Package 'liver' reference manual

Title:	Toolkit and Datasets for Data Science
Description:	Provides a collection of helper functions and illustrative datasets to support learning and teaching of data science with R. The package is designed as a companion to the book <https://book-data-science-r.netlify.app>, making key data science techniques accessible to individuals with minimal coding experience. Functions include tools for data partitioning, performance evaluation, and data transformations (e.g., z-score and min-max scaling). The included datasets are curated to highlight practical applications in data exploration, modeling, and multivariate analysis. An early inspiration for the package came from an ancient Persian idiom about "eating the liver", symbolizing deep and immersive engagement with knowledge.
Authors:	Reza Mohammadi [aut, cre] (ORCID: <https://orcid.org/0000-0001-9538-0648>), Jeroen van Raak [aut] (ORCID: <https://orcid.org/0000-0002-2190-0126>), Kevin Burke [aut] (ORCID: <https://orcid.org/0000-0001-8724-809X>)
Maintainer:	Reza Mohammadi <[email protected]>
License:	GPL (>= 2)
Version:	1.29
Built:	2026-06-03 06:59:43 UTC
Source:	https://github.com/cran/liver

liver: Foundations Toolkit and Datasets for Data Science

Description

The liver package provides a collection of helper functions and illustrative datasets to support learning and teaching of data science with R. The package is designed as a companion to the book Data Science Foundations and Machine Learning Using R, making key data science techniques accessible to individuals with minimal coding experience. Functions include tools for data partitioning, performance evaluation, and data transformations (e.g., z-score and min-max scaling). The included datasets are curated to highlight practical applications in data exploration, modeling, and multivariate analysis. An early inspiration for the package came from an ancient Persian idiom about "eating the liver," symbolizing deep and immersive engagement with knowledge.

Author(s)

Reza Mohammadi [email protected]
Amsterdam Business School
University of Amsterdam

Kevin Burke [email protected]
Departement of Statistics
University of Limerick

Maintainer: Reza Mohammadi [email protected]

Average classification accuracy

Description

Computes average classification accuracy.

Usage

accuracy(pred, actual, cutoff = NULL, reference = NULL)
accuracy(pred, actual, cutoff = NULL, reference = NULL)

Arguments

pred

a numerical vector of estimated values.

actual

a numerical vector of actual values.

cutoff

cutoff value for the case that pred is vector of probabilites.

reference

a factor of classes to be used as the true results.

Value

the computed average classification accuracy (numeric value).

Author(s)

Reza Mohammadi [email protected] and Kevin Burke [email protected]

Examples

pred   = c("no", "yes", "yes", "no", "no", "yes", "no", "no")
actual = c("yes", "no", "yes", "no", "no", "no", "yes", "yes")

accuracy(pred, actual)
pred   = c("no", "yes", "yes", "no", "no", "yes", "no", "no")
actual = c("yes", "no", "yes", "no", "no", "no", "yes", "yes")

accuracy(pred, actual)

adult data set

Description

the adult dataset was collected from the US Census Bureau and the primary task is to predict whether a given adult makes more than $50K a year based attributes such as education, hours of work per week, etc. the target feature is income, a factor with levels "<=50K" and ">50K", and the remaining 14 variables are predictors.

Usage

 data(adult) data(adult)

Format

the adult dataset, as a data frame, contains $48598$ rows and $15$ columns (variables/features). the $15$ variables are:

age: age in years.
workclass: a factor with 6 levels.
demogweight: the demographics to describe a person.
education: a factor with 16 levels.
education.num: an ordinal encoding of the 'education' feature.
marital.status: a factor with 5 levels.
occupation: a factor with 15 levels.
relationship: a factor with 6 levels.
race: a factor with 5 levels.
gender: a factor with levels "Female","Male".
capital.gain: capital gains.
capital.loss: capital losses.
hours.per.week: number of hours of work per week.
native.country: a factor with 42 levels.
income: yearly income as a factor with levels "<=50K" and ">50K".

Source

The data are based on the Adult, or Census Income, dataset from the UCI Machine Learning Repository. The original extraction was performed by Barry Becker from the 1994 Census database.

The dataset is also associated with DOI:

doi:10.24432/C5XW20

References

Kohavi, R. and Becker, B. (1996). Adult. UCI Machine Learning Repository. doi:10.24432/C5XW20

Kohavi, R. (1996). Scaling up the accuracy of naive-bayes classifiers: A decision-tree hybrid. Kdd.

Reza Mohammadi (2025). Data Science Foundations and Machine Learning with R: From Data to Decisions. https://book-data-science-r.netlify.app.

Examples

data(adult)
str(adult)
data(adult)
str(adult)

advertising data set

Description

the dataset is from an anonymous organisation's social media ad campaign. the advertising dataset contains $11$ features and $1143$ records.

Usage

 data(advertising) data(advertising)

Format

the advertising dataset, as a data frame, contains $1143$ rows and $11$ columns (variables/features). the $11$ variables are:

ad.id: an unique ID for each ad.
xyz.campaign.id: an ID associated with each ad campaign of XYZ company.
fb.campaign.id: an ID associated with how Facebook tracks each campaign.
age: age of the person to whom the ad is shown.
gender: gender of the person to whim the add is shown.
interest: a code specifying the category to which the person's interest belongs (interests are as mentioned in the person's Facebook public profile).
impressions: the number of times the ad was shown.
clicks: number of clicks on for that ad.
spend: amount paid by company xyz to Facebook, to show that ad.
conversion: total number of people who enquired about the product after seeing the ad.
approved: total number of people who bought the product after seeing the ad.

Details

For more information related to the dataset see:
https://www.kaggle.com/loveall/clicks-conversion-tracking

Source

This dataset is from:
https://www.kaggle.com/loveall/clicks-conversion-tracking

References

Reza Mohammadi (2025). Data Science Foundations and Machine Learning with R: From Data to Decisions. https://book-data-science-r.netlify.app.

Examples

data(advertising)
str(advertising)
data(advertising)
str(advertising)

Bank marketing data set

Description

the data is related to 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 (or not) subscribed. the classification goal is to predict if the client will subscribe a term deposit (variable deposit).

Usage

 data(bank) data(bank)

Format

the bank dataset, as a data frame, contains $4521$ rows (customers) and $17$ columns (variables/features). the $17$ variables are:

Bank client data:

age: numeric.
job: type of job; categorical: "admin.", "unknown", "unemployed", "management", "housemaid", "entrepreneur", "student", "blue-collar, "self-employed", "retired", "technician", "services".
marital: marital status; categorical: "married", "divorced", "single"; note: "divorced" means divorced or widowed.
education: categorical: "secondary", "primary", "tertiary", "unknown".
default: has credit in default?; binary: "yes","no".
balance: average yearly balance, in euros; numeric.
housing: has housing loan? binary: "yes", "no".
loan: has personal loan? binary: "yes", "no".

Related with the last contact of the current campaign:

contact: contact: contact communication type; categorical: "unknown","telephone","cellular".
day: last contact day of the month; numeric.
month: last contact month of year; categorical: "jan", "feb", "mar", ..., "nov", "dec".
duration: last contact duration, in seconds; numeric.

Other attributes:

campaign: number of contacts performed during this campaign and for this client; numeric, includes last contact.
pdays: number of days that passed by after the client was last contacted from a previous campaign; numeric, -1 means client was not previously contacted.
previous: number of contacts performed before this campaign and for this client; numeric.
poutcome: outcome of the previous marketing campaign; categorical: "success", "failure", "unknown", "other".

Target variable:

deposit: Indicator of whether the client subscribed a term deposit; binary: "yes" or "no".

Details

For more information related to the dataset see:
http://archive.ics.uci.edu/ml/datasets/Bank+Marketing

Source

This dataset comes from the UCI repository of machine learning databases:
http://archive.ics.uci.edu/ml/datasets/Bank+Marketing

References

Moro, S., Laureano, R. and Cortez, P. (2011) Using Data Mining for Bank Direct Marketing: An Application of the CRISP-DM Methodology. In P. Novais et al. (Eds.), Proceedings of the European Simulation and Modelling Conference.

Reza Mohammadi (2025). Data Science Foundations and Machine Learning with R: From Data to Decisions. https://book-data-science-r.netlify.app.

Examples

data(bank)
str(bank)
data(bank)
str(bank)

Seoul Bike Sharing Demand Data

Description

A dataset containing hourly bike rental demand in Seoul, South Korea, together with weather conditions, seasonal information, holiday status, and whether the bike sharing system was operating on that day.

