Package 'ibawds'

Description

Summary of data on restaurant bills from the dataset reshape2::tips. Labels are in German.

Usage

bills

Format

A data frame with 8 rows and 4 variables:

sex

sex of the bill payer

time

time of day

smoker

whether there were smokers in the party

mean_bill

mean of all the bills in dollars

Description

Breast cancer database obtained from the University of Wisconsin Hospitals, Madison from Dr. William H. Wolberg. The data were collected in 8 from 1989 to 1991 and are sorted in chronological order.

Usage

breast_cancer

Format

a tibble with 699 rows and 11 variables. All numerical values are integers in the range 1 to 10.

id

sample code number

clump_thick

clump thickness

unif_cell_size

uniformity of cell size

unif_cell_shape

uniformity of cell shape

marg_adh

marginal adhesion

ep_cell_size

single epithelial cell size

bare_nucl

bare nuclei

bland_chromat

bland chromatin

norm_nucl

normal nucleoli

mitoses

mitoses

class

"benign" (458) or "malignant" (241)

Source

The data is available on the UC Irvine Machine Learning Repository.

O. L. Mangasarian and W. H. Wolberg, Cancer diagnosis via linear programming, SIAM News, Volume 23(5) (1990) 1 & 18.

Description

Check if the current system is ready for the course by verifying the following:

• R and RStudio are up to date

• the ibawds package is up to date

• all the required packages are installed

The function must be run from RStudio in order to run properly.

Usage

check_ibawds_setup()

Value

a logical indicating whether the system is up to date (invisibly). Messages inform the user about the status of the system.

Description

ibawds offers the function install_ibawds() which installs all the packages that are required for the course. check_lecture_packages() finds all the packages that are used in the slides and exercise solution inside a directory. It then checks whether they are all installed by install_ibawds() and returns a tibble of those that are not. This can help to identify, if additional packages need to be installed by install_ibawds().

Usage

check_lecture_packages(path = ".")

Arguments

Value

a tibble with two columns:

file

the file where the package is used

package

the name of the package

Description

For a given dataset and given centres, cluster_with_centers() assigns each data point to its closest centre and then recomputes the centres as the mean of all points assigned to each class. An initial set of random cluster centres can be obtained with init_rand_centers(). These functions can be used to visualise the mechanism of k-means.

Usage

cluster_with_centers(data, centers)

init_rand_centers(data, n, seed = sample(1000:9999, 1))

Arguments

Value

a list containing two tibbles:

• centers: the new centres of the clusters computed after cluster assignment with the given centres

• cluster: the cluster assignment for each point in data using the centres that were passed to the function

Examples

# demonstrate k-means with iris data
# keep the relevant columns
iris2 <- iris[, c("Sepal.Length", "Petal.Length")]

# initialise the cluster centres
clust <- init_rand_centers(iris2, n = 3, seed = 2435)

# plot the data with the cluster centres
library(ggplot2)
ggplot(iris2, aes(x = Sepal.Length, y = Petal.Length)) +
 geom_point(data = clust$centers, aes(colour = factor(1:3)),
            shape = 18, size = 6) +
 geom_point() +
 scale_colour_brewer(palette = "Set1")

# assign clusters and compute new centres
clust_new <- cluster_with_centers(iris2, clust$centers)

# plot the data with clustering
clust$cluster <- clust_new$cluster
voronoi_diagram(clust, x = "Sepal.Length", y = "Petal.Length",
                data = iris2)

# plot the data with new cluster centres
clust$centers <- clust_new$centers
voronoi_diagram(clust, x = "Sepal.Length", y = "Petal.Length",
                data = iris2, colour_data = FALSE)

# this procedure may be repeated until the algorithm converges

Description

Table with the number of packages available on CRAN and the current R version for historic dates back to 21 June 2001.

