Strip Charts in ggplot2

How to make Strip Charts in ggplot2 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.

Basic stripchart

library(plotly)
library(ggplot2)

ToothGrowth$dose <- as.factor(ToothGrowth$dose)

ggplot(ToothGrowth, aes(x=dose, y=len)) + 
  geom_jitter()

p<-ggplot(ToothGrowth, aes(x=dose, y=len)) + 
  geom_jitter(position=position_jitter(0.2))
p <- p + coord_flip()

ggplotly(p)

Change point size

library(plotly)
library(ggplot2)

ToothGrowth$dose <- as.factor(ToothGrowth$dose)

p <- ggplot(ToothGrowth, aes(x=dose, y=len)) + 
        geom_jitter(position=position_jitter(0.2), cex=1.2)

ggplotly(p)

Change shape

library(plotly)
library(ggplot2)

ToothGrowth$dose <- as.factor(ToothGrowth$dose)

p <- ggplot(ToothGrowth, aes(x=dose, y=len)) + 
        geom_jitter(position=position_jitter(0.2), shape=17)

ggplotly(p)

Add summary statistics

library(plotly)
library(ggplot2)

ToothGrowth$dose <- as.factor(ToothGrowth$dose)

p <- ggplot(ToothGrowth, aes(x=dose, y=len)) + 
  geom_jitter(position=position_jitter(0.2), cex=1.2)
p <- p + stat_summary(fun.y=mean, geom="point", shape=18,
                 size=3, color="red")

ggplotly(p)

To add standard deviation use mean_sdl function which computes the mean plus or minus a constant times the standard deviation.

library(plotly)
library(ggplot2)

ToothGrowth$dose <- as.factor(ToothGrowth$dose)

p <- ggplot(ToothGrowth, aes(x=dose, y=len)) + 
    geom_jitter(position=position_jitter(0.2))
p + stat_summary(fun.data=mean_sdl, mult=1, 
                 geom="pointrange", color="red")

ggplotly(p)

You can change the representation of the statistics by changing geom, for example setting it to crossbar.

Add box plot

To add a box plot you can use geom_boxplot(). Likewise, you can add a notched boxplot with geom_boxplot(notch = TRUE) and a violin plot with geom_violin(trim = FALSE).

library(plotly)
library(ggplot2)

ToothGrowth$dose <- as.factor(ToothGrowth$dose)

p <- ggplot(ToothGrowth, aes(x=dose, y=len)) + 
        geom_boxplot()+
        geom_jitter(position=position_jitter(0.2))

ggplotly(p)

Colour data by groups

library(plotly)
library(ggplot2)

ToothGrowth$dose <- as.factor(ToothGrowth$dose)

p <- ggplot(ToothGrowth, aes(x=dose, y=len, color=dose)) +
  geom_jitter(position=position_jitter(0.2))

ggplotly(p)

library(plotly)
library(ggplot2)

ToothGrowth$dose <- as.factor(ToothGrowth$dose)

p <- ggplot(ToothGrowth, aes(x=dose, y=len, color=dose, shape=dose)) + 
  geom_jitter(position=position_jitter(0.2))+
  labs(title="Plot of length  by dose",x="Dose (mg)", y = "Length")
p + theme_classic()

ggplotly(p)

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)