Usage

data(bike_demand)
data(bike_demand)

Format

A data frame with 8760 observations and 14 variables:

date: Date of observation.
hour: Hour of the day, ranging from 0 to 23.
temperature: Temperature in degrees Celsius.
humidity: Humidity percentage.
wind_speed: Wind speed in meters per second.
visibility: Visibility in units recorded by the source dataset.
dew_point_temperature: Dew point temperature in degrees Celsius.
solar_radiation: Solar radiation in megajoules per square meter.
rainfall: Rainfall in millimeters.
snowfall: Snowfall in centimeters.
season: Season of the year: "spring", "summer", "autumn", or "winter".
holiday: Holiday status: "holiday" or "no holiday".
functioning_day: Whether the bike rental system was operating: "yes" or "no".
bike_count: Number of rented bikes (target variable).

Details

This dataset was obtained from the UCI Machine Learning Repository and renamed bike_demand for inclusion in the liver package. It can be used to illustrate methods for regression, exploratory data analysis, and predictive modeling in R.

Source

https://archive.ics.uci.edu/dataset/560/seoul+bike+sharing+demand

References

Reza Mohammadi (2025). Data Science Foundations and Machine Learning with R: From Data to Decisions. https://book-data-science-r.netlify.app.

Examples

data(bike_demand)

str(bike_demand)
summary(bike_demand)
data(bike_demand)

str(bike_demand)
summary(bike_demand)

Caravan insurance data set

Description

The contains $5822$ customer records from an insurance company, each described by $86$ variables. These include $43$ sociodemographic features based on zip codes and $43$ indicators of product ownership. The final variable, Purchase, indicates whether a customer bought a caravan insurance policy. Collected for the CoIL 2000 Challenge, the data was designed to address the question: Can you predict who would be interested in buying a caravan insurance policy and explain why?

Usage

data(caravan)data(caravan)

Format

A data frame with $5822$ observations (rows) and $86$ features (columns).

Details

For more information related to the dataset see
https://www.kaggle.com/datasets/uciml/caravan-insurance-challenge

Source

The data was supplied by Sentient Machine Research: https://www.smr.nl

References

P. van der Putten and M. van Someren (eds) . CoIL Challenge 2000: The Insurance Company Case. Published by Sentient Machine Research, Amsterdam. Also a Leiden Institute of Advanced Computer Science Technical Report 2000-09. June 22, 2000.
James, G., Witten, D., Hastie, T., and Tibshirani, R. (2013). An Introduction to Statistical Learning with applications in R, https://www.statlearning.com, Springer-Verlag.

Reza Mohammadi (2025). Data Science Foundations and Machine Learning with R: From Data to Decisions. https://book-data-science-r.netlify.app.

Examples

data(caravan)
str(caravan)
data(caravan)
str(caravan)

Cereal data set

Description

This dataset contains nutrition information for $77$ breakfast cereals and includes $16$ variables. the "rating" column is our target as a rating of the cereals (Possibly from Consumer Reports?).

Usage

 data(cereal) data(cereal)

Format

the cereal dataset, as a data frame, contains $77$ rows (breakfast cereals) and $16$ columns (variables/features). the $16$ variables are:

name: Name of cereal.
manuf: Manufacturer of cereal, coded into seven categories: "A" for American Home Food Products, "G" for General Mills, "K" for Kelloggs, "N" for Nabisco, "P" for Post, "Q" for Quaker Oats, and "R" for Ralston Purina.
type: cold or hot.
calories: calories per serving.
protein: grams of protein.
fat: grams of fat.
sodium: milligrams of sodium.
fiber: grams of dietary fiber.
carbo: grams of complex carbohydrates.
sugars: grams of sugars.
potass: milligrams of potassium.
vitamins: vitamins and minerals - 0, 25, or 100, indicating the typical percentage of FDA recommended.
shelf: display shelf (1, 2, or 3, counting from the floor).
weight: weight in ounces of one serving.
cups: number of cups in one serving.
rating: a rating of the cereals (Possibly from Consumer Reports?).

Details

For more information related to the dataset see
https://www.openml.org/search?type=data&status=any&id=1095&sort=runs

Source

This dataset is originally from
https://lib.stat.cmu.edu/DASL/Datafiles/Cereals.html

References

Reza Mohammadi (2025). Data Science Foundations and Machine Learning with R: From Data to Decisions. https://book-data-science-r.netlify.app.

Examples

data(cereal)
str(cereal)
data(cereal)
str(cereal)

Churn dataset for Credit Card Customers

Description

The churn data set contains $10127$ rows (customers) and $21$ columns (features). The churn column is our target which indicate whether customer churned (left the company) or not.

Usage

 data(churn) data(churn)

Format

the churn dataset, as a data frame, contains $10127$ rows (customers) and $21$ columns (variables/features). the $21$ variables are:

customer.ID: Unique identifier for each account holder.
age: Age of the customer, in years.
gender: Gender of the account holder.
education: Educational qualification (high-school, college, graduate, uneducated, post-graduate, doctorate, unknown).
marital: Marital status (married, single, divorced, unknown).
income: Annual income bracket (less than $40K, $40K-$60K, $60K-$80K, $80K-$120K, over $120K, unknown).
card.category: Credit card type (blue, silver, gold, platinum).
dependent.count: Number of dependents.
months.on.book: Tenure with the bank, in months.
relationship.count: Total number of products held by the customer (1-6).
months.inactive: Number of inactive months in the past 12 months.
contacts.count.12: Number of customer service contacts in the past 12 months.
credit.limit: Total credit card limit.
revolving.balance: Current revolving balance on the credit card.
available.credit: Available credit line, representing the unused portion of the credit limit. Calculated as credit.limit - revolving.balance.
transaction.amount.12: Total transaction amount in the past 12 months.
transaction.count.12: Total number of transactions in the past 12 months.
ratio.amount.Q4.Q1: Ratio of total transaction amount in the fourth quarter to that in the first quarter.
ratio.count.Q4.Q1: Ratio of total transaction count in the fourth quarter to that in the first quarter.
utilization.ratio: Average credit utilization ratio, defined as revolving.balance / credit.limit.
churn: Indicator of whether the account was closed (yes) or remained active (no).

Details

For more information related to the dataset see:
https://www.kaggle.com/sakshigoyal7/credit-card-customers

Source

This dataset is originally from https://leaps.analyttica.com/home

References

Reza Mohammadi (2025). Data Science Foundations and Machine Learning with R: From Data to Decisions. https://book-data-science-r.netlify.app.

Examples

data(churn)

str(churn)
data(churn)

str(churn)

Churn data set from MLC++ machine learing

Description

This dataset originates from the MLC++ machine learning software and is used for modeling customer churn. Customer churn, also known as customer attrition, refers to the event in which customers stop doing business with a company. The dataset contains $5000$ rows (customers) and $20$ columns (features). The churn column serves as the target variable, indicating whether a customer has churned (left the company) or not.

Usage

data(churn_mlc)data(churn_mlc)

Format

A data frame with $5000$ rows (customers) and $20$ columns (variables/features). the $20$ variables are:

state: Categorical, for the $51$ states and the District of Columbia.
area.code: Categorical.
account.length: count, how long account has been active.
voice.plan: Categorical, yes or no, voice mail plan.
voice.messages: Count, number of voice mail messages.
intl.plan: Categorical, yes or no, international plan.
intl.mins: Continuous, minutes customer used service to make international calls.
intl.calls: Count, total number of international calls.
intl.charge: Continuous, total international charge.
day.mins: Continuous, minutes customer used service during the day.
day.calls: Count, total number of calls during the day.
day.charge: Continuous, total charge during the day.
eve.mins: Continuous, minutes customer used service during the evening.
eve.calls: Count, total number of calls during the evening.
eve.charge: Continuous, total charge during the evening.
night.mins: Continuous, minutes customer used service during the night.
night.calls: Count, total number of calls during the night.
night.charge: Continuous, total charge during the night.
customer.calls: Count, number of calls to customer service.
churn: Categorical, yes or no. Indicator of whether the customer has left the company (yes or no).

