Title: Ascent Training Datasets
Version: 1.0.0
Description: Datasets to be used primarily in conjunction with Ascent training materials but also for the book 'SAMS Teach Yourself R in 24 Hours' (ISBN: 978-0-672-33848-9). Version 1.0-7 is largely for use with the book; however, version 1.1 has a much greater focus on use with training materials, whilst retaining compatibility with the book.
URL: https://www.ascent.io/
Depends: R (≥ 3.5.0)
Suggests: testthat
License: GPL-2
LazyLoad: yes
LazyData: yes
Encoding: UTF-8
RoxygenNote: 7.1.1
BugReports: https://github.com/HarryJAlexander/ascentTraining/issues
NeedsCompilation: no
Packaged: 2022-03-24 18:18:40 UTC; harry.alexander
Author: Ascent [aut], Harry Alexander [aut, cre, ctb, dtc, rev]
Maintainer: Harry Alexander <harry.alexander@ascent.io>
Repository: CRAN
Date/Publication: 2022-04-27 07:20:05 UTC

Ascent Training Datasets

Description

Datasets designed to be used in conjunction with Ascent training materials.

Details

Datasets designed to be used in conjunction with Ascent training materials and book, SAMS Teach Yourself R in 24 Hours (ISBN: 978-0-672-33848-9). The data covers a range of applications and has been collected together from a number of sources. The airquality dataset, from the Core R datasets package is also provided in xlsx format in the extdata directory of this package.

Author(s)

Ascent

Contact: Ascent rin24hours@mango-solutions.com

Auto MPG Data Set

Description

Data concerns city-cycle fuel consumption - revised from CMU StatLib library.

Usage

auto_mpg

Format

A matrix containing 398 observations and 10 attributes.

mpg

Miles per gallon of the engine. Predictor attribute

cylinders

Number of cylinders in the engine

displacement

Engine displacement

horsepower

Horsepower of the car

weight

Weight of the car (lbs)

acceleration

Acceleration of the car (seconds taken for 0-60mph)

model_year

Model year of the car in the 1900s

origin

Car origin

make

Car manufacturer

car_name

Name of the car

Source

http://archive.ics.uci.edu/ml/datasets/Auto+MPG

References

Quinlan,R. (1993). Combining Instance-Based and Model-Based Learning. In Proceedings on the Tenth International Conference of Machine Learning, 236-243, University of Massachusetts, Amherst. Morgan Kaufmann.

BBC articles data

Description

A collection of BBC news articles from the business or politics sections. There are a total of 927 articles used.

Usage

bbc_articles

Format

A tibble with 201,571 observations, each a word on a document.

word

A word in an article

document

The document/article ID where the word was taken from

Source

Full BBC Articles data

Description

Full BBC Articles data

Usage

bbc_articles_full

Format

A tibble, with 927 observations of separate documents and their contents. This results in two columns.

words

The words from a given article

document

The 'document' (article) ID

Details

A collection of business and politics BBC news articles. Each row represents each article (document), with a document ID and a string of the text content with stop words removed. This is a 'dirty' version of the bbc_articles dataset, where we now have a string of text for each observation, as opposed to a single word.

Source

BBC Business article data

Description

A single BBC Business article (not included in the full BBC articles dataset), given in tidy, one word per row format.

Usage

bbc_business_123

Format

A tibble with 107 observations, each a word on a document.

word

A word in an article

document

The document/article ID where the word was taken from. Note: this only has one unique value, however the column is kept for comparison with other BBC datasets.

Source

BBC Politics article data

Description

A single BBC Politics article (not included in the full BBC articles dataset), given in tidy, one word per row format.

Usage

bbc_politics_123

Format

A tibble with 86 observations, each a word on a document.

word

A word in an article

document

The document/article ID where the word was taken from. Note: this only has one unique value, however the column is kept for comparison with other BBC datasets.

