Problem in Aligning a Violin Plot Alongside Boxplot Inside

As data visualization continues to play an increasingly important role in scientific research and analysis, the need for effective and informative plots has never been more pressing. In this article, we will delve into a common problem encountered when creating violin plots with boxplots within each violin plot: misalignment.

Understanding Violin Plots and Boxplots

A violin plot is a type of density plot that displays the distribution of data points in three dimensions, providing a clear visual representation of the shape and orientation of the data. On the other hand, a boxplot (also known as a box-and-whisker plot) is a graphical representation showing the distribution of data based on the quartiles of the data set.

When combining violin plots with boxplots within each violin plot, we aim to create a visual representation that showcases both the shape and distribution of the data points, while also providing insight into the median, interquartile range, and outliers.

The Problem: Misalignment

In the provided code snippet, the author attempts to align a violin plot alongside a boxplot using the position_dodge function from the ggplot2 package. However, instead of achieving the desired result, the plots appear misaligned.

Upon closer inspection, it becomes apparent that the problem lies in the placement and positioning of the elements within the violin plot layer.

The Correct Solution

To correct this issue, we need to adjust the position argument for both the geom_violin and geom_boxplot layers. By setting position = position_dodge(0.9), we ensure that the boxplots are placed at a sufficient distance from each other, allowing them to align properly with the violin plots.

## Customizing the Plot

### Geom Violin Layer

The `geom_violin` layer is used to create the main violin plot. By setting `trim = FALSE`, we remove any unnecessary whitespace around the plot.

```markdown
ggplot(df, aes(x = Genes, y = Expression (logCPM), group = Group, fill = Group)) + 
  geom_violin(trim = FALSE, alpha = 0.5, draw_quantiles=c(0.5), position = position_dodge(1))

Geom Boxplot Layer

The geom_boxplot layer is used to add the boxplots within each violin plot. By setting position = position_dodge(1), we place the boxplots at the same x-coordinate as the corresponding violin plots, ensuring proper alignment.

  geom_boxplot(width = 0.1, position = position_dodge(1))

Additional Customization Options

To further enhance the plot’s appearance and readability, we can add additional theme settings to adjust the font sizes, colors, and other visual elements.

theme_bw(base_size = 14) + 
  xlab("") + ylab("Expression (logCPM)") +
  theme(axis.text=element_text(size=15, face = "bold", color = "black"),
        axis.title=element_text(size=15, face = "bold", color = "black"),
        strip.text = element_text(size=15, face = "bold", color = "black"),
        axis.text.x = element_text(angle = 0),
        legend.text=element_text(size=12, face = "bold", color = "black"),
        legend.title=element_text(size=15,face = "bold", color = "black"))

Conclusion

By understanding the basics of violin plots and boxplots, we can create effective and informative visualizations that showcase both shape and distribution. By adjusting the position argument for the geom_violin and geom_boxplot layers, we can ensure proper alignment between the two elements.

In addition to these adjustments, we can further customize our plot by adding theme settings to adjust font sizes, colors, and other visual elements.

With this knowledge, you should be able to create high-quality violin plots with boxplots that accurately represent your data.

Last modified on 2024-07-13