Why advanced features enable complex work in R Programming - Test Your Understanding

Practice - 5 Tasks

1fill in blank

easy

Complete the code to create a vector of numbers from 1 to 5.

R Programming

numbers <- [1](1, 5)

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Arep

Bseq

Cc

Dlist

Attempts:

3 left

💡 Hint

Common Mistakes

Using c creates a vector but does not generate a sequence automatically.

Using rep repeats values instead of creating a sequence.

The seq function generates a sequence of numbers from the first to the second argument.

2fill in blank

medium

Complete the code to calculate the mean of the vector 'values'.

R Programming

average <- [1](values)

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Amean

Bsum

Cmedian

Dlength

Attempts:

3 left

💡 Hint

Common Mistakes

Using sum returns the total, not the average.

Using length returns the count of elements, not the average.

The mean function calculates the average value of a numeric vector.

3fill in blank

hard

Fix the error in the code to subset the vector 'data' for values greater than 10.

R Programming

subset <- data[data [1] 10]

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A>

B<=

C==

D<

Attempts:

3 left

💡 Hint

Common Mistakes

Using == selects only values exactly equal to 10.

Using <= or < selects smaller or equal values.

The operator > selects elements greater than 10 from the vector.

4fill in blank

hard

Fill both blanks to create a named list with elements 'a' and 'b'.

R Programming

my_list <- list([1] = 1, [2] = 2)

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Aa

B1

Cb

D2

Attempts:

3 left

💡 Hint

Common Mistakes

Using numbers as names instead of letters.

Confusing values with names.

In R, you name list elements by using name = value. Here, 'a' and 'b' are the names.

5fill in blank

hard

Fill all three blanks to create a data frame with columns 'name' and 'age' and filter rows where age is greater than 20.

R Programming

df <- data.frame([1] = c("Alice", "Bob"), [2] = c(25, 19))
adults <- df[df$[3] > 20, ]

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Aname

Bage

Attempts:

3 left

💡 Hint

Common Mistakes

Mixing up column names and values.

Using the wrong column name in the filter condition.

The data frame columns are named 'name' and 'age'. To filter rows where age is greater than 20, use df$age > 20.