inst/app/server/projection/plot-functions.R
In jackolney/CascadeDashboard: A Shiny App for Investigating The Current Trajectory of HIV Care
GenYourCascadePlot <- function(h) {
t0 <- ExtractCascadeData(1)
c.fill <- rev(brewer.pal(9, "Blues")[3:8])
c.fill[h] <- "red"
output$outPLHIV <- renderInfoBox({
infoBox(
title = "Mean number of PLHIV",
value = scales::comma(round(t0[t0$def == "# PLHIV", "res"], 0)),
subtitle = paste(scales::comma(round(t0[t0$def == "# PLHIV", "min"], 0)), "-", scales::comma(round(t0[t0$def == "# PLHIV", "max"], 0))),
color = "blue",
fill = TRUE,
icon = icon("hashtag", lib = "font-awesome")
)
})
output$outDIAG <- renderInfoBox({
infoBox(
title = "Mean number of PLHIV who have been diagnosed",
value = scales::comma(round(t0[t0$def == "# Diagnosed", "res"], 0)),
subtitle = paste(scales::comma(round(t0[t0$def == "# Diagnosed", "min"], 0)), "-", scales::comma(round(t0[t0$def == "# Diagnosed", "max"], 0))),
color = "blue",
fill = TRUE,
icon = icon("hashtag", lib = "font-awesome")
)
})
output$outCARE <- renderInfoBox({
infoBox(
title = "Mean number of PLHIV in HIV care (including ART)",
value = scales::comma(round(t0[t0$def == "# In Care", "res"], 0)),
subtitle = paste(scales::comma(round(t0[t0$def == "# In Care", "min"], 0)), "-", scales::comma(round(t0[t0$def == "# In Care", "max"], 0))),
color = "blue",
fill = TRUE,
icon = icon("hashtag", lib = "font-awesome")
)
})
output$outART <- renderInfoBox({
infoBox(
title = "Mean number of PLHIV in HIV care and on ART",
value = scales::comma(round(t0[t0$def == "# Treatment", "res"], 0)),
subtitle = paste(scales::comma(round(t0[t0$def == "# Treatment", "min"], 0)), "-", scales::comma(round(t0[t0$def == "# Treatment", "max"], 0))),
color = "blue",
fill = TRUE,
icon = icon("hashtag", lib = "font-awesome")
)
})
output$outSUPP <- renderInfoBox({
infoBox(
title = "Mean number of PLHIV in HIV care, on ART and virally suppressed",
value = scales::comma(round(t0[t0$def == "# Suppressed", "res"], 0)),
subtitle = paste(scales::comma(round(t0[t0$def == "# Suppressed", "min"], 0)), "-", scales::comma(round(t0[t0$def == "# Suppressed", "max"], 0))),
color = "blue",
fill = TRUE,
icon = icon("hashtag", lib = "font-awesome")
)
})
ggOut <- ggplot(t0, aes(x = def, y = res))
ggOut <- ggOut + geom_bar(aes(fill = def), position = 'dodge', stat = 'identity')
ggOut <- ggOut + geom_errorbar(mapping = aes(x = def, ymin = min, ymax = max), width = 0.2, size = 1)
ggOut <- ggOut + scale_y_continuous(labels = scales::comma, expand = c(0, 0), breaks = scales::pretty_breaks(n = 5))
ggOut <- ggOut + expand_limits(y = round(max(t0$max), digits = -4) + 1e5)
ggOut <- ggOut + scale_fill_manual(values = c.fill)
ggOut <- ggOut + ggtitle("Care Cascade in 2015")
ggOut <- ggOut + theme_classic()
ggOut <- ggOut + theme(plot.title = element_text(hjust = 0.5))
ggOut <- ggOut + theme(title = element_text(size = 18))
ggOut <- ggOut + theme(axis.title = element_blank())
ggOut <- ggOut + theme(axis.text.x = element_text(size = 17))
ggOut <- ggOut + theme(axis.text.y = element_text(size = 18))
ggOut <- ggOut + theme(legend.position = "none")
ggOut <- ggOut + theme(plot.background = element_blank())
ggOut <- ggOut + theme(panel.background = element_blank())
ggOut <- ggOut + theme(axis.line.y = element_line())
ggOut <- ggOut + theme(text = element_text(family = figFont))
ggOut
}
GenCascadePlot <- function() {
t0 <- ExtractCascadeData(1) # t0 = 1
t5 <- ExtractCascadeData(251) # t5 = (5 / 0.02) + 1 [t0]
c.fill <- rev(brewer.pal(9,"Blues")[3:8])
t0$year <- 2015
t5$year <- 2020
out <- rbind(t0, t5)
ggOne <- ggplot(out, aes(x = def, y = res))
ggOne <- ggOne + geom_bar(aes(fill = as.factor(year)), position = 'dodge', stat = 'identity')
ggOne <- ggOne + geom_errorbar(mapping = aes(x = def, ymin = min, ymax = max, fill = as.factor(year)), position = position_dodge(width = 0.9), stat = "identity", width = 0.2, size = 1)
ggOne <- ggOne + scale_y_continuous(labels = scales::comma, expand = c(0, 0), breaks = scales::pretty_breaks(n = 5))
ggOne <- ggOne + scale_fill_manual(values = c(c.fill[2],c.fill[5]), guide = guide_legend(title = "Year"))
ggOne <- ggOne + theme_classic()
ggOne <- ggOne + theme(title = element_text(size = 18))
ggOne <- ggOne + theme(axis.title = element_blank())
ggOne <- ggOne + theme(axis.text.x = element_text(size = 17))
ggOne <- ggOne + theme(axis.text.y = element_text(size = 18))
ggOne <- ggOne + theme(axis.ticks.x = element_blank())
ggOne <- ggOne + theme(legend.position = "right")
ggOne <- ggOne + theme(legend.title = element_text(size = 17))
ggOne <- ggOne + theme(legend.text = element_text(size = 15))
ggOne <- ggOne + theme(plot.background = element_blank())
ggOne <- ggOne + theme(panel.background = element_blank())
ggOne <- ggOne + theme(axis.line.y = element_line())
ggOne <- ggOne + theme(text = element_text(family = figFont))
ggOne <- ggOne + expand_limits(y = round(max(out$max), digits = -4) + 1e5)
ggOne
}
GrabLegend <- function(a.ggplot) {
tmp <- ggplot_gtable(ggplot_build(a.ggplot))
leg <- which(sapply(tmp$grobs, function(x) x$name) == "guide-box")
legend <- tmp$grobs[[leg]]
return(legend)
}
GenPowersCascadePlot <- function() {
t0 <- ExtractPowersCascadeData(1)
t5 <- ExtractPowersCascadeData(251) # t5 = (5 / 0.02) + 1 [t0]
cols <- brewer.pal(9,"Set1")
p.col <- c(cols[3], cols[2], cols[4], cols[5], cols[1], cols[9], cols[8])
ggOne <- ggplot(t0, aes(x = order, y = res, fill = state))
