From 5e08597bf9242dad843a56dd67dbaa38143ef167 Mon Sep 17 00:00:00 2001 From: "Kyla Belisario (she/her)" <71769015+kbelisar@users.noreply.github.com> Date: Tue, 1 Nov 2022 21:09:59 -0400 Subject: [PATCH] First iteration of the dashboard (version 2) Basic structure is there with easy-to-navigate tabs at the top of the page - one tab for each data type. Still require a few items to attend to prior to making public, which will be updated in #41. --- data_updater.R | 21 ++--- global.R | 180 +++++++++++++++++++++++++++++++++++---- server.R | 115 +++++++++++++++++++------ ui.R | 106 +++++++++++++++-------- www/ccodwg_logo_bold.png | Bin 0 -> 505644 bytes 5 files changed, 323 insertions(+), 99 deletions(-) create mode 100644 www/ccodwg_logo_bold.png diff --git a/data_updater.R b/data_updater.R index 247fb86..d9b470a 100644 --- a/data_updater.R +++ b/data_updater.R @@ -13,27 +13,16 @@ curl::curl_download("https://raw.githubusercontent.com/ccodwg/CovidTimelineCanad curl::curl_download("https://raw.githubusercontent.com/ccodwg/CovidTimelineCanada/main/geo/health_regions.geojson", "data/health_regions.geojson") # 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")) -} +curl::curl_download(paste0("https://github.com/ccodwg/CovidTimelineCanada/raw/main/data/hr/deaths_hr.csv"), paste0("data/deaths_hr.csv")) # 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" + "hospitalizations", + "icu" )) { curl::curl_download(paste0("https://github.com/ccodwg/CovidTimelineCanada/raw/main/data/pt/", d, "_pt.csv"), paste0("data/", d, "_pt.csv")) } -# 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")) -} +# wastewater data +curl::curl_download("https://raw.githubusercontent.com/ccodwg/CovidTimelineCanada/main/raw_data/active_ts/can/can_wastewater_copies_per_ml_subhr_ts.csv","data/wastewater_data.csv") \ No newline at end of file diff --git a/global.R b/global.R index 6d3b718..fa6dda4 100644 --- a/global.R +++ b/global.R @@ -58,24 +58,168 @@ for (f in files) { } } -# set constants - -## PT colour palette -palette_pt <- list( - "AB" = "#00E676", - "BC" = "#304FFE", - "MB" = "#FF80AB", - "NB" = "#76FF03", - "NL" = "#B388FF", - "NS" = "#00E5FF", - "NT" = "#00ACC1", - "NU" = "#FFFF00", - "ON" = "#FF6F00", - "PE" = "#1B5E20", - "QC" = "#D50000", - "SK" = "#AA00FF", - "YT" = "#D500F9" -) +### FINDS start state for this calendar year (simplified function of MMWRweek package) +### Start of week is Monday + +start_week_calc <- function(week, year){ + begin_date <- function(year) { + jan1 <- as.Date(paste0(year, "-01-01")) + wday <- as.numeric(lubridate::wday(jan1, week_start = 1)) + date <- jan1 - (wday-1) + 7*(wday>4) + return(date) + } + jan1 <- begin_date(year) + return(jan1 + (week-1)*7) +} + +##### ----- FUNCTION to process data: PROVINCE/ TERRITORIES +### Variable names are standardized but include option to identify different value (cumulative vs daily) +### TYPE == type of data: "mean" (wastewater); or "total" (mortality, hospitalizations) +### ALSO ADDS week 53 to week 52 data (and if week 53 is NAN, then == 0 for addition) + +process_pt_data <- function(data, val, type){ + + names(data)[names(data) == val] <- "val" + + if(type == "mean"){ + + data <- data %>% + dplyr::mutate(year = lubridate::year(date), week = lubridate::week(date)) %>% + dplyr::group_by(region, year, week) %>% dplyr::summarise(mean = mean(val, na.rm = T), .groups = "keep") + + data <- data %>% dplyr::rename(val = mean) %>% + dplyr::mutate(date = start_week_calc(week, year)) + + } + + if(type == "total"){ + + data <- data %>% + dplyr::mutate(year = lubridate::year(date), week = lubridate::week(date)) %>% + dplyr::group_by(region, year, week) %>% dplyr::summarise(total = sum(val, na.rm = T), .groups = "keep") + + data <- data %>% dplyr::rename(val = total) %>% + dplyr::mutate(date = start_week_calc(week, year)) + } + + ### Separates out week 53 so it can be added to week 52 + w53 <- data[(data$week==53),] + w53$week <- 52 + w53 <- w53[c("week","year","region","val")] + colnames(w53) <- c("week","year","region","val_add") + + data <- data[(data$week!