),
-      data_ts_vaccine_distribution() %>%
-        rename(date_vaccine = date_vaccine_distributed) %>%
-        select(province, date_vaccine, cumulative_dvaccine),
-      by = c("province", "date_vaccine")
-    ) %>%
-      replace_na(list(
-        avaccine = 0,
-        cumulative_avaccine = 0
-        )) # 2020-12-13 is NA for avaccine
-    
-    ### collapse observations into one row per date
-    dat %>%
-      select(date_vaccine, cumulative_dvaccine, avaccine, cumulative_avaccine) %>%
-      group_by(date_vaccine) %>%
-      summarize(across(everything(), sum), .groups = "drop")
-    
-  })
-  
-  ## vaccine gap title
-  output$title_vaccine_gap <- renderText({
-    
-    ### don't run without inputs defined
-    req(input$prov)
-    
-    if (input$prov != "all") {
-      paste("Vaccine gap in", input$prov)
-    } else {
-      "Vaccine gap in Canada"
-    }
-    
-  })
-  
-  ## vaccine gap plot
-  output$plot_vaccine_gap <- renderPlotly({
-    
-    ### don't run without inputs defined
-    req(input$prov, input$date_range)
-    
-    ### get merged vaccine data
-    dat <- data_vaccine_gap()
-    
-    ### calculate smoothed vaccine distribution
-    cumulative_dvaccine_min <- dat %>% filter(cumulative_dvaccine != 0) %>% pull(cumulative_dvaccine) %>% min
-    cumulative_dvaccine_max <- dat %>% filter(cumulative_dvaccine != 0) %>% pull(cumulative_dvaccine) %>% max
-    dat <- dat %>%
-      mutate(
-        cumulative_dvaccine_smooth = gam(cumulative_dvaccine ~ s(as.integer(date_vaccine), bs = "tp")) %>%
-          fitted %>% round(0),
-        cumulative_dvaccine_smooth = ifelse(cumulative_dvaccine_smooth < cumulative_dvaccine_min,
-                                            cumulative_dvaccine_min,
-                                            cumulative_dvaccine_smooth), # avoid negative values
-        cumulative_dvaccine_smooth = ifelse(cumulative_dvaccine_smooth > cumulative_dvaccine_max,
-                                            cumulative_dvaccine_max,
-                                            cumulative_dvaccine_smooth) # avoid smoothed values larger than true max value
-        )
-    ### make sure final value of smoothed distribution is not smaller than true maximum
-    if (dat[nrow(dat), "cumulative_dvaccine_smooth"] < cumulative_dvaccine_max) {
-      dat[nrow(dat), "cumulative_dvaccine_smooth"] <- cumulative_dvaccine_max
-    }
-    
-    ### calculate 7-day average vaccine administration
-    admin_avg <- dat %>%
-      slice_tail(n = 7) %>%
-      pull(avaccine) %>%
-      mean
-    
-    ### project vaccine administration forward 7 days
-    vaxx_projection <- data.frame(
-      date_vaccine = seq.Date(from = date_max + 1, to = date_max + 7, by = "day"),
-      cumulative_avaccine = dat %>%
-        slice_tail(n = 1) %>%
-        pull(cumulative_avaccine) %>%
-        {. + admin_avg * 1:7}
-    ) %>%
-      bind_rows(
-        dat %>%
-          slice_tail(n = 1) %>%
-          select(date_vaccine, cumulative_avaccine)
-      ) %>%
-      rename(projected = cumulative_avaccine) %>%
-      arrange(date_vaccine)
-    
-    ### join projection data
-    dat <- dat %>%
-      full_join(
-        vaxx_projection,
-        by = "date_vaccine"
-      )
-    
-    ### setup update menus
-    updatemenus <- list(
-      list(
-        type = "buttons",
-        direction = "right",
-        xanchor = "center",
-        yanchor = "top",
-        x = 0,
-        y = 1.21,
-        showactive = TRUE,
-          buttons = list(
-            list(method = "restyle",
-                 args = list("y", list(as.formula(~cumulative_dvaccine_smooth)), 2),
-                 args2 = list("y", list(as.formula(~cumulative_dvaccine)), 2),
-                 label = "Smoothed<br>distributed")
-          )
-  ))
-    ### plot
-    dat %>%
-      plot_ly() %>%
-      add_trace(
-        type = "scatter",
-        mode = "none",
-        x = ~date_vaccine, 
-        y = ~cumulative_avaccine, 
-        name = "Administered",
-        fill = "tozeroy",
-        fillcolor = "rgba(0, 0, 255, 0.3)"
-      ) %>%
-      ### only add projection if showing most recent data
-      {
-        if (input$date_range[2] == date_max) {
-          add_trace(
-            .,
-            type = "scatter",
-            mode = "lines",
-            x = ~date_vaccine,
-            y = ~projected,
-            line = list(color = "black", dash = "dash"),
-            name = "Projected",
-            fill = "tozeroy",
-            fillcolor = "rgba(0, 0, 255, 0.1)",
-            stackgroup = "one", # prevent weirdness with filling the other two traces
-            visible = "legendonly" # off by default
-          )
-        } else {
-          ### blank trace to keep numbering of traces the same
-          add_trace(.,
-                    type = "scatter",
-                    mode = "markers")
-        }
-        } %>%
-     add_trace(
-       type = "scatter",
-       mode = "none",
-       x = ~date_vaccine, 
-       y = ~cumulative_dvaccine_smooth,
-       name = "Distributed",
-       fill = "tonexty",
-       fillcolor = "rgba(0, 0, 0, 0.7)"
-      ) %>%
-      layout(
-        xaxis = list(title = "Report date", fixedrange = TRUE, showgrid = FALSE),
-        yaxis = list(title = "Vaccine doses", fixedrange = TRUE),
-        legend = list(
-          orientation = "v",
-          yanchor = "bottom",
-          y = 1.02,
-          xanchor = "right",
-          x = 1
-        ), # override default legend to work better with button,
-        hovermode = "x unified",
-        updatemenus = updatemenus
-      ) %>%
-      config(displaylogo = FALSE,
-             modeBarButtonsToRemove = plotly_buttons)
-    
-  })
-  
-  ## percent at least one dose vaccinated data
-  data_at_least_one_dose <- reactive({
-    
-    ### don't run without inputs defined
-    req(input$prov, input$date_range)
-    
-    ### merge data
-    dat <- data_ts_vaccine_administration() %>%
-      rename(date_vaccine = date_vaccine_administered) %>%
-      left_join(data_ts_vaccine_completion(),
-                by = c("date_vaccine" = "date_vaccine_completed", "province" = "province")) %>%
-      select(province, date_vaccine, cumulative_avaccine, cumulative_cvaccine) %>%
-      replace_na(list(cumulative_avaccine = 0,
-                      cumulative_cvaccine = 0)) # 2020-12-13 is NA for avaccine
-    
-    ### collapse observations into one row per date
-    dat %>%
-      ### merge short names
-      left_join(map_prov %>% select(province, province_short, pop),
-                by = c("province")) %>% 
-      select(date_vaccine, pop, cumulative_avaccine, cumulative_cvaccine, province_short) %>%
-      group_by(date_vaccine, province_short) %>%
-      summarize(across(everything(), sum), .groups = "drop") %>% 
-      mutate(
-        at_least_one_dose_vaxx = 100 * (cumulative_avaccine - cumulative_cvaccine) / pop,
-        lab_percent = paste0(formatC(at_least_one_dose_vaxx, format = "f", digit = 2), "%")
-      ) %>% 
-      ungroup() %>% 
-      group_by(province_short) %>% 
-      filter(date_vaccine == max(date_vaccine))
-    
-  })
-  
-  ## percent fully vaccinated title
-  output$title_at_least_one_dose <- renderText({
-    
-    ### don't run without inputs defined
-    req(input$prov)
-    
-    if (input$prov != "all") {
-      paste("Percent with at least one dose in", input$prov)
-    } else {
-      "Percent with at least one dose in Canada"
-    }