Source

Body image dataset

Description

Body image dataset

Usage

body_image

Format

A tibble of 246 observations on 8 attributes.

ethnicity

Subject's ethnicity (Asian, Europn, Maori, Pacific)

married

How many times have they been married?

bodyim

Subject's rating of themselves (slight.uw, right, slight.ow, mod.ow, very.ow)

sm.ever

Have they ever smoked?

weight

Weight in kilograms

height

Height in centimetres

age

Age in years

stressgp

What stress group are they in?

Details

A simulated dataset containing data on the self-image of subjects with differing body aesthetics

Source

Simulated data

Gutenberg Project books dataset

Description

A mixed up collection of words from different book sections of two books.

Usage

book_sections

Format

A tibble with 108,657 observations, each a word on a document. This data set is designed to show how LDA can be used to separate a set of mixed documents into two distinct "topics" (or books).

word

Words from a given section within a book.

document

The book section ID that the word came from.

Source

Data taken from two books of the Gutenberg Project

Boston housing dataset

Description

Dataset containing housing values in the suburbs of Boston.

Usage

boston

Format

This data frame contains the following columns:

tract

Census tract

medv

Median value of owner-occupied homes in $1,000s.

crim

Per capita crime rate by town.

zn

Proportion of residential land zoned for lots over 25,000 sq.ft.

indus

Proportion of non-retail business acres per town.

chas

Charles River dummy variable (= 1 if tract bounds river; 0 otherwise).

nox

Nitrogen oxides concentration (parts per 10 million).

rm

Average number of rooms per dwelling.

age

Proportion of owner-occupied units built prior to 1940.

dis

Weighted mean of distances to five Boston employment centres.

rad

Index of accessibility to radial highways.

tax

Full-value property-tax rate per $10,000.

ptratio

Pupil-teacher ratio by town.

b

1000(Bk - 0.63)^2 where Bk is the proportion of blacks by town.

lstat

Lower status of the population (percent).

Details

The boston data frame has 506 rows and 15 columns.

Source

Harrison, D. and Rubinfeld, D.L. (1978) Hedonic prices and the demand for clean air. J. Environ. Economics and Management 5, 81–102.

Belsley D.A., Kuh, E. and Welsch, R.E. (1980) Regression Diagnostics. Identifying Influential Data and Sources of Collinearity. New York: Wiley.

Wisconsin Diagnostic Breast Cancer (WDBC)

Description

The data contain measurements on cells in suspicious lumps in a women's breast. Features are computed from a digitised image of a fine needle aspirate (FNA) of a breast mass. They describe characteristics of the cell nuclei present in the image. All samples are classified as either benign or malignant.

Usage

breast_cancer

Format

breast_cancer is a tibble with 22 columns. The first column is an ID column. The second indicates whether the sample is classified as benign or malignant. The remaining columns contain measurements for 20 features. Ten real-valued features are computed for each cell nucleus. The references listed below contain detailed descriptions of how these features are computed. The mean, and "worst" (or largest - mean of the three largest values) of these features were computed for each image, resulting in 20 features. Below are descriptions of these features where * should be replaced by either mean or worst.

*_radius

mean of distances from center to points on the perimeter

*_texture

standard deviation of gray-scale values

*_perimeter

perimeter value

*_area

area value

*_smoothness

local variation in radius lengths

*_compactness

perimeter^2 / area - 1.0

*_concavity

severity of concave portions of the contour

*_concave_points

number of concave portions of the contour

*_symmetry

symmetry value

*_fractal_dimension

"coastline approximation" - 1

Note

This breast cancer database was obtained from the University of Wisconsin Hospitals, Madison from Dr. William H. Wolberg.

Source

https://archive.ics.uci.edu/ml/datasets/Breast+Cancer+Wisconsin+(Diagnostic)

Bache, K. & Lichman, M. (2013). UCI Machine Learning Repository. Irvine, CA: University of California, School of Information and Computer Science.