Usage

cran_history

Format

A data frame with 69 rows and 4 variables.

date

date

n_packages

the number of available R packages on CRAN

version

the then current version of R

source

source of the data (see 'Details')

Details

Data on the number of packages on CRAN between 2001-06-21 and 2014-04-13 is obtained from CRANpackages from the package Ecdat. This data was collected by John Fox and Spencer Graves. Intervals between data points are irregularly spaced. These data are marked with "John Fox" or "Spencer Graves" in the column source. They are licenced under GPL-2/GPL-3.

Data between 2014-10-01 and 2023-03-06 was collected by the package author from CRAN snapshots on Microsoft's MRAN, which was retired on 1 July 2023. Data was collected on the first day of each quarter. These data are marked with "MRAN" in the column source.

Newer data has been collected in irregular intervals using the functions n_available_packages() and available_r_version(). These data are marked with "CRAN" in the column source.

Examples

library(ggplot2)
ggplot(cran_history, aes(x = date, y = n_packages)) +
  geom_point()

Description

Add the definitions for various useful LaTeX equation symbols for statistics to an RMarkdown document.

Usage

define_latex_stats()

Details

Run this function from within a code chunk in a RMarkdown document with options results = "asis" and echo = FALSE (see "Examples"). It only works for pdf output.

It defines the following macros: ⁠\E⁠, ⁠\P⁠, ⁠\Var⁠, ⁠\Cov⁠, ⁠\Cor⁠, ⁠\SD⁠, ⁠\SE⁠, ⁠\Xb⁠, ⁠\Yb⁠.

Value

The function returns NULL invisibly. The command definitions are output as a side effect.

Examples

## Not run: 
# add this code chunk to a RMarkdown document
```{r results = "asis", echo = FALSE}
  define_latex_stats()
```

## End(Not run)

Description

Dental formulas for various mammals. The dental formula describes the number of incisors, canines, premolars and molars per quadrant. Upper and lower teeth may differ and are therefore shown separately. The total number of teeth is twice the number given.

Usage

dentition

Format

Data frame with 66 rows and 9 variables:

name

name of the mammal

I

number of top incisors

i

number of bottom incisors

C

number of top canines

c

number of bottom canines

P

number of top premolars

p

number of bottom premolars

M

number of top molars

m

number of bottom molars

Source

The data have been downloaded from https://people.sc.fsu.edu/~jburkardt/datasets/hartigan/file19.txt

They come from the following textbook:

Hartigan, J. A. (1975). Clustering Algorithms, John Wiley, New York.

Table 9.1, page 170.

Description

Create plots of the density and distribution functions of a probability distribution. It is possible to mark points and shade the area under the curve.

Usage

distribution_plot(
  fun,
  range,
  ...,
  points = NULL,
  var = "x",
  title = "Verteilungsfunktion",
  is_discrete = NULL
)

density_plot(
  fun,
  range,
  ...,
  from = NULL,
  to = NULL,
  points = NULL,
  var = "x",
  title = "Dichte",
  is_discrete = NULL
)

Arguments

Value

a ggplot object

Examples

# plot density of the normal distribution
density_plot(dnorm, c(-5, 7),
             mean = 1, sd = 2,
             to = 3)

# plot distribution function of the Poisson distribution
distribution_plot(ppois, c(0, 12),
                  lambda = 4,
                  points = c(2, 6, 10),
                  var = "y")

Description

Downgrade packages to an older version available on CRAN. This can be useful when debugging problems that might have arisen due to a package update.

Usage

downgrade_packages(pkg, dec_version = c("any", "patch", "minor", "major"))

Arguments

Details

Using the argument dec_version, the user can control which version will be installed. The possible values are:

"any"

The previous available version will be installed.

"patch"

The newest available version with a smaller patch version number will be installed. For packages with three version numbers, this is the same as using "any".

"minor"

The newest available version with a smaller minor version number will be installed.

"major"

The newest available version with a smaller major version number will be installed.

Downgrading is only possible for packages that are currently installed. For packages that are not installed, a warning is issued.