-    
-  })
-  
-  ## percent partially vaccinated plot
-  output$plot_at_least_one_dose <- renderPlotly({
-    
-    ### don't run without inputs defined
-    req(input$prov, input$date_range)
-    
-    ### get merged vaccine data
-    dat <- data_at_least_one_dose()
-    
-    ### plot data
-    dat %>%
-      plot_ly() %>%
-      add_trace(
-        x = ~ province_short,
-        y = ~ at_least_one_dose_vaxx,
-        type = "bar",
-        color = ~ province_short,
-        colors = palette_province_short,
-        hoverinfo = "text",
-        hovertext = ~ paste0(
-          "Province: ", dat$province_short, "\n",
-          "Received at least one dose", ": ", dat$cumulative_cvaccine, "\n",
-          "Percent of total population: ", dat$lab_percent
-        )
-      ) %>% 
-      layout(
-        xaxis = list(title = "Province", fixedrange = TRUE),
-        yaxis = list(title = "Percent", fixedrange = TRUE),
-        showlegend = FALSE
-      ) %>%
-      config(displaylogo = FALSE,
-             modeBarButtonsToRemove = plotly_buttons)
-    
-  })
-  
-  ## percent fully vaccinated data
-  data_fully_vaccinated <- reactive({
-    
-    ### don't run without inputs defined
-    req(input$prov, input$date_range)
-    
-    ### merge data
-    dat <- full_join(
-      data_ts_vaccine_completion() %>%
-        rename(date_vaccine = date_vaccine_completed) %>%
-        select(province, date_vaccine, cumulative_cvaccine),
-      data_ts_vaccine_administration() %>%
-        rename(date_vaccine = date_vaccine_administered) %>%
-        select(province, date_vaccine, cumulative_avaccine),
-      by = c("province", "date_vaccine")
-    ) %>%
-      replace_na(list(cumulative_avaccine = 0,
-                      cumulative_cvaccine = 0)) # 2020-12-13 is NA for avaccine
-    
-    ### collapse observations into one row per date
-    dat %>%
-      ### merge short names
-      left_join(map_prov %>% select(province, province_short, pop),
-                by = c("province")) %>% 
-      select(date_vaccine, pop, cumulative_cvaccine, province_short) %>%
-      group_by(date_vaccine, province_short) %>%
-      summarize(across(everything(), sum), .groups = "drop") %>% 
-      mutate(
-        full_vaxx = 100 * cumulative_cvaccine / pop,
-        lab_percent = paste0(formatC(full_vaxx, format = "f", digit = 2), "%")
-      ) %>% 
-      ungroup() %>% 
-      group_by(province_short) %>% 
-      filter(date_vaccine == max(date_vaccine))
-    
-  })
-  
-  ## percent fully vaccinated title
-  output$title_fully_vaccinated <- renderText({
-    
-    ### don't run without inputs defined
-    req(input$prov)
-    
-    if (input$prov != "all") {
-      paste("Percent w/ 2 doses in", input$prov)
-    } else {
-      "Percent w/ 2 doses in Canada"
-    }
-    
-  })
-  
-  ## percent fully vaccinated plot
-  output$plot_fully_vaccinated <- renderPlotly({
-    
-    ### don't run without inputs defined
-    req(input$prov, input$date_range)
-    
-    ### get merged vaccine data
-    dat <- data_fully_vaccinated()
-    
-    ### plot data
-    dat %>%
-      plot_ly() %>%
-      add_trace(
-        x = ~ province_short,
-        y = ~ full_vaxx,
-        type = "bar",
-        color = ~ province_short,
-        colors = palette_province_short,
-        hoverinfo = "text",
-        hovertext = ~ paste0(
-          "Province: ", dat$province_short, "\n",
-          "2 doses", ": ", dat$cumulative_cvaccine, "\n",
-          "Percent of total population: ", dat$lab_percent
-        )
-      ) %>% 
-      layout(
-        xaxis = list(title = "Province", fixedrange = TRUE),
-        yaxis = list(title = "Percent", fixedrange = TRUE),
-        showlegend = FALSE
-      ) %>%
-      config(displaylogo = FALSE,
-             modeBarButtonsToRemove = plotly_buttons)
-    
-  })
-  
-  ## function: title cumulative (v2)
-  title_daily_cumulative_v2 <- function(fun_data, var_date, var_val, lab_title, input_scale, input_plot, exclude_repatriated = FALSE, by_province = FALSE, filter_province = input$prov) {
-    
-    ### don't run without inputs defined
-    req(input$prov, input$date_range)
-    
-    ### get data
-    dat <- fun_data %>%
-      ### remove repatriated cases from count (if applicable)
-      {if (exclude_repatriated) filter(., province != "Repatriated") else .}
-    
-    ### calculate n
-    n <- sum(dat[, var_val])
-    
-    if (input_scale == "per-capita") {
-      lab_title <- paste0(capitalize(lab_title), " per 100,000")
-    }
-    
-    ### render title
-    if (n == 0) {
-      
-      paste0(lab_title, " in ", filter_province)
-      
-    } else if (input_plot == "bar-graph") {
-      
-      if (input_scale == "per-capita") {
-        lab_title <- paste0("Average ", tolower(lab_title))
-      }
-      
-      if (filter_province == "Canada" & by_province) {
-        paste0(capitalize(lab_title), " in ", filter_province, " by province (n = ", format(n, big.mark = ","), ")")
-      } else {
-        paste0(capitalize(lab_title), " in ", filter_province, " (n = ", format(n, big.mark = ","), ")")
-      }
-
-    } else if (input_plot == "time-series") {
-      
-      if (filter_province == "Canada" & by_province) {
-        paste0("Cumulative ", tolower(lab_title), " in ", filter_province, " by province (n = ", format(n, big.mark = ","), ")")
-      } else {
-        paste0("Cumulative ", tolower(lab_title), " in ", filter_province, " (n = ", format(n, big.mark = ","), ")")
-      }
-      
-    }
-
-  }
-  
-  ## function: plot cumulative (v2)
-  plot_cumulative_v2 <- function(fun_data, var_date, var_val, 
-                                 input_scale, input_window, input_plot, 
-                                 lab_x, lab_y) {
-    
-    ### get data
-    dat <- fun_data
-    
-    ### choose y-axis label
-    if (input_scale == "per-capita") {
-      lab_y <- paste(lab_y, "per 100,000")
-    } else {
-      lab_y <- lab_y
-    }
-    
-    # input_plot accepts input$plot_type
-    if (input_plot == "bar-graph") {
-      
-      dat <- get_var_vaccine(fun_data, var_date, var_val, comparison_scale = input_scale)
-      
-      lab_y_var <- paste0("lab_", var_val)
-      
-      if (input_scale == "per-capita") {
-        y_var <- paste0(var_val, "_per_capita")
-      } else {
-        y_var <- var_val
-      }
-      
-      ### plot data
-      dat %>%
-        plot_ly() %>%
-        add_trace(
-          x = ~ province_short,
-          y = as.formula(paste("~", y_var)),
-          type = "bar",
-          color = ~ province_short,
-          colors = palette_province_short,
-          hoverinfo = "text",
-          hovertext = paste0(
-            dat$province_short, ": ", dat[[lab_y_var]]
-          )
-        ) %>% 
-        layout(
-          xaxis = list(title = "Province", fixedrange = TRUE),
-          yaxis = list(title = lab_y, fixedrange = TRUE),
-          showlegend = FALSE
-        ) %>%
-        config(displaylogo = FALSE,
-               modeBarButtonsToRemove = plotly_buttons)
-      
-    } else if (input_plot == "time-series") {
-      
-      if (input_scale == "per-capita") {
-        dat <- dat %>% 
-          group_by(province_short) %>% 
-          mutate(!!sym(var_val) := !!sym(var_val) / pop * 100000) %>% 
-          ungroup()
-      }
-      
-      ### transform data
-      dat <- dat %>%
-        filter(province != "Repatriated") %>%
-        ### create labels for values
