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# geom_boxplot in ggplot2

How to make a box plot in ggplot2. Examples of box plots in R that are grouped, colored, and display the underlying data distribution.

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.

### Basic Boxplot

library(plotly)

set.seed(1234)
dat <- data.frame(cond = factor(rep(c("A","B"), each=200)), rating = c(rnorm(200),rnorm(200, mean=.8)))

p <- ggplot(dat, aes(x=cond, y=rating)) + geom_boxplot()

fig <- ggplotly(p)

fig


### Colored Boxplot

library(plotly)

set.seed(1234)
dat <- data.frame(cond = factor(rep(c("A","B"), each=200)), rating = c(rnorm(200),rnorm(200, mean=.8)))

p <- ggplot(dat, aes(x=cond, y=rating, fill=cond)) + geom_boxplot()

fig <- ggplotly(p)

fig


### Flipped Boxplot

library(plotly)

set.seed(1234)
dat <- data.frame(cond = factor(rep(c("A","B"), each=200)), rating = c(rnorm(200),rnorm(200, mean=.8)))

p <- ggplot(dat, aes(x=cond, y=rating, fill=cond)) + geom_boxplot() +
guides(fill=FALSE) + coord_flip()

fig <- ggplotly(p)

fig


### Boxplot w/ Stats

library(plotly)

set.seed(1234)
dat <- data.frame(cond = factor(rep(c("A","B"), each=200)), rating = c(rnorm(200),rnorm(200, mean=.8)))

p <- ggplot(dat, aes(x=cond, y=rating)) + geom_boxplot() +
stat_summary(fun.y=mean, geom="point", shape=5, size=4)

fig <- ggplotly(p)

fig


### Boxplot Facets

library(plyr)
library(reshape2)
library(plotly)

set.seed(1234)
x<- rnorm(100)
y.1<-rnorm(100)
y.2<-rnorm(100)
y.3<-rnorm(100)
y.4<-rnorm(100)

df<- (as.data.frame(cbind(x,y.1,y.2,y.3,y.4)))

dfmelt<-melt(df, measure.vars = 2:5)

p <- ggplot(dfmelt, aes(x=factor(round_any(x,0.5)), y=value,fill=variable))+
geom_boxplot()+
facet_grid(.~variable)+
labs(x="X (binned)")+
theme(axis.text.x=element_text(angle=-90, vjust=0.4,hjust=1))

fig <- ggplotly(p)

fig


### Time Series Facets

library(foreign)
library(MASS)
library(Hmisc)
library(reshape2)
library(plotly)

invisible(lapply(dat[, c("apply", "pared", "public")], table))
invisible(ftable(xtabs(~ public + apply + pared, data = dat)))

p <- ggplot(dat, aes(x = apply, y = gpa)) +
geom_boxplot(size = .75) +
facet_grid(pared ~ public, margins = TRUE)

fig <- ggplotly(p)

fig


### Outliers

library(plotly)
set.seed(123)

df <- diamonds[sample(1:nrow(diamonds), size = 1000),]

p <- ggplot(df, aes(cut, price, fill = cut)) +
geom_boxplot(outlier.shape = NA) +
ggtitle("Ignore outliers in ggplot2")

# Need to modify the plotly object and make outlier points have opacity equal to 0
fig <- plotly_build(p)

fig$data <- lapply(fig$data, FUN = function(x){
x$marker = list(opacity = 0) return(x) }) fig  ### Linewidth library(plotly) set.seed(123) df <- diamonds[sample(1:nrow(diamonds), size = 1000),] p <- ggplot(df, aes(cut, price, fill = cut)) + geom_boxplot(size = 1) + ggtitle("Adjust line width of boxplot in ggplot2") # Need to modify the plotly object to make sure line width is larger than default fig <- plotly_build(p) fig$data <- lapply(fig$data, FUN = function(x){ x$line = list(width = 10)
return(x)
})

fig


### Whiskers

library(plotly)
set.seed(123)

df <- diamonds[sample(1:nrow(diamonds), size = 1000),]

# This is how it needs to be done in ggplot
p <- ggplot(df, aes(color, price)) +
stat_boxplot(geom ='errorbar') +
geom_boxplot()+
ggtitle("Add horizontal lines to whiskers using ggplot2")

# Note that plotly will automatically add horozontal lines to the whiskers
p <- ggplot(df, aes(cut, price, fill = cut)) +
geom_boxplot()+
ggtitle("Add horizontal lines to whiskers using ggplot2")

fig <- ggplotly(p)

fig


These example were inspired by Cookbook for R.

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)
)
)
) 