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# geom_ribbon in ggplot2

How to make plots with geom_ribbon in ggplot2 and R.

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### Line & Ribbon

library(plotly)

set.seed(1)
y <- sin(seq(1, 2*pi, length.out = 100))
x <- 1:100
plotdata <- data.frame(x=x, y=y, lower = (y+runif(100, -1, -0.5)), upper = (y+runif(100, 0.5, 1)))

p <- ggplot(plotdata) + geom_line(aes(y=y, x=x, colour = "sin"))+
geom_ribbon(aes(ymin=lower, ymax=upper, x=x, fill = "band"), alpha = 0.3)+
scale_colour_manual("",values="blue")+
scale_fill_manual("",values="grey12")

fig <- ggplotly()

fig


Inspired by ggplot2 docs

### Facets

library(plotly)

set.seed(1987)
pkgs <- c("ggplot2", "mgcv", "MASS")
invisible(lapply(pkgs, require, character.only = TRUE))

titanic3 <- na.omit(titanic3[, -c(3,8:14)])
titanic3$class_sex <- apply(titanic3, 1, function(x) paste(x[1], x[3], collapse = "_")) titanic3$class_sex <- factor(titanic3$class_sex) train <- titanic3[sample(row.names(titanic3), size = round(nrow(titanic3) / 2)), ] test <- titanic3[!(row.names(titanic3) %in% row.names(train)), ] sim.data <- expand.grid(sex = c("male", "female"), sibsp = 0, age = seq(1, 80), pclass = c("1st", "2nd", "3rd")) glm.fit <- glm(survived ~ poly(age, 2) * sex * pclass + sibsp, "binomial", train) inv.logit <- function(x) exp(x) / (1 + exp(x)) glm.pred <- predict(glm.fit, newdata = test, se.fit = TRUE) pred <- data.frame(mean = inv.logit(glm.pred$fit),
lo = inv.logit(glm.pred$fit - 2 * glm.pred$se.fit),
hi = inv.logit(glm.pred$fit + 2 * glm.pred$se.fit),
survived = test$survived) pred <- pred[order(pred$mean), ]
pred$id <- seq_along(pred$mean)
row.names(pred) <- NULL

pred <- predict(glm.fit, newdata = sim.data, se.fit = TRUE)
sim.data$mean <- inv.logit(pred$fit)
sim.data$lo <- inv.logit(pred$fit - 2 * pred$se.fit) sim.data$hi <- inv.logit(pred$fit + 2 * pred$se.fit)

p <- ggplot(titanic3, aes(x = age, y = survived))
p <- p + geom_point()
p <- p + facet_grid(sex ~ pclass)
p <- p + geom_line(data = sim.data, aes(y = mean))
p <- p + geom_ribbon(data = sim.data, aes(y = mean, ymin = lo, ymax = hi),
alpha = .25)
p <- p + labs(x = "Passenger Age", y = "Probability of Survival")

fig <- ggplotly(p)

fig


Inspired by Zachary Jones

### Facetwrap & Smooth

library(plotly)

set.seed(42)
n <- 100

df <- data.frame(location = rep(LETTERS[1:4], n),
score    = sample(45:80, 4*n, replace = TRUE))

df$fig <- inv.logit(0.075 * df$score + rep(c(-4.5, -5, -6, -2.8), n))
df$pass <- sapply(df$fig, function(x){rbinom(1, 1, x)})

g <- glm(pass ~ location + score, data = df, family = 'binomial')

new.data <- expand.grid(score    = seq(46, 75, length = n),
location = LETTERS[1:4])

preds <- predict(g, newdata = new.data, type = 'response',se = TRUE)
new.data$pred.full <- preds$fit

new.data$ymin <- new.data$pred.full - 2*preds$se.fit new.data$ymax <- new.data$pred.full + 2*preds$se.fit

p <- ggplot(df,aes(x = score, y = pass)) +
facet_wrap(~location) +
geom_point(size=1) +
geom_ribbon(data = new.data,aes(y = pred.full, ymin = ymin, ymax = ymax),alpha = 0.25) +
geom_line(data = new.data,aes(y = pred.full),colour = "blue")

fig <- ggplotly(p)

fig


Inspired by Stack Overflow

### Prediction Bands

library(plotly)

set.seed(42)
x <- rep(0:100,10)
y <- 15 + 2*rnorm(1010,10,4)*x + rnorm(1010,20,100)
id<-rep(1:10,each=101)

dtfr <- data.frame(x=x ,y=y, id=id)

library(nlme)

model.mx <- lme(y~x,random=~1+x|id,data=dtfr)

