Data frame creation in R Programming - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is a data frame in R?

A data frame is a table-like structure in R that holds data in rows and columns. Each column can have different types like numbers, text, or factors.

beginner

How do you create a simple data frame with columns 'Name' and 'Age' in R?

Use the data.frame() function like this:
data.frame(Name = c("Alice", "Bob"), Age = c(25, 30))

beginner

What happens if columns in a data frame have different lengths?

R will give an error because all columns in a data frame must have the same number of rows.

beginner

How can you check the structure of a data frame after creating it?

Use the str() function to see the structure, types, and sample data of the data frame.

beginner

Can a data frame column contain different data types?

No, each column must have the same data type, but different columns can have different types.

Which function is used to create a data frame in R?

A data.frame()

B create.df()

C make.data()

D frame.create()

The correct function to create a data frame in R is data.frame().

What will happen if you try to create a data frame with columns of unequal length?

A R will throw an error

B R will fill missing values with zeros

C R will recycle shorter columns automatically

D R will create a list instead

R requires all columns in a data frame to have the same length; otherwise, it throws an error.

Which of these is a valid way to create a data frame with two columns 'x' and 'y'?

A data.frame(x = c(1,2), y = c('a','b','c'))

B data.frame(x = 1:3, y = c('a', 'b'))

C data.frame(x = 1:3, y = c('a', 'b', 'c'))

D data.frame(x = 1:3, y = 1:2)

Only option D has columns of equal length (3), which is required for data frames.

How do you view the structure of a data frame named df?

A head(df)

B view(df)

C summary(df)

D str(df)

The str() function shows the structure of the data frame including types and sample data.

Can a single column in a data frame contain both numbers and text?

A Yes, columns can mix types freely

B No, each column must have one data type

C Only if you convert the column to a list

D Only if you use factors

Each column in a data frame must have the same data type for all its values.

Explain how to create a data frame in R with three columns: Name, Age, and City.

Think about how you combine vectors into a table.

Describe what happens if you try to create a data frame with columns of different lengths.

Imagine a table where rows must line up.

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