ggOne <- ggOne + geom_bar(stat = 'identity')
ggOne <- ggOne + scale_y_continuous(labels = scales::comma, expand = c(0, 0), breaks = scales::pretty_breaks(n = 5))
ggOne <- ggOne + scale_fill_manual(values = p.col)
ggOne <- ggOne + ggtitle("2015")
ggOne <- ggOne + theme_classic()
ggOne <- ggOne + theme(plot.title = element_text(hjust = 0.5))
ggOne <- ggOne + theme(title = element_text(size = 18))
ggOne <- ggOne + theme(axis.title = element_blank())
ggOne <- ggOne + theme(axis.text.x = element_text(size = 13))
ggOne <- ggOne + theme(axis.text.y = element_text(size = 15))
ggOne <- ggOne + theme(legend.text = element_text(size = 13))
ggOne <- ggOne + theme(legend.title = element_text(size = 15))
ggOne <- ggOne + theme(legend.position = "right")
ggOne <- ggOne + theme(plot.background = element_blank())
ggOne <- ggOne + theme(panel.background = element_blank())
cols <- brewer.pal(9,"Set1")
p.col <- c(cols[3], cols[2], cols[4], cols[5], cols[1], cols[9], cols[8])
ggTwo <- ggplot(t5, aes(x = order, y = res, fill = state))
ggTwo <- ggTwo + geom_bar(stat = 'identity')
ggTwo <- ggTwo + scale_y_continuous(labels = scales::comma, expand = c(0, 0), breaks = scales::pretty_breaks(n = 5))
ggTwo <- ggTwo + scale_fill_manual(values = p.col)
ggTwo <- ggTwo + ggtitle("2020")
ggTwo <- ggTwo + theme_classic()
ggTwo <- ggTwo + theme(plot.title = element_text(hjust = 0.5))
ggTwo <- ggTwo + theme(title = element_text(size = 18))
ggTwo <- ggTwo + theme(axis.title = element_blank())
ggTwo <- ggTwo + theme(axis.text.x = element_text(size = 13))
ggTwo <- ggTwo + theme(axis.text.y = element_text(size = 15))
ggTwo <- ggTwo + theme(legend.text = element_text(size = 13))
ggTwo <- ggTwo + theme(legend.title = element_text(size = 15))
ggTwo <- ggTwo + theme(legend.position = "right")
ggTwo <- ggTwo + theme(plot.background = element_blank())
ggTwo <- ggTwo + theme(panel.background = element_blank())
my.legend <- GrabLegend(ggOne)
l.width <- sum(my.legend$width)
grid.arrange(
arrangeGrob(
ggOne + theme(legend.position = "none"),
ggTwo + theme(legend.position = "none"),
ncol = 2),
my.legend,
widths = grid::unit.c(unit(1, "npc") - l.width, l.width),
nrow = 1)
}
Gen909090Plot <- function() {
out <- Extract909090Data()
vbOut1 <- round(out[out$def == "Diagnosed / PLHIV", "res"] * 100, digits = 0)
vbOut2 <- round(out[out$def == "On Treatment / Diagnosed", "res"] * 100, digits = 0)
vbOut3 <- round(out[out$def == "Virally Suppressed / On Treatment", "res"] * 100, digits = 0)
output$vb_90 <- renderValueBox({ valueBox(paste0(vbOut1, "%"), "Diagnosed / PLHIV", color = "red", icon = icon("medkit", lib = "font-awesome")) })
output$vb_9090 <- renderValueBox({ valueBox(paste0(vbOut2, "%"), "On Treatment / Diagnosed", color = "red", icon = icon("medkit", lib = "font-awesome")) })
output$vb_909090 <- renderValueBox({ valueBox(paste0(vbOut3, "%"), "Virally Suppressed / On Treatment", color = "red", icon = icon("medkit", lib = "font-awesome")) })
output$vb_90_wizard <- renderValueBox({ valueBox(paste0(vbOut1, "%"), "Diagnosed / PLHIV", color = "red", icon = icon("medkit", lib = "font-awesome")) })
output$vb_9090_wizard <- renderValueBox({ valueBox(paste0(vbOut2, "%"), "On Treatment / Diagnosed", color = "red", icon = icon("medkit", lib = "font-awesome")) })
output$vb_909090_wizard <- renderValueBox({ valueBox(paste0(vbOut3, "%"), "Virally Suppressed / On Treatment", color = "red", icon = icon("medkit", lib = "font-awesome")) })
# Blue override for colour
cfill <- rev(brewer.pal(9,"Blues")[6:8])
ggOut <- ggplot(out, aes(x = def, y = res))
ggOut <- ggOut + geom_bar(aes(fill = def), position = 'dodge', stat = 'identity')
ggOut <- ggOut + geom_errorbar(mapping = aes(x = def, ymin = min, ymax = max), width = 0.2, size = 1)
ggOut <- ggOut + scale_y_continuous(limits = c(0, 1), breaks = seq(0, 1, 0.1), labels = scales::percent, expand = c(0, 0))
ggOut <- ggOut + scale_fill_manual(values = cfill)
ggOut <- ggOut + geom_abline(intercept = 0.9, slope = 0)
ggOut <- ggOut + theme_classic()
ggOut <- ggOut + theme(plot.title = element_text(hjust = 0.5))
ggOut <- ggOut + theme(title = element_text(size = 20))
ggOut <- ggOut + theme(axis.title = element_blank())
ggOut <- ggOut + theme(axis.ticks.x = element_blank())
ggOut <- ggOut + theme(axis.text.x = element_text(size = 18))
ggOut <- ggOut + theme(axis.text.y = element_text(size = 18))
ggOut <- ggOut + theme(legend.position = "none")
ggOut <- ggOut + theme(plot.background = element_blank())
ggOut <- ggOut + theme(panel.background = element_blank())
ggOut <- ggOut + theme(axis.line.y = element_line())
ggOut <- ggOut + theme(text = element_text(family = figFont))
ggOut <- ggOut + geom_label(aes(x = def, label = scales::percent(round(out$res, digits = 2))), size = 8)
ggOut
}
GenNewInfPlot <- function(wizard) {
result <- CallModel()
out <- c()
min <- c()
max <- c()
for (j in 1:251) {
dat <- Quantile_95(unlist(lapply(result, function(x) sum(x$NewInf[j]))))
out[j] <- dat[["mean"]]
min[j] <- dat[["lower"]]
max[j] <- dat[["upper"]]
}
NI_out <- c(0, diff(out))
NI_min <- c(0, diff(min))
NI_max <- c(0, diff(max))
times <- seq(0, 5, 0.02)
combo <- cbind(times, NI_out, NI_min, NI_max)
# Calculate time intervals
yr2015 <- times[times >= 0 & times <= 1]
yr2016 <- times[times > 1 & times <= 2]
yr2017 <- times[times > 2 & times <= 3]
yr2018 <- times[times > 3 & times <= 4]
yr2019 <- times[times > 4 & times <= 5]
