21 Strategies for Data Cleaning and Preprocessing in R

Photo by Monti Timpanogus on Unsplash

Data cleaning and preprocessing in R involve various tasks, such as handling missing values, removing duplicates, transforming variables, and scaling data. Here’s a list of common R codes for data cleaning and preprocessing:

1. Handling Missing Values:

• Removing rows with missing values:

data <- na.omit(data)

• Imputing missing values with mean:

data$variable <- ifelse(is.na(data$variable), mean(data$variable, na.rm = TRUE), data$variable)

• Imputing missing values with median:

data$variable <- ifelse(is.na(data$variable), median(data$variable, na.rm = TRUE), data$variable)

2. Removing Duplicates:

• Removing duplicate rows from a data frame

data <- unique(data)

• Removing duplicate rows based on specific columns:

data <- data[!duplicated(data[, c("col1", "col2")]), ]

• Removing duplicate columns:

data <- data[, !duplicated(colnames(data))]

3. Transforming Variables:

• Log transformation:

data$log_variable <…