Nothing
dosedesignR_green <- "#5cb85c"
dosedesignR_blue <- "#3a6791"
dosedesignR_red <- "#c94053"
dosedesignR_white <- "#ebebeb"
performSimulation <- function(
input,
tab,
inp,
UserValue,
sdsim,
dmin,
dmax,
clevel,
sim = input$sim,
model= input$model
) {
for(i in 1:length(inp)) {
assign(paste0("inp", i), inp[[i]])
assign(paste0("o_doses", i), inp[[i]]$optValdoses())
assign(paste0("o_pats", i), inp[[i]]$optValpats())
assign(paste0("u_doses", i), inp[[i]]$UserValdoses())
assign(paste0("u_pats", i), inp[[i]]$UserValpats())
assign(paste0("model"), inp[[tab]]$model())
}
sim <- input$sim
if (!is.null(sim)) {
nam1 <- rlang::sym(paste0(str_sub(tolower(sim), 1, 1), "_doses", str_sub(sim, str_length(sim), str_length(sim))))
nam2 <- rlang::sym(paste0(str_sub(tolower(sim), 1, 1), "_pats", str_sub(sim, str_length(sim), str_length(sim))))
simdesign <- data.frame(doses = eval(nam1), pats = eval(nam2))
if (length(simdesign$doses) == 0) {
pmodel <- ggplot2::ggplot(data = data.frame()) +
ggplot2::geom_point() +
ggplot2::xlim(-1,1) +
gplot2::ylim(-1,1) +
ggplot2::theme_minimal() +
ggplot2::theme(
plot.background = ggplot2::element_rect(fill = "#383838")
) +
ggplot2::geom_text() +
ggplot2::annotate(
"text",
y = 0,
x = 0,
label = "No user defined model found.",
angle = 0,
colour = "white"
) +
ggplot2::theme(
legend.position = "none",
axis.text = ggplot2::element_blank(),
axis.title = ggplot2::element_blank(),
panel.border = ggplot2::element_blank(),
panel.grid.major = ggplot2::element_blank(),
panel.grid.minor = ggplot2::element_blank(),
text = ggplot2::element_text(
family = "",
face = "plain",
colour = "white",
size = 12,
lineheight = 0.9,
hjust = 0.5,
vjust = 0.5,
angle = 0,
margin = ggplot2::margin(),
debug = FALSE
)
)
} else {
if (model == "Linear") {
Int <- inp[[tab]]$parameter1()
Slp <- inp[[tab]]$parameter2()
doses <- simdesign$doses
pats <- floor(simdesign$pats)
x <- rep(simdesign$doses, pats)
y <- DoseFinding::linear(x, Int, Slp) + rnorm(sum(pats), 0, sdsim)
datasim <- data.frame(x, y)
model <- DoseFinding::fitMod(x, y, model = "linear")
level <- clevel/100
doseSeq <- seq(0, dmax, length = 201)
truem <- DoseFinding::linear(doseSeq, Int, Slp)
pred <- predict(model, predType = "ls-means", doseSeq = doseSeq, se.fit = TRUE)
quant <- qt(1 - (1 - level)/2, df = model$df)
lbnd <- pred$fit - quant * pred$se.fit
ubnd <- pred$fit + quant * pred$se.fit
predval <- pred$fit
plotdata <- data.frame(doseSeq, predval, lbnd, ubnd, truem)
ylimlow <- min(c(truem[length(truem)] - 3 * sdsim, truem[1] - 3 * sdsim))
ylimhigh <- max(c(truem[1] + 3 * sdsim, truem[length(truem)] + 3 * sdsim))
p <- ggplot2::ggplot(plotdata, aes(x = doseSeq, group = 1)) +
ggplot2::theme_minimal() +
ggplot2::theme(plot.background = element_rect(fill = "#383838")) +
ggplot2::theme(
legend.position = "none",
axis.text = ggplot2::element_text(colour = "white"),
panel.border = ggplot2::element_blank(),
panel.grid.major = ggplot2::element_line(colour = "#474747"),
panel.grid.minor = ggplot2::element_line(colour = "#474747", size = 0.25),
text = ggplot2::element_text(
family = "",
face = "plain",
colour = "white",
size = 12,
lineheight = 0.9,
hjust = 0.5,
vjust = 0.5,
angle = 0,
margin = margin(),
