Handling missing values (drop_na, fill) in R Programming - Mini Project: Build & Apply

Handling missing values (drop_na, fill)

📖 Scenario: You work as a data analyst. You have a small table of sales data, but some values are missing. You want to clean the data by removing rows with missing values and then fill missing values in another column.

🎯 Goal: Build an R script that creates a data frame with missing values, removes rows with missing values in one column, fills missing values in another column, and prints the cleaned data.

📋 What You'll Learn

Create a data frame called sales with columns product, price, and quantity including some missing values

Create a variable called cleaned_sales that removes rows with missing price using drop_na()

Fill missing quantity values in cleaned_sales using fill() from the tidyr package

Print the final cleaned_sales data frame

💡 Why This Matters

🌍 Real World

Data cleaning is a common task in data analysis to prepare data for accurate insights.

💼 Career

Knowing how to handle missing data is essential for data analysts, scientists, and anyone working with real-world data.

1

Create the sales data frame with missing values

Create a data frame called sales with these exact columns and values: product as c("A", "B", "C", "D"), price as c(10, NA, 15, 20), and quantity as c(5, 3, NA, 8).

R Programming

# Create the sales data frame with missing values
# Your code here

Need a hint?

Use data.frame() to create the table. Use NA for missing values.

2

Remove rows with missing price values

Load the tidyr package and create a new variable called cleaned_sales by removing rows with missing price from sales using drop_na(price).

R Programming

sales <- data.frame(
  product = c("A", "B", "C", "D"),
  price = c(10, NA, 15, 20),
  quantity = c(5, 3, NA, 8)
)

# Load tidyr and remove rows with missing price
# Your code here

Need a hint?

Use library(tidyr) to load the package. Use drop_na() with the price column.

3

Fill missing quantity values

Use fill() from the tidyr package on cleaned_sales to fill missing quantity values downward. Update cleaned_sales with the result.

R Programming

library(tidyr)

sales <- data.frame(
  product = c("A", "B", "C", "D"),
  price = c(10, NA, 15, 20),
  quantity = c(5, 3, NA, 8)
)

cleaned_sales <- drop_na(sales, price)

# Fill missing quantity values downward
# Your code here

Need a hint?

Use fill(cleaned_sales, quantity) to fill missing values downward.

4

Print the cleaned sales data frame

Write a line to print the cleaned_sales data frame to show the final cleaned data.

R Programming

library(tidyr)

sales <- data.frame(
  product = c("A", "B", "C", "D"),
  price = c(10, NA, 15, 20),
  quantity = c(5, 3, NA, 8)
)

cleaned_sales <- drop_na(sales, price)

cleaned_sales <- fill(cleaned_sales, quantity)

# Print the cleaned_sales data frame
# Your code here

Need a hint?

Use print(cleaned_sales) to display the data frame.