Why ggplot2 creates publication-quality graphics in R Programming - Test Your Understanding

Practice - 5 Tasks

1fill in blank

easy

Complete the code to create a basic ggplot2 scatter plot.

R Programming

library(ggplot2)
ggplot(data = mtcars, aes(x = wt, y = mpg)) + geom_[1]()

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

Apoint

Bpoints

Cscatter

Dgeom_point

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'scatter' instead of 'point' in the geom function.

Using 'geom_point' without parentheses.

Using 'geom_point' instead of 'point'.

The correct function to add points in ggplot2 is geom_point(). The code uses geom_point() to create a scatter plot.

2fill in blank

medium

Complete the code to add a title to the ggplot2 plot.

R Programming

ggplot(data = mtcars, aes(x = wt, y = mpg)) + geom_point() + ggtitle([1])

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

AMy Plot

B'My Plot'

C"My Plot"

DMy_Plot

Attempts:

3 left

💡 Hint

Common Mistakes

Not using quotes around the title string.

Using variable names without quotes.

Using single quotes inconsistently.

The title text must be a string, so it needs to be enclosed in quotes. Double quotes or single quotes work, but here double quotes are used.

3fill in blank

hard

Fix the error in the code to correctly map color to the 'cyl' variable.

R Programming

ggplot(mtcars, aes(x = wt, y = mpg, color = [1])) + geom_point()

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

Acylinders

Bcyl

Cmpg

D"cyl"

Attempts:

3 left

💡 Hint

Common Mistakes

Using a non-existent variable name.

Putting the variable name in quotes inside aes().

Using a different variable like 'mpg' instead of 'cyl'.

The variable name inside aes() should be the exact column name without quotes. Here, 'cyl' is the correct variable name.

4fill in blank

hard

Fill both blanks to create a plot with points sized by 'hp' and shaped by 'gear'.

R Programming

ggplot(mtcars, aes(x = wt, y = mpg, size = [1], shape = [2])) + geom_point()

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

Ahp

Bgear

Ccyl

Dmpg

Attempts:

3 left

💡 Hint

Common Mistakes

Mixing up size and shape variables.

Using variables not in the dataset.

Putting variable names in quotes inside aes().

To size points by horsepower, use 'hp'. To shape points by gear, use 'gear'. Both are column names in mtcars.

5fill in blank

hard

Fill all three blanks to create a plot with customized labels and theme.

R Programming

ggplot(mtcars, aes(x = [1], y = [2])) + geom_point() + labs(title = [3]) + theme_minimal()

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

Awt

Bmpg

C"Fuel Efficiency vs Weight"

Dhp

Attempts:

3 left

💡 Hint

Common Mistakes

Swapping x and y variables.

Not quoting the title string.

Using variables not related to the plot.

The x-axis is 'wt' (weight), y-axis is 'mpg' (miles per gallon), and the title is a string describing the plot.

Practice

1. Why is ggplot2 considered to create publication-quality graphics?

easy

A. Because it uses a layered grammar of graphics to build clear and customizable plots

B. Because it automatically exports graphs as PDF files only

C. Because it only works with large datasets

D. Because it requires no coding to create plots

Solution

1. Step 1: Understand ggplot2's design philosophy

   ggplot2 uses a layered grammar of graphics, allowing users to add components like points, lines, and labels step by step.

2. Step 2: Recognize why this matters for quality

   This layering approach helps create clear, customizable, and visually appealing plots suitable for publication.

3. Final Answer:

   Because it uses a layered grammar of graphics to build clear and customizable plots -> Option A

4. Quick Check:

   Layered grammar = clear, customizable plots [OK]

Hint: Remember: ggplot2 builds plots layer by layer [OK]

Common Mistakes:

• Thinking ggplot2 only exports PDFs
• Believing it works only with big data
• Assuming no coding is needed

2. Which of the following is the correct way to start a basic plot using ggplot2 in R?

easy

A. ggplot(data = df) + geom_point(aes(x = var1, y = var2))

B. plot(df, x = var1, y = var2)

C. ggplot(df, x = var1, y = var2)

D. ggplot(data = df, x = var1, y = var2)

Solution

1. Step 1: Recall ggplot2 syntax

   The correct syntax starts with ggplot(data = df) and adds layers like geom_point() with aesthetics inside.