debug = FALSE
)
) +
ggplot2::geom_ribbon(ggplot2::aes(ymin = lbnd, ymax = ubnd), fill = "#212121") +
ggplot2::geom_line(ggplot2::aes(y = predval), colour= dosedesignR_green, lwd = 1.1) +
ggplot2::geom_line(ggplot2::aes(x = doseSeq, y = truem), colour = dosedesignR_red, lwd = 1.1) +
ggplot2::xlim(dmin, dmax) +
ggplot2::ylim(ylimlow, ylimhigh) +
ggplot2::theme(
axis.text = ggplot2::element_text(size = rel(1.1)),
axis.title = ggplot2::element_text(size = rel(1.1))
)
tcec <- c("true curve", "estimated curve")
datleg <- data.frame(tcec, test = 1:2, y = 1:2)
pmodel <- p + ggplot2::geom_point(data = datasim, aes(x,y), colour = "grey70") +
ggplot2::labs(x = "Dose", y = "Response") +
ggplot2::geom_line(data = datleg, aes(colour = tcec, x = test, y = y, group = 1), alpha = 0) +
ggplot2::scale_colour_manual(values = c(dosedesignR_red, dosedesignR_green), labels = c("true curve", "estimated curve")) +
ggplot2::theme(
legend.text = ggplot2::element_text(size = rel(1.1)),
legend.key = ggplot2::element_rect(fill = "#383838"),
legend.margin = margin(l = -10),
legend.position = "bottom",
legend.title = ggplot2::element_blank()
) +
ggplot2::guides(
colour = ggplot2::guide_legend(override.aes = list(size = 1.05, alpha = 1))
)
} else if (model == "Emax") {
E0 <- inp[[tab]]$parameter1()
ED50 <- inp[[tab]]$parameter2()
Emax <- inp[[tab]]$parameter3()
doses <- simdesign$doses
pats <- floor(simdesign$pats)
x <- rep(simdesign$doses, pats)
y <- DoseFinding::emax(x, E0, Emax, ED50) + rnorm(sum(pats), 0, sdsim)
datasim <- data.frame(x,y)
model <- DoseFinding::fitMod(x,y, model="emax", bnds = defBnds(max(doses))$emax)
level <- clevel/100
model <- fitMod(x, y, model = "emax", bnds = defBnds(max(simdesign$doses))$emax)
doseSeq <- seq(0, dmax, length = 201)
truem <- emax(doseSeq, E0, Emax, ED50)
pred <- predict(model, predType = "ls-means", doseSeq = doseSeq, se.fit = TRUE)
quant <- qt(1 - (1 - level)/2, df = model$df)
lbnd <- pred$fit - quant * pred$se.fit
ubnd <- pred$fit + quant * pred$se.fit
predval <- pred$fit
plotdata <- data.frame(doseSeq, predval, lbnd,ubnd, truem)
ylimlow <- min(c(truem[length(truem)] - 3 * sdsim, truem[1] - 3 * sdsim))
ylimhigh <- max(c(truem[1] + 3 * sdsim, truem[length(truem)] + 3 * sdsim))
p <- ggplot2::ggplot(plotdata, aes(x = doseSeq, group = 1)) +
ggplot2::theme_minimal() +
ggplot2::theme(
plot.background = ggplot2::element_rect(fill = "#383838")
) +
ggplot2::theme(
legend.position = "none",
axis.text = ggplot2::element_text(colour = "white"),
panel.border = ggplot2::element_blank(),
panel.grid.major = ggplot2::element_line(colour = "#474747"),
panel.grid.minor = ggplot2::element_line(colour = "#474747", size = 0.25),
text = ggplot2::element_text(
family = "",
face = "plain",
colour = "white",
size = 12,
lineheight = 0.9,
hjust = 0.5,
vjust = 0.5,
angle = 0,
margin = margin(),
debug = FALSE
)
) +
ggplot2::geom_ribbon(aes(ymin = lbnd, ymax = ubnd), fill = "#212121") +
ggplot2::geom_line(aes(y = predval), colour = dosedesignR_green, lwd = 1.1) +
ggplot2::geom_line(aes(x = doseSeq, y = truem), colour = dosedesignR_red, lwd = 1.1) + xlim(dmin, dmax) +
ggplot2::ylim(ylimlow, ylimhigh) +
ggplot2::theme(
axis.text = element_text(size = rel(1.1)),
