select() for column selection in R Programming - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of select() with tidyselect helpers

What is the output of the following R code using dplyr::select() with tidyselect helpers?

R Programming

library(dplyr)
df <- tibble(a = 1:3, b = 4:6, c = 7:9, d = 10:12)
result <- df %>% select(starts_with("b"), ends_with("d"))
print(result)

A A tibble with columns a, b, c, d

B A tibble with columns a and c

C A tibble with columns b and d

D An error because ends_with() is used incorrectly

💡 Hint

Think about which columns start with 'b' and which end with 'd'.

The select() function picks columns starting with 'b' (which is 'b') and ending with 'd' (which is 'd'). So the result has columns 'b' and 'd'.

❓ Predict Output

intermediate

Result of select() with numeric range

What columns are selected by this code snippet?

R Programming

library(dplyr)
df <- tibble(x1 = 1:2, x2 = 3:4, y1 = 5:6, y2 = 7:8)
selected <- df %>% select(x1:y1)
print(selected)

A Columns x1, x2, y1, y2

B Columns x1, x2, y1

C Columns x1 and y1 only

D Error because x1:y1 is invalid

💡 Hint

The colon operator selects columns between two names in order.

The select(x1:y1) picks columns from x1 through y1 in the order they appear: x1, x2, y1.

🔧 Debug

advanced

Why does this select() call fail?

This code throws an error. What is the cause?

R Programming

library(dplyr)
df <- tibble(a = 1:3, b = 4:6)
selected <- df %>% select(a, c)
print(selected)

A Column 'c' does not exist in the data frame

B select() cannot take multiple arguments

C The pipe operator %>% is used incorrectly

D The tibble function is missing parentheses

💡 Hint

Check if all columns you select exist in the data frame.

The error occurs because column 'c' is not present in df, so select() cannot find it.

❓ Predict Output

advanced

Output of select() with helper functions and renaming

What is the output of this code?

R Programming

library(dplyr)
df <- tibble(name = c("Ann", "Bob"), age = c(25, 30), score = c(88, 92))
result <- df %>% select(person = name, age)
print(result)

A A tibble with columns 'age' and 'score' only

B A tibble with columns 'name' and 'age' unchanged

C An error because select() cannot rename columns

D A tibble with columns 'person' and 'age', where 'person' has the names

💡 Hint

select() can rename columns by using new_name = old_name syntax.

The select() call renames 'name' to 'person' and keeps 'age'. So the output has columns 'person' and 'age'.

🧠 Conceptual

expert

Understanding select() with complex tidyselect expressions

Which option correctly describes the columns selected by this code?

R Programming

library(dplyr)
df <- tibble(a1 = 1, a2 = 2, b1 = 3, b2 = 4, c1 = 5)
selected <- df %>% select(matches("^a[12]$"), -a2)
print(selected)

A Only column 'a1' is selected because 'a2' is excluded

B An error occurs because negative selection cannot be combined with matches()

C Columns 'a1', 'a2', and 'b1' are selected

D Columns 'a1' and 'a2' are selected because matches() includes both

💡 Hint

Negative selection removes columns after positive selection.

matches("^a[12]$") selects 'a1' and 'a2'. Then '-a2' removes 'a2', leaving only 'a1'.

Practice

1. What does the select() function do in R's dplyr package?

easy

A. It adds new columns to the data frame.

B. It filters rows based on conditions.

C. It chooses specific columns from a data frame.

D. It sorts the data frame by a column.

Solution

1. Step 1: Understand the purpose of select()

   The select() function is designed to pick columns from a data frame, not rows or sorting.

2. Step 2: Compare with other functions

   Functions like filter() handle rows, arrange() sorts, and mutate() adds columns, so these are not what select() does.

3. Final Answer:

   It chooses specific columns from a data frame. -> Option C

4. Quick Check:

   select() = pick columns [OK]

Hint: Remember: select() picks columns, filter() picks rows [OK]

Common Mistakes:

• Confusing select() with filter()
• Thinking select() sorts data
• Assuming select() adds columns

2. Which of the following is the correct syntax to select columns name and age from a data frame df using dplyr?

easy

A. select(name, age)

B. df.select(name, age)

C. select(name, age, df)

D. df %>% select(name, age)

Solution

1. Step 1: Recall dplyr pipe syntax

   In dplyr, the pipe operator %>% passes df as the first argument to select().

2. Step 2: Check each option

   A: select(name, age) misses the data frame df. B: df %>% select(name, age) uses pipe correctly. C: select(name, age, df) has wrong argument order. D: df.select(name, age) is not valid R syntax.

3. Final Answer:

   df %>% select(name, age) -> Option D

4. Quick Check:

   Use pipe %>% then select columns [OK]

Hint: Use pipe %>% before select() for clean code [OK]

Common Mistakes:

• Wrong argument order in select()
• Using dot notation like df.select()
• Forgetting to load dplyr package

3. Given the data frame df with columns id, name, age, and score, what will be the output of this code?

library(dplyr)
df %>% select(name, score)

medium

A. A data frame with only columns name and score.

B. A data frame with all columns except name and score.

C. An error because select() needs row filtering.

D. A data frame with columns id and age only.

Solution

1. Step 1: Understand select() behavior

   The select() function picks only the columns named inside it, here name and score.

2. Step 2: Confirm output columns

   The output will be a data frame with just these two columns, dropping id and age.

3. Final Answer:

   A data frame with only columns name and score. -> Option A

4. Quick Check:

   select(name, score) = only those columns [OK]

Hint: select() keeps only listed columns, drops others [OK]

Common Mistakes:

• Thinking select() drops listed columns
• Expecting row filtering from select()
• Confusing select() with filter()

4. Identify the error in this code snippet:

library(dplyr)
df %>% select('name', 'age')

medium

A. Missing pipe operator before select().

B. Using quotes around column names causes an error.

C. select() cannot be used with dplyr.

D. The data frame df is not defined.

Solution

1. Step 1: Check column name syntax in select()

   In dplyr's select(), column names should be unquoted bare names, not strings.

2. Step 2: Understand consequences of quotes

   Using quotes treats them as strings, causing select() to fail to find columns and throw an error.

3. Final Answer:

   Using quotes around column names causes an error. -> Option B

4. Quick Check:

   Column names unquoted in select() [OK]

Hint: Do not put quotes around column names in select() [OK]

Common Mistakes:

• Quoting column names inside select()
• Forgetting to load dplyr
• Assuming select() filters rows

5. You have a data frame df with columns name, age, score, and grade. You want to select only name and score but rename score to points in the output. Which code does this correctly?

hard

A. df %>% select(name, points = score)

B. df %>% select(name, score = points)

C. df %>% rename(points = score) %>% select(name, score)

D. df %>% select(name, score) %>% mutate(points = score)

Solution

1. Step 1: Understand renaming in select()

   dplyr allows renaming columns inside select() by using new_name = old_name syntax.

2. Step 2: Check each option

   A: df %>% rename(points = score) %>% select(name, score) renames score to points, then selects score which no longer exists, error. B: df %>% select(name, score = points) reverses names, no points column exists, error. C: df %>% select(name, points = score) correctly selects name and renames score to points. D: df %>% select(name, score) %>% mutate(points = score) adds points but keeps score.

3. Final Answer:

   df %>% select(name, points = score) -> Option A

4. Quick Check:

   Rename inside select() with new = old [OK]

Hint: Rename columns inside select() using new_name = old_name [OK]

Common Mistakes:

• Swapping new and old names
• Using rename() without dropping old column
• Using mutate() instead of select() for renaming