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Copy pathserver_copy_paste.R
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server_copy_paste.R
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output$th09Sevo <- renderInfoBox({
Th09_case_summary <- Th09_case_summary %>%
filter(date_time_2 >= input$th09dates[1] & date_time_2 <= input$th09dates[2])
infoBox(title = "Sevo Consumption",
value = paste(sum(Th09_case_summary$consumption_sev, na.rm = T), "ml"),
fill = T,
color = "yellow")
})
output$th09n2o <- renderInfoBox({
Th09_case_summary <- Th09_case_summary %>%
filter(date_time_2 >= input$th09dates[1] & date_time_2 <= input$th09dates[2])
infoBox(title = "N2O Consumption",
value = paste(sum(Th09_case_summary$consumption_n2o, na.rm = T), "L"),
fill = T,
color = "blue")
})
output$th09O2 <- renderInfoBox({
Th09_case_summary <- Th09_case_summary %>%
filter(date_time_2 >= input$th09dates[1] & date_time_2 <= input$th09dates[2])
infoBox(title = "Oxygen Consumption",
value = paste(sum(Th09_case_summary$consumption_o2, na.rm = T), "L"),
fill = T,
color = "black")
})
output$th09air <- renderInfoBox({
Th09_case_summary <- Th09_case_summary %>%
filter(date_time_2 >= input$th09dates[1] & date_time_2 <= input$th09dates[2])
infoBox(title = "Air Consumption",
value = paste(sum(Th09_case_summary$consumption_air, na.rm = T), "L"),
fill = F,
color = "black")
})
output$th09FGF_nonTIVA <- renderInfoBox({
Th09_case_summary <- Th09_case_summary %>%
filter(date_time_2 >= input$th09dates[1] & date_time_2 <= input$th09dates[2])
mean_Th09 <- Th09_case_summary %>%
group_by(TIVA) %>%
mutate(mean_FGF = mean(FGF, na.rm = T)) %>%
filter(TIVA == FALSE)
infoBox(title = "Mean FGF for Volatile Cases",
value = paste(round(mean_Th09[1, "mean_FGF"],2), "L/min"),
fill = T,
color = "navy")
})
output$th09FGF_TIVA <- renderInfoBox({
Th09_case_summary <- Th09_case_summary %>%
filter(date_time_2 >= input$th09dates[1] & date_time_2 <= input$th09dates[2])
mean_Th09 <- Th09_case_summary %>%
group_by(TIVA) %>%
mutate(mean_FGF = mean(FGF, na.rm = T)) %>%
filter(TIVA == TRUE)
infoBox(title = "Mean FGF for TIVA Cases",
value = paste(round(mean_Th09[1, "mean_FGF"],2), "L/min"),
fill = T,
color = "navy")
})
output$th09SevoCO2 <- renderValueBox({
Th09_case_summary <- Th09_case_summary %>%
filter(date_time_2 >= input$th09dates[1] & date_time_2 <= input$th09dates[2])
valueBox(
value = paste("Sevo", round(sum(Th09_case_summary$sevo_co2e, na.rm = T), 2)),
subtitle = "Kg",
icon = icon("industry"),
color = "yellow"
)
})
output$th09efficiency <- renderValueBox({
Th09_case_summary <- Th09_case_summary %>%
filter(date_time_2 >= input$th09dates[1] & date_time_2 <= input$th09dates[2])
valueBox(
value = round(mean(Th09_case_summary$sevo_efficiency, na.rm = T), 2)*100,
subtitle = "Sevo Efficiency",
color = "yellow",
icon = icon("percentage")
)
})
output$th09N2OCO2 <- renderValueBox({
Th09_case_summary <- Th09_case_summary %>%
filter(date_time_2 >= input$th09dates[1] & date_time_2 <= input$th09dates[2])
valueBox(
value = paste("Nitrous Oxide", round(sum(Th09_case_summary$n2o_co2e, na.rm = T), 2)),
subtitle = "Kg",
icon = icon("industry"),
color = "blue"
)
})
output$th09miles <- renderValueBox({
Th09_case_summary <- Th09_case_summary %>%
filter(date_time_2 >= input$th09dates[1] & date_time_2 <= input$th09dates[2])
valueBox(
value = paste(round(sum(Th09_case_summary$miles_driven_per_case, na.rm = T), 2), "miles"),
subtitle = "based on Ford Focus 1.5L EcoBoost with 277 mg/km CO2 Emissions",
icon = icon("car"),
color = "red"
)
})
output$th09sevocost <- renderValueBox({
Th09_case_summary <- Th09_case_summary %>%
filter(date_time_2 >= input$th09dates[1] & date_time_2 <= input$th09dates[2])
valueBox(
value = paste0("£", round(sum(Th09_case_summary$sevo_cost_per_case, na.rm = T), 2)),
subtitle = "Cost of sevo over period",
icon = icon("pound-sign"),
color = "green"
)
})
output$th09_monthly_cons <- renderPlot({
Th09_case_summary <- Th09_case_summary %>%
filter(date_time_2 >= input$th09dates[1] & date_time_2 <= input$th09dates[2])
Th09_case_summary %>%