axis.title = element_text(size = rel(1.1))
)
tcec<- c("true curve", "estimated curve")
datleg <- data.frame(tcec, test = 1:2, y = 1:2)
pmodel <- p +
ggplot2::geom_point(data = datasim, aes(x, y), colour = "grey70") +
ggplot2::labs(x = "Dose", y = "Response") +
ggplot2::geom_line(data = datleg, aes(colour = tcec, x = test, y = y,group = 1), alpha = 0) +
ggplot2::scale_colour_manual(values = c(dosedesignR_red, dosedesignR_green), labels = c("true curve", "estimated curve")) +
ggplot2::theme(
legend.text = ggplot2::element_text(size = rel(1.1)), legend.key = element_rect(fill = "#383838"),
legend.margin = margin(l = -10), legend.position = "bottom", legend.title = element_blank()
) +
ggplot2::guides(colour = guide_legend(override.aes = list(size = 1.05, alpha = 1)))
} else if (model == "Sigmoidal Emax") {
E0 <- inp[[tab]]$parameter1()
ED50 <- inp[[tab]]$parameter2()
Emax <- inp[[tab]]$parameter3()
Hill <- inp[[tab]]$parameter4()
doses <- simdesign$doses
pats <- floor(simdesign$pats)
x <- rep(simdesign$doses, pats)
y <- DoseFinding::sigEmax(x, E0, Emax, ED50, Hill) + rnorm(sum(pats), 0, sdsim)
datasim <- data.frame(x, y)
model <- DoseFinding::fitMod(x, y, model = "sigEmax", bnds = defBnds(max(simdesign$doses))$sigEmax)
level <- clevel/100
doseSeq <- seq(0, dmax, length = 201)
truem <- DoseFinding::sigEmax(doseSeq, E0, Emax, ED50, Hill)
pred <- predict(model, predType = "ls-means", doseSeq = doseSeq, se.fit = TRUE)
quant <- qt(1 - (1 - level)/2, df = model$df)
lbnd <- pred$fit - quant * pred$se.fit
ubnd <- pred$fit + quant * pred$se.fit
predval <- pred$fit
plotdata <- data.frame(doseSeq, predval, lbnd, ubnd, truem)
ylimlow <- min(c(truem[length(truem)] - 3 * sdsim, truem[1] - 3 * sdsim))
ylimhigh <- max(c(truem[1] + 3 * sdsim, truem[length(truem)] + 3 * sdsim))
p <- ggplot2::ggplot(plotdata, aes(x = doseSeq, group = 1)) +
ggplot2::theme_minimal() +
ggplot2::theme(plot.background = element_rect(fill = "#383838")) +
ggplot2::theme(
legend.position = "none",
axis.text = ggplot2::element_text(colour = "white"),
panel.border = ggplot2::element_blank(),
panel.grid.major = ggplot2::element_line(colour = "#474747"),
panel.grid.minor = ggplot2::element_line(colour = "#474747", size = 0.25),
text = ggplot2::element_text(
family = "",
face = "plain",
colour = "white",
size = 12,
lineheight = 0.9,
hjust = 0.5,
vjust = 0.5,
angle = 0,
margin = margin(),
debug = FALSE
)
) +
ggplot2::geom_ribbon(ggplot2::aes(ymin = lbnd, ymax = ubnd), fill = "#212121") +
ggplot2::geom_line(ggplot2::aes(y = predval), colour = dosedesignR_green, lwd = 1.1) +
ggplot2::geom_line(ggplot2::aes(x = doseSeq, y = truem), colour = dosedesignR_red, lwd = 1.1) + xlim(dmin, dmax) +
ggplot2::ylim(ylimlow, ylimhigh) +
ggplot2::theme(
axis.text = element_text(size = rel(1.1)),
axis.title = element_text(size = rel(1.1))
)
tcec <- c("true curve", "estimated curve")
datleg <- data.frame(tcec, test = 1:2, y = 1:2)
pmodel <- p +
ggplot2::geom_point(data = datasim, aes(x,y), colour = "grey70") +
ggplot2::labs(x = "Dose", y = "Response") +
ggplot2::geom_line(data = datleg, aes(colour = tcec, x = test, y = y, group = 1), alpha = 0) +