#create data.frame with new values for predictors
#more than one predictor is possible
new.dat <- data.frame(x=0:100)
#predict response
new.dat$pred <- predict(model.mx, newdata=new.dat,level=0) #create design matrix Designmat <- model.matrix(eval(eval(model.mx$call$fixed)[-2]), new.dat[-ncol(new.dat)]) #compute standard error for predictions predvar <- diag(Designmat %*% model.mx$varFix %*% t(Designmat))
new.dat$SE <- sqrt(predvar) new.dat$SE2 <- sqrt(predvar+model.mx$sigma^2) library(ggplot2) p <- ggplot(new.dat,aes(x=x,y=pred)) + geom_line() + geom_ribbon(aes(ymin=pred-2*SE2,ymax=pred+2*SE2),alpha=0.2,fill="red") + geom_ribbon(aes(ymin=pred-2*SE,ymax=pred+2*SE),alpha=0.2,fill="blue") + geom_point(data=dtfr,aes(x=x,y=y), size=1) + scale_y_continuous("y") fig <- ggplotly(p) fig  Inspired by Stack Overflow ### Confidence Bands library(plotly) require(nlme) set.seed(101) mp <- data.frame(year=1990:2010) N <- nrow(mp) mp <- within(mp, { wav <- rnorm(N)*cos(2*pi*year)+rnorm(N)*sin(2*pi*year)+5 wow <- rnorm(N)*wav+rnorm(N)*wav^3 }) m01 <- gls(wow~poly(wav,3), data=mp, correlation = corARMA(p=1)) fit <- predict(m01) V <- vcov(m01) X <- model.matrix(~poly(wav,3),data=mp) se.fit <- sqrt(diag(X %*% V %*% t(X))) predframe <- with(mp,data.frame(year,wav, wow=fit,lwr=fit-1.96*se.fit,upr=fit+1.96*se.fit)) p <- ggplot(mp, aes(year, wow))+ geom_point()+ geom_line(data=predframe)+ geom_ribbon(data=predframe,aes(ymin=lwr,ymax=upr),alpha=0.3) fig <- ggplotly(p) fig  Inspired by Stack overflow ### Multiple Layers library(plotly) x=seq(1,10,length=100) data=data.frame(x,dnorm(x,mean=6.5,sd=1)) names(data)=c('x','new.data') x.ribbon=seq(1,10,length=20) ribbon=data.frame(x.ribbon, dnorm(x.ribbon,mean=5,sd=1)+.01, dnorm(x.ribbon,mean=5,sd=1)-.01, dnorm(x.ribbon,mean=5,sd=1)) names(ribbon)=c('x.ribbon','max','min','avg') p <- ggplot()+geom_ribbon(data=ribbon,aes(ymin=min,ymax=max,x=x.ribbon,fill='lightgreen'))+ geom_line(data=ribbon,aes(x=x.ribbon,y=avg,color='black'))+ geom_line(data=data,aes(x=x,y=new.data,color='red'))+ xlab('x')+ylab('density') + scale_fill_identity() + scale_colour_manual(name = 'the colour', values =c('black'='black','red'='red')) fig <- ggplotly(p) fig  Inspired by Stack Overflow ### What About Dash? Dash for R is an open-source framework for building analytical applications, with no Javascript required, and it is tightly integrated with the Plotly graphing library. Learn about how to install Dash for R at https://dashr.plot.ly/installation. Everywhere in this page that you see fig, you can display the same figure in a Dash for R application by passing it to the figure argument of the Graph component from the built-in dashCoreComponents package like this: library(plotly) fig <- plot_ly() # fig <- fig %>% add_trace( ... ) # fig <- fig %>% layout( ... ) library(dash) library(dashCoreComponents) library(dashHtmlComponents) app <- Dash$new()
app$layout( htmlDiv( list( dccGraph(figure=fig) ) ) ) app$run_server(debug=TRUE, dev_tools_hot_reload=FALSE)