# count between years to calculate bars
bar1 <- combo[times %in% yr2015,]
bar2 <- combo[times %in% yr2016,]
bar3 <- combo[times %in% yr2017,]
bar4 <- combo[times %in% yr2018,]
bar5 <- combo[times %in% yr2019,]
NewInf <- c(
sum(bar1[,"NI_out"]),
sum(bar2[,"NI_out"]),
sum(bar3[,"NI_out"]),
sum(bar4[,"NI_out"]),
sum(bar5[,"NI_out"])
)
min <- c(
sum(bar1[,"NI_min"]),
sum(bar2[,"NI_min"]),
sum(bar3[,"NI_min"]),
sum(bar4[,"NI_min"]),
sum(bar5[,"NI_min"])
)
max <- c(
sum(bar1[,"NI_max"]),
sum(bar2[,"NI_max"]),
sum(bar3[,"NI_max"]),
sum(bar4[,"NI_max"]),
sum(bar5[,"NI_max"])
)
timeOut <- seq(2015, 2019, 1)
df <- data.frame(timeOut, NewInf, min, max)
c.fill <- rev(brewer.pal(9,"Blues")[4:8])
ggOut <- ggplot(df, aes(x = timeOut, NewInf))
ggOut <- ggOut + geom_bar(stat = "identity", size = 2, fill = c.fill)
ggOut <- ggOut + geom_errorbar(mapping = aes(x = timeOut, ymin = min, ymax = max), width = 0.2, size = 1)
ggOut <- ggOut + expand_limits(y = round(max(df$max), digits = -4))
ggOut <- ggOut + theme_classic()
ggOut <- ggOut + scale_y_continuous(labels = scales::comma, expand = c(0, 0), breaks = scales::pretty_breaks(n = 5))
ggOut <- ggOut + theme(axis.line.y = element_line())
ggOut <- ggOut + scale_x_continuous(breaks = seq(2015, 2019, 1), labels = seq(2015, 2019, 1))
ggOut <- ggOut + theme(text = element_text(family = figFont))
ggOut <- ggOut + theme(axis.ticks.x = element_blank())
if (wizard) {
ggOut <- ggOut + theme(axis.text.x = element_text(size = 12))
ggOut <- ggOut + theme(axis.text.y = element_text(size = 12))
ggOut <- ggOut + theme(axis.title = element_blank())
} else {
ggOut <- ggOut + theme(axis.text.x = element_text(size = 18))
ggOut <- ggOut + theme(axis.text.y = element_text(size = 18))
ggOut <- ggOut + theme(axis.title = element_blank())
}
ggOut
}
GenAidsDeathsPlot <- function(wizard) {
result <- CallModel()
out <- c()
min <- c()
max <- c()
for (j in 1:251) {
dat <- Quantile_95(unlist(lapply(result, function(x) sum(x$HivMortality[j]))))
out[j] <- dat[["mean"]]
min[j] <- dat[["lower"]]
max[j] <- dat[["upper"]]
}
HM_out <- c(0, diff(out))
HM_min <- c(0, diff(min))
HM_max <- c(0, diff(max))
times <- seq(0, 5, 0.02)
combo <- cbind(times, HM_out, HM_min, HM_max)
# Calculate time intervals
yr2015 <- times[times >= 0 & times <= 1]
yr2016 <- times[times > 1 & times <= 2]
yr2017 <- times[times > 2 & times <= 3]
yr2018 <- times[times > 3 & times <= 4]
yr2019 <- times[times > 4 & times <= 5]
# count between years to calculate bars
bar1 <- combo[times %in% yr2015,]
bar2 <- combo[times %in% yr2016,]
bar3 <- combo[times %in% yr2017,]
bar4 <- combo[times %in% yr2018,]
bar5 <- combo[times %in% yr2019,]
HivMortality <- c(
sum(bar1[,"HM_out"]),
sum(bar2[,"HM_out"]),
sum(bar3[,"HM_out"]),
sum(bar4[,"HM_out"]),
sum(bar5[,"HM_out"])
)
min <- c(
sum(bar1[,"HM_min"]),
sum(bar2[,"HM_min"]),
sum(bar3[,"HM_min"]),
sum(bar4[,"HM_min"]),
sum(bar5[,"HM_min"])
)
max <- c(
sum(bar1[,"HM_max"]),
sum(bar2[,"HM_max"]),
sum(bar3[,"HM_max"]),
sum(bar4[,"HM_max"]),
sum(bar5[,"HM_max"])
)
timeOut <- seq(2015, 2019, 1)
df <- data.frame(timeOut, HivMortality, min, max)
c.fill <- rev(brewer.pal(9,"Blues")[4:8])
ggOut <- ggplot(df, aes(x = timeOut, HivMortality))
ggOut <- ggOut + geom_bar(stat = "identity", size = 2, fill = c.fill)
ggOut <- ggOut + geom_errorbar(mapping = aes(x = timeOut, ymin = min, ymax = max), width = 0.2, size = 1)
ggOut <- ggOut + expand_limits(y = round(max(df$max), digits = -4))
ggOut <- ggOut + theme_classic()
ggOut <- ggOut + scale_y_continuous(labels = scales::comma, expand = c(0, 0), breaks = scales::pretty_breaks(n = 5))
ggOut <- ggOut + theme(axis.line.y = element_line())
ggOut <- ggOut + scale_x_continuous(breaks = seq(2015, 2019, 1), labels = seq(2015, 2019, 1))
ggOut <- ggOut + theme(text = element_text(family = figFont))
ggOut <- ggOut + theme(axis.ticks.x = element_blank())
if (wizard) {
ggOut <- ggOut + theme(axis.text.x = element_text(size = 12))
ggOut <- ggOut + theme(axis.text.y = element_text(size = 12))
ggOut <- ggOut + theme(axis.title = element_blank())
} else {
ggOut <- ggOut + theme(axis.text.x = element_text(size = 18))
ggOut <- ggOut + theme(axis.text.y = element_text(size = 18))
ggOut <- ggOut + theme(axis.title = element_blank())
}
ggOut
}
GenSinglePlot <- function() {
result <- CallModel()
out <- c()
min <- c()
max <- c()
for (j in 1:251) {
out[j] <- mean(unlist(lapply(result, function(x) sum(x[j,input$y]))))
min[j] <- range(unlist(lapply(result, function(x) sum(x[j,input$y]))))[1]
max[j] <- range(unlist(lapply(result, function(x) sum(x[j,input$y]))))[2]
}
time <- seq(0, 5, 0.02)
df <- data.frame(time, out, min, max)
ggOut <- ggplot(df, aes(x = time, y = out))
ggOut <- ggOut + geom_ribbon(aes(ymin = min, ymax = max), alpha = 0.2, fill = ggColorHue(3)[3])
ggOut <- ggOut + geom_line(size = 1.5, color = ggColorHue(1))
ggOut <- ggOut + theme_classic()
ggOut <- ggOut + scale_y_continuous(labels = scales::comma, breaks = scales::pretty_breaks(n = 5))
ggOut <- ggOut + scale_x_continuous(limits = c(0, 5), breaks = seq(0, 5, 1), labels = seq(2015, 2020, 1))
ggOut <- ggOut + theme(axis.text.x = element_text(size = 18))
ggOut <- ggOut + theme(axis.text.y = element_text(size = 18))
ggOut <- ggOut + theme(axis.title = element_text(size = 18))
ggOut <- ggOut + theme(axis.line.x = element_line())
ggOut <- ggOut + theme(axis.line.y = element_line())
ggOut <- ggOut + theme(axis.title.y = element_blank())
ggOut <- ggOut + xlab("Year")
ggOut
}