-        mutate(lab_val = formatC(!!(sym(var_val)), big.mark = ","))
-      
-      ### plot data
-      dat %>%
-        plot_ly(
-          x = as.formula(paste("~", var_date)),
-          y = as.formula(paste("~", var_val)),
-          color = ~ province_short,
-          colors = palette_province_short
-        ) %>%
-        add_lines() %>%
-        layout(
-          xaxis = list(title = lab_x, fixedrange = TRUE),
-          yaxis = list(title = lab_y, fixedrange = TRUE),
-          hovermode = "x unified",
-          legend = plotly_legend
-        ) %>%
-        config(displaylogo = FALSE,
-               modeBarButtonsToRemove = plotly_buttons)
-      
-    } else if (input_plot == "comparisons") {
-      
-      # Run plot_comp
-      plot_comp(fun_data, var_val, input_window, input_scale, lab_y)
-      
-    } else {
-      
-      ### if no matching data, return blank plot
-      if (sum(dat[, var_val]) == 0) {
-        return(plot_no_data())
-      }
-    }
-  }
-  
-  ## doses administered by province (absolute/per-capita) data
-  get_var_vaccine <- function(fun_data, var_date, var_val, comparison_scale) {
-    
-    dat <- fun_data
-    
-    ### convert to per-capita (if applicable)
-    if (comparison_scale == "per-capita") {
-      
-      var_comp <- paste0(var_val, "_per_capita")
-      lab_var_comp <- paste0("lab_", var_val)
-      
-      dat %>% 
-        group_by(province) %>% 
-        ### convert to per-capita (if applicable)
-        filter(!!sym(var_date) == max(!!sym(var_date))) %>% 
-        ungroup() %>% 
-        dplyr::mutate(!!sym(var_comp) := !!sym(var_val) / pop * 100000) %>%
-        ### create labels for values
-        dplyr::mutate(!!sym(lab_var_comp) := formatC(!!sym(var_comp), digits = 2, format = "f", big.mark = ","))
-      
-    } else {
-      
-      var_comp <- var_val
-      lab_var_comp <- paste0("lab_", var_val)
-      
-      dat %>% 
-        group_by(province) %>% 
-        ### convert to per-capita (if applicable)
-        filter(!!sym(var_date) == max(!!sym(var_date))) %>% 
-        summarize(pop = max(pop), 
-                  !!sym(var_comp) := mean(!!sym(var_val)), .groups = "drop") %>% 
-        ### create labels for values
-        dplyr::mutate(!!sym(lab_var_comp) := formatC(!!sym(var_comp), digits = 2, format = "f", big.mark = ",")) %>% 
-        ### merge short names
-        left_join(map_prov %>% select(province, province_short), by = c("province"))
-      
-    }
-    
-  }
-  
-  data_avaccine_per_capita <- reactive({
-    get_var_vaccine(data_ts_vaccine_administration(), "date_vaccine_administered", "cumulative_avaccine", comparison_scale = input$scale_comp_avacc)
-  })
-  
-  ## doses administered by province (absolute/per-capita) title
-  output$title_avaccine_per_capita <- renderText({
-    
-    title_daily_cumulative_v2(data_ts_vaccine_administration(), "date_vaccine_administered", "avaccine", "doses administered", input_scale = input$scale_comp_avacc, input_plot = input$plot_type_avacc, exclude_repatriated = TRUE, by_province = TRUE)
-  
-  })
-  
-  ## doses administered by province (absolute/per-capita) plot
-  output$plot_avaccine_per_capita <- renderPlotly({
-    
-    plot_cumulative_v2(
-      data_ts_vaccine_administration(),
-      var_val = "cumulative_avaccine", 
-      var_date = "date_vaccine_administered",
-      input_scale = input$scale_comp_avacc, 
-      input_plot = input$plot_type_avacc, 
-      lab_x = "Province",
-      lab_y = "Cumulative doses administered"
-    )
-    
-  })
-  
-  ## doses distributed by province (absolute/per-capita) data
-  data_dvaccine_per_capita <- reactive({
-    get_var_vaccine(data_ts_vaccine_distribution(), "date_vaccine_distributed", "cumulative_dvaccine", input$scale_comp_dvacc)
-  })
-  
-  ## doses distributed by province (absolute/per-capita) title
-  output$title_dvaccine_per_capita <- renderText({
-    
-      title_daily_cumulative_v2(data_ts_vaccine_distribution(), "date_vaccine_distributed", "dvaccine", "doses distributed", input_scale = input$scale_comp_dvacc, input_plot = input$plot_type_dvacc, exclude_repatriated = TRUE, by_province = TRUE)
-    
-  })
-  
-  ## doses distributed by province (absolute/per-capita) plot
-  output$plot_dvaccine_per_capita <- renderPlotly({
-    
-    plot_cumulative_v2(
-      data_ts_vaccine_distribution(),
-      var_val = "cumulative_dvaccine", 
-      var_date = "date_vaccine_distributed",
-      input_scale = input$scale_comp_dvacc, 
-      input_plot = input$plot_type_dvacc, 
-      lab_x = "Province",
-      lab_y = "Cumulative doses distributed"
-    )
-    
-  })
-  
-  # summary table for time to percent vaccinated by province
-  output$table_prov_time_to_pct_vaccination <- renderDT({
-    
-    # req(input$prov, input$date_range, input$pct_vaccination)
-    req(input$prov, input$date_range)
-    
-    ## create dataframe by province
-    table_time_to_pct_vaccination <- data_ts_vaccine_administration() %>% 
-      select(date_vaccine_administered, avaccine, cumulative_avaccine, province) %>%
-      dplyr::group_by(date_vaccine_administered, province) %>% 
-      dplyr::summarize(
-        avaccine = sum(avaccine),
-        cumulative_avaccine = sum(cumulative_avaccine),
-        .groups = "drop") %>%
-      ungroup() %>% 
-      mutate(province = as.factor(province)) %>% 
-      group_by(province) %>% 
-      dplyr::mutate(roll_avg = rollapply(avaccine, 7, mean, align = "right", partial = TRUE)) %>% 
-      arrange(date_vaccine_administered) %>% 
-      dplyr::mutate(
-        current_cum = last(cumulative_avaccine)
-      ) %>% 
-      filter(date_vaccine_administered == max(date_vaccine_administered)) %>% 
-      ungroup() %>% 
-      left_join(map_prov, by = "province") %>% 
-      # dplyr::mutate(
-        # total_needed = (input$pct_vaccination/100) * 2 * pop,
-        # total_remaining = total_needed - current_cum,
-        # date_to_vaxx = max(date_vaccine_administered) + round((total_remaining) / roll_avg, 0),
-        # days_to_vaxx = date_to_vaxx - date_vaccine_administered,
-        # weeks_to_vaxx = as.numeric(days_to_vaxx) / 7,
-        # mths_to_vaxx = as.numeric(days_to_vaxx) / 12
-      # ) %>% 
-      mutate_if(is.numeric, round, 1) %>%
-      select(province, pop, current_cum, roll_avg) %>% #total_remaining, date_to_vaxx) %>% 
-      dplyr::rename(
-        "Province" = province,
-        "Population" = pop,
-        "Total doses administered" = current_cum,
-        "7-day rolling average of doses administered" = roll_avg)
-        # "Remaining doses needed<sup> a</sup>" = total_remaining,
-        # !!paste0("Expected date to reach ", input$pct_vaccination, "% w/ 2 doses", "<sup> b</sup>") := date_to_vaxx)
-    
-    table_time_to_pct_vaccination %>%
-      DT::datatable(class = "stripe compact hover", 
-                    rownames = FALSE, 
-                    extensions = "FixedColumns",
-                    escape = FALSE,
-                    # caption = tags$caption(
-                      # style = "caption-side: bottom; text-align: left; margin: 8px 0;",
-                      # p(tags$sup("a "), paste0("Assuming ", input$pct_vaccination, "% of the population receives 2 vaccine doses.")),
-                      # p(tags$sup("b "), "The expected date column is calculated based on the 7-day average rate of daily vaccine doses administered and assumes that every individual receives 2 doses. This calculation does not account for delays between doses.")
