# Distplots in R

How to make interactive Distplots in R with Plotly.

New to Plotly?

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.

## Combined statistical representations with histogram

Several representations of statistical distributions are available in plotly, such as histograms, violin plots, box plots (see the complete list here). It is also possible to combine several representations in the same plot.

library(plotly)
library(ggplot2)
library(reshape2)
data(tips)

p <- ggplot(tips, aes(x=total_bill, weight = tip, color=sex, fill = sex)) +
geom_histogram(binwidth=2.5) +
ylab("sum of tip") +
geom_rug(sides="t", length = unit(0.3, "cm"))
fig <- ggplotly(p)
fig


## Combined statistical representations with distplot figure factory

The distplot figure factory displays a combination of statistical representations of numerical data, such as histogram, kernel density estimation or normal curve, and rug plot.

#### Basic Distplot

A histogram, a kde plot and a rug plot are displayed.

library(ggplot2)
library(plotly)

set.seed(1)
hist_data <- data.frame(rnorm(1000, mean = 0, sd = 1))
colnames(hist_data) = c('x')
gg <- ggplot(hist_data,aes(x = x, color = 'density')) +
geom_histogram(aes(y = ..density..), bins = 7,  fill = '#67B7D1', alpha = 0.5) +
geom_density(color = '#67B7D1') +
geom_rug(color = '#67B7D1') +
ylab("") +
xlab("")  + theme(legend.title=element_blank()) +
scale_color_manual(values = c('density' = '#67B7D1'))

ggplotly(gg)%>%
layout(plot_bgcolor='#e5ecf6',
xaxis = list(
title='Time',
zerolinecolor = '#ffff',
zerolinewidth = 2,
gridcolor = 'ffff'),
yaxis = list(
title='Value A',
zerolinecolor = '#ffff',
zerolinewidth = 2,
gridcolor = 'ffff'))


#### Plot Multiple Datasets

library(ggplot2)
library(plotly)
set.seed(1)
x1 <- rnorm(200, mean = 0, sd = 1) - 2
x2 <- rnorm(200, mean = 0, sd = 1)
x3 <- rnorm(200, mean = 0, sd = 1) + 2
x4 <- rnorm(200, mean = 0, sd = 1) +4
group_labels = c('Group 1', 'Group 2', 'Group 3', 'Group 4')

df1 <- data.frame(x1, group_labels)
colnames(df1) <- c('x', 'Group')
df2 <- data.frame(x2, group_labels)
colnames(df2) <- c('x', 'Group')
df3 <- data.frame(x3, group_labels)
colnames(df3) <- c('x', 'Group')
df4 <- data.frame(x4, group_labels)
colnames(df4) <- c('x', 'Group')
df <- rbind(df1,df2,df3,df4)
colnames(df) <- c('x', 'Group')

gg <- ggplot(data = df ) +
geom_histogram(aes(x=x, y = ..density.., fill=Group),bins = 29, alpha = 0.7) +
geom_density(aes(x=x, color=Group)) + geom_rug(aes(x=x, color=Group))+
ylab("") +
xlab("")

ggplotly(gg)%>%
layout(plot_bgcolor='#e5ecf6',
xaxis = list(
title='Time',
zerolinecolor = '#ffff',
zerolinewidth = 2,
gridcolor = 'ffff'),
yaxis = list(
title='Value A',
zerolinecolor = '#ffff',
zerolinewidth = 2,
gridcolor = 'ffff'))


#### Use Multiple Bin Sizes

Different bin sizes are used for the different datasets with the bin_size argument.

library(ggplot2)
library(plotly)

set.seed(1)
x1 <- rnorm(1000, mean = 0, sd = 1) - 2
x2 <- rnorm(1000, mean = 0, sd = 1)
x3 <- rnorm(1000, mean = 0, sd = 1) + 2
x4 <- rnorm(1000, mean = 0, sd = 1) +4
group_labels = c('Group 1', 'Group 2', 'Group 3', 'Group 4')

df = data.frame(x1,x2,x3,x4, group_labels)

gg <- ggplot(df,aes() ) +
geom_histogram(aes(x = x1, y = ..density.., fill = '#67B7D1'), alpha = 0.7, bins = 29) +
geom_histogram(aes(x = x2, y = ..density.., fill = '#ff8080'), alpha = 0.7, bins = 20) +
geom_histogram(aes(x = x3, y = ..density.., fill = '#ff99dd'), alpha = 0.7, bins = 10) +
geom_histogram(aes(x = x4, y = ..density.., fill = '#ff9900'), alpha = 0.7, bins = 5) +
geom_density(aes(x = x1),color = '#67B7D1') +
geom_density(aes(x = x2),color = '#ff8080') +
geom_density(aes(x = x3),color = '#ff99dd') +
geom_density(aes(x = x4),color = '#ff9900') +
geom_rug(aes(x = x1),color = '#67B7D1') +
geom_rug(aes(x = x2),color = '#ff8080') +
geom_rug(aes(x = x3),color = '#ff99dd') +
geom_rug(aes(x = x4),color = '#ff9900') +
theme(legend.title=element_blank()) +
scale_fill_identity(labels = c('Group 1', 'Group 2', 'Group 3', 'Group 4'),
guide = "legend")  +
labs(x = '',
y = '')

ggplotly(gg)%>%
layout(plot_bgcolor='#e5ecf6',
xaxis = list(
title='Time',
zerolinecolor = '#ffff',
zerolinewidth = 2,
gridcolor = 'ffff'),
yaxis = list(
title='Value A',
zerolinecolor = '#ffff',
zerolinewidth = 2,
gridcolor = 'ffff'))