Box plots and violin plots in R Programming - Step-by-Step Execution

Concept Flow - Box plots and violin plots

Start with data

↓

Calculate summary stats

↓

Draw box plot

↓

Calculate density

↓

Draw violin plot

↓

Display plots

We start with data, calculate statistics for box plots, and density for violin plots, then draw and display them.

Execution Sample

R Programming

library(ggplot2)
data <- data.frame(group = rep(c('A','B'), each=10), value = c(rnorm(10), rnorm(10,1)))
ggplot(data, aes(x=group, y=value)) + geom_violin(alpha=0.3) + geom_boxplot(width=0.1)

This code creates box and violin plots for two groups with random normal data.

Execution Table

Step	Action	Data/Variable	Result/Output
1	Create data frame	group, value	Data frame with 20 rows, 2 groups A and B
2	Calculate boxplot stats	value by group	Median, quartiles, whiskers for each group
3	Draw boxplot	boxplot stats	Boxplot shapes for groups A and B
4	Calculate density	value by group	Density curves for each group
5	Draw violin plot	density curves	Violin shapes overlayed with transparency
6	Display plot	boxplot + violin plot	Combined plot shown with box and violin for each group

💡 All steps complete, plot displayed with box and violin plots for groups A and B

Variable Tracker

Variable	Start	After Step 1	After Step 2	After Step 4	Final
data	none	data frame with groups and values	unchanged	unchanged	unchanged
boxplot_stats	none	none	median, quartiles, whiskers calculated	unchanged	used for boxplot
density	none	none	none	density curves calculated	used for violin plot

Key Moments - 2 Insights

Why do box plots show only summary statistics while violin plots show density?

Box plots summarize data with median and quartiles (see step 2 in execution_table), while violin plots show the full distribution shape using density curves (step 4).

How can box plots and violin plots be combined in one plot?

They can be layered using ggplot2 by adding geom_violin() and geom_boxplot() together (step 6), with transparency to see both.

Visual Quiz - 3 Questions

Look at the execution_table, what is calculated at step 2?

A Summary statistics for box plot

B Final combined plot

C Density curves for violin plot

D Raw data creation

💡 Hint

Check the 'Action' and 'Result/Output' columns at step 2 in execution_table

At which step are density curves calculated for violin plots?

A Step 1

B Step 4

C Step 3

D Step 6

💡 Hint

Look for 'Calculate density' in the 'Action' column in execution_table

If the data had only one group, how would the execution_table change?

A There would be no boxplot stats calculated

B Density calculation would be skipped

C Boxplot and violin plots would be drawn for one group only

D Plot display step would be removed

💡 Hint

Consider how grouping affects the number of plots drawn in execution_table steps

Concept Snapshot

Box plots show median, quartiles, and whiskers summarizing data.
Violin plots show data distribution density as a shape.
Use ggplot2 with geom_boxplot() and geom_violin() to create them.
They can be combined by layering with transparency.
Useful to compare groups visually by distribution and summary.

Full Transcript

We start with data containing groups and values. Then, we calculate summary statistics like median and quartiles for box plots. Next, we draw box plots using these stats. After that, we calculate density curves for violin plots to show data distribution shapes. We draw violin plots using these densities with some transparency. Finally, we display the combined plot showing both box and violin plots for each group. This helps us see both summary and distribution of data visually.