-                    # ),
-                    options = list(
-                      dom = "t",
-                      paging = FALSE,
-                      searching = FALSE,
-                      scrollX = FALSE,
-                      compact = TRUE,
-                      autoWidth = TRUE,
-                      ### centre all but the first column
-                      columnDefs = list(list(className = 'dt-center', targets = 1:(ncol(.) - 1))),
-                      ### freeze province column
-                      fixedColumns = list(leftColumns = 1:(ncol(.) - 1))
-                    )) %>%
-      # formatRound(columns = c("Population", "Total doses administered", "Remaining doses needed<sup> a</sup>"), digits = 0) %>%
-      formatRound(columns = c("Population", "Total doses administered"), digits = 0) %>%
-      formatRound(columns = "7-day rolling average of doses administered", digits = 1)
-    
+  shiny::onStop(function() {
+    Sys.setFileTime("app.R", lubridate::now())
   })
   
 }
diff --git a/tabs/tab_about.R b/tabs/tab_about.R
deleted file mode 100644
index fb4c911..0000000
--- a/tabs/tab_about.R
+++ /dev/null
@@ -1,6 +0,0 @@
-tab_about <- tabItem(tabName = "tab_about",
-                     fluidPage(
-                       title = "About the data",
-                       width = 12,
-                       includeHTML("text/about.html")
-                     ))
\ No newline at end of file
diff --git a/tabs/tab_cases.R b/tabs/tab_cases.R
deleted file mode 100644
index b9341a6..0000000
--- a/tabs/tab_cases.R
+++ /dev/null
@@ -1,38 +0,0 @@
-tab_cases <- tabItem(tabName = "tab_cases",
-                     fluidPage(tabsetPanel(
-                       type = "tabs",
-                       tabPanel("Daily reported cases",
-                                fluidRow(box(
-                                  title = textOutput("title_daily_cases"),
-                                  width = 12,
-                                  plotlyOutput("plot_daily_cases")
-                                ))),
-                       tabPanel("Cumulative reported cases",
-                                fluidRow(box(
-                                  title = textOutput("title_cumulative_cases"),
-                                  width = 10,
-                                  plotlyOutput("plot_cumulative_cases")
-                                ),
-                                box(
-                                  title = "More options",
-                                  width = 2,
-                                  radioButtons(
-                                    "plot_type_cases",
-                                    "Plot type",
-                                    choices = c("Bar" = "bar-graph", "Line" = "time-series"),
-                                    selected = "time-series"
-                                  ),
-                                  radioButtons(
-                                    "scale_cases",
-                                    "Absolute/per-capita",
-                                    choices = c("Absolute" = "absolute", "Per-capita" = "per-capita"),
-                                    selected = "absolute"
-                                  )
-                                ))),
-                       tabPanel("Pie chart",
-                                fluidRow(box(
-                                  title = textOutput("title_pie_cases"),
-                                  width = 12,
-                                  plotlyOutput("plot_pie_cases")
-                                )))
-                     )))
\ No newline at end of file
diff --git a/tabs/tab_comparisons.R b/tabs/tab_comparisons.R
deleted file mode 100644
index 7c1f6db..0000000
--- a/tabs/tab_comparisons.R
+++ /dev/null
@@ -1,88 +0,0 @@
-tab_comparisons <- tabItem(tabName = "tab_comparisons",
-                           fluidPage(
-                             p(HTML("<center><b><h1 style='font-size:27px'>Provincial Comparisons</h1></b></center><br>The <b>Comparisons</b> tab is intended to provide recent average <b>daily</b> values for cases, deaths, and testing by province. These values are available as either raw numbers (absolute scale) or adjusted for population (per-capita scale). For example, the 7-day average for daily per-capita cases indicates the average number of new cases reported <b>each day</b> in a province over the past 7 days.</p>")),
-                             fluidRow(
-                               HTML("<br>"), # blank line
-                               p(HTML(
-                                 paste0("<center><b><h1 style='font-size:27px'>COVID-19 Cases</h1></b></center>"))),
-                               box(
-                                 title = textOutput("title_comp_cases"),
-                                 width = 9,
-                                 plotlyOutput("plot_comp_cases")
-                               ),
-                               box(
-                                 title = "More options",
-                                 width = 3,
-                                 radioButtons(
-                                   "scale_comp_cases",
-                                   "Absolute/per-capita",
-                                   choices = c("Absolute" = "absolute", "Per-capita" = "per-capita"),
-                                   selected = "per-capita"
-                                 ),
-                                 radioButtons(
-                                   "window_comp_cases",
-                                   "Window",
-                                   choices = c("1 day" = 1, "7 days" = 7, "14 days" = 14),
-                                   selected = 7
-                                 ),
-                                 downloadButton("download_comp_cases", "Download")
-                               )
-                             ),
-                           fluidRow(
-                             HTML("<br>"), # blank line
-                             p(HTML(
-                               paste0("<center><b><h1 style='font-size:27px'>COVID-19 Mortality</h1></b></center>"))),
-                             
-                             box(
-                               title = textOutput("title_comp_mortality"),
-                               width = 9,
-                               plotlyOutput("plot_comp_mortality")
-                             ),
-                             box(
-                               title = "More options",
-                               width = 3,
-                               radioButtons(
-                                 "scale_comp_mortality",
-                                 "Absolute/per-capita",
-                                 choices = c("Absolute" = "absolute", "Per-capita" = "per-capita"),
-                                 selected = "per-capita"
-                               ),
-                               radioButtons(
-                                 "window_comp_mortality",
-                                 "Window",
-                                 choices = c("1 day" = 1, "7 days" = 7, "14 days" = 14),
-                                 selected = 7
-                               ),
-                               downloadButton("download_comp_mortality", "Download")
-                             )
-                           ),
-                           fluidRow(
-                             HTML("<br>"), # blank line
-                             p(HTML(
-                               paste0("<center><b><h1 style='font-size:27px'>COVID-19 Testing</h1></b></center>"))),
-                             
-                             box(
-                               title = textOutput("title_comp_testing"),
-                               width = 9,
-                               plotlyOutput("plot_comp_testing")
-                             ),
-                             box(
-                               title = "More options",
-                               width = 3,
-                               radioButtons(
-                                 "scale_comp_testing",
-                                 "Absolute/per-capita",
-                                 choices = c("Absolute" = "absolute", "Per-capita" = "per-capita"),
-                                 selected = "per-capita"
-                               ),
-                               radioButtons(
-                                 "window_comp_testing",
-                                 "Window",
-                                 choices = c("1 day" = 1, "7 days" = 7, "14 days" = 14),
-                                 selected = 7
-                               ),
-                               downloadButton("download_comp_testing", "Download")
-                             )
-                           )
-))
-                           
\ No newline at end of file
diff --git a/tabs/tab_maps.R b/tabs/tab_maps.R
deleted file mode 100644
index 7e93828..0000000
--- a/tabs/tab_maps.R
+++ /dev/null
@@ -1,42 +0,0 @@
-tab_maps <- tabItem(tabName = "tab_maps",
-                    fluidPage(
-                               p(HTML(
-                                 paste0("<center><b><h1 style='font-size:27px'>Map of COVID-19 in Canada</h1></b></center>
-                                        <br>The map provides data on both COVID-19 cases and deaths specific to local health regions, 
-                                        and are available as either raw numbers (absolute scale) or adjusted for population (per-capita scale).