=53),] + data <- merge(data,w53, by = c("week","year","region"), all.x = T) + data$val <- rowSums(data[c("val","val_add")], na.rm = T) + + data <- data[c("week","year","date","region","val")] + names(data)[names(data) == "val"] <- val + + ### ADD AREAS - FACTOR ACCORDINGLY + + data$area <- NA + data$area[data$region=="NL" | data$region=="NS" | data$region=="PE" | data$region=="NB"] <- "Atlantic Canada" + data$area[data$region=="ON" | data$region=="QC"] <- "Central Canada" + data$area[data$region=="MB" | data$region=="AB" | data$region=="SK" | data$region=="BC"] <- "Western / Prairie Provinces" + data$area[data$region=="NT" | data$region=="NU" | data$region=="YT"] <- "Northern Canada" + + return(data) + +} + + +process_hr_data <- function(data, hr, pt, val, type){ + + names(data)[names(data) == val] <- "val" + names(data)[names(data) == hr] <- "hr" + names(data)[names(data) == pt] <- "pt" + + areas <- data[c("hr","pt")] + areas$unique <- duplicated(areas$hr) + areas <- areas[(areas$unique==FALSE),] + + if(type == "mean"){ + + data <- data %>% + dplyr::mutate(year = lubridate::year(date), week = lubridate::week(date)) %>% + dplyr::group_by(hr, year, week) %>% dplyr::summarise(mean = mean(val, na.rm = T), .groups = "keep") + + data <- data %>% dplyr::rename(val = mean) %>% + dplyr::mutate(date = start_week_calc(week, year)) + + } + + if(type == "total"){ + + data <- data %>% + dplyr::mutate(year = lubridate::year(date), week = lubridate::week(date)) %>% + dplyr::group_by(hr, year, week) %>% dplyr::summarise(total = sum(val, na.rm = T), .groups = "keep") + + data <- data %>% dplyr::rename(val = total) %>% + dplyr::mutate(date = start_week_calc(week, year)) + + } + + ### Separates out week 53 so it can be added to week 52 + w53 <- data[(data$week==53),] + w53$week <- 52 + w53 <- w53[c("week","year","hr","val")] + colnames(w53) <- c("week","year","hr","val_add") + + data <- data[(data$week!=53),] + data <- merge(data,w53, by = c("week","year","hr"), all.x = T) + data$val <- rowSums(data[c("val","val_add")], na.rm = T) + + data <- data[c("week","year","date","hr","val")] + names(data)[names(data) == "val"] <- val + + ### ADD AREAS - FACTOR ACCORDINGLY + + areas$area <- NA + areas$area[areas$pt=="NL" | areas$pt=="NS" | areas$pt=="PE" | areas$pt=="NB"] <- "Atlantic Canada" + areas$area[areas$pt=="ON" | areas$pt=="QC"] <- "Central Canada" + areas$area[areas$pt=="MB" | areas$pt=="AB" | areas$pt=="SK" | areas$pt=="BC"] <- "Western / Prairie Provinces" + areas$area[areas$pt=="NT" | areas$pt=="NU" | areas$pt=="YT"] <- "Northern Canada" + + atlantic_can <- areas$hr[areas$area=="Atlantic Canada"] + central_can <- areas$hr[areas$area=="Central Canada"] + western_can <- areas$hr[areas$area=="Western / Prairie Provinces"] + northern_can <- areas$hr[areas$area=="Northern Canada"] + + data$area[data$hr %in% atlantic_can] <- "Atlantic Canada" + data$area[data$hr %in% central_can] <- "Central Canada" + data$area[data$hr %in% western_can] <- "Western / Prairie Provinces" + data$area[data$hr %in% northern_can] <- "Northern Canada" + + names(data)[names(data) == "hr"] <- "region" # rename to region for simplicity + + return(data) + +} + +##### ----- WASTEWATER DATA + +#!!!