Package 'bcscr'

Description

Find and graph the Collatz sequence from a given initial number.

Usage

collatz(n, limit = 10000)

Arguments

Value

side effects

Author(s)

Homer White [email protected]

Examples

## Not run: 
collatz(1757)

## End(Not run)

Description

In this version all five judges vote independently.

Usage

courtSim(reps = 10000,
                seed = NULL,
                table = FALSE,
                probs = c(0.95, 0.94, 0.90, 0.90, 0.80))

Arguments

Value

side effects

Author(s)

Homer White [email protected]

Examples

courtSim(seed = 3030)

Description

In this version the weakest judge always votes with the strongest one.

Usage

courtSim2(reps = 10000,
                seed = NULL,
                table = FALSE,
                probs = c(0.95, 0.94, 0.90, 0.90, 0.80))

Arguments

Value

side effects

Author(s)

Homer White [email protected]

Examples

courtSim2(seed = 3030)

Description

Manipulate parameters of a chosen distribution.

Usage

distExplore(options = NULL)

Arguments

Value

side effects

Author(s)

Homer White [email protected]

Examples

## distExplore()

Description

A drunken turtle starts at the origin and takes unit steps, turning through a randdom angle after each step. We are interested in the distribution of the number of close-returns to the origin, for a fixed number of steps and a fixed measure of closeness.

Usage

drunkenSim(steps = 1000, reps = 10000, close = 0.5,
                       seed = NULL, table = FALSE)

Arguments

Value

side effects

Author(s)

Homer White [email protected]

Examples

drunkenSim(seed = 3030)

Description

A drunken turtle starts at the origin and takes unit steps, turning through a randdom angle after each step. We graph the distance from the origin as a function of step-number.

Usage

drunkenSim2(steps = 1000, seed = NULL)

Arguments

Value

side effects

Author(s)

Homer White [email protected]

Examples

## Not run: 
drunkenSim2(seed = 3030)

## End(Not run)

Description

A British Ford Escort was driven along a prescribed course. Each drive was done at a different speed, and the fuel efficiency was recorded for each drive.

Format

A data frame with 15 observations on the following 2 variables.

speed

in kilometers per hour.

efficiency

fuel efficiency, measured in liters of fuel required to travel 100 kilometers.

Source

The Basic Practice of Statistics, by Moore and McCabe.

Description

See how density plots are built from kernels.

Usage

kdExplore(data, options = NULL)

Arguments

Value

side effects

Author(s)

Homer White [email protected]

Examples

## small custom dataset:
## myData <- c(1, 3, 5, 6, 6.2, 7, 9)
## kdExplore(myData)

## random exponential data:
## kdExplore(rexp(50, rate = 0.2))

Description

Results of a survey of MAT 111 students at Georgetown College.

• height. How tall are you, in inches?

• ideal_ht. A numeric vector How tall would you LIKE to be, in inches?

• sleep. How much sleep did you get last night?

• fastest. What is the highest speed at which you have ever driven a car?

• weight_feel. How do you feel about your weight?

• love_first. Do you believe in love at first sight?

• extra_life. Do you believe in extraterrestrial life?

• seat. When you have a choice, where do you prefer to sit in a classroom?

• GPA. What is your college GPA?

• enough_Sleep. Do you think you get enough sleep?

• sex. What sex are you?

• diff. Your ideal height minus your actual height.

Format

A data frame with 71 rows and 12 variables

Source

Georgetown College, MAT 111.

Description

Utility function to make small directed acyclic graphs, using the data.tree package. Nodes are provided with randomly-selected numerical values.

Usage

make_val_tree(min_children = 1,
                     max_children = 4,
                     min_depth = 3,
                     values = 1:5,
                     fillcolor = "palegreen",
                     fontcolor = "black",
                     seed = NULL)

Arguments

Value

A tree object of class "Node" and "R6"

Author(s)

Homer White [email protected]

Examples

## Not run: 
tr <- make_val_tree(
  values = 1:10,
  seed = 4040,
  fillcolor = "lightblue"
)
library(DiagrammeR)
plot(tr)

## End(Not run)

Description

Sample data for regular expression practice.

• name. Last name followed by first.

• phone. Phone number with area code, in several formats.

Format

A data frame with 50 rows and 2 variables

Description

You pick random numbers from 0 to 1, until their sum exceeds some target number. What's the expected value of the number of numbers you have to pick?

