Box plots and violin plots in R Programming - Mini Project: Build & Apply

Box plots and violin plots

📖 Scenario: You are a data analyst working with a dataset of students' test scores from different classes. You want to visualize the distribution of scores to understand how students performed in each class.

🎯 Goal: Build box plots and violin plots in R to compare the score distributions across classes.

📋 What You'll Learn

Create a data frame called scores with columns class and score using the exact data provided.

Create a variable called classes that stores the unique class names from the data frame.

Use a boxplot() function to create box plots of score grouped by class.

Use a vioplot::vioplot() function to create violin plots of score grouped by class.

Print the plots so they are visible in the R plotting window.

💡 Why This Matters

🌍 Real World

Box plots and violin plots are common ways to understand how data is spread out and where most values lie. They help compare groups easily.

💼 Career

Data analysts and scientists use these plots to summarize and communicate data insights clearly to teams and stakeholders.

1

Create the data frame with scores

Create a data frame called scores with two columns: class and score. Use these exact values: class contains "A", "A", "B", "B", "C", "C", "C" and score contains 85, 90, 78, 82, 88, 91, 85.

R Programming

# Create the data frame called scores with class and score columns
# Your code here

Hint

Use data.frame() with two vectors named class and score.

2

Extract unique class names

Create a variable called classes that stores the unique class names from the class column of the scores data frame using the unique() function.

R Programming

scores <- data.frame(
  class = c("A", "A", "B", "B", "C", "C", "C"),
  score = c(85, 90, 78, 82, 88, 91, 85)
)
# Create a variable called classes with unique class names
# Your code here

Hint

Use unique() on the class column of scores.

3

Create box plots for scores by class

Use the boxplot() function to create box plots of score grouped by class from the scores data frame. Use the formula score ~ class and set the main title to "Box plot of Scores by Class".

R Programming

scores <- data.frame(
  class = c("A", "A", "B", "B", "C", "C", "C"),
  score = c(85, 90, 78, 82, 88, 91, 85)
)
classes <- unique(scores$class)
# Create a box plot of score by class with a main title
# Your code here

Hint

Use the formula score ~ class inside boxplot() and set main for the title.

4

Create violin plots for scores by class

Load the vioplot package using library(vioplot). Then use the vioplot() function to create violin plots for each class's scores. Use the exact order of classes stored in classes. Set the main title to "Violin plot of Scores by Class" and label the x-axis as "Class" and y-axis as "Score".

R Programming

scores <- data.frame(
  class = c("A", "A", "B", "B", "C", "C", "C"),
  score = c(85, 90, 78, 82, 88, 91, 85)
)
classes <- unique(scores$class)
boxplot(score ~ class, data = scores, main = "Box plot of Scores by Class")
# Load vioplot package and create violin plots for each class in classes
# Your code here

Hint

Use library(vioplot) to load the package. Use vioplot() with scores filtered by class using scores$score[scores$class == classes[i]] for each class.

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