Data frame creation in R Programming - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

What is the output of this data frame creation?

Consider the following R code that creates a data frame. What will be the output when printed?

R Programming

df <- data.frame(Name = c("Anna", "Bob"), Age = c(25, 30))
print(df)

A

  Name Age
1 Anna  25
2 Bob   30

B

  Name Age
1 Anna  30
2 Bob   25

C

  Name Age
1 25   Anna
2 30   Bob

D Error: object 'df' not found

💡 Hint

Look at how vectors are assigned to columns in data.frame.

The data frame is created with two columns: Name and Age. The vectors are assigned in order, so Anna is 25 and Bob is 30.

❓ Predict Output

intermediate

What is the number of rows in this data frame?

Given this R code, how many rows does the data frame 'df' have?

R Programming

df <- data.frame(ID = 1:4, Score = c(88, 92, 79, 85))
nrow(df)

A 1

B 2

C 4

D Error: object 'df' not found

💡 Hint

nrow() returns the number of rows in a data frame.

The data frame has 4 rows because the vectors have length 4.

🔧 Debug

advanced

Why does this data frame creation code produce an error?

Examine the code below. Why does it cause an error when run?

R Programming

df <- data.frame(Name = c("Alice", "Bob"), Age = c(30))

A Because Age vector must be a factor, not numeric

B Because the vectors for columns have different lengths

C Because the Name vector is missing a closing quote

D Because the data.frame function requires numeric columns only

💡 Hint

Check if all columns have the same number of elements.

In R, data frame columns must have equal length. Here, Name has length 2 but Age has length 1, causing an error.

🧠 Conceptual

advanced

What happens if you create a data frame with a list column?

Consider this R code creating a data frame with a list column. What is the structure of the resulting data frame?

R Programming

df <- data.frame(ID = 1:2, Values = I(list(c(1,2), c(3,4))))
str(df)

A A data frame with 2 rows and a list column where each cell contains a numeric vector

B A data frame with 2 rows and a numeric column with combined values

C An error because data frames cannot have list columns

D A data frame with 4 rows, each with a single numeric value

💡 Hint

The I() function inhibits conversion and allows list columns.

Using I() wraps the list so the data frame keeps each list element as a cell, allowing complex data in columns.

❓ Predict Output

expert

What is the output of this complex data frame creation?

What will be printed when running this R code?

R Programming

df <- data.frame(
  Name = c("Tom", "Jerry"),
  Age = c(5, 7),
  stringsAsFactors = FALSE
)
df$Age <- df$Age + 1
print(df)

A

   Name Age
1   Tom  51
2 Jerry  71

B

   Name Age
1   Tom   5
2 Jerry   7

C Error: object 'df' not found

D

   Name Age
1   Tom   6
2 Jerry   8

💡 Hint

Adding 1 to the Age column increases each age by one.

The Age column values are incremented by 1, so 5 becomes 6 and 7 becomes 8.

Practice

1. What is the main purpose of a data.frame in R?

easy

A. To store data in rows and columns like a spreadsheet

B. To perform mathematical calculations only

C. To create graphical plots

D. To write text files

Solution

1. Step 1: Understand the structure of a data frame

   A data frame organizes data in rows and columns, similar to a spreadsheet.

2. Step 2: Identify the main use

   It is mainly used to store and analyze mixed types of data together.

3. Final Answer:

   To store data in rows and columns like a spreadsheet -> Option A

4. Quick Check:

   Data frame = tabular data storage [OK]

Hint: Think spreadsheet style data storage [OK]

Common Mistakes:

• Confusing data frames with plotting functions
• Thinking data frames only do math
• Assuming data frames are for text files

2. Which of the following is the correct way to create a data frame with columns age and name in R?

easy

A. data.frame(age = [25, 30], name = ["Alice", "Bob"])

B. data.frame(c(25, 30), c("Alice", "Bob"))

C. data.frame(age: c(25, 30), name: c("Alice", "Bob"))

D. data.frame(age = c(25, 30), name = c("Alice", "Bob"))

Solution

1. Step 1: Check syntax for named columns

   In R, data.frame() uses the syntax column_name = vector to name columns.

2. Step 2: Identify correct vector syntax

   Vectors are created with c(), so c(25, 30) and c("Alice", "Bob") are correct.

3. Final Answer:

   data.frame(age = c(25, 30), name = c("Alice", "Bob")) -> Option D

4. Quick Check:

   Correct syntax uses = and c() [OK]

Hint: Use = to name columns and c() for vectors [OK]

Common Mistakes:

• Using colons (:) instead of =
• Using brackets [] instead of c()
• Omitting column names

3. What will be the output of the following R code?

df <- data.frame(x = c(1, 2), y = c("a", "b"))
print(df)

medium

A. x y
1 1 a
2 2 b

B. b 2 2
a 1 1
y x

C. [1] 1 2
[1] "a" "b"

D. Error: object 'df' not found

Solution

1. Step 1: Understand data frame creation

   The code creates a data frame with two columns: x (numbers) and y (characters).

2. Step 2: Understand print output format

   Printing a data frame shows row numbers and columns with their values in a table format.

3. Final Answer:

   x y 1 1 a 2 2 b -> Option A

4. Quick Check:

   Print data frame shows rows and columns [OK]

Hint: Print shows row numbers and columns in table form [OK]

Common Mistakes:

• Ignoring row numbers in output
• Confusing vector print with data frame print
• Expecting error when none occurs

4. Identify the error in the following R code that tries to create a data frame:

df <- data.frame(age = c(20, 25), name = c("John"))

medium

A. Missing comma between vectors

B. Incorrect use of quotes around names

C. Vectors have different lengths causing recycling warning

D. data.frame() cannot mix numeric and character vectors

Solution

1. Step 1: Check vector lengths

   The age vector has length 2, but name vector has length 1.

2. Step 2: Understand recycling rule

   R recycles shorter vectors to match longer ones, causing a warning when lengths differ.

3. Final Answer:

   Vectors have different lengths causing recycling warning -> Option C

4. Quick Check:

   Unequal vector lengths cause recycling warning [OK]

Hint: Ensure all vectors have same length in data.frame() [OK]

Common Mistakes:

• Assuming data.frame() needs all vectors same type
• Thinking missing commas cause error here
• Confusing quotes usage

5. You want to create a data frame from two vectors: ids = c(101, 102, 103) and scores = c(88, 92, 85). Then add a new column passed which is TRUE if score ≥ 90, otherwise FALSE. Which code correctly does this?

hard

A. All of the above

B. df <- data.frame(ids, scores, passed = scores >= 90)

C. df <- data.frame(ids, scores)
df$passed <- if(scores >= 90, TRUE, FALSE)

D. df <- data.frame(ids, scores)
df$passed <- passed >= 90

Solution

1. Step 1: Check the one-line data.frame creation

   df <- data.frame(ids, scores, passed = scores ≥ 90) correctly names columns from variable names and computes passed as logical vector.

2. Step 2: Eliminate incorrect options

   The two-line option using if(scores ≥ 90, TRUE, FALSE) has invalid syntax for if. The other two-line uses undefined passed in passed ≥ 90. All of the above is false.

3. Final Answer:

   df <- data.frame(ids, scores, passed = scores ≥ 90) -> Option B

4. Quick Check:

   Valid: column computation inside data.frame() [OK]

Hint: Create computed columns directly in data.frame() [OK]

Common Mistakes:

• Trying to add columns before creating data frame
• Using incorrect logical syntax
• Not using vectorized operations