Data frame creation in R Programming - Mini Project: Build & Apply

Data frame creation

📖 Scenario: You are working as a data analyst and need to organize some survey data into a table format for easy analysis.

🎯 Goal: Create a data frame in R with specific columns and data, then display it.

📋 What You'll Learn

Create a data frame with exact column names and values

Add a variable for the number of rows

Use the data.frame() function to create the data frame

Print the data frame to show the result

💡 Why This Matters

🌍 Real World

Data frames are used to organize and analyze data collected from surveys, experiments, or business records.

💼 Career

Knowing how to create and manipulate data frames is essential for data analysts, statisticians, and anyone working with data in R.

1

Create vectors for survey data

Create three vectors called names, ages, and cities with these exact values: names = c("Alice", "Bob", "Charlie"), ages = c(25, 30, 35), cities = c("New York", "Los Angeles", "Chicago")

R Programming

# Create vectors for names, ages, and cities
# Your code here

Hint

Use the c() function to create vectors with the exact values given.

2

Create a variable for number of rows

Create a variable called num_rows that stores the number of elements in the names vector using the length() function

R Programming

names <- c("Alice", "Bob", "Charlie")
ages <- c(25, 30, 35)
cities <- c("New York", "Los Angeles", "Chicago")
# Create num_rows variable with length of names vector
# Your code here

Hint

Use length(names) to find how many names are in the vector.

3

Create the data frame

Create a data frame called survey_data using the data.frame() function with columns Name, Age, and City using the vectors names, ages, and cities respectively

R Programming

names <- c("Alice", "Bob", "Charlie")
ages <- c(25, 30, 35)
cities <- c("New York", "Los Angeles", "Chicago")
num_rows <- length(names)
# Create data frame survey_data with columns Name, Age, City
# Your code here

Hint

Use data.frame(Name = names, Age = ages, City = cities) to create the data frame with correct column names.

4

Print the data frame

Use print() to display the survey_data data frame

R Programming

names <- c("Alice", "Bob", "Charlie")
ages <- c(25, 30, 35)
cities <- c("New York", "Los Angeles", "Chicago")
num_rows <- length(names)
survey_data <- data.frame(Name = names, Age = ages, City = cities, stringsAsFactors = FALSE)
# Print the survey_data data frame
# Your code here

Hint

Use print(survey_data) to show the data frame in the console.

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