# For now, fix missing regional data: +wastewater_data$region[wastewater_data$sub_region_1=="Saskatoon"] <- "SK" +wastewater_data$region[wastewater_data$sub_region_1=="Prince Albert"] <- "SK" +wastewater_data$region[wastewater_data$sub_region_1=="North Battleford"] <- "SK" +wastewater_data$region[wastewater_data$sub_region_1=="Moncton"] <- "NB" + +wastewater_pt <- process_pt_data(wastewater_data,"value","mean") +wastewater_hr <- process_hr_data(wastewater_data,"sub_region_1","region","value","mean") + +wastewater_pt$area <- factor(wastewater_pt$area, levels = c("Western / Prairie Provinces","Central Canada","Atlantic Canada")) +wastewater_hr$area <- factor(wastewater_hr$area, levels = c("Western / Prairie Provinces","Central Canada","Atlantic Canada")) + +##### ----- HOSPITALIZATION DATA + +hospitalization_data <- process_pt_data(hospitalizations_pt,"value","total") +hospitalization_data$area <- factor(hospitalization_data$area, levels = c("Western / Prairie Provinces","Central Canada","Atlantic Canada","Northern Canada")) + +##### ----- MORTALITY DATA + +mortality_data <- process_pt_data(deaths_pt,"value_daily","total") +mortality_data$area <- factor(mortality_data$area, levels = c("Western / Prairie Provinces","Central Canada","Atlantic Canada","Northern Canada")) # load analytics (if available) analytics <- if (file.exists("google-analytics.html")) { diff --git a/server.R b/server.R index faddd40..47b1406 100644 --- a/server.R +++ b/server.R @@ -1,33 +1,5 @@ server <- function(input, output, session) { - # JavaScript function to format number with a thousands separator - format_thousands <- apexcharter::JS( - "function(value) {if (typeof(value) == 'number') {return value.toString().replace(/\\B(?=(\\d{3})+(?!\\d))/g, \",\");} else {return value;}}") - - # render Apex chart of PT timeseries for "value" or "value_daily" - ts_chart_pt <- function( - dat, val = c("value", "value_daily"), xlab, ylab, remove_negative_values = FALSE) { - match.arg(val, c("value", "value_daily"), several.ok = FALSE) - if (remove_negative_values) {dat[!is.na(dat[val]) & dat[val] < 0, val] <- NA} - apexcharter::renderApexchart( - apexcharter::apex(dat, type = "line", mapping = apexcharter::aes( - x = date, y = !!rlang::sym(val), colour = region)) %>% - apexcharter::ax_labs(x = xlab, y = ylab) %>% - apexcharter::ax_chart(animations = list(enabled = FALSE)) %>% - apexcharter::ax_yaxis(labels = list(formatter = format_thousands)) %>% - apexcharter::ax_tooltip(y = list(formatter = format_thousands)) %>% - apexcharter::ax_colors_manual(palette_pt) - ) - } - - # render case time series chart - output$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 shiny::observe({ ## run only if app is running on the server, not locally @@ -50,10 +22,97 @@ server <- function(input, output, session) { } }) + ##### ----- WASTEWATER PLOT + + output$pt_wastewater_ts <- apexcharter::renderApexfacet(wastewater_pt %>% + apexcharter::apex(mapping = apexcharter::aes(date,value,colour = region),type = "area", synchronize = "value") %>% + apexcharter::ax_facet_wrap(apexcharter::vars(area), ncol = 1, scales = "free_y") %>% ## don't facet by province but instead areas + apexcharter::ax_chart(animations = list(enabled = FALSE)) %>% + apexcharter::ax_legend(showForSingleSeries = TRUE) %>% + apexcharter::ax_stroke(lineCap = "round", curve = "smooth") %>% + apexcharter::ax_fill(gradient = list(enabled = TRUE, opacityFrom = 0.9, opacityTo = 0.3)) %>% + apexcharter::ax_xaxis(tooltip = list(enabled = FALSE), title = list(text = ""), + crosshairs = list( + opacity = 0.75, + width = 2, + fill = list(color = "#1C4579"), + stroke = list(width = 0))) %>% + apexcharter::ax_yaxis(labels = list(formatter = apexcharter::JS("function(val) {return val.toFixed(1);}")), + tooltip = list(enabled = FALSE), title = list(text = "Weekly Average of Raw copies per / ml")) %>% + apexcharter::ax_tooltip(y = list(formatter = apexcharter::JS("function(val) {return val.toFixed(1);}")), followCursor = FALSE) %>% + apexcharter::ax_colors_manual(list("AB" = "#5C4742","BC" = "#8D5B4C","MB" = "#948375","SK" = "#5A2A27", + "ON" = "#5C4742","QC" = "#948375", + "NB" = "#5C4742","NL" = "#8D5B4C","NS" = "#948375","PE" = "#5A2A27")) + ) + + output$hr_wastewater_ts <- apexcharter::renderApexfacet(wastewater_hr %>% + apexcharter::apex(mapping = apexcharter::aes(date,value,colour = region),type = "area", synchronize = "value") %>% + apexcharter::ax_facet_wrap(apexcharter::vars(area), ncol = 1, scales = "free_y") %>% ## don't facet by province but instead areas + apexcharter::ax_chart(animations = list(enabled = FALSE)) %>% + apexcharter::ax_legend(showForSingleSeries = TRUE) %>% + apexcharter::ax_stroke(lineCap = "round", curve = "smooth") %>% + apexcharter::ax_fill(gradient = list(enabled = TRUE, opacityFrom = 0.9, opacityTo = 0.3)) %>% + apexcharter::ax_xaxis(tooltip = list(enabled = FALSE), title = list(text = ""), + crosshairs = list( + opacity = 0.75, + width = 2, + fill = list(color = "#1C4579"), + stroke = list(width = 0))) %>% + apexcharter::ax_yaxis(labels = list(formatter = apexcharter::JS("function(val) {return val.toFixed(1);}")), + tooltip = list(enabled = FALSE), title = list(text = "Weekly Average of Raw copies per / ml")) %>% + apexcharter::ax_tooltip(y = list(formatter = apexcharter::JS("function(val) {return val.toFixed(1);}")), followCursor = FALSE) %>% + apexcharter::ax_theme(palette = "palette9") + ) + + output$pt_hospitalizations_ts <- apexcharter::renderApexfacet(hospitalization_data %>% + apexcharter::apex(mapping = apexcharter::aes(date,value,colour = region),type = "area", synchronize = "value") %>% + apexcharter::ax_facet_wrap(apexcharter::vars(area), ncol = 1, scales = "free_y") %>% + apexcharter::ax_chart(animations = list(enabled = FALSE)) %>% + apexcharter::ax_legend(showForSingleSeries = TRUE) %>% + apexcharter::ax_stroke(lineCap = "round", curve = "smooth") %>% + apexcharter::ax_fill(gradient = list(enabled = TRUE, opacityFrom = 0.8, opacityTo = 0.2)) %>% + apexcharter::ax_xaxis(tooltip = list(enabled = FALSE), title = list(text = ""), + crosshairs = list( + opacity = 0.75, + width = 2, + fill = list(color = "#1C4579"), + stroke = list(width = 0))) %>% + apexcharter::ax_yaxis(labels = list(formatter = apexcharter::JS("function(val) {return val.toFixed(1);}")), + tooltip = list(enabled = FALSE), title = list(text = "Weekly Total Hospitalizations")) %>% + apexcharter::ax_tooltip(y = list(formatter = apexcharter::JS("function(val) {return val.toFixed(0);}"))) %>% + apexcharter::ax_colors_manual(list("ON" = "#4A62C4","QC" = "#70B3D1", + "AB" = "#4A62C4","BC" = "#0393D2","MB" = "#70B3D1","SK" = "#0327Bf", + "NB" = "#4A62C4","NL" = "#0393D2","NS" = "#70B3D1","PE" = "#0327Bf", + "NT" = "#4A62C4", "NU" = "#70B3D1", "YK" = "#0327Bf")) + ) + + output$deaths_pt_ts <- apexcharter::renderApexfacet(mortality_data %>% + apexcharter::apex(mapping = apexcharter::aes(date, value_daily, colour = region), type = "area", synchronize = "value") %>% + apexcharter::ax_facet_wrap(apexcharter::vars(area), ncol = 1, scales = "free_y") %>% + apexcharter::ax_chart(animations = list(enabled = FALSE)) %>% + apexcharter::ax_legend(showForSingleSeries = TRUE) %>% + apexcharter::ax_stroke(lineCap = "round", curve = "smooth") %>% + apexcharter::ax_fill(gradient = list(enabled = TRUE, opacityFrom = 0.8, opacityTo = 0.2)) %>% + apexcharter::ax_xaxis(tooltip = list(enabled = FALSE), title = list(text = ""), + crosshairs = list( + opacity = 0.75, + width = 2, + fill = list(color = "#1C4579"), + stroke = list(width = 0))) %>% + apexcharter::ax_yaxis(labels = list(formatter = apexcharter::JS("function(val) {return val.toFixed(1);}")), + tooltip = list(enabled = FALSE), title = list(text = "Weekly Total Deaths")) %>% + apexcharter::ax_tooltip(y = list(formatter = apexcharter::JS("function(val) {return