Grammar of Graphics concept in R Programming - Mini Project: Build & Apply

Building a Simple Plot Using Grammar of Graphics in R

📖 Scenario: You are a data analyst who wants to visualize the relationship between two variables in a small dataset. You will use the Grammar of Graphics concept to build a plot step-by-step in R.

🎯 Goal: Create a scatter plot using the Grammar of Graphics approach with ggplot2 in R. You will start by setting up the data, then configure the plot, add points, and finally display the plot.

📋 What You'll Learn

Create a data frame called data with exact columns height and weight and given values

Create a ggplot object called plot with data and aesthetic mapping for height and weight

Add a geom_point() layer to plot to show points

Print the plot object to display the scatter plot

💡 Why This Matters

🌍 Real World

Data analysts and scientists use the Grammar of Graphics to build clear and customizable visualizations to understand data patterns.

💼 Career

Knowing how to use ggplot2 and the Grammar of Graphics is essential for roles in data analysis, data science, and reporting.

1

DATA SETUP: Create the data frame

Create a data frame called data with two columns: height containing values 150, 160, 170, 180, 190 and weight containing values 65, 70, 75, 80, 85.

R Programming

# Create the data frame called data with height and weight columns
# Your code here

Hint

Use data.frame() with height = c(...) and weight = c(...) to create the data.

2

CONFIGURATION: Create the ggplot object

Create a ggplot object called plot using the data data frame, and map height to the x-axis and weight to the y-axis using aes().

R Programming

data <- data.frame(
  height = c(150, 160, 170, 180, 190),
  weight = c(65, 70, 75, 80, 85)
)
# Create the ggplot object called plot with data and aes mapping
# Your code here

Hint

Use ggplot(data, aes(x = height, y = weight)) to create the plot object.

3

CORE LOGIC: Add points layer

Add a points layer to the plot object by adding geom_point().

R Programming

library(ggplot2)
data <- data.frame(
  height = c(150, 160, 170, 180, 190),
  weight = c(65, 70, 75, 80, 85)
)
plot <- ggplot(data, aes(x = height, y = weight))
# Add geom_point() layer to plot
# Your code here

Hint

Add + geom_point() to the ggplot object to show points.

4

OUTPUT: Display the plot

Print the plot object to display the scatter plot.

R Programming

library(ggplot2)
data <- data.frame(
  height = c(150, 160, 170, 180, 190),
  weight = c(65, 70, 75, 80, 85)
)
plot <- ggplot(data, aes(x = height, y = weight)) + geom_point()
# Print the plot object
# Your code here

Hint

Use print(plot) to show the plot in R.

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