2. Step 2: Check each option

   ggplot(data = df) + geom_point(aes(x = var1, y = var2)) correctly uses ggplot(data = df) + geom_point(aes(x = var1, y = var2)). Others misuse parameters or functions.

3. Final Answer:

   ggplot(data = df) + geom_point(aes(x = var1, y = var2)) -> Option A

4. Quick Check:

   Correct ggplot syntax = ggplot(data = df) + geom_point(aes(x = var1, y = var2)) [OK]

Hint: Start with ggplot(data=) then add geom layers [OK]

Common Mistakes:

• Passing x and y directly to ggplot()
• Using base plot syntax inside ggplot2
• Omitting aes() inside geom functions

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

library(ggplot2)
df <- data.frame(x = 1:3, y = c(4, 5, 6))
ggplot(df, aes(x, y)) + geom_line() + geom_point()

medium

A. A plot with only points but no lines

B. A plot with points connected by lines showing x vs y

C. A plot with only lines but no points

D. An error because geom_line and geom_point cannot be combined

Solution

1. Step 1: Understand the code layers

   The code creates a plot with geom_line() to connect points and geom_point() to show points.

2. Step 2: Predict the visual output

   The plot will show points at (1,4), (2,5), (3,6) connected by lines.

3. Final Answer:

   A plot with points connected by lines showing x vs y -> Option B

4. Quick Check:

   geom_line + geom_point = connected points [OK]

Hint: geom_line + geom_point shows connected points [OK]

Common Mistakes:

• Thinking geom_line removes points
• Assuming geom_point removes lines
• Believing combining layers causes error

4. Identify the error in this ggplot2 code snippet:

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

medium

A. The ggplot() function cannot take data and aes together

B. The aes() inside geom_point() is not allowed

C. There is no error; the code will run correctly

D. The geom_line() layer is missing aesthetics and will cause an error

Solution

1. Step 1: Analyze the code layers

   The main ggplot call sets data and aesthetics for x and y. geom_point() adds color aesthetic, which is allowed.

2. Step 2: Check if geom_line needs aes

   geom_line() inherits aesthetics from ggplot call, so no error occurs.

3. Final Answer:

   There is no error; the code will run correctly -> Option C

4. Quick Check:

   Layer inheritance allows this code [OK]

Hint: Layers inherit aesthetics unless overridden [OK]

Common Mistakes:

• Thinking geom_line must have aes explicitly
• Believing aes inside geom_point is invalid
• Assuming ggplot can't take data and aes together

5. You want to create a publication-quality plot with ggplot2 that shows the average value of a variable by group with error bars. Which approach best uses ggplot2's features to achieve this?

hard

A. Plot raw data with geom_line() without summarizing

B. Calculate means and errors manually, then plot with base R barplot()

C. Use geom_point() only with raw data points

D. Use stat_summary() with geom = 'bar' and geom_errorbar() layers to add means and error bars

Solution

1. Step 1: Identify ggplot2 tools for summary plots

   stat_summary() can compute summary statistics like mean and add error bars with geom_errorbar().

2. Step 2: Understand why this is publication-quality

   This method creates clear, summarized visuals with error bars, ideal for reports and presentations.

3. Final Answer:

   Use stat_summary() with geom = 'bar' and geom_errorbar() layers to add means and error bars -> Option D

4. Quick Check:

   stat_summary + error bars = clear summary plot [OK]

Hint: Use stat_summary and geom_errorbar for summaries [OK]

Common Mistakes:

• Plotting raw data without summarizing
• Using base R instead of ggplot2 for publication plots
• Omitting error bars in summary plots