Distplots in R
How to make interactive Distplots in R with Plotly.
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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[1])
colnames(df1) <- c('x', 'Group')
df2 <- data.frame(x2, group_labels[2])
colnames(df2) <- c('x', 'Group')
df3 <- data.frame(x3, group_labels[3])
colnames(df3) <- c('x', 'Group')
df4 <- data.frame(x4, group_labels[4])
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'))
Plot Normal Curve
library(ggplot2)
library(plotly)
set.seed(1)
x1 <- rnorm(200, mean = 0, sd = 1)
x2 <- rnorm(200, mean = 0, sd = 1) + 2
group_labels = c('Group 1', 'Group 2')
df1 <- data.frame(x1, group_labels[1])
colnames(df1) <- c('x', 'Group')
df2 <- data.frame(x2, group_labels[2])
colnames(df2) <- c('x', 'Group')
df <- rbind(df1,df2)
colnames(df) <- c('x', 'Group')
gg <- ggplot(data = df , aes(x=x)) +
geom_histogram(aes(y = ..density.., fill=Group),bins = 30, alpha = 0.7)+
geom_density(aes(color=Group))+
geom_rug(aes(color=Group))+
labs(x = '',
y = '',
title = 'Distplot with Normal Distribution')
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 Only Curve and Rug
library(ggplot2)
library(plotly)
set.seed(1)
x1 <- rnorm(200, mean = 0, sd = 1) - 1
x2 <- rnorm(200, mean = 0, sd = 1)
x3 <- rnorm(200, mean = 0, sd = 1) + 1
group_labels = c('Group 1', 'Group 2', 'Group 3')
df1 <- data.frame(x1, group_labels[1])
colnames(df1) <- c('x', 'Group')
df2 <- data.frame(x2, group_labels[2])
colnames(df2) <- c('x', 'Group')
df3 <- data.frame(x3, group_labels[3])
colnames(df3) <- c('x', 'Group')
df <- rbind(df1,df2,df3)
colnames(df) <- c('x', 'Group')
gg <- ggplot(data = df ) +
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'),
title = 'Curve and Rug Plot')
Plot Only Hist and Rug
library(ggplot2)
library(plotly)
set.seed(1)
x1 <- rnorm(200, mean = 0, sd = 1) - 1
x2 <- rnorm(200, mean = 0, sd = 1)
x3 <- rnorm(200, mean = 0, sd = 1) + 1
group_labels = c('Group 1', 'Group 2', 'Group 3')
df1 <- data.frame(x1, group_labels[1])
colnames(df1) <- c('x', 'Group')
df2 <- data.frame(x2, group_labels[2])
colnames(df2) <- c('x', 'Group')
df3 <- data.frame(x3, group_labels[3])
colnames(df3) <- c('x', 'Group')
df <- rbind(df1,df2,df3)
colnames(df) <- c('x', 'Group')
gg <- ggplot(data = df ) +
geom_histogram(aes(x=x, y = ..density.., fill=Group),bins = 29, alpha = 0.7) +
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'),
title = 'Hist and Rug Plot')
Plot Hist and Rug with Different Bin Sizes
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
df <- data.frame(x1, x2, x3)
gg <- ggplot(df,aes() ) +
geom_histogram(aes(x = x1, y = ..density.., fill = '#67B7D1'), alpha = 0.7, bins = 5) +
geom_histogram(aes(x = x2, y = ..density.., fill = '#ff8080'), alpha = 0.7, bins = 17) +
geom_histogram(aes(x = x3, y = ..density.., fill = '#ff99dd'), alpha = 0.7, bins = 29) +
geom_rug(aes(x = x1),color = '#67B7D1') +
geom_rug(aes(x = x2),color = '#ff8080') +
geom_rug(aes(x = x3),color = '#ff99dd') +
labs(x = '',
y = '',
title = 'Hist and Rug Plot') +
theme(legend.title=element_blank()) +
scale_fill_identity(labels = c('Group 1', 'Group 2', 'Group 3'),
guide = "legend")
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'),
title = 'Hist and Rug Plot')
Plot Only Hist and Curve
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[1])
colnames(df1) <- c('x', 'Group')
df2 <- data.frame(x2, group_labels[2])
colnames(df2) <- c('x', 'Group')
df3 <- data.frame(x3, group_labels[3])
colnames(df3) <- c('x', 'Group')
df4 <- data.frame(x4, group_labels[4])
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)) +
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'),
title = 'Hist and Curve Plot')
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)