The function uses remotes::install_version() to install a version of a package that is older than the currently installed version.

Value

A character vector with the names of the downgraded packages, invisibly.

Description

Find the named colour that is most similar to a given colour.

Usage

find_similar_colour(
  colour,
  distance = c("euclidean", "manhattan"),
  verbose = interactive()
)

Arguments

Value

a character of length one with the name of the most similar named colour.

Examples

find_similar_colour("#d339da")
find_similar_colour(c(124, 34, 201))

# suppress additional output
find_similar_colour("#85d3a1", verbose = FALSE)

# use Manhattan distance
find_similar_colour(c(124, 34, 201), distance = "manhattan")

Description

Two tables of father's heights with heights of one of their sons (galton_sons) or daughters (galton_daughters), respectively. All heights are given in centimetres. It is created from HistData::GaltonFamilies by randomly selecting one son or daughter per family. Since some families consist of only sons or only daughters, not all families are contained in both tables.

Usage

galton_sons

galton_daughters

Format

Two data frames with 179 (galton_sons) or 176 (galton_daughters)$ rows, respectively, and 2 variables:

father

size of the father in cm.

son/daughter

size of the son or daughter, respectively, in cm.

Description

Copy the files for an exercise for reading files to a directory.

Usage

get_reading_exercise_files(path, unzip = TRUE)

Arguments

Details

There are 8 files in total. Apart from a few errors that were introduced for the purpose of the exercise, they all contain the same data: information about 100 randomly selected Swiss municipalities. The full file can be downloaded from https://www.bfs.admin.ch/bfsstatic/dam/assets/7786544/master.

Value

Logical indicating the success of the copy operation.

Description

These functions create two tables that can be used for the grading of the student's papers.

Usage

create_minreq_table(repro, n_tab, n_plot_kinds, n_plots, n_stat)

create_grading_table(p_text, p_tab, p_plot, p_code, p_stat)

Arguments

Details

The tables are created using knitr::kable() and kableExtra::kableExtra is used for additional styling.

create_minreq_table() creates a table that checks that the minimal requirements are satisfied:

• the paper must be reproducible

• there must be at least one table and two kinds of plots

• there must be at least 5 plots and tables

• there must be at least two statistical computations

The table lists for each of those requirement whether it is satisfied or not.

create_grading_table() creates a table that gives grades in percent for each of five categories:

• Text

• Tables

• Plots

• Code

• Statistical computations

In each category, up to five points may be awarded. The last row of the table gives the percentage over all categories.

Value

both functions return an object of class kableExtra.

Description

A number of R-packages are used in the courses and the video lectures. They are also dependencies of this package. Use install_ibawds() to install the packages that are not yet installed.

Usage

install_ibawds()

Details

This function checks whether all the packages that ibawds depends on, imports or suggests are installed. In interactive sessions, it either informs the user that all packages are installed or asks to install missing packages. The function relies on rlang::check_installed().

Value

nothing or NULL invisibly

Description

In the mtcars dataset, the names of the car models are stored as row names. However, when working with ggplot2 and other packages from the tidyverse, it is convenient to have all data in columns. mtcars2 is a variant of mtcars that contains car models in a column instead of storing them as row names. mtcars_na is the same dataset as mtcars2, but some of the columns contain missing values.

Usage

mtcars2

mtcars2_na

Format

A data frame with 32 rows and 12 variables. The format is identical to mtcars and details can be found in its documentation. The only difference is that the car model names are stored in the column model instead of the row names.

Description

Obtain the number of available packages on CRAN and the current R version.

Usage

n_available_packages(cran = getOption("repos"))

available_r_version(cran = getOption("repos"))

Arguments

Details

The number of packages on CRAN and the R version can be obtained for selected dates in the past from the dataset cran_history.

