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healthyR.ai

The goal of healthyR.ai is to create a verb framework that allows for easy exploration, transformation and modeling of data.

Installation

You can install the released version of healthyR.ai from CRAN with:

install.packages("healthyR.ai")

And the development version from GitHub with:

# install.packages("devtools")
devtools::install_github("spsanderson/healthyR.ai")

Example

This is a basic example which shows you how to solve a common problem:

library(healthyR.ai)
#> 
#> == Welcome to healthyR.ai ===========================================================================
#> If you find this package useful, please leave a star: 
#>    https://github.com/spsanderson/healthyR.ai'
#> 
#> If you encounter a bug or want to request an enhancement please file an issue at:
#>    https://github.com/spsanderson/healthyR.ai/issues
#> 
#> Thank you for using healthyR.ai
library(ggplot2)

data_tbl <- tibble::tibble(
        day = sample(c("Monday", "Tuesday", "Wednesday", "Thursday", "Friday"),
                     100, TRUE),
        person = sample(c("Tom", "Jane", "Alex"), 100, TRUE),
        count = rbinom(100, 20, ifelse(day == "Friday", .5, .2)),
        date = Sys.Date() - sample.int(100))

my_chart <- hai_control_chart(data_tbl, count, date)
my_chart +
    ylab("Number of Adverse Events") +
    scale_x_date(name = "Week of ... ", date_breaks = "week") +
    theme(axis.text.x = element_text(angle = -90, vjust = 0.5, hjust=1))

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Version

Install

install.packages('healthyR.ai')

Monthly Downloads

335

Version

0.1.0

License

MIT + file LICENSE

Issues

Pull Requests

Stars

Forks

Maintainer

Steven Sanderson

Last Published

September 11th, 2024

Functions in healthyR.ai (0.1.0)

hai_data_impute

Data Preprocessor - Imputation
hai_data_poly

Data Preprocessor - Polynomial Function
hai_data_scale

Data Preprocessor - Scale/Normalize
hai_data_transform

Data Preprocessor - Transformation Functions
hai_default_regression_metric_set

Metric Set
hai_fourier_augment

Augment Function Fourier
hai_auto_xgboost

Boilerplate Workflow
hai_control_chart

Create a control chart
hai_cubist_data_prepper

Prep Data for Cubist - Recipe
hai_c50_data_prepper

Prep Data for C5.0 - Recipe
hai_data_trig

Data Preprocessor - Trigonometric Functions
hai_earth_data_prepper

Prep Data for Earth - Recipe
hai_distribution_comparison_tbl

Compare Data Against Distributions
hai_auto_wflw_metrics

Collect Metrics from Boilerplat Workflows
hai_histogram_facet_plot

Histogram Facet Plot
hai_density_plot

Density Histogram Plot
hai_kmeans_tidy_tbl

K-Means Object Tidy Functions
hai_glmnet_data_prepper

Prep Data for glmnet - Recipe
hai_fourier_discrete_augment

Augment Function Fourier Discrete
hai_polynomial_augment

Augment Polynomial Features
hai_auto_svm_rbf

Boilerplate Workflow
hai_density_hist_plot

Density Histogram Plot
hai_density_qq_plot

Density QQ Plot
hai_kmeans_user_item_tbl

K-Means User Item Tibble
hai_hyperbolic_augment

Augment Function Hyperbolic
hai_hyperbolic_vec

Vector Function Hyperbolic
hai_range_statistic

Get the range statistic
hai_default_classification_metric_set

Metric Set
hai_get_dist_data_tbl

Get Distribution Data Helper
hai_get_density_data_tbl

Get Density Data Helper
hai_scale_fill_colorblind

Provide Colorblind Compliant Colors
hai_scale_zero_one_augment

Augment Function Scale Zero One
hai_umap_list

UMAP Projection
hai_svm_rbf_data_prepper

Prep Data for SVM_RBF - Recipe
hai_fourier_discrete_vec

Vector Function Discrete Fourier
hai_fourier_vec

Vector Function Fourier
hai_kmeans_scree_plt

K-Means Scree Plot
hai_kmeans_scree_data_tbl

K-Means Scree Plot Data Table
hai_kmeans_mapped_tbl

K-Means Mapping Function
hai_skewed_features

Get Skewed Feature Columns
hai_scale_zscore_vec

Vector Function Scale to Zero and One
hai_scale_zero_one_vec

Vector Function Scale to Zero and One
step_hai_hyperbolic

Recipes Step Hyperbolic Generator
step_hai_fourier_discrete

Recipes Step Fourier Discrete Generator
hai_kmeans_automl

Automatic K-Means H2O
hai_kmeans_automl_predict

Automatic K-Means H2O
hai_kmeans_obj

K-Means Object
hai_svm_poly_data_prepper

Prep Data for SVM_Poly - Recipe
hai_skewness_vec

Compute Skewness of a Vector
hai_scale_zscore_augment

Augment Function Scale Zero One
hai_umap_plot

UMAP and K-Means Cluster Visualization
hai_ranger_data_prepper

Prep Data for Ranger - Recipe
hai_scale_color_colorblind

Provide Colorblind Compliant Colors
required_pkgs.step_hai_fourier_discrete

Requited Packages
hai_winsorized_move_augment

Augment Function Winsorize Move
hai_knn_data_prepper

Prep Data for k-NN - Recipe
hai_kurtosis_vec

Compute Kurtosis of a Vector
step_hai_winsorized_truncate

Recipes Step Winsorized Truncate Generator
step_hai_winsorized_move

Recipes Step Winsorized Move Generator
hai_winsorized_truncate_vec

Vector Function Winsorize Truncate
step_hai_fourier

Recipes Step Fourier Generator
hai_xgboost_data_prepper

Prep Data for XGBoost - Recipe
%>%

Pipe operator
pca_your_recipe

Perform PCA
step_hai_scale_zero_one

Recipes Data Scale to Zero and One
step_hai_scale_zscore

Recipes Data Scale by Z-Score
hai_winsorized_truncate_augment

Augment Function Winsorize Truncate
hai_winsorized_move_vec

Vector Function Winsorize Move
tidyeval

Tidy eval helpers
hai_auto_svm_poly

Boilerplate Workflow
get_juiced_data

Get the Juiced Data
color_blind

Provide Colorblind Compliant Colors
hai_auto_c50

Boilerplate Workflow
hai_auto_earth

Boilerplate Workflow
hai_auto_knn

Boilerplate Workflow
hai_auto_cubist

Boilerplate Workflow
hai_auto_glmnet

Boilerplate Workflow
generate_mesh_data

Generate Mesh Data
hai_auto_ranger

Boilerplate Workflow