R for Data Science: Quick 100 One-liner Codes

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Navigating the world of machine learning often involves dealing with diverse datasets, and effective data preprocessing is crucial. In this curated list, you’ll find 100 concise R codes to streamline data cleaning, exploratory analysis, and statistical operations.

Data Loading and Inspection

1. Load Data: data <- read.csv('file.csv')
2. View Data Structure: str(data)
3. Summary Statistics: summary(data)

Handling Missing Values

1. Handle Missing Values: na.omit(data)
2. Impute Missing Values: data$variable[is.na(data$variable)] <- mean(data$variable, na.rm = TRUE)

Data Cleaning and Transformation

1. Remove Duplicates: unique_data <- unique(data)
2. Subset Data: subset_data <- data[data$condition == 'value', ]
3. Rename Columns: colnames(data) <- c('new_name', 'new_name2')
4. Convert Factor to Numeric: data$variable <- as.numeric(as.character(data$variable))
5. Convert Date: data$date <- as.Date(data$date, format='%Y-%m-%d')
6. Convert Character to Factor: data$variable <- as.factor(data$variable)