GenAllPlot <- function() {
result <- CallModel()
UnDx <- c()
Dx <- c()
Care <- c()
PreLtfu <- c()
Tx <- c()
Vs <- c()
Ltfu <- c()
N <- c()
NewInf <- c()
TotalCost <- c()
HivMortalityProp <- c()
NaturalMortalityProp <- c()
for (j in 1:251) {
UnDx[j] <- mean(unlist(lapply(result, function(x) sum(x$UnDx[j]))))
Dx[j] <- mean(unlist(lapply(result, function(x) sum(x$Dx[j]))))
Care[j] <- mean(unlist(lapply(result, function(x) sum(x$Care[j]))))
PreLtfu[j] <- mean(unlist(lapply(result, function(x) sum(x$PreLtfu[j]))))
Tx[j] <- mean(unlist(lapply(result, function(x) sum(x$Tx[j]))))
Vs[j] <- mean(unlist(lapply(result, function(x) sum(x$Vs[j]))))
Ltfu[j] <- mean(unlist(lapply(result, function(x) sum(x$Ltfu[j]))))
N[j] <- mean(unlist(lapply(result, function(x) sum(x$N[j]))))
NewInf[j] <- mean(unlist(lapply(result, function(x) sum(x$NewInf[j]))))
TotalCost[j] <- mean(unlist(lapply(result, function(x) sum(x$TotalCost[j]))))
HivMortalityProp[j] <- mean(unlist(lapply(result, function(x) sum(x$HivMortalityProp[j]))))
NaturalMortalityProp[j] <- mean(unlist(lapply(result, function(x) sum(x$NaturalMortalityProp[j]))))
}
time <- seq(0, 5, 0.02)
gridExtra::grid.arrange(
ggplot(data.frame(time, UnDx), aes(x = time, y = UnDx)) + geom_line(color = ggColorHue(1)) +
theme_classic() + theme(axis.text.x = element_text(size = 18)) + theme(axis.text.y = element_text(size = 18)) +
theme(axis.title = element_text(size = 18)) + xlab("Year") + theme(axis.line.x = element_line()) +
theme(axis.line.y = element_line()),
ggplot(data.frame(time, Dx), aes(x = time, y = Dx)) + geom_line(color = ggColorHue(1)) +
theme_classic() + theme(axis.text.x = element_text(size = 18)) + theme(axis.text.y = element_text(size = 18)) +
theme(axis.title = element_text(size = 18)) + xlab("Year") + theme(axis.line.x = element_line()) +
theme(axis.line.y = element_line()),
ggplot(data.frame(time, Care), aes(x = time, y = Care)) + geom_line(color = ggColorHue(1)) +
theme_classic() + theme(axis.text.x = element_text(size = 18)) + theme(axis.text.y = element_text(size = 18)) +
theme(axis.title = element_text(size = 18)) + xlab("Year") + theme(axis.line.x = element_line()) +
theme(axis.line.y = element_line()),
ggplot(data.frame(time, PreLtfu), aes(x = time, y = PreLtfu)) + geom_line(color = ggColorHue(1)) +
theme_classic() + theme(axis.text.x = element_text(size = 18)) + theme(axis.text.y = element_text(size = 18)) +
theme(axis.title = element_text(size = 18)) + xlab("Year") + theme(axis.line.x = element_line()) +
theme(axis.line.y = element_line()),
ggplot(data.frame(time, Tx), aes(x = time, y = Tx)) + geom_line(color = ggColorHue(1)) +
theme_classic() + theme(axis.text.x = element_text(size = 18)) + theme(axis.text.y = element_text(size = 18)) +
theme(axis.title = element_text(size = 18)) + xlab("Year") + theme(axis.line.x = element_line()) +
theme(axis.line.y = element_line()),
ggplot(data.frame(time, Vs), aes(x = time, y = Vs)) + geom_line(color = ggColorHue(1)) +
theme_classic() + theme(axis.text.x = element_text(size = 18)) + theme(axis.text.y = element_text(size = 18)) +
theme(axis.title = element_text(size = 18)) + xlab("Year") + theme(axis.line.x = element_line()) +
theme(axis.line.y = element_line()),
ggplot(data.frame(time, Ltfu), aes(x = time, y = Ltfu)) + geom_line(color = ggColorHue(1)) +
theme_classic() + theme(axis.text.x = element_text(size = 18)) + theme(axis.text.y = element_text(size = 18)) +
theme(axis.title = element_text(size = 18)) + xlab("Year") + theme(axis.line.x = element_line()) +
theme(axis.line.y = element_line()),
ggplot(data.frame(time, N), aes(x = time, y = N)) + geom_line(color = ggColorHue(1)) +
theme_classic() + theme(axis.text.x = element_text(size = 18)) + theme(axis.text.y = element_text(size = 18)) +
theme(axis.title = element_text(size = 18)) + xlab("Year") + theme(axis.line.x = element_line()) +
theme(axis.line.y = element_line()),
ggplot(data.frame(time, NewInf), aes(x = time, y = NewInf)) + geom_line(color = ggColorHue(1)) +
theme_classic() + theme(axis.text.x = element_text(size = 18)) + theme(axis.text.y = element_text(size = 18)) +
theme(axis.title = element_text(size = 18)) + xlab("Year") + theme(axis.line.x = element_line()) +
theme(axis.line.y = element_line()),
ggplot(data.frame(time, TotalCost), aes(x = time, y = TotalCost)) + geom_line(color = ggColorHue(1)) +
theme_classic() + theme(axis.text.x = element_text(size = 18)) + theme(axis.text.y = element_text(size = 18)) +
theme(axis.title = element_text(size = 18)) + xlab("Year") + theme(axis.line.x = element_line()) +
theme(axis.line.y = element_line()),
ggplot(data.frame(time, HivMortalityProp), aes(x = time, y = HivMortalityProp)) + geom_line(color = ggColorHue(1)) +
theme_classic() + theme(axis.text.x = element_text(size = 18)) + theme(axis.text.y = element_text(size = 18)) +
theme(axis.title = element_text(size = 18)) + xlab("Year") + theme(axis.line.x = element_line()) +
theme(axis.line.y = element_line()),
ggplot(data.frame(time, NaturalMortalityProp), aes(x = time, y = NaturalMortalityProp)) + geom_line(color = ggColorHue(1)) +
theme_classic() + theme(axis.text.x = element_text(size = 18)) + theme(axis.text.y = element_text(size = 18)) +
theme(axis.title = element_text(size = 18)) + xlab("Year") + theme(axis.line.x = element_line()) +
theme(axis.line.y = element_line()),
nrow = 4,
ncol = 3
)
}
jackolney/CascadeDashboard documentation built on May 18, 2019, 7:56 a.m.