Usage

numberNeededSim(target = 1, reps = 1000,
                seed = NULL, table = TRUE)

Arguments

Value

side effects

Author(s)

Homer White [email protected]

Examples

numberNeededSim(seed = 3030)

Description

R6 Object for simulating a population of whales.

Format

R6Class object.

Value

Object of R6Class with methods for simulation.

Properties

• dimensions: A vector of length two giving the dimensions of the ocean.

• males: A list of R6 objects of class Male containing the current population of male whales.

• females: A list of R6 objects of class Female containing the current population of female whales.

• malePop: Current number of males in the population.

• femalePop: Current number of females in the population.

• starveParameter: Helps determine probability for each whale to die by starvation in the current generation.

• distance: Computes distance between any two whales.

Methods

• new: Instantiates an Ocean object. Parameters are:

  • dims: A numeric vector of length 2 setting the length and width of the ocean.

  • males: An integer giving the number of males (to be created with defaults) or a list of Male whale objects.

  • females: An integer giving the number of females (to be created with defaults) or a list of Female whale objects.

  • starve: A non-negative number, used to determine probability that an individual starves in a given generation. The larger the value, the lower the carrying-capacity of the population will be.

• advance: Advances the simulation by one generation. Takes no arguments.

• plot: Plots the current population. Takes no arguments.

Methods

Public methods

• Ocean$distance()

• Ocean$new()

• Ocean$starvationProbability()

• Ocean$advance()

• Ocean$plot()

• Ocean$clone()

Method distance()

Usage

Ocean$distance(a, b)

Method new()

Usage

Ocean$new(dims = c(100, 100), males = 10, females = 10, starve = 5)

Method starvationProbability()

Usage

Ocean$starvationProbability(popDensity)

Method advance()

Usage

Ocean$advance()

Method plot()

Usage

Ocean$plot()

Method clone()

The objects of this class are cloneable with this method.

Usage

Ocean$clone(deep = FALSE)

Arguments

Author(s)

Homer White [email protected]

Examples

## Not run: 
library(ggplot2)
oceanSim <- function(
  steps = 100, males = 10,
  females = 10, starve = 5,
  animate = FALSE, seed = NULL
  ) {
 if ( !is.null(seed) ) {
   set.seed(seed)
  }
  ocean <- Ocean$new(dims = c(100, 100), males = males,
                     females = females, starve = starve)
  population <-numeric(steps)
  for ( i in 1:steps ) {
    population[i] <- ocean$malePop + ocean$femalePop
    if ( animate ) ocean$plot()
    if ( population[i] == 0 ) break
    ocean$advance()
    if ( animate ) {
        ocean$plot()
        Sys.sleep(0.5)
      }
  }
  pop <- population[1:i]
  df <- data.frame(time = 1:length(pop),
                  pop)
  ggplot(df, aes(x = time, y = pop)) + geom_line() +
    labs(x = "Time", y = "Whale Population")
}
oceanSim(seed = 5050)

## End(Not run)

Description

Understand why qq-plots bend the way they do.

Usage

qqExplore(data, options = NULL)

Arguments

Value

side effects

Author(s)

Homer White [email protected]

Examples

## bimodal data:
## bimodal <- c(rnorm(50, 5, 1), rnorm(50, 10, 1))
## qqExplore(bimodal)

## random exponential data:
## qqExplore(rexp(300, rate = 0.2))

Description

This data table is modifed slightly from mosaicData::RailTrail, (see http://cran.r-project.org/web/packages/mosaicData/mosaicData.pdf). Description below is drawn from the mosaicData help file.

Usage

data(railtrail)

Format

A data frame with 90 observations on the following variables.

• hightemp daily high temperature (in degrees Fahrenheit)

• lowtemp daily low temperature (in degrees Fahrenheit)

• avgtemp average of daily low and daily high temperature (in degrees Fahrenheit)

• season spring, summer or fall

• cloudcover measure of cloud cover (in oktas)

• precip measure of precipitation (in inches)

• volume estimated number of trail users that day (number of breaks recorded)

• weekday logical indicator of whether the day was a non-holiday weekday

• dayType one of "weekday" or "weekend"

Details

The Pioneer Valley Planning Commission (PVPC) collected data north of Chestnut Street in Florence, MA for ninety days from April 5, 2005 to November 15, 2005. Data collectors set up a laser sensor, with breaks in the laser beam recording when a rail-trail user passed the data collection station.

There is a potential for error when two users trigger the infrared beam at exactly the same time since the counter would only logs one of the crossings. The collectors left the motion detector out during the winter, but because the counter drops data when the temperature falls below 14 degrees Fahrenheit, there is no data for the cold winter months.

