Remove Outliers and Perform Data Cleaning in R

Remove Outliers and Perform Data Cleaning in R

Key Points Outliers are data points that are significantly different from the rest of the data and can affect the results of statistical tests and machine learning models. There are different ways to detect outliers, such as graphical methods (boxplots and histograms) and statistical methods (z-scores, interquartile range, Dixon’s test, and Rosner’s test). There are different ways to remove outliers from a dataset, such as using logical operators and subsetting, using the subset() function, or using the filter() function from the dplyr package. There are different ways to impute missing values in a dataset, such as mean, median, or mode imputation, multiple imputations by chained equations (MICE), or K-nearest neighbours (KNN) imputation. There are different ways to encode categorical variables in a dataset, such as label encoding, one-hot encoding, or ordinal encoding. Description of Functions and Packages Function/Package Description boxplot() Creates a boxplot for a numeric variable hist() Cre…