-                                        These data can be filtered using the date range selector in the side menu. The bins are based on quintiles.</p><br>"))),
-                               
-                      fluidRow(column(
-                        width = 4,
-                        offset = 4,
-                        align = "center",
-                        radioButtons(
-                          "metric_choropleth_hr",
-                          NULL,
-                          choices = c("Cases" = "cases",
-                                      "Deaths" = "mortality"),
-                          inline = TRUE,
-                          selected = "cases"
-                        )
-                      )),
-                      fluidRow(column(
-                        width = 4,
-                        offset = 4,
-                        align = "center",
-                        radioButtons(
-                          "scale_choropleth_hr",
-                          NULL,
-                          choices = c("Absolute" = "absolute",
-                                      "Per-capita" = "per-capita"),
-                          inline = TRUE,
-                          selected = "absolute"
-                        )
-                      )),
-                      fluidRow(box(
-                        title = textOutput("title_choropleth_hr"),
-                        width = 12,
-                        leafletOutput("choropleth_hr", height = 700)
-                      )),
-                      includeHTML("text/sk_map.html"),
-                      uiOutput("text_choropleth_hr")
-                    ))
\ No newline at end of file
diff --git a/tabs/tab_mortality.R b/tabs/tab_mortality.R
deleted file mode 100644
index 0bcd757..0000000
--- a/tabs/tab_mortality.R
+++ /dev/null
@@ -1,38 +0,0 @@
-tab_mortality <- tabItem(tabName = "tab_mortality",
-                         fluidPage(tabsetPanel(
-                           type = "tabs",
-                           tabPanel("Daily reported deaths",
-                                    fluidRow(box(
-                                      title = textOutput("title_daily_mortality"),
-                                      width = 12,
-                                      plotlyOutput("plot_daily_mortality")
-                                    ))),
-                           tabPanel("Cumulative reported deaths",
-                                    fluidRow(box(
-                                      title = textOutput("title_cumulative_mortality"),
-                                      width = 10,
-                                      plotlyOutput("plot_cumulative_mortality")
-                                    ),
-                                    box(
-                                      title = "More options",
-                                      width = 2,
-                                      radioButtons(
-                                        "plot_type_deaths",
-                                        "Plot type",
-                                        choices = c("Bar" = "bar-graph", "Line" = "time-series"),
-                                        selected = "time-series"
-                                      ),
-                                      radioButtons(
-                                        "scale_deaths",
-                                        "Absolute/per-capita",
-                                        choices = c("Absolute" = "absolute", "Per-capita" = "per-capita"),
-                                        selected = "absolute"
-                                      )
-                                    ))),
-                           tabPanel("Pie chart",
-                                    fluidRow(box(
-                                      title = textOutput("title_pie_mortality"),
-                                      width = 12,
-                                      plotlyOutput("plot_pie_mortality")
-                                    )))
-                         )))
\ No newline at end of file
diff --git a/tabs/tab_overview.R b/tabs/tab_overview.R
deleted file mode 100644
index e9c918b..0000000
--- a/tabs/tab_overview.R
+++ /dev/null
@@ -1,139 +0,0 @@
-tab_overview <- tabItem(tabName = "tab_overview",
-                        fluidPage(
-                              fluidRow(
-                                column(
-                                  width = 8,
-                                  offset = 2,
-                                  valueBoxOutput("value_box_summary_cases", width = 3),
-                                  valueBoxOutput("value_box_summary_active", width = 3),
-                                  valueBoxOutput("value_box_summary_recovered", width = 3),
-                                  valueBoxOutput("value_box_summary_mortality", width = 3),
-                                )
-                              ),
-                              fluidRow(
-                                column(
-                                  width = 8,
-                                  offset = 2,
-                                  valueBoxOutput("value_box_summary_doses_administered", width = 3),
-                                  valueBoxOutput("value_box_summary_fully_vaccinated", width = 3),
-                                  valueBoxOutput("value_box_summary_hosp", width = 3),
-                                  valueBoxOutput("value_box_summary_testing", width = 3)
-                                )
-                              ),
-                              p(HTML("<br><br>")),
-                              p(HTML(
-                                paste0("<b><h1 style='font-size:18px'>Last updated: ", update_time, "</h1></b>"))),
-                              fluidRow(box(HTML(gsub("\n", "<br>", data_notes)), title = "Daily update notes (click + to expand)", width = 12, collapsible = TRUE, collapsed = TRUE)),
-                              p(
-                                "Reported cases are cumulative and include both confirmed and presumptive positive cases. Repatriated cases are included in the total but not shown in the map below."
-                              ),
-                              HTML("<br>"),
-                              p(HTML(
-                                paste0("<center><b><h1 style='font-size:27px'>Summary of COVID-19 in Canada</h1></b></center>"))),
-                              tags$head(
-                                tags$style(".nav-tabs {font-size: 16px; font-weight: bold; font-color: black}")),
-                              tabsetPanel(
-                                tabPanel(
-                                  "EpiView",
-                                  tabsetPanel(
-                                    type = "tabs",
-                                    tabPanel(
-                                      "Cases",
-                                      uiOutput("ui_plot_choropleth_overview_cases"),
-                                      uiOutput("ui_window_choropleth_overview_cases"),
-                                    ),
-                                    tabPanel(
-                                      "Mortality",
-                                      uiOutput("ui_plot_choropleth_overview_deaths"),
-                                      uiOutput("ui_window_choropleth_overview_deaths"),
-                                    ),
-                                    tabPanel(
-                                      "Recovered",
-                                      uiOutput("ui_plot_choropleth_overview_recovered"),
-                                      uiOutput("ui_window_choropleth_overview_recovered"),
-                                    )
-                                  )
-                                ),
-                                tabPanel(
-                                  "VaxView",
-                                  tabsetPanel(
-                                    type = "tabs",
-                                    tabPanel(
-                                      "% 2 doses",
-                                      uiOutput("ui_plot_choropleth_overview_vaccine_full_pct"),
-                                      uiOutput("ui_window_choropleth_overview_vaccine_full_pct"),
-                                    ),
-                                    # tabPanel(
-                                    #   "% At least one dose",
-                                    #   uiOutput("ui_plot_choropleth_overview_vaccine_at_least_one_dose"),
-                                    #   uiOutput("ui_window_choropleth_overview_vaccine_at_least_one_dose"),
-                                    # ),
-                                    tabPanel(
-                                      "Administered",
-                                      uiOutput("ui_plot_choropleth_overview_vaccine_administration"),
-                                      uiOutput("ui_window_choropleth_overview_vaccine_administration"),
-                                    ),
-                                    tabPanel(
-                                      "Distributed",
-                                      uiOutput("ui_plot_choropleth_overview_vaccine_distribution"),
-                                      uiOutput("ui_window_choropleth_overview_vaccine_distribution"),
-                                    ),
-                                    tabPanel(
-                                      "% Administered of total distributed",
-                                      uiOutput("ui_plot_choropleth_overview_vaccine_admin_pct"),
-                                      uiOutput("ui_window_choropleth_overview_vaccine_admin_pct"),
-                                    )
-                                  ))),
-                              HTML("<br>"), # blank line
-                              p(HTML(
-                                paste0("<center><b><h1 style='font-size:27px'>Daily COVID-19 Trends</h1></b></center>"))),
-                                tabsetPanel(
-                                  type = "tabs",
-                                  tabPanel(
-                                    "Cases",
-                                    fluidRow(box(
-                                      title = textOutput("title_flattening_cases"),
-                                      plotlyOutput("plot_flattening_cases"),
-                                      width = 12))
-                                  ),
-                                  tabPanel(
-                                    "Mortality",
-                                    fluidRow(box(
-                                      title = textOutput("title_flattening_mortality"),
-                                      plotlyOutput("plot_flattening_mortality"),
-                                      width = 12))
-                                  ),
-                                  tabPanel(
-                                    "Daily doses administered",
-                                    fluidRow(box(
-                                      title = textOutput("title_flattening_avaccine"),
-                                      plotlyOutput("plot_flattening_avaccine"),
-                                      width = 12))
-                                )
-                                )
-                              ),
-                              box(
-                                title = NULL,
-                                width = 12,
-                                align = "center",
-                                radioButtons(
-                                  "scale_flattening",
-                                  "Scale",
-                                  choices = c("Linear" = "linear", "Logarithmic" = "logarithmic"),
-                                  selected = "linear",
-                                  inline = TRUE
-                                ),
-                                radioButtons(
-                                  "scale_trends",
-                                  "Absolute/per-capita",
-                                  choices = c("Absolute" = "absolute", "Per-capita" = "per-capita"),
-                                  selected = "absolute"
-                                )),
-                              HTML("<center>These charts may be viewed on a <b>linear scale</b> (the default) or a <b>logarithmic scale</b>. The choice of scale changes the interpretation of the plot. Select the scale using the button above. Additionally, the time range of data displayed may be changed using the range slider below the plot. By default, the entire range of data is shown.</center>"),
-                              tags$p(""), # blank line
-                              htmlOutput({"text_flattening"}),
-                              tags$p(""), # blank line
-                              HTML(paste0("<br><center><b><h1 style='font-size:27px'>Metrics by Province/Territory</h1></b></center>")),
-                        fluidRow(DTOutput("table_prov_overview")),
-                        "RP = repatriated travellers."