ggplot2::scale_colour_manual(values = c(dosedesignR_red, dosedesignR_green), labels = c("true curve", "estimated curve")) +
ggplot2::theme(
legend.text = ggplot2::element_text(size = rel(1.1)),
legend.key = ggplot2::element_rect(fill = "#383838"),
legend.margin = margin(l = -10),
legend.position = "bottom",
legend.title = ggplot2::element_blank()
) +
ggplot2::guides(colour = guide_legend(override.aes = list(size = 1.05, alpha = 1)))
}
}
return(pmodel)
}
}
#for rmarkdown report:
performSimulation2 <- function(
tab,
inp,
sdsim,
dmin,
dmax,
clevel,
sim = input$sim,
model = input$model
) {
for(i in 1:length(inp)){
assign(paste0("inp", i), inp[[i]])
assign(paste0("o_doses", i), inp[[i]]$optValdoses())
assign(paste0("o_pats", i), inp[[i]]$optValpats())
assign(paste0("u_doses", i), inp[[i]]$UserValdoses())
assign(paste0("u_pats", i), inp[[i]]$UserValpats())
assign(paste0("model"), inp[[tab]]$model())
}
if (!is.null(sim)) {
nam1 <- rlang::sym(paste0(str_sub(tolower(sim), 1, 1), "_doses", str_sub(sim, str_length(sim), str_length(sim))))
nam2 <- rlang::sym(paste0(str_sub(tolower(sim), 1, 1), "_pats", str_sub(sim, str_length(sim), str_length(sim))))
simdesign <- data.frame(doses = eval(nam1), pats = eval(nam2))
if (length(simdesign$doses) == 0) {
stop("No user defined model found.")
}
if (model == "Linear") {
Int <- inp[[tab]]$parameter1()
Slp <- inp[[tab]]$parameter2()
doses <- simdesign$doses
pats <- floor(simdesign$pats)
x <- rep(simdesign$doses, pats)
y <- DoseFinding::linear(x, Int, Slp) + rnorm(sum(pats), 0, sdsim)
datasim <- data.frame(x, y)
model <- DoseFinding::fitMod(x, y, model = "linear")
level <- clevel/100
doseSeq <- seq(0, dmax, length = 201)
truem <- DoseFinding::linear(doseSeq, Int, Slp)
pred <- predict(model, predType = "ls-means", doseSeq = doseSeq, se.fit = TRUE)
quant <- qt(1 - (1 - level)/2, df = model$df)
lbnd <- pred$fit - quant * pred$se.fit
ubnd <- pred$fit + quant * pred$se.fit
predval <- pred$fit
plotdata <- data.frame(doseSeq, predval, lbnd, ubnd, truem)
ylimlow <- min(c(truem[length(truem)] - 3 * sdsim, truem[1] - 3 * sdsim))
ylimhigh <- max(c(truem[1] + 3 * sdsim, truem[length(truem)] + 3 * sdsim))
p <- ggplot2::ggplot(plotdata, aes(x = doseSeq, group = 1)) +
ggplot2::theme_minimal() +
ggplot2::theme(plot.background = element_rect(fill = "#f0f0f0")) +
ggplot2::theme(
legend.position = "none",
axis.text = ggplot2::element_text(colour = "black"),
panel.border = ggplot2::element_blank(),
panel.grid.major = ggplot2::element_line(colour = "#fafafa"),
panel.grid.minor = ggplot2::element_line(colour = "#fafafa", size = 0.25),
text = ggplot2::element_text(
family = "",
face = "plain",
colour = "black",
size = 12,
lineheight = 0.9,
hjust = 0.5,
vjust = 0.5,
angle = 0,
margin = margin(),
debug = FALSE
)
) +
ggplot2::geom_ribbon(aes(ymin = lbnd, ymax = ubnd), fill = "grey70") +
ggplot2::geom_line(aes(y = predval), colour= dosedesignR_green, lwd = 1.1) +
ggplot2::geom_line(aes(x = doseSeq, y = truem), colour = dosedesignR_red, lwd = 1.1) + xlim(dmin, dmax) +
ggplot2::ylim(ylimlow, ylimhigh) +
ggplot2::theme(
axis.text = ggplot2::element_text(size = rel(1.1)),
axis.title = element_text(size = rel(1.1))
)
tcec <- c("true curve", "estimated curve")
datleg <- data.frame(tcec, test = 1:2, y = 1:2)
