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server.R
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server.R
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shinyServer(function(input, output, session) {
# Labels ------------------------------------------------------------------
y_label <- reactive({
req(input$selectedvariable)
if(input$selectedvariable == "cases"){
y_label <- "Casos sospechosos"
} else if (input$selectedvariable == "deaths"){
y_label <- "Muertes sospechosas"
}})
# MINSA
y_label_minsa <- reactive({
req(input$selectedvariable_minsa)
if(input$selectedvariable_minsa == "cases"){
y_label <- "Casos confirmados"
} else if (input$selectedvariable_minsa == "deaths"){
y_label <- "Muertes confirmadas"
}})
# Boxes -------------------------------------------------------------------
## Observatorio ----
max_date <- df_country %>%
filter(date == max(date))
max_casos <- max_date$cases
max_muertes <- max_date$deaths
max_fecha <- max_date$date
output$vbox1 <- renderInfoBox({
infoBox(
title = tags$p(format(max_casos, big.mark = ","), style = "font-size: 150%;"),
subtitle = tags$p("Casos sospechosos", style = "font-size: 120%;"),
icon = icon("users"),
color = "black",
fill = TRUE
)
})
output$vbox2 <- renderInfoBox({
infoBox(
title = tags$p(format(max_muertes, big.mark = ","), style = "font-size: 150%;"),
subtitle = tags$p("Muertes sospechosas", style = "font-size: 120%;"),
icon = icon("users"),
color = "black",
fill = TRUE
)
})
## MINSA ----
max_date_minsa <- df_minsa %>%
filter(date == max(date))
max_casos_minsa <- max_date_minsa$cases
max_muertes_minsa <- max_date_minsa$deaths
max_fecha_minsa <- max_date_minsa$date
output$vbox1_minsa <- renderInfoBox({
infoBox(
title = tags$p(format(max_casos_minsa, big.mark = ","), style = "font-size: 150%;"),
subtitle = tags$p("Casos confirmados", style = "font-size: 120%;"),
icon = icon("users"),
color = "black",
fill = TRUE
)
})
output$vbox2_minsa <- renderInfoBox({
infoBox(
title = tags$p(format(max_muertes_minsa, big.mark = ","), style = "font-size: 150%;"),
subtitle = tags$p("Muertes confirmadas", style = "font-size: 120%;"),
icon = icon("users"),
color = "black",
fill = TRUE
)
})
# Acumulado ---------------------------------------------------------------
output$plot <- renderPlotly({
## Departmental ----
if (input$dataset == "state"){
plot <- df %>%
filter(!is.na(cases)) %>%
filter(departamento %in% input$dep) %>%
group_by(departamento) %>%
mutate(
cases = cumsum(cases),
) %>%
ungroup() %>%
ggplot(aes(x = date, group = 1, color = departamento)) +
geom_line(aes_string(y = input$selectedvariable), size = 1.125) +
scale_y_continuous(labels = comma) +
scale_x_date(breaks = date_breaks("2 months"), labels = date_format("%m/%Y")) +
labs(color = "",
x = "Fecha",
y = y_label(),
title = y_label())
if(input$logarithmicY) {
plot <- plot + scale_y_log10(labels = comma_format(accuracy = 1))
}
ggplotly(plot, height = 600, width = 900, margin = list(b = 50, l = 50))
}
## Country level ----
else if (input$dataset == "country") {
plot <- df_country %>%
filter(!is.na(cases)) %>%
ggplot(aes(x = date)) +
geom_line(aes_string(y = input$selectedvariable), size = 1.125) +
scale_y_continuous(labels = comma) +
scale_x_date(breaks = date_breaks("2 months"), labels = date_format("%m/%Y")) +
