Shiny app does not work once deployed - rnaturalearthhires? - r

Question

I have built a shiny dashboard with Covid19 data for Switzerland.
The dashboard works well when I run it from RStudio, but after being deployed I get this:
**An error has occurred
The application failed to start: exited unexpectedly with code 1
Attaching package: ‘dplyr’
The following objects are masked from ‘package:stats’:
filter, lag
The following objects are masked from ‘package:base’:
intersect, setdiff, setequal, union
Loading required package: ggplot2
Attaching package: ‘plotly’
The following object is masked from ‘package:ggplot2’:
last_plot
The following object is masked from ‘package:stats’:
filter
The following object is masked from ‘package:graphics’:
layout
Registered S3 method overwritten by 'quantmod':
method from
as.zoo.data.frame zoo
── Attaching packages ─────────────────────────────────────── tidyverse 1.3.0 ──
✔ tibble 3.0.3 ✔ stringr 1.4.0
✔ tidyr 1.1.2 ✔ forcats 0.5.0
✔ purrr 0.3.4
── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
✖ plotly::filter() masks dplyr::filter(), stats::filter()
✖ dplyr::lag() masks stats::lag()
Attaching package: ‘lubridate’
The following objects are masked from ‘package:base’:
date, intersect, setdiff, union
Linking to GEOS 3.5.1, GDAL 2.2.2, PROJ 4.9.2
Attaching package: ‘maps’
The following object is masked from ‘package:purrr’:
map
Google's Terms of Service: https://cloud.google.com/maps-platform/terms/.
Please cite ggmap if you use it! See citation("ggmap") for details.
Attaching package: ‘ggmap’
The following object is masked from ‘package:plotly’:
wind
Attaching package: ‘shinydashboard’
The following object is masked from ‘package:graphics’:
box
Attaching package: ‘rsconnect’
The following object is masked from ‘package:shiny’:
serverInfo
Parsed with column specification:
cols(
date = col_date(format = ""),
time = col_time(format = ""),
abbreviation_canton_and_fl = col_character(),
ncumul_tested = col_double(),
ncumul_conf = col_double(),
new_hosp = col_double(),
current_hosp = col_double(),
current_icu = col_double(),
current_vent = col_double(),
ncumul_released = col_double(),
ncumul_deceased = col_double(),
source = col_character(),
current_isolated = col_double(),
current_quarantined = col_double(),
current_quarantined_riskareatravel = col_double(),
TotalPosTests1 = col_character(),
ninst_ICU_intub = col_character()
)
Warning: 8254 parsing failures.
row col expected actual file
1 -- 17 columns 15 columns 'https://raw.githubusercontent.com/openZH/covid_19/master/COVID19_Fallzahlen_CH_total_v2.csv'
2 -- 17 columns 15 columns 'https://raw.githubusercontent.com/openZH/covid_19/master/COVID19_Fallzahlen_CH_total_v2.csv'
3 -- 17 columns 15 columns 'https://raw.githubusercontent.com/openZH/covid_19/master/COVID19_Fallzahlen_CH_total_v2.csv'
4 -- 17 columns 15 columns 'https://raw.githubusercontent.com/openZH/covid_19/master/COVID19_Fallzahlen_CH_total_v2.csv'
5 -- 17 columns 15 columns 'https://raw.githubusercontent.com/openZH/covid_19/master/COVID19_Fallzahlen_CH_total_v2.csv'
... ... .......... .......... .............................................................................................
See problems(...) for more details.
The rnaturalearthhires package needs to be installed.
Installing the rnaturalearthhires package.
Error in value[[3L]](cond) :
Failed to install the rnaturalearthhires package.
Please try installing the package for yourself using the following command:
install.packages("rnaturalearthhires", repos = "http://packages.ropensci.org", type = "source")
Calls: local ... tryCatch -> tryCatchList -> tryCatchOne -> <Anonymous>
Execution halted**
It seems like the rnaturalearthhires package is the problem, but I doi not need it to build the leaflet maps and tu run the app on RStudio. I have tried to call library(rnaturalearthhires) in the shiny dashboard code and even to add install.packages("rnaturalearthhires", repos = "http://packages.ropensci.org", type = "source"), but it does not work, I get an error message even before the end of deployment.
Does anyone had the same problem or know where is the issue?
