Bird
Raised Fist0
R Programmingprogramming~5 mins

select() for column selection in R Programming - Cheat Sheet & Quick Revision

Choose your learning style10 modes available
LearnWhyDeepVisualTryChallengeProjectRecallTime
Recall & Review
beginner
What does the select() function do in R's dplyr package?
It chooses specific columns from a data frame, making it easy to work only with the data you need.
Click to reveal answer
beginner
How do you select columns name and age from a data frame df using select()?
Use select(df, name, age) to get only those two columns.
Click to reveal answer
intermediate
Can select() use helper functions like starts_with()?
Yes! You can select columns that start with a pattern, for example select(df, starts_with('a')) picks all columns starting with 'a'.
Click to reveal answer
beginner
What happens if you use select() with a column name that does not exist?
You get an error telling you the column is not found, so you need to check column names carefully.
Click to reveal answer
intermediate
How can you deselect a column using select()?
Use a minus sign before the column name, like select(df, -age) to drop the age column.
Click to reveal answer
What does select(df, name, age) do?
ARenames columns 'name' and 'age'
BDeletes columns 'name' and 'age' from df
CFilters rows where name and age are present
DSelects only the columns 'name' and 'age' from df
How do you exclude the column 'salary' using select()?
Aselect(df, salary)
Bselect(df, -salary)
Cselect(df, exclude(salary))
Dselect(df, drop(salary))
Which helper function selects columns starting with 'temp'?
Acontains('temp')
Bends_with('temp')
Cstarts_with('temp')
Dmatches('temp')
What will happen if you try select(df, unknown_column)?
AThrows an error about missing column
BReturns an empty data frame
CReturns all columns
DAutomatically creates the column
Which of these is a correct way to select columns 'x' and 'y'?
Aselect(df, x, y)
Bselect(df, c('x', 'y'))
Cselect(df, ['x', 'y'])
Dselect(df, x & y)
Explain how to use select() to pick specific columns from a data frame.
Think about how you tell R which columns you want to keep.
You got /3 concepts.
    Describe how to exclude columns using select() and how helper functions can help select columns by pattern.
    Remember you can both pick and drop columns easily.
    You got /3 concepts.

      Practice

      (1/5)
      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
      Recommended

      Ready to prove it?

      Pass the test and this pattern moves unknown strong while earning +1 Grip toward your certificate.

      10 questions~5 mineasy hard