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Type:Package
Title:Tools for Binning Data
Version:0.2.1
Description:Manually bin data using weight of evidence and information value. Includes other binning methods such as equal length, quantile and winsorized. Options for combining levels of categorical data are also available. Dummy variables can be generated based on the bins created using any of the available binning methods. References: Siddiqi, N. (2006) <doi:10.1002/9781119201731.biblio>.
License:MIT + file LICENSE
URL:https://github.com/rsquaredacademy/rbin,https://rbin.rsquaredacademy.com
BugReports:https://github.com/rsquaredacademy/rbin/issues
Depends:R (≥ 3.3)
Imports:data.table, ggplot2, stats, utils
Suggests:covr, graphics, knitr, miniUI, rmarkdown, rstudioapi, shiny,testthat (≥ 3.0.0), vdiffr
VignetteBuilder:knitr
Encoding:UTF-8
LazyData:true
RoxygenNote:7.3.2
Config/testthat/edition:3
NeedsCompilation:no
Packaged:2024-11-05 10:44:29 UTC; HP
Author:Aravind Hebbali [aut, cre]
Maintainer:Aravind Hebbali <hebbali.aravind@gmail.com>
Repository:CRAN
Date/Publication:2024-11-05 11:50:03 UTC

rbin package

Description

Tools for binning data.

Details

See the README onGitHub

Author(s)

Maintainer: Aravind Hebbalihebbali.aravind@gmail.com

See Also

Useful links:

Bank marketing data set

Description

The data is related with direct marketing campaigns of a Portuguese banking institution.The marketing campaigns were based on phone calls. Often, more than one contact to thesame client was required, in order to access if the product (bank term deposit) wouldbe ('yes') or not ('no') subscribed.

Usage

mbank

Format

A tibble with 4521 rows and 17 variables:

age

age of the client

job

type of job

marital

marital status

education

education level of the client

default

has credit in default?

housing

has housing loan?

loan

has personal loan?

contact

contact communication type

month

last contact month of year

day_of_week

last contact day of the week

duration

last contact duration, in seconds

campaign

number of contacts performed during this campaign and for this client

pdays

number of days that passed by after the client was last contacted from a previous campaign

previous

number of contacts performed before this campaign and for this clien

poutcome

outcome of the previous marketing campaign

y

has the client subscribed a term deposit?

Source

[Moro et al., 2014] S. Moro, P. Cortez and P. Rita. A Data-Driven Approach to Predict the Success of Bank Telemarketing. Decision Support Systems, Elsevier, 62:22-31, June 2014

Bin continuous data

Description

Manually bin continuous data using weight of evidence.

Usage

rbinAddin(data = NULL)

Arguments

data

Adata.frame ortibble.

Examples

## Not run: rbinAddin(data = mbank)## End(Not run)

Custom binning

Description

Manually combine categorical variables using weight of evidence.

Usage

rbinFactorAddin(data = NULL)

Arguments

data

Adata.frame ortibble.

Examples

## Not run: rbinFactorAddin(data = mbank)## End(Not run)

Create dummy variables

Description

Create dummy variables from bins.

Usage

rbin_create(data, predictor, bins)

Arguments

data

Adata.frame ortibble.

predictor

Variable for which dummy variables must be created.

bins

An object of classrbin_manual orrbin_quantiles orrbin_equal_length orrbin_winsorized.

Value

data with dummy variables.

Examples

k <- rbin_manual(mbank, y, age, c(29, 39, 56))rbin_create(mbank, age, k)

Equal frequency binning

Description

Bin continuous data using the equal frequency binning method.

Usage

rbin_equal_freq(data = NULL, response = NULL, predictor = NULL, bins = 10)## S3 method for class 'rbin_equal_freq'plot(x, print_plot = TRUE, ...)

Arguments

data

Adata.frame ortibble.

response

Response variable.

predictor

Predictor variable.

bins

Number of bins.

x

An object of classrbin_quantiles.

print_plot

logical; ifTRUE, prints the plot else returns a plot object.

...

further arguments passed to or from other methods.

Value

Atibble.

Examples

bins <- rbin_equal_freq(mbank, y, age, 10)bins# plotplot(bins)

Equal length binning

Description

Bin continuous data using the equal length binning method.

Usage

rbin_equal_length(  data = NULL,  response = NULL,  predictor = NULL,  bins = 10,  include_na = TRUE)## S3 method for class 'rbin_equal_length'plot(x, print_plot = TRUE, ...)

Arguments

data

Adata.frame ortibble.

response

Response variable.

predictor

Predictor variable.

bins

Number of bins.

include_na

logical; ifTRUE, a separate bin is created for missing values.

x

An object of classrbin_equal_length.

print_plot

logical; ifTRUE, prints the plot else returns a plot object.

...

further arguments passed to or from other methods.

Value

Atibble.

