Continuous Error Bands in ggplot2

How to make Continuous Error Bands in ggplot2 with Plotly.

Plotly Studio: Transform any dataset into an interactive data application in minutes with AI. Sign up for early access now.

Default error bar plot

To create continuous errorbar plot we need to use df.summary. To add lower and upper error bars, use ymin = len-sd and ymax = len+sd.

library(plotly)
library(ggplot2)
library(dplyr)

df <- ToothGrowth
df$dose <- as.factor(df$dose)
df.summary <- df %>%
  group_by(dose) %>%
  summarise(
    sd = sd(len, na.rm = TRUE),
    len = mean(len)
  )

p <- ggplot(df.summary, aes(dose, len)) +
      geom_line(aes(group = 1)) +
      geom_errorbar( aes(ymin = len-sd, ymax = len+sd),width = 0.2) +
      geom_point(size = 2)

ggplotly(p)

Add jitter

library(plotly)
library(ggplot2)
library(dplyr)

df <- ToothGrowth
df$dose <- as.factor(df$dose)
df.summary <- df %>%
  group_by(dose) %>%
  summarise(
    sd = sd(len, na.rm = TRUE),
    len = mean(len)
  )

p <- ggplot(df, aes(dose, len)) +
  geom_jitter( position = position_jitter(0.2), color = "darkgray") + 
  geom_line(aes(group = 1), data = df.summary) +
  geom_errorbar(
    aes(ymin = len-sd, ymax = len+sd),
    data = df.summary, width = 0.2) +
  geom_point(data = df.summary, size = 2)

ggplotly(p)

Create groups

To make sure groups do not overlay, use position_dodge()

library(plotly)
library(ggplot2)
library(dplyr)

df <- ToothGrowth
df$dose <- as.factor(df$dose)
df.summary <- df %>%
  group_by(dose, supp) %>%
  summarise(
    sd = sd(len),
    len = mean(len)
  )

p <- ggplot(df.summary, aes(dose, len)) +
        geom_errorbar(
          aes(ymin = len-sd, ymax = len+sd, color = supp),
          position = position_dodge(0.3), width = 0.2
          )+
        geom_point(aes(color = supp), position = position_dodge(0.3)) +
        scale_color_manual(values = c("#00AFBB", "#E7B800")) 

ggplotly(p)

Add line with geom_line(), remember to apply position_dodge() to make sure groups do not overlay each other.

library(plotly)
library(ggplot2)
library(dplyr)

df <- ToothGrowth
df$dose <- as.factor(df$dose)
df.summary <- df %>%
  group_by(dose, supp) %>%
  summarise(
    sd = sd(len),
    len = mean(len)
  )

p <- ggplot(df.summary, aes(dose, len)) +
      geom_line(aes(linetype = supp, group = supp), position = position_dodge(0.3))+
      geom_errorbar(
        aes(ymin = len-sd, ymax = len+sd, color = supp),
        position = position_dodge(0.3), width = 0.2
        )+
      geom_point(aes(color = supp), position = position_dodge(0.3)) +
      scale_color_manual(values = c("#00AFBB", "#E7B800")) 


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)