Package 'rbin'

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 the same client was required, in order to access if the product (bank term deposit) would be ('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

---

Create dummy variables

Description

Create dummy variables from bins.

Usage

rbin_create(data, predictor, bins)

Arguments

data	A data.frame or tibble.
predictor	Variable for which dummy variables must be created.
bins	An object of class rbin_manual or rbin_quantiles or rbin_equal_length or rbin_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	A data.frame or tibble.
response	Response variable.
predictor	Predictor variable.
bins	Number of bins.
x	An object of class rbin_quantiles.
print_plot	logical; if TRUE, prints the plot else returns a plot object.
...	further arguments passed to or from other methods.

Value

A tibble.

Examples

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

# plot
plot(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	A data.frame or tibble.
response	Response variable.
predictor	Predictor variable.
bins	Number of bins.
include_na	logical; if TRUE, a separate bin is created for missing values.
x	An object of class rbin_equal_length.
print_plot	logical; if TRUE, prints the plot else returns a plot object.
...	further arguments passed to or from other methods.

Value

A tibble.

Examples

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

# plot
plot(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	A data.frame or tibble.
response	Response variable.
predictor	Predictor variable.
include_na	logical; if TRUE, a separate bin is created for missing values.
x	An object of class rbin_factor.
print_plot	logical; if TRUE, prints the plot else returns a plot object.
...	further arguments passed to or from other methods.

Examples

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

# plot
plot(bins)

---

Combine levels

Description

Manually combine levels of categorical data.

Usage

rbin_factor_combine(data, var, new_var, new_name)

Arguments

data	A data.frame or tibble.
var	An object of class factor.
new_var	A character vector; it should include the names of the levels to be combined.
new_name	Name of the combined level.

Value

A tibble.

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	A data.frame or tibble.
predictor	Variable for which dummy variables must be created.

Value

A tibble 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	A data.frame or tibble.
response	Response variable.
predictor	Predictor variable.
cut_points	Cut points for binning.
include_na	logical; if TRUE, a separate bin is created for missing values.
x	An object of class rbin_manual.
print_plot	logical; if TRUE, 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_bins 10 bins, the app will show you only 9 input boxes. The interval for the 10th bin is automatically computed. For example, if you want the first bin to have all the values between the minimum and including 36, then you will enter the value 37.

Value

A tibble.

Examples

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

# plot
plot(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	A data.frame or tibble.
response	Response variable.
predictor	Predictor variable.
bins	Number of bins.
include_na	logical; if TRUE, a separate bin is created for missing values.
x	An object of class rbin_quantiles.
print_plot	logical; if TRUE, prints the plot else returns a plot object.
...	further arguments passed to or from other methods.

Value

A tibble.

Examples

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

# plot
plot(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	A data.frame or tibble.
response	Response variable.
predictor	Predictor variable.
bins	Number of bins.
include_na	logical; if TRUE, 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 in quantile() to be used.
remove_na	logical; if TRUE NAs will removed while calculating quantiles
x	An object of class rbin_winsorize.
print_plot	logical; if TRUE, prints the plot else returns a plot object.
...	further arguments passed to or from other methods.

Value

A tibble.

Examples

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

# plot
plot(bins)

---

Bin continuous data

Description

Manually bin continuous data using weight of evidence.

Usage

rbinAddin(data = NULL)

Arguments

data	A data.frame or tibble.

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	A data.frame or tibble.

Examples

## Not run: 
rbinFactorAddin(data = mbank)

## End(Not run)

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