Thanks

Answer 1

Here is the code for the app:
library(readr)
library(readxl)
library(dplyr)
library(plotly)
library(forcats)
library(ggplot2)
library(tidyverse)
library(lubridate)
library(rnaturalearth)
library(rnaturalearthdata)
library(sf)
library(maps)
library(gifski)
library(leaflet)
library(ggmap)
library(htmlwidgets)
library(htmltools)
library(leaflet.extras)
library(purrr)
library(shiny)
library(shinydashboard)
library(RColorBrewer)
library(rsconnect)
# Data sets
# Load Covid data for Switzerland from GitHub repository
data_swiss <- read_csv("https://raw.githubusercontent.com/openZH/covid_19/master/COVID19_Fallzahlen_CH_total_v2.csv")
# Load Canton population data from excell csv file made from Wikipedia data
canton_swiss <- read_xlsx("swiss_cantons.xlsx")
# Load Switzerland spatial data (canton polygons)
switzerland <- ne_states(country = 'switzerland', returnclass = 'sf')
switzerland <- st_as_sf(switzerland)
# Join data frames
data_swiss = left_join(data_swiss, canton_swiss, by = c(abbreviation_canton_and_fl = "Canton_abbr"))
# Modify dataframe by adding more variables
data_swiss <- data_swiss %>% group_by(abbreviation_canton_and_fl) %>%
mutate(new_cases = ncumul_conf - lag(ncumul_conf, default = first(ncumul_conf), order_by = date))
data_swiss <- data_swiss %>% group_by(abbreviation_canton_and_fl) %>%
mutate(new_deaths = ncumul_deceased - lag(ncumul_deceased, default = first(ncumul_deceased), order_by = date))
data_swiss <- data_swiss %>%
mutate(pop_10thous = Pop/10000)
data_swiss <- data_swiss %>%
mutate(new_cases_per_10thous = new_cases/pop_10thous)
data_swiss <- data_swiss %>%
mutate(new_deaths_per_10thous = new_deaths/pop_10thous)
data_swiss <- data_swiss %>%
mutate(new_cases_smoothed = zoo::rollmean(new_cases, k = 7, fill = NA))
data_swiss <- data_swiss %>%
mutate(new_deaths_smoothed = zoo::rollmean(new_deaths, k = 7, fill = NA))
data_swiss <- data_swiss%>%
mutate(ncumul_deceased_per_10thous = ncumul_deceased/pop_10thous)
data_swiss <- data_swiss%>%
mutate(ncumul_conf_per_10thous = ncumul_conf/pop_10thous)
# Merge with geo data
data_swiss_geo <- left_join(switzerland, data_swiss, by = c(postal = "abbreviation_canton_and_fl"))
# Create new data frame with Switzerland totals
data_swiss_noNA <- data_swiss %>%
mutate_if(is.numeric, funs(ifelse(is.na(.), 0, .)))
switzerland_new_cases <- data_swiss_noNA %>%
group_by(date) %>%
summarize(switzerland_new_cases = sum(new_cases, na.rm = TRUE))
switzerland_new_cases_smoothed <- data_swiss_noNA %>%
group_by(date) %>%
summarize(switzerland_new_cases_smoothed = sum(new_cases_smoothed, na.rm = TRUE)) %>%
select(-date)
switzerland_new_deaths <- data_swiss_noNA %>%
group_by(date) %>%
summarize(switzerland_new_deaths = sum(new_deaths, na.rm = TRUE))%>%
select(-date)
switzerland_new_deaths_smoothed <- data_swiss_noNA %>%
group_by(date) %>%
summarize(switzerland_new_deaths_smoothed = sum(new_deaths_smoothed, na.rm = TRUE)) %>%
select(-date)
data_total_swiss <- cbind(switzerland_new_cases, switzerland_new_cases_smoothed, switzerland_new_deaths, switzerland_new_deaths_smoothed)
# Calculate trend
tot14days_last <- data_swiss %>%
group_by(abbreviation_canton_and_fl) %>%
filter(date <= max(date), date >= max(date)-14) %>%
summarize(tot14days_last = sum(new_cases, na.rm = TRUE))
tot14days_previous <- data_swiss %>%
group_by(abbreviation_canton_and_fl) %>%
