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.

Practice

1. What does a box plot primarily show about a dataset?

easy

A. The median, quartiles, and potential outliers

B. The exact frequency of each data point

C. The shape and density of the data distribution

D. Only the minimum and maximum values

Solution

1. Step 1: Understand box plot components

   A box plot displays the median line, the box showing the interquartile range (Q1 to Q3), and whiskers for minimum and maximum values excluding outliers.

2. Step 2: Compare options to box plot features

   The median, quartiles, and potential outliers correctly describes median, quartiles, and outliers. The shape and density of the data distribution describes violin plots, not box plots.

3. Final Answer:

   The median, quartiles, and potential outliers -> Option A

4. Quick Check:

   Box plot = median + quartiles + outliers [OK]

Hint: Box plots show median and spread, violin plots show density [OK]

Common Mistakes:

• Confusing violin plot features with box plot
• Thinking box plot shows exact data frequencies
• Ignoring outliers in box plot

2. Which of the following R commands correctly creates a violin plot using ggplot2 for a dataset df with numeric variable value and group category?

easy

A. ggplot(df, aes(x=category, y=value)) + geom_violin()

B. ggplot(df, aes(x=value, y=category)) + geom_boxplot()

C. plot(df$value, df$category, type='violin')

D. ggplot(df, aes(x=category, y=value)) + geom_histogram()

Solution

1. Step 1: Identify correct ggplot2 syntax for violin plot

   The correct syntax uses ggplot with aes mapping x to category and y to value, then adds geom_violin() to create violin plots.

2. Step 2: Check each option

   ggplot(df, aes(x=category, y=value)) + geom_violin() matches correct syntax. ggplot(df, aes(x=value, y=category)) + geom_boxplot() swaps x and y and uses boxplot, not violin. plot(df$value, df$category, type='violin') uses base plot with invalid type. ggplot(df, aes(x=category, y=value)) + geom_histogram() uses histogram, not violin plot.

3. Final Answer:

   ggplot(df, aes(x=category, y=value)) + geom_violin() -> Option A

4. Quick Check:

   Violin plot = ggplot + aes(x=group, y=num) + geom_violin() [OK]

Hint: Use geom_violin() with x=group, y=value in ggplot [OK]

Common Mistakes:

• Swapping x and y aesthetics
• Using geom_boxplot() instead of geom_violin()
• Trying base plot with invalid type argument

3. Given the following R code, what will be the output plot type?

library(ggplot2)
df <- data.frame(group = rep(c('A', 'B'), each=10), value = c(rnorm(10), rnorm(10, 2)))
ggplot(df, aes(x=group, y=value)) + geom_boxplot() + geom_violin(alpha=0.3)

medium

A. An error because you cannot combine geom_boxplot and geom_violin

B. Only box plots for groups A and B

C. Only violin plots for groups A and B

D. A combined plot showing both box plots and transparent violin plots for groups A and B

Solution

1. Step 1: Understand layering in ggplot2

   ggplot2 allows adding multiple geoms. Here, geom_boxplot() adds box plots, and geom_violin(alpha=0.3) adds violin plots with transparency.

2. Step 2: Interpret the combined plot

   The plot will show box plots overlaid on transparent violin plots for each group, allowing comparison of distribution shape and summary statistics.

3. Final Answer:

   A combined plot showing both box plots and transparent violin plots for groups A and B -> Option D

4. Quick Check:

   Multiple geoms = combined plot [OK]

Hint: Multiple geoms layer plots; transparency shows both [OK]

Common Mistakes:

• Thinking layering causes error
• Expecting only one plot type to show
• Ignoring alpha transparency effect

4. Identify the error in this R code snippet intended to create a box plot:

library(ggplot2)
df <- data.frame(group = c('X', 'Y', 'X', 'Y'), value = c(5, 7, 6, 8))
ggplot(df, aes(x=value, y=group)) + geom_boxplot()

medium

A. geom_boxplot() cannot be used with categorical variables

B. Missing + sign before geom_boxplot()

C. The x and y aesthetics are swapped; x should be group and y should be value

D. The data frame is missing numeric values

Solution

1. Step 1: Check aesthetic mappings for box plot

   Box plots require the categorical variable on the x-axis and numeric variable on the y-axis to show distribution per group.

2. Step 2: Identify the swapped aesthetics

   The code uses x=value (numeric) and y=group (categorical), which is reversed. It should be x=group and y=value.

3. Final Answer:

   The x and y aesthetics are swapped; x should be group and y should be value -> Option C

4. Quick Check:

   Box plot: x=category, y=numeric [OK]

Hint: Box plot x = group, y = numeric value [OK]

Common Mistakes:

• Swapping x and y in aes()
• Assuming geom_boxplot needs numeric x
• Ignoring axis roles in box plots

5. You have a dataset with three groups and numeric scores. You want to visualize both the distribution shape and summary statistics for each group in one plot. Which R code snippet best achieves this?

hard

A. ggplot(data, aes(x=group, y=score)) + geom_histogram() + geom_boxplot()

B. ggplot(data, aes(x=group, y=score)) + geom_violin(trim=FALSE) + geom_boxplot(width=0.1, fill='white')

C. ggplot(data, aes(x=score, y=group)) + geom_violin() + geom_point()

D. ggplot(data, aes(x=group, y=score)) + geom_point() + geom_line()

Solution

1. Step 1: Understand the goal

   The goal is to show distribution shape (violin plot) and summary statistics (box plot) together per group.

2. Step 2: Evaluate each option

   ggplot(data, aes(x=group, y=score)) + geom_violin(trim=FALSE) + geom_boxplot(width=0.1, fill='white') overlays violin plot with trim=FALSE (shows full distribution) and a narrow box plot with white fill for clarity. ggplot(data, aes(x=score, y=group)) + geom_violin() + geom_point() swaps axes and adds points but misses box plot summary. ggplot(data, aes(x=group, y=score)) + geom_histogram() + geom_boxplot() mixes histogram (not grouped) with box plot incorrectly. ggplot(data, aes(x=group, y=score)) + geom_point() + geom_line() shows points and lines, not distribution or summary.

3. Final Answer:

   ggplot(data, aes(x=group, y=score)) + geom_violin(trim=FALSE) + geom_boxplot(width=0.1, fill='white') -> Option B

4. Quick Check:

   Violin + boxplot overlay = full distribution + summary [OK]

Hint: Overlay violin and narrow boxplot for full view [OK]

Common Mistakes:

• Using histogram instead of violin for distribution
• Swapping x and y axes
• Not combining plots to show both shape and stats