References

O. L. Mangasarian and W. H. Wolberg: "Cancer diagnosis via linear programming",
SIAM News, Volume 23, Number 5, September 1990, pp 1 & 18. William H. Wolberg and O.L. Mangasarian: "Multisurface method of pattern separation for medical diagnosis applied to breast cytology",
Proceedings of the National Academy of Sciences, U.S.A., Volume 87, December 1990, pp 9193-9196. K. P. Bennett & O. L. Mangasarian: "Robust linear programming discrimination of two linearly inseparable sets",
Optimization Methods and Software 1, 1992, 23-34 (Gordon & Breach Science Publishers).

Wisconsin Breast Cancer Database

Description

Wisconsin Breast Cancer Database

Usage

breast_cancer_clean_features

Format

A list containing a training and test dataset. These come from a data frame with 699 observations on 11 variables, however the ID and class columns have been removed. There is a train to test ratio of 0.8.

Cl.thickness

Clump Thickness

Cell.size

Uniformity of Cell Size

Cell.shape

Uniformity of Cell Shape

Marg.adhesion

Marginal Adhesion

Epith.c.size

Single Epithelial Cell Size

Bare.nuclei

Bare Nuclei

Bl.cromatin

Bland Chromatin

Normal.nucleoli

Normal Nucleoli

Mitoses

Mitoses

Source

These data have been taken from the UCI Repository Of Machine Learning Databases at

and were converted to R format by Evgenia Dimitriadou.

References

1. Wolberg,W.H., \& Mangasarian,O.L. (1990). Multisurface method of pattern separation for medical diagnosis applied to breast cytology. In Proceedings of the National Academy of Sciences, 87, 9193-9196.
- Size of data set: only 369 instances (at that point in time)
- Collected classification results: 1 trial only
- Two pairs of parallel hyperplanes were found to be consistent with 50% of the data
- Accuracy on remaining 50% of dataset: 93.5%
- Three pairs of parallel hyperplanes were found to be consistent with 67% of data
- Accuracy on remaining 33% of dataset: 95.9%

2. Zhang,J. (1992). Selecting typical instances in instance-based learning. In Proceedings of the Ninth International Machine Learning Conference (pp. 470-479). Aberdeen, Scotland: Morgan Kaufmann.
- Size of data set: only 369 instances (at that point in time)
- Applied 4 instance-based learning algorithms
- Collected classification results averaged over 10 trials
- Best accuracy result:
- 1-nearest neighbor: 93.7%
- trained on 200 instances, tested on the other 169
- Also of interest:
- Using only typical instances: 92.2% (storing only 23.1 instances)
- trained on 200 instances, tested on the other 169

Newman, D.J. & Hettich, S. & Blake, C.L. & Merz, C.J. (1998). UCI Repository of machine learning databases [http://www.ics.uci.edu/~mlearn/MLRepository.html]. Irvine, CA: University of California, Department of Information and Computer Science.

Wisconsin Breast Cancer Database

Description

Wisconsin Breast Cancer Database

Usage

breast_cancer_clean_target

Format

A list containing a training and test dataset. These come from a data frame with 699 observations on 11 variables, however only the target classes have been kept. There is a train to test ratio of 0.8.

Class.Benign

Whether the sample was classified as benign

Class.malignant

Whether the sample was classified as malignant

2. Zhang,J. (1992). Selecting typical instances in instance-based learning. In Proceedings of the Ninth International Machine Learning Conference (pp. 470-479). Aberdeen, Scotland: Morgan Kaufmann.
- Size of data set: only 369 instances (at that point in time)
- Applied 4 instance-based learning algorithms
- Collected classification results averaged over 10 trials
- Best accuracy result:
- 1-nearest neighbor: 93.7%
- trained on 200 instances, tested on the other 169
- Also of interest:
- Using only typical instances: 92.2% (storing only 23.1 instances)
- trained on 200 instances, tested on the other 169

Newman, D.J. & Hettich, S. & Blake, C.L. & Merz, C.J. (1998). UCI Repository of machine learning databases [http://www.ics.uci.edu/~mlearn/MLRepository.html]. Irvine, CA: University of California, Department of Information and Computer Science.