Details

For more information related to the dataset see
- OpenML: https://www.openml.org/search?type=data&sort=runs&id=40701&status=active
- data.world: https://data.world/earino/churn

Source

This dataset is originally from http://www.sgi.com/tech/mlc

References

Saha, S., Saha, C., Haque, M. M., Alam, M. G. R., and Talukder, A. (2024). ChurnNet: Deep learning enhanced customer churn prediction in telecommunication industry. IEEE access, 12, 4471-4484.
Umayaparvathi, V., and Iyakutti, K. (2016). A survey on customer churn prediction in telecom industry: Datasets, methods and metrics. International Research Journal of Engineering and Technology (IRJET), 3(04), 1065-1070
Reza Mohammadi (2025). Data Science Foundations and Machine Learning with R: From Data to Decisions. https://book-data-science-r.netlify.app.

Examples

data(churn_mlc)
str(churn_mlc)
data(churn_mlc)
str(churn_mlc)

churn_tel dataset

Description

The churn_tel data set contains $7043$ rows (customers) and $21$ columns (features). The churn column is our target which indicate whether customer churned (left the company) or not.

Usage

 data(churn_tel) data(churn_tel)

Format

the churn_tel dataset, as a data frame, contains $7043$ rows (customers) and $21$ columns (variables/features). the $21$ variables are:

customer.ID: Customer ID.
gender: Whether the customer is a male or a female.
senior.citizen: Whether the customer is a senior citizen or not (1, 0).
partner: Whether the customer has a partner or not (yes, no).
dependent: Whether the customer has dependents or not (yes, no).
tenure: Number of months the customer has stayed with the company.
phone.service: Whether the customer has a phone service or not (yes, no).
multiple.lines: Whether the customer has multiple lines or not (yes, no, no phone service).
internet.service: Customer's internet service provider (DSL, fiber optic, no).
online.security: Whether the customer has online security or not (yes, no, no internet service).
online.backup: Whether the customer has online backup or not (yes, no, no internet service).
device.protection: Whether the customer has device protection or not (yes, no, no internet service).
tech.support: Whether the customer has tech support or not (yes, no, no internet service).
streaming.TV: Whether the customer has streaming TV or not (yes, no, no internet service).
streaming.movie: Whether the customer has streaming movies or not (yes, no, no internet service).
contract: the contract term of the customer (month to month, 1 year, 2 year).
paperless.bill: Whether the customer has paperless billing or not (yes, no).
payment.method: the customer's payment method (electronic check, mail check, bank transfer, credit card).
monthly.charge: the amount charged to the customer monthly.
total.charges: the total amount charged to the customer.
churn: Whether the customer churned or not (yes or no).

Details

For more information related to the dataset see:
https://www.kaggle.com/blastchar/telco-customer-churn

Source

This dataset comes from the IBM Sample Data Sets:
https://community.ibm.com/community/user/blogs/steven-macko/2019/07/11/telco-customer-churn-1113

References

Reza Mohammadi (2025). Data Science Foundations and Machine Learning with R: From Data to Decisions. https://book-data-science-r.netlify.app.

Examples

data(churn_tel)
str(churn_tel)
data(churn_tel)
str(churn_tel)

Confusion Matrix

Description

Create a Confusion Matrix.

Usage

conf.mat(pred, actual, cutoff = 0.5, reference = NULL, 
         proportion = FALSE, dnn = c("Actual", "Predict"), ...)
conf.mat(pred, actual, cutoff = 0.5, reference = NULL, 
         proportion = FALSE, dnn = c("Actual", "Predict"), ...)

Arguments

pred

a vector of estimated values.

actual

a vector of actual values.

cutoff

cutoff value for the case that pred is vector of probabilites.

reference

a factor of classes to be used as the true results.

proportion

Logical: FALSE (default) for a confusion matrix with number of cases. TRUE, for a confusion matrix with the proportion of cases.

dnn

the names to be given to the dimensions in the result (the dimnames names).

...

options to be passed to table.

Value

the results of table on pred and actual.

Author(s)

Reza Mohammadi [email protected] and Kevin Burke [email protected]

Examples

pred   = c("no", "yes", "yes", "no", "no", "yes", "no", "no")
actual = c("yes", "no", "yes", "no", "no", "no", "yes", "yes")

conf.mat(pred, actual)
conf.mat(pred, actual, proportion = TRUE)
pred   = c("no", "yes", "yes", "no", "no", "yes", "no", "no")
actual = c("yes", "no", "yes", "no", "no", "no", "yes", "yes")

conf.mat(pred, actual)
conf.mat(pred, actual, proportion = TRUE)

Plot Confusion Matrix

Description

Plot a Confusion Matrix.

Usage

conf.mat.plot(pred, actual, cutoff = 0.5, reference = NULL, conf.level = 0, 
              margin = c(1, 2), color = c("#F4A582", "#A8D5BA"), ...)
conf.mat.plot(pred, actual, cutoff = 0.5, reference = NULL, conf.level = 0, 
              margin = c(1, 2), color = c("#F4A582", "#A8D5BA"), ...)

Arguments

pred

a vector of estimated values.

actual

a vector of actual values.

cutoff

cutoff value for the case that pred is vector of probabilites.

reference

a factor of classes to be used as the true results.

conf.level

confidence level used for the confidence rings on the odds ratios. Must be a single nonnegative number less than 1; if set to 0 (the default), confidence rings are suppressed.

margin

a numeric vector with the margins to equate. Must be one of 1, 2, or c(1, 2) (the default), which corresponds to standardizing the row, column, or both margins in each 2 by 2 table. Only used if std equals "margins".

color

a vector of length 2 specifying the colors to use for the smaller and larger diagonals of each 2 by 2 table.

...

options to be passed to fourfoldplot.

Author(s)

Reza Mohammadi [email protected] and Kevin Burke [email protected]

Examples

pred   = c("no", "yes", "yes", "no", "no", "yes", "no", "no")
actual = c("yes", "no", "yes", "no", "no", "no", "yes", "yes")

conf.mat.plot(pred, actual)
pred   = c("no", "yes", "yes", "no", "no", "yes", "no", "no")
actual = c("yes", "no", "yes", "no", "no", "no", "yes", "yes")

conf.mat.plot(pred, actual)

CPU Specifications and Market Prices

Description

A dataset containing detailed specifications, integrated graphics availability, and market price information for a range of computer processors (CPUs). It includes hardware characteristics such as core counts, thread counts, clock speeds, cache size, and thermal design power (TDP), along with price data. The dataset is suitable for studying price-to-performance trade-offs across different CPU models.

Usage

data(cpu_price)
data(cpu_price)

Format

A data frame with 45 observations and 12 variables:

model: The model name of the processor.
brand: The brand of the CPU: "AMD" or "Intel".
gpu: Whether the CPU includes integrated graphics: "yes" or "no".
architecture: The microarchitecture or generation family of the CPU.
base_ghz: The base operating frequency of the CPU in gigahertz.
boost_ghz: The maximum turbo or boost frequency of the CPU in gigahertz.
p_cores: The number of performance cores (P-cores).
e_cores: The number of efficiency cores (E-cores).
threads: The number of logical threads the CPU can execute simultaneously.
cache: The total cache size in megabytes.
tdp: The typical thermal design power (TDP) of the CPU in watts under standard load conditions.
price: The approximate retail market price of the CPU in US dollars.

Details

The dataset was assembled to support exploratory and predictive analyses of CPU pricing. For example, it can be used in regression models relating CPU price to processor characteristics such as clock speed, thread count, graphics support, and brand.

Source

The dataset was collected by the package authors. Hardware specifications are based on publicly available manufacturer information. Price data was collected through Google searches during Spring 2026 and reflects approximate retail market prices at that time.

References

Reza Mohammadi (2025). Data Science Foundations and Machine Learning with R: From Data to Decisions. https://book-data-science-r.netlify.app.

Examples

data(cpu_price)

str(cpu_price)
summary(cpu_price)
data(cpu_price)

str(cpu_price)
summary(cpu_price)

South German Credit Data

Description

A dataset containing information on credit applicants, including account status, credit history, loan purpose, credit amount, savings, employment duration, personal characteristics, property, housing, and other financial attributes. The outcome variable indicates whether the applicant represents a good or bad credit risk.

