aRidanalysis

DRY out your regression analysis!

R Package for Inferential Regression and EDA Analysis!

As Data Scientists, being able to perform Exploratory Data Analysis as well as Regression Analysis are paramount to the process of analyzing trends in data. Moreover, following the DRY (Do Not Repeat Yourself) principle is regarded as a majority priority for maximizing code quality. Yet, often times Data Scientists facing these tasks will start the entire process from scratch, wasting both time and effort while compromising code quality. The aRidanalysis package strives to remedy this problem by giving users an easy-to-implement EDA function alongside 3 functions to generate statistical model classes that will simplify these analytical processes and produce an easy to read interpretation of the input data. Users will no longer have to write many lines of code to explore their data effectively!

Package Functions

arid_eda

This function takes in the data frame of interest and generates summary statistics as well as basic exploratory data analysis plots to helps users understand the overall behaviour of the explanatory and response variables.

arid_linreg

This function takes in user specified linear regression model hyperparameters of interest and returns an arid_linreg class linear regression model with a sci-kit learn style interface. This model class has appropriate fit, predict, and score methods to provide linear regression analysis with the model specified.

arid_logreg

This function takes in a data frame of input features, a response vector, and regression model parameters to perform either a binomial or multinomial classification and returns an arid_logreg class logistic regression model with a sci-kit learn style interface. This model class has appropriate fit, predict, and score methods to provide logistic regression analysis with the model specified.

arid_countreg

This function takes a dataframe, its categorical and continuous variables and other user input parameters to return a fitted arid_countreg class Poisson count regression model with a sci-kit learn style interface along with important inferential statistics. This model class has appropriate fit, predict, and score methods to provide Poisson count regression analysis with the model specified.

R Ecosystem Role

This package will build off the EDA and statistical analysis provided by ggplot2 included in the Tidyverse package as well as base R package to streamline data visualization and model analysis functionality. There are some existing packages that help you with this, however the aridanalysis package aims to ease the job to provide different regression analysis interpretations.

Related Packages

• SmartEDA: SmartEDA 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 summary and graphical form.
• MLR: This package contains a large number of classification and regression techniques, including machine-readable parameter descriptions.
• caret: The caret package (short for Classification And REgression Training) is a set of functions that attempt to streamline the process for creating predictive models.

Installation

The aRidanalysis package is not currently available on CRAN, but can be installed from GitHub using the following commands:

# install.packages("devtools")
devtools::install_github("UBC-MDS/aridanalysis")

Dependencies

dplyr,
tidyr,
glmnet,
palmerpenguins,
ggplot2,
GGally,
tidyverse,
MASS,
broom,
AER,
tibble,
rlang,
stringr,
magrittr

Usage

For usage examples please refer to the aRidanalysis vignette page.

Documentation

Documentation files located on GitHub here.

These instructions are available during development after package installation through help() and ? commands.

Contributors

Group 8 Members:
Craig McLaughlin : @cmmclaug
Daniel Ortiz Nunez : @danielon-5
Neel Phaterpekar : @nphaterp
Santiago Rugeles Schoonewolff : @ansarusc