Factor in analysis and plotting in R Programming - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

What is the output of this factor level count?

Consider the following R code that creates a factor and counts its levels.
What will be the output of table(f)?

R Programming

f <- factor(c("apple", "banana", "apple", "cherry", "banana", "banana"))
table(f)

A

apple 2
banana 3
cherry 1

B

apple 3
banana 2
cherry 1

C

apple 2
banana 2
cherry 2

D

apple 1
banana 3
cherry 2

💡 Hint

Remember that table() counts how many times each factor level appears.

The factor f has levels apple, banana, and cherry. The vector has apple twice, banana three times, and cherry once. So table(f) counts these occurrences.

❓ Predict Output

intermediate

What does this plot show about factor levels?

Given this R code, what will the barplot display?

R Programming

f <- factor(c("red", "blue", "red", "green", "blue", "blue"))
barplot(table(f), col = c("red", "blue", "green"))

A A barplot with 3 bars: red=3, blue=2, green=1, colored incorrectly

B A barplot with 3 bars: red=2, blue=3, green=1, colored accordingly

C A barplot with 2 bars: red=2, blue=3, green missing

D An error because colors do not match factor levels

💡 Hint

Check the counts from table(f) and the colors vector length.

The factor has levels red, blue, green with counts 2, 3, and 1. The colors vector matches these levels in order, so the barplot shows 3 bars with correct colors.

🔧 Debug

advanced

Why does this factor releveling code fail?

This code tries to reorder factor levels but causes an error. What is the error?

R Programming

f <- factor(c("small", "medium", "large"))
levels(f) <- c("large", "medium", "small")
print(f)

A It changes the levels but does not reorder the factor values, so output is incorrect

B It causes a warning but prints factor with new levels

C It causes an error: 'replacement has length zero' because levels assignment is invalid

D It silently fails and keeps original levels

💡 Hint

Changing levels directly does not reorder factor values; use factor(..., levels=...) instead.

Assigning new levels directly changes the labels but does not reorder the underlying factor codes, so the factor values become mismatched with levels. This does not cause an error but leads to incorrect factor values.

📝 Syntax

advanced

Which option correctly creates an ordered factor?

You want to create an ordered factor with levels: low < medium < high. Which code does this correctly?

A f <- factor(c("low", "high", "medium"), levels = c("low", "medium", "high"), ordered = TRUE)

B f <- factor(c("low", "high", "medium"), levels = c("high", "medium", "low"), ordered = TRUE)

C f <- factor(c("low", "high", "medium"), ordered = TRUE)

D f <- ordered(c("low", "high", "medium"), levels = c("low", "medium", "high"))

💡 Hint

Use ordered() or factor(..., ordered=TRUE) with correct levels order.

Option D uses ordered() with levels in correct ascending order, creating an ordered factor. Option D is valid but less idiomatic. Option D misses levels so order is default. Option D reverses levels order.

🚀 Application

expert

How many bars will this ggplot show?

Given this R code using ggplot2, how many bars will appear in the plot?

R Programming

library(ggplot2)
data <- data.frame(
  category = factor(c("A", "B", "A", "C", "B", "B"), levels = c("A", "B", "C", "D")),
  value = c(5, 3, 6, 2, 4, 1)
)
ggplot(data, aes(x = category, y = value)) + geom_bar(stat = "identity")

A 4 bars: one for each level A, B, C, D (D with height zero)

B 3 bars: only for levels A, B, C present in data

C 6 bars: one for each row in data

D Error because level D has no data

💡 Hint

Remember that geom_bar with stat="identity" plots one bar per row, not per factor level.

With stat="identity", ggplot plots one bar per row in the data frame, so 6 bars appear. The factor levels control x-axis order but do not reduce bars. Level D has no data, so no bar for D.

Practice

1. What is the main purpose of using a factor in R when analyzing data?

easy

A. To convert text data into categories for easier analysis and plotting

B. To perform mathematical calculations on numeric data

C. To store large datasets efficiently

D. To create complex machine learning models

Solution

1. Step 1: Understand what factors represent

   Factors in R are used to represent categorical data, turning text into categories.

2. Step 2: Recognize the benefit of factors

   Using factors helps in organizing data for analysis and plotting by grouping similar values.