Source

Pioneer Valley Planning Commission

References

http://www.fvgreenway.org/pdfs/Northampton-Bikepath-Volume-Counts

Examples

data(railtrail)

Description

Break a unit length at two random points: what's the chance that the three segments produced can form a triangle?

Usage

triangleSim(reps = 10000, table = FALSE, seed = NULL)

Arguments

Value

side effects

Author(s)

Homer White [email protected]

Examples

triangleSim(seed = 3030)

Description

A turtle walks randomly, but bounces back from the edge of its containing field.

Usage

turtle_bounce(side = 60, step= 10)

Arguments

Value

side effects

Author(s)

Homer White [email protected]

Examples

## turtle_bounce(side = 80, step = 10)

Description

A turtle takes steps of a fixed length, but turns at a random angle after each step.

Usage

turtle_drunk(side, step)

Arguments

Value

side effects

Author(s)

Homer White [email protected]

Examples

## turtle_drunk(side = 100, step = 10)

Description

R6 Objects for modelling a whale. Female and Male inherit from whale. Use Female and Male if you want to provide custom lists of male and female whales when you instantiate an ocean.

Format

R6Class object.

Value

Object of R6Class with methods for simulation.

Methods

• new: Instantiates an Whale object. Parameters are:

  • position: A numeric vector of length 2 giving the initial position. (Make sure that it's within the dimensions of the ocean.)

  • age: Initial age of the whale.

  • lifespan: Lifespan of the whale.

  • range: Distance at which a female can detect an eligible male.

  • maturity: Age of whale at which reproduction is possible.

  • stepSize: Number of units the whale move sin each gneration.

Methods

Public methods

• Whale$new()

• Whale$move()

• Whale$clone()

Method new()

Usage

Whale$new(
  position = NA,
  age = 3,
  lifespan = 40,
  range = 5,
  maturity = 10,
  stepSize = 5
)

Method move()

Usage

Whale$move(dims, r = self$stepSize)

Method clone()

The objects of this class are cloneable with this method.

Usage

Whale$clone(deep = FALSE)

Arguments

Super class

bcscr::Whale -> Male

Methods

Public methods

• Male$clone()

Method clone()

The objects of this class are cloneable with this method.

Usage

Male$clone(deep = FALSE)

Arguments

Super class

bcscr::Whale -> Female

Methods

Public methods

• Female$maleNear()

• Female$mate()

• Female$clone()

Method maleNear()

Usage

Female$maleNear(males, dist)

Method mate()

Usage

Female$mate()

Method clone()

The objects of this class are cloneable with this method.

Usage

Female$clone(deep = FALSE)

Arguments

Author(s)

Homer White [email protected]

Examples

## Not run: 
initialMales <- vector(mode = "list", length = 10)
ages <- c(rep(3, 5), c(rep(10, 5)))
for (i in 1:10) {
  initialMales[[i]] <- Male$new(
    position = runif(2, min = 0, max = 100),
    age = ages[i],
    lifespan = 40,
    range = 12,
    maturity = 10,
    stepSize = 7
  )
}
initialFemales <- vector(mode = "list", length = 10)
for (i in 1:10) {
  initialFemales[[i]] <- Female$new(
    position = runif(2, min = 0, max = 100),
    age = ages[i],
    lifespan = 40,
    maturity = 10,
    range = 12,
    stepSize = 3
  )
}

library(ggplot2)
oceanSim <- function(
  steps = 100,
  males = 10,
  females = 10,
  starve = 5,
  animate = FALSE,
  seed = NULL
  ) {
 if ( !is.null(seed) ) {
   set.seed(seed)
  }
  ocean <- Ocean$new(dims = c(100, 100), males = males,
                     females = females, starve = starve)
  population <-numeric(steps)
  for ( i in 1:steps ) {
    population[i] <- ocean$malePop + ocean$femalePop
    if ( animate ) ocean$plot()
    if ( population[i] == 0 ) break
    ocean$advance()
    if ( animate ) {
        ocean$plot()
        Sys.sleep(0.5)
      }
  }
  pop <- population[1:i]
  df <- data.frame(time = 1:length(pop),
                  pop)
  ggplot(df, aes(x = time, y = pop)) + geom_line() +
    labs(x = "Time", y = "Whale Population")
}
oceanSim(males = initialMales, females = initialFemales, seed = 5050)

## End(Not run)