Handling missing values (drop_na, fill) in R Programming - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to remove rows with missing values from the data frame.

R Programming

clean_data <- [1](data)

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

Adrop_na

Bfill_na

Cna.omit

Dreplace_na

Attempts:

3 left

💡 Hint

Common Mistakes

Using fill_na which is not a function to drop rows.

Using na.omit which works but is base R, not tidyverse.

The drop_na() function removes rows with missing values from a data frame.

2fill in blank

medium

Complete the code to replace missing values in the 'score' column with 0.

R Programming

data$score <- [1](data$score, 0)

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

Afill_na

Breplace_na

Cdrop_na

Dna.omit

Attempts:

3 left

💡 Hint

Common Mistakes

Using drop_na which removes rows instead of replacing values.

Using fill_na which is not a base or tidyverse function.

The replace_na() function replaces missing values with a specified value.

3fill in blank

hard

Fix the error in the code to fill missing values in the 'age' column with the previous non-missing value.

R Programming

library(tidyr)
data <- [1](data, age, .direction = "down")

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

Areplace_na

Bdrop_na

Cna.locf

Dfill

Attempts:

3 left

💡 Hint

Common Mistakes

Using replace_na which replaces with a fixed value, not previous values.

Using drop_na which removes rows instead of filling.

The fill() function from tidyr fills missing values using the previous non-missing value when direction is "down".

4fill in blank

hard

Fill both blanks to create a new data frame without missing values and then replace missing values in 'height' with 170.

R Programming

clean_data <- data %>% [1]() %>% mutate(height = [2](height, 170))

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

Adrop_na

Breplace_na

Cfill

Dna.omit

Attempts:

3 left

💡 Hint

Common Mistakes

Using fill() instead of replace_na() to replace values.

Using na.omit inside the pipe which is less common.

First, drop_na() removes rows with missing values, then replace_na() replaces missing 'height' values with 170.

5fill in blank

hard

Fill all three blanks to create a dictionary of counts for non-missing 'category' values, filtering out categories with zero count.

R Programming

counts <- table(data$category) %>% as.data.frame() %>% filter(Freq [1] 0) %>% mutate(category = as.character([2]), count = [3])

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

A>

BVar1

CFreq

D<

Attempts:

3 left

💡 Hint

Common Mistakes

Using < instead of > in filter condition.

Mixing up column names in mutate.

The code filters categories with frequency greater than 0, converts category to character, and assigns frequency to count.