Source

These data have been taken from the UCI Repository Of Machine Learning Databases at

and were converted to R format by Evgenia Dimitriadou.

Carrier data

Description

This data comes from the RITA/Transtats database

Usage

carriers

Format

A dataframe with 1492 observations and 2 variables

Code

A character string giving the IATA code for the carrier

Description

Carrier name/description

R For Data Science tidytuesday commute dataset

Description

Data from the ACS Survey detailing the use of different transport modes

Usage

commute

Format

A tibble containing 3,496 observations of 9 variables

city

City

state

State

city_size

City Size -

  • Small = 20K to 99,999

  • Medium = 100K to 199,999

  • Large = >= 200K

mode

Mode of transport, either walk or bike

n

Number of individuals

percent

Percent of total individuals

moe

Margin of Error (percent)

state_abb

Abbreviated state name

state_region

ACS State region

Source

American Community Survey, United States Census Bureau

Demographics data

Description

A simulated dataset containing demographic data about a number of subjects.

Usage

demo_data

demoData

Format

A data frame with 33 observations on the following 7 demographic variables. This data is designed so that it can be merged with the dataset pk_data.

Subject

A numeric vector giving the subject identifier

Sex

A factor with levels F M

Age

A numeric vector giving the age of the subject

Weight

A numeric vector giving weight in kg

Height

A numeric vector giving height in cm

BMI

A numeric vector giving the subject body mass index

Smokes

A factor with levels No Yes

Details

This dataset has be renamed using tidyverse-style snake_case naming conventions. However the original name of the dataset has been kept to ensure backwards compatibility with the book SAMS Teach Yourself R in 24 Hours (ISBN: 978-0-672-33848-9).

Source

Simulated data

Dow Jones Index Data

Description

Dataset containing the Dow Jones Index between 2014-01-01 and 2015-01-01, which is a stock market index that measures the stock performance of 30 large companies listed on stock exchanges in the United States.

Usage

dow_jones_data

dowJonesData

Format

A data frame with 252 observations on the following 7 variables containing data from 2014-01-01 to 2015-01-01.

Date

Date of observation in character string format "%m/%d/%Y"

DJI.Open

Opening value of DJI on the specified date

DJI.High

High value of the DJI on the specified date

DJI.Low

Low value of the DJI on the specified date

DJI.Close

Closing value of the DJI on the specified date

DJI.Volume

the number of shares or contracts traded

DJI.Adj.Close

Close price adjusted for dividends and splits

Details

This dataset has be renamed using tidyverse-style snake_case naming conventions. However the original name of the dataset has been kept to ensure backwards compatibility with the book SAMS Teach Yourself R in 24 Hours (ISBN: 978-0-672-33848-9).

Source

Data obtained using yahooSeries from the fImport package.

Repeated Measures Drug data

Description

Repeated Measures Drug data

Usage

drugs

Format

A data frame with 20 observations on the following 3 variables.

Subj

A numeric vector, giving the subject ID

Drug

A numeric vector giving the drug ID, numbered 1 to 4

Value

A numeric vector, giving the observation value

Source

Generated from example data used in https://www.stattutorials.com/SAS/TUTORIAL-PROC-GLM-REPEAT.htm

Data that can be used to fit or plot Emax models

Description

Data that can be used to fit or plot Emax models

Usage

emax_data

emaxData

Format

A data frame with 64 observations on the following 6 variables.

Subject

a numeric vector giving the unique subject ID

Dose

a numeric vector giving the dose group

E

a numeric vector giving the Emax

Gender

a numeric vector giving the gender

Age

a numeric vector giving the age of the subject

Weight

a numeric vector giving the weight

Details

This dataset has be renamed using tidyverse-style snake_case naming conventions. However the original name of the dataset has been kept to ensure backwards compatibility with the book SAMS Teach Yourself R in 24 Hours (ISBN: 978-0-672-33848-9).