labs(x = "Fecha",
y = y_label(),
title = y_label())
if(input$logarithmicY)
plot <- plot + scale_y_log10(labels = comma_format(accuracy = 1))
ggplotly(plot, height = 600, width = 900, margin = list(b = 50, l = 50))
}
})
## MINSA ----
output$plot_minsa <- renderPlotly({
plot <- df_minsa %>%
ggplot(aes(x = date)) +
geom_line(aes_string(y = input$selectedvariable_minsa), size = 1.125) +
scale_y_continuous(labels = comma) +
scale_x_date(breaks = date_breaks("2 months"), labels = date_format("%m/%Y")) +
labs(x = "Fecha",
y = y_label_minsa(),
title = y_label_minsa())
if(input$logarithmicY_minsa)
plot <- plot + scale_y_log10(labels = comma_format(accuracy = 1))
ggplotly(plot, height = 600, width = 900, margin = list(b = 50, l = 50))
})
# Diario ------------------------------------------------------------------
output$plot2 <- renderPlotly({
## Departmental ----
if (input$dataset == "state"){
plot <- df %>%
group_by(departamento) %>%
mutate(
deaths2 = deaths - lag(deaths),
deaths2 = ifelse(date == "2020-03-18", deaths, deaths2)
) %>%
select(-deaths) %>%
rename(deaths = deaths2) %>%
filter(departamento %in% input$dep) %>%
ggplot(aes(x = date, group = departamento, fill = departamento)) +
geom_bar(aes_string(y = input$selectedvariable),
stat = "identity",
width = 1,
alpha = 0.9) +
scale_y_continuous(labels = comma) +
scale_x_date(breaks = date_breaks("2 months"), labels = date_format("%m/%Y")) +
labs(fill = "",
x = "Fecha",
y = y_label(),
title = y_label())
if(input$logarithmicY)
plot <- plot + scale_y_log10(labels = comma_format(accuracy = 1))
ggplotly(plot, height = 600, width = 900, margin = list(b = 50, l = 50))
}
## Country level ----
else if (input$dataset == "country") {
plot <- df_country %>%
mutate(
cases2 = cases - lag(cases),
deaths2 = deaths - lag(deaths)
) %>%
mutate(
cases2 = ifelse(date == "2020-03-18", cases, cases2),
deaths2 = ifelse(date == "2020-03-18", deaths, deaths2)
) %>%
select(date, cases2, deaths2) %>%
rename(cases = cases2, deaths = deaths2) %>%
ggplot(aes(x = date)) +
geom_col(aes_string(y = input$selectedvariable),
width = 1,
fill = "grey",
alpha = 0.9) +
scale_y_continuous(labels = comma) +
scale_x_date(breaks = date_breaks("2 months"), labels = date_format("%m/%Y")) +
labs(x = "Fecha",
y = y_label(),
title = y_label())
if(input$logarithmicY)
plot <- plot + scale_y_log10(labels = comma_format(accuracy = 1))
ggplotly(plot, height = 600, width = 900, margin = list(b = 50, l = 50))
}
})
## Minsa ----
output$plot2_minsa <- renderPlotly({
plot <- df_minsa %>%
mutate(
cases2 = cases - lag(cases),
deaths2 = deaths - lag(deaths)
) %>%
mutate(
cases2 = ifelse(date == "2020-03-19", cases, cases2),
deaths2 = ifelse(date == "2020-03-19", deaths, deaths2)
) %>%
select(date, cases2, deaths2) %>%
rename(cases = cases2, deaths = deaths2) %>%
ggplot(aes(x = date)) +
geom_col(aes_string(y = input$selectedvariable_minsa), width = 1, fill = "gray", alpha = 0.9) +
scale_y_continuous(labels = comma) +
scale_x_date(breaks = date_breaks("2 months"), labels = date_format("%b/%Y")) +
labs(x = "Fecha",
y = y_label_minsa(),
title = y_label_minsa()) +
theme_ipsum_rc()
if(input$logarithmicY_minsa)
plot <- plot + scale_y_log10(labels = comma_format(accuracy = 1))
ggplotly(plot, height = 600, width = 900, margin = list(b = 50, l = 50))
})