filter(date <= max(date)-15, date >= max(date)-29) %>%
summarize(tot14days_previous = sum(new_cases, na.rm = TRUE)) %>%
select(-abbreviation_canton_and_fl)
trend <- cbind(tot14days_last, tot14days_previous)
trend <- trend %>%
mutate(change_percemt = round((tot14days_last-tot14days_previous)/tot14days_last*100, 0))
trend_swiss_geo <- left_join(switzerland, trend, by = c(postal = "abbreviation_canton_and_fl"))
trend <- left_join(canton_swiss, trend, by = c(Canton_abbr = "abbreviation_canton_and_fl"))
# App
header <- dashboardHeader(title = "Covid-19 Switzerland")
sidebar <- dashboardSidebar(
sidebarMenu (
menuItem("Timeline", tabName = "Timeline", icon = icon("calendar-alt")),
menuItem("Maps and Stats", tabName = "Maps", icon = icon("chart-bar")),
menuItem("14 days trend", tabName = "Trend", icon = icon("chart-line")),
menuItem("About", tabName = "About", icon = icon("comment-alt")),
menuItem("Source code", icon = icon("code"),
href = "https://github.com/vivvi87/Swiss-Covid-Shiny-Dashboard"),
menuItem("Source data", icon = icon("database"),
href = "https://github.com/openZH/covid_19")
)
)
body <- dashboardBody(
tabItems(
tabItem(tabName = "Timeline",
fluidRow(
valueBoxOutput("box_cases"),
valueBoxOutput("box_deaths"),
valueBoxOutput("box_canton")
),
fluidRow(
tabBox(width = 10, title ="Switzerland Covid-19 timeline",
tabPanel("Cases", plotlyOutput("swiss_timeline")),
tabPanel("Deaths", plotlyOutput("swiss_timeline_d"))
),
box(width = 2,
sliderInput("dates", "Select dates:",
min(data_total_swiss$date), max(data_total_swiss$date),
value = c(as.Date("2020-09-20"), max(data_total_swiss$date))
)
)
),
fluidRow(
tabBox(width = 10, title ="Swiss cantons Covid-19 timeline",
tabPanel("Cases", plotlyOutput("canton_timeline")),
tabPanel("Deaths", plotlyOutput("canton_timeline_d"))
),
box(width = 2,
sliderInput("dates_canton", "Select dates:",
min(data_swiss$date), max(data_swiss$date),
value = c(as.Date("2020-09-20"), max(data_swiss$date))
),
selectInput("canton", "Select canton:",
selected = "Geneva",
choices = c(levels(as.factor(data_swiss$Canton))),
multiple = FALSE
)
)
)
),
tabItem(tabName = "Maps",
fluidRow(
tabBox(title = "Total cases",
tabPanel("Absolute", leafletOutput("map_cases_abs")),
tabPanel("Every 10000 people", leafletOutput("map_cases"))
),
tabBox(title = "Total deaths",
tabPanel("Absolute", leafletOutput("map_deaths_abs")),
tabPanel("Every 10000 people", leafletOutput("map_deaths"))
)
),
fluidRow(
tabBox(title = "Total cases",
tabPanel("Absolute", plotlyOutput("cases_abs")),
tabPanel("Every 10000 people", plotlyOutput("cases"))
),
tabBox(title = "Total deaths",
tabPanel("Absolute", plotlyOutput("deaths_abs")),
tabPanel("Every 10000 people", plotlyOutput("deaths"))
)
)
),
tabItem(tabName = "About",
fluidRow(
box(width = 12,
h2("About"),
"This dashboard has been built using the data found in the GitHub repository ", em("https://github.com/openZH/covid_19"), " which collect Covid-19 data for Switzerland and Lichtenstain.",
"The data is updated at best once a day at varying times, but in order to avoid missing values and errors, the data in Maps and stats are displayed with a 2 days delay, as indicated when hovering on the data.",
"The data analysis as well as the source code of the dashboard can be found at ", em("https://github.com/vivvi87/Swiss-Covid-Shiny-Dashboard"), ". Both source code and data can be directly accessed from the sidebar."
