correlationfunnel <img src="man/figures/logo-correlationfunnel.png" width="147" height="170" align="right" />

Speed Up Exploratory Data Analysis (EDA)

The goal of correlationfunnel is to speed up Exploratory Data Analysis (EDA). Here’s how to use it.

Installation

You can install the latest stable (CRAN) version of correlationfunnel with:

install.packages("correlationfunnel")

You can install the development version of correlationfunnel from GitHub with:

devtools::install_github("business-science/correlationfunnel")

Correlation Funnel in 2-Minutes

Problem: Exploratory data analysis (EDA) involves looking at feature-target relationships independently. This process is very time consuming even for small data sets. Rather than search for relationships, what if we could let the relationships come to us?

Solution: Enter correlationfunnel. The package provides a succinct workflow and interactive visualization tools for understanding which features have relationships to target (response).

Main Benefits:

Speeds Up Exploratory Data Analysis
Improves Feature Selection
Gets You To Business Insights Faster

Example - Bank Marketing Campaign

The following example showcases the power of fast exploratory correlation analysis. The goal of the analysis is to determine which features relate to the bank’s marketing campaign goal of having customers opt into a TERM DEPOSIT (financial product).

We will see that using 3 functions, we can quickly:

Transform the data into a binary format with binarize()
Perform correlation analysis using correlate()
Visualize the highest correlation features using plot_correlation_funnel()

Result: Rather than spend hours looking at individual plots of capaign features and comparing them to which customers opted in to the TERM DEPOSIT product, in seconds we can discover which groups of customers have enrolled, drastically speeding up EDA.

Getting Started

First, load the libraries.

library(correlationfunnel)
library(dplyr)

Next, collect data to analyze. We’ll use Marketing Campaign Data for a Bank that was popularized by the UCI Machine Learning Repository. We can load the data with data("marketing_campaign_tbl").

# Use ?marketing_campagin_tbl to get a description of the marketing campaign features
data("marketing_campaign_tbl")

marketing_campaign_tbl %>% glimpse()
#> Observations: 45,211
#> Variables: 18
#> $ ID           <chr> "2836", "2837", "2838", "2839", "2840", "2841", "28…
#> $ AGE          <dbl> 58, 44, 33, 47, 33, 35, 28, 42, 58, 43, 41, 29, 53,…
#> $ JOB          <chr> "management", "technician", "entrepreneur", "blue-c…
#> $ MARITAL      <chr> "married", "single", "married", "married", "single"…
#> $ EDUCATION    <chr> "tertiary", "secondary", "secondary", "unknown", "u…
#> $ DEFAULT      <chr> "no", "no", "no", "no", "no", "no", "no", "yes", "n…
#> $ BALANCE      <dbl> 2143, 29, 2, 1506, 1, 231, 447, 2, 121, 593, 270, 3…
#> $ HOUSING      <chr> "yes", "yes", "yes", "yes", "no", "yes", "yes", "ye…
#> $ LOAN         <chr> "no", "no", "yes", "no", "no", "no", "yes", "no", "…
#> $ CONTACT      <chr> "unknown", "unknown", "unknown", "unknown", "unknow…
#> $ DAY          <dbl> 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, …
#> $ MONTH        <chr> "may", "may", "may", "may", "may", "may", "may", "m…
#> $ DURATION     <dbl> 261, 151, 76, 92, 198, 139, 217, 380, 50, 55, 222, …
#> $ CAMPAIGN     <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, …
#> $ PDAYS        <dbl> -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1,…
#> $ PREVIOUS     <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
#> $ POUTCOME     <chr> "unknown", "unknown", "unknown", "unknown", "unknow…
#> $ TERM_DEPOSIT <chr> "no", "no", "no", "no", "no", "no", "no", "no", "no…

Response & Predictor Relationships

Modeling and Machine Learning problems often involve a response (Enrolled in TERM_DEPOSIT, yes/no) and many predictors (AGE, JOB, MARITAL, etc). Our job is to determine which predictors are related to the response. We can do this through Binary Correlation Analysis.

Binary Correlation Analysis

Binary Correlation Analysis is the process of converting continuous (numeric) and categorical (character/factor) data to binary features. We can then perform a correlation analysis to see if there is predictive value between the features and the response (target).

Step 1: Convert to Binary Format

The first step is converting the continuous and categorical data into binary (0/1) format. We de-select any non-predictive features. The binarize() function then converts the features into binary features.

Numeric Features: Are binned into ranges or if few unique levels are binned by their value, and then converted to binary features via one-hot encoding
Categorical Features: Are binned by one-hot encoding

The result is a data frame that has only binary data with columns representing the bins that the observations fall into. Note that the output is shown in the glimpse() format. THere are now 80 columns that are binary (0/1).

marketing_campaign_binarized_tbl <- marketing_campaign_tbl %>%
    select(-ID) %>%
    binarize(n_bins = 4, thresh_infreq = 0.01)

marketing_campaign_binarized_tbl %>% glimpse()
#> Observations: 45,211
#> Variables: 74
#> $ `AGE__-Inf_33`       <dbl> 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0…
#> $ AGE__33_39           <dbl> 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0…
#> $ AGE__39_48           <dbl> 0, 1, 0, 1, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0…
#> $ AGE__48_Inf          <dbl> 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 1…
#> $ JOB__admin.          <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0…
#> $ `JOB__blue-collar`   <dbl> 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
#> $ JOB__entrepreneur    <dbl> 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0…
#> $ JOB__housemaid       <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
#> $ JOB__management      <dbl> 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0…
#> $ JOB__retired         <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0…
#> $ `JOB__self-employed` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
#> $ JOB__services        <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1…
#> $ JOB__student         <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
#> $ JOB__technician      <dbl> 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0…
#> $ JOB__unemployed      <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
#> $ `JOB__-OTHER`        <dbl> 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
#> $ MARITAL__divorced    <dbl> 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0…
#> $ MARITAL__married     <dbl> 1, 0, 1, 1, 0, 1, 0, 0, 1, 0, 0, 0, 1, 1, 1…
#> $ MARITAL__single      <dbl> 0, 1, 0, 0, 1, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0…
#> $ EDUCATION__primary   <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0…
#> $ EDUCATION__secondary <dbl> 0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 1…
#> $ EDUCATION__tertiary  <dbl> 1, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0…
#> $ EDUCATION__unknown   <dbl> 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0…
#> $ DEFAULT__no          <dbl> 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1…
#> $ DEFAULT__yes         <dbl> 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0…
#> $ `BALANCE__-Inf_72`   <dbl> 0, 1, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0…
#> $ BALANCE__72_448      <dbl> 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 1, 1, 0, 0, 1…
#> $ BALANCE__448_1428    <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0…
#> $ BALANCE__1428_Inf    <dbl> 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
#> $ HOUSING__no          <dbl> 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
#> $ HOUSING__yes         <dbl> 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1…
#> $ LOAN__no             <dbl> 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1…
#> $ LOAN__yes            <dbl> 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0…
#> $ CONTACT__cellular    <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
#> $ CONTACT__telephone   <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
#> $ CONTACT__unknown     <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1…
#> $ `DAY__-Inf_8`        <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1…
#> $ DAY__8_16            <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
#> $ DAY__16_21           <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
#> $ DAY__21_Inf          <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
#> $ MONTH__apr           <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
#> $ MONTH__aug           <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
#> $ MONTH__feb           <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
#> $ MONTH__jan           <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,

Correlationfunnel

Install / Use

README