Row and column indexing in R Programming - Mini Project: Build & Apply

Row and column indexing

📖 Scenario: You work in a small shop and keep track of daily sales in a table. You want to learn how to find specific sales data by looking at rows and columns.

🎯 Goal: Learn how to select specific rows and columns from a data frame in R using row and column indexing.

📋 What You'll Learn

Create a data frame called sales with exact values

Create a variable called day_index to select a specific row

Use row and column indexing to select the sales amount for that day

Print the selected sales amount

💡 Why This Matters

🌍 Real World

Selecting specific rows and columns from tables is common when working with sales data, customer data, or any spreadsheet-like information.

💼 Career

Data analysts and scientists often need to extract specific data points from large tables using row and column indexing.

1

Create the sales data frame

Create a data frame called sales with these exact columns and rows:
Day: "Monday", "Tuesday", "Wednesday"
Sales: 150, 200, 175

R Programming

# Create the sales data frame with Day and Sales columns
# Your code here

Hint

Use data.frame() with named vectors for columns.

2

Set the day index

Create a variable called day_index and set it to 2 to select the second row (Tuesday).

R Programming

sales <- data.frame(Day = c("Monday", "Tuesday", "Wednesday"), Sales = c(150, 200, 175))
# Create day_index variable and set it to 2
# Your code here

Hint

Just assign the number 2 to day_index.

3

Select the sales amount for the day

Use row and column indexing with sales and day_index to select the sales amount for that day. Store it in a variable called selected_sales. Use sales[day_index, 2] to get the sales column value.

R Programming

sales <- data.frame(Day = c("Monday", "Tuesday", "Wednesday"), Sales = c(150, 200, 175))
day_index <- 2
# Select the sales amount for the day using row and column indexing
# Your code here

Hint

Use sales[row, column] format to get the value.

4

Print the selected sales amount

Print the value stored in selected_sales using print().

R Programming

sales <- data.frame(Day = c("Monday", "Tuesday", "Wednesday"), Sales = c(150, 200, 175))
day_index <- 2
selected_sales <- sales[day_index, 2]
# Print the selected sales amount
# Your code here

Hint

Use print(selected_sales) to show the value.

Practice

1. In R, what does data[2, 3] select from a data frame named data?

easy

A. All rows and columns

B. The entire 2nd row

C. The entire 3rd column

D. The value in the 2nd row and 3rd column

Solution

1. Step 1: Understand the indexing format

   In R, data[row, column] selects the element at the specified row and column.

2. Step 2: Apply the given indices

   Here, row = 2 and column = 3, so it selects the value at 2nd row and 3rd column.

3. Final Answer:

   The value in the 2nd row and 3rd column -> Option D

4. Quick Check:

   data[2, 3] = value at row 2, column 3 [OK]

Hint: Use data[row, column] to pick exact cell [OK]

Common Mistakes:

• Confusing row and column order
• Thinking it selects entire row or column
• Using parentheses instead of brackets

2. Which of the following is the correct syntax to select all rows and the 4th column from a data frame df in R?

easy

A. df[, 4]

B. df[4, ]

C. df[4]

D. df[4, 4]

Solution

1. Step 1: Recall indexing rules for rows and columns

   Leaving the row index blank selects all rows; specifying a column index selects that column.

2. Step 2: Match syntax to select all rows and 4th column

   df[, 4] means all rows, 4th column, which is correct.

3. Final Answer:

   df[, 4] -> Option A

4. Quick Check:

   Blank row index means all rows [OK]

Hint: Blank row means all rows; specify column number [OK]

Common Mistakes:

• Using df[4, ] selects 4th row, not column
• Using df[4] selects 4th column but drops dimensions
• Using df[4, 4] selects single cell, not all rows

3. Given the data frame df <- data.frame(A=1:3, B=4:6, C=7:9), what is the output of df[1:2, 2]?

medium

A. [1] 7 8

B. [1] 4 5

C. [1] 1 2

D. [1] 4 5 6

Solution

1. Step 1: Understand the data frame structure

   df has columns A=1,2,3; B=4,5,6; C=7,8,9.

2. Step 2: Select rows 1 and 2, column 2

   Rows 1 and 2 of column B (2nd column) are 4 and 5.

3. Final Answer:

   [1] 4 5 -> Option B

4. Quick Check:

   df[1:2, 2] = c(4, 5) [OK]

Hint: Rows 1:2, column 2 picks values 4 and 5 [OK]

Common Mistakes:

• Confusing column numbers with letters
• Selecting wrong rows or columns
• Expecting output as a data frame instead of vector

4. What is wrong with this R code snippet to select the 3rd row and all columns from data frame df?

df[3]

medium

A. It selects the 3rd column, not the 3rd row

B. It causes a syntax error

C. It selects the 3rd row but drops columns

D. It returns all rows and columns

Solution

1. Step 1: Understand single bracket indexing with one index

   Using one index like df[3] selects the 3rd column, not row.

2. Step 2: Identify correct way to select 3rd row

   To select 3rd row and all columns, use df[3, ].

3. Final Answer:

   It selects the 3rd column, not the 3rd row -> Option A

4. Quick Check:

   df[3] = 3rd column, not row [OK]

Hint: Use df[row, ] to select rows, not df[row] [OK]

Common Mistakes:

• Using single index to select rows
• Expecting df[3] to select 3rd row
• Confusing data frame and vector indexing

5. You have a data frame df with 5 rows and 4 columns named A, B, C, D. How do you select rows 2 to 4 and columns B and D using row and column indexing?

hard

A. df[2:4, c(2, 4)]

B. df[c(2,3,4), 2:4]

C. df[2:4, c("B", "D")]

D. df[2:4, c("2", "4")]

Solution

1. Step 1: Identify rows and columns to select

   Rows 2 to 4 means indices 2, 3, 4; columns B and D are selected by names "B" and "D".

2. Step 2: Use correct syntax for row and column indexing

   Use df[2:4, c("B", "D") ] to select rows 2-4 and columns named B and D.

3. Final Answer:

   df[2:4, c("B", "D") ] -> Option C

4. Quick Check:

   Row indices and column names combined correctly [OK]

Hint: Use row range and column names vector inside brackets [OK]

Common Mistakes:

• Using numeric column indices instead of names
• Passing column names as numbers in quotes
• Selecting wrong rows or columns