)
)
),
tabItem(tabName = "Trend",
fluidRow(
valueBoxOutput("swiss_trend")
),
fluidRow(
box(title = "Map - 14 days variation %", width = 6,
leafletOutput("variation_map")
),
box(title = "Chart - 14 days variation %", width = 6,
plotlyOutput("variation_chart")
)
),
fluidRow(
DT::dataTableOutput("trend_table")
)
)
)
)
server <- function(input, output) {
output$swiss_timeline <- renderPlotly({
data_total_swiss %>%
filter(date >= input$dates[1] & date <= input$dates[2]) %>%
plot_ly() %>%
add_bars(x = ~date,
y = ~switzerland_new_cases,
color = I("black"),
opacity = 0.5,
text = ~paste(date, "<br>", "New cases: ", round(switzerland_new_cases, 1)),
hoverinfo = "text",
name = "New cases") %>%
add_lines(x = ~date,
y = ~switzerland_new_cases_smoothed,
color = I("orange"),
text = ~paste(date, "<br>", "New cases (7-days average): ", round(switzerland_new_cases_smoothed, 0)),
hoverinfo = "text",
name = "new cases (7-days average)") %>%
layout(yaxis = list(title = "Number of Covid-19 cases",
showgrid = F,
range = c(0, 11500)),
xaxis = list(title = " "),
legend = list(x = 0, y = 1)) %>%
config(displayModeBar = FALSE, displaylogo = FALSE)
})
output$swiss_timeline_d <- renderPlotly({
data_total_swiss %>%
filter(date >= input$dates[1] & date <= input$dates[2]) %>%
plot_ly() %>%
add_bars(x = ~date,
y = ~switzerland_new_deaths,
color = I("black"),
opacity = 0.5,
text = ~paste(date, "<br>", "New deaths: ", round(switzerland_new_deaths, 1)),
hoverinfo = "text",
name = "New deaths") %>%
add_lines(x = ~date,
y = ~switzerland_new_deaths_smoothed,
color = I("orange"),
text = ~paste(date, "<br>", "New deaths (7-days average): ", round(switzerland_new_deaths_smoothed, 0)),
hoverinfo = "text",
name = "new deaths (7-days average)") %>%
layout(yaxis = list(title = "Number of deaths",
showgrid = F,
range = c(0, 125)),
xaxis = list(title = " "),
legend = list(x = 0, y = 1)) %>%
config(displayModeBar = FALSE, displaylogo = FALSE)
})
output$canton_timeline <- renderPlotly({
data_swiss %>%
filter(date >= input$dates_canton[1] & date <= input$dates_canton[2]) %>%
filter(Canton == input$canton) %>%
plot_ly() %>%
mutate(Canton = as.character(Canton)) %>%
add_lines(x = ~date,
y = ~new_cases,
fill = "tozeroy",
fillcolor= 'rgba(153,102,204,0.5)',
line = list(color = 'rgba(153,102,204,0.6)'),
text = ~paste(Canton, "<br>", "Date: ", date, "<br>", "New Cases: ", new_cases),
hoverinfo = "text") %>%
layout(yaxis = list(title = "Number of Covid-19 Cases",
showgrid = F),
xaxis = list(title = " ", showgrid = F)) %>%
config(displayModeBar = FALSE, displaylogo = FALSE)
})
output$canton_timeline_d <- renderPlotly({
data_swiss %>%
filter(date >= input$dates_canton[1] & date <= input$dates_canton[2]) %>%
filter(Canton == input$canton) %>%
plot_ly() %>%
mutate(Canton = as.character(Canton)) %>%
add_lines(x = ~date,
y = ~new_deaths,
fill = "tozeroy",
fillcolor= 'rgba(153,102,204,0.5)',
line = list(color = 'rgba(153,102,204,0.6)'),
text = ~paste(Canton, "<br>", "Date: ", date, "<br>", "New deaths: ", new_deaths),
hoverinfo = "text") %>%
layout(yaxis = list(title = "Number of deaths",
showgrid = F),
xaxis = list(title = " ", showgrid = F)) %>%
config(displayModeBar = FALSE, displaylogo = FALSE)
})
output$variation_map <- renderLeaflet({
rc1 <- colorRampPalette(colors = c("purple", "white"), space = "Lab")(length(which(trend_swiss_geo$change_percemt < 0)))
rc2 <- colorRampPalette(colors = c("moccasin", "orange"), space = "Lab")(length(which(trend_swiss_geo$change_percemt > 0)))
rampcols <- c(rc1, rc2)
pal <- colorNumeric(palette = rampcols, domain = trend_swiss_geo$change_percemt)
trend_swiss_geo %>%