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GenYourCascadePlot <- function(h) {
t0 <- ExtractCascadeData(1)
c.fill <- rev(brewer.pal(9, "Blues")[3:8])
c.fill[h] <- "red"
output$outPLHIV <- renderInfoBox({
infoBox(
title = "Mean number of PLHIV",
value = scales::comma(round(t0[t0$def == "# PLHIV", "res"], 0)),
subtitle = paste(scales::comma(round(t0[t0$def == "# PLHIV", "min"], 0)), "-", scales::comma(round(t0[t0$def == "# PLHIV", "max"], 0))),
color = "blue",
fill = TRUE,
icon = icon("hashtag", lib = "font-awesome")
)
})
output$outDIAG <- renderInfoBox({
infoBox(
title = "Mean number of PLHIV who have been diagnosed",
value = scales::comma(round(t0[t0$def == "# Diagnosed", "res"], 0)),
subtitle = paste(scales::comma(round(t0[t0$def == "# Diagnosed", "min"], 0)), "-", scales::comma(round(t0[t0$def == "# Diagnosed", "max"], 0))),
color = "blue",
fill = TRUE,
icon = icon("hashtag", lib = "font-awesome")
)
})
output$outCARE <- renderInfoBox({
infoBox(
title = "Mean number of PLHIV in HIV care (including ART)",
value = scales::comma(round(t0[t0$def == "# In Care", "res"], 0)),
subtitle = paste(scales::comma(round(t0[t0$def == "# In Care", "min"], 0)), "-", scales::comma(round(t0[t0$def == "# In Care", "max"], 0))),
color = "blue",
fill = TRUE,
icon = icon("hashtag", lib = "font-awesome")
)
})
output$outART <- renderInfoBox({
infoBox(
title = "Mean number of PLHIV in HIV care and on ART",
value = scales::comma(round(t0[t0$def == "# Treatment", "res"], 0)),
subtitle = paste(scales::comma(round(t0[t0$def == "# Treatment", "min"], 0)), "-", scales::comma(round(t0[t0$def == "# Treatment", "max"], 0))),
color = "blue",
fill = TRUE,
icon = icon("hashtag", lib = "font-awesome")
)
})
output$outSUPP <- renderInfoBox({
infoBox(
title = "Mean number of PLHIV in HIV care, on ART and virally suppressed",
value = scales::comma(round(t0[t0$def == "# Suppressed", "res"], 0)),
subtitle = paste(scales::comma(round(t0[t0$def == "# Suppressed", "min"], 0)), "-", scales::comma(round(t0[t0$def == "# Suppressed", "max"], 0))),
color = "blue",
fill = TRUE,
icon = icon("hashtag", lib = "font-awesome")
)
})
ggOut <- ggplot(t0, aes(x = def, y = res))
ggOut <- ggOut + geom_bar(aes(fill = def), position = 'dodge', stat = 'identity')
ggOut <- ggOut + geom_errorbar(mapping = aes(x = def, ymin = min, ymax = max), width = 0.2, size = 1)
ggOut <- ggOut + scale_y_continuous(labels = scales::comma, expand = c(0, 0), breaks = scales::pretty_breaks(n = 5))
ggOut <- ggOut + expand_limits(y = round(max(t0$max), digits = -4) + 1e5)
ggOut <- ggOut + scale_fill_manual(values = c.fill)
ggOut <- ggOut + ggtitle("Care Cascade in 2015")
ggOut <- ggOut + theme_classic()
ggOut <- ggOut + theme(plot.title = element_text(hjust = 0.5))
ggOut <- ggOut + theme(title = element_text(size = 18))
ggOut <- ggOut + theme(axis.title = element_blank())
ggOut <- ggOut + theme(axis.text.x = element_text(size = 17))
ggOut <- ggOut + theme(axis.text.y = element_text(size = 18))
ggOut <- ggOut + theme(legend.position = "none")
ggOut <- ggOut + theme(plot.background = element_blank())
ggOut <- ggOut + theme(panel.background = element_blank())
ggOut <- ggOut + theme(axis.line.y = element_line())
ggOut <- ggOut + theme(text = element_text(family = figFont))
ggOut
}
GenCascadePlot <- function() {
t0 <- ExtractCascadeData(1) # t0 = 1
t5 <- ExtractCascadeData(251) # t5 = (5 / 0.02) + 1 [t0]
c.fill <- rev(brewer.pal(9,"Blues")[3:8])
t0$year <- 2015
t5$year <- 2020
out <- rbind(t0, t5)
ggOne <- ggplot(out, aes(x = def, y = res))
ggOne <- ggOne + geom_bar(aes(fill = as.factor(year)), position = 'dodge', stat = 'identity')
ggOne <- ggOne + geom_errorbar(mapping = aes(x = def, ymin = min, ymax = max, fill = as.factor(year)), position = position_dodge(width = 0.9), stat = "identity", width = 0.2, size = 1)
ggOne <- ggOne + scale_y_continuous(labels = scales::comma, expand = c(0, 0), breaks = scales::pretty_breaks(n = 5))
ggOne <- ggOne + scale_fill_manual(values = c(c.fill[2],c.fill[5]), guide = guide_legend(title = "Year"))
ggOne <- ggOne + theme_classic()
ggOne <- ggOne + theme(title = element_text(size = 18))
ggOne <- ggOne + theme(axis.title = element_blank())
ggOne <- ggOne + theme(axis.text.x = element_text(size = 17))
ggOne <- ggOne + theme(axis.text.y = element_text(size = 18))
ggOne <- ggOne + theme(axis.ticks.x = element_blank())
ggOne <- ggOne + theme(legend.position = "right")
ggOne <- ggOne + theme(legend.title = element_text(size = 17))
ggOne <- ggOne + theme(legend.text = element_text(size = 15))
ggOne <- ggOne + theme(plot.background = element_blank())
ggOne <- ggOne + theme(panel.background = element_blank())
ggOne <- ggOne + theme(axis.line.y = element_line())
ggOne <- ggOne + theme(text = element_text(family = figFont))
ggOne <- ggOne + expand_limits(y = round(max(out$max), digits = -4) + 1e5)
ggOne
}
GrabLegend <- function(a.ggplot) {
tmp <- ggplot_gtable(ggplot_build(a.ggplot))
leg <- which(sapply(tmp$grobs, function(x) x$name) == "guide-box")
legend <- tmp$grobs[[leg]]
return(legend)
}
GenPowersCascadePlot <- function() {
t0 <- ExtractPowersCascadeData(1)
t5 <- ExtractPowersCascadeData(251) # t5 = (5 / 0.02) + 1 [t0]
cols <- brewer.pal(9,"Set1")
p.col <- c(cols[3], cols[2], cols[4], cols[5], cols[1], cols[9], cols[8])
ggOne <- ggplot(t0, aes(x = order, y = res, fill = state))
ggOne <- ggOne + geom_bar(stat = 'identity')