Source

Simulated data

Function to calculate Emax

Description

Calculation used for Emax in Ascent materials. Note: This function has be renamed using tidyverse-style snake_case naming conventions. However the original name of the function has been kept to ensure backwards compatibility with the book SAMS Teach Yourself R in 24 Hours (ISBN: 978-0-672-33848-9).

Usage

emax_fun(Dose, E0 = 0, ED50 = 50, Emax = 100)

Arguments

Dose

User provided dose values

E0

Effect at time 0

ED50

50% of maximum effect

Emax

Maximum effect

Value

Numeric value/vector representing the response value.

Examples

emax_fun(Dose = 100)

Function to fit logistic model

Description

Simple logistic function as used in Ascent training materials. Note: This function has be renamed using tidyverse-style snake_case naming conventions. However the original name of the function has been kept to ensure backwards compatibility with the book SAMS Teach Yourself R in 24 Hours (ISBN: 978-0-672-33848-9).

Usage

logistic_fun(Dose, E0 = 0, EC50 = 50, Emax = 1, rc = 5)

Arguments

Dose

The dose value to calculate at

E0

Effect at time 0

EC50

50% of maximum effect

Emax

Maximum effect

rc

rate constant

Value

Numeric value/vector representing the response value.

Examples

logistic_fun(Dose = 50)

Messy clinical trial data

Description

Simulated dataset for examples of reshaping data

Usage

messy_data

messyData

Format

A data frame with 33 observations on the following 7 variables. This data has been designed to show reshaping/tidying of data.

Subject

A numeric vector giving the subject ID

Placebo.1

A numeric vector giving the subjects observed value on treatment Placebo at time 1

Placebo.2

A numeric vector giving the subjects observed value on treatment Placebo at time 2

Drug1.1

A numeric vector giving the subjects observed value on treatment Drug1 at time 1

Drug1.2

A numeric vector giving the subjects observed value on treatment Drug1 at time 2

Drug2.1

A numeric vector giving the subjects observed value on treatment Drug2 at time 1

Drug2.2

A numeric vector giving the subjects observed value on treatment Drug2 at time 2

Details

This dataset has be renamed using tidyverse-style snake_case naming conventions. However the original name of the dataset has been kept to ensure backwards compatibility with the book SAMS Teach Yourself R in 24 Hours (ISBN: 978-0-672-33848-9).

Source

Simulated data

Clinical trial data

Description

Clinical trial data

Usage

missing_pk

missingPk

Format

A data frame with 165 observations on the following 4 variables.

Subject

a numeric vector giving the subject identifier

Dose

a numeric vector giving the dose group

Time

a numeric vector giving the observation times

Conc

a numeric vector giving the observed concentration

Details

This dataset has be renamed using tidyverse-style snake_case naming conventions. However the original name of the dataset has been kept to ensure backwards compatibility with the book SAMS Teach Yourself R in 24 Hours (ISBN: 978-0-672-33848-9).

Source

Simulated from 'pk_data'

Typical PK data

Description

Typical PK data

Usage

pk_data

pkData

Format

A data frame with 165 observations on the following 4 variables.

Subject

a numeric vector giving the subject identifier

Dose

a numeric vector giving the dose group

Time

a numeric vector giving the observation times

Conc

a numeric vector giving the observed concentration

Details

This dataset has be renamed using tidyverse-style snake_case naming conventions. However the original name of the dataset has been kept to ensure backwards compatibility with the book SAMS Teach Yourself R in 24 Hours (ISBN: 978-0-672-33848-9).

Source

Simulated data

Insurance Policy Data

Description

Insurance Policy Data

Usage

policy_data

policyData

Format

A data frame with 926 observations on the following 13 variables.