leaflet(options = leafletOptions(minZoom = 7.2)) %>%
setView(lat = 46.9, lng = 8.3, zoom = 7.2) %>%
addProviderTiles("CartoDB") %>%
addPolygons(fillColor = ~pal(change_percemt),
weight = 1,
opacity = 1,
color = "white",
dashArray = "3",
fillOpacity = 0.7,
highlight = highlightOptions(weight = 2,
color = "#666",
dashArray = "",
fillOpacity = 0.7,
bringToFront = TRUE),
label = ~paste0(name_en, ": ",
round(change_percemt, 0), " %")) %>%
addLegend(pal = pal,
values = ~change_percemt,
opacity = 0.7,
title = NULL,
position = "bottomright")
})
output$variation_chart <- renderPlotly({
trend %>%
plot_ly() %>%
add_bars(x = ~Canton,
y= ~change_percemt,
color = ~change_percemt < 0, colors = c("darkorange3", "mediumpurple3"),
opacity = 0.6,
text = ~paste(Canton, "<br>", round(change_percemt, 0), "% variation"),
hoverinfo = "text") %>%
layout(yaxis = list(title = "14 days variation %",
showgrid = F),
xaxis = list(title = " ",
showgrid = F)) %>%
hide_legend() %>%
config(displayModeBar = FALSE, displaylogo = FALSE)
})
output$map_cases_abs <- renderLeaflet({
data_swiss_geo_last <- data_swiss_geo %>%
filter(date == max(date)-2)
labels2 <- sprintf(
"%s<br/><strong>%s</strong><br/> %s total Covid-19 cases",
data_swiss_geo_last$date, data_swiss_geo_last$Canton, data_swiss_geo_last$ncumul_conf
) %>% lapply(htmltools::HTML)
data_swiss_geo_last %>%
leaflet(options = leafletOptions(minZoom = 7.2)) %>%
setView(lat = 46.9, lng = 8.3, zoom = 7) %>%
addProviderTiles("CartoDB") %>%
addPolygons(weight = 1, col = "grey") %>%
addCircleMarkers(~longitude, ~latitude,
radius = ~data_swiss_geo_last$ncumul_conf/2200,
stroke = TRUE,
color = "orange",
weight = 2,
fillOpacity = 0.5,
label = labels2)
})
output$map_cases <- renderLeaflet({
data_swiss_geo_last <- data_swiss_geo %>%
filter(date == max(date)-2)
labels1 <- sprintf(
"%s<br/><strong>%s</strong><br/> %s Covid-19 cases every 10000 people",
data_swiss_geo_last$date, data_swiss_geo_last$Canton, round(data_swiss_geo_last$ncumul_conf_per_10thous,0)
) %>% lapply(htmltools::HTML)
data_swiss_geo_last %>%
leaflet(options = leafletOptions(minZoom = 7.2)) %>%
setView(lat = 46.9, lng = 8.3, zoom = 7) %>%
addProviderTiles("CartoDB") %>%
addPolygons(weight = 1, col = "grey") %>%
addCircleMarkers(~longitude, ~latitude,
radius = ~(data_swiss_geo_last$ncumul_conf_per_10thous/50),
stroke = TRUE,
color = "orange",
weight = 2,
fillOpacity = 0.5,
label = labels1)
})
output$map_deaths_abs <- renderLeaflet({
data_swiss_geo_last <- data_swiss_geo %>%
filter(date == max(date)-2)
labels4 <- sprintf(
"%s<br/><strong>%s</strong><br/> %s total deaths",
data_swiss_geo_last$date, data_swiss_geo_last$Canton, data_swiss_geo_last$ncumul_deceased
) %>% lapply(htmltools::HTML)
data_swiss_geo_last %>%
leaflet(options = leafletOptions(minZoom = 7.2)) %>%
setView(lat = 46.9, lng = 8.3, zoom = 7) %>%
addProviderTiles("CartoDB") %>%
addPolygons(weight = 1, col = "grey") %>%
addCircleMarkers(~longitude, ~latitude,
radius = ~data_swiss_geo_last$ncumul_deceased/40,
stroke = TRUE,
color = "mediumorchid",
weight = 2,
fillOpacity = 0.4,
label = labels4)
})
output$map_deaths <- renderLeaflet({
data_swiss_geo_last <- data_swiss_geo %>%
filter(date == max(date)-2)
labels3 <- sprintf(
"%s<br/><strong>%s</strong><br/> %s deaths every 10000 people",
data_swiss_geo_last$date, data_swiss_geo_last$Canton, round(data_swiss_geo_last$ncumul_deceased_per_10thous,0)
) %>% lapply(htmltools::HTML)
data_swiss_geo_last %>%
leaflet(options = leafletOptions(minZoom = 7.2)) %>%
setView(lat = 46.9, lng = 8.3, zoom = 7) %>%
addProviderTiles("CartoDB") %>%
addPolygons(weight = 1, col = "grey") %>%
