Grammar of Graphics concept in R Programming - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

What is the output of this ggplot2 code?

Consider the following R code using ggplot2. What will be the output when this code runs?

R Programming

library(ggplot2)
data <- data.frame(x = 1:3, y = c(3, 2, 1))
ggplot(data, aes(x = x, y = y)) + geom_point() + geom_line()

A A histogram of y values

B A bar chart showing counts of x values

C A scatter plot with points connected by lines in order of x values

D A boxplot of y grouped by x

💡 Hint

Think about what geom_point() and geom_line() do in ggplot2.

The code creates a scatter plot with points at (1,3), (2,2), and (3,1). The geom_line() connects these points in order of x, forming a line.

🧠 Conceptual

intermediate

Which component of Grammar of Graphics defines how data variables map to visual properties?

In the Grammar of Graphics, which component describes the mapping of data variables to visual aesthetics like color, size, or position?

A Aesthetics

B Statistics

C Geoms

D Facets

💡 Hint

Think about what controls the look of points or lines based on data.

Aesthetics define how data variables are mapped to visual properties such as color, size, shape, or position.

🔧 Debug

advanced

What error does this ggplot2 code produce?

Examine the code below. What error will it raise when run?

R Programming

library(ggplot2)
data <- data.frame(x = 1:5, y = c(2, 4, 6, 8, 10))
ggplot(data, aes(x = x, y = y)) + geom_point() + geom_line(aes(color = z))

A No error, plot displays with default color

B Error: geom_line() requires a numeric y aesthetic

C Error: geom_point() missing required aesthetics

D Error: object 'z' not found

💡 Hint

Check if all variables used in aes() exist in the data frame.

The variable 'z' is not defined in the data frame, so ggplot2 throws an error that object 'z' is not found.

📝 Syntax

advanced

Which option correctly adds a title to a ggplot?

Which of the following R code snippets correctly adds the title 'My Plot' to a ggplot object named p?

A p + ggtitle('My Plot')

B p + title('My Plot')

C p + labs(title = 'My Plot')

D p + set_title('My Plot')

💡 Hint

ggplot2 uses specific functions to add titles and labels.

The function ggtitle() adds a title to a ggplot. labs() can also add titles but requires the argument name 'title'.

🚀 Application

expert

How many layers does this ggplot have?

Given the code below, how many layers are added to the ggplot object?

R Programming

library(ggplot2)
data <- data.frame(x = 1:4, y = c(4, 3, 2, 1))
p <- ggplot(data, aes(x, y)) + geom_point() + geom_smooth(method = 'lm') + facet_wrap(~x)

A 3

B 2

C 4

D 1

💡 Hint

Count only the layers that add visual elements, not facets.

geom_point() and geom_smooth() each add one layer. facet_wrap() adds facets but does not add a layer. So total layers = 2.

Practice

1. In the Grammar of Graphics, which component defines how data variables are mapped to visual properties like color or size in a plot?

easy

A. Data

B. Aesthetics

C. Layers

D. Facets

Solution

1. Step 1: Understand the role of aesthetics

   Aesthetics specify how data variables correspond to visual elements such as color, size, or shape.

2. Step 2: Differentiate from other components

   Data is the raw information, layers are the graphical elements, and facets split plots; only aesthetics map variables visually.

3. Final Answer:

   Aesthetics -> Option B

4. Quick Check:

   Mapping variables to visuals = Aesthetics [OK]

Hint: Remember: aesthetics = visual mapping of data [OK]

Common Mistakes:

• Confusing data with aesthetics
• Thinking layers define variable mapping
• Mixing facets with aesthetics

2. Which of the following is the correct way to start a ggplot object in R using the Grammar of Graphics?

easy

A. ggplot(aes(x = var1, y = var2), data = mydata)

B. plot(data = mydata, aes(x = var1, y = var2))

C. ggplot(data = mydata, aes(x = var1, y = var2))

D. ggplot(mydata, x = var1, y = var2)

Solution

1. Step 1: Recall ggplot syntax

   The correct syntax is ggplot(data = ..., aes(...)) where data is first, then aesthetics.

