eda.utilsR

<!-- README.md is generated from README.Rmd. Please edit that file --> # eda.utilsR <!-- badges: start --> <!-- badges: end --> As data rarely comes ready to be used and analyzed for machine learning right away, this package aims to help speed up the process of cleaning and doing initial exploratory data anslysis (EDA). The package focuses on the tasks of dealing with outlier and missing values, scaling and correlation visualization. ## Installation You can install the released version of eda.utilsR from [CRAN](https://CRAN.R-project.org) with: ``` r install.packages("eda.utilsR") ``` And the development version from [GitHub](https://github.com/) with: ``` r # install.packages("devtools") devtools::install_github("UBC-MDS/eda.utilsR") ``` ## Functions The four functions contained in this package are as follows: - `cor_map`: A function to plot a correlation matrix of numeric columns in the dataframe - `outlier_identifier`: A function to identify and deal with outliers - `scale` A function to scale numerical values in the dataset - `imputer`: A function to impute missing values ## Our Place in the R Ecosystem While R packages with similar functionalities exist, this package aims to simplify the amount of code necessary for these functions and outputs. Packages with similar functionality are as follows: - [scaler](https://www.rdocumentation.org/packages/coop/versions/0.6-2/topics/scaler) - [ggcorr](https://www.rdocumentation.org/packages/GGally/versions/1.5.0/topics/ggcorr) - [mice](https://cran.r-project.org/web/packages/mice/mice.pdf) - [OutlierDetection](https://cran.r-project.org/web/packages/OutlierDetection/OutlierDetection.pdf) ## Dependencies - TBD ## Usage The eda.utilsR package help you to build exploratory data analysis. eda.utilsR includes multiple custom functions to perform initial exploratory analysis on any input data describing the structure and the relationships present in the data. The generated output can be obtained in both object and graphical form. The eda.utilsR is capable of : - Diagnose data quality : Resolve skewed data by identifying missing data and outlier and provide corresponding remedy. - Discover data: Plot correlation matrix to help explore data to understand the data and find scenarios for performing the analysis. - Machine learning perpetration : Perform column transformations, derive scaler automatically to fulfill further machine learning need ## Documentation The official documentation is hosted on Read the Docs: <https://eda.utilsR.readthedocs.io/en/latest/> ## Contributors This package is authored by Chuang Wang, Fatime Selimi, Jiacheng Wang, and Micah Kwok as part of the course project in DSCI-524 (UBC-MDS program). You can see the list of all contributors in the [contributors tab](https://github.com/UBC-MDS/eda.utilsR/graphs/contributors). We welcome and recognize all contributions. If you wish to participate, please review our [contributing guidelines](https://github.com/UBC-MDS/eda.utilsR/blob/main/CONTRIBUTING.rst).