-                        )
diff --git a/tabs/tab_recovered.R b/tabs/tab_recovered.R
deleted file mode 100644
index 4685f59..0000000
--- a/tabs/tab_recovered.R
+++ /dev/null
@@ -1,36 +0,0 @@
-tab_recovered <- tabItem(tabName = "tab_recovered",
-                         fluidPage(tabsetPanel(
-                           type = "tabs",
-                           tabPanel("Daily recovered",
-                                    fluidRow(box(
-                                      title = textOutput("title_daily_recovered"),
-                                      width = 12,
-                                      plotlyOutput("plot_daily_recovered")
-                                    )),
-                                    HTML("On July 17, Quebec <a href='https://opencovid.ca/work/data-faq/#6' target='_blank'> revised their definition of recovered</a>, causing a massive spike on that day. We are reviewing the feasibility of retroactively editing this time series.")
-                                    ),
-                           tabPanel("Cumulative recovered",
-                                    fluidRow(box(
-                                      title = textOutput("title_cumulative_recovered"),
-                                      width = 10,
-                                      plotlyOutput("plot_cumulative_recovered")
-                                    ),
-                                    box(
-                                      title = "More options",
-                                      width = 2,
-                                      radioButtons(
-                                        "plot_type_recovered",
-                                        "Plot type",
-                                        choices = c("Bar" = "bar-graph", "Line" = "time-series"),
-                                        selected = "time-series"
-                                      ),
-                                      radioButtons(
-                                        "scale_recovered",
-                                        "Absolute/per-capita",
-                                        choices = c("Absolute" = "absolute", "Per-capita" = "per-capita"),
-                                        selected = "absolute"
-                                      )
-                                    )),
-                                    HTML("On July 17, Quebec <a href='https://opencovid.ca/work/data-faq/#6' target='_blank'> revised their definition of recovered</a>, causing a massive spike on that day. We are reviewing the feasibility of retroactively editing this time series.")
-                           )
-                         )))
\ No newline at end of file
diff --git a/tabs/tab_testing.R b/tabs/tab_testing.R
deleted file mode 100644
index 9edfb77..0000000
--- a/tabs/tab_testing.R
+++ /dev/null
@@ -1,39 +0,0 @@
-tab_testing <- tabItem(tabName = "tab_testing",
-                       fluidPage(
-                         
-                         tabsetPanel(
-                         type = "tabs",
-                         tabPanel("Daily testing",
-                                  fluidRow(box(
-                                    title = textOutput("title_daily_testing"),
-                                    width = 12,
-                                    plotlyOutput("plot_daily_testing")
-                                  ))
-                                  ),
-                         tabPanel("Cumulative testing",
-                                  fluidRow(box(
-                                    title = textOutput("title_cumulative_testing"),
-                                    width = 10,
-                                    plotlyOutput("plot_cumulative_testing")
-                                  ),
-                                  box(
-                                    title = "More options",
-                                    width = 2,
-                                    radioButtons(
-                                      "plot_type_testing",
-                                      "Plot type",
-                                      choices = c("Bar" = "bar-graph", "Line" = "time-series"),
-                                      selected = "time-series"
-                                    ),
-                                    radioButtons(
-                                      "scale_testing",
-                                      "Absolute/per-capita",
-                                      choices = c("Absolute" = "absolute", "Per-capita" = "per-capita"),
-                                      selected = "absolute"
-                                    )
-                                  ))),
-                         p("Provinces occasionally report negative cumulative testing numbers. We aim to reconcile these inconsistencies in future data updates, if possible."),
-                         fluidRow(
-                           HTML(paste0("<br><center><b><h1 style='font-size:27px'>Specific Testing Metrics Used by Province/Territory</h1></b></center>")),
-                           DTOutput("table_info_testing"))
-                       )))
\ No newline at end of file
diff --git a/tabs/tab_trends.R b/tabs/tab_trends.R
deleted file mode 100644
index a30d349..0000000
--- a/tabs/tab_trends.R
+++ /dev/null
@@ -1,2 +0,0 @@
-tab_trends <- tabItem(tabName = "tab_trends",
-                      fluidPage())
\ No newline at end of file
diff --git a/tabs/tab_vaccines.R b/tabs/tab_vaccines.R
deleted file mode 100644
index 60e411e..0000000
--- a/tabs/tab_vaccines.R
+++ /dev/null
@@ -1,122 +0,0 @@
-tab_vaccines <- tabItem(tabName = "tab_vaccines",
-                        fluidPage(
-                          p(HTML(
-                            paste0("<center><b><h1 style='font-size:27px'>Vaccines Administered/ Distributed</h1></b></center>
-                                        <br></p>"))),
-                          HTML("<br>"),
-                          tabsetPanel(
-                            type = "tabs",
-                            tabPanel("Daily vaccine doses administered",
-                                     fluidRow(box(
-                                       title = textOutput("title_daily_vaccine_administration"),
-                                       width = 12,
-                                       plotlyOutput("plot_daily_vaccine_administration")
-                                     ))
-                            ),
-                            tabPanel("Cumulative vaccine doses administered",
-                                     fluidRow(
-                                       box(
-                                         title = textOutput("title_avaccine_per_capita"),
-                                         width = 10,
-                                         plotlyOutput("plot_avaccine_per_capita")
-                                       ),
-                                       box(
-                                         title = "More options",
-                                         width = 2,
-                                         radioButtons(
-                                           "plot_type_avacc",
-                                           "Plot type",
-                                           choices = c("Bar" = "bar-graph", "Line" = "time-series"),
-                                           selected = "time-series"
-                                         ),
-                                         radioButtons(
-                                           "scale_comp_avacc",
-                                           "Absolute/per-capita",
-                                           choices = c("Absolute" = "absolute", "Per-capita" = "per-capita"),
-                                           selected = "absolute"
-                                         )
-                                       )
-                                     ),
-                            ),
-                            tabPanel("Cumulative vaccine doses distributed",
-                                     fluidRow(
-                                       box(
-                                         title = textOutput("title_dvaccine_per_capita"),
-                                         width = 10,
-                                         plotlyOutput("plot_dvaccine_per_capita")
-                                       ),
-                                       box(
-                                         title = "More options",
-                                         width = 2,
-                                         radioButtons(
-                                           "plot_type_dvacc",
-                                           "Plot type",
-                                           choices = c("Bar" = "bar-graph", "Line" = "time-series"),
-                                           selected = "time-series"
-                                         ),
-                                         radioButtons(
-                                           "scale_comp_dvacc",
-                                           "Absolute/per-capita",
-                                           choices = c("Absolute" = "absolute", "Per-capita" = "per-capita"),
-                                           selected = "absolute"
-                                         )
-                                       )
-                                     ),
-                            )
-                          ),
-                          HTML("<br>"), # blank line
