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.