Note: Previously, these functions could obtain the number of packages on CRAN and the then current R version also for past dates by using snapshots from Microsoft's MRAN. However, MRAN shut down on 1 July 2023 such that this functionality is no longer available.

Value

the number of available packages as an integer or the R version number as a character

See Also

cran_history

Description

Training and test data created from a tenth order polynomial with added noise. The polynomial is given by

$f(x) = 2 x - 10 x^5 + 15 x^{10}$

The noise follows a standard normal distribution. The data can be used to demonstrate overfitting. It is inspired by section II. B. in A high-bias, low-variance introduction to Machine Learning for physicists

Usage

noisy_data

Format

a list of two tibbles with two columns each. $x$ stands for the independent, $y$ for the dependent variable. The training data (noisy_data$train) contains 1000 rows, the test data (noisy_data$test) 20 rows.

References

P. Mehta et al., A high-bias, low-variance introduction to Machine Learning for physicists Phys. Rep. 810 (2019), 1-124. arXiv:1803.08823 doi:10.1016/j.physrep.2019.03.001

Description

Protein Consumption from various sources in European countries in unspecified units. The exact year of data collection is not known but the oldest known publication of the data is from 1973.

Usage

protein

Format

Data frame with 25 rows and 10 variables:

country

name of the country

red_meat

red meat

white_meat

white meat

eggs

eggs

milk

milk

fish

fish

cereals

cereals

starch

starchy foods

nuts

pulses, nuts, oil-seeds

fruit_veg

fruits, vegetables

Source

The data have been downloaded from https://raw.githubusercontent.com/jgscott/STA380/master/data/protein.csv

They come from the following book:

Hand, D. J. et al. (1994). A Handbook of Small Data Sets, Chapman and Hall, London.

Chapter 360, p. 297.

In the book, it is stated that the data have first been published in

Weber, A. (1973). Agrarpolitik im Spannungsfeld der internationalen Ernährungspolitik, Institut für Agrarpolitik und Marktlehre, Kiel.

Description

rand_with_cor() creates a vector of random number that has correlation rho with a given vector y. Also mean and standard deviation of the random vector can be fixed by the user. By default, they will be equal to the mean and standard deviation of y, respectively.

Usage

rand_with_cor(y, rho, mu = mean(y), sigma = sd(y))

Arguments

Value

a vector of the same length as y that has correlation rho with y.

Source

This solution is based on an answer by whuber on Cross Validated.

Examples

x <- runif(1000, 5, 8)

# create a random vector with positive correlation
y1 <- rand_with_cor(x, 0.8)
all.equal(cor(x, y1), 0.8)

# create a random vector with negative correlation
# and fixed mean and standard deviation
y2 <- rand_with_cor(x, -0.3, 2, 3)
all.equal(cor(x, y2), -0.3)
all.equal(mean(y2), 2)
all.equal(sd(y2), 3)

Description

Rescale Mean And/Or Standard Deviation of a Vector

Usage

rescale(x, mu = mean(x), sigma = sd(x))

Arguments

Details

By default, mean and standard deviation are not changed, i.e., rescale(x) is identical to x. Only if a value is specified for mu and/or sigma the mean and/or the standard deviation are rescaled.

Value

a numeric vector with the same length as x with mean mu and standard deviation sigma.

Examples

x <- runif(1000, 5, 8)

# calling rescale without specifying mu and sigma doesn't change anything
all.equal(x, rescale(x))

# change the mean without changing the standard deviation
x1 <- rescale(x, mu = 3)
all.equal(mean(x1), 3)
all.equal(sd(x1), sd(x))

# rescale mean and standard deviation
x2 <- rescale(x, mu = 3, sigma = 2)
all.equal(mean(x2), 3)
all.equal(sd(x2), 2)

Description

Extract of the data in the Seatbelts dataset as a data frame. The original dataset is a multiple time series (class mts). Labels are in German.

