Box plots and violin plots in R Programming - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of a basic boxplot in R

What will be the output of this R code snippet?

R Programming

data <- c(5, 7, 8, 5, 6, 9, 10, 12, 7, 6)
boxplot(data, main = "Simple Boxplot")

A An error because boxplot requires a data frame

B A boxplot showing median around 7 with whiskers extending roughly from 5 to 12

C A scatter plot of data points against their index

D A histogram of the data with bars representing frequency

💡 Hint

Boxplots summarize data distribution with median and quartiles.

The boxplot function in R creates a box showing the median and quartiles. The whiskers extend to the min and max within 1.5 times the interquartile range. The data vector is valid input.

🧠 Conceptual

intermediate

Difference between boxplot and violin plot

Which statement correctly describes the difference between a boxplot and a violin plot?

A A violin plot is a 3D version of a boxplot with added colors.

B Both plots show the same information but use different colors.

C A boxplot shows individual data points, while a violin plot only shows the median.

D A boxplot shows summary statistics like median and quartiles, while a violin plot shows the data distribution shape using a kernel density estimate.

💡 Hint

Think about what each plot reveals about the data.

Boxplots summarize data with median, quartiles, and outliers. Violin plots add a smooth shape showing the full distribution density.

🔧 Debug

advanced

Identify the error in violin plot code

What error will this R code produce?

R Programming

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

A No error; the code runs and produces a violin plot with boxplot overlay

B Error: geom_violin requires a numeric x aesthetic, but group is a factor

C Error: ggplot2 not loaded properly

D Error: geom_boxplot cannot be added after geom_violin

💡 Hint

Check if the code syntax and library usage are correct.

The code loads ggplot2, creates a data frame with groups and values, then plots violin and boxplot layers. This is valid and runs without error.

❓ Predict Output

advanced

Output of customized boxplot with notch

What does the following R code produce?

R Programming

set.seed(123)
data <- rnorm(50)
boxplot(data, notch = TRUE, col = "lightblue", main = "Notched Boxplot")

A A boxplot with a notch around the median indicating confidence interval, colored light blue

B A histogram of data colored light blue

C A boxplot without notch and default color

D An error because notch requires a data frame

💡 Hint

Notches show confidence intervals around the median.

The notch parameter adds a notch shape around the median to indicate a confidence interval. The color changes the box fill.

❓ Predict Output

expert

Combined violin and boxplot with jittered points

What will be the output of this R code?

R Programming

library(ggplot2)
set.seed(42)
data <- data.frame(category = rep(c('X', 'Y'), each = 20), value = c(rnorm(20), rnorm(20, 1)))
ggplot(data, aes(x = category, y = value)) + geom_violin(trim = FALSE, fill = "pink") + geom_boxplot(width = 0.1, fill = "white") + geom_jitter(width = 0.15, alpha = 0.5)

A Only jittered points plotted without violin or boxplot

B A violin plot trimmed at tails with no boxplot or points

C A violin plot with full tails (not trimmed), white narrow boxplots inside, and jittered points scattered horizontally

D Error: geom_jitter requires a numeric x aesthetic

💡 Hint

Look at each geom layer and their parameters.

The code creates a violin plot with tails shown (trim=FALSE), overlays a narrow white boxplot, and adds jittered points with some transparency.

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