Pipe operator (%>% and |>) in R Programming - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to use the pipe operator to pass 'data' to the 'summary' function.

R Programming

result <- data [1] summary()

Drag options to blanks, or click blank then click option'

A->

B|>

C<-

D%>%

Attempts:

3 left

💡 Hint

Common Mistakes

Using assignment operators like <- instead of a pipe.

Confusing the pipe with other operators.

The %>% operator pipes the left side value as the first argument to the function on the right.

2fill in blank

medium

Complete the code to pipe 'numbers' into the 'mean' function using the native R pipe operator.

R Programming

average <- numbers [1] mean()

Drag options to blanks, or click blank then click option'

A->

B%>%

C|>

D<-

Attempts:

3 left

💡 Hint

Common Mistakes

Using %>% when the question asks for the native pipe.

Using assignment operators instead of pipes.

The native R pipe operator is |> and it passes the left side value as the first argument to the function on the right.

3fill in blank

hard

Fix the error in the code by choosing the correct pipe operator to chain 'data' to 'head' function.

R Programming

data [1] head(3)

Drag options to blanks, or click blank then click option'

A%>%

B->

C<-

D|>

Attempts:

3 left

💡 Hint

Common Mistakes

Using assignment operators instead of pipes.

Confusing the native pipe with magrittr pipe.

The %>% operator correctly pipes 'data' as the first argument to head().

4fill in blank

hard

Fill both blanks to create a pipeline that filters 'df' for values > 10 and then selects the 'score' column.

R Programming

result <- df [1] filter(value [2] 10)

Drag options to blanks, or click blank then click option'

A%>%

B>

C|>

D<-

Attempts:

3 left

💡 Hint

Common Mistakes

Using assignment operators instead of pipes.

Using wrong comparison operators like < or =.

The %>% pipes 'df' into filter(), and the condition uses the greater than operator >.

5fill in blank

hard

Fill all three blanks to create a pipeline that takes 'data', filters rows where 'age' is less than 30, then selects 'name' column.

R Programming

result <- data [1] filter(age [2] 30) [3] select(name)

Drag options to blanks, or click blank then click option'

A%>%

B<

C|>

D->

Attempts:

3 left

💡 Hint

Common Mistakes

Using wrong pipe operators in wrong places.

Mixing assignment and pipe operators incorrectly.

The %>% pipes 'data' into filter() with the condition <, and then pipes the result into select().