addCircleMarkers(~longitude, ~latitude,
radius = ~data_swiss_geo_last$ncumul_deceased_per_10thous,
stroke = TRUE,
color = "mediumorchid",
weight = 2,
fillOpacity = 0.4,
label = labels3)
})
output$cases_abs <- renderPlotly({
data_swiss %>%
plot_ly() %>%
filter(date == max(date)-2) %>%
add_bars(x = ~reorder(Canton, -ncumul_conf),
y = ~ncumul_conf,
color = I("black"),
opacity = 0.5,
hoverinfo = "text",
text = ~paste(date, "<br>", Canton, "<br>", ncumul_conf, "total cases")) %>%
layout(yaxis = list(title = "Total Covid-19 cases", showgrid = F),
xaxis = list(title = " ")) %>%
config(displayModeBar = FALSE, displaylogo = FALSE)
})
output$cases <- renderPlotly({
data_swiss %>%
plot_ly() %>%
filter(date == max(date)-2) %>%
add_bars(x = ~reorder(Canton, -ncumul_conf_per_10thous),
y = ~ncumul_conf_per_10thous,
color = I("black"),
opacity = 0.5,
hoverinfo = "text",
text = ~paste(date, "<br>", Canton, "<br>", round(ncumul_conf_per_10thous, 0), "total cases every 10000 people")) %>%
layout(yaxis = list(title = "Total Covid-19 cases", showgrid = F),
xaxis = list(title = " ")) %>%
config(displayModeBar = FALSE, displaylogo = FALSE)
})
output$deaths_abs <- renderPlotly({
data_swiss %>%
plot_ly() %>%
filter(date == max(date)-2) %>%
add_bars(x = ~reorder(Canton, -ncumul_deceased),
y = ~ncumul_deceased,
color = I("black"),
opacity = 0.5,
hoverinfo = "text",
text = ~paste(date, "<br>", Canton, "<br>", ncumul_deceased, "total deaths")) %>%
layout(yaxis = list(title = "Total deaths", showgrid = F),
xaxis = list(title = " ")) %>%
config(displayModeBar = FALSE, displaylogo = FALSE)
})
output$deaths <- renderPlotly({
data_swiss %>%
plot_ly() %>%
filter(date == max(date)-2) %>%
add_bars(x = ~reorder(Canton, -ncumul_deceased_per_10thous),
y = ~ncumul_deceased_per_10thous,
hoverinfo = "text",
color = I("black"),
opacity = 0.5,
text = ~paste(date, "<br>", Canton, "<br>", round(ncumul_deceased_per_10thous, 0), "total deaths every 10000 people")) %>%
layout(yaxis = list(title = "Total deaths", showgrid = F),
xaxis = list(title = " ")) %>%
config(displayModeBar = FALSE, displaylogo = FALSE)
})
output$box_cases <- renderValueBox({
box_cases_val <- summarize(data_total_swiss, sum(switzerland_new_cases))
valueBox(box_cases_val, "Total cases in Switzerland", color = "yellow", icon = icon("virus"))
})
output$box_deaths <- renderValueBox({
box_deaths_val <- summarize(data_total_swiss, sum(switzerland_new_deaths))
valueBox(box_deaths_val, "Total deaths in Switzerland", color = "yellow", icon = icon("skull"))
})
output$box_canton <- renderValueBox({
a <- data_swiss %>% filter(date == max(date))
box_canton_val <- a$Canton[which.max(a$ncumul_conf)]
valueBox(box_canton_val, "Canton with highest number of cases", color = "yellow", icon = icon("arrow-up"))
})
output$swiss_trend <- renderValueBox({
tot14days_last_swiss <- summarize(trend, sum(tot14days_last))
tot14days_previous_swiss <- summarize(trend, sum(tot14days_previous))
swiss_trend_val <- (tot14days_last_swiss-tot14days_previous_swiss)/tot14days_last_swiss*100
valueBox(paste0(round(swiss_trend_val, 0), "%"), "14 days variation of cases in Switzerland", color = "yellow", icon = if(swiss_trend_val >= 0){icon("arrow-alt-circle-up")} else {icon("arrow-alt-circle-down")})
})
output$trend_table <- DT::renderDataTable({
trend_table <- trend %>%
select(-Pop, -Canton_abbr)
DT::datatable(trend_table,
rownames = FALSE,
class = "hover",
colnames = c("Canton", "total cases last 14 days", "total cases previous 14 days", "variation %"))
})
}
ui <- dashboardPage(skin = "purple", header, sidebar, body)
shiny::shinyApp(ui, server)