Usage

data(credit)
data(credit)

Format

A data frame with 1000 observations and 21 variables:

status: Status of the debtor's checking account with the bank.
duration: Credit duration in months.
credit_history: History of compliance with previous or concurrent credit contracts.
purpose: Purpose for which the credit is needed.
amount: Credit amount in Deutsche Mark (DM).
savings: Debtor's savings.
employment_duration: Duration of the debtor's employment with the current employer.
installment_rate: Credit installments as a percentage of the debtor's disposable income.
personal_status_sex: Combined information on personal status and sex.
other_debtors: Whether there is another debtor or a guarantor for the credit.
present_residence: Length of time the debtor has lived in the present residence.
property: The debtor's most valuable property.
age: Age in years.
other_installment_plans: Installment plans from providers other than the credit-giving bank.
housing: Type of housing the debtor lives in.
number_credits: Number of credits the debtor has or had at this bank, including the current one.
job: Quality of the debtor's job.
people_liable: Number of persons financially dependent on the debtor.
telephone: Whether a telephone landline is registered in the debtor's name.
foreign_worker: Whether the debtor is a foreign worker.
credit_risk: Credit risk outcome: "good risk" or "bad risk".

Details

The South German Credit data are a corrected and documented version of the widely used German credit data. The dataset contains 700 good and 300 bad credits and covers actual credit data from 1973 to 1975, with bad credits heavily oversampled. It can be used to illustrate methods for classification, exploratory data analysis, and predictive modeling in R.

Source

UCI Machine Learning Repository: https://archive.ics.uci.edu/dataset/573/south+german+credit+update

South German Credit [Dataset]. (2020). UCI Machine Learning Repository. doi:10.24432/C5QG88

References

Gr\"omping, U. (2019). South German credit data: Correcting a widely used data set.

Examples

data(credit)

str(credit)
summary(credit)
data(credit)

str(credit)
summary(credit)

Credit Card Transactions for Fraud Detection

Description

A dataset containing credit card transactions for illustrating fraud detection and class imbalance in binary classification. The data include anonymized predictors derived from a principal component analysis, together with transaction time, transaction amount, and a binary fraud indicator.

Usage

data(creditcard_fraud)
data(creditcard_fraud)

Format

A data frame with 10000 observations and 31 variables:

Time: Seconds elapsed between each transaction and the first transaction in the dataset.
V1: Anonymized predictor obtained from a PCA transformation of the original variables.
V2: Anonymized predictor obtained from a PCA transformation of the original variables.
V3: Anonymized predictor obtained from a PCA transformation of the original variables.
V4: Anonymized predictor obtained from a PCA transformation of the original variables.
V5: Anonymized predictor obtained from a PCA transformation of the original variables.
V6: Anonymized predictor obtained from a PCA transformation of the original variables.
V7: Anonymized predictor obtained from a PCA transformation of the original variables.
V8: Anonymized predictor obtained from a PCA transformation of the original variables.
V9: Anonymized predictor obtained from a PCA transformation of the original variables.
V10: Anonymized predictor obtained from a PCA transformation of the original variables.
V11: Anonymized predictor obtained from a PCA transformation of the original variables.
V12: Anonymized predictor obtained from a PCA transformation of the original variables.
V13: Anonymized predictor obtained from a PCA transformation of the original variables.
V14: Anonymized predictor obtained from a PCA transformation of the original variables.
V15: Anonymized predictor obtained from a PCA transformation of the original variables.
V16: Anonymized predictor obtained from a PCA transformation of the original variables.
V17: Anonymized predictor obtained from a PCA transformation of the original variables.
V18: Anonymized predictor obtained from a PCA transformation of the original variables.
V19: Anonymized predictor obtained from a PCA transformation of the original variables.
V20: Anonymized predictor obtained from a PCA transformation of the original variables.
V21: Anonymized predictor obtained from a PCA transformation of the original variables.
V22: Anonymized predictor obtained from a PCA transformation of the original variables.
V23: Anonymized predictor obtained from a PCA transformation of the original variables.
V24: Anonymized predictor obtained from a PCA transformation of the original variables.
V25: Anonymized predictor obtained from a PCA transformation of the original variables.
V26: Anonymized predictor obtained from a PCA transformation of the original variables.
V27: Anonymized predictor obtained from a PCA transformation of the original variables.
V28: Anonymized predictor obtained from a PCA transformation of the original variables.
Amount: Transaction amount.
Class: Fraud indicator: 0 for non-fraudulent transactions and 1 for fraudulent transactions.

Details

This dataset is a teaching subset derived from the original Credit Card Fraud Detection dataset available on Kaggle. The original dataset is highly imbalanced. For inclusion in the liver package, we created a smaller subset with 10000 observations that retains all fraud cases and a random sample of non-fraud cases. This version is intended for illustrating class imbalance, resampling strategies, and model evaluation in binary classification.

Source

https://www.kaggle.com/datasets/mlg-ulb/creditcardfraud

References

Andrea Dal Pozzolo, Olivier Caelen, Reid A. Johnson, and Gianluca Bontempi (2015). Calibrating Probability with Undersampling for Unbalanced Classification. In 2015 IEEE Symposium Series on Computational Intelligence.

Reza Mohammadi (2025). Data Science Foundations and Machine Learning with R: From Data to Decisions. https://book-data-science-r.netlify.app.

Examples

data(creditcard_fraud)
str(creditcard_fraud)

table(creditcard_fraud$Class)
data(creditcard_fraud)
str(creditcard_fraud)

table(creditcard_fraud$Class)

Doctor Visits and Health Care Utilization Data

Description

A dataset containing information on individuals' doctor visit counts, demographic characteristics, income, illness burden, reduced activity days, self-reported health status, and indicators of health care coverage and chronic conditions.

Usage

data(doctor_visits)
data(doctor_visits)

Format

A data frame with 5190 observations and 12 variables:

age: Age of the individual.
income: Income level of the individual.
illness: Number of illnesses experienced by the individual.
reduced: Number of days with reduced activity.
health: Self-reported health score.
gender: Gender of the individual: "male" or "female".
private: Whether the individual has private health insurance: "yes" or "no".
freepoor: Whether the individual is covered by free government health care due to low income: "yes" or "no".
freerepat: Whether the individual is covered by free government health care due to repatriation status: "yes" or "no".
nchronic: Whether the individual has a chronic condition that is not limiting: "yes" or "no".
lchronic: Whether the individual has a chronic condition that is limiting: "yes" or "no".
visits: Number of doctor visits (target variable).

Details

This dataset was adapted for inclusion in the liver package and can be used to illustrate methods for count data modeling, exploratory data analysis, and regression techniques such as Poisson regression in R.

Source

Originally distributed with the AER package.

References

Mullahy, J. (1997). Heterogeneity, Excess Zeros, and the Structure of Count Data Models. Journal of Applied Econometrics, 12:337–350.

Cameron, A.C. and Trivedi, P.K. (1986). Econometric Models Based on Count Data: Comparisons and Applications of Some Estimators and Tests. Journal of Applied Econometrics, 1:29–53.

Cameron, A.C. and Trivedi, P.K. (1998). Regression Analysis of Count Data. Cambridge: Cambridge University Press.

Reza Mohammadi (2025). Data Science Foundations and Machine Learning with R: From Data to Decisions. https://book-data-science-r.netlify.app.

Examples

data(doctor_visits)

str(doctor_visits)
summary(doctor_visits)
data(doctor_visits)

str(doctor_visits)
summary(doctor_visits)

drug data set

Description

synthetically generated dataset of $200$ patients includes their age, sodium-to-potassium (Na/K) ratio, and the prescribed drug type.

Usage

 data(drug) data(drug)

Format

the drug dataset, as a data frame, contains $200$ rows (customers) and $3$ columns (variables/features). the $3$ variables are:

age: age of patients.
ratio: sodium-to-potassium (Na/K) ratio.
type: the prescribed drug type in three levels (A, B, and C).

References

Reza Mohammadi (2025). Data Science Foundations and Machine Learning with R: From Data to Decisions. https://book-data-science-r.netlify.app.

Examples

data(drug)
str(drug)
data(drug)
str(drug)

find.na

Description

Finding missing values.

Usage

find.na(x)
find.na(x)

Arguments

x

a numerical vector, matrix or data.frame.

Value

A numeric matrix with two columns.

Author(s)

Reza Mohammadi [email protected] and Kevin Burke [email protected]

Examples

x = c(2.3, NA, -1.4, 0, 3.45)

find.na(x)
x = c(2.3, NA, -1.4, 0, 3.45)

find.na(x)

Gapminder Data on Global Health, Income, and Population

Description

The gapminder dataset provides global health, income, and population indicators for $195$ countries over the period $1950$ – $2019$ .