val.toFixed(0);}"))) %>% + apexcharter::ax_colors_manual(list("ON" = "#5D704D","QC" = "#8CA38B", + "AB" = "#5D704D","BC" = "#4E8852","MB" = "#8CA38B","SK" = "#20431D", + "NB" = "#5D704D","NL" = "#4E8852","NS" = "#8CA38B","PE" = "#20431D", + "NT" = "#5D704D", "NU" = "#8CA38B", "YK" = "#20431D")) + ) + # when app stops, update file time of app.R # trick to update cache: https://stackoverflow.com/a/55476883 shiny::onStop(function() { Sys.setFileTime("app.R", lubridate::now()) }) + } diff --git a/ui.R b/ui.R index 7184ed3..5d0487b 100644 --- a/ui.R +++ b/ui.R @@ -4,41 +4,73 @@ analytics <- if (file.exists("google-analytics.html")) { } else {} # ui + ui <- function(req) { - shinydashboardPlus::dashboardPage( - 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 = 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"), - analytics, # included if available - shiny::fluidPage( - shiny::fluidRow( - shiny::column( - width = 8, offset = 2, - shiny::tags$h2("Confirmed COVID-19 cases by province/territory"), - apexcharter::apexchartOutput("pt_cases_ts") - ) - ) - ) - ), - controlbar = NULL, # no right sidebar - footer = shinydashboardPlus::dashboardFooter(), - title = "COVID-19 in Canada") -} \ No newline at end of file + +shiny::tagList( + shinydashboard::dashboardHeader( + # modify size of header and magin whitespace + tags$li(class = "dropdown", + tags$style(".navbar {margin-top: -50px;}"), + tags$style(".navbar {max-height:80px !important}"), + tags$style(".navbar {min-height:80px !important}") + ) + ), + metathis::meta() %>% + metathis::meta_social( + title = "COVID-19 in Canada", + description = "An interactive visualization of the ongoing epidemic of COVID-19 in Canada", + url = "https://art-bd.shinyapps.io/covid19canada/", + image = "https://source.unsplash.com/VhjsGKMefkk/640x426", + image_alt = "COVID-19"), + analytics, # included if available + shiny::navbarPage(theme = bslib::bs_theme(bootswatch = "flatly", primary = "#1C4579",success = "#73B6DE"), # flatly bootswatch customization of colours + title = div(tags$a(img(src="ccodwg_logo_bold.png", height=65), href= "https://opencovid.ca")), + collapsible = TRUE, + windowTitle = "COVID-19 in Canada", # browser tab title + + ### WASTEWATER TAB + shiny::tabPanel( + title = "Wastewater", icon = shiny::icon("vial-virus", lib = "font-awesome"), + shiny::column( + width = 8, offset = 2, + shiny::fluidRow( + shiny::tags$h3("COVID-19 Wastewater Signal in Canada by Province", align = "center"), + shiny::tags$body("", align = "center"), + apexcharter::apexfacetOutput("pt_wastewater_ts") + ), + shiny::fluidRow( + shiny::tags$h3("COVID-19 Wastewater Signal in Canada by Region", align = "center"), + apexcharter::apexfacetOutput("hr_wastewater_ts") + ) + ) + ), + + ### HOSPITALIZATIONS TAB + shiny::tabPanel( + title = "Hospitalizations", icon = shiny::icon("circle-h", lib = "font-awesome"), + shiny::column( + width = 8, offset = 2, + shiny::fluidRow( + shiny::tags$h3("COVID-19 Hospitalizations in Canada", align = "center"), + apexcharter::apexfacetOutput("pt_hospitalizations_ts") + ) + ) + ), + + ### MORTALITY TAB + shiny::tabPanel( + title = "Mortality", icon = shiny::icon("heart-pulse", lib = "font-awesome"), + shiny::column( + width = 8, offset = 2, + shiny::fluidRow( + shiny::tags$h3("COVID-19 Mortality in Canada", align = "center"), + apexcharter::apexfacetOutput("deaths_pt_ts") + ) + ) + ) + ), # navbar page bracket + tags$footer("© 2022 CCODWG (COVID-19 Canada Open Data Working Group)", align = "center", style = "background-color: #C2DFF1") +) #taglist bracket + +} diff --git a/www/ccodwg_logo_bold.png b/www/ccodwg_logo_bold.png new file mode 100644 index 0000000000000000000000000000000000000000..edece63a4945b692f82fbfb75ee6aede0c4342a9 GIT binary patch literal 505644 zcmdSBi9gis`!-&wRFdvgiqL%*l2F;Q49Z=S^(IS}AvbT1YQopjJ59_DCxbl2>j(Z^x+fOfdi+$vVRUH 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