Examples

bins <- rbin_equal_length(mbank, y, age, 10)bins# plotplot(bins)

Factor binning

Description

Weight of evidence and information value for categorical data.

Usage

rbin_factor(data = NULL, response = NULL, predictor = NULL, include_na = TRUE)## S3 method for class 'rbin_factor'plot(x, print_plot = TRUE, ...)

Arguments

data

Adata.frame ortibble.

response

Response variable.

predictor

Predictor variable.

include_na

logical; ifTRUE, a separate bin is created for missing values.

x

An object of classrbin_factor.

print_plot

logical; ifTRUE, prints the plot else returns a plot object.

...

further arguments passed to or from other methods.

Examples

bins <- rbin_factor(mbank, y, education)bins# plotplot(bins)

Combine levels

Description

Manually combine levels of categorical data.

Usage

rbin_factor_combine(data, var, new_var, new_name)

Arguments

data

Adata.frame ortibble.

var

An object of classfactor.

new_var

A character vector; it should include the names of the levels to be combined.

new_name

Name of the combined level.

Value

Atibble.

Examples

upper <- c("secondary", "tertiary")out <- rbin_factor_combine(mbank, education, upper, "upper")table(out$education)out <- rbin_factor_combine(mbank, education, c("secondary", "tertiary"), "upper")table(out$education)

Create dummy variables

Description

Create dummy variables for categorical data.

Usage

rbin_factor_create(data, predictor)

Arguments

data

Adata.frame ortibble.

predictor

Variable for which dummy variables must be created.

Value

Atibble with dummy variables.

Examples

upper <- c("secondary", "tertiary")out <- rbin_factor_combine(mbank, education, upper, "upper")rbin_factor_create(out, education)

Manual binning

Description

Bin continuous data manually.

Usage

rbin_manual(  data = NULL,  response = NULL,  predictor = NULL,  cut_points = NULL,  include_na = TRUE)## S3 method for class 'rbin_manual'plot(x, print_plot = TRUE, ...)

Arguments

data

Adata.frame ortibble.

response

Response variable.

predictor

Predictor variable.

cut_points

Cut points for binning.

include_na

logical; ifTRUE, a separate bin is created for missing values.

x

An object of classrbin_manual.

print_plot

logical; ifTRUE, prints the plot else returns a plot object.

...

further arguments passed to or from other methods.

Details

Specify the upper open interval for each bin. 'rbin'follows the left closed and right open interval. If you want to create_bins10 bins, the app will show you only 9 input boxes. The interval for the 10th binis automatically computed. For example, if you want the first bin to have all thevalues between the minimum and including 36, then you will enter the value 37.

Value

Atibble.

Examples

bins <- rbin_manual(mbank, y, age, c(29, 31, 34, 36, 39, 42, 46, 51, 56))bins# plotplot(bins)

Quantile binning

Description

Bin continuous data using quantiles.

Usage

rbin_quantiles(  data = NULL,  response = NULL,  predictor = NULL,  bins = 10,  include_na = TRUE)## S3 method for class 'rbin_quantiles'plot(x, print_plot = TRUE, ...)

Arguments

data

Adata.frame ortibble.

response

Response variable.

predictor

Predictor variable.

bins

Number of bins.

include_na

logical; ifTRUE, a separate bin is created for missing values.

x

An object of classrbin_quantiles.

print_plot

logical; ifTRUE, prints the plot else returns a plot object.

...

further arguments passed to or from other methods.

Value

Atibble.

Examples

bins <- rbin_quantiles(mbank, y, age, 10)bins# plotplot(bins)

Winsorized binning

Description

Bin continuous data using winsorized method.

Usage

rbin_winsorize(  data = NULL,  response = NULL,  predictor = NULL,  bins = 10,  include_na = TRUE,  winsor_rate = 0.05,  min_val = NULL,  max_val = NULL,  type = 7,  remove_na = TRUE)## S3 method for class 'rbin_winsorize'plot(x, print_plot = TRUE, ...)

Arguments

data

Adata.frame ortibble.

response

Response variable.

predictor

Predictor variable.

bins

Number of bins.

include_na

logical; ifTRUE, a separate bin is created for missing values.

winsor_rate

A value from 0.0 to 0.5.

min_val

the low border, all values being lower than this will be replaced by this value. The default is set to the 5 percent quantile of predictor.

max_val

the high border, all values being larger than this will be replaced by this value. The default is set to the 95 percent quantile of predictor.

type

an integer between 1 and 9 selecting one of the nine quantile algorithms detailed inquantile() to be used.

remove_na

logical; ifTRUE NAs will removed while calculating quantiles

x

An object of classrbin_winsorize.

print_plot

logical; ifTRUE, prints the plot else returns a plot object.

...

further arguments passed to or from other methods.

Value

Atibble.

Examples

bins <- rbin_winsorize(mbank, y, age, 10, winsor_rate = 0.05)bins# plotplot(bins)
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