Year

The four digit year of the policy

PolicyNo

The policy number

TotalPremium

The total insurance premium

BonusMalus

Discount level

WeightClass

The weight class of the car

Region

Region of the car owner

Age

Age of the main driver

Mileage

Estimated annual mileage

Usage

Car usage

PremiumClass

Class of the car

NoClaims

Number of previous claims

GrossIncurred

Claim cost

Exposure

How long they have been driving

Details

This dataset has be renamed using tidyverse-style snake_case naming conventions. However the original name of the dataset has been kept to ensure backwards compatibility with the book SAMS Teach Yourself R in 24 Hours (ISBN: 978-0-672-33848-9).

Source

Simulated based on details of how to simulate car insurance data in Modern Actuarial Risk Theory Using R 2nd Edition (Rob Kaas, Marc Goovaerts, Jan Dhaene, Michel Denuit)

Typical PK data

Description

Typical PK data

Usage

qtpk2

Format

A data frame with 2061 observations on the following 8 variables.

subjid

A numeric vector giving the subject ID

treat

A factor giving the treatment

time

A numeric vector giving the observation times

qt

A numeric vector giving the QT interval value

qtcb

A numeric vector giving corrected QT interval

hr

A numeric vector giving the heart rate

rr

A numeric vector giving the R-R interval

sex

A factor giving the subject sex

Source

A subset of the data qtpk originally provided in the QT package

An example of NONMEM run data

Description

This dataset has be renamed using tidyverse-style snake_case naming conventions. However the original name of the dataset has been kept to ensure backwards compatibility with the book SAMS Teach Yourself R in 24 Hours (ISBN: 978-0-672-33848-9).

Usage

run_data

runData

Format

A data frame with 73 observations on the following 10 variables.

ID

a numeric vector giving the subject ID

DAY

a numeric vector giving the day of the observation

CL

a numeric vector giving the clearance value

V

a numeric vector giving the volume of distribution

WT

a numeric vector giving the weight

DV

a numeric vector giving the dependent variable

IPRE

a numeric vector giving the individual prediction

PRED

a numeric vector giving the population prediction

RES

a numeric vector giving the residual

WRES

a numeric vector giving the weighted residual

Source

Simulated Data

Students simulated data

Description

Students simulated data

Usage

students

Format

A tibble with 146 observations of 15 variables.

Grade

Final grade (A, B, C, D)

Pass

Did they pass the course? (No, Yes)

Exam

Mark in final exam (out of 100)

Degree

The degree type undertaken by the student

Gender

Gender of the student

Attend

Did they regularly attend class? (No, Yes)

Assign

Score obtained in mid-term assignment (out of 20)

Test

Score obtained in previous term test (out of 20)

B

Mark for short answer section (out of 20)

C

Mark for long answer section (out of 20)

MC

Mark for multiple choice sectionC (out of 30)

Colour

Colour of exam booklet (Blue, Green, Pink, Yellow)

Stage1

Stage one grade (A, B, C)

Years.Since

Number of years since doing Stage 1

Repeat

Where they repeating the paper? (No, Yes)

Source

Simulated data

London Tube Performance data

Description

London Tube Performance data

Usage

tube_data

tubeData

Format

A data frame with 1050 observations on the following 9 variables.

Line

A factor with 10 levels, one for each London tube line

Month

A numeric vector indicating the month of the observation

Scheduled

A numeric vector giving the scheduled running time

Excess

A numeric vector giving the excess running time

TOTAL

A numeric vector giving the total running time

Opened

A numeric vector giving the year the line opened

Length

A numeric vector giving the line length

Type

A factor indicating the type of tube line

Stations

A numeric vector giving the number of stations on the line

Details

This dataset has be renamed using tidyverse-style snake_case naming conventions. However the original name of the dataset has been kept to ensure backwards compatibility with the book SAMS Teach Yourself R in 24 Hours (ISBN: 978-0-672-33848-9).