pmodel <- p +
ggplot2::geom_point(data = datasim, aes(x,y), colour = "#212121") +
ggplot2::labs(x = "Dose", y = "Response") +
ggplot2::geom_line(data = datleg, aes(colour = tcec, x = test, y = y, group = 1), alpha = 0) +
ggplot2::scale_colour_manual(values = c(dosedesignR_red, dosedesignR_green), labels = c("true curve", "estimated curve")) +
ggplot2::theme(
legend.text = ggplot2::element_text(size = rel(1.1)),
legend.key = ggplot2::element_rect(fill = "#f0f0f0"),
legend.margin = margin(l = -10),
legend.position = "bottom",
legend.title = ggplot2::element_blank()
) +
ggplot2::guides(colour = guide_legend(override.aes = list(size = 1.05, alpha = 1)))
} else if (model == "Emax") {
E0 <- inp[[tab]]$parameter1()
ED50 <- inp[[tab]]$parameter2()
Emax <- inp[[tab]]$parameter3()
doses <- simdesign$doses
pats <- floor(simdesign$pats)
x <- rep(simdesign$doses, pats)
y <- DoseFinding::emax(x, E0, Emax, ED50) + rnorm(sum(pats), 0, sdsim)
datasim <- data.frame(x,y)
model <- DoseFinding::fitMod(x,y, model="emax", bnds = defBnds(max(doses))$emax)
level <- clevel/100
model <- fitMod(x, y, model = "emax", bnds = defBnds(max(simdesign$doses))$emax)
doseSeq <- seq(0, dmax, length = 201)
truem <- emax(doseSeq, E0, Emax, ED50)
pred <- predict(model, predType = "ls-means", doseSeq = doseSeq, se.fit = TRUE)
quant <- qt(1 - (1 - level)/2, df = model$df)
lbnd <- pred$fit - quant * pred$se.fit
ubnd <- pred$fit + quant * pred$se.fit
predval <- pred$fit
plotdata <- data.frame(doseSeq, predval, lbnd,ubnd, truem)
ylimlow <- min(c(truem[length(truem)] - 3 * sdsim, truem[1] - 3 * sdsim))
ylimhigh <- max(c(truem[1] + 3 * sdsim, truem[length(truem)] + 3 * sdsim))
p <- ggplot2::ggplot(plotdata, aes(x = doseSeq, group = 1)) +
ggplot2::theme_minimal() +
ggplot2::theme(plot.background = element_rect(fill = "#f0f0f0")) +
ggplot2::theme(
legend.position = "none",
axis.text = ggplot2::element_text(colour = "black"),
panel.border = ggplot2::element_blank(),
panel.grid.major = ggplot2::element_line(colour = "#fafafa"),
panel.grid.minor = ggplot2::element_line(colour = "#fafafa", size = 0.25),
text = ggplot2::element_text(
family = "",
face = "plain",
colour = "black",
size = 12,
lineheight = 0.9,
hjust = 0.5,
vjust = 0.5,
angle = 0,
margin = margin(),
debug = FALSE
)
) +
ggplot2::geom_ribbon(aes(ymin = lbnd, ymax = ubnd), fill = "grey70") +
ggplot2::geom_line(aes(y = predval), colour = dosedesignR_green, lwd = 1.1) +
ggplot2::geom_line(aes(x = doseSeq, y = truem), colour = dosedesignR_red, lwd = 1.1) + xlim(dmin, dmax) +
ggplot2::ylim(ylimlow, ylimhigh) +
ggplot2::theme(
axis.text = element_text(size = rel(1.1)),
axis.title = element_text(size = rel(1.1))
)
tcec <- c("true curve", "estimated curve")
datleg <- data.frame(tcec, test = 1:2, y = 1:2)
pmodel <- p +
ggplot2::geom_point(data = datasim, aes(x, y), colour = "#212121") +
ggplot2::labs(x = "Dose", y = "Response") +
ggplot2::geom_line(data = datleg, aes(colour = tcec, x = test, y = y,group = 1), alpha = 0) +
ggplot2::scale_colour_manual(values = c(dosedesignR_red, dosedesignR_green), labels = c("true curve", "estimated curve")) +
ggplot2::theme(
legend.text = ggplot2::element_text(size = rel(1.1)),