filter(TIVA == F) %>%
mutate(consumption_sev = ifelse(is.na(consumption_sev), 0, consumption_sev),
consumption_n2o = ifelse(is.na(consumption_n2o), 0, consumption_n2o)) %>%
group_by(year_m) %>%
summarise(mean_sevo = mean(consumption_sev, na.rm = T),
mean_n20 = mean(consumption_n2o, na.rm = T)) %>%
ggplot(aes(x = year_m)) +
geom_line(aes(y = mean_sevo, colour = "Sevo"), size = 2) +
geom_line(aes(y = mean_n20, colour = "Nitrous"), size = 2) +
labs(x = "Time",
y = "Average Consumption per case (mL Sevo; L N2O)") +
coord_cartesian(ylim = c(0, 30)) +
theme(aspect.ratio = 0.4, plot.caption = element_text(size = 12, face = "italic"),
plot.title = element_text(size = 16),
axis.title = element_text(size = 14),
axis.text = element_text(size = 12)) +
scale_colour_manual(name = "Agent",
values = c("Sevo" = "#FABD2F",
"Nitrous" = "#0095E6"),
labels = c("Nitrous", "Sevo")) +
theme_minimal()
})
output$th09_monthly_co2e <- renderPlot({
Th09_case_summary <- Th09_case_summary %>%
filter(date_time_2 >= input$th09dates[1] & date_time_2 <= input$th09dates[2])
Th09_case_summary %>%
mutate(sevo_co2e = ifelse(is.na(sevo_co2e), 0, sevo_co2e),
n2o_co2e = ifelse(is.na(n2o_co2e), 0, n2o_co2e)) %>%
group_by(year_m) %>%
summarise(mean_sevo = mean(sevo_co2e, na.rm = T),
mean_n20 = mean(n2o_co2e, na.rm = T)) %>%
ggplot(aes(x = year_m)) +
geom_line(aes(y = mean_sevo, colour = "Sevo"), size = 2) +
geom_line(aes(y = mean_n20, colour = "Nitrous"), size = 2) +
labs(x = "Time",
y = "Average CCO2 Eq (Kg) per Case for Sevo and N2O)") +
coord_cartesian(ylim = c(0, 10)) +
theme(aspect.ratio = 0.4, plot.caption = element_text(size = 12, face = "italic"),
plot.title = element_text(size = 16),
axis.title = element_text(size = 14),
axis.text = element_text(size = 12)) +
scale_colour_manual(name = "Agent",
values = c("Sevo" = "#FABD2F",
"Nitrous" = "#0095E6"),
labels = c("Nitrous", "Sevo")) +
theme_minimal()
})
output$th09_monthly_FGF <- renderPlot({
Th09_case_summary <- Th09_case_summary %>%
filter(date_time_2 >= input$th09dates[1] & date_time_2 <= input$th09dates[2])
Th09_case_summary %>%
group_by(year_m, TIVA) %>%
summarise(monthly_FGF = mean(FGF, na.rm = T)) %>%
ggplot(aes(x = year_m)) +
geom_line(aes(y = monthly_FGF, color = TIVA), size = 2) +
labs(x = "Time",
y = "Average FGF (L/min)") +
coord_cartesian(ylim = c(0, 10)) +
theme(aspect.ratio = 0.4, plot.caption = element_text(size = 12, face = "italic"),
plot.title = element_text(size = 16),
axis.title = element_text(size = 14),
axis.text = element_text(size = 12)) +
scale_color_manual(name = "Anaesthetic Type",
values = c('TRUE' = '#8ec09c','FALSE' = '#693a7c'),
labels = c("Volatile","TIVA")) +
theme_minimal()
})
output$th09_monthly_miles <- renderPlot({
Th09_case_summary <- Th09_case_summary %>%
filter(date_time_2 >= input$th09dates[1] & date_time_2 <= input$th09dates[2])
Th09_case_summary %>%
group_by(year_m) %>%
summarise(monthly_miles = sum(miles_driven_per_case, na.rm = T)) %>%
ggplot(aes(x = year_m)) +
geom_line(aes(y = monthly_miles), colour = "#393E3F", size = 2) +
labs(x = "Time",
y = "Monthly Total (Miles)") +
coord_cartesian(ylim = c(0, 900)) +
theme(aspect.ratio = 0.4, plot.caption = element_text(size = 12, face = "italic"),
plot.title = element_text(size = 16),
axis.title = element_text(size = 14),
axis.text = element_text(size = 12)) +
theme_minimal()
})
output$th09_monthly_sevo_cost <- renderPlot({
Th09_case_summary <- Th09_case_summary %>%
filter(date_time_2 >= input$th09dates[1] & date_time_2 <= input$th09dates[2]) %>%
filter(TIVA == FALSE)
Th09_case_summary %>%
group_by(year_m) %>%
summarise(monthly_cost = mean(sevo_cost_per_case_min, na.rm = T)*100) %>%
ggplot(aes(x = year_m)) +
geom_line(aes(y = monthly_cost), colour = "#40926d", size = 2) +
labs(x = "Time",
y = "Cost of Sevo per minute (p)") +
coord_cartesian(ylim = c(0, 20)) +
theme(aspect.ratio = 0.4, plot.caption = element_text(size = 12, face = "italic"),
plot.title = element_text(size = 16),
axis.title = element_text(size = 14),
axis.text = element_text(size = 12)) +
theme_minimal()
})