Usage

seatbelts

Format

A data frame with 576 rows and 3 variables:

date

data of the first data of the month for which the data was collected.

seat

seat where the persons that were killed or seriously injured were seated. One of "Fahrer" (driver's seat), "Beifahrer" (front seat), "Rücksitz" (rear seat).

victims

number of persons that were killed or seriously injured.

Description

Set options for ggplot plots and tibble outputs for IBAW slides.

Usage

set_slide_options(
  ggplot_text_size = 22,
  ggplot_margin_pt = rep(10, 4),
  tibble_print_max = 12,
  tibble_print_min = 8
)

Arguments

Details

The function uses ggplot2::theme_update() to modify the default theme for ggplot and options() to set base R options that influence the printing of tibbles.

Note that if you make changes to these options in a R Markdown file, you may have to delete the knitr cache in order for the changes to apply.

Value

a named list (invisibly) with to elements containing the old values of the options for the ggplot theme and the base R options, respectively. These can be used to reset the ggplot theme and the base R options to their previous values.

Description

Evaluation of the student papers, lecture slides and some exercises are all done in the form of Rmd files. These function find all the relevant Rmd-files in a directory and check the spelling using the package spelling.

Usage

spell_check_evaluation(path = ".", students = NULL, use_wordlist = TRUE)

spell_check_slides(path = ".", use_wordlist = TRUE)

Arguments

Details

spell_check_evaluation() finds Rmd-files with evaluations in subfolders starting from the current working directory or the directory given by path. The file names must be of the form "Beurteilung_Student.Rmd", where "Student" must be replaced by the student's name. By default, words contained in a wordlist that is part of the package as well as all the students' names are excluded from the spell check, but this can be turned off by setting use_wordlist = FALSE. (Note that the students' names will still be excluded.)

spell_check_slides() finds Rmd-files with evaluations in subfolders starting from the current working directory or the directory given by path. In order to exclude a file from the spell check, make sure it's first line contains the term "nospellcheck", typically in the form of an html-comment:

<!-- nospellcheck -->

By default, words contained in a wordlist that is part of the package are excluded from the spell check, but this can be turned off by setting use_wordlist = FALSE.

Description

Create a Voronoi diagram for a given clustering object.

Usage

voronoi_diagram(
  cluster,
  x,
  y,
  data = NULL,
  show_data = !is.null(data),
  colour_data = TRUE,
  legend = TRUE,
  point_size = 2,
  linewidth = 0.7
)

Arguments

Details

The function uses the deldir package to create the polygons for the Voronoi diagram. The code has been inspired by ggvoronoi, which can handle more complex situations.

References

Garrett et al., ggvoronoi: Voronoi Diagrams and Heatmaps with ggplot2, Journal of Open Source Software 3(32) (2018) 1096, doi:10.21105/joss.01096

Examples

cluster <- kmeans(iris[, 1:4], centers = 3)
voronoi_diagram(cluster, "Sepal.Length", "Sepal.Width", iris)

Description

Physicochemical data and quality ratings for red and white Portuguese Vinho Verde wines.

Usage

wine_quality

Format

a tibble with 6497 rows and 13 variables:

colour

colour of the wine; "red" (1'599) or "white" (4'898)

fixed_acidity

tartaric acid per volume in $g/dm^3$

volatile_acidity

acetic acid per volume in $g/dm^3$

citric_acid

citric acid per volume in $g/dm^3$

residual_sugar

residual sugar per volume in $g/dm^3$

chlorides

sodium chloride per volume in $g/dm^3$

free_sulfur_dioxide

free sulphur dioxide per volume in $mg/dm^3$

total_sulfur_dioxide

total sulphur dioxide per volume in $mg/dm^3$

density

density in $g/dm^3$

pH

pH value

sulphates

potassium sulphate per volume in $g/dm^3$

alcohol

alcohol content per volume in %

quality

quality score between 0 (worst) and 10 (best) determined by sensory analysis.

Source

The data is available on the UC Irvine Machine Learning Repository.

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