Source

This data was taken from "https://data.london.gov.uk/dataset/tube-network-performance-data-transport-committee-report"

Iris predictors data for Species classification

Description

This data was taken from Edgar Anderson's famous iris data set. This gives the measurements (in centimeters) of the variables sepal length and width and petal length and width, respectively, for 50 flowers from each of 3 species of iris. The species are Iris setosa, versicolor, and virginica. However, the species is seen as the target variable, and as such has been removed from this dataset, whilst being added to the counterpart y_iris dataset. Furthermore, the 4 remaining 'predictor' variables have been separated into a training and test set with a ratio of 4:1, followed by centering and scaling.

Usage

x_iris

Format

A list of two named matrices, 'train' and 'test', representing the training and test sets for the predictors. These have 4 columns each, with 120 and 30 rows respectively.

Sepal.Length

Sepal length

Sepal.Width

Sepal width

Petal.Length

Petal length

Petal.Width

Petal width

Source

Fisher, R. A. (1936) The use of multiple measurements in taxonomic problems. Annals of Eugenics, 7, Part II, 179-188. The data were collected by Anderson, Edgar (1935). The irises of the Gaspe Peninsula, Bulletin of the American Iris Society, 59, 2-5

References

Becker, R. A., Chambers, J. M. and Wilks, A. R. (1988) The New S Language. Wadsworth & Brooks/Cole.

Typical NONMEM data

Description

Typical NONMEM data

Usage

xp_data

xpData

Format

A data frame with 1061 observations on the following 23 variables.

ID

a numeric vector giving the subject ID

SEX

a numeric vector giving the subject sex

RACE

a numeric vector giving the subject race

SMOK

a numeric vector giving the subject smoking status

HCTZ

a numeric vector giving the treatment status

PROP

a numeric vector giving the treatment status

CON

a numeric vector giving the treatment status

DV

a numeric vector giving the dependent variable

PRED

a numeric vector giving population prediction

RES

a numeric vector giving the residual

WRES

a numeric vector giving the weighted residual

AGE

a numeric vector giving the subject age

HT

a numeric vector giving the subject height

WT

a numeric vector giving the subject weight

SECR

a numeric vector giving the serum creatinine value

OCC

a numeric vector giving the occasion

TIME

a numeric vector giving the time of the observation time

IPRE

a numeric vector giving individual prediction

IWRE

a numeric vector giving the individual weighted residual

SID

a numeric vector giving the site ID

CL

a numeric vector giving the clearance

V

a numeric vector giving the volume of distribution

KA

a numeric vector giving the absorption rate constant

Details

This dataset has be renamed using tidyverse-style snake_case naming conventions. However the original name of the dataset has been kept to ensure backwards compatibility with the book SAMS Teach Yourself R in 24 Hours (ISBN: 978-0-672-33848-9).

Source

Simulated Data

Iris class data for Species classification

Description

This data was taken from Edgar Anderson's famous iris data set. This gives the measurements (in centimeters) of the variables sepal length and width and petal length and width, respectively, for 50 flowers from each of 3 species of iris. The species are Iris setosa, versicolor, and virginica. This is the target dataset (as a counterpart to the x_iris dataset) and thus only retains the Species information. As with the x_iris dataset, the data has been split into a training and test set with a ratio of 4:1. Following this the species class has been one-hot encoded to give three columns, one for each species level.

Usage

y_iris

Format

A list of two named matrices, 'train' and 'test', representing the training and test sets for the predictors. These have 3 indicator columns each, with 120 and 30 rows respectively.

Species.setosa

Indicator column for the species class setosa

Species.versicolor

Indicator column for the species class versicolor

Species.virginica

Indicator column for the species class virginica

Source

Fisher, R. A. (1936) The use of multiple measurements in taxonomic problems. Annals of Eugenics, 7, Part II, 179-188. The data were collected by Anderson, Edgar (1935). The irises of the Gaspe Peninsula, Bulletin of the American Iris Society, 59, 2-5

References

Becker, R. A., Chambers, J. M. and Wilks, A. R. (1988) The New S Language. Wadsworth & Brooks/Cole.