Usage

 data(gapminder) data(gapminder)

Format

The gapminder dataset, provided as a data frame, contains $13{,}650$ rows and $8$ columns (features) as follows:

country: Country name.
year: Calendar year of observation (1950–2019).
gdp: Gross domestic product (GDP) in USD, based on World Bank data.
life_expectancy: Average life expectancy at birth (in years).
population: National population size.
continent: Continent to which the country belongs.
iso_alpha: ISO 3166-1 alpha-3 country code.
world_group: A five-category geopolitical grouping of countries used for visualization, with levels The West, Asia, Latin America, Africa, and Others.

Details

For more information related to the dataset see:
https://www.gapminder.org/data/documentation/

Source

This dataset is originally from https://www.gapminder.org/resources/

References

Reza Mohammadi (2025). Data Science Foundations and Machine Learning with R: From Data to Decisions. https://book-data-science-r.netlify.app.

Examples

data(gapminder)

str(gapminder)
data(gapminder)

str(gapminder)

house data set

Description

the house dataset contains $6$ features and $414$ records. the target feature is unit.price and the remaining 5 variables are predictors.

Usage

 data(house) data(house)

Format

the house dataset, as a data frame, contains $414$ rows and $6$ columns (variables/features). the $6$ variables are:

house.age: house age (numeric, in year).
distance.to.MRT: distance to the nearest MRT station (numeric).
stores.number: number of convenience stores (numeric).
latitude: latitude (numeric).
longitude: longitude (numeric).
unit.price: house price of unit area (numeric).

Details

For more information related to the dataset see:
https://archive.ics.uci.edu/dataset/477/real+estate+valuation+data+set
https://www.kaggle.com/quantbruce/real-estate-price-prediction

Source

This dataset originally comes from the UCI repository of machine learning databases:
https://archive.ics.uci.edu/dataset/477/real+estate+valuation+data+set

References

Reza Mohammadi (2025). Data Science Foundations and Machine Learning with R: From Data to Decisions. https://book-data-science-r.netlify.app.

Examples

data(house)
str(house)
data(house)
str(house)

house_price dataset

Description

This data set contains $1460$ rows and $81$ columns (features). the "SalePrice" column is the target.

Usage

 data(house_price) data(house_price)

Format

the house_price dataset, as a data frame, contains $1460$ rows and $81$ columns (variables/features).

Details

For more information related to the dataset see:
https://www.kaggle.com/datasets/lespin/house-prices-dataset

Source

This dataset comes from:
https://www.kaggle.com/competitions/house-prices-advanced-regression-techniques

References

Reza Mohammadi (2025). Data Science Foundations and Machine Learning with R: From Data to Decisions. https://book-data-science-r.netlify.app.

Examples

data(house_price)
str(house_price)
data(house_price)
str(house_price)

insurance data set

Description

the insurance dataset contains $7$ features and $1338$ records. the target feature is charge and the remaining 6 variables are predictors. This dataset is simulated on the basis of demographic statistics from the US Census Bureau.

Usage

 data(insurance) data(insurance)

Format

the insurance dataset, as a data frame, contains $1338$ rows (customers) and $7$ columns (variables/features). the $7$ variables are:

age: age of primary beneficiary.
bmi: body mass index, providing an understanding of body, weights that are relatively high or low relative to height, objective index of body weight (kg / m ^ 2) using the ratio of height to weight, ideally 18.5 to 24.9.
children: Number of children covered by health insurance / Number of dependents.
smoker: Smoking as a factor with 2 levels, yes, no.
gender: insurance contractor gender, female, male.
region: the beneficiary's residential area in the US, northeast, southeast, southwest, northwest.
charge: individual medical costs billed by health insurance.

Details

For more information related to the dataset see:
https://www.kaggle.com/mirichoi0218/insurance

Source

This dataset comes from:
https://github.com/stedy/Machine-Learning-with-R-datasets

References

Brett Lantz (2019). Machine Learning with R: Expert techniques for predictive modeling. Packt Publishing Ltd.

Reza Mohammadi (2025). Data Science Foundations and Machine Learning with R: From Data to Decisions. https://book-data-science-r.netlify.app.

Examples

data(insurance)
str(insurance)
data(insurance)
str(insurance)

k-Nearest Neighbour Classification

Description

kNN is used to perform k-nearest neighbour classification for test set using training set. For each row of the test set, the k nearest (based on Euclidean distance) training set vectors are found. then, the classification is done by majority vote (ties broken at random). This function provides a formula interface to the class::knn() function of R package class. In addition, it allows normalization of the given data using the scaler function.

Usage

kNN(formula, train, test, k = 1, scaler = FALSE, type = "class", l = 0, 
    use.all = TRUE, na.rm = FALSE)
kNN(formula, train, test, k = 1, scaler = FALSE, type = "class", l = 0, 
    use.all = TRUE, na.rm = FALSE)

Arguments

formula

a formula, with a response but no interaction terms. For the case of data frame, it is taken as the model frame (see model.frame).

train

data frame or matrix of train set cases.

test

data frame or matrix of test set cases.

k

number of neighbours considered.

scaler

a character with options FALSE (default), "minmax", and "zscore". Option "minmax" means no transformation. This option allows the users to use normalized version of the train and test sets for the kNN aglorithm.

type

either "class" (default) for the predicted class or "prob" for model confidence values.

l

minimum vote for definite decision, otherwise doubt. (More precisely, less than k-l dissenting votes are allowed, even if k is increased by ties.)

use.all

controls handling of ties. If true, all distances equal to the kth largest are included. If false, a random selection of distances equal to the kth is chosen to use exactly k neighbours.

na.rm

a logical value indicating whether NA values in x should be stripped before the computation proceeds.

Value

When type = "class" (default), a factor vector is returned, in which the doubt will be returned as NA. When type = "prob", a matrix of confidence values is returned (one column per class).

Author(s)

Reza Mohammadi [email protected] and Kevin Burke [email protected]

References

Ripley, B. D. (1996) Pattern Recognition and Neural Networks. Cambridge.
Venables, W. N. and Ripley, B. D. (2002) Modern Applied Statistics with S. Fourth edition. Springer.

Examples

data(risk)

train = risk[1:100, ]
test  = risk[  101, ]

kNN(risk ~ income + age, train = train, test = test)
data(risk)

train = risk[1:100, ]
test  = risk[  101, ]

kNN(risk ~ income + age, train = train, test = test)

Visualizing the Optimal Number of k

Description

Visualizing the Optimal Number of k for k-Nearest Neighbour (kNN) algorithm based on accuracy or Mean Square Error (MSE).

Usage

kNN.plot(formula, train, test = NULL, ratio = c(0.7, 0.3), k.max = 10, 
         scaler = FALSE, base = "accuracy", reference = NULL, cutoff = NULL, 
         type = "class", report = FALSE, set.seed = NULL, ...)
kNN.plot(formula, train, test = NULL, ratio = c(0.7, 0.3), k.max = 10, 
         scaler = FALSE, base = "accuracy", reference = NULL, cutoff = NULL, 
         type = "class", report = FALSE, set.seed = NULL, ...)

Arguments

formula

a formula, with a response but no interaction terms. For the case of data frame, it is taken as the model frame (see model.frame).

train

data frame or matrix of train set cases.

test

Data frame or matrix containing the test set observations. If NULL, the train data are partitioned according to ratio.

ratio

Numeric vector of length 1 or 2 specifying the proportions used by partition() to split the train data into training and validation sets.

k.max

the maximum number of neighbors to consider can either be a single value, with a minimum of 2, or a vector representing a range of values k.

scaler

base

base measurement: accuracy (default), error, or MSE for Mean Square Error.

reference

a factor of classes to be used as the true results.

cutoff

cutoff value for the case that the output of knn algorithm is vector of probabilites.

type

either "class" (default) for the predicted class or "prob" for model confidence values.

report

a character with options FALSE (default) and TRUE. Option TRUE reports the values of the base measurement.

set.seed

a single value, interpreted as an integer, or NULL.

...

options to be passed to kNN().

Author(s)

Reza Mohammadi [email protected] and Kevin Burke [email protected]

References

Ripley, B. D. (1996) Pattern Recognition and Neural Networks. Cambridge.
Venables, W. N. and Ripley, B. D. (2002) Modern Applied Statistics with S. Fourth edition. Springer.