2. Step 2: Check each option

   ggplot(data = mydata, aes(x = var1, y = var2)) matches the correct syntax; using plot() refers to base R graphics, not ggplot2; swapping data and aes() arguments assigns them incorrectly due to positional matching; omitting aes() fails to map variables to aesthetics.

3. Final Answer:

   ggplot(data = mydata, aes(x = var1, y = var2)) -> Option C

4. Quick Check:

   ggplot(data, aes()) syntax = ggplot(data = mydata, aes(x = var1, y = var2)) [OK]

Hint: ggplot(data=..., aes(...)) is the standard start [OK]

Common Mistakes:

• Using base plot() instead of ggplot()
• Swapping data and aes() arguments
• Not wrapping variables inside aes()

3. What will be the output of this R code using ggplot2?

library(ggplot2)
data <- data.frame(x = 1:3, y = c(2, 4, 6))
ggplot(data, aes(x = x, y = y)) + geom_point()

medium

A. An error because geom_point() is missing arguments

B. A line plot connecting points (1,2), (2,4), and (3,6)

C. A bar chart showing counts of x values

D. A scatter plot with points at (1,2), (2,4), and (3,6)

Solution

1. Step 1: Understand the code components

   ggplot() sets data and aesthetics; geom_point() adds points for each (x,y) pair.

2. Step 2: Predict the plot output

   Since geom_point() is used, the plot shows points at (1,2), (2,4), and (3,6) as a scatter plot.

3. Final Answer:

   A scatter plot with points at (1,2), (2,4), and (3,6) -> Option D

4. Quick Check:

   geom_point() = scatter plot points [OK]

Hint: geom_point() always plots points, not lines or bars [OK]

Common Mistakes:

• Confusing geom_point() with geom_line()
• Expecting a bar chart instead of points
• Thinking geom_point() needs extra arguments

4. Identify the error in this ggplot2 code snippet:

ggplot(data = df, aes(x = var1, y = var2)) + geom_point(color = var3)

medium

A. color should be inside aes() to map to var3

B. geom_point() cannot have color argument

C. aes() must include color outside geom_point()

D. data argument is missing in ggplot()

Solution

1. Step 1: Understand color mapping in ggplot2

   To map a variable to color, it must be inside aes(), not as a fixed argument.

2. Step 2: Analyze the code

   color = var3 is outside aes(), so ggplot treats it as a fixed color, causing an error or wrong plot.

3. Final Answer:

   color should be inside aes() to map to var3 -> Option A

4. Quick Check:

   Variable color mapping requires aes() [OK]

Hint: Put variable color inside aes(), fixed color outside [OK]

Common Mistakes:

• Placing variable color outside aes()
• Thinking geom_point() disallows color
• Omitting data argument in ggplot()

5. You want to create a layered plot in ggplot2 showing points and a smooth trend line for data frame df with variables x and y. Which code correctly applies the Grammar of Graphics to achieve this?

hard

A. ggplot(df, aes(x = x, y = y)) + geom_point() + geom_smooth()

B. ggplot(df) + geom_point(aes(x = x, y = y)) + geom_smooth()

C. ggplot() + geom_point(df, aes(x = x, y = y)) + geom_smooth(df, aes(x = x, y = y))

D. ggplot(df, aes(x = x)) + geom_point() + geom_smooth()

Solution

1. Step 1: Understand layering in ggplot2

   Layers add graphical elements; setting data and aesthetics once in ggplot() is best practice.

2. Step 2: Evaluate each option

   ggplot(df, aes(x = x, y = y)) + geom_point() + geom_smooth() sets data and aesthetics once, then adds point and smooth layers correctly. Specifying aes(x = x) globally without y in layers causes missing y aesthetic error. Starting with bare ggplot(df) and aes only in first geom leaves smooth without mappings. Passing df directly to geoms after empty ggplot() misuses argument positions.

3. Final Answer:

   ggplot(df, aes(x = x, y = y)) + geom_point() + geom_smooth() -> Option A

4. Quick Check:

   Layered plot with shared aes() = ggplot(df, aes(x = x, y = y)) + geom_point() + geom_smooth() [OK]

Hint: Set data and aes() once, then add layers [OK]

Common Mistakes:

• Incomplete aesthetics when splitting across layers
• Omitting aes() in later layers
• Passing data inside geom_ functions incorrectly