-                          p(HTML(
-                            paste0("<center><b><h1 style='font-size:27px'>Vaccine Gap (Administered versus Distributed)</h1></b></center></p><br>
-                                  <b>By default, the vaccine gap plot shows smoothed values of the vaccine distribution time series to account for 
-                                  reporting delays in the distribution numbes. This can be disabled by clicking the \"Smoothed distributed\" button.</b><br><br>")))),
-                        fluidRow(box(
-                          title = textOutput("title_vaccine_gap"),
-                          width = 12,
-                          plotlyOutput("plot_vaccine_gap")
-                        )
-                        ),
-                        # HTML("<br>"),
-                        # p(HTML(
-                        #   paste0("<center><b><h1 style='font-size:27px'>Percent Vaccinated</h1></b></center>"))),
-                        #   tabsetPanel(
-                        #     type = "tabs",
-                        #     tabPanel("Percent at least one dose",
-                        #              fluidRow(box(
-                        #                title = textOutput("title_at_least_one_dose"),
-                        #                width = 12,
-                        #                plotlyOutput("plot_at_least_one_dose")
-                        #              )
-                        #              )
-                        #     ),
-                        #     tabPanel("Percent fully vaccinated",
-                        #              fluidRow(box(
-                        #                title = textOutput("title_fully_vaccinated"),
-                        #                width = 12,
-                        #                plotlyOutput("plot_fully_vaccinated")
-                        #              )
-                        #              )
-                        #     )
-                        #   ),
-                        #   HTML("Note that it is possible for doses administered to temporarily exceed doses distributed for two reasons: 1) Doses distributed is updated less frequently than doses administered and 2) <a href='https://www.saskatchewan.ca/government/news-and-media/2021/february/02/covid19-update-for-february-2-35575-vaccines-delivered-223-new-cases-266-recoveries-eight-deaths' target='_blank'>extra doses can sometimes be obtained beyond the labelled amount</a>.<br>"),
-                        #   HTML("<br>"), # blank line
-                        #   p(HTML(
-                        #     paste0("<center><b><h1 style='font-size:27px'>Time to Percent Fully Vaccinated</h1></b></center></p>"))),
-                        #       HTML("<br>"), # a little whitespace
-                        #       fluidRow(
-                        #         column(
-                        #           sliderInput(
-                        #             "pct_vaccination",
-                        #             "Specify desired level of % vaccination:",
-                        #             min = 30,
-                        #             max = 100,
-                        #             step = 1,
-                        #             value = 70
-                        #           ),
-                        #           width = 12,
-                        #           align = "center"
-                        #         )
-                        #       ),
-                              fluidRow(
-                                div(style="overflow-x:auto", DTOutput("table_prov_time_to_pct_vaccination")) # scrollX=TRUE is buggy with when table is updated with slider
-                                )
-                        )
\ No newline at end of file
diff --git a/text/about.html b/text/about.html
deleted file mode 100644
index 2b2d0f8..0000000
--- a/text/about.html
+++ /dev/null
@@ -1,33 +0,0 @@
-
-<p>The <b>COVID-19 Canada Open Data Working Group</b> collects information on confirmed and presumptive positive cases of COVID-19 in Canada. Our datasets are updated nightly. All data are exclusively collected from publicly available sources including government reports, government websites and news media.</p>
-
-<b><h1 style='font-size:18px'>Raw Data</h1></b>
-
-<p>Raw data can be downloaded from <a href='https://github.com/ishaberry/Covid19Canada' target='_blank'> GitHub</a>. Recommended citation for the dataset: Berry I, Soucy J-PR, Tuite A, Fisman D. Open access epidemiologic data and an interactive dashboard to monitor the COVID-19 outbreak in Canada. CMAJ. 2020 Apr 14;192(15):E420. doi: <a href='https://doi.org/10.1503/cmaj.75262' target='_blank'> https://doi.org/10.1503/cmaj.75262</a>.</p>
-
-
-<b><h1 style='font-size:18px'>Interpreting Maps</h1></b>
-
-<p>The case/mortality maps show case and mortaltiy absolute or per capita counts by health region. Darker colours indicate a higher number of cases/mortality in that health region than ligher colours. If health region is not reported for a case/mortality, it is not included in the map but is detailed at the bottom of the page in text.</p>
-
-<b><h1 style='font-size:18px'>Data Notes</h1></b>
-
-<p>Detailed information about our <a href='https://opencovid.ca/work/dataset/' target='_blank'> data collection methodology</a> and <a href='https://opencovid.ca/work/data-sources/' target='_blank'> sources</a>, <a href='https://opencovid.ca/work/data-faq/' target='_blank'> answers to frequently asked questions</a>, specific data notes and more information about the <b>COVID-19 Canada Open Data Working Group</b> is available on our <a href='https://opencovid.ca/' target='_blank'> website</a>.</p>
-
-<b><h1 style='font-size:18px'>Vaccine Data</h1></b>
-
-<p>Sources for our vaccine data can be found <a href='https://docs.google.com/spreadsheets/d/1zebsxvOPw8gJ-38r9Wbs_tY0Sk5lvfr0khun9_p3gmY/htmlview' target='_blank'>here</a>.</p>
-
-<b><h1 style='font-size:18px'>Additional Data Sources Used on This Dashboard</h1></b><br>
-
-<ul>
-    <li><p>Hospitalization data from Noah Little <a href='https://covid19tracker.ca' target='_blank'> (COVID-19 Tracker Canada)</a> <a href='https://creativecommons.org/licenses/by/4.0/' target='_blank'> (CC BY 4.0)</a>.</p>
-    <li><p>Provincial and health region population data from <a href='https://www150.statcan.gc.ca/t1/tbl1/en/cv.action?pid=1710013401' target='_blank'> Statistics Canada</a> 2019 estimates.</p>
-    <li><p>The new Saskatchewan health regions (introduced 2020-08-04) use <a href='https://www150.statcan.gc.ca/t1/tbl1/en/cv.action?pid=1710013401' target='_blank'> unofficial estimates</a> of 2020 population values provided by Statistics Canada and may differ from official data released by Statistics Canada at a later date.</p>
-    <li><p>Health region map from <a href='https://resources-covid19canada.hub.arcgis.com/datasets/regionalhealthboundaries-1' target='_blank'> Esri Canada</a>. Health region estimates have been adjusted to the most recent health region boundaries in the map above.</p>
-</ul>
-
-<b><h1 style='font-size:18px'>Additional Questions or Comments</h1></b>
-
-
-<p>If you have any additional questions and/or comments, or find any errors, please do not hesitate to <a href='https://opencovid.ca/contact-us/' target='_blank'> contact us</a>.</p>
diff --git a/text/alert_text.html b/text/alert_text.html
deleted file mode 100644
index 7275282..0000000
--- a/text/alert_text.html
+++ /dev/null
@@ -1 +0,0 @@
-<b>Updates to the dashboard are suspended temporarily as we transition to our new dataset but will return shortly.</b> For more information on our new dataset, see: <a href="https://github.com/ccodwg/CovidTimelineCanada" target="_blank">https://github.com/ccodwg/CovidTimelineCanada</a>
diff --git a/text/alert_title.html b/text/alert_title.html
deleted file mode 100644
index 5648255..0000000
--- a/text/alert_title.html
+++ /dev/null
@@ -1 +0,0 @@
-Important message
diff --git a/text/info_testing.csv b/text/info_testing.csv
deleted file mode 100644
index 02c6324..0000000
--- a/text/info_testing.csv
+++ /dev/null
@@ -1,14 +0,0 @@
-"province","unit","definition","source","notes"
-"AB","Tests","Total tests completed","https://www.alberta.ca/coronavirus-info-for-albertans.aspx"," "
-"BC","Tests","Total tests completed","https://experience.arcgis.com/experience/a6f23959a8b14bfa989e3cda29297ded"," "
-"MB","Tests","Total tests completed","https://experience.arcgis.com/experience/f55693e56018406ebbd08b3492e99771"," "
-"NB","Tests","Total tests","https://experience.arcgis.com/experience/8eeb9a2052d641c996dba5de8f25a8aa"," "
-"NL","People","Total number of people tested","https://covid-19-newfoundland-and-labrador-gnl.hub.arcgis.com/"," "
-"NT","Tests","Total tests completed","https://www.gov.nt.ca/covid-19/"," "
-"NS","Tests","Total tests completed","https://novascotia.ca/coronavirus/data/","Total is calculated as sum of positive and negative tests."
-"NU","Tests","Total tests completed","https://health-infobase.canada.ca/covid-19/epidemiological-summary-covid-19-cases.html","Total tests completed from the Public Health Agency of Canada dataset."
-"ON","Tests","Total tests completed","https://www.ontario.ca/page/how-ontario-is-responding-covid-19#section-0","From March 15 to 30, total patients approved for testing (persons) is reported; From March 31 to April 14, total patients tested (persons) is reported; Beginning on April 15, tests completed (not persons) is reported."
-"PE","Tests","Total tests completed","https://www.princeedwardisland.ca/en/covid19","Sum of confirmed cases and negative tests."