Examples

data(risk)

partition_risk <- partition(data = risk, ratio = c(0.6, 0.4))

train <- partition_risk$part1
test  <- partition_risk$part1

kNN.plot(risk ~ income + age, train = train, test = test)
kNN.plot(risk ~ income + age, train = train, test = test, base = "error")
data(risk)

partition_risk <- partition(data = risk, ratio = c(0.6, 0.4))

train <- partition_risk$part1
test  <- partition_risk$part1

kNN.plot(risk ~ income + age, train = train, test = test)
kNN.plot(risk ~ income + age, train = train, test = test, base = "error")

Loan Application and Approval Data

Description

A dataset containing information on loan applicants and their financial profiles, including demographic characteristics, employment status, income, loan details, credit score, asset values, and loan approval outcome.

Usage

data(loan)
data(loan)

Format

A data frame with 4269 observations and 13 variables:

loan_id: Unique identifier for each loan application; not intended as a predictor in modeling.
no_of_dependents: Number of dependents of the applicant.
education: Education level of the applicant: "graduate" or "not-graduate".
self_employed: Whether the applicant is self-employed: "yes" or "no".
income_annum: Annual income of the applicant.
loan_amount: Requested loan amount.
loan_term: Loan term.
cibil_score: Applicant's CIBIL credit score.
residential_assets_value: Value of the applicant's residential assets.
commercial_assets_value: Value of the applicant's commercial assets.
luxury_assets_value: Value of the applicant's luxury assets.
bank_asset_value: Value of the applicant's bank assets.
loan_status: Loan application outcome: "approved" or "rejected".

Details

This dataset was obtained from Kaggle and renamed loan for inclusion in the liver package. It can be used to illustrate methods for classification, exploratory data analysis, and predictive modeling in R.

Source

https://www.kaggle.com/datasets/architsharma01/loan-approval-prediction-dataset

References

Reza Mohammadi (2025). Data Science Foundations and Machine Learning with R: From Data to Decisions. https://book-data-science-r.netlify.app.

Examples

data(loan)
str(loan)
summary(loan)

table(loan$loan_status)
data(loan)
str(loan)
summary(loan)

table(loan$loan_status)

Mean Absolute Error (MAE)

Description

Computes mean absolute error.

Usage

mae(pred, actual, weight = 1, na.rm = FALSE)
mae(pred, actual, weight = 1, na.rm = FALSE)

Arguments

pred

a numerical vector of estimated values.

actual

a numerical vector of actual values.

weight

a numerical vector of weights the same length as pred.

na.rm

a logical value indicating whether NA values in pred should be stripped before the computation proceeds.

Value

the computed mean squared error (numeric value).

Author(s)

Reza Mohammadi [email protected] and Kevin Burke [email protected]

Examples

pred   = c(2.3, -1.4, 0, 3.45)
actual = c(2.1, -0.9, 0, 2.99)
  
mae(pred, actual)
pred   = c(2.3, -1.4, 0, 3.45)
actual = c(2.1, -0.9, 0, 2.99)
  
mae(pred, actual)

marketing data set

Description

the marketing dataset contains $8$ features and $40$ records as 40 days that report how much we spent, how many clicks, impressions and transactions we got, whether or not a display campaign was running, as well as our revenue, click-through-rate and conversion rate. the target feature is revenue and the remaining 7 variables are predictors.

Usage

 data(marketing) data(marketing)

Format

the marketing dataset, as a data frame, contains $40$ rows and $8$ columns (variables/features). the $8$ variables are:

spend: daily send of money on PPC (apy-per-click).
clicks: number of clicks on for that ad.
impressions: amount of impressions per day.
display: whether or not a display campaign was running.
transactions: number of transactions per day.
click.rate: click-through-rate.
conversion.rate: conversion rate.
revenue: daily revenue.

Details

For more information related to the dataset see:
https://github.com/chrisBow/marketing-regression-part-one

Source

This dataset comes from:
https://github.com/chrisBow/marketing-regression-part-one

References

Reza Mohammadi (2025). Data Science Foundations and Machine Learning with R: From Data to Decisions. https://book-data-science-r.netlify.app.

Examples

data(marketing)
str(marketing)
data(marketing)
str(marketing)

Min-Max scaling of numerical variables

Description

Performs Min-Max tranformation for numerical variables.

Usage

minmax(x, col = "auto", min = NULL, max = NULL, na.rm = FALSE)
minmax(x, col = "auto", min = NULL, max = NULL, na.rm = FALSE)

Arguments

x

a numerical vector, matrix or data.frame.

col

a character vector of column names or indices. If "auto", all numeric columns will be transformed. If "all", all columns will be transformed.

min

a numerical value or vector indicating the minimum value(s) to use for Min-Max tranformation; if NULL, the default is based on x.

max

a numerical value or vector indicating the maximum value(s) to use for Min-Max tranformation; if NULL, the default is based on x.

na.rm

a logical value indicating whether NA values in x should be stripped before the computation proceeds.

Value

transformed version of x.

Author(s)

Reza Mohammadi [email protected] and Kevin Burke [email protected]

Examples

x = c(2.3, -1.4, 0, 3.45)

minmax(x)

minmax(x, min = 0, max = 1)
x = c(2.3, -1.4, 0, 3.45)

minmax(x)

minmax(x, min = 0, max = 1)

Mortgage data set

Description

The mortgage dataset contains 850 records and 8 variables. The target variable is risk, a factor with two levels, "low" and "high". The remaining seven variables serve as predictors. The dataset was simulated to represent a realistic mortgage application setting.

Usage

data(mortgage)
data(mortgage)

Format

A data frame with $850$ rows (applicants) and $8$ variables:

age: Age in years.
income: Annual income.
savings: Total savings.
employment_status: A factor with levels "permanent", "temporary", "self_employed", and "unemployed".
credit_history: A factor with levels "poor", "average", and "good".
debt_level: A factor with levels "low", "medium", and "high".
loan_amount: Requested loan amount.
risk: A factor with levels "low" and "high".

Details

The dataset was generated using a hybrid latent simulation approach. Continuous variables were simulated with dependence, and categorical variables were derived from latent scores to create realistic relationships among applicant characteristics, financial indicators, and mortgage risk.

Source

Simulated data generated for illustration and teaching purposes.

References

Reza Mohammadi (2025). Data Science Foundations and Machine Learning with R: From Data to Decisions. https://book-data-science-r.netlify.app.

Examples

data(mortgage)
str(mortgage)
data(mortgage)
str(mortgage)

Mean Squared Error (MSE)

Description

Computes mean squared error.

Usage

mse(pred, actual, weight = 1, na.rm = FALSE)
mse(pred, actual, weight = 1, na.rm = FALSE)

Arguments

pred

a numerical vector of estimated values.

actual

a numerical vector of actual values.

weight

a numerical vector of weights the same length as pred.

na.rm

a logical value indicating whether NA values in pred should be stripped before the computation proceeds.

Value

the computed mean squared error (numeric value).

Author(s)

Reza Mohammadi [email protected] and Kevin Burke [email protected]

Examples

pred   = c(2.3, -1.4, 0, 3.45)
actual = c(2.1, -0.9, 0, 2.99)
  
mse(pred, actual)
   
pred   = c(2.3, -1.4, 0, 3.45)
actual = c(2.1, -0.9, 0, 2.99)
  
mse(pred, actual)

One Hot Encoder

Description

One-Hot-Encode unordered factor columns of a data.frame, matrix, or data.table, using the mltools::one_hot() mltools::one_hot function.

Usage

one.hot(data, cols = "auto", sparsifyNAs = FALSE, naCols = FALSE, 
                   dropCols = FALSE, dropUnusedLevels = FALSE)
one.hot(data, cols = "auto", sparsifyNAs = FALSE, naCols = FALSE, 
                   dropCols = FALSE, dropUnusedLevels = FALSE)

Arguments

data

a numerical vector, matrix, data.frame, or data.table.

cols

a character vector of column names or indices to one-hot-encode. If "auto", all unordered factor columns will be one-hot-encoded.

sparsifyNAs

a logical value indicating whether to converte NAs to 0s.

naCols

a logical value indicating whether to create a separate column for NAs.

dropCols

a logical value indicating whether to drop the original columns which are one-hot-encoded.

dropUnusedLevels

a logical value indicating whether to drop unused factor levels.

Author(s)

Reza Mohammadi [email protected] and Kevin Burke [email protected]

Examples

data(risk)
str(risk)

risk_one_hot <- one.hot(risk, cols = "auto")
str(risk_one_hot)
data(risk)
str(risk)

risk_one_hot <- one.hot(risk, cols = "auto")
str(risk_one_hot)

Partition the data

Description

Randomly partitions the data (primarly intended to split into "training" and "test" sets) according to the supplied probabilities.

