Statistics with R

6.

Data Cleaning and Preprocessing

6.1.

Data Quality Assessment

6.1.1.

Identifying Data Issues

6.1.2.

6.1.3.

Completeness Checks

6.1.4.

Consistency Checks

6.2.

Missing Data Handling

6.2.1.

Types of Missing Data

6.2.2.

Identifying Missing Values

6.2.2.1.

is.na() Function

6.2.2.2.

complete.cases() Function

6.2.3.

Missing Data Patterns

6.2.4.

Removing Missing Values

6.2.4.1.

na.omit() Function

6.2.4.2.

drop_na() Function

6.2.5.

Imputation Methods

6.2.5.1.

Mean Imputation

6.2.5.2.

Median Imputation

6.2.5.3.

Mode Imputation

6.2.5.4.

6.2.5.5.

6.3.

6.3.1.

Identifying Duplicates

6.3.1.1.

duplicated() Function

6.3.1.2.

distinct() Function

6.3.2.

Removing Duplicates

6.3.2.1.

unique() Function

6.3.2.2.

Deduplication Strategies

6.4.

Data Type Conversion

6.4.1.

6.4.2.

Numeric Conversion

6.4.3.

Character Conversion

6.4.4.

Factor Conversion

6.4.5.

Date and Time Conversion

6.4.5.1.

as.Date() Function

6.4.5.2.

lubridate Package

6.4.5.3.

6.5.

Variable Transformation

6.5.1.

Renaming Variables

6.5.2.

Recoding Variables

6.5.3.

Creating New Variables

6.5.4.

Binning Continuous Variables

6.5.5.

Standardization

6.5.6.

6.6.

Text Data Cleaning

6.6.1.

String Manipulation

6.6.2.

Regular Expressions

6.6.3.

Case Conversion

6.6.4.

Trimming Whitespace

6.6.5.

Pattern Matching

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7. Data Manipulation with dplyr