-"QC","People","Number of people tested","https://www.inspq.qc.ca/covid-19/donnees",
-"SK","People","Number of patients with tests ordered","https://dashboard.saskatchewan.ca/health-wellness/covid-19/cases","From March 15 to 24, total tests completed (not persons) is reported; Beginning March 25, people tested (persons) is reported."
-"YT","People","Total people tested","https://yukon.ca/en/case-counts-covid-19","Sum of confirmed cases, negative results, and pending results."
diff --git a/text/sk_map.html b/text/sk_map.html
deleted file mode 100644
index de8d0e9..0000000
--- a/text/sk_map.html
+++ /dev/null
@@ -1 +0,0 @@
-Saskatchewan transitioned to using new health region (HR) boundaries on August 4, 2020, going from 6 HRs to 13. These new bounaries will be displayed on the map for date ranges beginning 2020-01-25 and ending on 2020-08-04 or later and date ranges beginning after 2020-08-03. Otherwise, the old HR boundaries will be used. Note that when displaying the old boundaries, values for dates after 2020-08-03 rely on case and death numbers that have been approximately mapped from the new HR boundaries to the old HR boundaries.<br><br>
diff --git a/ui.R b/ui.R
index 141b27a..7184ed3 100644
--- a/ui.R
+++ b/ui.R
@@ -1,145 +1,44 @@
-# load tabs
-invisible(sapply(list.files("tabs", full.names = TRUE), source))
-
-# JS for getting screen width
-js_get_width <-
-  '$(document).on("shiny:connected", function(e) {
-  var jsWidth = screen.width;
-  Shiny.onInputChange("screen_width", jsWidth);
-});
-'
+# load analytics (if available)
+analytics <- if (file.exists("google-analytics.html")) {
+  shiny::tags$head(shiny::includeHTML("google-analytics.html"))
+} else {}
 
 # ui
 ui <- function(req) {
   shinydashboardPlus::dashboardPage(
-    title = "COVID-19 in Canada",
-    header = dashboardHeader(title = "COVID-19 in Canada"),
-    sidebar = dashboardSidebar(
-      useShinyjs(),
-      sidebarMenu(
-        id = "tab",
-        uiOutput("sidebar_controls"),
-        menuItem("Overview",
-                 tabName = "tab_overview",
-                 icon = icon("table")),
-        # menuItem(
-        #   "Trends",
-        #   tabName = "tab_trends",
-        #   icon = icon("chart-line")
-        # ),
-        menuItem(
-          "Comparisons",
-          tabName = "tab_comparisons",
-          icon = icon("chart-bar")
-        ),
-        menuItem(
-          "Maps",
-          tabName = "tab_maps",
-          icon = icon("map-marked-alt")
-        ),
-        menuItem("Vaccines",
-                 tabName = "tab_vaccines",
-                 icon = icon("syringe")
-        ),
-        menuItem(
-          "Cases",
-          tabName = "tab_cases",
-          icon = icon("calendar-plus")
-        ),
-        menuItem(
-          "Mortality",
-          tabName = "tab_mortality",
-          icon = icon("heartbeat")
-        ),
-        menuItem(
-          "Recovered",
-          tabName = "tab_recovered",
-          icon = icon("file-medical")
-        ),
-        menuItem("Testing",
-                 tabName = "tab_testing",
-                 icon = icon("vial")
-        ),
-        menuItem(
-          "About the data",
-          tabName = "tab_about",
-          icon = icon("question-circle")
-        ),
-        tags$footer(
-          HTML(
-            paste0(
-              "<b>Please visit our <a href='https://opencovid.ca/work/data-faq/' target='_blank'> frequently<br/>asked questions</a> page</br>about our dataset for answers</br>to questions like this one:<br/></b>",
-              sapply(strwrap(faq, 30, simplify = FALSE), paste, collapse = "<br/>"),
-              "<b><br/><br/><a href='https://github.com/ishaberry/Covid19Canada' target='_blank'> Download our dataset</a><br/><br/>Last updated:<br/>",
-              update_time,
-              "</b><br/><br/>All data used to produce this<br/>dashboard are exclusively<br/>collected from publicly available<br/>sources including government<br/>reports and news media.<br/>"
-            )
-          ),
-          align = "left",
-          style = "
-              position:absolute;
-              width:100%;
-              height:0px;
-              color: white;
-              padding: 10px;
-              background-color: transparent;
-              word-wrap: break-word;
-              z-index: 1000;"
+    md = TRUE,
+    skin = "blue",
+    header = shinydashboardPlus::dashboardHeader(
+      title = "COVID-19 in Canada"
+    ),
+    sidebar = shinydashboardPlus::dashboardSidebar(
+      shinydashboard::sidebarMenu(
+        shinydashboard::menuItem(
+          "Navigation", startExpanded = TRUE, icon = shiny::icon("map", lib = "font-awesome"),
+          shinydashboard::menuSubItem(text = "Top of page", href = "#", newtab = FALSE)
         )
       )
     ),
-    body = dashboardBody(
-      meta() %>%
-        meta_social(
+    body = shinydashboard::dashboardBody(
+      metathis::meta() %>%
+        metathis::meta_social(
           title = "COVID-19 in Canada",
           description = "An interactive visualization of the ongoing epidemic of COVID-19 in Canada",
           url = "https://art-bd.shinyapps.io/covid19canada/",
           image = "https://source.unsplash.com/VhjsGKMefkk/640x426",
-          image_alt = "COVID-19"
-        ),
-      ### google-analytics.html is not needed to run app locally
-      analytics,
-      tags$script(js_get_width),
-      tags$head(tags$style(
-        HTML(
-          '.myClass {
-        font-size: 20px;
-        line-height: 50px;
-        text-align: left;
-        font-family: "Helvetica Neue",Helvetica,Arial,sans-serif;
-        padding: 0 5px;
-        overflow: hidden;
-        color: white;
-      }
-    '
+          image_alt = "COVID-19"),
+      analytics, # included if available
+      shiny::fluidPage(
+        shiny::fluidRow(
+          shiny::column(
+            width = 8, offset = 2,
+            shiny::tags$h2("Confirmed COVID-19 cases by province/territory"),
+            apexcharter::apexchartOutput("pt_cases_ts")
+          )
         )
-      )),
-      tags$script(
-        HTML(
-          '
-      $(document).ready(function() {
-        $("header").find("nav").append(\'<span class="myClass"> Menu </span>\');
-      })
-     '
-        )
-      ),
-      tabItems(
-        tab_overview,
-        # tab_trends,
-        tab_comparisons,
-        tab_maps,
-        tab_vaccines,
-        tab_cases,
-        tab_mortality,
-        tab_recovered,
-        tab_testing,
-        tab_about
       )
     ),
-    footer = dashboardFooter(
-      left = HTML(
-        "Created by <a href='https://twitter.com/JPSoucy' target='_blank'> Jean-Paul R. Soucy</a> and <a href='https://twitter.com/ishaberry2' target='_blank'> Isha Berry</a>, Dalla Lana School of Public Health, University of Toronto. Developed by Jean-Paul R. Soucy, Isha Berry, <a href='https://twitter.com/benkcwong' target='_blank'> Ben Wong</a> and <a href='https://github.com/kbelisar' target='_blank'> Kyla Belisario</a>, with contributions from <a href='https://www.linkedin.com/in/wenyu-huang-11b211124/' target='_blank'> Wenyu Huang</a>, <a href='https://www.linkedin.com/in/angelinazhu' target='_blank'> Angelina Zhu</a>, <a href='https://github.com/mattwarkentin' target='_blank'> Matthew T. Warkentin</a> and <a href='https://mountainmath.ca/' target='_blank'> Jens von Bergmann</a>. Data curated by the <a href='https://opencovid.ca' target='_blank'> COVID-19 Canada Open Data Working Group</a> (see \"About the data\" for sources)."
-      )
-    )
-  )
+    controlbar = NULL, # no right sidebar
+    footer = shinydashboardPlus::dashboardFooter(),
+    title = "COVID-19 in Canada")
 }
\ No newline at end of file