ggOne <- ggOne + scale_y_continuous(labels = scales::comma, expand = c(0, 0), breaks = scales::pretty_breaks(n = 5))
ggOne <- ggOne + scale_fill_manual(values = p.col)
ggOne <- ggOne + ggtitle("2015")
ggOne <- ggOne + theme_classic()
ggOne <- ggOne + theme(plot.title = element_text(hjust = 0.5))
ggOne <- ggOne + theme(title = element_text(size = 18))
ggOne <- ggOne + theme(axis.title = element_blank())
ggOne <- ggOne + theme(axis.text.x = element_text(size = 13))
ggOne <- ggOne + theme(axis.text.y = element_text(size = 15))
ggOne <- ggOne + theme(legend.text = element_text(size = 13))
ggOne <- ggOne + theme(legend.title = element_text(size = 15))
ggOne <- ggOne + theme(legend.position = "right")
ggOne <- ggOne + theme(plot.background = element_blank())
ggOne <- ggOne + theme(panel.background = element_blank())
cols <- brewer.pal(9,"Set1")
p.col <- c(cols[3], cols[2], cols[4], cols[5], cols[1], cols[9], cols[8])
ggTwo <- ggplot(t5, aes(x = order, y = res, fill = state))
ggTwo <- ggTwo + geom_bar(stat = 'identity')
ggTwo <- ggTwo + scale_y_continuous(labels = scales::comma, expand = c(0, 0), breaks = scales::pretty_breaks(n = 5))
ggTwo <- ggTwo + scale_fill_manual(values = p.col)
ggTwo <- ggTwo + ggtitle("2020")
ggTwo <- ggTwo + theme_classic()
ggTwo <- ggTwo + theme(plot.title = element_text(hjust = 0.5))
ggTwo <- ggTwo + theme(title = element_text(size = 18))
ggTwo <- ggTwo + theme(axis.title = element_blank())
ggTwo <- ggTwo + theme(axis.text.x = element_text(size = 13))
ggTwo <- ggTwo + theme(axis.text.y = element_text(size = 15))
ggTwo <- ggTwo + theme(legend.text = element_text(size = 13))
ggTwo <- ggTwo + theme(legend.title = element_text(size = 15))
ggTwo <- ggTwo + theme(legend.position = "right")
ggTwo <- ggTwo + theme(plot.background = element_blank())
ggTwo <- ggTwo + theme(panel.background = element_blank())
my.legend <- GrabLegend(ggOne)
l.width <- sum(my.legend$width)
grid.arrange(
arrangeGrob(
ggOne + theme(legend.position = "none"),
ggTwo + theme(legend.position = "none"),
ncol = 2),
my.legend,
widths = grid::unit.c(unit(1, "npc") - l.width, l.width),
nrow = 1)
}
Gen909090Plot <- function() {
out <- Extract909090Data()
vbOut1 <- round(out[out$def == "Diagnosed / PLHIV", "res"] * 100, digits = 0)
vbOut2 <- round(out[out$def == "On Treatment / Diagnosed", "res"] * 100, digits = 0)
vbOut3 <- round(out[out$def == "Virally Suppressed / On Treatment", "res"] * 100, digits = 0)
output$vb_90 <- renderValueBox({ valueBox(paste0(vbOut1, "%"), "Diagnosed / PLHIV", color = "red", icon = icon("medkit", lib = "font-awesome")) })
output$vb_9090 <- renderValueBox({ valueBox(paste0(vbOut2, "%"), "On Treatment / Diagnosed", color = "red", icon = icon("medkit", lib = "font-awesome")) })
output$vb_909090 <- renderValueBox({ valueBox(paste0(vbOut3, "%"), "Virally Suppressed / On Treatment", color = "red", icon = icon("medkit", lib = "font-awesome")) })
output$vb_90_wizard <- renderValueBox({ valueBox(paste0(vbOut1, "%"), "Diagnosed / PLHIV", color = "red", icon = icon("medkit", lib = "font-awesome")) })
output$vb_9090_wizard <- renderValueBox({ valueBox(paste0(vbOut2, "%"), "On Treatment / Diagnosed", color = "red", icon = icon("medkit", lib = "font-awesome")) })
output$vb_909090_wizard <- renderValueBox({ valueBox(paste0(vbOut3, "%"), "Virally Suppressed / On Treatment", color = "red", icon = icon("medkit", lib = "font-awesome")) })
# Blue override for colour
cfill <- rev(brewer.pal(9,"Blues")[6:8])
ggOut <- ggplot(out, aes(x = def, y = res))
ggOut <- ggOut + geom_bar(aes(fill = def), position = 'dodge', stat = 'identity')
ggOut <- ggOut + geom_errorbar(mapping = aes(x = def, ymin = min, ymax = max), width = 0.2, size = 1)
ggOut <- ggOut + scale_y_continuous(limits = c(0, 1), breaks = seq(0, 1, 0.1), labels = scales::percent, expand = c(0, 0))
ggOut <- ggOut + scale_fill_manual(values = cfill)
ggOut <- ggOut + geom_abline(intercept = 0.9, slope = 0)
ggOut <- ggOut + theme_classic()
ggOut <- ggOut + theme(plot.title = element_text(hjust = 0.5))
ggOut <- ggOut + theme(title = element_text(size = 20))
ggOut <- ggOut + theme(axis.title = element_blank())
ggOut <- ggOut + theme(axis.ticks.x = element_blank())
ggOut <- ggOut + theme(axis.text.x = element_text(size = 18))
ggOut <- ggOut + theme(axis.text.y = element_text(size = 18))
ggOut <- ggOut + theme(legend.position = "none")
ggOut <- ggOut + theme(plot.background = element_blank())
ggOut <- ggOut + theme(panel.background = element_blank())
ggOut <- ggOut + theme(axis.line.y = element_line())
ggOut <- ggOut + theme(text = element_text(family = figFont))
ggOut <- ggOut + geom_label(aes(x = def, label = scales::percent(round(out$res, digits = 2))), size = 8)
ggOut
}
GenNewInfPlot <- function(wizard) {
result <- CallModel()
out <- c()
min <- c()
max <- c()
for (j in 1:251) {
dat <- Quantile_95(unlist(lapply(result, function(x) sum(x$NewInf[j]))))
out[j] <- dat[["mean"]]
min[j] <- dat[["lower"]]
max[j] <- dat[["upper"]]
}
NI_out <- c(0, diff(out))
NI_min <- c(0, diff(min))
NI_max <- c(0, diff(max))
times <- seq(0, 5, 0.02)
combo <- cbind(times, NI_out, NI_min, NI_max)
# Calculate time intervals
yr2015 <- times[times >= 0 & times <= 1]
yr2016 <- times[times > 1 & times <= 2]
yr2017 <- times[times > 2 & times <= 3]
yr2018 <- times[times > 3 & times <= 4]
yr2019 <- times[times > 4 & times <= 5]
# count between years to calculate bars
bar1 <- combo[times %in% yr2015,]
