Package: funModeling 1.9.4

funModeling: Exploratory Data Analysis and Data Preparation Tool-Box

Around 10% of almost any predictive modeling project is spent in predictive modeling, 'funModeling' and the book Data Science Live Book (<https://livebook.datascienceheroes.com/>) are intended to cover remaining 90%: data preparation, profiling, selecting best variables 'dataViz', assessing model performance and other functions.

Authors:Pablo Casas [aut, cre]

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funModeling.pdf |funModeling.html✨
funModeling/json (API)

# Install 'funModeling' in R:

install.packages('funModeling', repos = c('https://pablo14.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/pablo14/funmodeling/issues

Datasets:

data_country - People with flu data
data_golf - Play golf
heart_disease - Heart Disease Data
metadata_models - Metadata models data integrity

On CRAN:

8.31 score 100 stars 638 scripts 2.1k downloads 36 exports 81 dependencies

Last updated 1 years agofrom:c04e72293d. Checks:OK: 7. Indexed: yes.

Target	Result	Date
Doc / Vignettes	OK	Oct 29 2024
R-4.5-win	OK	Oct 29 2024
R-4.5-linux	OK	Oct 29 2024
R-4.4-win	OK	Oct 29 2024
R-4.4-mac	OK	Oct 29 2024
R-4.3-win	OK	Oct 29 2024
R-4.3-mac	OK	Oct 29 2024

Exports:auto_grouping categ_analysis compare_df concatenate_n_vars convert_df_to_categoric coord_plot correlation_table cross_plot data_integrity data_integrity_model desc_groups desc_groups_rank df_status discretize_df discretize_get_bins discretize_rgr entropy_2 equal_freq export_plot fibonacci freq gain_lift gain_ratio get_sample hampel_outlier infor_magic information_gain plot_num plotar prep_outliers profiling_num range01 status tukey_outlier v_compare var_rank_info

Dependencies:backports base64enc bitops bslib cachem caTools checkmate cli cluster colorspace data.table digest dplyr entropy evaluate fansi farver fastmap fontawesome foreign Formula fs generics ggplot2 glue gplots gridExtra gtable gtools highr Hmisc htmlTable htmltools htmlwidgets isoband jquerylib jsonlite KernSmooth knitr labeling lattice lazyeval lifecycle magrittr MASS Matrix memoise mgcv mime moments munsell nlme nnet pander pillar pkgconfig plyr R6 rappdirs RColorBrewer Rcpp reshape2 rlang rmarkdown ROCR rpart rstudioapi sass scales stringi stringr tibble tidyselect tinytex utf8 vctrs viridis viridisLite withr xfun yaml

funModeling quick-start

Pablo Casas

Rendered fromfunModeling_quickstart.Rmdusingknitr::rmarkdownon Oct 29 2024.

Last update: 2023-07-11
Started: 2018-01-17

Citation

Development and contributors

Readme and manuals

Help Manual

Help page	Topics
funModeling: Exploratory data analysis, data preparation and model performance	funModeling-package funModeling
Reduce cardinality in categorical variable by automatic grouping	auto_grouping
Profiling analysis of categorical vs. target variable	categ_analysis
Compare two data frames by keys	compare_df
Concatenate 'N' variables	concatenate_n_vars
Convert every column in a data frame to character	convert_df_to_categoric
Coordinate plot	coord_plot
Get correlation against target variable	correlation_table
Cross-plotting input variable vs. target variable	cross_plot
People with flu data	data_country
Play golf	data_golf
Data integrity	data_integrity
Check data integrity model	data_integrity_model
Profiling categorical variable	desc_groups
Profiling categorical variable (rank)	desc_groups_rank
Get a summary for the given data frame (o vector).	df_status
Discretize a data frame	discretize_df
Get the data frame thresholds for discretization	discretize_get_bins
Variable discretization by gain ratio maximization	discretize_rgr
Computes the entropy between two variables	entropy_2
Equal frequency binning	equal_freq
Export plot to jpeg file	export_plot
Fibonacci series	fibonacci
Frequency table for categorical variables	freq
Generates lift and cumulative gain performance table and plot	gain_lift
Gain ratio	gain_ratio
Sampling training and test data	get_sample
Hampel Outlier Threshold	hampel_outlier
Heart Disease Data	heart_disease
Computes several information theory metrics between two vectors	infor_magic
Information gain	information_gain
Metadata models data integrity	metadata_models
Plotting numerical data	plot_num
Correlation plots	plotar
Outliers Data Preparation	prep_outliers
Profiling numerical data	profiling_num
Transform a variable into the [0-1] range	range01
Get a summary for the given data frame (o vector).	status
Tukey Outlier Threshold	tukey_outlier
Compare two vectors	v_compare
Importance variable ranking based on information theory	var_rank_info