Mirror bar plot with plotly in R

Creating a mirror bar plot in R using the Plotly library can be a visually effective way to compare two sets of data, such as positive and negative values, in a single chart. This article will guide you through the process of creating a mirror bar plot with Plotly in R, including setting up the environment, preparing the data, and customizing the plot for better visualization.

What is a Mirror bar plot?

A mirror bar plot typically consists of the following:

1. Two sets of bars: Each set represents different categories that are mirrored along the same axis.
2. Positive and negative scales: One category’s values are plotted as positive bars, while the other is plotted as negative to mirror the two datasets effectively.
3. Comparison: The mirrored effect allows a clear visual comparison between the two categories.

This plot helps understand the differences or similarities between two groups, such as population distributions or survey responses.

Now we will discuss step by step implementation of Mirror bar plot with plotly in R Programming Language.

Step 1: Install and Load Necessary Libraries

Ensure you have the plotly and dplyr libraries installed.

# Install necessary libraries if not already installed
# install.packages("plotly")
# install.packages("dplyr")

# Load libraries
library(plotly)
library(dplyr)

Step 2: Prepare Data for the Plot

For this example, we will use a dataset comparing male and female populations in different age groups. Let’s create a sample dataset:

# Sample data
age_group <- c("0-10", "11-20", "21-30", "31-40", "41-50", "51-60", "61-70")
male_population <- c(3000, 5000, 6000, 7000, 5500, 4500, 3000)
female_population <- c(2800, 4900, 6200, 6800, 5700, 4600, 3200)

# Create a data frame
df <- data.frame(
  age_group = age_group,
  male_population = male_population,
  female_population = female_population
)

Step 3: Create the Mirror Bar Plot

Now, let's create the mirror bar plot. To mirror the plot, we will represent one of the groups (e.g., male) as negative values and plot them horizontally using Plotly's plot_ly() function.

# Create the mirror bar plot
plot <- plot_ly(df, x = ~male_population, y = ~age_group, type = 'bar', orientation = 'h',
                name = 'Male Population', marker = list(color = 'blue')) %>%
  add_trace(x = ~-female_population, y = ~age_group, type = 'bar', orientation = 'h',
            name = 'Female Population', marker = list(color = 'pink')) %>%
  layout(title = "Male vs Female Population by Age Group",
         xaxis = list(title = "Population", tickvals = seq(-7000, 7000, 1000),
                      ticktext = c(seq(7000, 0, -1000), seq(1000, 7000, 1000))),
         yaxis = list(title = "Age Group"),
         barmode = 'overlay',
         bargap = 0.1,
         legend = list(x = 1, y = 1))

# Display the plot
plot

Output:

• plot_ly(): This function is used to initialize the plot and specify the data. Here, we create two traces, one for males and one for females.
• add_trace(): Adds a second set of bars (female population), mirrored by making the values negative.
• layout(): Customizes the layout of the plot, including axis titles, tick marks, and other properties.
  • The tickvals and ticktext parameters ensure that the negative values are displayed as positive numbers for better readability.
  • barmode = 'overlay' ensures the bars from both categories are overlaid in the plot.

This code will display a mirror bar plot that compares male and female populations by age group. The bars will be interactive, allowing users to hover over each bar to view population counts for both genders.

Step 4: Customizing the Mirror Bar Plot

To customize the mirror bar plot, you can adjust various aspects such as colors, bar width, axis labels, grid lines, and other layout elements.

# Customized mirror bar plot
plot <- plot_ly(df, x = ~male_population, y = ~age_group, type = 'bar', orientation = 'h',
                name = 'Male Population', marker = list(color = '#1f77b4', 
                                                        line = list(color = '#000000', width = 1)),
                hoverinfo = 'x+y+name') %>%
  add_trace(x = ~-female_population, y = ~age_group, type = 'bar', orientation = 'h',
            name = 'Female Population', marker = list(color = '#ff7f0e', 
                                                      line = list(color = '#000000', width = 1)),
            hoverinfo = 'x+y+name') %>%
  layout(
    title = list(text = "Population Distribution: Male vs Female", 
                 font = list(size = 24, family = "Arial, sans-serif")),
    xaxis = list(title = "Population",
                 tickvals = seq(-7000, 7000, 1000),
                 ticktext = c(seq(7000, 0, -1000), seq(1000, 7000, 1000)),
                 gridcolor = 'rgba(200, 200, 200, 0.7)',  # Gridline customization
                 zerolinecolor = 'rgba(100, 100, 100, 0.7)',  # Zero line customization
                 tickfont = list(size = 12, color = 'black')),
    yaxis = list(title = "Age Group",
                 tickfont = list(size = 14, color = 'black')),
    bargap = 0.05,  # Reduced gap between bars for clearer comparison
    barmode = 'overlay',  # Overlay bars
    legend = list(x = 0.85, y = 0.95, bgcolor = 'rgba(255, 255, 255, 0.5)', 
                  bordercolor = 'black', borderwidth = 1),
    margin = list(l = 70, r = 40, b = 50, t = 80, pad = 10)  # Adjusted margins for title and axis
  )

# Display the customized plot
plot

Output:

This customized version of the mirror bar plot adds a more polished and professional look, improving readability, comparison, and overall aesthetics. You can further adjust parameters such as colors, fonts, and sizes based on your specific use case and audience preferences.

Conclusion

Creating a mirror bar plot in R using Plotly is a straightforward process that involves setting up the environment, preparing the data, and customizing the plot. By following the steps outlined in this article, you can create interactive and visually appealing mirror bar plots for your data analysis tasks.