cleanR: Streamlined Data Cleaning in R

Overview

cleanR is an R package designed to simplify and streamline the data cleaning process. It provides a set of functions to handle common data cleaning tasks, including removing duplicates, standardizing categorical variables, converting data types, and removing outliers. This package aims to make data preparation more efficient and consistent across projects.

Installation

You can install the development version of cleanR from GitHub with:

# install.packages("devtools")
devtools::install_github("Steven-Nanga/cleanR")

Features

cleanR offers the following main functions:

• remove_duplicates(): Remove duplicate rows from a data frame
• standardize_categories(): Standardize categorical variables
• convert_types(): Convert column types in a data frame
• remove_outliers(): Remove outliers from numeric columns
• clean_data(): A wrapper function that applies multiple cleaning steps

Usage

Here's a basic example of how to use cleanR:

library(cleanR)

# Sample data
data <- data.frame(
  id = c(1, 2, 2, 3, 4, 5),
  category = c("Cat ", "DOG", "dog", "FISH", "Bird", "CAT"),
  value = c(10, 20, 20, 100, 40, 50),
  date = c("2023-01-01", "2023-02-01", "2023-02-01", "2023-03-01", "2023-04-01", "2023-05-01")
)

# Clean the data
cleaned_data <- clean_data(
  data,
  duplicate_cols = c("id", "category"),
  categorical_cols = "category",
  type_list = list(value = "numeric", date = "date"),
  outlier_cols = "value"
)

print(cleaned_data)

Detailed Function Usage

Remove Duplicates

remove_duplicates(data, cols = NULL)

• data: A data frame
• cols: Columns to consider when identifying duplicates (default is all columns)

Standardize Categories

standardize_categories(data, cols)

• data: A data frame
• cols: Categorical columns to standardize

Convert Types

standardize_categories(data, cols)

• data: A data frame
• type_list: A named list specifying column names and their desired types

Remove Outliers

remove_outliers(data, cols, method = "iqr", threshold = 1.5)

• data: A data frame
• cols: Numeric columns to check for outliers
• method: Method to use for outlier detection ("iqr" or "zscore")
• threshold: Threshold for outlier detection

Clean Data (Wrapper Function)

clean_data(data, 
           duplicate_cols = NULL, 
           categorical_cols = NULL,
           type_list = NULL,
           outlier_cols = NULL,
           outlier_method = "iqr",
           outlier_threshold = 1.5)

This function applies all the above cleaning steps in one go.

Contributing

Contributions to cleanR are welcome! Here are some ways you can contribute:

• Report bugs and request features by opening an issue
• Submit pull requests to fix bugs or add new features
• Improve documentation or add examples
• Share your experience using cleanR

Please read CONTRIBUTING.md for details on our code of conduct and the process for submitting pull requests.

License

This project is licensed under the MIT License - see the LICENSE file for details.

Authors

• Steven Nanga

Acknowledgments

Inspired by common data cleaning challenges in R

To do

• Missing values handling ie imputation etc
• Converting between long and wide datasets
• Column transformation ie logarithmic, square root etc