# Table -------------------------------------------------------------------
## Observatorio ----
tableData <- reactive({
if (input$dataset == "state"){
df %>%
select(departamento, date, input$selectedvariable) %>%
mutate(source = "Observatorio Ciudadano COVID-19") %>%
select(source, date, departamento, input$selectedvariable) %>%
filter(., departamento %in% input$dep) %>%
arrange(desc(date))
}
else if (input$dataset == "country") {
df_country %>%
mutate(source = "Observatorio Ciudadano COVID-19") %>%
select(source, date, input$selectedvariable) %>%
arrange(desc(date))
}
})
## MINSA ----
tableData_minsa <- reactive({
df_minsa %>%
ungroup() %>%
mutate(source = "MINSA") %>%
select(source, date, input$selectedvariable_minsa, -id) %>%
arrange(desc(date))
})
## Observatorio ----
output$table <- renderDT(
tableData(),
class = "display nowrap compact",
filter = "top",
options = list(
"pageLength" = 20)
)
## MINSA ----
output$table_minsa <- renderDT(
tableData_minsa(),
class = "display nowrap compact",
filter = "top",
options = list(
"pageLength" = 20)
)
# Vaccines ----------------------------------------------------------------
y_label_vacunas <- reactive({
req(input$selectedvariable_vaccines)
if(input$selectedvariable_vaccines == "total_vaccinations"){
y_label <- "# de vacunas administradas"
} else if (input$selectedvariable_vaccines == "people_vaccinated"){
y_label <- "Personas vacunadas"
} else if (input$selectedvariable_vaccines == "people_fully_vaccinated"){
y_label <- "Personas completamente vacunadas"
}
})
output$plot_vacunas <- renderPlotly({
plot <- vaccines %>%
filter(!is.na(!!rlang::sym(input$selectedvariable_vaccines))) %>%
filter(location %in% c(input$countries, "Nicaragua")) %>%
ggplot(aes(x = date, group = location, color = location)) +
geom_line(size = 1.2, alpha = 0.8, aes_string(y = input$selectedvariable_vaccines)) +
geom_point(alpha = 0.6, aes_string(y = input$selectedvariable_vaccines)) +
scale_y_continuous(labels = comma) +
scale_color_viridis_d(option = "turbo") +
scale_x_date(breaks = date_breaks("2 months"), labels = date_format("%m/%Y")) +
labs(
y = y_label_vacunas(),
x = "Fecha",
color = "",
title = y_label_vacunas()
)
ggplotly(plot, height = 600, width = 900, margin = list(b = 50, l = 50))
})
## Descargar datos de vacunas ----
tableData_vacunas <- reactive({
vaccines %>%
ungroup() %>%
mutate(source = "Our World in Data") %>%
select(source, everything()) %>%
arrange(desc(date))
})
output$data_vacunas.csv <- downloadHandler(
filename = function() {
paste("vacunas-", Sys.Date(), ".csv", sep="")
},
content = function(file) {
write.csv(tableData_vacunas(), file, row.names = FALSE)
}
)
# Health Workers ----------------------------------------------------------
## BOXES ----
max_date_workers <- workers %>%
filter(date == max(date))
max_casos_workers <- max_date_workers$cases
max_muertes_workers <- max_date_workers$deaths
output$vbox1_workers <- renderInfoBox({
infoBox(
title = tags$p(format(max_casos_workers, big.mark = ","), style = "font-size: 150%;"),
subtitle = tags$p("Casos sospechosos", style = "font-size: 120%;"),
icon = icon("users"),
color = "black",
fill = TRUE
)
})
output$vbox2_workers <- renderInfoBox({
infoBox(
title = tags$p(format(max_muertes_workers, big.mark = ","), style = "font-size: 150%;"),
