library(ggplot2) library(plotly) library(plyr) library(flexdashboard) set.seed(955) dat <- data.frame(cond = rep(c("A", "B"), each=10), xvar = 1:20 + rnorm(20,sd=3), yvar = 1:20 + rnorm(20,sd=3)) p <- ggplot(dat, aes(x=xvar, y=yvar)) + geom_point(shape=1) # Use hollow circles p ggplotly(p) p <- ggplot(dat, aes(x=xvar, y=yvar)) + geom_point(shape=1) + # Use hollow circles geom_smooth(method=lm) # Add linear regression line ggplotly(p) p <- ggplot(dat, aes(x=xvar, y=yvar)) + geom_point(shape=1) + # Use hollow circles geom_smooth() # Add a loess smoothed fit curve with confidence region ggplotly(p) n <- 20 x1 <- rnorm(n); x2 <- rnorm(n) y1 <- 2 * x1 + rnorm(n) y2 <- 3 * x2 + (2 + rnorm(n)) A <- as.factor(rep(c(1, 2), each = n)) df <- data.frame(x = c(x1, x2), y = c(y1, y2), A = A) fm <- lm(y ~ x + A, data = df) p <- ggplot(data = cbind(df, pred = predict(fm)), aes(x = x, y = y, color = A)) p <- p + geom_point() + geom_line(aes(y = pred)) ggplotly(p) dfGamma = data.frame(nu75 = rgamma(100, 0.75), nu1 = rgamma(100, 1), nu2 = rgamma(100, 2)) dfGamma = stack(dfGamma) p <- ggplot(dfGamma, aes(x = values)) + stat_density(aes(group = ind, color = ind),position="identity",geom="line") ggplotly(p) dim1 <- c(rnorm(100, mean=1), rnorm(100, mean=4)) dim2 <- rnorm(200, mean=1) cat <- factor(c(rep("a", 100), rep("b", 100))) mydf <- data.frame(cbind(dim2, dim1, cat)) p <- ggplot(data=mydf, aes(x=dim1, y=dim2, colour=as.factor(cat))) + geom_point() + stat_density(aes(x=dim1, y=(-2+(..scaled..))), position="identity", geom="line") stuff <- ggplot_build(p) xrange <- stuff[[2]]$ranges[[1]]$x.range # extract the x range to make the # new densities align with y-axis ## Get densities of dim2 ds <- do.call(rbind, lapply(unique(mydf$cat), function(lev) { dens <- with(mydf, density(dim2[cat==lev])) data.frame(x=dens$y+xrange[1], y=dens$x, cat=lev) })) p <- p + geom_path(data=ds, aes(x=x, y=y, color=factor(cat))) ggplotly(p) dd<-data.frame(matrix(rnorm(144, mean=2, sd=2),72,2),c(rep("A",24),rep("B",24),rep("C",24))) colnames(dd) <- c("x_value", "Predicted_value", "State_CD") dd <- data.frame( predicted = rnorm(72, mean = 2, sd = 2), state = rep(c("A", "B", "C"), each = 24) ) grid <- with(dd, seq(min(predicted), max(predicted), length = 100)) normaldens <- ddply(dd, "state", function(df) { data.frame( predicted = grid, density = dnorm(grid, mean(df$predicted), sd(df$predicted)) ) }) p <- ggplot(dd, aes(predicted)) + geom_density() + geom_line(aes(y = density), data = normaldens, colour = "red") + facet_wrap(~ state) ggplotly(p) df <- data.frame(x <- rchisq(1000, 10, 10), y <- rnorm(1000)) p <- ggplot(df, aes(x, y)) + geom_point(alpha = 0.5) + geom_density_2d() + theme(panel.background = element_rect(fill = '#ffffff')) ggplotly(p)