bar2 <- combo[times %in% yr2016,]
bar3 <- combo[times %in% yr2017,]
bar4 <- combo[times %in% yr2018,]
bar5 <- combo[times %in% yr2019,]
NewInf <- c(
sum(bar1[,"NI_out"]),
sum(bar2[,"NI_out"]),
sum(bar3[,"NI_out"]),
sum(bar4[,"NI_out"]),
sum(bar5[,"NI_out"])
)
min <- c(
sum(bar1[,"NI_min"]),
sum(bar2[,"NI_min"]),
sum(bar3[,"NI_min"]),
sum(bar4[,"NI_min"]),
sum(bar5[,"NI_min"])
)
max <- c(
sum(bar1[,"NI_max"]),
sum(bar2[,"NI_max"]),
sum(bar3[,"NI_max"]),
sum(bar4[,"NI_max"]),
sum(bar5[,"NI_max"])
)
timeOut <- seq(2015, 2019, 1)
df <- data.frame(timeOut, NewInf, min, max)
c.fill <- rev(brewer.pal(9,"Blues")[4:8])
ggOut <- ggplot(df, aes(x = timeOut, NewInf))
ggOut <- ggOut + geom_bar(stat = "identity", size = 2, fill = c.fill)
ggOut <- ggOut + geom_errorbar(mapping = aes(x = timeOut, ymin = min, ymax = max), width = 0.2, size = 1)
ggOut <- ggOut + expand_limits(y = round(max(df$max), digits = -4))
ggOut <- ggOut + theme_classic()
ggOut <- ggOut + scale_y_continuous(labels = scales::comma, expand = c(0, 0), breaks = scales::pretty_breaks(n = 5))
ggOut <- ggOut + theme(axis.line.y = element_line())
ggOut <- ggOut + scale_x_continuous(breaks = seq(2015, 2019, 1), labels = seq(2015, 2019, 1))
ggOut <- ggOut + theme(text = element_text(family = figFont))
ggOut <- ggOut + theme(axis.ticks.x = element_blank())
if (wizard) {
ggOut <- ggOut + theme(axis.text.x = element_text(size = 12))
ggOut <- ggOut + theme(axis.text.y = element_text(size = 12))
ggOut <- ggOut + theme(axis.title = element_blank())
} else {
ggOut <- ggOut + theme(axis.text.x = element_text(size = 18))
ggOut <- ggOut + theme(axis.text.y = element_text(size = 18))
ggOut <- ggOut + theme(axis.title = element_blank())
}
ggOut
}
GenAidsDeathsPlot <- function(wizard) {
result <- CallModel()
out <- c()
min <- c()
max <- c()
for (j in 1:251) {
dat <- Quantile_95(unlist(lapply(result, function(x) sum(x$HivMortality[j]))))
out[j] <- dat[["mean"]]
min[j] <- dat[["lower"]]
max[j] <- dat[["upper"]]
}
HM_out <- c(0, diff(out))
HM_min <- c(0, diff(min))
HM_max <- c(0, diff(max))
times <- seq(0, 5, 0.02)
combo <- cbind(times, HM_out, HM_min, HM_max)
# Calculate time intervals
yr2015 <- times[times >= 0 & times <= 1]
yr2016 <- times[times > 1 & times <= 2]
yr2017 <- times[times > 2 & times <= 3]
yr2018 <- times[times > 3 & times <= 4]
yr2019 <- times[times > 4 & times <= 5]
# count between years to calculate bars
bar1 <- combo[times %in% yr2015,]
bar2 <- combo[times %in% yr2016,]
bar3 <- combo[times %in% yr2017,]
bar4 <- combo[times %in% yr2018,]
bar5 <- combo[times %in% yr2019,]
HivMortality <- c(
sum(bar1[,"HM_out"]),
sum(bar2[,"HM_out"]),
sum(bar3[,"HM_out"]),
sum(bar4[,"HM_out"]),
sum(bar5[,"HM_out"])
)
min <- c(
sum(bar1[,"HM_min"]),
sum(bar2[,"HM_min"]),
sum(bar3[,"HM_min"]),
sum(bar4[,"HM_min"]),
sum(bar5[,"HM_min"])
)
max <- c(
sum(bar1[,"HM_max"]),
sum(bar2[,"HM_max"]),
sum(bar3[,"HM_max"]),
sum(bar4[,"HM_max"]),
sum(bar5[,"HM_max"])
)
timeOut <- seq(2015, 2019, 1)
df <- data.frame(timeOut, HivMortality, min, max)
c.fill <- rev(brewer.pal(9,"Blues")[4:8])
ggOut <- ggplot(df, aes(x = timeOut, HivMortality))
ggOut <- ggOut + geom_bar(stat = "identity", size = 2, fill = c.fill)
ggOut <- ggOut + geom_errorbar(mapping = aes(x = timeOut, ymin = min, ymax = max), width = 0.2, size = 1)
ggOut <- ggOut + expand_limits(y = round(max(df$max), digits = -4))
ggOut <- ggOut + theme_classic()
ggOut <- ggOut + scale_y_continuous(labels = scales::comma, expand = c(0, 0), breaks = scales::pretty_breaks(n = 5))
ggOut <- ggOut + theme(axis.line.y = element_line())
ggOut <- ggOut + scale_x_continuous(breaks = seq(2015, 2019, 1), labels = seq(2015, 2019, 1))
ggOut <- ggOut + theme(text = element_text(family = figFont))
ggOut <- ggOut + theme(axis.ticks.x = element_blank())
if (wizard) {
ggOut <- ggOut + theme(axis.text.x = element_text(size = 12))
ggOut <- ggOut + theme(axis.text.y = element_text(size = 12))
ggOut <- ggOut + theme(axis.title = element_blank())
} else {
ggOut <- ggOut + theme(axis.text.x = element_text(size = 18))
ggOut <- ggOut + theme(axis.text.y = element_text(size = 18))
ggOut <- ggOut + theme(axis.title = element_blank())
}
ggOut
}
GenSinglePlot <- function() {
result <- CallModel()
out <- c()
min <- c()
max <- c()
for (j in 1:251) {
out[j] <- mean(unlist(lapply(result, function(x) sum(x[j,input$y]))))
min[j] <- range(unlist(lapply(result, function(x) sum(x[j,input$y]))))[1]
max[j] <- range(unlist(lapply(result, function(x) sum(x[j,input$y]))))[2]
}
time <- seq(0, 5, 0.02)
df <- data.frame(time, out, min, max)
ggOut <- ggplot(df, aes(x = time, y = out))
ggOut <- ggOut + geom_ribbon(aes(ymin = min, ymax = max), alpha = 0.2, fill = ggColorHue(3)[3])
ggOut <- ggOut + geom_line(size = 1.5, color = ggColorHue(1))
ggOut <- ggOut + theme_classic()
ggOut <- ggOut + scale_y_continuous(labels = scales::comma, breaks = scales::pretty_breaks(n = 5))
ggOut <- ggOut + scale_x_continuous(limits = c(0, 5), breaks = seq(0, 5, 1), labels = seq(2015, 2020, 1))
ggOut <- ggOut + theme(axis.text.x = element_text(size = 18))
ggOut <- ggOut + theme(axis.text.y = element_text(size = 18))
ggOut <- ggOut + theme(axis.title = element_text(size = 18))
ggOut <- ggOut + theme(axis.line.x = element_line())
ggOut <- ggOut + theme(axis.line.y = element_line())
ggOut <- ggOut + theme(axis.title.y = element_blank())
ggOut <- ggOut + xlab("Year")
ggOut
}
