Box Plots in R

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

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Basic Boxplot

library(plotly)
fig <- plot_ly(y = ~rnorm(50), type = "box")
fig <- fig %>% add_trace(y = ~rnorm(50, 1))

fig

Choosing The Algorithm For Computing Quartiles

By default, quartiles for box plots are computed using the linear method (for more about linear interpolation, see #10 listed on http://www.amstat.org/publications/jse/v14n3/langford.html and https://en.wikipedia.org/wiki/Quartile for more details).

However, you can also choose to use an exclusive or an inclusive algorithm to compute quartiles.

The exclusive algorithm uses the median to divide the ordered dataset into two halves. If the sample is odd, it does not include the median in either half. Q1 is then the median of the lower half and Q3 is the median of the upper half.

The inclusive algorithm also uses the median to divide the ordered dataset into two halves, but if the sample is odd, it includes the median in both halves. Q1 is then the median of the lower half and Q3 the median of the upper half.

library(plotly)
fig <- plot_ly(y = list(1,2,3,4,5), type = "box", quartilemethod="exclusive") # or "inclusive", or "linear" by default

fig

Modifying The Algorithm For Computing Quartiles

For an explanation of how each algorithm works, see Choosing The Algorithm For Computing Quartiles

library(plotly)
fig <- plot_ly(y = list(1,2,3,4,5), type = "box", quartilemethod="linear", name="Linear Quartile Mode")
fig <- fig %>% add_trace(y = list(1,2,3,4,5), quartilemethod="inclusive", name="Inclusive Quartile Mode")
fig <- fig %>% add_trace(y = list(1,2,3,4,5), quartilemethod="exclusive", name="Exclusive Quartile Mode")
fig <- fig %>% layout(title = "Modifying The Algorithm For Computing Quartiles")

fig

Box Plot With Precomputed Quartiles

You can specify precomputed quartile attributes rather than using a built-in quartile computation algorithm.

This could be useful if you have already pre-computed those values or if you need to use a different algorithm than the ones provided.

library(plotly)
fig <- plot_ly(y = list(1,2,3,4,5,6,7,8,9), type = "box", q1=list(1, 2, 3), median=list(4, 5, 6),
                  q3=list(7, 8, 9 ), lowerfence=list(-1, 0, 1),
                  upperfence=list(5, 6, 7), mean=list(2.2, 2.8, 3.2 ),
                  sd=list(0.2, 0.4, 0.6), notchspan=list(0.2, 0.4, 0.6))

fig

Horizontal Boxplot

library(plotly)
fig <- plot_ly(x = ~rnorm(50), type = "box")
fig <- fig %>% add_trace(x = ~rnorm(50, 1))

fig

Adding Jittered Points

fig <- plot_ly(y = ~rnorm(50), type = "box", boxpoints = "all", jitter = 0.3,
        pointpos = -1.8)

fig

Several Box Plots

fig <- plot_ly(ggplot2::diamonds, y = ~price, color = ~cut, type = "box")

fig

Grouped Box Plots

fig <- plot_ly(ggplot2::diamonds, x = ~cut, y = ~price, color = ~clarity, type = "box")
fig <- fig %>% layout(boxmode = "group")

fig

Styling Outliers

library(plotly)

y1 <- c(0.75, 5.25, 5.5, 6, 6.2, 6.6, 6.80, 7.0, 7.2, 7.5, 7.5, 7.75, 8.15,
       8.15, 8.65, 8.93, 9.2, 9.5, 10, 10.25, 11.5, 12, 16, 20.90, 22.3, 23.25)
y2 <- c(0.75, 5.25, 5.5, 6, 6.2, 6.6, 6.80, 7.0, 7.2, 7.5, 7.5, 7.75, 8.15,
        8.15, 8.65, 8.93, 9.2, 9.5, 10, 10.25, 11.5, 12, 16, 20.90, 22.3, 23.25)
y3 <- c(0.75, 5.25, 5.5, 6, 6.2, 6.6, 6.80, 7.0, 7.2, 7.5, 7.5, 7.75, 8.15,
        8.15, 8.65, 8.93, 9.2, 9.5, 10, 10.25, 11.5, 12, 16, 20.90, 22.3, 23.25)
y4 <- c(0.75, 5.25, 5.5, 6, 6.2, 6.6, 6.80, 7.0, 7.2, 7.5, 7.5, 7.75, 8.15,
        8.15, 8.65, 8.93, 9.2, 9.5, 10, 10.25, 11.5, 12, 16, 20.90, 22.3, 23.25)

fig <- plot_ly(type = 'box')
fig <- fig %>% add_boxplot(y = y1, jitter = 0.3, pointpos = -1.8, boxpoints = 'all',
              marker = list(color = 'rgb(7,40,89)'),
              line = list(color = 'rgb(7,40,89)'),
              name = "All Points")
fig <- fig %>% add_boxplot(y = y2, name = "Only Whiskers", boxpoints = FALSE,
              marker = list(color = 'rgb(9,56,125)'),
              line = list(color = 'rgb(9,56,125)'))
fig <- fig %>% add_boxplot(y = y3, name = "Suspected Outlier", boxpoints = 'suspectedoutliers',
              marker = list(color = 'rgb(8,81,156)',
                            outliercolor = 'rgba(219, 64, 82, 0.6)',
                            line = list(outliercolor = 'rgba(219, 64, 82, 1.0)',
                                        outlierwidth = 2)),
              line = list(color = 'rgb(8,81,156)'))
fig <- fig %>% add_boxplot(y = y4, name = "Whiskers and Outliers", boxpoints = 'outliers',
              marker = list(color = 'rgb(107,174,214)'),
              line = list(color = 'rgb(107,174,214)'))
fig <- fig %>% layout(title = "Box Plot Styling Outliers")

fig

Reference

See https://plotly.com/r/reference/#box for more information and chart attribute options!

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)