geom_density in ggplot2

Add a smooth density estimate calculated by stat_density with ggplot2 and R. Examples, tutorials, and code.

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Plotly is a free and open-source graphing library for R. We recommend you read our Getting Started guide for the latest installation or upgrade instructions, then move on to our Plotly Fundamentals tutorials or dive straight in to some Basic Charts tutorials.

Basic Density Plot

library(plotly)

library(ggplot2)
set.seed(1234)

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")

fig <- ggplotly(p)

fig

Density & Facet

library(plotly)

require(plyr)
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)

fig <- ggplotly(p)

fig

Multiple Density Plot

library(plotly)

carrots <- data.frame(length = rnorm(100000, 6, 2))
cukes <- data.frame(length = rnorm(50000, 7, 2.5))

#Now, combine your two dataframes into one.  First make a new column in each.
carrots$veg <- 'carrot'
cukes$veg <- 'cuke'

#and combine into your new data frame vegLengths
vegLengths <- rbind(carrots, cukes)

#now make your lovely plot
p <- ggplot(vegLengths, aes(length, fill = veg)) + geom_density(alpha = 0.2)

fig <- ggplotly(p)

fig

Stacked Density Plot

library(plotly)
set.seed(123)

df <- data.frame(x <- rchisq(1000, 5, 10),
                 group <-  sample(LETTERS[1:5], size = 1000, replace = T))

p <- ggplot(df, aes(x, fill = group)) + 
  geom_density(alpha = 0.5, position = "stack") + 
  ggtitle("stacked density chart")

fig <- ggplotly(p)

fig

Overlay Histogram

library(plotly)
set.seed(123)

df <- data.frame(x <- rchisq(1000, 5, 10),
                 group <-  sample(LETTERS[1:5], size = 1000, replace = T))

p <- ggplot(df, aes(x)) + 
  geom_histogram(aes(y = ..density..), alpha = 0.7, fill = "#333333") + 
  geom_density(fill = "#ff4d4d", alpha = 0.5) + 
  theme(panel.background = element_rect(fill = '#ffffff')) + 
  ggtitle("Density with Histogram overlay")

fig <- ggplotly(p)

fig

Overlay Scatterplot

library(plotly)
set.seed(123)

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')) + 
  ggtitle("2D density plot with scatterplot overlay")

fig <- ggplotly(p)

fig

Kernel Density Estimate

library(plotly)

p <- ggplot(diamonds, aes(x = price)) + 
  geom_density(aes(fill = "epanechnikov"), kernel = "epanechnikov") + 
  facet_grid(~cut) + 
  ggtitle("Kernel density estimate with Facets")

fig <- ggplotly(p)

fig

Kernel Density Plot

library(plotly)

p <- ggplot(diamonds, aes(x = price)) + 
  geom_density(aes(fill = color), alpha = 0.5) + 
  ggtitle("Kernel Density estimates by group")

fig <- ggplotly(p)

fig

These plots were inspired by ggplot2 documentation.

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)