Box plots and violin plots in R Programming - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to create a basic box plot of the variable 'mpg' from the 'mtcars' dataset.

R Programming

boxplot(mtcars$[1])

Drag options to blanks, or click blank then click option'

Ampg

Bcyl

Cgear

Dhp

Attempts:

3 left

💡 Hint

Common Mistakes

Using a categorical variable like 'cyl' instead of a numeric variable.

Forgetting to use the $ operator to access the variable.

The boxplot function requires the variable name to plot. Here, 'mpg' is the variable representing miles per gallon.

2fill in blank

medium

Complete the code to create a violin plot of 'mpg' grouped by 'cyl' using ggplot2.

R Programming

library(ggplot2)
ggplot(mtcars, aes(x = factor([1]), y = mpg)) + geom_violin()

Drag options to blanks, or click blank then click option'

Ahp

Bcarb

Ccyl

Dgear

Attempts:

3 left

💡 Hint

Common Mistakes

Using a numeric variable like 'gear' without converting to factor.

Not converting the grouping variable to a factor, causing continuous x-axis.

To group the violin plot by the number of cylinders, we use 'cyl' as the x-axis variable, converted to a factor for categorical grouping.

3fill in blank

hard

Fix the error in the code to add a box plot layer to a ggplot object 'p' that plots 'mpg' by 'cyl'.

R Programming

p + geom_boxplot(aes([1] = factor(cyl), y = mpg))

Drag options to blanks, or click blank then click option'

Ax

By

Cfill

Dcolor

Attempts:

3 left

💡 Hint

Common Mistakes

Using y instead of x for grouping.

Using fill or color inside aes() without specifying x or y.

The x aesthetic should be mapped to the grouping variable 'factor(cyl)' to create separate boxes for each cylinder group.

4fill in blank

hard

Fill both blanks to create a combined violin and box plot of 'mpg' grouped by 'cyl' with ggplot2.

R Programming

ggplot(mtcars, aes(x = factor([1]), y = mpg)) + geom_violin() + geom_[2]()

Drag options to blanks, or click blank then click option'

Acyl

Bboxplot

Dpoint

Attempts:

3 left

💡 Hint

Common Mistakes

Using geom_point() instead of geom_boxplot().

Not converting 'cyl' to a factor for grouping.

The x-axis groups by 'cyl' and the second layer adds a box plot with geom_boxplot().

5fill in blank

hard

Fill all three blanks to create a violin plot with customized fill color by 'cyl' and add box plot overlay.

R Programming

ggplot(mtcars, aes(x = factor([1]), y = mpg, fill = factor([2]))) + geom_violin() + geom_[3](width = 0.1)

Drag options to blanks, or click blank then click option'

Acyl

Cboxplot

Dpoint

Attempts:

3 left

💡 Hint

Common Mistakes

Using different variables for x and fill causing inconsistent grouping.

Using geom_point() instead of geom_boxplot() for overlay.

Both x and fill aesthetics use 'cyl' as a factor to group and color the plot. The box plot is added with geom_boxplot() and a narrow width for overlay.

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