Row and column indexing in R Programming - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of selecting rows and columns by index

What is the output of the following R code?

R Programming

df <- data.frame(A = 1:4, B = 5:8, C = 9:12)
result <- df[2:3, c(1,3)]
print(result)

A

  A  C
2 2 10
3 3 11

B

  A  C
1 1 9
4 4 12

C

  B  C
2 6 10
3 7 11

D

  A  B
2 2 6
3 3 7

💡 Hint

Remember that df[row_indices, column_indices] selects rows and columns by their positions.

The code selects rows 2 and 3, and columns 1 and 3 from the data frame. Column 1 is 'A' and column 3 is 'C'. Rows 2 and 3 have values 2,3 in 'A' and 10,11 in 'C'.

❓ Predict Output

intermediate

Output of logical indexing on rows and columns

What will be the output of this R code?

R Programming

df <- data.frame(X = c(10,20,30), Y = c(5,15,25), Z = c(1,2,3))
result <- df[df$X > 15, c(TRUE, FALSE, TRUE)]
print(result)

A

   X  Y  Z
2 20 15  2
3 30 25  3

B

   X  Z
2 20  2
3 30  3

C

   Y
2 15
3 25

D

   X  Z
1 10  1
2 20  2
3 30  3

💡 Hint

Logical vectors can be used to select columns; TRUE means select that column.

Rows where X > 15 are rows 2 and 3. Columns selected are the first and third columns (X and Z) because the logical vector is TRUE, FALSE, TRUE.

🔧 Debug

advanced

Identify the error in row and column indexing

What error does this R code produce?

R Programming

df <- data.frame(A = 1:3, B = 4:6)
result <- df[1:4, 1]
print(result)

A Error: subscript out of bounds

B No error, prints 1 2 3 4

C Error: object 'result' not found

D No error, prints 1 2 3 NA

💡 Hint

Check how R handles indexing beyond the number of rows in a data frame.

R allows indexing beyond existing rows and fills missing rows with NA values. So row 4 does not exist, but R returns NA for that row without error.

❓ Predict Output

advanced

Output of negative indexing on rows and columns

What is the output of this R code?

R Programming

df <- data.frame(M = 1:5, N = 6:10, O = 11:15)
result <- df[-(2:4), -2]
print(result)

A

  M  O
1 1 11
5 5 15

B

  N  O
1 6 11
5 10 15

C

  M  N
1 1 6
5 5 10

D

  M  O
2 2 12
3 3 13
4 4 14

💡 Hint

Negative indices exclude the specified rows or columns.

Rows 2 to 4 are excluded, so only rows 1 and 5 remain. Column 2 (N) is excluded, so columns M and O remain.

🧠 Conceptual

expert

Number of items after complex indexing

Given the data frame below, how many elements are in the result of df[1:3, c(FALSE, TRUE, TRUE)]?

R Programming

df <- data.frame(A = 1:5, B = 6:10, C = 11:15)

A 3

B 9

C 6

D 15

💡 Hint

Count rows selected times columns selected.

Rows 1 to 3 means 3 rows. The logical vector selects columns 2 and 3 (B and C), so 2 columns. Total elements = 3 * 2 = 6.