Usage

partition(data, ratio = c(0.7, 0.3), set.seed = NULL)
partition(data, ratio = c(0.7, 0.3), set.seed = NULL)

Arguments

data

an ( $n \times p$ ) matrix or a data.frame.

ratio

a numerical vector in range of [0, 1].

set.seed

a single value, interpreted as an integer, or NULL.

Value

a list which includes the data partitions.

Author(s)

Reza Mohammadi [email protected] and Kevin Burke [email protected]

Examples

data(iris)

partition(data = iris, ratio = c(0.7, 0.3))
data(iris)

partition(data = iris, ratio = c(0.7, 0.3))

Confdidence interval for proportion

Description

Compute a confidence interval for the proportion of a response variable using the normal distribution.

Usage

prop.conf(x, n, conf = 0.95, ...)
prop.conf(x, n, conf = 0.95, ...)

Arguments

x

a vector of counts of successes, a one-dimensional table with two entries, or a two-dimensional table (or matrix) with 2 columns, giving the counts of successes and failures, respectively.

n

a vector of counts of trials; ignored if x is a matrix or a table.

conf

confidence level of the interval.

...

further arguments to be passed to prop.test.

Value

A vector with two values: lower and upper confidence limits for the proportion of the response variable.

Author(s)

Reza Mohammadi [email protected]

Examples

data(churn_mlc)

prop.conf(table(churn_mlc$churn), conf = 0.9)
data(churn_mlc)

prop.conf(table(churn_mlc$churn), conf = 0.9)

Online Shopper Purchase Intention Data

Description

A dataset containing session-level information from an e-commerce website, including page visit counts, time spent in different page categories, Google Analytics metrics, visitor characteristics, and a binary outcome indicating whether the session ended in a purchase. The dataset can be used to illustrate binary classification, exploratory data analysis, model comparison, and supervised learning methods in R.

Usage

data(purchase_intention)
data(purchase_intention)

Format

A data frame with 12330 observations and 18 variables:

administrative: Number of administrative pages visited during the session.
administrative_duration: Total time spent on administrative pages during the session.
informational: Number of informational pages visited during the session.
informational_duration: Total time spent on informational pages during the session.
product_related: Number of product-related pages visited during the session.
product_related_duration: Total time spent on product-related pages during the session.
bounce_rates: Average bounce rate associated with the visited pages.
exit_rates: Average exit rate associated with the visited pages.
page_values: Average page value for pages visited before a completed transaction.
special_day: Closeness of the session date to a special shopping day, scaled between 0 and 1.
month: Month of the session.
operating_systems: Visitor operating system, recorded as a categorical factor.
browser: Visitor browser, recorded as a categorical factor.
region: Visitor region, recorded as a categorical factor.
traffic_type: Traffic source type, recorded as a categorical factor.
visitor_type: Visitor type: "New_Visitor", "Returning_Visitor", or "Other".
weekend: Whether the session occurred on a weekend: "no" or "yes".
revenue: Whether the session ended in a purchase: "no" or "yes".

Details

This dataset was obtained from the UCI Machine Learning Repository and renamed purchase_intention for inclusion in the liver package. It contains session-level records from an online shopping website and is well suited for illustrating modern binary classification problems in which the goal is to predict whether a browsing session will end in a purchase.

The predictors combine behavioral measures such as page visit counts and time spent on different types of pages with summary metrics such as bounce_rates, exit_rates, and page_values, as well as visitor and session characteristics including month, visitor_type, traffic_type, and weekend. The outcome variable revenue indicates whether the session resulted in a completed transaction.

The dataset is particularly useful for demonstrating classification workflows such as partitioning data into training and test sets, fitting logistic regression, Naive Bayes, k-nearest neighbors, and tree-based models, and evaluating predictive performance using confusion matrices, ROC curves, and AUC.

Source

UCI Machine Learning Repository: https://archive.ics.uci.edu/dataset/468/online+shoppers+purchasing+intention+dataset

Sakar, C. and Kastro, Y. (2018). Online Shoppers Purchasing Intention Dataset. doi:10.24432/C5F88Q

References

Sakar, C. O., Polat, S. O., Katircioglu, M., and Kastro, Y. (2019). Real-time prediction of online shoppers' purchasing intention using multilayer perceptron and LSTM recurrent neural networks. Neural Computing and Applications, 31, 6893–6908. doi:10.1007/s00521-018-3523-0

Reza Mohammadi (2025). Data Science Foundations and Machine Learning with R: From Data to Decisions. https://book-data-science-r.netlify.app

Examples

data(purchase_intention)

str(purchase_intention)

summary(purchase_intention)
data(purchase_intention)

str(purchase_intention)

summary(purchase_intention)

Red wines data set

Description

the red_wines datasets are related to red variants of the Portuguese "Vinho Verde" wine. Due to privacy and logistic issues, only physicochemical (inputs) and sensory (the output) variables are available (e.g. there is no data about grape types, wine brand, wine selling price, etc.).

the dataset can be viewed as classification or regression tasks. the classes are ordered and not balanced (e.g. there are many more normal wines than excellent or poor ones). Outlier detection algorithms could be used to detect the few excellent or poor wines. Also, we are not sure if all input variables are relevant. So it could be interesting to test feature selection methods.

Usage

 data(red_wines) data(red_wines)

Format

the red_wines dataset, as a data frame, contains $1599$ rows and $12$ columns (variables/features). the $12$ variables are:

Input variables (based on physicochemical tests):

fixed acidity
volatile acidity
citric acid
residual sugar
chlorides
free sulfur dioxide
total sulfur dioxide
density
pH
sulphates
alcohol

Output variable (based on sensory data)
quality: score between 0 and 10.

Details

For more information related to the dataset see the UCI Machine Learning Repository:
https://archive.ics.uci.edu/dataset/186/wine+quality

Source

This dataset comes from the UCI repository of machine learning databases:
https://archive.ics.uci.edu/dataset/186/wine+quality

References

Cortez, P., Cerdeira, A., Almeida, F., Matos, T., and Reis, J. (2009). Modeling wine preferences by data mining from physicochemical properties. Decision support systems, 47(4), 547-553.

Reza Mohammadi (2025). Data Science Foundations and Machine Learning with R: From Data to Decisions. https://book-data-science-r.netlify.app.

Examples

data(red_wines)
str(red_wines)
data(red_wines)
str(red_wines)

Risk data set

Description

The risk dataset contains 246 records and 6 variables. The target variable is risk, a factor with two levels ("good risk" and "bad risk"). The remaining five variables serve as predictors. The dataset was simulated to reflect a realistic real-world scenario.

Usage

 data(risk) data(risk)

Format

the risk dataset, as a data frame, contains $246$ rows (customers) and $6$ columns (variables/features). the $6$ variables are:

age: age in years.
marital: A factor with levels "single", "married", and "other".
income: yearly income.
mortgage: A factor with levels "yes" and "no".
nr_loans: Number of loans that constomers have.
risk: A factor with levels "good risk" and "bad risk".

References

Larose, D. T. and Larose, C. D. (2014). Discovering knowledge in data: an introduction to data mining. John Wiley & Sons.

Reza Mohammadi (2025). Data Science Foundations and Machine Learning with R: From Data to Decisions. https://book-data-science-r.netlify.app.

Examples

data(risk)
str(risk)
data(risk)
str(risk)

Feature scaling

Description

Performs feature scaling such as Z-score and min-max scaling.

Usage

scaler(x, scale = c("minmax", "zscore"), col = "auto", 
       par1 = NULL, par2 = NULL, na.rm = FALSE)
scaler(x, scale = c("minmax", "zscore"), col = "auto", 
       par1 = NULL, par2 = NULL, na.rm = FALSE)

Arguments

x

a numerical vector, a matrix or a data.frame.

scale

a transfer for x.

col

a character vector of column names or indices. If "auto", all numeric columns will be transformed. If "all", all columns will be transformed.

par1

a numerical value or vector that for the case scale = "minmax" indicating the maximum value(s) and for the case scale = "zscore" indicating the mean value(s).

par2

a numerical value or vector that for the case scale = "minmax" indicating the maximum value(s) and for the case scale = "zscore" indicating the sd value(s).

na.rm

a logical value indicating whether NA values in x should be stripped before the computation proceeds.