legend.key = ggplot2::element_rect(fill = "#f0f0f0"),
legend.margin = margin(l = -10),
legend.position = "bottom",
legend.title = ggplot2::element_blank()) +
ggplot2::guides(colour = guide_legend(override.aes = list(size = 1.05, alpha = 1)))
} else if (model == "Sigmoidal Emax") {
E0 <- inp[[tab]]$parameter1()
ED50 <- inp[[tab]]$parameter2()
Emax <- inp[[tab]]$parameter3()
Hill <- inp[[tab]]$parameter4()
doses <- simdesign$doses
pats <- floor(simdesign$pats)
x <- rep(simdesign$doses, pats)
y <- DoseFinding::sigEmax(x, E0, Emax, ED50, Hill) + rnorm(sum(pats), 0, sdsim)
datasim <- data.frame(x, y)
model <- DoseFinding::fitMod(x, y, model = "sigEmax", bnds = defBnds(max(simdesign$doses))$sigEmax)
level <- clevel/100
doseSeq <- seq(0, dmax, length = 201)
truem <- DoseFinding::sigEmax(doseSeq, E0, Emax, ED50, Hill)
pred <- predict(model, predType = "ls-means", doseSeq = doseSeq, se.fit = TRUE)
quant <- qt(1 - (1 - level)/2, df = model$df)
lbnd <- pred$fit - quant * pred$se.fit
ubnd <- pred$fit + quant * pred$se.fit
predval <- pred$fit
plotdata <- data.frame(doseSeq, predval, lbnd, ubnd, truem)
ylimlow <- min(c(truem[length(truem)] - 3 * sdsim, truem[1] - 3 * sdsim))
ylimhigh <- max(c(truem[1] + 3 * sdsim, truem[length(truem)] + 3 * sdsim))
p <- ggplot2::ggplot(plotdata, aes(x = doseSeq, group = 1)) +
ggplot2::theme_minimal() +
ggplot2::theme(plot.background = element_rect(fill = "#f0f0f0")) +
ggplot2::theme(
legend.position = "none",
axis.text = ggplot2::element_text(colour = "black"),
panel.border = ggplot2::element_blank(),
panel.grid.major = ggplot2::element_line(colour = "#fafafa"),
panel.grid.minor = ggplot2::element_line(colour = "#fafafa", size = 0.25),
text = ggplot2::element_text(
family = "",
face = "plain",
colour = "black",
size = 12,
lineheight = 0.9,
hjust = 0.5,
vjust = 0.5,
angle = 0,
margin = margin(),
debug = FALSE
)
) +
ggplot2::geom_ribbon(ggplot2::aes(ymin = lbnd, ymax = ubnd), fill = "grey70") +
ggplot2::geom_line(ggplot2::aes(y = predval), colour = dosedesignR_green, lwd = 1.1) +
ggplot2::geom_line(ggplot2::aes(x = doseSeq, y = truem), colour = dosedesignR_red, lwd = 1.1) + xlim(dmin, dmax) +
ggplot2::ylim(ylimlow, ylimhigh) +
ggplot2::theme(
axis.text = ggplot2::element_text(size = rel(1.1)),
axis.title = ggplot2::element_text(size = rel(1.1))
)
tcec <- c("true curve", "estimated curve")
datleg <- data.frame(tcec, test = 1:2, y = 1:2)
pmodel <- p +
ggplot2::geom_point(data = datasim, aes(x,y), colour = "#212121") +
ggplot2::labs(x = "Dose", y = "Response") +
ggplot2::geom_line(data = datleg, aes(colour = tcec, x = test, y = y, group = 1), alpha = 0) +
ggplot2::scale_colour_manual(values = c(dosedesignR_red, dosedesignR_green), labels = c("true curve", "estimated curve")) +
ggplot2::theme(
legend.text = ggplot2::element_text(size = rel(1.1)),
legend.key = ggplot2::element_rect(fill = "#f0f0f0"),
legend.margin = margin(l = -10), legend.position = "bottom", legend.title = ggplot2::element_blank()
) +
ggplot2::guides(colour = guide_legend(override.aes = list(size = 1.05, alpha = 1)))
}
return(pmodel)
}
}
Any scripts or data that you put into this service are public.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.