Data Cleaning Basics
Learn to deal with missing values and correct data types to ensure data quality.
Data Cleaning Basics is a free R Academy lesson on CoddyKit. This is lesson 2 of 3. Any 3 lessons of this learning path are free to read in full — after that, CoddyKit PRO unlocks every lesson, plus hands-on practice with a built-in code editor and a 24/7 AI tutor. It is part of the R Academy learning path, and your progress syncs across the web and the CoddyKit app. The R Academy course includes 3 lessons in total.
Introduction to Data Cleaning in R
Cleaning data is an essential step before analysis. In this lesson, you'll learn how to handle missing values, correct data types, and remove duplicates.
Handling Missing Values
Missing values in R are represented by NA. You can check for missing values using is.na().
sum(is.na(data))Removing Missing Values
You can remove missing values from a dataset using na.omit().
clean_data <- na.omit(data)Replacing Missing Values
Sometimes, replacing missing values with a default value or the mean of a column is useful.
data$column[is.na(data$column)] <- mean(data$column, na.rm = TRUE)Identifying Duplicates
You can check for duplicate rows in your dataset using duplicated().
sum(duplicated(data))Removing Duplicates
To remove duplicate rows, use distinct() from the dplyr package.
install.packages('dplyr')
library(dplyr)
data <- distinct(data)Converting Data Types
Sometimes, data may be in the wrong format. You can convert data types using functions like as.numeric() and as.character().
data$column <- as.numeric(data$column)Renaming Columns
You can rename columns in a data frame using the rename() function from the dplyr package.
data <- rename(data, NewName = OldName)Summary
In this lesson, you learned:
- How to handle missing values.
- How to remove duplicates.
- How to clean and convert data types.
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Frequently Asked Questions
Is the “Data Cleaning Basics” lesson free?
Yes — any 3 lessons of the R Academy learning path, including “Data Cleaning Basics”, are free to read in full here on the web. After that, CoddyKit PRO unlocks every lesson, plus interactive practice with a built-in code editor and a 24/7 AI tutor. The R Academy course includes 3 lessons in total.
What will I learn in “Data Cleaning Basics”?
Learn to deal with missing values and correct data types to ensure data quality. You practise R Academy with hands-on code you run directly in the browser, and a 24/7 AI tutor answers your questions as you work through the lesson.
Do I need any experience to start R Academy?
No prior experience is required. R Academy on CoddyKit is structured for beginners through advanced learners, so you can start here or from the beginning and move at your own pace. This is lesson 2 of 3.
How long does the “Data Cleaning Basics” lesson take?
Most CoddyKit lessons take about 5–10 minutes. Each one is bite-sized and interactive, so you make steady progress and pick up exactly where you left off across the web and the app.
Can I write and run code in this R Academy lesson?
Yes. Every R Academy lesson includes a built-in code editor, so you write and run real code right in your browser and get instant AI feedback — no local setup required.
All lessons in this course
- Importing Data
- Data Cleaning Basics
- Combining and Reshaping Data