subtitle = tags$p("Muertes sospechosas", style = "font-size: 120%;"),
icon = icon("users"),
color = "black",
fill = TRUE
)
})
## Y LABEL WORKERS ----
y_label_workers <- reactive({
req(input$selectedvariable_workers)
if(input$selectedvariable_workers == "cases"){
y_label <- "Casos sospechosos"
} else if (input$selectedvariable_workers == "deaths"){
y_label <- "Muertes sospechosas"
}
})
## PLOTS ----
output$plot_workers <- renderPlotly({
## Country level ----
plot <- workers %>%
ggplot(aes(x = date)) +
geom_line(aes_string(y = input$selectedvariable_workers), size = 1.125) +
scale_y_continuous(labels = comma) +
scale_x_date(breaks = date_breaks("2 months"), labels = date_format("%m/%Y")) +
labs(
x = "Fecha",
y = y_label_workers(),
title = y_label_workers()
)
if(input$logarithmicY_workers)
plot <- plot + scale_y_log10(labels = comma_format(accuracy = 1))
ggplotly(plot, height = 600, width = 900, margin = list(b = 50, l = 50))
})
## Descargar datos de vacunas ----
tableData_workers <- reactive({
workers %>%
ungroup() %>%
mutate(source = "Observatorio Ciudadano COVID-1") %>%
select(source, everything()) %>%
arrange(desc(date))
})
output$data_workers.csv <- downloadHandler(
filename = function() {
paste("health-workers-", Sys.Date(), ".csv", sep = "")
},
content = function(file) {
write.csv(tableData_workers(), file, row.names = FALSE)
}
)
# Download bottom ---------------------------------------------------------
## Observatorio ----
output$data.csv <- downloadHandler(
filename = function() {
paste("covd-nic-observatorio-", Sys.Date(), ".csv", sep="")
},
content = function(file) {
write.csv(tableData(), file, row.names = FALSE)
}
)
## MINSA ----
output$data_minsa.csv <- downloadHandler(
filename = function() {
paste("covd-nic-minsa-", Sys.Date(), ".csv", sep="")
},
content = function(file) {
write.csv(tableData_minsa(), file, row.names = FALSE)
}
)
#----------------#
#### MAPAS ####
# Map data
# mapdata <- get_data_from_map(download_map_data("https://code.highcharts.com/mapdata/countries/ni/ni-all.js")) %>%
# write_rds("data/mapdata.rds")
# MINSA
y_label_map <- reactive({
req(input$selectedvariable_map)
if(input$selectedvariable_map == "cases"){
y_label <- "Casos sospechosos"
} else if (input$selectedvariable_map == "deaths"){
y_label <- "Muertes sospechosas"
}})
# Prepare data
last_day <- df %>%
group_by(departamento) %>%
filter(date == max(date)) %>%
mutate(name = departamento)
# Codes to merge
codes <- mapdata %>%
select(code = `hc-a2`, `name`) %>%
mutate(
name = ifelse(code == "AS", "RACCS", name),
name = ifelse(code == "AN", "RACCN", name),
name = ifelse(code == "RS", "Río San Juan", name),
departamento = name
) %>%
left_join(last_day)
# Chart
output$hchart <- renderHighchart(
hcmap("countries/ni/ni-all",
data = codes,
value = input$selectedvariable_map,
joinBy = c("hc-a2", "code"),
dataLabels = list(enabled = TRUE, format = "{point.departamento}"),
borderColor = "#AFAFAF",
borderWidth = 1
) %>%
hc_tooltip(headerFormat = "") %>%
hc_title(text = paste0(y_label_map())) %>%
hc_subtitle(text = paste0("Actualizado al ", format(max_fecha, "%d %b %Y"))) %>%
hc_caption(text = "Datos: Obsevatorio Ciudadano COVID-19.") %>%
hc_mapNavigation(enabled = T) %>%
hc_exporting(enabled = TRUE, filename = "custom")
) %>%
bindCache(input$selectedvariable_map)
})