GenAllPlot <- function() {
result <- CallModel()
UnDx <- c()
Dx <- c()
Care <- c()
PreLtfu <- c()
Tx <- c()
Vs <- c()
Ltfu <- c()
N <- c()
NewInf <- c()
TotalCost <- c()
HivMortalityProp <- c()
NaturalMortalityProp <- c()
for (j in 1:251) {
UnDx[j] <- mean(unlist(lapply(result, function(x) sum(x$UnDx[j]))))
Dx[j] <- mean(unlist(lapply(result, function(x) sum(x$Dx[j]))))
Care[j] <- mean(unlist(lapply(result, function(x) sum(x$Care[j]))))
PreLtfu[j] <- mean(unlist(lapply(result, function(x) sum(x$PreLtfu[j]))))
Tx[j] <- mean(unlist(lapply(result, function(x) sum(x$Tx[j]))))
Vs[j] <- mean(unlist(lapply(result, function(x) sum(x$Vs[j]))))
Ltfu[j] <- mean(unlist(lapply(result, function(x) sum(x$Ltfu[j]))))
N[j] <- mean(unlist(lapply(result, function(x) sum(x$N[j]))))
NewInf[j] <- mean(unlist(lapply(result, function(x) sum(x$NewInf[j]))))
TotalCost[j] <- mean(unlist(lapply(result, function(x) sum(x$TotalCost[j]))))
HivMortalityProp[j] <- mean(unlist(lapply(result, function(x) sum(x$HivMortalityProp[j]))))
NaturalMortalityProp[j] <- mean(unlist(lapply(result, function(x) sum(x$NaturalMortalityProp[j]))))
}
time <- seq(0, 5, 0.02)
gridExtra::grid.arrange(
ggplot(data.frame(time, UnDx), aes(x = time, y = UnDx)) + geom_line(color = ggColorHue(1)) +
theme_classic() + theme(axis.text.x = element_text(size = 18)) + theme(axis.text.y = element_text(size = 18)) +
theme(axis.title = element_text(size = 18)) + xlab("Year") + theme(axis.line.x = element_line()) +
theme(axis.line.y = element_line()),
ggplot(data.frame(time, Dx), aes(x = time, y = Dx)) + geom_line(color = ggColorHue(1)) +
theme_classic() + theme(axis.text.x = element_text(size = 18)) + theme(axis.text.y = element_text(size = 18)) +
theme(axis.title = element_text(size = 18)) + xlab("Year") + theme(axis.line.x = element_line()) +
theme(axis.line.y = element_line()),
ggplot(data.frame(time, Care), aes(x = time, y = Care)) + geom_line(color = ggColorHue(1)) +
theme_classic() + theme(axis.text.x = element_text(size = 18)) + theme(axis.text.y = element_text(size = 18)) +
theme(axis.title = element_text(size = 18)) + xlab("Year") + theme(axis.line.x = element_line()) +
theme(axis.line.y = element_line()),
ggplot(data.frame(time, PreLtfu), aes(x = time, y = PreLtfu)) + geom_line(color = ggColorHue(1)) +
theme_classic() + theme(axis.text.x = element_text(size = 18)) + theme(axis.text.y = element_text(size = 18)) +
theme(axis.title = element_text(size = 18)) + xlab("Year") + theme(axis.line.x = element_line()) +
theme(axis.line.y = element_line()),
ggplot(data.frame(time, Tx), aes(x = time, y = Tx)) + geom_line(color = ggColorHue(1)) +
theme_classic() + theme(axis.text.x = element_text(size = 18)) + theme(axis.text.y = element_text(size = 18)) +
theme(axis.title = element_text(size = 18)) + xlab("Year") + theme(axis.line.x = element_line()) +
theme(axis.line.y = element_line()),
ggplot(data.frame(time, Vs), aes(x = time, y = Vs)) + geom_line(color = ggColorHue(1)) +
theme_classic() + theme(axis.text.x = element_text(size = 18)) + theme(axis.text.y = element_text(size = 18)) +
theme(axis.title = element_text(size = 18)) + xlab("Year") + theme(axis.line.x = element_line()) +
theme(axis.line.y = element_line()),
ggplot(data.frame(time, Ltfu), aes(x = time, y = Ltfu)) + geom_line(color = ggColorHue(1)) +
theme_classic() + theme(axis.text.x = element_text(size = 18)) + theme(axis.text.y = element_text(size = 18)) +
theme(axis.title = element_text(size = 18)) + xlab("Year") + theme(axis.line.x = element_line()) +
theme(axis.line.y = element_line()),
ggplot(data.frame(time, N), aes(x = time, y = N)) + geom_line(color = ggColorHue(1)) +
theme_classic() + theme(axis.text.x = element_text(size = 18)) + theme(axis.text.y = element_text(size = 18)) +
theme(axis.title = element_text(size = 18)) + xlab("Year") + theme(axis.line.x = element_line()) +
theme(axis.line.y = element_line()),
ggplot(data.frame(time, NewInf), aes(x = time, y = NewInf)) + geom_line(color = ggColorHue(1)) +
theme_classic() + theme(axis.text.x = element_text(size = 18)) + theme(axis.text.y = element_text(size = 18)) +
theme(axis.title = element_text(size = 18)) + xlab("Year") + theme(axis.line.x = element_line()) +
theme(axis.line.y = element_line()),
ggplot(data.frame(time, TotalCost), aes(x = time, y = TotalCost)) + geom_line(color = ggColorHue(1)) +
theme_classic() + theme(axis.text.x = element_text(size = 18)) + theme(axis.text.y = element_text(size = 18)) +
theme(axis.title = element_text(size = 18)) + xlab("Year") + theme(axis.line.x = element_line()) +
theme(axis.line.y = element_line()),
ggplot(data.frame(time, HivMortalityProp), aes(x = time, y = HivMortalityProp)) + geom_line(color = ggColorHue(1)) +
theme_classic() + theme(axis.text.x = element_text(size = 18)) + theme(axis.text.y = element_text(size = 18)) +
theme(axis.title = element_text(size = 18)) + xlab("Year") + theme(axis.line.x = element_line()) +
theme(axis.line.y = element_line()),
ggplot(data.frame(time, NaturalMortalityProp), aes(x = time, y = NaturalMortalityProp)) + geom_line(color = ggColorHue(1)) +
theme_classic() + theme(axis.text.x = element_text(size = 18)) + theme(axis.text.y = element_text(size = 18)) +
theme(axis.title = element_text(size = 18)) + xlab("Year") + theme(axis.line.x = element_line()) +
theme(axis.line.y = element_line()),
nrow = 4,
ncol = 3
)
}
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