Remove Whiskers and Outliers in R plotly

Boxplots are a powerful tool for visualizing the distribution of data. They highlight the median, quartiles, and potential outliers, providing insights into data spread. However, sometimes you may want to customize your boxplots by removing whiskers and outliers for cleaner visualizations. This article will guide you through the process of removing whiskers and outliers using the plotly package in R.

Why Use Plotly for Boxplots in R?

The plotly library in R allows for creating interactive and highly customizable boxplots. It provides flexibility in adjusting various elements of the plot, including the ability to show or hide whiskers and outliers. This can be particularly useful when you want a simplified representation of your data.

Prerequisites

Before diving into the examples, make sure you have the plotly package installed and loaded:

# Install plotly if you haven't already
install.packages("plotly")

# Load the library
library(plotly)

Understanding Whiskers and Outliers in a Boxplot

A standard boxplot contains the following elements:

• Median: The middle value of the data.
• Quartiles: The first (Q1) and third quartiles (Q3).
• Whiskers: Lines extending from the quartiles to the maximum and minimum values within a specified range.
• Outliers: Data points that fall outside the whisker range, typically displayed as individual points.

In some cases, you might want to remove the whiskers and outliers to make the plot cleaner, especially when focusing on the main data distribution using R Programming Language.

1: Creating a Basic Boxplot with Plotly

Let's start with a basic boxplot using the plotly package:

# Create a sample data frame
data <- data.frame(
  category = rep(c("A", "B", "C"), each = 50),
  values = c(rnorm(50, mean = 10, sd = 3), 
             rnorm(50, mean = 15, sd = 4), 
             rnorm(50, mean = 20, sd = 5))
)

# Create a basic boxplot
basic_boxplot <- plot_ly(data, x = ~category, y = ~values, type = "box")
basic_boxplot

Output:

This will generate a standard boxplot with whiskers and outliers.

2: Removing Whiskers in Plotly

To remove whiskers from the boxplot, you can adjust the whiskerwidth attribute. Setting it to 0 effectively hides the whiskers:

# Boxplot without whiskers
no_whiskers <- plot_ly(data, x = ~category, y = ~values, type = "box", whiskerwidth = 0)
no_whiskers

Output:

The whiskerwidth = 0 parameter hides the whiskers, focusing only on the box's median and quartiles.

3: Removing Outliers in Plotly

To remove outliers, set the boxpoints attribute to "false":

# Create a boxplot without outliers
no_outliers <- plot_ly(data, x = ~category, y = ~values, type = "box", boxpoints = FALSE)
no_outliers

Output:

4: Customizing the Boxplot

You can further customize your boxplot using additional plotly parameters. For example, let's change the box color and remove the gridlines:

# Customized boxplot without whiskers and outliers
custom_boxplot <- plot_ly(data, x = ~category, y = ~values, type = "box", 
                          whiskerwidth = 0, boxpoints = "false",
                          marker = list(color = 'lightblue'), 
                          line = list(color = 'blue')) %>%
  layout(title = "Customized Boxplot without Whiskers and Outliers",
         xaxis = list(title = "Category"),
         yaxis = list(title = "Values", zeroline = FALSE),
         showlegend = FALSE)
custom_boxplot

Output:

When to Remove Whiskers and Outliers

Here is the main reason for When to Remove Whiskers and Outliers:

• Data Presentation: Removing whiskers and outliers can make the visualization less cluttered, focusing on the median and quartiles.
• Non-Standard Data: If your data contains many outliers, removing them might make the boxplot more representative of the core data distribution.
• Report Simplification: For presentations or reports where simplicity is key, removing extra elements can make the plot easier to interpret.

Conclusion

The plotly package in R offers extensive customization options for creating interactive boxplots. You can easily remove whiskers and outliers by adjusting parameters like whiskerwidth and boxpoints, providing a clean and focused visualization of your data. By mastering these techniques, you can tailor your boxplots to meet your analysis or presentation needs.