3. Final Answer:

   To convert text data into categories for easier analysis and plotting -> Option A

4. Quick Check:

   Factors = Categories [OK]

Hint: Factors group text into categories for analysis and plots [OK]

Common Mistakes:

• Thinking factors perform calculations
• Confusing factors with numeric vectors
• Assuming factors store data efficiently

2. Which of the following is the correct way to create a factor with levels ordered as "Low", "Medium", "High" in R?

easy

A. factor(x, order = c("Low", "Medium", "High"))

B. factor(x, levels = c("Low", "Medium", "High"))

C. factor(x, labels = c("Low", "Medium", "High"))

D. factor(x, sort = c("Low", "Medium", "High"))

Solution

1. Step 1: Recall factor syntax for setting levels

   The correct argument to set the order of categories is levels.

2. Step 2: Check the options

   Only factor(x, levels = c("Low", "Medium", "High")) uses levels correctly to order the factor levels.

3. Final Answer:

   factor(x, levels = c("Low", "Medium", "High")) -> Option B

4. Quick Check:

   Use levels= to order categories [OK]

Hint: Use levels= to set factor order, not order= or sort= [OK]

Common Mistakes:

• Using order= instead of levels=
• Confusing labels= with levels=
• Trying to sort levels with sort= argument

3. Consider the following R code:

x <- c("apple", "banana", "apple", "cherry")
f <- factor(x, levels = c("banana", "apple", "cherry"))
summary(f)

What will be the output of summary(f)?

medium

A. banana: 1apple: 2cherry: 1

B. apple: 2banana: 1cherry: 1

C. apple: 2cherry: 1banana: 1

D. cherry: 1banana: 1apple: 2

Solution

1. Step 1: Understand factor levels order

   The factor levels are set as "banana", "apple", "cherry" which determines the order in summary output.

2. Step 2: Count occurrences of each level

   In x, "banana" appears once, "apple" twice, and "cherry" once.

3. Final Answer:

   banana: 1
   apple: 2
   cherry: 1 -> Option A

4. Quick Check:

   Summary shows counts in levels order [OK]

Hint: Summary counts follow factor levels order [OK]

Common Mistakes:

• Assuming summary sorts alphabetically
• Ignoring levels order in summary
• Counting values incorrectly

4. The following R code is intended to create a factor with levels "Small", "Medium", "Large" and plot a bar chart. However, the plot shows the levels in alphabetical order instead of the specified order.

sizes <- c("Medium", "Large", "Small", "Medium")
f <- factor(sizes, levels = c("Small", "Medium", "Large"))
barplot(table(f))

What is the likely cause of this issue?

medium

A. The factor levels are correct, but barplot plots in alphabetical order by default

B. The barplot function ignores factor levels and sorts alphabetically

C. The factor was not created with the correct levels argument

D. The table function converts factor to character and loses order

Solution

1. Step 1: Check factor creation syntax

   The factor is created with levels specified correctly, so this is not the issue.

2. Step 2: Understand barplot and table behavior

   The table() function converts the factor to a named vector which may lose the factor level order, causing barplot to plot in alphabetical order.

3. Step 3: Identify common mistake

   Using table() directly on factor can lose order; using barplot with factor levels preserved requires careful handling.

4. Final Answer:

   The table function converts factor to character and loses order -> Option D

5. Quick Check:

   Use methods preserving factor order for plotting [OK]

Hint: Always set levels when creating factor to control plot order [OK]

Common Mistakes:

• Assuming barplot ignores factor levels
• Not setting levels argument in factor()
• Using character vector instead of factor in table()

5. You have a dataset with a column rating containing values "Poor", "Average", "Good", and "Excellent". You want to create a factor that orders these levels from worst to best and then plot a bar chart showing the counts in this order. Which R code correctly achieves this?

hard

A. rating_factor <- factor(rating, levels = sort(unique(rating)))
barplot(table(rating_factor))

B. rating_factor <- factor(rating)
barplot(table(rating_factor))

C. rating_factor <- factor(rating, levels = c("Poor", "Average", "Good", "Excellent"))
barplot(table(rating_factor))

D. rating_factor <- factor(rating, labels = c("Poor", "Average", "Good", "Excellent"))
barplot(table(rating_factor))

Solution

1. Step 1: Set factor levels in desired order

   To order from worst to best, levels must be explicitly set as "Poor", "Average", "Good", "Excellent".

2. Step 2: Plot counts respecting factor order

   Using table() on the factor and plotting with barplot() will show bars in factor level order.

3. Final Answer:

   rating_factor <- factor(rating, levels = c("Poor", "Average", "Good", "Excellent")) barplot(table(rating_factor)) -> Option C

4. Quick Check:

   Explicit levels order controls plot order [OK]

Hint: Set levels explicitly to control order in plots [OK]

Common Mistakes:

• Not setting levels, so order is alphabetical
• Using labels= instead of levels= to order
• Sorting unique values instead of custom order