select() for column selection in R Programming - Mini Project: Build & Apply

Selecting Columns with select() in R

📖 Scenario: You have a small table of fruit sales data. You want to look at only some columns to understand the sales better.

🎯 Goal: Learn how to use the select() function from the dplyr package in R to pick specific columns from a data frame.

📋 What You'll Learn

Create a data frame called fruit_sales with exact columns and values

Create a vector called columns_to_select with exact column names

Use select() with fruit_sales and columns_to_select to create a new data frame

Print the new data frame

💡 Why This Matters

🌍 Real World

Selecting specific columns from data is common when you want to focus on important information and ignore the rest.

💼 Career

Data analysts and scientists often use select() to prepare data for reports, charts, or further analysis.

1

Create the fruit sales data frame

Create a data frame called fruit_sales with these exact columns and values:
Fruit: "Apple", "Banana", "Cherry"
Quantity: 10, 15, 7
Price: 0.5, 0.3, 0.8

R Programming

# Create the fruit_sales data frame with Fruit, Quantity, and Price columns
# Your code here

Hint

Use data.frame() with named vectors for each column.

2

Create a vector of columns to select

Create a character vector called columns_to_select with these exact values: "Fruit" and "Price"

R Programming

fruit_sales <- data.frame(
  Fruit = c("Apple", "Banana", "Cherry"),
  Quantity = c(10, 15, 7),
  Price = c(0.5, 0.3, 0.8)
)

# Create columns_to_select vector with "Fruit" and "Price"
# Your code here

Hint

Use c() to create a vector of column names.

3

Select columns using select()

Load the dplyr package with library(dplyr). Then create a new data frame called selected_data by using select() on fruit_sales with the columns in columns_to_select.

R Programming

fruit_sales <- data.frame(
  Fruit = c("Apple", "Banana", "Cherry"),
  Quantity = c(10, 15, 7),
  Price = c(0.5, 0.3, 0.8)
)

columns_to_select <- c("Fruit", "Price")

# Load dplyr and select columns from fruit_sales
# Your code here

Hint

Use all_of() inside select() to select columns by name from a vector.

4

Print the selected data

Print the selected_data data frame to see only the selected columns.

R Programming

library(dplyr)

fruit_sales <- data.frame(
  Fruit = c("Apple", "Banana", "Cherry"),
  Quantity = c(10, 15, 7),
  Price = c(0.5, 0.3, 0.8)
)

columns_to_select <- c("Fruit", "Price")

selected_data <- select(fruit_sales, all_of(columns_to_select))

# Print selected_data
# Your code here

Hint

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

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