Package: correlationfunnel 0.2.0

Matt Dancho

correlationfunnel: Speed Up Exploratory Data Analysis (EDA) with the Correlation Funnel

Speeds up exploratory data analysis (EDA) by providing a succinct workflow and interactive visualization tools for understanding which features have relationships to target (response). Uses binary correlation analysis to determine relationship. Default correlation method is the Pearson method. Lian Duan, W Nick Street, Yanchi Liu, Songhua Xu, and Brook Wu (2014) <doi:10.1145/2637484>.

Authors:Matt Dancho [aut, cre]

correlationfunnel_0.2.0.tar.gz
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correlationfunnel_0.2.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
correlationfunnel/json (API)

# Install 'correlationfunnel' in R:
install.packages('correlationfunnel', repos = c('https://business-science.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/business-science/correlationfunnel/issues

Pkgdown/docs site:https://business-science.github.io

Datasets:

On CRAN:

Conda:

correlationexploratory-analysisexploratory-data-analysisexploratory-data-visualizationstidyverse

7.32 score 140 stars 148 scripts 516 downloads 4 exports 99 dependencies

Last updated from:e592ef37ff. Checks:7 NOTE, 2 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64NOTE203
source / vignettesOK288
linux-release-x86_64NOTE175
macos-release-arm64NOTE105
macos-oldrel-arm64NOTE132
windows-develNOTE151
windows-releaseNOTE138
windows-oldrelNOTE165
wasm-releaseOK129

Exports:%>%binarizecorrelateplot_correlation_funnel

Dependencies:askpassbase64encbslibcachemclasscliclockcodetoolscpp11crayoncrosstalkcurldata.tablediagramdigestdplyrevaluatefarverfastmapfontawesomeforcatsfsfuturefuture.applygenericsggplot2ggrepelglobalsgluegowergtablehardhathighrhtmltoolshtmlwidgetshttripredisobandjquerylibjsonliteKernSmoothknitrlabelinglaterlatticelavalazyevallifecyclelistenvlubridatemagrittrMASSMatrixmemoisemimennetnumDerivopensslotelparallellypillarpkgconfigplotlyprodlimprogressrpromisespurrrR6rappdirsRColorBrewerRcpprecipesrlangrmarkdownrpartrstudioapiS7sassscalesshapesparsevctrsSQUAREMstringistringrsurvivalsystibbletidyrtidyselecttimechangetimeDatetinytextzdbutf8vctrsviridisLitewithrxfunyaml

Introducing Correlation Funnel - Customer Churn Example
Problem | Solution | Main Benefits | Correlation Funnel Process | Example - Customer Churn | Step 1 - Prepare Data as Binary Features | Step 2 - Correlate to the Target | Step 3 - Plot the Correlation Funnel | Business Insights | Conclusion | More Information
Rendered fromintroducing_correlation_funnel.Rmdusingknitr::rmarkdown

Last update: 2020-06-09
Started: 2019-07-16

Methodology, Key Considerations, and FAQs
Methodology | Key Considerations | Prior to Binarization Step | During Binarization | Prior to Correlation Step | After Plotting the Correlation Funnel | FAQs | 1. How does the Correlation Funnel Find Relationships in Numeric Data? | 1.1 Linear Relationships | 1.2 Non-Linear Relationships | 2. What About Skewed Numeric Data? | 2.1 Skewed Data | 2.2. Highly Skewed Data | 3. How does the Correlation Funnel Work With Categorical Data? | 3.1 One-Hot Encoding vs Dummy Encoding | 3.2 Reducing Dimensionality (Preventing Irrelevant Factor Levels) | 3.3 Assessing Correlations With Categorical Data | References
Rendered fromkey_considerations.Rmdusingknitr::rmarkdown

Last update: 2020-06-09
Started: 2019-07-16