Interactive Plots with plotly

Learn how to create interactive plots using the plotly package in R. This lecture covers essential techniques for enhancing data visualizations with interactivity using plotly in R.

Introduction

Interactive plots provide a dynamic way to explore data, allowing users to zoom, pan, hover, and click to get more information. The plotly package in R enables the creation of interactive plots with ease. In this lecture, we will learn how to create interactive plots using plotly in R.

Key Concepts

1. What is plotly?

plotly is a graphing library that makes interactive, publication-quality graphs online. In R, the plotly package allows you to create interactive plots directly from R code.

2. Advantages of Interactive Plots

• Enhanced data exploration.

• Improved user engagement.

• Ability to reveal more details on demand.

3. Basic Structure of plotly

Creating a plotly plot involves:

• Loading the plotly library.

• Creating a plotly object using the plot_ly() function or converting an existing ggplot2 plot to plotly using the ggplotly() function.

Creating and Customizing Interactive Plots with plotly

1. Installing and Loading plotly

If you haven’t installed plotly yet, you can install it using the following command:

install.packages("plotly")

Load the plotly package:

library(plotly)

Warning: package 'plotly' was built under R version 4.3.3

Loading required package: ggplot2

Warning: package 'ggplot2' was built under R version 4.3.3

Attaching package: 'plotly'

The following object is masked from 'package:ggplot2':

    last_plot

The following object is masked from 'package:stats':

    filter

The following object is masked from 'package:graphics':

    layout

2. Basic Scatter Plot

A basic scatter plot in plotly can be created using the plot_ly() function.

# Creating sample data

set.seed(123)

data <- data.frame(x = rnorm(100), y = rnorm(100))



# Creating a basic scatter plot with plotly

plot_ly(data, x = ~x, y = ~y, type = 'scatter', mode = 'markers')

3. Adding Titles and Labels

You can add titles and labels to a plotly plot using the layout() function.

# Adding titles and labels

plot_ly(data, x = ~x, y = ~y, type = 'scatter', mode = 'markers') %>%

  layout(title = 'Basic Scatter Plot with Titles and Labels', 

         xaxis = list(title = 'X Axis Label'), 

         yaxis = list(title = 'Y Axis Label'))

# Plot result

plot_ly(data, x = ~x, y = ~y, type = 'scatter', mode = 'markers') %>%

  layout(title = 'Basic Scatter Plot with Titles and Labels', 

         xaxis = list(title = 'X Axis Label'), 

         yaxis = list(title = 'Y Axis Label'))

4. Customizing Colors and Symbols

You can customize colors and symbols using the marker parameter.

# Creating sample data with groups

data$group <- sample(c("Group 1", "Group 2"), 100, replace = TRUE)



# Customizing colors and symbols

plot_ly(data, x = ~x, y = ~y, type = 'scatter', mode = 'markers', 

        marker = list(color = ~group, colorscale = c('red', 'blue'), symbol = ~group))

5. Converting ggplot2 Plots to plotly

You can convert an existing ggplot2 plot to an interactive plotly plot using the ggplotly() function.

# Creating a ggplot2 plot

library(ggplot2)

p <- ggplot(data, aes(x = x, y = y, color = group)) +

  geom_point() +

  labs(title = "ggplot2 Plot")



# Converting the ggplot2 plot to plotly

ggplotly(p)

# Plot result

p <- ggplot(data, aes(x = x, y = y, color = group)) +

  geom_point() +

  labs(title = "ggplot2 Plot")

ggplotly(p)

6. Adding Hover Text

You can add hover text to provide more information when the user hovers over a data point.

# Adding hover text

plot_ly(data, x = ~x, y = ~y, type = 'scatter', mode = 'markers', 

        text = ~paste("X:", x, "<br>Y:", y, "<br>Group:", group), hoverinfo = 'text')

# Plot result

plot_ly(data, x = ~x, y = ~y, type = 'scatter', mode = 'markers', 

        text = ~paste("X:", x, "<br>Y:", y, "<br>Group:", group), hoverinfo = 'text')

Example: Comprehensive Interactive Plotting with plotly

Here’s a comprehensive example of creating and customizing interactive plots using plotly in R.

# Creating sample data

data <- data.frame(x = rnorm(100), y = rnorm(100), group = sample(c("Group 1", "Group 2"), 100, replace = TRUE))



# Basic scatter plot

plot_ly(data, x = ~x, y = ~y, type = 'scatter', mode = 'markers')



# Scatter plot with titles and labels

plot_ly(data, x = ~x, y = ~y, type = 'scatter', mode = 'markers') %>%

  layout(title = 'Basic Scatter Plot with Titles and Labels', 

         xaxis = list(title = 'X Axis Label'), 

         yaxis = list(title = 'Y Axis Label'))



# Customizing colors and symbols

plot_ly(data, x = ~x, y = ~y, type = 'scatter', mode = 'markers', 

        marker = list(color = ~group, colorscale = c('red', 'blue'), symbol = ~group))



# Converting ggplot2 plot to plotly

library(ggplot2)

p <- ggplot(data, aes(x = x, y = y, color = group)) +

  geom_point() +

  labs(title = "ggplot2 Plot")

ggplotly(p)



# Adding hover text

plot_ly(data, x = ~x, y = ~y, type = 'scatter', mode = 'markers', 

        text = ~paste("X:", x, "<br>Y:", y, "<br>Group:", group), hoverinfo = 'text')

# Plot results

plot_ly(data, x = ~x, y = ~y, type = 'scatter', mode = 'markers')

plot_ly(data, x = ~x, y = ~y, type = 'scatter', mode = 'markers') %>%

  layout(title = 'Basic Scatter Plot with Titles and Labels', 

         xaxis = list(title = 'X Axis Label'), 

         yaxis = list(title = 'Y Axis Label'))

plot_ly(data, x = ~x, y = ~y, type = 'scatter', mode = 'markers', 

        marker = list(color = ~group, colorscale = c('red', 'blue'), symbol = ~group))

p <- ggplot(data, aes(x = x, y = y, color = group)) +

  geom_point() +

  labs(title = "ggplot2 Plot")

ggplotly(p)

plot_ly(data, x = ~x, y = ~y, type = 'scatter', mode = 'markers', 

        text = ~paste("X:", x, "<br>Y:", y, "<br>Group:", group), hoverinfo = 'text')

Summary

In this lecture, we covered how to create interactive plots using the plotly package in R. We explored various techniques for adding titles, labels, colors, symbols, and hover text. We also learned how to convert ggplot2 plots to plotly for enhanced interactivity. Interactive plots are a powerful tool for data exploration and user engagement.

Further Reading

For more detailed information, consider exploring the following resources:

• plotly for R Documentation

• R for Data Science

• Interactive web-based data visualization with R, plotly, and shiny

Call to Action

If you found this lecture helpful, make sure to check out the other lectures in the R Graphs series. Happy plotting!