Value

transformed version of x.

Author(s)

Reza Mohammadi [email protected] and Kevin Burke [email protected]

Examples

x = c(2.3, -1.4, 0, 3.45)

scaler(x, scale = "minmax")

scaler(x, scale = "zscore")
x = c(2.3, -1.4, 0, 3.45)

scaler(x, scale = "minmax")

scaler(x, scale = "zscore")

Skewness

Description

Computes the skewness for each field.

Usage

skewness(x, na.rm = FALSE)
skewness(x, na.rm = FALSE)

Arguments

x

a numerical vector, matrix or data.frame.

na.rm

a logical value indicating whether NA values in x should be stripped before the computation proceeds.

Value

A numeric vector of skewness values.

Author(s)

Reza Mohammadi [email protected] and Kevin Burke [email protected]

Examples

x = c(2.3, -1.4, 0, 3.45)

skewness(x)
x = c(2.3, -1.4, 0, 3.45)

skewness(x)

Skim a data frame to get useful summary statistics

Description

skim() provides an overview of a data frame asan alternative to summary(). This function is a wrapper for the skimr::skim() function of R package skimr.

Usage

  skim(data, hist = TRUE, ...)
skim(data, hist = TRUE, ...)

Arguments

data

a data frame or matrix.

hist

Logical: TRUE (default) to report the histogram of each variable.

...

columns to select for skimming. the default is to skim all columns.

Author(s)

Reza Mohammadi [email protected] and Kevin Burke [email protected]

Examples

data(risk)

skim(risk)
data(risk)

skim(risk)

Confdidence interval for mean

Description

Compute a confidence interval for the mean of a response variable using the t-distribution.

Usage

t_conf(x, conf = 0.95, ...)
t_conf(x, conf = 0.95, ...)

Arguments

x

a (non-empty) numeric vector of data values.

conf

confidence level of the interval.

...

further arguments to be passed to t.test.

Value

A vector with two values: lower and upper confidence limits for the mean of the response variable.

Author(s)

Reza Mohammadi [email protected]

Examples

data(churn_mlc)

t_conf(churn_mlc$customer_calls, conf = 0.9)
data(churn_mlc)

t_conf(churn_mlc$customer_calls, conf = 0.9)

White wines data set

Description

the white_wines datasets are related to white variants of the Portuguese "Vinho Verde" wine. Due to privacy and logistic issues, only physicochemical (inputs) and sensory (the output) variables are available (e.g. there is no data about grape types, wine brand, wine selling price, etc.).

Usage

 data(white_wines) data(white_wines)

Format

the white_wines dataset, as a data frame, contains $4898$ rows and $12$ columns (variables/features). the $12$ variables are:

Input variables (based on physicochemical tests):

fixed acidity
volatile acidity
citric acid
residual sugar
chlorides
free sulfur dioxide
total sulfur dioxide
density
pH
sulphates
alcohol

Output variable (based on sensory data)
quality: score between 0 and 10.

Details

For more information related to the dataset see the UCI Machine Learning Repository:
https://archive.ics.uci.edu/dataset/186/wine+quality

Source

This dataset comes from the UCI repository of machine learning databases:
https://archive.ics.uci.edu/dataset/186/wine+quality

References

Cortez, P., Cerdeira, A., Almeida, F., Matos, T., and Reis, J. (2009). Modeling wine preferences by data mining from physicochemical properties. Decision support systems, 47(4), 547-553.

Reza Mohammadi (2025). Data Science Foundations and Machine Learning with R: From Data to Decisions. https://book-data-science-r.netlify.app.

Examples

data(white_wines)
str(white_wines)
data(white_wines)
str(white_wines)

Wholesale Customer Spending Data

Description

A dataset containing annual spending information for clients of a wholesale distributor, along with the customer's sales channel and geographic region. The dataset can be used to illustrate customer segmentation, clustering, exploratory data analysis, and unsupervised learning methods in R.

Usage

data(wholesale_customers)
data(wholesale_customers)

Format

A data frame with 440 observations and 8 variables:

fresh: Annual spending on fresh products (in monetary units).
milk: Annual spending on milk products (in monetary units).
grocery: Annual spending on grocery products (in monetary units).
frozen: Annual spending on frozen products (in monetary units).
detergents_paper: Annual spending on detergents and paper products (in monetary units).
delicassen: Annual spending on delicatessen products (in monetary units).
channel: Customer sales channel: "Horeca" or "Retail".
region: Customer region: "Lisbon", "Oporto", or "Other".

Details

This dataset was obtained from the UCI Machine Learning Repository and renamed wholesale_customers for inclusion in the liver package. It refers to clients of a wholesale distributor and records their annual spending in several product categories. The dataset is well suited for illustrating methods for clustering, customer profiling, and multivariate data exploration.

In clustering applications, the numerical spending variables are typically used to define the clusters, while channel and region can be used afterward to help interpret the resulting customer groups.

Source

UCI Machine Learning Repository: https://archive.ics.uci.edu/dataset/292/wholesale+customers

References

B. Jaya Lakshmi, K. B. Madhuri, and M. Shashi (2017). An Efficient Algorithm for Density Based Subspace Clustering with Dynamic Parameter Setting. International Journal of Information Technology and Computer Science, 9(6), 27–33. doi:10.5815/ijitcs.2017.06.04

Reza Mohammadi (2025). Data Science Foundations and Machine Learning with R: From Data to Decisions. https://book-data-science-r.netlify.app.

Examples

data(wholesale_customers)

str(wholesale_customers)

summary(wholesale_customers)
data(wholesale_customers)

str(wholesale_customers)

summary(wholesale_customers)

Confdidence interval for mean using z-distribution

Description

Compute a confidence interval for the mean of a response variable using the z-distribution.

Usage

z.conf(x, sigma = NULL, conf = 0.95)
z.conf(x, sigma = NULL, conf = 0.95)

Arguments

x

a (non-empty) numeric vector of data values.

sigma

the population standard deviation. If NULL, the sample standard deviation is used. This is useful when the population standard deviation is known, otherwise it should be left as NULL.

conf

confidence level of the interval.

Value

A vector with two values: lower and upper confidence limits for the mean of the response variable.

Author(s)

Reza Mohammadi [email protected]

Examples

data(churn_mlc)

z.conf(x = churn_mlc$customer_calls, conf = 0.9)
data(churn_mlc)

z.conf(x = churn_mlc$customer_calls, conf = 0.9)

Z-score scaling of numerical variables

Description

Performs Z-score tranformation for numerical variables.

Usage

zscore(x, col = "auto", mean = NULL, sd = NULL, na.rm = FALSE)
zscore(x, col = "auto", mean = NULL, sd = NULL, na.rm = FALSE)

Arguments

x

a numerical vector, matrix or data.frame.

col

a character vector of column names or indices. If "auto", all numeric columns will be transformed. If "all", all columns will be transformed.

mean

a numerical value or vector indicating the mean to use for Z-score calculation; if NULL, the default is the mean of x.

sd

a numerical value or vector indicating the standard deviation(s) to use for Z-score calculation; if NULL, the default is the standard deviation of x.

na.rm

a logical value indicating whether NA values in x should be stripped before the computation proceeds.

Value

transformed version of x.

Author(s)

Reza Mohammadi [email protected] and Kevin Burke [email protected]

Examples

x = c(2.3, -1.4, 0, 3.45)

zscore(x)
zscore(x, mean = 1, sd = 2)
x = c(2.3, -1.4, 0, 3.45)

zscore(x)
zscore(x, mean = 1, sd = 2)

Package 'liver'

Help Index

liver: Foundations Toolkit and Datasets for Data Science

Description

Author(s)

Average classification accuracy

Description

Usage

Arguments

Value

Author(s)

See Also

Examples

adult data set

Description

Usage

Format

Source

References

See Also

Examples

advertising data set

Description

Usage

Format

Details

Source

References

See Also

Examples

Bank marketing data set

Description

Usage

Format

Details

Source

References

See Also

Examples

Seoul Bike Sharing Demand Data

Description

Usage

Format

Details

Source

References

See Also

Examples

Caravan insurance data set

Description

Usage

Format

Details

Source

References

See Also

Examples

Cereal data set

Description

Usage

Format

Details

Source

References

See Also

Examples

Churn dataset for Credit Card Customers

Description

Usage

Format

Details

Source

References

See Also

Examples

Churn data set from MLC++ machine learing

Description

Usage

Format

Details